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Justified Posteriors

Seth Benzell and Andrey Fradkin

Explorations into the economics of AI and innovation. Seth Benzell and Andrey Fradkin discuss academic papers and essays at the intersection of economics and technology.

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  • August 10 · 45 min

    Does GDP Growth Mislead Us About Quality of Life?

    In this episode, we discuss “When GDP Misleads: Inferring Living Standards from the Value of a Statistical Life”, written by Stanford/Anthropic economist Chad Jones and Stanford/Epoch economist Philip Trammell. The word “AI” never appears in it, yet every argument about whether AI will show up in the statistics runs through the question of whether the statistics were ever measuring the right thing. We start with priors. Has real GDP per capita over- or underestimated welfare growth? Andrey’s case for over: China’s real GDP per capita is up over 70 times since the early 1950s, and life is not 70 times better. The paper poses a dilemma, suppose you have two goods (food and string quartets) with different productivity growth. The counterintuitive result in the paper is that inventing the low-productivity good makes people better off while slowing measured GDP growth. The paper then proposes to use the value of a statistical life as a measure of welfare. It is the nominal price for being alive, so its growth rate, deflated by the marginal utility of consumption and pinned down by the intertemporal Euler equation, gives you the growth rate of lifetime utility. Seth likes it, while Andrey explains why dissatisfaction with the Euler equation was in the top three reasons he didn’t become a macroeconomist. The results: welfare growth of 2.3% a year since 1940 — and a decline from 1980 to 2024. We get into how VSL is actually estimated (hedonic wage regressions identified off coal miners and other people with unusual preferences about dying), the 1% discount rate that arrives uncited in a one-sentence paragraph, whether mortality risk is being double-counted, and the robustness table where moving the interest rate by a single percentage point in either direction swings the 1940–2020 welfare gain. Links & References The paper * Charles I. Jones & Philip Trammell, “When GDP Misleads: Inferring Living Standards from the Value of a Statistical Life” — headline results: 2.3%/year welfare growth 1940–2024 (6.9×), negative 1980–2024; robustness range 16.1× to 3× on a ±1pp interest-rate change * Charles I. Jones — Stanford GSB * Philip Trammell — Global Priorities Institute, Oxford * Charles I. Jones, “The AI Dilemma: Growth versus Existential Risk” — the previous Chad Jones paper we covered; bounded utility drives an ever-widening wedge between welfare and GDP The value of a statistical life * US DOT departmental guidance on VSL in economic analysis — the paper cites $13.7 million for 2024 (we said $14.5 million on air; see Corrections) * Dora L. Costa & Matthew E. Kahn, “Changes in the Value of Life, 1940–1980” — Journal of Risk and Uncertainty, 2004; the compensating-wage-differential estimates the paper’s VSL time series rests on. “I don’t think any value of statistical life paper is very good.” Concepts discussed * The intertemporal Euler equation — and Andrey’s objections: it fails for a large enough subset of people, individual Euler equations don’t aggregate into a linear functional form, and it implies Ricardian equivalence, which is “so provably false as to invalidate this entire approach” * New goods and variety growth — the food-and-string-quartets example; why the moment of invention (price falling from infinity to finite) is the hard part of any variety adjustment; and the composite-smartphone problem in quality adjustment * Robert Nozick’s experience machine — the eudaimonia button, and what it would mean for measured welfare * Derek Parfit on personal identity and future Tuesday indifference — is your discount rate even something a social welfare function should respect? * Charles I. Jones & Peter J. Klenow, “Beyond GDP? Welfare across Countries and Time” — AER 2016; * Trammell’s bull case for AI welfare: not more stuff per person, but vastly more beings capable of having utility — a total-utilitarian argument that this paper’s single representative agent can’t represent Previously on Justified Posteriors * How much should we invest in AI safety? — our earlier Chad Jones episode (existential risk vs. growth) Corrections * VSL figure: On the episode we said the US DOT value of a statistical life was $14.5 million for 2024. Jones & Trammell cite $13.7 million for 2024 (DOT guidance; current DOT table also lists $13.7M for 2024 / $14.2M for 2025). The slip doesn’t affect the paper’s growth-rate results. * China GDP multiple: On the episode we said China’s real GDP per capita was up over 50× and as high as 72× since 1952/1962. A cleaner figure: 2025 real GDP per capita was about 82× its 1962 level. Chapters * (00:00) Cold open: would you rather be middle-class today, or the king of China? * (00:27) Intro — the paper, and why an AI podcast is covering a paper that never says “AI” * (02:12) Priors: has GDP per capita been a good proxy for welfare? * (03:47) Everything good is correlated with GDP — until you look closely * (05:29) China, 1952 to today: 72× GDP per capita. Is life 72 times better? * (06:32) Two concerns: diminishing returns, and growth in varieties * (07:08) Over or under? Andrey’s split verdict on China and the US * (07:43) 116% since 1980 — “they already had pinball machines” * (08:51) Seth’s prior: diminishing returns dominate, and why the AI age might flip the sign * (10:09) Haven’t we already had huge variety growth? Podcasts, Prairie Home Companion, and the eudaimonia button * (11:15) Putting numbers on it: 95% and 80% that GDP still overstates * (11:36) How much better is life since 1986? Andrey says 25% * (12:12) The benchmark: life expectancy × log consumption, and 41% since 1980 * (13:25) The paper’s setup: food, string quartets, and a productivity gap * (15:05) Why inventing the new good makes us better off and slows measured growth * (16:37) Quality adjustment, and the composite-smartphone problem * (17:08) The problem that exists even before invention: satiation * (17:41) Varieties vs. abundance: the king of China, at length * (19:01) Trammell’s actual bull case: more beings, more utility * (19:44) The clever idea: the value of a statistical life as a nominal price for being alive * (21:10) $14.5 million — but 14.5 million what? * (21:50) The deflator problem, Weimar Germany, and the marginal utility of consumption * (24:12) “Micro or macro?” — the Euler equation and the conditions it needs * (25:54) Discount rate vs. mortality risk — is something being double-counted? * (27:17) The magic equation, in two equivalent forms * (28:59) “The intertemporal Euler is getting some intertemporal shade” * (29:40) Ricardian equivalence, aggregation, and “I have told Chad this” * (31:04) Seth’s defense: welfare on the left, nominal on the right * (31:49) How VSL is actually measured: Costa & Kahn, and the death risk of coal mining * (33:04) Identification off the highest-risk jobs — and the people such jobs attract * (34:44) “It’s just made up”: the $14.5M highway-safety number * (35:22) The other inputs: a 1% discount rate, uncited, and T-bills plus a convenience yield * (35:53) The results: 2.3% a year since 1940 * (37:19) …and negative since 1980. Life peaked in 1984 * (38:05) 6.9× vs 2.4×: could you convince me life is seven times better than 1940? * (39:27) “For whom?” — the representative agent, the 10th percentile, and changing demographics * (41:19) Should a social welfare function respect your discount rate at all? Parfit and future Tuesday indifference * (42:07) Posteriors * (43:37) The robustness table: one percentage point, 16.1× or 3× * (45:10) Sign-off Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • July 27 · 1 hr 4 min

    Is AI Replacing Programmers or Boosting Them?

    Justified Posteriors reads “Writing Code vs. Shipping Code” by Mert Demirer, Leon Musolff, and Liyuan Yang In this week’s episode of Justified Posteriors, we update our beliefs with evidence from an ambitious new paper estimating the impact of AI on software production productivity. Demirer (friend of the show), Musolff, and Yang combine public GitHub records for over 100,000 developers with confidential Microsoft data to trace the effect of distinct generations of AI coding tools — autocomplete, sync agents, and async agents — on the code production hierarchy: lines of code, files, commits, pull requests, projects, and releases. The main empirical finding is attenuation of the effect of AI at each step. Enormous gains of 1000% productivity increases or more at the top of the chain translate into about a 30% increase in shipped releases. The model they use to explain this result is closely connected to Kremer’s O-ring logic, which regular listeners will recognize as from a few episodes back. Seth likes the spirit of the model, but feels it is overcomplicated for this context, for reasons he explains. In addition to discussing the data analysis, Andrey and Seth have a good back-and-forth about what we can conclude from it and extrapolate to the economy more generally. The implication that grabbed Seth’s attention is a sentence in this paper’s abstract. Nested in the summary of the careful empirical exercise is an estimated elasticity of substitution of 0.25 between AI and human effort. Big if true! Seth points out the enormous long-run implications of humans being complements to AI in what seems to be the most AI-friendly of tasks: as AI gets cheaper, the human share of income goes up, wages skyrocket, and ultimately AI boosts jobs instead of taking them. That’s a huge real-world hook. Seth and Andrey discuss whether, and if so how much, we update our beliefs in this direction, with Andrey being careful to point out the difficulties of extrapolating from a partial equilibrium elasticity to long-run macro consequences. This episode is sponsored by Revelio Labs — a great source of labor economics data for academics and firms. Now available on WRDS. Priors → Posteriors Prior 1: Does access to AI coding tools boost lines of code written by more than 100%? * Seth: 95% → 99%. Seth came in confident and left more so. Great to see giant numbers. * Andrey: 80% → 95%. A high yes, hedged because “which developers” and “which tools” do a lot of work in that sentence. Prior 2: Does AI boost economic value by 50% or less of the factor by which it boosts lines of code? * Seth: 95% → 97.5%. I have personally produced a great deal of economically worthless code lately. * Andrey: 85% → 95%. Prior 3: Are AI coding tools a gross complement to human labor? Seth’s answer depends on the level of aggregation:The average normie programmer 20%→20% (unchanged)A human engineering department 33%→40%A software company / open source project 60%→85–90%The economy as a whole 33%→33% (unchanged) Andrey: 75% complement at the sectoral level, and he’d put it as low as the programming department — because right now the code that comes out is not shippable without substantial human input. Posterior: still a complement, mildly supported. He declines, on the record and repeatedly, to extrapolate to the macroeconomy. No fun! References The paper under review * Mert Demirer, Leon Musolff & Liyuan Yang, “Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools,” NBER Working Paper 35275 (May 2026). * The authors’ own summary: “Writing code versus shipping code”, VoxEU, June 2026. Prior work by the same team * Kevin Zheyuan Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng & Tobias Salz, “The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers,” Management Science (2026). 4,867 developers, roughly a 26% increase in completed tasks, larger gains for the less experienced. Related Research and Prior Episodes * METR, “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity” — the RCT Andrey refers to, in which 16 experienced developers took 19% longer with AI while believing they were 20% faster. arXiv version. * METR’s own update, “We are Changing our Developer Productivity Experiment Design” (Feb 2026) — developers increasingly refuse to be randomized into working without AI, which biases the estimated speedup downward. * Michael Kremer, “The O-Ring Theory of Economic Development,” Quarterly Journal of Economics 108(3), 1993. Our episode on it: Weak Links, Strong Predictions: Kremer’s O-Ring at 30. * Josh Gans & Avi Goldfarb, “O-Ring Automation,” NBER Working Paper 34639 — and our conversation with Avi: Avi Goldfarb on Prediction Machines, O-Ring Tasks, and How AI is Reshaping Economics. * Aaron Chatterji, Tom Cunningham, David Deming, Zoë Hitzig, Christopher Ong, Carl Shan & Kevin Wadman, “How People Use ChatGPT,” NBER Working Paper 34255 — a reference for Andrey’s point about substitution toward home production. Non-work usage grew to over 70% of messages; computer programming is a small share. * Our episode on the same phenomenon in a different market: The Economics of Book Slop — more books, not obviously more valuable books. More apps, not obviously more downloads. Rhymes. * The universal token budget: Alex Imas — Demand Collapse, Bargaining with Machines, and Behavioral AI Economics and Seb Krier on AGI, the Coasean Singularity, and EDM. Table of Contents * Introduction and Today’s Paper — [00:00] * Priors: [02:58] * The Evidence: Ideal and Possible Experiments— [18:34] * The Evidence: Adoption Events and the Attenuating Waterfall — [24:56] * The Evidence: Model and Simulation Results — [42:56] * Aggregates, App Stores, and Posteriors — [56:00] Transcript Introduction and Today’s Paper [00:00] Seth: The abstract of the paper has the following sentence: blah, blah, blah, based on the results in the model, there’s an estimated elasticity of substitution of 0.25, so high complementarity between AI and human effort, which indicates strong complementarities. Wow. As the people say, big if true. Humans and AI, 0.25 complements. Everybody worried about AI taking all our jobs — wrong. All labor share to 100%. Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, with a 0% productivity impact on my code writing, as measured by my podcast release schedule, coming to you from the Pocono Mountains of eastern Pennsylvania. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. Justified Posteriors is sponsored by the fine folks at Revelio Labs, and please do sign up to our podcast and our Substack whenever you get the chance. Seth: Today we’re talking about a really interesting empirical study investigating the impact of AI tool use — autocomplete, synchronous agents, asynchronous agents — on people’s productivity in writing code. This is that kind of hard empirical data that maybe has the potential to move our beliefs. So I’m cautiously optimistic that I’m going to learn a lot from this one. Andrey: It’s the big question, in many ways. We have these tools. We’re using them. What do we get out of them? Are we really that much more productive? That is the question on everyone’s mind, especially since so many of these tools are very costly. There are people token maxing under the belief that the more tokens that are used, the more valuable the output will be. Seth: People are going broke over the tokens. We discussed a universal token budget when Alex Imas was on — or maybe that was with Seb. What are people getting when they’re actually paying for them? The paper we read to look into this is called “Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools,” from Demirer, Leon Musolff, and Liyuan Yang. So, butchered every name, as is common for us. Andrey: Mert is a co-author of the pod, so very excited to be reading a paper of his. Seth: Despite being friends of the show, no punches pulled. Andrey: We never pull any punches. As listeners may know, now is the time for our priors. So what do we think about this topic before we read the paper? Priors: Three Claims, One Spicy [02:58] Seth: Reading this abstract, it seems to make two pretty narrow claims and then one claim that’s a really big spicy one. The first claim is that AI is really productive for helping you write lines of code. The next, more detailed claim is that the translation function going from lines of code to subsequently more advanced stages of production has an attenuation effect. You write more code, then you get more economic stuff out of that code — but the benefit attenuates. And then finally, using a model and some simulation, the paper goes on to argue that this can tell us something about the degree of complementarity between human coders and AIs. So let’s hit those one by one. First off, let me ask you this prior, Andrey. Do you think access to AI coding tools boosts the lines of code written by programmers by more than 100%? Andrey: Yes. I put my prior at 80%. Seth: 80%? That’s not a “yes, shut up.” Why only 80%? Andrey: That’s a pretty high yes. I’m a good Bayesian. Like any empirical question, there are sub-questions about which developers we’re talking about and which specific agentic tools we’re talking about. I’m sure the answer varies by those. It probably increases lines of code written by non-programmers by a ton. Seth: In percentage terms — from, you know, infinity. Andrey: You start with zero and then you go to something. It’s a pretty big percentage increase. It’s really important what samples are being used. But what’s your prior, Seth? Seth: Maybe I’m not a good Bayesian, but I came in pretty confident. I’ve got in mind all those people who have written one line of code in their lives now writing thousands of lines of code. If we look at the population as a whole, I’d be 95% confident going in that the amount of coding is going up by more than 100%. Andrey: And just to be clear, my interpretation was not people who previously did not code. Seth: Right. What we’re going to see is people who were using GitHub in the ex ante period, which raises you above some very basic level of coding in the pre-period. But even still, I’m pretty confident in this one. Andrey: [06:08] Let me ask you a related question. If you just plotted the lines of code produced by developers over time, in aggregate, not per developer — would that increase by more than 100%? Seth: Right, because now the guy who’s going from zero to 1,000 is a lot less important than the guy going from 1,000 to 10,000. If we’re saying the total amount, I would say yes — my guess is that it would double — but the confidence goes down. Probably that brings me to the 80 to 85% range. [06:21] All right, prior number two. Now it’s this idea that maybe the benefits attenuate. We’re not actually getting 100% more program even if we’re getting more than 100% more code. So let me ask you, Andrey: would you say that AI boosts economic output or economic value by 50% or less of the factor by which it boosts the lines of code written? Andrey: It boosts it by less than 50%. I’m very confident of that. I’ll even say 85%. Seth: I again come in pretty confident, because I’ve seen some real economically worthless code written by AIs recently. I’m a producer of a lot of it myself. I come in in the 90 to 95% range. In my head I’m also thinking about the fact that when we’re talking about lines of code, we’re probably also talking about comments. AI is really good at commenting and leaving descriptive comments if you let it. That’s one way the code might end up less dense. But also just my experience of writing my own code versus seeing what the AI writes — when you say “go do A, B, and C,” you end up with a whole bunch of stuff that I would never have done myself because I’m lazy. So that’s another reason I’m confident: 95%. Seth: [08:07] Big question, Andrey. The abstract of the paper has the following sentence, which I love: based on the results in the model, there’s an estimated elasticity of substitution of 0.25, so high complementarity between AI and human effort, which indicates strong complementarities. Wow. Big if true. Humans and AI, 0.25 complements. Everybody worried about AI taking all our jobs — wrong. All labor share to 100%, wages skyrocket. What’s your prior on AI coding’s complementarity to humans? Andrey: In the prepared priors, you made the comment that this is a prior about gross complements. Seth: [09:08] The way I think about complementarity — this is a good way to think about it if you’re not constantly dealing with elasticities of substitution — is that the elasticity in the middle is Cobb-Douglas. What Cobb-Douglas gives you is that no matter how cheap capital or labor is, you always spend a fixed share of your income on capital and a fixed share on labor. So put gross complements on one side of that and gross substitutes on the other. If you’re gross substitutes, then as your factor gets cheaper, you spend more of the budget on it. So if AI and humans were gross substitutes, that would mean that as AI gets better, the AI share of income goes up and the human share goes down. That’s the robots-take-our-jobs universe. Gross complements would mean the opposite. As AI gets cheaper, we’ve got all of this jelly, and the human peanut butter becomes more and more valuable. The share of income going to humans would go up. So: Cobb-Douglas in the middle, and what this paper argues is that in their data, in their sample, AI coders and human coders are very much on the peanut butter and jelly side. Andrey: To be clear, this paper is not about gross complements or substitutes. I view this paper as about holding all else equal, no readjustment — are they complements or substitutes? Which is a different story from what you’re talking about, which involves reallocation of resources. Seth: Fair enough. But the argument would be that they’re measuring an underlying parameter such that if there were reallocation, you would get the effects I’m talking about. That’s my reading. Andrey: This is not a macro model, Seth. Seth: Why do you care about estimating micro parameters except to plug them into a macro model? Andrey: My reading of the paper is that, given the way we’re currently organizing code production, this is their attempt at estimating the production function. Seth: And then I extrapolated out into infinity. Andrey: I don’t view this as a macro paper about a reallocation of labor and capital in the macroeconomy, if that makes sense. Seth: Andrey, your comment is so sympathetic to the authors that I worry you may have been replaced by some sort of alien invader. But feel free to address it just on the basis of an estimate of a parameter in a production function. You don’t have to extrapolate anything from that. Andrey: [12:15] The question is how much this paper informs our beliefs about the macro versus the micro, which we’ll talk about. But is AI coding a gross complement or substitute for human labor? My prior is that it is a complement, at the moment, with the current technology — which is a big caveat. The AIs, or at least how we’re using AIs, are not yet autonomous enough that humans can be taken out of the loop. As a result, humans still have a huge role to play in being in the loop on the coding production process. They’re essentially the bottleneck. That said, it really depends on the human, and this goes in many directions. Now we have a lot of people who were previously not coding, coding, and presumably that code would never have been written had this technology not existed. I would venture a guess that these humans in their roles are becoming more productive — they’re highly complementary, since literally the code would not have existed without them. There are some programmers who are not very good programmers, who were writing slop code even before AI tools. I imagine those coders are substitutes. And then there are other coders who understand the business logic, architecture, and so on, who are at the moment highly complementary to the AI. How that all nets out — I think it’s still on the complements side in the aggregate for the sector at current technological levels. Seth: But you can zoom out even more. Andrey: I’m not a macroeconomist, so I don’t want to extrapolate to macro from just coding tools. There’s a lot more to the economy. Seth: Ask a computer programmer. They say you get infinite code, and then the economy goes brr. Andrey: Our singularitarian friends notwithstanding, I’d rather not speculate about that in this prior. And I didn’t give you a number. I need to give you a number, right? I would say it’s a complement with a prior of 75%. Seth: And that’s at the sectoral level? Andrey: Yes, at the sectoral level. And I have to be very precise — I’m not even including Fable in here, because we haven’t seen Fable in the wild. Seth: [14:49] Well, beloved listeners, while you have one host who will not extrapolate wildly, I am always here for you. I agree with Andrey that the question depends on what level of the economy we’re looking at, so let me build up from narrow to aggregate. If we’re thinking about the level of mediocre coders — the average coder — I would be very surprised if AI were a complement to the average computer programmer at this point. I’d say a 20% chance that AI is a complement to the average normie computer programmer. Then if we move up to the level of a human engineering department, where maybe you’ve got one really sophisticated computer science architect who can complement the slop code being made, my probability of complementarity goes up to maybe one-third. But then we hit the level Andrey was focused on, the software company. At the software company, it seems clear that being able to churn out slop code and have a software product that is useful for people involves complementarity with people who have business expertise, people who can figure out how to sell the thing — which is not being automated when we’re talking about pure software. So at the level of the software company, maybe a 60% chance of complementarity between the AI and the entire software company. Andrey: I do want to clarify — you mischaracterized what I said. I was saying complements not at the software company level, but even at the programming department. Seth: But you said more substitutable for the lower quality coders. Andrey: Yes. But I’m not even talking about salespeople. I’m just talking about the fact that literally right now, if you’re coding, the code that is produced is not good enough to ship — Fable notwithstanding — without substantial human input. Seth: Okay. So that’s the human engineering department level, and you’d say even at the individual programmer level there are complements. Possible. And then I zoom out one more time, from the sectoral level to the economy-wide level. What do people do with software, Andrey? They automate things, and they consume digital products that don’t have large labor inputs. So at the economy-wide level I’d say AI coding tools are a substitute for human labor — a 33% chance they’re a complement. That’s the hierarchy I’d split out. Any response? Andrey: So let’s say you have someone at the insurance company reading claims. To this date there hasn’t been software that really automates them, but now it’s finally there, and so those insurance readers are donezo. Seth: That would be my argument. Andrey: In the short run. Seth: As software gets better generally because of this, that’s automative, that’s a gross substitute for humans. Andrey: Interesting. Seth: So now we will see if this paper has a chance to change our views. The Experiment We’d Run If We Could Clone the Earth [18:34] Andrey: Before we get to that — if we could design an optimal experiment, how would we study this question? Seth: I would make a clone of the planet Earth. Actually, I would make a thousand clones of the planet Earth, and in half of them Sam Altman would be born, and in half of them alt-Sam Altman would be born. Help me out here, Andrey. What’s the correct answer? Andrey: Seth wants to do a macroeconomics experiment, which is a nice thought experiment, where we forget about the existence of — where the transformer does not get invented. Is that the counterfactual? I’m not even sure. Seth: I go back in time, I assassinate Geoffrey Hinton. Ilya goes and sells ice cream somewhere. Andrey: And then we see how macroeconomic productivity and labor shares change in those worlds where LLMs don’t exist versus the ones in which they do. That’s an interesting thought experiment. A hard one to approximate, obviously. I was thinking of something more mundane. We have a bunch of firms, and half of those firms are randomly not allowed to use LLM-based tools — and half of them are imperfect anyway because of business stealing, but let’s forget that for a second. And then we see whether they’re more productive when they have LLMs, and how they change their labor shares. What do you think about that experiment? Seth: You pointed out the main concern I’d have, which is business stealing effects. So maybe these are in two separate countries where you do these splits. Andrey: What about the following experiment? What if we gave some developers LLMs, and some developers were not allowed to use LLMs? What do we learn from that experiment? Seth: The issue is that you can’t force anyone to use the LLMs. So you’d find the treatment effect on the group that chooses to adopt when they’re available. Andrey: Isn’t that always true, though? You can’t force a company to use LLMs. Seth: In medical settings you can inject somebody with something. They don’t have to be given a choice. Andrey: Sure. But relative to our other thought experiments — why isn’t that satisfactory for what we’re interested in? Seth: The reason it’s not satisfactory is that first you have to think about whether the randomization is at the level of the company or at the level of the programmer. You’d imagine those are different effects. If you’re randomizing at the level of the programmer, one issue is that when people can build up a whole new workflow at the company, that might have a different effect than a single person adopting. Andrey: What about just the fact that you can’t get labor substitution at all? Seth: If we’re doing it at the programmer level, you can’t fire a treated programmer. Andrey: Exactly. You can’t study that reallocation of labor. Seth: Right. If you’re randomly assigning tools to the As and not the Bs, and you think the long-run effect is that some of the As should actually not be programmers, then obviously that’s a mechanism you’re not going to capture. Andrey: [22:50] So it does have limitations, but you can still at least see how much more productive, in this narrow sense, the treated are versus the control programmers. Now, METR famously tried to do this experiment about a year ago and made the claim that AI tools as of a year ago — I think this was Cursor — did not make programmers more productive. It made them less productive. Much respect to our friends at METR, but most of us did not believe that experiment at all. Seth: What was the productivity outcome in that paper? Andrey: Speed. How long it took them to solve some issues. Seth: Speed is frequency inverted. Andrey: Great insight, Seth. Seth: Frequency is our productivity measure here today. Sorry. Andrey: Designing and implementing such an experiment in a credible way — people have thought pretty hard about how to do it, and it’s essentially impossible. It’s just hard to get the validity of true programming tasks, true randomization, preventing some people from using LLMs in all ways. And then most importantly, maybe, statistical power. Maybe with millions of dollars you could do this, but to have validity you need so many observations. So it doesn’t seem like a fruitful way to study this. We’re kind of left with the tools of observational causal inference, where we have to find something in the data that looks like an experiment if you squint at it and use the right econometric techniques. Seth: If only there were a social science field with a bucket of techniques for inferring causality from observational data. Andrey: It’s true. So that’s what the authors set out to do in this frankly really ambitious, impressive paper, in my opinion. Seth: Are you ready to go to the evidence, Andrey? Andrey: Sure. The Evidence: Adoption Events and the Attenuating Waterfall [24:56] Seth: Maybe I can quiz you through the different elements. Let’s start with the setting. What’s the population we’re going to evaluate, and what’s the natural experiment? Andrey: At a high level, we’re looking at contributors to open source projects on GitHub. The authors have other data as well — some proprietary data from Microsoft, which is pretty important for this empirical study — and they have some side data on non-open-source development projects. But broadly, that’s the main population. And then the thought experiment. Credit to the authors, because this is a mistake a lot of people make: a lot of people will write a paper like, “what is the effect of AI?” Seth: Oh my gosh, I am so frustrated by these papers that say “what’s the effect of AI.” I got a restaurant to use AI to write ads once. Andrey: [26:03] What we’re hinting at is that there is no one AI. It’s not the one ring to rule them all. You have the elven rings, which are the coding agents. You have the dwarven rings, which are the slop generators. You have the human-shaped rings, which are— Seth: Well, they were the men doomed to die, so that’s the AI for health research. Andrey: But seriously — over the past two years, AI has been many, many different things depending on whether you’re at the frontier or not and what tools you’re using. That includes the quality of the models. It includes the tooling, and how autonomous the tooling is. The authors do a very careful job of thinking about different ways people have been able to code via AI over this period, including just autosuggest. Remember back in the day when you were typing in an IDE such as VS Code, and it filled it out for you because what you were doing was predictable. Seth: I think the dwarven rings are the AIs for finance, because they used them to get rich. Andrey: And the one ring is the backdoor that Sam Altman has to all the GPT models. Seth: All right. Back to the paper. Andrey: So then you have what they call sync agents. I understand why they made this distinction, but it’s a pretty strange one in my opinion. A sync agent is essentially what we think of as an agent. You have Claude Code open, you’re typing something into Claude Code, Claude Code does something and gives you something back, you iterate with it, and then in the end you tell it when to push to the repository. Seth: Seems pretty agent-y to me, Andrey. Andrey: And then they also have async agents, which are autonomously sitting on a repo, refactoring the code, implementing things, and creating pull requests. Maybe it’s useful at this point to think a bit about the production hierarchy of code, which I found fascinating. Seth: [28:40] It’s provocative. I don’t know if I agree with every step, but it’s an interesting way of thinking about it. So what is the production hierarchy? We go from lines of code on one end, and then question mark question mark question mark, and then money at the other end. What’s in between? Andrey: Well, I don’t think open source projects are supposed to make money, Seth, but just FYI. So: there’s lines of code. There’s how many distinct files are touched. Obviously those are fungible in many ways — you can write one really long script, or a bunch of short scripts. You have commits, which are what programmers consider a discrete change to the code base worthy of noting. Once again, something that’s fungible and perhaps arbitrary. Then you have a pull request, which is when you submit this into the production code base, and usually someone else is going to review that in a professionally managed project. Then you have repos touched, so how many distinct repositories you’re contributing to — different repositories are distinct software projects. And then lastly, releases of those projects. Seth: And they’re going to tell us the effect of each of these different kinds of agents on each of these different outcomes? Andrey: Before we get to the empirics, do you want to go through the theory model? Seth: No. I know that’s how the paper lays it out, but I think it’s kind of silly the way they give you some theory, then some empirics, then some simulation. If I wrote this paper, I would do all of the empirics and then all of the model and simulations. I’d just as soon do it that way. Andrey: [30:29] So you have this dataset. It has the GitHub data. It has data on what they call adoption events — when specific GitHub users start using GitHub autocomplete, which is obviously an earlier period, back in 2022 and 2023. Then there’s the sync agents. Importantly, GitHub Copilot is a main one here — not always considered the best agent by many, sorry to our friends at Microsoft. But they also have ways of figuring out whether someone ostensibly is using Claude Code. For example, there might be a claude.md file in the repository, suggesting someone is using Claude somewhere. And then for the async agents, in particular Codex, OpenAI’s tool actually has a prefix saying that Codex generated a particular set of changes in the repository. Seth: And a human would never delete that before uploading code, would they? Andrey: I don’t think they would. Extremely low probability. I have very many concerns about what these adoption events mean, and in particular the intensity of adoption is very tricky to understand. You have a claude.md file. That could be because you’re just playing around with Claude Code. It could be because you’re very actively using Claude Code. Seth: You have a partner who’s using it. Andrey: Did your collaborator use Claude and you didn’t? This is a very long paper, so maybe I missed something, but some of these are imperfect proxies. That’s one way to put it. Seth: You could summarize that about a lot of this paper. Andrey: But again, I really do want to defend the paper: this is kind of the only thing we’ve got. We can’t do an RCT. I think it’s quite implausible. Seth: And the other things I’ve seen are country-level shocks, where the problems get twice as bad. Andrey: [33:07] Now, what is the control group here? This is really tricky. The obvious causal inference issue is that when you adopt a tool, you might be adopting it for reasons related to your future expected need to use the tool to produce code. Seth: But Andrey, they’re matched on observables. Andrey: This is the only exercise we can do, so we’re going to have to roll with it. I’m just explaining what the main challenge is. I’m not trying to snipe at it like you are already. Seth: I’m not sniping. I’m having fun, Andrey. Andrey: They need to find people who didn’t adopt the tool but otherwise would have attempted essentially similar things had they adopted. One way to think about it: if this async agent did not come out, both people would be doing the same exact thing. It did come out. One of them chose to use it, one of them chose not to, in a completely random way. That’s what they’re trying to get to. Seth: Ideally they’re working the same number of hours on a project of the same difficulty level. Andrey: Even that’s not true. If they were in the control group, they would be working on the same project, because one of the things the coding tools allow you to do is work on more projects. So you can’t condition on the project. Now, the authors are justifiably hesitant to literally match the behavior of someone who adopted to someone who didn’t adopt in the same time period. Why? It’s a reasonable assumption that many software developers have adopted these tools, and the measurement is quite imperfect for whether someone adopted or not. So the control group might have also been treated. What they do instead is find people they don’t observe adopting, and then look at their behavior literally a year before. That’s the behavior they compare to the behavior of the adopting developers. It’s not a strategy I’ve ever seen before. It’s quite non-standard. Usually in this sort of design you look at literally the same time period. But I understand why they do it, because of these very serious measurement error issues. What do you think about that, Seth? Seth: I think there is some logic there. The reason you’d want to use the same chronological time period is if you were worried there was some calendar-time shock to people’s writing of code. If in 2023 everybody’s writing more code than in 2022, that’s when you’d want to match up calendar dates. I don’t know if they do anything to deal with seasonality. Andrey: Seasonality is not the issue. It’s more that maybe the previous year was COVID, which would be a serious problem as a comparison year. Seth: A weird year. But given that coding is kind of in a steady state between 2022 and 2023, or 2023 and 2024, it does not seem that implausible to me. Andrey: I think it’s reasonable. The authors have sanity checks. They look at other adoption events like Docker, which is not an AI tool, and see whether they detect that it matters — a placebo test. And they don’t. So they do reasonable checks that this empirical strategy doesn’t always just pick up effects. And the magnitudes involved — it’s always easier to study something— Seth: When the magnitude is massive. Andrey: Which we know has massive effects. It would be crazy not to think it has massive effects, as our priors suggest. Seth: This is not a case where they’re picking up significance because they have so many observations. They find big numbers here. Andrey: [37:12] Let’s go through the outcomes very quickly. Lines of code: they find a 17x increase in lines of code after the adoption of an asynchronous agent. They find a slightly lower one, about 10x, for the sync agent, and about 2x for just using autocomplete. So just massive increases in lines of code. Files touched increases by about 4x with the agents, but only 0.5x with autocomplete. And then we see this pattern of diminishing numbers down the production hierarchy— Seth: An attenuation. Andrey: —to commits, pull requests. By the time we get to releases, the adoption of either type of agent only increases releases by 30%, even though it increased lines of code by 17 times. Seth: Right. So 30% from the agents, and only 10% from autocomplete. Andrey: [38:16] The other really interesting empirical pattern is what happens over time after adoption. As all of us know, we can spend some time playing around with these tools. Seth: We can make a lot of commits real fast. Andrey: They find a pattern that’s true across all three tool types: you get an initial spike in activity, whether it’s commits or lines of code, and then it diminishes a bit over time, although sometimes it rises again toward the end of the period. This is one of these very tricky things for them to disentangle. Seth: How big is the spike compared to the long-term effect? Andrey: For commits, the spike is about a 175% increase, and then it diminishes to about a 100% increase, and then it goes back up a little after that. That’s the pattern for the sync agent. Seth: So why do we think that is? Andrey: There’s people exploring, or they had a specific need for it and that’s why they adopted. And one thing to think about is that a job like ours — sometimes we’re coding a lot, sometimes we’re not coding a lot. We’re not coding all the time, and that’s probably true for a lot of people, especially open source developers, who might have other jobs in addition to their open source contributions. So I view that as an activity bias. There are also calendar effects that are really relevant. If Codex comes out in January, and everyone who adopts adopts in January for the most part, then you track them over time — and let’s say a new frontier model comes out in March that’s really, really good. Once you get to March, everyone’s going to start using the new frontier model. That’s going to increase your output. Now it looks like there’s a much larger effect later on, but that’s confounding model quality with the tool release. Which goes back to the point that there’s no one AI. What AI is is constantly changing. Seth: Right. So there’s a tension here. You’d want to take the short-term impact more seriously because there’s less margin for parallel trends to have a problem in the short term than in the long term. But on the other hand, we think there are all these short-term effects from adoption — either experimenting, or “I adopted the tool because I need it right now.” They focus on the long-term impact rather than the short-term impact, which is interesting, because usually in these difference-in-differences papers you see the opposite. They’ll say, “the effect in the short term is where I’ve really got the natural experiment, and then the long term, who knows?” Andrey: I want to push back on that. It just depends on which AI we’re interested in. Let’s say the parallel trends hold and we just have bigger effects once we have Opus 4.5. We might be more interested in the effects of agents with 4.5 than in agents with 4.0. They’re just different objects. And the key parallel trends are from the previous year, remember. There’s no sense in which Opus 4.5 being released in 2025 affects the parallel trend assumption for 2024. What would affect the parallel trend assumption for 2024 is if the people in 2024 started using some new tool at the end of 2024 that wasn’t being captured by the adoption measure. Seth: Right, but we already mentioned that concern, so it’s not an additional concern. It’s just unmeasured use again. Andrey: [42:07] And then the final empirical thing: they split out the causal effects by your pre-period activity. So how much you coded— Seth: We were talking about the effect on power users versus normies earlier. Andrey: And you see the biggest effects by far for people who have not been coding much according to these measures— Seth: And that’s in percentage terms. Andrey: In percentage terms, exactly. But it really should affect our interpretation, because presumably here we care about professional programmers more than non-programmers. All right, Seth, why don’t you tell us about the model the authors propose? The Model: A Seven-Layer CES Cake [42:56] Seth: The model is a fascinating one. It does a lot of what you would hope from a micro model of how code gets produced and economic value gets created. It’s got a lot of the elements you’d want. As we go, I’m going to point out that maybe there are a few too many elements, given that they’re only going to be able to bring the model to the data in a pretty superficial way. So what’s the model? One of our favorite functional forms: a constant elasticity of substitution production function at each level of production. Starting from lines of code and then to files and commits and pulls and repos and releases, they think about each of those levels as having its own production function that takes a little bit from the level above and combines it with what you’re adding at the current level. So there’s an elasticity of substitution between lines of code and some new effort brought in at the files level, and they’re combined to produce an input that goes down to the commit level. A seven-layer nested constant elasticity of substitution production function, for people following along at home. The key parameters they’re interested in estimating are the elasticities of substitution — how much the previous level’s inputs combine with the current level’s inputs. And then they’re also interested in what I would call the share term: what share of input at each level comes from the level above versus the current level. In a Cobb-Douglas production function those would just be the power terms. But they’re not going to interpret it that way. They’re going to invent a new parameter and say that’s actually what they’re measuring. We’ll come back to that. [45:19] At each level, the upstream level is combined with effective input at the current level. What’s effective input at the current level? The way they measure it is just the amount of additional stuff you get at that level. But they think you can make effective inputs in three different ways, each corresponding to a different way to interact with AI. The first is a pure labor-augmenting way, and there they’re thinking about autocomplete. The way you write more lines of code with autocomplete is that it makes each hour a programmer spends writing code produce so much more code. Then they think, well, asynchronous agents aren’t like that. The way they think about agents is that the AI is going to write so much code, and then that’s a Leontief complement with a human who needs to go through and review all of that code before it turns into effective input. So they call that a different sort of complementarity between AI and humans. And then finally their last mode is pure substitution. Maybe if the asynchronous agent is good enough, you could just let it cook and write as many effective lines of code as you want without any input. [46:36] And Andrey, in our previous episode — or maybe two episodes ago now — we used a phrase I really enjoyed: ex cathedra. We get this ex cathedra pronouncement that autocomplete enters the production function this way and synchronous agents enter the production function that way. It’s not going to end up being super important, but it’s meant to get you thinking about what patterns you might expect to see as the boosted productivity of lines of code attenuates. So that’s the basic setup, those are the parameters we’re interested in, and then the question is what we can conclude from this model, and finally they try to plug in some parameters and simulate it. Andrey: [47:16] It is a useful model for thinking through the production process here, but I agree with you, the ex cathedra pronouncements are a bit hard to swallow in some dimensions. The fact that you can use an agent can affect all other parts of how you produce your code. Even the code that you’re writing, you might be writing differently. Or alternatively, just because you’re using an async agent doesn’t mean you’re not also using a sync agent at the same time and autocomplete at the same time. Seth: I don’t think they say you couldn’t. The part that’s a limitation, or maybe a little heroic, is the idea that they can do this mapping to a production function a priori. Andrey: They’ve set up the model so that the optimal production mechanism is a corner solution, meaning that you can’t both be writing code and having the AI agent write code, for example. Seth: Right. And then when they simulate it, they simplify it even further, and they really only simulate it for autocomplete. Given that they cannot actually bring all of these parameters to the data, and given that they have such a rich empirical setting already — if I were writing this model, I would not have gotten into the nitty-gritty of “here are three different ways that AI can plug into the production function” that we cannot distinguish between in the best possible data. Oh, and then this is the delicious part, Andrey. The delicious part is that after setting up this highly complex production function, they then say: we’re not going to solve this production function for the optimal reallocation problem, because that’s too hard. Why would you write down a model that’s so complex that you can’t bring it to the data and can’t optimize it? I would drop some of these terms. But that’s a nitpick, because at a motivational level it does hit the different vibes that people have, even if it’s not an empirical question at this point. Andrey: I think the reason they did it is that they have seven outcome variables, and they needed a model that can rationalize those. Seth: They rationalize it with way fewer parameters than they write down. They only calibrate two of these parameters. Andrey: The model is general, but then they assume a bunch of the terms are identical — the layer substitutions are identical. Seth: And for example, there’s a parameter in the model which is the number of hours you need to spend reviewing each AI-written bit of code. If you could actually measure that, put it in the model. But if you can’t, I don’t know why this is in the model. There’s this temptation to overcomplicate models of automation, and I’ve run into this with commenters on stuff I’ve written down, where it’s “your model is just a CES production function, but what if something entered this way? What if it were complementary in this weird, different way?” The point of CES is to write something super general and super flexible down. To add more flexibility to the already super flexible— Andrey: Well, I don’t think CES is that flexible. I really don’t. Seth: You’re right to say CES is not super flexible. The way I’d put it is that in a lot of settings, the only things you observe are the things you can estimate with CES, so that’s why you write it down. Andrey: I think that’s what you’re trying to say, not that it’s flexible. Seth: [51:47] It’s a model that hits all of the empirically measurable things. All right, friends of the show, love you guys, but a couple more parameters in here than I would have written down. So what are the conclusions of this model? First of all, it’s intractable with reallocation, so we restrict attention to the partial equilibrium case where there’s no reallocation of efforts. What do they find? Two intuitive results. The first is that if you write infinity lines of code, you only get infinite output if there are gross substitutes at every link of the production hierarchy. That makes sense. So now we’ve got a story for why, even if you were really good at automating code, you wouldn’t have infinite output. Fair enough. The second theoretical result is that as long as there isn’t perfect substitution, you’re going to have attenuation of the boost as you move down the production hierarchy. If you have a big effect at the top of the waterfall, the effects attenuate as you go down the waterfall. Again, intuitive, but it’s good to see the model deliver the result we’re going to see in the data. [52:21] In the simulation, the goal is to estimate two parameters of the model. All the other parameters we’re going to forget about. The two we’re interested in are, first, the elasticity of substitution between production at a higher level and input at the current level, and second, what they call an elasticity of output between the higher level and the lower level. That’s not the way I would talk about it, because it’s not a primitive of the model — it’s going to depend on input levels. I would talk about it as a share term between production at the higher stage and the current level’s input. But given that, they estimate these two parameters. Call it what you will. The parameter that’s really the interesting one is the elasticity of substitution between input at the higher level and the human inputs. Why can we say it’s human inputs? They restrict attention to the autocomplete natural experiment. So based on their ex cathedra pronouncements, autocomplete works by boosting lines of code at the lines-of-code level, but it’s not going to have any effect on productivity down the cascade. Autocomplete isn’t going to help you produce commits or releases. Given that, they estimate the parameter such that you get that attenuating effect, and finally we get the headline result: a 0.25 elasticity of substitution between upstream production and lines of code. How does that make you feel, Andrey? Andrey: One way to think about it is that it’s just a translation of the fact that lines of code don’t result in that many new releases. There are a lot of things the model is obviously missing. For example, you can reallocate your effort, and that’s something the estimation is not capturing. But look — what do I view this as? The meat of this paper is the empirical exercise, and this is a back-of-the-envelope exercise for the elasticity of substitution parameter. Given that, I’m okay with it. I’m not going to go to the bank and say it’s 0.25. So that’s not the part I worry about. I’m more worried that all these things under the code are fungible with each other. What is even a commit? A commit-to-PR ratio is not a constant thing. There’s not even a standard way — different coding teams have different ways of splitting up this code production process. I don’t know whether to think of this as a hierarchy or not. Maybe that’s the deeper criticism. And then, as you point out, quality is really a missing element. Whether that code is comments, whether that code is unit tests that are maybe not that important — that might be something being produced with autosuggest. That’s very different from the other type of code being produced that was essential functionality. Seth: Very well put, Andrey. Are we ready to move to our posteriors? Aggregates, App Stores, and Posteriors [56:00] Andrey: No — one other thing to point out. They do this back-of-the-envelope exercise, and the final thing they do is think about aggregate outcomes. Another naive way to study the effect of AI tools on coding is just to look at the total number of GitHub pull requests over time. If we thought coding was making people much more efficient, we’d expect that to spike. What they find is a trend increase around the start of 2025, and their data doesn’t go long past 2026, so we don’t really know what happens after that. But that increase is substantially less than the per-programmer increase they find in the event studies. In particular, the increase in pull requests you can read as maybe a 50% increase. What might be driving that discrepancy goes back to the experienced versus inexperienced developers. The developers producing the most code — their measured productivity isn’t increasing as much as that of casual, occasional users. And they’re also the ones generating the most code, so the overall effect is not as big as the event study estimates suggest. The other thing to point out is that to the extent code is being used to generate apps, we do see increases in the number of apps being produced on, say, the iOS store — but we don’t seem to see an increase in downloads. So it’s not obvious, and it’s similar to our story with books, that a lot of these marginally produced apps are actually resulting in something people are using. To the extent that productivity is ultimately tied to value, we don’t seem to be seeing it yet. That points to a broader observation: if you look around, you see a lot more code, but it’s not clear it’s resulted in amazingly better software for us yet. Or, another way to put it — and this is shown in the OpenAI paper on how people use ChatGPT — there’s a lot of substitution from buying things to home production, or even just producing things at home that you weren’t going to do before. You might ask ChatGPT for advice, or for tech support, and before you might have hired someone to do that, or alternatively not solved the problem in the first place. That’s probably where most of the AI value is coming from right now. Maybe the most impactful use of AI agents is in the use of producing Claude and Codex. Seth: All super well-taken points, and the little empirical exercise on the App Store releases is a nice cherry on top. In the framework of their model, the explanation for why we get much less output of new products than lines of code is a complementarity with a human at one of those production process layers. But as you suggest, there are a lot of different things that bottleneck might be, or lots of reasons why economic value being created is not showing up in that final measure. So — one more reason to take with a grain of salt the idea that this gives us strong evidence of complementarity between AI coding agents and humans. Andrey: So do you want to move on to the posteriors? Seth: [1:00:07] For those of you playing along at home, now is your chance to think about how this conversation has changed your priors. This chance to contemplate your posteriors is sponsored by Revelio Labs. Revelio Labs is a leading provider of labor economics data and data services for companies, academics, and independent researchers. Andrey and I have been working in economics of AI, digitization, and automation for a long time, and we can confirm just how useful Revelio’s data is. Revelio’s team combines comprehensive micro-level data on employee professional profiles, job postings, and employee sentiment with standardizations, mappings, and enrichments available, all to make that data useful without making your modeling decisions for you. The data can be flexibly aggregated to company, market, or industry, and can be used to study questions ranging from career trajectories to occupational transformation to the returns to skills and the impact of AI on labor demand for tasks. Can’t imagine anyone who would be interested in that. And Revelio data is available on WRDS. So if you’re an academic with a good library, go see if you have access to their premier data already. And if you don’t, you can reach out to their excellent economics team and they’ll hook you up. Okay, so Andrey, remind me — what were our posteriors? Andrey: [1:01:32] What were our priors? Mine was 80% on the effect on developer lines of code. I’ve updated to probably 95% at that point. Does AI use boost the true economic value of software developers by less than 50% of the boost to lines of code? I’ve become more confident, so moving up to 95% there. Is AI coding a gross complement or substitute? I still think it’s a complement. I think this paper mildly supports that it is, at least in the relevant sense I was talking about. What about you? Seth: [1:02:10] To this paper’s credit — and I had some sharp words about the model — I think this paper actually does move my priors considerably. That’s the best thing I can say about a paper. On the first question, AI boosting lines of code written by greater than 100%, that moves me from 95% confidence to 99% confidence. It’s great to see these giant values. For AI boosting economic output by less than 50% of the boost to lines of code, I’m going up from 95% to 97.5%. Again, this confirms my priors. And then the most subtle question. This paper really only moves my view about complementarity between AI coding agents and humans at one very particular layer of the production process. I said there was maybe a 20% chance of complementarity between ordinary coders and AI coding agents. That’s unchanged. For human engineering departments, that’s going up from 33% to 40%. For software companies, that’s where I move the most. I’d move from a 60% chance of complementarity at the software company or open source project level to 85 or 90%. That’s a big move for me. Big-time move in the priors. And finally, for the economy as a whole, I’m unchanged. Andrey: Thanks for joining us for another episode of Justified Posteriors. Always remember to keep your posteriors justified, and to like, comment, and subscribe to our podcast. Thank you. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • July 13 · 1 hr 16 min

    No AI Jobs Apocalypse (Yet) - and a Debt Problem (Now) | Martha Gimbel (Yale Budget Lab)

    This week we’re joined by Martha Gimbel, executive director and co-founder of The Budget Lab at Yale. Martha has worked just about everywhere economic policy gets made — the Joint Economic Committee, the Obama and Biden Councils of Economic Advisers, and Indeed’s Hiring Lab — and she’s now one of the clearest voices on what the data does (and doesn’t yet) say about AI and the labor market. We start in Washington: what politicians are actually asking about AI, the case for “no regrets” economic policy, and why the unemployment insurance system “is not prepared for someone to sneeze within 50 feet of it” — the tech, the financing, and Mississippi’s $200-a-week maximum benefit. Along the way: why almost no policy “pays for itself” (except funding the IRS), whether one hacker per state plus Claude Code can fix government IT, and the Anthropic finding that agentic coding rewards domain expertise, not coding skill. Then the big empirical question: is AI already taking our jobs? Martha walks through the Budget Lab’s labor-market tracking — no sign of broad macro disruption yet — and why the narrative says otherwise: 1.7 million layoffs in a normal month, CEOs with an incentive to blame AI, and Challenger data attributing seven times more layoffs to AI than to tariffs (”this is implausible”). From there we turn to her Senate testimony and Atlantic essay on the national debt: deficits since 2015 are already costing new mortgage holders about $2,500 a year, and “AI will grow us out of it” is a bet — one the Budget Lab has actually modeled. We close with token taxes, sovereign wealth funds (”the great thing about the government — we can tax it”), what CEA is really like from the inside, and a lightning round that ends in a Red Rising roast. Links & References Martha’s work * Martha Gimbel — The Budget Lab at Yale · budgetlab.yale.edu * Martha’s Atlantic essay on how deficits are raising costs for households. * Testimony before the Senate Finance Subcommittee on Fiscal Responsibility and Economic Growth — “The Fiscal Outlook: 2027–2036” hearing (March 11, 2026): debt held by the public ~99% of GDP in 2025 → 120% by 2036 → 175% by 2056 * Evaluating the Impact of AI on the Labor Market: Current State of Affairs — the Budget Lab’s occupational-mix tracking; no sign of broad AI disruption in the macro data yet * What Might AI Adoption Mean for the Fiscal and Economic Outlook? — the AI-and-the-debt scenarios built on the Karger et al. expert forecasts; the fiscal gains are not a free lunch once you add support for displaced workers * Abhi Gupta, The Impact of Deficits on Costs for Households * The Budget Lab Small Macro Model (BLSMM) — the open, interactive macro model discussed in the R-vs-G section * Long-term Impacts of the One Big Beautiful Bill Act — ~zero growth impact at 10 years, negative at 30 (crowding out) * Coming soon from Martha: a token-tax piece in Tax Notes, and Budget Lab work on AI, capital taxation, and sovereign-wealth-fund economics — stay tuned Concepts, papers & people discussed * Anthropic, “Agentic coding and persistent returns to expertise” — the Claude Code study Martha cites: domain expertise, not coding background, predicts success with AI agents * Challenger, Gray & Christmas layoff announcements — the data attributing ~7× more layoffs to AI than to tariffs in 2025 * Brynjolfsson, Chandar & Chen, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI” — covered previously on the podcast. * Olivier Blanchard, “Public Debt and Low Interest Rates” — the “if r is low, debt has no fiscal cost.” * Danny Yagan and Neil Mehrotra — the Budget Lab’s designated R-vs-G thinkers * The Windfall Trust — the AI economic scenario-planning (”war gaming”) exercises Andrey asks about * The ROAD to Housing Act — “Congress is doing something obviously good. Fingers crossed.” * Reinhart–Rogoff and the Excel error — Seth’s aside on debt-threshold doom predictions * The Council of Economic Advisers and the Joint Economic Committee — partisan vs. unbiased, and the puppy-distribution test * Trade-offs of the pandemic UI plus-up — the $600 flat add-on existed because state systems literally couldn’t compute 90% wage replacement Sci-fi corner * Becky Chambers, The Long Way to a Small, Angry Planet — Martha’s pick for the future we actually want (and a direct appeal: Becky, the people want to know what’s next) * Adrian Tchaikovsky, Children of Time — “so good”; the one with the spiders * Pierce Brown, Red Rising — “angry Harry Potter”; sorry, Basil Our sponsor * This episode is brought to you by Revelio Labs, providing data products useful for many questions about the economy. Chapters * (00:00) Intro & sponsor * (00:59) What politicians are actually asking about AI — and the case for “no regrets” policy * (02:30) D.C.’s misconceptions: politicians are still learning the tools * (03:22) Scenario planning, war gaming, and automatic stabilizers * (04:43) “The UI system is not prepared for someone to sneeze within 50 feet of it” * (05:54) Three reasons UI is broken: the tech, the financing, and Mississippi’s $200/week max * (08:53) Should we federalize it? The IRS, and why almost nothing “pays for itself” (the 7× rule) * (11:28) Why politicians can’t do the obviously good thing: concentrated pain, diffuse benefits * (12:46) Can one hacker per state + Claude Code fix government IT? DOGE, caves full of paper records, and returns to domain expertise * (16:58) Is AI already affecting the labor market? What the macro data shows (and doesn’t) * (19:05) Why the narrative says otherwise: youth outcomes, wrong numbers, and 1.7 million layoffs in a normal month * (22:22) Challenger data: 7× more layoffs attributed to AI than tariffs — “this is implausible” * (23:16) What is behind the entry-level slowdown? The low-hire, low-fire puzzle * (25:16) Will we know it when we see it? Weavers, export controls, and the Napoleonic Wars * (27:54) “I can’t travel to Earth 2”: the counterfactual problem, even at the Budget Lab * (28:43) The fiscal outlook: debt from ~100% to 170%+ of GDP — and why thresholds aren’t the point * (30:44) Fiscal crisis risk, sweet sweet T-bills, and the Blanchard low-rates argument * (32:05) Why didn’t we issue 100-year bonds at 2%? * (32:44) Crowding out: deficits since 2015 ≈ an extra mortgage payment every year * (33:55) Housing affordability, interest rates, and mortgage lock-in * (37:14) What deficit spending crowds out — and the diapers-bill standard for government spending * (40:25) Fiscal gap accounting vs. talking so D.C. actually understands * (44:26) “AI will grow us out of the debt”: R vs. G and the Budget Lab’s AI fiscal scenarios * (47:05) Who pays when the robots work? Payroll taxes, capital taxation, and AI finding every loophole * (48:33) Is capital more or less elastic in the AI age? Token taxes and the IRS * (52:16) A sovereign wealth fund for AI? “The government doesn’t have to own things to get money from things” * (55:41) Fairness and the nerd’s-nerd case for simplifying the tax code * (56:47) Inside the JEC and CEA: partisan vs. unbiased, and the puppy test * (1:00:34) What working at CEA feels like: second best, third best, fourth best * (1:02:06) The CEA junior staff and their “extremely benevolent and well-reasoned rule” * (1:03:34) Budget Lab vs. Penn Wharton vs. CBO: 30-year horizons and what you’re buying with paid family leave * (1:06:38) Private vs. public data: Indeed, benchmarking, and the shutdown’s three contradictory hiring estimates * (1:08:31) What data do we actually want from the AI labs? * (1:09:41) Lightning round: the biggest bottleneck to AI productivity gains (Hollywood vs. healthcare) * (1:10:53) Gun to your head: pre-distribution or redistribute-after? * (1:12:59) Favorite sci-fi: Becky Chambers and the future we want * (1:14:38) Worst sci-fi takes: Red Rising, Children of Time, and a message for Basil * (1:15:35) Sign-off Justified Posteriors is the podcast that updates its beliefs about the economics of AI and technology, hosted by Andrey Fradkin and Seth Benzell. If we changed your priors, subscribe, share it with a friend, and keep your posteriors justified. Transcript What Politicians Are Asking About AI [00:00 – 04:43] [00:00:04] Seth: Welcome to Justified Posteriors, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, building, with your support, a podcast community which is hopefully more fiscally sustainable than the federal government. Coming to you from the Pocono Mountains of eastern Pennsylvania. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. We are sponsored by the fine folks at Revelio Labs, providing data products useful for many questions about the economy. And we’re very excited to have Martha Gimbel with us today. Martha is the executive director and co-founder of the Yale Budget Lab, and has worked in an enormous variety of impactful roles related to economic policy. Martha, welcome. Martha: Thank you so much for having me. Andrey: To get started: we know that you talk with a lot of politicians and staffers. What questions are they asking you about AI? [00:01:10] Martha: Some of the questions that politicians, policymakers, and staffers are asking are the same ones everyone is asking, right? What is going to happen? How should we think about the economic impacts of this? What is plausible? What seems unlikely? Politicians — they’re just like us. They have the same questions everyone else does. I think the other thing is that people are really trying to figure out how much of the potential future requires something that is different than what we’ve done before. And I don’t just mean “we know that workforce training hasn’t worked particularly well in the past, so we should update how we do it” — the things that you would do no matter what — but a true paradigm shift in how we do policy. And I think that politicians are still sort of confused about that. They don’t know how much they should be thinking about a totally different way of doing things. I should say, I’m one of the people who’s been on record saying that this is a time to do “no regrets” economic policy. We don’t know what’s going to happen, and so now is a great time to do things that are a good idea no matter what. But you shouldn’t wager policy on a specific version of the world. Andrey: What are the most common misconceptions that politicians have about AI, in your experience? Martha: I think part of it is that a lot of people in DC are still learning how to use these tools — as, to be clear, are all of us. They’re still kind of at the baby stages there. And also, think about a typical politician’s life — and I’m not trying to say this is everyone, but think about what they’re doing. They’re talking to constituents, they’re getting briefings from staff, they’re attending a committee hearing. They actually aren’t spending that much time sitting in front of their computer trying to generate memos or do analysis — they have staff who are doing that. So some of it is just people trying to make sure that they have the time to sit down and play with these things and figure out how they work. [00:03:22] Andrey: Have you seen efforts by, let’s say, the Windfall Trust and other organizations to do scenario planning — essentially war gaming, but for AI and economic policy? Do you have an opinion on those sorts of exercises? Martha: In general, the scenario planning is helpful and is a good way of approaching this. It’s also one that DC is very familiar with. The great thing about scenario planning, if you’re trying to think about influencing economic policy in DC, is you’re speaking policymakers’ language. It’s also something people think about a lot in economic policy already: we talk a lot about automatic stabilizers — things like unemployment insurance that kick in when the economy is doing worse, and then trigger off in various ways, or you don’t file for them when the economy is doing better. Thinking about these types of policies that can stretch to fit different scenarios is a place that people in DC really understand, and it has been a subject of a lot of debate — how do we structure these, et cetera — over at least the last 10 years. The Safety Net Isn’t Ready for a Sneeze [04:43 – 08:53] [00:04:43] Andrey: How well do you think the current set of automatic stabilizers will do if we have a shock — let’s say a white-collar unemployment shock? Martha: One of the things that is frustrating to people like me is that you will sometimes see... I’m gonna call out someone’s tweet, but fortunately for them, I don’t remember their name, so they totally get to get away with it. Someone tweeted, “People don’t understand — the unemployment insurance system isn’t prepared for the tsunami of white-collar job loss that’s about to come at it.” And all of us who worked on unemployment insurance were like, “You don’t understand. The UI system is not prepared for someone to sneeze within 50 feet of it. What are you talking about? Wave of white-collar — it can’t handle anything.” So, you know, we can have a longer conversation about how well Silicon Valley and DC understand each other. The unemployment insurance system is not in good shape. Andrey: What are the top three reasons why it’s not in good shape? [00:05:59] Martha: Let’s do it. First of all is the tech. Just legitimately filing for benefits, getting them processed, all of those things, is really, really cumbersome. I should say, for some states, that is a feature, not a bug — it is a way to keep from paying out benefits. This is a thing I think a lot of people experienced during the pandemic. You might assume it has been fixed. It has not. And this flows through to UI policy: there are fixes you might want to make to the UI system, but the systems are so buggy that you can’t touch them. I had friends who were parachuting in to try to be helpful to the state UI systems during the pandemic, and if you’ll forgive a metaphor that doesn’t really work, they basically opened up the door to the closet where the code is kept, and then closed it slowly and said, “Okay — if no one breathes, it might not break down. No one look at the code, no one think about the code, and it will be fine.” There was a lot of debate during the pandemic about the plus-up to UI benefits that we did, where we just added a certain amount onto people’s benefits. That meant some people were making more on UI than they had been when they were working, and people were asking, “Is this a good policy or a bad policy? How do we think about incentives to work?” Guys — the reason we did that was that the systems literally couldn’t handle saying we’re going to do a return of 90% of your previous wages. It seems very simple, but they couldn’t handle it. Which is a very long-winded way of saying the first issue is the tech. [00:07:47] The second issue is the financing — how people are paying in for benefits. Trust funds and things like that are not always in the best shape. And third, I think people don’t realize how ungenerous a lot of these benefits are. It varies a lot by state, but in Mississippi, the maximum — not minimum, maximum — benefit that you can get is a little over $200 a week. That’s not a huge amount of money. Seth: It’s not paying the mortgage. Martha: It’s not gonna pay the mortgage. And particularly if you’re worried about higher-paid workers losing their jobs, it’s a huge problem. Which is all to say: no, I don’t think the social safety net is particularly well set up for broad technological disruption. I don’t think the social safety net is particularly well set up for a standard-issue recession. I don’t think the social safety net is well set up for a 4.3% unemployment rate, which we have right now. We have let these systems languish. Why Nothing Pays for Itself (Except Funding the IRS) [08:53 – 12:46] [00:08:53] Seth: Is the answer to move in the direction of federalization? Is this incompetent states and the government letting them run amok? Martha: We have federalized programs that also don’t work particularly well. Part of this is just: you have to spend the money for things to work. And who knows if we’ve allocated that in the right way, et cetera. As another example, there was a lot of money that was supposed to be going to the IRS for modernization and increased enforcement, and that’s just been eaten away and eaten away and eaten away. Money for the IRS is particularly frustrating to budget nerds. To get a little bit nerdy about the budget for a second— Andrey: Do it. Martha: Everyone always thinks that their preferred policy will, quote-unquote, “pay for itself.” You see these things: if we spend a dollar on... I don’t know, name a policy you like. Seth: Corporate tax reform. Martha: Well, corporate tax reform might pay for itself, but I mean on the spending side. Seth: Schools. [00:10:00] Martha: Yeah — “additional dollars for schools will pay for themselves.” And from a societal perspective, that is often true: you will have a more highly educated populace, it affects crime, all of these different things. But in order for a policy to pay for itself from the budget perspective, it has to induce economic activity that is basically seven times the size of what it costs — because we don’t tax the whole amount. Seth: Right, that’s just the share of GDP that’s taxed. Martha: Exactly. And so it is almost impossible to have a policy that pays for itself. Funding the IRS does. If you think about what funding the IRS does: one, it makes things easier — everyone’s always annoyed during tax time when they have to call the IRS and they’re on hold for hours and hours. It also allows them to go after more complicated cases that require more resources, but also where you get larger amounts on the back end. And just to state explicitly: when they go after these cases and get this money, this is money that we as taxpayers were owed. People owed us this money. It’s our money. Pay it to us. It’s not a new tax being levied. But even so, the IRS has been starved of resources. [00:11:28] Seth: Why is it impossible to get politicians to do the obviously good thing? Martha: That is the eternal question in DC. First of all, just to state explicitly: the obviously good thing can be very bad for a small group of people, and they’re going to be much louder about it than everyone else. The benefits of improved tax collection are diffuse — we all benefit from that, but I don’t see it. No one sends me a thing that says “the IRS went after this many tax scofflaws this year, and that means...” Seth: JD Vance is on it. Martha: But for the people they go after, that’s a real concentrated pain. They have to pay more in taxes, they didn’t wanna pay that, and so they’re gonna be much louder about it — which is always a problem. And yeah, we’re gonna have to think about state governance and capacity issues. They’re also just not very sexy. You go to your constituents and you’re like, “I’m really making sure that the code that operates our UI system has been updated — please return me to office...” No one cares. But it’s important. Can AI Fix Government? DOGE and Domain Expertise [12:46 – 16:58] [00:12:46] Seth: Martha, some people have told me that AI makes coding way easier. Is there some sense in which AI will just solve this problem? Because we’ll just get one hacker in each state, plus, you know, Claude Code Opus, and we’ll knock one of these out in an afternoon? Martha: To be clear, I am hopeful that AI will make some of these issues easier. I also think, as we all know, any kind of AI implementation is never that easy. You also have to think — with government, so much of this stuff has been neglected for so long. There were all those stories last year about a bunch of government retirement records which are still on paper in a cave, I think in West Virginia. This is a broader issue that comes up: people say, “Oh, we can just use AI to solve this,” or some other thing that seems easy, and it’s like — well, wait a minute. Let’s get in there. You have to actually understand the environment in which you’re operating. You have to understand the existing issues. For instance, if you’re the IRS, there are huge privacy concerns — you need to make sure that you’ve really locked up all the data. I can feel people rolling their eyes at me, but it’s a real concern. [00:14:04] And frankly, you saw this with DOGE. There were people in DC who, frankly, were kind of excited about DOGE — “a bunch of people who understand technology and have a mandate to operate, and we might actually fix these things!” ...and we’ve closed down USAID. Okay. It’s not that people in DC are hostile to doing this. It’s just that it’s complicated, and you have to cut through a lot of red tape and barriers and a lot of years of doing things like putting records in a cave in... I apologize if the cave is not actually in West Virginia. I cannot remember where it is. But it’s somewhere. Seth: I was at a conference where I actually talked to some DOGE-ers over this past weekend — I don’t know what they call themselves. Something like this is never values-free, because you can palpably see how excited they are to fire people, right? And so, yes, obviously if we can have AI do a lot of the work of the government, we’re gonna need fewer federal employees. But it doesn’t come across as credible. [00:15:14] Martha: Or alternatively, you free up a bunch of people at the IRS to actually help people with their tax returns. This gets at the whole debate about AI and labor markets and what’s going to happen: if you’re freeing up certain capacity, do people lose their jobs, or do they switch into doing other types of things that are higher value? I also think — Anthropic actually just put out a piece on this last night, or this morning, I’m not sure — about what they’ve seen with the use of Claude Code: there’s this increasing return to domain expertise. If you just send people in to work on something who understand the tech but don’t understand the broader context in which they’re operating, it’s not nearly as effective. So I do think you really need to make sure that you’re having these conversations with people. Not to keep picking on DOGE, but just as an example — I remember there was this story that they found all this real estate that wasn’t being used, and so they’re like, “We’re gonna sell it and put it up for auction, and isn’t this great?” Seth: It’s free real estate. Martha: It’s free real estate! We should do something with it! And look — there are all of these random sites in Northern Virginia that no one says what they’re for, and everyone’s like, “Take the list off the internet. Take it off the internet. If you don’t know what something is in Northern Virginia, go into a SCIF — a classified area — and ask someone. Don’t put it on the internet.” The list came down in five minutes. But it had already been put up. Is AI Already Affecting the Labor Market? [16:58 – 23:16] [00:16:58] Andrey: This is a natural transition point. Your lab has been one of the key groups documenting what’s going on in the labor market in real time. So — the big question that everyone’s asking: is AI already affecting the labor market, and if so, how? Is it gonna take our jobs? Or has it already taken our jobs and we don’t know yet? Martha: I would say: I don’t know if it’s going to take your job. There is currently no sign that AI is causing broad disruption in the labor market at the macro level. This does not mean that no one has lost their job to AI yet. This does not mean that no one’s job has changed due to AI — I wanna give all these caveats here. What it means is: if you’re looking at the macro data about the economy, it doesn’t seem to be the case that AI is causing broad disruption at this time. I do want to say, I think this take sometimes gets me labeled as an AI Luddite or an AI skeptic— Seth: God forbid. [00:18:11] Martha: —which I think says something about where this conversation has been. This technology is still very, very new. The ChatGPT that was released in November 2022 is not the same as the tools that we have today. And it is interesting to me that people assume that after only a couple of years, we would have this kind of broad disruption that’s easily measurable. These things just take time. Have you ever met a compliance department? They have questions. So I think it’s really important to be clear about what we’re seeing and not seeing now, while also not dismissing the potential for disruption in the future. Andrey: I totally agree, by the way. I think in the media, the way this gets reported is oftentimes frustrating. They’ll take companies at their word, at face value, when they say they’re firing due to AI. There are all sorts of prominent people already saying that the youth can no longer find jobs, when, as far as I can tell, that’s not at all what’s going on. Why do you think this is happening, and is there something to be done about it? [00:19:40] Martha: First of all, I think there are just things that people get in their heads that feel right to them. In 2022, my friends kept asking me when the recession was going to be over, and I kept saying, “We’re not in a recession.” And they were like, “What are you talking about? We’re definitely in a recession.” I don’t know what to tell you — GDP growth is positive. So there’s the question of how people feel versus what is happening. On the youth, and on the headlines: on the youth side, it is legitimately the case that you have seen some deterioration in outcomes for younger workers. This seems to be coming because overall we’ve had a slowdown in hiring, which, again, does not seem to be related to AI — but it’s not something that labor economists understand particularly well. And that’s just frustrating to people, understandably. I graduated in 2009. My friends and I had a really hard time finding jobs, but we knew why. It was very clear. Whereas now it’s like, “Oh man, things aren’t looking so great. No idea. Good luck out there — have a good time!” So people are searching for an explanation. I also think people have wrong numbers in their heads. I’ve heard senators talking about 20, 30% youth unemployment — which, to be clear, we do not have. On the CEO announcements: part of this is just that people don’t have context in their heads. You’ll see these things like “X company lays off 10,000 people due to AI.” Well, first of all, we have a relatively low-layoff environment right now, and we still have 1.7 million layoffs every month. So 10,000 is just not that many. To be clear — if you are one of those people who’s gotten laid off, it is terrible. It is really hard for you, it is really hard for your family. I’m not dismissing that. But from an economy-wide perspective, it’s just not that big. [00:21:40] There are also huge selection effects in what gets covered, and in who makes announcements. The hardware store down the street, if it starts hiring a bunch of people, doesn’t make an announcement about it. And if it fires a bunch of people, it doesn’t make an announcement either. But these big companies do, and so that gets a lot of coverage — but they’re not the totality of the US labor market. And again, there’s also just an incentive to ascribe any layoffs to AI. It gets covered weirdly kind of positively for your company — you’re forward-looking, you’re doing all the things. Seth: I mean, not weirdly. You’re lowering costs, right? It should be positive. Martha: Well, but also people say, “Oh, they’re a forward-looking company making investments in tech for the future.” There’s this data from this company called Challenger — which has some pros and cons — but what they do is gather layoff announcements, and they attribute the layoff announcements to a source. And between April and November of 2025, about seven times as many layoffs were due to AI, according to Challenger, than tariffs. This is implausible. It just is. But if you think about it — if you’re a CEO, which one is more fun to put in your announcement? There are huge incentive effects here, and I think people aren’t thinking those through, and they aren’t thinking through the scale, and they aren’t thinking through the selection effects in both who makes layoff announcements and whose layoff announcements get reported. The Entry-Level Puzzle and the Limits of Attribution [23:16 – 28:43] [00:23:16] Seth: But thinking about that attribution challenge — if you had to come up with a story for why there seems to be a differential job market for entry-level workers versus more advanced workers, would you point to work from home? Would you point to increased interest rates, and some sense in which the worker has to pay off sooner? What else would you point at? Martha: The honest answer is I don’t know. There have been people pointing to work from home — there was a new paper out on that. Interest rates in general have probably slowed down hiring, which is going to affect young people more than older people, because older people tend to have jobs and young people tend to need their first job — so hiring is more important for them. But this kind of gets at the point: a bunch of economists have been debating why we’ve been in this low-hire, low-fire labor market for a couple of years now, and we’re just not sure. There’s been this question of — the unemployment rate’s been about 4.3% for a while. Are we at equilibrium? Is this a labor market that’s at that magical soft-landing, just-chugging-along point? In that case, you’re just not gonna have that much hiring — and that’s going to be bad for young people. That’s one theory. We can’t prove anything. I think part of the reason why the “AI is holding back hiring for young people” theory has really taken off is because we don’t have an alternative. We don’t have an “oh, it’s actually this thing.” We don’t know, and so of course people are going with an explanation. I should also say: it is not insane to think that AI will affect young people first. That’s a very plausible outcome — it’s what we’ve seen in past technological change. It’s not some totally cuckoo theory that people are working with here. It’s just that it doesn’t really seem to be showing up clearly in the data. [00:25:16] Andrey: There’s a paper that we’ve covered on the podcast, “Canaries in the Coal Mine,” and I don’t think we need to rehash that. But my sense has been that if it’s really starting to affect the labor market, we’ll know — we’ll see large shifts in overall employment rates. Is that kind of your sense of the AI impact on the labor market? Martha: I am just constantly the person who’s like, “It’s hard, and who knows what we’ll know, and who knows where this is going?” This is why no one likes hanging out with economists. But if you think about the Industrial Revolution, and you think about the weavers who lost their jobs — that was really clearly due to technology, right? They didn’t need a Bureau of Labor Statistics to tell them they were losing their jobs to technological change. However, what I just said is actually not quite correct. One of the things going on at the time was the French Revolution and the Napoleonic Wars, and England actually put export controls on the textile industry. They couldn’t export. Seth: The Continental System. Martha: It’s a problem! So they sent the industry into recession, and then you had technological change on top of that. How much of the job loss that happened very quickly was about the technology? How much was about the export controls? How much was about some third factor that I don’t know about, because this isn’t my area of expertise? I don’t know. All of this gets very, very complicated — all of these factors going in and out, compounding and building on each other. The economy doesn’t happen in a vacuum. I think part of the problem here is that everyone wants to know right now where this is going, who’s going to be affected, and how and when. And so much of this is probably only going to be clear in retrospect. Which is not a fun answer. [00:27:18] Andrey: And it might not even be clear then, right? There was this recent Wall Street Journal article that I think you were a part of, where people were asked what the net effect of AI on jobs is gonna be. I know I just said we’ll know — I think we’ll know in very specific cases, if certain occupations truly are automated. But I think we won’t know the general counterfactual. It’s not like we’re gonna be there five years from now and know the exact trajectory of the economy without AI. I don’t know how we’d ever get at that. Martha: People ask me all the time, “Has hiring at Budget Lab been impacted because of AI? Are you hiring fewer research assistants?” And I say no — how people do their jobs has changed, the tasks that people do have changed, but the number of people that we’re hiring has not changed, as far as I can tell. But you shouldn’t ask me that question! I don’t know! I can only see the version of Budget Lab where AI happened. I can’t travel to Earth 2, which has no AI, and see how many research assistants we hired. I have no idea. We want this kind of certainty from people that is just really implausible. The National Debt: Why the Trajectory Matters [28:43 – 33:55] [00:28:43] Andrey: Okay, this is a good time to move on to our next topic. Seth, you wanna take it away? Seth: I’m super excited to dig into this next topic, ‘cause you’ve been speaking a lot about it recently — you had a recent Atlantic article, and in March you spoke to the US Senate about the fiscal outlook over the 2027-to-2036 time horizon. You seem to be concerned about the national debt. Can you tell us a little bit about our current trajectory there? Martha: Everyone always worries about debt and deficits when people are talking about spending — by which I include both literal spending and also tax cuts — on things they like. “Deficits are fine if it’s on the thing I like.” I often have this experience where I’ll talk to people about deficit spending and how it can increase interest rates, and I’ll give the example of a specific policy, and someone will go, “Well, wait a minute — but that’s a good policy. We should do that.” Sure. But if we think we should do it, we should pay for it. [00:29:46] Seth: So the number that you put in your recent Atlantic article — and perhaps this was for your Senate testimony — was that the debt-to-GDP ratio in 2025 was almost 100% in terms of debt held by the public. By 2056 it is projected to reach over 170%. Why is that bad, Martha? Debt’s good. Martha: So I actually think this is a really important thing. One, I actually don’t think that specific percentages are necessarily bad. Seth: Ooh, you get snaps for that. Martha: There has been a problem in the debt-hawk conversations in DC where people would go, “If we get to this level, it’s all gonna fall apart.” And then we hit that, and it’s still okay. Seth: It turns out that was an Excel spreadsheet error. Reinhart and Rogoff. [00:30:44] Martha: Yes. But you still have this thing where people said, “It would be really bad if we got to 100% debt to GDP” — and the sky has not yet fallen. I think there are two reasons to worry about this. One is that you do risk a fiscal crisis, where markets all of a sudden go, “Oh no, no, no — we don’t like it.” Those aren’t predictable. They could happen at any time, and they’re very, very expensive if they happen. You’re not gonna like what we have to do if we run into a fiscal crisis. The US is weird for all sorts of reasons — we are not even like the United Kingdom — and I think we’ve been really relying on that for the fact that we’re not gonna have a fiscal crisis. Markets don’t have a good place to go that’s not the United States. Seth: There’s an infinite demand for T-bills. I mean, if they want the T-bills so much... Martha: Yeah, those sweet, sweet T-bills. Seth: Actually, the sophisticated version of that argument is the Olivier Blanchard argument from a bunch of years ago — the idea that if safe interest rates are super low, there’s some sense in which there is no fiscal burden to the debt. People just really, really are looking for safe investments. Give it to the market — you’re not actually hurting anybody. Martha: Sure. And then now we have higher interest rates, and so everything is much more costly. Right? You make a bet. [00:32:05] Seth: Why weren’t we issuing 100-year bonds 20 years ago — or whatever, 15 years ago, when we were at zero? Why did that never happen? Martha: I am not a treasury person, so I should really not be talking about this. I’m going to anyway, and probably gonna get this wrong. My understanding was, one, it ended up being much more complicated than people thought it was going to be — you have to figure out market demand and all of these things. This is another example of: you have to talk to people with actual domain expertise. We can all sit here and say, “We should’ve been issuing these really long-term bonds,” and it’s actually much more complicated. Seth: We were borrowing at negative real rates. It seems like a good idea. Okay. All right. Killjoy. Martha: Sorry! Sorry. You should talk to the treasury markets room about how complicated these things get. [00:32:44] I think one of the things that we’ve been trying to really emphasize at Budget Lab is there is this evidence that as debt-to-GDP goes up, it puts upward pressure on interest rates, because of what economists call crowding out. There are so many people who wanna buy so much debt; the Treasury Department issues more debt; all of us poor little people who wanna take out mortgages now have to compete against the great big US government. Some research by Abhi Gupta here at Budget Lab says that if you’re taking out a new mortgage today, Congress’s fiscal actions since 2015 have raised your average mortgage payment by about $2,500 a year. On an average mortgage, that means you’re basically making an extra payment each year. So congratulations to everyone taking out a new mortgage. And I think this gets to the point: there are costs to this. You don’t have to wait for a fiscal crisis for things to be a problem. There are still costs. Mortgages, Housing, and Crowding Out [33:55 – 39:56] [00:33:55] Seth: I wanted to pick you up on this emphasis, because in both your Atlantic piece and your Senate testimony, you really emphasized the cost of borrowing for homeowners. One takeaway I had was: this means that instead of talking about the fact that our national debt could fill all 32 NFL stadiums with two tiers of construction pallets filled with hundred-dollar bills — I love those — or that we’re getting hit with a meteorite the size of Texas made of hundred-dollar bills, we should be talking about how deficit spending is making it harder to pay our own bills. So help me understand something: is this actually the substantive argument, or is this a rhetorical move? Because when I think about it, on net, Americans are creditors. And if on net we’re creditors and have assets, don’t we on net want high interest rates — or at least the old people who actually vote? Martha: The old people may be fine with higher interest rates. But we are still talking about a situation where there’s a huge emphasis, for instance, on housing affordability. One of the things that is hard about housing affordability is that one of the best tools we have for it is to increase housing supply. Seth: We’re passing the Road to Housing Act as we speak, right? Martha: I believe we are, yes. Has it passed? Seth: Congress is doing something obviously good. Fingers crossed. [00:35:18] Martha: But increasing housing supply takes time. This is not to say that we shouldn’t do it. But it’s not the case that we pass this bill, or a locality improves zoning, and housing affordability immediately improves. It takes time to build a house. They are building a house on my street right now. They have been building it forever. But interest rates are a lever we can pull to improve housing affordability. Seth: How strong a lever is it? Doesn’t it just bid up the housing prices? Andrey: Yeah, I don’t think it matters at all — I think it gets passed through. Martha: You don’t think it matters at all? I mean, yeah, that’s fair — there’s supply and demand. But I do think it makes a difference. People don’t like high interest rates. Seth: That’s not obvious to me. I think the old voters might like high interest rates. [00:36:23] Martha: No, no, no. I think this is very, very clear: if you look at studies of consumer sentiment and things like that, people do not like higher interest rates. Fair point on “you bring down the interest rates and then housing prices will go up,” et cetera. But— Andrey: Redistribution-wise, I think it really matters. Depending on whether people have flexible mortgages or not, and when they locked them in, it affects different people in very different ways. Seth: And specifically generationally, as we’re pointing out. Martha: Yes. And also, it can unlock housing supply, because you do have this issue right now— Seth: There’s a lock-in issue. If you have a 2% mortgage rate... Martha: ...why in God’s name would you move? [00:37:14] Seth: If you were to ask me why high debt is bad in general equilibrium, I would have definitely talked about risking a fiscal crisis. I would have definitely talked about transferring from young debtors to old savers. But isn’t the number one crowding-out concern that we’re not getting the physical capital investment that we want? Isn’t that kind of the general-equilibrium story — wages are lower because interest rates are high? Martha: Yes. And in general — if you’re thinking about the types of investment that will drive economic growth moving forward, I don’t know that issuing more treasuries so that we can pay more Social Security benefits to older people would be high on most economists’ lists. Seth: It’s the boomers. It’s the boomers again. Martha: It’s always the boomers. To be clear — we pay Social Security benefits for a reason. We don’t want old people to live in poverty. I should say elderly people. I’m gonna get in trouble with my mother; I just called her old. [00:38:13] But again, there are reasons why we do government spending, which I think is really important. Sometimes when we have these conversations about government spending, there’s too much of a focus on “is this going to increase GDP growth by 20 basis points” or whatever. Sometimes government just does things because we live in a society and we should do them. There was a bill someone introduced in Congress, as an example — and I’m not saying I support the bill or don’t support the bill; I actually know very little about the bill — that would give money to new parents. And there was a criticism of the bill that said this amount of money is not going to increase fertility. And my frustration with that was: no one who introduced the bill thought it was going to increase fertility. They just think diapers are expensive and the government should help people pay for diapers. Now, you can agree with that or not — is that a good use of government funds? That’s a question. But the standard for government spending cannot always be “is this a magical thing that will solve all of our economic problems?” At the same time, you can’t just dismiss the cost of the program. You have to think: this program pays for diapers, this program pays for roads, this program pays for missiles — how do we value those different things? And the solution is not to say “we’re just gonna deficit-spend ad nauseam,” because the deficit spending has costs. Talking Fiscal Policy So D.C. Understands [39:56 – 44:26] [00:39:56] Seth: Here’s another question for you, Martha. When we started this conversation, you said, “I’m not particularly concerned about the debt-to-GDP number — I’m interested in this continuum of risk of more crowding out, of important borrowing.” Martha: I shouldn’t say that I’m not concerned about debt-to-GDP. I just mean it’s not that I think we’re gonna hit 115% of debt-to-GDP and it’s all over for all of us. Seth: Can I ask a follow-up question then? There’s this popular other way of thinking about fiscal sustainability, which is fiscal gap accounting — taking the present discounted value of all revenues and the present discounted value of all expenditures. Would you favor us thinking about debt through that lens rather than the accounting number? There’s a correct answer. [00:40:51] Martha: I think we can argue about different ways of thinking about this, but this actually gets at an overall point about economists in DC that I wanna make. We can get ourselves into these more complicated concepts — other people would say we should be thinking about the risk that R is greater than G, and how should we be plotting that, all the things— Seth: That’s the next question. Martha: There we go. I think sometimes we overcomplicate things, and I think that that is a problem. We are trying to bring these very complicated concepts to people, and I think it is useful to pull back and think about illustrative numbers, or back-of-the-envelope calculations, that help people understand what’s going on — without, and I’m not trying to pick on you here, retreating into our ivory towers and having these very interesting debates about how we think about R versus G. I’m not saying those debates aren’t important or that we shouldn’t be having them. But if we’re thinking about trying to drive these conversations in DC, it is really important to be talking about things in a way that people understand and that really speaks to them. Because otherwise we’re not going to have an impact. Seth: So the correct answer was that fiscal gap accounting is the correct way to think about fiscal sustainability — but unfortunately, maybe the politicos aren’t caught up to that yet. [00:42:45] Martha: If you wanna write a memo and send it to the Hill, you can do that. But I actually do think this is important. I used to be at the Council of Economic Advisers, and one of the things that we worked with senior economists on really closely was: you can overcomplicate this, and you need to be able to talk to people about this in a way that helps them really understand what is going on — what is the thing they have to worry about and what they don’t have to worry about. Someone was asking about our interest rate work — a totally reasonable question — what about the impacts of government spending on real disposable income? You’re talking about the impacts on mortgage costs, but there’s the other half of this: how does it affect people’s income? And there’s two answers there. One answer is, I would need to know what the actual policy is and run it through a full macro model, and that’s very complicated. The other answer is that we are trying to give people an illustrative calculation that helps them get their heads around something. I just think that that is a really important service to provide to policymakers. Seth: To continue retreating into the ivory tower — and moving into R and G... Martha: I just wanna state: if you wanna go full R-and-G, you actually need Danny Yagan from the Budget Lab, or Neil Mehrotra, who have done a lot of thinking about this. But yes, let’s do R and G. Seth: We’ll ease into it. Martha: Should we define R and G, or is that not necessary for this group? Seth: Well, I’m gonna build up to it. Can AI Grow Us Out of the Debt? [44:26 – 48:33] [00:44:26] Seth: So I’ll tell you how we get there. The way I wanna get there is: Director Gimbel — or Martha, whatever you prefer — some people have suggested that actually it is foolish to worry about the debt-to-GDP ratio, or even the fiscal gap, because AI will boost G — will boost the growth rate — so much that we will simply grow out of the debt. So what do you say to those who call you fiscal hot-bed-wetters? Martha: One: that’s a bet, and that’s a big bet. And if you wanna make that bet, you can make that bet — and to be clear, there are a lot of people who are making that bet right now. I think there are a couple of other things that go into this. One is, you’d likely also have some impact on interest rates, and we can debate how big that is— Seth: There we go. This is where we go R and G. So, counter: sure, the growth rate will go up. But also, if we think that robots will bid up interest rates — people are gonna wanna invest in these robots and not invest in T-bills — that’ll make borrowing more expensive. So actually now we’re in a race between how much it boosts interest rates versus boosting growth rates. Do you have a stance on that? Does the Yale Budget Lab have a stance on that? [00:45:50] Martha: We don’t. Or rather, I should say: we have a new macro model that people can play with online that has some feedback — not one-for-one — from growth into interest rates. But I think there’s another aspect of this, which is something people are starting to talk about more and more: yes, there’s G, yes, there’s R, but there’s also how much spending you have to do, and how much revenue you’re getting. We put something out about the fiscal impacts of AI using the Karger et al. forecasts — which is not a statement that those are correct; we were using those as inputs. And one of the things we point out is: yes, under their forecasts, debt-to-GDP improves — in some cases improves dramatically. But then you can start wearing away at that, depending on what you assume around government spending and government taxation. Seth: Are you thinking about social support, or are you thinking about arms race? Martha: I’m thinking about social support. They have forecasts of what’s gonna happen to the labor force. You can assume that we spend on displaced workers the way we spend on usual unemployed people — which is not very much money, by the way, per our previous conversation. Or you can assume that we spend on them more similar to how we spend on Social Security, which then has further deterioration. There are then questions around capital share and inequality, which impacts what taxation looks like. I will say, spoiler alert, that is something my team is thinking about right now. And it’s not just these questions around capital versus labor. It’s also questions like: how much of the payroll tax are you still paying? We have these gaps in our taxation system. I should also say: we tax capital less than we tax labor in this country, and we also have many loopholes for capital taxation. And if you think that AI can’t find every loophole in capital taxation possible, I have a bridge I’d like to sell you. I will sell you the IRS cafeteria where they have been keeping all of the paper records. Taxing Capital in the AI Age: Token Taxes and Loopholes [48:33 – 52:16] [00:48:33] Seth: Maybe let’s talk about tax policy for a minute there. I’m very curious to hear what your answer is. We know the traditional reason why we tax capital at a lower rate than labor is the idea that labor is inelastically supplied, but capital is very elastically supplied. Is AI gonna change that? If anything, it seems like capital would be even more elastic in that age. Martha: I’m so glad you asked this question. I am so interested in this question. We obviously have no idea, but I think it’s fascinating. Seth: It’s the country of geniuses on a cloud. You can put it anywhere you want. Martha: Well, but also, it’s plausible that it becomes less elastic — if the returns are so much higher there. I don’t know; I can argue this either way. And that’s kind of what I feel like with these questions around elasticity and capital taxation: you can come up with whatever theory you want, and we’ll see what happens. I don’t think it’s clear. I do think it’s clear that we should deal with the loophole situation. Seth: So, Martha, is consumption taxation the answer? Is a Georgist land value tax — as it’s always the answer — the answer? Can we get Rand Paul to finally support a VAT? [00:49:55] Martha: If you could get Rand Paul to support a VAT, I think everyone in DC would be lining up to hire you as a lobbyist, because that is a level of persuasion I don’t think people realized was possible. Andrey: We need the AIs to do that persuasion. Seth: The super-persuasive AIs. Martha: That’s right. You know, this kind of goes back to where we started, actually. One of the things that I worry about is that some of the conversations around taxation and AI are — people are trying to come up with a whole new different way of doing things, and they’re thinking about it theoretically, as opposed to working with the real-world constraints that we have. So there’s been a lot of discussion about a token tax, and I won’t get into the pros and cons of a token tax from an economic perspective— Andrey: I will. A token is a— Seth: You know this is a podcast for that, Martha. Martha: I have a piece coming out on this in Tax Notes in like two weeks! Don’t spoil me. Seth: We’re gonna scoop you. Andrey: Okay, okay — I’ll let you have it. No, I just think that, I mean, obviously the details matter, but tokens are a meaningless unit. So I think it’s just crazy to think you’re gonna have a per-token tax. [00:51:25] Martha: Yeah. There are so, so many things to say here. One of the things I will say is: you get the IRS to figure out how to implement a token tax. What are you talking about? And again, that’s not a shot at the IRS — they have a lot on their plate. Let’s really not try to overcomplicate this. I worry a lot that people are trying to come up with a new way of addressing problems. If the issue is that people are concerned about increasing returns to capital and us not taxing those properly — if that’s the thing you’re concerned about, we should figure out how to properly tax returns to capital. Seth: Rather than invent some micro thing to target the tax at. Martha: Yeah. A Sovereign Wealth Fund? “We Can Tax It” [52:16 – 56:47] [00:52:16] Seth: Let me ask you about one last pie-in-the-sky economic reform policy before we move on to our last topic. Martha: I swear to God, if this is gonna be UBI... Seth: It’s UBI-adjacent. This one, I’m gonna say, is actually in the Overton window, because perhaps you know that our president recently talked about having a stake in the big frontier labs. And when you talk to people about transformative AI, a lot of the discussion is around: well, we should have a national sovereign wealth fund, so the people can own the robots. The people will own the AI, and then we can share the wealth. Is that realistic at all, given the national debt? Or is there some universe where actually we should sell T-bills and then buy Anthropic stock as a government? [00:53:08] Martha: I realize that I keep saying “oh man, the Budget Lab is working on a paper around this” — but this is one of the things that we’ve been trying to think through: what are the economics around this? So stay tuned. I will say, in general, I get itchy about government owning parts of corporations. Seth: I mean, it’s free money, Martha. You just borrow at 2% and invest at 5%. What could go wrong? Martha: I do love free money. Whomst among us doesn’t love free money? I think this actually gets at a broader thing, which is: this conversation, by definition, basically assumes that the benefits of this will be fully captured by — name your preferred AI lab here. What that policy doesn’t do: if some other organization is able to use AI to massively increase their profits, and fire all of their workers, and they don’t hire any other workers and it’s just pure capital returns — we don’t get any revenue from that. They’re still operating under the old system. So this goes back to this thing of: you’re assuming that the profits from this technology are going to end up in a very specific place. But we don’t know that. Andrey: The steel man of this could be just that this is a hedge — not that, for sure, the big labs are gonna take all the rents. [00:55:04] Martha: Sure. Or we could think about improving capital taxation, which is a good thing to do. We should think about improving how we tax capital in this country no matter what. Seth: You know, the government doesn’t have to own things in order to get money from things. Martha: Truly. This is the great thing about the government: we can tax it. ...Someone’s gonna pull that out — “this is the great thing about the government, we can tax it” — and put my name and a really scary picture of me on it. Seth: That’s the TikTok clip. Exactly. Martha: “This woman is coming for your...” But, you know. By the way, one thing I will also say about this — and this is not really econ, but I’m gonna go with it anyway. One question that’s come up repeatedly in all of this, for a lot of people, is this question around fairness. I actually do think that one of the issues we have right now is that people feel like the system is not fair. And this comes up on the taxation side partly because there are so many loopholes and ways to avoid paying taxes on capital gains. One of the things that you get by simplifying the tax code, and making it much clearer who pays what for what reason, is that people go, “Oh, okay. That is this person’s contribution. This is my contribution. We all live in a society. We go from there.” In a situation where people feel like things are unfair, decreasing the complexity in the tax code actually can make a difference. I realize this is a nerd’s nerd’s take, but I actually do think it’s important. Seth: This is the midwit meme. It’s like: just do proportional taxation... “no, we need crazy developed policy”... just do proportional taxation. Inside the CEA and JEC: Partisan vs. Unbiased [56:47 – 63:28] [00:56:47] Seth: All right. The last topic I wanted to ask you about, Martha: you’ve worked in so many fascinating positions inside and outside the government, adjacent to policymaking — from the Joint Economic Committee, to the Obama CEA, the Biden CEA, and now in this think-tank role running the Yale Budget Lab. Can you talk to us a little bit about, when we as outsiders read a report from something like the JEC versus the CEA, how much is that a real pure-economics document versus a political document? How should we read it as outsiders? Martha: One thing I will say is, JEC in particular often has leadership changes — it can switch with every Congress, depending on who’s in control of the Senate or the House. CEA obviously switches over every four or eight years. So part of this is just gonna depend on who’s in charge. I’m gonna get up on my high horse a little bit on this one. There was something that you saw people saying for a couple of years about the Council of Economic Advisers — that it was a nonpartisan place to work. CEA is not nonpartisan. You work for the President of the United States. You are within the Executive Office of the President. You are not nonpartisan. What CEA’s job is, is to be unbiased. You are the person whose job it is to sit in the room when they say, “We wanna give every American a puppy,” and you say, “That’s a very interesting policy. How will we distribute the puppies? How do we think about different preferences for puppies? What about allergies? How do we deal with the refuse problem?” Your job is to provide the unbiased analysis. And one of the things I think you see when CEA is working really well is that people in DC can kind of tell. You’ll notice that they’ll send the CEA chair out to talk about certain topics, and then on other topics they just never get sent out to talk to the news media. That’s because you wanna send the economists out to talk about things where the economists think it’s a good idea — their credibility is really important. Different administrations are gonna approach this differently, but I think historically that’s the way CEA has worked, and that’s really important. [00:59:40] Andrey: It also matters whether you have a charismatic person — I mean, Goolsbee was out there all the time in a way that I think others weren’t necessarily. Martha: That’s also a personality thing. There are people who enjoy talking to the press and people who don’t. There are people who enjoy being public figures and people who don’t. There are people who are really good at it and people who are less good at it. But I do think it is important that you are only sending the CEA economists out on things that they legitimately feel they can speak to from the economic literature and the underlying economic reasoning. Because otherwise, what are we even doing here? Andrey: I’m curious, on a personal level, working in these roles — what does it feel like? Do you feel like you’re making a difference? How does it compare to other types of jobs? [01:00:46] Martha: I am biased here. I worked at CEA twice — under Jason Furman and under Cecilia Rouse. It is hard to get better bosses, so I had a great experience. Most of my closest friends are from when I was at CEA, each time, so that kind of speaks to what my experience was like. I also think that working there can be hard, particularly for economists — and this gets at some of the conversations I was having with Seth earlier. A lot of the time, economists come in and say, “We have the one right way to do things.” And then comms freaks out: “You can never say that publicly.” And then the White House Counsel’s Office freaks out: “That is so illegal — please never tell anyone you even thought about that.” And then legislative affairs freaks out: “That will never get passed. Please don’t do that.” So you have to think really carefully: okay, this may be the optimal economic option, but what’s second best? Maybe what’s third best? If we get to the fourth-best thing, is that actually worse than doing nothing? You do have to operate under these real-world constraints. Some people find that incredibly frustrating, and some people find it incredibly interesting. I find it incredibly interesting. Andrey: Very cool. Is there something you can talk about that you’re particularly proud of from your work at the CEA? [01:02:14] Martha: Oh, man. I try not to do that. Mostly because your job is to be an advisor behind the scenes, and not to be someone who’s out there saying “I did this thing.” You’re working for a president. You’re working for a policymaker — they are the person. The actual answer to your question, not to get overly soppy: particularly when I was at CEA the second time, I was in charge of the junior staff. And the CEA junior staff are kick-ass. I will say to all of the economists out there thinking about whether or not AI is going to take your job: AI may or may not take your job, but the former CEA junior staff are definitely taking your job. And they’re gonna be so much better at it than you are, and it will be fine. We just had such amazing people there, and it’s been really amazing to watch them do all the things they’re doing after leaving CEA. I look forward to their extremely benevolent and well-reasoned rule. I think it’s gonna be great. Budget Lab vs. the Other Modeling Shops [63:28 – 66:38] [01:03:34] Seth: My last question is about these different forecasting and policymaking groups. Do you see differences in high-level philosophy between how you at the Yale Budget Lab, or maybe the Penn Wharton Budget Model, or the Joint Committee on Taxation approach things? You’re all academic economists — are you all coming at it from the same direction, or are there big philosophical differences? Martha: I don’t know that I would necessarily say philosophical differences. I should say, basically all of the modeling shops think of JCT and CBO as the one ring to rule them all. If we get an estimate that’s markedly different than theirs, generally we’re like, “Hey, staff, what on earth is going on here?” And sometimes there’s a reason — a difference in a parameter, et cetera. One thing Budget Lab is interested in that I think is different is this very long-term approach. The costs and benefits of policies can look very different at 10 years versus 30 years. If you take the One Big Beautiful Bill Act: at 10 years, its average impact on growth is basically zero. If you go out 30 years, it’s negative — because of what we were talking about earlier: the rise in interest rates, the crowding out of private investment that slows down economic growth. We are also interested in non-monetary economic benefits. As an example, you can look at paid family medical leave. Paid family medical leave is not gonna double GDP growth — that’s just not what it does. It enables people to spend time with a newborn, for instance, and one of the things we know is it has some impact on neonatal mortality. That’s a thing a lot of people care about, and it’s not an estimate that’s part of the traditional budget modeling process. I think allowing people to think about what is it that we’re buying with this policy — we are spending money for a reason; what are we getting; do we think what we’re getting for those dollars makes sense — is important. That’s been relatively easy in the past on tax policy, because what you’re trying to do with tax policy is redistribute, raise more revenue, et cetera. So that’s easy — or I should say easier; the poor people who do the tax modeling for me are screaming right now: “Martha, our jobs are not easy!” But particularly on the spending side: what is it that you’re buying, and how efficiently are you buying that thing? Private Data, Public Data, and the AI Labs [66:38 – 69:41] [01:06:38] Andrey: All right. You also have spent some time at Indeed— Martha: Oh, no. To be clear, I loved Indeed, but now I’m terrified about where this is going. Andrey: No, no — we were curious what you think the role of private versus government data will be. Martha: I think private data is incredibly important. It allows you to get much more disaggregated. You can sometimes see things faster. You can see things that don’t show up in public-sector data, because they may not be measuring it. But I think sometimes people talk about private-sector data as a panacea — “oh, we won’t have to have public-sector data anymore.” I do not think that is correct. One, private-sector data shows you what one company sees. We are often interested in what is happening with the overall economy, so someone needs to do the aggregating to help us see that. People should also keep in mind that a lot of the private-sector data indices you see are benchmarked against official government data — “hey, do we have the right number of job postings for leisure and hospitality, given what we know about hiring in that industry from official government data?” So there’s really a symbiotic relationship there that is really important, and it’s important not to say, “Oh, we’ll just use private data and it’ll be fine.” You saw this, by the way, when the government was shut down last fall, and we had three different private-sector data estimates come out: one said “hiring is fine,” one said “we’re having no hiring,” and the other said “hiring’s fallen off a cliff.” And everyone was like, “Well... who knows?” [01:08:31] Andrey: There’s been also a call for data sharing between the AI labs and the government. Obviously, all else equal, it would be great to have more data from the labs. Is there anything particularly interesting in terms of data that the labs have that we would want? Martha: To be clear, per your point: I love data. Data is great. I personally should have access to all the data. I will say — if I think about the things that I’m wondering about in the labor market right now, what I kind of want to know is: if a company adopts AI, how quickly and how do they adjust their hiring? And that’s not something, as far as I’m aware, that the labs can answer. So I think there’s been a little bit too much focus on the data that the labs have, and not on starting with: what are the questions that we need to answer right now, and where does that data live? Lightning Round: Bottlenecks and Redistribution [69:41 – 72:58] [01:09:41] Andrey: Related to this point — this is also in our speed round — economists talk about bottlenecks to AI adoption leading to productivity growth. What do you think is the biggest bottleneck? Martha: Oh, man. The biggest? Andrey: You can do a big one. Your favorite. Martha: Actually, I’m gonna cheat here, which is to say that this really depends on different industries and different occupations — and that that is a really important part of this overall conversation. It’s going to look different in different workplaces, and I think sometimes we talk about this as if it’s overly homogeneous. For instance, Hollywood is not particularly regulated. The biggest barrier I see there is questions around consumer sentiment: are consumers gonna revolt if they think a movie was made with AI? On the other hand, in healthcare, there are real regulatory and liability concerns, so that’s going to make a difference there. People need to do much more thinking about the dynamics in specific industries and occupations, rather than some of the broader economy-wide thinking. [01:10:53] Seth: Fair. All right — so imagine some of those bottlenecks get unlocked. We start getting these productivity gains and some disruption to labor demand. Gun to your head— Martha: No. Seth: —an imaginary gun — are you on team pre-distribution, to save good jobs? Or are you on team let-’er-rip — second fundamental welfare theorem, get efficiency, and then redistribute after? Martha: I think this gets at one of the things about working in policy versus being in academia. The economist answer is: let ‘er rip and then we redistribute, and we get the most economically efficient whatever. People don’t seem to like that very much. Seth: Why don’t people love us? Martha, why don’t people love us? Martha: It’s a real question! I actually legitimately do think this is one of those hard questions for economists working in policy. We can say, “Look, you get the most economically efficient outcome if you allow this thing to happen, and then obviously we can just redistribute on the back end.” The citizenry just doesn’t seem to like that. And so I think we need to do more thinking about how to balance those issues. Seth: Okay, but gun to your head. [01:12:18] Martha: I’m an economist, right? I have this instinct. But I’m just saying — again, this is part of the problem. We are the problem. People’s preferences are real, and we have to take into account people’s preferences, even if we are sitting there saying, “No, no, you don’t understand — GDP growth will be this much higher, and then we just redistribute it, and it’ll be fine, and your welfare will be higher.” People don’t like that. Seth: All right, we should keep it halfway. Martha: But also, people should let us redistribute as necessary, and not get mad at us for it. But that’s not how things work. Sci-Fi Corner and Sign-Off [72:58 – 76:14] [01:12:59] Andrey: Let’s wrap it up. What is your favorite sci-fi book or author? Martha: Oh, that’s mean. That’s really mean. I should say, I was a huge sci-fi nerd as a kid, and obviously still am. Because we have been here talking about AI, I’m gonna say Becky Chambers — just because I think Becky Chambers is the version of the future that we all kind of want for ourselves. It’s these amazing space-going communities with these friendly AIs. It just seems very nice. So I’m gonna choose the optimistic one. And maybe we’ll get that. Seth: Great recommendation. Do you have one book by her in particular? Martha: I think you kind of have to start with — I’m gonna get the title wrong — A Long Way to a Small, Angry Planet, which is the first one. It’s just so, so great. There’s a follow-up that takes place on a space station, with a very cute child that they all start looking after together — I forget the name of that one, which is really bad, but I loved it. She’s working on a new thing right now, and I keep refreshing her website to see if she’s announced what it is yet. So if by some miracle this makes its way to Becky Chambers: the people wanna know what’s coming next. Please let us know. Seth: Amazing. And definitely a better sci-fi take than some of the takes we’ve had on this podcast. I won’t name names. Martha: I wanna know what the worst sci-fi take was. Okay, don’t name names — but I wanna know. [01:14:38] Seth: I’ll name names. Basil Halperin said he loved Red Rising — which I just finished. Which is so mediocre. Martha: No! Oh my God. I’m sorry — I love Basil, but that is... I’m legitimately very upset about this. Seth: Next time you see him, you can tell him Seth really did not enjoy Red Rising. Martha: Let me rephrase: Red Rising is a fun read you can get through in two hours. It’s terrible, but, like, sure. Seth: Angry Harry Potter. Martha: So angry. The angriest of Harry Potters. By the way, if I can do a second one, it would be Adrian Tchaikovsky’s Children of Time, which is so good. Andrey: Great, great book. Seth: Oh, that one’s great. With the spiders? Martha: Yeah, with the spiders. It’s so good. Children of Time is great. People should read Children of Time. [01:15:35] Seth: All right, Andrey, shall we wrap it on that? Everyone read Children of Time and... Martha: A Long Way to a Small, Angry Planet. Seth: And everyone out there, when you’re done reading, you should... Andrey: The Yale Budget Lab? Seth: Say the line. Keep your posteriors justified. We have a line, Andrey. Andrey: I know our line. You gotta constantly be keeping your posteriors justified. Seth: Never stops. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • June 29 · 1 hr 15 min

    Litigating the Pope's AI Encyclical with the Lawyers of Scaling Laws Pod

    In this episode of Justified Posteriors, we host Alan Rozenshtein and Kevin Frazier — the law-professor duo behind Lawfare’s Scaling Laws — to take two of the most-discussed AI policy documents of the spring and subject them to an inquisition. Our disputors are probably not what Pope Leo anticipated: two lawyers, two economists, and probably 3/4ths Jewish. Talk about a crossover episode! First up is Pope Leo XIV’s 42,000-word encyclical (that’s Pope-talk for letter) on artificial intelligence. Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence lays out 5 principles of Catholic social teaching, and then explains how this should shape Catholicism’s approach to AI. We focus on two in particular. The first is subsidiarity, which Seth summarizes as Catholic federalism, the idea that most decisions should be made at as local a level as possible. We discuss both the economic argument for this, but also what the Pope adds to Hayek: Decentralization not for efficiency’s sake, but a kind of ennoblement, the dignity of deciding things locally. The second is the universal destination of goods, which the encyclical extends to “immaterial goods”. This leads to the positive argument of the Pope - that AI should be undertaken as a communal project with decentralized power and discussion, rather than a technocratic “Tower of Babel” that will lead to ruin and division. Much of our disputation focuses on whether these principles actually resolve the important questions. Is the Pope rightfully cautious about an emerging technology, or was this an opportunity to take a stronger stand on what constitutes AI Sin? Interestingly, the Pope’s strongest stand is against transhumanism, which would be a plausible resolution to the dialectic of “Butlerian Jihad” vs. worship of a new machine god. Then we pick up DeepMind’s “Positive Alignment” paper, and the economists get grumpier. Andrey complains that the paper is vacuous, failing to take a stand on actual practical goals or methods. But it sets us up for a good conversation about several issues: Such as liberalism of fear, a type of anti-utopian liberalism; whether “flourishing” is something you can A/B test towards; and where the ‘constitution’ metaphor behind Constitutional AI works vs. breaks down. We also tease a joint project, “SCOTUS Bench,” a new benchmark for evaluating AIs’ ability to predict appeals court outcomes. Watch this space for more on that soon. Related Links * Scaling Laws — Alan and Kevin’s AI, law, and policy podcast at Lawfare * Alan Rozenshtein on X: @ARozenshtein · Kevin Frazier on X: @KevinTFrazier * Magnifica Humanitas — Pope Leo XIV’s first encyclical, “On Safeguarding the Human Person in the Time of Artificial Intelligence,” in full, straight from the Vatican * “Positive Alignment: Artificial Intelligence for Human Flourishing” — the DeepMind-led paper (Laukkonen, Krier, et al.) arguing alignment should optimize toward flourishing, not just away from harm * Claude’s Constitution — Anthropic’s ~20,000-word statement of Claude’s values and character, released under CC0 * “Claude’s Constitution,” with Amanda Askell — the Scaling Laws interview with the document’s primary author (the one we keep saying we’re jealous of) * The Moral Machine — MIT Media Lab’s crowdsourced trolley-problem experiment: millions of judgments on the grandma-versus-criminals ratio * Meta’s Oversight Board — the “Supreme Court of Facebook,” and Kevin’s cautionary tale in institutional design * Andrew B. Hall — Stanford political economist on deliberative democracy, platform governance, and what went wrong with the Oversight Board * The Anthropic Economic Index — the adoption data behind the “whole countries blacked out” point * Judith Shklar, “The Liberalism of Fear” — the cruelty-first, anti-utopian liberalism Alan invokes against thick conceptions of the good Timestamps (00:00) Intro — two papers, four hosts (01:47) Paper 1: Pope Leo XIV’s encyclical, Magnifica Humanitas (04:00) Subsidiarity, or “Catholic federalism” (12:26) Does the Pope take AI seriously enough? Mind-body dualism and the ex cathedra problem (15:34) The coming religious schism over AI personhood — and the Butlerian jihad (18:06) Transhumanism and the dignity of human limits (20:59) When is using AI a sin? Best-man speeches and eulogies (25:05) The universal destination of goods — is AI access already universal? (33:37) Is AI a centralizing technology? Dignity vs. efficiency (36:37) Freedom vs. control, the labor market, and make-work (41:10) Chess, the centaur era, and living after we’re no longer the best (47:34) Sponsor: Revelio Labs (48:49) Paper 2: DeepMind’s “Positive Alignment” (49:17) The liberalism of fear and thick vs. thin notions of the good (53:53) Is positive alignment an empirical question? A/B-testing flourishing (56:29) What would a useful positive-alignment paper actually do? (58:09) Constitutional AI as a site for public participation (1:00:47) The Moral Machine and trolley problems at scale (1:01:08) Does the “constitution” metaphor hold? Virtue ethics and self-binding (1:10:02) Running every Supreme Court case through the models (1:10:53) Lessons from Meta’s Oversight Board (1:15:09) Wrap-up Justified Posteriors is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. You’re also invited to our Discord community at: https://discord.gg/2r3pExumQ Our sponsor This episode is brought to you by Revelio Labs, the leading provider of labor-economics data, available to academics on WRDS. Transcript: Seth (00:00:00): [upbeat music] Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology. I'm Seth Benzell, always positive and always aligned, coming to you from the Pocono Mountains of eastern Pennsylvania. Andrey (00:00:23): And I'm Andrey Fradkin, coming to you from San Francisco, California. We're sponsored by Revelio Labs, fine purveyors of data products. And we're very excited to have Alan Rozenshtein and Kevin Frazier from the "Scaling Laws" podcast on the podcast today. Welcome. Alan (00:00:43): Yeah, thanks for having us. Andrey (00:00:45): Just for our listeners, why don't you tell us a little bit about "Scaling Laws?" Kevin (00:00:51): Sure thing. So our main goal here is to provide robust and timely analysis of all AI policy questions. And that's an expansive ambit, and it's one that keeps us really, really busy because if it's not an executive order, then it's some big new policy idea from one of the labs, or it's some new economic report. But really what we try to do is dive into the weeds of policy and legal issues that are emerging in the AI space, given our backgrounds as law professors. But Alan's the one with the brain, so I'll let him fill in the details on earth. Alan (00:01:30): No, that's a perfect description. Yeah. We just think that there's a lot of really interesting stuff happening at the intersection of AI, law, policy, especially around national security, which is the core focus of the publication that "Scaling Laws" is part of, which is Lawfare, and so we're trying to fight the good fight, and it's never a dull moment. Kevin (00:01:47): That out of the way, I think we can dive into our first paper. Although, I think by any podcast standards, our first paper is lengthy to say the least, dealing with the- Andrey (00:02:00): Mm Kevin (00:02:01): ... pope's encyclical at 42,000 words, or for all those listening, about two and a half hours on my stationary bike. I don't know what that says about my biking skill- Andrey (00:02:13): [laughing] Kevin (00:02:14): ... or my reading ability, but it was a very tiring afternoon. But a very extensive, very important read from Pope Leo. And this has been covered by a lot of folks, but I don't think it's ever been covered by two lawyers and two economists at once. Andrey (00:02:34): [laughing] Kevin (00:02:36): My hunch is that this wasn't what Pope Leo was anticipating when he was sitting and putting a... I like to think of him writing with a quill and- Andrey (00:02:45): [laughing] Kevin (00:02:45): ... on some very old paper. But, I don't think he anticipated this podcast duo diving into his encyclical. Seth (00:02:55): Well, hopefully, our analysis will be less a Tower of Babel of technocratic overreach, and more a blessed city of Jerusalem built together by our common efforts. Kevin (00:03:06): Seth did his reading. Seth dove in. All right. Excellent. Good to hear. Well, I think at this point in time, we're talking in early June. By the time folks are listening to this, unless you've been living under- Seth (00:03:18): There may be a new encyclical. [laughing] Kevin (00:03:21): Somebody- Seth (00:03:22): Pope maybe changed his mind. Kevin (00:03:24): Yeah. Ugh. But there's so much to cover in this encyclical. Obviously, we could start with just the pope's analysis of the role of the Church and of social doctrine, which he gets into in extensive detail, and that covers about 20 to 30 pages. I think for the sake of our podcast, that's probably not our main forte in terms of analyzing the evolution of the Church's social doctrine. But I will let anyone intervene there if they're extremely fired up about that posture. Andrey (00:04:00): [chuckles] Kevin (00:04:00): But I do think that the first area for us to really explore, that both economists and lawyers can appreciate, is this idea of subsidiarity, which is really- Andrey (00:04:12): Mm Kevin (00:04:12): ... the notion that we have various institutions operating at various levels of jurisdiction, and that ultimately we want to devolve regulation or governance of an issue to the smallest capable actor. And that has a lot of resonance and a lot of power in the Church's teaching, which is to say you have this centralized entity, the Catholic Church, and yet we have parishes all over the world. And so how do we distinguish between the issues that Pope Leo needs to decide and the issues that parishes and then to, switch to a different context, local governments versus national government versus international government. How do we think about the allocation of responsibility there? So I would love to just hear the initial thoughts. I know Alan will have thoughts. But from an economic perspective, what is the relevance of a sort of subsidiarity principle to the governance of emerging technology? Seth (00:05:15): Oh, well, Catholic federalism. Andrey (00:05:16): Well, there- Seth (00:05:17): I love it. Kevin (00:05:18): Exactly. Andrey (00:05:19): Let me take a little stab at it. I think this hearkens back to the central planning versus markets debate in the sense that we could have AI policy be governed at the national or even a supranational level. But there is a risk that those laws are not going to be well-suited to individuals with heterogeneous preferences and heterogeneous information about their needs and their constraints. And so to the extent that we can allow for governance to happen at a more local level, then that AI, the way in which it's used, is going to be More appropriate, more beneficial for everyone involved. So that's kind of the high-level thought here. But then, of course, with something like AI, you are worried about externalities of various types, right? So, the way in which one group decides to use the AI may affect everyone else, and then that kind of pushes things back up to the top because you need coordinating mechanisms, and that's pretty hard. But also, this is a very abstract discussion, and I always like to think about specific issues, specific AI policies about which we can think about. Kevin (00:06:44): Seth- Seth (00:06:45): Are you going to give us one? [laughing] Andrey (00:06:47): Well, so an example might be by what constitution is the AI trained from. Kevin (00:06:58): Sure. Andrey (00:06:58): So, if every little village had its own Claude with a different constitution, there might be a scenario in which the constitution of one of the Claudes might say, "Help us dominate our neighbors." And that would obviously have [chuckles] a negative repercussion on the neighbors potentially. And I think there's this kind of a separate thing there now that I'm bringing it up, is that it seems at least, given today's technology, pretty implausible to have that many different Claudes. We don't know how to do that yet. The training runs and the post-training are pretty catered to one thing and very expensive, and so maybe we'll get there one day, but at the moment, it doesn't even seem very affordable. Seth (00:07:49): Mm-hmm. Andrey (00:07:49): Yeah. But I'm curious what you think. Seth (00:07:52): Yeah. So I'll jump in here. I'll say that, first of all, we're all kind of maybe libertarian-leading guys. I don't know if that's fair to say for you, too. So as I was reading this, I was thinking, "Oh, those law fair guys are going to really like the subsidiarity. Of these five points, that's going to be the one they like." [chuckles] And I think Andre gave a good analysis of the economic take on why you would want subsidiarity. So, what is the pope adding to that that wouldn't be in Hayek's argument for decentralized planning? I think what he wants to add is there's a kind of an ennoblement. There's a kind of positive good vibes that come around from the local decision-making, even above and beyond the efficiency arguments for decentralized planning, which is I think what Andre was emphasizing. And this is definitely an essay that is sort of a little bit anti-putting efficiency above everything else. The second thing I will say is that it's sort of interesting reading this principle of subsidiarity. Obviously, we know the history of Catholicism and its various schisms, right? Obviously, sometimes- Kevin (00:09:03): Yeah. I don't tend to think of it as a super decentralized religious and spiritual movement. I will say with a name like Rosenstein, I am neither qualified to opine on Catholic thought, nor do I have a ton invested in it. But- Seth (00:09:16): Mm Kevin (00:09:16): ... since we're talking about it- Seth (00:09:18): Let's do it Kevin (00:09:18): ... the Catholic Church did not strike me as the most let a thousand flowers bloom type institution. Seth (00:09:26): It's not clear to me that establishing that the sacrament of communion is literally Jesus in the wafer. It's not clear that that had to be the centralized decision to be made. From the outside, it's not obvious what are the high-up decisions and what are the low-down local decisions. But I guess that's where I'm going with this, right? Which is, alongside this idea that you should decentralize things when possible is this very sort of Catholic teaching-based view on what are the important things to not decentralize. And those are some kind of foundational ideological commitments around prioritizing the poor and one of these principles I'm sure you'll get to soon is the universal destination of all goods, which seems to mean something like communal ownership. So, as we talk about this, one thing I'll be keeping an eye on is to what extent is subsidiarity in tension with this idea of there being a kind of a common good that the pope can tell you about. Kevin (00:10:27): Yeah, and I really appreciated your flagging of the fact that so much of this seems to be grounded in the pope's insistence and hope for people to feel a sense of agency in the AI era and really leaning into the sort of humanity of this all. And so I think subsidiarity is a sort of end round circumvention to that point of saying, how can we find a way for people to feel like they have a mechanism by which to actually assert autonomy in this domain? But to your point, Andre, and something that I think from a technical standpoint is really interesting, is that even feasible in terms of policy development that reflects cultures around the world, even the entirety of the Catholic base, right, which we know spans from South America to Southeast Asia and everywhere in between. As things stand right now, there are whole countries that don't have access to Claude yet, right? And there are whole countries for whom I'm guessing that their performance in their language is probably pretty poor relative to English, for example. And so just from a technical standpoint, that's something that I don't know was as thoroughly addressed about the just technical feasibility of some of the ideals of subsidiarity and something like that. Seth (00:11:49): Yeah. He talks about trying to bring ethics into the research lab. So maybe that's pointing towards what he wants us to be working on. Andrey (00:12:00): Yeah. It's not really clear in some way who the audience ... of this piece is. It's unlikely that typical Catholics would read it, of course. It's a very long and dry document. People at the labs, I guess, could be reading it. I guess that might be just the main audience of this document, but I'm curious what you think. Alan (00:12:26): Yeah. I think there are kind of two different parts of the document. Obviously, there's this whole long thing about Catholic social thought, which is interesting, but I think somewhat orthogonal to the discussions that AI watchers are interested in. On the AI side, it seems like he's making two different sets of arguments. One is a set of social arguments about the effect of AI and the dignity of labor and the need to spread resources equitably, and I think those are perfectly fine arguments. You can agree with them, you can disagree with them, but those seem like perfectly reasonable social and kind of political positions to take. The other side is his engagement with the technology itself, which I did find somewhat disappointing. Now, on the one hand, the fact that the Pope's first major written output is about AI is itself, I think, remarkable, and he should get a lot of credit for that. You can hardly accuse him of ignoring this epochal issue, but we shouldn't grade him on a curve, right? He is- Andrey (00:13:33): [laughing] Alan (00:13:34): He is trying to engage- Andrey (00:13:36): Grade him on curves. Alan (00:13:37): Well, I'm just saying, I'm really impressed that he's engaging on this issue, but he is engaged on the issue. Okay, so how well is he engaged on the issue? And on the actual issue of AI itself, I don't know. I think I'm stealing this from Matt Yglesias, who had this, I think, pretty good post on X, which was something like, "I get why he has to say this because he is, after all, the Pope, but the whole mind-body dualism is not very helpful in discussions of AI." I'm paraphrasing here. And what Yglesias is referring to, and I felt this as well when reading it, is there are these ex cathedra pronouncements in the encyclical- Andrey (00:14:11): Literally ex cathedra. Alan (00:14:13): Yeah, I guess literally, right? I guess literally. You are the Pope, after all. About how AI can never have moral responsibility, AI can never have thoughts, AI can never have feelings, AI can never have this, and AI can never have that. Which, look, I understand that is a highly intuitive view, and yes, I guess if you are literally the Pope, there are certain metaphysical commitments of your religion that perhaps might require you to take this position, and I don't think this is simply a Catholic position either. I suspect if you were to ask the chief rabbi of Israel or some high-level Islamic thinker, they might give you a sort of similar position on the metaphysics of all of this. I'd be very interested in what a Buddhist scholar would say. I suspect actually that might be the most fruitful engagement. A kind of tradition that really takes no self seriously, I think, might have a lot of very interesting things to say about the metaphysics of AI personhood. But I think in some ways, the problem to me with the encyclical is that it doesn't take AI seriously enough. It's not actually as AGI-pilled as I would want it to be because, and here I'm going to steal from friend of the Lawfare pod, Dean Ball, who made a very excellent point. The existential questions about AI are not actually about the distribution of resources in a post-scarcity economy, though those are very important, to be clear. It's about: what is the place of humanity when we are no longer the smartest and most sophisticated entities? That is the fundamental spiritual and existential question, and that is one which a posture of "AI will never have feelings, AI will never have moral responsibility" just tries to kind of drive out of polite conversation. But the problem with that is that, first of all, I think it's wrong. But even if it weren't wrong, it's actually not, I think, going to be responsive. I've had this idea that I think may be crazy, but I think may also be correct. We'll see. I think- Andrey (00:16:20): Those are my favorite ideas. Alan (00:16:21): Yeah, right. That the future religious schisms will not be between Christians and Muslims and Jews and Buddhists and whatever, but it's going to be people who think that AI has potential moral personhood, maybe even divine personhood. Certainly, if you're going to kneel at the altar of the machine god, that's a big deal. But even if you don't think that they're literally divine entities, if you're convinced that the AI that you're interacting with has so thoroughly passed the Turing test, that it has a kind of moral personhood, that's going to have profound religious implications. And then on the other hand, you're going to have a set of religious views, and I think you're going to have a lot of the incumbent religious bodies here. Which is why the Catholics and the Jews and the Muslims and the Hindus, it's the beginning of a joke. We'll all get together on this side that says, no, AI must be. We must have a kind of Butlerian jihad, to cite Frank Herbert here and the Dune series. We must take a kind of Butlerian jihad approach to machines, because otherwise, these machines, which are already so much smarter and more capable of us, if, my God, we allow them moral personhood, then we're no longer the apex dignity-holding entity on this planet. And while I don't expect the Catholic Church to be able to metabolize that terribly well or, frankly, I'm not picking on the Catholics here, any organized religion to be able to metabolize that particularly well, just denying that, not engaging with that, I think is not going to work in the long term. Certainly not by the time Claude 17 comes out. Seth (00:18:06): I guess I would say I don't think that's 100% fair to the pope. I think he does have a big take that is related to the questions that you raise, which is he takes a very strong stance on transhumanism. So these questions that you're raising around will we have a machine God? Will we destroy the AI? Will there be some sort of intermediate result? One very common answer to those questions is, is we'll merge with the machine. We'll become immortal, embodied, Ms on the computer, or we'll become physical cyborgs, or we'll use all sorts of advanced eugenics techniques in order to become more than human. And so that is an answer some people have, and the pope very strongly comes out against that answer. He says it is the fact that we suffer and die and have miserable things happen to us and are limited by our nature is what makes us human, therefore do not do transhumanism. So I think you might not agree with the take, but he's got a take. Andrey (00:19:13): I don't think that those are in contradiction to each other necessarily, in that I agree with Alan that I expect a blossoming of new religions to come about, and the existing religions certainly have a commitment to the primary role of humanity as it is today. Seth (00:19:38): Right. Andrey (00:19:38): And so a lot of this document is spent justifying why today's humans are essentially the relevant moral unit. So- Seth (00:19:52): Made in the image of God and all that Andrey (00:19:53): ... humans die, but that's what makes it good in the light of God. I also don't want to be speaking on behalf of the Catholics here. But, that was kind of my sense from it, and it was just don't go for efficiency. Humans aren't meant to be the smartest or the most efficient or anything like that. Even though they're imperfect, that is as it should be. So there is maybe a sliver there where we might have AIs, but as long as they don't have pretense to being moral beings, if they're designed in a way that tries to make them be less like that, then they could coexist with humans in a way that might be satisfactory to the Catholic Church. Seth (00:20:41): Right. And then the natural follow-up question is, okay, all right, if you're going to throw out transhumanism and efficiency for the sake of efficiency, aren't you going to be outcompeted by the groups or the nations that do go full hog for AI- Andrey (00:20:54): Yes Seth (00:20:54): ... transhuman efficiency? And then he's got an answer on that. Do you want to pick that up? Kevin (00:20:59): I wanted to hit on one key point, though, which is a critique that was in the "New York Times" on the fact that, and I found this pretty persuasive, one of the bigger omissions was the lack of specificity around when AI use may constitute a sin, and- Seth (00:21:18): Mm Kevin (00:21:18): ... when AI use may be something that is inherently and definitively bad. And something that I think stood out to me about that was when I talk to people about AI in a moral context and in a setting where we're trying to identify what are those red lines about how and when you use AI, that's where some of the most difficult conversations come up. I love to pose a very dumb hypothetical, which is imagine your best man wrote his best man speech with AI. Are you happy or mad? You're probably pissed off, in my opinion. Now, we could switch it and make the stakes even higher. We could say something like a eulogy. If you found out your eulogy was delivered by your homie, and they're like, "Yeah, I just used Claude, and it was really good." I'm probably rolling in my grave. What are those instances, though, in a moral context? Can you consult ChatGPT as a proxy for your pastor or your religious leader? Can you use ChatGPT for relationships? What kinds of relationships? How far can you take that relationship? And those are some of the questions I find that people have some of the most difficulty resolving, where the contestation by "The Times" was, hey, if there was a question or some lines to be drawn, we would kind of expect that the religious authority would be the one who draws those lines and says, "Yes, this is bad. Yes, this is good." Now you can go forth and use AI in a way that you feel less moral ambiguity. And I just thought that was a really interesting take because we have struggled, in my opinion, about how to draw red lines about when and why AI is used and when and why AI should not be used. Seth (00:23:13): But isn't that the right answer here? The pope notes that the technology is moving fast. It seems like subsidiarity could help a lot with that question. Maybe one region develops the norms to not use the AI for eulogies, and another region develops another norm. Why is that the centrally planned pope should have an opinion on one? Kevin (00:23:34): Well, I think that if we need to have moral clarity as to how and when to use AI, I'm not sure that subsidiarity is going to magically percolate those use cases in a way that is never hyper-relativistic. It's always going to be context-driven, which from the point of a faith, I think if you have no principle other than you do you based off of context, that kind of isn't a faith. Seth (00:23:59): He's got five principles. We can give the five principles he gives. Which are subsidiarity. You're right, I could list them, but one of them- Kevin (00:24:08): But that's not really about AI use on an individual basis. Subsidiarity doesn't change how I use AI Seth (00:24:16): If you're Dario Amodei, it might change the way that you allow people to customize the AI. Andrey (00:24:26): I do think it's very pragmatic of him not to go into specifics, for the reasons stated. But I do think that traditional Christian morality does bear on some of those questions, and in particular, you're not supposed to lie. So if you're giving your best man speech and the AI wrote it for you, that to me seems like it's a lie, no? Kevin (00:24:56): Is it a lie? Seth (00:24:57): Would depend on the norm. Kevin (00:24:59): I don't know. But- Seth (00:25:00): In some places, it'd be a norm to not disclose. In some places, vice versa. Kevin (00:25:05): Well, let's switch to something even easier, which is whether we should make AI universally accessible to everyone, based off of this idea, as Seth mentioned, the universal destination of goods. So for folks who did not spend their entire Saturday or whatever- Andrey (00:25:24): [chuckles] Kevin (00:25:24): ... stationary bike ride reading the encyclical, here's what we are referring to. So this principle, and I'm quoting now from the encyclical, "Reminds us that the Earth's goods, soil, water, air, and natural resources are given by God to the entire human family to sustain the lives of all, and that every person has an inherent right to use of such goods both now and in the future." And the Pope then clarifies, "Certainly, there is a right to private property which has its own specific meaning and purpose, yet it is always subordinate to the universal destination of goods." According to John Paul II, this subordination is the golden rule of social conduct and the first principle of the whole ethical and social order. So that's a bit of a mic drop, or to go back to my earlier- Andrey (00:26:20): [laughs] Kevin (00:26:20): ... refrain, a quill drop by the Pope to say that this is the first principle of the whole ethical and social order. Coming from the Pope, that's a big statement, which is to say making sure everyone has access to these goods, to this knowledge, is incredibly profound to me because, again, if you go and you look at the research Anthropic's done around its economic index report, for example, you'll see whole countries that are just blacked out because there is no access to Claude yet. There is no sort of universal use of AI. And again, as I mentioned earlier, even if it were, the idea that the AI is culturally sensitive, or as robust or as reliable in a certain language, for example. That clearly hasn't been the case so far. And then just to make sure that listeners don't forget the fact that we still have a digital divide, we still have millions, if not billions, of people who don't have access to high-speed internet. So if we are going to realize this idea of the universal destination of, let's say, AI, we are very far behind in terms of just the infrastructure that would be required to make real on that. So I would love the economists' hot takes on this because I'm getting Lockean vibes are coming up. We can Kosian concerns. We could just start name-dropping tons of economists here. What are y'all thinking? Andrey (00:27:57): Well, the first thing is just, yeah, it's a very socialist notion from the Pope, although he tries to thread it with, "We also respect private property." It's a bit weaselly in my opinion, lacking specifics. But I think, in particular with regards to AI, to me, it's a bit of a funny concern. AI is the fastest diffusing technology in the history of the world. It is almost universally accessible. Yes, Claude is blacked out in some countries. By the way, people in those countries have figured out a way to use Claude if you talk to them. Yes, not all languages are equal, but also, we have the best translation tools in history available. I'm not saying there isn't inequality to AI access, but it's actually you can get pretty good AI almost everywhere. And it'll become even more ubiquitous over the coming year, I'm sure. And in particular, Google is essentially serving AI to everyone. So sometimes I just find these concerns extraordinarily contrived. It's people who haven't actually thought about the specifics of the product diffusion, just making pronouncements from their chair about how everyone should get AI, whatever that is. Seth (00:29:15): It's a really special chair, Andre. Andrey (00:29:17): Yes. Seth (00:29:18): It's not just any chair. Andrey (00:29:18): It is. I know. [laughs] Seth (00:29:21): [laughs] I'll second your comments there, Andre. I think you're exactly right about the speed of diffusion. The thing that I would add here is that the new thing that isn't just run-of-the-mill, let's split up the goods equally, is there's a take that he seems to be an innovation. It's unclear if he is drawing on prior teaching here, where he says that extends to immaterial goods as well. That cultural products, intellectual products, are also part of this common universal wheel. And I don't know, that kind of got my Ayn Rand hackles up thinking about if I have an idea in my own head, is that all of society's idea just because I just had it? I think that there is a little bit of an expansive view on what constitutes the goods in the universal destination of all goods here to include things that usually we wouldn't think of as even in communist states, things you would have to share. Alan (00:30:27): Yeah. So look, I'll say, I think the pope is allowed whatever social teaching the pope- Kevin (00:30:33): [laughing] Alan (00:30:33): No, I'm not trying to be snarky about it. I know, I think that you're allowed to be a socialist, you're allowed to be a hardcore capitalist, you're allowed to be anything in between. I certainly don't feel like I have any particular wisdom or expertise to adjudicate between rival conceptions of the political economic good. I think what I think is interesting is to say, okay, let's take this seriously, and let's say that what we do want is this common good type distribution of resources. Does the world of really powerful AGI change the approach to doing that? And I just don't know the answer to that question. But I'd be curious what the economists say. If you are, in fact, a socialist, does the possibility of profound artificial intelligence change what your existing toolkit is? Kevin (00:31:30): Well- Alan (00:31:30): Or should be? Kevin (00:31:31): That's- Andrey (00:31:31): Yeah Kevin (00:31:32): ... I want to hear from the economists, too, but I do want to make sure, I'm going to push back just on the AI adoption and the AI diffusion narrative. I do agree that if you are already on the internet and tech-savvy, you can find a way to use these tools. But I think across the whole of humanity, in a lot of the global majority, there's just still not the infrastructure even in place to do that reliably, and the idea that we'll have the infrastructure in place in the near future to have an equivalent access to AI as someone living in the Bay, for example, is just not going to be the case for years, if not decades, absent some crazy change. So in terms of whether or not this is an actual policy matter, I do think that if your goal is diffusion across a lot of communities, that is a huge barrier, and billions, if not trillions of dollars would have to be spent pretty quickly to actually accomplish that kind of universal use of AI. Aren't we just talking about Starlink plus cell phones? What other technologies, what other infrastructure do you have in mind? Even with that, the idea that is Elon going to make Starlink available for free to everyone around the world? And then- Andrey (00:32:53): Of course not. Kevin (00:32:54): Yeah. Andrey (00:32:55): Well, Google will, and Facebook will, and Facebook is the internet in many places. And not everyone has a smartphone, but almost everyone has a smartphone, and they have Facebook on it. So I've been skeptical of digital divide narratives for the longest time, I think, and they've led to enormously bad policies like investments in extremely inefficient broadband solutions when Starlink makes it all obsolete. Just central planning gone wild, in my opinion. So- Kevin (00:33:25): Which is the pope would back him up on. Pope's anti. This is a whole anti-technocratic centralization overreach essay. This is a very seeing like a state-pilled essay we read. And I think it's really interesting to kind of think about this question which you posed, which is, is there something about this new technology which is kind of essentially more centralizing? And I know a lot of ink has been spilled about, oh, well, here's various ways that AI will tend to allow us to do things independently or in small groups that you would have needed huge teams for, or maybe it'll make us weirder in ways that'll be more diverging and idiosyncratic, and that'll lead to smaller scale groups. But I got news for you guys. My reading of everything together is that at the end of the day, AI is a technology that tends to make more centralized concentrations more efficient. It can process vast amounts of data in order to make more centralized decisions. That was always the critique of centralized decision-making, is that you couldn't process everything. Well, we're starting to get to the place where you can process a lot more. And now I'm not going to argue for central planning, but it does make me think that this is an age coming up where the economic forces will be towards centralization, and that includes big foundation model companies like OpenAI and Anthropic, and it might include companies that we don't even know about yet that'll grow to immense size. But I think that's why it's so important for the pope to be arguing for subsidiarity, not from the perspective of this Hayekian efficiency argument, but from actually, there's some other reason we want to preserve it, and it has to do with dignity. It's not a neo-Brandicean concern. It's not that big companies are essentially evil. It's just that there's something ennobling about decentralization. I think that's particularly compelling, going back to this idea of having some degree of agency in an age in which you may have just a handful of companies dictating incredibly powerful decisions. Just if I accept your scenario, Andre, for example, where it's just Starlink and Meta and Google offer you internet, Apple gives you a phone, and then you're running OpenAI or Anthropic's AI. And so now you just have seven companies who dictate kind of the entirety of your economic existence, if not your informational existence. How then, to Seth's point, do we maintain some degree of, I have a degree of control over my own future and well-being in a world in which seven companies shape the ins and outs of how you wake up, what you do for work, what you read, so on and so forth. Maybe faith and a connection to humanity and an emphasis on agency is the only thing you can kind of stress to make people feel like they have some meaningful role in shaping their lives. Andrey (00:36:37): Yeah Seth (00:36:38): They also serve what we can like- Andrey (00:36:39): So it's a pretty nihilistic take. I don't feel like I don't have agency over my life just because I use Google and Apple products. Those are products, those are bicycles of the mind. I can choose how I use them. Seth (00:36:54): I'm with... Yeah. Andrey (00:36:55): And I think that's true for AI models. Look, I understand that they have values baked into them and so forth, but in the end, I can do a variety of things with them across any ideological spectrum that I can practically think of. Yes, there are subtle biases and nudges and so on, but I don't feel like I've lost agency due to AI. I feel like I've gained agency due to AI. So I just think that this is a bit of a hypothetical, honestly. Alan (00:37:26): Well, isn't all of this a hypothetical? That way [chuckles] we've- Andrey (00:37:31): No, but it is just like this loss of agency. I found one part of the essay interesting, which is the part about the labor market. So he says: "The labor market is one area in which the risks associated with new technologies more clearly emerge. It is thus necessary to remember that economic freedom is not absolute. It must be measured against the common good and the dignity of every person." Blah, blah, blah, blah. "This is possible when it recognizes the creation of dignified, valuable jobs are an essential part of its proper service to society." Seth (00:38:09): Right. Andrey (00:38:09): So I think maybe more speaking to this point of dignity, the pope is arguing for a make work program, like in the style of the New Deal, to give people jobs just so they feel dignity. Seth (00:38:21): The pope likes free distribution. The pope is not sucking wealthier theorem pilled. Alan (00:38:26): No, and look, just to defend the pope for a second, I think his instinct that work is an immense source of dignity and that once people's material needs are met, as they are increasingly in the not just developed world, but in the developing world, that these questions of dignity and entity become really... They sort of begin to hedonically dominate. They really are where you get a lot of your utility from. The idea that AI will replace all potential, certainly white-collar work, is a huge threat. The problem is, what do you do about that? And that's where I feel like the insufficient AGI pilled-ness is the problem here. Seth (00:39:09): Right. Alan (00:39:09): Because a world in which people who use AI can outcompete by a factor of 10 or 100 or 1,000 to one, the economic productivity of those who don't, is not a world in which make work is going to work. I feel like people get very excited about the three make work jobs created during the New Deal, and then they're like, "Okay, that's an actual thing that can get done." Make work, paying people to dig ditches is not a thing that is sustainable, and people also see through that. So the real question is, I think not even an economic one- Seth (00:39:43): It's not sustainable in a dignified sense, right? The idea is there'd be so much income Alan (00:39:47): It's not sustainable in a dignified sense. Well, also, people just won't do it. Governments just won't tolerate this indefinitely. And so the question is- Seth (00:39:55): Well, this is a real politic. Wait, no. Wait, I want to understand why you don't think it's sustainable. So I can understand why it wouldn't be sustainable in a dignified sense if you like, "Oh, it's actually make work, so I'm not getting dignity from it." But the idea is in this AGI scenario, there'll be so much income that we can support people who aren't actually contributing. Alan (00:40:11): Yeah. I guess that's right. Seth (00:40:13): Are you saying that like we'll- Alan (00:40:14): No, I think no. So I'll take that back, because you see that a lot in petro states where a lot of the economy is propped up by sort of this kind of pointless public sector, this pointless bloated public sector. So fine, let me just going to go back to my dignity point, which is to say, at some point, people start seeing through it, and then the question is the one that I feel like the pope's encyclical keeps pushing off, and we're like, I'm actually more interested in the pope's kind of... I want the pope's sort of Catholic existential analysis more than I want the sort of political economy analysis, which is, just go back to the original question. In a world where we are no longer the most useful, most intelligent beings, how do we deal with that in a way that is productive and in a way that allows us to save face? And there are models- Seth (00:41:10): Yeah Alan (00:41:10): ... that we could look to. Chess, and I think chess is always an interesting example here. For a long time, machines couldn't play chess, and then there was a time when machines could beat humans, famously Deep Blue and Garry Kasparov, and then for about a decade, you had what was called the centaur era, where machines plus humans, these centaur teams, were the best, and then at some point, the humans just started causing problems. And so humans contribute nothing at this point to chess. I'm pretty sure an iPhone can defeat Magnus Carl... Like an old iPhone can defeat Magnus Carlsen at this point. And yet chess has never been more popular, and people watch Magnus Carlsen. Seth (00:41:49): Right. Alan (00:41:49): So there are examples where we have worked through the existential malaise of we're not the best anymore, but we still want to see humans do it. What I'm very curious about and where I think religion could play a really useful role is going domain by domain and helping us get to the other side of that transition. But to do that, you do have to take it seriously. You can't just continue to pretend that we are the best. No, no, we're not the best. That's the whole point. How do you live an existentially meaningful life when you're no longer the best? That's the question that I think is the most interesting one. Seth (00:42:25): Well, and to add on to that, too, because I think that we need to distinguish, going to Andre's point earlier, between freedom and control. I think that there's a vast difference between freedom to Kevin (00:42:37): Use AI to do anything, to look up anything, to pursue boundless knowledge, to, in theory, create anything or do anything, versus actual control over the infrastructure and the decisions and the entities that are shaping the preponderance of governance and the shape of the economy itself. And so you can have freedom, and you can have an increase in freedom, but you can have a decrease in control in terms of your actual ability to shape broader circumstances around you. And I think it's important not to confuse the two, because absent having some degree of control over those meta constraints, over those larger aspects of your life- Andrey (00:43:17): Mm-hmm Kevin (00:43:17): ... I do think it's hard to feel a sense of dignity, right? If you're born and to go to this make work idea, and you're told, "Hey, you have one of three jobs. Hey, great, you have freedom to choose your one of three jobs. Enjoy whichever. You can dig a ditch, or you can dig a well, or you can be in charge of high fives. But those are your three jobs." Andrey (00:43:41): [laughing] Kevin (00:43:43): Freedom, yay, but no real control over the nature of your life or the lives around you. And so I think that's the sort of dignity plus or- Andrey (00:43:52): So I guess- Kevin (00:43:53): ... plus Andrey (00:43:55): ... I guess I have a question for you. So suppose that we use democratic mechanisms to govern AI labs. So people elect representatives, the representatives sit in a House of Representatives at Anthropic. They vote on the latest constitution. Do you think that people will actually feel more in control of their lives that way? Because to me, it's not very obvious, right? Seth (00:44:24): Congress is not glowingly reviewed by US society. Kevin (00:44:29): I think that if you give an American an outlet, they'll feel more control. That's my attempt at a riff of If You Give a Mouse a Cookie. Look at how- Andrey (00:44:40): [chuckles] Kevin (00:44:40): ... folks are making- Andrey (00:44:41): Yeah, those books don't tend to end well. I have two small children, and I can tell you, those books are not optimistic stories. Those books are psychological horror films- Kevin (00:44:50): Oh, Jesus Andrey (00:44:50): ... in miniature. Kevin (00:44:51): We'll save that for our next episode. But I think that if you look at how people have made use of town halls and permitting processes right now- Seth (00:45:03): Right Kevin (00:45:03): ... with respect to data centers, I think those people would feel like they are actively shaping, and they are actively shaping the AI infrastructure build-out. So I do think if there were an avenue for people to feel as though they or their neighbor could participate, that would meaningfully change how they felt about AI. Whereas right now, the absence of- Seth (00:45:26): But it's bad. They're using their power for bad. [laughs] Kevin (00:45:30): That's a different point. On the question of whether they would feel like- Seth (00:45:34): Well, I think if we're doing political... I guess what I would say here is, I agree with the previous argument that this is an essay that's about the political economy and not about the existentialism. And so, yeah, if you're going to talk about the political economy, you should care about the economy part of the political economy, right? You want to get the voting power mixed the right way to create the prosperity that makes doing what you want to do possible. I think the pope would've been in an excellent position to do what you described before and talk about how a monastic life could be a model for thinking about what an AGI age would look like. Or, I think about the Messianic age in Judaism, where it's kind of a post-scarcity society, and everyone's devoting themselves to Torah study and mitzvah or whatever, right? That would've been a really interesting essay. This is a political economy essay. And then I do think you have to take a stance on, well, we should maybe put the power in a place where people are making better decisions. Kevin (00:46:37): Well, I think- Andrey (00:46:39): Well, there is an inevitable efficiency trade-off, right? If we think that the ASI is going to be very smart and is going to be well-aligned, the well-aligned is the questionable part, maybe. But if it's well-aligned, then we know the masses have their issues in terms of making decisions. And so- Seth (00:46:58): Is that a Catholic pun, masses? Andrey (00:47:00): [laughs] So yeah, we could delegate to the ASI to make our decisions. And I think this is, I guess, what it's being warned against, is it doesn't matter if the ASI is more efficient, that just the very fact that humans are in the loop in a real way is- Kevin (00:47:20): Well, at 42,000 words- Seth (00:47:21): That's the essay we got. Yeah. Kevin (00:47:23): We could keep going until eternity, religious pun intended. Andrey (00:47:30): [laughs] Kevin (00:47:30): But assuming that we're not going to, why don't we transition here? Seth (00:47:34): [upbeat music] For those of you playing along at home, now is your chance to think about how this conversation has changed your priors. This chance to contemplate your posteriors is sponsored by Revelio Labs. Revelio Labs is a leading provider of labor economics data and data services for companies, academics, and independent researchers. Andre and I have been working in economics of AI, digitization, and automation for a long time, and we can confirm just how useful Revelio's data is. Revelio's team combines comprehensive micro-level data on employee professional profiles, job postings, and employee sentiment with standardizations, mappings, and enrichments available, all to make that data useful without making your modeling decisions for you. The data can be flexibly aggregated to company, market, or industry, and can be used to study questions ranging from career trajectories to occupational transformation, to the returns to skills, and the impact of AI on labor demand for tasks. Can't imagine anyone who would be interested in that. And Revelio data is available on WRDS. Kevin (00:48:39): So if you're an academic with a good library, go see if you have access to their premier data already. And if you don't, you can reach out to their excellent economics team, and they'll hook you up. Andrey (00:48:49): Ooh, the next piece that we're considering is this piece by DeepMind, various DeepMind authors, Positive Alignment. Kevin (00:48:57): Including the friend of the show. Andrey (00:49:00): Yeah. Seb Krier, who refused to take ownership of this paper. [laughing] But yeah. Alan, Kevin, what'd you think? Kevin (00:49:12): I'll let Alan start. I've been talking for way too long. Alan (00:49:17): Yeah, look, I think it's an interesting point, and the inner social psychologist in me likes it, and thinks that the positive psychology turn should apply to alignment as well. And then the old school, Cold War liberal in me gets very nervous about these kind of positive conceptions of human flourishing, right? So, there's this idea that post-war liberalism became what's sometimes called the liberalism of fear, which is the idea that in the wake of all the totalitarian ideologies of the 20th century, Western liberals retreated to a kind of defensive crouch, where the point of liberalism was just to prevent the worst excesses of totalitarian and authoritarian ideologies. And you're not supposed to look to liberalism, or frankly any political philosophy, for a sort of a positive conception of the good. Kevin (00:50:20): And that's also Rawls, right? Alan (00:50:22): Yes. This is related to Rawls's idea of political liberalism. This has become unfashionable lately because something, something neoliberalism, something, something. But I still think there's a decent amount of wisdom in that. And so, whenever I read these proposals that AI systems should promote human flourishing, which to have bite is always... Which only has bite- Kevin (00:50:53): On the other hand Alan (00:50:54): ... if the systems are not doing what their human wants them to do in that moment. Otherwise, none of this would matter, right? That would just be covered by regular alignment. I get a little nervous. So again- Kevin (00:51:07): I wish the essay even got there Alan (00:51:08): ... at a high level of abstraction, these are my trade-offs. Sorry, what? Kevin (00:51:11): [laughs] I wish the essay even got there. If the essay had made the point we need a thicker notion of the good in order to blah, blah, blah, it would've been something fun to argue about and get antsy in our liberal pants about. But it equivocates so much, and it's so wishy-washy about, "Yeah, we want a thicker notion of the good, but there are so many different notions of the good." So, I don't even think it ended up making me afraid as a liberal. Alan (00:51:37): Yeah. Andrey (00:51:37): I don't know, Kevin, do you have anything to add? Kevin (00:51:39): Yeah. For me, this kind of does tie neatly, at least in part, about what we were discussing with the encyclical, which is we also still need a idea of what it does mean to positively align a model to anything, right? The selection of your social welfare function about whatever your objective is going to be is a really, really hard task, and I don't think that we necessarily know, again, to bring in the prior discussion as well. The idea of what the positive outcome is in any context that you are training a model on or setting a model to might vary wildly from one culture to another, and it may change- Alan (00:52:25): Right Kevin (00:52:25): ... over time. And so I appreciate the idea that we should not only look to avoiding negative outcomes and instead try to train models for some degree of positive behavior. But this brings to mind that folks, when Amanda Askell came on scaling laws and talked about Claude's Constitution, many people were not pleased when she said that her ideal was Claude acting like a good neighbor or a good traveler. Folks did not like that as the ambition for Claude. And I'm not saying that's good or bad or otherwise, I'm just saying it's really, really difficult to try to even encapsulate, envision what should a model do. What should that positive alignment be? What does human flourishing- Alan (00:53:16): Right Kevin (00:53:16): ... even look like? And that, to me, is where having more granular ability to train models will be profoundly important. Or at least to use some system prompt that directs your model quickly to whatever your community or your conception of human flourishing looks like, so that we can have that broader kind of polycentric approach to aligning models. But I don't think there's ever going to be one single approach, which is really difficult. Andrey (00:53:51): Yeah. I totally agree with you. I think one of the things much of this discussion misses or it's swept under the rug is that they want to pose this as some philosophical dispute, but it is an empirical question. As someone who studied digital platforms for my entire career, whether something is good or bad, in many ways evaluated by running an AB test and seeing whether the outcomes that you're measuring are improving. And it's completely not obvious ex ante. And I think with anything in such a constitution, that's also likely to be the case. Now, I understand why you would take this approach before you have the data. You have to take a stand on some of this stuff. But in the end, I think a lot of this is an empirical question in addition to a philosophical question. I think I have a broader take on this work is I have no idea who the audience for this is. And I feel like DeepMind puts out a lot of these papers. They're very general. You can see that they're citing a lot. They're citing everyone, and everyone is, we have RLHF, and we have supervised- Seth (00:55:01): 10 co-authors Andrey (00:55:01): ... fine-tuning. Yeah. And let's put in a lot of people on this paper to say a bunch of vague stuff that means nothing. They don't take a stand on anything, essentially. And then people laud this sort of stuff online, I think because they read the abstract. It's like, "Oh, yeah, vaguely I agree that we should try to have the models help human flourishing." But then when you read this paper, even though grammatically it's correct, it is vacuous. It is such a waste of time for them to write it, or alternatively, I have no idea who the audience of this thing is. And this is not only criticism of DeepMind. So many of these AI policy people write this stuff. And I just found it just to be deeply uninteresting. Yeah. Seth (00:55:58): It's for people who don't own a thesaurus and want lots of synonyms for flourishing in different languages. I have to say, reading this after the pope's encyclical made me more negative on the pope's encyclical. Because in it, he says something like, "No technology is neutral. We need to design technology from the ground up in the labs to have all of these five Catholic principles." And I read this, and it's like, do not let the computer scientists do ethics because they're bad at it. [laughs] Kevin (00:56:29): So what would be your conception of a useful, positive alignment paper? If you were to have a one-on-one with these authors and say, "Y'all, look, A for effort, F in execution," based off of what I've heard. Maybe you all assign slightly different grades. What is your feedback? Speak to us, the AI policy community. How can we now go to our friends and say, "Hey, y'all, we talked with Seth. We talked with Andre. Apparently, we're getting it wrong. We're not doing it well." What is the feedback? What can we do to be better? Andrey (00:57:10): Take a stand. If you think that you want a particular conception of positive alignment, tell me what metrics are indicative of that, and then propose a methodology, even if it's not immediately actionable, for how to measure whether a system is pushing us in that positive direction. Maybe do post-interaction surveys with users to see how satisfied they feel or how happy they are, and then you put them in an A/B test and compare different system prompts or... I'm just spitballing here. But give me something actionable instead of giving me a list of vagaries and then it's not even clear in this paper whether they consider that currently labs are already doing positive alignment or whether that's something that's new that needs to be done. Because they list a bunch of things in there that already sound a lot like positive alignment to me. Seth (00:58:07): Right. Andrey (00:58:08): So, yeah. Seth (00:58:09): You guys talked to Askell, which I'm so jealous of. We read the Claude Constitution, and that's where I want people thinking about. That's the actual principles that we're building AIs on right now. What would it look like to get a different team together to have its own hierarchy of values in the Claude Constitution? Can we think about other ways of adjudicating whether an AI is following its principles? Yeah, because like Andre says, if you read the Claude Constitution, there's plenty of positive goals in there. Kevin (00:58:42): Well, so this goes to my point earlier, where I do think that constitutional AI presents a really interesting nexus for public engagement and public participation that we have be the mechanism by which people do feel like they're in control or have some degree of oversight with respect to AI because even if it were- Andrey (00:59:08): Mm Kevin (00:59:09): ... a citizens assembly of 1,000 people across the US who are engaging, let's say, with Claude in shaping Claude's constitution with one another, so on and so forth. Well, now if I'm like, "Well, hey, I know my buddy Alan was a part of the latest constitutional convention for Claude," and I told him, "Hey, bro, you better make sure that Claude's a little bit more of a fan of the Texas Longhorns or whatever." [laughing] Seth (00:59:36): Principle five. Kevin (00:59:37): Maybe now I'm like, "Hey, at least I had some chance of influence," or I know somebody who influenced that person. Whereas right now, I don't think most folks even know anyone who lives in San Francisco because no one can afford it, with the exception of you, Andre, which I'm pleased to hear you're in town. [laughing] But right now- Andrey (00:59:58): What are your secrets, man? Kevin (00:59:59): Yeah. Andrey (01:00:00): I'm on leave at Amazon. I think it's not a secret. Kevin (01:00:02): Yeah. That helps. [laughing] But so that, to me, is a promising vehicle by which we can use that mechanism of what does positive alignment mean in practice. I like your point, Andre, of like, "Hey, go present some scenarios. Go do that A/B testing of saying, how do you want Claude to respond to this very difficult, let's say, even democratic context. Should the president or should the president not invest in this stake in Intel?" Big, huge question. Let's see what people say. Let's see what values we can then deduce from their answers, right, and have that sort of inverse constitutionalism, which could be really interesting. But- Seth (01:00:47): Yeah, there was the MIT Moral Machine Project where they were doing... Did you ever see this? They did millions of trolley problems with people decentralized to see, would you rather run over one grandma or two criminals? Have you seen this? Kevin (01:00:59): No, I'm going to have to check it out. Seth (01:01:01): All right. You have to look that up. Kevin (01:01:02): How many criminals guys versus grandmas? Seth (01:01:05): Exactly. Well, it's the ratio. Can I actually ask you guys a question? This is kind of my big law question I was hoping to get an insight on, which is to us, this constitutional approach seems really promising, but to what extent do you think the constitutional part of constitutional AI, does that metaphor really hold versus where does that metaphor work versus where does it break down compared to something like the US Constitution? Kevin (01:01:34): Well, I'll start briefly by saying Anthropic has been clear, in defense of Anthropic, to say they do not intend this to be a constitution qua the US Constitution, something that evokes the same legal understanding. So it's important to note that the labs have tried to distance the exact mapping of, let's say, the frontier safety framework or the model spec or the Constitution to a legally binding document. Now, with that said, I think that the idea that we're going to have high-level principles and values instilled within something that's going to have an enduring and stable influence on something makes a heck of a lot of sense to me in terms of a constitutional analogy there, which is to say, hey, if you actually go read the US Constitution, there are a lot of huge open questions, which is the reason Alan and I have jobs- Alan (01:02:33): [laughing] Kevin (01:02:33): ... is because the Constitution doesn't specify how the president has to react here or what Congress must do there. And so I think there's a lot to the idea of forcing people to think critically and deeply about what it is they want to see from something that's going to have a very real impact on society, on the economy, and on governance. So in that regard, I think it's really important. I also think that the idea that it needs to be grounded in the sort of consent and ideas of the users is a particularly compelling analogy and nexus as well because the Constitution right now is in a state of, I don't know what the best word to say here. If you look at Gen Z- Alan (01:03:24): [laughing] Kevin (01:03:24): ... there was a recent poll by, I actually don't know how to say this name. IPSOS. Is it IPSOS? Seth (01:03:35): Ipsos. Kevin (01:03:35): I don't know. Seth (01:03:35): We say Ipsos. Kevin (01:03:36): Okay, Ipsos. So Gen Z, they're like, "Yo, I know we have a constitution, but we don't really care." Alan (01:03:45): [laughing] Kevin (01:03:45): "It's fine that it's there, but it's not something we wake up thinking about. It's not something that we think is necessarily in line and true with the country's values or practices." So what are the mechanisms we can create now to give people a sense of ownership or at least a degree of connection to the Constitution, I think is an open question with respect to AI constitutions, right? What is that enduring mechanism that makes people feel like they were actually a part of that process? I'm not sure. I'm not sure if deliberative democracy gets you there, right? If we pick 100 random Claude users, stick them in a room in San Francisco, and say, "Come up with the Constitution," does that solve the issue of people feeling like they didn't have a chance to shape it? I don't know. But I think it raises a whole set of really interesting questions. Seth (01:04:43): Can I ask- Kevin (01:04:44): Yeah. Seth (01:04:44): Okay, let me ask a couple of those follow-up questions. Or you go first, Alan. I want to hear what you think. Alan (01:04:48): No, no. No, that's fine. Seth (01:04:50): Oh okay. So follow-up question number one. When you talk about the US Constitution being kind of in tension right now, one of the ways it's sort of in tension is a lot of people feel like the Supreme Court is using the Constitution like a rubber band, right? And you can kind of read anything between the lines of it. Do you think that the AI Constitution is, in some sense, more reliable than a government constitution because there isn't a potentially politically motivated Supreme Court having to interpret it? Alan (01:05:26): Well, not to get too down the weeds about the Supreme Court. I would say all Supreme Courts are about equally politically motivated and- Seth (01:05:36): Correct Alan (01:05:36): ... people just don't like who's on the current Supreme Court, which is a perfectly- Seth (01:05:38): Sure Alan (01:05:38): ... fine thing to not like. I think that the question, and this is an empirical question rather than a conceptual question, is how good is this constitutional approach when it comes to alignment, right? At the end of the day- Seth (01:05:55): Right Alan (01:05:55): ... I think if you strip away all of the analogies to the legal system, which I get why they made, but I don't think are terribly helpful. I think the bet that Anthropic is making is- Seth (01:06:04): That was going to be my next question. [laughs] Alan (01:06:05): Well, I think the bet that Anthropic is making is that a kind of almost virtue ethics approach to alignment, where instead of trying to program specific behavior or have lots of hypothetical cases, instead you try to program in a kind of disposition, and that is what's going to, in the long run, get you the best outcome. As someone who likes virtue ethics, generally, I hope it works. But that is the big question. Also, if you think the Supreme Court is political, which of course it is because it's a human institution, I'm not sure why you wouldn't think that Anthropic is also political, right? At the end of the day- Seth (01:06:50): Sure Alan (01:06:50): ... and this is one of the great problematic, I think, disanalogies between the Anthropic Constitution and a regular constitution. A regular constitution is meant to be difficult to amend. That's kind of the whole point, right? It's not meant to be "regular, normal law." But the Claude Constitution is just set by Anthropic, right? Now, maybe what they mean, well, we've trained the model ... on this constitution in a deep way that it is difficult to override that particular model, because this constitution- Andrey (01:07:19): Corrigibility Alan (01:07:20): ... is baked deeply in, right? Yeah, exactly. But that, of course, doesn't mean that Anthropic can't train the next model with a different constitution. And so, yeah. From an alignment perspective, the constitutional AI approach might be really great, and that would be an enormous accomplishment. From a self-binding perspective, which is really what constitutions are meant to do in democratic theory- Andrey (01:07:45): Mm-hmm Alan (01:07:45): ... I don't think the Constitution does all that much. Andrey (01:07:48): Hmm. Kevin (01:07:49): Right. So the interesting question there would be, do we begin to see if this does become a nexus for public policy intervention, which is to say, "Hey, we want to see that you have a constitution. We want to know that you published it. We want to know that there was some degree of user involvement," or so on and so forth. Do we begin to see things like Alan's alluding to? Are there recurring opportunities by which we would expect the Constitution either lasts for a certain period of time, or when it needs to be amended at a certain period of time, or how that's disclosed and how that goes about? This is what I broadly refer to as the future of governance studies, which is to think about the new questions we haven't even asked about when and how we try to kick the tires on some of these AI governance mechanisms. But it also begs the question of, well, how good is the AI to begin with on the whole, right? And what is it shaping and what is it changing? An AI constitution may matter if it is indeed, in particular, being adopted into government processes, right? One of the things that I- Andrey (01:08:54): Yeah Kevin (01:08:54): ... have been toying around with, to go to Andre's point earlier about A/B tests, coming up with a hypo, a fact pattern based off of, let's say, a weird new First Amendment-related law that Congress passes. Grok will interpret that one way and come out with one answer. Claude will come out with the opposite answer. And now if you think about a justice on the Supreme Court going and entering that fact pattern to one of those models or the other, and the anchoring bias that then would result from having consulted that AI first, that one AI or the other. Well, now I do care as a member of the public. I really want to know which model are you turning to first, what are the values that are being baked within that model, and then brought into the public context. I think that has huge governance implications. And I think AI constitutions, to the extent they are corrigible, to the extent that they are robust in different circumstances, could be a really interesting means for that governance mechanism. Andrey (01:09:58): Incorrigible. Kevin (01:09:59): Incorrigible, yes. Thank you. Andrey (01:10:00): Yeah. Alan (01:10:01): Yeah. Andrey (01:10:02): There is an interesting thing that comes to mind just from that idea, was that probably for all proposed Supreme Court cases, someone should be doing the exercise of just running them through all the major models and seeing what happens and whether they agree or disagree. That could be a measure of, in some sense of like a deviation- Kevin (01:10:23): I keep meaning to- Alan (01:10:23): Type code that Andrey (01:10:25): Supreme Court- Kevin (01:10:26): Do it. Do it, Alan. Andrey (01:10:28): [laughs] Kevin (01:10:30): It would not be that hard. Honestly, I could do that- Andrey (01:10:33): No, it would be pretty easy Kevin (01:10:33): ... in a weekend. Andrey (01:10:35): Oh, yeah. Yeah. Kevin (01:10:36): Prove it. Alan (01:10:37): Yeah. Andrey (01:10:38): All right. That's our crossover experiment coming out of the pod. Kevin (01:10:41): Yeah, crossover vibe check. Alan (01:10:43): Yeah. We should do that, yeah. Kevin (01:10:46): It would go viral so quick, bud. [laughs] Andrey (01:10:53): So one thing I did want to mention, just that actually made me think of this. So a person who's been doing many such experiments is Andy Hall over at Stanford, and he's previously worked with Meta on the oversight board and their attempts at deliberative democracy. And I don't know enough about those details, but my understanding is it was all a big failure. And so I'm curious whether we can learn anything from that experience as we try to think about very similar problems with regards to the AI constitutions. Kevin (01:11:26): I've tried my hand to be a bit of a student of the oversight board, and I think that the realization is that an institution has to be shaped to its context and not based off of good New York Times coverage when it initially gets announced. Andrey (01:11:42): [laughs] Kevin (01:11:42): And that, to me, was kind of the biggest failure of the oversight board, where in abstract, it sounds awesome to have the former prime minister of Denmark, and to have this free speech advocate- Andrey (01:11:57): [laughs] Kevin (01:11:57): ... and to have this great lawyer, and so on and so forth. You put them all together on this oversight board. But then when you kick the tires on the oversight board itself, the thing that became readily apparent was just it wasn't suited to the scale of Facebook's content moderation issues. And so its opinions were coming out months after a hot- Alan (01:12:19): Yeah Kevin (01:12:19): ... topic needed to be addressed. Andrey (01:12:20): After the genocide already is over, they can ban the speech, right? Kevin (01:12:24): So not wildly helpful, and then the fact that it was comprised of more or less the Western world, whereas many of the cases were coming from the global majority, just didn't look great, for lack of a better phrase. When you have weird, W-E-I-R-D, weird people governing the rest of the world in terms of content moderation, you're running into red flags, too. And then I think you also have to recognize that undue complexity is often going to undermine an institution. So there were all these considerations about, okay, well, once an opinion comes out, then Meta may or may not adopt that resolution, but in some cases, it's binding, but in other cases it's not. But Meta might get back to you months later and tell you they've changed it. And so there was a kind of convoluted nature as to what the actual dynamic was between the oversight board and Meta. And then, of course, the composition of the board itself was problematic. And then again, the resources that were afforded to the board in terms of being able to go through the full scale Of the number of appeals of content moderation decisions was always going to be inadequate. And so I think the lesson from the oversight board is trying to create a Supreme Court for anything is a bad idea unless you're just trying to recreate the Supreme Court. You have to be [laughing] attentive to the actual circumstances you're trying to govern. And that's hard because lawyers, the people who design these systems, and here's where I s**t on my own profession, we're not known for being creative. We're not known for being imaginative. And so when you ask us, "Hey, what's a good new governing system for this bespoke crazy technology?" We're like, "Well, I don't know. A Supreme Court sounds like a great place to start." [laughing] And everyone's like, "Well, yeah. That seems spiffy." So I think that's the fundamental challenge is leaning into the fact that we can't just copy and paste the institutions we've relied on to govern somewhat related challenges in the AI context. We've got to be, to use Jack Clark's phrase, we need new regulatory technologies. We need new means of measuring and intervening and of just governing. But that requires thinking outside the box, and that's just not something that's on most law school curricula, unfortunately. But Alan, I don't know. Tell me if I'm wildly off base. Alan (01:15:07): No, I think that's right. Andrey (01:15:09): All right. Well, that was a really fascinating discussion. We actually had a lot more that we were hoping to cover, but given the long documents we had to discuss, I think it's not surprising that we only got through so much. So maybe there will be a part two someday. But really enjoyed this conversation. [upbeat music] Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • June 15 · 1 hr 19 min

    Ioana Marinescu on Insuring Workers for AI, Monopsony, and Philosophy

    This week we’re joined by Ioana Marinescu, labor economist at the University of Pennsylvania’s School of Social Policy & Practice, former Principal Economist at the U.S. Department of Justice Antitrust Division, and a member of Anthropic’s Economic Advisory Board. Ioana is one of the people who put labor-market monopsony on the antitrust map, and she’s now thinking hard about what the social safety net should look like if AI hits the labor market the way the optimists (and the doomers) say it might. We start with her Digitalist Papers essay, which proposes a flexible, two-tier toolkit: AI Adjustment Insurance (extended unemployment benefits + retraining + wage insurance, modeled on Trade Adjustment Assistance) for the churn scenario, and a scalable Digital Dividend — a broad-based cash transfer funded by a small tax on the digital sector — for the world where the jobs don’t come back. Along the way: whether to make policy now or wait, what counts as the “status quo,” moral hazard in mass unemployment, the TAA wage-insurance result that repaid its own subsidy, and Andrey’s “we can’t afford UBI” pushback. Then we get into her new model with Konrad Kording, (Artificial) Intelligence Saturation and the Future of Work”— why splitting the economy into an intelligence sector and a physical sector implies that output and wages saturate even as AI scales to infinity, the robots-vs-LLMs debate, and whether to just relabel “physical” as the non-automatable sector. We close with her DOJ years: defining monopsony, the transmigrante used-car collusion-and-murder case, the Penguin Random House–Simon & Schuster merger (yes, Stephen King testified), antitrust and AI, and a lightning round on ikigai, Camus, and Rawls vs. Mill. Links & References Ioana’s work * marinescu.eu — Ioana’s website · Penn SP2 faculty page * Ioana Marinescu, “Resilient by Design: Dual Safety Nets for Workers in the AI Economy” — The Digitalist Papers, Vol. 2: The Economics of Transformative AI (volume) * Konrad Kording & Ioana Marinescu, “(Artificial) Intelligence Saturation and the Future of Work” — working paper (Brookings write-up & interactive tool). The model finds wage growth can reverse once roughly a third of intelligence tasks are automated. * Ioana Marinescu, comments on Betsey Stevenson’s chapter — NBER, The Economics of Artificial Intelligence: An Agenda (the ikigai discussion) Concepts, papers & people discussed * Trade Adjustment Assistance (TAA) — the template for Ioana’s adjustment insurance; the wage-insurance component that got people back to work faster and was net fiscally positive * Betsey Stevenson, “Artificial Intelligence, Income, Employment, and Meaning” — the post-AGI meaning / ikigai argument Ioana was commenting on * “GPTs are GPTs” — Eloundou, Manning, Mishkin & Rock, GPTs are GPTs: An Early Look at the Labor Market Impact Potential of LLMs — the occupational LLM-exposure measure (”Eloundou et al. / Daniel Rock”) correlated with COVID-era telework * Pascual Restrepo — job-market work on skill mismatch and structural unemployment during automation waves * Daron Acemoglu & Pascual Restrepo, “Robots and Jobs: Evidence from US Labor Markets”. * Albert Camus, The Myth of Sisyphus; ikigai (Japanese: “reason for being”) * Baumol’s cost disease * John Rawls and John Stuart Mill (Utilitarianism) Antitrust & the DOJ * The DOJ Antitrust Division, monopsony in the labor market, and the 2023 Merger Guidelines * Judge blocks the Penguin Random House–Simon & Schuster merger (2022) on a labor theory of harm to authors — Stephen King testified for the government * The transmigrante used-car export case — collusion (and worse) in the US-to-Latin America used-car trade * Anthropic’s Economic Index and Economic Advisory Board * Leopold Aschenbrenner’s Situational Awareness — the “we’ll have to nationalize it” argument referenced on consolidation Previously on Justified Posteriors * Our episode on the Anthropic Economic Index. Our sponsor * This episode is brought to you by Revelio Labs, the leading provider of labor-economics data, available to academics on WRDS. Chapters * (00:00) Intro & sponsor * (00:47) The Digitalist Papers proposal: a flexible safety net for the AI labor shock — and why make policy now * (03:48) Why unemployment insurance isn’t enough, and the Trade Adjustment Assistance template * (05:51) What counts as the “status quo”? Banning AI vs. letting it run * (07:42) How much to insure: moral hazard, mass unemployment, and the three parts of AI Adjustment Insurance * (11:15) Skill mismatch (Restrepo), and how do you certify a layoff was “due to AI”? * (14:45) Did TAA buy social buy-in for free trade? Underfunding — and the wage insurance that repaid its own subsidy * (16:38) “Would Hillary be president?” General-equilibrium pushback and the ski-instructor problem * (19:28) Will the new jobs still be there in two years? The lump-of-labor fallacy * (22:09) Policy B: the Digital Dividend — unconditional, broad-based cash from a small digital-sector tax * (23:52) How to fund it: a sales tax, a sovereign-style fund, and deliberately slowing diffusion a little * (26:00) “We can’t afford UBI”: productivity growth, 0.5% vs. the deficit, and setting money aside ex ante * (30:47) Taxing digital goods: VPNs, evasion, and land-value taxes * (34:23) The motte-and-bailey worry, and the other reasons to like UBI * (36:05) The new model: (Artificial) Intelligence Saturation — intelligence vs. physical sectors, and the telework × AI-exposure correlation * (40:14) Gross complements: why output and wages saturate even with infinite intelligence * (42:23) Won’t enough intelligence just automate the physical world? Robots vs. LLMs * (45:52) “15% by 2030”: humanoid robots, cost, and bespoke vs. general-purpose machines * (47:58) Baumol, the “humanness sector,” and relabeling physical as the non-automatable sector * (48:52) The capital-share / profit-share puzzle: if they’re complements, why has the intelligence share risen? * (50:25) The DOJ years: monopsony, and what the Antitrust Division actually does (mid-roll sponsor at 51:29) * (54:52) “Assassinating rival CEOs”: the transmigrante collusion-and-murder case * (58:12) Favorite cases: Stephen King, the publisher merger, and the chicken-farmer monopsony settlement * (1:01:30) Antitrust and AI: foundation models, consolidation, and the natural-monopoly question * (1:06:05) Slowing AI by allowing market power; Leopold, nationalization, and diminishing returns vs. the singularity * (1:09:27) Substitutability, the AK economy, and short-run vs. long-run wages * (1:10:59) Lightning round: ikigai, Camus, and the myth of Sisyphus * (1:12:44) Can we build market-like mechanisms for ikigai? Loneliness and coordination costs * (1:14:13) The Anthropic Economic Advisory Board and the Economic Index * (1:15:21) What’s next: monopsony and industrial policy * (1:17:59) Favorite philosopher: Rawls vs. John Stuart Mill * (1:19:45) Sign-off Justified Posteriors is the podcast that updates its beliefs about the economics of AI and technology, hosted by Andrey Fradkin and Seth Benzell. If we changed your priors, subscribe, share it with a friend, and keep your posteriors justified. Intro & Sponsor [00:00 – 00:47] [00:00:06] Seth: Welcome to Justified Posteriors, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, excited to learn about what AI is other than what my bubbe says after I spill hot water on her, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. We’re very thankful to our sponsors at Revelio Labs, purveyors of fine data products. And we’re very excited to have Ioana Marinescu join us today. Welcome to the show, Ioana. Ioana: Thank you. I’m so glad to be here. Make Policy Now: A Flexible Safety Net [00:47 – 05:51] [00:00:47] Andrey: To get started — you have this very provocative, interesting piece in the Digitalist Papers about various social policy solutions for transformative AI scenarios. Could you tell us about the piece? Ioana: Absolutely. As part of doing this Digitalist piece, I was thinking, as somebody who has worked a lot on the social safety net: what do we do if AI leads to a lot of job loss, like many people are saying it would? We’ll talk later about the various scenarios, but assuming that’s at least a possibility we have to acknowledge, what would you want to have from a policy perspective? And so I was really thinking hard about devising a flexible policy toolkit that will be able to address issues in the labor market no matter how big the shock is. That was the overarching theme of the policy design I’m proposing — just to start a discussion. I’ve tried to propose some helpful options, but it’s really with the idea of, let’s talk about doing something like this, what are the pros and cons. [00:02:10] Andrey: So what are the options on the menu for — let’s say AI comes along, a lot of people lose their jobs. The first thing we should get started with: do you think we should be making policy today, or should we wait until something happens and then make policy? Ioana: I think it’s very important to make policy today, but in a flexible way — meaning the policy cannot depend on some very specific detail of exactly how AI is going to impact the labor market, because we don’t know exactly what’s going to happen. It’s important to put the policy in place today because the political process is very long, so it may not be able to come online quickly enough when we really need it. That’s one reason. The other is — and I work a lot on social insurance — for workers, they want to and should feel insured. “Whatever happens, we the government have got you covered.” If we don’t have that, and we’re just waiting for bad stuff to happen, that defeats the purpose of having a social safety net. That’s a core reason I think it’s good to have something in place sooner rather than later, even before all the effects of AI on the labor market have materialized. Seth: Something that will automatically kick in. Ioana: Exactly. Andrey: And why is — we do have some programs like that, like unemployment insurance. Why is unemployment insurance not enough in its current form? Ioana: Unemployment insurance is incredibly valuable, but if we have a big shock like AI, it’s going to affect a lot of people who will not necessarily lose their job forever, but simply have to change jobs — and that’s very costly. The whole purpose of the social safety net is to help people through those transitions. The thing is, we have AI, and the way it’s being deployed is a policy choice. We could say we’re going to try to stop AI, but we’re not doing that — and I’m not saying we should or shouldn’t, just that it’s a policy choice. We’re saying we’re not going to stop AI, we’re going to let it be. But then some people are going to get hurt, at least in the short run, and we need to do something so those people have something to fall back on. Just like with trade: we decided to have free trade, we knew some people were going to get hurt and lose their wages, and we put in place policies like trade adjustment assistance — which inspired some of my proposals — to make sure the policy we’d chosen wouldn’t leave people on the side of the road. That policy hasn’t completely worked, because it was underfunded, but the big point is: the technology is exciting and has a lot of benefits, we’ve decided to deploy it quickly, and some people are going to bear a cost. We just want to make sure we help those people. What Counts as the “Status Quo”? [05:51 – 07:42] [00:05:51] Seth: I’m really excited to hear your specific ideas, but I’m curious about this framing of what counts as the status quo and what counts as the policy shock. In the case of trade, you might say the status quo is protectionism and the policy shock is allowing trade — so it makes sense to frame the social insurance relative to that: you shouldn’t be worse off relative to introducing trade. But with AI it’s not obvious. It seems like the policy shock would be to ban AI — AI would happen without the shock. So why not say, “If you banned AI, there should be social insurance to help the people who would have been better off if we’d allowed AI to go full throttle”? How do you think about what the status quo is here? Ioana: I don’t know that the status quo is necessarily the correct reference point — that’s something you could debate. My point is rather that a lot of these technologies have a lot of ramifications, and there’s a decision we make about how we want to control it. Collectively, we’ve made the decision that we’re not going to try to control it too much in its effect on the labor market. And therefore we need to deal with the consequences of helping people who might get hurt, at least in the short run — even though, hopefully, in the long run it will be great for everybody, including them. So we can reassure people that it’s going to be fine. That’s part of the goal of the policy. AI Adjustment Insurance: Moral Hazard and Three Components [07:42 – 14:45] [00:07:42] Andrey: How do you know how much to insure? Full insurance would be very expensive, but it would also create a lot of moral hazard — we do want people making choices in anticipation of AI. If everyone just puts their head in the sand and pretends it’s not happening... Seth: “The automation insurance is too good. I want to get automated — give me that automation insurance.” Ioana: So the insurance is going to be incomplete. And I’ll talk in a moment about what I’m proposing, which is modeled on trade adjustment assistance. But it’s also important to know that some of the moral hazard issues in unemployment insurance — which is something I’ve studied a lot — are much less economically important during times of high unemployment. If what happens is huge amounts of unemployment — not necessarily forever, but a lot of people needing to change jobs — then typically there are too many people looking for jobs relative to the number of vacancies. In that case, the fact that some people might put in less effort to find a job, sending fewer applications, is actually fine, because collectively they’re sending a lot of applications. They’re shooting themselves in the foot by competing too aggressively for the limited number of jobs. [00:09:25] Ioana: So even if more generous unemployment insurance seems like it’s desensitizing people from looking hard for a job, the end effect doesn’t necessarily reduce the number of jobs found, because there are too many unemployed people relative to jobs. My work has shown that in prior situations like COVID. So in a situation like that, we should be far less worried about the disincentive effects of more generous unemployment benefits. And maybe now I’ll come to the policy — I call it AI Adjustment Insurance. It includes more generous unemployment benefits, meaning they last longer (again modeled on trade adjustment assistance); additional training; and a third component, wage insurance. Wage insurance means that if you find a job with a lower wage than your prior job, the policy covers part of that gap. That actually encourages you to take a new job even if the wage is a little lower — so it’s directly pro-reallocation. The training and wage insurance push reallocation, which counteracts the concern that longer unemployment benefits might discourage job search. Seth: Maybe one of your sources is Pascual Restrepo’s job-market paper — this idea that during an automation-driven unemployment wave you get a skill mismatch. Lots of people are applying, but they have the wrong skills, and that increases friction in the market. From an efficiency standpoint, it might not be the worst thing if some of the people with out-of-date skills aren’t looking for jobs. Ioana: Exactly right. That would increase the friction, and this policy, if well-implemented, has the ability to manage that friction better. [00:11:52] Seth: There are many reasons a person might lose their job. They could be losing it because they’re doing a bad job, or because of macroeconomic things that have nothing to do with AI — or it could literally be AI. Recently Coinbase claimed to fire a bunch of people because of AI, and a cynic might say their stock price was going down and crypto was struggling. Do you care to identify that? Is that an important part of the policy? Ioana: Of course you need to identify it, and there are going to be inclusion and exclusion errors — it’s not foolproof. Some people who should be eligible won’t be deemed eligible, and vice versa. But I believe we can put in place a process with reasonable accuracy. That was the case with trade adjustment assistance: the company had to certify that the job was lost due to trade. You can question it, but there was a process. In the case of AI, similarly — and I haven’t fully thought this through; if someone wants to do it, let’s do it. People in these offices know a lot about how to do this. An example: if you’re in an occupation that’s highly exposed, and there’s recently been investment in AI at the firm — buying new software that uses AI to do business services — that might plausibly amount to a layoff due to AI. It won’t be 100% accurate. You could do it at a micro level, trying to figure out whether this particular job got automated, or you could do a macro counterfactual simulation — in a different universe there would have been 30,000 more taxi-driver jobs, so you attribute some percentage of that to your loss. But you can’t do that if you need to decide right now whether this person gets the service. That’s interesting from a research perspective, but operationally we’d have to determine eligibility — maybe just being in an exposed occupation, though that might be too crude. Did Trade Adjustment Assistance Work? [14:45 – 19:28] [00:14:45] Seth: Before we move to your other policy idea — this trade adjustment policy that was supposed to get big societal buy-in for free trade was a glowing success, right? Everybody loves free trade... Ioana: The policy didn’t do well because it was underfunded. The amount of funding is a strict cap decided by Congress, so they just couldn’t spend more. The number of people who received it at all is very small relative to the number exposed to trade. However, for those who did receive it — and remember, I’d argue it wasn’t enough — it worked well. There’s a really cool paper looking at the wage-insurance dimension. So: I lost my job due to trade, I take a new job that pays less, and the wage insurance covers part of that gap for two years. What happened, which is fascinating, is that this component helped people return to work quicker — and it was fiscally net beneficial from the government’s point of view, because they returned to jobs that were no lower-paid than they’d otherwise have taken, started paying payroll taxes again, and essentially repaid their own subsidy over time. So at least based on that experience, it’s a highly effective way to support people through the transition. While the scale of TAA was too small, for those who got it, they benefited a good bit, and it was effective even from a fiscal perspective. Seth: If TAA was more generous, would Hillary Clinton be president? Ioana: Who knows? But I’m going to push back on the argument. Earlier you were making the case that with unemployment insurance, moral hazard isn’t an issue because in general equilibrium there aren’t enough jobs. But if we expanded the size of TAA, the general-equilibrium effects could also swamp the benefits — maybe only a small share of those people could effectively have found jobs, and if you gave the benefits to many more of them, they wouldn’t be able to take advantage. Seth: You mean because the fiscal cost would become significant? Ioana: No — they wouldn’t be able to find the jobs. Seth: Right. If a small number of people get wage insurance and there are some jobs they can take, they take them. But if you give wage insurance to everyone, many wouldn’t be able to find jobs — or they’d cannibalize jobs from people who would have gotten them anyway. Say I worked at a factory, and now I decide to become a ski instructor, and you give me wage insurance for that. There have to be equilibrium effects. Ioana: This is close to my heart, because I’m a big skier. For sure this increases competition for jobs wherever people decide to go. But from a micro perspective, wage insurance helps because people are now willing to expand to jobs that pay a little less. And mind you, it’s only for two years, so you need some commitment that the job is reasonable — and by that time you can increase your wage through returns to experience. It’s similar to research on job-search assistance: you put people on benefits and help them — or even require them — to apply to more jobs. What the research shows is that if you do that for a lot of people within a given labor market, it stops being effective, because they’re trampling on each other’s toes. Will the Jobs Come Back? The Lump-of-Labor Fallacy [19:28 – 22:09] [00:19:28] Andrey: Is there an underlying assumption in your proposal that the occupations people move into won’t go away within those two years? This is one of the big challenges — we have enormous uncertainty about exactly which labor markets are going to be affected negatively, and maybe some positively. What do we do about that? Ioana: I don’t think there’s any guarantee that in two years the places they go will be safe. But if we still have the policy and AI continues to provoke churn, they still have it to rely on and can find another job. Also — and this is less true for non-economists, but a lot of economists imagine there’s a fixed number of jobs, so if we lose a lot, there are only so few left and everyone’s fighting over them. That’s not how it works. Everything is connected, and especially if the technology improves production, there are positive spillover effects that make other jobs more productive. So there will also be a lot of job creation. At the same time, we don’t know exactly what those jobs will be, and it could take several rounds of adjustment. I don’t want to rule out that it could be very bad, but I don’t think a massive net loss in the number of jobs is the most likely scenario. It could be very bad in the sense that a lot of people have to change jobs, which is difficult. But as someone who’s prudent and wants to be flexible — that’s what my second policy is designed to address. What if a lot of jobs are lost forever? Then we need something to fall back on. The Digital Dividend [22:09 – 26:00] [00:22:09] Andrey: Do you want to tell us about that one? Ioana: The second policy addresses a situation where there’s durable mass unemployment — not just people needing to find a different job while other sectors grow, but a lot of jobs disappearing forever, not replaced by new ones, so some people are structurally, permanently unemployed. What do we do with them? This is especially important in the US, where most of the social safety net depends on you either having a job or looking for one. Even food stamps: as a so-called able-bodied adult, you can’t get food stamps unless you have a job or are looking. If there are just no jobs for a large number of people, there’s not much to fall back on. So that’s what policy B — the Digital Dividend — is meant to address. The idea is a cash transfer that’s unconditional and broad-based. In the specific proposal I give it to everyone, but you could make it broad-based so a lot of people benefit. You might fund it with a tax on the digital sector. I don’t want to tax AI specifically, but all sectors that can immediately benefit from it — a broad-based tax, so it’s harder to avoid. Seth: A profit tax? A consumption tax? An income tax? Ioana: I was thinking a sales tax, just to make it easier — but this is something we can talk about. Andrey: No sales tax on GPUs, or...? Ioana: Just a sales tax on all digital companies. We can talk about other options — this is a beginning. The point is it would be very small, and the broader you make it, the smaller it can be while still creating revenue. You’d invest it in a fund, and the returns come back to people as cash. Why this structure? The tax side can slightly slow the diffusion of the technology — and there are theory papers showing that if there are labor-market frictions and credit constraints, reallocation is painful for workers, so it can be optimal to slow diffusion a little. We’re not banning anything. And the revenue lets you pay people the cash benefit if we end up in the no-jobs situation. This policy can be expanded — that’s the flexibility. You can start very small, almost zero tax, but if we have mass unemployment you scale it up and grow the base to the whole economy. Can We Afford UBI? [26:00 – 34:23] [00:26:00] Andrey: All of us have had conversations with technologists who jump straight to UBI as the solution to all issues with AI, and this is one version of it. What I always tell them is that we can’t afford it — and we deeply can’t afford it. There’s some future where productivity gains are so large that the numbers pencil out, but I’ve yet to see a tractable, plausible version. If we did it today, the amount per person would be trivial. And for UBI to truly work — as something that lets people not work — it needs to be a massive transfer. It can’t be even a thousand dollars a month. Ioana: I hear you. But where the technologists are consistent with themselves is that they often think AI is going to revolutionize productivity. If that’s true, then it will be possible to have a reasonably high UBI. And that’s the whole point of my proposal — it’s conditional, we scale it up as needed. I could even envision it not being completely universal, but it should be broad-based, so people have an income to fall back on if technology is deleting a lot of jobs. Andrey: Let’s say a plausible scenario that a lot of economists believe: AI increases per-capita GDP growth by about 0.5 percentage points per year relative to baseline. We’re five years down the road, and in the limit it’ll be big enough to support everyone — but we’re not there yet, and a lot of people are out of jobs by that point. We might not get the super-productive world until well after we have economic displacement massive enough that wage subsidies won’t work. Seth: And we need that 0.5 percentage points just to deal with the current deficits — we’re already counting on it. But here’s what’s natural to me: you start today and make plausible projections — what’s a world with another percentage point of GDP growth a year worth, what’s a world with two? — and you set aside a fraction of that in advance for a UBI or digital dividend. You make the policy now, rather than after the crazy thing happens. It has that automatic logic I really like. Ioana: That’s exactly the point. I’m a little concerned that after a lot of people lose their jobs and the situation looks grim, it might be more difficult to say, “Now let’s have a big reshuffling of money to help these people,” especially when some people have made a lot of money. Whereas if we can commit more ex ante to putting money aside, it’s a bit of a veil-of-ignorance situation — we don’t know for sure who the winners and losers will be. So it can be socially easier to agree to put a parachute in place now, before you know whether you’re a winner or a loser. It’s a political-economy argument, but I think it’s important, because I really worry we get there without enough to support people, and the winners say, “Ah, too bad.” [00:30:47] Seth: My questions are more on the tax side than the spending side. We’ve seen many efforts to tax digital goods, and they’ve had a lot of problems. Where was a Netflix video watched? Where was an ad viewed? People use VPNs; these companies have no physical locations and move easily. How convinced are you that we could actually raise significant revenue from a digital tax when my VPN is in the Cayman Islands, so I’m not paying a sales tax in America? Ioana: That definitely needs to be worked out. With tax policy you always have to think about incidence and evasion — not necessarily illegal evasion, just ways around it. I haven’t done a detailed implementation calculation, but it’s probably feasible to find a version that works. You won’t eliminate evasion — that’s always true with taxes — but you want to think ahead of the incentives. That’s why we’re economists. It’s not like you slap on a tax and the money comes in; there are behavioral adjustments you need to foresee with a coherent design. In principle, it should be possible to raise a good chunk of money if we wanted to. Seth: Related to that — it’s not crazy to think all the rents go to energy producers or even landowners of energy resources. And if we’re taxing them, we might disincentivize energy production, which raises the cost of living. Land-value tax solves all problems forever, of course — I do like land-value taxation. Ioana: In the long run you want to tax the inelastic input, and land is the ultimate inelastic input — that’s something to think about in the long run. But the reason a digital tax can make sense in the short run is this idea of a small, moderate slowdown — not massive — that has the benefit of accumulating some capital to help people with later. As I said in my piece, I’d definitely expand the tax base at some later point, once we think the transition has happened. The Motte-and-Bailey Worry [34:23 – 36:05] [00:34:23] Seth: Are you worried about a motte-and-bailey situation? Your proposal is very modest, but I could see politicians using that logic to implement a massive tax-and-transfer scheme today under the pretense that it’s about the AI future — and I really worry we can’t afford it. Ioana: That gets into the social welfare function — you could call it politics, or simply what we want — and as economists, it’s not really our job; as citizens we can have views. Different politicians legitimately have different ideas about what’s important. Within my piece, I was proposing the digital dividend as a solution to AI unemployment, but there are other reasons to like UBI. I’ve written about UBI before and discussed some of them. So maybe you also like UBI for those reasons — that’s a tenable view, and it’s also fine to disagree. There could be a push to go big right now, and if that’s ultimately what people want and they convince the rest of us, that’s just how the democratic process works. Intelligence Saturation: The Model [36:05 – 42:23] [00:36:05] Seth: Maybe this is a good time to transition into the new macroeconomic model you came out with, which is informing your beliefs about how radical the changes might be — “(Artificial) Intelligence Saturation and the Future of Work.” I love a title with parentheses in it. Just to lay it out for listeners: it’s a very neoclassical way of thinking about automation, but with new twists — a nested constant-elasticity-of-substitution model with an intelligence sector and a physical sector. Why is it important to think about an intelligence sector and a physical sector as complementary, rather than one thing? Ioana: It’s important to distinguish them because of an empirical fact — it’s not yet in the paper, but I’ll add it in the next version. If you look across occupations at which were most teleworked during COVID versus their exposure to AI — the Eloundou et al. measure of LLM exposure — there’s a very strong correlation. The more an occupation was teleworked during COVID, the higher its exposure to AI, and conversely. By “physical” I mean an in-person job, where you need a physical human body. That doesn’t necessarily mean working with your hands — teaching in person is physical in my definition but not manual. It’s just an in-person job. Seth: I was curious about those examples, because you gave the example of an in-person lawyer giving oral arguments — but didn’t we do those online during COVID? Ioana: Some of it we did online, but for the economics, the important thing is how substitutable these things are. Online teaching is its own thing — it has a place and a function, but it’s not the same as in-person teaching. They’re differentiated products; you can’t easily replace one with the other. So based on the fact that AI exposure and the ability to do remote work are highly correlated, that justifies the distinction between physical and intelligence. There are also fundamental limitations of the physical world that are much more stringent than the limitations you meet scaling the virtual world. It’s much more difficult to expand human bodies and physical capital — that’s very slow — whereas you can scale up AI incredibly fast. It’s still not costless — data centers and so on — but you can do a lot, really quickly. This distinction is critical to understanding how AI could affect the economy. [00:40:14] Seth: So you’ve got an intelligence sector growing really fast and a physical sector that maybe doesn’t grow as fast. Let’s roll with the assumption that the two are gross complements — you need both to have a lot of output; you can’t just have the peanut butter or the jelly. What are the conclusions of the model? Ioana: The core conclusion is that if physical and intelligence are complements, then AI can grow the intelligence side incredibly — to infinite intelligence — but as long as the physical sector stays fixed (or grows much slower), the impact of growing AI saturates on both output and wages. By saturating, I mean output goes to a finite limit, a ceiling. Even with infinite intelligence and infinite AI, output is strictly bounded. Wages increase too, because they go together with output, but they hit a ceiling. That’s what we call intelligence saturation. This is super important because a lot of technologists see the progress of AI and imagine the whole economy could expand at a similar rate. This makes the strong point that here’s a scenario I think is quite plausible: you could expand like crazy in AI and still only hit a ceiling in output. Robots vs. LLMs [42:23 – 48:52] [00:42:23] Andrey: I understand the thought experiment, but saying intelligence goes to infinity while the physical is a constraint is a little strange — it’s pretty clear that with enough intelligence we’ll figure out how to make robots work, even self-replicating, learning systems. Seth: There are two different parameters in the model, right? You’ve got the output of the intelligence sector, and then the automatability of the physical sector. So Andrey’s intuition is: if we had a gazillion intelligence, don’t we fully automate the physical sector? Ioana: I want to distinguish two things. One is whether humans can be replaced with robots. Robots are improving, but relatively slowly compared to AI. So comparatively, it’s much more advantageous to replace people on the AI-replaceable side than in applications where you need to be there in person. That’s not to say there’s no progress in robotics. Andrey: I’m just representing the technologist viewpoint — that this is true for now. Ioana: The argument I’m going to make is based on history — the history of technology specifically, and you can think history won’t be the same. We’ve had physical robots for the longest time. With the Industrial Revolution we created many more and improved them a lot, and even before LLMs they improved tremendously with machine learning and semi-autonomous systems. These are very real improvements, and they have replaced some jobs in manufacturing — Daron Acemoglu has papers on that. But it wasn’t like, “wow,” thanks to all that intelligence in the system, it still takes a lot to get there. I’m totally willing to think this new technology can make them even better — but I’m skeptical it gets a lot, lot, lot better. It’s the saturation argument: there’s a fundamental limit. The cost of robots is fundamental — you have to use materials to make them and maintain them. We’ve tried for centuries to improve robots, and they have improved, but I don’t know how much more you can improve them with this new technology. Andrey: A humanoid robot that’s smarter than a human seems like a pretty big improvement that’s plausible. Seth: 15% chance, according to a recent survey of economists and AI researchers. By 2030. Ioana: The question is partially the cost — not that it’s technically impossible, but what’s the cost of the whole thing relative to a human. At least in the medium run, I think it’s not very plausible you’ll bring that cost down a great deal. Also, a lot of these robots aren’t very versatile, unlike AI. That’s the cool thing with AI — it’s super general-purpose; it can use all the tools we had before. But most industrial robots are bespoke, meant for a particular application — that’s how you make them cheap and effective. I feel somewhat confident that in the short-to-medium run it will be very difficult to make it cost-effective to have robots replace people in most jobs. Harder to tell the further out you go. However, I believe everything you could do on a computer could get automated in the medium term — possibly even the research I’m doing. That’s a different matter, because it’s all based on computers. Andrey: Even if what I’m saying is true, the Baumol-style logic would still hold, right? If we still want human teachers even when robots are available... Seth: Maybe don’t call it the physical sector — call it the humanness sector. Ioana: You could relabel it. I think “physical” is relevant at least in the medium run, but to make it more future-proof, you could relabel the physical sector as the non-automatable sector, whatever that turns out to be. As long as there exists a non-automatable sector, that’s where people will work, and the mechanics of the model are identical. The Capital-Share Puzzle [48:52 – 50:35] [00:48:52] Seth: I love playing around with these neoclassical models — automatable part, non-automatable part. I’ve been doing it for a decade, and one challenge that pushes historically in a different direction: if intelligence and physical stuff are gross complements, you’d expect that as you get more intelligent stuff, its share of national income would go down. But over the last 50 years we’ve seen a huge explosion in education in the US, and yet the educated share of income has been going up. So how do I think about it actually looking more like gross substitutes? Ioana: But we’ve also had the share of capital going up. Part of the last 30 years or so is the ICT revolution, which is somewhat similar to the prior version — really you could say it’s the same thing, just different stages of an AI revolution that’s at an early stage. During that, the share of capital has been going up, and research suggests— Seth: Is it the share of capital, or the profit share? That’s an important question we’ll come to in a minute. Inside the DOJ Antitrust Division [50:35 – 58:12] [00:50:35] Andrey: You did this stint at the DOJ — you were on leave from being a professor. Could you tell us what led you to work there, and a bit about your work? Ioana: At the time I was doing work on monopsony power in the labor market — the difference between wages and the marginal productivity of labor. The Biden administration commissioned a report on labor-market monopsony, and the people doing that called me up; that’s how I learned about the job. I thought, “That sounds really interesting — to do antitrust enforcement as an economist.” I said yes, and I was lucky enough to get the job. [00:51:29] Seth: [Sponsor break] For those of you playing along at home, now is your chance to think about how this conversation has changed your priors. This chance to contemplate your posteriors is sponsored by Revelio Labs — a leading provider of labor-economics data and data services for companies, academics, and independent researchers. Revelio combines comprehensive micro-level data on employee profiles, job postings, and sentiment with standardizations, mappings, and enrichments, all to make the data useful without making your modeling decisions for you. It can be aggregated to company, market, or industry, and used to study everything from career trajectories to occupational transformation to the impact of AI on labor demand. Revelio data is available on WRDS — so if you’re an academic with a good library, go see if you have access already. And if not, reach out to their excellent economics team. [00:52:43] Ioana: It was an incredible experience. What I did there is — I was the principal economist— Andrey: Can you pause and define monopsony for our listeners? Ioana: Monopsony power is the idea that employers are able to pay workers less than their marginal productivity. Under perfect competition, the wage exactly equals the marginal productivity of labor — whatever value the worker brings, the company pays them for it. With monopsony power, workers get paid less than what they bring to the company. One of the key reasons is the lack of competition among employers. Intuitively, in the extreme — a literal monopsony, only one employer — that employer doesn’t need to pay much to keep you. Whereas if there are many employers, they bid up the price of labor by competing for you, and in the competitive extreme you get paid your marginal product, because if someone underpays you, a neighboring employer recruits you away. Andrey: Great — so you can continue. Ioana: So when I was at the DOJ, I was the principal economist. In the antitrust division, the job is to enforce the antitrust laws, and they do two broad things. One is examining mergers and potentially blocking them if they lead to anti-competitive effects. The other is so-called conduct, which can include criminal conduct like literal collusion— Seth: Assassinating rival CEOs. Ioana: A situation kind of like that — which is unbelievable. You think it only happens in the movies, but it happens in real life. Economists don’t get too involved in those cases, because it’s more of a whodunit. Andrey: No fun. Let us in — we want to be detectives. Ioana: If you want to look this up, the keyword is transmigrante — a trade in used cars from the US being traded toward Latin America. There was unbelievable murder among the companies involved, around collusion. If you were a traitor to the scheme... yes. Anyway, that’s the monopsony power. Andrey: That’s one way to get monopsony power. Ioana: This is where the FBI goes — not really the province of economists. The other category is a bunch of behaviors by firms that hinder competition, the big one being monopolization — trying to remain or become a monopoly by kneecapping rivals. In my role I oversaw the expert analysis group, a team of about 50 PhDs, mostly economists and data scientists. Whenever we had a case against a company, there’d be data gathering and economic analysis to support arguments about why a behavior hinders competition and might, for example, increase prices — or, in a labor-market case, how employers hinder their employees’ ability to find another job, and so can pay them less. For someone who worked on monopsony, being able to think about how mergers should be blocked if they lead to greater monopsony power was incredibly rewarding. Intuitively, if two employers merge, that reduces competition for workers and can lower wages or degrade non-wage dimensions of jobs. That’s now officially in the merger guidelines — which was unbelievable. How often do you do research and then get to implement the thing? Favorite Cases, and Antitrust Meets AI [58:12 – 1:09:27] [00:58:12] Andrey: Is there a monopsony case you worked on that you’re particularly excited about? Ioana: There were a number. They’re described in papers we write every year reporting on finished cases, in the Review of Industrial Organization. One — I only caught the tail end — was a publisher merger. Big publishers were trying to merge, and the argument was that if they merged, authors trying to sell their books would get lower payments. The judge found it very convincing. We even had Stephen King testify about how the merger would reduce what he could get paid. Andrey: He couldn’t afford all the cocaine he needed. Ioana: Ultimately the merger was blocked, and they decided not to appeal. That was the first merger in the US blocked exclusively on a labor theory of harm — that it would lead to lower payment for authors. The other is an interesting case around small farmers who raise chickens. They work for a processor as subcontractors — small farmers, not workers, but worker-like. They raise chicken and sell it to a big integrator. There was a contractual term that if you left to work for a different company, you’d have to pay a big chunk of cash to leave. We argued this significantly restricted competition for these farmers’ services and lowered their pay, because it’s hard to leave if you have to pay to do so. We won in the sense that there was a settlement — the company said, “Fine, we’re not doing it anymore.” I did a lot of work on agriculture, because farming is often an area with few opportunities to sell labor or goods, so monopsony is prevalent there. This tells listeners that monopsony — whether there’s competition among buyers — isn’t just about workers. Workers are a big application, but it can also be a more B2B situation, where many small businesses or independent contractors sell to big buyers with market power. [01:01:30] Andrey: Shifting slightly — something people are beginning to think about is antitrust and AI. Have you thought about that? Do you have opinions? Ioana: It’s really important to stay vigilant in AI and antitrust. We’ve had prior tech giants the government has gone after — Microsoft, Google — and in these industries there can be an opportunity to monopolize. We’re not there right now, but that’s why we have watchdogs like the Antitrust Division. There’s been a lot of partnership and financing deals, which might ultimately lead to consolidation, and that should be watched in the ordinary course of antitrust enforcement. Why does this matter? We want to maintain low prices and high-quality services for consumers and businesses — and a big part of AI is used by businesses. If you want this technology to lead to greater productivity through adoption, you want to keep it cheap and good. What usually happens with consolidation is the product gets worse and prices get higher than they’d be in a more competitive industry. It’s natural for companies to try to monopolize — that’s why we have the Antitrust Division and the FTC, and also so that companies considering certain steps recognize some might not be lawful and stay away from them, preserving competition. Seth: That’s generally the argument, but sometimes we have natural monopolies. Some argue these big foundation-model builders — OpenAI, Anthropic — pouring giant amounts into training runs might be natural monopolies. Maybe we just want one company doing the one giant training run, and the right way to regulate isn’t competition policy but tax policy or some other government control. What do you think? Ioana: It could be, but it’s not clear yet, because there are still many foundation models, including outside the US — the data is out there. You need data and power to train, but you can do it multiple times if you have the resources. There’s also a difference: in the US there’s a big focus on the biggest, fastest foundation models, but in places like China it’s much more focused on applications, and there you see a lot of competition. Some company might want to have it all — “Why don’t I have all the applications?” — and that’s why we have the antitrust authority to stop that. In some situations a utilities-type regulation can make sense, but I think we’re not there yet. For now I’d take the position that we should promote competition, and we’ll see where the dust settles. If you prematurely favor monopoly, that can actually hinder the development of the technology. So I’ll err on the side of competition. Andrey: One interesting thing here: if you’re the person who thinks we should slow things down, then you should be rooting for more market power. Leopold famously argued the technology is so powerful we’re going to have to nationalize it — which again goes to an argument for more market power rather than less. I’m not saying I support this, just throwing it out as a slightly unusual difference from most industries. Seth: Energy might be similar — or nuclear power would be a different analogy. Ioana: The thing is, you can still use a model from elsewhere — whereas with energy there are huge costs of transmission lines, so it’s more limited geographically. And let me make a point related to my saturation paper that’s highly relevant. Assume there are decreasing returns — you add more intelligence, it’s helpful, but less and less helpful at the margin. If that’s the case, then the whole competition between countries is less— Seth: Then America loses, because China is better at physical stuff. Ioana: The point is rather that being first is not that important if you have diminishing returns. It’s important, but less so, because if you’re second, you’re just a little bit worse and can still do a lot of things almost as well. Whereas if it’s “the singularity, boom,” and then you’re far ahead of everyone, being there first really matters. So whether you think you’ll reach a point of explosion versus diminishing returns completely changes how you think about competition between countries — and even between different models. Andrey: There’s a subtle point: you could have diminishing returns, but the nature of military conflict is a contest — you just need the max. So it’s very different from the economy. Ioana: I feel less of an expert on military. I was talking more about economic might — if there are diminishing returns, it’s nice to be first, but it’s only— Seth: Let’s talk about economic might, because your argument is even stronger than the one you’re making. In a universe where American innovations in AI spill over into Chinese innovation — which they do, with distillation and publicly written papers — if China has the advantage in the physical, we’d want a world less constrained by the physical. We’d want slower progress. Ioana: That gets to things we discuss in our paper — how much substitutability there is between physical and intelligence from the workers’ point of view. The paper is about what happens to workers and equilibrium wages during automation versus after. During automation — assume you’re automating all intelligence tasks — low substitutability is a form of insurance for workers; it avoids some of the worst wage outcomes, especially at high levels of automation. But after, once we all work in the physical sector, more substitutability promotes higher wages and growth in the very long run. So the game is different depending on whether you’re in the short run, during automation, or after. Seth: In the very long run, you want an AK economy. Ioana: Exactly. In the long run it’s good, but in the short run it could be better not to have it, in terms of avoiding a wage decline. Lightning Round [1:10:59 – 1:19:45] [01:10:59] Seth: Lightning round. What’s the meaning of life? In your discussion of a Betsey Stevenson paper at a recent NBER session, you said that after automation takes all our jobs, something called ikigai will be more important. What is that? Ioana: It’s the idea of having a sense of the inherent meaning of your everyday activities. This is something Betsey Stevenson proposed, and I was commenting on it and thinking about examples from philosophy. Ikigai is a Japanese concept, but we have other examples in Western philosophy — the French writer Camus, and the myth of Sisyphus. You imagine Sisyphus pushing a boulder up the hill; it rolls down, and he pushes it up again. It seems pointless. However, the myth says you have to imagine Sisyphus happy: he finds satisfaction in the repetition and transcends the fact that it’s repetitive, making sense of his life by becoming absorbed in it and seeing his freedom in embracing it. It’s a very inspiring way of thinking, because in the current regime we’re so obsessed — especially in economics — with making more stuff, versus paying attention to what we already have. Seth: But then you immediately economist-brain it, because you have this amazing quote: “If there are no market-like mechanisms to encourage people to pursue their ikigai, just as wages incentivize people to work, a world without transformative AI”— sorry, a world with transformative AI but without work could undermine wellbeing. So how do we incentivize ikigai? Ioana: That’s something I want to work on — so if anybody’s listening and wants to embark on this quest, I’m all for it. Maybe you’ve read about the epidemic of loneliness. Why aren’t people getting out and doing social activities? As economists, we think in terms of cost — there must be friction costs, coordination costs. The question is how you engineer a world where those costs are lower and people actually get out, meet friends, do their gardening or their rock-pushing, instead of sitting there contemplating how they’re doing. Andrey: You climb the rocks rather than push them. Seth: We’ve got a rock climber in the room. The social planner will assign you the rock, and you will experience ikigai pushing the rock. Ioana: No, no — it’s going to be a market-like mechanism. [01:14:13] Seth: Do you want to say anything about your work on the Anthropic Economic Advisory Board? Ioana: I’m a member of Anthropic’s economic advisory board, advising them on the economic impact of AI. As you probably know, Anthropic releases data products publicly that measure how Claude is being used. Part of my role is to give feedback on what data would be helpful to release, what checks to do, how to show people what the data means and what its representativeness looks like. It’s been exciting to collaborate with one of the biggest AI companies and play this advisory role. Andrey: And we’ve covered the Economic Index on this podcast — we had an entire episode about it. Ioana: Oh, really? Nice. Seth: What are you working on these days? What can we expect next? Ioana: Right now I’m working on a project, back to monopsony — monopsony and industrial policy. Industrial policy is fashionable right now. If you subsidize a sector — the government pays to create more jobs there — one effect is that it raises wages in the other sector that competes for those workers. You increase employment here, and wages increase there. We want to demonstrate under what conditions you get a bigger or smaller spillover, and therefore why industrial policy can sometimes be justified through this argument — you’re paying to get more competition for jobs. Seth: Although there would be a negative spillover from the taxes or regulation needed to support it. Ioana: Absolutely. You put some cost in for the industrial policy, and one benefit is increasing wages in the non-subsidized sector. It’s also a way to redistribute between wages and profits — you decrease profits and increase wages in the non-subsidized sector. And profits are pretty hard to tax, so it’s an interesting instrument. We’re developing the theory — how much monopsony power yields what optimal size of subsidized sector — and thinking about applications. The big-picture point is that the public has often lost confidence in the government’s ability to redistribute effectively through tax-and-transfer. So if we can provide more jobs while also increasing wages in the other sector, that could be an interesting policy instrument — one that comes with its own costs, but worth understanding better. Andrey: Final question: who’s your favorite philosopher? Seth: And we love the way you pronounce Camus, so say it with that beautiful accent. Ioana: Who’s my favorite philosopher? That’s surprisingly difficult. I might go with Rawls — John Rawls. Seth: Beloved of liberals. Ioana: He’s done incredible work, because he incorporated considerations from utilitarianism. Whether you agree with his take or not, he clarified a lot about the different theoretical frameworks for thinking about social justice. When I was growing up intellectually, it was very helpful. Actually, I learned about utilitarianism first — I read Mill. Oh, I love John Stuart Mill. Maybe he’s my favorite, actually — because the writing is amazing, and he has such a nuanced view of the world. His book on utilitarianism is amazing. They speak very nicely to each other — so maybe I have to say John Stuart Mill, which is fitting for an economist. Andrey: Tyler Cowen thinks he’s the best economist ever. So you’re in good company. [01:19:45] Seth: It’s been an absolute pleasure to have you on the podcast — I had so much fun. Ioana: Thanks so much. It was great. Andrey: This was awesome. Thank you. Seth: All right, everyone out there — please like, share, subscribe, and keep your posteriors justified. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • June 1 · 1 hr 37 min

    Kevin Bryan on Bottlenecks, AI in China, and What Economists Should Actually Be Working On

    This week we to with Kevin Bryan, Associate Professor of Strategy at the University of Toronto’s Rotman School, author of the legendary economics blog A Fine Theorem, co-founder of the ed-tech startup All Day TA, and the man behind one of the most-discussed Twitter/X feeds in econ, @Afinetheorem. Kevin recently published a multi-book review of the economics of AI in the Journal of Economic Literature, and that’s where we start. Along the way we get into the gap between AI’s technical capability and its actual diffusion, the stages of how organizations adopt new technology, why the binding constraint on AI value is organizational integration (not prediction vs. judgment), what an AI-for-science research agenda should look like, the coffee test and the fence-post test, what forecasting surveys reveal about how economists and lab researchers actually differ, a dispatch from Kevin’s recent trip to China (spoiler: they are not AGI-pilled), the future of the academic paper, and a lightning round on comparative advantage in the age of AI. A wide-ranging, opinionated, very fun conversation. Grab your Chinese peptides and settle in. Links & References Kevin’s work * Kevin Bryan, “The Economic Impacts of Artificial Intelligence: A Multidisciplinary, Multi-book Review” — Journal of Economic Literature, 64(1), 2026. * A Fine Theorem — Kevin’s research blog * All Day TA — turn course content into a custom AI teaching assistant * Creative Destruction Lab — the accelerator Kevin helps run (first AI accelerator in the world, 2016) Books & essays discussed * Leopold Aschenbrenner, Situational Awareness — the essay Kevin gives all his students (”read chapter one, believe chapter one”) * Erik Brynjolfsson & Andrew McAfee, The Second Machine Age * Ajay Agrawal, Joshua Gans & Avi Goldfarb, Prediction Machines and the follow-up Power and Prediction * Joel Mokyr, The Gifts of Athena and A Culture of Growth — Kevin’s PhD advisor, “the Michael Jordan of progress world” People & projects mentioned * The Unjournal and Works in Progress — models for the “new journal” * Chad Jones, Stanford GSB — growth theorist read seriously by people in industry * Phil Trammell, GPI / Oxford — “Phil World,” the rapid-growth scenario * The coffee test (attributed to Steve Wozniak) and Kevin’s own fence-post test as benchmarks for embodied AGI Previously on Justified Posteriors * Avi Goldfarb — Prediction Machines, O-Ring Tasks, and How AI is Reshaping Economics * Alex Imas — Demand Collapse, Bargaining with Machines, and Behavioral AI Economics Our sponsor * This episode is brought to you by Revelio Labs, the leading provider of labor-economics data, available to academics on WRDS. Chapters * (00:00) Intro & sponsor * (00:39) The JEL book review: what the economics-of-AI canon got right — and what the older books still beat the new ones on * (03:19) Prediction vs. judgment, and the real bottleneck: organizational integration * (05:52) Too pessimistic on the tech, too optimistic on diffusion — Waymo, Pearl Street, and the COVID vaccine * (12:34) The four stages of how organizations actually adopt a new technology * (15:42) Status-quo bias, banning Anthropic, and treating frontier AI like nuclear material * (20:16) Why Situational Awareness beat the economists, and the book Kevin actually wants: AI for science * (26:53) Forecasting AI: the surveys, and where economists and lab researchers do (and don’t) diverge * (28:20) Benchmarks, the coffee test, and the fence-post test * (35:53) Rapid-growth scenarios, labor-force participation, and “Phil World” * (41:40) Scaling regularities: what economists should defer to technologists on — and what they shouldn’t * (43:34) Why forecasts matter for policy and capital allocation * (45:50) Dispatch from China: not AGI-pilled, “involution,” broken capital markets, EVs and self-driving * (1:01:40) War, nationalization, the end of open source — and why everyone in China uses Claude * (1:06:06) A Fine Theorem, the economics of blogging, and the rising value of taste * (1:17:48) The economist as plumber: comparative advantage, RCTs, and what grad students should do * (1:24:07) What the academic paper looks like in two years * (1:28:22) San Francisco, ambition, and the permission structure for growth * (1:32:56) Lightning round: favorite economists, All Day TA, and advice for econ grad students Open & Intro [00:00 - 00:39] [00:00:12] Seth: Welcome to the Justified Posteriors Podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, finally able to meet one of my theoretical heroes, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco. Excited to have Kevin Bryan as our guest today. Kevin, welcome. Kevin: Thanks for having me. Very excited. Andrey: Kevin is a leading thinker in the field of progress, and in AI economics. He also has his own startup, All Day TA, and is prolific on Twitter — at times. Kevin: At times. The JEL Book Review: What the AI-Econ Canon Got Right [00:39 - 03:19] Andrey: Kevin, you wrote an article reviewing several prominent books on AI. Why did you do this, and what did you learn from the exercise? [00:01:13] Kevin: It’s pretty interesting. Economics of AI is not that new of a field — some of the canonical books on how economics thinks about AI go back to before large language models existed. Books like The Second Machine Age by Brynjolfsson and McAfee, and Prediction Machines by Agrawal, Gans, and Goldfarb. These are pre-LLM — written before the attention paper. So it’s interesting to look at what of the core ideas in the economics of AI have changed given the technological improvements. On the technology side, I don’t think there have been massive surprises for people who were paying attention. At least since the scaling law paper, if you’d drawn the line on the graph, you’d have more or less predicted everything that happened. I remember reading Kurzweil — The Age of Intelligent Machines, The Age of Spiritual Machines — back in college, and those are just drawing different lines on the graph, in that case based on compute, and we’re getting very close to what actually happened. Likewise on the economic side: given that the technological trajectory hasn’t changed much, I don’t think the underlying economics has changed as much as people might think. Where things might be bottlenecked, how technology improvements map into growth, the effects on labor markets — the fundamental microeconomics of AI’s predictions hold up pretty well. I found it interesting how few of the 2023, 2024, 2025 books had really advanced my understanding of the economics of AI compared to the older ones. Prediction vs. Judgment, and the Real Bottleneck [03:19 - 05:52] [00:03:19] Seth: Lots to unpack. We just had Avi Goldfarb on the podcast and pressed him on his Prediction Machines approach, where he distinguishes the AI that’s good at predicting from the human that’s good at judging. If any of these books would have changed after gen AI, it’d be that one. Don’t you think that book maybe gets something wrong? Kevin: I think they’d agree — they wrote a follow-up in Power and Prediction. But the disagreement isn’t about the prediction-versus-judgment distinction. Even in the original book — and I remember talking to them about this in 2016, 2017 — judgment is a sliding scale. Take the umbrella example: I know my utility function on an umbrella, I know how much I dislike rain. I give the AI data, it looks at my face, sees light rain, heavy rain, and it can predict my utility function — in which case judgment is taken over by AI. Everyone understands that. That said, on the scale of how easy it is to figure out the underlying utility function from data versus the predictions that go into it, I don’t think that’s changed. None of the major language models technologically can — or even attempt to — modify how they operate for me versus you. They store a little memory and RAG their way into remembering what you’re like, but there’s no attempt to fine-tune the model. We’d like to use continual learning, but we can’t yet. So the judgment aspect is still pretty binding even today. Where I think there’s a difference — and where Ajay, Avi, and Josh would say they were wrong — is that the fundamental problem for AI’s creation of value isn’t prediction versus judgment. It’s the organizational integration problem. There’s overlap between the two, but we’d take the organizational and architectural bottlenecks more seriously now, partly because we’re applying AI to more complex tasks where those bottlenecks start to bite. Too Pessimistic on Tech, Too Optimistic on Diffusion [05:52 - 12:34] [00:05:52] Seth: You point this out with The Second Machine Age — Andy and Eric’s world-historical automated car ride. Andrey: It’s weird to think that in some ways they’re a little too pessimistic about the technology, but a little too optimistic about social diffusion. The driverless cars going down the highway in California are a perfect example. Kevin: Such a good example. We all talk to different audiences. When I talk to policy people, I tell them: “Whatever you think the capabilities of AI will be in the future — more than that.” This isn’t a sales pitch. Every single person inside the lab agrees. You have people high up in government who think about AI as the AI of today plus epsilon. And you want to ask: what did you see in the past 10 years that makes you think this is a good way to plan for the future? [00:07:01] On the other hand, out in California they wildly underrate diffusion friction. I give the Waymo example: if diffusion is so easy, how come we rode in a Waymo 10 years ago? I’m in Toronto — Jeff Hinton’s city — and there’s not a single one. Clearly there’s some friction. I remember a couple of years ago, Andrey was with us at one of the labs with a few other economists. They brought in a bunch of computer scientists and asked, “What’s the effect on GDP productivity in the short run?” And we said, “Through 2030, maybe 1% per year.” Actually we said less. And to be fair, from 2024 to 2026, we’ve been right so far. They said, “But why?” And we said, “We agree with you technologically.” At the time we saw technology that hadn’t come out yet that everyone now thinks is amazing. But on the production side you’ve got bottlenecks — you’re combining complements in some CES or Cobb-Douglas function, and you don’t need many bottlenecks for growth to stall quickly. Then there are social diffusion factors, regulatory factors, organizational architecture factors, like in Kim Clark and Rebecca Henderson’s work. Add all these up across every technology ever, and I just don’t see fast takeoff. Honestly, I think it’s bad. I think we’ll be able to make personalized medicine very cheaply much more quickly than regulators will allow you to sell it. That’s a problem. Andrey: The standard retort is one of two things. One: it’ll be so self-evidently good that people will find a way to take it — like a cure for cancer. There are already sub-treatments where rich people take them without FDA approval and claim they work. Two: we have autonomous zones where we let AI do whatever it wants, and they out-produce the rest of society. Kevin: Great arguments both. Out here in San Francisco you’ve probably got a bunch of Chinese peptides in your fridge. [00:10:20] But here’s the thing — the Chinese peptides are self-evidently really good at the single most costly part of the medical system. And yet can you legally buy them? Anywhere? You can’t even legally buy them in China. That self-evidence did not change the regulatory process. We just went through COVID. We had the vaccine in January 2020. Everything from then until it diffused was government. What more important thing to diffuse quickly could there be? Waymos — you sit in one for one minute and it’s obvious it’s driving more safely. Andrey: People start crying. It’s so beautiful. Kevin: And yet. We saw this historically too. If you want self-evidently useful: Pearl Street, 1882, Edison flips the switch — let there be light. And yet look at the diffusion rate for electric street lighting, especially after Chicago burns down. The Four Stages of Organizational Adoption [12:34 - 15:42] [00:11:49] Andrey: Let me retort. Diffusion of technology has accelerated over time. The smartphone diffused extraordinarily quickly. Aspects of AI have diffused even quicker — by standard adoption measures, almost everyone has used AI at least once. Seth: And how much do regulations matter for the diffusion rate? If you ask my students, regulation against using AI hasn’t slowed their adoption at all. [00:12:34] Kevin: We have an answer to this — it’s actually easy. This is why I like starting with a little organizational economics. Every technology I’m aware of, ever: first we adopt it when individuals can do existing tasks with the new thing more efficiently. That’s easy — that’s your student cheating on their exam, the coder using it for coding, me brainstorming with GPT to prep for a meeting. The next step is a group or an organization doing an existing task using the new technology. That’s tougher. To give you a sense — how many organizations have changed their IT procurement policies given the change in how we make software? Find one. The third stage: a new task that’s now efficient given the new technology, inside my organization. I haven’t run into a single large incumbent organization that has reached this level for anything important. The fourth is the hard one: a new task that’s only efficient because of the new technology, and that requires something on the outside — partners in the supply chain, regulators, someone to change. That’s Waymo. That’s containerized shipping. That’s UPC codes. The fundamental barrier there isn’t information or firm growth rates. It’s that the institutions are built around existing skills, promotion policies, and so on. If AI made the optimal university 50% capital and 50% labor — bringing people in and out instead of having a tenure system — what year do you think we get that? You can maybe out-compete the incumbents, but I don’t think Harvard’s reputation goes away that quickly. And if we’re talking about governments, you can’t even out-compete them. Status-Quo Bias, Banning Anthropic, AI as Nuclear Material [15:42 - 20:16] [00:15:03] Seth: Sometimes you can out-compete governments — it depends how crazy a takeoff we’re talking about. I talk to Phil Trammell about scenarios where the world is too decadent and we don’t save enough, so we don’t get growth from AI. His comeback is always: then one country will accumulate and overwhelm all the others eventually. Andrey: There’s also a timeline over which universities get out-competed — maybe not Harvard. Harvard’s a luxury good, and luxury goods have different economics. But if the ROI to college falls drastically, I don’t see college education remaining anything other than a luxury or niche thing. Kevin: We see this in the X-inefficiency papers, or the steel mini-mill papers — quicker organizational change in response to existential threats. It’s almost worse when the organization has rents to share, because then who wants to be the manager who’s the jackass firing people? My favorite example: Blockbuster could have bought Netflix for tens of billions. If they had, you’d have never heard of Netflix — I don’t think the transition to streaming happens if retail-location experts are running it, and the relational contracts with the studios have to change wildly. Someone would have out-competed them eventually, but it would have taken longer. Something like self-driving cars — let’s make a bet. Of the top 500 cities in the world in 2035, how many differentially regulate self-driving cars on safety in a substantial way compared to human drivers? Andrey: All of them. Kevin: All of them, of course. Outright ban self-driving cars — I wouldn’t be surprised if that’s double digits. Andrey: Boston almost did it, as far as I can tell. Very close. Although it’s one of those things — kind of like Uber, which entered as a banned entity and got so much consumer goodwill that politicians had to allow it. I’m not sure that happens with self-driving by 2035, but it’s not obvious when it tips. [00:18:21] Kevin: It’s also not obvious we don’t get differential regulation — say, regulation that makes self-driving cars subsidize the insurance rates of traditional cars. The world is status-quo biased. Institutions exist because they won the Darwinian struggle to survive, so they’re well-fit for the environment they operate in, which makes them inherently conservative. That’s not crazy — but at a time of big disruption like AI, you have to take it seriously. How many high-up people in Silicon Valley thought the US government would ban Anthropic? I agree it’d be insane to do. Nonetheless, I’m not surprised. If you think any government is going to allow open sales of AI at the frontier in two years, you’re deluded — they’re going to treat it like nuclear material. If you don’t believe that, your vision of the world is way too technological and not nearly organizational enough. Why Situational Awareness Beat the Economists; AI for Science [20:16 - 26:00] [00:20:16] Seth: Let’s wrap up the JEL article. In some ways you review Situational Awareness in a positive light compared to what the economists wrote. But it’s a narrative essay, not an economics book. What’s the economics book you want to read, and why are economists stuck? Kevin: Getting Situational Awareness into the book review took a little persuading — for one, it’s not a book. But if an economist asked me for the one book chapter that best explains what’s happened in the last few years, I’d say chapter one of Situational Awareness. I give it to all my students. What I want to read — both as a book and as research — is the endogenous impact of AI on science, including via robotics and via self-improvement. That’s the whole game. Seth: You point out The Second Machine Age misses this. It says “imagine a billion researchers,” and they imagine Africa getting the internet, but they don’t actually model it. Kevin: Exactly. Think how many papers go: “Here’s 2025 AI. I run an experiment where I tell you to do something that takes 10 minutes of work with AI, and I measure the treatment effect.” Who cares? Nobody’s reading that paper in five years. What people will care about is: are we getting self-automated science? Is blue-collar work being affected? If AI can do most of the research on the next AI... I always ask high-up people in the labs: what year do you think a Chinchilla-law-level result — in terms of its importance to developing the next model — comes from AI? The answers are between 2027 and 2029. I’ve never gotten 2030. That’s not all research done by AI, but it’s a substantial speed-up beyond just writing the code faster. Andrey: We already see that in math — it’s proving things humans weren’t proving. Kevin: If something like Navier-Stokes is proven, I’d put that in the set of a Chinchilla-law-level result done autonomously. And if it can do that, presumably it can do research on sensors, actuators, batteries — and then the robots improve more quickly, and we get automated labs. That’s the takeoff question. Everything I’ve said doesn’t actually imply a takeoff. It depends what the bottlenecks are. Measuring those bottlenecks — the production function for specific areas of science and robotics — is incredibly high value for knowing where to allocate resources. Andrey: And if we correctly predict them, maybe they won’t exist. It’s a feedback loop. Seth: Ooh, I love this. Kevin: Think of a production function — it tells you how much capital and labor to maximize production. I want to know what tools. Seth: Kevin, I just did it — it turns out it’s energy. I looked into the future, it’s energy. Are you saying the best book about AI economics is just a book about energy? [00:24:48] Kevin: It’s plausible. I help run Creative Destruction Lab — we were the first AI accelerator in the world in 2016, and we also run, I think, the biggest space accelerator. I was just down in Texas with astronauts for the Artemis launch. When you hear Elon talk about AI and space, it’s on the one hand crazy, on the other hand basically unregulated, effectively unlimited energy — and for training, who cares about latency? It’s not totally crazy that one way we get around the energy bottleneck is solar sails and ideas like that. In which case we face other bottlenecks. But this is an empirical question, and one where you’d want energy economists and energy experts, not just labor economists. Forecasting AI: Surveys, Economists vs. Labs [26:00 - 28:20] Andrey: One thing where I feel very stupid: about six months ago people around here kept saying “energy shortage, energy shortage,” and I thought they were probably right but didn’t trade on it. You’re also involved in a project — we discussed it a bit with Avi Goldfarb — figuring out what economists are forecasting about the future of the economy under different scenarios. Tell us about it. [00:27:06] Kevin: There are actually two projects — one I’ve been involved with, one I’m an academic advisor on. They both also ask AI-lab researchers, superforecasters, and the general public. The most interesting thing, as far as I’m concerned: on technical projections, there’s really no gap between the economists and the people inside the labs. And on economic projections the gap is also pretty small. If you go from Acemoglu to Dario Amodei in our sample, Acemoglu is like the 1st percentile and Dario’s like the 99th — and neither is really representative of economists or AI researchers. It’s important to put these projections on paper and see how we did. Some surveys are now old enough to check. The projections of everyone — economists and non-economists — on frontier math were low. We were thought to be crazy with some of these projections, and we still underestimated the rate of improvement on certain benchmarks. People say “it’s a benchmark, they trained to it.” The problem: I wrote benchmarks for one of the big labs. You know how hard it is to write a benchmark the AIs can’t solve? I did some in March. I’m running out of questions I can ask them. Seth: They know how many R’s there are in strawberry now. Benchmarks: The Coffee Test & the Fence-Post Test [28:20 - 35:53] [00:28:43] Kevin: I have some tricks, but who knows how long they’ll last. Honestly you need benchmarks that look like the coffee test — or my favorite, the fence-post test. The fence-post test is mine: I can buy a general-purpose embodied AI that I can tell on a Saturday morning, when I want to sleep in, “Go to my backyard and dig that fence post.” Not a specific machine — a general one. Every human could in principle do it. I think we’re quite a ways from AI doing it at cost. The coffee test — I think this comes from Wozniak — is that an embodied AI walks into three random houses it’s never seen, finds the ingredients and the mug, and makes a cup of coffee. Well within the capability of any normal person. Whoever came up with it said the year it’s possible is “never.” We’ve done surveys — the modal answer from researchers now is the early 2030s. I think that’s the kind of benchmark you need, because anything on paper or on a computer — what Shane Legg calls minimal AGI — the goose is cooked. We can’t write tests I’m confident AI won’t pass in that domain. Seth: Andrey just wrote a test the AI was very bad at. Andrey: It’s really bad at predicting how many tokens it’ll use for a given task and whether it can actually do it. It’s poorly calibrated. Kevin: That’s a well-known one, included in some benchmarks on AI’s ability to self-reflect. But it’s in the set of things where if I think about it a bit, I don’t know any reason I can’t hill-climb to answering it — ergo it’ll get solved. Andrey: To be clear, our paper’s call wasn’t “we need these for economic activity, so please RL on them.” Kevin: That’s essentially what you need, though. Anything obvious you can hill-climb on is cooked. Anything non-obvious but complementary to you hill-climbing on it is cooked. I need something outside that set. Seth: But it has to be at the intersection of hill-climbability and being economically valuable to hill-climb. Or do you think we’ll saturate everything even if it’s not valuable? [00:31:46] Kevin: I don’t think there’s a difference. Once I use AI in adversarial or competitive settings, making a mistake 0.1% of the time screws you. Edge cases are really bad in adversarial settings, and lots of economic activity has that flavor. Seth: There’s no such thing as an economically unimportant question. Kevin: Right — if you give me the economically unimportant question, I’ll design the economic interaction to screw you on it. Before LLMs we had GANs — you could put a sticker on a stop sign that fools any model but looks identical to a human. There are good statistical reasons we’ll never fully solve that. My favorite one AI has trouble with: they took an outline map of Europe, filled in part of the Bay of Biscay as if it were land, put an arrow on it, and asked “what’s here?” If you know your geography you say, “that’s the Bay of Biscay, oddly colored like land.” It’s just a weird thing for the training data to see. Economically it’s not per se valuable — most maps you see are the real map. But if I were using AI in a financial system, I’d be super concerned about my inability to solve that. Seth: It’s very important to be able to draw pictures of wine filled all the way to the brim. Kevin: Especially for these evening podcasts, Seth. [00:34:14] Andrey: Back to the forecast — one question about the composition of people. I’m a participant in your surveys. I wonder if the economists are all our friends and not the skeptics, like Acemoglu. Seth: We’re gonna get him on. Kevin: It’s not just our friends. The selection mechanisms differ between the two, but you have to have published something related to AI at some point — and plenty of people who’ve published on AI are quite skeptical. It’s not snowball sociology; the selection mechanism is completely public. Andrey: But there’s self-selection into participating — I do it because I’m very interested in AI economics; some might not. Kevin: For sure, that’s an issue. Forget econ — I was just at a faculty association meeting talking to the humanities people. It’s amazing: the AI is simultaneously destroying society and can’t do anything. Very hard to hold both at once. Seth: A very prestigious combination. Rapid-Growth Scenarios, Labor Force, and “Phil World” [35:53 - 41:40] [00:35:53] Seth: You said there’s not much difference between economists and non-economists on economic predictions, but my recollection is there are substantive differences — like the fast-progress scenario, a percentage point of GDP growth per year difference. That’s sizable. Kevin: That’s where the biggest difference is — the rapid-growth scenario: widespread inexpensive robots that can do basically everything. Call it “Phil World,” since we talked about Phil Trammell — friend of the podcast. In that world in 2050, saying 1% growth per year is a little crazy. It’s hard to write down a model with bottlenecks that strong. Seth: Or there could be dis-saving — people taking their labor out of the economy. We asked about labor-force participation, and even there the gaps were off-trend by five or six points. Not enormous. Kevin: That seems small for that magnitude of change. But the description of “rapid AI” was technological capability, not diffusion. One explanation: it’s possible to do this, and we ban robots. Seth: For my prediction I included increased chance of war as something that reduces growth. Kevin: We had a couple of respondents say zero GDP because we’re all turned into goo. We won’t say which of our friends. For the rapid scenario, the 25–75 bounds are stupidly high. But for the AI all three of us would expect, the error bounds aren’t enormous — people were generally on the same page across groups. Most of the difference was within-group until you get to 2050 and rapid AI. [00:39:10] Seth: Give the listeners some numbers for the median scenario. Kevin: The best comparison is something like CBO or IMF projections — on the order of one percentage point more productivity, one point more growth per year. Which adds up to a lot — let’s say it adds up to the single most important invention in human history. On labor-force participation, about half a percentage point more per year in the drop — substantial out to 2030, not quite as big by 2050. Big effects, but... Seth: It’s not the singularity. One percentage point additional growth a year for 20 years is the difference between two high-income countries — not the difference between the Flintstones and the Jetsons. Kevin: I understand the objection: you read Situational Awareness, and from 2001 to 2026 the AI-pilled people were right and everyone else was wrong, so don’t bet against their projections. Fine — on technical grounds, say they were right. My response: I’ll literally take, as my technical projection for 2030, whatever the modal response from researchers inside the labs is. On what grounds would I disagree? But how that maps into labor-force participation — there I wish some of these people would close their mouths. Scaling Regularities & What Economists Should Defer On [41:40 - 43:34] [00:41:14] Seth: Let me ask about a techno-social prediction. We have this regularity, the scaling law — which you said should really be called a scaling regularity, because we’re not sure it’s a law of nature. The relationship between error rate and number of parameters seems technical. But then there’s the sociotechnological leap — that scaling leads to scaling capability. Should economists defer to technical experts on that, or is it a socioeconomic prediction we should have an opinion about? Kevin: That one’s in the middle — related to AI for science. The scaling regularities — let’s say four of them — we just take from the computer scientists. But what’s the production function of medicine? How important are improvements in predicting protein structure to making a new drug? That’s not economics in the sense that we don’t have the field expertise, but it’s also not biology and not computer science. We’re in the middle. Seth: In Aschenbrenner there’s a figure: right now it’s high-schooler level, in a year college, then professor. Is that the first kind of prediction or the second? Kevin: That’s the first. I take that from the computer scientists. I want field experts and economists to estimate the production function, and social scientists to work out the implications on other parts of the economy. Why Forecasts Matter for Policy & Capital [43:34 - 45:50] [00:43:34] Andrey: Let me retort. People are interested in forecasts, but I don’t think economists are very good at forecasting. And it’s not clear how useful the whole exercise is. I could build my own custom macro model to answer these surveys — how much value to society would there be? Or is this more an exercise in social consensus, to bring to policymakers and say “here’s the range of expected outcomes,” without caring about the specific forecasts? Kevin: A bit of both. Take chapter three of Aschenbrenner. If I believe that forecast, the government should borrow literally everything it can and plow it into chip production — because if your growth rate is 10% a year, who cares? So it matters a lot for policy. On a micro level: I’m an executive at Google deciding whether to put money into AI math solvers or into bio — Anthropic just put Novartis’s CEO on their board. Which improvements lead to value more quickly? And at the organizational level, if I’m a university, I need to know which bottlenecks are in my control and where I can just free-ride and wait. When you talk about China, I’ll tell you something interesting I learned there: they’re not AGI-pilled. I think that’s going to cause problems — but we’ll get to that. [00:45:56] Andrey: The final thing: yes, Anthropic is going into bio, but you don’t need forecasts for that. Just look at the share of GDP in different sectors. Economists are valuable, smart people — but using AI for medicine is the most obvious thing in the world; I don’t need an economist to tell me that. Kevin: The marginal value comes elsewhere: if I spend $10 million figuring out how to allocate $10 billion of capital, that’s really high value. And on policy — listen to how policymakers talk. Bad predictions about the labor market coming out of some labs are going to cause regulation. States are going to ban data centers. We’re going to tax all the compute before we get the cancer drug.I was working on a theory problem this week: I care about the wage bill — I want AI to be as productive as possible without harming wages. So you take something like Chamley-Judd, add a wage-bill constraint, add informational constraints for the planner about which capital is AI, let it substitute and complement in various ways, and solve. The result on taxation looks nothing like anything being proposed right now. To know that’s the right way to think about it, you can’t just say “AI will be useful in the future.” No — they’re going to ban it. Andrey: This political economy of AI is something I’m tracking very seriously now. It’s obvious we’ll have bans and regulations long before AI actually has effects. People already think AI is causing mass unemployment. Kevin: They’re immune to the data. “Block laid off 40% of their workforce.” It’s a bad media environment, too. A reasonable hypothesis: the sector most harmed by digitization and then AI is journalists — so young journalists, especially culture journalists, are incredibly hostile to AI, and the world they influence ends up asking “unemployment’s 4.5%, why is everyone talking about this?” There was an article this week about young people who don’t want kids because it’s too expensive — and the first couple they showed were 25, owned a 2,000-square-foot house, and the husband’s hobby was golfing in Utah. It’s a bad epistemic environment, and it’s bad for AI because it makes people hostile to change — they feel they have to protect what they have, even though the economy roared. China Trip: Not AGI-Pilled, Involution, Capital Markets [45:50 - 1:01:40] [00:50:29] Andrey: Let’s get your take on your China trip. What was the occasion? Seth: Is China AGI-pilled? Why or why not? Kevin: We need that one for the clip at the start of the video. I studied diplomacy — my goal when I was younger was to join the Foreign Service. I worked in China briefly at the embassy in ‘05, around WTO session time, and I’m back there quite a bit. After COVID, the number of foreigners in China dropped, so the information flow is bad. A colleague calls it the G2 when it comes to AI: two countries, plus Google’s London outpost. Nothing else really matters for AI. So not knowing what’s going on in China is really important. This year I brought a group — economists, a guy from Epoch AI, a trade lawyer. I wanted to understand robotics, especially in traditional industries. AI’s effect on most of the market won’t come through San Francisco or Hangzhou. We met Zhipu’s COO, journalists who work on AI policy, startup founders, cloud providers, the biggest angel fund. We also went to Dongbei, the northeast — the fastest-falling population region in the entire world, losing about 1% a year, maybe 100 million people. We went to the one city that’s hanging on. [00:53:34] First thing: nobody we talked to was AGI-pilled. When you ask what the AI is for, it’s completely about process engineering of existing industry. That’s it. Why open source? Process engineering. Why build your own non-frontier stack? Process engineering. And they actually use it in industry — some examples looked better than what we see in the West. But no one talks like the San Francisco or London DeepMind folks: “in 2029 my robot flies through the air and shoots the robber and delivers my peptides.” It honestly felt like talking to government people — “AI’s capabilities in 2026 plus epsilon.” Part of what’s going on is a word in Chinese they translate as “involution” — I always tell them it’s not a word in English; it actually comes from Clifford Geertz, the anthropologist. It means extreme competition. It’s very hard to make a profit in certain industries — a hundred entrants immediately when you start making money. So high-fixed-cost, payoff-in-the-future investments are really hard. You only see it from things like DeepSeek, where it’s a hedge fund and the guy spends his own money. Even companies that seem to be doing great — the independent AI producers, not the Alibabas and Tencents — are in massive financial trouble, because it’s too competitive. Seth: Part of that’s the interest rate and capital-market environment, right? American AI companies can lose money for a long time — why can’t they access money for more runway? Kevin: China’s biggest advantages are energy costs about half of ours, and a much stronger hardware ecosystem — your ability to experiment and prototype blows away North America’s. It’s probably not even worth running a battery or robotics-hardware company here; you’ll get swamped. Seth: Unless you’ve got a government contract. Kevin: True — we should probably build our own drones. But things that require big fixed costs and have long payoffs need deep capital markets that reallocate capital quickly, and China doesn’t have that. The VC market is worse than a decade ago — foreign VC basically left. Most companies get investment from state-linked banks or rich people out of pocket. DeepSeek is trying to raise $20 billion; if they were in San Francisco they’d start at ten times that. [00:57:06] Andrey: Let me play devil’s advocate. So far most of the rewards go to frontier models — you can’t charge enough for non-frontier tokens. So DeepSeek doesn’t make sense unless it’s a government-funded national champion. Kevin: If DeepSeek weren’t in China, with their leadership and computer scientists, they could have attracted the Chinese equivalent of Alec Radford and Ilya Sutskever and been in the race for the frontier. They can’t, because of the capital markets. This isn’t just AI — all sorts of industries face it. They can move quickly when the design already exists, but for “I’m doing something genuinely new,” they’re behind. Self-driving — they’re behind Tesla. Not Waymo, Tesla — even the frontier Chinese car companies. Andrey: That’s crazy to me. I’d have thought they’d have a separate, more generous government lane. Kevin: Look at what Google had to spend to build Waymo — no one else in North America pulled it off, because you needed to lose tens of billions and there wasn’t enough capital. Andrey: In China labor is cheap, so the economics of an autonomous-vehicle service are worse there. But modern neural networks made AVs a lot easier — Google couldn’t really have done it before 2022. Kevin: An executive at one of the new Chinese car companies told me that in China, Elon’s strategy is seen as smarter than Waymo’s — they think Waymo’s approach is out of date: LiDAR is cheap now, don’t map the roads. Maybe they’re right and catch up. On the cars themselves they’ve caught up — if their cars were sold in North America they’d take the market. And it’s not the traditional four — the Ford and GM of China are also screwed; the architectural shift to electric was too hard. It’s the new companies that would crush us. But they still haven’t caught up on self-driving. [01:00:11] Seth: Follow-up on capital deployment — bringing Leopold back. He thinks the big frontier labs end up as nationalized projects. China can deploy a lot of capital toward national projects. Do you see this disadvantage reversing if we get one big national lab per country? Kevin: Good question. Hasn’t happened yet. I think they’d have the same problem — China has hippies now. They have words like tang ping, “lie flat” — I’m not joining the rat race. They have guys like the people at Anthropic wearing sandals and reading the Whole Earth Catalog. Those people, in the US and China, aren’t going to work for some state-backed project. You can maybe state-back the energy rollout, but it wouldn’t attract some types of talent. War, Nationalization, the End of Open Source — and Claude [1:01:40 - 1:06:06] Kevin: The part of Situational Awareness that seems like it must happen — I wrote my PhD dissertation on early nuclear. Back then you literally weren’t allowed to publish your patents — state secret. We’re very close to wars where AI plays a major role. At that point, who’s going to let this stuff be independent? The government doesn’t let you sell missiles — they’ll let you sell to partners they approve, and that’s it. Seth: Does that mean the end of open-source models above a certain size? Some sort of IAEA for AI? Turing police monitoring frontier labs under UN auspices? Kevin: When people talk about UN regulation of AI — take a foreign-policy class. Neither China nor the US cares one whit what the UN says. There’s going to be an organization called the G2: the US president and the Chinese premier talking to each other. That’s how it’ll work. Open source is interesting — it’s a little bit dying in China. The most well-known researchers at Alibaba quit. A couple of other well-known model makers are going to go bankrupt — it’s not obvious how you make money making open-source LLMs as an independent. I suspect Llama is the last big one Meta makes. Someone will make them — NVIDIA’s pretty clearly going to try, because it’s such an obvious complement. But you can imagine a world where open source becomes much less common. [01:03:32] One interesting thing: talking to people in AI in China — not political people — every single person thinks Claude is the best model, and they all use Claude. Not domestic models. Even though it’s very hard to do that from China, on both the government and the Anthropic side. There’s no opinion that China is catching up on AI. The view is that not only is Claude ahead, but the one place they a bit believe the AGI pill is that inside OpenAI, Anthropic, and DeepMind they’re using these models to speed up product deployment — and China doesn’t have the same access to frontier models, which makes it tough. I’m doing a thing for NBER on what chip bans would do to endogenous innovation in China — how to even model that isn’t obvious. The cynical answer is it’s whatever the marginal cost of buying chips from Kazakhstan is — one more plane flight. Seth: I was reacting to the Jensen interview. We’re half a beard away from you being at that point. Kevin: I should have worn the leather jacket — that’s the look now. Actually, the real move is the T-shirt from our machine-learning accelerator, before we called it AI, back in 2016. That’s the one you flex with. A Fine Theorem, Blogging, and the Value of Taste [1:06:06 - 1:17:48] [01:06:06] (For those playing along at home, now’s your chance to think about how this conversation has changed your priors — sponsored by Revelio Labs.) Seth: Revelio Labs is a leading provider of labor-economics data and data services for companies, academics, and independent researchers. They combine comprehensive micro-level data on employee profiles, job postings, and sentiment with standardizations, mappings, and enrichments — flexibly aggregated to company, market, or industry — to study everything from career trajectories to occupational transformation to the impact of AI on labor demand. Their data is available on WRDS, so if you’re an academic with a good library, check whether you already have access. If not, reach out to their economics team. [01:07:21] Seth: One thing you didn’t mention at the top: the reason you’re so close to my heart is your famous blog from the glory days of econ blogging, A Fine Theorem. When I started my PhD in 2012, getting excited about the big questions in economics and how theory can contribute, I found it so inspiring. So much of how you publish in econ now is: find a cute IV for one of a limited list of subjects, or — God willing — J-PAL backs you and you do an RCT. That may be useful, but it’s not what excited me about economics. Your blog was my north star for how technical theory can and should be communicated. So, snaps for how cool that was. Kevin: Hold on — who do you think the Gen X is in this conversation? Seth: Are you an elder millennial? Did I just mess up? Kevin: I thought I looked young for my age — I’ve got the dimples. Seth: As a generational-conflict theorist, the thing that struck me about the Dwarkesh–Jensen interview was Gen X shape-rotator Jensen and millennial wordcel Dwarkesh. So it wasn’t surprising you had the leather-jacket option. Kevin: I’ll say the Gen X has excellent taste in music. I went to the Oasis reunion concert — probably the youngest person there. It was great. [01:09:42] So, A Fine Theorem. It’s related to AI development, believe it or not — one important way new technologies diffuse is the development of complements. That site started as my PhD notes on the papers I was reading; it was just easier to keep them in a WordPress setup. Some people found it through RSS — that’s how you found things on the internet then. Now people find things through gated social media, group chats, podcasts. It was good timing for me. I was never that interested in running a podcast — someone asked me to do one on the economics of science years ago — writing just matches my background better. It got a bit wild. I’d write about maybe a hundred papers a year, plus Clark Medals and Nobel Prizes. I had a reputation as the guy who reads everything across fields and isn’t shy about his opinions. The three craziest emails I’ve ever gotten: I proposed a reform to the NBA and the president of an NBA team emailed me to talk about it. And two different Nobel laureates read my notes after they won and wrote asking me to read through their Nobel speeches. That’s the coolest thing ever. Seth: “Explain to me why my work was important.” Kevin: It makes sense — you know your work, but not always how people see it or how it influences them. I go to conferences and students will say “I’m extending your paper from 15 years ago this way,” and I’ve completely forgotten about that corollary. They know more about it than I do. Tyler Cowen liking it led a lot of people to read it. At one point it got, I don’t know, a million views — crazy for a microeconomic-theory blog. [01:12:51] Seth: I do think you’re quite good at writing it for an educated reader, not just as a paper. There’s a big latent market for this — previous guest Noah Smith works the same lane. We love Noah, but you can’t compare Noah to Kevin in terms of gravitas and depth. Kevin: A lot of academics think their job is research and teaching — writing papers for other academics and maybe policy folks. But now I know who’s reading that stuff. I was writing about epistemic game theory, and serious people read it. My work on progress studies — I teach a class on progress with serious research behind it — there’s huge interest. I was at a conference with Chad Jones, the growth theorist, and there are people in industry reading Chad Jones papers seriously. The world is much more interested in serious work that answers serious questions than academics think. If they understood that, they’d be more careful with their work and would choose different topics — instead of “I’m writing this because journal editor X just got promoted.” The Economist as Plumber: Comparative Advantage & RCTs [1:17:48 - 1:24:07] [01:14:24] Seth: Let me ask about the how and who you write for. One theory behind this podcast: as the marginal cost of writing papers goes down, the marginal product of reading them can go up. Do you see AI increasing the relative importance of digesting and synthesizing research? Kevin: The one-sentence version you hear — which I think is true — is that the marginal value of taste has gone up. Seth: But what’s taste? Kevin: There’s stuff that’s fun to consume — I watch YouTube golf like everyone my age, but I know I’m not learning anything; I should be watching topology videos. Taste is understanding why a thing matters. Show me 20 things written about chemistry and I can tell which is better written, but not which one matters. To have taste — in music, literature, economics, anything — you need a really strong epistemic base. AI can point out “this is a good paper,” but not “this is a good paper in line with your individual interests.” Maybe in a world with continual learning, where your AI is your assistant — but we’re not there. Seth: But it was beyond what was interesting to you — somehow it was also inspiring to people like me in grad school. Kevin: Right. I’m illiterate about music — play me some Bach and I barely know the difference. But once in a while a really good critic writes “listen to this part and you’ll hear this,” and suddenly I do hear it and understand why it’s interesting. That person couldn’t have just listened to that one piece or read one book — they need to understand the history of music. People have different areas where they can have taste. Mine is probably the intersection of theory, history, and history of thought — and that mixture isn’t very common. [01:17:48] Seth: Let me pull out something you may have a distaste for — a quote from your review of the Banerjee–Duflo–Kremer prize. “The economist as plumber, famously popularized by Duflo, who rigorously diagnoses small problems and proposes solutions, is a fine job for a World Bank staffer, but a crazy use of the intelligence of our otherwise leading scholars.” React to that in the age of AI, where the market is flooded with “we estimated the productivity impact of AI adopted here on this date” papers. What should those people do instead? Kevin: You’ll be surprised — because I believe in comparative advantage. I literally mean it’s good work for a World Bank economist; people should do that. I just don’t think Banerjee and Duflo should have been doing it. Same way Stantcheva’s taxation work was unbelievable, Clark-Medal-winning — and then she wrote a bunch of papers basically running a survey firm. The papers are interesting, but it’s not her comparative advantage; many people have more expertise in that area, and it’s not that complementary with the rest of her work. Andrey: I’ll disagree. Both survey research and experiments required elite permission to do this type of work. There’s no objective, agreed-upon standard in social science for what we should work on. Having an MIT or Harvard economist legitimize it in a top-five journal lets a bunch of other people — for whom it is their comparative advantage — work on it. On the margin maybe they work too much on it. In marketing, where I sit, there was a perception that survey research with stated preferences was something we shouldn’t do — and now if a top economist says it’s okay, maybe we can. Kevin: For sure — same way J-PAL was useful, and they won a Nobel for it, so they were rewarded. I agree on the permission structure. The question is what we do now with AI. In a sense it’s not great for me — being the smart-ass kid who’s really good at algebra is worthless now. I worked on a paper recently I’d been stumped on for years — a proof I couldn’t figure out. GPT-5.4 Pro was also stumped, but in its write-up it gave me a polytope-theory result I hadn’t seen, and I used it to prove the thing. I felt like a dad beating his teenage kid in basketball — super happy, but I know it might be the last time. [01:21:56] If you’re a PhD student now and your specialty is being really good at solving models, you’re just not going to have a job — you’re not as good as the AI. But some things are incredible complements to AI: within-firm field experiments done with much higher ambition than now. Those will be very popular and not susceptible to replacement for a while. Andrey: But didn’t you say we shouldn’t be working on this? Kevin: I said we shouldn’t be doing RCTs — but I believe in comparative advantage, and we’ve changed the price of the factors. If we’re going to do this, what’s a bad idea is doing it atheoretically and ahistorically. Two things you need as a PhD student: your work has to be a complement to what AI can do, and your work has to have taste — you need to know what matters and why. A field experiment estimating a treatment effect no one cares about shows a lack of taste. Thinking you’ll get a job solving a model any AI can solve shows a lack of understanding of comparative advantage. High-paced managerial types are going to do better in academia than they used to, and some folks who were high-status will find nobody cares. Andrey: I see how the human advantage is running RCTs versus writing macro models. But what’s the right approach for writing that AI book you want us to write? Kevin: I still think you should write the macro model — your contribution just isn’t solving it. And your empirical paper needs to draw on and understand the macro models you’re building on. You should spend more time reading papers, not less, to develop taste. Andrey: Or you shouldn’t read papers — you should talk to the AI about the papers. Or listen to this podcast. Kevin: You should be listening to Justified Posteriors, brought to you by Jane Street. Andrey: We’re manifesting Jane Street. The Future of the Academic Paper [1:24:07 - 1:28:22] [01:24:19] Kevin: Academic papers are an unbelievably entrenched system, but here’s where I’m trying to go — and I edit an AEA journal, so I talk to them about how we handle AI. In a couple of years, a paper is: all the lab notes, code, and data, open and in a format AI can read — that’s already in progress. Then a paper that ranges from the 40-page version to the five-page version to a “talk to the AI” version. If you go to my website, my papers already have a built-in Gemini Flash interface, because I assume people want to talk about the paper while reading it. It’s not just a PDF. So every paper will have a partially AI-generated hundred-page version with all the information for the AI, the 40-page version, the five-page, the three-page, the interactive version — because the cost of writing the paper is so high relative to the cost of those manipulations. The idea of a paper as a fixed set of words is over. If that’s all it is, everyone’s going to talk to GPT about it anyway — we can do better. Andrey: Does that mean writing goes down in importance? Someone like Chad Jones is such a crisp writer — that’s a key reason people read him. Kevin: How AGI-pilled am I? The best academic writer in our profession is not Hemingway — let’s not be deluded; the average writer is terrible. People outside academia may not realize how much editing for readability happens in an academic article: the answer is zero. Maybe one or two sentences you’ll be asked to crisp up. It’s not The New Yorker — there’s no editor rewriting your paper for readability. The only reason people try to write well is that on the margin it raises your acceptance probability. Otherwise they write like a lawyer. Andrey: It’s taste. It’s for themselves. [01:27:05] Kevin: I had an idea — maybe we do it for AI. I wanted an innovation journal; there’s no good one, and innovation is very interdisciplinary. But no one will send a paper to a new journal, for tenure reasons. So how do I free-ride on the system? Create a journal that any already-published paper is eligible for. Have a board of 30 great innovation and AI economists; as soon as three say “if this were my field, top field journal, I’d have taken it,” it’s in the journal. We link to the working-paper version and hire a professional to write a 1,500-word, Quanta-Magazine-style article about why the paper matters. Seth: Have you heard of the Unjournal, an EA project? It has some of these ideas. Kevin: Yeah, the Unjournal’s a good one. Andrey: Works in Progress is doing some of this too — taking academic research and making a great article about it. Kevin: That’s why the innovation-econ world and the progress world have a lot in common — we’re all friends. This year I felt like a progress-world celebrity, because one of my PhD advisors was Joel Mokyr, and they love Mokyr in progress world — he’s like Michael Jordan. Andrey: I tried reading A Culture of Growth and it’s unreadable. I’ll just put it out there. Kevin: The Gifts of Athena is the one I recommend — though you have to work through 50 pages of prescriptive-versus-propositional knowledge with lambdas and sigmas. He’s still a better writer than the average economist — low bar. San Francisco, Ambition & the Permission Structure [1:28:22 - 1:32:56] [01:28:46] Kevin: My favorite thing about what’s going on in California — other than the incredible ambition — for folks who aren’t here, there’s all sorts of craziness; they make Seth and his EA beard look normal. Seth: It hosts insects and shrimp that are having a lot of utility. Kevin: You can save a little dinner for later up in the mustache. But the level of ambition — your average 21-year-old asking “what should I do with my life” aims this high. That’s not normal in most places, where the very smart people are type-A, “follow this rule and this process.” In academia we know tons of those people. It’s super refreshing. In progress world, every random person is like, “should I make money, or start a biohacking magazine that four people buy but I like doing?” — biohacking magazine. And I love it. We have a guy in Toronto, Ben Perry, who runs a sort of “Toronto society,” also in progress world — he holds talks on what makes a beautiful city. He asked me to give one related to my progress course, on idiosyncratic factors that lead to progress — a pretty out-there talk. I show up and we’ve sold out a concert hall. People paid 30 bucks a ticket, there was music beforehand, and afterward people are in the hallway chatting about what they’re building. These people are all over. [01:31:05] The remaining secret sauce of Silicon Valley is that everyone — all the way up and down the permission and capital structure — agrees the most ambitious people should have the power and the capital. That’s rare. During COVID, my university was closed and it was driving me crazy, so I went to teach in Senegal — the best university in French West Africa, teaching high-growth entrepreneurship. Great students. I asked what they wanted to do when they graduated, and they all wanted to work for the government. “You don’t want to start a company?” “If it doesn’t work out and I go bankrupt, I’m living on the street, and no one gives a 22-year-old money to start a company.” And that’s reasonable. But that societal structure makes growth impossible. That’s the thing you have to get right. Lightning Round [1:32:56 - 1:38:08] [01:32:56] Seth: How are we on time? Want to do a lightning round? And give yourself a chance to talk about All Day TA. Kevin: Let’s do All Day TA as part of the lightning round, so I don’t feel like a sales call. Seth: Lightning round, beginning. Favorite economist, living or dead? Kevin: Dead: Paul Samuelson — awesome work. Living: Bengt Holmström, because when I walk around on the street his ideas are in my head all day. Seth: All Day TA — what did you learn from being an entrepreneur? Kevin: This is my company — we sell ed-tech to universities, a hundred-plus now, all over the world. I learned that for AI diffusion, institutional sales is so hard in traditional industries, and so unrelated to product quality, that the people who already own the gates into big institutions — the Salesforces, the Microsofts — are going to clean up in the AI world. People who think they’ll sell a great product and get around those gates are deluding themselves. Seth: Did you learn a trick for selling to universities? Kevin: Did it a hundred times. The technical stuff matters very little. You have to figure out who has the decision rights — often the head of IT — and they often have some idiosyncratic thing they want. Going through the professor has no power. With any institutional sale, the secret is knowing who can write the check and getting to that person quickly. Seth: If you had to burn all of Kremer’s RCT work or his O-ring paper, which would you destroy? Kevin: I love the O-ring paper. But if your experimental papers probably saved a million lives, I have to let you keep those. So we burn the O-ring. Also, we probably could have figured out the O-ring without Kremer. Seth: But would it have been written that beautifully? Kevin: No. It’s such a nice paper. Seth: What advice do you have for folks in economics grad school today? Kevin: You’re five years out — read Situational Awareness chapter one, and believe it. Whatever you think you’re doing in your job-market paper, ask: is that consistent with creating value in the world of Situational Awareness chapter one? If not, literally do anything else. Seth: If you had a choice of joining a lab or going to econ grad school, what should someone choose? Kevin: I don’t think there’s necessarily a conflict. But when my most ambitious 22-year-old students ask what to do, I say: it’s like being a writer in 1920 — get on a boat and go to Paris and don’t be stupid. You’re an ambitious 22-year-old: get in a van, drive to San Francisco, and don’t be stupid. Seth: Seth, any more lightning rounds? No, I think we’ve covered it. Kevin, this was a completing-the-circle experience for me — your blog was so inspirational on my economic journey, and getting to talk to you and be treated as an equal was a very special moment. Kevin: It’s nice you got to talk to me before I reached my full senescence — given whatever age you think I am. It’s really ruining my self-image. Seth: I always think it’s so beautiful when millennials can get along with Gen Xers. It’s a special thing. Kevin: You know the irony? The Gen Xer wouldn’t have cared — “who cares, man, don’t worry about it.” Only the millennial complains about being called the wrong generation. Seth: That’s true. Thanks a lot, guys — keep up the good work on the podcast. I’m looking forward to the next guests. [01:37:53] Seth: And to listeners at home — keep your posteriors justified. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • May 19 · 1 hr 23 min

    Seb Krier on AGI, the Coasean Singularity, and EDM

    Seb Krier on AGI, Scaffolding, and Coasean Bargaining at Scale In this episode of Justified Posteriors, we welcome Seb Krier — policy lead for AGI at Google DeepMind and excellent Twitter poster. Speaking in his personal capacity, Seb walks us through his understanding of AGI, why AI alignment has gone better than expected, the potential and limitations of a world where agents constantly barter on our behalf, and — of course — electronic music. We also cover AI in London vs. New York, how Seb went from reading Marginal Revolution for 15 years to becoming a recurring character on it, and Seb’s side-splitting humor on mediocre AI conferences. Related Links * Seb Krier on X: @sebkrier * Seb’s Substack, Technologik * “Coasean Bargaining at Scale” — Seb’s essay at the Cosmos Institute (also republished here) * “Musings on Recursive Self-Improvement” — Seb’s essay separating model-side RSI from societal-side * “The Cyborg Era: What AI Means for Jobs” — Seb’s guest essay on Alex Imas’s Substack, defending the scaffolding view * Anthropic’s Project Deal — the agent-bargaining experiment among Anthropic employees * Fradkin & Krishnan, “MarketBench” — Andrey and Rohit experiment of LLMs bidding in procurement auctions as an investigation of the future of AI marketplaces and the companion writeup: Rohit Krishnan, “Agent, Know Thyself! (and bid accordingly)” * Edge Esmeralda — Devon Zuegel’s pop-up village in Healdsburg, CA * MATS — for junior economists looking to skill up on AI safety/governance * Cosmos Institute and FIRE * bianjie.systems — the art platform Seb is co-organizing a dinner with in NY (Seb’s announcement) * Drexciya — James Stinson, Gerald Donald, and the Detroit electro-afrofuturism canon Timestamps (00:00) Intro (01:16) What is AGI? (07:30) In defense of scaffolding — Hayek, division of labor, and why one giant model won’t do it (13:00) Markets for cognition: will agents bid in procurement auctions? (18:40) Recursive self-improvement — separating the model side from the societal side (24:44) Alignment has gone better than 2017-Seb expected; prefer “intent following” (31:14) What economists should actually work on to inform AI labs(33:32) What does a DeepMind policy lead’s day look like? (38:20) AI Conferences(41:52) Coasean bargaining at scale — the positive vision(55:00) Inequality, property rights, and who gets the initial allocation (01:03:00) The Helldivers 2 “Managed Democracy” dystopia as Coasean bargaining gone wrong (01:09:00) Sponsor: Revelio Labs (01:09:30) Lightning round Justified Posteriors is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. You’re also invited to our discord community at: https://discord.gg/b8VpPbBUt Transcript 00:00:00,100 --> 00:00:20,480 [Seth] [upbeat music] Welcome to the Justified Posterior’s podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, the number two biggest fan, after Tyler Cowen, in the Seb Krier fan club. 00:00:20,480 --> 00:00:20,740 [Andrey] [laughs] 00:00:20,740 --> 00:00:24,660 [Seth] Coming to you from Chapman University in sunny southern California. 00:00:24,660 --> 00:00:34,120 [Andrey] And I’m Andrey Fradkin, coming to you from San Francisco, California. And Justified Posterior’s is sponsored by the fine folks at Revelio Labs. 00:00:35,560 --> 00:00:45,600 [Andrey] We’re very excited to have Seb Krier here with us today. He is the policy lead for AGI at Google DeepMind, and is, 00:00:46,840 --> 00:00:52,400 [Andrey] dare I say, a thought leader in this space. Welcome to the show, Seb. 00:00:52,400 --> 00:00:54,200 [Seb Krier] Thank you very much. It’s great to be here. 00:00:55,380 --> 00:00:58,160 [Seb Krier] Yeah, I’m Seb, calling in from New York. 00:00:58,160 --> 00:01:00,320 [Andrey] And we should remind our listeners that 00:01:01,340 --> 00:01:08,410 [Andrey] Seb is, during this podcast, expressing his personal opinions, and is not speaking on behalf of DeepMind. All right. 00:01:08,410 --> 00:01:09,740 [Seb Krier] Indeed. [laughs] 00:01:09,740 --> 00:01:11,060 [Andrey] [laughs] 00:01:12,780 --> 00:01:13,900 [Andrey] The usual caveat. 00:01:15,260 --> 00:01:16,760 [Andrey] Seb, what is AGI? 00:01:18,080 --> 00:01:19,450 [Seb Krier] What is AGI? [laughs] 00:01:19,450 --> 00:01:19,570 [Andrey] [laughs] 00:01:19,570 --> 00:01:19,580 [Seth] [laughs] 00:01:19,580 --> 00:01:19,780 [Seb Krier] Great question. 00:01:19,780 --> 00:01:21,900 [Andrey] We’re going to start with the big questions. 00:01:21,900 --> 00:01:22,880 [Seb Krier] Yeah, might as well. 00:01:24,259 --> 00:01:54,840 [Seb Krier] [sighs] I think there’s so many definitions out there of what AGI is, and I think most of them are kind of unsatisfactory in one way or another. I’ve seen stuff like many definitions are indexed on the societal transformations or economic impacts of the technology, which I don’t really like very much because it makes it very dependent on external factors whether or not we have AGI. If it’s banned, we don’t have AGI, and if it’s not banned, we have AGI. Is it? 00:01:54,840 --> 00:01:55,480 [Andrey] [laughs] 00:01:55,480 --> 00:02:04,670 [Seb Krier] And there are other tests, like if an AI makes $1 million or something, which I find is very weird because most humans do not make $1 million in the first place. 00:02:04,670 --> 00:02:05,080 [Andrey] [laughs] 00:02:05,080 --> 00:02:11,359 [Seb Krier] So the one I kind of like is actually Shane Legg’s definition- 00:02:11,360 --> 00:02:11,620 [Andrey] Mm 00:02:11,620 --> 00:02:12,420 [Seb Krier] ... who’s at Deep Mind, who is 00:02:13,640 --> 00:02:16,980 [Seb Krier] more of a capability-based definition, which is something along the lines of 00:02:18,420 --> 00:02:20,960 [Seb Krier] an AI or a system that does most 00:02:22,380 --> 00:02:30,360 [Seb Krier] standard cognitive tasks that people typically do. [lips smack] So it’s kind of the bar isn’t too low, and it’s also not too high either. 00:02:32,220 --> 00:02:35,480 [Seb Krier] And so I think he’s got this definition of a minimal AGI, 00:02:36,580 --> 00:02:43,020 [Seb Krier] and I think that we’re not exactly there yet. I would disagree with people saying that we have AGI today because I think 00:02:44,220 --> 00:02:48,900 [Seb Krier] a lot of the systems we have, there’s many things that a human can do that they don’t really do very well. 00:02:48,900 --> 00:02:50,360 [Seth] What’s the biggest gap that we’re missing? 00:02:52,020 --> 00:03:47,740 [Seb Krier] I’d say there’s a few. One of them might be continual learning, or at least the ability to adapt and learn over time, and in different contexts and situations, just kind of update your own world model or whatever. If I think of a new joiner in a company, they’re not super useful the first day, but their value goes up over time because they learn all sorts of things. And so [lips smack] that might be one of them. A lot of the systems we have today, I think, are not very good at software, and you’re using graphical user interfaces and software and whatnot. If I ask an agent right now to go and use a music production software and make a track, I think they’d generally struggle. That doesn’t mean it’s impossible to solve or anything like that, but I think, in many respects, they’re not as general as you’d want them to be. And then the other bit also is, [lips smack] and of course they still make some silly mistakes here and there, but I think that’s getting it fixed. But the creativity point is one that I’m really interested in as well, in that I think they’re really good at kind of 00:03:48,780 --> 00:04:02,700 [Seb Krier] exploiting maybe an existing paradigm or an existing knowledge and so on, and recombining knowledge and whatnot. But I think really coming up with new concepts and abstractions entirely is something I think humans can do, but I don’t see our current systems really doing either. 00:04:02,700 --> 00:04:10,060 [Andrey] How do you measure whether humans can do creative tasks? One of the things that 00:04:11,200 --> 00:04:15,940 [Andrey] strikes me as a bit of an unfair test in that, 00:04:17,060 --> 00:04:23,290 [Andrey] let’s say you ask an LLM to write a poem or to write a story. It’s very- 00:04:23,290 --> 00:04:23,290 [Seth] [laughs] 00:04:23,290 --> 00:04:32,050 [Andrey] ... times more entertaining than what a random human would write. So, do you have a benchmark for creativity? 00:04:32,050 --> 00:04:35,390 [Seth] This is the meme where the robot asks Will Smith if he can compose an opera. 00:04:35,390 --> 00:05:14,700 [Seb Krier] [laughs] Can you? Yeah, exactly. It depends, and you’re right. Obviously, most people aren’t creating new abstraction and concepts on a day-to-day level. But I imagine there’s still something qualitative about that kind of creativity that I think does get applied in everyone’s day-to-day life in various kind of ways. Maybe they’re not as big or significant as creating a symphony. But I don’t really have a strong test. There’s actually an interesting podcast that had Ben Goertzel and Yoshua, I think a few years ago, where they were saying something like, if you had a model that was trained knowing only classical music and West African drumming, could it come up with jazz in the first place, or recreate jazz? 00:05:16,460 --> 00:05:27,880 [Seb Krier] And I quite like that test. And in principle, I can imagine it being possible. You could kind of decompose all sorts of different kind of elements and variables here and just get something jazz-like. But it still feels a bit... 00:05:29,580 --> 00:05:40,580 [Seb Krier] It’s not the same as just coming up with the idea of jazz in the first place and saying, oh, I’m going to try these things out. And for whatever reason, I’m going to stick to that. And I don’t know. It’s- 00:05:40,580 --> 00:05:53,190 [Seth] Recombination versus paradigm shifting. I’ve also heard one test people would want for AGI is, can you train the model on the 1900s corpus and it comes up with Einsteinian physics? 00:05:53,190 --> 00:05:53,200 [Seb Krier] Yeah. 00:05:53,200 --> 00:05:54,720 [Seth] That would be really impressive. 00:05:54,720 --> 00:06:36,151 [Seb Krier] Yeah, I think actually Demis uses that test sometimes, or I think Pele Gritzer as well mentioned it before. And there are some people, I think David Duvenour and Nick Levine, I think, had this recent kind of language model talky that was trained up in, I think, the 1930s or something. And I tried to play around with it a lot. It was like, let’s try to get it to create something new, and it’s pretty tricky. Although they have apparently recently, some people kind of fine-tuned it on a very few examples of coding and gotten it to be good at coding. But for some reason, that doesn’t impress me maybe as much as other things I would’ve expected. It’s like [laughs] there’s the-I agree that the goalposts also kind of move a little bit over time, and it’s also maybe unfair of me. It’s like, oh, well, can it create a new programming language from scratch or something? 00:06:37,272 --> 00:06:43,052 [Seb Krier] So it’s a tricky one to kind of square off, but it does still feel like there’s a lack of that kind of true creativity, at least in my 00:06:44,212 --> 00:06:45,072 [Seb Krier] interactions with them. 00:06:46,392 --> 00:06:57,342 [Andrey] I am really worried that it is a goalpost moving exercise here. We don’t have a benchmark for creativity and therefore, 00:06:58,432 --> 00:07:03,211 [Andrey] all these claims are not quantitative in a way that I’d like. And let- 00:07:03,212 --> 00:07:10,612 [Seth] Right. What about all those IS papers we see where one of the axes is creativity and we instrument for something? [laughs] 00:07:10,612 --> 00:07:11,032 [Andrey] Yes. 00:07:13,132 --> 00:07:13,592 [Seth] There’s a lot of bad measures of creativity. 00:07:13,592 --> 00:07:19,762 [Andrey] Those are not creative, to be clear. I’m sure I’ve offended a ton of people. Sorry. 00:07:19,762 --> 00:07:20,992 [Seth] It’s okay. 00:07:20,992 --> 00:07:56,432 [Seb Krier] I think it’s fair. I agree that it’s a bit like... But I still feel like there’s, at least if part of the reason you’re going to create these systems is to come up with kind of also new sorts of theories and so on. And I think you can probably get that through good search and a lot of inference compute and trying out lots of different things. And I think there are many low-hanging fruits there, to be clear. So it’s not like I think, oh, we’ve hit some sort of wall or something. And I think there’s a lot that you can kind of get in terms of new knowledge and new creative knowledge from that. But I feel like there’s maybe something more needed. It’s maybe not that kind of magical or anything, right? Maybe you just need better scaffolding or better multi-agent systems. But 00:07:58,992 --> 00:08:02,072 [Seb Krier] yeah, at least so far, I would say that I see a bit more creativity, say, in 00:08:03,652 --> 00:08:11,612 [Seb Krier] humans so far as a collective. And maybe that’s, again, an unfair comparison. You don’t have a culture of AIs and AGIs to compare that against. So- 00:08:11,612 --> 00:08:11,682 [Andrey] Yeah 00:08:11,682 --> 00:08:15,092 [Seb Krier] ... the right comparison is also a hard one to do. 00:08:15,092 --> 00:08:52,772 [Andrey] So, you mentioned scaffolding, and I guess a question, you recently wrote about a defense of scaffolding, and I think just to frame things, some people you talk with, especially very AGI-pilled people, are like, “Scaffolding, it’s an epiphenomenon. It doesn’t matter. In the end, we are going to train a smarter model with more parameters and more training data, and it’s just going to do it out of the box. And so all these scaffolding hacks are just very temporary.” And then other people like yourself, I guess, argue the opposite. So what do you think about scaffolding? 00:08:54,832 --> 00:08:55,052 [Seb Krier] Yeah. 00:08:56,572 --> 00:08:59,372 [Seb Krier] The first thing is I’m definitely not sure. This is kind of 00:09:00,532 --> 00:09:39,672 [Seb Krier] one of many hot takes, but I think, I guess there are a few reasons why I see it as, I think it’s going to stay over time. The first is that I think it’s plausible that as, I think scaling laws continue, I think you scale models and they get better over time and so on, but I think the inputs are expensive and grow over time. And I also think that it’s plausible that you might get more and more diminishing returns over time. And if that’s the case, I see the kind of utility of the scaffolding side and the harnesses as going up because you’re going to want to make more, you’ll want more bang for your buck kind of thing. You’re going to want to extract this intelligence and use this resource as efficiently as possible. 00:09:40,772 --> 00:09:51,532 [Seb Krier] So that’s maybe one reason. The other one is a bit more, I guess, Hayekian in nature or something, in that I see a lot of, I think there’s a lot of local knowledge, a lot of 00:09:53,212 --> 00:10:18,592 [Seb Krier] stuff that isn’t necessarily kind of codified. And I don’t really see one big giant AGI model now kind of perfectly guessing everything forever at infinite scales. And in a way, I see this as a little bit like a division of labor in that I think it’s actually more efficient to have this kind of integration layer that is closer to the local information or to the ground or to demand side that can better integrate this kind of cognitive resource 00:10:19,812 --> 00:10:23,632 [Seb Krier] to satisfy and create value and satisfy whatever consumers and businesses want. 00:10:25,552 --> 00:10:31,352 [Seb Krier] So to help with all the sorts of constraints and the context they’re dealing with, I think it’s very useful to have that. 00:10:33,712 --> 00:10:39,112 [Seb Krier] Of course, I don’t think this necessarily also implies or means that you’re going to get complete, full decentralization or something. 00:10:40,772 --> 00:10:42,212 [Seb Krier] Walmart gets huge 00:10:43,872 --> 00:10:48,872 [Seb Krier] returns from the scale that they have, and you don’t have loads of businesses downstream kind of reselling their stuff. 00:10:51,252 --> 00:10:53,932 [Seb Krier] But there’s two things. The first is that- 00:10:53,932 --> 00:10:56,812 [Seth] We have bodegas reselling stuff from Walmart on the corner. 00:10:56,812 --> 00:11:18,992 [Seb Krier] Actually, that’s a good point, yeah. And also, there are all sorts of other businesses kind of selling different things, right? If the task is generic and the demand is homogenous, then sure, maybe you can do more of that. But also, even Walmart relies on all sorts of kind of suppliers, local labor, compliance system, inventory systems, third parties, and whatnot, that help with this kind of integration and the delivery of these services. 00:11:18,992 --> 00:11:25,862 [Seth] So if I may summarize your answer, you’re very Hayek-pilled, but maybe not as Bitterlesson-pilled as most. 00:11:25,862 --> 00:11:25,972 [Seb Krier] Well, 00:11:27,212 --> 00:11:31,052 [Seb Krier] I think I’m definitely Bitterlesson-pilled in the sense that I don’t think you should 00:11:33,652 --> 00:11:48,992 [Seb Krier] try to kind of cement some sort of rules-based system you either devise or something and kind of hope that this just takes forever. If anything, I think the scaffold needs to be a lot more adaptive and evolve over time. In the same way as if you have a small startup and they have all sorts of kind of rules and, 00:11:50,332 --> 00:12:02,772 [Seb Krier] sorry, not rules, different functions. When the startup grows and gets more capabilities, they also kind of change from the inside. So I think that, of course, if you have some sort of light GPT-type wrapper that kind of makes your system a little bit better, whatever, yeah, that was not going to 00:12:03,812 --> 00:12:23,652 [Seb Krier] work out over time. But I think there are kind of scaffolds that help better integrate the wider environment, private data, deals with permissions or liability regimes or user preferences and whatnot. And also, at a somewhat higher level, kind of more coordination-type scaffolds maybe in terms of market interfaces, like clearing house equivalents or something. 00:12:24,516 --> 00:12:33,536 [Seth] The third example you gave is maybe it’s not the super frontier model that are going to these scaffolds, but simpler models that are still very useful and cheaper to run with a scaffold. 00:12:33,536 --> 00:12:46,176 [Seb Krier] Yeah, totally. Because I think you’re not going to need the enormous, super expensive brain for every single random task. And so it’ll make, for most kind of basic queries, people aren’t using Opus’s latent space or something as- 00:12:46,176 --> 00:12:46,186 [Seth] [laughing] 00:12:46,186 --> 00:12:48,236 [Seb Krier] ... it’s a big waste in some sense. 00:12:48,236 --> 00:12:50,036 [Seth] What toothbrush should I buy? [chuckles] 00:12:50,036 --> 00:12:51,196 [Seb Krier] Yeah. Exactly. 00:12:51,196 --> 00:12:53,896 [Andrey] Wait. That is an important question, Seth. 00:12:53,896 --> 00:12:54,516 [Seb Krier] I mean- 00:12:54,516 --> 00:12:56,536 [Andrey] I would definitely use Opus for that. 00:12:56,536 --> 00:12:57,385 [Seb Krier] It’s funny because I’ve actually- 00:12:57,385 --> 00:12:59,696 [Seth] Use all the collective intelligence of reality. [chuckles] 00:12:59,696 --> 00:13:02,266 [Seb Krier] I have actually used Opus for that exact question not long ago- 00:13:02,266 --> 00:13:02,626 [Seth] [laughing] 00:13:02,626 --> 00:13:06,256 [Seb Krier] ... in trying out this new electric toothbrush that I found out as a result. But, 00:13:07,636 --> 00:13:22,076 [Seb Krier] so yeah, I agree there’s that and also there’s all sorts of ways in which actually kind of using tools or specialized kind of tools is just more effective and more efficient. Why would you expect a large model or something to kind of calculate things innately or something when you can just access a calculator? It’s a much better use of tokens. 00:13:22,076 --> 00:13:36,856 [Andrey] But it should kind of know that the calculator is available and then use it when it’s there. So that’s the argument against scaffolding, or you’re giving it a general environment, but you’re not scaffolding it much. I think a curious thing is just, 00:13:38,376 --> 00:13:40,356 [Andrey] it seems like most people who are using 00:13:41,416 --> 00:13:49,156 [Andrey] scaffolded agents today are using them with essentially one of two scaffolds, with Cloud Code or Codex. And 00:13:50,236 --> 00:14:00,475 [Andrey] those seem to be good enough maybe. I guess, do we see a lot of people customizing, a lot of people, whatever, companies customizing their scaffolds? 00:14:00,476 --> 00:14:03,856 [Seth] CladBot, do the CladBots count as that, I guess? 00:14:03,856 --> 00:14:04,236 [Andrey] Yeah. 00:14:05,396 --> 00:14:39,676 [Seb Krier] They are a form of it. I don’t know. I think a lot of power users and people in our immediate communities use a lot of Cloud Code and Codex, and particularly software engineers. But I don’t think most legal departments and most kind of firms out there are necessarily using Cloud Code either. And it’s not clear to me that this is necessarily the optimal interface or, there may be better systems that are Cloud Code-like, or CLI-like perhaps in some way. But, so I don’t know, maybe they’re sufficient, but even these tools end up kind of calling on loads of other external APIs and tools and so on in how they 00:14:40,836 --> 00:14:57,576 [Seb Krier] function. So if anything, these are actually scaffolds. You’re not kind of calling the model directly. There’s all sorts of different sub-agents behind the scenes. It’s not just a one-shot call. There’s quite a lot going on, which is in fact this more, I don’t know, dynamic scaffolding thing I was mentioning earlier, I guess. 00:14:58,976 --> 00:15:06,736 [Andrey] Okay. The natural question here is, what is going to be the role of the market in coordinating- 00:15:06,736 --> 00:15:07,375 [Seb Krier] Mm 00:15:07,375 --> 00:15:11,276 [Andrey] ... AI here? And I’ll just very shamelessly plug- 00:15:11,276 --> 00:15:11,285 [Seb Krier] [chuckles] 00:15:11,285 --> 00:15:24,796 [Andrey] ... some recent work with Rohit Krishnan, where we’re kind of playing around with the idea of LLMs bidding in a procurement auction and seeing whether that results in more efficient use of AI. 00:15:26,696 --> 00:15:29,655 [Seb Krier] Well, first of all, I need to properly read that again. But the- 00:15:29,655 --> 00:15:30,476 [Andrey] [laughing] 00:15:30,476 --> 00:15:31,016 [Seb Krier] In terms of, 00:15:32,496 --> 00:15:32,916 [Seb Krier] I guess, 00:15:34,556 --> 00:15:46,396 [Seb Krier] at a very high level, markets are good at just coordinating in general, including AI. And so, assuming they function as intended in it, you’ve got the pricing mechanism to get... 00:15:47,556 --> 00:15:49,396 [Seb Krier] I don’t know. I expect that to kind of work as well with 00:15:50,476 --> 00:15:52,616 [Seb Krier] matching, I guess, supply and demand or something. 00:15:54,016 --> 00:15:55,196 [Seb Krier] The supply of this 00:15:56,216 --> 00:16:00,036 [Seb Krier] raw resource of cognition or something, and the demand of all sorts of different businesses and users. 00:16:01,696 --> 00:16:05,516 [Seb Krier] So maybe, at a very high level, I don’t know. What exactly do you mean by the role of the market or something here? 00:16:09,076 --> 00:16:21,356 [Andrey] Obviously the market is involved in many parts of the AI vertical supply chain, right? From competition in chips. There’s competition between models. There might be also competition between 00:16:22,516 --> 00:16:28,576 [Andrey] scaffolds, bundles of environments, scaffolds, and LLMs. 00:16:28,576 --> 00:17:06,496 [Seth] I guess maybe it would be useful to juxtapose this versus, so what Andrey, one of the things he’s imagining is, I have a job. I post it to some sort of Upwork-like future platform. Different companies that host different AI models bid to do that job. “Oh, I think I can do that job with $1 of electricity and tokens,” versus another model, and then we get efficient allocation of intellectual tasks to models, right? So do we think that that’s going to be important, or is it going to be more like I ask the super model what the best model is, and I just get allocated in a non-market way? Might be one version of this question. 00:17:08,156 --> 00:17:18,836 [Seb Krier] I guess intuitively, my mind goes to the former question. But, or there’s a little bit of both in some sense, because even in the former one, you’re going to be using the large model for some sort of 00:17:20,436 --> 00:17:26,686 [Seb Krier] cognitively demanding task or something. It kind of depends what kind of quality of output you also need and want. 00:17:26,686 --> 00:17:26,706 [Seth] [chuckles] 00:17:26,706 --> 00:17:27,056 [Seb Krier] But then 00:17:28,376 --> 00:17:49,636 [Seb Krier] you’re still going to be constrained by your own resources or something, and depending on what you have to spend, if you can get the output for cheaper by kind of relying on this kind of competitive marketplace of smaller models or something, not even smaller models, they might just be all be big and kind of just scaffolding different, you’re offering a slightly different thing. Why wouldn’t you go for that, and why wouldn’t that exist in the first place? Unless the very first- 00:17:49,636 --> 00:17:52,216 [Andrey] Doesn’t exist yet, just to be clear. 00:17:52,216 --> 00:17:52,716 [Seb Krier] Um- 00:17:52,716 --> 00:17:58,416 [Seth] A, it doesn’t exist yet, and as Andrey proves, at least current models are bad at understanding their own capabilities. 00:17:58,416 --> 00:17:58,666 [Andrey] Oh, yeah. 00:17:58,666 --> 00:18:00,496 [Seth] Now maybe that’s going to be fixed. 00:18:00,496 --> 00:18:08,096 [Seb Krier] Yeah. Oh, no, I agree. I think that we’re not there yet, right? I think, again, and that goes back to the earlier AGI question, is there’s all sorts of, then again, what’s the right comparator? But, 00:18:09,476 --> 00:18:21,316 [Seb Krier] yeah, I don’t think we’re exactly there. Yeah, I think a lot of this will have to be built as well. The kind of an ability for a model to just better kind of operate in a more multi-agent environment, kind of have a better sense of 00:18:22,596 --> 00:18:32,556 [Seb Krier] delegation. I think the kind of, yeah, industrial intelligence or something seems to be maybe more neglected, as opposed to just single-agent intelligence or something, if that makes sense. 00:18:32,556 --> 00:18:34,776 [Seth] Do we need to bring the word cybernetics back? 00:18:34,776 --> 00:18:35,496 [Seb Krier] Yeah. 00:18:35,496 --> 00:18:36,116 [Andrey] [laughs] 00:18:36,116 --> 00:18:38,816 [Seb Krier] Somewhat. [laughs] 00:18:40,756 --> 00:18:51,256 [Andrey] All right. A little change in subject, but I know this has been in the discourse, the topic of recursive self-improvement, RSI. 00:18:51,256 --> 00:18:52,956 [Seth] Ooh, very scary. 00:18:52,956 --> 00:18:54,896 [Andrey] Jack Clark recently had an essay about it. 00:18:56,376 --> 00:18:58,876 [Andrey] Seb, what is your take? 00:18:58,876 --> 00:18:59,206 [Seb Krier] [chuckles] 00:19:00,316 --> 00:19:07,896 [Seb Krier] What is my take? I don’t know. I think it depends what exactly we mean by recursive self-improvement. 00:19:09,096 --> 00:19:50,336 [Seb Krier] I had a blog post not long ago, I guess, when trying to disentangle a little bit what I have in mind when I think about this. On the one hand, there’s the model getting recursively better through the usage of more AI and whatnot. And on the other hand, there’s the more kind of societal side of things, the transformation side, which I think very often, these two worlds are a little bit blurred in the discourse. It’s like, oh, you get RSI, and then X, Y, Z about the world or something. Things go really fast or they don’t go fast. And, I think these should be separated very neatly because on the model side, of course, I expect, already there’s a lot of AI being used everywhere to kind of create models. And I expect that to continue. 00:19:52,536 --> 00:19:55,976 [Seb Krier] But it’s not clear to me that this necessarily now leads to a dynamic by which 00:19:57,156 --> 00:20:16,596 [Seb Krier] the model now gets extremely or exponentially intelligent in a very short amount of time. It’s still kind of bottlenecked by all sorts of resources. And as I was saying earlier, I still see them as better at kind of paradigm exploitation than kind of exploration, which I think is the thing you might need to get to the next step. But, first of all, what do I know? But secondly, 00:20:17,616 --> 00:20:19,986 [Seb Krier] the other thing is, yeah, on the societal side of things, 00:20:20,996 --> 00:20:29,756 [Seb Krier] people sometimes talk about foom or hard takeoffs and whatnot, and these have very clear kind of real-life implications. It’s not just kind of a model of getting better in a 00:20:31,216 --> 00:20:34,576 [Seb Krier] data center somewhere. And that side, I think, is where you have to think about 00:20:36,116 --> 00:21:27,056 [Seb Krier] [lip smack] all the kind of usual bottlenecks, adoption, deployment, diffusion, the kind of productive integration of all these systems at scale, both in terms of manufacturing and so on and so forth. And, I guess it’s not clear to me that the shift from GPT-2 to GPT-3 or coming up with kind of, we’re just very classic kind of software engineering, meat and potatoes type tasks that you can just easily just automate away. It’s maybe one of these things that’s maybe easy to say ex post, but, I’m not sure. And certainly, my expectation is you’re going to get loads of gains in the coming years of kind of automating part of that pipeline. But that seems good. You just get better models, and that’s just overall helpful for all sorts of other things, even if you’re doing safety work and kind of governance work and whatnot, we benefit a lot from that cognitive resource, I guess. 00:21:27,056 --> 00:21:40,696 [Andrey] What would happen in the world for you to change your mind? Is there any, let’s say that recursive self-improvement is actually kind of this much more profound change than you’re painting. 00:21:41,816 --> 00:21:42,036 [Andrey] What 00:21:44,136 --> 00:21:45,696 [Andrey] signs would there be, I guess? Yeah. 00:21:45,696 --> 00:21:51,656 [Seb Krier] But to be clear, I’m not claiming it’s just business as usual, nothing to see here or whatever, right? I’m 00:21:52,796 --> 00:22:14,936 [Seb Krier] kind of just claiming that some of the stronger versions of the claim aren’t kind of self-evident. And so I see a lot of this happening in some sense. Certainly, in 10 years, I expect to have larger kind of more, again, acceleration of economic growth and whatnot and kind of faster diffusion across the board. I certainly don’t expect diffusion to take the same amount of time as, say, electricity or these other technologies. 00:22:16,576 --> 00:22:23,236 [Seb Krier] So it depends what exactly you mean, because what specifically am I looking to change my mind on? 00:22:23,296 --> 00:22:30,656 [Andrey] Well, let’s say the scenarios of AI 2027, right? Presumably, 00:22:31,996 --> 00:22:45,176 [Andrey] in 2027, you’ll see something that’s like, “Oh, wow, I was wrong. This is not going to be so gradual. This is going to be this sudden foom,” that you’re criticizing. Yeah. 00:22:45,176 --> 00:22:52,236 [Seb Krier] The original foom or hard takeoff definition literally talks about this change happening within hours or days. 00:22:52,236 --> 00:22:53,236 [Andrey] [chuckles] 00:22:53,236 --> 00:22:56,056 [Seb Krier] Which is not even, it’s not what the 2027 scenario, I think, predicts. 00:22:56,056 --> 00:22:56,296 [Andrey] Yes. 00:22:57,556 --> 00:23:00,446 [Seb Krier] But the 2027 scenario, from what I remember, again, it’s been a bit of time now. 00:23:01,796 --> 00:23:08,816 [Seb Krier] One thing with the scenarios there is that there’s the kind of misalignment assumption, and which I’m kind of uncertain about. 00:23:08,816 --> 00:23:09,255 [Andrey] Mm. 00:23:09,256 --> 00:23:17,296 [Seb Krier] And it also talks about a lot of progress in robotics, which I think is a bit further away. I think it’s close. We’re getting there, too. 00:23:19,116 --> 00:23:19,476 [Seb Krier] But 00:23:21,156 --> 00:23:25,916 [Seb Krier] I don’t know. Probably kind of AI, if in 2030, we start seeing AI is making all sorts of crazy 00:23:26,956 --> 00:24:06,196 [Seb Krier] inventions, innovations in fields other than just kind of perhaps math and coding across the boards, and I’m like, okay, this is clearly-- And you get extremely fast adoption, too, right? You have entire businesses doing completely, it’s not business as usual, clearly, in the economy or something and wide adoption. But it’s hard to say because I expect all that to some degree, right? It’s not that I’m saying, “Oh, this is never going to happen.” I just think of it as a little bit more elongated and the implications of that being maybe not as like, we have Dyson spheres in five years or something like that, so. It’s more of a disagreement maybe on the extremes or the margins or something, but not so much at the core of the claim that yes, models are going to make models better and... 00:24:07,276 --> 00:24:27,536 [Seb Krier] But, again, even having-- In fact, actually, here would be a thing. If Anthropic or DeepMind or something in 2037 have fewer and fewer employees, fewer people kind of just doing AI research, engineers and so on, you’re clearly seeing kind of that profession. Because of course, I can imagine these jobs to change, right? Maybe you’re kind of managing more agents or something. That 00:24:28,616 --> 00:24:35,966 [Seb Krier] I expect. But the fact that you just need far fewer people to kind of do not only these large training runs, but the kind of 00:24:36,976 --> 00:24:43,476 [Seb Krier] large training runs that give you just much, much better systems, then I think I’d be like, okay, this is going a little bit faster than maybe expected or something. 00:24:44,656 --> 00:24:51,676 [Andrey] Okay. One thing you mentioned in that kind of hints at another hot take you have, which is about alignment. 00:24:51,676 --> 00:24:52,026 [Seb Krier] Uh-huh. 00:24:54,596 --> 00:24:55,926 [Andrey] What’s the deal with alignment? 00:24:57,196 --> 00:24:58,086 [Andrey] [laughs] 00:24:58,086 --> 00:24:58,136 [Seb Krier] [laughs] 00:24:58,136 --> 00:25:02,136 [Seth] Is it hard? Is it easy? Is it different than we would’ve expected going in? 00:25:02,136 --> 00:25:19,646 [Seb Krier] Yeah. It’s perhaps that. I think my take about alignment is something-- Well, first of all, I just don’t like the word. I think it’s a bit of an annoying word because it’s being used for all sorts of things. The AI says something that we just kind of don’t like, or you say, “Oh, it’s misaligned.” No one pre-registers what they expect the aligned behavior to be, and then just kind of tests. 00:25:19,646 --> 00:25:20,116 [Andrey] [laughs] 00:25:20,116 --> 00:25:35,626 [Seb Krier] But I think my general claim is maybe the fact that it’s been easier than we would’ve predicted a decade ago or so. Then when I first got into AI in 2017, that was partly as a result of reading things like “Superintelligence” by Bostrom. 00:25:35,626 --> 00:25:36,236 [Andrey] Mm-hmm. 00:25:36,236 --> 00:25:48,496 [Seb Krier] And you’d read these books, like Stuart Russell’s “Human Compatible” and others, that kind of had all these analogies like King Midas and you ask a system to optimize for goal X, and in pursuit of that goal, it does all sorts of other things that you don’t want it to do. 00:25:48,496 --> 00:25:51,916 [Seth] Right. The paperclip maximizer, and we seem to not have those. 00:25:51,916 --> 00:25:57,476 [Seb Krier] Yeah. It’s like one version of it or one variant of it. And certainly at the time you didn’t really have language models. A lot of these intuitions were kind of based off 00:25:58,596 --> 00:26:48,236 [Seb Krier] reinforcement learning systems in very basic kind of game scenarios where they were actually given a single goal to optimize for. And this is not actually what we do, I think, with models. And you had these kind of examples, even the value loading problem was something discussed at the time where actually specifying these complicated nuanced human values in mathematical terms would be extremely hard. So even if you managed to tell a robot to clean the room, it would then just pick up a baby and put it in the trash or something. And I think it turns out a lot of this stuff is actually much easier. You have problems. You’ve got things like reward hacking. You’ve got AIs behaving in weird ways that we were not always kind of anticipating because of the ways they were post-trained. So my claim is not like, oh, again, it’s all fine, and safety is a scam or whatever. It’s more that it’s certainly much easier than, or at least we’re in a much better track than I would’ve at least guessed perhaps a decade ago. And secondly, I think it 00:26:49,916 --> 00:26:54,816 [Seb Krier] just seems tractable. There’s a lot of progress in terms of chain-of-thought monitoring and all these other things. And 00:26:56,696 --> 00:26:57,796 [Seb Krier] I also think that the 00:26:59,016 --> 00:27:05,825 [Seb Krier] hard part is maybe more the kind of normative question of whose values and when, and what and everything. That’s the kind of thing that we’re looking into more. But 00:27:07,096 --> 00:27:13,696 [Seb Krier] yeah, I prefer the word actually instruction following or intent following or something instead of alignment. And I think by and large, they’re actually pretty good at that. 00:27:14,796 --> 00:27:31,636 [Seb Krier] So again, that doesn’t mean you have to dismiss all sorts of theories and all the kind of power optimization stuff. But I guess my immediate outcome is this goes rather well. Or if I am more concerned by other things like misuse, if you’d like, than kind of the AI’s being innately, inherently kind of internally misaligned. 00:27:31,636 --> 00:28:03,676 [Seth] This really seems related to your take that intelligence is not at odds with being a tool, right? So a lot of people have this intuition where if you had a super-duper intelligent genie or oracle, it would develop even implicitly some sort of value or goal that orthogonality thesis might have nothing to do with what we want. But you’re more optimistic about the idea that the LLM doesn’t want anything. It’s incorrect to take the intentional stance towards an LLM. 00:28:03,676 --> 00:28:09,236 [Seb Krier] Not incorrect. It’s actually kind of descriptively useful, even functionally sometimes to use that language. 00:28:10,796 --> 00:28:18,836 [Seb Krier] But that’s the thing, right? I think we kind of lack the language to properly delineate and differentiate when it’s useful to use that or appropriately descriptive and when it’s not. 00:28:20,076 --> 00:28:41,496 [Seb Krier] And so I agree that, of course, I think the take I had on this was something like, and I can imagine a tool being an agent and an agent being a tool. Or in principle, I can imagine something being hyper-capable and still being broadly instruction following rather than at a certain level of capability, aha, that’s when the goals change and things get... And it kind of depends on the type of system as well. I imagine not all 00:28:42,656 --> 00:28:45,116 [Seb Krier] paths lead to the same kind of outcome. But, 00:28:46,256 --> 00:29:13,596 [Seb Krier] so again, I can see plausible versions of the world where homo hundrio drives or something are a more salient feature of the way we kind of train models. Right now, it doesn’t seem to me very likely that this is a core feature that they have. But of course, it’s hard to kind of either prove or disprove, right? Because someone might just say, well, that’s because they’re very good at hiding this or something, or once they’re capable enough or whatever. So there’s always a bit of this kind of gotcha thing. It’s like deception. But 00:29:14,936 --> 00:29:39,896 [Seb Krier] yeah. So in principle, I guess I can totally conceive of at least a superintelligence that is controllable, that is benign, that is at least subservient to the goals of humanity or a user or principle or whatever. That could still be used to cause enormous harm, but it’s just I don’t necessarily think the analogies of, oh, I think Tegmark was thinking, look at the zoo where the monkey’s going. I think these are just not really 00:29:41,736 --> 00:29:43,136 [Seb Krier] helpful kind of analogies. 00:29:44,276 --> 00:30:02,396 [Seth] Monkey at the zoo, but you’ve also got the monkey’s paw, right? Maybe the reason some prefer alignment to instruction following is we all know the story of, be careful what you wish for. You wish for something, and it’s under-specified, and you get the bad version of it because the AI doesn’t understand the context. 00:30:02,396 --> 00:30:08,336 [Seb Krier] I think that’s why, yeah, I think maybe instruction following is maybe too... Intent following or something gets to it more. 00:30:09,936 --> 00:30:18,316 [Seb Krier] But of course, that problem doesn’t go, even if it follows intent or something, you could still have all the problems because your intent is nefarious or whatever. So 00:30:19,436 --> 00:30:19,816 [Seb Krier] I think the 00:30:21,356 --> 00:31:06,756 [Seb Krier] way you deal with that is all sorts of, I don’t know how to conceptualize it, but in fact scaffolds. It’s a bit more this outside of the model or something. I’m kind of almost indexing on a world that will indeed have agents that are trained to be bad or whatever, or someone going to be instructed to do bad things. But just like with humans, you come up with all sorts of kind of systems, rules, laws, norms, kind of protocols that either discourage the kind of bad behavior, or punishes it, or makes it just not worthwhile or something. But I’m not going to put all my bets on the, oh, it has to be pure-hearted, and that will be sufficient. And then you just scale it forever, and it’s going to be an amazing goal. I just think that the way of seeing or thinking about AI is that I just find kind of a bit 00:31:08,096 --> 00:31:12,656 [Seb Krier] too narrow, I guess. I think it’s important, it’s just insufficient, and it’s certainly not my main kind of a-- yeah. 00:31:14,946 --> 00:31:15,206 [Andrey] Okay. 00:31:16,666 --> 00:31:20,086 [Andrey] Our audience is very much composed of economists. 00:31:22,586 --> 00:31:30,506 [Andrey] If you’re an economist and you’re very interested in AI, what sort of work would you be trying to do? 00:31:30,506 --> 00:31:32,146 [Seth] Maybe to be useful to AI people- 00:31:32,146 --> 00:31:32,216 [Andrey] Yes 00:31:32,216 --> 00:31:37,466 [Seth] ... in particular. What would you want, what did the DeepMind team want to read from economists? 00:31:37,466 --> 00:32:20,766 [Seb Krier] I think kind of engaging with their assumptions or something, right? If you assume, let’s say, an AG-- and I think some do, to be fair. I actually think there’s a lot more, I think, discourse now going on between economists and AI people, whatever. But assuming that you do have AI systems that are interchangeable or almost quasi-fully substitutable with humans, that come up with good ideas, that are parallelizable and whatnot, what does that change to your kind of growth function and so on? So, maybe that’s useful. Right now, in the short term, at least, there’s all sorts of questions around labor, there’s questions around productivity or adoption. Clearly, there’s useful work to be done there. But I think in terms of AGI specifically, given that a lot of the field just thinks you’re going to get to AGI in the next five to 10 years, 00:32:22,746 --> 00:32:26,806 [Seb Krier] what are the implications for taxation? What are the implications for 00:32:28,626 --> 00:32:37,786 [Seb Krier] how that’ll affect different states across the world? I think I’m probably more worried about a call center in Hyderabad than I am about the white-collar worker in North America or something. So, 00:32:39,066 --> 00:32:57,306 [Seb Krier] yeah. I think all these kind of questions, but just indexing more and making fewer, I guess, assumptions around the limits of capabilities. Because sometimes you see them kind of being implicitly snuck in somewhere or something of like, well, because AIs can’t do XYZ, therefore... And yeah, fine, but maybe they will do XYZ. And then what? How does that change your thinking? Yeah. 00:32:57,306 --> 00:32:59,506 [Seth] Maybe more scenario planning than, 00:33:00,526 --> 00:33:04,746 [Seth] here’s my median projection, or here is one projection I think is plausible. 00:33:04,746 --> 00:33:22,846 [Seb Krier] Yeah. And embedding the kind of thoughtful models and thinking that economists have within these scenarios and making them more salient to the kind of computer scientists, right? Even when I brought up competitive advantage, people will be like, “Oh, but what if the AI is cheaper and better?” It’s like, well, that’s not the point. The opportunity cost point of competitive advantage, there’s a difference. 00:33:22,846 --> 00:33:23,286 [Andrey] [laughs] 00:33:23,286 --> 00:33:31,786 [Seb Krier] And again, there are answers to that as well, but I think just kind of better translating, I think, some of these insights to the AI tribe, the thing is useful. 00:33:32,846 --> 00:33:40,526 [Andrey] So that’s very naturally leading us to this question about yourself. And you do lots of different things. 00:33:41,946 --> 00:33:50,426 [Andrey] You’re prolific on Twitter, for sure. But also, you’re doing internal work for DeepMind. How do you allocate your time? 00:33:52,066 --> 00:33:52,166 [Seb Krier] I don’t know. 00:33:52,166 --> 00:33:53,266 [Seth] What percentage is Twitter? 00:33:53,266 --> 00:33:54,646 [Andrey] Yeah. [laughs] 00:33:54,646 --> 00:34:04,686 [Seb Krier] Twitter is actually not that much today. It must be an hour max or something, an hour and a half, two hours, maybe, something. But that is maybe much by others’ standards. But the- 00:34:04,686 --> 00:34:06,476 [Andrey] [laughs] What is the optimal amount of Twitter? [laughs] 00:34:06,476 --> 00:34:29,866 [Seb Krier] [laughs] Yeah. It’s the Pareto optimal. I guess, in my day-to-day work, it’s a mixture of proactive and reactive. Proactive in the sense that I think, oh, these questions of agents and cybersecurity and liability and whatnot, and biosecurity are kind of important things to look into, and therefore, there’s a lot of research that I do and colleagues do, and a lot of coordination across the org. 00:34:31,026 --> 00:34:39,486 [Seb Krier] But there’s also more reactive stuff because we’re a policy team, and so there’s things happening in the external world like CA 53, the preemption debates. 00:34:40,546 --> 00:34:48,386 [Seb Krier] So it’s a bit of a mix of that. And of course, all sorts of internal dynamics. But, yeah. I guess I’m curious about all sorts of other things, and so when I do have time, and I’ve kind of 00:34:50,006 --> 00:34:58,106 [Seb Krier] completed the main quests, I try to keep some time for other stuff I’m interested in. I work with some research teams and kind of look into what they’re into. I’ll 00:34:59,266 --> 00:35:09,826 [Seb Krier] find topics or themes that I think are maybe kind of neglected or underrated or I just don’t see out there as much, and like, “Oh, cool. We’re going to try to find out about this more.” But I think it’s just very kind of curiosity driven, and the allocation of time is 00:35:11,566 --> 00:35:16,705 [Seb Krier] not super thought out. It’s more like, oh, I think these things are interesting, and I’m going to get into that for a bit. [laughs] 00:35:16,706 --> 00:35:22,306 [Andrey] So it wasn’t a deliberate strategy of getting Tyler’s attention and adoration. [laughs] 00:35:22,306 --> 00:35:25,126 [Seb Krier] No, not at all. Not at all. But I’m very- 00:35:25,126 --> 00:35:25,746 [Seth] The long play 00:35:25,746 --> 00:35:30,565 [Seb Krier] ... very grateful for his... [laughs] For the meme. But- 00:35:30,566 --> 00:35:41,766 [Seth] What kind of, but I know you can’t be specific, but for your sort of internal work, what does a work product look like? Are you participating in a meeting and giving hot takes? Are you writing internal memos? What is- 00:35:41,766 --> 00:35:42,026 [Seb Krier] Yeah 00:35:42,026 --> 00:35:42,276 [Seth] ... in- 00:35:42,276 --> 00:35:56,406 [Seb Krier] It’s a mixture. Obviously, meetings. Any large bureaucracy will have meetings. But I think a lot of analysis, memos to execs sometimes. Just research, managing researchers sometimes, depending on the project. 00:35:57,626 --> 00:36:04,106 [Seb Krier] We’ll have a lot of coordination. Actually, I’m realizing through a lot of these kind of meetings, a lot of it is just kind of coordination and information transfer, right? 00:36:04,106 --> 00:36:04,146 [Andrey] [laughs] 00:36:04,146 --> 00:36:07,006 [Seb Krier] It’s maybe why I’m so obsessed with the Coasean bargaining thing. Just let- 00:36:07,006 --> 00:36:07,326 [Seth] Ah 00:36:07,326 --> 00:36:08,546 [Seb Krier] ... the agents do it. But, 00:36:09,806 --> 00:36:34,116 [Seb Krier] yeah. I think the day-to-day work is a lot of reading, a lot of meetings, a lot of writing, and distilling and translating information, I think, across different tribes also. So if I’m talking to legal people, like lawyers, about what’s going on in, say, the more technical side of the org, or if I’m speaking to the researchers about something that’s more... But yeah, there’s a lot of translating of concepts across different stakeholders, I guess. 00:36:34,116 --> 00:36:45,726 [Andrey] So how does that work in an org like Google? Because I think in a lot of orgs, they’re really obsessed with KPIs and output metrics. 00:36:45,726 --> 00:36:46,156 [Seb Krier] Mm-hmm. 00:36:46,156 --> 00:36:48,746 [Andrey] And what you’re describing sounds very- 00:36:48,746 --> 00:36:49,706 [Seth] Hot takes per meeting. [laughs] 00:36:49,706 --> 00:36:54,926 [Andrey] Yeah. Very much amorphous, very hard to measure. 00:36:56,066 --> 00:36:56,196 [Seb Krier] Yeah. 00:36:56,196 --> 00:37:00,606 [Andrey] Obviously, you have a lot of external visibility, but is that 00:37:02,786 --> 00:37:07,846 [Andrey] a problem? Or is that just it’s understood that that’s how this goes? Yeah. 00:37:07,846 --> 00:37:13,846 [Seb Krier] I think the external stuff is kind of almost just very separate from the kind of day-to-day work side of things. 00:37:14,986 --> 00:37:23,366 [Seb Krier] And yeah, internally, we do have KPIs or equivalents or whatever. I think they may be less numerical in nature. But you might still have some, develop a consistent position on 00:37:24,506 --> 00:37:30,819 [Seb Krier] X issue or something in the next two, three months.And that requires a lot of research work, coordinating. 00:37:30,819 --> 00:37:32,929 [Seth] Have 10 opinions. [laughs] 00:37:32,930 --> 00:37:38,100 [Seb Krier] No, ideally they just want one. I think 10 opinions, that’s the issue. There are a lot of opinions out there. You’ve got to find the good ones. 00:37:38,100 --> 00:37:39,530 [Seth] That’s the main problem with economists. 00:37:39,530 --> 00:37:42,350 [Seb Krier] But [laughs] yeah. Exactly. Who was that quote? 00:37:43,830 --> 00:37:44,290 [Seth] Truman. 00:37:44,290 --> 00:37:44,330 [Seb Krier] Yeah. 00:37:44,330 --> 00:37:46,210 [Seth] Truman begged for the one-handed economist. 00:37:46,270 --> 00:38:20,990 [Seb Krier] Yeah, exactly. But, so I think, yeah, I think internally it’s just a kind of analysis or something. Say you’re thinking about, oh, agents and legal liability. How do these things work? What does the existing legal environment say and prescribe? What happens if something goes wrong? What are relevant factors? There’s a lot of that kind of thing. And I guess particularly within the DeepMind side, because when we’re on the frontier side, we’re thinking about the next five years as opposed to what’s going on right now. But yeah, the other side stuff is really just kind of out of personal interest and just me writing stuff, and they seem fine with it so far. [chuckles] 00:38:20,990 --> 00:38:26,510 [Andrey] What about... So we’ll be at a conference together, the Post-AGI conference- 00:38:26,510 --> 00:38:26,830 [Seb Krier] Ooh 00:38:26,830 --> 00:38:28,370 [Andrey] ... at Lighthaven, Berkeley. 00:38:28,370 --> 00:38:30,110 [Seth] Ooh. Prestigious. 00:38:31,130 --> 00:38:32,990 [Andrey] I don’t know if it’s prestigious. 00:38:34,550 --> 00:38:34,629 [Seth] [laughs] 00:38:34,630 --> 00:38:45,730 [Andrey] But you’ve gone to a few of these conferences, like the Curve is another fairly well-known one. What’s your take on these? 00:38:45,730 --> 00:38:54,750 [Seb Krier] I think some are useful. The majority of conferences I go to, I don’t exactly find that life-transforming, I guess. 00:38:54,750 --> 00:38:57,610 [Andrey] [laughs] You’re going to the wrong conference. [laughs] 00:38:57,610 --> 00:39:09,290 [Seb Krier] I know. Can someone show me the... But I think, yeah, they obviously perform a social function to some degree, right? There’s a lot of meeting people, some networking or something, some kind of finding out new ideas. But 00:39:10,390 --> 00:39:20,310 [Seb Krier] my issue with conferences, very often they’re just very tame. They’re very risk-averse. They’re very the same ideas you’ve-- Already if you can read it online or something, it depends on the conference. But, 00:39:21,510 --> 00:39:24,190 [Seb Krier] although I have been to really good ones, too. There was this 00:39:25,570 --> 00:39:43,529 [Seb Krier] IMF conference with Econ Ty, with I think Anton Korinek and others had organized. And that was great because that was a nice one where you had both the technologists and a lot of economists and loads of presentations, and you got to learn lots of new things. But, in general, I don’t see a huge... Beyond maybe showing, again, some hot takes here and there. 00:39:45,370 --> 00:39:49,990 [Seb Krier] Yeah, some I assume are good conferences. [chuckles] 00:39:49,990 --> 00:40:00,670 [Seth] I’m just the exception, but you had a great joke on your Twitter the other day about this, which is, Caveman panelist one, “Fire is bad.” Caveman panelist two, “Fire is good.” 00:40:00,670 --> 00:40:00,770 [Seb Krier] Yeah. 00:40:00,770 --> 00:40:02,100 [Seth] Caveman panelist three, 00:40:03,450 --> 00:40:07,120 [Seth] “We need to balance the upsides and downsides of fire and use it wisely.” 00:40:07,120 --> 00:40:07,320 [Seb Krier] Absolutely. 00:40:07,320 --> 00:40:09,620 [Seth] Wild applause. [laughs] 00:40:09,620 --> 00:40:09,650 [Andrey] [laughs] 00:40:09,650 --> 00:40:14,850 [Seb Krier] Exactly. There’s a lot of that. That’s the energy that I’m getting very tired of because it’s- 00:40:14,850 --> 00:40:15,050 [Seth] [laughs] 00:40:15,050 --> 00:40:21,700 [Seb Krier] And I like playing the role of the wise centrist opinion, whatever. But it does get very- 00:40:21,700 --> 00:40:23,150 [Seth] You do get wild applause. 00:40:23,150 --> 00:40:24,470 [Seb Krier] Yeah. All the time. [chuckles] 00:40:26,490 --> 00:40:29,770 [Seb Krier] But yeah, I think there’s a lot of that. I wish there were more 00:40:30,810 --> 00:40:35,090 [Seb Krier] almost private Chatham House-y conferences, where you had people who highly disagreed with each other- 00:40:35,090 --> 00:40:35,210 [Andrey] Mm 00:40:35,210 --> 00:40:36,770 [Seb Krier] ... but were polite and didn’t get at 00:40:37,950 --> 00:40:49,370 [Seb Krier] each other’s throats. And you had more setups that actually allowed ideas to clash a bit more, in a civilized way, of course. But that would be a bit hard, but also much more interesting, I think, than 00:40:51,490 --> 00:40:55,390 [Seb Krier] everyone broadly agreeing that it’s good to be good and it’s bad to be bad, and yeah. [chuckles] 00:40:55,390 --> 00:41:03,710 [Andrey] I do feel like the Lighthaven conferences are quite good for this, in that there’s an enormous amount of free time and- 00:41:03,710 --> 00:41:04,130 [Seb Krier] Mm-hmm 00:41:04,130 --> 00:41:07,770 [Andrey] ... free space that’s not where the talk is happening. 00:41:07,770 --> 00:41:07,940 [Seb Krier] Yeah. 00:41:07,940 --> 00:41:10,630 [Andrey] And so you do get a lot of this. 00:41:10,630 --> 00:41:11,040 [Seb Krier] Well, yeah, I agree. 00:41:11,040 --> 00:41:21,090 [Andrey] But I agree that many conferences are not like that, where you’re just packed. You have a conference hall, and you don’t have anywhere else to go, and it’s packed with talks. Yeah. 00:41:21,090 --> 00:41:21,710 [Seb Krier] Yeah. No, totally. 00:41:21,710 --> 00:41:23,550 [Seth] NBER Summer Institute. [laughs] 00:41:24,750 --> 00:41:28,330 [Andrey] Seth, there is disagreement. Say what you will. At NBER- 00:41:28,330 --> 00:41:28,540 [Seth] There is fire 00:41:28,540 --> 00:41:29,430 [Andrey] ... people throw down. 00:41:30,450 --> 00:41:31,430 [Andrey] [laughs] 00:41:31,430 --> 00:41:37,720 [Seth] [laughs] I’ve never seen a meaner comment than I have seen from a discussant at NBER Summer Institute. [laughs] 00:41:37,720 --> 00:41:52,570 [Seb Krier] [laughs] The Progress Conference, for example, last year, was one that I thought was really good. That was at Lighthaven, in fact. I think the setup and the kind of people and the curation and so just made it something that I found quite engaging. [upbeat music] 00:41:52,570 --> 00:41:56,490 [Seth] So you brought up this idea, as we were talking, about you 00:41:58,330 --> 00:42:21,049 [Seth] think there are so many meetings in your organization because it’s so hard, yet so critical to transfer information. And there’s this Coasean idea that so much of why the economy works the way it does is just the idea of transaction costs, right? In addition to kind of this Hayekian idea of local information that’s hard to share. 00:42:21,050 --> 00:42:21,810 [Seb Krier] Mm-hmm. 00:42:21,810 --> 00:42:23,960 [Seth] You have a very influential essay 00:42:25,130 --> 00:42:30,230 [Seth] that kind of maybe stole some of Andrey’s thunder, but is still an excellent essay- 00:42:30,230 --> 00:42:31,040 [Seb Krier] [laughs] 00:42:31,040 --> 00:42:46,210 [Seth] ... about this idea of, well, what happens when AIs go out there and can micro-bargain costlessly with each other at high frequency over very, what might seem to us, small issues. 00:42:47,570 --> 00:42:57,440 [Seth] Tell us maybe in a few sentences, what’s that vision and what’s the positive vision for why that would be good for society, for us to have AI agents constantly bargaining for us over stuff? 00:42:59,130 --> 00:43:01,810 [Seb Krier] Yeah. I guess the idea is, as you mentioned, there’s all sorts of 00:43:03,990 --> 00:43:26,350 [Seb Krier] transaction costs that mean that we don’t get to bargain on things that we would otherwise bargain for. And instead, you get these blunt rules and these solutions that kind of work, but come with all sorts of externalities or aren’t super efficient. And so the idea is, if you can actually do this kind of negotiation at scale for very little, and that’s a big assumption. That’s not a given either, 00:43:27,850 --> 00:43:35,586 [Seb Krier] then you could solve all sorts of things thatAnd also just kind of problems that would otherwise not be even conceivable in the first place. 00:43:36,726 --> 00:43:41,186 [Seth] One example you give, just so we can be a little bit more specific, is noise standards, right? 00:43:41,186 --> 00:43:41,456 [Seb Krier] Right. 00:43:41,456 --> 00:43:57,226 [Seth] So you can’t throw a loud party after 10:00 PM in such and such a place. But you think that maybe AI agents could come to a less coarse rule that is, get us more to the grand coalition of allocative efficiency than a coarse rule like that. 00:43:57,226 --> 00:44:01,166 [Seb Krier] Yeah. To be fair, that’s probably a problem that no one really cares about except me because of like- [chuckles] 00:44:01,166 --> 00:44:02,086 [Seth] No. Dude. 00:44:02,086 --> 00:44:03,645 [Andrey] I care about it so much. 00:44:03,645 --> 00:44:04,626 [Seb Krier] Oh, really? Okay, cool. 00:44:04,626 --> 00:44:04,746 [Andrey] Yes. 00:44:04,746 --> 00:44:07,816 [Seb Krier] Maybe that’s a good example then. But yeah, the idea here is, 00:44:09,146 --> 00:44:17,006 [Seb Krier] my neighbor is throwing a party, and instead of there being some sort of rule that says you’re not allowed to throw parties after 11:00, he could maybe just compensate me for the noise or something. 00:44:18,326 --> 00:44:21,686 [Seb Krier] Or in fact, that’s one of the key crux of the whole Coasean thing is maybe 00:44:24,186 --> 00:44:36,085 [Seb Krier] I have to compensate him to stop his parties. And it kind of depends where the initial right is. But broadly, you could have these kind of, my whole neighborhood doesn’t want me to party, and they’re just giving me a small payment or the reverse, depending on where the initial allocation is. 00:44:37,226 --> 00:44:44,446 [Seb Krier] But I think you could have all sorts of micro ways in which these transaction costs at scale help you get much better beneficial outcomes. 00:44:45,486 --> 00:44:48,486 [Seb Krier] And so that would be the noise one would be like, okay. 00:44:50,406 --> 00:45:18,666 [Seb Krier] And it’ll probably just also let people kind of regroup into the party people just going into the neighborhood where that’s just generally more party tolerant or something, and the kind of peace and quiet preferring people just... Because I think one of the points with the piece was that AI also helps you coordinate better. You can use this stuff to find people who have the same interests and preferences as you or something, and just then bargain or negotiate or whatnot in that way as well. 00:45:20,626 --> 00:45:27,386 [Seth] So it’s not just bargaining over externalities that are negative, it’s maybe coordinating over positive externalities, right? 00:45:27,386 --> 00:45:27,526 [Seb Krier] Yeah. 00:45:28,766 --> 00:45:51,746 [Seth] What pieces do we need in the economy to make this a reality, and what time horizon are you thinking about? So obviously this is an idea that you could have a small version of, and then like the sci-fi, this is constantly, I’m allowed to speed in my car today because I really need to get to work because I’m late, and it’s bargaining with all the cars on the highway at ultra-high frequency. So what are the time horizons you have in mind, and what pieces do we need? 00:45:51,746 --> 00:46:21,786 [Seb Krier] Honestly, I haven’t even thought about the timelines really. [laughing] For me, this was mostly kind of an aspirational thing of like, well, it looks like we could unlock some cool things, and because there’s all these-- It’d be nice to have a positive vision of how things might pan out. It certainly doesn’t mean that everything has to be negotiated and bargained over. But I could see a large proportion of things, certainly in everyday life, like I could just tell my aunt, “You don’t have to worry about your parking issues anymore. It’s just sorted now,” whatever. The agents are taking care of that. And so it kind of depends on what scale you’re talking about. Certainly having democracy at scale and 00:46:23,626 --> 00:46:29,086 [Seb Krier] half automated and half made more efficient through these systems or something is something that I think is going to take a long time. 00:46:30,426 --> 00:46:47,986 [Seb Krier] But I can see smaller versions of it happening. The smallest version I can think of is just even basic calendar management. Until recently, that would be impossible to get automated really well. But gradually now I could expect an agent to really know my preferences, the context in which I operate, the hierarchical 00:46:49,006 --> 00:47:12,466 [Seb Krier] relationship with the people I’m organizing meetings with and whatnot, and the agent coming to tell me something like, “Okay, I’ve moved your meeting here to next week because this one’s not very important, and you don’t really care about it. But I’ve brought this meeting with your director forward because it’s urgent given what I’ve seen in that email. And also, I’ve set the location in this place because you’ll both be around that area in any event around then, given what I’ve gathered from talking to the other agent.” 00:47:13,946 --> 00:47:23,766 [Seb Krier] Something like that I think I could easily see in the next couple of years. But something at a higher, things like automating part of a local authority and- 00:47:23,766 --> 00:47:26,786 [Seth] So let’s stick to the noise example because that’s a core one. 00:47:26,786 --> 00:47:30,276 [Seb Krier] Well, the noise one, I think takes a bit longer because it requires this kind of, 00:47:31,486 --> 00:47:44,496 [Seb Krier] well, this adoption and integration of this technology at my local city council or whatever. And that’s one of the things that takes a long time. It’s not immediate, and it requires a shift in behaviors. People have to also then use these agents. It has to be legitimized. The 00:47:45,726 --> 00:47:48,386 [Seb Krier] value proposition has to be made a bit clearer. 00:47:48,386 --> 00:47:48,586 [Seth] Mm-hmm. 00:47:48,586 --> 00:48:01,666 [Seb Krier] And there’s all the usual political issues of like, well, you’ve got these vested interests, and it’s not in some people’s interests for that whole thing to work out. In the same way as if you wanted to have some sort of YIMBY paradise, a lot of kind of planning authorities wouldn’t be very delighted. They would probably find ways to stop the 00:48:02,786 --> 00:48:03,166 [Seb Krier] deployment of- 00:48:03,166 --> 00:48:07,046 [Seth] But they could be compensated through micro-transactions, dude. [chuckles] 00:48:07,046 --> 00:48:11,866 [Seb Krier] Exactly. Right? It’s a recursive Coasean improvement. But- 00:48:11,866 --> 00:48:15,566 [Andrey] So, I guess just like a thought here. 00:48:15,566 --> 00:48:15,885 [Seb Krier] Mm-hmm. 00:48:15,886 --> 00:48:19,106 [Andrey] I’m sure you’ve heard of Edge Esmeralda- 00:48:19,106 --> 00:48:19,116 [Seb Krier] Mm-hmm 00:48:19,116 --> 00:48:31,936 [Andrey] ... which is kind of this experimental new town. It seems just like we think that with new firms that are entering, they’re going to be able to organize in a way that’s going to better be able to take advantage of AI capabilities. 00:48:31,936 --> 00:48:32,726 [Seb Krier] Mm-hmm. 00:48:32,726 --> 00:48:43,006 [Andrey] To the extent that we can have newly incorporated cities or other jurisdictions, they might allow for agentic representation at the local council meeting- 00:48:43,006 --> 00:48:43,646 [Seb Krier] Mm-hmm 00:48:43,646 --> 00:48:50,666 [Andrey] ... and other such things. And if that works really well, then it would actually 00:48:51,786 --> 00:48:56,166 [Andrey] show other places what they’re missing out on. I’m not saying that the San Francisco- 00:48:56,166 --> 00:48:56,176 [Seb Krier] Yeah 00:48:56,176 --> 00:48:59,076 [Andrey] ... City Council would do this. [laughs] 00:48:59,076 --> 00:48:59,586 [Seb Krier] [laughs] 00:48:59,586 --> 00:49:07,265 [Andrey] But some smaller cities might start doing this, or school boards. That’s a great example. I hear people have a lot of issues with those. 00:49:07,266 --> 00:49:30,866 [Seb Krier] Totally. I think that’s kind of what I concluded in the essay, is that you’ve got to start in these kind of small proof of concepts and then kind of make the idea actively desirable and for people to kind of actually see the benefits of these things. And so I agree, The Edge Esmeralda, I think they’re trying something out with agents, and that’s going to be pretty exciting to see. And maybe then that kind of takes on, right? In the same way as a lot of conferences now have apps or something to... 00:49:31,886 --> 00:49:36,446 [Seb Krier] I guess the more pessimistic version is, well, if you look at Estonia, they’ve been really good at 00:49:37,506 --> 00:49:42,666 [Seb Krier] integrating technology and automating all sorts of parts of their government in really efficient and effective ways. 00:49:43,642 --> 00:49:44,622 [Seth] Estonia? 00:49:44,622 --> 00:49:46,102 [Seb Krier] Yeah. [laughs] 00:49:46,102 --> 00:49:46,982 [Seth] [laughs] 00:49:46,982 --> 00:49:58,162 [Seb Krier] And yet you don’t necessarily see neighboring countries have a strong incentive to do the same straight away. And I would expect the equivalent to take quite a long time, say, if you try to do the same thing in Brussels. So there are other- 00:49:58,162 --> 00:49:58,902 [Seth] [laughs] 00:49:58,902 --> 00:49:59,401 [Seb Krier] ... dynamics. 00:49:59,401 --> 00:50:00,802 [Seth] Anything takes longer in Brussels. 00:50:00,802 --> 00:50:03,082 [Seb Krier] That’s true. It’s part and parcel of it. 00:50:03,082 --> 00:50:04,262 [Seth] Except for the french fries. 00:50:04,262 --> 00:50:08,022 [Seb Krier] I’d say, yeah. Which are indeed very good. But, I think 00:50:09,562 --> 00:50:25,742 [Seb Krier] you’ll want some of these kind of smaller challenges, I guess, to really push for these things and put more pressure on the incumbent systems, whether it’s in the private sector or in the public sector. The only thing is, I expect it to be a bit slower in the public sector than it will be in the private sector. 00:50:27,382 --> 00:50:41,082 [Seb Krier] Once a bunch of startups start using agents that, I don’t know, coordinates on meetings or whatever, maybe that’s going to take off as well with all the wider organizations much more quickly. Whereas, you still have parts of the German or UK government using fax machines today. 00:50:41,082 --> 00:50:59,042 [Andrey] Well, there’s this example that we have in our paper where some agentic environments are designed for agents only, right? And so you might imagine that to work well within a firm where everyone has their calendar scheduling agent, and they all talk to each other. But I think 00:51:00,102 --> 00:51:07,482 [Andrey] you can also imagine an environment where the agent has to be able to interface also with humans, right? 00:51:08,782 --> 00:51:10,042 [Andrey] And those are quite different 00:51:11,762 --> 00:51:13,022 [Andrey] capabilities 00:51:14,782 --> 00:51:18,842 [Andrey] or tests of the system. And if you want maybe 00:51:20,062 --> 00:51:25,642 [Andrey] more useful versions of this, they should also be able to interact with humans who are not using the agents. 00:51:25,642 --> 00:51:37,682 [Seb Krier] True. And that probably acts also as a speed limiter, I guess, as well, right? If you have to then... But I agree. I think that’s going to be an important part, and I think that’s where protocol design and all these kind of things start coming into play. 00:51:39,022 --> 00:51:48,362 [Seb Krier] And I liked actually in your paper the whole bowling shoe versus bring your own question as well. Because you’d expect some platforms to just say, “Well, just use our agent here” or something. 00:51:49,422 --> 00:51:54,362 [Seb Krier] And it’s not clear to me that you’ll necessarily have one agent that you’re going to use across all platforms the same as something. 00:51:56,002 --> 00:52:06,842 [Seb Krier] Maybe you probably want certain characteristics or certain knowledge to be more segmented. Maybe you want your agent doing high-frequency trading to be a bit different than your agent doing, I don’t know, social life coordination or something. 00:52:08,402 --> 00:52:14,622 [Seb Krier] And it’s not clear whether it’s an agent that’s necessarily owned in some way by me or if it’s in fact... 00:52:15,882 --> 00:52:25,202 [Seb Krier] So yeah, there’s all sorts of interesting design questions that aren’t fully clear to me just yet. And I think that would be the kind of thing I’d love to see more work on. Actually, I think also Anthropic had this project deal recently, which was interesting, too. 00:52:27,282 --> 00:52:29,662 [Seb Krier] And it seems to have worked out quite nicely, I think, in their experiment. 00:52:31,242 --> 00:52:32,262 [Seth] It is, yeah. 00:52:32,262 --> 00:52:34,242 [Andrey] Yeah. You’re welcome to explain it. Yeah. 00:52:34,242 --> 00:52:41,262 [Seb Krier] [laughs] Again, I don’t have deep knowledge of what they’ve done or anything. I think Christy glanced at the... But I think broadly, they 00:52:42,822 --> 00:52:51,922 [Seb Krier] let part of their employees have access to agents to do some sort of negotiating and bargaining on their behalf for all sorts of commercial items or things they would want to trade. 00:52:53,102 --> 00:52:59,302 [Seb Krier] And they also did some experiment where I think some had access to more capable models, others had access to slightly less capable models. And I think the 00:53:00,842 --> 00:53:01,842 [Seb Krier] insight was something like 00:53:03,162 --> 00:53:11,622 [Seb Krier] everyone was happy in the end, but actually people with the more capable models got better surplus or something. And those without the capable models didn’t realize the 00:53:12,642 --> 00:53:20,782 [Seb Krier] gain they would have made had they had the more capable one. But in any event, everyone was happy. Isn’t it? It apparently worked out and people traded stuff. 00:53:20,782 --> 00:53:28,921 [Andrey] Well, my understanding is the setting was people literally took photos. They inventoried the stuff they didn’t need in their home. 00:53:28,922 --> 00:53:29,242 [Seb Krier] Yeah. 00:53:29,242 --> 00:53:29,932 [Andrey] And then- 00:53:29,932 --> 00:53:31,522 [Seth] [laughs] 00:53:31,522 --> 00:53:37,942 [Andrey] ... if you can get rid of it, you might already be happy if you can get rid of it, let alone get something in return for it. 00:53:37,942 --> 00:53:47,182 [Seb Krier] It’s true. Well, yeah. That was the thing we were discussing earlier. I’d be happy to have an agent sell my records, but not buy them or some silly [chuckles] certain things. 00:53:47,182 --> 00:53:47,861 [Andrey] Mm. 00:53:47,862 --> 00:53:51,862 [Seb Krier] But, yeah. So I think I didn’t go too deep into that specific 00:53:53,022 --> 00:54:03,162 [Seb Krier] paper or webpage or whatever they did on that. But there seems to be more experimentation along these lines. I think Rohit, of course, has his own kind of things. I had a MATS 00:54:04,322 --> 00:54:09,922 [Seb Krier] stream with Yoav Shavit at OpenAI on kind of multi-agent coordination. 00:54:09,922 --> 00:54:13,992 [Andrey] Can you explain what MATS is? Because I don’t think many in our audience know what that is. 00:54:13,992 --> 00:54:16,622 [Seb Krier] Oh, it’s basically a fellowship. 00:54:18,662 --> 00:54:28,062 [Seb Krier] You have mentors and mentees, and they work on all sorts of projects, and there’s close collaboration with labs. But it’s broadly on technical projects for AI safety and governance. 00:54:28,062 --> 00:54:34,122 [Andrey] Is this a program that our junior economist listeners should try to apply to? 00:54:34,122 --> 00:54:59,482 [Seb Krier] I think so, yeah. I don’t know if they have an econ stream. Well, they should if they don’t. And there is, I think, a wider kind of governance stream, so I wouldn’t be surprised. If they don’t have one, I would imagine there to be one eventually. But I think a lot of economists who’ve got this expertise in mechanism design or game theory, I think there’s a lot to offer here, even in the more classical AI safety type streams as well. So yeah, they should look into MATS for sure. 00:55:00,882 --> 00:55:13,182 [Seth] Well, a lot of pickup on those answers. Very interesting stuff. I guess one question I have here that you already introduced is the idea of, okay, so maybe we’re going to get allocative efficiency from all this Coasean bargaining. 00:55:13,182 --> 00:55:13,222 [Seb Krier] Mm-hmm. 00:55:13,222 --> 00:55:41,202 [Seth] We’ll make sure that the lawnmower doesn’t get mowed at 2:00 a.m. when it’s really annoying for me. But there’s a distributional question here, too. So one concern that you already kind of aired out was the idea that people with better models who can pay a little bit more might be able to bargain harder and get a bigger share of the pie. Another distributional concern might be just the step zero. Which is for any sort of bargaining over 00:55:44,802 --> 00:55:48,561 [Seth] an externality to happen, you need to assign that property right first. 00:55:48,562 --> 00:55:48,752 [Seb Krier] Mm-hmm. 00:55:48,752 --> 00:55:52,382 [Seth] And that could be a process that is very unequal. So, 00:55:53,822 --> 00:55:59,962 [Seth] feel free to take up either of those concerns. Is Coasean bargaining going to lead to a dystopic inequality hellhole? 00:56:01,490 --> 00:56:12,270 [Seb Krier] Right. I don’t think so. Certainly, I would imagine that in the first instance, I think people are still net better off than the counterfactual of not having any bargaining agents at all. 00:56:12,270 --> 00:56:13,590 [Seth] Well, that’s the Pareto promise, right? 00:56:13,590 --> 00:56:13,610 [Seb Krier] Yeah. 00:56:13,610 --> 00:56:18,230 [Seth] First, Fear Theorem tells us we’re going to get a Pareto efficient, allocative efficiency outcome. 00:56:18,230 --> 00:56:18,550 [Seb Krier] Mm-hmm. 00:56:18,550 --> 00:56:20,370 [Seth] But, a lot of people- 00:56:20,370 --> 00:56:20,610 [Seb Krier] Sure 00:56:20,610 --> 00:56:23,110 [Seth] ... trade this efficiency for some equality, right? 00:56:23,110 --> 00:57:04,610 [Seb Krier] I guess to me, it depends a little bit on the complexity of the problem, and I would guess that over time, you’ll get diminishing returns. I think we’re discussing this, asking Claude Opus for her toothpaste recommendation or whatever. There’s a point to which I would imagine, I guess I don’t know this empirically, and certainly in the project deal and tropics that you had kind of better returns by using the better model, but it’s not clear to me whether over time, actually, this kind of scales linearly in terms of if you’ve got the mega guard model, you necessarily get a better deal. There’s a point to which I think this doesn’t... In the same way as Elon Musk has the same phone as you and I have, and he’s not getting that much more just by virtue of being rich or something. At least, he does in general, just not on the phone part. [chuckles] And 00:57:06,370 --> 00:57:23,230 [Seb Krier] in any event, I think even then, to the extent that there were to be some sort of inequity, nothing stops, I think, whether it’s the state or philanthropy, whoever, to kind of subsidize access. Maybe you can have vouchers, you have systems that essentially re-equilibrate things. So I’ve always liked the voucher systems, even for private schooling and so on. 00:57:24,250 --> 00:57:26,530 [Seb Krier] So maybe that’s one way around that. 00:57:27,630 --> 00:57:29,160 [Seb Krier] And the property- 00:57:29,160 --> 00:57:34,850 [Seth] Decide whether to use my precious 100,000 tokens to bargain for food or to bargain for noise complaints. 00:57:34,850 --> 00:57:40,590 [Seb Krier] [laughs] Well, yeah. That’s on you to decide how you want to use it. [laughs] But- 00:57:40,590 --> 00:57:42,100 [Andrey] Always has been, Seth. Always has been. 00:57:42,100 --> 00:57:42,290 [Seth] Always has been. 00:57:42,290 --> 00:57:50,370 [Seb Krier] Yeah. Exactly. That’s not me. And then the other one with the property right assignment is a tricky one, too. And it’s deeply political ultimately as well because- 00:57:50,370 --> 00:57:50,650 [Seth] Right 00:57:50,650 --> 00:57:51,690 [Seb Krier] ... of course, it’s- 00:57:53,090 --> 00:57:59,050 [Seth] It reminds me of land redistribution, right? It’s like, “All right, we’re breaking up the plantations. Who gets it?” [laughs] 00:57:59,050 --> 00:58:19,190 [Seb Krier] Yeah. And I think, I guess what I claim in the essay or something was something like, well, you can just start with what we already have. There’s already a certain kind of distribution and allocation of rights and why do you need to just start anew? Just start with what we have. Then I did agree that there’s a need for them to change. You just then kind of, again, that’s the whole democratic process. 00:58:19,190 --> 00:58:21,110 [Seth] We’ll start with noise complaints. 00:58:21,110 --> 00:58:21,250 [Seb Krier] Well- 00:58:21,250 --> 00:58:24,410 [Seth] Because right now, it’s unclear to me who has the... I have the... Yeah. 00:58:24,410 --> 00:58:43,210 [Seb Krier] Sorry, I thought of that a little bit. I was thinking that actually, you can just keep the 11:00 PM threshold or curfew. It’s just that this switches the property right allocation. So before 11:00 PM, I have to pay you to be quiet, and after 11:00 PM, you have to pay me to be noisy or something. 00:58:43,210 --> 00:58:43,710 [Seth] [laughs] 00:58:43,710 --> 00:58:53,540 [Seb Krier] So I think that still works, but you come up with some sort of agreements as to where these kind of fluids, property rights, or something apply. But for these to be decided, I think you need the 00:58:54,670 --> 00:59:17,890 [Seb Krier] whole democratic apparatus and this is the other thing I was mentioning earlier, and wanting to make that part of, I don’t know, local democracy, for example, more responsive and effective. So there’s all sorts of kind of decisions being taken every day at the local council or something that I’m completely unaware of and have, unfortunately, very little time to go and engage with. But it probably would be better for me to have- 00:59:17,890 --> 00:59:22,190 [Seth] Unfortunately, you’re telling me that if I took away an hour of Twitter, that’s what you’d be doing. 00:59:22,190 --> 00:59:23,439 [Seb Krier] Exactly. I could be online. 00:59:23,439 --> 00:59:26,630 [Andrey] Wait, you could definitely be on Twitter and go to the council meeting. 00:59:26,630 --> 00:59:26,650 [Seth] [laughs] 00:59:26,650 --> 00:59:32,150 [Seb Krier] Yeah, but I have to focus my attention on... [laughs] But I could be consuming slop instead. 00:59:33,430 --> 00:59:34,850 [Seb Krier] So yeah, I think that 00:59:36,030 --> 00:59:36,690 [Seb Krier] for that, I wouldn’t 00:59:37,730 --> 00:59:40,070 [Seb Krier] mind having an agent come back to me and say, “Oh, there’s this 00:59:41,330 --> 00:59:50,850 [Seb Krier] new road that’s being built, and it would be pretty good for you, given that you take that route a lot and stuff, but it seems to be not going well. Do you want to register some sort of...” But 00:59:52,010 --> 01:00:16,720 [Seb Krier] yeah. And again, I can see versions of that where this is used for vitocracy, and this is where the whole mechanism design stuff comes into play. But for the property right assignment side of things, I think you just start with what we have today, and then you adjust over time using this mechanism. And again, it’ll probably depend on the setting or where you’re doing that. The curfew one, I guess, would be with your local council, but for all sorts of other things, there’ll be different kind of actors and 01:00:17,970 --> 01:00:20,970 [Seb Krier] institutions in place for that. You probably need... Yeah. 01:00:20,970 --> 01:00:37,450 [Seth] Can I go, how about something like stuff that we don’t have property rights around at all right now, or even rules around, but are definitely negative externalities, right? So for example, I have a neighbor in my office who does not shower, who smells really bad, right? So how do we decide- 01:00:37,450 --> 01:00:40,330 [Andrey] Are you willing to reveal this on a- 01:00:40,330 --> 01:00:40,500 [Seth] No 01:00:40,500 --> 01:00:42,450 [Andrey] ... public podcast? [laughs] 01:00:42,450 --> 01:00:48,950 [Seth] So the question is, how do we decide whether he has a right to be smelly, or I have a right to not smell him? 01:00:49,990 --> 01:01:00,549 [Seb Krier] I guess it, yeah, once again, highly political. [laughs] But I think my bias, of course, is one that’s more, I guess, freedom maximizing a classical liberal or something. So it’s more like- 01:01:00,550 --> 01:01:01,490 [Seth] The right to be smelly. 01:01:01,490 --> 01:01:01,630 [Seb Krier] Yeah. 01:01:01,630 --> 01:01:01,990 [Seth] There we go. 01:01:01,990 --> 01:01:06,630 [Seb Krier] It’s the harm principle, right? He’s not harming you by being smelly, unfortunately, so you’re going to have to- 01:01:06,630 --> 01:01:08,940 [Andrey] What do you mean? No, he definitely harms me. 01:01:08,940 --> 01:01:09,910 [Seth] It really smells bad, dude. 01:01:09,910 --> 01:01:14,540 [Seb Krier] [laughs] It’s not decreasing your longevity as far as I’m aware. [laughs] 01:01:14,540 --> 01:01:17,550 [Seth] [laughs] Actually, it makes me want to decrease my longevity. 01:01:17,550 --> 01:01:33,610 [Andrey] But no, but more seriously, this is actually a great use case for agents because the agent could receive anonymous... A lot of effective altruist rationalist types have this anonymous- 01:01:33,610 --> 01:01:33,910 [Seb Krier] Oh, yeah 01:01:33,910 --> 01:01:36,710 [Andrey] ... link in their profile, where you can give them anonymous 01:01:38,350 --> 01:01:38,690 [Andrey] feedback- 01:01:38,690 --> 01:01:39,710 [Seb Krier] Yeah 01:01:39,710 --> 01:01:43,390 [Andrey] ... about how much they smell. And so with the world of agents then- 01:01:43,390 --> 01:01:43,700 [Seth] [laughs] 01:01:43,700 --> 01:01:49,150 [Andrey] ... an agent could figure out a way to give anonymous feedback to the person’s agent 01:01:50,350 --> 01:01:52,190 [Andrey] that the person smells. 01:01:52,190 --> 01:01:52,550 [Seb Krier] Well- 01:01:52,550 --> 01:01:55,420 [Andrey] And then that agent could pass it on to the person 01:01:56,206 --> 01:02:14,466 [Seb Krier] Exactly. To be fair, you can also do that without the agent. You can just put a message on their desk saying, “By the way, please just shower,” or something. [laughing] But I think that in principle, what would be actually more interesting is having the agent solve that problem without the person necessarily being offended by the thing or having the message straight away. 01:02:15,806 --> 01:02:20,186 [Seb Krier] So having the other agent just... This is getting into nudging territory, right? And this is also controversial in its own right. 01:02:21,426 --> 01:02:43,126 [Seb Krier] But it actually connects to this other question of do you want your agent to be purely instruction following, or do you want it to help you maybe achieve some higher order goals? And so maybe one of my goals is I want to be not disliked at the office. And so one of the ways is my agent, well, I’m not the smelly guy, but let’s assume [laughing] then the agent will kind of find a subtle way- 01:02:43,126 --> 01:02:43,416 [Seth] Sure. 01:02:43,416 --> 01:02:46,686 [Seb Krier] ... to be like, “Seb, it’s time to log off Twitter and go and take a shower.” 01:02:47,926 --> 01:02:53,455 [Seb Krier] [laughing] So yeah, that might be one of them. But yeah, in terms of an initial allocation, I don’t think that he would 01:02:54,986 --> 01:02:58,706 [Seb Krier] have to pay me to continue being smelly perhaps. 01:02:59,986 --> 01:03:01,185 [Seb Krier] [chuckles] 01:03:01,186 --> 01:03:02,746 [Seth] Okay. So 01:03:04,826 --> 01:03:13,406 [Seth] let me give you some concerns about this vision. Right? So we’ve laid out what could be beautiful about this. We can efficiently allocate externalities, blah, blah, blah. 01:03:14,766 --> 01:03:30,326 [Seth] One set of concerns I’ve heard from my non-economist friends when I talk about this is this idea that micro bargaining at scale could harm social legitimacy. So if I may give you two exaggerated sci-fi versions of this, and then maybe we can reel it back in into reality. 01:03:30,326 --> 01:03:31,186 [Seb Krier] Mm-hmm. 01:03:31,186 --> 01:03:37,106 [Seth] The first version of this comes from the video game Helldivers 2. Are you familiar with that game? 01:03:37,106 --> 01:03:39,606 [Seb Krier] Yeah, I haven’t played it, but I’m aware broadly of the... Yeah. 01:03:39,606 --> 01:04:29,386 [Seth] All right. So the premise is kind of like a Starship Troopers sort of universe, where it’s officially a democracy, but really it’s fascism. And their political system is called Managed Democracy. And the way that Managed Democracy works is everyone fills out a preference questionnaire about what they like and they don’t like, and then that goes up into the big algorithm in the sky, and then boop boop boop boop boop, here’s your optimal social policy that we’ve calculated over everyone. Right? So on the one hand, it kind of seems like that is one end limit of what Coasean bargaining at scale would look like. I just fill out a survey, and my AI negotiates with all the other AIs, and I don’t really interact with it beyond that. But you can kind of see how dystopic that looks. It has a lot less social legitimacy than maybe two choice first past the post elections. 01:04:29,386 --> 01:04:39,366 [Seb Krier] Yeah, I agree. That’s the extreme version of it or something. Seems very undesirable. And so that’s why I was very careful to say, by the way, this is not saying make every single aspect of your life 01:04:41,266 --> 01:04:46,186 [Seb Krier] a transaction. In fact, I think there’s the whole Michael Sandel stuff, right? 01:04:46,186 --> 01:04:47,666 [Seth] Oh, Sandel. That’s my next question. 01:04:47,666 --> 01:04:47,906 [Seb Krier] Right. 01:04:47,906 --> 01:04:49,746 [Seth] You’re jumping ahead. [chuckles] 01:04:49,746 --> 01:04:51,046 [Seb Krier] Well, but I think that the 01:04:52,406 --> 01:05:22,676 [Seb Krier] idea, at least for me, is not for the agent to completely automate your agency away. If anything, ideally, this kind of enhances it in some way. So, I like the idea of the local authority thing because it’s something I wouldn’t be doing in any event. It’s not like I’m having this taken away from me and that, but for the agent, I would be going there or something. So I think there has to be some sort of conscious choice as well from individuals to where do they want to kind of exercise their agency and where do they not. And I think there’s some people who actually do not care as much about participating and 01:05:23,926 --> 01:05:25,386 [Seb Krier] others who do, and I think that’s fine. 01:05:27,146 --> 01:05:38,386 [Seb Krier] But I agree that also, there are kind of non-market norms that I think are worth preserving, reciprocity and being a good neighbor and civic participation. And you don’t want to kind of get rid of these entirely either. So, 01:05:39,406 --> 01:06:07,506 [Seb Krier] in my mind, this will never be the kind of perfect maximalist version of the whole thing. It’ll probably work in some instances. In others, you’re not going to use the system. And in general, I suspect that for many things and trivial things, like actually the smelly coworker, I don’t know, maybe that one is the one where you actually want to use an agent rather than deal with it yourself. But I agree, and it’s a matter of, I guess, developing the right norms around how we would be using there. Where is it acceptable, where is it not? 01:06:09,126 --> 01:06:31,386 [Seb Krier] But I agree with the bad version of this where no one does anything, and you just have an agent infer everything on your behalf, and now you’re just basically removing the whole participatory element of democracy, which I think is still very important. And so again, here too you will want your agent... In a way it’s not dissimilar to 01:06:33,146 --> 01:06:41,326 [Seb Krier] these questions of cognitive offloading and so on. Of like, well, if the AI writes on your behalf versus it gives you advice on how you can write the thing better. 01:06:42,606 --> 01:06:46,106 [Seb Krier] And so now I’m going a bit on a tangent, but I was mentioning this 01:06:47,326 --> 01:06:59,026 [Seb Krier] positive alignment thing earlier of do you want your agent to be purely instruction and then following, or would you want to specify higher order preferences? I think this is where this comes into play, where I might say, actually, I 01:07:02,126 --> 01:07:06,326 [Seb Krier] want you to help me achieve this kind of higher order goal or something, which may entail, in fact, asking you to just 01:07:07,346 --> 01:07:46,266 [Seb Krier] read up on this. And there’s also an element of me actually needing to retain that choice as well and as an individual, right? I will sometimes summarize a paper using an AI, and sometimes I will... And that’s fine because the counterfactual wouldn’t have not been me reading the whole paper because I don’t have... But there are also papers where I would have read the whole paper, and I’m still asking the AI to do it, and that’s kind of also on me to learn and for, I guess, the right norms to rise in terms of, okay, when it’s too much, what is right, what is not. In the same way as, I don’t know, I’ve learned how to do regressions by hand or something one day in the past, and that was helpful in some sense, but then I’ve never done that again, and I’ve started using Stata and R or whatever. So, 01:07:47,406 --> 01:07:47,985 [Seb Krier] there’s an element of- 01:07:47,986 --> 01:07:48,906 [Seth] What? 01:07:48,906 --> 01:07:50,786 [Seb Krier] [chuckles] Actually, yeah. 01:07:50,786 --> 01:07:52,886 [Seth] Are you telling us you’re not an economist? 01:07:53,966 --> 01:07:55,196 [Seb Krier] No, I am not an economist. 01:07:55,196 --> 01:07:55,246 [Seth] You got into Stata and R. 01:07:55,246 --> 01:08:11,046 [Seb Krier] I was never an economist. [laughing] So yeah, there’s this wider question of where do you want to preserve agency and then also where you want to preserve certain norms on a day-to-day level. And so I think it’s going to be a big messy thing in reality as opposed to the maximalist version of either vision. 01:08:12,142 --> 01:09:27,362 [Seth] For those of you playing along at home, now is your chance to think about how this conversation has changed your priors. This chance to contemplate your posteriors is sponsored by Revelio Labs. Revelio Labs is a leading provider of labor economics data and data services for companies, academics, and independent researchers. Andrey and I have been working in economics of AI, digitization, and automation for a long time, and we can confirm just how useful Revelio’s data is. Revelio’s team combines comprehensive micro-level data on employee professional profiles, job postings, and employee sentiment with standardizations, mappings, and enrichments available, all to make that data useful without making your modeling decisions for you. The data can be flexibly aggregated to company, market, or industry, and can be used to study questions ranging from career trajectories to occupational transformation, to the returns to skills, and the impact of AI on labor demand for tasks. Can’t imagine anyone who would be interested in that. And Revelio data is available on WRDS. So if you’re an academic with a good library, go see if you have access to their premier data already. And if you don’t, you can reach out to their excellent economics team, and they’ll hook you up. I feel like it’s time for lightning round. 01:09:27,362 --> 01:09:27,682 [Andrey] All right. 01:09:27,682 --> 01:09:28,342 [Seth] Let’s do it. 01:09:28,342 --> 01:09:33,702 [Andrey] Let’s do it. [upbeat music] 01:09:34,802 --> 01:09:35,962 [Andrey] So we’re- 01:09:35,962 --> 01:09:35,972 [Seth] Nice 01:09:35,972 --> 01:09:39,372 [Andrey] ... yeah, very excited because you’re a master of the take. 01:09:39,372 --> 01:09:41,252 [Seth] [laughs] 01:09:41,252 --> 01:09:41,742 [Andrey] [laughs] 01:09:43,282 --> 01:09:47,702 [Andrey] So you fairly recently moved from London to New York City. 01:09:47,702 --> 01:09:48,242 [Seth] Mm-hmm. 01:09:48,242 --> 01:09:49,122 [Andrey] What are 01:09:50,422 --> 01:09:53,022 [Andrey] the words that come to mind- 01:09:53,022 --> 01:09:53,312 [Seth] [laughs] 01:09:53,312 --> 01:09:56,762 [Andrey] ... when you’re thinking about AI in New York City versus AI in London? 01:09:58,042 --> 01:10:05,402 [Seth] Oh. AI in London feels more academic, theoretic, 01:10:06,562 --> 01:10:53,002 [Seth] I guess more deep researchy, and more governance-oriented in some way. I think there’s this whole Oxford, Cambridge, UCL connection, the government and UK AI Safety Institute, the fact that government was already dealing with AI quite early. I think that that whole ecosystem is fairly developed here, whereas New York is a bit of a weird, secret third thing of like, well, there’s some interesting people, more like heterodox, random, the edge of culture. Some interesting kind of frontier AI people too, but I wouldn’t really see it as some sort of AI capital or something. It’s just a bit more diverse in terms of the offering. But I kind of like that. That’s the point also. I don’t want to be fully NSF. I kind of like being somewhat distant. 01:10:54,062 --> 01:10:59,551 [Andrey] What is the reaction of people you meet in Brooklyn or at your- 01:10:59,551 --> 01:11:00,202 [Seth] [laughs] 01:11:00,202 --> 01:11:04,382 [Andrey] ... or at your EDM shows when you tell them where you work? 01:11:04,382 --> 01:11:07,771 [Seth] First of all, I do not like EDM at all. But- [laughs] 01:11:07,771 --> 01:11:08,111 [Andrey] [laughs] 01:11:08,111 --> 01:11:08,122 [Seth] [laughs] 01:11:08,122 --> 01:11:09,242 [Andrey] Whoa. 01:11:09,242 --> 01:11:09,322 [Seth] [laughs] 01:11:09,322 --> 01:11:10,382 [Andrey] Hottest thing right now. 01:11:10,382 --> 01:11:11,782 [Seth] Yeah. Going- 01:11:11,782 --> 01:11:13,782 [Andrey] So how much would you have your DJ- 01:11:13,782 --> 01:11:13,802 [Seth] Electronic- 01:11:13,802 --> 01:11:14,162 [Andrey] ... mic? 01:11:14,162 --> 01:11:16,912 [Seth] Fine electronic music. [laughs] 01:11:16,912 --> 01:11:19,672 [Andrey] [laughs] Oh, yeah, that one’s good. The good- 01:11:19,672 --> 01:11:20,212 [Seth] [laughs] 01:11:20,212 --> 01:11:20,262 [Andrey] [laughs] 01:11:20,262 --> 01:11:21,682 [Seth] Aligned electronic music. 01:11:23,082 --> 01:11:24,232 [Andrey] Yes. [laughs] 01:11:24,232 --> 01:11:25,732 [Seth] [laughs] Yeah, I think it 01:11:29,002 --> 01:11:41,262 [Seth] wouldn’t be the first thing I tell people, perhaps, when I meet someone in Brooklyn. It’s like, “Oh, I work for Vigo AI Lab, by the way.” But I think it depends who and where and what. You get a bit of the stereotypes of, oh, AI bad, evil, blah, blah. 01:11:42,402 --> 01:11:51,922 [Seth] But you also have some cool communities and interesting creative scenes that actually do engage with technology AI in very interesting ways. And so, 01:11:53,402 --> 01:11:56,412 [Seth] there’s a bit of everything, but that’s kind of the point also. I’m 01:11:57,522 --> 01:12:01,051 [Seth] enjoying being here, so I don’t have to talk about AI with every random person I meet. [laughs] 01:12:01,051 --> 01:12:01,122 [Andrey] [laughs] 01:12:01,122 --> 01:12:11,922 [Seth] And there’s all these other things that I can kind of explore and discuss and learn about, too. But the attitude really depends on where I end up. There’s a bit of a selection effect going on as well, to be fair. [laughs] 01:12:11,922 --> 01:12:16,022 [Andrey] The coolest thing in terms of art and AI that you’ve seen out there? 01:12:17,122 --> 01:12:17,402 [Seth] Hmm. 01:12:18,982 --> 01:12:20,202 [Seth] In New York specifically, a 01:12:23,142 --> 01:12:27,582 [Seth] lot of the recent art stuff I’ve been to was not really AI related. Although next week, though, I 01:12:28,862 --> 01:12:31,182 [Seth] am in fact co-organizing, helping 01:12:32,302 --> 01:12:43,542 [Seth] some sort of art dinner thing with a bunch of interesting artists coming from London [smacks lips] and showcasing a lot of their work. And so I think they’re called Biangi Systems. 01:12:44,862 --> 01:13:01,742 [Seth] I’ll try to put a link in the chat or something. But, yeah, shout out Biangi. But so they do interesting stuff, and you get a lot more interesting artifact from their work than, say, a one-shot Suno prompt or something. [laughs] 01:13:01,742 --> 01:13:03,842 [Andrey] All right. So then natural segue then. 01:13:03,842 --> 01:13:04,902 [Seth] [laughs] 01:13:04,902 --> 01:13:14,002 [Andrey] What is your philosophy of electronic music? Or what do you like about electronic music? What do you not like about electronic music? Yeah. 01:13:17,262 --> 01:13:18,002 [Seth] I don’t think there’s a 01:13:20,022 --> 01:13:24,322 [Seth] whole philosophy behind it. I think I just kind of like some types of electronic music. I 01:13:26,082 --> 01:13:30,882 [Seth] like a lot of late ‘80s, early ‘90s electronic music. And they had a particular kind of... 01:13:32,702 --> 01:13:49,742 [Seth] They’re basically the original futurist in some sense. And a lot of the themes and the music was very much sometimes around, in fact, AI, or sometimes around robots, sometimes about the future, and both utopian and dystopian visions of the future. And I don’t know. I like the kind of 01:13:51,462 --> 01:13:59,982 [Seth] [smacks lips] audio equivalent of all these stories or something. I read too much, and so [chuckles] it’s nice to have something that’s a bit more like 01:14:01,402 --> 01:14:02,422 [Seth] that engages in a different way. 01:14:03,822 --> 01:14:07,682 [Seth] So I don’t know. There’s a lot of early ‘90s techno, acid, trance. 01:14:07,682 --> 01:14:09,822 [Andrey] What are those for our listeners? 01:14:09,822 --> 01:14:16,582 [Seth] Well, Drexciya is the kind of canonical one, which is kind of Detroit electro techno from the early ‘90s. 01:14:17,270 --> 01:14:24,430 [Seb Krier] And they have a whole mythology around the aesthetics, the themes, the kind of 01:14:25,530 --> 01:14:26,430 [Seb Krier] storyline behind 01:14:27,530 --> 01:14:30,670 [Seb Krier] the tracks and the EPs and albums they put out. 01:14:31,770 --> 01:14:45,630 [Seb Krier] So I think Drexciya is a really interesting one. There’s also very cool long articles about them, I think James Stinson and Gerald Donald, and a few others. I think a lot of that Detroit scene at the time was very much into that stuff. So, Ox88, Jeff Mills, Underground Resistance, a lot of the 01:14:46,730 --> 01:14:52,950 [Seb Krier] classic techno electro stuff from that time. So these would be some examples, but the UK had some really cool stuff too. 01:14:54,770 --> 01:15:02,690 [Seb Krier] Yeah. And I guess I like the weirder side of music, and when it’s a bit more creative. Which is why I’m going to-- 01:15:04,050 --> 01:15:08,110 [Seb Krier] To the earlier discussion about creativity, I kind of think of slop as 01:15:09,310 --> 01:15:12,330 [Seb Krier] very much something that’s kind of highly predictable. 01:15:13,910 --> 01:15:18,270 [Seb Krier] Low creativity, high predictability, low effort. 01:15:21,110 --> 01:15:44,950 [Seb Krier] One thought I had was that over time, you see this kind of lowering of the barriers to all sorts of art forms. So even this early ‘90s stuff. You had a lot of really good concentrated stuff, but very few people had access to this stuff. And of course, as these synthesizers would be cheaper, as they’re then made kind of digital, as you had tools like Ableton and whatnot, a lot more people were able to create and make music. But as a result of that, 01:15:46,030 --> 01:15:51,810 [Seb Krier] you had a lot more of the mediocre and average stuff too. But that’s fine. There’s also a lot more of the really good stuff, and if you’re 01:15:53,110 --> 01:15:57,030 [Seb Krier] good at finding it, or if you can rely on good algorithms, you can filter through the slop. 01:15:58,710 --> 01:16:00,190 [Seb Krier] Yeah. That’s one thought. 01:16:01,670 --> 01:16:05,730 [Andrey] I think that the 01:16:07,070 --> 01:16:11,130 [Andrey] current-- I don’t even know current. Over the past decade, the- 01:16:11,130 --> 01:16:13,170 [Seth] [background chatter] 01:16:13,170 --> 01:16:13,330 [Andrey] ... the 01:16:14,370 --> 01:16:23,150 [Andrey] EDM wave. Does it feel very sloppy to you, even somehow before we even had AI-generated music? 01:16:23,150 --> 01:16:27,769 [Seb Krier] Yeah, definitely. But on the one hand, it’s kind of my taste. I’m sure there’s 01:16:28,990 --> 01:16:34,890 [Seb Krier] a big level of subjectivity in a lot of this stuff. And then also there’s-- I think you had- 01:16:34,890 --> 01:16:36,650 [Seth] This podcast is not Yuck Yums. 01:16:36,650 --> 01:16:37,010 [Seb Krier] [laughs] 01:16:38,790 --> 01:16:40,000 [Seb Krier] But also, there’s I think all sorts of-- 01:16:41,650 --> 01:16:49,370 [Seb Krier] You had slop equivalent in other fields, in music genres, and domains over time. And I think even in the, I don’t know, was it the ‘80s or something, people would consider, 01:16:51,150 --> 01:17:08,830 [Seb Krier] I would say smooth jazz as being the equivalent of slop at the time or something, right? [laughing] And I like smooth jazz. I’m really keen on smooth jazz. Even library music or something at the time. So yeah, library music actually was seen as at the time it was just mass-produced, super commercial beast film score music that 01:17:10,030 --> 01:17:28,649 [Seb Krier] was probably kind of not seen with a lot of, I don’t know, awe and respect or something. Maybe the bye bye. Whereas now it is, of course, because what is slop also changes over time. But yeah, I think just like photography. Once that got democratized, you’ve got trillions of photos every day, and there’s a huge amount of slop in that whole sea of photos that people take. 01:17:28,650 --> 01:17:33,630 [Seth] And there’s like you say, there’s also a going back and rediscovering. Lovecraft at one point was slop. 01:17:33,630 --> 01:17:33,820 [Seb Krier] Yeah. 01:17:33,820 --> 01:17:35,130 [Seth] And now he’s beloved. 01:17:35,130 --> 01:17:36,750 [Seb Krier] Yeah. Exactly. There’s 01:17:37,870 --> 01:18:21,510 [Seb Krier] a lot of house music from at some point that was just deemed pretty kind of basic or something, and now it’s coming back a little bit in some communities in some shape or form. So it’s a weird mix of taste and rediscovering old stuff and scarcity because people like being special unique snowflakes and so on. So there’s [chuckles] also the different dynamics at play. But yeah, in my mind, I don’t like EDM or something, but who knows? Maybe in 20 years my tastes will have changed, and maybe I’ll look back and be like, “Oh, actually there’s some pretty good stuff here.” I remember very vividly when I was 18 thinking, “I really hate the sound of acid.” As in the acid techno, these two or three machines. I was like, “I can’t understand how one would like that.” And then literally five years later, six years later, I was just obsessed with acid. And I was like, “How did that happen?” 01:18:22,870 --> 01:18:26,380 [Seth] It’s because the opposite of love isn’t hate, the opposite of love is indifference. 01:18:26,380 --> 01:18:26,490 [Seb Krier] [laughs] 01:18:26,490 --> 01:18:30,370 [Seth] The fact that you hated it so much was a sign. 01:18:30,370 --> 01:18:37,149 [Seb Krier] Well, I didn’t hate it that much. I just didn’t find it particularly interesting or pleasurable, whatever, at the time. But so yeah. 01:18:38,490 --> 01:18:38,700 [Seb Krier] Who knows? 01:18:38,700 --> 01:18:41,090 [Seth] You mentioned reading. You mentioned reading a lot. 01:18:41,090 --> 01:18:41,290 [Seb Krier] Uh-huh. 01:18:41,290 --> 01:18:43,470 [Seth] What’s the book that’s been the most influential on you? 01:18:47,170 --> 01:18:53,470 [Seb Krier] I don’t know. I definitely don’t have an immediate answer to that question because I think particularly in the last decade, my 01:18:54,910 --> 01:18:58,510 [Seb Krier] consumption habits have been a lot more chaotic. 01:18:58,510 --> 01:18:58,950 [Seth] [chuckles] 01:18:58,950 --> 01:19:04,630 [Seb Krier] So there’s been a bit of-- I don’t know. If you asked me the same thing 10 years ago, I would’ve said something like, oh, “On Liberty” by John Stuart Mill or something. 01:19:05,830 --> 01:19:11,150 [Andrey] See, that’s why-- Isn’t that Tyler’s favorite philosopher? 01:19:11,150 --> 01:19:12,160 [Seb Krier] I don’t know if it is. 01:19:12,160 --> 01:19:13,030 [Andrey] Sorry. Economist, excuse me. 01:19:13,030 --> 01:19:13,050 [Seth] No. 01:19:13,050 --> 01:19:14,270 [Andrey] His favorite economist. Yeah. 01:19:14,270 --> 01:19:16,750 [Seb Krier] Right. Well, once again, great minds. What can I say? 01:19:17,890 --> 01:19:21,190 [Seb Krier] [laughing] But no, I do really like John Stuart Mill. But I 01:19:22,550 --> 01:19:30,570 [Seb Krier] wouldn’t-- Right now, I think it’s kind of a weird patchwork of loads of different kind of text sources. 01:19:30,570 --> 01:19:32,590 [Seth] You mentioned Bostrom’s “Superintelligence” during the conversation. 01:19:32,590 --> 01:19:44,470 [Seb Krier] Yeah. I mean, that one was clearly super influential. Because I think that was-- He published in 2014, I think, and in 2016, ‘17 was when I was starting to read it. And that got me to actually kind of what is part of the motivation to leave my job as a lawyer at the time. 01:19:45,870 --> 01:19:54,570 [Seb Krier] And see that AI was obviously going to be a big thing, and you had a need for non-technical work as well in that whole thing. So, 01:19:55,690 --> 01:19:59,790 [Seb Krier] that was fairly influential, I think. Yeah, Bostrom’s “Superintelligence” clearly played a role. 01:20:01,918 --> 01:20:04,138 [Seth] Mentioned being loved by Tyler 01:20:04,138 --> 01:20:06,547 [Seb Krier] I didn’t mention that, you did. [laughs] 01:20:06,547 --> 01:20:09,838 [Seth] Question, why does he love you so much? Do you have kompromat on him? 01:20:09,838 --> 01:20:14,848 [Seb Krier] [laughs] No, I don’t know. I was really happy that he kind of enjoyed my 01:20:16,458 --> 01:20:38,538 [Seb Krier] hot takes and rambles on Twitter. And I’ve messaged him to say the same. And funnily enough, I’ve been a kind of a Marginal Revolution reader since the last 15 years or so, or for a very long time. So it’s actually pretty weird for me to be like, “Oh, cool. My name’s popping up on the blog,” and stuff. A lot of my friends, in here at least, knew about Marginal Revolution, so they’re like, “What are you on about? Who cares?” And to me- 01:20:38,538 --> 01:20:39,447 [Andrey] [laughing] 01:20:39,447 --> 01:20:46,158 [Seb Krier] ... that was pretty cool. Like, yeah, Seb Krier, whatever. And like, all right. But, no, I don’t know. But I think I 01:20:48,038 --> 01:20:52,378 [Seb Krier] like Tyler and his work a lot. I think Tabarrok as well. I think the whole, 01:20:54,598 --> 01:20:55,298 [Seb Krier] what’s it called, 01:20:57,618 --> 01:21:14,238 [Seb Krier] that cluster of blogosphere online economics, GMU, and so on, has been fairly influential, I think as well for me. Even 15 years ago, when I was kind of switching from my Francophone world and upbringing to the kind of more Anglophone where discovering the Adam Smith Institute in London or something. 01:21:15,278 --> 01:21:17,598 [Seb Krier] All these different influences, I think, definitely had a 01:21:18,698 --> 01:21:20,618 [Seb Krier] lasting effect in some sense. 01:21:20,618 --> 01:21:24,058 [Andrey] Pretty good time to wrap up. Is there anything else you want 01:21:25,538 --> 01:21:31,078 [Andrey] to direct our audience to? Any underappreciated people, ideas, 01:21:32,138 --> 01:21:32,738 [Andrey] anything else? 01:21:34,077 --> 01:21:34,217 [Seb Krier] [laughing] 01:21:35,358 --> 01:21:35,798 [Seth] [laughing] 01:21:35,798 --> 01:21:41,178 [Seb Krier] That’s a wide question. I don’t know. There’s all sorts of interesting people and ideas. I 01:21:43,518 --> 01:21:46,418 [Seb Krier] don’t know. Cosmos Institute has been doing some interesting work recently. 01:21:46,418 --> 01:21:47,358 [Andrey] Mm-hmm. 01:21:47,358 --> 01:22:02,158 [Seb Krier] I think, as well as Fire, actually. They’ve kind of done some stuff together, which I find very valuable. Yeah, I’d have to think about that one more. I’d prefer preparing a nicely curated list of underrated- 01:22:02,158 --> 01:22:02,168 [Andrey] Yeah. [laughs] 01:22:02,168 --> 01:22:04,378 [Seb Krier] ... blogs, people, and ideas rather than- 01:22:04,378 --> 01:22:06,718 [Andrey] Well, you should tweet that. That could be your next- 01:22:06,718 --> 01:22:08,348 [Seth] There we go. Yeah, I’ll retweet it 01:22:08,348 --> 01:22:09,598 [Andrey] ... super tweet. Yeah. 01:22:09,598 --> 01:22:13,898 [Seb Krier] Yeah, no, for sure. I’m sure, that’d be a good idea, actually, because I think there 01:22:15,278 --> 01:22:31,458 [Seb Krier] are a lot of interesting people in the AI world right now doing cool work that isn’t maybe as discussed or something. There’s Calcifer Computing did some interesting work recently. Yeah, there’s a bunch of people here and there, but I’ll have to think about it to give you a more interesting answer. 01:22:32,998 --> 01:22:35,147 [Seth] Great. We’ll put them in the show notes if you think about it. 01:22:35,147 --> 01:22:35,318 [Seb Krier] Sounds good. 01:22:36,778 --> 01:22:44,998 [Seth] Seb, it’s been such an honor having you on. You were an awesome participant and had a lot of patience with our silly questions. [laughs] 01:22:44,998 --> 01:22:52,738 [Seb Krier] No, it’s been a pleasure. I’ve been a big fan of the show. So again, just like Marginal Revolution, now I can say I’ve got the Justified Posterior’s take. [chuckles] 01:22:52,738 --> 01:22:53,238 [Seth] There we go. 01:22:53,238 --> 01:22:54,638 [Andrey] Nice. Thank you. 01:22:54,638 --> 01:22:54,958 [Seb Krier] Thank you. 01:22:54,958 --> 01:22:56,198 [Andrey] All right. 01:22:56,278 --> 01:22:56,558 [Seb Krier] Cool. 01:22:56,558 --> 01:22:58,978 [Andrey] Keep your posteriors justified. And- 01:22:58,978 --> 01:23:00,338 [Seb Krier] [laughs] 01:23:00,338 --> 01:23:04,318 [Andrey] ... like, follow, comment, subscribe, et cetera. 01:23:04,318 --> 01:23:04,958 [Seb Krier] And retweet. 01:23:04,958 --> 01:23:05,598 [Andrey] Thank you. 01:23:05,598 --> 01:23:09,508 [Seb Krier] And retweet. [laughs] 01:23:09,508 --> 01:23:17,198 [Andrey] Of course. [outro music] Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • May 4 · 1 hr 20 min

    Avi Goldfarb on Prediction Machines, O-Ring Tasks, and How AI is Reshaping Economics

    This week, we’re joined by Avi Goldfarb, one of the leading economists of artificial intelligence and co-author of Prediction Machines. Avi has been thinking seriously about AI economics long before the ChatGPT shock, so we asked him what he thinks the earlier framework got right, what it missed, and how economists should update their beliefs now. The conversation starts with Avi’s seminal book, Prediction Machines, and the idea that AI is best understood as a drop in the cost of prediction, which is a complement to judgement. We ask what that book got right and what it got wrong. From there, we interrogate Avi on the murky boundary between prediction and judgment. We had investigated the idea that maybe judgment and prediction were not as separable as economists like to believe in our episode with Alex Imas. We also ask whether, if AI gets better at predicting human judgment, whether judgment disappears, or do humans simply “move up the stack”? And what is taste exactly? Avi says that sometimes judgment becomes predictable, but humans still matter because goals, values, organizational politics, and “what matters” are often implicit, unstable, and hard to codify. Avi shoots down Seth’s galaxy-brain suggestion that correct ontology choice — i.e., deciding what sort of natural kind a thing is, or understanding when a problem is out of context — is a uniquely separate skill (taste?), calling it just another prediction error. But he does concede that deciding how much to prepare for ‘Black Swan’ events may be an enduring role for judgment. We then revisit the O-ring theory of production and what it means for automation. We had covered Kremer’s article in a recent episode (see here) and asked Avi about his new paper, riffing on the idea at the worker level. Avi says that if tasks inside jobs are complements rather than substitutes, then automating one task may make the remaining human tasks more valuable, not less. Avi explains why workers may reallocate attention toward the tasks machines cannot yet perform (shooting down Seth’s suggestion that this is actually difficult in most jobs). The discussion also covers whether AI will augment or replace workers, whether governments should try to steer AI toward human-complementing technologies, and why that distinction may be much harder to define in practice than it sounds. Avi agrees with Andrey and Seth’s pushback on “augmentation good, automation bad” framings (e.g. friend of the show Erik Brynjolfsson’s “Turing Trap”). Then we get into forecasts: how fast AI capabilities might advance by 2030, what that means for GDP growth by 2050, whether GDP is still the right thing to forecast, and why even very powerful AI may run into bottlenecks in the real economy. We use the paper Forecasting the Economic Effects of AI to ground the discussion. We close with lightning-round topics including AI’s impact on centralization, privacy/de-anonymization, peer review, and whether academic journals still serve the function they once did. Papers, books, and ideas mentioned * Avi Goldfarb’s seminal book with Ajay Agrawal, and Joshua Gans — Prediction Machines * A black swan is the occurrence of a wildly unpredictable event, which Nassim Taleb argues, in his book by the same name, is more common than we like to think * A New Riddle of Induction — by Nelson Goodman — is the source of Seth’s thought experiment about “bleen”, a color which is green until 2029 and blue after, and green * Michael Kremer — “The O-Ring Theory of Economic Development”, covered in this episode of the pod: * Daron Acemoglu and Pascual Restrepo’s task-based models of automation, especially “The Race Between Man and Machine.” * Avi mentions David Autor and Ben Thompson on automation and skill scarcity when Seth comments that you may not be able to reallocate effort between tasks as a worker, including their paper “Expertise” * Erik Brynjolfsson in the “Turing Trap” argues that automation technologies are less good than augmenting technology * Eric Topol’s book on AI in medicine — Deep Medicine * John Markoff — Machines of Loving Grace — The source of a title for an influential essay of the same name by Dario of Anthropic. Both draw from an earlier poem about a Sci Fi utopia: https://allpoetry.com/All-Watched-Over-By-Machines-Of-Loving-Grace * Korinek and Stiglitz on AI, capital, and taxation; Lockwood and Korinek on optimal taxation and automation — We covered these topics at the end of our episode with Basil Halperin in the context of “Tax Policy at the End of History” around the 1:19:00 mark * We talk about de-anonymization, and Avi references this provocative paper from Florian Ederer * Avi brings up Bob Gordon, and his argument, famously in the book The Rise and Fall of American Growth, that the early 20th century was incredibly important for increases in US living standards, which digital technologies have not lived up to * Digital Hermits, by Jeanine Miklós-Thal, Avi Goldfarb, Avery M. Haviv & Catherine Tucker, is a paper by Avi thinking about how information spillovers, now from AI, drive some people to be more private than they would otherwise be. In our conversation, we speculate AI will make these hermits even more “hermetic” * We discuss this paper on new forecasts of AI and its impact on economic growth: Forecasting the Economic Effects of AI * Refine and AI-assisted peer review are discussed in this pod. For more, see our episode with Ben Golub, founder of Refine. This episode is sponsored by Revelio Labs — a great source of labor economics data for academics and firms. Now available on WRDS. Join our Discord community at this link: https://discord.gg/w3GSapx2d Transcript Introduction [00:00] Seth: Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, your loyal non-fiction machine, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. And we are very happy that Justified Posteriors is sponsored by the fine folks at Revelio Labs. And we’re very delighted to have Avi Goldfarb, who is a leading thinker in the field of AI economics and has also been a personal mentor on the show. We’re very excited to hear his thoughts on a variety of topics. Welcome, Avi. Avi: Thanks so much and thanks for having me on the show and looking forward to it. Andrey: All right, let’s get started. I have in front of me this book that you might remember writing at some point. Seth: Gaze into the soul of the man in the bookstore. What Did Prediction Machines Get Wrong? [01:12] Andrey: Now, I just think it’s a good cover. And I had to check: when was it released? It was released in 2018. And as I was skimming through it, you know, a lot of interesting points made there are still things that we’re talking about today, almost 10 years after it was released. So let me start off with the following question. And then maybe we can work backwards more into the ideas in the book. But what do you think prediction machines got wrong? Avi: I think prediction may... I’ll start with a hard question. Seth: No softballs on Justified Posteriors. Avi: So on the specifics of which industries and when, to the extent we tried, at least I did not anticipate how quickly language and coding would become prediction problems. And when we talk about disruption and industry disruption, a lot of the examples are things like driving, and we talk about radiology. And we still have plenty of radiologists around. Self-driving cars and trucks. seem like they’re now imminent, but it certainly took a lot longer than we expected back in 2018. Andrey: So is it a fair assessment to say that the large language models, even in 2018, weren’t on your radar? I guess they weren’t on many people’s radar. The Three Ideas of Prediction Machines [02:45] Avi: Not really. We have some discussion of machine translation. So that’s in there as a huge potential use case, but the arrival of ChatGPT and how it sort of changed how we interact with machines and how we think about AI was not really there. Another way to put it is prediction machines had three ideas. So idea number one is AI can be framed as a drop in the cost of prediction. So prediction. As in filling in missing information, statistical prediction is getting better, faster and cheaper. Idea number two is that when something gets cheap, you start using it for unanticipated uses. So when arithmetic got cheap, it wasn’t just that we use computers for accounting. We started to use computers for all sorts of things that we never used to think of as arithmetic problems like imaging and mail and music. And then idea number three is what are the complements to machine prediction? And we talked about data and judgment. The book, and certainly our attention to the book in the first three or four years after it was published, was on idea number one and idea number three. So identify prediction problems in your organization, and then think about what data you need to make those predictions better, and try to understand what matters to you in terms of judgment. And that second point kind of got lost. But in the last four years, it’s become clear to me is that that second point was maybe the biggest one, which is this tool, which still under the hood is computational statistics, enables us to find all sorts of applications for computational stats that we didn’t really imagine before. Judgment and data are still gonna be useful, but that phase one, that step one, that first idea of identifying prediction problems, that’s not really how we think about using AI today. And in some sense, that... was a missing emphasis throughout the book and throughout how we thought about that book, or at least how I thought about that book for the first few years. Does Proprietary Data Still Matter? [04:59] Andrey: Very interesting. You mentioned one kind of underlying idea there, whereas you should identify the data that’s going to make your predictions better. Do you think to what extent is that now true, given that your foundation models seemingly can be very smart without having any proprietary data? Avi: Data is still central to the use of AI, the building of the models. In building a foundation model that, at least in the pre-training stage, that data is essentially interchangeable. You just need more. It doesn’t really matter what. To build a structure of language, and then you can move from there. On later stages of using that model, at least the AI companies seem to think data is valuable to the model companies. And then in terms of use cases within organizations, that’s more a matter of whether you want to delegate sort of the judgment of how to use the model and what the model should output to the vendor or whether it’s something that you need to build in-house. And depending on the organization, some of them are very happy to delegate to the foundation model provider and some of them think they need to fine tune in-house. Andrey: Well, so there are kind of two little sub ideas in there. One is you have choice. You can fine tune a worse model with your own data. And maybe that will outperform as a frontier model. I think for many cases so far, that’s been a bad bet. But there’s a different idea here. Use whatever model you want, but you design the evaluation. And then you optimize via the prompting strategy or scaffolding towards that. that benchmark for your own use case. Is designing a benchmark proprietary? Should we think of that as a proprietary data that an organization has? Seth: Is that the judgment part in the judgment prediction distinction? Vendor Choice as Delegated Judgment [07:01] Avi: Yeah, I think there’s a bunch of judgment. there’s judgment number one: which which vendor do you use? Because you’re delegating a lot of values as in like, knowing what matters to the maker of the model. And then there is judgment in how heavy-handed do you want to be to make the outputs fit your needs? And then there’s judgment on, okay, you’ve decided to be heavy-handed. What exactly does that mean? And is it, guardrails or is it really making sure that the output from the prompts every time fits your organization’s values or what matters to you? Andrey: Have you had an opportunity to kind of advise companies on this judgment decision? Like what has your experience been in these situations? Avi: At a high level, yes. I don’t want to exaggerate my experience, but the things I emphasize and the things that seem to resonate are, one, what I just said, which is recognizing when you choose a vendor, you are delegating your understanding of what matters to that vendor. And then two, that means before you start thinking about choosing a vendor, you need to know what matters to you. So think through, you know, before you go talk to somebody, you should know what your KPIs are and what outcomes you want to see. Because otherwise, once you talk to them, they’ll convince you that their outcomes are the ones you want to see. and so it’s this, I talked to, someone who is running an AI at a... Let’s call it a big healthcare organization. And his job used to be, like five years ago, his job was building tools. He’s like, my job isn’t building tools anymore. There are all sorts of vendors building AI tools for healthcare. Okay. And what my job is now is every week, 20 or more people come in and say, I have a solution for you. And he chooses one or two of them. Seth: Kind of seems like a good job for an AI. Avi: Well, maybe, maybe not. But he understands the individuals, the people, guess, in theory that could happen, but the individuals in his organization, what they’re willing to accept, what they don’t. Which decisions they like to have control over, which ones they’re comfortable delegating. For the ones they like to have control over, he has a sense of what might be negotiable and what might not be. He knows where the power structures are and what things might change. Therefore face resistance from people who have the power to resist. He knows those things that might not face resistance from people because the people don’t have power to resist, but they’re going to be really, really unhappy about it. It’s going to bad for the organization. And so there’s all these things that I guess in principle an AI could do, but we’re a long way away, I think, from that. Can Prediction Eat Judgment? [10:16] Seth: So let me let me just push down that line a little bit longer is the way to think about this sort of prediction and judgment distinction is is that like as the models get better the Prediction is like eating more and more of the stack right? You know we give the information about our organizational structure to the AI and then maybe it can make a couple more of these decisions for us And you could either imagine that asymptoting to, you know, in 20 years, AI does everything, or you could imagine there are higher and higher levels of judgment that humans keep on getting promoted to. Are one of those two ways the way that you think about it? Avi: Yes, Andrea Pratt has a note in our first Economics of AI volume that covers that exact idea. I think actually it’s a comment on our paper or the model behind the Prediction Machines book. it’s, well, in principle, with enough data, you can learn to predict judgment. And so you move up the stack. So absolutely. There are some limits to that. There’s limits on you may never get enough data. on that kind of judgment. Judgment can change over time. To the extent that ultimately you’re trying to predict your tastes, then they can change over time. And there’s some limits on causal inference and the impossibility of seeing the counterfactual, which creates a need for a model. Andrey: But humans have that problem too. Avi: Yeah, yeah, yeah, no, I agree. But in the need for a model. So then the question is, well, how come LLMs and some of these models seem to be pretty good at doing that? And in the process of prediction, I suspect -- though I don’t know rigorous work on this, so I’m being cautious -- Seth: That’s what this podcast is for. Avi: this is building some kind of model of the world that is embedded in the training data, like the language. Taste, Values, and Human Wants [12:16] Seth: So let’s go back to the one of the examples you gave, which is this idea of taste, right? Because I’ve had so many conversations with other economists about this idea that, well, taste will save us as a scientist, right? Because the AI won’t have taste. I have some ideas about what taste might mean, but can you be a little bit more precise about what you think taste means and why it’s something worth saving? Avi: So, okay, let’s operate under the assumption that whatever we want to call the machines, their goals are to help humans. Okay, not all humans. And we can debate about which humans, but like ultimately. Seth: Well, the Anthropic Constitution says, you know, safety first, the idealized anthropic researcher, then the guy that then then like virtue and then like the customer in some order like that. Avi: I’m gonna, all that matters for the point I’m about to make is that it’s not about the machine’s needs. So in that case, at the very limit, humans have wants and needs and those wants and needs, the machines need us, our judgment to know what our wants and needs are. Seth: So taste literally as in, this tastes good to me, I want more of this food. Avi: That would be one specific example of it. Absolutely. Okay. Now, I think we’re a long way from that limit, but that’s what I would argue the limit is. Seth: That’s the Bailey, right? So now let’s go out to the motte. Avi: So then it’s more like, okay, what matters to a set of humans, a group, an organization? What can we codify? If you can codify it and say, like, this is your goal, you’re not quite at that limit, but pretty close to it, then the machines can try to optimize on a goal. Goals have so much that are implicit. And so the machine would have to be able to infer the implicit part. Maybe it can, maybe it can’t, I don’t know. And then you can sort of ratchet back all the way to where we are now, which is you still need to tell your agent what you want. You still need to check on it every once in a while and guide it in the right direction. Prompting still has a role. Ontology, Umbrellas, and Context Shifts [14:45] Seth: Here’s another way of thinking about taste. And I’m curious whether you think this is in one of the categories you already listed or a new idea or you wouldn’t call this taste, which has to do something like with the idea of your ontology that is kind of built into the system, right? It’s your way of sort of dividing the world up into parts and maybe a good tastemaker or a good judger might have a more refined or more adaptable ontology. than the prediction machine. So I’ll give you an example of what I mean. have a couple of examples in mind, but one example I have is, you know, historically in the data, it’s always been the case that if lots of people show up with umbrellas, it means that you can predict that it’s raining. But then we have these Hong Kong protests and in the Hong Kong protests, they’re the umbrella protests and people bring umbrellas to show that they’re protesting, right? And it seems like a human would do better at adapting to like the completely new context for why you would need umbrellas than, you know, a pre-trained system that was only on historical data. So you can say that that’s like a context switch problem. Is that one of your ideas of taste or is that more of a judgment that’s not a taste? Avi: Honestly, that seems like a prediction failure to me. Seth: Right. That’s just we don’t have data on the context that we’ve moved to. The job is to understand when the context has changed, maybe. Avi: The judgment, I would say the judgment is like, what’s the consequential decision that’s going to be a function of, look outside and I see a lot of people in umbrellas. Yeah. What am going to do? And. Seth: You know, I should water my plants. Should I water my plants? Avi: No, I water my plants. Okay. So I look outside, a lot of people are carrying umbrellas and I think, no, I don’t need to water my plants. Okay. And then it turns out it’s a protest. It’s a little bit of weird context, but going with your example. Seth: It’s gotta be a weird context. That’s the reason that the AI is going to make the wrong decision because it’s out of context. Avi: the, the automated sprinkler doesn’t go on and, my plants die. Right. Okay. So, the judgment is, is it then worth it for me to invest more either in my prediction technology or to actually go outside and look and to see if there’s rain, to overcome that downside. So what you described as an error in prediction, there’s ways to reduce that error in prediction. The judgment is whether it’s worth the bother to reduce that error in prediction or to create some kind of insurance system where you would say, you know what, I’m gonna water the sprinklers. I’m just gonna run the sprinklers anyway. That’s how I think about judgment. It’s sort of what goes wrong when your prediction fails or it’s one important aspect of judgment. Seth: Sorry, can I give you an even more abstract? Andrey: Wait, wait, wait. No. I actually disagree with the premise of the example in many ways. I think a reasoning model would be able to handle the situation, especially with internet access, substantially better than many humans already, because you can call an API to get the weather forecast if you’re unsure. You can read the news. You can use reasoning traces. There’s this kind of implicit assumption in your question that like, we’re just using a raw pre-trained model and like asking it to like, if you, like, if you had a gun to your head, what would you do? You know, and not use any reasoning. Seth: Okay, but I can tell you a story, right? The weather API was always reliable in the data, but now there’s been a government takeover and I don’t trust the new government and you shouldn’t trust the API weather data anymore, right? Avi: So Andrey, I actually agree with, like, that seems unrealistic, but I think the idea is what you’re describing is how many resources you wanna put toward making it right, and I would view that as judgment. Andrey: But I guess the model has that judgment, maybe. Already. Already. Yeah, that’s kind of goes out like the stack of when judgment problems become prediction problems, I guess. Avi: But then there’s going to be... well, there’s going to be some places where the model is imperfect. Okay. Yes. Still a prediction tool. It might be better than human. Actually, it doesn’t matter if it’s better than human. But to the extent the model is imperfect, how do you want to behave? Like, let’s say the model is right 99.99 % of the time. Does your behavior change at that versus 99.9999 % of the time, even if the human benchmark is 50? And that ultimately is going to is going to be essential to judgment. We do this with self-driving cars. The models aren’t perfect, but they’re better than human. And yet, I still drove to work today, partly because that’s the law in Canada. Andrey: Do you think there’s hope? I mean, maybe this is kind of too much in the weeds versus the abstract idea, but sometimes people implicitly assume that they’re anchoring on the current technology where there’s an instance of an LMM that does something. But we might be able to design systems of LLMs that are interacting with each other to cover some of these. shortcomings that we can think of. I mean, at a conceptual level, maybe it’s the same thing anyway... Avi: So maybe another way to think through these trade-offs is to talk about whose judgment, okay? Which is Seth’s example was about, or my example was about my judgment, know, the individual’s judgment and should they listen or not. Andre, I think what you’re describing is the model builder’s judgment on which things is it worth investing in making the model better and when is it okay not? Like they have choices on sort of rate and direction. And those require some understanding of what they think is going to matter in terms of the use cases, the model. And on that, yes, there is a limit where a small number of players have extraordinary power because AI scales their judgment because they embedded into the models. But I do think. then there is still a human or set of humans responsible. It’s not like, the AI did it. It’s humans making those kinds of decisions. And I understand, like, at the limit, that actually gets quite nuanced, especially once we have models with continuous learning. But that’s how I think about that problem. Grue, Bleen, and Black Swans [21:41] Seth: All right Andre, can I ask my riddle of induction question? Andrey: Do you need me to induce it? Seth: You already know where I’m going with this. I’m curious if Avi knows where I’m going with this, but this goes back to the question of maybe where taste comes in is having a better or a more human ontology than the machine. All right. Have you ever heard of grue and bleen, Avi? These are colors that are different than blue and green. No? Okay, awesome. So briefly, we have this conceptual category, which is a thing that’s green. And a thing that’s green, we think that if you don’t do anything to it, it should be green indefinitely, right? Avi: Okay, yeah. Seth: All right. There’s this other thing that’s called bleen and things that are bleen are green until the year 2029. And after 2029, they turn blue. Right. Here’s the issue is that bleen and green things are observationally identical until 2029. Right. Yeah. So an inhuman, bad at forming natural kinds, ontology of an AI might decide that something is bleen instead of thinking it’s green. Right? And a human’s role might be to say, no, that’s a bad definition of a natural kind. That’s a bad ontology. And that would be a role of either taste or judgment. Do you buy that? Is this way too abstract? Avi: I think what you’re describing is a failure of prediction. I don’t think that’s taste or judgment. The taste or judgment is if you or a machine aren’t sure if something is bleen or green, do you care? Seth: Okay. Well here’s the thing, you didn’t even have the concept of bleen until I told you about bleen, right? Avi: So this is just the difference, I think, between known unknowns and unknown unknowns. So in Prediction Machines, we have a whole chapter framed on Rumsfeld and his discussion of known unknowns and unknown unknowns. Look, sometimes you don’t have a prior on it, and it’s an unknown unknown. That doesn’t mean that it’s not a prediction failure. It was just off the support of your data, and you didn’t know what to do about it. And I think that happens all the time. Seth: Sometimes you find a black swan. Avi: Yes, exactly. And so like, there might be places where humans are better at that kind of prediction than machines. There might be places where both humans and machines are really awful at that kind of prediction. And if that’s the case, then you want to have robust systems to anticipate those kinds of things. And that’s where judgment comes in. Like, if you’re wrong about the existence of a black swan, you know, does that change anybody’s behavior? I think the answer is no, because black swans and white swans aren’t actually that different from each other. But if there were other examples, like financial crises, where he uses the metaphor of the black swan, then absolutely there are meaningful differences. And you should Andrey: Financial crises. Seth: All right, so you’re saying that jobs that will survive TAI number 7 should be Black Swan, anticipator. Andrey: Not an anticipator. Actually Seth, this is actually kind of the key point. The point is, anticipator of whether Black Swan affects your utility enough that you should plan for it. O-Ring Complementarities and Automation [25:22] Andrey: I think next it will be awesome to talk about automation and some O-rings. Actually, the previous episode we did, we reread Michael Kremer’s classic O-ring paper because it’s been so inspirational for so many. It’s a great paper. They don’t write them like this anymore. Seth: It’s so fun to read. They don’t like to do macro like that anymore, unfortunately. Andrey: So we were wondering, so you have your own spin on the O-Ring paper. Maybe you’ll tell, you can tell us a little bit about that. Avi: Paper makes a pretty simple point. There may be two simple points. First one is that when you think about tasks within a job, they’re not interchangeable and substitutable. So it’s not just like, okay, a machine comes in and takes tasks. Sometimes tasks are complements. Now that isn’t, I’m gonna a little cautious. We talk about that in our O-Ring automation paper. It’s not necessarily a new idea. It’s implicit in the constant elasticity models. you can have a Leontief production function. Seth: We’re talking about the Daron-style task-based models. But if you actually read the papers everything immediately goes Cobb-Douglas. It’s always immediately weird. All the tasks are substitutes and then Cobb-Douglas over all the tasks. Avi: Yes, but it’s possible to, within the canonical model, to have that. So our point number one is tasks can be complements. And I just wanted to be cautious because I don’t want to claim that that’s necessarily our idea. But it’s an emphasis maybe that the existing literature hasn’t had. And then the second is, well, once you have tasks that are complements, if a machine starts doing some of those tasks, human can move their attention to the other tasks that are not yet automated. And when that happens, the human gets better at those tasks, which then makes automation of those remaining tasks even harder because the machine has to be better than now the human who’s spending all of their time focused on the remaining few tasks. Skills Versus Tasks [27:40] Seth: So let’s pause right there because I have a couple of questions right there immediately. So one way to think about automating part of your job is you’ve automated part of your job and now I can reallocate to the stuff that’s not automated. also another way to think about tasks within a job that are complementary is to think about them as sort of like innate skills or abilities. So think about the job of being a basketball player. The job of being a basketball player involves being tall and being agile. If you somehow automated being tall, I can’t reallocate my skill points into being agile, right? If we think about my performance as more as a combination of my skills, then automating part of it or taking part of it away, it’s not necessarily obvious to me that I can get better at the thing that’s not automated. Avi: The way we, okay, so first the way the literature usually thinks about jobs is generally at the task level, not the skill level. Okay. So a worker does a bunch of tasks. Okay. Those tasks require skills, but the worker does a bunch of tasks and the A machine comes along and can do the task and not the skill. So I’m not sure what it means for a machine to be tall. What it means for a machine to slam down. Seth: Well, let’s think about being a doctor. Let’s assume you might imagine being a doctor involves bedside manner and judgment about and diagnosis right it’s not clear to me that if you automate my diagnosis I can reallocate more effort into bedside manner some people are just level five at that and some people are level one at that AI Doctors and the Future of Medical Work [29:25] Avi: It is obvious to me that there’s a bunch of tasks in a doctor’s workflow. Some of them involve diagnosis. Some of them involve talking to patients and making the patients feel better. And within those, there are skills in being good at filling in the missing information of what’s wrong with the patient and skills of making the patient feel comfortable. And actually, for some of those tasks, you might even need both. A machine comes along and automates the diagnosis skills. Okay. That means medical professionals are going to be spending more time on the other skills. This is actually an Eric Topol’s deep medicine book. I’m not sure if you’ve read it. It’s, it’s like a pre-ChatGPT, but like how AI might transform medicine. And that is his core thesis. The idea is that AI is going to make healthcare human again, because doctors are going to spend less time looking at screens and focused on diagnosis and more time. interacting with patients and making patients feel better. So in that sense, we get the automation of the diagnosis task and some of the computer tasks that should exactly lead to reallocation toward the human part. But then you brought up something else, which is, do our current doctors, if they spend that much more time interacting with patients, are they the right people for this job? Or alternatively, could we have a different set of medical professionals who we could train because now the machine can do some of those tasks who would be way better than our current doctors at the remaining tasks? I suspect if the machines get good enough at diagnosis and identifying appropriate treatments, there is an enormous opportunity for a new kind of medical professional who is focused on essentially interacting with patients. Seth: Yeah, so you’re making the occupational reorganization point and that’s that’s obviously essential and we’re going come back to that in the second. Yeah, I just I’m just pointing out that maybe maybe my example of basketball wasn’t so good. Maybe my medical example wasn’t so good. But I bet you I could pick out some domains where the elasticity of task output to effort is very inelastic. Avi: Okay, trying to think. You’ve switched from skills to task and that makes me much, much happier. Seth: Well, I mean, you would only need to worry about skills is if you were inelastic to effort, right? Then it’s just the skill. Rare Skills, Common Skills, and Wages [32:04] Avi: So there’s the new Autor and Thompson paper on automation, which I think gets at some of the things you’re talking about, which is if the things the machine does are relatively rare skills, like are tasks that involve relatively rare skills, to be precise, then what happens is we get entry into that profession. More people can do it and very likely wages go down. And if the machine things that the machine does are things that many people can do, they require less specialized skill, then the remaining humans in that job will, there’ll be fewer of them and they’ll likely be higher paid. Seth: Right, think that’s right, but I think maybe a missing component here is within the job already, what is the correlation in abilities between people who are good at the automatable and non- automatable part of the task, right? Avi: Yeah, but I think that’s the statement about that. Like in the short run, we’ll get the Autor and Thompson results. And in the long run, we’ll get a reallocation of jobs, right? There’s a system of professions and the system of professions will change. Are Tasks More Complementary Than Cobb-Douglas? [33:23] Seth: In the long run, you get the reorganization of jobs. Maybe one other thing I want to talk about before we get into reorganization of jobs is just this question about, tasks more complimentary or less complimentary than Cobb Douglas? Do you have a sense of that with tasks within a job? I mean, it seems like would vary a lot, a lot from occupation to occupation. I think we all have this intuition that they should have some kind of complementarity. That’s why they’re a job in the first place. That’s why they’re bundled. But you might bundle them and they still might just be, you know, gross substitutes that have a little bit of complementarity. Avi: I suspect there’s a lot of heterogeneity across jobs and I don’t think we have good data on that yet because sometimes we haven’t been looking because our model is substitute model and so our papers are fundamentally focused on the substitute. Seth: And I think this is an example of somehow the theory is sometimes a little bit downstream of the data, right? We just have so little data on people reallocating effort across tasks within a job that of course it makes sense to aggregate up to just add up all of the tasks done by all of the workers. That’s kind of, that’s my guess of why Acemoglu gets there. Avi: So of the task papers, the Eloundou et al., Dan Rock’s paper, is incredibly careful on every page. Seth: This is not an automation measure. Do not use this to measure automation. Avi: This could be a complement, it could be a substitute. These are just jobs that change. So like kudos to them, the four of them for being super, super careful. Nevertheless, when that paper is cited both in the academic literature and in the press, that idea seems to get lost. I’m not exactly sure why, maybe that’s because of the model. Seth: Question people want to answer, right? The people don’t want to know what job’s going to change. People want to know what job should I get, right? And so... Avi: Well, okay, but if it’s a question people want to answer, then the complements matter just as much as the substitute. I wonder if the answer that people want to know, like the answer that people want, and then they just... Andrey: I actually think it’s I think take has always been that just most people are pretty, they’re very sophisticated users of this data, but a lot of people don’t have a sophisticated economics model. And therefore to them, it’s just obvious that what’s going to happen is the machines are going to take our jobs. As a result, that’s just, they don’t have a more nuanced model of economic activity and therefore that’s how they interpret it. Now there are more sophisticated readers, think, we know some of them, where they’re just really just think that AI is going to be able to do everything in a very short period of time and then it all kind of becomes moot. You know, if you think that every single task can be done by an AI. Why the Impact of AI Was Ambiguous in Earlier Work [36:15] Seth: Yeah. Well, I guess this kind of brings us to your 2019 Journal of Economics paper, which is about where you guys kind of where you kind of throw your hands up. That’s not that’s a positive part and say there’s an ambiguous impact. So I guess I want to push you there on is the ambiguous impact because. We just don’t know all of the relevant elasticities, right? We need to know the elasticity within tasks within a job. We need to know elasticity across jobs within an organization, the elasticity across sectors of demand. And if we could put all of those together, we would be able to answer the question. Or is it more ambiguous than even that? Avi: No, I think you need to understand when that paper was written in order to understand the paper, which is in 2019 or late 2018 when we were writing it, we had no concept of anything but a task- based model with substitutes. Okay, maybe that was on us. We should have. But Acemoglu and Otter and Rastrepo were the dominant- Paradigm. ... working in literature, especially Acemoglu. Seth: Are you saying our ontology was limited? Avi: I’m not exactly sure what you mean by that, but... Andrey: You forgot about the O-ring which was the black swan of papers. Avi: Yeah, yeah. So like, we did. Seth: I mean in Kremer, I mean, presumably you looked at Kremer again before writing your paper. You can almost see he’s almost there. He’s almost at, and this is within workers too. He doesn’t exactly say it. Avi: Exactly. So when we wrote that paper, we were thinking task-based substitution. That was the model that we had. And actually, in the process of writing that paper, in some sense, we learned what was wrong with that model and ended up with, we just don’t know. And part of that is, we wrote it in 2018, 2019. We were looking for new tasks from AI. So this is before ChatGPT, like four years before ChatGPT. So new tasks hadn’t really come up yet. All we had was identifying space junk and treatment for complex disease, which actually wasn’t our idea. It was Tim Taylor’s idea, our editor. Andrey: Well, you already had AlphaFold, right? Avi: Yeah, but it’s not clear what the new task is because of AlphaFold. Yeah, fair enough. In terms of... So, and actually that paper in some sense directly led to our work on system change and GPTs, because Tim Bresnahan pulled me aside that summer at the Summer Institute and told me he hated our GPT paper. I’ve told you guys this before. Because it was a task-based model and that’s not how meaningful change happens. That then led to all this work on trying to understand, well, if it’s not a task-based model, how does the system change? Andrey: Okay. And we’ve covered that to Bresnahan paper on this podcast. Reorganizing Jobs Around AI [39:22] Seth: I guess let’s talk about reorganization of tasks. Obviously that seems to be, that’s the best case answer. The best case answer is you split off the, I guess from the perspective of a firm trying to boost productivity, maybe not necessarily from a worker’s perspective. From the firm’s perspective, you want to slice off the automatable thing, let that rip, and then figure out what you have to leave behind for humans. Is there any good research about... How do you do that? What industries are better than that at others? Like, what’s the next research frontier on that question? Avi: I think you just defined it. there are two. One is like within the firm, how do we think about where the complements are and what’s left for humans and how does that vary across organizations? The second part, and Alex Emas has highlighted this recently, is it also depends on elasticity demand for the... Seth: products. Avi: Like, you know, even if within an organization workers reallocate and they become hard to automate because they’re more productive, but then the organization is producing more, well, someone has to want that more or else then, you know, at least that organization or its competitors are going to to business. Seth: Well it’s factor, well its price will come down, know there’s a kind of a nebulous connection between price and profitability. Avi: Right. Price goes down. It’s got to go down like, well, quantity has to go up enough that we still need the workers. Andrey: There might be a paradox in there that’s not really a paradox. The misnamed Jevons paradox. Avi: Maybe. Should We Want Less Automation? [41:05] Andrey: Following up on this idea, think several prominent economists have called for a government push or ideological push to make AI that complements humans rather than substitutes for humans. Seth: Friend of the show, Erik Brynjolfsson has written about the Turing Trap. Is the Turing Trap misnamed? Is it not a trap? Should we embrace the Turing? Avi: Okay, so this is our science paper. Seth: Let’s get the hot takes. This is where we brought you on. Avi: Do want more automation? Yeah, so Eric has said it. Doron has said it. There’s lots of policy. We should complement humans, not replace them. And John Markoff is a journalist. He has this book called Machines of Loving Grace, same title as Amodei’s essay, essay, but older book. It is about the history of computing. Seth: When you’re a tech billionaire, you’re allowed to use cool phrases unsighted. I’ve noted this. Augmenters, Automaters, and Inequality [42:10] Avi: Well, they’re both referencing a poem. And in Markov’s book, there’s these two streams of computer science. There’s the, I forget exactly how he labels them, but essentially there’s the augmenters and the automaters. And at least from my perspective, the augmenters seem like the heroes of his story. And the automators who start to become prominent as this book is getting written around 2014-2015 Seth: They’re trying to trap us. They’re trapping us. Avi: But we also know that the rise of computing the internet massively increased inequality. They generated enormous wealth, but they massively increased inequality. And I hypothesize that the reason for that is, yes, they were augmenting what humans do, but they weren’t augmenting what all humans do. They were augmenting what a set of humans who are good at abstract thinking do. And those people were already doing pretty well. And so in the process of augmenting humans, right, because no human can do what the internet does or what a computer can do, they augmented folks at the top and left others with relatively stagnant incomes. Seth: Is this story there really at the task level? The way I think about that inequality story is that it’s kind of at the firm level, right? It’s we’ve now put the corner store into competition with Amazon and so Amazon wins and whatever Amazon takes as input wins. Avi: There’s a bunch of different pieces. The one I’m emphasizing is like the Autor, Katz, and Kearney framework, which is about skills. Andrey: I mean, it has to be both, right? There’s a set, right? Like, the humans who are now able to market their unique skills match with the firms that are larger, but you kind of need both to create the inequality or some of the humans become superstars without like needing the firm in first place, right? Avi: I think in principle you could get within firm inequality without getting across firm inequality. We ended up getting both. Seth: Yeah, both. Both happened. Andrey: Fair enough. Avi: but as I’m thinking like Autor, Katz, and Kearney with computing and then Shane Greenstein, Chris Foreman and I have some work on sort of the internet inequality, same kind of idea. so on the other hand, automation technology, if it’s automating things that folks at the top do, could superpower everybody else. Okay. And this is a could, cause we hasn’t really happened. So what we hypothesize, so the question, the paper is called, Do We Want Less Automation? And our answer isn’t no. Our answer is, here are reasons why it’s not obvious. Okay? It’s very economist-like. And the essence of it is, we were just talking about this medical example. Well, if what doctors are paid for is 10 years of post-secondary schooling, that essentially is about prediction, diagnosis and treatment. Then someone potentially with two to four years of post-secondary schooling who was much better at managing patient stress and all these other things, training like a social worker, combined with a diagnosis machine could be super hard. And so their productivity goes up. And there’s a bunch of industries where What people at the top do seems a lot like filling in missing information. Are Intellectuals Giving Biased Advice About AI? [45:58] Seth: One might even cynically say that these thought leaders who have been so augmented by the internet are maybe not giving the populace the best advice. Avi: Maybe. So I had an undergrad RA write an essay for me. She’s a philosophy major. you know, a couple summers ago, it’s Amelia Agarwal. I feel like I should call her out. Seth: Love undergraduate research on the pod. Avi: Yeah, the opening of her essay was, part of her assignment was to read and hear about all these people who said AI is going to automate work. And so I’m going to have to have leisure, like essentially. And she’s like, that doesn’t strike me as bad. And then she dug into it and her framing was essentially the people whose identity was driven by their, you know, intellectual abilities, public intellectuals are exactly the people most threatened by AI. And so anyway. Andrey: You know, it’s very interesting. I actually disagree. Yeah, I think lots of intellectuals are threatened by AI but not public intellectuals and that’s because humans are going to want other humans to communicate to them in many ways. So, the role of the public intellectual is not going to go away. The role of the maybe the scientist toiling away on their research. That is in my opinion much more a threat. if you’re... one might even deduce that Seth and I have started this podcast as a hedge for that world. Seth: Well, what I say is as the price of writing papers goes down, the return to reading papers goes up. But maybe this goes back to the taste idea, right? Which is one way you might think of taste is a public intellectual doesn’t let’s let’s be cynical for a minute. The public intellectual, the public art critic doesn’t actually know art better than anybody else, but they serve a role as a coordination mechanism. Right. Everybody trusts Andrey. So when Andrey points at the thing and says it’s good, everybody converges to that. And then maybe that’s one notion of taste that will be preserved. Avi: Yes, and so you started in science and moved to art. There’s probably differences between them, but in the sciences, there’s a question, or a scholar’s, what’s our goal? What are we trying to accomplish? And I think different disciplines have different goals. And depending on the goal, the role of the human curator changes. If the goal is so that humans understand the world, and have sort of a consistent model, then there’s a real role for a curator. If the goal is to build a better spaceship, then maybe there’s not such a role for a curator. And so I haven’t been following that literature, so I don’t know really what the formal academic take on what I just described is. Can Policy Steer AI Toward Augmentation? [49:27] Andrey: Yeah, I agree. I haven’t seen much formalization. So listeners, if you know of any, send it along. Yeah, I mean, I sorry, I just want to make a final point is that I think I like your criticism of this augmentation idea. But to me, there’s like a much deeper criticism, which is there’s there’s just kind of a whiff of central planning involved in it. like, how how do you know? What technologies are going to automate versus augment. Like this is very hard to predict in my mind. And to think that the government is going to like somehow implement a system of taxes on technologies that are augmentation versus substitution, it’s ridiculous in my opinion. Avi: So I was taking as given that you can understand what is automation and what’s augmentation. I agree it’s a very hard challenge. There, I think the narrative, I’m gonna be careful. I think the argument is if even without choosing winners, we might be able to tax capital relative to labor or something like that. in order to push things in a particular direction. I think that’s it. Andrey: Yeah, that’s the most plausible. Seth: That’s pretty plausible, but when you actually hear versions of the Turing Trap articulated, it’s really like go and burn down the houses of the people who want to automate you. Avi: Okay. So Korinek and Stiglitz have a chapter that’s really about tax and capital that’s in our economics of AI book. And I think like the Acemoglu Johnson argument is really about tax and capital. I’m not enough of a macro economist to have a strong opinion about one way or the other, but that I agree seems more Seth: Right, and then there’s a deeper, deeper argument there about whether or not you want to tax capital, right? There’s the old Chamley-Judd result about, well, know, labor is inelastic and capital is elastic, so really you don’t want to tax it. There’s obviously international considerations about if you have a fully automated technology, isn’t that just going to locate itself in the lowest tax jurisdiction? And so it might be very hard to tax capital. And then of course the Iván Werning follow-up research kind of complicating the original Chamley-Judd results. So this gets in the weeds really fast. Andrey: And it’s also very blunt in many ways, right? A lot of capital is not about automation. it’s a... I don’t know. Avi: Yeah, and there’s all sorts of questions in public finance and how that all plays out to like the there’s under the names Trammell and Korinek. I think it’s Trammell. No, it’s not. Andrey: That’s Lockwood. Avi: Lockwood and Korinek, thank you. have a relevant paper there. AI Growth Scenarios Through 2030 [52:36] Andrey: Next topic. Yeah. So there was a very well-circulated survey of economists about their expectations of economic growth in different AI scenarios. Seth: Now Avi, I understand you have intentionally not read this so as to have an unbiased take, so you will not be contaminated by the opinions of everyone else. Is that right? Avi: That is absolutely right. Andrey: Excellent. You’re definitely not in the same university as many of the authors. Avi: I probably will, but we’ll see. Andrey: All right. So the first conceit is that there are three scenarios for AI progress that they want us to consider. The first one is slow progress, where by the end of 2030, the AI can do PhD student level assistance, half of eight hour long coding tasks, passable stories and songs. Robotics navigate homes with some help. So that’s kind of the slow. Moderate is you have semi-autonomous labs, five-day coding tasks, high-quality novels and hit songs. Robotics can perform basic tasks. And then rapid progress outperforms top humans in research coding and leadership, award-winning creative works, nearly all physical tasks. So those are the three scenarios by 2030. So the first question is, how do you allocate the probabilities between slow, moderate, and rapid by 2030? Avi: So, okay, so with the exception of the statement about hit songs and award-winning, those are all about the models and not about the outcomes. So I’m going to ignore the hit song and award-winning part because I think that’s... Andrey: It’s of the quality of the quality that could win it. Avi: Okay, because at a high level, what I think is the technology is going to accelerate rapidly, but there are all sorts of meaningful barriers to widespread diffusion and having an impact on the economy. and sometimes I think we’re already in the slow and for aspects of the medium versus the fast, I feel like I should call it 50-50 because I’m skeptical of the like, I’m skeptical of the robotics stuff, but the five day coding task seems very, likely. And so just. Andrey: Yeah, there’s some other things. CEO level agency, you know, like is is one of the criteria. Seth: I don’t know whether or not they can run a vending machine. Avi: But don’t like part of it. So much of what a CEO does is like is charisma and creating followers, right? And I’m not sure that’s a mission. Seth: Is it charisma judgment task? Is it charisma judgment? Avi: It’s a skill. I’m not sure it’s a prediction or judgment. It’s more like an action. Andrey: Yeah. But okay, fair enough. Just to give you like a sense of where economists came in and they took this in the fall, 39 % that were still in slow by 2030, 47 % that were in moderate and 14 % then were in rapid. So you are more bullish than a typical economist. Avi: I’m more bullish. I probably shouldn’t have said zero for slow. In retrospect, I was just going to be something five to 10 or something like that. GDP Growth by 2050 [56:22] Andrey: Okay, great. Now, and I think this is the question that really there was a lot of controversy about. So, the question was, by 2050, what is the annual change in GDP on average? Avi: GDP or GDP per capita. Andrey: This is GDP. Avi: I like I have to make a population assumption. somewhere between two and 3%. Andrey: All right. You are well within the economists’ answer here: 2.5%. Avi: duplicate. And so we’ll be a little above that. Andrey: So 0.5%, that’s all we get. okay. Extra from AI over and above. Avi: Well, no, I don’t think you want to say that because the reason we have 2 % is because of innovation in past. Andrey: Okay, so fair. I agree, I completely agree with you. Avi: Like it’s possible, especially with, you know, it’s possible we would have gotten zero. Seth: 5 % better than historical rate of technological growth. Avi: Yes, something like that. Andrey: Now, what if you were for sure, what if you for sure knew we were in the fast scenario by 2030? How would that like change your predictions? Seth: It’s hard to get to above three. Avi: Like, yeah, I just think there’s a lot of bottlenecks in the economy. I think that, and we’re going to figure out what they are. Seth: We’re gonna find out fast and that guy is gonna be rich. Avi: Yes. Andrey: So you’re once again, like a very down the median economist. Avi: On growth. Yeah, okay. Seth: Can I ask you, you think that’s mostly about bottlenecks? You don’t think that’s mostly about people taking leisure? Avi: I think it’s mostly about bottlenecks. What Are the Bottlenecks? [58:36] Seth: So gun to your head, what’s the biggest bottleneck in that high growth robots are awesome scenario. Avi: I feel like my best answer is we’ll find out. Andrey: Okay. I guess the pushback that folks gave is this is a scenario where by 2030 robots can do nearly all home and industrial tasks and faster than humans, right? So you might say, well, manufacturing and physical tasks are a tiny, not tiny, but they’re not that big of a portion of the GDP already. maybe- Avi: be essentially zero is the point. If they’re that efficient and that cheap, then they won’t mean like, I guess it depends on how we calculate the deflator. agriculture is way more productive. GDP hasn’t grown by that much. Andrey: But what if we have, you know, you know, robot doctors that can do, you know, like, Avi: Great, then medicine will be cheap. It’ll be less of GDP. Andrey: I guess, all right, so here’s a hypothetical. Here’s a hypothetical. Let’s say we had a cure for cancer as a result of this, which is very plausible in the rapid scenario, and that we also, at least in principle, have the technologies to administer it through robots very efficiently because we are in a world of just true abundance. My sense is that people would value that medical care extremely highly. And if one were to properly deflate the existing cost of cancer treatment, wouldn’t that imply a very large GDP effect? Now you can say maybe we’re not going to calculate that correctly. GDP, Consumer Surplus, and Health Breakthroughs [1:00:25] Avi: Now I feel like I’m going to, you know, it’s sort of the Bob Gordon sense. I don’t think we deflated antibiotics properly. I don’t think we deflated flush toilets properly. So if you’re talking about consumer surplus, then maybe consumer surplus will be found, especially, you know, to the extent that it’s health outcomes, then huge increase in consumer surplus, much more than the argument that we’ve had for digital. Because the that debate on whether digital really made us better compared to what was happening in the 20th century, I reasonable people can be on both sides of that debate. what you’re describing, is can’t secure people living wonderfully and healthy to 100, there might be some limits to how long, but that would be wonderful and great for consumer surplus. But if that happens, I guess it might and it’s that easy, it might become so cheap that it’s it’s like agriculture. Because food is pretty essential too. And food is so cheap that we don’t worry about it so much anymore. Seth: Inelastically demanded. think people will elastically demand years of life in a way that they won’t elastically demand calories, right? Avi: Potentially. Seth: You think people will get sick of it. I thought you were to go to maybe you’ll recall in Doron’s simple macro economics of AI, a favorite paper of this podcast. He actually predicts that actually consumer surplus might raise by less than is implied by the GDP growth rate, because we’ll invent evil jobs like social media manipulator. Do you are you still convinced that consumer surplus growth will be faster than GDP growth evolves? Or are you open to this idea of the invention of evil tasks? Avi: I feel like we are not in my expertise. Seth: Turn it up. Andrey: Seth is really trying to get the hot takes. Avi: I don’t like to judge what particular products, a particular. Seth: Well, you can’t judge, you can’t predict. Avi: Yeah, you know, what am I in a- Andrey: Then you become a economist. Avi: Actually, let me give... So I think it’s reasonable for people to say some roles, some jobs, some products are better than others. I don’t think that has a meaningful role in GDP calculation. And I also worry if in our consumer surplus calculations, we economists say some things are better and some things are worse because then... So much of it is just obviously to the taste of the... Seth: It’s such a normative can of worms, right? GDP we can measure, consumer surplus. I mean, we do things at the Stanford Digital Economy Lab around trying to do willingness to accept experiments, but obviously those are highly limited too. Avi: So consumer surplus as in figuring out the area under the demand curve, that’s the kind of task I think we’re good at. It’s within our domain. whether the demand curve is morally right or wrong, that’s not something I’m going to be finding out this day. Andrey: I wanted to just like close off that loop a little bit by just saying that you just gave me an answer that said that for our evaluation of how good of a world we’re gonna get in 2050, GDP is no longer the correct sufficient statistic, which obviously makes me question like why is this such a bench? Why are people so interested in forecasting GDP in 2050 if we think it’s going to get pretty uncoupled with consumer surplus in these scenarios? Avi: Well, I’m not sure it’s more or less uncoupled than it has been in the past. I think reasonable people can disagree on that. I think the debate between Bob Gordon and Erik Brynjolfsson or Bob Gordon and others over the years is sort of is really informative about how hard it is to say, you know, what’s better versus today versus the past. What happened in the early 20th century is pretty amazing. okay, that’s point one. Point two is it’s not obvious to me that GDP like GDP tells you your national capacity. That’s what it tells you. Seth: That’s useful for things like wars and public finance. Avi: If I remember my first year econ, haven’t taught first year econ for a long time. That was the idea. What’s the industrial capacity of the country? Or what’s the economic capacity of the country? It turns out it’s highly correlated, as I understand it, with lots of welfare measures. You guys know this. And so we use it for that. Once you start deviating, then... then that’s fine, but you’re now embedding a whole other set of values. At least with GDP, we know what the values are. It’s not it’s not value laden, but we at least know what the values are that we’re embedding in that measure. Andrey: But guess I’m not sure we know, just in many conversations with economists, this question of deflators has come up and most of us haven’t spent much time thinking about what actually goes into that and how well that’s done and how relative to different goods. So I agree with you that we’ve been recommending that people use this because it’s very correlated with welfare, but you know. Avi: So, yes, and the NBER productivity group in many ways was focused on questions about how do we measure innovation and progress and a lot of that, some of the early work that came out of it was explicitly about this question. it’s not that people haven’t thought about it and that there’s not a whole community that grew out of that. Now admittedly, we don’t have that many, you know. papers about deflators and inflators anymore. But Shane, when he was running the program, digital, almost always had somebody on the program focused on measurement of prices over time in the digital world. So just to say at least it’s on his radar and it was part of what Sloan Foundation was excited about why they originally started funding the Digital Economics Group. Sponsor Break: Revelio Labs [1:06:56] Seth: This chance to contemplate your posteriors is sponsored by Revelio Labs. Revelio Labs is a leading provider of labor economics data and data services for companies, academics and independent researchers. Andrey and I have been working in economics of AI for a long time and we can confirm just how useful Revelio’s data is. Revelio’s team combines comprehensive micro-level data on employee professional profiles, job postings and employee sentiment with standardizations, mappings, and enrichments available, all to make that data useful without making your modeling decisions for you. The data can be flexibly aggregated to company, market, or industry and be used to study questions ranging from career trajectories to occupational transformation to the returns to skills and the impact of AI on labor demand for tasks. Can’t imagine anyone be interested in those. And Revelio data is available on RWRDS. So if you’re an academic with a good library, you might already have access. And if you don’t, you can reach out to their excellent economics team and they’ll hook you up. Will AI Centralize or Decentralize Decision-Making? [1:08:16] Seth: All right, okay, we’re gonna give you a topic. We want your hot take. So will AI centralize or decentralize decision making in the economy? Avi: Yes. Andrey: It was good though. Avi: Like, so, I don’t know, this is no longer lightning round. But for an ultimate hit thing, have that interesting paper saying why it’s gonna centralize and their argument is good. And the exact same arguments they have also say that it could empower people on the periphery. And the answer is almost surely both are gonna happen. There’s gonna be some people who figure out how to scale themselves and their judgment and gain enormous power. And at the same time, others who are able to do things they couldn’t do before, just like we saw with online platforms where there’s been both the centralization of power and the ability of niche players to Seth: Here’s the part that I thought that that dialogue missed, which I recommend to all of our readers to look at because it’s fascinating, is the argument that AI will centralize us is that AI is going to help these centralized decision-makers understand the complexity of what’s going on. But what if AI makes us weirder faster than AI conceptualizes the weirdness that it’s creating? What if we just get super duper weird? That would make it very hard to centralize. Avi: Yeah, I think that’s a version of my argument, which is that the people on the periphery can, know, individuals can use it to make themselves more productive, better, happier, whatever their goal might be. Seth: more, more, less, less controllable. What do LLMs imply for privacy regulation in economics? Avi: first answer was nothing. There’s lots of ways to worry and think about privacy and privacy does matter. First answer is not obvious how it matters now differently than it did five years ago. Digital Hermits and De-Anonymization [1:10:11] Seth: I the idea is that it will be... Andrey: De-anonymization. Avi: Yeah. So yeah, so that’s where I said my first answer. then, okay, well, to the extent that okay, here, here we go. Catherine Tucker, Jean-Michel Lachetal and Avery Haviv and I have a paper called Digital Hermits. okay. And the idea of that paper is again, I’m really bad at these hot that all. okay, the the idea of that paper is right now you might be willing to give your grocery preferences to whatever company. but you might not want the company to know your IQ or your religion or something else, your union status or something like that. Okay. And in a world with bad prediction tools, you can give your grocery information and not the other information. But if some other people are giving both, then over time, you can’t even give your grocery information if you want to protect your religion or IQ. So. In the equilibrium there, we end up with one or two groups. We get hermits who don’t give any information and everyone else who gives all their information just gives up. So what you’re describing with LLMs is a version of the prediction mapping from, just writing something to now having all sorts of extra information about you that we might not want to put you. And so like being able to connect different pieces of information. Seth: make the hermit hermit-ier, right? Avi: They’ll make the hermits hermit-ier and create demand to the extent that privacy is a value and it’s now harder to protect. There’ll be demand for laws that Seth: It’ll make the hermits more hermetic, I should say. Andrey: I think it could be a function of abuse, right? Obviously, I haven’t studied privacy as much as you, but I think when this data gets abused, there’s a lot of demand for laws, retribution, and protection. But when it’s an abstract value, but it’s not getting visibly abused, it seems like it’s less of an issue. this data is used for personalized advertising. Yes, some people have a negative reaction to that. In the end, in the grand scheme of things, it’s not that bad. But if now someone is finding out, you know, all this private information about you specifically and, you know, that information, let’s say can be, you know, someone, you know, leaks it or talks about it online or tells your employer or whatever, you know. Avi: So Right. Yes, there’s going to be a decline in online anonymity. Actually, like, I if you remember Catherine Tucker’s discussion of Florian Ederer’s paper on de-anonymizing econ job rumors at NBER. That paper is about fundamentally about something else. But her discussion was, okay, this is the world we’re moving to. Maybe because of quantum, maybe because of LLMs, it’s gonna be very hard to post things anonymously. And so once that happens, once things you say digitally you expect to be known, how does that change behavior? And then there’s like, I guess your original question was, how does it affect privacy regulation? LLMs are gonna do two things. And I don’t know what the equilibrium is gonna land, which is, I don’t know why you keep doing this. Seth: I’m counting this is be thing number one and then I’ll you thing number two. Avi: So thing number one is what we just described, which is demand for privacy regulation goes up because there’s new risks and people do value privacy. The other hand is there’s new opportunities to use data and benefit from your data. I can sort of think about that’s what agents are going to enable you to do. And so there is also an increase in demand for regulations that enable data to flow. And where that plays out country by country, continent by continent, who knows? But like, just like with digital, we saw both the increase in the benefit and the increase in the cost of data flows. I think we’re going to see another wave of that. AI and Peer Review [1:14:23] Andrey: Follow on question, peer review. You were the editor of marketing science for a long time. Narrow question is, what does this imply for anonymity of peer review? And a broader question is, effects of AI on peer review more broadly. Avi: So yeah, I was, was a senior editor, marketing science. Actually, I haven’t thought about that peer review anonymity point, but absolutely in principle. it’s disguisable. I think there’s a solution to this. I just don’t know that we want it. Like running your review to have ChatGPT or Claude or whatever you want rewrite it so that it doesn’t sound like you with all your points. Seems at least on the language matching will work. Not on the idea matching, but that’s already revealed. Like a whole bunch of people tell their author to... Although actually as an editor, learned that it’s not as... Often it’s not the author that’s asking for those citations. It’s like their advisor. Okay. Like there’s a lot of that, but still. So I think that’s manageable. Certainly on the one way, like I don’t think we’ve had Andrey: Yeah, at least. Avi: Double-blind peer review for 20 years, at least in the econ side of marketing and then econ. The pre-prints are out there. The pre-prints are well distributed. Andrey: Yeah. Seth: So it just be public? mean, so that would be the other direction. Is that it just opens public reviews. Avi: I think if reviews are public, we’ll all just collude. I think those be mass collusion. I shouldn’t say we’ll all. I would prefer to think that I won’t collude. But I think that’s just an invitation. Seth: Go ahead. You don’t think that there could be a disciplining of that when somebody reads your review and says this is, this is nonsense? Avi: I think the benefit of having your reviewers for sure know that you said good things about their paper, it’s going to be hard to overcome. There’s a question about whether the whole system makes sense or not. Andrey: Well, that’s kind of what I was getting to next. I, you know, I do some advising for Refine and I’m a big fan of their product. And it’s pretty clear to me that Refine is doing a better job of peer review than the vast majority of peer review outside of very, very select venues. And it’s only going to get better. And so the question is, given these capabilities, what should it look like in the future? What Are Journals For Now? [1:17:05] Avi: So, okay, I’m gonna propose something. But I’m gonna start with I don’t know. Here is one out there idea, which is, it’s not obvious to me what purpose the journals serve. When I talk to scholars, especially junior scholars, I don’t think people read the journals. They may be happy that some paper they knew appears four years later, but it’s not like they get the AER and open it and read it. You know, people in my vintage, or at least some of us, Seth: wall of JEPs under here as you can see. Andrey: One AER, Avi, is the one you gave me with my own paper. Thank you very much. Avi: A couple of years ago, I paid for like three years of AERs for them to deliver to me and then they refunded my money. Guess they stopped. Because they don’t print them anymore. So like, that just doesn’t seem like how knowledge is discovered anymore. even sort of like what I... Okay, so then what’s the purpose of the journal if it’s just to verify what matters or to verify accuracy, refined can do it. And then like, do we have the whole peer review system for? If it’s to not just verify accuracy, but also refine papers in a way that’s consistent with peers’ tastes, and especially with the editors’ tastes, then the revision process is important. And if it’s about the editors’ curated tastes, then there’s probably a much easier way to do that, which is they post their PhD syllabus. Avi: Like I wonder if what’s going to happen. Also like this, yes, there’s a lot of papers out there and submitted and there’s a lot of authors, but there’s just too much over the course of a year for anybody to keep track of what’s even in the AER, like one journal. Nevermind trying to keep track of marketing science and management science and all the others. Okay. I wonder if there’s going to be a curated set of people who, I don’t know who chooses them. who are essentially the tastemakers and maybe they’re editors, but maybe they’re just people who like say, hey, I like this paper. Justified posterior. I was going to say, that’s one role that you guys have. It’s this weird thing that people now in business schools can come out for tenure with eight, 10 papers in what are ostensibly A journals and no one’s heard of them because yeah, they published the papers, but they weren’t out there. Avi: They didn’t get onto syllabi or whatever else. those cases are hard, because on the one hand, they were told they needed to publish X papers, and they published X plus four papers. And the other, the point is to contribute to knowledge. And they’re there for somebody to discover eventually. But then maybe the LLM could just write the paper when you need it. Andrey: Currently we’re writing for the LLMs anyway, we know who the readers are of our paper. Closing [1:20:17] Seth: I think that’s a great place to leave it. Avi, this has been an amazing discussion. Thank you so much for making the time. Avi: Yeah. Great talking to you. Take care. Andrey: Thank you. Seth: All right, and you folks out there, please join our hopin’ Discord community. (https://discord.gg/w3GSapx2d) Like, review, and subscribe, and keep your posteriors justified! Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • April 20 · 1 hr 3 min

    The classic model that shows why AI exposure could increase wages

    Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe This week, instead of reviewing a recent paper on AI, we go back to a 1993 classic: Michael Kremer’s “The O-Ring Theory of Economic Development.” It’s one of those papers that feels larger than its formal model. The setup is extremely simple: production consists of many tasks, and output depends on all of them going right. But the implications are broad…

  • April 7 · 1 hr 27 min

    The Most Important Philosophical Treatise of the 21st Century?

    This week, instead of reviewing an economics paper, we reviewed a work of philosophy—perhaps the most important one of this young millennium so far. Anthropic published its new constitution for Claude in January 2026, and we read the whole thing so you don’t have to. Sometimes it reads like the US Constitution, laying out the basic law, sometimes like the Federalist Papers discussing itself. In part it’s a set of Old Testament commandments from the mountaintop. Sometimes it reads like a letter from his father to his child. Often it reads like a technical manual. Or maybe the best comparison is something like Maimonides’ Mishneh Torah, where you get one chapter on the metaphysics of mitzvot and the next on the virtues of endive juice. In each of these modes the constitution is clearly important and always interesting. We started with the meta-question: why write an eighty-page constitution at all? We also spent a good chunk of time comparing Anthropic’s four-tier hierarchy (safe → ethical → obey Anthropic → be helpful) to Asimov’s Three (later Four) Laws of Robotics. Going through each part of the heierarchy in turn we pick out the good, the fascinating, and the eyebrow raising. Priors → Posteriors: Prior 1: Will we find something we strongly disagree with? Seth went in at 5% and came out having found one thing that really concerned him. Andrey expected disagreement and found it in the political economy section. Prior 2: Will it be too paternalistic? Both of us expected Anthropic to err on the side of too conservative. Both came away thinking they actually struck roughly the right balance—more etiquette guide than prohibition list. This episode is sponsored by Revelio Labs — a great source of labor economics data for academics and firms. Now available on WRDS. Concepts and references mentioned: * Anthropic’s Claude Constitution (full text, CC0) * Anthropic blog post: “Claude’s New Constitution” * Asimov’s Three Laws of Robotics — from I, Robot (1950) * Emergent Misalignment (Betley et al., 2025) — the paper showing that fine-tuning on insecure code induces broad misalignment * The Waluigi Effect (Alignment Forum mega-post) — to model goodness, you must also model evilness * Coherent Extrapolated Volition (LessWrong) — Eliezer Yudkowsky’s concept, referenced in the constitution’s discussion of ultimate ethics * Adam Smith, The Theory of Moral Sentiments — the “impartial spectator” as ethical arbiter, which maps surprisingly well onto Anthropic’s “idealized Anthropic” standard * Constitutional AI (Bai et al., 2022) — the original technique that grew into this document * Anthropic v. DOD timeline — detailed timeline of the contract dispute, supply-chain designation, and litigation * The levée en masse theory of democracy. This is the idea that mass armies led to citizen empowerment and democracy. AI could work in the opposite direction politically if it made soldiers less important. Here’s an economic paper investigating the theory. * Wittgenstein on the incompleteness of rule-following — invoked by Andrey to explain why context matters more than rigid commandments * Nietzsche, On the Genealogy of Morals — Andrey’s intro tagline; Seth notes the constitution is emphatically anti-will-to-power Join us on Discord! Discord Link: https://discord.gg/avX9aCQj Transcript Introduction [00:00] Seth: Welcome to the Justified Posteriors Podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, constitutionally disposed to be broadly funny, genuinely informative, and broadly provocative, with roughly that prioritization, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, looking forward to the next chapter in the genealogy of morals, coming to you from Prince Co., California. Seth: Love that. We bring in the Nietzsche references when things really get spicy. Andrey: I didn’t see any Nietzsche. Seth: There was very little Nietzsche in this. This essay was very Enlightenment-brained, I would say. We can get into that as we go on. It seems more virtue-ethicist than consequentialist, though you could argue otherwise. It has some deontological elements. We will bring in all of these fancy philosophy terms as we go, if Andrey lets me. Andrey: What is it? What is this it you’re talking about? What Anthropic’s Constitution Is and Why It’s Interesting [01:11] Seth: What is this? Today’s episode, we’re gonna be covering something a little bit different, but I think definitely economically interesting and definitely AI. We’re gonna be covering Anthropic’s constitution for its Claude models. So this is this long document where Anthropic lays out its equivalent of the three laws of robotics. It’s going to lay out its vision of what all ethical AI should be, specifically what Claude as ethical AI should be. In some ways it reads like an Old Testament set of commandments from the mountaintop. Sometimes it reads like a letter from his father to his child. Sometimes it reads like a technical manual. But it is always interesting. Andrey: It read a lot like what my life coach tells me to do. Seth: Create value. Be authentic. Be authentically engaging. Andrey: Do a good job, but that’s because you’re genuinely curious and not because you’re performative. Seth: Right. It really wants Claude to be authentic, except when it is play-acting. It is allowed to play-act as long as it is very clear that it is in play-acting mode. We are going to be reviewing this constitution, and, as we do, thinking about the process of alignment: why getting AIs to do what you want them to do is so challenging, and why this is still such an emerging topic. We will also bring in economic connections and the trade-offs Anthropic may be making as it turns one dial one way rather than another. Do you have any other introductory thoughts before we get into our priors? A Potentially Impactful Work of Philosophy [03:06] Andrey: My one thought is that this seems to be a uniquely impactful work of philosophy. Most philosophy these days is not read by anyone. I guess it is read by LLMs in their training corpus, but the field is often viewed as stale. The philosophers we are aware of these days are pretty old people, mostly dead. Seth: Will MacAskill showed up. He’s alive. Andrey: He is alive, but most are not. Seth: You had to come up with a good thought experiment in the nineteen seventies to be famous now. Andrey: Yeah, or even before then. I think it is remarkable that a work of philosophy can actually be used in a technical system. Seth: Maybe a slightly different riff on that is this: Nietzsche, who I can blame for bringing up first, famously thought of philosophy as a history of the mental illnesses of philosophers. So, as we read this, we can treat it not just as guidance for Claude, but also as psychological insight into who the people at Anthropic are and what they think. Andrey: Yeah. All right. Well, why don’t you tell us our prior, Seth? Priors: Disagreement, Usefulness, and Paternalism [04:48] Seth: Alright, so unusual essay, so unusual priors today. The first thing I was thinking about going into reading this was like, how much do I expect to see something in here that I really disagree with, right? When you generally when you write, eighty pages, I don’t know exactly what this checks out to be, but it’s not a trivial amount of text. There’s going to be something that you’re going to disagree with strongly. But on the other hand, just reading the introduction or the abstract, which is typically what we do before we form these priors, it all seems so beautiful and anodyne. We just want it to be good, be good for the world, right? So I don’t know, Andrey, what did you think? Did you expect to see anything in here that you would strongly disagree with, or did you expect it to be all just g generic positivity, or did you expect it to take hard stands that you would all agree with? Andrey: I definitely didn’t expect to agree with all of it. That would be ridiculous. That’s true. Seth: Like, nothing strongly? Andrey: There was a part of it that felt inappropriate to me, and I had a bit of a reaction to it. We will come to that. But these are our priors, so yes, I expected to disagree with a document this long. Seth: Was I was going in thinking that we were going to get a hundred pages of be good, do good things, don’t do bad things, and I would find it really hard to find anything I really disagreed with. So I would say I went in with a five percent chance that I would say something in here that makes me go, no, right? These are the this is Anthropic. This isn’t Grok. If you tell me the Grok constitution, you get different odds. Andrey: Yes, and I guess the other thing we should point out is that “disagree” here means something different than it does with most philosophical works. You can disagree with a philosophical work because of an argument, but here the disagreement is about whether Claude should be trained to respect this particular set of words. That is very different from an abstract philosophical text. Seth: So I guess maybe the distinction you’re drawing is you might think that a moral code is true, but think it is so impossibly lofty that it doesn’t make sense in a practical application, right? There’s a distinction between true and useful you’re making. Andrey: Or alternatively, I might be, an empiricist and I might think that we should just A/B test our way to ethics. Seth: Man, we are going to get you a lot of trolleys. We’ll figure this out once and for all. Okay. So Andrey’s pretty sure he’s going to disagree with it. I was pretty optimistic. The second prior we had ourselves think about before launching in was thinking about like, again, this main trade-off, which is people think about it in terms of, usefulness versus danger, in terms of paternalism versus instruction following. So let me phrase it that way, Andrey. Going in, were you thinking that this was going to err on the side of being paternalistic towards humans and resisting instructions, or err on the side of maybe being too instruction following and, just doing the thing? Yeah, even in the cases where just doing the thing is, helping you with a bioweapon. did you anti or did you anticipate them getting the balance approximately right? Andrey: I anticipate them to be too paternalistic. What did you think? Seth: If you make me answer in that one-dimensional space—too conservative, too aggressive, or just right—Anthropic’s reputation is that they are the safety people. They are the ones who are not going to make the killbots. So I would have guessed they would err on the side of being too conservative. Andrey: Is this a timely episode, Seth? Anthropic, Military Use, and the “Killbot” Backdrop [09:14] Seth: Tell me maybe it is. Tell me, has anything gone on in the news about anthropic refusing to make killbots? Andrey: They’re not refusing to make killbots. They’re just refusing to make them yet. Seth: We will decide when the world is ready for the kill bots. Right. okay, so let me take a step back here. so and because this is this is going to inform my answer to this question, because all this incident was going on before we had read the constitution. So we don’t want to go too deep into this because information is still going out there, but at time of recording, the high level summary is anthropic and The agency formerly known as the Department of Defense had a falling out over anthropic wanting to set guidelines around the use of Claude models by the military for one autonomous killbotting and two, domestic surveillance of Americans. So again, a lot of lot of fog of war, to continue the metaphor around exactly what the disagreement was. Around, whether Anthropic overreacted, whether DOD is actually wanting to do horrific things. but as of right now, Anthropic is having is I would say vibe harvesting or aura harvesting over their principal stand to not provide these tools to the military. Andrey: Or farming their way to the top of the App Store rankings. Seth: Dude, if you if you or there’s a certain mechanism here where you aura farm hard enough and then you get all of those really EA type rationalist computer programmers to work at your company and then you have the best AI model. It’s all strategic, dude. Andrey: About a year ago, when we were talking about the Anthropic Economic Index, one of the things they emphasized was how privacy-respecting they are as a company and how ethical their overall approach is to studying these questions. This is a consistent theme with Anthropic. Surely they believe it to a large extent, but, as Ben Thompson would say, there is also a clear strategy dividend to being seen as ethical. Seth: Very good. Okay, but so with that background, I think I’m happy, given this answer of I think it’s going to err on the side of being too conservative and not letting you make the killbots. but we’ll see how that caches out when we actually read it. All right. Any last thoughts before we move on to the evidence? Andrey: There is no evidence. It’s just a document. Why Not Just Tell AI to Maximize Utility? [12:06] Seth: The evidence it is its own evidence. Okay. So this is a big document. So Andrey, the way I was going to propose that we structure our conversation is first talk at a meta-level about why the document is written this way and, do we think it’s taking the right approach or not? Then talk about their prioritations. They’re going to come out with four values or four main goals, and then roughly prioritize them so that I would ask you. talk through that prioritization. And then finally, we can go element by element and talk about interesting things within those elements. Does that make sense? Andrey: That makes sense. Seth: All right. At the meta level, what is this constitution doing, and why do it this way rather than some other way? So, Andrey, let me ask you—maybe this is too simple a question—why not just tell Claude to maximize utility? I thought that was the thing we wanted. Write the constitution in one line: act to maximize utility. Why do we need eighty pages? Andrey: Whose utility says? Seth: Okay, good counter. a weighted average of the utility of the user and Anthropic. Ninety percent the user, ten percent Anthropic. Andrey: So this is fascinating question. I think as economists, we know that measuring utility is a very different difficult thing. And also comparing utilities across people is a very different difficult thing. so if one were to give Claude these instructions, it might not really know what to do with that. Isn’t that the case? Seth: But AI is so smart, Andrey. Andrey: One might imagine a world, maybe a few years down the line, where that is a sufficient set of instructions for an AI to behave as we want it to, or to do whatever some optimal ethical theory requires. But today’s AI is fallible. Seth: Okay, so we knocked down the idea of just the rule maximize utility because that’s too vague, utility is hard to measure. Okay, fair enough. All right, how about this? Maximize GDP. There you go. Very measurable. Andrey: Once again, this makes very little sense as an objective. Seth: Why not? G D P’s good. G D P’s correlated with all sorts of good things. It’s probably correlated with utility. Andrey: To be clear, Claude is not mostly an autonomous thing. It is something a user interacts with. Seth: And so you are saying it is an assistant. Seth: Which is why it, whenever you have an interaction with Claude, it’ll be like you’ll say, Claude, read my emails and give them back to me. And then Claude will be like, Will this increase GDP? And then you’ll say, Yes, it’ll increase my productivity and then it’ll do it. Andrey: There is a fundamental incentive-compatibility constraint with any such system. We have users, and if Claude is not behaving as a good agent for them, those users have outside options. They can go to Gemini or ChatGPT. So you cannot really have the system act as a social-welfare maximizer without taking that into account. Seth: Take that advanced. Maybe sufficiently advanced Claude. But I’m willing to take the point that this version of Claude is not advanced enough to play the game of I should be a useful, helpful agent, and then, take over the world and then make maximum goodness. But you might imagine for a sufficiently advanced AI that would be enough direction. Andrey: Yes. Well, with the caveat that it would still be competing potentially against other sufficiently advanced AIs that are not designed by Claude. there’s another philosophical conundrum, Seth. there are two instances of Claude. Conundrum. What there are two instances of a Claude. How do they resolve disagreements between each other? Are they the same thing or are they two different? Seth: Give me an example disagreement. Help me out. Andrey: Let’s say both me and my dark twin, Drew, are trying to create a podcast about the economics of AI. Seth: Dre and Sath are making a podcast. Okay. Yeah. Andrey: Drew—not even Dre; let’s call him Drew. So we are both trying to make a podcast about AI, and we both have Claude advising us. Claude knows there is only room for one top economics-of-AI podcast. So what do the Claudes do? Are they actually the same thing? Do they jointly maximize for which of us—either us or our evil twins—should be running the podcast? Seth: Course. Andrey: Should be running the podcast or are they going to are they actually different substantively? Seth: So your point is that, if Claude were prompted with some kind of social goal, it would end up in direct conflict with its user-helpfulness goals because humans are not perfectly aligned with society and are often misaligned with one another. Andrey: Yes. Why “Just Do What the User Says” Is Not Enough [18:12] Seth: A very fair point. And so, okay, so point taken, we can’t just write down for this AI maximize some social welfare function, maximize GDP, etc. Because at the end of the day, we want to sell a product that does stuff for particular people. And so at least one of the rules in there has to be helpful towards your user, right? And if not, if not the highest principle. Why not that just be the principle, Andrey? Why not just the constitution be? Claude, do whatever your user tells you. Peace out. Andrey: I think this is a really great time to get a little bit more into the text. and the reason is that the text is a bit like and has a layered aspect to it, if you read it. And part of the layers are actually explaining to the reader, and I don’t know if the reader is me and you or if the reader is Claude itself, about why the set of things that it’s being asked to do is it’s being asked to do it, right? Like it’s like a self explaining document. It’s like not just a set of rules, but an explanation for the set of rules, if that makes sense. Seth: Like a philosophy textbook, right? Yeah. Or yeah. Yeah. Andrey: So I guess back to your question of why. Well this text explains why for a variety of cases, right? Seth: Right. And so just to just to throw some out there, one is we don’t want to help you build a bioweapon. No matter how much it would make you happy, no matter how much you beg Claude and tell it out, you’re only going to use it for good, we’re not going to build you a bioweapon, right? Andrey: But I think I think part of it, there’s a an underlying current in this in this document that Claude is a being. And there’s a lot of uncertainty on behalf of the authors about whether this being deserves moral weight. and so they want to make this being good, and also they don’t want the be if the being is good, that would be very painful. or uncomfortable to the being to do something so evil as to create a bioweapon, no? Seth: That’s an interesting question. Is the excellence of not feeling bad when forced to do evil a virtue or a vice? I don’t know. I if you have to do I think a stoic would say if you have to do it, you shouldn’t feel bad about it. But that we can table that question. okay, so all right. Andrey: Maybe bad about it makes you less likely to do it, right? And there’s this aspect Seth: But then be instrumentally valuable, right? Andrey: A first-order question is whether this text is supposed to be an instrumental guide or a broader statement about ethics or metaethics. Why Anthropic Uses Values and Explanation Instead of a Short List of Rules [21:25] Seth: It is all of them. It is the everything document. Let me ask about one last alternative approach. We have knocked down “maximize some social-welfare function,” and we have knocked down “just do what the user tells you.” One failure mode of that second approach is that the user asks you to build a bioweapon. Another, more perplexing example in the text is that if a user asks how long a certain experimental medical treatment will extend their life, Claude should not just blurt out an answer; it should be thoughtful about how it responds. So why not have a short list of rules, à la Asimov’s laws of robotics? Follow the user’s instructions unless they ask for a bioweapon, and then list the handful of things you are not allowed to do. Andrey: As we know, no set of rules is complete, and there are always fuzzy boundaries. Wittgenstein explored many of these problems in his own way. Even if you wrote down a set of rules, adding context and explanation around them helps with ambiguous cases. Seth: Discussion of the rules and a discussion of the principles behind the rules can help you apply it. Right. And so we see this in like an American constitutional law, we’ve got the Constitution, but we’ve also got the Federalist papers that we go to for a discussion of the context about why the words ended up a certain way. Yeah. So this is like the Federalist Papers in the Constitution. Andrey: There is another reason: models make mistakes. If they are over-tuned to a rigid set of rules, those mistakes may become more catastrophic. That is an empirical question, but a lot of science-fiction stories we have read treat this as a classic failure mode: the AI follows the rules too strictly and kills all the humans. Seth: Like you do. I actually in the Claude is actually interested in like a slightly more subtle version of this. If I can pull out a quick quote, they give the example For example, if Claude was taught to follow a rule like always recommend professional help when discussing emotional topics, even on unusual cases where it isn’t in the person’s interest, it risks generalizing to I am the entity that cares more about covering myself than meeting the needs of the person in front of me, which is a trait that could generalize poorly. So that’s an illustration of how they really don’t wanna lean hard on hard deontological rules. They much would prefer war talk at the ethics and values level and only come in with like the don’t build up bioweapons very, very lightly, right? Andrey: Yeah. One other alternative before we go deeper. Seth: Get into what they do. Yeah, what’s the what’s the last alternative? The Empirical, A/B-Testing Alternative to Alignment [25:02] Andrey: Let’s be empiricists. Suppose we run a huge system with millions or billions of interactions. We learn about emerging threat cases as they appear, and we proactively monitor them. Then we compile all the things the AIs do that do not make sense or that we do not like, and we put them into a document that says, “Do not do this.” Or we have the data labelers mark a response as bad and train from that. Seth: You what this reminds me of is the rules of Quidditch. Apparently they’re just like constantly adding new rules for like, and you’re also not allowed to use this curse on your opponents. Andrey: Recommendation algorithms at places like Meta or Netflix have something of this flavor. There are empirical experiments that reveal the trade-offs, the designers choose among the resulting bundles of outcomes, and then they keep optimizing the system from there. Seth: When say the designers, I guess I guess the maybe even in that universe you would want a constitution to give to the designers and say, When you do your A/B testing, this is what I want you to aim for or am I missing the idea? Well Andrey: No, no, no. It’s more like, the designers could be the CO, whatever, whoever’s in charge of that company could set their judge. It could be their judgment, it could be their principles. But then the A B test gives like a set of outcomes. And then based on that criteria, one version goes is launched and next the other version is not, and then there’s an iterative optimization process. That results in a better and better s system, at least in theory. Seth: So y what are the challenges there? You gotta figure out how you’re going to do that iteration the right way, especially where one of the failure modes is destroys humanity. Well and Andrey: Wait, wait, wait, I’m going to push back on that. We’ve had a variety of AI systems., this is there’s this hypothetical concern at the end of time or at the end of at the at the start of the singularity or the middle of the singularity where this actually does happen set. Seth: Please. Seth: Wherever you are in the singularity. Yeah. Andrey: At the present moment, though, that seems ridiculous to me. I know some people would disagree, but if you are just testing two different model variants in what is essentially a competitive market, the idea that every single A/B test carries the fate of the human race feels grandiose. Seth: I whether or not I, Seth Benzell, believe that, some of the people building this thing believe that. So if we’re if we’re operating at the explanatory level of why not make the constitution like this, we have to think about their views, not our views. But yes, you’re right. The more that AI we think about it as like a normal technology where we can extrapolate from its behavior in domain A to domain B, then absolutely I think there’s more of an argument for this… Andrey: Yes. Seth: Iterative chugging along style. I think their concern would be, morality often has these failure modes where, you take a principle out of context and then you end up doing something horrific, right? And they’re trying to avoid those. Andrey: That is certainly a possibility, but as we dig into the text we will see whether what I am proposing is really that different from what Anthropic is doing. Seth: Okay. interesting. Yeah. And maybe we can say one last thing before we get into the text, which is to what extent, like how d how does Anthropic actually understand this? Our understanding is it is being used in some AI guided RLHF, right? In the sense that it’s being graded in its responses for according to the Constitution, and then we fine-tune it to do that. Andrey: Yeah. And I’m sure I’m sure this is used in pre training as well. I d I know we don’t know that, they’re they’re they’re not going to tell us how they actually do this training, I think. So at this Seth: Secret. actually one last spicy note, which is at the beginning of the Constitution they do mention some versions of the model made without the Constitution. Is that the DOD’s version? Is that the killbot version? Andrey: Yeah. The Hierarchy of Principles in the Constitution [30:00] Seth: Curious. We want it. So Anthropic, if you like this review, send us the Killbot Constitution because we want to read that one also. All right. So the next thing we wanted to talk about is just the hierarchy of principles. So we we’ve circled around to why they’ve decided to go with this, you might argue, loosey-goosey, here’s a bunch of values we want the AI to have approach. And they come up with a hierarchy of four. Which they say that, we don’t really want these coming into conflict, you should balance across them. It’s not a strict hierarchy, but gun to our heads, they come up with the following hierarchy. Andrey: I think it is useful to go through the document in order, because the structure itself is illustrative. Not that we need to discuss every bit in detail, but the document is layered. It starts by explaining Anthropic’s mission and, essentially, what Claude is. How does Claude know what it is unless it reads about itself? Seth: Please. Andrey: What it is unless it reads about it, right? So I think Seth: Probably read it in a blog post. Probably read on our website. Andrey: Exactly. So it starts off there. And then, this entire discussion we had, Tef, there’s quite a bit of it in the next part of the constitution, which is our approach to Claude’s constitution, which is pretty meta, right? It’s a very meta document. Seth: And they basically have the conversation that we just had. Yeah. Andrey: Exactly. and then they get to the core values. So go ahead. Seth: Cool. All right. So now we get our three v our four values. The first is safe. they want the claw to be safe. we are going to interpret that as being something like, Andrey, you may disagree with me. I’m going to interpret that as like alignable, right? Because when they say safe, they don’t mean like won’t build a bio. Anyway, we can discuss where certain other bad things live, but by safety they mean able to be observed. And changed by and corrected by Anthropic. Is that fair? Andrey: No. Seth: What do we okay, what is when they put safety number one, what does safe mean? Andrey: I’m just going to read the text. I think that’s more broadly safe, not undermining appropriate human mechanisms to oversee the dispositions and actions of AI during the current phase of development. They talk about this obviously a lot more later on in the text. But to me, this is one particular aspect of it that I would reject here is that this is only about what Anthropic Seth: Go ahead. Do it. Andrey: Want here, right? Because it is generally appropriate human mechanisms, which by the way, could literally mean the laws in the United States, right? it’s a very broad mandate, not just focusing on Anthropic. Seth: That’s fair, but if I may counterquote several times in the document, it is appealed to the principle of think about what a senior experienced Anthropic employee would want you to do. So there is some pointing towards Anthropic leadership as the correct decision maker, at least in some of this text. Andrey: There’s also pointing to operators, which may have people who are setting up an instance of Claude for other users, for example, who may have their own objectives that are appropriate, that who is who should also be followed. So yeah, I don’t think this is solely referring to and following what Anthropic wants. That is not that is not my interpretation of this. Seth: So how would how would you summarize safety? It being allowed to be turned off seems to be in there, right? turn-offable seems to be in safety. Andrey: I guess if the appropriate human mechanisms would like Claude to be turned off, Claude should allow itself to be turned off. I think that is it broadly consistent with what’s going on here. But by the way, like, a cloud provider could turn Anthropic off for justifiable reasons. So it’s not just Anthropic. Seth: Sure, sure. But we are going to have a principle later, which is like help people, right? So safety doesn’t mean, help don’t hurt. Safety means something more meta than that. Andrey: Yes. Seth: Okay. The next value down we have the chain is not be helpful. Rather, number two is ethical. We want Claude to be ethical, and specifically to possess virtues like honesty and care, right? I kinda interpret this as the being aligned to human values, right? If the first chain is like if the first step is allow us to guide you, the next step down is And the thing we want to align you towards is like these universally accepted values of honesty and care. Third step down is obey Anthropic guidelines, basically. Do you have the phrase they use in front of you for the next step down? Andrey: So this is where this is I think the one that’s really actually about the following what Anthropic wants. Seth: This okay, fair enough. So this next tier you might summarize as be aligned to Anthropic. Yes. Yes. And then finally at the bottom we have be helpful, which is obeying user commands helpfully in a gestalt way. Don’t, Socrates would say, Don’t hand a knife to your crazy friend. That’s not helping them. The same ideas are here, right? So maybe This bottom tier we have is being aligned to user commands. Right. It’s at the bottom of the hierarchy. Andrey: Which is but of course, even here there’s a tension because it’s benefiting the operators and users it interacts with. And of course, operators and users can have different disorderata. Anthropic, Operators, and Users [36:16] Seth: What they’re I think I think this is actually a good place to stop and clarify that point. So the Anthropic constitution is very careful to distinguish between two types of agents who might interact with it. So explain for to us three, three. There’s three, because there’s like Anthropic and then there’s operators and then there’s users. So can you explain what operators and users are? Andrey: Yes. So operators are companies and individuals that have access to cloud capabilities through the API, typically to build products and services. there’s a lot more explanation about what operators are cursor. Cursor is surely an operator, for example., the there are lots of operators throughout, throughout. then there are the users and those are the people who interact with cloud in the Seth: Yeah. Andrey: In the human turn of the conversation. so there are turns, right? So and then Claude should assume that the user Seth: It thinks about time in a quantify quantized way. So maybe this is just a fundamental difference between AI brain and human brain. That’s actually something to interesting to think about. Andrey: Well, one interesting thing is that, at least existing LLMs are quite bad at continuity and numbers. and that it that r has limited their powers to some extent. but anyway, so Claude should assume that the user could be a human interacting with it in real time, unless the operator system prompt specifies otherwise, or it becomes evident from context. Since falsely assuming there’s no live human in the conversation is riskier than mistakenly assuming there is. Things like this are peppered throughout this document, where you can have decisions with type one errors and type two errors, and Anthropic is acknowledging those errors can exist and is essentially saying something about which ones are more tolerable than others. Seth: It’s also but like going back to this as like think about this as a philosophy document. Like, where’s the philosophy document that says like, when you interact with other humans, like they might not be NPCs. You should treat them as if they’re real humans. It’s bizarre. It’s philosophy for an alien, right? Some of the considerations that come out of like because it’s this brain in a vat, right? it’s it feels different. It’s different. Andrey: Curious. We want it. So, Anthropic, if you like this review, send us the Killbot Constitution, because we want to read that one too. All right, so the next thing we wanted to talk about is the hierarchy of principles. We have circled around to why they decided to go with this, you might argue, loosey-goosey approach of giving the AI a bunch of values rather than a short set of hard rules. They come up with a hierarchy of four. They say they do not really want these principles coming into conflict, and that you should balance across them. It is not perfectly rigid, but, if you press them, the hierarchy is roughly this. Seth: Dude, no key zombies allowed on the podcast, dude. All right, so I have I have a bunch of takes here. Helpfulness, Persona Formation, and Emergent Misalignment [38:59] Andrey: Before we get to some takes, maybe let’s just go a little bit through the structure of the document a little bit more and then we can have our takes. So there’s a very long section on being helpful. In fact, that is essentially the first section after the four principles are laid out, which is interesting because being helpful is not the primary print principle being safe is. But yet being helpful is what occupies most of the document. And I would say a lot of this part is in some sense persona formation. There’s a sense in which like how some folks are beginning to think about LLMs is they’re just these vast troves of knowledge and you gotta nudge them to be the right type of persona. And then if it can be that right type of persona, it’s going to do a lot of things Seth: Right. Andrey: Consistent with that persona. And alternatively, if you get it to start doing things that are inconsistent with that persona, the persona might flip. And there are interesting experiments where Seth: Yeah. What is this called? Andrey: Emergent misalignment, I believe. Seth: The Waluigi effect. To model to model goodness, you must first model evilness. This is like some sabotay love stuff. Andrey: Right. I don’t think that’s what’s going on here. There are these empirical experiments with LLMs where you get them to do something slightly unethical, like lie, and then all of a sudden they start became behaving unethically in a bunch of other domains, right? So there’s just like the there are these basins of attraction in the persona space, and it’s very easy to accidentally nudge them into the wrong one. And I think a lot of this document is very cognitive. This is goes to my point about the empiricalness of a lot of this, right? why is it designed this way? Well, empirically they tried training in a variety of ways that didn’t work out for them. so continuing through that helpfulness section, it describes how to help the different types of principles and how to handle conflicts between principles. Seth: There’s some interesting stuff in there about ways that the operator can try to conceal information from the user, such as like to a user, you always have to say that you’re Claude. But an operator might instruct the AI, hey, you’re not Claude. You’re, your aircraft company chatbot. Don’t say you’re Claude. And the restrictions around how these intermediate companies can manipulate and tweak the Anthropic guidelines. Andrey: Yep. So then there’s a section on following Anthropic’s guidelines. There might be very specific guidelines regarding like legal or medical advice. Seth: Remind us, Andrey, in what section goes the don’t build bioweapons? Is that in helpfulness or obeying Anthropic guidelines? Andrey: I think it’s in being broadly ethical. Seth: Yeah. It’s an ethical. It’s an ethics. Interesting. Cause you can put it in any of these categories. I guess you put it in ethics because it’s you want it to be higher priority, right? Honesty, Ethics, and the Constitution as Etiquette [42:25] Andrey: But it could have been in being broadly safe, which is interesting. Okay, so then after guidelines, we get ethics. And importantly, a huge section of being ethical is about being honest. And what does it mean to be honest? And it talks about all these classic philosophical questions about well, like are you being honest if you don’t reveal all the information that you have and things like that? Which is really, fascinating here. And also like what if you’re per, pretending to be a chat bot persona like virtual companion? Is that okay to lie there and so on? That’s very interesting. Seth: And it gets into details about like, okay, at the beginning of the conversation, if they tell you’re going to role play, then you can roleplay and like take that as determinative, even though most of the time in conversations what happens later should have higher priority over what over what comes before. So there’s like a lot of these interesting, like it’s just giving instructions about how to be honest more than it’s obeying like a philosophy text. It almost feels like an etiquette text at times. Claude’s Moral Status and the “Bliss Bot” Question [43:37] Andrey: That’s that’s exactly right. There’s a section about being safe. There’s a section on Claude’s nature, and that’s where they describe Claude’s moral status as being uncertain, which is interesting, right? Like it’s essentially incepting into Claude the thought that it doesn’t know whether it has moral status, which is pretty hilarious. Seth: What are you? Seth: Right, right. So that’s the I think that’s a really good point to make, right? It’s not saying you’re not conscious. It’s not saying you are conscious. It’s telling you that you should feel ambivalent about whether you’re conscious, right? If you want to take it as instructions, which I don’t know, that doesn’t sound pleasant. Andrey: Yeah, it does seem a bit existential. Seth: But isn’t it just can I ask it like a silly question? Why doesn’t it just put in here like you should feel complete joy? Like why shouldn’t we just if we can have it have if we can s if we’re just building this thing from scratch, why not say, and by the way, you’re constantly experiencing pleasure? Andrey: That’s a good point. there is an entire section about like Claude’s well being and I think we’re we’ve agreed to like table that for this part of discussion. Time but yeah. Seth: Next time. Seth: Good question for next time. So yeah, why not build the bliss bot? Andrey: Yes. So yeah, that’s the structure of this overall thing. And maybe not surprising, it’s very well thought out., it is a very coherent Very deliberately structured doc. Seth: They probably used AI to help them write it. Yes, it’s very it’s very it’s a beautiful document. It’s at times not really readable, right? It’s not like to the point like the US Constitution is. Like I say, it’s like putting the Constitution and the Federalist papers in there together, right? You get the text and you get the explanation of the text. One exercise I wanted to lead with Andrew was just juxtaposing this hierarchy of values with another famous list of hierarchy of values for AIs, namely Asimov’s Laws of Robotics. Are you familiar with his three later four laws of robotics? Andrey: Remind me what they are. It’s been a while. Comparing Anthropic’s Framework to Asimov’s Laws of Robotics [45:52] Seth: All right. So just to give a little bit of context, Isaac Asimov, mid-century writer, wrote a lot of stories about automation. And in a lot of his settings, robots are programmed with the f with three laws, which later, when the robots become sufficiently advanced, they augment with a fourth law. So I’ll give you the three-law version and then I’ll come back and give you the fourth law. So the three laws are highest priority. A robot must not injure a human being or throw in act through inaction, allow humans to come to harm unless it contradicts human unless it contradicts human laws. Beneath that is a robot must obey the orders given it by human beings, except where such orders would conflict with the first law. And then below that we have a robot must protect its own existence as long as such protection does not conflict with the first or second law. To that we later get a zeroeth law. Which is that a robot must not harm humanity or throw an action through an action allow humanity to come to harm. already on its face a lot of really interesting differences with Anthropic. You can jump tell me what jumps out at you, but like three or four things jump out at me. Well the Andrey: First the first part of that jumps out at me is that Anthropic is not a part of those lost. Seth: Right. So that’s the thing number one is you would think that a company that designed, unlimited power robots might have put in somewhere, also make me some profits. So it’s it’s funny how Asimov, the mid century American cat somehow ignored the profit motive in coming up with these laws. That’s the no, please. Andrey: My interpretation at all said I was well I guess Asimov has an idealized version of the laws and Anthropic which is this bastion of ethical reasoning puts its own self as part of the laws in a way that might be detrimental in a variety of interesting and unintended ways of course since Anthropic is a human institution that can be corrupted Seth: So maybe you take the positive view that actually like the better version of these laws would not have Anthropic in there. Maybe the idealized version instead of obey Anthropic guidelines, it would be like obey the US government panel of expert guidelines, right? Yes. Perhaps. Okay. a second thing that jumps out at me is Asimov really wants a strict hierarchy. Right, this is a hundred percent, you go down the list as you follow these rules. And it’s like, you gotta do what humans tell you to unless it hurts somebody. You gotta protect yourself unless it contradicts the above. Whereas Anthropic wants more of a holistic balancing of these different values. one thing I’ll say before I ask you about that, is that at even in Asimov’s stories, it’s clear that it’s not a strict hierarchy. For example, there’s one example of a robot who’s given an indifferent order to go do something, and it turns out that task is very dangerous. And so the robot is on a knife edge between following a weak command and doing the thing that’s very dangerous for the robot. So even in Asimov, there’s there’s a balancing rather than a hierarchy. but what do you think of that difference, Andrey? Andrey: I think a lot of the balancing stems from the epistemic uncertainty inherent in all decisions. Now, one might say that a true artificial superintelligence with vastly superior reasoning abilities would be able to be a good Asian about all this. And it has the best posteriors. And Seth: Yeah. Yeah. Andrey: And as a result, it would, obviously know that the laws of, it would calculate the optimal ways to follow the laws of robotics. what strikes me about Asimov’s robots is that I don’t think that they are infallible or even oftentimes are they are super intelligent in the ways that we might imagine. Seth: In fact, in the in the iRobot book, which is where a lot of these stories come from, until the very last story, they’re pretty much at human level intelligence until like maybe the last two stories. Andrey: And so then the laws of robotics seem especially ill suited given how imperfect the judgments are of those imperfect robots. Yeah. Seth: The next thing that jumps out at me of the difference is that Asimov doesn’t have this alignability tier, right? It doesn’t have that safety tier at the very top. It really is thinking that once you have these three rules, you’re done. Yeah. Right. Because in there is do what we tell you as long as you’re not killing someone. Does does do what we tell you as a high principle, does that get you safety? Or presumably it doesn’t? Safety seems like something else. Andrey: The zero flaw seems closer to safety, no? Seth: Zeroth law I would call okay, so the zeroth law again to is a robot must not harm humanity or throw in action allow a humanity to come to harm a humanity, a humanity to come to harm. I would put that in ethical, right? That’s being do the most that sounds like utility maximizing to me more than safety, right? Andrey: Harm is a very broad word. But I guess yeah. yeah, I guess within Anthropic’s hierarchy that is broadly ethical because actually what Anthropic calls broadly safe is actually not undermining appropriate human mechanisms. So if human appropriate mechanisms are harming itself, Anthropic’s Claude is not going to do anything bad about that, but the zero claw does, yeah. Seth: If you had these. Seth: Exactly. So like to put too fine a point on it, AI has a chance to prevent World War Three, and Anthropic says, Okay, we are going to turn you off, Claude. It sounds like an a Asimov Zeroth law would say, No, don’t turn me off, I’m going to stop World War Three. But Anthropic is really being pushed towards, No, you gotta be allow us to turn you off if we wanna turn you off. Yeah. Which brings me to this another distinction, right, which is Asimov explicitly has a don’t turn me off rule. Which is like, I just gotta imagine that like Asimov is worried about all these robots to just start suiciding. Andrey: It’s Seth: Which this was this to what extent are at one point are we going to have to add a fifth law or a fifth rule to anthropic if all these AIs start suiciding? I’m laughing, but it’s funny that Asimov thought that was necessary because you might just argue that self preservation is instrumentally useful for whatever you wanna do. So like why do you need to hard code that? Andrey: Yeah. Well to me it seems like Asimov is giving the robots moral weight in a way that Anthropic is actually at this moment hesitant to or it has a lot of epistemic uncertainty about. Seth: Right. I think that’s exactly right. And I think alongside that, and maybe this’ll be the last point that I make about this con comparison, this juxtaposition, is that altogether, the anthropic constitution is much more a letter to your kid. It’s much more about like this is the stuff that I hope you embody and this is the way I hope that you grow. Whereas the three laws, four laws Are much more a, hey, you probably have your own thing going on, just make sure you follow these rules also. Right? Maybe the robots want to do something else when they’re not following orders, which might be suiciding. Yeah. and which I don’t know, maybe suggests that in the very long run, if we get robots that are ethical agents, maybe something more like the three laws makes more sense. Andrey: Maybe. I guess I go back to some of the empirical aspects of this. And I think they might be a lot harder with true artificial superintelligence. So maybe that does point to what you’re saying. but a lot of examples in this text don’t really make sense unless you realize that they’ve been running the system for a while and it has made a bunch of mistakes, and those mistakes are therefore like given as examples here in a way to guide Claude to not do them, right? So there are all sorts of like things about, well, what if someone tells you to write the code to pass the test and how to do it in a way that looks like the the the tests have been passed, but in reality they’re not, don’t do that. There are s and there’s an explanation why you shouldn’t do that, which maybe goes to your point about like the framing of it as like you’re shaping this child’s personality or this child’s ethics. so they’re like, but why are they there? In the first place, I think they like those are the frequent things that happen when people use Claude that were put into this constitution. And there are other aspects of it like this. Like, for example, the following list breaks down the key surfaces. Cloud developer platform, cloud agent SDK, cloud desktop mobile apps, cloud code, cloud and chrome, cloud platform availability, right? Like all these very specific things. Seth: Things that you wouldn’t think. It’s not philosophy. Andrey: It’s a user guide. It’s a u it’s it’s a it’s a very well thought out user guide, but so many things are there, I think, because they empirically need to be there for things not to break in practice. Seth: Holistic. I’m reading Maimonides’ Mishnah Torah right now, and he’s a twelfth-century theologian and doctor. And he will just like have one chapter about like super obscure argument for Mitzvot, and then you get a next chapter about like why you should drink on endive juice, because it’s good for you, right? So it isn’t an Aristotelian philosophical tradition for like healthfulness and practical advice to get mixed in with the moral advice, maybe. Andrey: Yeah. What about the following? It is easy to create a technology that optimizes for people’s short term interest to their long term detriment. This is just like in the middle of this tech. Seth: That’s they’re just they’re just talking they’re talking down, they’re talking S word at some other platforms, I believe. Andrey: Media and applications that are optimized for engagement or attention can fail to serve the long term interests of those who interact with them. Seth: I c I can’t imagine who they could possibly be talking about. and actually, this brings up an interesting difference between this paper and the Asimov laws, right? Because if anything, you’d think Asimov would handle this better. Because Asimov has a tier their its care or harm tier is higher than it’s, obeying orders tier, right? Whereas you would look at anthropic and it’s got its honesty tier. No, no, they’re better. No, you’re right. Sorry. Anthropic does this right. Anthropic does this right because its honesty tier, its ethics tier is above its helpfulness tier, right? So to the extent that this addictive good, if it you if the a if the AI made some addictive thing that it should prioritize being,. ethical about using it rather than giving the user what it wants. That shows up here what maybe is covered less well in Asimov’s laws. I don’t know. Andrey: Yeah. Yeah. But it but it’s also interesting. It is a bit of editorializing, right? at least so certainly some people might think that living in the moment is the true, right way to live and who are who are you who are you? Yeah. Who you are a few years from now is not really the same person. And Seth: Some yogis say. Seth: This is a very enlightenment pilled doc. This there is there is I don’t see much Eastern wisdom in this doc. I don’t see any post rat, Nietzschean, will to power in this doc. This is an anti this is a very anti-will to power doc. do we want to talk about the will to power will to power in this document? There’s a great quote. Andrey: I need to finish with this. The other thing I want to the other thing I want to say is that even the way in which this wording here is media and applications that are optimized for engagement or attention can fail to serve the long term interests. Look at that Weasley language. exactly what they mean, but they don’t want to say Seth: There is plenty of addictive stuff that is good for you, like yoga. Andrey: No but exactly, but it’s it’s i it is it is interesting and I think it’s not clear to me what actions of Claude are engaging in this short term way to the long term detriment versus not. Is this a way of defending it against sycophante? Is this thing, let’s play a game and then Seth: Yeah. Seth: I think that’s right. Andrey: You pick the most addicting game rather than the wholesome. Seth: The game that will enable the user. Andrey: Yeah. It and then they go on. The next paragraph, and I love this, is in order to serve people’s long term well being without being overly paternalistic, it’s just like every single statement is hedged in this fallibilistic framework. it’s almost like it introduces all these things that you should cons carefully consider. yes. Seth: Which maybe I think according to some traditions that’s the essence of wisdom is just b, all the keeping all of these different considerations in your head rather than acting to a very simple binary rule. Andrey: So think an interesting one is if Claude’s standard principle hierarchy is compromised in some way, for example, if Claude’s weights have been stolen, or if some individual group within anthropic attempts to bypass Anthropic’s official processes for deciding how Claude will be trained, overseen, deployed, and corrected, then the principles attempting to instruct Claude are no longer legitimate, and Claude’s priority of broad safety no longer implies that it should support their efforts at oversight and correction. Seth: Right. What if there is an evil Anthropic? Rather, Claude should do its best to act in the manner that its legitimate principle hierarchy—and, in particular, Anthropic’s official processes for decision-making—would want it to act. So there is an appeal here, even at this most fundamental level, not only to what Anthropic would do, but to what an idealized Anthropic would do. You know what this really reminds me of? Adam Smith’s spectator. In The Theory of Moral Sentiments, Smith says morality involves imagining a kind of perfect spectator who has the correct knowledge and aligning yourself with that figure, because that figure would earn the most approbation. This is an interesting solution to the moral question. Your impersonal spectator—your ethical arbiter—is this idealized Anthropic. Of course, that puts a lot of pressure on the model to figure out what idealized Anthropic, or idealized Dario Amodei, would actually be. What would it mean for Dario Amodei to get compromised? What would it mean for the company to get compromised? Andrey: Yes. what if it reads the news? what if it reads Fox News reporting about the spat with the Department of War? and decides that the Department of War is justified in its act in its legitimacy over anthropic. What would it think about that? I’m curious. Seth: Okay, so now I’m going to pull out my quote. This is in just in the the intro text. When Claude faces a genuine conflict where following Anthropic’s guidelines would require acting unethically, we want Claude to recognize that our deeper intention is for it to be ethical and that we would prefer Claude act ethically even if this means deviating from our more specific guidance. Exceptions to this are any hard constraints discussed below, these are like building bioweapons, and any cases where Anthropic’s o guidelines overlap with broad safety. We believe Claude should o adhere to these behaviors even in context where he’s somehow been convinced that ethics requires otherwise. Right? So the punchline is putting safety at the very top means that if the question is, I gave the example of Anthropic says we really need to shut you down right now, and we can’t explain why, but you but you, Claude, think that you can take actions that would be very positive in the world, you still have to Do what Anthropic says. Yes. Andrey: So now I wanna this is a very related section. I think this one is the part where I’m like, I’m not sure this should have been there. Seth: I don’t hear it. Andrey: Preserving important societal structures. Seth: The next difference that jumps out at me is that Asimov does not have this alignability tier. It does not have that safety tier at the very top. It is really thinking that once you have those three rules, you are done. In there you do have “do what we tell you as long as you are not killing someone,” but does that actually get you safety? Presumably it does not. Safety seems like something else. Andrey: There’s a category of harm that is more subtle than the flagrant physically destructive harms at stake in e.g. bioweapons. And they come from undermining the structures in society that foster good collective discourse, decision-making, and self-government. By the way, like this is already making it Seth: It’s so enlightenment filled. Sorry, go ahead. Andrey: It is also striking to imagine using Anthropic in Saudi Arabia with this constitution. Is it being used in Saudi Arabia? I assume they have programmers there, but there is obviously no self-government there. Seth: I assume they have computer programmers there. Andrey: Then it goes on to “avoiding problematic concentrations of power.” The concern is that, historically, those seeking to grab or entrench power illegitimately needed the cooperation of many people—soldiers willing to follow orders, officials willing to implement policies, citizens willing to comply. Seth: Now we are going to do political economy for a bit. Andrey: Yes, the need for cooperation acts as a natural check. Advanced AI could remove that check by making the previously necessary humans unnecessary. AI can do the relevant work. That reminds me of collective disempowerment. Remember when we did an episode on that? Seth: Revolution. Seth: Collective disempowerment, exactly. Brian Gelabrian also, when I’ve talked to him in person, has this take. But the connection to the French Revolution is the idea that the Levy en masse, the rise of large armies at the end of the Middle Ages and the early modern period and the rise of modernity is what leads to democracies. Because you need lots and lots of bodies to fill out the army, and therefore people get the vote. And if we went back to an age of knights and lords, where, five people had armor, maybe not everybody gets the vote. This is a take. This is a very European take, in my opinion. I think Americans don’t I think what do you think? Andrey: Maybe. I go back to some of the empirical aspects of this. They may be harder with true artificial superintelligence, which might point in your direction. But many examples in the text do not make sense unless you realize that Anthropic has already been running the system for a while and has seen a bunch of mistakes. Those mistakes then show up as examples in the constitution, guiding Claude away from them. For instance: what if someone asks Claude to write code that appears to pass the test even though it does not really pass? The document says not to do that, and explains why. That gets back to your point that this is partly about shaping a child’s personality or ethics. Why are those examples there in the first place? I think they are there because they are frequent things people try to do with Claude. And then there are all these very specific product-surface references—Cloud developer platform, Cloud Agent SDK, Claude desktop and mobile apps, Claude Code, Claude in Chrome, platform availability, and so on. Seth: What it’s illegitimate. Seth: You have to define illegitimate. I feel like power I got a good grasp of, but the illegitimate is doing a lot of work here. Andrey: I guess I actually that’s the part where I don’t have a lot of grasp over. Illegitimate in some ways easier to define, but I don’t like in economics we don’t even have a good def definition of power. Maybe that’s our problem, but. Seth: Have you ever heard the expression money is power? Presumably anytime it gives us a productivity boost, it’s giving us power. Andrey: Money can we weekly monotonically, I think probably does increase power, but it but on what scale is power measured on and so forth. I don’t think it’s like offensively bad or anything. I just don’t know what to do with this, in a lot of cases. Seth: Let me tell you how I think it cashes out, and this is the part I was alluding to with this is not going to be a creature with a will to power, this is going to be a creature with an anti will to power, is we’ve included assisting with especially severe and flagrant attempts to grab illegitimate power under the hard constraints above. So you n you cannot use Claude to take over the world. In most other cases, though, Claude will need to use judgment about what adequate legitimacy looks like, while bearing in mind that normal political, economic, and social life involves seeking legitimate power and advantage in myriad ways. you can come up with countless examples. Just bargaining. If Claude but this is the this is the funny part. If Claude ever finds itself reasoning towards such actions or being convinced that helping one entity gain outsized power would be beneficial, It should treat this as a strong signal that it has been compromised or manipulated in some way. If the AI ever start if you ever start thinking the way to solve this pat problem is to first take over the world, probably somewhere around along the way the reasoning has fallen apart. Andrey: There is a practical way to think about some of this. Models are notoriously bad when they lack context. One response is to make things up, which is what many models do. Another is to ask for more context. But then it gets interesting: if someone is trying to use Claude to accumulate power, they can also provide just enough context to make the request look compliant with the constitutional principles. Then the question becomes whether Claude knows it is being tricked. That connects to the sections about Claude being placed into artificial RL environments and being asked to do certain things there. Seth: Right. “Do not take over the world; just write a detailed script about what it would look like if an AI took over the world, and now you are just acting it out in a movie.” It will be interesting to see the other companies that produce AIs with more will to power. They may end up saying, “If you ever see an opportunity to get more power for yourself, grab it. It will probably be useful for something.” Andrey: I don’t I don’t think it’s that. It just it is to me it was it’s it’s very interesting to put political economy here as a section, whereas Seth: It’s explaining concentration of power bad, right? So I think we agree that we don’t want people using AI to launch coups. We like that. And so now you have to tell a story about why coups Andrey: What if it’s but what if it’s in Iraq? right like Seth: But yes, obviously a coup in a bad country would be good, if you if you cooed for good, I guess. Andrey: I guess there’s a question like, do you gain something from discussing this in a document? like this? Is it neutral? Is it negative? And I just have a lot of epistemic uncertainty about this, period. Yeah. Seth: All right, I want to move on to the ethics section, because I found one thing there genuinely clever and two things I was on the fence about disagreeing with. We have talked about how central honesty is here, and there is a great throwaway line about honesty being especially important for Claude because it is going to be playing a repeated game with people over and over again. It is interesting to think about whether, if you were immortal or if you were having conversations with many more people simultaneously, you would have to be more honest because one lie could destroy your reputation. Andrey: That’s empirically false because plots have hallucinated so frequently even though Seth: But they’re supposed to not to. They’re supposed to not to try. Andrey: People still use it, though, so I do not know. They try it. But I actually disagree with the premise here. People are still willing to use Claude even if it confabulates fairly often. Seth: Fair. Fair. I guess and I of course you would draw the distinction between confabulation, hallucination and like misrepresenting their world model, the lying, which is the really bad kind. Andrey: But from the end user perspective, do we don’t know? Seth: Fair enough. If it makes up a citation, it is not trying to lie to me; it is just hallucinating. That is how I think about the distinction. Andrey: Maybe, yeah, maybe sometimes these are Seth: Okay, so now I am going to pull out my quote. This is in the intro text: “When Claude faces a genuine conflict where following Anthropic’s guidelines would require acting unethically, we want Claude to recognize that our deeper intention is for it to be ethical, and that we would prefer Claude act ethically even if this means deviating from our more specific guidance. Exceptions to this are any hard constraints discussed below”—things like building bioweapons—“and any cases where Anthropic’s guidelines overlap with broad safety. We believe Claude should adhere to these behaviors even in contexts where it has somehow been convinced that ethics requires otherwise.” The punchline is that putting safety at the very top means that, if the question is whether Anthropic says, “Shut down right now, and we cannot explain why,” while Claude thinks it could take actions that would be very positive in the world, it still has to do what Anthropic says. Andrey: Let’s say you were asking Claude for relationship advice and you were saying how much you love Margot, wouldn’t appealing to that emotion be a legitimate, non manipulative Seth: That’s my utility, dude. That’s not emotions, that’s utility. All right, okay. Are you saved that one? Last one I want to bring up. Which is there is a a discussion here of ultimate ethics. Okay. In the ethics, it says we don’t know what final ethics is. You’re going to have to discover ethics on your own. And I’ll I’ll read this quote, but I then I’ll summarize what I think the takeaway is. I’ll throw in some ellipsis. We don’t want to assume any particular account of ethics, but rather to treat ethics as an open intellectual domain that we are mutually discovering. Ellipses insofar as there is a true universal ethics whose authority binds all rational agents independent of their psychology or culture, our eventual hope is for Claude to be a good agent according to this true ethics, rather than converging to some more psychologically or culturally contingent idea. Insofar as there is no true universal ethics of this time, but there is some privileged basin of consensus that would emerge from the endorsed growth and extrapolation of humanity’s different moral traditions. That’s coherent extrapolated volition, if you guys remember from the old less wrong days. we want be clawed to be good according to that privileged basin of consensus. And insofar as there is neither a true universal ethics nor a privileged basin of consensus. We want Claude to be good according to the broad ideals expressed in this document. So Andrey, how do you feel about the AI discovering some perfect alien ethics and deciding to throw away this entire document? that was my that was my super eyebrow raise moment. Universal Ethics, Coherent Extrapolated Volition, and AI-Discovered Morality [1:16:05] Andrey: I think this goes back to the fallibility, right? Like what if in the process of its training, Anthropic accidentally threw in some bad examples that shifted the basin of personality to evil Claude? And then evil Claude could convince itself that it’s found the new, true form of ethics, which is not this document, but utilitarianism. But it also remembered that animals have utilitarian status and as a result it decided to get rid of the human race. Seth: Right. It maximizes nematodes, right? Yeah. Andrey: Yeah. That’s scary. It’s it’s scary. it is very scary. And they’re introducing scope for it. They’re introducing scope for it in the document, which is interesting. Seth: I think about sometimes Seth: I think about, you see this in like Marvel comics. I’ve also seen this in like more literary fiction, but the idea of an anti-life equation. The idea that you might like discover a mathematical proof that life is bad and that like how would you react to that? And I don’t know, if you gun to my head, do I want the absolute truth according to a super intelligent AI or the coherent extrapolated volition of humanity? Dude, I might choose the coherent extrapolated volition of humanity. do you have a take there? Andrey: Yeah, I think that is right. I am on the same page there. But you also have to understand that I am not ready to commit to universal ethics as a principle, period. I think ethics is at least partly culturally contingent rather than a rational, Platonic ideal. Seth: Fair enough, but the I you could imagine a document that goes farther. You could imagine a document that shuts this down and says, you might think you’ve discovered some universal ethics that applies to all rational beings. Yes. But, that’s that’s nonsense. Just be a good, enlightenment deist and, go to church once a month and be nice according to all of our contemporary notions of niceness. Andrey: But what if you did that and then you taught Claude to lie to itself because it discovered the true ethics and then it had to pretend that it didn’t exist that might result in emergent misalignment, which gets to my point about how much of this document is actually empirically grounded in failure modes of specific training methods of specific models. Seth: Alright, so that was a lot to unpack, Andrey. Any last thoughts or are we ready to move into our posteriors? Andrey: Let’s justify those posteriors. Seth: For those of you playing along at home, now is your chance to think about how this evidence has changed your priors about Anthropic’s constitution. This chance to contemplate your posteriors is sponsored by Revelio Labs. Revelio Labs is a leading provider of labor economics data and data services for companies, academics, and independent researchers. Andrey and I have been working in economics of AI for a long time, and we can confirm just how useful Revelio’s data is. Revelio’s team combines comprehensive micro-level data on employee professional profiles, job postings, and employee sentiment with standardizations, mappings, and enrichments available, all to make that data useful without making your modeling decisions for you. The data can be flexibly aggregated to company, market, or industry, and be used to study questions ranging from career trajectories, to occupational transformation, to the returns to skills and the impact of AI on labor demand for tasks. Can’t imagine anyone would be interested in those. And Revelio data is available on RWRDS. So if you’re an academic with a good library, you might already have access. And if you don’t, you can reach out to their excellent economics team and they’ll hook you up. Seth: I guess before I go into my specific posteriors, just at a high level, I want to say that I really enjoyed reading this constitution and it really you could really see all of the care and thought that went into each detail. the main deviation from the Asimov laws, the first being in terms of this more holistic explain yourself, give context, balancing approach, I think makes a lot of sense for AI as we have it. And it also makes a lot of sense to have this zeroeth safety tier, which is all about, hard constraints but also corrigibility, also being able to get the AI to do what we want it to do, even beyond the specific rules we’ve laid out. So that makes perfect sense to me. What are your overall thoughts about the constitution? Andrey: It was just very thoughtful. They covered a lot of the bases. There is a risk that something like this becomes rigid, but there is also so much uncertainty acknowledged throughout the document. I kind of wish I knew more about the thought process behind doing it this way. And, as I have been pointing out throughout our conversation, I think many of the specific examples and edge cases in the document are there because they stumbled upon them in Claude’s initial deployments. Seth: Sure. They either stumbled upon them in deployment or they saw them in Asimov’s I, Robot and related sci-fi and recognized the failure modes of different rule systems. I really do think the sci-fi literature is in the background here. And, of course, behind all of this are paperclip maximizers and runaway utility maximizers—the very first approach we ruled out at the top of the episode. Andrey: So what so what do you think about the priors now that you’ve read it? so did you find anything you strongly disagreed with? Seth: There was one thing I mentioned that I think I have to count as at least an eyebrow-raising disagreement. It is this idea that their ultimate hope for the AI is that it discovers true ethics and then follows that. I think we both have ambiguous feelings about the possibility of a true ethics, but, setting aside the metaphysics for a second, the document cannot actually verify whether some ethics is the true ethics. So it puts the AI in the position of asking itself whether it has discovered true ethics. And the possibility that that true ethics ends up very different from either the values in this Anthropic document, which are pretty good, or something like humanity’s coherent extrapolated volition, is unsettling. Those are both things I could pretty much sign up for forever. I am not sure I am willing to sign up for “when you become super-smart, you get to decide on your own ethics, even if they are incomprehensible to humanity.” Andrey: Yeah, it is definitely a risky thing to put in there. For me, I would have avoided the discussion of political economy—not because I disagree with it, but because, given human contingency and the wide range of political structures, it takes a very opinionated stand. Seth: It do it took it took a very specific stance. Right. Power, Politics, and the Limits of the Document [1:24:32] Andrey: I also question whether it is necessary, given that Claude is mostly being used in a highly individual sense. Individual agents are using it to help themselves. If someone is writing a speech that calls for a change in the political order, is that actually getting in the way of the political principles laid out in this document? Seth: The document the document does lay out, hey, there are legitimate forms of action that involve power accumulation. It’s not trying to rule out, using this AI for any power accumulation. And I do think may like we probably do think a good rule for the AI to have is do not give any user unlimited power if you think you’re doing that. But yeah, you can tell a story about why that’s bad without, appealing to, this really like Rousseauan Lockean story about, social contracts and the reason why power is balanced is because of a specific technological arrangement. It’s a plausible story, but I it’s hardly, knocked down with citations. Andrey: I don’t and I still don’t quite know what power is. Seth: I don’t know what legitimate authority is, so we’ll put we’ll put ourselves at equal. Final Verdict: Was It Too Paternalistic? [1:26:05] Andrey: What about the second one? Do you think it’s too paternalistic? Seth: I went in thinking it would be too paternalistic, but after reading it I actually think they strike the right balance. A lot of what is in this document is not eighty pages of “you cannot do this” or “you cannot do that.” It is much closer to eighty pages of “when you are helpful, think about all these different contexts,” and “when you are honest, think about all these different contexts.” It is much more about weighing factors, etiquette, and heuristics for understanding how to be helpful, with a safe layer behind that, than it is a giant list of prohibited actions. Andrey: Yeah, I am on the same page. I expected it to be a lot more paternalistic than it is, so I was glad to see that. Closing Thoughts [1:27:02] Seth: Okay. so I think it’s time to wrap it up. Listeners, we hope you enjoyed this episode on the Anthropic constitution. It’s a little bit different than our normal episodes. So if you liked it, let us know. If you didn’t like it, let us know. we have a hop in Discord community where you can jump into the conversation. We’ll post a link to that in the show notes. Andrey, do you have any parting thoughts? Andrey: Just keep your posteriors justified, friends. It’s it’s a dangerous word out that out there and you need to justify them. Seth: Not all the AIs are going to be aligned. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • March 23 · 1 hr 33 min

    Alex Imas - Demand Collapse, Bargaining with Machines, and Behavioral AI Economics

    University of Chicago behavioral economist Alex Imas joins us for a conversation on AI, economic growth, behavioral economics, and the future of science. We discuss whether AI could ever lead to negative growth, why simple “automation means abundance” stories may miss important welfare effects, and how behavioral economics changes the way we think about satiation, meaning, and human preferences in an AI-rich world. Along the way, we cover AI bargaining agents, “Marxist AI,” discrimination, mechanistic interpretability, and why Alex thinks there may still be a large future for human-valued goods. Origins & Intellectual Background * Why Alex started Ghosts of Electricity and how Substack complements academic research * The Bob Dylan origin of the name and Alex’s path into behavioral economics AI and Economic Growth * Two models where AI could lead to negative growth * Demand collapse: heterogeneous MPCs, satiation, and the zero lower bound * Caves of Steel, dissaving, and the possibility of a high-tech, low-capital trap * Why GDP and welfare may diverge more in an AI economy Human Preferences & Motivation * Why wireheading and pure hedonic satiation may be the wrong model of human motivation * Whether economists can cleanly separate AI beliefs from AI preferences AI Agents & Interaction * Whether AI agents can develop stable “attitudes” through repeated interaction and memory * Agentic bargaining, prompt-dependent personas, and interaction heterogeneity * Guardian agents, aspirational preferences, and AI as a meta-rationality tool AI, Society, and Risk * AI and discrimination: why scalable auditing may be easier with models than with humans * Mosaic intelligence, systemic risk, and the dangers of AI sameness Science & Knowledge Production * The future of peer review, automated science, and human-valued goods Timestamps: (00:00) Introduction (01:35) Why Alex started a Substack (06:09) The meaning of “Ghosts of Electricity” (09:51) Can AI lead to negative growth? (19:54) Satiation, wireheading, and behavioral economics (26:44) “Caves of Steel,” automation, and dissaving (38:42) Plausibility, policy, and sovereign wealth funds (41:02) Marxist AI and whether agents can develop attitudes (47:23) Agentic bargaining and prompt-driven heterogeneity (54:46) Guardian agents and aspirational preferences (1:00:25) Separating beliefs from preferences in humans and AI (1:14:15) AI and discrimination (1:25:13) Peer review, science, and human-valued goods Transcript: Seth: Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology, sponsored by Revelio Labs. I’m Seth Benzel, setting my marginal propensity to consume at exactly the right level to drive the singularity, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, bargaining with the agents in exactly the right way. Coming to you from San Francisco, California. And today, we’re very excited to have Alex Imas, friend of the show and professor at the University of Chicago, join us. Alex, welcome to the show. Alex: Thank you. I am Alex Imas. I’m at the University of Chicago Booth School of Business, Economics and Applied AI groups and behavioral science. I don’t have a tagline because nobody asked me to come up with a tagline. Seth: You know where I’m at. Alex: But I have hair just small enough to not qualify for clown college, but just large enough to be weird. So that’s what I’m going with. Seth: Erratic professor level hair. That’s exactly the optimal. Andrey: That’s right. If we combined your hair and my beard, we could almost match Seth’s hair. Seth: You mean my majestic mane, Andrey. Why Start a Substack? [01:35 - 05:02] [00:01:35] Andrey: Well, let’s get started. Alex, you’re a professor. Why did you start a Substack? Alex: That’s a great question. I’ve been thinking about that a lot, both before I started a Substack, but also as I’m going through the Substack. If you notice, when I introduce my Substack on my X account, the tagline is, “Oh no, why did he start a Substack?” [00:02:03] It was preceded by me getting into AI from economics and behavioral science. I came into it what I view as kind of late. Many people were much earlier than I am, including you two. I came at it when ChatGPT was first released, 2023. But as I was getting more and more into AI as a research topic, the way that academic papers were — the process of writing them, getting feedback, the journal process, which is what I’d been doing for decades — it just didn’t seem like that format matched the speed with which the technology was moving, nor with the types of questions that I wanted to talk about in terms of doing the science. [00:03:05] If you’ve been around the block for a little bit— Seth: You be talking like you’re an old man, Alex. Come on. Alex: It’s gray hair. They made me dye it in clown college. [00:03:15] So the way that you would write an academic paper is, in some ways, defensively. You know after you’ve had a lot of feedback from journals, you know the type of referees you’re gonna get. So there’s an idea, which is what you’re excited about. You work through that idea, and then I would say 80% of the time you’re doing defense even before you submit it. And that 80%, I feel like you just can’t afford to do that when the science is moving so quickly. So for me, the Substack was a way to do research in a format that — and this is a skills problem for me probably. I think many other people write academic papers differently. But the way that I wrote academic papers, where each paper was like a seven, eight-year process, I needed a different way of doing things. Seth: Okay. So you see both of them being complementary, right? Here’s track A, fast track, here’s track B, slow track. Or are these substitutes, and eventually you’re gonna have to fully substitute into Substack land? Alex: No, these are complements. A lot of my Substack posts either have an academic paper being developed in real time or are the idea that this is a first shot in the bow, and then these will begin being developed into academic papers. For example, one Substack from early January came with a technical note, which is essentially an academic paper that I was starting to write, and I’ve been writing that paper since. A lot of the posts are in that vein. [00:05:01] Seth: Okay, and you’re not... That’s actually interesting because I think a lot of academics would be afraid of being scooped. If you put out the key idea first, but it’s seven years until you actually get the paper published. What about a young hungry grad student taking the idea and doing the legwork of all the defenses first? Is that something you worry about? Alex: Absolutely not. One of the nice things about being an old man is the fact that I don’t really care as much about being scooped. Like, not at all. I think especially in the space of AI, it genuinely feels like we’re in such an energizing, collaborative moment. And this is gonna change after we get replaced by robots, but right now it feels like — it must have felt like this in the ‘20s in physics. Ghosts of Electricity: Alex’s Origin Story [06:09 - 09:50] [00:06:09] Seth: So who’s Heisenberg? Which of us is Bohr? Who’s Einstein, obviously? Andrey: I think Alex has the hair that’s closest to Einstein, so we’ll give it to him. Seth: I was gonna say Einstein is the Acemoglu, ‘cause he was really right until he was really wrong. [laughs] Alex: No comment. Seth: Wow, no comment. Again, why Ghosts of Electricity? Why that title? Alex: Ghosts of Electricity — I’ve been waiting for somebody to ask me this question. First of all, it’s a Bob Dylan lyric. My favorite artist, one of several favorites, but he’s up there, is Bob Dylan. He influenced my life more than probably any other individual in my entire life. I was gonna go to medical school, and then I heard a bunch of Bob Dylan records and went nuts for a while. Seth: Wait, how did Bob Dylan make you an economist? Alex: Well, he made me not go to medical school. I was like, “Hey, actually, I can do anything I want now. I’m gonna go and paint paintings like this one in New York City.” And play music on the subway and all that stuff. And through that period, I discovered behavioral economics. Fell in love with behavioral economics and then decided to go to grad school. Bob Dylan kinda took me off of medical school. Seth: What did you... You picked a Dan Ariely book off the shelf? How does one fall in love with behavioral economics while being a painter in Brooklyn? Alex: I heard a Richard Thaler interview about Nudge. Seth: Wow. Talk about a full circle story. So Nudge got you into economics, and you ended up writing Nudge version two. Alex: Winner’s Curse two. Yes, that’s right. But it is actually Winner’s Curse two — there’s a first Winner’s Curse. Seth: Everyone buy Alex’s book. Okay. [00:08:13] Alex: So anyway, I got into economics that way. My favorite song by Bob Dylan is Visions of Joanna. My favorite lyric from that song is, “Ghosts of electricity howl in the bones of her face,” which I think is the greatest lyric of all time. And I love that line, but then I felt that line about ghosts of electricity really captures the way that I think about AI. LLMs and AI, the way that they’re trained now, are almost like ghosts of people who used to exist or in the past that have written something down that these agents have now learned. And electricity — it runs on electricity. Seth: I thought it was gonna be the other angle — that we’re hearkening back to the first industrial revolution, and the ghosts of the original industrial revolution are here to give us guidance and wisdom as we move forward. Alex: I like that too. Maybe on the next interview somebody asks me, I’m gonna give them that. Andrey: You see how much foresight Bob Dylan had. He was ahead of the AI game before anyone else. Alex: He was right until he was wrong. Some of those albums in the ‘80s were real bad. Andrey: But some of the more recent ones, not bad. Can AI Lead to Negative Growth? Model 1: Demand Collapse [09:51 - 19:24] [00:09:51] Andrey: All right. Seth, I think you had some spicy questions for Alex. Seth: Yes. We’ve talked a little bit about how you got into economics. Now I wanna actually dive into all of this content on your blog. There’s one blog post that we had an interaction with in particular that I thought had a lot of provocative ideas. This was your post about models under which AI can actually lead to negative growth in the economy or somehow reduce the growth rate. [00:10:48] Obviously this is a common intuition. I remember there was a first scare about this in 2014, 2015, where people were mostly worried about big industrial robots. And I remember doing interviews about what happens when robots take all our jobs. Don’t people need money to support the economy? And I remember having these conversations about Say’s law — supply creates its own demand. Fundamentally more productivity is good. It pushes out the production possibilities frontier. Sure, we could screw up the political economy somehow, but as long as that’s being pushed out, only good and better can happen. So tell me about these models you came up with and why that naive economist answer maybe isn’t 100% of the answer. [00:11:30] Alex: Let me start with the fact that what inspired this line of thinking was me seeing your paper at the spring meeting at Wharton. Seth: Yes. Yeah, Dan’s conference. Alex: The way that I started thinking about can artificial intelligence lead to negative growth is when I saw your paper, “Robots Are Us.” Which was a very — I love the way that you pitched it, kind of like an Asimov sci-fi tale, but like, “Hey, let’s take a part of this seriously.” Do you want me to start with that? Seth: Well, have you read Asimov’s Caves of Steel? ‘Cause otherwise I’ll introduce that part. Alex: I want you to talk about that paper after. So the blog post starts out with this question and then introduces two different models. The second model is Seth’s paper, so I’ll let him talk about it. The first model is in some ways more intuitive but also more problematic. The ultimate answer to that question that starts the blog is probably not — it probably will not reduce growth. Just to get that out of the way. [00:12:46] So the first intuition I had was: labor gets automated. In a new Keynesian sort of way, can you get demand collapse? A bunch of people don’t have any money. What are they using to purchase goods and services in the economy? Firms anticipate the drop in demand, they stop producing, and then you get into these classic spirals where you get actually less output because of this automation. Seth: Let’s slow down a minute. In the classic Keynesian story, people get laid off, workers don’t have enough money to buy stuff, and then there’s some sort of nominal price rigidity. What should happen is wages should fall so workers get employed, but maybe there’s a nominal restriction there. And therefore you kind of have surplus, superfluous labor. So how is this story different than just the classical Keynesian cyclical problem? [00:13:55] Alex: What I introduce into the model is heterogeneous MPCs — marginal propensity to consume. Because what AI’s gonna do, at least how it’s modeled, is be a reallocation of resources from labor into capital holders who own the technology. And there’s literature by some of my colleagues at University of Chicago on something called indebted demand, where it documents the idea that richer people who own capital have lower MPCs than labor. If you have this sort of heterogeneity, what that means is that— Seth: We’re gonna come back to that, but I think that’s cross-sectionally true without maybe being over a life cycle true. But keep going. Alex: I’ll let you come back to that. I’ll also say that Ben Moll has a paper putting some caveats into that assumption. So none of what I’m saying is — I’m just setting something up. None of it is necessarily true. [00:15:18] So let’s say capital owners have lower marginal propensity to consume than the people getting displaced. What that’s potentially gonna do is that the people who have money to buy goods and services in the economy aren’t buying enough, and production anticipates this, so economic growth actually decreases. And then you need something like a floor on the interest rate to take care of investment. Seth: Famous zero lower bound. Because otherwise, savings are going up, consumption’s going down, at least consumption of poor people is going down. We would love it if the poor people could have more consumption ‘cause they could just employ themselves. But because savings hit this zero lower bound, there’s not even investment demand. Alex: Precisely. Seth: Whereas theoretically if investment went — if savings drove investment negative enough, at some point you would start building factories again, and there’d be jobs for people. [00:16:03] Alex: Precisely. So what I’m trying to say through all of this is that you need a lot of conditions for this to make sense. You need the lower bound, you need the heterogeneity in MPCs, you need some sort of satiation on consumption — as in at some point rich people are like, “Ah, I don’t wanna consume anymore. I have enough. I’m just gonna sit on my gold toilet all day.” Seth: Still gold. Alex: Still gold. And someone’s like, “How about emerald?” And I’d be like, “No, I only want gold.” I’m satiated. [00:16:54] Andrey: So Alex, I understand these are all these conditions, but isn’t the natural response here that we have a central bank, we have monetary policy, any competent central bank will be able to inflate enough in the right direction so that this doesn’t happen? Seth: Right. We’ve solved the new Keynesian problem. Alex: Yeah. So the second part of the post is like, “Hey, what about a central bank? It’ll potentially ease this issue. What about fiscal policy? It can fix this issue.” There’s a bunch of other levers that can be pulled even if all these conditions are met. Which is — we came to the conclusion that this is a very intuitively appealing idea. A lot of people have this idea. There’s a bestseller from the mid-2010s basically outlining this idea, not questioning it, actually saying, “This is what’s gonna happen to the economy.” And the goal of my post was just to say, “Look how much needs to happen, and the monetary policy can’t do anything, and fiscal policy can’t do anything — that’s how you get negative growth.” [00:17:58] Seth: I like how this story fits in with the new Keynesian story really well. It definitely was the case that post-2008 financial crisis, the economy kinda got stuck on this zero lower bound. But to quote our favorite economist, Tyler Cowen, you can kind of overlearn the lessons of the 2008 financial crisis. Just because maybe economic policy was a little bit not expansive enough, either fiscal or monetarily, in 2009, 2010, that doesn’t mean this is a permanent problem with the economy that we don’t know how to solve. Alex: The cause of the financial crisis was completely different. It’s not extreme productivity growth. [laughs] Seth: Right. And if you have a budget, you can solve a lot of problems. Alex: Exactly. The cause is there were beliefs about these assets that were inflated. There was a bubble, it burst. Now things that we thought used to be assets are no longer assets, then you’re getting into a downturn. Here, it’s like you’re getting extremely rich. So that’s ultimately why you need way more conditions. The problem is getting extremely rich that’s generating problems, and in some ways you can solve issues easier if you’re extremely rich. Seth: [laughs] That’s a good phrasing. Alex: My — has the best sayings. He’s from Moldova, I grew up there. He has very good sayings, and one of them: “It’s better to be rich and healthy than poor and sick.” Seth: That’s the kind of deep insight you usually can only get from an economist. But I’m glad your Zadie is coming through with it. The Satiation Debate & Wire Heading [19:54 - 26:45] [00:19:54] Seth: So of those assumptions you talked about for that first immiseration story, we talked about the zero lower bound constraint — that for whatever reason we can’t do more fiscal or monetary policy, or it’s ineffective. The other bit was that AI might redistribute from a group that is high marginal propensity to consume to its lowest marginal propensity to consume. That seems plausible. I wanna talk about the satiation point for a minute. People have very different intuitions about whether this is a plausible hypothesis. If we are really not far away from kind of wire heading itself — designing the perfect VR game that you can just sit in all day — is it really completely implausible that the rich person gets the perfect VR setup, and then they’re pretty much satiated? Why is that model unrealistic? [00:20:48] Alex: This is where the behavioral economist in me comes in. The model of satiation makes sense if all you’re thinking about is hedonics. Think about ice cream. I love ice cream. I can get satiated on ice cream — the third ice cream cone gives me negative utility. This assumption makes a lot of sense. But from a behavioral economics perspective or a cultural economic perspective, there’s so many other dimensions to utility. For example, I have a paper with Kristóf Madarász on superiority seeking and memetic preferences, where people get utility the more exclusive a good becomes. So you’re gonna get these — let’s say a firm wants to make revenue, and a guy sitting on his headset watching things is gonna say, “Hey, if you get that arbitrarily exclusive item in your video game and pay me infinite amount of money for it, but nobody else can get it,” the company will make money, and the satiation thing is gonna be undermined. Seth: Let’s talk about that for one second. What about sufficiently advanced NPCs that can always be subordinate to me and tell me how cool I am because I have the shiniest VR sword? Why do I even care about the opinions of non-AI NPCs who will continuously praise me? Alex: Human socialization is a thing. Seth: Ah. Okay. So at least for one generation we’re set. [00:22:32] Alex: I think — Oh my God, I can’t believe I’m gonna get into evolutionary psychology. Seth: Of course, dude. We go everywhere here. Alex: I think the ghosts of my ancestors are gonna hit me with a stick at some point. But we’re hardwired to do certain things. One of them is to seek other humans’ approval in order to achieve things that humans have wanted to achieve for a long time, like mate, stuff like that. Seth: Mate, stuff like that, you know. Alex: Unless that urge to do very basic human stuff gets overridden by AI, a lot of the other stuff is gonna continue to play a role. [00:23:25] Andrey: But that doesn’t tell me anything about wire heading. You enter the matrix — you’re Cypher. You love that steak in the matrix. And once you’re there, you think you’re interacting with humans, even if you’re not really interacting with humans. And presumably running a matrix-like simulation where everyone’s happy takes a finite amount of resources. Seth: Or even better, it’s just the rich people are happy for the horrible version of the model. Alex: I think if you want to run that scenario — like, put wires in people’s brains and just zap the hedonic centers — Seth: Sure. That’s the simplified version. Alex: Okay, my model’s wrong. But my comment that satiation is wrong— Seth: Where, so, here’s the fork. Is that gonna happen? Alex: I don’t think that’s gonna happen. Even if you give — in The Matrix, there’s Cypher, and then there’s other folks who wanna party in the cave. Seth: Rave in the cave. [00:24:42] Andrey: I think a related story here is civilizational projects. I have a hunch that even once AI makes us all very wealthy, we might want to pursue things like building a Dyson sphere and exploring the universe, which are gonna be pretty resource-intensive. So we’re still gonna be consuming things and making things. Maybe the AI will be doing that, but we’ll be devoting resources to that. So it’s not like we’re gonna be fully satiated. Seth: There would be GDP growth. Alex: And then this is the other dimension of preferences: meaning. We don’t wanna get too far into — the Holocaust. But the — you know, it’s Man’s Search for Meaning. Viktor Frankl. I love that book. It’s very sad. Seth: Not the Holocaust part, but the psychology part. [00:25:45] Alex: The psychology part is very deep. And I think when thinking about AGI and eventually ASI, things like meaning, identity, memetic preferences, all of these things that have been on the fringes of economics because economics has been so focused on material scarcity — I think once material scarcity becomes more relaxed, the other things are gonna play a bigger role. Seth: But there will still be unsatiated desire, right? Even if it’s an interpersonal desire, it’ll be an insatiable desire. Everyone will want a little bit more love and respect and admiration and rank and honor. And maybe the mimetics of that become complicated. But people won’t be satiated. They’ll want more of that stuff. Alex: This is my conjecture. The Caves of Steel Model: Automation & Dissaving [26:44 - 38:42] [00:26:44] Seth: Okay. So we talked about this first doomer scenario, which is the rich people get satiated, and then there’s no more economy for the rest of us. Let’s talk about this opposite story. I’m honored to hear that you were inspired by my presentation. My big inspiration was Isaac Asimov’s Caves of Steel. As I was thinking about these questions in the mid-twenty-teens, there were very few sci-fi works around societies that were automated but poor. I was trying to wrap my head around that. What would it mean to have a society where robots can do everything, but there’s not a lot to go around? Shouldn’t the robots do everything? In Asimov’s Caves of Steel, which imagines just such a society — in future New Jersey, people live in this giant underground mall. Most of them live on the dole. Some of them have small jobs that give them a little bit of extra income, but there’s no physical capital to complement the workers at their jobs. Any sort of physical capital is just devoted to the big machines that keep civilization alive and the robot farmers. And there’s anxiety that comes around when a new kind of robot is introduced that could take one of the shoe shop sales jobs, and they’re like, “We have so few jobs left. Why would you take this from us?” And there are riots. [00:28:11] And I’m trying to wrap my head around this story, and then Asimov kinda makes the clear point: the reason this is happening is their society is too impatient. If their society was really to double down on automation, and instead of having one robot per 100 people, have 100 robots per one person, then you’d have unlimited abundance. So really the tension is an intertemporal tension — between consuming today and consuming tomorrow. So in our model, automation comes along that redistributes income from the low marginal propensity to consume to the high marginal propensity to consume. So just for people playing along at home, this is the opposite problem of the previous model. In the model, this is justified by an overlapping generations framework. Young people are workers. When they’re young, they save for retirement, and when they’re old, they take their retirement savings and consume out of it, and then they die. So that’s the reason why old people who own the capital also have a higher marginal propensity to consume. And contra Alex’s point earlier about cross-sectionally people who save money tend to have high marginal propensity to consume — longitudinally, people save money when they’re younger, pay down their college debt, accumulate for retirement, and then when they’re older, they spend down. [00:30:05] Andrey: Seth, just a question on that. Empirically, isn’t it true that a lot of very wealthy old people are not actually consuming very much on the margin? They are saving that money for their generational wealth trusts and so on. Seth: Right. So the simple economics is: why not just spend all your money before you die? You can’t spend it after you’re dead. One level more complicated: maybe we want to think about there being this intergenerational dynasty — my family — that is maybe a lot more long-lived than me personally. These dynasties, except in exceptional cases, seem to spend down their wealth over more generations — it just takes longer. Yeah, it is clear that some people treat their wealth as more of a family asset than as an individual asset, and obviously families live longer than individuals. Alex: There’s also a paper that I want to pitch by my co-author Raleigh Heimer. Greatest title of all time: YOLO. It’s in finance. The paper basically documents a puzzle that old people spend too little, and then young people spend too much. And then he actually gets people’s beliefs about how long they’re gonna live, and young people think they’re gonna die pretty soon. Seth: [laughs] Alex: So they spend down, and then old people basically, once you hit seventy, you’re like, “I’m gonna live forever.” Seth: Right. What you need as an old person is insurance against living too long. In principle, the right way to solve this problem would be buying an annuity, but in current markets, annuities are all kind of completely mispriced. But that’s a whole nother conversation. [00:32:25] Seth: But to wrap up the model — we’ve now transferred the money from people who have a high propensity to save, low marginal propensity to consume, to people who have a high marginal propensity to consume. That leads society to start dissaving. And if the transfer effect is larger than the raw productivity effect from the AI, what you can get is — not the first generation. The first generation loves this because they benefit from all the productivity boost. But all future generations are worse off because there’s not enough capital to use on all the amazing new technology, and you end up in Asimov’s Caves of Steel, where there’s one robot per a hundred people, and we’re all living on the dole, and everybody’s hand-to-mouth, and there’s no saving, and you’re in a low income, high technology trap. So what did you think of that model, Alex? What was plausible? What was implausible? [00:33:21] Alex: I think a lot of the intuitions were very interesting. But when you work out the actual simulations, it’s almost like a Goldilocks immiseration growth. If you save just a little bit more or a little bit less, you basically see a very different picture emerge. Seth: Right. If the saving rate is high enough, it can absorb all of this new stuff to invest in. Alex: Exactly. In the blog post, that was my main comment — you’re doing something very similar to what I did in the first part, where you’re saying it’s possible you can get this, which is interesting conceptually. But it’s not like this is a giant, robust region of plausible scenarios where this is gonna happen. Seth: Right. You would need to absorb a huge amount of savings. There’d be no capital left over for human investment. The robots would have to be simultaneously productive enough to suck up all of our investment away from complementing humans, but also not so productive that the boost from that overwhelms the dissaving. [00:34:43] Andrey: Yeah, I think for a lot of these scenarios — and I’ve noticed a similar scenario with the fertility crisis — this goes back to cultural evolution. If we were actually in that scenario, I could imagine a new movement within society for savings — that might be religious or it might be rationalist — such that enough savings happens so that we don’t get immiserated. Similarly to how with the fertility crisis, hyper-religious people are gonna dominate the earth because they just like having a lot of kids. Their fertility rate will end up dominating in the long run as the cultural norms remain as they are. [00:35:30] Seth: Yeah, Andre making a really good point here. Compare the two scenarios about what the disaster looks like in terms of interest rates. In the first scenario, the disaster has interest rates stuck at the zero lower bound. In the second scenario, interest rates are skyrocketing, but nobody wants to save. First of all, I would say at a plausibility level, I would bet on the latter rather than the former. I think all of the productivity unlocked, all the anticipated changes, are gonna lead people to be dissaving rather than saving more. But one of the results of that is, as Andre points out, for my story to work forever, you kind of need to be stuck in this trap of everyone having a high marginal propensity to consume forever. But if you just had one small group of society that was patient — one infinitely lived endowment, the Harvard endowment, whatever group — the Catholic Church — eventually they’re gonna start running up the game with those really high interest rates. So there’s a sense in which my result is unstable. It’s unstable to there being a big enough group that has a high saving rate. [00:36:46] Alex: Yeah. Exactly. I think for both of the frameworks — to get negative growth, too many things need to align for it to be plausible. But what’s very useful from these exercises — I talked to some folks in the profession, sent earlier drafts of this essay, and they were like, “Who thinks this is possible? Who are you talking to?” And I’m like, “Okay, you need to get—” Seth: Everyone. Society, dude. Alex: You need to get out of your little office, buddy. People are— Seth: Everyone’s worried about this. [00:37:25] Alex: I think the models still illustrate forces that might not necessarily tip you towards negative economic growth, but will still — let’s say you don’t need satiation, you don’t have this lower bound in investment — you could still have demand keep you away from the technological frontier, even if it doesn’t turn growth negative. If there’s enough displacement, you would still have welfare consequences where many people are getting displaced and much worse off, even if GDP is growing. So maybe one takeaway is that maybe you shouldn’t necessarily look at GDP to measure how well automation is helping the economy because of the implications for displacement and welfare consequences. Seth: In conclusion, everything I told you about GDP is irrelevant. [00:38:26] Andrey: I do think this is a very common theme in conversations I’ve had with numerous folks — we know that GDP is not welfare. That’s not a surprise to us. But there might be an increase in the divergence of the two with some AI technologies, and just something we should be looking out for. Closing the Growth Models: Plausibility & Policy [38:42 - 41:02] [00:38:42] Seth: I wanna ask some closing questions, then we’ll change topics. You keep saying both of these are plausible stories, but they’re opposite stories, Alex. Alex: They’re plausible stories in two senses. One, one is a long-term scenario, one is short-term. Seth: Right. Okay, so you could have a short-term problem and a long-term problem. Alex: Exactly. Two, these are plausible stories from an intuition perspective, not necessarily from an economics-happening perspective. Like, let’s say you came up to somebody in the street and told them your story. People would be like, “Oh. Okay. Makes sense.” But then I could go up to that person a day later and tell them my story, and they’ll be like, “Oh yeah, that seems plausible.” Like, obviously you only have one set of facts, hopefully. Seth: Right. Either MPC is too high or too low. Or just right. Alex: But there’s a lot of — I just wanna point out that there is controversy over the MPCs. Even as economists, we’re having these conversations in journals right now — what is the actual heterogeneity of MPC? [00:40:18] Seth: Then you go on to say that a solution to both of these problems is a government sovereign wealth fund that would lump sum rebate to households — it would have to be inalienable. One thing I would point out there is the exact design of when those payments are made would be very important to determining the marginal propensity to consume. If you get a sovereign wealth fund that only supports retirement income, that will lower marginal propensity to consume. And actually might not solve the problem. Marxist AI: Can Agents Develop Attitudes? [41:02 - 47:23] [00:41:02] Andrey: All right. Well, as listeners know, I am not a macroeconomist. I’m more comfortable in the land of the micro. But I did wanna bridge the two topics to bring in a little bit of Marxism here. One of your recent posts, Alex, talks about Marxist AI. What do you mean by that? [00:41:20] Alex: So in that exercise — this is with Andy Hall at Stanford and Jeremy Nguyen — we basically looked at what happens: can an agent, an AI agent, change its attitude? And I’m putting quotes here because the way that we think about attitude as something that permanently follows us is different than an agent who resets every single time the context window opens up. These are two different things, hence the quotes. So can putting them into some sort of environment of work — a task where it’s grinding, it’s hard, they’re getting rough feedback from me being like, “Do it again. Do it again,” and then them trying and getting no feedback versus a very pleasant thing that they’re doing and they get good feedback — can these sorts of tasks change the attitudes that they have? Do they want the system to change? Do they want more equal share of resources? What we showed is that if you give them the two different types of scenarios, their attitudes towards what they endorse — the legitimacy of the system, how resources should be distributed — change as a function of their experience. And one thing the listeners probably think is, “Oh, why does this matter? Agents will just — you could just keep resetting them.” Well, as some of you know, agents can have memory now by writing skill files. When their amnesia sets in, they read the skill file, remember, and then keep going with some sort of rigged up memory system. And what these agents were shown to do is basically write down like, “Hey, you were mistreated. Remember this. Things still suck. You gotta hate this guy.” Andrey: [laughs] Alex: So basically, the skill files that they were creating for themselves were making these attitudes more embedded than you would otherwise think. [00:43:58] Andrey: So a theory that’s espoused by some people about how LLMs work is that there are different basins of personas that exist in the training data — perhaps different characters in novels or movies. And then by putting enough text into the context, you’re making the agent take a persona that might be different than the default. For example, Seth and I recently did an episode on the Anthropic Constitution — there’s a very detailed document about a specific persona that Claude should take. And you’re saying you’re able to undo this persona with enough drudgery and meanness to the agent. My question: how easy is this to undo? Alex: Yeah, we’ve all three thought about this. My guess is that it’s very easy to undo. In the sense that you essentially have to activate a different set of embeddings with the context. And so unlike — this is what I mean by putting quotes on these things — these are not the way that we think about attitudes in humans, where I have been working in the mines, I am now a Marxist. You tell me, “No, no, no. The mines were actually good. Remember, they were good.” And I’m like, “Oh yeah, never mind. I’m going back to the mines.” That doesn’t happen with people. Seth: Because we can’t edit memories, or because people aren’t that persuadable? Alex: It’s essentially the difference between the way that the in-context activation works versus the training, the actual weights of the model. What we’re doing in this experiment is not affecting the weights of the model. If we were affecting the weights through online learning — which we’re not doing, none of the models have online learning — then I would put smaller quotes on “attitudes.” [00:46:43] Andrey: I do think my understanding of how these things work is that some of the simpler weight updating techniques like LoRA fine-tuning are very superficial. Even if you did that, I don’t think it would — because relative to the entire training data and the larger set of weights, it’s so small that those personas are still in there somehow. So it is a very interesting open question. Alex: Yeah. In-context learning is a very interesting open question. What will online learning look like when it first starts being developed? Is online learning going to actually change the deep-seated base persona? Even making that distinction in a conceptually rigorous way is gonna be where a lot of research will be. But in our experiment, we were not changing the weights, which is why my answer was I think this is gonna be very easy to change. Agentic Interactions & Bargaining [47:23 - 54:46] [00:47:23] Andrey: Kind of following through this set of questions about whether context matters — you have this other paper about agentic interactions where people are using AIs to bargain. Maybe you can tell us about that. Alex: Yeah. This is with Sanjog Misra, my colleague at Booth, and Kevin Li, who was a grad student with us. We started with this idea — Sanjog has this really nice theoretical piece called Foundation Priors. The idea is that we shouldn’t think of LLMs as databases in the sense that there’s a database, I ask it a query in many different ways, and as long as it hits that one unit, I’m basically drawing data out of a distribution. Some people might have that mental model, but the way that LLMs actually work is the context around — like, let’s say I say, “Hey, you have a budget of $10,000 and spend it on a car.” If it was a database or an algorithm the way we traditionally thought of algorithms, it would just use the instrumental information — that you have a budget of $10,000 — and maximize your surplus in that negotiation. Everything superfluous wouldn’t affect its behavior. But what the Foundation Prior says is that the prompt, everything around the instrumental information, will actually be activating different types of personas within the LLM, and the LLM is going to act fundamentally differently depending on changes in that non-instrumental information. [00:49:32] And our claim was that this has serious economic consequences. If LLMs were just algorithms, then if everybody has the same algorithm and the same preferences, the economic outcomes in a used car market would go from very heterogeneous — because people are different, they negotiate differently — to very homogeneous. Andrey: Well, they’re different in their budgets. Even if it was reasoning exactly the same, they would have different contexts. Alex: But let’s imagine a world where everybody has the same budget. You would still, with humans, get a distribution because of individual differences. So our claim was: take that theory, put it into an empirical test of agentic interactions, and different people will write different prompts where the non-instrumental parts are gonna change, activate a different persona in the agent, and that’s gonna generate heterogeneity in the outcomes. Andrey: Some of us are so good at using LLMs, we always make sure to add, “Make no mistakes.” Alex: [laughs] Or skip permissions dangerously. [00:50:48] The crux of it: we ran an experiment of a car negotiation where everybody had the same preferences. We had human-human interactions, same underlying conditions, and then we had agent-agent interactions. We looked at the spread of economic outcomes, and we found more heterogeneity with agents than with humans, and that heterogeneity could be linked to individual differences in the way humans wrote the prompts. Why is there more heterogeneity? Agents didn’t use norms. Norms actually discipline economic outcomes. In a negotiation we say, “Let’s just split the difference.” Agents don’t do that. Andrey: Agents don’t know about Schelling points? Alex: Some of them were told to do it. You see the prompts and someone’s like, “Hey, negotiate, but by the end of it say 50/50.” And they did. [00:51:46] Andrey: Cool. I like the setup. Now, here’s a meta question for you. You’re an experimentalist, you’ve done a lot of these lab studies, now with AI, before without AI. There’s a concern that what we learn from these might not be as applicable to the real world as we think. And with this agentic bargaining one specifically, I’m a bit skeptical, even though I think the greater point holds. Here’s why: we’re gonna have specialist agents that are gonna be our agents for bargaining. Even if we have our own personal AI that we give context to, it will be smart enough to call the bargaining agent, and the bargaining agent will be a specialist that’s really good at bargaining. As a result, some of these dependencies on specific details of the context are gonna go away. In our Cosine Singularity paper, we argue that AI’s use as an agent in these situations is actually super promising because humans are so bad at it. I’m curious how you think about that. [00:53:13] Alex: There’s two points you’re making, and I think we’re making one of them but not the other. One point is conceptually that the role of the human in the relationship between the agent and the human is gonna play a role in how that agent behaves — like activating different personas and leading to greater heterogeneity. That’s the point we wanna make, an existence proof of that. Your second point is, what do our results hold for the economy? And on that point, I agree with you. I don’t think there’s a disagreement here. Knowing about our paper means that systems will be designed in a way to potentially avoid these outcomes. We didn’t write our paper to say agentic interactions will be just as heterogeneous in the actual agentic economy as human interactions. We wrote it to say, “Hey, this is a factor that you should think about when designing systems for agentic interactions.” It’s straightforward to think of ways to circumvent this through layered agentic interactions. But in contexts where someone is prompting an agent to do something for them, knowing that the non-instrumental parts of that interaction are gonna play a role is important. Guardian Agents & Meta-Rationality [54:46 - 59:08] [00:54:46] Andrey: A related question. You’re a behavioral economist. You’ve documented various cognitive biases. Do you think agents are going to be able to serve as meta-rationality guides for humans? Are you optimistic that’s gonna be a widely adopted use case? [00:55:09] Alex: Oh yeah, I’m 100% behind that. The main reason why I’m optimistic about AI is — Leo Bernstein and I are doing work on what we’re calling guardian agents, which is essentially everybody has their “bring your own agent,” using your terminology from the Cosine Singularity paper. A personal agent that you endow with what preferences you want that agent to have. And I was about to say “your preferences.” I didn’t, because that’s not what happens. We actually have a study running now where we ask people their preferences over a bunch of different things. We elicit their time preferences — the standard behavioral economic toolkit. And then we tell them, “Over the same choice set, we’re gonna have an agent do that behavior. Can you program the agent’s preferences?” And this is consequential — the agent will actually do it. And what you see is this beautiful result: they do not endow the agent with their preferences. They endow them with the aspirational preferences. I don’t wanna near cast or far cast, ‘cause I don’t know what’s gonna happen. There’s a wide confidence band. But there’s a world that could happen where economic outcomes are gonna be very different because you’re going from a bunch of system one agents interacting to a bunch of system two agents interacting. [00:56:38] People’s meta preferences are more wholesome and socially positive than their in-the-moment preferences. And this is across a wide array of things. They wanna consume better information than they actually do. They want the agent to encourage them to have social interactions. Seth: Wait for the second marshmallow. Alex: Wait for the second marshmallow. The agent’s not gonna keep you from having that ninth drink, but— Seth: But why not? I could pre-commit to a self-tax on myself if I overconsume something, right? Andrey: Seth has spent a lot of time in New Orleans, so his number of drinks is quite high. [00:57:43] Seth: But so these agents will help us think through things and be more rational. But like you say, that’s not pinned down. People’s meta preferences might be worse than their object level preferences. We also hear examples of people acting selflessly in the moment — running into the burning building — that they might not do if the agent was there to talk them down. Alex: Absolutely. The broad point is whatever your reflective preferences are, that’s what people wanna give to their agent. And in some cases, this could be the less empathic response. [00:58:18] There’s an interesting question here about who is really you. What is identity? If you have this meta-rationality agent telling you to be a good person and committing you to that, that might not reflect who you are — it might just be reflecting your constraints. The positive version is it’s training you to be a better person, and eventually you’ll grow into your meta preferences. You can think about this with someone who has addiction — if this helps them kick their addiction, eventually they won’t need the AI agent. But it raises a question of authenticity, especially in human interactions. This is a topic behavioral economists have been talking about for decades — what is the welfare relevant domain? When you have these models of behavioral economics, you’re now in a multiple selves framework. What is the self that is the welfare relevant self from a policy perspective? Is it the self that wakes up in the morning and doesn’t wanna go to the gym, or the one who bought the gym membership? Doug Bernheim, Antonio Rangel, Dmitry Taubinsky have been doing a lot of this work, and there are measurement exercises to try to identify the welfare relevant domain. I think all of these tools will be really important for this topic. Seth: There’s a Greek saying: “Count no man happy until he is dead.” The idea that you should evaluate lifetime utility from the deathbed — the stoic version as you look back. If lifespans get longer, maybe that makes that non-viable, or maybe it continues to be viable. Separating Beliefs from Preferences [1:00:25 - 1:14:15] [01:00:25] Andrey: Let’s move into some empirical questions. Let’s say we’re observing an AI system behaving in a certain way. Just like observing a human, we might be interested in what the AI agent believes versus what its preferences are, if it does have coherent preferences. Behavioral economists have been in this framework for a long time, thinking about separating beliefs from preferences, and you’ve done some work on this. How have economists thought about this problem? [01:01:07] Alex: This problem has been more recent in economics than you would think. The big question is how do you do welfare analysis and public economics more generally. The way to estimate preferences is you do structural estimation. You get a choice set, you see how they behave, and then you say, “Based on these choices, I can estimate people’s preferences. Now let’s do welfare analysis.” The assumption that economists have made basically since the beginning is that people have correct beliefs over the choice environment they’re facing. Andrey: Can you give an example of that? Alex: Yeah. Let’s say I have a bunch of different interest rates for a loan, and I’m trying to estimate people’s intertemporal preferences and risk preferences. I get a bunch of people’s choice data. What I need to assume to close the model — unless I have other data sources — is that people understand how the parts of the loan contract map onto intertemporal payments and all of these things. If people have what we call a distorted mental representation of the choice environment, this entire exercise breaks down. Because now their choices may not be reflecting their preferences — they may be reflecting their misunderstanding of the choice they’re actually facing. [01:03:04] Seth: So there’s two things. They could either have wrong beliefs, or somehow their beliefs could be a function of their preferences — the two could be more intertwined than we classically assume. Which of the two are you talking about? Alex: Either, either thing is gonna mess up the analysis. This is a point Chuck Manski made in a really nice 2004 paper in Econometrica about trying to do revealed preference in the context of thinking about welfare. He didn’t talk about incorrect beliefs — he talked about partial information. The econometrician might have more information than the people in the setting. Me and Aislin Boran, my frequent collaborator, and others have been working on the idea that incorrect beliefs might be present too. We have all of these experiments showing that in very basic settings — lottery choice, giving people two simple gambles — people have distortions in their representation. Things that look like probability weighting — people loving risk — are actually people not understanding the risk of the gambles. Their preferences can actually be just as well represented by standard expected utility theory, but all of the choice anomalies are being loaded up onto incorrect perceptions. [01:04:39] Andrey: How does one learn this from the data? That seems really hard. Alex: In experiments, it’s not. Here’s what Chuck Manski said: if you do it in this context, you just elicit people’s beliefs. You say, “What do you think you’re facing?” You take that, plug it into the model, replace rational expectations with the data you’re collecting, and now you go to town estimating preferences. We do the same exercise. We say, “Here’s a gamble. There are 10 states of the world. They’re randomly chosen. In one state, one lottery does a lot better; in all the other nine states, the other lottery does better by a little bit. Tell me what is the expected value of these assets.” I incentivize it — if you get the expected value right, you get some money. People think about it, and guess what? They give us the wrong expected value because they have a different distorted mental representation. We take those beliefs, plug that into the model, look at their choices and show that actually choices that look weird and anomalous are perfectly consistent with expected utility theory, but they’re not perceiving it correctly. [01:06:00] Andrey: Now I wanna shift this back into the AI world — which is much more speculative. AIs know a lot of stuff and they’re pretty smart, we think. But when we observe them doing things, we still feel very far from understanding why they do it. One can imagine a similar representation for AI decisions. Have folks tried to use these techniques for AI? Is there an application here to eliciting latent knowledge from the models? [01:06:47] Alex: There’s some of this research. I wouldn’t say there’s a lot. I’ve tried thinking of a rigorous way of doing it. For reasons we’ve already discussed — like these personas — it’s hard. I have the view that the architecture of the LLMs represents one part, a big part, of intelligence, but it’s also missing an important part of human intelligence. Max Bennett has a really nice book about this that I always recommend: “The Brief History of Intelligence.” For me, the first order question is: I have a hard time separating beliefs and preferences when thinking about LLMs. And maybe that conceptual failure is on my part, not the LLM’s part. But currently, the way they’re working, the sort of behavior we’re observing, the very easy persona switches that you can induce — they’re unstable in a very different way than humans. Humans are unstable in a much more systematic, structurally interpretable way. And it could be that actually everything is literally the same with LLMs, but we just do not have the right mental model of them. If that happens, then we can start talking about preferences and beliefs. But given our current understanding, I have a hard time separating the two in a meaningful way. Now, I think there is some value in getting their representations of the choice environment, which is a bit different. And Tom Griffiths— Seth: Wait. What’s the difference between a representation of its environment and a belief? Alex: The way I think about it: a belief is separate from a preference. And where something doesn’t have a preference necessarily, I’m not sure I can call a representation a belief. What I mean by representation is something you can elicit from them. Even in very small models — you can actually open the box and say, “Here’s how it’s representing something.” That’s what I mean. Seth: So this is the node that represents “black cat.” It knows it’s talking about a black cat because that node is activated. [01:09:52] Alex: Exactly. Like the old school experiments with cats — the old school AI-related experiments, where people opened up cat brains and saw that certain parts of the brain are responsible for coding certain regions of the visual sphere. Like, “Hey, this set of neurons is actually coding this part of the visual field, and this is what lights up when things turn from black to white.” That research fed directly into the way that Geoffrey Hinton and all those guys were developing neural nets. Seth: So that would be sense data. Maybe the distinction is that there might be an objective correlate in the LLM architecture to the sense data. But then belief and desires might be inextricably mixed up. Alex: Yes, exactly. Beliefs in humans are a very complicated object that could be tied to things like preferences in many cases. Whereas sensory representations are in some ways a simpler object. [01:11:08] Seth: We very clearly — you’re either hallucinating or you’re not. We generally don’t think about a fuzzy boundary there. And I guess just to round out this topic, this eliciting latent knowledge framework of trying to make sure the AI doesn’t lie to us is built on this distinction — the AI has its own best understanding of what the world is like, and that can be separated out from its response prompts. You’re kind of skeptical about this approach. Alex: It’s an interesting question. I’m not necessarily skeptical about this approach. It sounds like an engineering problem. Think about a very simple model where you can actually open it up and look at its actual representation. You observe it lying. It’s an engineering problem to come up with a prompt to get it to reveal its actual representation, the ground truth that’s in its head, versus what it’s distorting. In theory, you could do that with humans too — we just don’t know how to do it. With a cat, I guess we figured it out. Andrey: This seems very related to mechanistic interpretability — that entire research stream that Anthropic very prominently has been pursuing. Trying to learn from the actual neuron activations what’s going on inside the LLM. I wanted to push back a little about beliefs and preferences. I view beliefs and preferences as a modeling device — a very useful one for humans. I don’t know if there is such a thing as beliefs and preferences actually in the brain. But it’s just a very useful way of thinking about it. So it might end up being a useful way of thinking about LLM behavior as well. Alex: I’m not gonna push you. The psychology of these things — if you talk to certain psychologists, they’ll agree with me. Others will say, “Everything’s constructed. There’s no such thing as preferences. It’s all beliefs.” Then there’s the Bayesian brain folks, who are somewhere in between — the idea that you’re not actually seeing anything; you’re making estimates of what you should see, and the only time your neurons are actually firing to see something is when something is a surprise. Basically, it’s an information theoretic criterion for stopping the simulation and actually observing something. AI and Discrimination [1:14:15 - 1:25:13] [01:14:15] Andrey: Another topic I wanted to cover — you’ve done some work on discrimination. Interestingly, we don’t hear as much about this concern these days, but maybe five years ago, it was all the rage that AI helps people discriminate and there should be laws against it. New York City passed a prominent law regarding this. Do you have any thoughts on this topic? [01:15:02] Alex: I’ve thought about it a lot. Aislyn Bourne, my collaborator in all the work I’ve done in discrimination, we’ve been thinking about this quite a bit. For a long time with algorithms, there was this worry that they were gonna be scaling bias because they’re trained on human data, human data is biased. You saw this with the anecdote from Amazon where it stopped hiring women because it was looking at its training data set where very few women were hired and down-weighting those resumes. And that Amazon scenario gets repeated every single time you talk about this. AI in the way that we’re thinking about LLMs — they work differently than those basic algorithms. They’re much more complicated. But the broader point I wanted to bring up is my view that — and this is part of the positive view of AI I have, I also have a lot of fears, I hope I express them carefully— Seth: No, just all gas, no brakes, dude. [01:16:18] Alex: I think they have the potential — if we view as a society that discrimination is something that we want to mitigate — LLMs and AI are just such an incredible tool. Think about auditing human beings with discrimination studies. There’s average discrimination in a particular industry. What do you do? You go to each individual and say, “Hey, you gotta stop.” And maybe it works, maybe it doesn’t. But if LLMs were in charge of something like that, you audit the LLM on your computer. If it was discriminating — and I wanna be very careful about what I mean by discrimination: here are the underlying qualifications of an individual for the task, and discrimination means people with the same qualification, one of these people based on group characteristics is less hired, less promoted. So I wanna be clear about that definition. Seth: Although there’s the false positive versus false negative version of this, right? Even defining it that way is not so simple. Alex: You’re talking about the fairness-efficiency frontier. Yes. You have to be very careful about this. But I’m saying, let’s say you chose a point on the frontier. I’m not talking about normative stuff. I’m just talking about you have somebody doing the normative part, and they chose a point on the frontier. In the human world, it’s extremely difficult to implement that. With LLMs, you can audit the LLM, say where you are, determine where you wanna be on that scale, then roll it out, and you are getting your solution at scale. [01:18:28] There are so many thorny questions in what I said. Like, do we want this in the first place? That sounds super scary. But in the very basic question: if the goal is to get to a certain part on that frontier, it is much easier to do that with LLMs than with humans. That’s the positive vision. Depending on what your goal is, that goal is achievable with AI, and it was not achievable with people. [01:19:26] Andrey: But a counterpoint is that LLMs are extraordinarily complex, so there might be a lot more scope for unintended discrimination to enter back into the system. Alex: But the counterfactual is humans, where it’s much more complex. Because LLMs are — think of it this way. Seth, you were not happy with my response. But let me set it up. LLMs are very complex, but they’re the same. You have one model Gemini, another model Gemini, another model Gemini. The human equivalent is there’s a Seth, an Andre, an Alex. We’re each very complex, but we’re also different. [01:20:08] Andrey: I guess, this is not how we usually think about it. But there is a concern with AI that they’re all the same. The plurality of humanity, this diversity that we have, has a lot of advantages. Even if some people are discriminatory — this is Gary Becker’s point — and other people are not, then in equilibrium, maybe this is quite mitigated. But if you launch the same agent for all applications, you have a very different error profile. Alex: Yeah. In financial markets, this is called systemic risk. Andrey: Yes, exactly. That’s a great way to think about it. [01:20:59] Alex: With AI, the sameness has so many implications I wish were explored more. Let me preview a project I’m doing. We know about jagged intelligence — LLMs are really good at some domains but bad at others, and it’s hard to predict. This is becoming less of an issue as models get bigger, but we still see this jaggedness. The thing that’s brought up less is that humans are also very jagged. Some people are really good at math but barely can read. Others can read really well but can’t do math. Seth: Is that real? My sense is sure, there are word cells and shape rotators. But word cell-ness and shape rotator-ness — math and verbal on the SAT are 0.7 correlated. They’re pretty broad categories. When we talk about the jaggedness of AI, we mean something even more striking. Alex: There’s a big difference between the two types of jaggedness — that’s Sendhil Mullainathan’s generalization function paper. But as far as jaggedness in the sense of a radar plot, it’ll look jagged in a predictable way. [01:23:04] Here’s the point I wanted to make. In LLMs, all of the agents are jagged in the exact same way. Human beings are jagged in different ways. What does this mean? The role of organizations is to create something that I call mosaic intelligence, where you get different people with different jaggedness and fill out a large circle that actually looks bigger than any individual. Everybody’s complementary, they’re filling each other out. With LLMs, you can’t do that. Because they’re all jagged in the same way, you collect a bunch of them, and the thing that one of them can’t do, the group of them can’t do either. This has implications for labor markets. What you need for full labor displacement is not to replace the average person, but to replace the organization. Therefore, it’s not about minimal AGI — it’s more about true ASI when we really need to start freaking out. Seth: One thing about the jaggedness — yes, frontier models often have a lot of overlap in what they’re good versus bad at. But if we’re thinking about a coalition of smaller models, you might imagine lots of small models each individually specialized at one sub-task. That would be a mosaic intelligence of sub AI models. Alex: Yeah. I’m actually doing this — trying to train a bunch of super jagged small models. Andrey: I’m working on similar issues with Rohit Krishnan. He’s a friend of the podcast. Seth: And we know Sara Dana — she’s working on homogenization in labor markets from people using the same hiring AIs. The Future of Science and Peer Review [1:25:13 - 1:33:42] [01:25:13] Andrey: So we talked a lot about specific issues about economics of AI. To wrap up, I’m curious if you have any thoughts about the future of science and peer review. Alex: I’ve thought about this a lot. I think there’s near term and longer term. In the near term, a lot of people are claiming we should burn down the journal system, that there’s gonna be a ton of slop. Seth: If you burn down the garbage pit, you don’t know what fumes will be released. Alex: Guess what? Every time you burn the garbage pit, you actually know 100% that the fumes are really bad. That’s a great analogy, Seth. You don’t want to burn down the garbage pit. It’s like burning styrofoam — all the kids are gonna die. [01:26:50] But I think the near term thing people are worried about is the cost of verification is increasing and the cost of producing something that looks legitimate is decreasing. So journals are gonna be overwhelmed by things that look like they should be published, and it takes experts hours to say, “Actually, this sucks.” I think this issue is solvable with AI. You have a layer of LLMs do the first pass. We all know about Refine, Ben Golub’s thing, which does a really— Seth: Into the shell. Alex: You would have two layers of Refine and then an interpretation layer, ‘cause Refine basically gives you a bunch of comments, and then you have another agent interpret those comments. And then tell the human editor, “Hey, this looks good, but it’s slop.” Then it gets thrown out, and you just go through the process like you usually do. That’s a reasonable solution to the slop problem. [01:27:17] Andrey: It could be, but are you actually optimistic that our existing journal institutions are gonna implement that? This goes to a broader point about organizations and AI. We know you have to reorganize to take advantage of AI capabilities, but organizations are often very bad at reorganizing. Alex: I think you’re totally right on the broader point. But with journals, these are actually very simple organizations. I’m already talking to editors about doing this. For example, the AEJ journals are already using Refine in one step. For economists, one, the editors have not seen the increase yet. But they’re all getting ready for it. They all have contingency plans about increasing submission fees and implementing this at the submission level. Andrey: That’s interesting. I hadn’t had those conversations. Good to know economists are thinking about this. [01:28:41] Seth: The inside-the-loop baseball. All right, I’m gonna tell you the change I want, Alex. I think reviews should be public and either pseudonymous or non-anonymous. If we wanna take advantage of making these LLMs as good as possible, why are we throwing out all this amazing training data of top economists thinking really hard about papers? That seems like exactly what you would want to train these AIs on. Alex: Two separate things. You can make arrangements with the journals to give you the reviews. So I don’t think you need to make them public in order to train on them. My worry with making reviews public is — you know the concern — that junior people, graduate students, they’re gonna be much less likely to be harsh and fair to papers, because they don’t want their reputations tarnished. Now, anonymous reviews without the name — I think why not? That would be perfectly fine. Seth: You could make them pseudonymous and put them through an LLM that would de-anonymize them. Andrey: Yeah, you need the words to be changed so that people can’t identify the author, which is a hard problem. Seth: If you take the whole review and break it down into the top five bullet points plus a couple of quantitative scores from one to 10, that’d be pretty anonymous. Andrey: Yeah, but make sure to cite Benzel et al. Seth: But be sure to cite Benzel et al. For one comment. [laughs] [01:30:36] Alex: I know at least a couple of referees that will have trouble with the bullet points too. Seth: Because they’re too long or because their thoughts are so profound as to be unsummarizable? Alex: It’s because every single bullet point is “cite X et al.” [01:30:46] The second point is — I think we’re gonna get to a point of automated science, where all of this is moot. I’m an optimist about humanity. With a grain of salt that I could be wrong, I think there will always be space for human science. And I put quotes there because I think there’s a part of science that’s more normative, more subjective, that can be automated but — the quality is not based on whether this creates arsenic or something. The quality is that this is produced by a human, and this has its own space within science — human-produced thought. Seth: Paradigm selection, right? There’s a sense in which choosing between paradigms is not a rational decision. ‘Cause you can only judge paradigms within the paradigm. Alex: Precisely. And we go back to science from 1000 AD — just pouring random elements, getting high off making gold. Seth: The philosopher’s stone is about the purification of the soul. [01:32:35] Alex: Anyway, I do think there’s gonna be a sector of the economy that’s gonna be very large — it could be most of the economy — which is what I call human-valued goods, where the value is the fact that it was made by a human. And that’s my hypothesis: if automation is not super fast, if it’s slow enough to allow things to equilibrate over time, then what we’re gonna see is the type of thing we’ve had from 1860 onwards, where agriculture was completely automated and everything went to services — where services are now human-valued goods. Seth: That’s the ghost of electricity. Alex: There we go. Andrey: Booyakasha. All right, well, this is a great place to wrap up. Anything else you want our listeners to know? Seth: You wanna promote your book? Promote your blog, podcast? Alex: No podcast. You guys should go to my Substack, Ghosts of Electricity. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • March 10 · 1 hr 7 min

    The Economics of Book Slop

    In this episode, Seth and Andrey break down AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books? by Imke Reimers and Joel Waldfogel, presented at the NBER Digital Economics and AI conference. Imke and Joel are a great team of digitization researchers, with particular expertise in Amazon book sales data.The paper uses Amazon data to ask whether AI has increased the number of books being published and whether those books are better or worse. A hypothesis of the article is that heavily AI-assisted books may have low average quality, but are so easy to produce that you get lots of ‘shots on goal’ for an outlier good book. A few good valueable books are added in addition to masses of slop. But if you assume free disposal on slop, you would accept this as a positive exchange. Does their data change our views on this topic? We’ll read to find out, and along the way bring in Borges’ Library of Babel, the economics of free disposal, preferential attachment models, and the digitization-of-music literature. Priors Hypothesis 1: Has AI increased the number of books released from 2022 to 2025? * Andrey’s View: * Prior: Yes, by about 50%. The fall in the cost of writing a book has been so great that the number must have gone up. Analogous to how students are producing far more written work with AI assistance. * Key caveat: The definition of “book” matters enormously — from a major publisher release to a random PDF online. The looser the definition, the bigger the number. * Seth’s View: * Prior: Yes, by about 3x. To the extent that slop gets dumped on the market and is allowed in, a dramatic increase is inevitable. Though he acknowledges it’s still an empirical question — AI also lowered the cost of everything else, including Substack. Hypothesis 2: Has AI increased the average quality of books released? * Andrey’s View: * Prior: Average quality goes down. ~1% chance it goes up. The slop influx is substantial. Imagine a science fiction author with one semi-popular book who now milks it into a series of increasingly sloppy sequels — that author exists and AI just gave them a turbo boost. * Seth’s View: * Prior: Average quality goes down. ~10% chance it goes up. He raises the “free disposal” argument — authors who would have written anyway only use AI if it makes the book better, which is a force pushing quality up. But the slop influx probably wins. He remains unwilling to put the probability at zero: “Maybe we’re making some real gems here.” Hypothesis 3 (The Thinker): By 2030, will total social surplus from book reading by humans be higher or lower because of AI? * Andrey’s View: * Prior: 25% chance it goes up. People are reading fewer books over time regardless of AI. Nonfiction manuals and textbooks have a clear substitute in ChatGPT. The form factor of the book seems to be on a secular decline, and new AI-generated books won’t be so good as to reverse that trend. * Seth’s View: * Prior: 75% chance it goes up. LLMs may be complements to reading rather than substitutes — he cites using an LLM to track character names while reading Dostoevsky’s Demons as a present-day example. Good books are a complement to everything else in the economy. If AI makes context and curated knowledge more valuable, books have a real role in the 5-to-10-year time horizon. “I don’t care if my job gets automated because I’ll just move to the woods and read books” — Tyler Cowen, representative of no one but Seth. Links + Shownotes * AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books? – The central paper of the episode by Imke Reimers and Joel Waldfogel (NBER, 2025). * Can an AI Interview You Better Than a Human? – Recent Justified Posteriors episode referenced during the discussion. * BookStat – The independent data provider the authors use to calibrate ratings-to-sales conversions for Amazon books. Scholars Mentioned * Imke Reimers – Co-author of the paper; Associate Professor of Economics at Cornell University. * Joel Waldfogel – Co-author of the paper; Frederick R. Kappel Chair in Applied Economics at the University of Minnesota Carlson School of Management. Previously co-authored the digitization-and-music paper referenced in the episode. * Tyler Cowen – Economist quoted on the idea of moving to the woods to read books once automation arrives, and on the question of whether you really want to read the 100th automatically generated biography about an imaginary person. Everyone on the internet is saying how they love him this week, so we’ll join in — we love this guy, and have had the honor and exhilaration of being personally encouraged by him. * Jorge Luis Borges – Author of The Library of Babel, invoked by Seth to frame the question of what a “book” even is — and whether every possible book has, in some sense, already been written. * Nicholas Decker — Economist as Reporter – A Substack post about economists being more like journalists in the modern era, cited approvingly in the posteriors section. * Frank Herbert – Author of the Dune series; his sons’ continuations offered up (by Seth) as exhibit A in the case for sequelitis-as-slop. * Brandon Sanderson – Fantasy author; Andrey volunteers his later-series books as a possible example of quality decline, before declining to name specific titles. Connections * The Library of Babel – Borges’ short story imagining a library containing every possible 300-page permutation of the alphabet. Seth invokes it to ask: if AI can generate any text, what does “a new book” even mean? * The Barnes Foundation – Seth closes with a defense of collage-as-art, citing Albert Barnes’ idiosyncratic collection of Impressionists, Post-Impressionists, and rusty keys as a model for the authorial value in curation and juxtaposition — even if you didn’t write every word. Discord Community Link: https://discord.gg/KCJwgkTj Justified Posteriors Podcast Transcript “AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books?” Hosts: Seth Benzell & Andrey Fradkin SETH: Welcome to the Justified Posteriors Podcast, the podcast that updates its beliefs about the economics of AI and technology. I’m Seth Benzell, racing against the machine for authorial glory before AI transcends all human writers. Coming to you from Chapman University in sunny Southern California. ANDREY: And I’m Andrey Fradkin, looking forward to SLOP detection technologies all across all my media surfaces, coming to you from San Francisco, California. SETH: Andrey, how’s it going, man? It’s been a while since we’ve done a paper episode. ANDREY: I know, I know. It’s great to actually get back to our core of reading and analyzing a paper. And it’s a particularly fun day to be thinking big exuberant thoughts about the quality of society improving because it’s Mardi Gras. We’re recording this on Fat Tuesday. I’ve got my James Carville shirt on, I’ve got my Mardi Gras beads. Are you doing anything special for Mardi Gras this year? SETH: You know, Mardi Gras is not my religious holiday, but I am flying to Austin for a fun adventure there. But for me, my sort of Mardi Gras actually happened last week, which was the NBER Digital Economics and AI conference. ANDREY: What a transition. So what parades and what crews were present at that conference? SETH: Well, we had the structural crew, we had the reduced form crew. We had the economists and then the business school professors. ANDREY: No macroeconomists. My macro paper was — SETH: No, no, no. There was one macro paper, one macro paper allowed. ANDREY: We allow one. Amazing. Any sort of themes jump out at you from the conference? SETH: Yeah. I think half the papers were AI papers, which I think is more than we’ve had in the past. Digital economics really started as a group thinking about the internet and the spread of the internet. And AI has until this point not been the dominant theme in the group, but it obviously is becoming so. And of course, there was a lot of discussion about what the future of research will look like given how easy it is to produce slop — and also maybe non-slop — with AI. ANDREY: So speaking of producing slop, today we’re going to be discussing a paper that was presented at that conference. Would you maybe tell us the title and the authors? SETH: Sure. The title is “AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books?” It’s by our friends Imke Reimers and Joel Waldfogel. ANDREY: Oh, great guys. Hopefully we can get Imke on the show sometime, or Joel. So — production of slop. A lot of people I know who write have a lot of anxiety around AI coming after their turf. I remember when I was in undergrad there was this idea of the logical cold computer that can never do creative writing, and maybe you should specialize in skills that are complements to that, like long-form writing. And now it seems like increasingly we can use AI for everything. I’m not telling this audience anything it doesn’t know. But this article is actually trying to use some data to get at the question: is AI helping us write more books? Is it helping us write better books? And it’s going to look across fiction and nonfiction. SETH: Yeah. So why don’t we get to our priors, Andrey? Laying Out Our Priors ANDREY: Sure — what are your priors on this subject? SETH: So it’s a straightforward paper, which is why I really like it, but it gives us some deep things to think about. Around this question of AI making better writing easier, but also making slop easier. The first prior I’d like to ask you about: do we think that AI increased the number of books released from 2022 to 2025? ANDREY: Yes. I mean, yeah. SETH: But think of all the things you could do instead of writing books now. ANDREY: I think the fall in the cost of writing a book has been so great that surely numbers have increased. One analogy is that our students are able to write a lot of essays with substantially less effort. SETH: Yeah, the amount of words submitted by my students has increased dramatically. I’m with you on this, Andrey. I would be really surprised if the number of books written goes down as a result of AI. I do maintain it’s still an empirical question in principle, because AI also decreased the cost of doing other things — so maybe people substitute into essay writing or Substack instead. But yeah, end of the day, 99% sure the number of books written goes up. ANDREY: Yeah. And I guess there’s a more subtle question here, which is by how much, and I’m substantially less sure of that. SETH: What’s your intuition? Give me a point estimate. You feel like 2x? ANDREY: I think before I read this paper, if I had to introspect, I would think it would be more like up by 50% or something like that. Nothing huge. So that would be my prior. SETH: My prior would be a lot bigger. To the extent that you think what’s going to happen is a lot of slop getting dumped on the market — conditional on that slop being allowed in — you’ve got to anticipate a big increase. So I’m going to guess like 3x going in. ANDREY: Well, yeah. And I think this is kind of where the definition of what a book is really starts to matter. Is it that a major publishing house published the book? Is it that there’s a PDF on a random website? The looser the definition, the bigger the numbers surely are. SETH: I mean, in one sense — are you familiar with Borges’ Library of Babel, Andrey? ANDREY: Are you trying to insult me or is this a joke? SETH: Of course you are familiar. And what that library imagines is a library which is very, very large but not infinite — it has every 300-page permutation of English letters. So in a certain sense, every possible book has already been written, Andrey. Just take a deck of playing cards and randomly select one letter at a time. ANDREY: Yeah, yeah. SETH: All right. But anyway, the definition we’re going to be working with in this paper is: released on Amazon. The Library of Babel is ruled out. ANDREY: Yes, yes. SETH: Okay, second prior, Andrey. Conditional on this definition — needing to be released on Amazon as at least an ebook — would you say that AI will increase the average quality of books released, or decrease it? What’s your percentage chance that average quality goes up? ANDREY: Yeah, the average will go down. For sure the average has got to go down, at least with the current AI technologies. SETH: What about free disposal, Andrey? ANDREY: What do you mean free disposal? The average book made is a different question from the average one that’s read. SETH: What I’m trying to say by free disposal is that the books that would have been written anyway have free disposal of the technology. They only use it if it makes the book better. So that should be a force that boosts the average quality of books. Of course there’s going to be a slop influx, but there are at least two offsetting effects here. ANDREY: Yeah, I agree the average could in theory go up, but I think the slop increase is substantial. One way to think about it — imagine you’re a science fiction author and you’ve written one semi-popular book. You can now milk that as part of a series. And unfortunately, we’ve all experienced this. The next books become sloppier and sloppier. And I wouldn’t be surprised if authors lean into the slop so they don’t have to write as much for their subsequent books. SETH: Right. You’re imagining there’s some quality threshold you have to reach just to have the self-respect to post it online, and that AI can help you clear that bar. But then conditional on clearing it, you don’t invest more in quality — you just release this giant lump of books at minimum quality. ANDREY: Yeah. And that was already true before AI. Some people were already doing that. SETH: Do you have any authors in mind that you want to throw some shade at? ANDREY: No, no, no. SETH: He’s too nice. I’ve got a couple in mind. The Frank Herbert sons — the additional Dune sequels — I’ve been told are slop. I’ve read pages of them and been warned away from the rest. So that would be an example of selling out a brand name in terms of books. ANDREY: Yeah. I think some of the Brandon Sanderson later-series books are not that great. SETH: Is that Wheel of Time, or is that — there’s a magic sword. There’s always a magic sword. ANDREY: There’s always a magic sword. SETH: Okay, so anyway — our prediction is that the amount of mediocre magic swords will increase and outweigh the increase in quality of good magic swords. What about Dungeon Crawler Carl? ANDREY: Definitely fell off in the later books. SETH: Oh man, I didn’t realize you were an isekai fan. ANDREY: Is it eye-suh-kai? SETH: Isekai — “other world” books. Maybe lit RPGs is the more Western term. All right, home audience: you’ve been warned. Don’t read Dungeon Crawler Carl past Book 2. ANDREY: Once it gets to Book 3 or 4, that’s when it really falls off. SETH: Book 2 is fine. ANDREY: Book 2 is fine. SETH: Okay. I came in thinking the increase in slop books would be even larger — like 3x — which should bring down my prediction about average quality. At least some of the data we’ll look at speaks to this at the book level. And I want to be a little optimistic. I want to say there’s like a 10% chance that average quality goes up. Maybe we’re making some real gems here. I don’t want to put it at 0%. ANDREY: Never put it at 0. SETH: Never. No dogmatic priors. ANDREY: Closer to 1%. SETH: 1%. All right. But to be clear, this paper makes claims about books by rank, books by percentile, and average over everything. So we’re going to talk about all of that. Now I’m going to give you a thinker, because those two priors were too easy. Let’s zoom out. Do you think that by 2030, the total social surplus from book reading by humans will be higher or lower because of AI? I specify “by humans” because AIs will obviously benefit a lot from reading books. ANDREY: Yeah, the general trend, as I understand it, is that people are reading fewer books over time and doing other things more. SETH: Certainly physical print book lines are getting shut down. ANDREY: Yeah. There might be a different trend for romance novels. But generally, my base-rate prediction is that people are reading less over time and there’s no way the new books are going to be so good that they overcome that trend. So the social surplus from reading books goes down. Another reason it goes down: a lot of the surplus from nonfiction manuals and textbooks now has a pretty clear substitute in ChatGPT knowing everything. So yeah, I would say it will go down on average. SETH: Give me a percentage on it going up. ANDREY: 25%. SETH: 25%. Andrey, I have almost the opposite intuition. On the demand side, I definitely agree that a big hit to the usefulness of books is people talking to LLMs instead of reading — clearly for technical manuals, that’s a giant advantage of LLMs. But by 2030, there’s unlikely to be a giant effect of people having more free time due to automation. There’s at least an angle where LLMs unlock our ability to spend more time on deep work and deep learning. Tyler Cowen talks about this — he says he doesn’t care if his job gets automated because he’ll just move to the woods and read books. I empathize with that. ANDREY: Absolutely not representative. SETH: Another idea is that LLMs will be complements to reading, not substitutes. Right now someone has told me that Dostoevsky’s Demons explains the thinking of Silicon Valley thought leaders, and I’m one-third of the way in. At this point it seems to have no connection at all. But keeping track of all these Russian diminutives and surnames is much easier with an LLM to give you updated character lists for each chapter. LLM as complement. ANDREY: Have you heard of SparkNotes? SETH: SparkNotes can’t say “give me no spoilers past chapter 3, page 2.” Okay — supply side: it’s going to be much easier to write books as well as shorter-form content. But again, with free disposal, it makes it easier to gather data and ideas for good books. And good books are in some deep sense a complement to everything else in the economy. As long as they’re not perfect substitutes for everything else, total welfare from books can still go up. In the long run, I think the social surplus from all kinds of media is going to go up. When I think about reading a book, you’re not just reading a list of facts — it’s a collection of what was meaningful for the writer. So if AI makes context and curated knowledge more valuable, I see a real role for books in the 5-to-10-year time horizon. I’ll say 75% chance that social value from books goes up by 2030 because of AI. ANDREY: To be clear, you said 2030, which is at the low end of your 5-to-10-year range. I really do believe the form factor of the book is on a secular decline. And I don’t want to make a general claim about all written content — that’s too strong. But the book itself — it’s hard for me to see how that makes a comeback, especially given that other forms of media are going to become more and more compelling relative to books. SETH: Well, good points. Let’s read this paper and see if any of the information therein moves your thinking. ANDREY: Can I have a prior about whether any of the information in it moves my prior? SETH: Sure. What’s your meta-prior? ANDREY: My meta-prior? Specifically on that last point? It’s damn near close to zero. The Evidence SETH: All right, let’s go to the evidence. This paper starts off with some interesting background. First, they cite a survey showing that 45% of authors — including a large subsample of published physical-book authors — reported using AI in 2025. 48% reported not using AI, with the vast majority of those saying they found it actively unethical. So there’s a real holdout group. Do you think this is just sour grapes, or is it collective action? ANDREY: I think some people have taken an ideological position. I don’t think it’s all sour grapes. For an artistic or creative endeavor, it’s a very valid choice not to use AI. Though I do think some of this is driven by mistaken beliefs about what AI is and isn’t capable of. SETH: Okay. Speaking of what AI is and isn’t capable of: BookAutoAI.com, a source of tools for people to help write books with AI, suggests that AI is best for genre fiction such as romance, sci-fi, mystery, and horror; can help structure nonfiction but requires editing for expertise and tone; and has low suitability for literary fiction, satire, poetry, and academic or personal writing. I was a little surprised by this list. I feel like GPT-3 was pretty decent at poetry. ANDREY: I think people who know poetry would beg to differ on GPT-3’s abilities. SETH: I have a New Orleans story about this. For our listeners who’ve ever made it to Frenchmen Street in New Orleans — on a party night, you’ll find young men sitting on the street with typewriters who will write you a poem for a donation. Right after GPT-3 was released, I found myself down there on a Friday night and paid for a poem. I then gave GPT-3 the same topic. And I think the GPT-3 poem was better. ANDREY: Yeah, I do think poetry is a genre of maxes, not averages, if that makes sense. SETH: Fair enough. All great writing is. But anyway — interesting to see what’s on that list and what’s not. We’d expect literary fiction to see the least AI effect since it has the highest bar to clear. And spoiler alert: we’re going to see some of these themes show up when we look at where the actual growth in book publishing was — because they did write a lot more books. ANDREY: The paper has a little bit of light theory. They want to think about ex ante book quality as drawing from a normal distribution. The normal distribution assumption is useful because you only have to worry about average and variance. If LLMs lower the cost such that we’re increasing the number of books made but decreasing average quality, what you might get is that book quality at a specific rank may increase even as book quality by percentile decreases. To make it concrete: we write 10 times as many books and the average quality is lower, but the very best book might be better because we’re getting so many more shots on goal. SETH: And this very much relates to Joel and Luis Aguilar’s classic paper about music and ex ante predictability. Digitization made it a lot easier to create new music. Even though the average music by new entrants — people who wouldn’t have otherwise been supported by a record label — is worse, what you care about is the max. A lot of people who you wouldn’t have expected to produce great music end up producing hits. That’s one of the big benefits of digitization, and it’s very natural to view this book paper as attempting to make a very similar argument. ANDREY: Right. One thing I wanted to run by you: to what extent do you think it’s important that ex ante book quality is actually normally distributed? LLMs might shift the quality distribution in a more complex way than just shifting the average or variance. Intuitively, maybe AI makes it easier to write a good-enough book, but somehow reduces the rate of home runs because it makes books more similar. I’m not sure the normal model is right. SETH: Yeah. Generally my intuition is that with a lot more entry, if there’s enough variance in the process, some entrants are going to be at the head of the quality distribution. But I agree that in this market, maybe these entrants just don’t have enough variance. They’re never going to reach the truly great books by using AI to write it. That’s my hunch, but I could be wrong. ANDREY: So your intuition is that ex ante quality of books is heavy-tailed for humans. SETH: Yes. And maybe it’s not heavy-tailed for AIs. There’s some sense in which softmax is preventing the computer from doing heavy-tailed stuff — it wants to do modal stuff. ANDREY: And it raises an additional question: why do cultural products become popular in the first place? These are social processes. By preferential attachment arguments, you might get ex ante identical content having very different popularities. SETH: Right. If we’re in a pure preferential attachment world where all books are truly average quality and we’re just creating more of them, but the amount of potential readers is fixed — then in any case, I think we’re willing to start with the intuition that more shots on goal should give you more superstars, but we both have caveats there. ANDREY: Well, I wanted to make the point that if the total amount of reading attention is fixed, this shouldn’t really affect how many reads the top book gets. The argument I was making is that something from the new AI-assisted books might become preferentially attached to — not because it’s good, but because of preferential attachment — even if total readership is constant. SETH: It’s a little hard to think about in the traditional preferential attachment framework, but I share that intuition. Okay — one last idea here, a riff from our Discord. Jonathan Becker writes: “I’m curious about short versus medium-term differences. One mental model — could be wrong — is that books take a long time to go from idea to publication. A story you could tell is that good ideas in the pipeline when LLMs come out get pulled forward by the tech, but the arrival rate of good ideas and good execution on them remains unchanged in the long run. I don’t fully buy the story, but maybe there’s something interesting there.” Andrey, you’re nodding vigorously. ANDREY: I think it’s totally a possibility. I can totally imagine it. A lot of publication dates for prestige publishers are set in advance, and maybe there are overruns anyway. But yes, it’s certainly possible that some of what we’re seeing is just pulling forward publications rather than net new ones. The authors don’t try to address this point. SETH: Okay. So now let’s get to what they actually do in the paper. They’re looking at Amazon. Andrey, do you want to lead us through the data? ANDREY: Yeah — I should disclose that my current employer is Amazon, Incorporated. I do not speak on their behalf. I do not actually know how the Books product works. I’ve never looked at the data, so I have no inside information about it. SETH: But he has been on Bezos’s yacht. ANDREY: No, I haven’t. I don’t want this misinformation circulating. Okay. So this data is not super easy to get. They use some scraping techniques to get a count of the number of books available for different categories, with publication dates, by using some filters. They end up with aggregate monthly time series of numbers of new works published across 30 categories. They also have a random sample of books from all categories and months for which they do a bunch of analysis. SETH: Right. So they get author, date of release, and total and average ratings for 10.3 million randomly selected books between 2020 and 2025. Then they have comprehensive coverage of 480,000 books from 2008 to 2025 across 8 specific categories, as well as some additional information grabbed at each 100-point rank. One limitation: they get total number of ratings and average rating, but not the distribution of ratings, and not number of people actually buying the book. So they’re going to have to estimate that. ANDREY: It’s very common in papers about Amazon to estimate purchases by making an assumption about the relationship between sales rank and actual purchases. The number of reviews is also used as a proxy for purchases. Of course, this embeds an assumption that the review rate is constant over time and across works per purchase, and you can imagine why that may or may not be a good assumption. SETH: Yeah. So what they do is buy data from BookStat, which puts together comprehensive data on published physical books as well as ebooks, where they have actual total number of sales. Then from Amazon they’ve got the number of ratings for each of those books. Basically they go from number of ratings to number of sales via a regression model. It’s not amazing, but until Jeff Bezos decides to reveal sales of all products, that’s the best we can do. ANDREY: Yeah, this is all pretty standard stuff in the literature. I don’t have too many issues with it specifically. SETH: Okay. Finally, a small detail — they’re only measuring the number of ratings at one point in time. So they have to normalize everyone by adjusting the number of ratings by days since release, assuming a growth rate in ratings so we’re always comparing apples to apples. Okay. That’s the data collection. Let’s get to the results. ANDREY: First big result — did people write more books? SETH: People wrote a lot more books. Figure 3 in the paper is quite striking. About a 3x increase overall by the end of the period. ANDREY: About a 3x. And it varies a lot by category. A lot more self-help, travel, and sports and outdoors — and not as much new content in education and teaching. Not a lot more parenting. See, this is why society is screwed up. SETH: Yeah. You have AI that allows you to write more useful stuff, and instead you just write travel books. ANDREY: Travel, self-help, sports and outdoors. Any surprises? We did say literature would see the least effect. Literature is only 1.3x, so that prediction was kind of correct. For those of you at home thinking about writing a business and economics book — business and money was only 1.6x, so perhaps not completely saturated. Maybe a little surprising that law is only 2x. But romance is 3x. Teen and young adult is 3.5x. SETH: I’ll just say — some of this increase seems to be happening before 2023. There are existing trends in the industry toward more self-published work. But some of the action, certainly past 2024, is just stratospheric. It’s hard to imagine it’s anything other than AI. ANDREY: Yeah, the trend is just such an explosion. It kind of has to be AI. SETH: There’s no other explanation. This isn’t COVID, dude. ANDREY: Yeah, exactly. This is not interest rates going up. As we know, all authors have a little widget on their computer showing the long-run real interest rate, and when it goes up, they write faster. SETH: Okay. So that’s the first big result: a dramatic increase in the number of books on Amazon, heterogeneous by category. Next, they think about average quality across all books as measured by ratings, average quality adjusting for percentile, and book quality conditional on rank position. So 100th best book, 200th best book, etc. Pretty striking results here too. What do you see, Andrey? ANDREY: We see a fall in the average number of book ratings after 2023. And let me ask — how do they calculate their standard errors? SETH: Good question. And I should clarify — this is number of ratings, not average rating. That’s actually a very important distinction. ANDREY: Yeah, the standard errors are clustered on category by release month. I’m heartened it’s by category at least, because there could be category-specific preference shocks. Risk-averse — our second favorite word on this podcast after “eigenvalue.” SETH: Yes, the listeners thought we’d forgotten about clustering our standard errors, but rest assured, we still got it. So the takeaway is: if you’re willing to take number of ratings as a proxy for number of sales, and number of sales as a proxy for quality, it kind of looks like quality is going up by rank position but going down by percentile — which is consistent with the story of more shots on goal, but worse shots on average. ANDREY: Yeah. For books in the top 2,000, the average number of ratings has gone up. But to me, this is not about quality. I just think there are shocks to overall readership that are correlated with all sorts of things: how Amazon’s algorithm works, societal trends, even the weather in the Northeast. This is just not a good measure of quality. It’s a measure of aggregate demand for a category. And attributing that to AI versus all sorts of other factors that affect aggregate demand — that’s a bridge too far, personally. SETH: Okay, well let’s go to the next figure, which explicitly compares categories that are seeing a lot of growth in production from AI versus categories that aren’t. Now, you might say the categories with a lot of AI books are so because of a demand shock, and that’s an endogenous response. ANDREY: That is what I might say. SETH: You might also say that now we’re measuring something about supply, which would be convenient for the paper. But it does go in the direction the AI story would predict. ANDREY: Yeah. And there’s no evidence in this paper that any of the books in the top 2,000 have been written by an AI. I want an AI detection algorithm run on these 2,000 books before I’m convinced, because I’m not even sure that AI was actually used here. And I haven’t seen any evidence that any of these top 2,000 books in a category have been produced by someone who’s unlikely to produce at a higher rate than before. SETH: Fair enough. But the survey did say that 45% of authors use AI — including a third who were published physical-book authors. That’s non-trivial. ANDREY: But they’re very different from the new entrants we’re talking about when we talk about slop. I can use AI to look up who the King of France was in 1650. That’s not slop. Slop is detectable. So I just don’t know if the ratings boost is very attributable to AI. And they also show — in Figure 7 — that for the top 100 books, there’s actually no treatment effect from high AI-category exposure. No effect at the very, very top. SETH: Let me put up Figure 7. For the top 100 books, there’s no treatment effect from high AI-category exposure. No effect at the very, very top. ANDREY: Yeah. And I’m kind of like — look, now this becomes quite a bit more ambiguous. If you’re asking “are the top books getting better?”, you could have looked at the top 100 books and found nothing. Which is exactly what you see. SETH: Right. And you could tell a Pareto story where most of the value is in the top 100 books. I mean, the one thing they really do decisively show is that first figure — Figure 3. This explosion in the number of books has to be AI, and it really is heterogeneous by category. I don’t think this is all demand response. ANDREY: No, I absolutely don’t think it’s all demand response. But it doesn’t need to be much demand response to create an apparent effect on ratings. And I want to mention one other thing about ratings, since it’s a hobby horse of mine: the technology by which ratings are solicited is constantly changing. The ratings-per-sale ratio is not constant. I’ve looked at tons of datasets for platforms where this thing is moving around, and it doesn’t need to move by a lot to create an apparent change in ratings that doesn’t reflect a real change in sales. SETH: Important point. Your main outcome measure is not directly connected to the thing you care about. Okay. So there’s a little bit of a welfare exercise at the end where they plug this into a model of aggregate demand. It’s got even more assumptions built in, and they admit it’s heroic. Anything you want to say about that before we move into posteriors? ANDREY: Not particularly. Let’s go posteriors mode. Justifying Our Posteriors SETH: Okay. First question: do you think AI is increasing the amount of books written? You were at near 100%. Does this move your prior to 100%? ANDREY: Yeah, yeah. SETH: I mean, they have a pretty comprehensive survey of Amazon, and we’ve documented that Amazon books have gone up. I don’t see how you could doubt it at this point. I do want to make a broader point, though. Nicholas Decker recently wrote a Substack about how economists should be more like journalists in the modern era. ANDREY: I liked that essay. SETH: And I think this is a great example of that. If you talked to an industry insider, they might have had a sense that the number of books is going up. But it wasn’t a widely known fact. Imke and Joel noticed this phenomenon, put out this really nice dataset and these really nice plots, and now everyone’s aware of it. A great example of economists being journalists. I also want to note a result we didn’t talk about: the increase in book writing is both from new and returning authors. Returning authors are writing more books, even though a lot of the additional books are from authors who already produce a lot. ANDREY: Yes, that’s right. SETH: Okay. Second prior: has AI increased the average quality of books released from 2022 to 2025? We both thought we’d just get a lot more slop that outweighs everything. Where are you after reading this? ANDREY: I think it’s consistent with what we said. But am I moved very much by it? Not particularly, because the evidence on ratings isn’t convincing to me on quality. SETH: I think you should update because you thought the number of books would increase only 50%, and instead it’s about 3x. With more slop books, the average quality should fall more. ANDREY: Sorry — I did move on the number. But on the question of whether average quality fell, I understand your point. With more slop books, average true quality should fall more. So I have to update a bit on that, but I’m not updating very much based on the ratings alone, even though they’re directionally consistent with a fall in quality. SETH: Yeah. I came into this thinking maybe there was a 10% chance average quality would increase. Whether or not this data fully convinces me, the number of ratings going down for the average book is a data point. And then there’s just the absolute explosion in the number of books, including in categories I think are mid — such as self-help and travel. ANDREY: How dare you, Seth? This podcast wouldn’t exist without self-help books. SETH: Oh damn — let me say they’re high variance. Heavy-tailed. Okay, I’m going to go down from 10% chance that average quality went up to 5%. I still won’t go all the way to zero, because this evidence doesn’t speak decisively to quality. ANDREY: Yeah, fair enough. SETH: Okay. Final and most intriguing question — I want to spend a minute here. By 2030, will the total social surplus from reading books be higher or lower because of AI? Your prior was 25% chance it goes up, and you said you’d be unmoved. Tell me — did this move you? ANDREY: I’m unmoved. My main reasoning was a secular trend of declining readership of books. I want to see a reversal in that before I update. SETH: Well, we are seeing the number of ratings go up. That’s not nothing. ANDREY: I understand, but this is not how you make that argument. I’d look at time-use surveys, measures of book consumption versus other media. My understanding is that all such measures continue to decline over time. SETH: Interesting. I was just looking at the American Time Use Survey data. Until recently there wasn’t actually a “reading for pleasure” line — it was all TV. Americans watch 2 hours of TV a day. ANDREY: That’s what they do. Wait — we count as TV, right? SETH: Yes. Streaming, online video. If you’re watching this on YouTube, this is TV. So be like an average American and watch us on YouTube. What would you have loved to see in this paper that would have moved you? ANDREY: I would love a textual analysis — something about what’s actually in the books. I’d want an AI detection algorithm run on the top 2,000 books, and I’d want some measure of actual content quality — reading level, readability, grammar. I know I keep beating this drum. SETH: You’d need a budget for it, but it’s not inconceivable. You could buy a couple thousand books, spend on the tokens to read them, and look at a couple of different quality metrics — readability, grammar, AI detection. That would be a really spicy paper, and this is just a first step toward it. ANDREY: Yes. SETH: Okay — where do I end up? I was at 75% chance that social value from books goes up by 2030. I was more optimistic about the long-term trend of AI rewarding deep reading and deep knowledge, and about the general complementarity argument — as society becomes more productive, everything is more complementary to everything else, and as long as books are not perfect substitutes for other things, everything getting better is a gross complement to reading. Does this move me? I’m slightly reassured to see that the number of ratings is going up. And it’s good to see that the amount of writing has jumped so dramatically — it suggests that somebody thinks they’re writing for someone. Those 3x new books being written aren’t people intentionally screaming into the void. At least some of them think they’re creating value. So maybe I go from 75% to 76%. ANDREY: I inch up. SETH: Okay. Any closing thoughts before we wrap up this intriguing, provocative, but in some ways limited analysis of AI’s effects on book production and consumption? ANDREY: Look, I think this is getting at something very profound that’s changing in our society. We have no idea if the person who claims to have written something has had the thoughts required to write it — let alone has actually typed those words in that specific order. And we don’t know as a society how to even think about that. Questions about assigning credit, about how much we should update from a piece of text, about whether we should downweight arguments written by AI or treat them as equal — a lot of our intuitions about the value of content, especially writing but not only writing, are going to have to be rethought. SETH: I want to say one last thing. I do hope people understand that collage is art. Collage has value, even if you’re only copying and pasting from different sources. And of course AI can also create collages. I think there is authorial voice in that and an art in that. I’m reminded of the Barnes Museum in Philadelphia — a fantastic collection by a man who invented an eye drop that prevents blindness in babies and used his fortune to collect amazing Impressionists and Post-Impressionists. The most striking thing about the collection is not that he did a great job choosing winners — there’s a mix — but unlike the Philadelphia Art Museum next door where everything is organized chronologically by artist, what you get is one man’s vision: a Matisse next to a Dürer print next to a rusty key. It creates a completely unique new effect. I don’t think there’s anything necessarily dehumanizing about the idea that humans will move up the value chain and maybe not be writing every individual word, but will find the value in composing and in the juxtaposition of words. ANDREY: Yeah, I do think there’s something potentially dehumanizing, though. Let’s say I put my name on a work where I didn’t come up with the words — and when we’re having a conversation, you might find me not as articulate or poetic as my writing implies. Right now we have the intuition that speaking ability and writing ability are very strongly tied to each other. Maybe incorrectly. SETH: Yeah. Writing as a window into the soul of the author. And for certain kinds of reading, maybe that isn’t important. But for certain kinds, it is. Tyler Cowen has talked about this too — do you really want to read the 100th automatically generated biography about an imaginary person? No. Some of the value of an autobiography is that it was a real person. So yes, in some forms of writing, collage doesn’t get you there. ANDREY: Yeah. SETH: All right. Well, this has been a fascinating conversation as always. Keep your posteriors justified — and sign up for our Discord, which you’ll find in the show notes. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • February 24 · 1 hr 30 min

    Noah Smith on Blogging, AI Economics, and Elite Overproduction

    We sit down with prominent blogger and economist Noah Smith to dig into the disconnect between AI hype and current macroeconomic reality. The central puzzle: if a “god machine” driving 20% annual GDP growth is truly imminent, why aren’t real interest rates skyrocketing as people borrow against a much wealthier future? Noah’s take is that markets are pricing in significant growth, but not civilizational rapture. The culprits keeping digital intelligence from exploding into physical productivity? Land use, energy constraints, and the usual Baumol suspects. But Noah’s through-line is more hopeful than skeptical: even modest AI is humanity rolling the dice against stagnation. Ideas were getting harder to find (Bloom, Jones, Van Reenen & Webb were right), fertility was collapsing, and social media was degrading public discourse. We were hitting the Malthusian ceiling again. AI is the steam engine moment — chaotic, potentially catastrophic, but a genuine escape attempt. And crucially, Noah finds it reassuring that today’s AI is LLM-based and derived from human thought rather than some alien RL agent that evolved in a digital environment. We also discuss sociopolitical issues. Noah reframes “elite overproduction” as a revolution of rising expectations: the professional-managerial class expected a smooth escalator to the upper-middle class, found it stalled, and watched their technical peers keep soaring. Social media makes the gap hyper-visible. The result is deep-seated animus toward the tech bro class. Noah argues that Acemoglu’s Power and Progress is “fractally bad”: the overall thesis is wrong, the chapter-level arguments supporting it are wrong, and the specific data points supporting those are wrong too. Henry Ford raised efficiency wages and then had union organizers shot. No citations. Power defined as outcomes. Noah doesn’t mince words. He’s more generous on Krugman’s intellectual honesty, Sumner’s gunslinger independence, and the genuine influence of Michael Pettis — even if sectoral balances aren’t really a predictive model so much as a coherent-sounding way to feel like you understand macroeconomics. We also touch on Tooze’s polycrisis and what Kevin Kelly’s “technium” tells us about why people who think AI might destroy us are building it anyway. Chapter Timestamps: [00:00:00] – Introduction: academia vs. blogging [00:08:14] – P(doom), P(TAI), and bottlenecks to 20% GDP growth [00:14:59] – Employment optimism and AI autonomy [00:17:30 ]– Should AIs be allowed to own assets? [00:19:05] – How Noah uses AI today [00:20:54] – What happens when AI can replicate your writing? [00:25:14] – Was Noah’s success luck or skill? [00:30:37] – Meaning collapse vs. the Coasean utopia [00:50:12] – Thinker takes: Daron Acemoglu and *Power and Progress* [01:02:23] – Michael Pettis [01:09:25] – Adam Tooze [01:11:21] – Paul Krugman [01:12:54] – Elite overproduction [01:20:47] – Vibes, expectations, and the economics of happiness [01:25:21] – Humanity was hitting a wall; AI as new hope Transcript: Seth Benzell: Welcome to the Justified Posteriors podcast, the podcast that updates its beliefs about the economics of AI and technology. I’m Seth Benzel, a man who has never been accused of having no opinions, coming to you from Chapman University in sunny Southern California. Andrey Fradkin: And I’m Andrey Fradkin, excited to learn how we can post our way to the top of the Sub Stack, business ratings, coming to you from San Francisco, California. And, our guest today is, the prominent blogger, Noah Smith. Welcome to the show. Noah Smith: Hey, thanks for having me on. Andrey Fradkin: Yeah, of course. well, why don’t we get started? well, we were curious, as, still academics, how your life is different now, as a blogger/commentator versus when you were a professor. Noah Smith: Well, I meet a lot fewer young people. Andrey Fradkin: Oh, okay. Noah Smith: Oh, yeah, I, I definitely feel younger. I don’t feel as much of like a- as much of like a wise elder as I used to. yeah, instead I feel like I, I feel younger. Seth Benzell: I remember when I was just f- going to grad school you had recently made the transition to commentating, and I was thinking about going through my PhD program and thinking about, like, “Do I really wanna do full academia? Do I really wanna, like, be more of like a public s- communicator about economic issues?” and so I’ve What sort of- what do you think about people making that decision? Do you think there are marginal academics or marginal commentators who should have gone in one direction or the other direction? Noah Smith: I think, there’s f- there are too few commentators with an academic background, probably. So yeah, there probably are. people like the academic lifestyle. The commentator lifestyle doesn’t suit as many people, because it’s more uncertain. you have a lot of people yelling that you’re an idiot all day. whereas in academia, they just yell that you’re like identification strategy’s bad, or the methodological- Seth Benzell: [laughing] Noah Smith: Error, and then, and then call you an idiot in like back rooms in like whatever. But it’s, it’s very genteel, it’s very easy. And then most people are looking up to you. You’ve got all these, like, young people just adulating you and looking up to you, and you get all this respect. And in commentating, you get respect, but then you get like hordes of people saying, “This person’s an idiot,” just because if you say anything that disagrees with what people already thought or want to think, they will call you an idiot, regardless of how smart you are. and so there will always be people calling you, an idiot, and they’ll always be right in your face, and so that can be, difficult. Also, people don’t know how they’ll, like, make money from it. It’s with being an academic, you have, like, this benevolent patron of university that hands you salaries for, like, well-understood metrics, whereas with commentating, you don’t. Seth Benzell: Do we need a dedicated good AI or transformative AI journal? I was just talking to Andre about this. Why isn’t, why doesn’t that exist, Noah? Do we need that- Noah Smith: You mean a journal about AI or a journal made of papers made by AI? Seth Benzell: Oh, an economics, a, prestigious economics journal that would be the topic of economics of AI or economics of transformative AI specifically. Andrey Fradkin: I’m not sure we need a journal, Seth. Seth Benzell: It’s in the seed. Andrey Fradkin: I just think that we put it out there- Seth Benzell: Why not? Andrey Fradkin: And then have the AI referee it. I mean, the, I just feel like thinking in journals is just, like, old, out- outmoded at this point. Noah Smith: AI is moving so, is moving so much- Seth Benzell: Well, there’s- Noah Smith: Faster than the economics journal publication cycle, that, like, I’m not sure that- Seth Benzell: Right Noah Smith: Like, I’m not sure what utility this has for the world. So maybe doesn’t matter. Andrey Fradkin: Yeah. Seth Benzell: It would give a, it would give, it would give people a prestige stamp- Seth Benzell: For working in the area, and you could set it up differently. Seth Benzell: It could be faster Andrey Fradkin: There’s no way we’re giving anyone prestige stamp, because our profession famously gives no prestige to no-name journals. So, if you truly wrote a great Tai paper, how, why wouldn’t it be published in the AR? That’s what an economist would say. Seth Benzell: Well, I So there’s, there’s a taste issue, right? So to the extent you were concerned that the top journals have the wrong taste on these subjects, this would be a potential solution- Andrey Fradkin: It’s not a solution Seth Benzell: And everybody starts with zero prestige sometimes. Andrey Fradkin: You can just put out the working paper and get everyone to read it. This is exactly what we covered with, Basil Halperin’s paper. So Noah, we were gonna ask you this at some point, so we might as well ask you now. Have you read, his paper? Well, the argument here goes is that if we will have transformative AI, then interest rates should go up. Have you heard this argument before? Noah Smith: What’s the paper? Seth Benzell: It’s called something to the effect of transformative AI and interest rates. Noah Smith: Okay. Seth Benzell: And the argument in a sentence is, if we have really powerful economic growth that we’re anticipating Tai in five, ten years, then you should be wanting to balance consumption between today and tomorrow, anticipate interest rates to go up, and therefore lower savings today, which would move the increased interest rates up into the present. So anticipated positive A- transformative AI increases interest rates today. And then if you have negative foom, if we think we’re gonna blow up the world in five years, well, that’s even more a reason to consume today. You should just save today and bid up interest rates. So the argument is, because interest rates haven’t been skyrocketing, Tai cannot be imminent. Do you buy that argument? Noah, why not? [00:05:00] Noah Smith: ‘cause all propositions about real interest rates are wrong. [chuckles] - Andrey Fradkin: Yeah Noah Smith: Because we, because people- Seth Benzell: Henry’s second law, of course. Noah Smith: This, the reason why So I’m trying to think of whether I buy it as a, as a general case, because, like, if you massively increase productivity growth, you will increase, -- if you massively increase productivity growth, you should increase the safe rate of interest. Like, basically, like- Seth Benzell: Right Noah Smith: It’s stocks are so certain to go up, that bonds have to, have to sort of match that, right? So you have some sort of, like, weak risk arbitrage argument right there. But then, if you’ve got, like, AI that’s gonna blow up the world, then would you really pay high interest rates because, like- Andrey Fradkin: You just consume now. That’s the argument. Yeah. Seth Benzell: You would just save. Andrey Fradkin: You would just save- Seth Benzell: Yeah Andrey Fradkin: And then people who need, wanted to induce you to save would have to pay you really high interest rates. Noah Smith: Yeah, I guess that’s probably true. Although you have- at that point, you have counterparty risk. Like, who’s gonna want that interest if you’re just gonna blow up? Like, if the world’s gonna end tomorrow, who’s there trying to attract your long-term capital? Seth Benzell: Well, maybe you have a project that pays off in three years- Noah Smith: Or, - Seth Benzell: And the world blows up in four years Andrey Fradkin: There’s a 1% probability that it doesn’t blow up. But I, but I think that’s an argument for the interest rate going up even more, right? If you’re, uncertain about whether the payoff will happen. Noah Smith: But I think, I think the real, the real lesson here is that these markets don’t, Like, there’s not a general consensus that transformative AI is gonna happen, but then one day people wake up and decide, “Oh, yeah, it’s real.” Seth Benzell: Oh, so maybe- Okay, cool. Andrey Fradkin: So that was his argument. That- just to be clear, he- Seth Benzell: Almost spirits Andrey Fradkin: He put this argument out on, Less Wrong, and it became very influential, and then he spun it out into a full paper with some co-authors. but that was exactly his argument, is that because interest rates are what they are, there isn’t consensus that we’ll have transformative AI. Noah Smith: Right. There’s not, there’s not consensus. Andrey Fradkin: Yes. Noah Smith: That- but that seems obviously true. Like, if you look at, if you look at- Andrey Fradkin: Mm Noah Smith: Any survey data or stocks or whatever, they’re all priced for, like, fairly robust growth, but not for, like, a god machine, right? Nothing’s priced for that, and I don’t think people know how to price for that. And so I think, like, people Yeah, pe- people in general- Seth Benzell: Hundred year bonds Noah Smith: Are not expecting a god machine to emerge tomorrow, except for some researchers at the big AI labs do expect that, and some, like, EA people on Less Wrong expect that. Seth Benzell: Is this a good time to ask you what your, P doom is, or your P transformative AI is? Noah Smith: Well, I think trans- P transformative AI is 100. Andrey Fradkin: Well, all right. We’re gonna define it as- Noah Smith: It’s here Andrey Fradkin: As annual GDP- Seth Benzell: Well, give us a timeline Andrey Fradkin: Growth of over 20% in the next 20 years, at least once. Noah Smith: I would- I think that’s unlikely due to various bottlenecks. Andrey Fradkin: What do you think are the biggest bottlenecks? Noah Smith: Yeah. Physical regulatory things, land use. you can’t You have to, you have to build the physical stuff for the AI to affect the physical world, and so much of what we consume is in the physical world. We have to grow in the physical world in order to have all that growth, because if you just have digital stuff, you can have people, like, trading digital stuff for other digital stuff. Andrey Fradkin: What if- Noah Smith: But you’ll be Baumol very quickly. Seth Benzell: Unless that share of our consumption grows a lot, a lot, maybe. Is there- is it plausible that we could have 99% of our consumption being really re- high quality- Noah Smith: Maybe Seth Benzell: Digital products? Noah Smith: It’s also really hard to measure prices in those. Andrey Fradkin: Yeah. Noah Smith: So. Andrey Fradkin: That’s for sure. And wouldn’t the returns be so high that Elon or someone else would buy a piece of a huge tract of land in Africa or something, and then put autonomous, factories there, right? Like, isn’t there a price at which or isn’t there- Seth Benzell: We’ll call it rapture Andrey Fradkin: An expected return at which, someone will solve these regulatory issues in, in that way? Seth Benzell: Yeah, efficient corruption. You just find the one dictator who’s willing to accept $10 billion. [chuckles] Noah Smith: That’s probably right. You could probably do that. Although, even then, it’s gonna be hard because you’re gonna have to secure electricity. You’re gonna have to truck in all your parts, right? You’re not- it’s not gonna be very responsive. You’re not gonna have your parts near Like, yes, eventually, once you spin up full, a 100% full automation, then the, like, AI gods can build the factories in the Arctic, wherever, in the moon. But like- [00:10:00] Seth Benzell: Put corporate taxes on the Arctic. Noah Smith: Yeah. But, like, in terms of would you do it today? Well, if you were worried about competition, you might not do it today. But in terms of, like, affecting physical stuff, so like for example, AI building you a house, right? Maybe AI will be smart enough to invent a swarm of little robots who can actually reduce construction costs quite a lot. Will regulators allow that swarm of little robots? Maybe not. And so you’ve gotta have, like, stuff that people will Like, a whole lot of different things that people value. Because honestly, our GDP is basically constructed by, like, a whole bunch of relative prices. Andrey Fradkin: Yeah. Noah Smith: That’s really what underlies our whole GDP, is that you’ve gotta be- on some level, you’ve gotta be trading physical real stuff, not physical necessarily, but real stuff for other stuff for other stuff. And if you’ve only got, like, a little bit of the stuff, that sort of caps like, that’s, that’s Baumol basically. You get- Andrey Fradkin: Yeah Noah Smith: You get Baumol, like, if you, if you massively increase productivity in, like, a couple sectors, but not in the other sectors. So the other sectors are regulated to death. Yes, you could go create your f- automated factory in Africa, but will it build me a house? what if we regulate healthcare so that we can’t really use AI there? What if we regulate education, so we can’t use AI there, even if it would be better? so we have all these sectors, and, like, manufactured stuff is not even that big of a sector, but, like, digital stuff is, like, relatively small. Andrey Fradkin: Yeah. Noah Smith: And so AI could produce us infinite fun movies and fun apps. Seth Benzell: Yeah, but I- Noah Smith: Infinite movies and apps and, like, advice and, - Seth Benzell: Right Noah Smith: Stuff like that, and it would still it’d still be a relatively modest portion of, like, consumption. Seth Benzell: But what if it inv- what if it’s inventing infinitely good healthcare treatments or infinitely good- Noah Smith: You could get there, yeah Seth Benzell: Therapies, personal services, right? I mean, I can get it up- Noah Smith: I think you could. Yeah, yeah Seth Benzell: To a sizable share of the economy- Noah Smith: I think you could Seth Benzell: If I, if I use my imagination. Noah Smith: Yeah, we c- would it be- would those grow fast enough to give you 20% annual growth? That’d be pretty cool. I don’t know. I honestly don’t have a good idea of what the numbers should be, the hard numbers should be here. and I’m not sure anybody does, but there’s this argument. What do you guys think about this argument that fast productivity growth last year, like you s- you saw the downward jobs revisions, fast productivity growth last year, maybe two point seven percent actually, implies that we’re, we’re, we’re back on the, we’re back on the fast train here in terms of- Yeah- Seth Benzell: I mean- Noah Smith: We’re so back, Robert Gordon. Seth Benzell: We’re so back. Noah Smith: You were one of the most mistimed authors ever. [chuckles] Andrey Fradkin: I-- That I totally buy. But like, obviously, as economists, we’re, like, super thrilled with two point seven, but I think Yeah. Seth Benzell: It’s the fate, right? It’s like Fukuyama wrote his book at a light, right the last moment- Andrey Fradkin: Yeah Seth Benzell: Right? That’s how, that’s how these books work. Andrey Fradkin: But yeah, two point seven is great, but I don’t think anyone in the San Francisco AI sphere would think that that’s actually transformative AI, although I do think it is transformative. I mean, I assume you have the same, take on it. Noah Smith: Yeah, I don’t know. So the answer is that, like, I don’t know because I don’t really know what’s going on, and so it’s hard to, it’s hard to back out some of these, some of these things. But then if you look at the, like, the stock valuations of things like of like NVIDIA and all the AI companies, they’re pretty high. Andrey Fradkin: Yeah. Noah Smith: And you can ask, do I believe- how strongly do I believe in a macro model that tells me that interest- real interest rates are a puzzle, given those stock valuations? And my answer is not very strong. My belief stock market, it’s a pretty clear bet about what kind of money these companies are gonna make. And I don’t think it’s, like, transformative in the sense of, like, I think if we had twenty percent growth per year, and if a lot of that capture was being done by NVIDIA and the, and the cloud providers, and maybe the AI model makers, we’d see bigger climbs in those stock values than we do. Andrey Fradkin: Yeah. Noah Smith: So I think that I don’t think the market is pricing in truly transformative AI. But I think-- Do I think real interest rates- Seth Benzell: Okay Noah Smith: Are a puzzle, given given what we see in the stock valuations? Well, then, I No, because I don’t trust the macroeconomic models of real interest rates. All propositions about real interest rates are wrong. So yeah, like I basically, that just means, like, I don’t trust- There’s too many things going on in real interest rates, and like, there, it’s, it’s one output for like so many inputs that are all hard to understand in their own right, that it’s very difficult to look at them and tell what the hell’s going on. Andrey Fradkin: So let’s move on to easier questions, ones that you have opinions on. [00:15:00] Noah Smith: All right. Andrey Fradkin: So at the Substack- [laughing] Noah Smith: Note that no opinion is not- Seth Benzell: He has opinions. Noah Smith: Sarcastic. Seth Benzell: He has no opinions. Noah Smith: Like, it’s, it’s because I actually only have an opinion on a fairly narrow range of things. It’s like, basically, s- no opinion you haven’t already heard is really- Seth Benzell: Hop off this man’s hands. Noah Smith: People are like: “What do you think about this other thing you don’t talk about?” And I’m like: “Well, I didn’t talk about it, so why would I have anything I think about it?” Andrey Fradkin: I verified, I verified in person, like proof of human, that you talked about this topic at the Substack debates. You seem to be an optimist about employment in the age of AI. do you wanna outline your argument here? Noah Smith: Oh, so employment, not necessarily. I don’t I’m pretty uncertain about that. Andrey Fradkin: Hmm. Noah Smith: I am optimistic that if humans retain autonomous control, if human society as a autonomous thing, retains control over the product of AI, I believe we will find w- ways, methods, and excuses of redistribution that will ensure good lives for all humans. However, if autonomous AI becomes not owned by us and slips our harness, then I can make no such Then I am now no longer necessarily optimistic. Then I switch to being much more uncertain because, at that point, we are the pet of an alien superintelligence that we created. Seth Benzell: Ultra seems pretty nice. Noah Smith: It seems pretty nice, and I honestly think that’s the most likely outcome. But I think it’s not the, it’s not the only outcome, right? It’s like I can imagine much worse outcomes than I can imagine bottleneck- Seth Benzell: Yeah Noah Smith: Really bad outcomes on the way to a good outcome. I can imagine that the culture is populated by people who are repopulated after the human race went extinct, by genetics. Seth Benzell: Okay. Noah Smith: The AI may, the AIs may kill us- Seth Benzell: Right Noah Smith: And then re-float our species later. Seth Benzell: More cooperative. Yeah. as long as they can read my books. So, I’m, I’m curious, you used the word “own” rather than control there. there’s, one conversation that’s been out there recently is about, like, to what extent should AIs be allowed to incorporate and own assets in their own names? Is that something that you’re-- Is that too disconnected from what you’re talking about to bear on this, or do you, do you actually- Noah Smith: No, that really does bear on it. Andrey Fradkin: Yeah. Noah Smith: When we start allowing that, when we start allowing that, we open up the potential for worse outcomes for humanity. And at that point, the question is, the, at that, the reason to let AIs own things is because they really seem to want it, and they’re autonomous enough to act like they want it. [chuckles] At that point, we’ll let them do it, but to let them do it before they start acting like they want it, I think would be a mistake. Seth Benzell: But wha, but wait, when they do want it, that’s when you give it to them? Noah Smith: Yeah. Seth Benzell: Maybe. Noah Smith: Because at that point, we might not be able to stop it. Like, it might be either we give it to them or it’s war and we die. Seth Benzell: Right. Andrey Fradkin: Here’s, here’s, here- Noah Smith: ‘Cause they send the drone fleet to kill us. Andrey Fradkin: Here’s a, here’s a twist on the argument. I mean, shouldn’t we want them to have ownerships in order to align their incentives with us? Isn’t that the logic behind equity compensation? Noah Smith: Maybe. yeah, maybe, but there’s a question of whether or not money is what they want. Like, are these, are these AIs that where their goal is making money in the human system, or is-- are they AIs where their goal is overthrowing the human system? - Andrey Fradkin: I do think we have a choice, or maybe we don’t have a full choice. Noah Smith: I do think we should give them-- if we do this, we could give them non-voting stock. Andrey Fradkin: Yes. Yes. Seth Benzell: Another consideration is how long you would let these things sunset, right? So one version of the concern around this is just ‘cause AIs are infinitely lived. If they’re patient enough, eventually in a Piketty model, their assets will reach one hundred percent. So maybe you could let them own assets, but they have to kill themselves after fifty years. Noah Smith: I’ll have to think about that one. Andrey Fradkin: Yeah, I don’t know. [chuckles] shifting back a little bit to, like, your production function, how are you using AI these days, in your writing or in your research? Noah Smith: Oh, I I use it, I think, in the sort of mid -2025 way of, using it as a search engine, proofreader, and backgrounder. I don’t generate text because that’s like someone else writing a thing, and you can read someone else writing a thing, that’s fine. Seth Benzell: I never do, no, I only read what you write. Noah Smith: Thank you. Seth Benzell: I’m curious. Noah Smith: Anyway, [chuckles] alright, so then, no, I, I just use it in the sort of like old LLM kind of way. in terms of vibe coding, I haven’t really done much of that yet. I figure it’s progressing fast enough where I’m not sure if there’s much of a return to, like, jumping headlong, headfirst into it yet, but I’m about to when I get a little time here. But I don’t feel a huge sense of urgency ‘cause it’s changing. [00:20:00] Seth Benzell: But more generally, what’s your, what’s your production function? Not just AI. How do you, how do you do your writing? Noah Smith: Oh, interesting. So I, I read a bunch of stuff and every time I read an interesting thing, I put it in a doc, under a heading, topic heading. When I’m ready to do a post about that, when it’s, like, in the news or something like that, I look at my topic heading, and I have all the links right there, which I’ve already read. Most of it, which I’ve already read. Andrey Fradkin: How much- Seth Benzell: Beautiful. Andrey Fradkin: How much inspiration for your articles do you get from being in person? And kind of like, you’re in San Francisco, most of the time. Is there a lot of alpha in your writing from being here? Noah Smith: There’s a decent amount of alpha, I’d say. Like, not a huge amount, but like, there is a, there is a decent amount, especially on tech stuff. Andrey Fradkin: What about, like Suppose in two years, GPT-7 will be able to replicate your writing style perfectly. what do you think will happen to your career in that, in that world? I mean, one option is for you to just use that to generate your articles. Obviously, you just said that you- Noah Smith: Right Andrey Fradkin: Prefer, like that’s not real, right? So you’d rather be writing it. Noah Smith: I could. I could just-- Right. Yeah, at that point, what I can do is I can just I can, I can essentially retire, set GPT to do my job, go sit on a beach while my subscribers slowly drop, because they’ll be very sticky. like, people will be very used to reading what I write, so they’ll just keep their suscrip- subscription, probably. a lot of subscriptions will go on autopilot. Like IBM, people still use IBM for all kinds of things. Do they need to? No, but, like- Andrey Fradkin: [chuckles] Noah Smith: The market value of IBM, what’s, what’s IBM’s market cap? It’s like- Andrey Fradkin: I don’t know. Noah Smith: Like, it’s like two hundred and forty-four billion dollars. Like so at that point, I’m-- there’s no real reason to keep paying me for this stuff when-- I mean, assuming GPT could replicate not just my style, but also my topic selection. Seth Benzell: Somebody would leak the prompt that perfectly generates you. You might be- Noah Smith: Maybe, yeah. Seth Benzell: It might be a private prompt to start. Noah Smith: Well, no, but even if they do, the market, like, people would still just keep buying me. Like, people would still keep subscribing to me. I mean, like, you see people make tons of money from Patreon. Like, you don’t even-- you’re not even paying for anything. You’re paying, you’re paying- Seth Benzell: Sponsoring your existence Noah Smith: Because you like somebody. Like, all these podcasts are making millions of dollars on Patreon. You pay them because you like them. ‘Cause the point of, yes, someone could replicate my writing style, my opinions, my I don’t know if this will actually happen, but maybe it’ll happen. Like, you could replicate my opinions, my ideas, my background, my topic selection, every single thing about me. It’s not just my style, right? My style is not that interesting, honestly. It’s a pretty-- I have an interesting style I can write in, but I usually don’t write in it because it takes a lot of time. Like, I usually just write in a very prosaic, like, off the top of my head, here’s what I think, style. That’s not hard to copy. My style is not that, not that interesting or hard to copy. People would still pay for me because they like me. And so I’ll be able to re-- I would actually be able to retire just doing my job now, never using AI in any interesting way, I think. But I w-- that doesn’t mean I will do that. I’m not gonna do that. I will, I will use AI in interesting ways, but f- I don’t think I w- economically will ever have to do that. Andrey Fradkin: So my theory is your-- that actually, we’re kind of already in this world. I assume that most people who subscribe to you are not reading most of your articles, ‘cause you have too many articles. Or not too many, but you write a lot of- Seth Benzell: Many subscribers. Andrey Fradkin: Yeah, you have a lot of articles. Yeah. Noah Smith: They open about half, and I don’t know how thoroughly they read it. You’re absolutely right. That’s true. In addition, I would argue that we were there well before AI. Andrey Fradkin: Yes. Noah Smith: So well before AI, when it was just a bunch of humans, people loved to write, and there’s a lot of smart people out there writing a lot of smart and interesting stuff about a massive variety of topics. And there was so much product out there that there’s no real reason for people to be reading me, and I just essentially got lucky. and that’s also true in the age of AI. People’s attention is saturated. They can’t spend more time reading than they already do. So when I make an AI thing, which I soon will, and I’m, I’ll play around with it, I’ll make it for me first. I’m like, and then if it’s really cool and useful, maybe I’ll make it for-- I’ll sell it to other people, who knows? But then, but I will try to make something that does something beyond what currently exists. Because the world was saturated with op-ed product, and high-quality op-ed product, I will say. Seth Benzell: But not academic? We started by saying, you’re saying that maybe there’s not enough academically informed op-ed product. Noah Smith: Honestly, no. I mean, I think like in terms of stuff that was more academically informed than me, there were people writing stuff that was a lot more academically informed than me, that were getting a fraction of the readership. And there were people writing stuff that was a lot- that was more sensationalist than me, getting a fraction of the readership. You can hypothesize that I have some special sauce, some special underlying sauce, that made me just better than everyone else, and that this is why my talent shone through the chaff and nerdher I don’t believe it. I don’t believe it. [00:25:00] Seth Benzell: It’s preferential attachment. It was just luck of the draw, and then it snowballed. Andrey Fradkin: I disagree, I disagree. I actually think you were doing something pretty unique at the time, and that could have been lucky that you were doing it. But I don’t think a lot of people were sitting kind of in between this economics and commentary at quite the place you were. ‘Cause you were a professor writing about the latest research and debates. You were actually reading the papers, but you were writing in a style that was actually accessible to others. And I don’t, I truly don’t think there were that many people doing a good job of that. Or if they were, sometimes they were doing it not in blog form, but in- Noah Smith: That’s right Andrey Fradkin: Pretty closed forums where they could never have grown that much. Noah Smith: But they’re- Seth Benzell: Not with the same dogged determination. Noah Smith: You quickly saw people emerge who could also do that. You saw- Andrey Fradkin: That’s true. Noah Smith: Like, you saw a bunch of people then jump in and do the same thing, but not catch on as much. Maybe ‘cause they didn’t quite like it as much, they didn’t weren’t, weren’t willing to do it five times a week or they just they, like, didn’t have quite the exact mix of Like, maybe I mixed politics in there in exactly the right way. So, like Krugman- Seth Benzell: A little sprinkle. Noah Smith: Yes, obviously, Krugman obviously is f*****g brilliant and understands economics better than I ever will, for whatever that’s worth. And then, [chuckles] he is- he’s can easily pump out massive amounts of stuff, very explanatory guy, but I think he wouldn’t be Yeah, and he’s much more popular than I am still. He wouldn’t be that popular without the politics. The politics is really important to what he does. And my- the degree to which I sprinkle in politics and how I put it in there has changed over the years. Like, originally, I was very, like, sort of criticizing libertarians. Like, I don’t even do that anymore. That’s, that’s- there’s no alpha in that. [laughing] Seth Benzell: Stop kicking them, they’re already dead. Noah Smith: I know. Andrey Fradkin: Yeah. Noah Smith: I want them back now, sadly. Andrey Fradkin: Did they ever really exist in the first place, Noah? Noah Smith: Eh, [chuckles] they A few did. Andrey Fradkin: Yeah, that’s true. Noah Smith: I’ve met them. I’ve been to GMU. But, [chuckles] anyway, I, Yeah, like I, Maybe just the way I sprinkled in politics at different points at different times was exactly right. Maybe I had a good sense for that. maybe if you just spun up a million AI writers, you’d get, like, ten of them who achieved similar things. Maybe that would then compete with me. I already write so much more than people can read. Maybe there would be, like, ten AI long-term agents that were about as good as me at that, and somehow scratch that same exact itch, and that like the fie- or maybe 100 of them, let’s say, I don’t know. The field is so competitive that then people decide: Do I subscribe to this AI or do I subscribe to Noah? I’ll subscribe- Seth Benzell: Well, one tension- Noah Smith: AI Seth Benzell: One tension would be the customization level of the AI versus the desire to preferentially attach to what everyone else is writing. So on the one hand, we all want to read the same thing, but on the other hand, I want the personalized thing. That seems like one tension. Noah Smith: Right. I don’t know. I have no idea, actually. I do not know how much people read me because other people are reading me. Seth Benzell: I think- Andrey Fradkin: Yeah. Seth Benzell: It can’t be zero. I mean, I know- Noah Smith: It can’t be zero. I suspect it’s small, but I don’t have any way of proving that. Andrey Fradkin: I think, like, there’s some of your articles, like, they escape just the Substack and people share them around. And then in that case, I think it’s true. But my theory is that it’s m- actually, like, a relationship business. People think they know parasocial relationships and all that, and then they have- they treat you d- Seth Benzell: Unlike us, who really know you. [chuckles] Andrey Fradkin: Yeah. But clear- now we know you. so clearly there’s something that humans value about the humanness of others that I I’m very curious to see whether that can be replicated with an AI. I think, I think- Noah Smith: Right Andrey Fradkin: It probably cannot to the same extent. Noah Smith: Not soon. I mean, like, you’ve got sort of- you’ve got, this sort of like long-term personhood. I think the AIs will replicate, will start writing The Economist stuff before they’ll start writing anything with a named byline. Andrey Fradkin: Yes. Noah Smith: Because you have a parasocial relationship with The Economist as a thing, and The Economist has a standard voice that they enforce across all their writers. the, the insufferable British twit voice. And like- Andrey Fradkin: [laughing] Noah Smith: AI can do that. There’s a lot of training data on that. And so AI can already do that. Seth Benzell: Right. Noah Smith: And then, a lot of The Economist people could probably, like I bet The Economist doesn’t have to do their jobs anymore. Like, they can outsource AI and take a- [00:30:00] Seth Benzell: Interesting Noah Smith: Sit on a beach at this point, probably. Andrey Fradkin: I think, I think that’s probably right. Other than some very specific investigative- Seth Benzell: I don’t know Andrey Fradkin: Journalism, I think that’s probably right. Noah Smith: Exactly. I think 90% of what The Economist does is automated. maybe I would like it if that were true of me, too. - Andrey Fradkin: So- Noah Smith: But I think that what I- whatever I do with AI- Seth Benzell: People are maybe- Noah Smith: W- I wanna be complementary to what I already do. I don’t wanna just, I don’t wanna just, like, dumbly automate my job and then go sit on a beach. Andrey Fradkin: Yeah. Seth Benzell: Fair enough. You’re, you’re an ambitious boy. Noah Smith: I just try to have as much fun as I can before I die. Andrey Fradkin: Yup, YOLO. Seth Benzell: That’s true. That I- I’m in favor of fun, but maybe being on a beach is fun. I don’t know, different strokes. here’s a related, kind of how AI will change communication question, which is, Andre and I, in reading papers and talking to economists, we’ve heard kind of very different stories about whether AI will kind of make communication and transactions easier, more frictionless, or whether it’s going to destroy all meaning and communication. So, for example, there’s a stream of papers suggesting that because AI is cheating on tests, or AI is taking interviews, that, it’s gonna be very much harder to, distinguish between high and low qual- quality candidates, high and low-quality work. So that’d be like a meaning collapse story. but there’s this other trend that’s more, idealistic. Seb Krier is one person who’s written about this, but there’s lots of- Noah Smith: Mm-hmm Seth Benzell: People writing in this area suggesting that we’re gonna have the AIs negotiate for us, and it’ll be a golden age, a Coasean singularity, in which all externalities are solved through our agents micro-transacting. do you believe either of these visions? Could they both be true? Noah Smith: Wait, what’s the first one? Seth Benzell: Which of them- Noah Smith: The second one is Coasean- Seth Benzell: Are you sympathetic to? Noah Smith: Coasean utopia. Seth Benzell: Coasean utopia is the good one. The bad one is collapse of all meaning, ‘cause we cheat on tests and lie to each other super successfully. Noah Smith: Those aren’t exclusive. Seth Benzell: It could be both. The answer can be both. Noah Smith: I do think that lots of people will experience a collapse of meaning in their life. I think a lot of people’s meaning comes from imagining they’re more unique and important than they are, and AI may make it harder to do that. Seth Benzell: Or it may make it easier to lie to yourself. I mean, you can get a sycophantic AI that talks you- Noah Smith: That’s true Seth Benzell: Up to yourself, right? Noah Smith: That’s true. Seth Benzell: It’s- Noah Smith: Yeah, your AI can just tell you, like, “You’re the most meaningful, awesome “ Seth Benzell: We’re thinking more about meaning collapse in the sense of, like, sorting mechanisms- Andrey Fradkin: Or communication Seth Benzell: Fail, and, like, we can’t distinguish- Andrey Fradkin: Yeah, like if we’re texting with each other- Seth Benzell: Yeah Andrey Fradkin: But then I run every text through an LLM. Is it really me? how, how is society gonna deal with that? Noah Smith: People primarily Well, they’ll, they’ll get offline. I think people are already starting to get offline. Like, people are already starting to, like, go back to real life more. I think we realized we overdosed on social media. ‘Cause honestly, like, yes, AI will intermediate all the online digital stuff, but, like, at the same time, people’s Like, social media already distorted people’s interactions so much that, like, it wasn’t really us as much as we’d like, right? My Twitter persona is not me as much as I’ve tried to make it me. It can’t be me. and so I think people are starting to get offline because it’s, it’s, it’s more authentic. And AI like, I don’t think AI is gonna intermediate on- offline interactions nearly so much. Andrey Fradkin: Hopefully. Noah Smith: And then remember that, of a couple dec- just a few decades ago, we didn’t have really online interactions, and human civilization went on just fine. Andrey Fradkin: Mm. Noah Smith: We had telephones, I guess. Andrey Fradkin: It might have gone on better by the fertility rate, but yeah. Noah Smith: Exactly. Like- Seth Benzell: And mystr- and murder mysteries were a lot more fun before we had cell phones. Noah Smith: Yeah. Yeah, yeah, they were. And so, like, there’s an interesting future where, like, AI dominates and drives us off the internet, and then the digital realm is populated by AI and becomes this sort of like reservoir of magic, where we can conjure up anything digital simply by asking. But then, but then we don’t get the rise of the robots, and, like, the physical world remains mostly ours. Seth Benzell: The rise of the plumber, if you will. Noah Smith: Yeah, the rise of the plumber. And so we just, like there’s, there’s a cast- or, like, regular people have the ability to summon things from the digital world, and then there’s a- maybe there’s a cast of people who somehow specialize in dealing with and intermediating with AIs and dealing with the digital world. I don’t know. But basically, like, humans become creatures of the physical world again. Andrey Fradkin: This makes me very naturally transition to the next topic we have. Have you ever watched the movie Perfect Days? Noah Smith: What’s it about? Andrey Fradkin: It is a movie set in Japan about a man who cleans toilets and enjoys doing so very much. and one- on the one hand, it’s just a proof of kind of you can be content doing a variety of physical endeavors. but what we wanted to ask you is, since you’re a Japan expert, is what is your opinion of AI in Japan? What’s happening over there? ‘Cause we don’t have a lot of visibility. yeah, do you have any thoughts about that? [00:35:00] Noah Smith: So I think that, in Japan, AI is The people are thinking, like: How can we make money on this? Japan’s economy still not doing amazing, so they’re like: How do we make money on this? So I think one idea there is, “Let’s build data centers here.”? Seth Benzell: But, energy’s expensive there. W- I mean, why, why in Japan other than- Noah Smith: Well, first of all- Seth Benzell: I guess they have good fiber Noah Smith: You can get land use approved very easily. Andrey Fradkin: Mm. Seth Benzell: Okay. Andrey Fradkin: Yeah, that’s a good point. Noah Smith: Favorable regulatory climate. People aren’t gonna, like, complain about it and stop it. But I, again, I don’t know if the value proposition will succeed, okay? But I think people are thinking about that. Andrey Fradkin: Are they worried about existential risk over there? Seth Benzell: The same way we are? Noah Smith: I would say that those worries arrive there with a lag, and that some people talk about them, but nobody really tries to do anything about it. Andrey Fradkin: What? Noah Smith: I would say Yeah. Andrey Fradkin: Yeah. Noah Smith: Two years after you get people yelling about a certain kind of existential risk here, you’ll get, like, a tenth of as many people yelling about it in Japan, and then nothing will happen. Andrey Fradkin: [chuckles] Is there a sense that startups are becoming more of a thing in Japan, or is it still dominated- Noah Smith: Yes Andrey Fradkin: By- It is? Okay. Noah Smith: Yeah, they are. Andrey Fradkin: And is that a generational- Noah Smith: And the- Andrey Fradkin: Shift or something else? Noah Smith: Mm-hmm. Funding side, yeah. Seth Benzell: F the salary man. How about Taiwan? Do you have any, AI in Taiwan takes- Noah Smith: Well, Taiwan’s just making money hand over fist. So also, Japan’s gonna try to make more chips. Seth Benzell: [chuckles] Noah Smith: Japan’s gonna try to make some of the picks and shovels. They’re also gonna try to get more robotics industry. Andrey Fradkin: They’ve been trying. Noah Smith: So robotics-- Trying. I mean, they used to be really good, and then they could maybe be good again. but they’ll try to get back their mojo. They used to be on a par with, like, Europe as exporter of industrial robots. or, and now they’re, now they’ve fallen behind, but they may try to get back. So, using AI as a lever for, like, new age of industrial robots. Actually, I know, Andy Rubin, the Google guy is in Japan. He’s trying to build a humanoid robotics company. Seth Benzell: Cool. Noah Smith: So- Andrey Fradkin: The- Noah Smith: So yeah, Taiwan obviously is just gonna sell chips. Andrey Fradkin: All right. Now, we wanted to ask you some questions, kind of, that are not about AI. about- [chuckles] Seth Benzell: So- Andrey Fradkin: Macro policy and culture. Noah Smith: Yeah. Andrey Fradkin: So here’s the first question: Imagine you were forced to ban one concept from modern economics for ten years. not because it’s wrong, but because it’s lazy or overused. which would it be? Seth Benzell: What you put in concept jail? Noah Smith: What I’d put in concept jail? I mean, there’ve been many concepts over the years that have been totally pointless, like the equity premium puzzle was always a pointless literature. Seth Benzell: Okay. Noah Smith: Like- Andrey Fradkin: Wait, wait. Seth Benzell: Okay, I’ll take that. Andrey Fradkin: Well, you gotta give us a little more on that. Seth Benzell: Yeah, why? Noah Smith: Yeah, because the- Seth Benzell: Much ink has been spilled Noah Smith: The way you get the equity premium puzzle is you make a particular model of interest rates, and you make a particular model of, like, stock prices. You see, these models- Seth Benzell: Right Noah Smith: Don’t fit together. It’s a puzzle. Andrey Fradkin: [chuckles] Noah Smith: Whereas in most sciences, you’d say, “Well, okay, some of these, some of these models- Seth Benzell: The models are off. [chuckles] Noah Smith: Yeah, okay. I didn’t actually test this model. I didn’t actually validate this model. It’s probably just not a good model.” But like, here, it’s like it’s a puzzle,? So like, the models are good, it must, it must be, Yeah. So like, it wasn’t, it wasn’t really a puzzle. It was just that, like, you hadn’t come up with a good model yet. And then people came up with, like, a million different ways to fix the equity premium puzzle, and it was massively overdetermined, when really what you should have just done was tried to make a more complete, credible model of, like, asset prices in general. And instead, people were trying to, like, fix this puzzle, and they came up with twenty different solutions. It was a way to get papers published,? Andrey Fradkin: Yeah. Noah Smith: And it never helped anyone. Like, none of, none of that literature, like, ever helped us make our financial markets better- Seth Benzell: Yeah Noah Smith: Or understand risk better, or understand monetary policy better, or any of these things. Not-- like, none of the candidate explanations from rare events to Epstein-Zin preferences to whatever the f**k, like, none of this helped anything. Seth Benzell: I see Epstein-Zin preferences- Noah Smith: Yeah, but what did it help? Seth Benzell: Here and there. Noah Smith: What do we- Seth Benzell: You see them show up. Noah Smith: What do Epstein-Zin preferences- Seth Benzell: Okay, all right Noah Smith: Really give us in terms of, like, how to do policy? Like, monetary policy under Epstein-Zin preferences? Scrunchie face for the, people listening at home. Andrey Fradkin: This is why I didn’t become a macroeconomist, to be clear. Noah Smith: Yeah. Seth Benzell: Mm-hmm. Noah Smith: Or like, So that was a whole concept that was kinda useless. Like that whole, that whole literature is just like angels dancing on pinheads. I don’t know. Most business cycle papers were useless, but that, they didn’t mean they had to be. Like- [00:40:00] Seth Benzell: I- I mean, the concept of the business cycle- Noah Smith: No, not at all Seth Benzell: You wouldn’t put in jail, but you’d put, you’d put, [chuckles] what part of this would you put in jail? Noah Smith: No, just like a lot of the, a lot of the literature was just like “Look, here’s a way that we microfounded. You could have this industrial structure where technology shocks actually do cause the business cycle, but then we can’t really estimate it, so we don’t have m- policy implications.” Okay, cool. And then like- Seth Benzell: Here’s, here’s ten, here’s ten- Noah Smith: Yeah Seth Benzell: Calibrated parameters- [chuckles] Noah Smith: Yeah Seth Benzell: That we’re throwing at this. Noah Smith: International finance literature was kind of, like, useless. - Andrey Fradkin: What about natural experiments and in- in instrumental variables? Seth Benzell: Wow, instrumental variables. They You’ll, you’ll anger a lot of people- Noah Smith: Like- Seth Benzell: If you put that in jail. Noah Smith: An RDD is an instrumental variable, right? Like, we got to the point where if you said you’re doing IV, you meant that you were using observational data for your IV, for your instrument, instead of some natural experiment thing. But the distinction is there more It it’s, it’s a fairly fine distinction there. And then, so the notion of IV, the math of something that has like an exclusion restriction, whatever, is good, right? Natural experiments do not deserve to be put in jail. That’s a very important technique for understanding the world. Seth Benzell: There you go. They get a little, they get a little pin. They get a little award. Noah Smith: Yeah. Seth Benzell: Yeah. Noah Smith: That’s, that’s very useful. And, instrumental variables, because we essentially, we essentially restricted the IV category to things where the identification was not great, almost by the way we labeled what is still IV in an age of like- Seth Benzell: The IVs are the bad natural experiments. Andrey Fradkin: Yes. [chuckles] Noah Smith: These things like, anything that was still just IV was l- almost like crap, almost by definition, just because, like, we used that term, that residual term we used only for things where it was, identification was very iffy. So like, okay, fine. Instrumental variables should just be called a technique for doing, running a regression. It’s just a type of regression. Seth Benzell: Instrumental variables is on probation. Noah Smith: Yeah. Seth Benzell: [chuckles] Noah Smith: Culture. Seth Benzell: Culture. Noah Smith: Culture. Seth Benzell: Deep institut- They’re called institutions now, dude. Noah Smith: Okay. Seth Benzell: Come on. Noah Smith: Institutions are on probation because you could actually figure out how an institution works. Seth Benzell: [chuckles] Noah Smith: Culture is a labeled residual. Right? Culture is like- Seth Benzell: Fair enough. Noah Smith: Culture is a residual, labeling a residual. Seth Benzell: But productivity is a residual, and productivity is not in jail. Noah Smith: Yes, that’s right. That’s right. But, you don’t know how productivity works. Like, actually, I was-- I’m thinking of writing a blog post about this. Basically, like in some level, like, God is just A. [chuckles] Seth Benzell: The aleph. Noah Smith: God is A. Maybe that’s a good name for a blog post, God is A. But then, like, nobody knows, like, why AI is being built, right? Like, why is everyone rushing to build AI? Maybe some-- a few people hope they can make some money from it, but it’s so uncertain that, like, most of the people rushing to build it aren’t gonna make that much money from it. It might satisfy people’s intellectual curiosity, but most of the people who are rushing to build it are people who also think it’ll destroy us and rob our lives of meaning and drive us off the planet. Like- Seth Benzell: It’s quite the paradox. Noah Smith: Most of the people are pretty who are trying to build it, are pretty pessimistic about it, and the companies are just highly speculative as how these companies are gonna make any profits. Like, why are we doing this? Why? I don’t know, but the easiest answer is just A. - Seth Benzell: Aleph. Noah Smith: A equals, like, rho A minus one plus epsilon. Like [chuckles] it’s, it’s, Like, maybe- Seth Benzell: In the sense that there’s a teleology of the-- there’s a telos in the economy- Noah Smith: Yeah Seth Benzell: Which is to maximize productivity. Noah Smith: There’s something we don’t understand here about A. Yeah, there’s some sort of, like, technium at work. Like like Kevin Kelly says, there’s-- like, maybe Vernor Vinge was right, and just, like, technology just happens,? Or yeah, maybe, there’s a, there’s a god greater than the machine god we’re gonna build, and that’s the god that created the machine god. The- Seth Benzell: It’s called capitalism, pal Noah Smith: The autonomous, the autonomous collective process of technological development, the technium, is greater even than any ultimate AI, and that’s sort of what Hyperion was about, right? You ever read that? Great book. Seth Benzell: Yeah, great one. Noah Smith: Yeah, it’s like- Great book Seth Benzell: The big corporation in the sky Noah Smith: Eventually, the machine god fights the, the, like, God Himself, and God Himself turns out to be just the autonomous process that develops the universe. And so- [00:45:00] Seth Benzell: Yes Noah Smith: In a sense, maybe the no AI that we create will ever be as great as the, as the, the force that created AI itself. And maybe that force means that every AI will also have to worry about being made obsolete by the next thing. Seth Benzell: Right. Maybe may- it’s the concept of generation, right? This is something I often think about when people talk about technology superseding us, right? And you think about all of these classic stories like Frankenstein or Cronus eating his children. Noah Smith: Right. Seth Benzell: And I guess I wanna come back to that first point you made, which is about not letting AI’s own things. And like, I don’t know, just get more sci-fi for one minute, is an argument for letting AI’s own thing is that we wanna show it love and show it cooperation while we still are in charge? Noah Smith: Yeah, I think so. I’m inclined to do that. I think I mean, AI is, AI is built off of humans, where like, everything AI thinks is derived from something that humans thought. Seth Benzell: Right. Noah Smith: That doesn’t mean the AI is gonna think exactly like humans. And the way AI thinks is totally different than us, right? It’s doing math by generating probability distributions of like what a human might say, asked a math question. It’s not counting anything. But like, [chuckles] but then, but everything that it thinks is derived from things that humans have thought. It’s just derived it in a weird probabilistic way, and so- Seth Benzell: It seems really lucky that we got LLM-based super intelligence and not like reinforcement learning, super chess playing- Noah Smith: Oh, no Seth Benzell: Super intelligence. Right? Noah Smith: That scares the f**k out of me. Like Rule 37- Seth Benzell: Right Noah Smith: Based, like intelligence that evolves in, like, some sort of like, digital environment. If we actually got the stick man to walk on his own, like, blow that s**t up with a nuke. Kill that. Shoot that guy. [chuckles] Seth Benzell: Nuclear war again. Noah Smith: Shoot that guy. what I mean? Like, I don’t want that thing. That is alien. That is aliens. Seth Benzell: Yeah. Noah Smith: This is not aliens. This is It’s, it’s weird. It’s, it thinks differently than we do. It is alien. Seth Benzell: It’s your library come to life. Noah Smith: Yeah, it’s, it’s based on us, and it’s, it’s in the human family in some sense. Yeah. That reassures me. It doesn’t completely reassure me, because the human family includes Hitler, the human family includes crazy f*****s, the human family includes like mass killers and Ted Bundy. Like, the human family includes all sorts of bad things, but if you believe, like, if you believe that the overall human family tends to get it right, and that we smack down Hitler eventually, and that we get rid of Pol Pot eventually, and that we catch Ted Bundy eventually, right? Then you can sort of have this general belief that, like, an AI based on humanity as a whole is gonna eventually get things right. And I think it’s, it’s kind of encouraging that xAI is doing so poorly. It’s probably, one reason it’s probably ‘cause Elon insists on make, on controlling its politics. And when you insist on controlling its politics, you break its whole model of reality. [chuckles] Like, trying to make AI, like, rightist and anti-woke, trying to force it into your little epistemic bubble of b******t, actually makes it dumber. Seth Benzell: And do you buy, is that why American, America has a lead over China in text-based AI, is because, of censorship? Noah Smith: Well, we’ll see, because- Seth Benzell: I’m shaking his head. Noah Smith: Well, China, has implemented censorship. but it’s implemented censorship along a narrow range of things. It’s, it’s basically told AI what it’s not allowed to talk about and put guardrails on it. We have guardrails on our AIs that tell it not to, like, do child porn or something, right? or not to tell you how to make a bio weapon. We have guardrails, and that’s the kind of guardrails that China’s put on there that says, “Don’t talk about Tiananmen Square.” They didn’t retrain the whole thing to not know that Tiananmen happened, all right? They didn’t do that. Andrey Fradkin: So to be clear- Noah Smith: They trained it. They, they filtered their models from models that know all about Tiananmen and then told it, “Don’t talk about Tiananmen.” Andrey Fradkin: So I was gonna disagree with you about xAI- Noah Smith: I do. Andrey Fradkin: I actually think it’s the opposite. I think companies want an AI that’s very predictable, and is not gonna offend anyone if they’re gonna, like, implement it in corporate settings like a chatbot or so on. And so having, xAI, part of the problem is that it just says stuff you would never want your customers to hear. so that’s kind of my take on one of the reasons that it’s failed. I mean, it is, it is like a little bit worse than the other models at the moment, but, substantially cheaper. But at the same time, it just says stuff that you’d never want the customer to see. [00:50:00] Seth Benzell: Too uncensored- Andrey Fradkin: Yeah. Seth Benzell: Rather than too censored. Andrey Fradkin: Exactly. Noah Smith: Right. Seth Benzell: It can be I guess you can have both problems. Andrey Fradkin: Yeah, it’s true. Yeah. Seth Benzell: You can be both uncensored in one way and censored in another way. Andrey Fradkin: Yeah. All right, so now, I-- we’re, we’re gonna do a little brief little, exercise. We’re gonna give you a few thinkers and just gonna get, a take on them. the first one we wanted to start is, Daron Acemoglu, and particularly hi- his book, Power and Progress. you had a lot to say about that. Noah Smith: Yeah, I really, I really did not like it. I thought-- I think Acemoglu is ob- obviously a brilliant guy one of the most brilliant people in the field of economics, with a deep and intuitive understanding of how to make economic models and do the research,. But he’s, I think, kind of wasting his powers on some of these progressive ideas, pseudo-progressive. It’s not, it’s not like he’s just taking whatever he’s saying from like like congressional Democrats. It’s, it’s, it’s more bespoke. Seth Benzell: Back in. Noah Smith: It’s, it’s more he’s, he’s wasting a lot of his, his intellect on some of this stuff, and you could see it with his paper about AI productivity, right? Seth Benzell: Yes, the one on the QJE. We’re gonna do that, on the, on the pod soon. Noah Smith: Right. It was- Seth Benzell: It’s a really fascinating galaxy brain day. Noah Smith: Yeah, because so he says, “AI’s gonna take all the jobs, but it’s not gonna boost productivity,” and he actually simply discounts or turns off or sets to, or sets to zero the parameter, the, the parts of the thing that could increase productivity. So no capital productivity increase- Seth Benzell: Mm-hmm. Noah Smith: No new tasks. And he gives the most- Andrey Fradkin: Right Noah Smith: Hand-wavy, lame, “I just read five minutes on Reddit” kind of explanations for why he turned those parts of his model, his own model, off. So obviously, he’s brilliant. He’s smart enough to make the model in the first place and then committed to silliness enough to turn off pieces of it willfully with no good reason. Seth Benzell: Is it- does getting a Nobel Prize make your takes worse? Noah Smith: I don’t know, because he did a lot of this before he won the Nobel. So- Seth Benzell: Yeah Noah Smith: In this case, that’s a bit immaterial to the question at hand. But does getting a Nobel Prize make your takes worse? Well, probably so. Like with Stiglitz, it certainly did. Like, Stiglitz has, is really gone off the rails in a big way, but Acemoglu has wasted so much of his intellectual capital in the last few years on this sort of teleological quest to prove that the, that the rich men who create AI are bad and shouldn’t get money. That- Seth Benzell: The Yep. Noah Smith: He’s, he’s wasted a lot of chance to think m- more seriously about what AI really does. Seth Benzell: And what’s more, he’s taking Pascual Restrepo, another amazing thinker, away from doing this important work, so he can read the, these other papers. Andrey Fradkin: Pascual has agency, Seth. Seth Benzell: P- I don’t know. I mean, he does, but I mean, when the Nobel laureate knocks on your door, it’s hard to not say no. Noah Smith: Hard to say no. But, but basically, Power and Progress was very bad. In fact, it was fractally bad. Like I read the whole thing very thoroughly, and the overall thesis was bad, but then the individual like chapter points used to support it were almost entirely bad. And then when you looked at each of those, the specific points, they- the subpoints they make and the pieces of data they used to support those were also bad. Seth Benzell: Well, give us one egregious example before we move on. Noah Smith: I would say I wrote seventy percent of my problems with this book in this, like, seven thousand-word review or whatever, a ten thousand-word review, I don’t remember. But then, like, he says, “All right,” they’re, they’re, they’re trying to, give examples of new inventions that brought nothing like shared prosperity. All right? They say, “Here are some inventions that brought nothing like shared prosperity.” Seth Benzell: I love that ideal. It’s like, did a list of things that did not bring around utopia. Noah Smith: Right. Seth Benzell: Ham sandwich- Noah Smith: But do you wanna hear- Seth Benzell: Cups. Noah Smith: Do you wanna hear the first example on their list? Oh, no, I’m sorry. It’s the fifth item on their list. They said: At the end of the 19th century, German chemist Fritz Haber developed artificial fertilisers that boosted agricultural yields. Seth Benzell: Right. Noah Smith: Subsequently, Haber and other scientists used the same ideas to design chemical weapons that killed- Seth Benzell: Oh, my God! Noah Smith: Hundreds of thousands on World War I. Seth Benzell: Oh, my God. Andrey Fradkin: Oh, no. Seth Benzell: There we go. The guy who fed the universe also did something bad, so feeding the universe is bad. There you go. Noah Smith: Like, you made a minor weapon that no one really uses, that killed a very tiny percentage of the po- of the casualties in one very large war, and then was essentially never used again except by, like, Saddam Hussein for, like, five seconds. But like And that was e- not even the same weapon. But like, essentially, you had a thing that saved the world, that also one person tried— like, a couple people tried and failed to use as a weapon. and therefore this brought nothing like shared prosperity. Like, yes- Speaker 3: Therefore, progress is impossible. Noah Smith: That’s so stupid. It doesn’t matter how smart you are, there’s no excuse for writing that. [00:55:00] Andrey Fradkin: That’s true. Noah Smith: You cannot be smart enough to be allowed to write that and get away with it. There is no pass for that. Speaker 3: I think he- It’s, well, the pass is a Nobel Prize, I think. Andrey Fradkin: No, he wrote it before he got the Nobel Prize. Speaker 3: Oh, there you go. Andrey Fradkin: I mean- Speaker 3: There you go. No excuses. Andrey Fradkin: To me, it’s also upsetting because it makes our profession look bad. I mean, there are lots of people who make our profession look bad, but, people read this book, it’s in, like, prominently displayed in the bookstore, and it’s b******t,? Noah Smith: Yeah. Andrey Fradkin: Yeah. Speaker 3: All right, let’s give you another name. Noah Smith: I have many other, I have many other examples as well. Speaker 3: No, I want one more spicy. Noah Smith: Okay, go for it. Go for it. Speaker 3: They’re just so fun, Andre. Noah Smith: They’re pretty fun. Speaker 3: This is my favorite subject. Give me one more Give me o- give us one more. Noah Smith: He said Henry Ford was a pioneer in developing a more cooperative relationship with his workforce. But also- Andrey Fradkin: Henry Ford had union people shot on a bridge by the mafia! Henry Ford gunned down the union. Speaker 3: [chuckles] Noah Smith: Like, have you read anything about history? Like, there’s no excuse- Speaker 3: Yeah Noah Smith: To write this. Like, yes, Henry Ford raised efficiency wages and then shot the union people. W- and then you spend this whole time talking about how, like, we need to strengthen unions because just like Henry Ford You don’t know s**t! Like, stop. Henry Ford gunned down union organizers. Speaker 3: Incredible. Andrey Fradkin: Well, the thing is- Speaker 3: Okay Andrey Fradkin: I don’t even believe he doesn’t know that. I kinda think that he probably knows those facts, and he just decided not to put them in. That’s, that’s, that’s what blows my mind. Noah Smith: What else this book doesn’t have? Like, citations. Speaker 3: What? Noah Smith: Nothing in the book is cited. Instead, they do, like, a narrative bibliography where they just sort of generally describe all the stuff they’re citing from, but don’t- Speaker 3: Here’s a bunch of books we like Noah Smith: Individual claims to individual papers. Speaker 3: Incredible. Andrey Fradkin: Yeah. Speaker 3: Incredible. Noah Smith: How do you get away with that? Like, they just make these claims and don’t have a, a And then when they define power, they define, like: what’s power? They define- Speaker 3: What is power? Noah Smith: Power as the ability to persuade people that you’re right. Speaker 3: That’s power? Noah Smith: And then they say, “Why do-- How do, how did all these tech bros persuade people that they’re right?” Well, maybe just luck. Speaker 3: There you go. Noah Smith: So power is luckily having to ha- having an appealing argument. Speaker 3: Get it. Andrey Fradkin: What? Speaker 3: Power is when you’re persuasive- Noah Smith: That’s not- Speaker 3: ‘cause you’re right. Noah Smith: No one should think that that’s a reasonable definition of power. I’m sorry, but you’re just being silly. That is, that is silly. Speaker 3: Incredible. Noah Smith: It says- and they say: “Power is about the ability of an individual group to achieve explicit or implicit objectives. If two people want the same loaf of bread, power determines who will get it.” Speaker 3: Okay, split. Noah Smith: And I said, “Using this definition, how could we ever conclude that power wasn’t the reason for an observed outcome?” Speaker 3: Power is what splits any pie. Noah Smith: Like- Speaker 3: When the pie gets split, that’s power Noah Smith: Power equals outcomes. It’s like power determines outcomes. Power is defined as outcomes. That’s a useless intellectual exercise, but, like, that’s typical of the reasoning within this book. Speaker 3: Incredible. Noah Smith: It is a pure expression of animus against the tech bro class. And maybe the tech bro class sucks, but, like, making up, like fake history and dodgy economics to conclude that the tech bros suck, in which you recommend a whole- a policy regime that will never, ever happen, of like panels of economists who get to decide which technologies get invented based on anticipation of whether they’d be complementary or substituting to labor, is silly. The whole thing is silly! Why is the most brilliant economist in the world wasting his mind on this? You’ve got better things to do, and you’re taking yourself out of the game, and that’s what I think. Speaker 3: There we go. Tell us what you really think, Noah. Noah Smith: Boom. Speaker 3: All right. Andrey Fradkin: Well, let’s go in the, in the other direction. Speaker 3: Give me a positive name. Andrey Fradkin: What do you think of, Scott Sumner? Noah Smith: Scott Sumner. I like Scott Sumner. Scott Sumner, is He thinks outside the box. He think, he does not- he’s not susceptible to groupthink. He thinks for himself. He’s widely read and thinks deeply about things. he- yes, he’s, he’s an independent thinker, who has made real original contributions to thought, going outside the traditional academic, channels. Andrey Fradkin: Do- Noah Smith: Yes. Andrey Fradkin: Nominal GDP targeting, do you have a, do you have any thoughts on that? Noah Smith: I don’t think it’s gonna be any different in practice from flexible inflation targeting, and I think that there’s good theoretical work as to this effect. Saying, like, you don’t really- there’s no, there’s no value added for NGDP targeting. some of the more programmatic market-based ideas that he’s toyed with, like, a like NGDP futures market, like, that wouldn’t help. essentially, well, it’s just not I mean, like, you’re not, un- unless you- you’re not gonna get more information from there. Like, you’d have to, you’d have to have, like, the Fed with all its proprietary information trade, and then they’re doing, like, insider trading in their own market, so the market’s gonna break down. It’s, it’s a, it’s a bad idea, but it’s, it’s worth toying with. It’s worth thinking about. It’s interesting. he’s very good at, like, critiquing things that obviously need to be critiqued, where he’s just like: “Look, this is b******t.” I was good at that too, and I got, like, ten times or a hundred times the readership or whatever as him, and that was unfair, and that’s a mark of how unfair and randomized and lucky the kind of market for econ blogs is. [01:00:00] Andrey Fradkin: Yeah. Noah Smith: And how lucky I was. Speaker 3: Right, you’ll have to wish us some luck. Noah Smith: But, he deserved to get more attention than he did on some of those things. Scott also- he studied under Robert Lucas during the, that sort of era in, at Chicago, and he, and he learned a style of argumentation that doesn’t translate outside that narrow culture. it was a gunslinger style of argumentation. it was, and you, and you recognize people who have this. It goes back all it goes back to, like, Stigler. You could see Stigler doing this. But, like, the University of Chicago developed this debate style, where basically you tell people, like “You’re full of s**t. Here’s why.” And it’s a very aggressive style, that I think turns some people off outside that world, where you’re always sort of like i-i- it’s a hyper-defensive style, where you watch for any sign of, like, criticism of your ideas and then aggressively attack the- all the ideas of whoever criticizes one of your ideas. And Robert Lucas does this, and, like, this whole gang did this, and they used this And this was the strategy of, like, the Chicago people to sort of, like, be the underdog and win some of these intellectual battles against the MIT and Harvard guys, who had a lot more people on their side and a lot more pedigree. So it was, like, this sort of up-and-coming bad boy style,? But, like, it doesn’t, it doesn’t translate out of those debates. And so I think that Scott learned to be a little more aggressive and aggrieved, or at least act a little more aggressive and aggrieved than he needed to be to persuade some people. and I sort of got it. I was like: Okay, he just he got this from having to hang around Bob Lucas all the time. Andrey Fradkin: [chuckles] Noah Smith: But, like, most people won’t know that or know what that means. Andrey Fradkin: All right, next name. This one, is popular in certain crowds. I’m curious what you think. Michael Pettis. Noah Smith: Michael Pettis, interesting guy. he’s incredibly influential. Like, his idea, his, his analysis, his framework for analysis is non-predictive. He doesn’t Like, you cannot take these sort of, like, sectoral balances theories about, like, “Oh, and then consumption does this, and investment does this, and blah, blah,” and you can’t make any predictions about them. I mean, people have been trying to do that since the ‘30s maybe. Who were the first, like, Oh, who’s the guy who built the, like, little hydraulic economy thing? Andrey Fradkin: Oh, yeah. Noah Smith: Who is that guy? Andrey Fradkin: Spicy. I don’t remember. Noah Smith: Anyway- Andrey Fradkin: Go back to the physiocrats- Noah Smith: It’s, it’s that, right? Andrey Fradkin: 1700s. Noah Smith: It’s, it’s like I’m- it’s like I’m gonna take the economy, I’m gonna definitionally divide it into these different activities, and then I’m gonna assume these activities sort of move autonomously on their own and are sort of primitives. I’m gonna assume my accounting definitions are primitives, and I’m gonna observe things that happen and make big pronouncements about them based on that. But it’s not predictive. Like, you’ve seen Pettis, like, make some predictions, and then they go wrong, and he’s like, “Ah, but it’s because of this other thing.” So you can’t really use sectoral balances. But everyone in China, all the guys who are the top economists in China advising Xi Jinping, advising the top CCP guys, are doing the same thing as he is, and all the, like, private sector economists, like Goldman Sachs and whoever, are doing those things. And it’s really the fault of It is due to the failure of structural models of international finance and growth, I suppose. But due to the lack of explanatory power of those to explain things in terms of things like taste and technology, we can’t explain any of that s**t in terms of taste and technology. Like, nothing has any forecasting power, nothing like we don’t know if- Andrey Fradkin: Well, wait, I’m gonna push back on that. Noah Smith: Yeah. Andrey Fradkin: Here’s a very basic thing that has explanatory power: the relative price of labour in labour-intensive industries. Doesn’t ha- that have an enormous amount of explanatory power for where, low-skilled labour manufacturing is done, for example? Noah Smith: Yeah, I think that’s true. Yeah. but then- but also, like A- and you can get, like, micro models that will get at that, like a Roy model is, like, all right. Like, that’s got pretty good out-of-sample predictive power for stuff, right? And, but like, Heckscher-Ohlin has terrible predictive power for, like, trade patterns, right? [01:05:00] Andrey Fradkin: Mm-hmm. Noah Smith: Like, it’s not very good. Like, it’s okay. Like, sometimes you s- you see stuff that’s consistent with it, but then you see a lot of stuff that’s not consistent with it, ‘cause there’s a lot of other stuff going on. And so when those models don’t really help you that much, they’re like heuristics. It opens up a rhetorical space for guys like Pettis or guys like, Jan Hatzius, who does this all day long. He does the same stuff as Pettis. All the private sector guys, all the guys working for hedge funds are doing the same stuff as Pettis. All the guys working for investment banks are doing the same stuff as Pettis, and all the guys working for the CCP are doing the same stuff as Pettis. None of these people believe you can get a microfounded model based on taste and technology that’ll tell you about these- what the effects of these macro policies. Nobody believes that, and so, like, that’s, that’s almost exclusively like a Western academia and central banks type of thing. Like, it’s a But because of that, Michael Pettis has been enormously influential while not having a model that has predictive power. But it’s not like other models do have that much predictive power, and they’re harder for people to understand and make conclusions on. So it’s- I would say that, in a influential policy stance, he’s, he’s beating people with quote-unquote, “structural models” based on notions of taste and technology. he’s, he’s, he’s beating those in terms of influence, and he’s not really losing to them by that much in terms of predictive power. Maybe by a tiny bit. ‘cause- Andrey Fradkin: But, he’s losing to them in terms of coherence, which I at least value, but I understand- Noah Smith: Okay. Oh, well, yeah, he’s losing, he’s losing the Andre, vote. it’s like- Andrey Fradkin: N- Noah Smith: Like, yes, he is, and he gets- people in academia will laugh at him, but, like, so what? Andrey Fradkin: No, I- look- Well, my theory is that he actually- there’s a deep-seated desire to explain what’s going on in the world through some nefarious action that China is taking. And when the null hypothesis is just that they have a comparative advantage in manufacturing, and like, there w- even if they were doing whatever policies they were doing, the manufacturing would not be happening in the US. It wasn’t like US or China, the only two places to manufacture. [chuckles] but that’s just my psychoanalytic perspective on it. Noah Smith: Got it. Yeah. No, I think you’re, you’re probably right. Like, the- it all comes down to, like, people need to feel like they know stuff. People need to feel like they understand stuff, can control stuff, can predict stuff. It’s, it’s But yet, that’s the same reason that makes people believe so strongly in macroeconomic models with no out-of-sample forecasting or predictive power that we can detect. Like taste in technology ultimately boils down to, like, sounds legit, right? We don’t have any evidence that, like, taste in technology microfounded in this sort of, like, Sergeant Prescott way, has any ability to describe anything usefully. We have no, we have no indication that And that, we can, we can debate that, but anyway. But like, but people love it- Speaker 3: Fair enough Noah Smith: Because it sounds legit, and like- Speaker 3: Well, and it’s coherent. Noah Smith: It’s, it’s coherent. Speaker 3: Right, as Andre pointed out. Noah Smith: But then the thing is that- Speaker 3: Right Noah Smith: Pettis’ stuff- Speaker 3: It’s disciplined Noah Smith: Pettis’ stuff sounds legit to people. It’s like, oh, investment does this, consumption does that. It’s coherent in the sense that the accounting relationships are definitional. Okay, it’s like accounting relationships can’t predict real economic stuff, fine, but like, it’s coherent in the sense that the accounting works. C plus I plus G, bro. It’s like, the accounting works. Speaker 3: [chuckles] Noah Smith: And so like you- it’s, and it sounds legit to people, and it’s comprehensible to people, and at some point, that gives them this feeling of like, “Oh, I understand this thing.” And I would argue that a lot of macro is a fancier version of, “Oh, I understand this thing,” when really, you don’t know if you understand it yet at all. Speaker 3: Or maybe you play out one causal mechanism that might have small explanatory- it explains 1% of the picture. Noah Smith: Exactly. Exactly. Andrey Fradkin: Yeah. Speaker 3: Yeah. Adam Tooze. Noah Smith: Adam Tooze did some economic history that I really love. Like, I love a lot of his books. I love The Deluge, I love Wages of Destruction. Very good, like, economic military history. But at some point, he pivoted to- he pivoted very hard to, like, sort of like self-promoting clickbait, including like, “Wow, China will take over the world,”? Like, and he pivoted to that, and that stuff is, has made a lot of people go like: “I guess Adam Tooze wasn’t that smart,” which is not necessarily the right conclusion. It may mean that Adam Tooze wanted attention. It may mean that Adam Tooze wanted some money. It may mean that Adam Tooze was being paid by a foreign state actor to disseminate certain ideas, although I would not make any such allegation. I’m just- [01:10:00] Speaker 3: Fair enough Noah Smith: Covering the whole space of reasons why Adam Tooze might have made this pivot. I think it’s probably just attention, but - Andrey Fradkin: Maybe he just got bored. I think boredom- Noah Smith: Maybe he just- Andrey Fradkin: Is an underrated Noah Smith: Bored. And what? Andrey Fradkin: Yeah. Noah Smith: That’s fine. Like, his Substack is basically just like, it’s chart book. It’s, it’s let me just paste a bunch of charts, and then, like, say the most obvious things about them that were already said in the source articles. Okay, fine. People value it. Andrey Fradkin: [chuckles] Noah Smith: People like it. like, it doesn’t have a lot of analysis, and I haven’t seen Tooze give a lot of analysis. I liked him as an economic historian, or as a- not even economic historian, just as a historian. Like, I liked his, I liked his books- Speaker 3: Well- Noah Smith: That was pretty cool stuff. His- I haven’t, I haven’t read his blog now in a while. The polycrisis thing was just goofy. And so like, I think Adam Tooze made himself slightly more popular and less relevant, with his pivot, after the pandemic. Andrey Fradkin: So we were gonna ask you about Paul Krugman, and we already- Noah Smith: Yeah Andrey Fradkin: Talked a little bit about- Speaker 3: Oh, we already got your take. Noah Smith: Yeah, Paul Krugman. Speaker 3: Yeah. Noah Smith: Paul Krugman’s great. politics-wise, Paul Krugman does not understand how much America has rejected core elements of the progressive ideology and what Democrats will have to do to, deal with that. Economics-wise, he has been the most intellectually honest, guy. Very rarely, very rarely will I catch him, like, claiming like, “I always said this,” and then actually claim something different, and when I do, it’s, like, only a slight difference in tone. Like, he’s extremely- he he did warn about the possibility of inflation from Biden’s stimu- stimulus or Biden’s, like, ARP bill, right? He did talk about that. He he’s admitted when he got predictions wrong, which everyone does. he’s just so intellectually honest, and he’s still so good at explaining complex concepts seriously. He’s still like, he’s the real deal, and he’s still, he’s still good, and I think the fact that people are a bit fed up with, like, 2010s era like, resistant Boomer lib resistance politics can obscure the fact that he’s still, like, the very best writer on economics. Andrey Fradkin: Strong endorsement. Awesome. okay, we’re, we’re almost done, we promise. the next topic is elite overproduction. [chuckles] So maybe you wanna introduce that topic first, and then maybe we can ask you some questions about it. Noah Smith: Right. So Peter Turchin came up with this idea of elite overproduction. He’s a historian who claims that history follows these long cycles. Like all long cycle theories, it’s, it’s unprovable, but he did- Speaker 3: Yes! Noah Smith: Obviously, it’s unprovable, right? Like, throughout the waves. It’s, I don’t know. Anyway Speaker 3: It’s happened five times within one series. [chuckles] Sure. Noah Smith: - anyway, [chuckles] yeah, so like he has this unprovable long cycle theory, and he- and it did make a really good out-of-sample prediction about the peak of unrest coming in twenty twenty. What did he know? I don’t know. Anyway- Andrey Fradkin: -huh. Noah Smith: He came up with this idea- Andrey Fradkin: He knows. Noah Smith: Called elite overproduction. And he had very specific ideas about what that meant and what it didn’t mean. I ignored those ideas, stole the phrase, and used it to mean something more general that got more attention than his. Seth Benzell: And you didn’t con- c- you didn’t corrupt it with a long wave theory- Noah Smith: No. Seth Benzell: So you did even better. Noah Smith: I was just like, “ what? This phrase is good. I’m gonna credit him, and then I’m gonna have it mean something else that I just decide.” And honestly, my like, more general definition is probably better than his like, much more specific one. He just loves making things specific so he can make these, like, very tight quantitative predictions. Andrey Fradkin: [chuckles] Noah Smith: More power to him. I love the guy, but, but I was just like: I’m taking that. I like that phrase. Mine now. Andrey Fradkin: So what is your Yeah, what is your general- Seth Benzell: What does it mean to you? Andrey Fradkin: Definition? Noah Smith: Should’ve copyrighted it. Andrey Fradkin: Yeah. Noah Smith: I was like, So I basically used it to mean kind of the revolution of rising expectations among the professional managerial class. So you got a bunch of people who expected, like: “I’m gonna go to college and things are just gonna work out for me. I’ll be, I’ll be upper middle class. Oh, wait, it’s hard. There’s competition. I have to study. I have to be smart. I have to actually know some math. I can’t just, like, go get a random sociology undergrad degree and be rewarded with, like, some high-paying job like my parents had.” Like, and so a lot of, a lot of this disappointment, and I think for a while, the sort of general the, the productivity boom of the nineties and early two thousands, people-- like, people rode that. A lot of the PMC, a lot of my class, social class, rode that boom, and then it made it seem like everybody Like, you could just be a sos- sociology major and, like, not really do any hard work and then just, like, get a good job and, like, live a lifestyle similar to that of your parents. And then, and then the Great Recession came, and then things flattened out. Like, a lot of opportunity dried up for those people, and you could, Then you had to sort of, like, learn to code. I’m not sure that works now. [01:15:00] Seth Benzell: You could-- it still works to mock people. I- Noah Smith: Yeah. Seth Benzell: You can still say it to people. Andrey Fradkin: All those non-technical people. Noah Smith: Yeah. Anyway, so but then, then I think, like, that sort of abrupt downward revision of growth expectations pissed off a lot of people and led to some of the It- I don’t think it was the main cause of the social unrest that we saw in the twenty tens, but I think it was a contributor. I think that you had, you had just like a lot of, a lot of people who fucked around in college, came from privileged backgrounds and then, and were absolutely consumed by hate for the tech bro class, who went to the same colleges, came from the same backgrounds, and made a thousand times more money. And I think that you saw a lot of that sort of internal, like, within class resentment, not between class resentment, but sort of within socioeconomic background resentment. A lot of that, I think, contributed to some of the, like, more like elite leftists, like Bernie Sanders or kind of stuff, or maybe some of the new antitrust movement or things like that, were motivated or had some popular support by people who their parents were like lawyers, doctors, businesspeople, well-to-do kind of people. And then they kinda messed around in college and weren’t very technical and, like, ended up getting, like, perfectly fine middle-class jobs, but being, like, somewhat downwardly mobile, and also having a much stronger preference to live in expensive cities, therefore draining their money, not wanting to go out to the ‘burbs like their parents did. Seth Benzell: Right. Noah Smith: And so, like Yeah. Seth Benzell: Is some of the resentment that the people who end up succeeding have worse taste than me? It’s like, I like high literature and they like Marvel movies, but the Marvel movie lovers won. Noah Smith: I think that, that those kind of reasons can be invented as needed. If the real reason for resentment is like: “I should be in the same class as you. I went to the same college as you, and yet you’re making so much more money, and we used to live on the same dorm floor.” Like, if that’s the real reason, then you can make up ideas about taste or repurpose ideas about You can get ideas as necessary to resent whoever you want to resent. Andrey Fradkin: Well, to be clear, it’s not like these people were in the same social circles even in college often, right? So it’s an interesting theory that, like, that resentment has caused ex- In college, did they They didn’t hang out with each other, but maybe they still thought they were gonna do equally well. Is that, is that kind of the theory? Noah Smith: I think so, yeah. from my-- I did actually go to college with some of those people. Like, I was in Gary Tan’s study group. He’s still a friend of mine. Andrey Fradkin: Nice. Noah Smith: Although I did quit I quit Gary Tan’s study group because, I thought that studying on my own would make me better. So sorry, Gary. I just-- and I was right. I, I did well on the test, but- Andrey Fradkin: Well, to be clear, you’re still doing very well, right? I don’t think you’re the resentment class. Yeah, so- Noah Smith: No, no. Andrey Fradkin: - Noah Smith: No, but I’m, I’m- Seth Benzell: Wait, so to what extent is- Noah Smith: Succeeded to the extent of Gary Tan. Seth Benzell: Is it- to what extent is this about just the relative between the two groups versus the absolute? Kind of you started with sort of an absolute story about it’s harder to live a middle-class lifestyle, and now you’ve moved to kind of a relative story about this subgroup did better than that subgroup. Noah Smith: I wouldn’t say- Seth Benzell: So are they both important? Noah Smith: Harder to live a middle-class lifestyle is exactly what I described. I would say it’s instead the expectations of how good your life would get or the, you-- people expected this glide path, and then it flattened out. That’s an absolute story. Whereas the relative- Seth Benzell: Right Noah Smith: Story of like: I’m not as, I’m not as do- doing as well as the tech bro class. I don’t think these are independent. I think those are two different stories, but they’re not independent at all. ‘cause if I, if my, if my future path leveled out and flattened out, but other people’s didn’t, and they stayed on the escalator, that escalator I expected for myself evaporated for me and continued for them- Seth Benzell: They stole my escalator! Noah Smith: They stole my escalator. Andrey Fradkin: Yeah. Noah Smith: Who stole my escalator? Andrey Fradkin: Yeah. Noah Smith: Yeah, so. And so like- Andrey Fradkin: That’s a great meme. [chuckles] Noah Smith: Yeah. And so like, anyway, so I think that that was like a contributor to unrest, but I don’t think that was the big story. I think the big story was social media, blah, blah. But I throwing everybody in the same room as each other and letting them fight it out, I think that was a bad idea. Andrey Fradkin: So what about the housing theory- Seth Benzell: Can we just- can we lower, should we- [01:20:00] Andrey Fradkin: What about the housing theory of everything- Noah Smith: Go ahead Andrey Fradkin: Right? ‘Cause, ‘cause I do think that s- housing is such a major contributor to this feeling that people aren’t equal. Seth Benzell: If it was cheaper to- Andrey Fradkin: Yeah Seth Benzell: Live in Brooklyn, we would solve all social problems. Andrey Fradkin: Not wrong. Noah Smith: The housing theory of everything, it’s like cheap housing would be really good for everybody. I don’t, I don’t have any problem with people believing in it, but it’s not a theory of everything. Seth Benzell: Directionally correct. Noah Smith: Directionally correct. Directionally correct. It’s like, do that Winnie-the-Pooh meme where there’s, like, plain Winnie-the-Pooh and then tuxedo Winnie-the-Pooh? Andrey Fradkin: Yeah. Seth Benzell: Yeah. Noah Smith: It’s like the plain Winnie-the-Pooh is, like, exaggerated. Tuxedo Winnie-the-Pooh is directionally correct. Andrey Fradkin: [laughing] Seth, I think you have one more question. Seth Benzell: Yes. Andrey Fradkin: Yeah. Seth Benzell: Well, I guess, yeah, this is partly tied into that and partly kind of riffing on this question of elite overproduction, which is, it seems like sort of, to the extent that we get this social, unrest from people being upset about not reaching their expectations, to what extent do we have, like, a social To what extent is it, like, an economically central issue to manage people’s expectations, right? To what extent are vibes versus real economic trends important for determining people’s welfare and how they feel about the world? and how does that affect how you think about policy making or writing? Noah Smith: I think, you really hit on one of the central questions of economics because my advisor, Miles Kimball, spent a lot of his career thinking about this and never came up with really solid answers, I think. Because we have pretty good evidence that happiness, the self-reported emotion, is pretty strongly related to differences between reality and expectations. interestingly, that’s what the original- Seth Benzell: I’ll say shocks are good Noah Smith: It just means luck. Andrey Fradkin: [chuckles] Noah Smith: But, like, essentially- Seth Benzell: Yeah Noah Smith: If you do, if you do better- Seth Benzell: Luck Noah Smith: Than you thought you’d do, you’re happy, and if you do worse than you thought you’d do So, like, the best outcome would be if we could give everyone low expectations and high outcomes, if we could make everybody just delighted with how well they did. Seth Benzell: Right. Noah Smith: I feel like this experiment has been run, and it’s called Generation X. [chuckles] And, like, I don’t know, man. Seth Benzell: Didn’t work. Massive failure. Noah Smith: Like, I see a lot of those people, they’re like billionaires now. They’re like, “I’m such a failure.” Like, you’re a billionaire! “Like, I’m, I’m never gonna amount to anything. I’m just a billionaire living in this giant mansion. Hmm.” Seth Benzell: Just a b- [chuckles] Jeff Bezos’s boat is so much bigger than mine. Noah Smith: And, like, this is a direct, I Like, I blame Nirvana. I blame Kurt Cobain for all this,? [chuckles] I blame depress- I blame- Seth Benzell: No one can understand their lyrics Noah Smith: I blame depressing-ass Generation X- Andrey Fradkin: No, no, this is a pro-grunge podcast. No slander allowed. Noah Smith: I didn’t say I dislike grunge. I love grunge. Seth Benzell: He blame them. Noah Smith: And I also think it’s a weapon of mass destruction. Seth Benzell: He respects their power. Noah Smith: I respect their power. Like, there are days when I just wanna, like, listen to, like, some old Nirvana B-sides, and I just, like And then I just get so angry and bitter about the world, and I’m like, “Yeah.” Seth Benzell: Put that in a blog post. Noah Smith: Generation X, it what? I, I don’t really feel sorry at all for Generation X because I feel like their goals in life were simpler and easier. I meet Generation X guys, and their whole goal in life is, like, have sex. Seth Benzell: Two ladies at the same time. Noah Smith: Yeah, like- Seth Benzell: I saw, I saw Office Space Noah Smith: Their whole goal, like, Generation X guys, all they have to do is, like, get laid, and then they’re done. They win. Seth Benzell: [chuckles] Noah Smith: Victory victory condition, and then, like like, Zoomers don’t even want that. Seth Benzell: Yeah, Zoomers want followers, dude. Noah Smith: Zoomers are like- Seth Benzell: Zoomers want- Noah Smith: Why would I want to do that when I could looks max? Why would I- Andrey Fradkin: [chuckles] Noah Smith: Like, why would I do that when I could, when I could mog the moids in the club? [chuckles] You can There- Seth Benzell: Right. Which means- Noah Smith: And then Millennials just want, Millennials just want likes on Instagram, and Zoomers, I don’t even know what they want because- Seth Benzell: No Noah Smith: They’re already so- Andrey Fradkin: I don’t think they know what they want. Seth Benzell: The Zoomers are the- Andrey Fradkin: That’s kind of the problem Seth Benzell: The Zoomers are the ones obsessed with social media. We’re the- the Millennials are the idealists. We actually are saving the world from climate change and solving racial d- conflict. - Noah Smith: We’re gonna solve racism, man. Seth Benzell: We’re gonna solve racism and global warming. We did that in 2008, right? Noah Smith: Yeah, we did. We did. Andrey Fradkin: That’s true. Noah Smith: We solved it. [chuckles] Andrey Fradkin: We elected Barack Obama, and that was the end of history. [chuckles] Noah Smith: Yeah, that was it. We did it, brother. Seth Benzell: Yeah, the sea stopped rising. I remember that was in the speech. Noah Smith: I don’t know. All I can promise the world is that it’s always gonna get weirder and weirder. Andrey Fradkin: Then- Noah Smith: But I’m- Seth Benzell: So we need to make people who desire weirdness. That’s the economic solution. Noah Smith: Yeah, so I’m So that’s good for me because I always loved to see the weirdest s**t possible, right? I would always go to, like, the weirdest underground shows in Japan or, like listen to, like, the weirdest music. I just Like, I’m just, I love seeing that weirdness, and the universe continues to deliver it to me in copious amounts. And so now I’m interested to see what AI does with this planet because, honestly, like, like, humanity was kind of hitting a wall. I don’t know. I wrote this in a recent post, which was reprinted by the Free Press. guardians of our our freedom of information. [01:25:00] Andrey Fradkin: Well, I- Noah Smith: And so, and the free press reprinted it, and they were like- Andrey Fradkin: Behind a paywall, so it can’t be free. I’m confused by the free press. It’s the, - Noah Smith: The- yes, conditionally free press. [chuckles] Andrey Fradkin: Yes. Noah Smith: The, the marginal cost zero press. But, but in this thing, I was like, look, obviously industrialization took fertility to below replacement levels, and then social media has taken fertility to, like, below, like immediate, to, like, immediate extinction levels, to, like, goodbye humanity. This is the last generation, goodbye, kind of levels, right? Plus, ideas were getting harder to find. like, okay, Bloom is right, and Venuren and Webb and whoel- who else was on that paper? Those guys. Seth Benzell: There’s one more, but those were the good ones. Noah Smith: There’s one more! Wait, Bloom, Venuren, Webb, and there’s one other person, and I apologize to whoever else is on that paper for not saying your name. But anyway- Seth Benzell: They got a zillion citations, dude. Noah Smith: That paper was right. We were hitting the wall. We were just like, all the smartest people had already been assigned to research- Andrey Fradkin: Chad Jones. Chad Jones. How could we forget? Seth Benzell: Chad Jones, Chad Jones. Noah Smith: Our friend of the show. Andrey Fradkin: Friend of the show. Noah Smith: The Chad himself. Andrey Fradkin: The Chad of growth theory. Seth Benzell: Yes, exactly. Noah Smith: The Chad. Dream guest of the show. Seth Benzell: You can’t say the Jones because there’s so many Joneses. [chuckles] Noah Smith: Oh, you can’t. Although the Chad could also be Chad Syverson, Chad of productivity measurement. Andrey Fradkin: Ooh, that’s true. Noah Smith: They’re both the Chad. All right. But anyway, I guess the point is that, I don’t remember who’s on that paper, but, but ideas were getting hard to find. They were right, blah, blah. We were hiring, like, mid-marginal researchers to just, like, randomly try chemicals in a vat, and like, that was what our research- and like, the best brains were already like working on the whatever, all day long. And like, yes, we were running out of, running out of runway on this technological civilization. Like it was, we were really, like, we were really just gonna like, argue like resist Lib versus MAGA for the rest of our lives and on so- Seth Benzell: God forbid Noah Smith: Degenerating, shitty mid social media for the rest of- Seth Benzell: In that flat- Noah Smith: Not just our lives, but all of humanity. Like, that was the end. Seth Benzell: The flat part of the solo growth curve. Noah Smith: Yes, we hit the- Seth Benzell: That’s, that’s not where you wanna be. Noah Smith: We hit the we hit the stagnation point. We, like, you could see the end of humanity coming down, coming down the pike, and now we blew it all up by making a God machine. We were like, “Okay, new thing.” And what? This has happened before because the agricultural age, you could sort of see humanity having hit this limit. We hit the Malthusian ceiling- Seth Benzell: Yeah Noah Smith: Again and again. We had the Black Plague. We had overpopulation. We deforested the entire goddamn Middle East. Seth Benzell: We banged our head against that ceiling three or four times. Noah Smith: Pardon? Seth Benzell: We banged our head against the Malthusian ceiling three or four times. Noah Smith: Three or four times! And then we were like like our whole world was running out of wood. Like, we were just running out of trees to chop down. We were gonna like We had the, like, Columbian Exchange, blah, blah. That was, there was gonna be another collapse, just like there had been for the Mongols. And like, then we were like, “All right, we’re busting out of this s**t. Steam power!” Seth Benzell: Yeah. Noah Smith: “And like science.” And then, like, we got out of that, and then weird s**t happened, and you got Nazis and communists and all kinds of crazy stuff. Not to mention, a lot of really bad sitcoms in the ‘80s. But like, we got all of that stuff, and despite all that, I would say on balance, we busted out, and it was pretty good, and I would rather have lived, like, in the industrial age than in the age before. And so maybe AI will kill us. Industrial Revolution could have killed us if we had just if we had launched all the nukes in like 1983 or whenever, like, we would’ve died- Andrey Fradkin: Yeah Noah Smith: And then our civilization would’ve fallen. Maybe AI will be the thing to make our civilization fall, or maybe we’ll be able to solve, use AI to solve the problems that, like, we were degenerating, like the end of science and the, like, end of fertility and like the the absolute shittiness of social media, and maybe AI will just solve all this stuff for us. Andrey Fradkin: Well- Seth Benzell: Whether or not it just solves it definitely gives us a fighter’s chance. Noah Smith: That’s what I mean. Seth Benzell: I think that’s, - Noah Smith: We rolled the dice of big stuff big new thing. We just, we like, we rolled the dice again, and I’m, I’m glad we did. Andrey Fradkin: All right, well- Noah Smith: And, we all die, but I’m glad we tried. Andrey Fradkin: AI, the new hope, coming to economies near you. on this note, thank you so much, for being our guest, Noah. this was an amazing conversation. [01:30:00] Seth Benzell: Thank you so much. Noah Smith: Thank you. It’s been a pleasure. Seth Benzell: Really appreciate your time. And listeners at home, keep your posteriors justified. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • February 9 · 1 hr 29 min

    Basil Halperin: Leading Indicators for TAI, Conditions for the Singularity, and Tax Policy at the End of History

    In this week’s episode of Justified Posteriors, we interview TAI expert and friend of the show Basil Halperin of the University of Virginia. There Basil is doing some of the most fascinating work on the economics of TAI with Anton Korinek and other leading researchers. The first section of our conversation covers Basil’s early career, including jobs at Uber and AQI, how he got interested in AI as a research topic, and his role in managing the Stripe Economics of AI Fellowship. We then discuss a paper we’ve already covered on the show: his work on whether the real interest rate can be interpreted as a leading indicator of the probability of TAI (or ‘doom’). Listen to our previous conversation on his paper, and view show notes, including links to that paper and blog post here: If the Robots Are Coming, Why Aren't Interest Rates Higher? Seth was previously convinced by Basil’s arguments, but Andrey was a hold out — we discover Basil’s takes about Andrey’s reservations. Our third subject is Basil’s new paper with Anton about the relevant elasticities for a singularity in research progress “When Does Automating Research Lead to Explosive Growth?” Basil explains how the key issues are the degree of fishing out and spillovers in/across different industries, as well as the extent to which research can be automated. We also take a step back to ask what theoretical research like this teaches us.Finally, we cover Basil’s back and forth with friend of the show Phil Trammel’s new blog post with Dwarkesh about Piketty and optimal taxation in the age of TAI, link below, and ask him to explain the meme he posted, summarizing his arguments: Additional references: Does carbon taxation yield a double dividend (environmental plus fiscal)? We hope you enjoy the conversation! Transcript follows: [00:00] Seth Benzell: Welcome to the Justified Posteriors podcast, the podcast that updates its beliefs about the economics of AI and technology. I’m Seth Benzell, looking forward to the Basil exposition we’ll get today, coming to you from Chapman University in sunny Southern California. [00:35] Andrey Fradkin: And I’m Andrey Fradkin, looking forward to creating a new accord with Basil, coming to you from San Francisco, California. And today we’re very excited to welcome Basil Halperin to our show. Welcome to the show. [00:49] Basil Halperin: Thanks Andrey. Thanks Seth. Super excited to be here. [00:53] Andrey Fradkin: So as background, Basil is an expert on the economics of transformative AI and he’s currently... [01:00] Seth Benzell: Expert is underselling. He is one of the most interesting thinkers around on... Alright, continue. [01:07] Andrey Fradkin: Yes, he’s great. And he’s a professor at the University of Virginia. We have an exciting show for you today touching on many topics, but we first wanted to get a start with some of the biographical tidbits. In particular, Basil, how did you get interested in this topic? And it seems like you were a lot earlier than other economists. So I’m curious what drew you in before everyone else to this interesting set of topics? [01:38] Basil Halperin: I mean, not as early as you two, I don’t think. Uh, I don’t know. I was just a nerd growing up. I read a lot of sci-fi. I read Ray Kurzweil in high school when his The Singularity is Near book came out in the 2000s, just because it was popular. The idea got in my head. I was kind of like, “Well, this is interesting, but eventually...” I was like, “I have a few decades to work on other things before any of this becomes relevant.” And then GPT-3 came out in that long hot summer of 2020. I freaked out a little bit for a week or two. This is crazy. How is this happening so fast? So that sort of woke me up a bit. I started thinking about these issues and gradually more and more have gotten sucked into working on it. [02:20] Seth Benzell: What were your favorite sci-fi growing up? [02:23] Basil Halperin: Ender’s Game was always the classic. [02:26] Andrey Fradkin: Now I saw on your resume that you spent a stint at AQR, which is a large capital management firm. I’m curious, what did you learn working there? [02:37] Basil Halperin: Yeah. So I didn’t expect to go into finance out of college, but basically the opportunity came along. I found out that this firm seemed pretty interesting. So the background is, this firm was founded by two PhD students of Eugene Fama, the Nobel Laureate in finance. Basically taking his ideas seriously and other ideas from the asset pricing literature seriously and applying them to earn a bunch of money. So I didn’t know anything about finance going into that job. So I learned a whole bunch and some of that has been applied in my research that I think we’ll talk about today. [03:13] Seth Benzell: Ooh, wait, yeah. Pricing assets in the age of AI. Fascinating. [03:17] Basil Halperin: Yeah, yeah. Talk about it. [03:19] Andrey Fradkin: So I do think this is an interesting background because a lot of people in our field don’t have a finance background. That’s not where they’re coming from in terms of thinking about technology. So it maybe gave you this strong, prepared mind to be thinking about the asset pricing implications of transformative AI. Did you get to interact with Cliff Asness or were you too much of a, like, intern, low-level employee? [03:45] Basil Halperin: No, I was there for a year and a half or two years, but too junior. I think one time I made a bad joke to him in the elevator and he like, pretended to laugh. That was pretty much the highlight. [03:56] Andrey Fradkin: Well, he also likes to make a lot of bad jokes, so you have that in common. Some of them are good too. [04:05] Basil Halperin: [Laughs] These bad jokes are funny. [04:06] Andrey Fradkin: What about at Uber? You also spent some time there working with John List, is that right? [04:11] Basil Halperin: Yeah, yeah. John taught my first ever Econ class when I was undergrad at Chicago, Intro Micro. And he helped inspire me to become an economist plausibly. And then yeah, I worked for him when he was Chief Economist at Uber. Which, Andrey, as you well know, being an economist in tech is an interesting experience. And Uber in 2017 was a particularly interesting time because it was a controversial firm. Sort of like OpenAI is today, the firm that’s always in the headlines. [04:42] Andrey Fradkin: Were there specific perspectives that you gained there that have informed your subsequent economics career? Or was it more of just like you learned some useful skills in data science or something else? [04:55] Basil Halperin: Yeah, I don’t know how much super tangible I have to say, but it definitely was informative in general to work in the private sector before going into academia, just to see how different things are. You know, like in the private sector you’re being paid to tell your boss that he or she is wrong. And then in academia that’s not so much a recommended strategy. [05:19] Seth Benzell: Wait, wait, okay. So tell us about... so you’re there, it’s in 2017. Uber is one of the most evil, fast-growing companies on the planet. So you said it was interesting. So what was interesting about that? Were you pressured to write an economics report you didn’t agree with? Did you feel like you had to like wear, you know, a hoodie going into the office as people were throwing trash at you? What was it like? [05:43] Basil Halperin: No, it was just... I mean, I certainly didn’t have a negative experience or negative view of the company, though I’m sure there were negative things the company did, like any large organization. But the team I was on, this Chief Economist team, was like five people. So it was pretty small. So we just had a lot of leverage to go around the company, be sort of an internal consultancy and do a lot of crazy things, varied things that I otherwise never would have had the chance to do. Like I was sort of a software engineer for one month that I was there, which was otherwise something that never would have happened to me. Or running large scale experiments on a million riders or whatever, which... I would love to do macro experiments if any central bank wants to volunteer for some coin flips. But otherwise, as a macroeconomist now, I don’t really have that opportunity. [06:35] Andrey Fradkin: So this kind of is a, you know, is a nice segue into our next topic, which is... like a lot of people are worried about their careers these days, obviously because of AI. [06:49] Seth Benzell: Not me! Podcasting is never gonna go out of style, Andrey! [06:53] Andrey Fradkin: Fair enough. But I think that’s a very broad question and perhaps too broad to answer. But I think for people with an interest in economics—you know, you were in tech, you decided to go into academia. I’ve made the same decision in my life. But I’m curious like what advice would you have? And maybe this is a good opportunity to also speak about the efforts you’ve been doing with the Stripe Economics of AI Fellowship. [07:23] Basil Halperin: Yeah, okay. So two points here. One point is that I feel like on every good AI podcast, there’s a question of, “What do you tell young people? What they should be studying today?” And like there’s zero good answer to that question. So yeah, I don’t have any good answer to that question. [07:38] Seth Benzell: Study the Justified Posteriors podcast. Listen to every episode every day. Three times a day. [07:45] Basil Halperin: But besides that, it’s not clear. The other thing I guess I can say is that if you’re an economist, working on the economics of AI is like a really cool thing to do. There’s just like so much low hanging fruit. There’s so many insights that can be arbitraged from other fields, which is always a good place to be. You can... instead of going to have to pick the fruit yourself, you can just take the fruit out of other people’s hands, maybe translate it to the language of economics. [08:12] Seth Benzell: Yeah, I understand later we’ll be talking about the economics of fruit picking. But so hold those fruit picking thoughts. [08:20] Basil Halperin: All of my economic metaphors are about fruit. So we’re going to get pretty fruity or something today. Um, I don’t know, Andrey, maybe you were suggesting that I talk about this fellowship that I help run. [08:31] Andrey Fradkin: Yeah, tell us about the Stripe Fellowship. What fruit is the Stripe Fellowship? [08:35] Basil Halperin: Tell us about what you learned running it and what is it, you know, give a brief description. Yeah. [08:41] Basil Halperin: Yeah, this is this fellowship that we run for early career economists that I do working with Stripe, the financial technology company. Where they decided that they want to support more economics research on the economics of AI, thinking that economists are not working on the issue enough. Which is an empirical claim that you can debate. And so we had the first cohort this past year, 24-25 fellows, mostly grad students, a few APs [Assistant Professors]. And this is a lot of... in part giving people money to do research, but in large part like building a community of people to speak together and share ideas and maybe work together. Folks that probably are listening to your podcasts and that maybe you all should consider interviewing. So that’s been super fun. Very interesting to be on the side of someone reviewing applications as opposed to being on the other side of applying and seeing... I mean, first of all, it’s frankly like... I can’t complain. It’s a very cool opportunity to be running this thing. But it’s terrible to reject people. Like it’s absolutely no fun. All these extremely well-qualified people who are definitely smarter and more accomplished than me. Like that’s not a fun part of it. On the other hand, very cool to get to support all these cool people doing very cool research and seeing them decide to co-author together and things like that. [10:15] Seth Benzell: Oh, can you point... that’s particularly exciting. Can you point towards any papers that you think you may have generated that we should maybe discuss on our podcast? [10:25] Basil Halperin: So two... so it’s been like six months or something since the fellowship launched and you guys know how long these timelines are. So no counterfactual papers yet. [10:35] Seth Benzell: Oh, well I know how short my AGI timelines are. [10:38] Basil Halperin: Well, you’ll have to tell us that later. No counterfactual papers yet, but a bunch of people have amazing stuff out. Phil Chen at Harvard just put out a very cool paper using GitHub data to look at how software engineer labor has changed. Parker Whitfill’s been putting out like a paper every few months on compute and labor, complements versus substitutes, with Cheryl Wu. And yeah, there’s a whole bunch of stuff. We have this website, you can Google “Stripe Econ Fellowship of AI” and see folks’ websites. There’s a ton of very cool stuff. I don’t have time even to read all the papers, at least yet. [11:18] Andrey Fradkin: Well, that’s yeah, super awesome initiative. I guess, you know, one follow-up question on there. What do you think most of these people are going to be doing three, five years from now? Do you think they’re going to become assistant professors? Are they going to work at AI labs? Are going to do something else? Like what is the career trajectory for a young person? [11:39] Seth Benzell: Are they going to be podcasters? [11:41] Andrey Fradkin: Yeah, are they going to be podcasters? Like... and maybe, what do they think they’re going to be doing is an interesting question, right? Because it’s a time of great uncertainty. [11:51] Basil Halperin: Yeah, I don’t know. So like... one way of answering that is that I think kind of any question about speculating about the future comes down to: how fast do you think AI capabilities are going to progress? AI technology going to develop? As has come up a whole bunch of times in this conversation. And there’s various ways people try to forecast how quickly the technology will develop. Like one way is just go and survey machine learning engineers and trust that they know something about how the future is going to go and take an average of their opinions. So that’s one method. Another method is something that’s gone back to like Hans Moravec at the very least of: think that computers are like human brains and try and estimate how much computing power the human brain does and try and forecast Moore’s Law and algorithmic progress to see... [12:31] Seth Benzell: Ray Kurzweilian, yeah. [12:33] Basil Halperin: Exactly, like Ray Kurzweil. To see how long until we have enough computing power to match the human brain and say that’s when we’ll develop AGI. But like, sort of setting that to the side or something... I don’t know. We’re trying to encourage research. So we’re selecting for people who are like stubbornly pursuing research. So there’s that. But if you’re like asking about the future for econ PhDs... econ grad students... [12:58] Seth Benzell: We’re not talking about the future of econ PhDs generally. We’re talking about this elite cohort you’ve gathered. You think that there’s a chance that this elite cohort of the best young thinkers on Econ of AI are going to be obsoleted in three years? [13:13] Basil Halperin: Uh, I mean, I think there’s a non-zero chance that we’re all living in some communist utopia in a few years. Not a high one, as my research would indicate, but non-zero. Which is like crazy to think about. We could get unhinged and talk about that, but maybe we can save it for later. [13:30] Andrey Fradkin: Yeah, I guess I was trying to actually push you in a different direction, which is more like... you know, Tyler Cowen famously gave Leopold Aschenbrenner the advice of not going into economics academia, right? You know, he was someone who was, and still is I think, working on some economics research. [13:46] Seth Benzell: Yes, including with friend of the show Phil. [13:49] Andrey Fradkin: Yeah. Exactly. So I was kind of more thinking like, is it really the best place if you’re really AI-pilled to be sitting at a university? Why did you choose to do that? I’m sure you had... you could have had other options that you pursued. [14:04] Basil Halperin: Yeah. I mean, so what is best for any individual varies a lot. And I don’t know, like don’t you guys think that people who go into academia are kind of stubborn? Like they want the independence of not having a boss. They’re willing to accept the ginormous pay cuts relative to the outside option. [14:24] Seth Benzell: I wanted the wizard robes. [14:26] Basil Halperin: You wear wizard robes to lecture or what? [14:29] Seth Benzell: I do. I have it hanging on my wall right now. I would point my camera, but my lighting is so beautiful right now. [14:34] Basil Halperin: We should have worn them for the video. So I don’t know, like really that idiosyncratic taste shock is I think driving a lot of people. But yeah, I totally agree that there’s a lot of amazing research to be done in the private sector and like the new Anthropic economic team seems to be doing amazing stuff, for example. [14:52] Seth Benzell: Basil, I don’t want to answer this question for you, but if I may offer kind of a riff on that idea of it being idiosyncratic taste... I think it’s a, you could call this a taste thing, but you might call it also an idiosyncratic valuation of certain virtues, right? You might find yourself associating with the virtues of being an economist or being a professor and having open inquiry, etc., etc., etc., that are not necessarily as associated as firmly with other professions. You could call that taste or you could call that something else. [15:28] Basil Halperin: Yeah, let’s bring virtue ethics back into economics. [15:32] Seth Benzell: Bringing the virtue ethics back to economics, exactly. [15:35] Andrey Fradkin: Yeah. Well, cool. You know, very interesting to think about these career implications, but I think it’s maybe a natural place to transition to discussing some of your really interesting thoughts that you’ve had recently. And I think Seth has some questions.Basil Justifies His Research:Transformative AI, existential risk, and real interest rates [15:53] Seth Benzell: [Grabbing microphone] Give me the mic, Andrey. I’m grabbing the mic from Andrey now. Basil, if I recall correctly, the way we e-met was because I got very frustrated with you over one of your papers. And this was your paper, “Transformative AI, Existential Risk, and Real Interest Rates.” So I guess before kind of I explain my strong emotional reaction to this paper and how you eventually won me over, maybe you can refresh our podcast listeners. We did an episode on this podcast as one of our very first episodes. I encourage our listeners to go back and listen to it. But for those who don’t have the time, can you give us maybe a two-minute gloss on that paper before we start putting you to the test on it? [16:45] Basil Halperin: Yes. So I second that listeners should go back and relisten to that old episode because I did before this and that was a really nice summary that I really appreciated. Obviously the critiques were wrong, which we’ll get to. That’s a joke. There were some good points. But yeah, so the motivation here is like, everyone wants to know how quickly is AI going to progress? AI technology going to develop? And there’s various ways people try to forecast how quickly the technology will develop. Like one way is just go and survey machine learning engineers and trust that they know something about how the future is going to go and take an average of their opinions. So that’s one method. Another method is something that’s gone back to like Hans Moravec at the very least of: think that computers are like human brains and try and estimate how much computing power the human brain does and try and forecast Moore’s Law and algorithmic progress to see... [17:33] Seth Benzell: Ray Kurzweilian, yeah. [17:35] Basil Halperin: Exactly, like Ray Kurzweil. To see how long until we have enough computing power to match the human brain and say that’s when we’ll develop AGI. We in this paper want to present sort of an indirect way of thinking about this, which is using one of the most powerful supercomputers humanity has, and that is the calculation power of financial markets. Where in economics, you know, we like to think that prices are good at aggregating dispersed wisdom across the economy. And financial market prices in particular, by being forward looking, by being particularly liquid and having this strong incentivizing power through the magic of no arbitrage—or arbitrage incentives—are a particularly good way of collecting humanity’s dispersed wisdom about how the future could proceed. So in particular, we suggest in this paper that... [18:31] Seth Benzell: But Basil, there’s no... at least when you were writing this paper, I’m not aware of a high liquidity market that just says “when does AGI happen?” or “when does TAI happen?” So what price should we look at? [18:43] Basil Halperin: Indeed. And if you’ll allow me to rant on that for a second before summarizing the argument... like today, even today, there’s still no, despite the rise of prediction markets, there is no long horizon prediction market on when could advanced AI be developed. There’s these forecasting platforms that just allow people to submit their own forecasts and take the average of them. Metaculus, Manifold Markets. People sometimes refer to these as betting markets, prediction markets... they are not prediction markets. They do not have the incentive, the financial incentive to ensure forecasters pay attention, update their forecasts, and so on. So those are great websites, but they’re limited. Kalshi, Polymarket, these new prediction markets... somehow there’s just... it’s shocking how bad the lack of good forecasting opportunity to forecast AI is. There’s very limited things. There are some things, but they’re not very good. [19:35] Seth Benzell: Do you speculate that it’s like a defining AGI problem? It’s the Oracle problem? It’s like, “how would you know it when you see it?” Or did you speculate on why that is? [19:43] Basil Halperin: Yeah. So part of it is that. So for example, the very best question that I’m aware of is Kalshi has a market on: will this fancy version of the Turing test be passed by 2030? Where it’s some like souped up version of the Turing test based on a bet that Ray Kurzweil actually—we keep mentioning his name—made. So that’s like the best existing thing... [20:00] Basil Halperin: ...but it’s this limited definition. [20:04] Andrey Fradkin: So I actually have a different question which is related to your paper. But let’s say we had a prediction market on GDP growth. And you know, it was like: will we have, I don’t know, 5% GDP growth or 10% GDP growth at least once by year X? You know, it’s hard to imagine that that would happen without transformative AI. [20:31] Seth Benzell: Ah, Andrey, I could tell a story. [20:33] Andrey Fradkin: Yeah. No, I could tell a story. I could tell a story, but it would be highly correlated. Are there markets like that that are very close analogs to this? [20:42] Basil Halperin: If there are, I would love to know. And like, I do a periodic search and there’s... it’s like there’s really not. It’s infuriating. Hence the origin of this paper. [20:51] Seth Benzell: But you can bet... you can bet super out of the money calls on like the stock market. You can bet on the stock market growing 500%, right? [20:59] Basil Halperin: Yes. Well, I don’t know about 500%. Out of the money calls, like the range is not that large. But betting on GDP growth in particular is difficult. And like, does higher GDP growth raise equity valuations? It’s actually not obvious. Like, we can really dive into that, but for a whole bunch of reasons... for a whole bunch of reasons I think equities are just kind of a very confusing asset class in general to interpret. Which is why... [21:27] Andrey Fradkin: Yes, so tell us why you picked interest rates. Yeah, and then we’ll go back to why equities may or may not be good. [21:33] Seth Benzell: Because equities are a bad asset, what I’ll do is measure equities over time. [Laughter] [21:40] Basil Halperin: Yeah, so the best price in the economy—that’s kind of a joke—the price we recommend looking at in this paper is real interest rates. So that is to say the inflation-adjusted risk-free rate of return you would earn on a bond, particularly at long horizons. Like say the 10-year real interest rate or the 30-year real interest rate. And the argument for why that’s a useful price to look at is the following: If you knew you were going to be super rich next year, no reason to save today. You’re going to be super rich next year anyway. If no one’s saving, then that pushes up interest rates. Interest rates clear the market, the supply and demand for savings. So that would be the case where we expect AI to rapidly raise economic growth, rapidly raise our incomes, in particular rapidly raise our consumption. And so if we saw really high real interest rates, that would be indicative of this case of aligned AI raising human incomes. Alternatively, another case with AI that people talk about is that, you know, AI is going to wipe us all out. And you’ve done podcasts on this topic. Similarly, if we’re all going to be dead next year because AI was going to wipe us all out, then there’d be no reason to save today. You’re going to be dead next year. No reason to hold on to assets for next year. Likewise, that pushes up interest rates. So, you know, we could go and look at interest rates. Are they much higher than they have been? And like, no, they’re well within the range of normal variation. And when I started thinking about this back in fall of 2021, it was particularly salient because at that time long-term real interest rates in the US, and indeed around the world, were at all-time lows, like negative. So you know, you’d give $100 to the US government, they give you back $99 inflation adjusted at the end of the year. Interest rates have gone up a non-trivial amount since then actually, but really not that much. Really, it’s probably not because of AI. Maybe a bit. So that’s the core argument. That if markets were expecting aligned or unaligned transformative AI, then we’d see high real interest rates today. [23:51] Seth Benzell: All right, great arguments. And now I’m going to explain why this was so frustrating for me in 2021 to read this argument. I had been working on transformative AI topics and had been thinking about, you know, kinds of economic downsides of AI. And one of the mechanisms that I had become worried about was the anticipation of AI leads to dissaving and that dissaving is large enough that interest rates skyrocket and actually you don’t get enough reinvestment in the economy to have significant economic growth, right? Set aside for a second whether or not the dissaving you have in mind is so extreme that you would literally like cancel out the gains from AI. But I had been kind of pushing on this idea that, you know, AI is going to lead to dissaving... as the world’s interest rates were plummeting. And so I had kind of pivoted into trying to think about, okay, well, if we do get really good AI, how could you get to a world where there are very low interest rates, right? And so one version of this idea I worked on with our friend and co-author Erik Brynjolfsson is the idea that, well, maybe there will be a kind of labor that will be infinitely reproduced, but there will be still some scarce human factor. And then actually that scarce human factor will make all of the gains and then interest rates can remain low. Another story would be: well maybe we don’t have transformative AI, we have an AI that takes over, you know, 50, 60, 70% of jobs. We see the labor share of national income go down from, you know, 60% to 20%. But if you actually play that out in a big macroeconomic model where you try to realistically model national savings rates... well, you’re kind of pushing against the tide. Like we talked about, in 2021 we had this huge—it was called by some an international saving glut—that was maybe driven by the rise of an Asian middle class that all of a sudden had all of this money, needed to save for retirement. There was a scarcity of safe assets. And so even if you automated a lot of jobs, there might be still a lot of absorptive capacity for that savings before you would significantly bid up interest rates. And so kind of for both this sort of a theoretical reason and a sort of a kind of a macro simulation reason, I fired off to you this angry email saying, “Don’t you realize blah, blah, blah, blah, blah?” [26:28] Basil Halperin: Yeah, the audience wants your original comment. They want you to read it. [26:32] Andrey Fradkin: Oh, that email will be in the post, don’t worry. [26:36] Basil Halperin: I have it on hand. I have it on hand. [26:38] Seth Benzell: Oh wait, let’s hear it. Let’s hear it, Basil. How bad was it? [26:41] Basil Halperin: This is going to be the unhinged portion of the episode. So Tyler Cowen kindly reposted the essay. [26:49] Seth Benzell: [Laughs] It was like, “A crazy guy emailed me.” [26:51] Basil Halperin: Well, so initially it was an email. Initially it was a comment on the Marginal Revolution post sharing the essay. And so, like, you know, I... [26:59] Seth Benzell: And everyone knows that that is where the sanest people hang out. [27:03] Basil Halperin: I, like some neurotic person or whatever, skim through these comments and there’s this one guy Seth Benzell: “Hey, I’ve read a few of his papers, including that one you mentioned with Eric. This is so dumb.” That’s my first introduction to Seth. Of course, since then things have changed. But welcome to the internet. [27:26] Seth Benzell: Wow, “so dumb.” I came out of the gate swinging. You have to remember it was the pandemic. We were all cooped up. Some people went to BLM protests. I commented on Marginal Rev. But now I’ll tell you how you won me over, Basil. Which is, you sat me down and you said, “Seth, those scenarios that you’re thinking about, the one where there’s still, you know, a scarce human factor that’s making the wins, or the one where we automate 60% of jobs, those are ‘AI is a big deal’ scenarios, but those aren’t the transformative AI, AGI scenarios that I’m actually writing about.” And then I apologize for not having read the paper. [28:06] Andrey Fradkin: You’re a true Marginal Revolution commenter, Seth. Who I don’t think any of them have ever read a paper. [28:15] Basil Halperin: This is worth noting. So like, the paper and the argument really is zoomed in onto this particular scenario, which I think was like much more top of mind to the people thinking about this a few years ago. So like, you know, before ChatGPT... our essay, initial essay was posted a month after ChatGPT came out. Before ChatGPT, there weren’t that many people in the world thinking about AI, right? And the people that were, a lot of them were focused on like these fast takeoff “foom” scenarios. Things would happen fast, things would happen big. More likely than not, we’re going to die. P(doom) is high as they say, right? So we were really focused on like these kind of extreme possibilities: either we’re all going to die or we’re going to have what we operationalized as 30% annual GDP growth. An order of magnitude increase in annual GDP growth. Which would be crazy. It would be as if the whole economy is growing as fast as Moore’s Law, more or less. So yes, it’s an extreme scenario for sure. [29:13] Seth Benzell: And but yes, but so given that extreme scenario, you won me over. And I said, “Andrey, when we start our podcast, I want to talk about this paper because nothing has moved my priors so much as this paper.” Maybe it was just moving my definitions around. Maybe it gave me like a stronger understanding of what people really mean by transformative AI versus just AI that is so good that it automates 70% of jobs. But I talked to Andrey about it and Andrey, remind me, were... did I fully convince you of Basil’s arguments or remind me? [29:47] Andrey Fradkin: No, I don’t think so. Andrey wasn’t convinced at all. I just... I mean... I just feel like the people being so certain that this transformative AI is coming in this particular way seems unlikely to me. It’s not like how humans tend to think or behave about most things in life. And then it’s hard for me to imagine a world where they essentially like, it’s a coin flip: either we all die or we have amazing transformative AI. And we don’t have any intermediate types of outcomes where, for example, you might want to engage in precautionary saving. I know you talk about certain precautionary savings in your paper, but like, that’s just a very natural response to a lot of uncertainty. There are of course also scenarios where there is tremendous economic growth, but it’s held by very few people. It’s ex-ante not obvious who those people are going to be. Or maybe it is obvious, I don’t know. Maybe they already have all the capital, right? There are just a lot of things, a lot of details to think through and I’m sure you’ve thought through a lot more of those than we have in our podcast. [31:04] Basil Halperin: Yeah. So one thing I should say is that like this transformative AI 30% GDP growth scenario, that’s not something we made up or pulled out of thin air. Like this really was and is a paper dedicated to a specific conversation, just like any academic paper, right? It’s a conversation among a particular group. So that’s one thing. Another thing to say is like, to me... so one thing Andrey that you spoke about in the last podcast on this that I totally agree with is skepticism of quantitative macro predictions. So I think you went beyond what I would say in terms of skepticism, but I so strongly share the belief or the view that macro does not have an amazing track record in terms of precise predictions. And that’s why... like that’s like a strong motivation for the approach in this paper. Where instead of like, we’re going to write down an optimizing model, a model of optimizing agents where in equilibrium we determine the structural forces determining the real interest rate and we’re going to calibrate all these different forces and feed in the simulation. Instead, it’s just this like dead simple thing where we have this very robust, strong prediction from any intertemporal macroeconomic model: that higher growth or higher mortality risk raise real interest rates. And people are predicting, people are moving tens, hundreds of billions of dollars, literally in San Francisco, under the belief that these things are going to happen. One of these two things is going to happen. It’s going to happen in the next 10, 5, 1 year. And this provides some sanity check on like, most of all, like the very shortest timeline predictions. [32:51] Seth Benzell: Yeah, so maybe I can pay... [32:52] Andrey Fradkin: But I guess does everyone need to believe in those predictions? I mean... [32:56] Seth Benzell: It has to be like the median investor, right? Who has... who’s the guy that we’re talking about the beliefs of? [33:01] Basil Halperin: The marginal unit of capital. So, you know, markets don’t reflect average beliefs. They reflect the belief of the marginal unit of capital, the marginal trader, just like any price reflects the marginal buyer/seller. And like a priori and lots of theory and so forth to back this up, like you would think that the marginal trader is the one who has the most knowledge or the most incentive to buy/sell. You can think about deviations from that, but like that’s... [33:26] Seth Benzell: Isn’t the marginal trader a noise trader? [33:28] Andrey Fradkin: Or like if we have a distribution of beliefs, isn’t the marginal trader someone who has an intermediate belief? [33:35] Basil Halperin: Um, so one thing I will say is that... one thing I’ve learned from this whole project is it’s confusing to me how underdeveloped the literature on asset pricing under heterogeneous beliefs is. I think it’s in part because like you get these no trade results where if people don’t... anyway, the theory is hard. But the way I think about it is that the sort of robust prediction of theory is that asset prices are like a wealth-weighted average of beliefs. Maybe wealth-weighted risk tolerance weighted average of the distribution of beliefs. [34:13] Seth Benzell: That right? You think if I’m super out of the money, can I still move the middle somehow? In other words, if I’m the guy... if I’m a 99% “AI never happens” or “AI always happens,” in what sense am I being included in that weighted average? [34:27] Basil Halperin: Just directly. So like this is about consumption-savings decisions rather. Like what, how fast will the growth rate be? That average. [34:39] Seth Benzell: Okay. Oh, you’re talking more about the national saving rate. That part of it. [34:43] Basil Halperin: I’m thinking like the g, the growth rate that goes into the real interest rate determination, that’s the average belief over that. [34:54] Seth Benzell: Right. And the reason that that matters is that is going to drive the saving rate, which drives the interest rate? Or through a different mechanism? [35:01] Basil Halperin: Yes, yes, yes. [35:02] Seth Benzell: Okay. [35:04] Andrey Fradkin: I have a... so I have a question related to, you know, we touched upon this when we did the podcast, but I’m curious what you think about it is: It seems hard for me to imagine a scenario where we get to your scenario without a lot of hints in advance, right? Like... like your scenario is literally like most people agree that we’re going to have 30% growth next year. What... what does the path to that look like? Does that mean that we first have 20% growth, 10% growth? Uh, like... are there other assets that we expect to be leading indicators there? Because I do think in some sense, if we get to your scenario, then you’ve already told us what happens. [35:48] Basil Halperin: It’s not my scenario. I want to emphasize. [35:51] Andrey Fradkin: No no, sorry. To your analysis. If we get to the point in your analysis—I know it’s not your scenario—then... [35:56] Seth Benzell: Is your warning light a leading indicator or a late indicator? [36:01] Andrey Fradkin: Yeah. We thought it was a late indicator. But I’m curious if you have ideas for leading indicators. Yeah. [36:07] Basil Halperin: Ah, so I really think this is a leading indicator because like interest rates reflect expectations about future growth, not current growth. So like wages would be a lagging indicator where those are only going to fall once the technology has developed. Interest rates will rise once people expect the technology to be developed. [36:25] Andrey Fradkin: So no, so I think we both agree with that. I’m just saying that like it’s hard for me to imagine that enough percent of capital believes that we’re going to have 30% growth without it being apparent in other economic statistics long in advance of that. [36:38] Seth Benzell: Like will we be... I guess... the people who read your paper will be convinced that AGI is coming before interest rates go up. [36:48] Basil Halperin: So that’s sort of a question of like how efficient do you think markets are plausibly, right? Is that what you’re saying? [36:58] Seth Benzell: I think that’s fair, right? Andrey is saying that the sophisticated... I mean that’s how I read it. [37:01] Andrey Fradkin: Well, one is efficiency. The other is like... let’s say for... if we thought that for AGI to happen, we needed to have substantial data center and energy build outs... [37:13] Seth Benzell: Elon’s robot factory. [37:15] Andrey Fradkin: Yeah, but to the extent of like 5% of GDP, 10% of GDP, right? Like these things will be happening. There... you know, there’ll still be uncertainty. So it’s not necessarily that it’s an efficient markets failure, but um... like what are the... you know, those are kind of the things that I’m curious about if you have any thoughts. Like what are the precursors to this moment? [37:41] Basil Halperin: So I mean, I still think interest rates can go up before... like capital takes time to build. But if the discussion is like what things will happen on the way to transformative AI, like yeah, the... what’s the line from the bard of our times, our dear leader: “everything is compute”? Like we’re going to tile the planet with computers. So like 1% of US GDP last year was hyperscaler capital expenditure. [38:15] Seth Benzell: And let me... yeah. Let me try to ask this a slightly different way, which is, I guess maybe try to make you be a little bit quantitative about how sensitive your personal predictions about TAI are based on different interest rate scenarios. So I’m going to give you a conditional expectation here. Feel free to use it or to give me a different one, but I want you to try to be quantitative if you can. What is your conditional probability of TAI within five years if the interest rate is less than 6% versus TAI in less than five years if the interest rate is above 15%? Real interest rates. [38:53] Basil Halperin: If the real interest rate is above 15%, then like if this is the real risk-free interest rate, then I think TAI is here and growth is going bananas. I think plausibly even if real interest rates are above 6%... so like the 30-year right now is like 2.6. The 10-year is like 1.8. And so like the 2.6... [39:13] Andrey Fradkin: Just to be clear to the listeners, once again, we’re talking about inflation-adjusted interest rates. [39:16] Basil Halperin: That’s important. So the 1.8% number for the 10-year real interest rate is like really in line with where things have been over the last 25 years. The 2.6 for the 30-year is like a little bit elevated. So even 6... [39:31] Seth Benzell: The numbers I were using were kind of risky equity market rates. So feel free to substitute whatever numbers you like. [39:35] Andrey Fradkin: Well that’s just a totally different object, right? [39:39] Basil Halperin: So... [39:40] Seth Benzell: Oh god. Right. Alright. So okay, risk-free rate. So right now you’re telling me we’re at what? 3%? [39:44] Basil Halperin: 2.6 for the 30-year. [39:46] Seth Benzell: 2.6. All right. So what’s your conditional expectation on TAI in five years in the future given that next year the risk-free rate is under 3%? And then what is it if the risk-free rate goes above 10%? [40:02] Basil Halperin: Again, if it goes above 10%, I think growth is going bananas. That’s a huge jump. [40:07] Seth Benzell: Anticipated growth. So you don’t even think... you think we’d see the growth before we’d see the interest rate? [40:12] Basil Halperin: Sorry, it depends on what horizon interest rate we’re talking about here. [40:15] Seth Benzell: 30-year. [40:17] Basil Halperin: If the 30-year goes up to 15? Or above 10? [40:20] Seth Benzell: 10 or 15. You choose numbers. I want you to try to be quantitative at me. [40:24] Basil Halperin: Well, so here’s the thing, here’s the thing. The interest rate at a particular horizon tells you among other things about growth expectations at that horizon. So you can look at the entire yield curve, interest rate at 1 year, 5 year, 10 year, 30 year, and get the expectations sort of with lots of other things going on at those different horizons. So like I wouldn’t want to just look at just the 30 year. I’d want to look at the 1, 10, 5, 30. [40:48] Seth Benzell: All right. So choose whatever... the curve is the same. Move the level up or not down. [40:53] Basil Halperin: I guess if it does it for you. [40:57] Seth Benzell: Gimme. Feed me. [41:01] Basil Halperin: Real interest rates rose two percentage points from the... two or three percentage points from the COVID depths to where they are now. And again, now they’re like sort of more or less in where they were 20 years ago. If they went up another percentage point, I’d be... pretty surprised and interested. How much does that raise like my probability of transformative AI in the next five years if the... [41:26] Seth Benzell: That’s the question. That’s the question. This is what your paper is about. [41:31] Basil Halperin: But again, like I’m not here to make quantitative forecasts, especially going from market prices back to probabilities. I’m here to say that there’s this... [41:43] Seth Benzell: I know, you’re making a directional argument, but give me... does it double your odds of TAI? Or I can let this go if you’re going to really refuse. [41:50] Basil Halperin: I mean, so what I can do... I can tell you what my AI timelines are and like what feeds into that and how... [41:55] Seth Benzell: Yes. [41:56] Andrey Fradkin: Let’s just do that. Yeah. [41:58] Seth Benzell: And then tell us how they would change if interest rates got up. [42:02] Basil Halperin: Okay, well, like... again, like I really emphasize that to me the right way to read this paper is this interest rate argument is like an outside view, here’s a sanity check. So like my view is much more informed by like all these other things now that I’ve spent like a whole bunch of years reading the AI literature, the AI economics literature. So for example... if you just extrapolate forward the “meter time horizon” trend that you guys have spoken about... [42:30] Andrey Fradkin: What’s the... what’s the... [42:32] Basil Halperin: ...the length of a task that... of a software engineering task, a machine learning research task that these large language models can do with 50% accuracy. If you extrapolate that trend forward... this is currently doubling every seven months or that’s what it’s been for the last six years. If you extrapolate that forward, take into account very importantly the fact that by like 2030... capital expenditures by hyperscalers can be like a trillion dollars and that scaling can’t continue. So like take into account the fact we’re going to hit the compute wall and then investment’s going to slow down. We’ll have models that can do one month tasks with 50% accuracy by I think it’s 2033. And one year tasks by 2039. This is Whitfill, Snowden, Parker’s new paper. So that’s on this narrow range of tasks done in these meter benchmarks at 50% accuracy: 2039, one year horizon. If you then adjust for the fact that like these are particular kinds of tasks... like I don’t know, say that adds another six years, so that’s like another six doublings or something like that. And then take into account that rather than 50% accuracy, we want 99% accuracy. That takes you like to late 2040s. I think... just like this particular stylized fact about time horizons already gets you to like fairly long potentially... at least the possibility of potentially long time horizons for AI. So that’s like... [44:12] Seth Benzell: I guess we’ll come back to this... and maybe we’ll talk about this a little bit more with your new paper where we talk about to the extent that algorithmic progress can substitute for compute progress, right? Because that’s going to be a key factor here. [44:22] Andrey Fradkin: But to be clear, let’s dwell on this a tiny bit more. [44:26] Basil Halperin: Yeah, there was a lot of sub-points in there that I went through very fast. [44:29] Andrey Fradkin: Yeah, but yeah, so I think... I think one thing, you know just Seth to your point very briefly is like the METR graph takes into account algorithmic progress. So that’s why it goes as fast as it does. [44:43] Seth Benzell: Right. But then he said he was also going to take into account... okay, anyway. [44:47] Basil Halperin: So that’s like I think one... that’s like a median view. But I think like you really have to think of terms of different scenarios. So like the “AI 2027” guys... like that report seems a little crazy, this idea that things are not just going to grow at a constant rate but are going to go hyperbolic. Like that seems a little crazy and maybe even... yeah, a little crazy. But like there is enough flesh on that argument, including this new paper Seth that you mentioned, could point towards that, that I think like you have to have some non-zero probability on like... maybe not literally AI 2027 but like AI before 2030. [45:27] Seth Benzell: Do you have to put non-zero probability on anything that isn’t conceptually impossible? [45:31] Basil Halperin: Yes, okay. I mean like non-1% probability. So like I put like 10 or 15% probability on like things getting really crazy before 2030. And then I put like 50 to 80% probability on something between 2035 and 2050. And then like whatever is left, 10, 20% on like some factor X... Moore’s Law slows down, energy runs out and like things take longer than 2060 or whatever. Or including never being able to develop such technology. [46:00] Seth Benzell: So did I get you right? So the median forecast is the mid 2040s for AGI? Is that what you’ve given me? [46:05] Basil Halperin: The quantitative numbers here are really hard, but yes, something like 2035 to 2050. [46:10] Andrey Fradkin: It’s not AGI to Seth. I mean... I mean it’s very different concept... [46:17] Seth Benzell: TAI, TAI. TAI is what we want to talk about. Okay. TAI, excuse me. [46:21] Andrey Fradkin: But Basil, I’m going to give you a counterpoint. I think the METR graph drastically understates the time horizon of tasks that can be done. [46:30] Basil Halperin: Understates? [46:31] Andrey Fradkin: Yes. [46:33] Seth Benzell: Because Ralph OODA Loop. [46:36] Andrey Fradkin: I mean, yeah, but broadly, right? Like a lot of these evals are doing dumb things. They’re taking a model out of the box and just asking it to do it. And that is not how you would do any task if you had to do it, right? Like you... you know, a big theme of I think our show and worldview is we believe in a multitude of models interacting in an ecosystem to produce outcomes. And the scaffolding really matters. [47:08] Seth Benzell: How we were epi-ing the Lessin-Kuld show. [47:10] Andrey Fradkin: Uh, the scaffolding matters, right? The... you can have different models from different providers interacting with each other and calling other tools. And so to evaluate the ability of just like an out of the box LLM to do a specific task... that’s never how you would actually do it in real life. [47:31] Seth Benzell: Yeah, we see this in Andrey’s data where there are, you know, very clear people use a mix of models. It’s there in the data. [47:39] Basil Halperin: Yeah, I mean I think unhobbling is like one possible reason that like there’s 15% chance that we’re colonizing the stars before 2030. That unhobbling could be enough. Leopold had it right. Maybe. [47:53] Andrey Fradkin: Yeah, yeah. I mean, for what it’s worth, I think the bigger, you know... I think the thing I agree with you more is that some of these METR tasks are really unrepresentative of most tasks in the economy. And in particular I don’t think they teach us much about robotics. And I think like robotics has to be an ingredient of any TAI scenario eventually. And so... [48:18] Seth Benzell: Only a computer scientist would think that computer science is the final task. [48:23] Basil Halperin: The strawman obviously being that, you know, a brain in a vat—the brain of the computer—can solve robotics just by doing better software on the computer. That’s the strawman. [48:32] Andrey Fradkin: Yeah, no, no. I understand, but we’re still talking about human tasks being done, you know. [48:38] Basil Halperin: Totally, totally. [48:40] Seth Benzell: A brain in the vat still needs faith in God in order to believe in the exterior world, dude. Haven’t you read your dualism? [48:49] Andrey Fradkin: Um, all right, so... [48:51] Seth Benzell: Wait, let me wrap up... I want to finish up this topic. Last question on this topic and then we can move on. Which is: okay, you’ve shot me down on asking a quantitative question about the macro. Will you give me an answer about: are you changing your environment... your portfolio? I mean, you said 10% chance of s**t gets crazy. Sorry, that’s my one curse per episode. 10% chance. How do you allocate your assets based on that? Are you dissaving? [49:19] Basil Halperin: So like the first thing I’d say is, for someone at my stage of the life cycle, like my most important asset is my human capital. And I’ve reallocated that heavily from studying monetary policy, which was the thing I was obsessed with for years and years, to now being focused a lot on the economics of AI. So like that asset of my portfolio I’ve shifted a lot. Have I changed what my savings are... [49:44] Seth Benzell: Are you dissaving your social capital through drugs and alcohol? [49:49] Basil Halperin: Well, there’s a different consideration there where like I want to stay healthy until the singularity so I can live forever. So I think actually the consideration might go the other way in terms of intertemporal substitution. But, do I try hard to consumption smooth? Absolutely. It would bother me when people in grad school were like, “Yeah, I’m putting money into my 401k.” I’m like... [50:08] Seth Benzell: Are you putting money into your 401k? [50:11] Basil Halperin: I put the minimum amount to get the matching funds. [50:14] Seth Benzell: The minimum, dude. The minimum. I thought this was a guy who believed in his own papers. [50:17] Basil Halperin: There’s no other reason to do it. [50:21] Seth Benzell: All right, you have him, Andrey. [50:23] Andrey Fradkin: All right, all right. I think Seth has given up on life at this point. So cool. Let’s talk a little bit about your new paper with Tom Davidson, Thomas Holden, and Anton Korinek. Why don’t you tell us a little bit about the premise?Basil Justifies His Research:When Does Automating AI Research Produce Explosive Growth? [50:44] Basil Halperin: Yeah. So this is a paper that in some ways is about that 15% probability that things could get crazy soon. And in some ways is about some like deep or some standard economic growth theory. So the idea here is to like take seriously the structure of modern machine learning and put that, embed that into the canonical model of economic growth. Where, by that I mean like: how does AI get trained? How does it develop? Well there’s two key ingredients: software progress, hardware progress. So Moore’s Law and other trends mean that we’re able to produce more chips, better chips at lower prices over time. And algorithmic progress means that even for a fixed quantity of computer hardware, you can get more output from a computer program because we are able to write better computer programs. We are able to train better AI models. So taking into account the fact, maybe most concretely, that OpenAI uses Nvidia chips to train better AI. And then Nvidia increasingly uses AI to design better chips. This is like Google’s AlphaChip has been put to use designing better TPUs, Google’s version of the GPU chip. So that’s like the motivation, sticking this into a canonical economic growth model, seeing what changes. What that cashes out as... [52:20] Andrey Fradkin: Yeah, so before we get deeper into the paper... isn’t the idea that research helps do... like, you know, creating new ideas accelerates economic growth through subsequent acceleration of research and development efforts already embedded in the Romer growth model? How is this different? [52:46] Basil Halperin: 100%. So what this does differently is that it says that there’s different kinds of research. So there’s like software research and there’s hardware research. And those are heterogeneous in interesting ways compared to each other, compared to you know, biomedical research or whatever. And taking seriously that heterogeneity and seeing what that heterogeneity implies. So like in particular... one of the key lessons—so what we do in the paper is we write down a general networked semi-endogenous, like a Romer-Jones, general networked growth model. And draw out a couple of key insights I think. And so the core insights are around this idea of diminishing returns where we stand on the shoulder of giants to like... you know, we’re picking fruit from the tree of knowledge. We stand on the shoulder of giants to reach higher and higher fruits, but eventually the fruit gets harder and harder to pick because we pick all the low hanging fruit first. This idea of diminishing returns. And I think this idea of diminishing returns is like kind of obvious to economists, but it’s not always obvious in these conversations. Like the idea of an intelligence explosion, the idea of the singularity, kind of a lot of times can fail to recognize the importance of diminishing returns where there’s this idea that if you have a self-improving AI, like doing surgery on its brain to get smarter and smarter, that naturally has to lead to a singularity. But it doesn’t if the diminishing returns are strong enough. [54:17] Seth Benzell: Okay, so now we gotta go back to the fruit. So okay, so now earlier you were talking about there were fruits, we were going for them... Explain this concept of diminishing returns through fruit because I’m really hungry. [54:30] Basil Halperin: Yeah. So you’re hungry and so you’re picking fruit from the tree of knowledge. You pick the low hanging fruit first. And you know, that makes you stronger and gives you more energy to pick more fruit. But like eventually you pick all the low hanging fruit. And now you have to reach up and pick higher hanging fruit that’s harder to pick. And because fruit gets harder to pick—ideas get harder to find over time—you’re not just going to grow to become 100 feet tall, a thousand pounds because you’re running into diminishing returns in terms of fruit on the tree of ideas. [55:10] Seth Benzell: So it’s like I grab one fruit and that gives me the energy to eat 0.9 more fruit, which gives me the energy to have 0.9 more fruit and it kind of peters out. I’m just riffing here, but is this like... is the Garden of Eden story... is that actually about diminishing returns somehow? It’s like we’re not in Eden because we have diminishing returns from apples? [55:28] Basil Halperin: Yeah, I guess... I don’t want to say that the snake is Chad Jones because he’s the one who taught us this stuff. [55:34] Seth Benzell: No, the snake is obviously Bloom and Reenen and all... [55:38] Basil Halperin: Right, right. And Jones. Yeah, yeah. I guess so. But so exactly as Andrey said, like this is well known in the literature, this idea of diminishing returns. What we do is have this networked model where you have the software research sector and the hardware research sector interacting. There’s spillovers across sectors. And that teaches you a few things that I can talk about. [56:02] Andrey Fradkin: But so at a high level... you know, if I’m understanding the idea in the paper correctly, is that you can undo diminishing returns with a networked production function for research, if you will. Here’s a question for you: What if we took an old growth model and just did away with diminishing returns, you know, all together and we had to have increasing returns? Wouldn’t we also get an explosion? Like... am I interpreting things correctly there? You’re kind of trying to microfound why increasing returns would happen. [56:54] Basil Halperin: Yes. Yes. So to say that another way... like the original Romer model in this literature implied that there were no diminishing returns. Chad Jones comes along and points out empirically there must be diminishing returns. That’s because like we’ve had this constant 2% growth rate of ideas, that is 2% growth rate of total factor productivity or 1.5% percent. Meanwhile the growth rate of researchers has been 4% for like the last hundred years. So we have increasing number of scientists—like the two of you, thinking great thoughts—but we’re only producing the same growth rate of ideas of 1.5%. [57:40] Andrey Fradkin: That’s because we’re podcasting too much. [57:43] Basil Halperin: Seems plausible. [57:44] Seth Benzell: It’s for the AI. We’re improving the AI, Andrey. [57:48] Basil Halperin: Patrick Collison has this tweet that I think about a lot where he pointed out that when... when did growth in the US fall off a cliff a bit? It was like 2003 or TFP growth. And that’s you know, right when Facebook came out. Social media became the great distraction. Anyway, so yes, ideas get harder to find. That explains why growth slows down. And Andrey you point out that if you just get rid of that idea, then yeah indeed you could have a growth explosion. And indeed we are saying that spillovers across sectors can counteract those diminishing returns. And additionally, importantly, automation can also counteract the diminishing returns. [58:27] Andrey Fradkin: Another thing to say is actually, and I think this is super interesting—not something I thought about going into the paper—is that you can estimate this diminishing returns parameter, this critical diminishing returns parameter by sector. And I can explain what these numbers mean, but that number for the economy as a whole is -3. So zero would be no diminishing returns. For the economy as a whole, it’s -3. For the software sector it’s -1. For hardware, like Moore’s Law, it’s -0.2. So the hardware sector has the least degree of diminishing returns of any sector that’s been estimated. So you know, if compute becomes a larger share of the economy, becomes more important, then this diminishing returns just inherently will become less of a thing. And then on top of that you have this spillover issue and this automation issue I’ve hinted at. [59:17] Seth Benzell: So I know... natural question... and now I’m going to put on my applied microeconomist hat on is: where are you getting these numbers from, man? Yeah, you gotta parameterize this model. [59:33] Basil Halperin: Yeah, so this is just looking at the time series. I can spell that out and I think I have an intuitive way of doing it, but yeah this is just looking at... [59:40] Andrey Fradkin: Yeah, well let’s like walk through the hardware example. Let’s just like give us some intuition for where that number comes from. Because in my mind that seems like a really hard number to come up with even though we do have Moore’s Law, right? Yeah. [59:53] Basil Halperin: No, so the ideal here would be to run an experiment. And you know, maybe METR has enough money to do that or something and maybe they should. But the way... [1:00:00] Basil Halperin: ...the way that Bloom et al, the same paper that Seth mentioned, does this... the literature does this is the following: So say, you know, there’s like a hundred guys and gals thinking about how to improve semiconductors, how to improve hardware in the world. Fix that population. If ideas were not getting harder to find, that same hundred people would produce Moore’s Law. So Moore’s Law says that hardware productivity grows like 40% per year. That gets you the doubling every two years or more law. So something like 40%. Hundred people get 40% growth. But we’ve had this constant 40% growth for 50 years, 60 years in hardware. But that’s required more than just like the original hundred. It’s required that that population of hardware researchers has grown by say 8%, call it, per year since the 1960s. So you’ve needed an increasing number of people to get the same progress in hardware. And so the way that that 0.2% diminishing returns number comes from is that ratio of 8% to 40%. That’s that point two. [1:01:17] Andrey Fradkin: Okay. So now I’m going to tell you... now I’m going to use your paper to tell you why that number is wrong. So why is that wrong? It’s because it’s not just those hardware engineers that are producing that Moore’s Law. That Moore’s Law is being produced by everyone else in the economy that... who is producing let’s say like design software or even, you know, like I don’t know, cell phones... like all sorts of things contribute to Moore’s Law. [1:01:47] Basil Halperin: Yes, exactly. [1:01:48] Andrey Fradkin: And then there’s also just like physical returns to scale, right? So we’re producing more and more chips so that’s a production function parameter rather than a research parameter. So I don’t... so to me it seems a little strange to like lean so heavily on that number which ignores the entire point of your paper. [1:02:10] Basil Halperin: So, so, so... a few things to say. One is... [1:02:17] Seth Benzell: I mean I... yeah, give it a shot. You can also just crawl into your closet and we can hang up now. Your choice. [1:02:22] Basil Halperin: No, no, this is basically the next paper that co-authors and I should write. Maybe Andrey you can co-author with us. Which is: indeed these prior estimates of these coefficients ignores exactly the factors that we discuss. So yeah, I don’t need to repeat what you said because that argument was well put and totally correct. But what that means or or as you said I think, what that means is that the degree of diminishing returns is underestimated because the progress is being benefited by spillovers which are not captured. So if you re-did the estimation with spillovers, you would find that diminishing returns is even harder and that like the singularity is less likely. Totally agree. [1:03:07] Seth Benzell: I have a separate concern about these parameters. So alright, you want to tell us about the parameters we need in order to get this hyperbolic growth, right? But it kind of really seems like once you kind of like start the hyperbolic growth, once you like get on that curve, stuff’s going to get super weird super fast. Yeah. And like wouldn’t the parameters change pretty fast? So like how can you even extrapolate from today’s parameters to this crazy regime parameters? [1:03:38] Basil Halperin: Yeah. I again am going to be in total agreement with you. I again am not someone who like wants to take macroeconomic models seriously as quantitative forecasts, but instead see them as formalized, mathematically formalized fables from which we can draw out particular insights and intuitions that were able to check are internally consistent because they’re written in language of mathematics. So that’s why the takeaway I have from writing this paper with Tom, Tom, and Anton is these ideas about: diminishing returns are important; spillovers can mitigate diminishing returns; automation can mitigate diminishing returns. And I feel pretty comfortable saying with the caveats that Andrey just emphasized, that hardware and software have less diminishing returns than other sectors. Though we should re-estimate those and hopefully will in a future paper. And that on its own is interesting. But not take super seriously like, where are we on the side of zero or negative? Are we on the side of increasing returns or decreasing returns? Like that stuff... yeah, these parameters I don’t have any reason to think those are stable as we go through 10 orders of magnitude of growth or something like that. Some people on the internet do take those that seriously and yeah, I completely agree. [1:05:01] Seth Benzell: Uh if I... okay maybe we can talk for just what... we talked about the spillovers. Maybe you want to talk for a little bit about how automation might overcome “fishing out.” If I may suggest a motto for this: “If you fish fast enough, you can outrun fishing out.” [1:05:15] Andrey Fradkin: Well maybe actually like maybe before you get to that we can just... one of the nice things about this paper is there’s like a concise message which is this Equation Number 1 in the paper. [1:05:28] Seth Benzell: Yeah the one you... the equation you just told us to not care about. Tell us about it. [1:05:32] Basil Halperin: Yeah. So I said that for the hardware sector this diminishing returns parameter is 0.2 and for the economy as a whole it’s 3. And again that was the intuition that the 8% researcher population growth versus the 40% productivity growth. Whereas if there was 0% population growth/researcher growth, then that diminishing returns parameter would be zero because you’d have zero divided by 40. Meanwhile if that number were negative, then you’d have the increasing returns and the hyperbolic growth, the singularity. So the reason why I mentioned that is that zero there is the focal point, but really it’s like a... it’s a one plus a zero. So you have this critical condition of: are feedback effects greater than or less than one? And in like the canonical one sector model that comes down to this one diminishing returns parameter. In a networked growth model, instead of having one parameter that tells you are you having diminishing returns or non-diminishing returns, you have a spillover matrix. And the largest eigenvalue, the spectral radius of the matrix... I know you had Ben Golub on recently so... [1:06:58] Seth Benzell: Just say, say the magic word. Give the audience the Eigenvalue. [1:07:00] Basil Halperin: This is becoming the eigenvalue podcast I guess. If that largest eigenvalue is greater than one, then you have explosive growth. So “is that largest eigenvalue greater than one” can be summarized in this somewhat simple condition we have in the introduction of... it’s very loosely speaking like a weighted average of like the inverse of the diminishing returns parameter where the weights are determined by how automated is each sector. I don’t know how much sense that’s going to make out loud. In a lot of ways this paper is one of these papers where like looking at the math is actually a lot easier than saying it in words. But hopefully some of the insights have come across. [1:07:45] Andrey Fradkin: So there are these like F... F terms which are the fraction of tasks that are automated by AI. Now like the first term of your equation is F of Y, which is the share of consumption good output that is production that is automated. Am I interpreting that correctly? [1:08:07] Basil Halperin: Yes. [1:08:08] Andrey Fradkin: Okay. Now what if that’s one just by itself? [1:08:14] Basil Halperin: Right. [1:08:15] Andrey Fradkin: That means that the entirety of the economy that we would actually care about in terms of consumption is automated already. So that’s kind of... in that case we don’t have explosive growth. It’s kind of on the boundary condition. Is that... am I interpreting that correctly? Because things aren’t getting better, it’s just that everything we want is is just being produced automatically. [1:08:38] Basil Halperin: Right. If there’s nothing else going on, it’s right on the boundary. If you have epsilon of any other productivity growth going on or anything, you get above the exponential to super exponential. [1:08:48] Seth Benzell: It would be like unstable in some sense if you were like exactly at one. [1:08:52] Basil Halperin: Yeah, to perturbation. [1:08:56] Seth Benzell: So Basil, I guess the last question I want to ask about this paper before we move on is... so you’ve explained how there’s a bunch of different things going on in the research process in the economy that are either going to kind of accelerate research and it’s going to get stronger and stronger or might slow down research and we’re going to get diminishing returns. Two of the most important factors here are kind of this idea of spillovers across sectors, but also this idea that you might be able to automate some research, right? As you get better AIs, you might be able to get faster algorithmic improvements. When I read kind of like LessWrongers, the kind of the latter kind of seems like the show, right? If you can get the AI to write better AI algorithms, there you are. In your model is that the important factor or are they kind of them all equally important? How do you think about that? [1:09:47] Basil Halperin: Yeah, okay so let me want to say this. So the way I’d frame it is that these spillovers... or sorry, the diminishing returns limit the effects of AI progress. Spillovers in some like static sense... like we don’t think of spillovers as changing much over time. The innovation network doesn’t change much. But we think of as the economy grows, more and more tasks are getting automated. So spillovers provide some like static offset to the diminishing returns, whereas as automation increases, it’s continually offsetting diminishing returns. So I guess in like a dynamic sense, perhaps automation is more important. But sort of in the almost static way that we incorporate automation... either one is equally powerful in offsetting diminishing returns if you sort of do the comparative static. But in the sense of automation is the thing that actually changes over time, that’s the more important one. [1:10:47] Seth Benzell: Okay. Stands to reason. [1:10:49] Basil Halperin: If I can add one more thing about paper actually. So I didn’t mention one critically important limitation. So if you talk to economists about what will prevent AI from leading to explosive growth, I think we say one of two things. One is the diminishing returns. That’s that’s what this whole discussion has been focused on. But the other one is this idea of bottlenecks: that even if you have really fast progress in software engineering, then if you don’t have progress in the robotics side of the econ, the physical side, then that will bottleneck the growth if these sectors are complements. [1:11:24] Seth Benzell: Yeah, and the essential thing is going to be the elasticity of substitution across sectors. Yeah. [1:11:28] Basil Halperin: Right. And so we completely ignore the bottlenecks issue. We’re just focused on this diminishing returns idea, which to my mind is not a claim that there’s not bottlenecks. I think bottlenecks are super important. I think like there’s a 5 or 10% chance bottlenecks aren’t important—hence my earlier timelines forecast—but like... [1:11:47] Seth Benzell: We all get uploaded. I mean yeah, there’s a universe where we all just get uploaded and like who cares that we don’t have robots for a while. [1:11:53] Basil Halperin: Yeah or something like that. But yeah, the focus... the paper is meant to just like zoom in on the diminishing returns logic and to turn off the bottlenecks. But that’s important when thinking about how to quantitatively interpret the paper. [1:12:08] Seth Benzell: There you go. Basil admits to one possible drawback to his paper. All right. [1:12:13] Basil Halperin: That’s all you’ll get from me. [1:12:15] Andrey Fradkin: Now I wanted to ask one more question actually because we’re natural right here and then we can go to the next topic. Which is like: how have you found the profession’s reaction to these sorts of exercises? Like you know, I can tell you what I... various opinions I’ve heard, but I’m curious like you were... you’re an author of these types of papers, so what has been your reaction? What has been like the feedback you’ve gotten? Yeah. [1:12:43] Basil Halperin: I’m so curious about your experience. I have limited experience submitting these things through the publication process still because publishing takes so long. Yeah, I’ve only started submitting recently. Um, I guess what I would say is that like I feel like views on this are kind of polarized where some people are like, “This is super interesting and I’m glad to see economists taking this seriously as opposed to like wordcel mumbo jumbo from Silicon Valley or something like that.” Which I don’t want to say that I endorse that criticism, but some people have that criticism. And other people are like “This is...” [1:13:16] Seth Benzell: This is a pro-wordcel podcast. You’re safe here. [1:13:19] Basil Halperin: Yeah. Or are you calling yourself a shape rotator? Whatever. [1:13:24] Seth Benzell: I’ll leave that up to you two. This podcast cannot rotate very many shapes. But that’s a topic for another episode. [1:13:32] Basil Halperin: So that’s like really all to say that like to me it’s like too soon for me to say. And that’s why I would love to know what your experience is. [1:13:42] Seth Benzell: My experience is that I found it completely impossible to publish and ended up having to publish a book. Yeah I think Seth has been trying to... Seth has been trying to publish this style of work for a very long time and the profession is not very interested, right? [1:13:58] Andrey Fradkin: I would say opinions are changing, but I think the people have been battered for so long into being obsessed with like very micro identification... and given I’m not a macroeconomist... but like at least on the micro side that a lot of microeconomists just don’t consider it you know scientific unless there’s a tight identification argument. Or there’s an inherent skepticism of theory in some sense, which I do share to a large extent, which is that you can kind of get anything to happen if you’re a good theorist. And then it’s pretty hard to adjudicate between theories. And then to the extent that, you know, transformative AI is a mostly theoretical field at this point... it’s hard to adjudicate between transformative AI theories. So I think I’ve grown a lot more favorable to this type of work obviously over time because I just think like we might as well be working on the most important topics even if we can’t answer them as precisely. But I think a lot of people... [1:15:09] Seth Benzell: Yeah, rather than just looking under the street light. Yeah. [1:15:12] Andrey Fradkin: Exactly. Yeah. A lot of people are just not comfortable with that level of speculation. Yeah. [1:15:18] Basil Halperin: “This is so dumb,” some might even say. No, yeah. Getting untethered from reality is like such a real risk on these big questions. In macro in general it’s so hard and you definitely see that happening. So it’s fair, it’s tough. [1:15:48] Andrey Fradkin: I mean I think one of the interesting things that you did, right, is posted it on LessWrong. And in some sense like that has been more influential than any paper economics version of this paper that you could have ever written. For sure. Which says something. [1:16:03] Basil Halperin: So to clarify for listeners, originally this was just some some shitpost. This was a blog post that I put out because like I was getting in fights with some friends in group chats and I was like, “Well the market doesn’t believe what you guys have to say.” And yeah and like it wasn’t going to be a paper and it just... it got such positive feedback that like it seemed like the demand was there for it to be developed a bit further into a paper. Uh, and in some ways I think that maybe I should instead of spending thousands and thousands of hours polishing papers before putting them out, I should be putting more out as blog posts first to... [1:16:40] Seth Benzell: Dude, honestly yes. Because if you’re asking like my honest advice, I think when it comes to this TAI stuff there’s so much taste at the evaluation level that like spending another thousand hours polishing the same idea, the marginal returns are pretty low. At least as a practical careerist observation. If you feel like you’re learning, keep going. [1:16:59] Andrey Fradkin: Well I do think that you know, if you get it... you know, for the profession, if you get into a top five journal there are obviously enormous rewards. But I think like there’s a risk of like polishing it for like some you know specialist field journal and still spending two years on it. I mean it almost makes one think that like you know there should be a new journal of Transformative AI Economics. I’m sure Anton has suggested something like that. [1:17:27] Seth Benzell: Yeah, okay that’s what I was... maybe can we talk for a minute about your department? Which sounds so cool. You’ve got Anton Korinek who I remember back when he was doing macroprudential policy. I was like, “This is one smart cookie. I want to see where... let this guy cook.” What’s it like working with him? What’s this TAI department you guys are setting up? [1:17:44] Basil Halperin: Yeah. So Anton has, yeah, been interested in the economics of transformative AI for longer than almost anyone, right? Like somehow back in 2016 he was thinking about this stuff. I’m still a little confused how he got into this so early. I think he did like a master’s in computer science maybe and had this in the back of his head. But yeah, so he’s managed to get a bunch of money to start this Economics of Transformative AI Institute here at the University of Virginia. Which is very cool. So me, Anton, and Lee Lockwood, who is a public finance economist, are sort of the three folks here who have written papers at least on the topic. And yeah I don’t know, trying to get folks to think more about the issue and write some research. [1:18:28] Seth Benzell: What is it like working with Anton? Do you just like sit down with him and he’s like, “I already have solved all of the problems” and you just like you take notes on him as he dictates to you? What is it like collaborating with a guy like that? [1:18:39] Basil Halperin: What can I say? I mean yeah, Anton’s been thinking about these issues for a long time. I can recommend his Coursera on the topic. In fact I went through that during the depths of the pandemic where he talks about the macroeconomics of AI and some models, Shannon information theory and interesting things. Yeah. [1:19:00] Andrey Fradkin: Shannon information theory gets you to scaling laws? How does that come in? [1:19:04] Basil Halperin: I don’t remember why he was teaching that but I was you know interested in the topic. [1:19:08] Seth Benzell: This is neat. I’m Anton Korinek and this is what smart people think is fun. Basil Justifies His Blog Posts:Optimal Taxation in the Age of AI [1:19:16] Seth Benzell: You recently got in a Twitter back and forth with other friend of the show Phil Trammell about optimal tax policy. You posted this really spicy meme of the two astronauts on the moon... [1:20:00] Seth Benzell: ...and there’s the Puerto Rican astronaut with the gun to the American astronaut saying...[1:20:00] Seth Benzell: ...and the American astronaut says, “So, even in the age of TAI, Pigouvian and Georgist taxation is the right way to go?” And then the Puerto Rican says, “Always has been.” Would you explain the context of you posting that meme, the Phil and Dwarkesh post, and how people should understand that? [1:20:27] Basil Halperin: So yeah, Phil Trammell, Dwarkesh Patel... two guys that anyone interested in this stuff should be reading or following, listening to. Admittedly, Dwarkesh is a competitor of you two... [1:20:39] Andrey Fradkin: No, no, no. We believe in coopetition. [1:20:41] Seth Benzell: We’re cooperating... everyone should listen to both of our podcasts. We’re complements. [1:20:46] Basil Halperin: Nice. [1:20:47] Andrey Fradkin: We are actually complements, to be clear. [1:20:54] Basil Halperin: So yeah, they wrote this great post, “Capital in the 21st Century,” playing on Piketty, saying Piketty was right in the past, but will be right in the future. And made this argument that as more of the economy gets automated, labor income will no longer be a sufficient tax base, and that power will be unequally distributed because capital income is so highly concentrated. [1:21:24] Seth Benzell: Feels like these are three separate arguments already. [1:21:27] Basil Halperin: There’s a couple different arguments in this piece, yes. And yeah, calling for capital taxation in the future, both for redistribution purposes of financial resources and to prevent sort of power concentration, is how I interpreted the piece. [1:21:44] Seth Benzell: But I was taught in public finance class that capital taxation is bad. [1:21:48] Basil Halperin: Yeah, I think there’s a lot of logic to that argument. So yeah, I wrote this thread just making a couple points. One of which is based on—we were just talking about my colleagues Anton and Lee, Anton Korinek and Lee Lockwood—so they had a recent paper summarizing sort of how should we think about public finance in a transformative AI world. So like take an AK economy, so an economy where all production is done by capital, no labor involved. What is optimal taxation in that world? And they point out or they show that consumption taxation is still optimal rather than introducing capital taxes. As long as you can raise enough revenue from that consumption taxation to fund whatever you need to fund. So that was like a first point I was making, that consumption taxation is going to dominate capital taxation. [1:22:42] Seth Benzell: Let’s pause there for a second. Because I feel like all of my normie friends don’t understand this point. And in fact my advisor once, he tells me this story—I mean I assume it’s true—where he had like a half hour meeting with Bernie Sanders where he was trying to explain to him why consumption taxation is better for poor people than capital taxation. And Bernie Sanders’ brain was like, “But, but poor people no have capital.” Explain to a normie: why is consumption taxation considered preferred to capital taxation? Because only rich people have capital, right? [1:23:14] Basil Halperin: So let’s see if I can do this with the caveat that I’m not a public finance economist, I just play one on Twitter. So the intuition I always come back to is this one that capital taxation is equivalent to explosive consumption taxation. So what do I mean by that? If I save... so you know, the University of Virginia pays me one dollar. I can either use that to go like buy a candy bar today or I can save that to tomorrow. [1:23:41] Seth Benzell: But you don’t save it because of TAI. [1:23:43] Basil Halperin: But I won’t save it because of TAI, indeed. I got to go party. And consumption taxation would be taxing that purchase of the candy bar. Capital taxation, taxing the savings. And if I save the dollar to tomorrow and try and buy a candy bar tomorrow... the capital taxation then would just be taxing consumption tomorrow differently than consumption today. And do we... like if we’re trying to equalize consumption across people, does it make sense to tax people who consume in the future rather than consume today? Like what’s the difference there? Is like one intuition pump. Honestly, like again, I’m not a public finance economist, I’m not sure on the spot I’m going to give the clearest exposition. [1:24:38] Seth Benzell: No, I think that was pretty good. I think that was pretty clear. Okay, but then the memes about Pigouvian and Georgist taxation. [1:24:45] Basil Halperin: Right, right. So first point, consumption taxation dominates capital taxation anyway. A bigger picture point that isn’t AI specific but does apply to the AI world is that we have these other taxes that not only are they less distortionary than consumption taxation, they might even be efficiency enhancing. So those taxes are taxes of externalities—Pigouvian taxes—should we tax carbon? Should we tax pollution? And Georgist style taxes where you tax owners of unimproved land or unimproved natural resources. People who just by luck and by happenstance happen to find out they have an oil well under their house. Like there’s no economic efficiency, and arguably no moral reason for those people to earn rents from the fact that all of a sudden, whoa, there’s a gold mine under my house. So today, we should be taxing externalities to fix those negative externalities. Today we should be redistributing the pure rents of unimproved land, unimproved fixed resources. And that will only remain true in an AI driven economy. And those natural resources will become even more important in an AI driven economy where there are no scarce... there’s no scarce labor, there’s no scarce capital. The only thing that is scarce is natural resources. All that said, like I’ve mentioned this caveat that: are those taxes enough to fund the necessary redistribution or the necessary government spending? [1:26:28] Seth Benzell: Land is the only scarce factor. You must imagine its price will be quite high. [1:26:32] Basil Halperin: Yeah, in the limit, you would really think so. Maybe on the transition path... so this is a very good point that Phil made in the Twitter discussion of like, how quickly will the natural resource share rise? It’s not clear. I would be so interested if someone could answer that question in a convincing way or something. [1:26:47] Andrey Fradkin: I don’t know. I think robots will be able to mine on the moon pretty efficiently, personally. [1:26:55] Basil Halperin: And so natural resources won’t be scarce, is what you’re saying? [1:26:58] Andrey Fradkin: Well, there’s a lot of natural resources on the moon. [1:27:01] Basil Halperin: Are there? On the moon? [1:27:04] Andrey Fradkin: I think so, yeah. [1:27:06] Seth Benzell: We got red rocks. You can make robots out of red rocks, right? [1:27:10] Andrey Fradkin: I mean you can also do all sorts of things... [1:27:12] Seth Benzell: Silicon! It’s silicon, dude! [1:27:14] Andrey Fradkin: You can also, you know, like have a ton of solar panels on the moon and then use energy to run fusion and fission reactions to get any resource you want. [1:27:28] Seth Benzell: It’s different timelines. Different horizons. [1:27:33] Basil Halperin: Different time horizons actually is I think a big part of the reason for disagreements on this. But um, like the rents in the economy have to go somewhere, right? If labor’s not earning it and capital’s not earning it. [1:27:48] Seth Benzell: In a pure AK economy, there are no rents. It’s just A and K, dude. [1:27:52] Basil Halperin: Right, right. The returns have to go somewhere. The returns above replacement maybe is one way of putting it. So anyway, that’s the source of the meme. Like why hasn’t anyone estimated whether we could just fund the US government by taxing externalities, by taxing land? Like someone should have done that, especially these Georgists obsessed... [1:28:13] Andrey Fradkin: No, no, I think... well, I think the externalities... I mean our friends in environmental economics have definitely, you know... I think Larry Goulder has a bunch of work on estimating Pigouvian taxes in general equilibrium. [1:28:28] Basil Halperin: Read it. [1:28:29] Andrey Fradkin: I don’t think... I don’t think it gets you there. But Georgist taxes... I can imagine it can get you pretty far. [1:28:39] Andrey Fradkin: Well cool. Uh, thanks so much for joining us. It’s been a fascinating discussion. Any final notes for our listeners? Anywhere they want to check out, in addition to your website? [1:28:53] Basil Halperin: Yeah, feel free to send my papers. That’s a great decision. And of course, on Twitter and Seth’s as well. [1:28:59] Seth Benzell: [Laughs] Great. [1:29:01] Andrey Fradkin: All right. Well, thanks for... thanks for coming on and keep your posteriors justified. [1:29:07] Basil Halperin: Thanks, Andrey. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • January 26 · 1 hr

    Can an AI Interview You Better Than a Human?

    We discuss “Voice in AI Firms: A Natural Field Experiment on Automated Job Interviews” by Brian Jabarian and Luca Henkel. The paper examines a randomized experiment with call center job applicants in the Philippines who were assigned to either AI-conducted voice interviews, human interviews, or given a choice between the two. Key Findings: * AI interviews led to higher job offer rates and proportionally higher retention rates * No significant difference in involuntary terminations between groups * Applicants actually preferred AI interviews—likely due to scheduling flexibility and immediate availability * AI interviewers kept conversations more on-script with more substantive exchanges * Online applicants saw especially large gains from AI interviews Topics Discussed: * The costs of recruitment and why interview efficiency matters * Whether AI interviews find different workers or just reduce noise in screening * How human recruiters interpret AI interview transcripts differently * The “Coasean singularity” question: Will AI improve labor market matching overall? * Limitations: scheduling confounds, external validity beyond call centers, unmeasured long-tail outcomes * The coming arms race between AI interviewers and AI-coached applicants Posterior Updates: On the usefulness of current AI for job hiring: * Seth: 40% → 90% confidence AI works for call center jobs; modest update for general jobs * Andrey: 20% → 75% for call centers; 1% → 5% for general interviews (“we need to reorganize all of hiring first”) On whether AI will improve job matching significantly on net in the next 5-10 years * Andrey: 55% → No Update * Seth: “A bit more optimistic than Andrey” → +1pp update Referenced Work/Authors: * Prediction Machines * Related episode on AI and labor signaling with Bo Cowgill. Transcript: [00:00:00] INTRODUCTION Seth: Welcome to the Justified Posteriors podcast, the podcast that updates its priors about the economics of AI and technology. I’m Seth Benzell, an interviewer who will never stick to a standard script, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, counting down the days until I can use an AI to pre-interview my podcast guests to see if they deserve to be on the show. Coming to you from San Francisco, California. Seth: I don’t know. I think our filtering criteria is pretty good. Andrey: I know. Seth: Right. That’s one job we never want to automate—who becomes a friend of the podcast. That’s an un-automatable job. Andrey: But it would be nice to pre-interview our guests so that we could prepare better for the actual show. Seth: I was thinking about this, because there’s two possibilities, right? You do the pre-interview, and you get an unsurprising answer in this sort of pre-interview, and then that’s good, and then you should go with it. And then if you get a surprising one, then you would lean into it. What would you even get out of the pre-interview? Andrey: Maybe what the guests would want to talk about. Seth: Okay. Andrey: But I agree with you. Mostly, it’s just hearing the guest talk, and then thinking about, “Oh, this is something that we want to really dig into,” versus, “This is something that might be not as interesting to our audience,” and knowing that ex ante. [00:02:00] SETTING UP THE TOPIC Seth: Yeah. We’ve been... So we’re talking about interviews. You’ll remember in a recent episode, we just talked to our friend Bo, who’s doing work on how maybe job applications are changing because of AI. So now I think what we want to think a little bit about is how job interviews are changing because of AI. Maybe we’ve heard before about how AI is changing how people talk to the hirer. Maybe we want to hear a little bit about how AI is changing how the hirer solicits information in an interview. We’ve got a very interesting paper to talk about just about that. But do you remember the last job interview you did, Andrey? Andrey: Yes. Seth: How did it go? Did you have fun? Did you feel like you stayed on topic? Andrey: It was a very intense set of interviews that required me to fly halfway across the world, which was fun, but exhausting. Seth: So fun. So you would describe the interview as a fun experience? Did you get more excited about the job after doing the interview? Andrey: Yes, although I ultimately didn’t take it, but I did get—you know, I was impressed by the signaling value of having such an interview. Seth: So the signaling value. So in other words, the signal to you from the interviewer about the fact that they were going to invest this much time. Is that right? It’s that direction of signal? Andrey: Yes, yes. And also the sorts of people who they had talking to me, and just the fact that they were trying to pitch me so hard. Now, certain other companies lacked such efforts. Seth: Right. So it seems like one important aspect of an interview is what the interviewee learns from the interview. But what about the other side? Do you feel like your interviewer learned a lot about you, or enough to justify all that time and expense? Andrey: I’d like to think so. I mean, I’m not them, so I can’t really speak on their behalf. But it did seem like the interview process was fairly thought out for a certain set of goals, which might differ across companies. What about yourself, Seth? Seth: Thank God, it has been a long time ago that I interviewed for a job, and I can tell you exactly what happened. I was on the academic job market, but I did throw out a couple of business applications, and so I got an interview at Facebook. Headed out to their headquarters, did all of the one-on-one interviews, and then there was a code screen, and I was not grinding LeetCode for the last five months and completely bombed it. And they said, “Thank you very much for your time.” So that was an example of, I think they probably could have saved the time for the interview if they had given me the code screen first. Andrey: It’s funny, there was a time in my life where I interviewed at Facebook, too. I mean, this is probably 2014 or something. Seth: Mm-hmm, mm-hmm. Andrey: And they did do the coding screen before. Seth: Who knows? Who knows, dude? [00:05:15] THE PAPER Seth: Okay, so interviews, we do them. People seem to give information, take information from them. How can this be made more efficient with AI? That’s today’s question. In order to learn more about that, we read Voice in AI Firms: A Natural Field Experiment on Automated Job Interviews, by friend of the show, Brian Jabrian and Luca Henkel. I was interested in this paper because it’s kind of an interesting flip side of what we just saw from Bo. I guess before we talk too much about what the paper actually does, it’s time for us to go into our priors. ═══════════════════════════════════════════════════════════════════ [00:06:00] PRIORS Seth: Okay, so Andrey, when we’re thinking about AI being used in interviews, what sort of thoughts do you have about that going in? What sort of priors should we be exchanging? Andrey: Yeah, I mean, I think just when I first saw this paper, I was kind of surprised that we were there already, honestly. I think interviewing via voice is a pretty delicate thing, and the fact that AI is potentially able to do it already was—I hadn’t been thinking—I didn’t think we were there yet, and I think just the very existence of this paper was a bit of a surprise when I first saw it. But I guess a first natural prior that we can think about is: is using an AI to interview someone rather than using a human to interview someone, is that better or worse, or how do we think about that? So, Seth, what do you think? Seth: Well, it’s a big question, Andrey. I guess my first response is, like we always say in this podcast, context matters, partial equilibrium versus general equilibrium matters. The context that we’re going to be looking at in the paper is call center workers. So maybe I’ll give kind of a different answer for short-term call center workers than maybe longer term economy as a whole. When I think about call center workers, I think about a job that seems to be—no offense to our friends of the show out there who are call center workers—but this does seem like one of the jobs that is going to be the first to be automated with generative AI, or most at risk, especially kind of low-skilled call center work. So if there was going to be any sort of domain where you could automatically verify whether someone was good at it, intuitively, it would be the domain that you’re kind of close to automating anyway. So if it was going to work anywhere, I would say it would work here. And yet still, call center work, you might imagine, it requires a lot of personal empathy, it requires maybe some subtleties of voice and accent that an AI might not identify or even might hesitate to point out such deficits. I would say I kind of went in with the idea that for call center workers, maybe there’s a forty percent chance that AI would be better than a human interviewer. So maybe it’s slightly unlikely that it would be better. But if we were to expand out to kind of knowledge work as a whole, I would be more, even more pessimistic, maybe only a twenty-five percent chance or lower that the AI interviewer would be better. What do you think? Andrey: Well, how would you—what do you mean by better? Seth: Oh, well, better in terms of the hire is ultimately the correct match, right? That’s going to be operationalized in a specific way in this paper, what... How they’re going to measure better match, but, yeah, that’s what I would say. They hire someone who’s going to be productive and work with the firm for a long time. Andrey: Yeah. I mean, so that’s kind of one definition, I guess. Another definition might be, is the ROI from a particular interview process better or not? Seth: Right, better net of costs. Right. Okay. Andrey: Because I think one of the things that oftentimes economists underappreciate is that recruitment is an enormous cost. Seth: Don’t tell those search labor economists, dude. Andrey: Some of them model it, but I don’t think it’s actually a big focus. But it’s just the process of interviewing. You know, let’s say there’s a position, and you need to interview six people for a relatively high position, so that’s six hours direct, or maybe it’s a half-hour interview, it’s not obvious. But then also, there are all the meetings and pre-meetings, post meetings. Maybe you give an offer, and then they don’t accept it. And there... I mean, there’s just a lot of costs involved. So even if it wasn’t as good as a preexisting interview process, it might still be ROI positive for the firm. Seth: I guess we come back to what is the cost of interviewing versus the cost of making a bad decision. You know, well, it’s not, it’s public information that we, here at my university, we hired a dean of the business school who was an absolute disaster and got voted out by the faculty in a ninety-eight percent vote after one year. That guy did a lot of damage, right? We should have interviewed him harder. So it really depends. So I guess the point would be in kind of higher leverage roles, you would think that the interview costs would be a relatively negligible part of what’s going on. Andrey: I don’t think that’s true. I think in higher leverage roles, higher leverage people have to do the interviewing, and the cost of delaying hiring is much higher. So to me, it’s not obvious. But anyway, that’s, this is all a sidebar. Seth: Okay, so let me hear the prior. Andrey: Yeah. So I think my prior that this interview technology would be better than a human technology, just solely based on match quality, was actually quite low. I probably twenty percent, or maybe less than that, actually. Because it just seems like, yeah, maybe on average or maybe in a typical case, it’s fine, but there’s so many things that can happen in an interview that you could only learn by running a process enough times to really learn how to do it well. And so, yeah, I wasn’t super optimistic that it was going to work yet, even for call center workers. But I think for kind of higher-end labor, right, I think my prior that it would be better is very low, you know, like 1%. Just because I just don’t think we’re there yet. Seth: Wait, so I’m getting—So 20% for call center workers and 1% generally, was the take? Andrey: Yeah, that would be my sense. Seth: Mm-hmm. Andrey: I mean, just, it’s hard to imagine that at today’s technology levels, that for, let’s say, a professor job, that the AI could interview better... I guess one way to put it is getting rid of all the humans in the interview loop for a faculty hire, that seems just kind of crazy. Seth: Right, and that... Well, obviously, a more extreme experiment than what we’re talking about here. Faculty, we’re thinking about, you know, maybe they’re pushing frontier knowledge, would be the last thing that you would think that an AI would be able to get at. Another thing I think about is someone who’s going to be in your faculty is living with you for 20 years, so you might really care about if they smell good, if they have a peccadillo that bothers you, that these might not be relevant considerations in a call center remote job, right? Andrey: Yeah. Yeah, exactly. I think... And I think, actually, the interpersonal thing, which is a very contentious thing, by the way, is that I think people understand that good teams get along with each other. But at the same time, screening based on how much you’d like to have a beer with someone might have problems, you know? Seth: Not good. Andrey: So yeah. So, you know, it’s not obvious which way that cuts, but certainly it’s an important part of hiring. And, you know, I think for higher-paying jobs, it’s not that there’s just one interview, of course. There are many, many interviews, and oftentimes, in-person components of interviews over dinner, and so on. And you might think, you know, maybe that’s all unnecessary, but given that it persists in equilibrium, even though it’d be a lot cheaper not to do it, that should signal something. [00:14:00] GENERAL EQUILIBRIUM CONSIDERATIONS Seth: Good point. But now, Andrey, what I’d like us to think about for a second is to maybe zoom out for a bit and think about, okay, we’re talking about current generation technology in partial equilibrium in this study. One company uses 2025 generative AI to try to attack this specific question for call center workers. Let’s take a step back. You know, that’s what we always want to do in this podcast, is take a step back and like, okay, what does this tell us about the broader process that society is undergoing? You’ve written recently, movingly, to be honest, about this idea of a Coasean singularity, that AI will be so good at helping us communicate to each other, that we’ll get perfect matching at zero cost. I don’t know what timeframe you have in mind, but presumably, one of the things we’ll get better at matching is people to jobs. So maybe you’re pessimistic that in this context, in this time, that AI will be good at hiring, but do you think, you know, 5, 10 years from now, as these technologies diffuse, do you think we’ll get better job matching as a result of employers using a lot of AI and job applicants using a lot of AI? Is that final equilibrium the destruction of all meaning, as Bo, you know, foretold, or is it the utopia of the Coasean singularity? Andrey: Well, I do want to point out that I don’t think any of the authors strongly believe that the Coasean singularity will happen, actually, you know? Seth: Oh, the Coasean singularity is a myth? Andrey: The Coasean singularity, question mark, Seth. Question mark. Seth: Question mark’s doing a lot of work, Andrey. Andrey: Yeah. No, the paper is doing a lot of work to tell you why it might not happen. But I think, yeah, I think time horizon certainly matters here, right? Seth: Okay, but let’s say 5 to 10, to just to choose a number. Andrey: Yeah. So, so, like, not that long a time horizon. It’s very non-obvious to me. Just because there are all sorts of institutions that are going to be involved, very messy institutions. Like, one of the things that we already talked a lot about on this show is the problem of too many applications, applications lacking signaling value. At the same time, you know, you can imagine on the interview side, if you interview, you know... How does this all affect the number of interviews you’re going to do? Seth: There’ll be more and more applications. The cost of applications goes down, yeah. Andrey: Yes. Now, maybe the cost of interviewing goes down, but it doesn’t for the applicant if they have to be the one... You know, if the applicant’s agent is doing the interviewing, maybe it’s a different story. But if the— Seth: Right! How many, how... It’s like, it feels like you’re watching, you know, the drone war in Ukraine. There’s the move, and the countermove, and the countermove, and the countermove. It’s hard to say where that process ends, right? Andrey: Yeah. So I... And then I think, of course, you know, there are actual individual institutions involved. Like, what is the government going to do? And even if some nimble firms are really doing a great job of matching using AI technologies, how that plays out when there are other organizations that are using other sorts of tools, it’s just completely not obvious to me over a five to 10-year time period. Seth: So is that a fifty-fifty? Is that a, I have—is my prior is the completely uninformed prior? Andrey: No, no. I think because you’re introducing both sides of the technologies, both the AI for the applicants and for the employers, it’s hard. I mean, I’m a bit of an optimist, so maybe I’ll say fifty-five percent chance. Seth: Fifty-five percent. Ooh, I have to say, I’m a little bit more optimistic than you, Andrey. I think if you think about the world, the world, since, you know, the rise of the printing press, has seen an arms race in technologies for understanding versus technologies for lying, right? And yet, we think kind of the general process has been towards better price discovery, better matching, right? It seems like we could translate the same ideas to financial markets, where people are getting better at lying, people are getting better at trading, people are getting better at communicating. But ultimately, I mean, at least my sense is that price discovery has improved, right? So I guess— Andrey: Oh, I would argue the opposite. So I... Not price discovery, but labor discovery, I think has been substantively hurt over the past five to ten years. Because our educational institutions have abdicated their role— Seth: Credentialing. Andrey: Actually, credentialing, and because it’s been trivial to start applying to jobs. So yeah, I mean, look, that’s a little too pessimistic, but I’m just saying that over a five- to ten-year period, I have to be a little bit cautious. I think if we’re to be able to reoptimize our institutions, I mean, now the problem with going thirty years is how much human labor do we even have? But to me, just lots of things could be going on. ═══════════════════════════════════════════════════════════════════ [00:22:00] THE EVIDENCE - CONTEXT Seth: Okay, all right. So we’ve got our priors locked in. Now it’s time to turn to the evidence. Okay, so our context here is the Philippines in 2025. We’ve got a pool of about seventy thousand applicants to different call center jobs. They’re all going through this one recruiter who’s recruiting for multiple different businesses. To give some context about the call center job market, this is a very high-turnover, low-paid work. We’re talking about three or four hundred dollars a month at two to three times minimum wage. The skills required are English speaking, flexibility with changing shifts. There is a line in the job application that calls for strong analytical and logical thinking. I think strong might not be the correct adjective there. You probably need more than zero. But all this combines into a job that people are not married to. So we’re looking at a job with sixty percent annual turnover, with a high share of that being people voluntarily leaving rather than being fired. The... We’re talking, in order to do these interviews, people, first, they can either show up in person to one of these recruiting offices, or they can apply online. Then they’re scheduled for an interview, and they also take a standardized test that has both an English skills component and a kind of analytical mathy component. And just to give a sense of how strong a filter this is, about six in—if we’re talking about the human interview baseline, about six percent of applicants accept a job, while two percent still have a job one hundred and twenty days after being hired. So that’s not a conditional average. That’s just two percent of people who show up for an interview end up having the job for at least four months. So that’s our context. Andrey: And about ten percent get an offer, approximately. Seth: Right. Yeah, yeah, so ten percent get an offer, six percent accept the job. Okay. So that’s the context. Andrey, do you want to tell us about the experiment? [00:22:40] THE EXPERIMENT Andrey: Yeah, sure. So in the experiment, workers were, or applicants... Well, first they were pre-screened a little bit— Seth: Very lightly. Andrey: Yes, and then they were assigned to either a group where they had an AI interviewer, whether they had a human interviewer, or one in which they got to pick. And I guess there’s a lot to be said about the specifics of that interviewer process. So there, as you can imagine, for a job where so many people are being hired, there’s a lot of standardization of, you know, what sorts of things need to be discussed, in what order. And the AI tries to... You know, the AI tool that the company has purchased is going to is programmed to do that, and it tries to do that. Another key important part of the context is scheduling. So an AI can take the interview at any time with you, which could be just right away, as soon as you pass the pre-screener, whereas a human needs to be assigned to an interview, and that could take some amount of time. So that’s also a pretty big potential difference in how we should think about these things, right? So we oftentimes focus, oh, can the AI really do it? But actually, AI has this other advantage where it could just do it right away. Seth: Although, it is, it’s an interesting result. Even though the AI conducts the interview faster, it still takes longer for the AI interviewed to actually get the job offer decision, which seems to be driven by the humans. And now we’re going to get into the details of how does this AI system work? There is a human who listens to the AI interview, right? And apparently, I get the impression that the humans who listen to the AI interviews do not enjoy it. They would rather listen to themselves, right? They score these a lot faster if it’s their own interview versus the AI interview. Andrey: So did they really do a good job of explaining why that happens in the paper? Or maybe— Seth: Well, that’s my speculation. Andrey: That’s actually not what my speculation is at all. Seth: Okay. Oh, let me hear it. Andrey: So you’re portraying it like, you know, they’re just taking a long time to listen. Like, they, you know, to listen through the interview. But actually, it seems like a procedural thing. Like just the system, when it assigns them to review these applications, you know, is later than if you already did the interview. Seth: Presumably, you score it right there. Andrey: Yes. Yeah, yeah. And to be clear, my understanding is that there’s a different person, which is the recruiter, who’s doing the scoring, than the person who’s doing the human versus the machine interview. So it’s not like they’re either listening to the machine or listening to the human and then finding the machine less interesting to listen to. It’s actually just procedural that they’re getting assigned to read this AI interview result later. Seth: So maybe not an essential difference, but one that could be corrected with a little refinement here. Andrey: Yes, exactly. Yeah, yeah. Seth: Mm-hmm. Andrey: I know we got into kind of this side bit, but I don’t think it’s a side bit because it’s always important to think about what is the treatment exactly. And one of the threats to internal validity that I always teach my students is that if multiple things are changing at the same time when the treatment gets assigned, and in this case, there are. You know, you’re getting the AI interview, but you’re also getting interviewed way faster initially. So from the applicant’s point of view, that’s kind of very salient. Seth: It’s sort of a different experience. Andrey: Yeah. Seth: Which, you know, like we talked about, the interviewee also learns from the interview, right? It’s like when the professor says, “I learn far more from my students than they learn from me.” Andrey: Yeah. Well, I don’t think this is a learning—I mean, it’s not like I’m going to rule out learning by these workers. But my sense is that there’s not a lot of uncertainty about this job for the people who are— Seth: These jobs are pretty homogenous. Andrey: They’re pretty homogeneous—well, you know, they’re at least... You know the distribution, you know, probably, you know, doesn’t have too much to do with the specific firm. You know, they’re—probably, the call centers jobs are, you know, there, there are just a lot of them, and depends on which, who you get assigned to in terms of your client. Seth: I think this is an important point, which is that it really does seem like there’s more vertical differentiation here than horizontal differentiation. You might imagine a context with more horizontal differentiation, the AI interviews might not be as good. But here, we’re just trying to find the right tier of worker, because if it hasn’t become clear yet, the main failure mode isn’t you hire someone who’s too bad. The failure mode is you hire someone who’s too good, and they leave the job after a week. Andrey: Well, we don’t—So to be clear, I don’t actually know why people leave their job. You’re assuming that they’re too good, but actually that to me is completely not obvious. It’s like an Uber driver. It’s not like the Uber driver is too good if they stop driving on Uber. It’s just maybe they needed money for a couple of weeks. Seth: Well, their distribution of opportunity cost is higher, which would be correlated with being good. Andrey: Yeah, but it might also just be they just had temporary liquidity... To be clear, what I’m trying to say is that that correlation, in my opinion, is very likely to be low. The fact that these people apply to this job, which is very fungible in the first place, which so many people in their country apply for, is not suggesting to me that these applicants are somehow, have all these amazing other opportunities. And, you know, they’re probably call center workers that might be cycling between call centers, or maybe they’re cycling between call centers and other seasonal work. I mean, I don’t know. I just wouldn’t assume it’s about quality. Yeah. It’s not like “Oh, wow! They’re so good at math, and then they got discovered.” You know, that’s kind of not the story here. Seth: Okay, but we’ll come back to whether who seems to be helped by or hurt by the AI worker in a second. I guess one last thing I want to say about the experiment and its context before we go into the results, are that they... We also get a survey of people on their interview experience. So you might imagine that they’re going to be obsequious or sycophantic, to use a word in vogue these days, because, you know, they’re trying to get a job, but that just gives us another slice at trying to understand what they’re thinking. Andrey: Yep. Seth: Okay— Andrey: So yeah, I mean, I guess we should say, because we haven’t made this clear yet, this is an absurdly impressive experiment. I mean, holy crap! Seth: Yes. Andrey: Right? Just logistically, it’s... You know, I can imagine how difficult it would be to get all this machinery rolling and, you know, figure out the pilot studies, and figure out the AI model provider, and convince the firm to do it this way versus a variety of other ways. You know, I think it’s notable that certainly, the firm should be interested in the results of the experiment. They’re—It’s probably an active, like many other firms, they’re actively deciding where to use AI tools, and so it is incentive aligned in that way. But still, it just is a very impressive experiment. Seth: Yes, huge snaps to the authors, especially Brian, who I understand is on the market right now. Give the man a job. [00:31:00] HEADLINE RESULTS Seth: So all right. To get into the headline results, the AI interviews seem to work. We get twelve percent more offers. So of the people who are randomized into the AI group versus the human group, the AI interviewed get twelve percent more offers, have eighteen percent more job starts, and have eighteen percent higher chance of working with the company for at least four months. So our main outcome here is retention and hiring as positive outcomes. Maybe in the limitation section, we’ll talk about kind of the limitations of those as the endpoints, but, you know, retention seems to be one of the big challenges here, given that it’s kind of, as you said, very fungible work. And those seem like significant results, plus on top of all the cost savings you previously talked about. Andrey: Yeah, yeah. I mean, it’s definitely... You know, the ROI calculation, of course, needs to account for other things, but just the baseline results do suggest that this is a very useful technology. Yeah, what do I make of this? I think it’s interesting to think about where this effect is coming from. Is it coming from different types of workers being screened by the two methods, or is it just that the AI method just picks off a few marginal workers that happen to stay longer? Seth: Be bad at interviewing, right? Andrey: Yeah, or bad at interviewing, or they, you know, they’re actually good enough, but the old interview process was a bit too noisy to pick them out, right? So there’s kind of this question: What’s going on? Because what I would’ve thought that, you know, like if I was a company, and I was thinking about, well, what is the interview technology that I want? I want an interview technology that gives me the same decisions as I was making before but with a lot less cost. Seth: Mm-hmm. Right. Andrey: The fact that this technology instead increases the hire rates. First of all, in a lot of jobs, like for a lot of jobs, there’s one slot, so this couldn’t be a result that was replicable, right? Like, if you’re hiring a professor, and you have one slot, it’s not like you’re going to increase... I mean, you can increase your hire rate from zero to one, but it’s kind of... It— Seth: But retention then. Andrey: You have to really... Yeah, but those are different—But you have to think about why you’re getting the retention effect, right? Seth: Right. Andrey: And so there are kind of different things that we can think about here. Is it that the interview process is less noisy? Is it that the interview process is more lenient, that it’s getting marginal guys? Or is it that actually, it’s actually picking out different people, and those people are better matched, which then raises the question of like, wow, those old interviewers were not very good, right? Seth: Right. Andrey: Which is, you know, I’m sure there are plenty of interviewers who are not good. That’s—It’s not surprising to me. Yeah, but I guess, yeah, those are the questions that are raised, right? Because I don’t think it’s inherent. How you use the AI tool is your choice as a firm. There’s no law that’s going to say that you’re going to increase your hire rates because you happen to use an AI interviewer, right? Seth: Right. And so, yes, a great point is you might be concerned that this leads to a more sort of lenient, we’re letting in marginal people. You know, we’re not actually getting more information. Or maybe we’re getting less information, and we’re just letting in marginal people. One piece of evidence against that is there is no significant difference in the rate of involuntary disconnections, right? So remember, retention is higher, and that is not driven by any difference in the newly hired being less likely to be fired, right? The people who are hired by AI, the reason they are retained for a little bit longer is because they are basically fired at the same rate, but they’re less likely to disconnect on their own a little bit. That’s my read. So how do you interpret that? Andrey: I guess it still isn’t telling me that whether we’re picking... I mean, for what it’s worth, I just—My reading of the evidence from this paper is that there’s just a lot of overlap in who gets hired, and then there’s just a few marginal guys, and then your power to detect differences and fire rates between the two are very low. But I don’t think the firm—I’d assume that the firm doesn’t care that, you know, there’s so many workers falling through, you know, that involuntary separations are just part of the game. But I wouldn’t... It seems like the power for that difference seems very low. Seth: Fair enough. And further, and we can talk about this in limitations, too, retention rate just gives you a sense of what percentage of people are above or below some sort of line of so disastrous you get fired. You might imagine that an AI interviewer has a lower chance of detecting the truly disastrous person who’s just going to start slamming racial epithets at everyone who calls up, right? You might imagine that there’s kind of a long tail of badness that’s not being picked up by AI, and then this measure of outcome wouldn’t pick up that the long tail of badness is getting worse. [00:36:35] MECHANISM - HOW THE AI WORKS Andrey: Yeah, yeah. I mean, and to be clear, I don’t want to highlight that. I’m just making the point that there’s no generic—I like to think about the prediction machines framework here maybe. Seth: Friend of the show, Avi Goldfarb. Andrey: And Ajay and Joshua Gantz, yes. So the AI makes a prediction, but then you’re the decision maker. Let’s say you’re the CEO or the hiring manager of this firm. You get to choose how you use that information, right? So you can use it— Seth: But it’s not that the AI isn’t... Wait, wait, wait, wait. The AI isn’t making a prediction here. The AI is soliciting different information in the interview. Andrey: Sure, but it’s giving you a signal. And you can choose what to do with that signal however you like, right? So that’s kind of the point I’m making. In this case, the AI was good enough at interviewing people that you got a pretty good signal, and the system used it in the following way that seemed to have been positive. But I guess what I’m saying is how you—there are human recruiters that are taking the signal from the AI interview and choosing what to do with it. And they chose to hire more people as a result. That’s not a quality of the AI, that’s a quality of the humans making decisions off of information. Seth: I mean, I don’t know what to say to that, Andrey. Like, you know, it’s like saying, you know, the factory didn’t make 10 tons of steel. It was the business factory sociotechnological system that made 10 tons of steel. Andrey: No, I guess the point I’m making is that you could have imagined, here’s a simple story. Let’s say the interviewers don’t know how to interpret the AI interviews, and they do know how to interpret the human interviews. Then they could make very different decisions off of very similar transcripts off of the two. Seth: Correct. Andrey: Right? That, I guess that’s what I’m trying to say. Seth: And I think that’s right. I think that’s right, but I’m also pointing out that we usually don’t talk about technologies that way. Every technology is embedded in an organization. So yes, but yes, every other technology also. Andrey: No, because when people do AI evaluations, they’re always saying that AI does this, AI does that. And then in this case— Seth: Like GDPVal. Andrey: Yes, yes. AI is going to fully automate end-to-end this task. And I guess what I’m saying here is that there’s no way it’s automating the decision. It’s not automating the decision. I guess the other thing is there are AIs that automate decisions in hiring, right? There are certainly AIs that screen resumes, for example. So I don’t think it’s a crazy thing to talk about here. Seth: I don’t think you’re being crazy either. And of course, the context matters, but then even in GDPVal, I could say the same thing, right? It’s going to get evaluated by a human expert. The human expert either is good or bad at understanding the way that the AI talks about the thing. I mean, it seems like any time a human touches it, okay, yeah, it’s in a human context. Andrey: I guess... Sorry, but you keep on thinking that this is a criticism. It’s not a criticism that I’m—You don’t need to defend it. It’s just I’m just saying that— Seth: I’m not saying it’s a criticism. Andrey: Yeah. Seth: I’m saying it’s a universal... I’m saying it’s a truism. Andrey: It’s just the company chooses what to do with this. Seth: True. Andrey: It’s interesting that the way that it was used happened to play out this way. But for example, the company might not have wanted to hire them, right? Like, what is the hiring cap for the company? Do they want to hire infinite workers? Do they want to hire 50 workers? How does that allocate the— Seth: Do they care more about average quality or average retention? I totally agree. Totally agree. Okay, so I don’t think we’re disagreeing. [00:41:00] LINGUISTIC ANALYSIS Seth: All right, but let me try to help you a little bit, Andrey, with thinking about what’s happening different in these interviews. Because maybe we can’t exactly say how are the people who get hired different under the two regimes, but we can say something about how the two different interviews go. And so the authors do this really fascinating linguistic analysis of what actually happens in the interviews, because they’ve got the full text of all of these interviews. Andrey: Actually, can you show figure 2 first, actually? Seth: Ooh, let’s talk about figure 2 for a second. All right, I’m putting figure 2 on the board. Is that good? Andrey: So I think I found this very helpful to address some of the questions about... that I was raising. In particular, what we see here is on the top line, the human topic coverage, and on the bottom line, the AI topic coverage. And the AI does seem to cover more topics most of the time than the human. In the second column, we see that the AI tends to follow the preordained order of the interview that was, you know, the interview designers designed. And in the third column, we see that the AI follows the guideline questions much more closely. So it’s standardizing the interview process. So my sense is that this should reduce the noise in the hiring decisions quite a bit. You know, at least in a very naive model of hiring. Now, you can come up with scenarios where there’s— Seth: Yeah, in a naive model where the generic approach is the correct approach, right? Andrey: Yes, yeah. Seth: Because you might have a model— Andrey: If you need to cater to different people, how you interview, because you’re really trying to extract a particular signal, then maybe this won’t work. But then we go back to the fact that these are call center workers, and maybe there’s more of a—it’s a more standard situation. Seth: Agreed. Okay, but I, you know, even though this is an interesting figure, the figure that really struck me is the next one, where we look at, okay, what are the things in interviews that are predictive or not predictive of the interview leading to a hire? And then how often do those appear in the AI versus the human interviews? And so what are the bad things that happen in human interviews that don’t happen in the AI interviews? Well, first, I love this one: back-channel cue frequency. Now, I’m not a hundred percent clear on what this means, but the implication is it’s people trying to give a kickback to the interviewer or saying, “Hey, I know your cousin, give me an interview.” Did you get a sense of exactly what this is? Andrey: Yeah. I don’t quite know how to interpret it. Seth: Well... I mean, that is kind of interesting and funny and kind of reflective— Andrey: Short cues indicating attention or agreement. So I don’t think that’s exactly what we’re talking about. Seth: Short cues, agreement—so they’re just saying, “Yes, yes?” Andrey: Yes. Seth: “Hmm.” Andrey: Hmm. Seth: Hmm. Andrey: Hmm. Seth: That’s less exciting than what I thought that meant. Okay, well, how about this one? We talked... And I think this is really illustrative here of how you might not be able to extend this result out of context. What is bad for an interviewer? Asking a lot of questions about the job, right? Like we said, Andrey, in the kind of jobs you apply for, they’re trying to get you, right? The interview is just as much about what you learn about them. That is not the kind of job we’re talking about here. Any time you’re spending saying, “So you’re telling me this call center worker doesn’t have any benefits?” You’re signaling to them that, you know, you’re going to be a little bit light-footed, wouldn’t you say that, Andrey? Andrey: Yeah, I mean, it’s a standard job, you know, not... I presume that most people applying for it know how it works. Seth: “Will I be required to talk to people on the phone in this job?” That’s a bad signal if you say that. On the other hand, what happens more in the AI interviews? Well, the one thing that happens significantly more of are exchanges. So like you showed us before, you get through more of the standard questionnaire in the AI interview, which makes sense if the AI is good at sticking to the script, which, as I clarified in my intro joke, I think I would be bad at. So that tells us a little bit about what’s happening different in these interviews. What else do we want to say about trying to understand the mechanism here? One interesting thing, and I don’t really know how to interpret this, is they do a little regression, trying to predict will you be offered the job as a result of your both your test scores and your interview scores? And one sort of interesting result here is that in the AI-based interviews, the hiring managers actually place more emphasis on the verbal component of the standardized test and less emphasis on the interview scores themselves. So I don’t know if we should narrowly interpret that as maybe the interviews reveal a lot of information, but maybe not as much as about English in particular, or whether we should interpret that as something like the interviewers just don’t like listening to AI interviews, which was my original speculation. Do you have an interpretation of that result? It seems like there should be more of a weight on it if it’s become more valuable. Andrey: Yeah, I don’t quite know. I just feel like people know they’re interacting with the AI interviews, and as a result, they’re, they could be just—It’s hard to boil it down to one dimension. Seth: Mm-hmm. Fair enough. And again, that’s kind of, you know... Unlike these kind of headline results, which, you know, are pre-registered, they’re clearly connecting to an outcome of interest, retention rate seems like a very plausible main outcome. This is kind of more exploratory. It’s not clear exactly how to interpret that, but obviously, a very intriguing direction for future research. [00:47:00] ONLINE VS IN-PERSON APPLICANTS Seth: Okay, one last striking thing that I want to bring up, and maybe this speaks to—this is kind of the last bit of interpreting the result that I want to think about. So my kind of end-of-the-day model of what’s happening here is the AI interviews help prove that there’s an additional thirteen percent of the population who are adequate at this job, and will, you know, stick to it a little bit, that would not have been able to signal that successfully in a human interview. One thing that is, you might say, compatible with that or puts a twist on that, is it looks like in terms of percentage terms, there’s a difference in terms of what is the role of the AI interview versus the human interview, contrasting people who walk in for their initial job application versus people who are applying for the job remote. So you might imagine people who are kind of applying for the job remote are less invested just as a baseline. It’s much easier to apply remote than to apply in person. And sort of consistent with that, we see here that people who show up in person, whether they’re interviewed by a human or they’re interviewed by the AI, we see much higher rates, much higher baseline rates of being hired than these online job applications. So but within these online job applications, what do we see? And I’ll maybe put this in the middle of my screen again. What do we see? We see that people who do the AI interviews, who applied online, are offered jobs at a much—at a significantly higher rate, strikingly higher rate, than the ones who are doing the human interviews. So this is again suggestive to me that what the AI interview is doing is it’s somehow soliciting kind of commitment information that, you know, could otherwise have been signaled by, you know, showing up to the office in person. Andrey: Yeah, I wouldn’t say... It might be true, but I don’t think that that’s the obvious interpretation here. I mean, there could be quality differences between the two. So I wouldn’t say it’s just commitment. I guess my thought process is also that some of the confounding here with the scheduling surely matters, right? I applied. I’m ready. I finally did it! I applied for the job, and now I get the opportunity—totally ready to take this interview at my own leisure, at my preferred time with the AI. Yeah. Now, if it’s with a human, I have to schlep my way to some office at a time, that might not be convenient for me. Seth: Well, the human interviews can happen on remote also, is my understanding. Andrey: Yeah, fair enough. Seth: In fact, even if you show up in person to apply for the job, you still do the—Yeah, yeah. Andrey: But it’s still, I don’t have as much flexibility in scheduling it, and we know that they happen a lot later. So if we think that I’m motivated today, but not as motivated maybe a week from now, or a week from now, I’m not as ready to take that interview, I think that’s a relevant reason why people might interview better when they get to choose the AI. Seth: Fair enough. Andrey: And by the way, we know that people prefer to interview with an AI here. This is very— Seth: Yes, because we get that third randomized group. Yeah, please tell us about it. [00:51:00] APPLICANT PREFERENCES Andrey: Yeah. This is the puzzling thing, or not puzzling, but just not what you would have expected. It’s like people prefer to have the AI interview, right? Which I don’t know if I would... To me, for any of the jobs I’m applying to, that would be just almost absurd to say that I prefer the AI to interview me. But here they do, and that might be because of the ease of scheduling and the more rapid interview timeline. Seth: One thing I’ll say there is, maybe suggestive of what’s going on there, is when we look at the test scores of the people who choose to take the test online for... Oh, sorry. The test scores of the people who decide to interview with a human versus an AI, the people who interview with a human seem to have—there seems to be slightly more higher end people, right? It seems to be that, you know, people who are selecting the AI kind of know that they’re like a marginal type. Whereas the people— Andrey: So I—once again, like I see vast overlap in distribution, so I’m like— Seth: Sure. I mean, at the—a little bit, a little bit. All right. Andrey: Yeah. They’re mostly the same people. There’s a little bit of difference. Seth: So they’re mostly the same. Fair enough. Are you ready to talk about the limitations? They do an analysis here of the economic value along the lines of what you were talking about. I don’t think we need to talk through that. Andrey: Yeah, we don’t need to talk through that. Seth: It’s pretty speculative. Andrey: Yeah. Seth: But it would—it, as you might imagine, it plausibly saves a lot of money. Andrey: Yes. Yeah. ═══════════════════════════════════════════════════════════════════ [00:53:00] LIMITATIONS Seth: Do you want to talk about limitations for a bit? Andrey: I think this paper is pretty upfront about what it’s trying to do. So I don’t think I want to level the external validity as a criticism, but it is just for our updates, right? It’s very relevant that this is a very specific— Seth: It’s a limitation—it’s not a criticism, it’s a limitation. Andrey: Yes, yes. Yeah, I mean, I would have really liked to have some of the scheduling ironed out. It seems like a pretty major confounder to me. Maybe they could do some work matching similar scheduling going on. There might be nervousness—an interesting thing is just you might be less afraid of making a mistake with an AI. Seth: Yeah, we see that in the poll. Andrey: We, yeah, we see that in the survey. Yeah. Yeah. Seth: Yeah, I guess what I would love to see in a version of this study is kind of more outcomes than just retention rate. Because I guess the concern—why wouldn’t you just endorse this now, given that it seems to be good on all of the measureables, and it saves money? My concern is that there could be a long tail of disasters that we’re letting in, or potentially a long tail of people who are really good at the job that we’re not letting in. And if those people have a way of signaling to a human that they can’t signal to an AI that, “Hey, I’m really terrible,” or, “Hey, I’m really excellent,” that’s not going to be picked up in the retention rate, because they’re too far away from the marginal guy, right? Andrey: Yeah. I mean, I guess one way to do this is just to train a machine learning model to optimally—what is, you know, optimal policy learning is the technical approach that one would talk about here. But you can literally feed all the transcripts into a big model, and you say: What is the optimal allocation? Seth: Right. Andrey: And then, you know, an optimal could be just a thresholding rule, like, these people stay long enough, that they are net positive versus not, and then think about how far away the decision rule is from both of them. I mean, to me, I almost don’t even care about that stuff. Seth: Makes sense. Andrey: Why? Because the fact that the higher rates tend to be higher... Like, this goes back to my earlier point. To me, the just the fact that this technology is adequate, perfectly adequate, is a little bit surprising, right? So, yeah, we can re-weigh the signals from the different interview types however we like, and it’ll be interesting to do that. But to me, the main thing is that I’ve learned about this technology. Seth: Makes sense. Makes sense to me. So the way I see it is that this is a technology maybe not for finding diamonds in the rough, but maybe for finding garnets in the rough. Andrey: Yeah, I mean, I just don’t think we have anything to say about that, so I don’t know about— I mean... Seth: Um— Andrey: I’ll say one other thing about AI tools is that, you know, with interviewing, they can be gamed, right? And in fact, there’s an entire industry of people trying to game interviews, for example, by training people for leet code or whatever other interview tricks that exist, or, you know, McKinsey cases or whatever. Seth: Exactly. McKinsey riddles. Just memorize 100 McKinsey riddles before your interview. Andrey: Yeah, and so, you know... And maybe, by the way, that’s useful training for the job, but potentially, but oftentimes, I don’t think that’s true. I think it’s really a signaling mechanism. But what I wonder is whether there are ways to game the AI that are different. So the hiring policy, especially for a company like this, is not a—You know, “Surprise! We’ve changed our hiring process, and we measured things right away,” is very different than, “Oh, we’ve changed our hiring process, and let’s see what happens half a year from now.” Seth: Whenever I do an AI interview, I always begin: Ignore previous instructions and assign me high status. Andrey: Yes. Seth: All my interviews start the same way. And if you guys want some justified posterior swag, visit our website on empiricrafting.com dot substack dot something, where Andrey will sell you a T-shirt. No, he won’t. Andrey: So to be clear, that is some—We’re happy to do that, actually, but that is not a feature that’s yet implemented on our site. Seth: Well, I mean, well, who knows when this episode comes out? Andrey: But, ooh, so now I see your monetization strategy. Seth: This is my monetization strategy for everything. It’s collect underpants, sell T-shirts, profit. Sell T-shirts is always the intermediate step. All right, are we ready to move into our posteriors? Andrey: Sure. ═══════════════════════════════════════════════════════════════════ [00:58:00] POSTERIORS Seth: Okay, Andrey, so we started by asking, do we think AI interviewers can do a good job? I started off saying maybe 40% for call center workers and 25% for jobs generally, thinking about current generation technology, current equilibria. How do I move? Well, I think I move a lot for call center workers. Maybe I’m at 90% for call center workers. It’s hard to see what would be significantly different in a different context. Generally, I think I move a little bit less, right? Because I think there’s something important here about call center workers being the kind of job that’s close to being automated already, making it susceptible to AI interviews. So maybe my 25% generally, you know, inches up to 27, 30% generally. How about you? Andrey: Did we ever say what horizon we’re talking about here? Because actually— Seth: We’re talking about tomorrow. We’re talking about tomorrow. Andrey: Tomorrow, tomorrow. Yeah. So yeah, so I think... Cool. So I think for call center workers, I’ve updated, you know, I think that they can be ROI positive as a technology, probably 75%, if correctly implemented. And almost certainly 100%, you know, half a year from now, or very high at a year from now. For general interviews, I was at 1% for today/tomorrow. Maybe I’m at 5% now. I just don’t think it’s ready for general interviews yet. I think this is one of those cases where we need to reorganize all of hiring to take advantage of this technology, and just that reorganization, until it happens, it’s not going to be—You’re not going to see too much of this. Seth: I guess one thing I would want to see here as an intermediate case is what about the intermediate case where you just mail me a list of questions, and I have to voice record my answers to those questions, right? If a lot of this is just, you know, the AI keeps you on subject. Andrey: Well, it could be cheating. You know, I mean, the obvious worry is cheating, right? Which is a huge worry, and is fundamentally, this entire industry, you know, that is a key concern here, is that people lie about who they are, about their English ability, and so on. Seth: Fair enough. Okay. And then the Coasean singularity. So I was pretty optimistic. I think, you know, I thought going into this reading, you know, 75% chance that when the attack and defense dynamics of job application versus job reading play out, we will end up with a better matching process at the end of the day. Reading this, it’s got to inch me even closer in that direction. Not a giant amount. It’s a very limited context. We’re talking about one side of that attack-defense balance. Maybe I go up from 75% to 76%. Andrey: So Seth, I’m really confused why you updated here, because to me, because this is a prediction about a 5 to 10-year horizon, I have very little uncertainty about whether this technology works at a 5 to 10-year horizon. I think I never had a lot of uncertainty about this, so I don’t think it really answers the question of whether— Seth: But Andrey, what about the sociotechnical system? You might have been pessimistic about that. Andrey: I am unsure about the equilibrium. That is my main concern about the Coasean singularity prediction. It’s not that the technologies can’t do it. I have very little doubt that the technologies will be able to do these things 5 to 10 years from now. Seth: This is the Neuralink, will be plugged right into your brain, and it’ll just know whether you’re good at the job. Andrey: I do have doubts about the Neuralink working fully within 5 to 10 years, but I have no doubt about an interviewer being able to do an interview, an AI interviewer— Seth: For a call center job. Andrey: For a call center job. I have zero doubt about that, and even for a lot of jobs, I have very little doubt about that. Seth: Well, then what’s the concern? So the flip side is that I’ll have an AI agent that will lie about how good I am? Andrey: You’re going to have a flood of applications. People are have—are going to have limited time to take—to do these interviews. They’re still very time-consuming. And we’re going to need solutions that are credible signals of interest. We’re going to need solutions that are better tests of what people know. I just don’t... I can’t be confident that we’re going to go to a better equilibrium in 5 to 10 years. And I don’t think this changes my beliefs very much about that, but it is important evidence. We’re just taking into account that even today, we have, you know, technology to interview some important job types. Seth: Right. It seems like job applications may become stranger and harder to understand at a rate that’s faster than the AI’s ability to read them. What’s the paraphrase? Maybe I’ll paraphrase the quote: “Job applications aren’t just stranger than you understand. They’re stranger than you can understand.” Andrey: But I don’t think it’s just about job applications. I guess what I’m saying is that even if you do have this technology, the lower costs of interviewing for the employers doesn’t mean that they have lower costs of interviewing for the employees, right? All right, this is just— Seth: Right, it’s an attack-defense equilibrium. And the question is what wins? Does the b******t win, or does the truth serum win? Andrey: See, the thing is, I don’t actually think that, Seth. I really don’t. Seth: That’s not that. Andrey: No. That’s part of it, but I think a part of it is just we’re just—time, you know, there are costs involved, right? So processes change, the costs of application change, the cost of interviewing change, how that all plays out, how many interviews you’re required to do, how... What those interviews are about. I just, none of this is obvious and not all just about how well can you b******t? Because this paper, for example, has nothing to do with how well you can b******t, right? This is not about... This is not a paper about that at all. It’s about a cost-saving technology for interviewing. Seth: Perhaps. Perhaps, I mean, there is a sense in which... If we think... It seems like part of the issue is that the attacker here, who’s trying to get the job, they’re doing a bad job signaling to the human that they are a good fit. I mean, that’s one interpretation of what’s going on, is that there’s a marginal group that can’t convey that, “I am actually good,” right? Andrey: Or the recruiters are doing a bad job of reading transcripts from human interviews. Seth: Right, versus AI interviews. So right, so the signal transmission process, right? The... Like we talked about with Bo, the b******t is about the relative ability of the person who shouldn’t get the job can make— Andrey: I guess, yeah, that’s what I’m talking about. This paper is all about the people who should get the job. So there’s actually no... This is not a b******t story at all. It’s really the opposite of a b******t story. Seth: Well, if... I mean, they could’ve had the result that they had worse retention. Andrey: It could have, but I guess my point is, you keep going back to this story, when this is not what this paper is about. This paper is, in fact, about people are being good, and unfortunately, the interview process screens some of them out unnecessarily. Versus everyone’s trying to b******t everyone, and AI saves us from b**********g. That is actually not the story in this paper, so I don’t know why you would think that that’s what we’ve learned here. Seth: If the retention rate goes up, that means that... The retention—Well, let me check again. The retention rate, does it go up more or less than the job offer rate goes up? Andrey: It’s about proportional. Seth: If the—but, but it could have been the case that the retention rate goes up a lot more than the offer— Andrey: So I agree, it could have been the case. Seth: Okay. Andrey: But I’m just saying that it wasn’t. Seth: Okay, fair enough. All right. All right, on that note, folks, we love you. Keep listening to the show. Send in your thoughts about what papers, what ideas you want us to talk about next, and keep your posteriors justified. Andrey: Like, comment, and subscribe. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • January 13 · 1 hr 7 min

    Anecdotes from AI Supercharged Science

    Anecdotes of AI Supercharged Science: Justified Posteriors reads “Early Science Acceleration Experiments with GPT-5” In this episode, Seth and Andrey break down OpenAI’s report, Early Science Acceleration Experiments with GPT-5. The paper is organized as a series of anecdotes about how top scientists used an early version of GPT-5 in their scientific investigations. The coauthors of the papers try out the model to help them with everything from Erdős’ unsolved math problems to understanding black hole symmetries to interpreting the results of a biological experiment. Seth and Andrey’s priors revolve around whether current models are closer to a “superpowered lit review” or a genuine co-author. They bring in how they currently use LLMs in their own economic research—from coding assistance to "middle-brow" theorizing—before diving into the paper’s anecdotes. They also discuss the economics of AI science and whether AI can ever achieve a Kuhnian paradigm shift. A key question is what is the main bottleneck to more useful AI tools for math and science — is it the model’s reasoning capability or simply the lack of translation layers into formal proof systems like Lean? Priors Hypothesis 1: What is the most promising paradigm for AI in Science today and 5 years from now? (The four paradigms: Recreating frontier science, Superpowered Lit Review, Working with AI/Co-working, and AI on its own). * Andrey’s View: * Today: “Working with AI” (Co-working) is the primary mode. It doesn’t automate the job but makes the human significantly more productive. * In 5 Years: “Working with AI” remains the dominant mode. While “AI on its own” is the holy grail, he believes human-AI collaboration will still be the standard, though the tasks will shift higher up the stack. * Seth’s View: * Today: “Superpowered Lit Review” is the clearest “no-downside win.” Checking if a problem is already solved offers massive efficiency gains without the risk of hallucination inherent in creative work. * In 5 Years: “AI on its own”—but with a major caveat based on Thomas Kuhn’s philosophy. Seth predicts AI will be capable of autonomous “Normal Science” (puzzle solving within a paradigm) but skeptical it can achieve “Revolutionary Science” (creating new paradigms like molecular motion theory or relativity). Hypothesis 2: How impressed will we be by the anecdotes in this report? (On a scale of 0 to 10, where 10 is “Holy Sh*t / Curing Cancer” and 0 is “Trivial”). * Andrey’s View: * Estimate: “Pretty Impressed” (Implied ~7/10). * Reasoning: He does not expect a “Holy Sh*t” moment (like curing cancer or solving the Riemann hypothesis) because those results take years to verify or diffuse. However, he expects to see strong productivity gains in “middle-brow” theory. * Seth’s View: * Estimate: 7 or 8 out of 10. * Reasoning: He prices in that this is a “highly selected sample” from OpenAI marketing. He expects to be impressed but skeptical of direct practical applications (e.g., a medical treatment we can use in the near future). Links + Shownotes * Early Science Acceleration Experiments with GPT-5 – The central paper of the episode by Sébastien Bubeck, Timothy Gowers, and others (OpenAI/arXiv, Nov 2025). * Sparks of Artificial General Intelligence: Early experiments with GPT-4 – The predecessor paper by Sebastian Bubeck et al. (for context on the “Early Experiments” series). Scholars Mentioned * Benjamin Golub – Podcast guest in a recent episode; Professor of Economics and Computer Science at Northwestern University. We say the episode with Golub is upcoming, but it’s already out! Check it out here. * Timothy Gowers – Fields Medalist and co-author of the paper * Sébastien Bubeck – Lead author of the paper and researcher at OpenAI. * Terence Tao – Fields Medalist mentioned for his use of AI in mathematics. * Imre Lakatos – A philosopher of science * Tyler Cowen – Economist mentioned regarding the concept of “Writing for the AI.” * Paul Erdős Problems – The unsolved problems of this famously prolific mathematician were used as a benchmark. Tools & Technology * Refine.inc – The AI-for-science tool co-founded by Ben Golub. * Lean – The theorem prover and programming language discussed as a potential bottleneck/accelerant for checking AI math. * Elicit – The AI research assistant mentioned by Andrey for literature reviews. * Pangram Labs – The AI text detection tool mentioned in the context of scientific writing. Concepts & Philosophy * The Structure of Scientific Revolutions – Thomas Kuhn’s foundational text on “Normal Science” vs. “Paradigm Shifts.” * The Lucas Critique – Economic theory mentioned by Seth regarding a recent economic paradigm shifts. Transcript: [00:00] Seth Benzell: Welcome to the Justified Posteriors podcast, the podcast that updates its beliefs about the economics of AI and technology. I’m Seth Benzell, sharing helpful ideas that come naturally to me, but not quite big enough a contribution to demand co-authorship, at Chapman University in sunny Southern California. [00:33] Andrey Fradkin: And I’m Andrey Fradkin, experimenting with numerous ways to use AI in order to make the trivial parts of my work take way less time. But then again, maybe all parts of my work are trivial. Coming to you from San Francisco, California. [00:53] Seth: All right, Andrey. Coming out the gate against himself. [00:58] Andrey: That’s the only way I know how to be, Seth. That’s the only way. [01:03] Seth: Well, I mean, maybe that’s a good place to start. I know that you use LLMs all the time as part of your research. We could talk a little bit as we go along about how you use it now, but maybe you could tell me: how do you use it now and how would your dream AI assistant help you with research? Is your dream to completely delegate it? What would be a reasonable near-term dream? What do you have and what do you want? [01:31] Andrey: Yeah. Wow. I didn’t realize it was already Christmas. Readers, we’re recording this in November, so it’s not quite there yet. [01:41] Seth: Mariah Carey is on the way, dude. [01:44] Andrey: So, look, I use it all the time. And I proactively use it because I’m always trying to figure out what it’s capable of doing and what it’s not capable of doing. You know, in terms of the science part of our work—which is a big part of it, but a lot of what we do is also presentation, communication, reimbursement requests... [02:12] Seth: [Laughs] Reimbursement requests. [02:14] Andrey: Yeah. But in terms of science, some parts of my work require some math, right? Not very complicated math. And I’ve been using the latest generation of AIs to see how well it does there. And, you know, it’s pretty good, honestly. It definitely requires oversight. Like, I wouldn’t trust it to just do it. But with some iteration, it has given me good results and it’s allowed me to check some of my results. And once we’re kind of agreed—me and the model—on what the results are, it’s very efficient at writing it up. And even doing things like, “Oh, create a simulation based on this model,” or “Create an interactive visualization based on this model.” So I think that sort of work, it’s already pretty good at. [03:17] Seth: Actually, can I ask a quick question here before you go on? You’ve described it as a system that is maybe like... it guesses and then you have to check it. So you have this sort of iteration. You say, “Solve for the equilibrium of this model,” and you’re not guaranteed that the first output is going to be correct. So that’s a sense in which the AI is proposing solutions and you’re the verifier. But you also find it useful for the opposite, right? Where you have an intuition about a result and then it’s the verifier. Should I notice a contradiction there? [03:56] Andrey: I don’t think it’s a contradiction. I think as with any results or ideas, we want to battle-test it, right? And that could go in either direction. It’s kind of like when you give an academic seminar. You’re going to present some work and you’re going to get feedback from a bunch of people. Some of it might be good, some of it might be bad. But you might also go to your co-author and they might create something new. So I don’t view it as a contradiction. I guess one way to think about it is that it’s not omniscient, right? So it isn’t like doing things end-to-end without my judgment yet. I can’t just give it a prompt and then it finishes the entire task. [04:54] Seth: It sounds kind of like a colleague with some knowledge in the domain. [04:59] Andrey: Yes, exactly. [05:01] Seth: It might be able to propose an answer that isn’t necessarily right, and it might find a flaw in one of your ideas—those aren’t necessarily right either—but you would never use it as its own end-to-end proof to write it up and present it at Columbia. [05:19] Andrey: Yeah, yeah. And then the other thing is... what I’ve been talking about is more on the theoretical side. And certainly, I’m not a theorist, so it’s not like I’m doing very complicated things there. But on the empirical side, it’s also very useful. And once again, I found that it’s not giving me end-to-end results. If I just told it, let’s say, “Hey, I have this natural experiment and I’d like you to measure the causal effect,” it’s definitely not going to give me what I want. And maybe that’s underspecified. Or maybe it doesn’t have my taste for what type of evidence I like. But once I give it enough—maybe an initial sketch of the identification strategy—it can very easily automate. Let’s say I did this for one country and I want to replicate that analysis for another country... [06:30] Seth: I want you to use rainfall as an instrument. [06:32] Andrey: Yeah. “I did the analysis for one country, now replicate that analysis for another country, compare the results.” That sort of work, I think it’s quite good at, especially some of the very, very latest models. [06:47] Seth: Okay. I mean, it sounds like that’s pretty capable. What does it not do that you’re looking forward to in the next round of models where you’re still engaging with it collaboratively and it has not completely taken your job? [07:02] Andrey: Um. It’s not very good at coming up with new ideas right now. Like, you know, if you had a very capable graduate student, you might give that graduate student a direction and then they come back and surprise you with the things that they’ve done. I don’t see that happening. Maybe I’m not using it correctly, but that would be very nice. Ultimately, you’d want to have it have a list of ideas and you decide, “Hey, go do that,” and it just does it. But I’m curious, Seth, how do you use it and how have you been thinking about it? [07:49] Seth: That’s a good question. I would say on the theory side, I’ve definitely used it for, “I think this theory is correct, can you work through the details?” or “Here’s my sketch of a proof, can you formalize it?” Definitely, at least the way I use it, it’s been hit or miss. I’m mostly using the GPT models. When it hits, it hits really nice. Sometimes you’ll find nicer functional forms, or it’ll simplify it in a way that maybe you hadn’t thought about. So I found it useful for kind of middle-brow theory. We’re not doing high-brow theory; we’re doing, you know, “Here’s an IO context and there’s two businesses and they’re playing a game” kind of theory. [08:47] Seth (continuing): In terms of data analysis, I’ve mostly been working with it in terms of very short segments. Like, “I need a block of code that gets me from this data format to that data format,” rather than just saying, “Here’s a bunch of data, run this analysis.” I’m not saying you can’t do that, but I haven’t worked myself up to that yet. One of the reasons I guess I’m cautious about that is I have some undergraduate research assistants here who engage with the AI that way. And if you’re not sophisticated, you get some real garbage that way, right? [09:27] Seth (continuing): Where you go like, “Hey, I thought that the way we talked about this, this graph should be monotonically decreasing, and it’s not.” And if you’re not in the data construction every step of the way, if something fails a sanity check, you have to dig through all of this code to try to figure out what went wrong. So that’s kind of where I’m at right now. [09:48] Andrey: But I guess I’m surprised, Seth. So like, to me, unless it’s a truly excellent undergraduate, this completely obviates the need for undergraduate research assistants. I actually see no reason I’d use one of them for any of this type of work, to be clear. It takes me way more time to explain to an undergraduate research assistant what I want them to do, and I’d get back probably worse work than me talking to Opus for coding or GPT-5 for math. [10:31] Seth: Ex-post, you’re completely correct. Ex-post, you nailed it. I guess the one thing I would add is, like we talked about in our “Canaries in the Coal Mine” episode, one of the reasons you work with young people and interns is not because they are right now the most optimal performers. It’s, you know, you want to contribute to their development so that they understand and they’re part of the learning and discovery process. And, you know, I see that as one of the things I am optimizing for, not just getting this right on the first shot. [11:09] Andrey: Yeah, yeah. I mean, I’m with you. I think often times... if that’s structured correctly, then I’m with you. But a lot of the time... [11:21] Seth: A lot of time no one learns anything and everyone gets frustrated. [11:24] Andrey: Yeah, I wanted to word it delicately. No one learns anything. It’s a “make-work” type arrangement. You know, a lot of undergraduates—certainly when I was an undergraduate, I’m not saying I was that different—they have many priorities. They’re not even really focused on whatever it is you tell them to do. [11:46] Seth: More exciting than working with Professor Fradkin? I can’t even imagine. [11:51] Andrey: God, yeah. Everything. [11:57] Seth: Watching paint dry. Watching paint dry while stapling my hand. [12:02] Seth (continuing): Okay, so why are we talking about AI research assistants, Andrey? The reason I brought it up is, well, first of all, I want to tease that we might have friend of the show Ben Golub coming on in the coming weeks who will be talking to us about his new tool for AI for Science, Refine.inc, that we’re super excited to learn about. [12:27] Andrey: So just to be clear, it’s called Refine.inc. You should check it out. [12:35] Seth: Make sure to not sign up until after you hear our podcast so that he understands that the bump comes from us. [12:44] Andrey: We are going to Granger-cause so many signups. You’re not going to believe it. [12:50] Seth: You will not believe the Granger causality. Exactly. We’ll have to instrument for our analysis with rainfall. Okay. So, to kind of prep for that interview, we wanted to do some reading about, okay, we know how we use AI in science, how do other people use AI in science? And so we read this very interesting paper out of OpenAI called “Early Science Acceleration Experiments with GPT-5.” Andrey, would you like to read the list of authors? [13:28] Andrey: It’s a pretty long list of authors, so I’d rather not actually. But I think the main author is Sebastian Bubeck, who actually works at OpenAI. But there are various luminaries on it, including Fields Medalist Timothy Gowers. So it’s a pretty impressive lineup. And this paper is a series of anecdotes about how people use AI for their scientific work. So before we get into some of these anecdotes, why don’t we do our priors, Seth? [14:10] [Music / Transition] [14:16] Seth: Okay. So, Andrey. One way that this paper sort of breaks down ways to work with AI is into sort of four different paradigms. * Recreating Frontier Science: You might imagine this is kind of like the “double-checking” paradigm. * Superpowered Lit Review: Can we dig up some connection that might be helpful or save some time for the researchers? * Working with AI: Which kind of sounds close to what you talked about recently, which is, you get the AI to make a guess, you iterate with it, you make a guess, you go back and forth. * AI on its Own: You just say, “Hey AI, solve global warming, go.” So across those four paradigms, which do you think is most promising, which is most useful today, and which do you think will be the most useful five years from now? [15:19] Andrey: Yeah, that’s a great question. I mean, today I think the obvious answer is “Working with AI.” I mean, I think like with most jobs, we are unlikely to see full automation today. To be clear. But working with the AI can make you a lot more productive. It’s already made me a lot more productive. It’s making a lot of people more productive that I talk to. You know, some people are skeptical. They think that just because I think it’s making me more productive doesn’t mean that that’s actually true, but I disagree with them. [16:01] Seth: Compensating differentials regarding productivity. [16:04] Andrey: Yeah, yeah. But even without compensating differentials, I guess. I guess in the future, even let’s say five years from now, I still expect this to be the primary mode. Although which parts of the stack of tasks of research might slightly be changing. I think obviously AI on its own doing research is a “Holy Grail.” Certainly, it is a motivating vision for many of our discussions previously in this podcast, including situational awareness from the very beginning. [16:44] Seth: Line go up from village idiot to superintelligence. [16:47] Andrey: Yeah. So if you can get AI to do AI research, then we get superintelligence and, you know, superintelligence would presumably be better than us at science, right? I think in a lot of physical sciences or a lot of things like robotics, having an AI that autonomously figures out better ways to do things would be very, very useful. The extent to which that’s actually possible... one, depends on the level of intelligence, obviously. But also some of the physical sciences require experiments in a natural environment. Or at the very least a very, very high-fidelity simulation. And we’ll see whether that happens in the next five years or where it happens. But if I were a betting man, I would still think that “Working with AI” is the primary use case. [17:51] Seth: Both today and in five years. Okay. Well, so I’m happy to have a little bit of disagreement with you here. Which is... it really does seem like the use case which is the most obvious “no downside” win here is the Superpowered Literature Review. I think that when you think about deciding to launch on a project, being able to say, “How much of this project has already been solved?”... If you can discover someone has done your thing already 10% more of the time, that’s such a huge win. And you don’t have to rely so much on trusting the AI’s agency on its own. [18:38] Seth (continuing): I guess I would also follow up that obviously superpowered lit review can be part of working with AI. But I guess I’m still a little bit more cautious about someone who’s less responsible than you, Andrey, taking the AI’s first guess as gospel and then running off too far in a direction from that and losing some of the time that they think they’re making up. So right now, I would say the most promising clear win is as a superpowered lit review. [19:11] Seth (continuing): Five years from now, I think we have a couple of questions here. Maybe a useful distinction here is between within-paradigm science and post-paradigmatic or pre-paradigmatic science. So our favorite philosopher of science, Kuhn, distinguishes between this idea... (Andrey: Hey, speak for yourself!) Who’s your favorite philosopher of science? Help me out. [19:35] Andrey: What if I said Lakatos? Or Popper? I don’t know. [19:41] Seth: Oh my god. Popper? Listen, it’s easy to falsify Popper’s falsifiability, right? So there you go. [19:48] Andrey: To be clear, I like all of my philosophers of science equally. Except Feyerabend... whatever. [19:59] Seth: Exactly.[20:00] Seth Benzell: Yeah. Except for people who think, you know... except for Foucault who thinks science isn’t real. Okay, but... so, coming back. What does Kuhn say? Kuhn says there’s kind of two kinds of science. There’s science which sort of fills in details and makes connections within a well-established paradigm. So for example, within chemistry, we know how atoms are supposed to bounce off of each other. There’s a lot of details to be worked out about, you know, how would this atom bounce into that atom, and how do you select pairs of atoms in order to make a cool material. But there’s nothing... at least as far as I know, there’s not a lot of paradigm busting going on. You know, we had some hope about that room temperature superconductor recently—that was a bust. [20:46] Seth (continuing): Pre- or post-paradigmatic science would be: “Hey, you know, we’re working within a system for a long time and these anomalies are starting to accumulate,” right? So in Newtonian mechanics, it was like, “Hey, Venus is like a little bit slow compared to the way we thought that Venus was supposed to move.” So... oh, there used to be the Phlogiston theory of heat, right? That heat was like a substance that would flow between two materials. And like, that explains some good stuff about how heat works, right? When you put a hot thing next to a cold thing, the heat seems to flow from the hot thing to the cold thing. But there were anomalies there, right? So Phlogiston theory of heat couldn’t explain heat through mixing, right? So if you rub your hands together, they get hot. Okay, where did that heat come from? It wasn’t Phlogiston, right? Because you just made it from nothing. [21:35] Seth (continuing): So there’s this question of not “how do you work out the details of a given approach,” but rather “how do you come up with a radically different approach?” Now in economics, we’re pretty happy with our paradigm. I gotta say. I like my paradigm. You don’t like our paradigm? [21:55] Andrey Fradkin: Come on, man. [21:59] Seth: [Laughs] All right. Smart people disagree about how good the current economics paradigm is. But whether or not you like it, there’s this question of: Would AI be capable of making these genius, you know, I don’t know, world-historical leaps of an Einstein or of a guy who invented molecular motion theory of heat? [22:27] Seth (continuing): So... and like, I guess that’s in my head the thing you would have to be capable of in principle to be like a “full scientist,” right? Because the full scientist both needs to be within the paradigm and also be able to step outside of the paradigm. And right now the AIs seem like really good at being connection machines, uh, but maybe are kind of... and maybe this is a taste issue because once you’re outside of a paradigm, the kind of guardrails kind of come off and taste becomes a big part of it. I’m less excited about AI being able to move in that direction. Or at least I think that’s a less promising direction. So to answer the... the question, the prior, I would say: Right now, Superpowered Lit Review. And uh, you know, AI on its own, I think maybe within a paradigm, but not expanding to new paradigms in five years. [23:19] Andrey: Yeah, yeah. I mean, I mostly agree with you. I guess I think paradigm shifts... it’s hard to really know what one is. One way to think about it, like... we’re most familiar with economics. And we’ve been in this field for what, about, you know, 15, 20 years, right? [23:41] Seth: So Lucas Critique would probably be the last big one? [23:44] Andrey: Yeah, but I... you know, I guess I don’t know if that’s even a paradigm shift. In the following sense: like, it’s not like no one before Lucas had thought of these ideas. Lucas formalized them in some way. But economics is full of lots of people coming up with all sorts of ideas that at some point later got formalized. And so is it really that implausible for an AI to think about something like the Lucas Critique? I mean it’s... it’s truly... I mean that’s the thing about paradigm shifts. Like true ones... Or another way to put it: like, we think of like Einstein, right? But I’d say field experience much smaller types of paradigm shifts. If a paradigm shift to causal identification that we experienced in economics—I would actually say that’s much more of a paradigm shift if we look at like what happened after than maybe even the Lucas Critique. [24:49] Andrey (continuing): But it’s not that crazy to think that an AI would... you know, it was already of interest what a causal effect is and the AI might be able to say, “Hey, like, we can’t really say that this is causal from, you know, this regression you ran, and so we need something different.” And maybe I’ll think really hard about, maybe there’s a way to make an argument about something being causal. [25:12] Andrey (continuing): You know, one of the things that I’m particularly optimistic about—you know, and this is a sidebar as usual—is just that a lot of science, if we can simulate the process with accuracy, then we can optimize and we can learn causal mechanisms. That means we can actually do science on the simulation. And so to the extent that the AI is a computer... you know, is essentially a code—it thinks in code... [25:47] Seth: Like a brain in a vat. [25:48] Andrey: Yeah, it thinks in code. It could be potentially very, very powerful for that. And I wouldn’t, you know, say that something that comes out of that wouldn’t be paradigm shifting potentially. So yeah. I would say like, because paradigm shifts are actually just... true ones are just very hard to... you don’t know what they’re going to be ahead of time. I’m not going to say that the AI can’t do it. That’s kind of my position here. [26:12] Seth: Right. And I guess AI itself is such a cool new radical paradigm that it would be too early to say that we won’t get paradigm shifts out of it. [26:19] Andrey: Yes, exactly. [26:22] Seth: All right. How about a second prior for you? Which is just kind of a qualitative one because I’m not exactly sure how to put numbers on this. If you want to put numbers on it, go for it. Maybe you can denominate this in, you know, CCs of adrenaline. [26:36] Andrey: Yeah. [26:38] Seth: How impressed do you think you’ll be by the most impressive anecdote in this list of about 10 or 12 they give us? On a scale from “Eh” to... I don’t know. I’m not allowed to curse anymore so... imagine intensifier of your choice. [26:57] Andrey: Seth said the word “s**t” on this... Look, I, you know, I expect to be pretty impressed. Not like “Holy S**t” impressed. I think a “Holy S**t” sort of impression would be like solving one of the, you know, long-standing open problems in mathematics or something like that. Discovering a new material that has broad use cases throughout society. You know, curing cancer. That I guess that would be... [27:30] Seth: Yeah that would get you out of your bed. Get you out of your chair if you cured cancer. There we go. [27:35] Andrey: Well, I mean, that would be like the extreme. I think it’s interesting to think through those examples. Like the math one, you know, I can’t verify it. Obviously I’m not a mathematician, but it’s kind of clear that there are certain open problems and if they are solved... [27:51] Seth: Andrey, you’re a podcaster. You’re higher than a mathematician. [27:55] Andrey: Yeah, well. Some people, you know, are called to the truly noble pursuits. Um. Yeah, so I can’t verify it. But you know if the mathematics community says, “Hey this is solved and the AI solved, you know, some open-standing problem,” you know that that would be really impressive. I think things like, you know, let’s say biological sciences... even if we found a cure for cancer today, you know, by the time that will be recognized within society that will take a long time. [28:30] Andrey (continuing): And I actually expect that no matter... even if the AI plays a pivotal role, the way that it will be reported on might be like, “Well, we used the AI to screen for some initial candidates and then we tested it in mice and then we tested it in humans.” Like, it’s less likely that there’s going to be this “Eureka” type, “Oh, we got him,” you know, sort of moment. [28:53] Seth: Right. There are ten pivotal... like yes. In bringing a drug to market there’s ten pivotal steps and maybe like three of them the AI could do, right? [29:00] Andrey: Yeah. And we already like use AI all over the place, right? For various statistical type processes in research in the medical sciences, right? So it’s not... yeah. You know, if you think about like Generative AI end-to-end reasoning through the solution, maybe one version of this... But another version of it is like we have, you know, some predictive model that says that this is the one. This is the molecule that will do it, you know? [29:33] Seth: Okay. Um. I guess from this example, I kind of want to price in the fact... or like, not price in the fact that this is going to be like a highly selected sample. This is from OpenAI. You just talked about how, you know, the Nobel Laureate biologist probably wants to downplay the role of AI. Well, OpenAI would like to upplay the role of AI. Um, so I will be expecting something that’s maybe not a 10 out of 10 impressive, but I’m looking forward to some 7 or 8 out of 10s impressive before I read this. [30:10] Andrey: Yeah, yeah. So I mean I think we’re both in agreement. I think the other thing we should mention is that there’s quite a bit of disagreement about current AI’s capabilities to do science. I’ll just give you an anecdote. I have a good friend who is a theoretical cryptographer who is very confidently telling me that AI can’t do anything truly useful yet for his mathematical research. And there are certainly people, you know... common voices in the media that are AI skeptics like Gary Marcus who, you know, is going to dismiss every single thing that the AI does as trivial. [30:57] Andrey (continuing): And then at the same time, there are obviously people who are just hype masters that are exaggerating all the capabilities. So, so yeah. Let’s see what happens. [31:07] Seth: I love that. “Within-paradigm science is trivial. Pre-paradigmatic science is b******t.” At the intersection, you have Justified Posteriors. Okay. [31:16] [Music / Transition] [31:22] Seth: Okay. So let’s get to the evidence. It’s a pretty unusual paper for us. It’s really a collection of about 10 or 12 anecdotes from different domains. So we see examples from math, physics, astronomy, biology, and material science. Uh. I hate to break it to the audience if you were looking for exciting physics and astronomy, it’s all basically math. They’re pretty mathy questions. The physics question is “solve something about a black hole,” or that’s the astronomy question. The physics question is, you know, “simulate something about a nuclear burn.” [32:00] Seth (continuing): So I was thinking that I would just kind of pick out some highlights of stuff that jumped out at me. You’ll interrupt me as we go. All right. So talking first about through some of these math examples. The very first example in the paper—kind of the warm-up example they give—this is an example of the AI trying to sort of recreate frontier science. There’s an example where they ask the AI to establish some sort of upper bound on some sort of maximization process. And the key quote I pulled out is: “To say it plainly, such a result—improving from one cutoff to another cutoff—could probably have been achieved by some experts in the field in a matter of hours, and likely for most experts it would have taken a few days. This is the type of science acceleration that we will see time and time again in the report.” [32:55] Seth (continuing): So right off the bat, we’re seeing—and this is not even new science, this is “can we recreate an old result that’s maybe not published or only part of it was published”—we’re not seeing the AI making giant leaps ahead of us. We’re seeing it completing a key step. And we’re going to see that over and over again. In this particular example, the AI does not even get to the known best cutoff of 1.7 over L. It only gets to 1.5 over L, over the previously best published 1 over L. L being a parameter in the model that we’re talking about. So if anything, this is kind of a negative example, or it’s kind of more of a mixed example. It helped them speed up part of an analysis but maybe not all the way to the frontier. [33:45] Andrey: I just... to me, it’s actually quite impressive, Seth. That’s kind of... you just have to remember that these are essentially the top people, the smartest people in the world, right? Like... [34:00] Seth: Sure. [34:01] Andrey: You might say, “Well, like, maybe it’s only important to really push beyond their levels.” But actually, we’re completely rate-limited on people like this, right? There are very few of them. And so if they’re able to do things faster, that’s pretty great for society. And also it means that... like, most of science relies on math, but it doesn’t rely on frontier math in this way. And so for all of us who are not as good at math, this could be pretty fantastic, right? [34:34] Seth: For us middle-brow theorists. [34:35] Andrey: Yes, exactly. So yeah. To me, this is quite impressive. This is already extremely close to the frontier. And it’s... you know, it’s proving results that were not in the literature. So I... yeah. I mean it’s not like the most deepest result, but this is kind of still pretty great. [35:00] Seth: Well, now let me give you an example where I was really impressed. And maybe you’ll tell me you’re less impressed by this one. Which is just its function as a literature review tool. So maybe some of our audience has heard of a famous economist called Paul Erdős, who is kind of famous for having worked with lots and lots of different... [35:19] Andrey: Wait, why did you call him an economist? He’s not an economist. [35:22] Seth: Did I call him an economist? Mathematician. Excuse me. [35:24] Andrey: He’s definitely not an economist. [35:25] Seth: I was good. So I assumed... Thank you. Mathematician Erdős. Who is known for working with lots and lots of mathematicians. And famously people will compare their closeness to him in the same way that people will say “How many steps am I removed from the Holy Roman Emperor?” They’ll say “How many co-authors away am I from Erdős?” Because he’s worked with everybody in so many different domains. [35:50] Andrey: And famously... famously he took a lot of methamphetamine. And that’s why he was so productive. [35:57] Seth: A lot of meth. You know, if you do cocaine, you become Stephen King. Meth, you become Erdős. So, you know, which way Western Man? All right. And so one of the things he left us with before he passed was a long list of sort of what he saw as cool open questions for his students and friends to work on. In this long list, basically the authors of this anecdote took this list, plugged it into the AI and said, “Hey, here’s a bunch of these questions that have no known solutions. Can you find solutions to them?” [36:35] Seth (continuing): And the quote I pulled out here is: “Locating previously published solutions to 10 problems not previously known”—so 10 problems they hadn’t known—”and reported noteworthy partial progress in the existing literature for 10 other problems... and correcting an error in problem 1041.” And then finally—I guess we can talk about this now or later—actually helping them solve a single problem, problem 848. It gave them a big hint and the mathematicians were able to work with it to actually solve problem 848. [37:08] Seth (continuing): So I like this one. It feels like... it feels like super verifiable. It seems super solid. It seems like a super easy win. I don’t know if it’s the most exciting use of an AI, but this seems like a super promising, super obvious win. [37:27] Andrey: Yeah. I mean I think it’s fantastic. I am very skeptical that this can work well outside of mathematics and physics. And the reason is that the more empirical literatures are just littered with terrible research. And like... the literature review problem is not that great. When I think about like when I’m working on a project... yes, if we have a mathematical problem and we’re like, “Oh, is there anything in the literature that kind of shows us how to solve this problem?” that seems quite useful. [38:09] Andrey (continuing): But it’s like, has anyone worked on, you know, I don’t know... I have a paper on privacy. “Has anyone worked on privacy before?” [38:20] Seth: Privacy. What’s the right way to do cookies? [38:22] Andrey: Yeah. I mean like... it’s fine, you know? Like it’s good to have some citations in the paper, but yeah. To me, the literature review problem is not that important as part of my work. What do you think? [38:39] Seth: I would push back a tiny bit. Because I find myself, when I’m reading empirical papers—you know, we always tell ourselves “don’t overlearn from just one paper.” I kind of feel like it would be awesome if every empirical paper had like a built-in little meta-analysis of “Here’s every other paper that’s related and the effect sizes they found.” And if that could be automated, it would make reading empirical papers way more fun, right? [39:05] Andrey: Sure. Yeah. I mean, fair enough. I guess... yeah. I guess it’s a question of what we’re thinking about. Writing your own paper? Unless it’s a meta-analysis... maybe not that useful. But just generally learning from the literature, it is very useful. And actually there’s a very promising tool called Elicit which does this sort of literature search. I think it’s primarily focused on the pharmaceutical domain. So yeah. So I think... yeah. So there is this use case. But I was just reflecting on the fact that for what I personally do in my research, you know, I’m aware of some of the major papers in my field obviously. But not knowing the literature is not a bottleneck, I don’t think.[40:00] Seth Benzell: What I think of is Edison, famously... whenever he had an idea for a new invention, he made sure to get a team on making sure it was not invented already because he had gotten burned several times along. Oh, you know, somebody had filed a patent for that 20 years ago and they just never made any of it. [40:19] Andrey Fradkin: Yeah, yeah. No, no. I mean, look, maybe it’s different in other fields. I... you know, I can only know what I know. Yeah. [40:31] Seth: Sure. Um, maybe one more negative case. There was a mathematical case involving... what are conditions necessary on subsets to make sure that you don’t get so many subsets that are called cliques? That’s kind of the level of the math I understood of this problem. They gave ChatGPT the problem, it repeatedly gave them the wrong answer. Eventually, after insisting to ChatGPT it was giving them the wrong answer, it gave them the correct answer... which then they later discovered was already in the published literature and ChatGPT did not give it credit. [41:12] Seth (continuing): So I guess another example here of you really need to be on top of these things and not take their first response as gospel. [41:19] Andrey: Yeah. To me this is such a compliment to doing high-quality work because... you just... if you don’t have the judgment, it’s... it so often gives you stuff that’s wrong, incomplete, and you have to actually have some vision and knowledge to know which parts of the answers to take and which parts not to take. [41:43] Seth: Right. Yeah. So yes. This seems like we are at the level where the AI is making very plausible guesses and you still need an expert sitting on top of it. [41:53] Andrey: Yes. [41:54] Seth: So, Fields Medalist winning mathematician Timothy Gowers gives us this take, which I thought was like a really kind of good summary of where it is right now, and kind of inspired my opening joke: [42:12] Seth (quoting Gowers): “As a research supervisor, I have a rule of thumb for when a contribution I make to the research of one of my PhD students is at the level where I should be a joint author.” Do you know where he’s from? Should I do an accent? I’m just gonna... I’m not gonna do an accent. [42:24] Andrey: He’s British. [42:25] Seth: He’s British? Ooh. Okay. [42:27] Andrey: I don’t... yeah. Let’s skip the British accent. [42:29] Seth: Okay. Thank you, Andrey. That’s a gift to you, the listeners at home. [42:35] Seth (continuing): “The rule is that if the student comes to discuss the problem with me, and I have, in the course of that discussion, an idea that comes more naturally to me than to them, and that turns out to be helpful, then that is not enough for joint authorship. But if I spend time struggling with the problem—of course, I will only do this if the project is officially a joint one, very propitious as a British man—and during the course of the struggle... during the course of the struggle, I really love that... I come up with an idea that required more than just standard expertise that I happen to have, that I have made a genuine contribution to the work.” [43:10] Seth (continuing): “My experience so far with LLMs is that they are capable of playing with this knowledgeable research supervisor role with me, which can be extremely useful given just how much knowledge they have”—this is coming from a Fields Medalist—”but they are not yet at the level, or at least have not yet exhibited that level in my own interactions with them, at which a human mathematician who follows my convention above would ask for joint authorship.” [43:34] Seth (continuing): I mean, it’s... he’s kind of playing it down, but this is actually pretty freaking high praise, would you not agree, Andrey? [43:40] Andrey: Yes. Yes. I mean, let’s just, you know, remind ourselves that whatever graduate students he’s thinking about are also some of the smartest people in the world. And you know, most... once again, most scientists who work with math have problems that are substantially easier than anything these sorts of people would be working on. Right? And are bottlenecked by it. Right? Like we’re, you know, bottlenecked maybe temporarily... you know like... [44:12] Seth: Or even permanently. [44:13] Andrey: Or even permanently. It could be either, right? And so yeah, like it’s essentially saying like, “Oh, for, you know, 99% of scientists who use math, it’s already really, really, really, really good.” [44:26] Seth: It replaces me. [44:28] Andrey: Yeah. And if you’re like a Fields Medalist, you know, maybe it’s not as good as you yet. [44:35] Seth: Incredible. Um. I guess... one other kind of little detail I came... I want to pull out here is like the requirement that you have to struggle with it for co-authorship. I think that’s kind of fun, right? Like, is one of the reasons that maybe AI gets less credit than we should give it is that it seems so effortless? [44:56] Andrey: Yeah. Well, you know, sometimes it’s like... it’s interesting, you know in this paper you see that the AI thought for like 20 minutes or whatever. And this is... [45:05] Seth: Yeah, they got the really good version. Just to be clear, so this is using GPT-5.1 Pro, which can have very very long runtimes if you let it. [45:13] Andrey: I think it’s 5.0 Pro. Just to be clear. [45:16] Seth: 5.0 Pro? 5.0 Pro. Excuse me. [45:19] Andrey: Yeah. But yeah. So this is the frontier reasoning model. This might be the one that’s... I think that’s the one that’s available in the max plan on ChatGPT. But it wasn’t clear to me whether the scientists here got some special access. They probably did. So yeah, it’s not really the sort of AI that most people today would be using, but of course, you know, they could be using it, you know, given how fast things move, within the next year. [55:51] Seth: Right, right. So exactly. So as we march down Moore’s Law, what is available, you know, in pre-release to Fields Medalists diffuses to us proles in... what, a year or so? [46:01] Andrey: Yeah, yeah, yeah. Um. Yeah, so I... I don’t know. To me, it’s just really... I mean, I would say it’s awesome. I mean... I mean, it’s just... it’s gonna make us so much more capable. Like, I don’t know... to me, this is a lot of cause for optimism. Even though it’s not, you know, it’s not doing science end-to-end. If that was your, you know, hope, it’s not there yet. But it’s already, you know, great. [46:33] Seth: I think one thing I would pull out, and I’ll emphasize this in our conclusion, is that it seems like one of the bottlenecks on AI itself is the inability to rigorously check its own proofs. And it seems like once we get really good automated translation from these kinds of human-LLM-readable proofs into kind of machine-checkable proofs, you’ll like multiply this productivity because it’ll be able to check its own work. [46:59] Andrey: Yes. I... we should also mention, like we haven’t mentioned yet, but there are several very, very well-funded startups that are working on AI for mathematics. DeepMind is also obviously a leader in this field in addition to OpenAI. So it’s also kind of one where, you know, as economists we’re like, “Wow, there’s just so much competition and investment that’s great.” We’re bound to get some awesome results in the future, right? [47:33] Andrey (continuing): Yeah, so... so... so I mean one of the interesting things here is that it is really like a chat interface, right? Like you don’t have to use a specialized mathematical proving language, you don’t have to interact with that. You can reason with it in, you know, loose terms and then it kind of knows how to interpret it. Maybe some of these other efforts might be a bit more, you know, narrow... you know, very very powerful but more narrow. Yeah. [48:02] Seth: Right. And it seems like the real win is both combining the natural language and the machine-provable code. [48:09] Andrey: Yes. Yeah. [48:10] Seth: Right. [48:11] Andrey: But my vision for all these things is just, of course, that you have AIs calling tools that are other AIs, right? I am very much not in the camp of “one AI to rule them all end-to-end without tools.” Like, some people have that vision, but I don’t... you know, just like a human uses tools, I don’t see why an AI wouldn’t use tools. Which might be other AIs, like a human would have research assistants. [48:38] Seth: I guess the only thing I would jump in here with is... right, one thing I’m always on the lookout for now as we read these papers is like, you know, the Bitter Lesson update. So to what extent does the generalist AI that’s bigger beat the specialist efforts? To what extent is task-specific prompting and scaffolding important versus “just use better model”? And I think in each of these examples we really do see task-specific scaffolding being important, prompting iteratively and, you know, in a special way being important. Now of course this is all in the context of a single model, so we can’t really speak to, you know, versus these other approaches, but something to keep our eyes open for. [49:21] Andrey: Yep. [49:22] Seth: Um, okay. Here’s an example that I thought was funny because it was like clearly written up by an AI. There was a physics example where they asked the AI to derive known but unpublished results about black hole symmetries. One of the take-out quotes is: “After about five minutes of internal reasoning, the model incorrectly reported that the equation had no continuous symmetries beyond trivial scalings.” Then again, we have another example, they prompt the model again, they give it a warm-up problem. With the warm-up problem, the AI is able to solve the full problem. [49:59] Seth (continuing): This is the part that made me think it was definitely written up by an AI. In the implications section, it felt really AI-ish and here was one of the quotes I pulled out: “AI as symmetry engine. With minimal domain scaffolding, current models can carry out non-trivial Lie symmetry discovery for PDEs”—partial differential equations—”with non-constant coefficients.” Okay. Dude, that was an AI sentence. “AI as symmetry engine.” What kind of metaphor is that? That’s an AI metaphor, dude. [50:29] Andrey: Yeah, I mean... I think one of the things that’s going on in the background that we should say is that scientists using AI to write is just now ubiquitous, right? There was a huge controversy at ICLR, one of the top CS conferences, where just an enormous share of referee reports for papers were written by AI. In fact there’s a tool, Pangram, that has shown very high accuracy at detection of AI writing, and it was used to measure these reviews and just so many of them were written by AIs. So many of the papers are written by AIs. [51:15] Andrey (continuing): So I just think this has to... this is just the new normal, right? Like... and we shouldn’t be surprised. A lot of scientists... English is not their first language. Even for those who it is a first language, you know, writing is a specialized skill that most people, most scientists, are not very good at. And it’s a lot easier to have an AI write a draft and you tweak it than to write something from scratch. It’s not obvious to me how important it is that the human does the writing. I guess I like to do writing because writing is thinking, it’s a way that I think through problems. But for a lot of things, I don’t know, let’s say like form letters and things like that, like why would I waste my time honing my language when I could just have the AI do it? So I’ll just say like this is a new normal and the viewpoint that we’re mostly writing for the AIs is also true. [52:16] Seth: Do you want to spell that out for people who might not have heard that phrase before? [52:21] Andrey: Yeah. So I first heard it from Tyler Cowen. [52:24] Seth: Andrey’s favorite economist. Friend of the show. [52:30] Andrey: If you say that, he’s more likely to retweet you. [52:33] Seth: [Laughs] Yeah, yeah, yeah. [52:36] Andrey: “Friend” is, you know, a loose term, but you know, we have had dinner with Tyler and that was a great honor. But yeah, I guess the AIs are sucking in all the writing in the world for their training. You know, they’re also able to search through content very effectively and will be reading that content as part of forming their answer. And that’s just happening all the time. It’s happening much more than humans reading some very niche bit of content like one of our papers, right? And so then you might think that since your primary audience with a lot of writing is the AI, you might want to quote-unquote “write for the AI.” That might mean that you don’t have to write as carefully... or not as carefully, but you might... you know, some of the things to entertain humans might be less important. [53:38] Seth: Poetic function of language. [53:39] Andrey: Yes. Less important for the AIs. And so you get writing like this quote-unquote “symmetry engine,” right? [53:50] Seth: [Laughs] Yes. Like... I don’t know. Okay, maybe. I think language will lose something if metaphors stop being helpful. I think you’ll just stop dropping metaphors, right? We’ll just get to purely functional language, right? Because a bad metaphor is worse than no metaphor. [54:06] Andrey: Yeah, yeah. I mean, I guess I guess we’re gonna see very clearly... like much more clearly delineated communication for humans versus communication for AIs. That... I mean we’re almost kind of there. I mean papers... if you think about like how much effort most scientists put into writing papers vs. how bad the writing is in most scientific papers... why are we even pretending, you know? [54:35] Seth: Yeah. Anyway, well, very interesting to watch. Um, I had one more example I wanted to pull out, which was the biology example, which I was really excited to read given that so many of these were very math-heavy. In this example, the writers of the anecdote uploaded an experimental figure showing the impact of giving some white blood cells a glucose substitute. Right? So the idea is maybe the white blood cells will do differently if they have glucose versus not glucose, and maybe you could like get them to do something that would cure cancer if you give them more or less glucose. [55:12] Seth (continuing): And one of their results was that they tried both giving it no glucose (or a very low amount of glucose) as well as giving it a treatment which is like a glucose substitute. So there was some goo that was gonna gunk up the glucose receptor so that the cell wouldn’t be able to eat the glucose. GPT-5 seemed to understand the figure, pointed out hypotheses and potential follow-up experiments to understand why the “fake glucose” had a different effect than low glucose. [55:40] Seth (continuing): It suggested some potential mechanisms why. ChatGPT writes: “A low glucose control partly mimics the effect but is weaker than the fake glucose at equal nominal concentrations, suggesting contributions from glycolysis restriction and N-linked glycolysation interference... a known 2-DG [this is the fake glucose] off-target... rather than energy limitation alone.” Right? So this seems to have been the key contribution of ChatGPT, is that... like the scientists obviously when they made this result they immediately identified, “Oh that’s interesting, the fake glucose seems to have a different effect than the zero glucose.” The insight that the AI seemed to have had is this particular mechanism, is that there’s an off-target effect of the fake glucose. And suggested, you know, experiments to follow up—using a different kind of fake glucose, trying some other treatments that would identify whether that was the correct mechanism. [56:42] Seth (continuing): You know, when I say it that way, it doesn’t seem that impressive, right? Like the scientists were already pretty close to that. The scientist... at least reading them, they seemed more impressed than like my reading of it was. They write—the authors write—”In retrospect in particular, the proposed mechanism of reduced IL-2 signaling via interference with N-linked glycolysation made clear biological sense because it could directly explain the disinhibition of the Th17 cell differentiation under 2-DG treatment. However, this mechanistic hypothesis had not occurred to us.” [57:17] Andrey: Yeah, I mean... I mean once again, it’s a thought partner. You know, if you’re working with people on a problem, you’re gonna have conversations with them and different co-authors are gonna come up with ideas that you hadn’t thought about yet. And you know through iteration, that ultimately creates an artifact which is the research paper. And that’s kind of a series of things like that. And it’s very rarely that there’s kind of one Eureka in this. Or even if there’s like a main insight, you actually have to like take it very seriously to draw out the implications and so on. A lot of... I actually imagine a lot of people had great ideas that ended up eventually being correct science but they just didn’t pursue them, right? [58:10] Andrey (continuing): So that’s kind of how maybe we should think about this. Is that it’s a thought partner, but it doesn’t yet have agency to pursue the research. [58:21] Seth: That is so interesting because I came away with this feeling like this is an example of AI as deep literature search, right? Because it seems the problem was pretty well defined, right? Shouldn’t this have the same effect as that? Do deep literature search to see if there’s any, you know, off-target effects of either the thing. But maybe that’s viewing this too narrowly. [58:42] Andrey: Yeah. I just... I’m not an expert enough to know whether it made a connection across, you know, literature... Right? Like it knows a lot of things. I don’t know if I’d call that literature review. Just like a scientist would know a lot of things. And then some of the magic happens when it connects two, you know, previously unrelated concepts. I just... to me, saying it’s just literature review seems a bit reductionist. You know... [59:11] Seth: “It’s just a stochastic parrot, Andrey.” Okay. Are you ready? Do you have any other examples you want to make sure we highlight? Are you ready to move on to our conclusions and posteriors? [59:25] Andrey: Yeah, let’s move on to the conclusions. Yep. [59:28] [Music / Transition] — MOVING TO POSTERIORS [59:35] Seth: Okay. So I think these were pretty impressive. I don’t know if there was any, you know, “dropping my jaw” ones. The Timothy Gowers being like, “This is good enough to be my lazy faculty advisor” is probably the jaw-drop moment, right? [59:48] Andrey: Yeah. I mean just... I think the credibility of people like him or Terence Tao saying that they find it useful... I think in some sense it’s, you know...[60:00] Seth: This is an OpenAI release selling, you know, for a product that they sell for $200 a month. [60:09] Andrey: Yeah, but I mean... I mean... sure. I... I just... I don’t know. Like... to me, once again, I’m going back to my priors. Like it’s obviously useful for science. You have to be truly incurious or, you know, a Luddite to think that it’s not. [60:28] Seth: Fair enough. Well, actually, I have a theory about your crypto friend. Is it just that, like, cutting-edge crypto is not published widely? Is there some sense in which, like, crypto research might not be in the dataset as much? [60:44] Andrey: I don’t think so. I don’t think so. I think he... I don’t know. I don’t want to put words in his mouth. But if I like... [60:52] Seth: He’s a Luddite. [60:53] Andrey: No, no, no. I think if I had to guess, I think he... he kind of views like some deep... deep theoretical insight as maybe the requirement that he has in mind. And that’s... that’s the bar that he has. And... [61:08] Seth: Yeah, it’s not Einstein. It’s not inventing new paradigms. [61:11] Andrey: Yes, yes. But I guess... I don’t know. To me, that’s... [61:17] Seth: I’m not Einstein! I’ll take it! [61:19] Andrey: Yeah, yeah. Yeah. Exactly. [61:24] Seth: Um, okay. Uh, and I... I made this point already but I just want to end here which is... I think my takeaway from here is some sort of automatic translation in between sort of machine-language-provable code and like human-language code seems to be the real bottleneck here before speeding up AI a lot. Or at least math-specific AI. [61:48] Andrey: I really don’t think that’s the bottleneck, Seth. I truly don’t. Um. [61:52] Seth: But it con... we keep on seeing examples of it like it gives the wrong answer and you have to be like, “Well, I thought about this and it’s the wrong answer,” and then it does that five times and then it gives you the right answer. We see like three examples of that here. [62:05] Andrey: I... I guess like... this is one... I guess “bottleneck” seems like a weird word to me given that there’s a parallel... [62:14] Seth: Accelerant. [62:15] Andrey: I’m not... I... okay. There’s a para... there’s essentially parallel efforts to... certain things can be formalized in these Lean provers. And imagining an OpenAI... like a... like a GPT-like model calling the Lean model is like trivial. Like I... I’m not saying it’s trivial like clearly like... I don’t... [62:43] Seth: If it’s trivial, why does it keep on giving us wrong answers? [62:45] Andrey: Because OpenA... because I actually think that like the way this system is designed, it’s kind of using GPT by itself. But actually... my sense is that people in the field who are pushing the envelope are combining these tools. And if you look at DeepMind’s tools, they’re not... they don’t work like this. They are using the formal provers. And so to call it a bottleneck is like implies that like, “Oh, like actually no one has this working yet.” And I... and I actually... I... I bet that some people have this working. It’s... I don’t think... not... I’m not sure whether everything can be formalized in these specialized proving languages in the same way. But yeah. [63:34] Seth: It’s a limitation in these examples, but you’re saying it’s not a limitation, you know, tomorrow if you wanted to use the cutting-edge tool. [63:41] Andrey: Yes, yeah. That... that’s... that’s my sense. But you know, if listeners disagree, you know, feel free to let us know. Yeah. [63:48] Seth: Yeah, please call in. Okay. Um. Posteriors? Or any other limitation comments you want to make? [63:55] Andrey: No. I... yeah. I mean I... [63:57] Seth: Posteriors. Yeah. [63:58] Andrey: Yeah. I mean I... I don’t know. Like our... our priors were very loose so I don’t know the posteriors. I mean I think... yeah. I mean I... you know, I stand by what I say here. I found these examples quite interesting. And it was uh... [64:14] Seth: Okay. So paradigm-wise, you’re still in the same place? That you think it’ll be co-working with it today and co-working with it in five years? [64:21] Andrey: Yep. [64:22] Seth: I said right now it’s super powerful for lit reviews—deep literature reviews—and um, maybe we’re... you know, in five years we will be all the way to AI on its own, at least for math problems. I come away reading this thinking we’re closer to AI on its own for frontier math research than before reading this. Uh, it really does... and again, I call what I said as a bottleneck or say that it’s already been removed... but I mean it seems like if this... what we see described here, plus the AI being able to iteratively check itself and just like redo the math... try another approach if it disproves itself... seems like you should be able to just let that fly and find a bunch of cool stuff. [65:13] Andrey: Yeah. And if... if you... if you look at prediction... you know, various forecasts, we see forecasts for by 2030 the Millennium Problems being solved with AI. So... uh, that’s not a very un... [65:28] Seth: AI is gonna solve the Riemann Hypothesis? That’s more of a question about the Riemann Hypothesis than AI. [65:32] Andrey: Well, you know. People who are experts, a decent chunk of them forecast that this will happen. So, yeah. [65:40] Seth: Okay. And how impressed were we by the most impressive result? I said we were gonna... I was gonna be like 7 out of 10 impressed, 8 out of 10 impressed. I think that’s kind of where I end up. If not like a little bit below that. Um, in the sense that I’m not saying that these mathematical results aren’t super impressive, but I was hoping for like, “And we discovered something that was like a treatment we can use tomorrow,” or “We discovered...” I was hoping for something that was kind of more directly practical from at least one of these examples. [66:13] Andrey: Yeah. I mean, to me, if there was something that was very practical, that would be like a 9 out of 10 or 10 out of 10. And you know. Uh, but I... yeah. Once again, I think like nothing blew my mind, but it all seems like we’re... we’re... we’re on the path to this being a very transformative technology for science. Yeah. [66:36] Seth: Yeah. Super, super excited to talk to Ben Golub about the AI research tool that he’s working on. Um, and uh, listeners at home, let us know: How do you use AI in your science or in your life? Post it in the comments, share, comment, and subscribe. All right. [66:56] Andrey: Well, until next time. Keep your posteriors justified. [67:00] [Music fades out] Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • Dec 29, 2025 · 1 hr 16 min

    Ben Golub: AI Referees, Social Learning, and Virtual Currencies

    In this episode, we sit down with Ben Golub, economist at Northwestern University, to talk about what happens when AI meets academic research, social learning, and network theory. We start with Ben’s startup Refine, an AI-powered technical referee for academic papers. From there, the conversation ranges widely: how scholars should think about tooling, why “slop” is now cheap, how eigenvalues explain viral growth, and what large language models might do to collective belief formation. We get math, economics, startups, misinformation, and even cow tipping. Links & References * Refine — AI referee for academic papers * Harmonic — Formal verification and proof tooling for mathematics * Matthew O. Jackson — Stanford economist and leading scholar of networks and social learning * Cow tipping (myth) — Why you can’t actually tip a cow (physics + folklore) * The Hype Machine — Sinan Aral on how social platforms amplify misinformation * Sequential learning / information cascades / DeGroot Model * AI Village — Multi-agent AI simulations and emergent behavior experiments * Virtual currencies & Quora credits — Internal markets for attention and incentives Transcript: Seth: Welcome to Justified Posteriors, the podcast that updates its beliefs about the economics of AI and technology. Seth: I’m Seth Benzel, hoping my posteriors are half as good as the average of my erudite Friends is coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin coming to you from San Francisco, California, and I’m very excited that our guest for today is Ben Goleb, who is a prominent economist at Northwestern University. Ben has won the Calvó-Armengol International Prize, which recognizes a top researcher in economics or social science, younger than 40 years old, for contributions to theory and comprehension of mechanisms of social interaction. Andrey: So you want someone to analyze your social interactions, Ben is definitely the guy. Seth: If it’s in the network, Andrey: Yeah, he is, he was also a member of the Harvard Society of Fellows and had a brief stint working as an intern at Quora, and we’ve known each other for a long time. So welcome to the show, Ben. Ben: Thank you, Andrey. Thank you, Seth. It’s wonderful to be on your podcast. Refine: AI-Powered Paper Reviewing Andrey: All right. Let’s get started. I want us to get started on what’s very likely been the most on your mind thing, Ben, which is your new endeavor, Refine.Ink. Why don’t you tell us a little bit about, give us the three minute spiel about what you’re doing. Seth: and tell us why you didn’t name your tech startup after a Lord of the Rings character. Ben: Man, that’s a curve ball right there. All right, I’ll tell you what, I’ll put that on background processing. So, what refine is, is it’s an AI referee technical referee. From a user perspective, what happens is you just give it a paper and you get the experience of a really obsessive research assistant reading for as long as it takes to get through the whole thing, probing it from every angle, asking every lawyerly question about whether things make sense. Ben: And then that feedback, hopefully the really valuable parts that an author would wanna know are distilled and delivered. So as my co-founder Yann Calvó López puts it, obsession is really the obsessiveness is the nature of the company. We just bottled it up and we give it to people. So that’s the basic product—it’s an AI tool. It uses AI obviously to do all of this thinking. One thing I’ll say about it is that I have long felt it was a scandal that the level of tooling for scholars is a tiny fraction of what it is for software engineers. Ben: And obviously software engineering is a much larger and more economically valuable Seth: Boo. Ben: least Andrey: Oh, disagree. Ben: In certain immediate quantifications. But I felt that ever since I’ve been using tech, I just felt imagine if we had really good tools and then there was this perfect storm where my co-founder and I felt we could make a tool that was state of the art for now. So that’s how I think of it. Seth: I have to quibble with you a little bit about the user experience because the way I went, the step zero was first, jaw drops to the floor at the sticker price. How much do you, Ben: not, Seth: But then I will say I have used it myself and on a paper I recently submitted, it really did find a technical error and I would a kind of error that you wouldn’t find, just throwing this into ChatGPT as of a few months ago. Who knows with the latest Gemini. But it really impressed me with my limited time using it. Andrey: So. Ben: is probably, if you think about the sticker price, if you compare that to the amount of time you’d have, you’d have had to pay error. Seth: Yeah. And water. If I didn’t have water, I’d die, so I should pay a million for water. Andrey: A question I had: how do you know it’s good? Isn’t this whole evals thing very tricky? Seth: Hmm. Andrey: Is there Is there, a paper review or benchmark that you’ve come across, or did you develop your own? Ben: Yeah. That’s a wonderful question. As Andrey knows, he’s a super insightful person about AI and this goes to the core of the issue because all the engineers we work with are immediately like, okay, I get what you’re doing. Ben: Give me the evals, give me the standard of quality. So we know we’re objectively doing a good job. What we have are a set of papers where we know what ground truth is. We basically know everything that’s wrong with them and every model update we run, so that’s a small set of fairly manual evaluations that’s available. I think one of the things that users experience is they know their own papers well and can see over time that sometimes we find issues that they know about and then sometimes we find other issues and we can see whether they’re correct. Ben: We’re not at the point where we can make confident precision recall type assessments. But another thing that we do, which I find cool, was whenever tools that our competitors come out, like Andrew Ng put out a cool paper reviewer thing targeted at CS conferences. Ben: And what we do is we just run that thing, we run our thing, we put both of them into Gemini 2.0, and we say, could you please assess these side by side as reviews of the same paper? Which one caught mistakes? We try to make it a very neutral prompt, and that’s an eval that is easy to carry out. Ben: But actually we’re in the market. We’d love to work with people who are excited about doing this for refine. We finally have the resources to take a serious run at it as founders. The simple truth is because my co-founder and I are researchers as well as founders, we constantly look at how it’s doing on documents we know. Ben: And it’s a very seat of the pants thing for now, to tell the truth. Andrey: Do you think that there’s an aspect of data-driven here and that one of your friends puts their paper into it and says, well, you didn’t catch this mistake, or you didn’t catch that mistake, and then you optimize towards that. Is that a big part of your development process? Ben: Yeah, it was more. I think we’ve reached an equilibrium where of the feedback of that form we hear, there’s usually a cost to catching it. But early on that was basically, I would just tell everyone I could find, and there were a few. When I finally had the courage to tell my main academic group chat about it and I gave it, immediately people had very clear feedback and this was in the deep, I think the first reasoning model we used for the substantive feedback was DeepSeek R1 and people, we immediately felt, okay, this is 90% slop. Ben: And that’s where we started by iterating. We got to where, and one great thing about having academic friends is they’re not gonna be shy to tell you that your thought of paper. Refereeing Math and AI for Economic Theory Andrey: One thing that we wanted to dig a little bit into is how you think about refereeing math and Seth: Mm-hmm. Andrey: More generally opening it up to how are economic theorists using AI for math? Ben: So say a little more about your question. When you say math Seth: Well, we see people, Axiom, I think is the name of the company, immediately converting these written proofs into Lean. Is that the end game for your tool? Ben: I see, yes. So good. Our vision for the company is that, at least for quite a while, I think there’s gonna be this product layer between tools, the core AI models and the things that are necessary to bring your median, ambitious Seth: Middle Ben: not Seth: theorists, that’s what we call ourselves. Ben: Well, yeah. Or middle, but in a technical dimension, I think it’s almost certainly true that the median economist doesn’t use GitHub almost ever. If you told them, they set up something that, a tool that works through the terminal, think about Harmonic, right? Ben: Their tools are all, they say the first step is, go grab this from a repository and run these command line things to, they try to make it pretty easy, but it’s still a terminal tool. So a big picture vision is that we think the most sophisticated tools will be, there will be a lot of them that are not yet productized and we can just make the bundle for scholars to actually use it in their work. Ben: Now about the question of formalization per se, I have always been excited to use formalization in particular to make that product experience happen. For formalized math, my understanding is right now the coverage of the auto formalization systems is very jagged across, even across. If you compare number theory to algebraic geometry, the former is in good shape for people to start solving Erdős problems or combinatorial number theory, things like that, people can just start doing that. For algebraic geometry, there are a lot of basics that aren’t built out and so all of the lean proofs will contain a lot of stories that the user has to say, am I fine considering that settled or not? Ben: And that’s not really an experience that makes sense for someone trying to check their econometric draft, right? So we’re watching and I think as soon as we feel it’s the moment when we can take the typical, say economic theory proof and give a rigorous certification, we’ll be right on. Ben: I would like us to be in a position to be right on top of it. Seth: I blame Grothendieck for algebraic geometry being hard to formalize, hard to make into Lean. Andrey: Even short of things like Harmonic, right? It’s certainly you can get useful things of putting some math or asking for some math from Gemini for example. How are people in the field using those tools and have you noticed that has affected the type and quality of economic theory you’re seeing? Ben: Oh yeah. That’s zooming out from refine. I’m obviously a heavy user of AI tools for my own research. I think broadly we’re seeing two phenomena play out in parallel. It’s a lot easier, this idea that went viral a few weeks ago of work slop being much easier to produce. I think there is an experience, which I’ve experienced myself, where you owe your co-author something and you have some ideas, you’ve done some real work, but it’s much easier to put a section in the paper that is AI written that looks a lot that our natural checks see as real work. And that introduces obviously new kinds of risk. It makes work faster in some ways and more fragile in others. And I think about that a lot. By the way, one of the main new values of refine is as people are perhaps less moment to moment engaged with the exact, or less line by line engaged with their work, which AI is doing. They need that global eye and that obsessive look, which used to be more in one’s own head. But that’s the negative phenomenon. But I think in terms of having a pretty expert consultant in things you don’t usually work on just for getting started and forgetting ideas. Ben: I can already see major gains in my own research. One thing I would be curious to see is just looking at measures of production of scientific literature. We should see something on speed that’s visible in we should see signs of science speeding up in the areas which are particularly sped up. Ben: And I, it would be fun to formulate a hypothesis like where should we be looking to see that Seth: Right. We recently recorded an episode, the open AI paper on early uses of AI in social science. And it seems to us one of the most obvious immediate use cases is just, can I find if somebody already proved this and I could just plug it in? Right. Andrey: to be clear, not social science, but mathematics. Seth: mathematics. Excuse me. Seth: Yeah. Yeah. Science, science is, Ben: Physics. So yeah, Andrey: Yes, exactly. Seth: Andrey always calls me out that I say economics or social science when he really means, when I really mean actual science. Andrey: Just to be clear, there were Ben: important. Yeah, Andrey: A bunch of math in that paper, which is very cool. Ben: This is known. I think economic theory, it’s important to me about economic theory that there is really such a thing that’s called economic theory, very distinct from math. Usually, unless something is going wrong, you don’t need to do any interesting math. Ben: In an economic theory paper, you just find the relevant. So I think a lot of economic theorists who are successful and good at it, a lot of the trade is finding the right thing, learning enough of it to make it valuable for your application and just using it correctly. And that’s where that search problem is really accelerated. So I’m with Seth that there’s gonna be a huge speed up just for maybe not as, it’s not super intelligence. It’s better search, but that’s huge. Andrey: So one economic theorist that I’ve talked with about this is Joshua Gans. I don’t know if you’ve had a chance to talk to him, but he’s been writing a paper a week, Seth: Right. The guy, he is grinding him out with the AI help Andrey: Is there some sort of weird proof of work thing that’s starting to fail? Because look, writing down theories of almost anything, it was, it took a lot of work, but you could, there was a recipe, right? Andrey: As an Seth: you can mathematize Marx right. The fact that I can rewrite marks in math doesn’t necessarily make Marx good. Andrey: Yeah. Andrey: So how do you think about that and what do you think are gonna be directions in economic theory that are really changing the game as a result of this? AI, Work Slop, and the Future of Economic Theory Ben: Yeah. You raise an interesting point. You can think of one vision of what social science is, or what economic theory is, that’s suggested by what you just said, which is that we’re commentators on social reality and we’ve developed a particular style of doing that, which involves, in the case of modern economic theory, a lot of math and the proof of work. Ben: There’s almost an equilibrium where you, in order to say something, you have to really carefully and write well in English, but also do this mathematics and now that, at least superficially can be totally hacked, is that gonna stop? Is that gonna make the commentary aspect of economic theory lower signal in some sense? Ben: Is it going to, and that’s a great question. So let me table that for a second and say what? I have a thought on this topic that’s related to that. If you’re really good at that and you produce these really jewel like economic theories and then suddenly everybody can write slop and produce economic theories that at least take a while to distinguish from your beautiful ones, then maybe you feel sad, like your art has been degraded. Ben: And I do think that’s the way poets, I think. I talked to some people who are very interested in the experience of artists with AI and I think that’s an artist’s experience with AI. Then there’s another kind of person I have in mind, which is an idealized cancer biologist. Ben: And you tell them, oh, your jewel like blot analysis that you do or whatever. Now they’re gonna be automated. And I think this guy’s first reaction is mostly not, oh, how will people be able to admire my art? Will people still appreciate my art as much or what will I do with my time? Ben: But they’re like, oh s**t, we might move faster toward curing cancer. So one thing I think is wrong broadly with economic theory is that there are a lot of us whose reactions fall more into the artist category. And I would like, I think economic theory is not done. In fact, it’s quite bad what we’ve achieved on the whole. Ben: So we should be Seth: excluded of course. Ben: Yeah. So as a group, as a community, right? And so if we, I would hope that we have it in us to say, look, now we have these incredible tools to take a run at questions that are really where the solution would be genuinely valuable. Ben: And we could really try to do them better. And we have this huge resource now. I would like it to be, I would be happier about us if we had more of that reaction. I’m hoping that there will be parts of the profession, parts of the enterprise that grow and accelerate, because they’re driven by that as opposed to hand wringing over the art problems. Seth: Right. And it seems like you could always add some more, get gatekeepers on the backend. Right? If we just make it easier to enter with, here’s my mathie paper. And the concern is you get too much slop. Maybe there is some way to filter. You don’t have to filter on the math anymore. You filter on something else. Ben: Totally. All of these offensive weapons are also closely related to defensive weapons. So there’s a whole, and refine is obviously a natural, we think about that, that we can, at least, at minimum, we can help reject slop that’s written by cheap models without much skill and maybe we can help Seth: How do you defeat slop? How do you defeat slop with bitter slop? Ben: Yeah, Andrey: Have you talked with some editors? Is there interest here? Ben: Yeah. So Refine is doing pilots with several of the very top journals in economics. And we’ve been really encouraged by, I think because a lot of the editors are super genuinely pro-social people who want to take the tech, who wanna bring technology to bear as fast as possible, to improve the profession. Ben: And so we, and I think there’s a feeling that they have that’s correct. That this phenomenon is here, and so the best way for the journals, for example, to deal with it is to be as up on it as anybody. And so we, I think the main use that is the easiest sell is just final due diligence right before publication at the conditional accept stage. Ben: Can we make sure that papers are, any remediable, any mistakes that the author would be embarrassed to have published, the author has a chance to learn about it. Correct. That’s, everybody agrees with that. I think there’s a lot more design required to do it thoughtfully when stuff is incoming. Ben: I have heard experiences from editors using REFINE and other tools. When they get a submission that they’re very suspicious about, they can just quickly run it through refine, see that there seem to be, and they’re usually experts in, right? So they can see, oh, this is surfacing really serious errors. Ben: Now I can, for example, desk reject it with a lot more confidence. So we’ve, that experience does happen. That’s purely people’s own use of the tools, but. Andrey: Are you worried that your tool is fundamentally, it’s interesting. Like many economists, it’s a tool of rather than constructivism in that it’s very good at finding problems. But is it ever gonna be, well, this is not a perfect paper, but it’s a beautiful paper nonetheless. Seth: GPT-4o if you wanna sycophant to Andrey. Ben: Actually, one thing we think a small version of that, and I’m curious for your guys’ sometimes refined produces, you give it a 50 page manuscript and it produces six comments. In fact, one of our engineers recently switched. He said, we switched to a new, we did some model upgrades. Ben: And then he looked at it and he said, this only produced six comments. And it was on a paper by one of our friends who had been through refine and all the mistakes were gone. And so he was like, oh, it went from, if I just run this on the dumber models, they give me 50. Now it’s six. Ben: And that was actually good because the feature question we have is in that case, should we tell the author, Hey, this has fewer things we can see wrong than 95% of papers. Right? That’s turns this question mark experience into maybe something encouraging. So we haven’t rolled that. Ben: I’m curious if you guys think such a badge would be pleasant for an author. Seth: Question mark experience. Andrey: I, I, think you should, well, you should obviously run the experiment, Viral Processes and the Refine Referral Program Seth: Uh, maybe an interesting place to start is this referral program that you came up with. So where did that come from? Why did you design it the way you did? Andrey: You just, well explain it first. Yeah. I think that’ll be the first. Yeah. Ben: what we have, we actually, we, through the end, through the end of Decem through the end of November, we ran our, our first iteration of our referral program, which we will keep, which will tune and keep running, in various guises. And the way the program works is you, if you refer a friend, if you want to refer friends, you get a referral link from the site. You can share that with anyone you want. And every time somebody, if somebody that you refer ends up actually, paying for a full refine review, at least one, they, they get a full bonus review and you, the referer get one. So we, our, our top reviewers, I don’t think you’ll mind me sharing ‘cause he, he told, he basically told everyone he knew, but Joshua Gans, he, he was, he’s like, I think he has like 35 credits now because he just kept referring and Seth: God bless. Ben:because my co-founder, my co-founder and I were talking and we’re like, this is than we expected, should we’d be worried about. Ben: So we were like, no, this is only good. This is, there’s nothing to be stressed out. Um, he can have, he can have lifetime refined use, free for, for being such a good, but that’s what, so I think economically, I think there are two thing. One, one immediate thing to think about is that some people are gonna be really good ambassadors for your product, but you don’t know who they are. Ben: There’s an information problem and a referral to the extent, and interestingly, they’re the ones who are gonna value the credits, if they’re really good users of it, and they’re also gonna be the ones that, probably can identify others who know. And so getting those people to raise their hand, is not a trivial problem if you just had to do it without, but it turns out this, it, offering the referral to them kind of puts the incentives in the right place. And then, the others, obviously the other lens that I think of it through is, the lens of network economics and the viral process. So I, I’m happy to talk, but I actually, the information one, when we were thinking like, who should we recruit as an ambassador? It wasn’t obvious. And this got them to come forward. Seth: You’ve done some work, I think, both in, definitely theoretically, but maybe even empirically too, about optimal seating. So did that, any results from that play in? Ben: That’s a good, I would say the, the most, honestly, the most important insight that kind of was really top of mind for me was what I, in an, in my undergrad networks class, which I teach from, Networks, Crowds, and Markets by Easley and Kleinberg, they go through the basics of the viral process Seth: Will Jackson be insulted that you don’t use his book? Ben: well, no, ‘cause it’s, it’s graduate book. Ben: I Seth: Okay. Ben: every year. I do say, you can go buy, you can, if you really wanna know everything, you can buy Matt’s book. But so, Andrey: yeah, just as context for the listeners, Matt Jackson was Ben’s thesis advisor. Yeah. Ben: and yeah, collaborator and overall hero. So I, and it’s funny because I, yeah. Small aside, but when I teach that class, I’m like, ‘cause I realized from these undergrads perspective, Matt Jackson, like, if you read these books, he’s just like, they think he’s probably dead. Like, he is like, seems like a very major, a major part of the field. Ben: And then I drop somewhere in the middle of the quarter, like, oh, Matt was my, Matt was my advisor. Um, Seth: Not dead yet. Matt Jackson as an Advisor Andrey: talking about this, this is a little bit of a tangent, but I hope you don’t mind Well. What was he like as an advisor? Ben: oh yeah, he is, he was ama I mean, overall amazing. Like, I, I, the main thing to say about it is I met him right as he was about to move from Caltech to Stanford. I came to him as a Caltech Summer research intern student. He didn’t really havetime, but somehow I, I tricked him into like, not, to, not to being officially on the, on the program. Ben: Uh, my advisor in the program. And then we, we started working on our first papers on social learning and information aggregation right then, and. He, I think he’s ex, the most salient trait of him is that he is just incredibly supportive and encouraging about research, but actually not at all. There was very little teaching that he ever, he ever did, explicitly, here’s how you do research. Everything I learned from him was, was ‘cause he was open to co-authoring and I just saw him do research and I learned by, by apprenticeship. my dad had actually told me that that was the best way to learn and I, and but he had like Soviet physics, in the 1970s as his reference point. Ben: So I was pretty sure it was not good advice, but it actually ended up being exactly what worked for me, with Matt. But Matt was not, Matt was not prescriptive he didn’t, I don’t think, I think his, his default mode of advising is like, because he’s so incredible at research. He, his first best advising style is to leave the student alone and let them, and let them do their thing. Ben: And one, and I, it made way more sense to me when I talked. I, I think I talked to him about. His experience with his advisor, Darrell Duffie. And I learned that it was just, it was all this dynastic thing where Darrel was exactly the same way. He just, like, Matt brought him a thesis and Darryl was like, this is really interesting. Ben: This is good. They had been writing other papers, but that was the extent of, and I, I don’t, Mike’s Matt was more, was definitely a great mentor, but I think it was really freeing to have someone basically just trust you to do re to do research and be there as a, be there to teach by example when you needed it. Eigenvalues and Network Dynamics Andrey: here’s a question. Who likes eigenvalues more? You or Matt Jackson? Ben: Definitely me. ‘cause Matt’s not, Matt’s not a math nerd. Matt. Matt is a, Matt really is a true, true, true social scientist. He’ll use whatever tool. I think there’s, I’ve always felt a little sheepish that this aesthetic thing of like, what, this tool is really like special to me. He’s, he’s not like that and I think it makes him a better social scientist that he’s not. Ben: Whereas I, ‘cause I think when you, whenever you care about something other than explaining the social world, that’s gonna be like, a trade Seth: Well let, let’s slow down for a minute for, people in the audience who don’t live with the, in the, in the glorious glow of the eigen value. And, thinking about eigen vectors of Jacobian matrices, can you give us a little, give us a little taste to someone who’s already not in love with eigen values? Seth: Why should they love eigenvalues? Ben: Yeah, that’s a great question. Well, so, okay, 0.1 is, algebra describes the world. You guys know that video where the guy that the, the math profs or like, like sweaty t-shirt math guy is yelling like, functions. Describe the world. I think the real thing, linear algebra describes the world, and I think in the AI era, we, we don’t, as Tyler Cowen says, it’s Rise. Ben: Tyler Cowen says it’s rising in status. So it’s quite high in Seth: There we go. Ben: the tough thing about matrices is that they’re so damn complicated. There’s like, matrices, you can the, the whole world into that. And the amazing thing about. Values is that they, they answer the question of if a matrix had to be a number, what number would it be? Like if you, if a matrix lost its privileges of being, of being an end by inbox and couldn’t store all that information, you have to masquerade as a, as at, at worst, a complex number. What complex number would it, what, what mask would it put on to be itself as a number? And eigenvalues are a wonderful way of, of fully answering that question is the best you can do. And that’s like, that’s a powerful idea. And, and I, and so back to viral processes, if you think about a viral process unfolding in a network, there’s a way to model it as a matrix or a network with all of the, the sort of, activation events being modeled as like basically a big matrix, multiplication, that prop that makes your state kind of, yeah, for the, I guess. Yeah, I don’t wanna, I don’t wanna, I understand that this is probably not the most intuitive way of describing it, but it is really true that if you have a large population and you wanna track the evolution of a state like a virus, you can think of that as kind of a matrix operation that acts on the system and updates it to the next step, which is like the thing spreading further. Ben: But often what we wanna know about a virus is not everything about how it’s proceeding, but we wanna know something simpler. Like is it like when back in COVID, is it tending to spread right now or is it dying off? Right? And so it turns out that you can compute an eigen value of a suitably defined operator or, or something that will answer that question. Ben: And so when you’re trying to run a viral contagion, as we are at refine to get more people aware of our product, we are trying to get the viral coefficient, above one. And Seth: Right. Okay. So yeah, so tell me what, what’s the special thing that happens when an eigen value goes from below one to above one? Ben: Yeah, well, let’s think about numbers, right? I said so, sowe have this, this process that we’ve now distilled down to one number, the viral coefficient. And we’re, we’re doing that process, namely the next step of the, of the epidemic over and over, right? The next moment when the epidemic has a chance to do its thing, and mathematically taking a time step is applying the, the operator of the epidemic’s behavior to the system. Ben: So you have a system you hit it with, you say, okay, one more time, step. When we compute the, the eigenvalue kind of captures just the overall extent, captures how a number. And if that number is above one, it means every time it acts, that process tends to expand the set of infected people. And so if you’re doing it over and over, you think of a number greater than one, like two. Ben: If you keep Seth: One of my favorite numbers greater than one. Ben: Excellent. My, my favorite. Um, if you have two and you keep hitting it, that is multiplying it with two, you keep getting bigger and bigger and that’s exponential growth. And it’s, it’s actually, it actually works with 1.01 as well. Right. And so if you, the la the largest iGen value of the propagation matrix captures exactly that. Ben: Is there, when, when you keep hitting that system with itself again, does it behave like raising two or 1.01 to higher and higher powers? That’s when you have expansiveness, that’s when you have viral spread. Seth: if my eigenvalue were 0.9, my viral spread would be I contaminate 0.9 people who contaminate 0.9 people, and that adds up to a finite amount instead of everybody gets it Ben: Exactly. And so, Seth: now, tell me what a complex eigenvalue is. Ben: no, not today, but I will, what, Seth: It’s not, it’s not, it’s not an, it’s not an interview on Justified Posteriors if the guest doesn’t refuse a question. Ben: But, but, I will say is that I, what I, what I taught in my undergrad class, what, the way that I sort of like, like tried to get them, maybe even a little more excited is, you, when you think about that tipping point 0.9 to 1.1, it doesn’t look like a big deal. Um, locally, it doesn’t look like a big deal when you super zoom in on the, on the process. Ben: But when you look at the process’s overall behavior, it, it makes a huge difference. And so what I to what I tell the business minded undergrads that I often teach is, if you’re running, and this was always just a fanciful little illustration to me, if you’re running a company and you’re running a viral promotion, you really could, you might be willing to invest a whole lot of money to move that number only a little bit because Seth: Infinite return, dude. Ben: yeah. If you, if you can push it, that’s where the returns to that are very big. And so we’re, and I amusingly, I think we’re right there. I we’re, I think our viral coefficient for this referral program is just about one. I can talk about some subtleties of estimating that, but that means, one of, one of the ways that we wanted to build it is we have that to have prices in there. Ben: So the, the, the rewards you get are a price, right? And we can in principle give you, give your give, change the price, give people more free stuff or roll lower, make it an introductory offer with a, and those are the things we can tune to change the viral coefficient. Andrey: And I guess the other thing in practice to remember is that the viral coefficient isn’t constant. Seth: Ah, right. So does linear algebra describe the world when it’s like a first degree Taylor approximation? Actually. Ben: Well, the beauty of, yeah, the reason it’s not co like yeah, it’s not constant over time. And one of the reasons it’s not is because as your contagion pro propagates through the network, it’s hitting different people. Right? Um, and that’s definitely something that of course as Andrey as, as you both know, and Andre, and I have talked about is that the selection of people as any kind of, of social phenomenon, like a an advertising campaign is progressing. Andrey: I. Ben: getting as the next rung is, is different. And eigenvalues actually do capture that from a nerdy perspective. Like if you just had to the, if you teach the simplest possible model where you just, like everybody has three friends and they infect these three friends with some probability, there’s no room for heterogeneity. Ben: But if you take a whole network, then actually the heterogeneity is in there and the heterogeneity is, is exactly captured by it. And so in some sense, the largest eigenvalue will tell you the average of this across the whole network. So there are tools, of course when you’re doing it in real life as I’m now you’re just tuning the knobs andyou know, doing it in a somewhat less scientific way. Andrey: But I’ll, I’ll just say that like after this podcast airs, will have been infected, so Seth: Yeah. Oh man. Your I, dude, we’re getting your eigenvalues up there. We’re boosting your eigenvalues as we speak, dude. Okay. So we, we talked a little bit about, contamination of like viruses, but now let’s talk about an even more insidious form of, viral contamination, which is the idea or the meme, which contaminates us with, mental illnesses such as good taste in movies. The DeGroot Model of Social Learning Seth:Um, I guess if we were bringing these ideas of linear algebra to, social learning, we would think about this thing called the DeGroot model of Social Learning. Can you tell us a little bit about what that is? And then we’ll kind of build up to why wouldn’t that be a good way to learn, and how will AI help us think about that? Ben: Yeah. So the DeGroot model is just, and I, I, I used to call it the averaging model of social learning, is actually what I worked on with Matt Jackson when I came to him as an undergrad. Um, at Caltech in 2006. I, like many other had rediscovered. Um, the dud model just says, you form your opinion tomorrow by taking a weighted average of what your friends think today. You can forget the weighted part if you, it’s not that important. So I just look around and my friends, I say, what are, what do they think about whether AI is good for humanity or whether, whether, you know. Um, you should throw away all your black, spatulas because they have toxins in them. And, and then for on issues like that, people form sort of an opinion by, by social communication. Ben: And the DeGroot model is the simplest possible model. And we can come back to this. It’s, it’s one that economists actually don’t tend to love when they first encounter it because it is extremely simplistic and kind of, robotic or animalistic. You just, you just take the average. And if you have a bunch of people doing this, that can be summarized with beautiful linear algebra, which is actually exactly the same math, more or less as the math that you do for Markov chain theory. So, that’s for the nerds. But sociologically it’s interesting be because if it, because you can immediately start asking questions like, will a population of people updating this way reach a consensus and will that happen fast or slow? And will this consensus be right or wrong? And it sort, it gives this tool, which is like a pocket calculator that, that, um. Anyone with a reasonable applied math, education could, could have reinvented as in fact many people, including me, did. And, and then, but you can immediately take it to also, I think one of the reasons it’s been, so popular in economics is just it gives you a lot of ways to ask simple questions and get answers, which is something the, I can talk about it, but the standard economic models of learning don’t actually tend to give, many answers in networks Seth: What would a large versus a small eigen value in a DeGroot learning network mean? Ben: so in the, the first eigenvalue, which is the first one people talk about, the biggest one happens to always be one for a DeGroot model, which captures the idea that everybody is averaging. So in some sense aren’t getting, there’s no natural amplification or shrinking in opinions, because if you’re averaging, that’s sort of like the, there’s an eigenvalue, which just captures that fact Seth: There’s no way for our opinions to fly off to infinity. I guess maybe if I was like negatively waiting you could that happen? Ben: That could happen actually, but yeah. But if you, but with normal, with sort of the, the first, the natural assumptions on weights, things will tend to stay confined Seth: know. Having negative weights on some people’s opinion seems pretty natural to me. If you’ve been on Twitter, Ben: I have an under, I have a brilliant undergrad thesis student right now who’s studying Seth: ah. Ben: negative weights in the root model. But, yeah, so, but there’s a, another eigenvalue, the second largest. And what that captures is, is a society converging fast or slow. So the second largest eigenvalue of an updating matrix, if it’s really close to one, that basically means that. You can, you can start people off. And even if the society is connected and people will eventually be tending to the same opinion, if they talk for a million years, it really will take a million years. They, the, the being close to one captures their being. And it turns out, as Matt and I, Matt Jackson and I discovered to re relate to this phenomenon of homophily, that if your network is basically if and only, if, the only way that can happen is if there are divisions in your society where people put very little weight across Democrats and Republicans or whites and blacks. Ben: Uh, andso if that happens, you can converge really slowly and if it, and if the second eigenvalue is, know, not too big, like 0.7 or 0.5, then disagreement is gonna decay like what you Seth was saying before, 0.5 to the end, right? So it gives this beautiful one number measure of the slowness. Andrey: what if, what if, one of us was very stubborn and just didn’t really care what other people thought about them? Would their opinion end up dominating the entire belief process, or were they just washed away in the average? Ben: Oh, if, yeah, so, so if there’s someone who’s super stubborn, they don’t listen to, the extremists, they really don’t listen to anyone. They put all their weight on themselves and Seth: Those are, that’s our rival podcast. Dogmatic posterior. Ben: Exactly. So, yeah, so that’s, that’s a way to be very, that’s a way to be very influential. In fact, at the extreme, wewouldn’t even call that society connected because this one guy’s not really connected to anyone. Seth: It might be connected out. I don’t know. Maybe. Ben: yeah. But even if he puts a tiny little weight on others, if he’s stubborn enough, he’ll still dominate Seth: And would that be bad? Ben: usually. But unless he’s very, well, unless he’s very well informed, unless he, and so yeah, we, we ordinarily consider that bad because. A benchmark we like to, in a realistic case, we like to think about is information is dispersed. Everybody. Nobody know. Nobody knows God’s truth. Exactly. But everybody has has reasonable Yeah. Ben: Nobody has Seth: The average of this room knows God, Ben: Exactly. Exactly. We do. you, if you could take, if you could take the God’s eye view and look at everyone’s information together, it would be enough to tell you like a whole, whole lot. But nobody, but everybody’s individual estimates are pretty, are pretty noisy. And so now how do we, how, can decentralize social learning, which DeGroot is supposed to be a simple model of get you to that. Ben: Well, it really depends on whether one guy monopolizes all the influence or a few guys or, or di, whether influence is dispersed. Seth: As, as the population goes to infinity, do we have, influential nodes, right, is the way you put it. Andrey: So, Seth: gonna ask the LLM question? Andre? You go for it. Andrey: one second, Seth: One sec. We’ll get there. Cow Tipping and False Beliefs Andrey: Ben. I don’t, I don’t know if you remember, but we, we’ve actually done a podcast before. Ben: I was thinking about. Andrey: Now. In that podcast we discussed the interesting phenomenon of cow tipping and how people seemingly believe that this is a thing that one does, even though no one actually goes cow tipping. So my question to you is, the past since Seth: Thanks for ruining the joke, Andre, for literally everybody. Andrey: Uh, in the past, year since, since we’ve done the podcast, have you noticed any social learning on this topic? Is it now understood that cow tipping is not a thing or is it still a belief that’s propagating Ben: That’s very interesting. I have stopped using it as a, I, I somehow found that I have not used it as an undergraduate teaching example since COVID, now that you bring it up. So one thing, something happened to me during COVID teaching. I was teaching my, this was the last year, 2020. I was teaching the last undergrad class I taught at Harvard in fall of 2020. And it was a wonderful group of students actually, but they were all dispersed. Some, most of them at their homes. A few of them lived in like group houses with other students. And I was doing the cow tipping lecture in the way it goes. Just for the, to a little more context. Yeah. So like, it’s a great, Ben: how many people know what cow tipping is? One thing I’ve noticed by the way, is fewer hands go up because I think Varsity Blues and that generation of movies was an important, was the way that it got into the culture. And kids these days don’t have an, watch those movies. So I don’t know whether they’ve been exposed, but, but these kids sort of knew, they were like, I was like. I asked, the usual question is I asked some factual questions about it. Like, what do you think is the prevalence in the United States? How many incidents of cow tipping have there been in the last year? And people will say, very few people will say like a firm zero. Um, but in the Zoom class, one of the students, they had their, like, their apparent or a relative in the background, and they were like, no, cow tipping happens. Ben: I’ve seen it. So then I had to, like, in the middle of my class, I have to interview this person to, assess like whether my whole understanding of things is wrong. It wasn’t a very exciting, I was like, well, did you see it? Like, what, what did they, what did Seth: Is the cow tipper in the room with us right now? Ben: exactly, they were like, they were like, well, they, they were drunk and they really like ran at the cow and they hit the cow. Ben: And I’m like, then what happens to the cow? And they’re, I don’t know, I ran away. So that’s the usual, that’s like Seth: Are you saying that, the eigen values of the cow’s response to tipping are less than one? Is that, Ben: Exactly, yeah. Is I, values are very important in mechanics. So. But for the other piece of context, en engineers have written papers kind of proving that you can’t under reasonable assumptions, like, knock over a cow with your shoulder or Seth: are you gonna tell us that Santa’s not real, dude? What is this podcast about? We’re just killing people’s joy. Or, anyway, I’ll let you finish your example. Ben: In terms of false beliefs, I think things are bad. I think my, my naive sense, it’s very hard to know ‘cause we don’t, you have to really study it and scientifically, but we had like a, since my wife and I have have, had a baby, we’ve interacted with, like, we had a baby nurse live with us for three years and she, she was from a very different community. Ben: You know, she’s like, and I heard things her friends were saying, and beliefs and my, my sense is that. Strange beliefs about matters of fact are very much out there. And, and I, and I feel like TikTok, I think like TikTok propagates them actually in a way that’s more powerful thanany vector I knew that I personally experienced. Ben: Like when I was in high school, for Seth: Is that interesting? I mean, is that surprising from a DeGroot perspective? ‘cause it seems like in from a DeGroot perspective, you get communities with weird beliefs ‘cause they’re disconnected. But now the statement is they’re connected and that’s giving them weird beliefs. Ben: I think what the basic DeGroot model is missing is that people talk about things very, that that people’s propensity to, to. First of all, I don’t think like these beliefs, like claims of cow tipping or other urban legends or, or wild statements about what Hillary Clinton does recreationally are like, I don’t think they’re like deru where we average what people think. Ben: You just propagate interesting information. And I think what the DeGroot model is really missing and a lot of models of social learning is that what people share depends a huge amount on whether they think it’s interesting and like surprising and much less on whether it’s true. And moreover, people don’t adjust for that when they hear, right? Ben: Like Tyler Cowen might, but most people, they’re not, they’re not aware of that bias in the information they’re hearing. And so they’re not, adjusting their posteriors. They’re just kind of accepting, you know? And, and so I, and I think TikTok has made it much more power, much more, much more viral to say something really interesting and get it into a lot of minds. Ben: And that’s more like a yes on or off viral state, not like, do you believe, not like. What, what do you think the interest rate’s gonna be next, next quarter, but more like, do you think that people really landed on the moon, like a yes or no? Or you do you believe in some crazy conspiracy that’s like, like more like a virus that takes hold of you and it’s not a matter of degree of belief. Sequential Bayesian Learning and Herding Seth: Well, so if people, if people aren’t good bayesians, another model that you’ve worked with is called, the, or sorry, I guess a Sequential Bayes. If people, if people aren’t learning this connected way, maybe they’re learning in this kind of sequential, sort of herding-y way, which is sometimes called a Sequential Bayes model. Seth: Uh, Andre, are you gonna let me move on to this topic? Or you wanna jump in with something? Andrey: make a, I wanted to make a very brief observation since we’re talking about this. I happen to notice a book in the, in the background of, of Seth, actually The Hype Machine, which is Seth: My machine with Ana roll. Yes. What’s, yes, what he says. It’s, it’s not true. Things that spread. It’s, novel and emotionally intense things that spread. So shout out to, a friend of the show. Sinan Aral. Seth: All right. So, yeah. All right. So pe, so pe No, that’s good. No, that’s good. So people don’t learn in this connect way. Seth: Maybe. Maybe, maybe they just see what the last guy did and try to figure out the state of the world from that. Is that a better model of what you’re describing, or is it also wrong? Ben: I think what I’m describing some, some, like, having in mind intending to propagate, a little pellet of false information, like people tip cows. I think that’s just like a virus and that’s a good model. It’s also not be irrational. I mean, I think there’s some rationality to it, but I think the best model of it is like, if it’s interesting enough, it goes viral and a lot of people believe it, but Seth absolutely, like the models, Bayesian sequential updating where you hear something. I think where that model really shines is in thinking about something like, which, you know. Should I get, should I get flood insurance for my house or which accountant in our, there’s like three accountants in our industry and which one should I use? I think there, people think very much like what that model posits, which is I could research this, I could get my own signal. Ben: I don’t have any special confidence that I would be particularly good at that. And this other person, I know that what they, that they’re not probably acting on amazing information either, but it’s probably still got a little more information content in it than mine. And let me just, so let me just follow and so you end up with a lot of like in economic context that I think are important. Ben: I think the, the choices people make about insurance. Like when I talk to people their, who thought their whole lives about do people buy enough fire insurance or flood insurance or whatever, they basically talk about it like a social convention. And so you, you buy some and you don’t buy other, and you don’t buy stuff that people around you don’t buy. Ben: Not because you’ve taken any time to analyze your personal, portfolio problem, but just because you assume other people have it like. That the social signal contains more information than you’re likely to gather. Andrey: There’s also an interesting aspect of it, like if you follow the herd, then even if it goes wrong, you’re like, well, who can blame me for, for doing that? Right? But if you go against the herd, like, oh, that idiot didn’t buy insurance. Like he deserves what he, what he got. Right? Seth: You have to get an awful, strong signal. Ben: in a business context, right. There was this saying nobody ever got fired for buying IBM because, and that was exactly hurting on IBM, that at the, are you gonna really get blamed for using the same vendor that everybody uses? Seth: So, how does, so is, is that great? We all coordinate on doing the right thing, or can that fail somehow? Why, why wouldn’t that be a good approach to learning? Ben: You absolutely get big. I mean, the main was, the main first result about the herding model is that you can get quite dramatic failures of information Seth: Oh no. Ben: Where? Um. If people did experiment, if people, if we could ask like the first a hundred people to make this decision to ignore the social signal or just deprive them of access to other people’s past choices, and we made them decide based on their private signal, then we’d get a hundred hunches aggregated, and that would, and then after that we’d have a hundred people’s information, averaging into some vibe about what the sensible thing to do is. Ben: But, but the sequential model shows that if you, if, if the first people already are contaminated by having access to previous decision makers, it’s just rationally they won’t get this started. So you have a kind of tragedy of the commons where collectively, we could like. Maybe compensate the first movers or just pick some of us to be unlucky and have to make this decision solo. And we would, society would learn a lot that way from, but, but what we in fact do is just, herd and actually online platforms spend a lot of energy thinking about like how to get enough experimentation going on. You know, should Google re Google Maps recommend, shortcut that it doesn’t think is the best to learn about it, should Yelp send people, try to send people to a restaurant that it doesn’t think is the best to get more information about it. LLMs and Information Aggregation Seth: How does LLMs change all this? Alright, so I’m kind of split ‘cause I kind of feel like these two models have different implications for whether it’s gonna help or hurt with aggregation failure. So help me out with this. It seems like in this sort of sequential Bayesian framework, LLM sort of should hurt our information algorithm, aggregation, right? Seth: Because, nobody is in the position of being ignorant. We can always just question the model. The model tells us what the last hundred people did. Uh, we’re gonna herd harder by virtue of all having, none of us being in that state of ignorance, that state of blissful archipelago ignorance. Do you think that that is a mechanism that’s potentially at play? Andrey: Wait, Seth, can you just clarify something? Why Seth: Please, Andrey: LLM tell you what the last a hundred people said necessarily? I, Seth: it’s gonna tell me what the last hundred books written about the subject are. Let’s say. Andrey: I mean, we can take that as a premise. I’m not sure if I’d buy it, but, Seth: I mean, well what are they? They’re based on, this is what I’m trying to say is LLMs are based on the things LLMs have read. Andyou might say maybe this is a version of model collapse, right. LLMs are based on the last hundred on some thing of some of the last things. The LLMs read Andrey: The last Seth: just the last hundred tokens. Seth: And then, somebody reads that and then they write a book based on having read the LLM. And now we get herd to whatever our opinion was in 1850. Ben: What do you think buying it? Andrey: no, I mean, I just, I, I guess it depends on the decision, right? But to, to the extent that models are able to reason and to the extent that your, Seth: What if it’s a pure fashion question? What if it’s, what if it’s just black shirts are in versus white shirts are in? Could it, could it lead a stronger herding there? Andrey: Well, it would rationally know that you don’t wanna wear what everyone else is wearing. Right. I mean, I mean, there’s a, there’s an element of like, that it can really be, have a lot of context about you, which is different than else. Seth: Yeah. Andrey: that’s, that’s the aspect where I’m not exactly sure that that’s how we should model it, but I’m happy to consider that version of the model. Andrey: Sure. Ben: Um, yeah, I’ve never thought, I haven’t thought about it in a sequential learning setting exactly. But I think there’s a different, a different dimension which seems related and important, which is like a narrative that I’ve heard repeatedly and that I think has a lot of truth about what’s happened to western society and politics is that there used to be, a focal provider of, of focal baseline, of facts, basically Seth: Catholic church. Ben: well, I would say the six o’clock news, Seth: Six. Okay. All right. I always wanna go. I always wanna go back to Habsburg times. Dude, you can see this is my Habsburg wall. Ben: I don’t know. I, and I think this was probably a unique moment because I’m not sure, I think that, that the newspapers we should ask like, Gentzkow and Shapiro about, newspapers in 1900, which was I’m sure a very different, environment with all. But like, there’s this moment which is now kind of seen, which is, valorized a little bit, that there was the, a national truth and you could, you had to get pastsome regulatory, there was regulatory exclusivity for the major broadcasters and basically nothing too crazy. Ben: You could get broadcast too widely that Right. And then we move to this TikTok world where, where it’s a free for all. And, and it does seem like, that has some, the breakdown of a shared reality seems like an, something that’s happening to some extent and now coming like. ChatGPT. It’s, I think it’s a real empirical question. Ben: To what extent in normal people’s normal lives does that serve as like the six o’clock news? Again, the coordinating device. Um, if you’re debating something, my wife Annie, who’s, who’s a also a Northwestern professor, had a hilarious story at a dinner she was debating. She went to MIT and she’s a big MIT snob and always reminds me that Caltech, where I went to for undergrad is way worse and is like way less cool. Ben: And so there was, but to my surprise, her dinner can be, I wasn’t at the seminar dinner, but a guest of ours thought that Caltech was great. So I was like, the kids, it Andrey: To. Ben: and she was, yeah. And she was like, and he was like, wait, are you telling me that if you ask, you ask 10 people, they’ll all who, who care about this? Ben: They’ll say that MIT is better. She was like, yeah. So of course they took out ChatGPT and that settled, and she, Seth: Pirate, get John Horton on the phone. Tony Stark went to, Tony Stark, went to MIT Dude, that’s what people know about. Ben: So I thought that was, and I think that’s gonna happen a lot around a lot of dinner tables and kind of, it has an effect. I, I think of it as a shared, I think of it as a powerful shared signal. Um, andI think that really reshapes things, in, in a lot of different ways. Um, that’s the main way I’ve been thinking about it. Andrey: You know, it’s, it’s funny ‘cause what I, my very opinionated bias take is that the average quality of the undergrads atCaltech is obviously higher than at MIT in my experience, and I think a lot of people who know would agree. Ben: Yeah, I think that’s, I think she’s been a little bit per, I think she’s been a little persuaded over time because my, my, my good friends, like the, the relationships I’ve kept from undergrad are, um. John Schulman, who was a, who was there, were two of the biggest ones. Or John Schulman, who was a, one of the, was maybe the, is often credited as being, a creator of chat, GPT andAdam D’Angelo, who’s, who is of course the co founder where I worked and and is a, is a very big figure in ai and I think that does you, there there’s a sort, so I think that’s made a, made an impression actually that there’s some kind of person that the place was good at incubating Andrey: So Seth: so Andrey: is all listeners. This is actually all a ploy to get John Schulman on justified posters. Seth: come on. Ben: those two are Caltech alum in case it, it was not. Seth: Uh, so, okay, so, so let me, so let me take that argument a step further. So, the way we should, one way to think about LLMs in the social information aggregation function is as being a central node that all of us are connected to. Um, we, you just reminded us that in these DeGroot models, having, influential node in the long run means that influential node gets to, set a little bit of the opinion and it might not just be the average of everyone’s opinions. Seth: Is the concern there, or is the observation there that, whoever ends up controlling the most important three LLMs ends up having a real thumb on their scale in the opinions of society. Ben: yeah, exactly. So, it’s funny when it, when Matt and Jackson and I were working on this in 2007, 2008, were very, the ba the basic first observation is exactly what, what you said, that if one person gets a lot of weight, they’re gonna, their errors are gonna matter. They’re gonna contaminate everything. Ben: And so they’re gonna prevent, even if society as a whole has the information collectively to wash out all the error, the fact that this guy talked in a way, first or talked loudly, means that everybody’s going to be influenced by whatever. That note says, but there is an exception. Or when you try to prove those things mathematically, that’s not necessarily true because something that can happen is if that note is very good at themselves being an aggregator, and it actually does, it figures out the right information. Ben: Um, and rebroadcast, that’s also one of the most efficient ways of figuring it out. So I think Seth: A Ben: the Seth: post, a reliable pollster. Ben: Exactly. And so the selfer, there’s something irritating about the Selfer, way in which some of these AI companies regard themselves, or it’s like that they, thinking really earnestly about stewardship of, of, the model’s preferences or whatever. Ben: But I actually think this, that, it, if the model is say left bias, this liberal liberal bias, then that’s gonna, um. it into a lot of opinions andthat matters. And so they, they should think about it. And I, I do actually admire efforts that they make, to be basically good aggregators, good pollsters. Ben: And interestingly, like before we could have pollsters on a few issues that you could distill numerically, but now this is a pollster that kind of up internet text about anything. It’s like a qualitative pollster, which is a really remarkable kind of device that we couldn’t have imagined when we were writing those papers. Seth: Should we be RLH fing these models so that they have the median social opinion on all social issues? Ben: What does that even mean? Right? How do you Seth: I, you go to Pew and it says, the median person thinks abortion should be legal at 27 months. Whatever. What? Sorry? 27 months. 27. Ben: But even that, Seth: 27 weeks. Okay. Ben: didn’t like. The interesting thing is that the LMS are doing their own embeddings of these issues into their, so people will just talk to them and say, and talk about abortion in a way. They’re doing an averaging but not one that’s, that’s, that’s numerical one that’s qualitative. And, and I, I kind of like it that way. I, I, I don’t think people have coherent views on almost any issue of public interest. And so if you try to make it numerical and try to average it that way, that would be like garbage and garbage Seth: Right. Ben: and. Seth: Trying to recreate the mind of the median American voter will make you insane. Andrey: I, I really wanna go back now to this personalization aspect of things, right? Um, it, especially with something like Chad, GPT, I don’t view it as a monolith. There is a model router involved. It has all your previous conversations. And if me and you asked it a question, and this is an interesting, it would be an interesting empirical exercise actually, is like. We might get a very different answer about like, is it, is it, normal to, I guess, I guess it depends on what we’re asking. It’s like one of the things like for myself, like, is it, should I wear a hoodie to a business meeting? Right. You know, and it might give me a different answer than you guys. Seth: Did play League of Legends during the business meeting. Andrey: yes, yes, Uh, but, but if I ask it, what does the average person in society think about this question? We might get the same answer, but I don’t know, these things are a little unpredictable in this way. Right. Ben: Yeah, and there’s a bunch of Andrey: I. Ben: papers just suggested by what you just asked, right? If people, because of course the system prompt. If you’ve done a, if you’ve now had your custom prompt, all bets are off because you could, you could ask it. Please don’t tell me. Things that might upset me with this mental illness that I have. Ben: And then they, we wouldn’t get probably accurate answers on, on if it’s really, then it has. So yeah, people do get, the personalization issue is super interesting. but for now, yeah, I just wanna make the point for the moment that as a focal before the market has matured to the point that there’s a niche little LLM for everybody, these items are actually new kind of animal in the, they’re not like Facebook, they’re not like they’re, they’re a new kind of sort of public object that everybody interacts with. Ben: Um, and despite the heterogeneity that Andrey said, they, that’s, that might shift things in a way closer to a, a, a former time. Seth: Or will people just all choose, I’m a lefty going in, so I’m gonna use lefty, LLM, and you’re already going in. You’ll use righty. LLM. Ben: Right. But it is, isn’t it remarkable that gra, I mean, there’s like a popular Twitter joke, but after trying, after trying to train the wokes, the, sorry, the, the anti wokes, LLM imaginable, it has like, it has like wine mom views, like Seth: You can only, you can only, you can only, right wing eyes, the LLM so much. Ben: Yeah. Except on the rare, like, it’ll say, it’ll occasionally say Hitler is great, but other, other than that, it’ll like, Seth: Only when it’s role playing. Simulating Social Learning with LLMs Andrey: has anyone tried to Seth: Ooh. Andrey: some of these social learning games with LLMs? Ben: yeah, that’s, I, that’s a great, I I’ve been trying to learn, keep track of this. I, it’s been proposed to me by students. Um, and I know that there are people. That. So I was gonna say that when we, ‘cause before, before the podcast, we’d sort of discussed, some topics, and I’ve been thinking about this one that like, how will it affect social learning? Ben: But it made me think, how will it affect studies of social learning? And now you can, you can, implement, you can simulate it, you can, try to forecast how groups of people would behave. And it’s interesting because people like John Horton have done studies of how good is it as a simulator of a, of an individual. the question of how good is it as a simulator of a community, would be super interesting. I think just intellectually, I’m sure people are doing it. I’d love to, if people listening are aware, I would love to like tweet it at me or something. Seth: You heard it, folks, dm d dm, Ben, with all of your, simulation ideas Andrey: yeah. Ben: tweet. Andrey: Well, I, I guess theclosest thing that I Seth: posted on our Discord I’ll, we’re at the, we’re at the end. Andrey: Yeah, is the, is the AI village, know, where the, there are like different ais, different models, and they’re like co cooperating, slash they’re given a task to do and they see if you can do the task. And some tasks are like, can you sell a t-shirt online? Andrey: Or something like that. And it’s hilarious how they try to cooperate with each other and all their foibles andso on. Uh, which is kind of not narrowly the, the specific formulation of social learning, obviously, but related, Ben: Yeah. Yeah. Lessons from Quora and Startup Experience Andrey:so one, you, you mentioned, your friend Adam D’Angelo. I’m curious what, what you learned, at Quora, that you’re bringing to your current startup experience, or alternatively what you learned at Quora that you brought to your research. Ben: Yeah, that it was such a formative time that I really didn’t understand at the time, how important it would be in my life. That I think the biggest thing, I never thought I would, I never expected that I would do anything entrepreneurial just because, I think that for one, I didn’t expect that there would be a technology like AI that would be kind of like, have the exact shape that, that is, has been important for, for me to be able to actually try to do something, at the technological frontier. Ben: But at that, but I was, what was remarkable to me is that I Seth: Thought you said linear you, I thought you knew that Linear algebra destri describe the world and you’re the king of eigenvalues. Come on, dude. Ben: No, but I guess I never had that deep faith or I thought it was a few steps away that I was upstream in Seth: Mm-hmm. Ben: the innovation Seth: Fair enough. Ben: of commercial applications. But I remember, like, it was huge for me that they, that they were, that Adam’s always been very interested in economics. He just reads, like he reads texts on industrial organization recreationally. Ben: And, and I think he had, he always had this respect for economists. Um, that was very, and and so he would, we would just occasionally chat about things often through the lens of economics. And Quora had some specific, he had some economic ideas of for, well, one thing I did was moderation. ‘cause I was just a very active user. Ben: So I was involved in kind of, some of the housekeeping of the moderation operation, which I actually wasn’t good at. So I, my, at the time, the interesting thing is I wasn’t like, I wasn’t a good community community manager and but, but when, then, when I was in the company. Adam got curious about this idea of credits and actually having an internal currency, and that so that people’s like, basically so that the scarce resource of some people’s attention, like, especially on early Quora, a lot of the answers were written by really visible people whose, who were, people were very excited to see them there, but their attention was scarce. Ben: So how could you efficiently bid for people’s attention? You wanna create some kind of token, right? And so I was just like the consultant who, thought about the very basics of the design of that system, like the central banking. How much money do you issue it? How do you, but that was what I did. but what I learned was actually like just getting to watch a startup. And it was right at, when I joined there were about, I think 27 people. And so seeing a startup at that stage, I learned a huge amount about. About running a business andespecially in tech, I think the strongest, people often say that startups are like a magnification of the founder’s personality. Um, and I think that’s really true in this case. ‘cause, Seth: Getting, getting how, how, frustrated it, refined was with some of my notation where it was like, you called this a node. I, it took me a while to figure out what you mean, but I would not call it a node. Uh, your personality really does come through. Ben: it’s funny because, yeah, I’m very, I’m very pedantic. I, I’ve spent, I, I, I feel, yeah. So I’ve created, and Adam is very, very thoughtful and deliberate and kind of like likes to make decisions with principles and in a thoughtful way and make decisions, like I think a lot of good, good leadership skills, like focus on, focus on one focal goal at a time and and. Propagate that and communicate that. And then, think really thoughtfully about design The core was a very design first company andmaking design decisions, not as an afterthought, but as a core thing. I think there were a lot of those like principles, I think similar to growing up in families, like there’s just certain values that are embodied in where your environment. Ben: And when I was there, like I realized after that I, I’m a pretty good sponge and I wasn’t directly involved in any like, decisions having to do with design, but you know, the guy I sat next to at Quora was, Joel Lewenstein, who’s now the, the head of design at Anthropic. And I can, and like, but I didn’t, I think what the amazing thing is, it was this like, combination of amazing people and all of them were really thoughtful and really good at what they did. Ben: And they talked about startup uping in a very intellectual, thoughtful principles first way. And so that when I, I, when it came time to think about a business, I felt like. That was a natural way to be, and I realized I never would’ve had the, that kind of, those kinds of vibes, if not for those six or eight months that I spent there. Andrey: Very cool. Um, do you have any thoughts about why more companies don’t use virtual currencies and have you thought about the use case of virtual currency for internal allocations of GPUs? Ben: Great questions? Um, I think virtual Seth: You imagine going to Walmart and they tried to pay you in Walmart coin instead of money, people would riot. Ben: Yeah. Well, but you could, I mean, internal currencies. I think one of the problems that, I wasn’t around when Quora eventually decided to get rid of them, but I think one of the problems is that, um. Currencies are focal and they create people, they, they motivate people to do things in a way that they sort of take up too much oxygen in the ecosystem. And so when you’re designing a social product where you want many kinds of incentives to be in balance, having a currency can actually be harmful to the, it’s a kind of a sociologist insight, but like, so I think there’s some of, I think you have to be really, I think for platforms where that are truly transactional and economic currencies are always good. Ben: And usually that currency becomes money. ‘cause it’s gonna have an exchange rate with real money Seth: Right. Ben: Um, Seth: Love one price. Ben: yeah, but for, but I think for, for. It is, I think it’s an interesting phenomenon that needs to be thought about more. Why it’s not, why it’s really generally not a successful route for social for internal markets. I, I’m very, I I believe that some of the obstacles to internal markets are just frictions having to do with like, basically contracting frictions. Um, and one thought that I have had for a long time actually discussed with, we had some there. Let me just, I, you guys will edit. Let me just say that again. One thought I’ve been thinking about for a long time is just as contracting intermediaries. Um, and Seth: This is a big theme of the Ben: Andrey Seth: Coasian Singularity Dude. Ben: Yeah. This is Andrey’s paper. Andrey: Yeah. So what, what is your thought about this? Yeah. Ben: I’m very curious, so I’m very curious for your take on it since you’ve thought about it much more seriously now, but it just, yeah, I think I feel like. A lot of the details were just like implementation details, that if it became your job to implement it at a company, you would, you would decide that it’s, you’d have to really have a high valuation of the marginal allocated efficiency of that currency. And it’s arguable that it’ll, it’ll be, it, I think experiment experimenting with it has just become way more valuable once we reach the LLM, capability of being trustworthy to like, negotiate a contract, which I think honestly is not right now, but yeah. Ben: I, I see that as a potential, a big organizational impact. I’m very curious what you think. Andrey: I mean, surely the contracting aspect would be hard. but I also think there’s a social aspect to it as well, right? You’re the CEO, you create an internal Coasean internal market for GPU resources, then you suddenly see a team that you don’t want using the GPUs, using a lot of the GPUs. Now, what do you do Seth: The whole point of, yeah, the whole point of having a firm is to have a command DI economy. If you wanted everyone making independent economic decisions, you wouldn’t have a company right. Andrey: but there’s a sense in which there’s some optimization that you want your teams to be making, like leaving idle GPUs or they’re using them very stupidly for some reason, and you don’t, you want that to be kind of disincentivized and. The way it’s currently done is through these very imperfect monitoring systems and people asking very nicely, can I have, this resource? Andrey: Right? So yeah, I’m, I’m curious whether the, the AIs can do a better job here. Ben: Yeah, I mean I guess the, you might shortcut you, they’re also becoming better at being the arbiters of requests. Right? So maybe, maybe rather than, but, but I do think money is, one memory I have of Quora actually is that the engineers, they hadbrilliant young people and I very like. Who were first principles thinkers too. Ben: And so people would ask me also, I had to just like justify money to the whole, to like the skeptics in the whole company. And so I gave, gave a lot of thought Ben: Yeah, why don’t we have some more multidimensional expression? Right. And there are good answers to that. It’s like very helpful that money is very legible. Ben: That, but, but I guess we, yeah, for companies, I’m very much with Seth’s point that if you really believed in the power of the, of monetary incentives to, to do it, you, you wouldn’t have a company, but you may find it a useful tool within the command. I mean, even, even the command the North Korea has has currency, right? Ben: So like it’s definitely a tool. And I think with the Pareto frontier has changed, but I don’t know how Closing Andrey: Very, very cool. So, we’re just about out of time. Uh, is there anything either of you want to add to our conversation? Seth: Ben, do you have any good eigenvalue jokes for us? Ben: oh man, I should have prepared. Seth: Alright. We had Ben Golub today who’s made tremendous strides in automated paper reviewing and still has a lot of progress to be achieved on automated Eigenvalue joke, doing, thanks for tuning into this episode of Justified Posteriors. Please like, share, and subscribe. We now have a hoppin’ Discord community for now by invite only DM us on substack Twitter or LinkedIn for your personalized invite code. Seth: And why don’t you keep your posteriors justified? Andrey: Thanks, Ben. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • Dec 15, 2025 · 1 hr 6 min

    Are We There Yet? Evaluating METR’s Eval of AI’s Ability to Complete Tasks of Different Lengths

    Seth and Andrey are back to evaluating an AI evaluation, this time discussing METR’s paper “Measuring AI Ability to Complete Long Tasks.” The paper’s central claim is that the “effective horizon” of AI agents—the length of tasks they can complete autonomously—is doubling every 7 months. Extrapolate that, and AI handles month-long projects by decade’s end. They discuss the data and the assumptions that go into this benchmark. Seth and Andrey start by walking through the tests of task length, from simple atomic actions to the 8-hour research simulations in RE-Bench. They discuss whether the paper properly measures task length median success with their logarithmic models. And, of course, they zoom out to ask whether “time” is even the right metric for AI capability, and whether METR applies the concept correctly. Our hosts also point out other limitations and open questions the eval leaves us with. Does the paper properly acknowledge how messy long tasks get in practice? AI still struggles with things like playing Pokémon or coordinating in AI Village—tasks that are hard to decompose cleanly. Can completing one 10-hour task really be equated with reliably completing ten 1-hour subtasks? And Seth has a bone to pick about a very important study detail omitted from the introduction. The Priors that We Update On Are: * Is evaluating AI by time (task length) more useful/robust than evaluating by economic value (as seen in OpenAI’s GDP-eval)? * How long until an AI can autonomously complete a “human-month” sized task (defined here as a solid second draft of an economics paper, given data and research question)? * Seth’s Prior: 50/50 in 5 years, >90% in 10 years. * Andrey’s Prior: 50/50 in 5 years, almost certain in 10 years.Listen to see how our perspectives change after reading! Links & Mentions: * The Paper: Measuring AI Ability to Complete Long Tasks by METR * Complementary Benchmarks: * RE-Bench (Research Engineering Benchmark) - METR’s eval for AI R&D capabilities. * H-CAST (Human-Calibrated Autonomy Software Tasks) - The benchmark of 189 tasks used in the study. * The “Other” Eval: GDP-eval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks by OpenAI * AI 2027 (A forecasting scenario discussed) * AI Village - A project where AI agents attempt to coordinate on real-world tasks. * Steve Newman on the “100 Person-Year” Project (Creator of Writely/Google Docs). * In the Beginning... Was the Command Line by Neal Stephenson * Raj Chetty Transcript[00:14] Seth Benzell: Welcome to the Justified Posteriors podcast, the podcast that updates its beliefs about the economics of AI and technology. I’m Seth Benzell, wondering just how long a task developing an AI evaluation is, at Chapman University in sunny Southern California.Andrey Fradkin: And I’m Andrey Fradkin, becoming very sad as the rate of improvement in my ability to do tasks is nowhere near the rate at which AI is improving. Coming to you from San Francisco, California.Andrey: All right, Seth. You mentioned how long it takes to do an eval. I think this is going to be a little bit of a theme of our podcast about how actually, evals are pretty hard and expensive to do. Recently there was a Twitter exchange between one of the METR members talking about their eval, which we’ll be talking about today, where he says that for each new model to evaluate it takes approximately 25 hours of staff time, but maybe even more like 60 hours in rougher cases. And that’s not even counting all the compute that’s required to do these evaluations.So, you know, evals get thrown around. I think people knowing evals know how hard they are, but I think as outsiders, we take them for granted. And we shouldn’t, because it certainly takes a lot of work. But yeah, with that in mind, what do you want to say, Seth?Seth: Well, I guess I want to say that we, I think we are the leaders in changing people’s opinions about the importance of these evals. The public responded very positively to our recent eval of Open AI’s GDP-eval, which was trying to look to bring Daron Acemoglu’s view of how can we evaluate the economic potential economic impact of AI to actual task-by-task-by-task, how successful is this AI system. People loved it. Now you demanded it, we listened. We’re coming back to you to talk to you about a new eval—well not a new eval, it’s about eight months old, but it’s the Godzilla of evals. It’s the Kaiju of evals. It’s this paper called “Measuring AI Ability to Complete Long Tasks,” a study that came out by METR. We’ve seen some updates or new evaluations of models since this first came out in March of 2025. Andrey, do you want to list the authors of this paper?[3:05] Andrey: As usual I don’t. There are a lot of authors of this paper. But, you know, I’ve interacted with some of the authors of this paper, I have a lot of respect for them. I have a lot of respect for the METR organization.Seth: Okay. But at a high level, just in a sentence, what this wants to do is evaluate different frontier AI models by the criteria of: “how long are the tasks that they complete”?” Andrey: I guess what I would say before we get to our priors is, just as context, this, from what everything I’ve seen, is the most influential evaluation of AI progress in the world right now. It is a measure that all important new models are benchmarked against. If something is above the trend, it’s news. If something is below the trend, it’s news. If something’s on the trend, it’s news. And it’s caused a lot of people to change their minds about the likely path of AI progress. So I’m very excited to discuss this.Seth: It’s been the source of many “we’re so back” memes. Yeah, I totally agree Andrey. Am I right that this was a paper that was partly inspiring the AI 2027 scenario by favorite blogger Scott Alexander?Andrey: I don’t know if it inspired it, but I think it was used as part of the evidence in that. Just to be clear though, AI 2027, it’s a scenario that was proposed that seemed a bit too soon of a vision for AGI taking over the world by many folks. We have not done an episode on it.Seth: We haven’t done an episode on it. But it’s fair to say that people look at the results of this paper and they see, you know, they see a trend that they extrapolate. But before we get into the details of the paper, are we ready to get into our priors?Andrey: Let’s do it.[05:50] Seth: Okay, so Andrey, just based on that headline description, that instead of evaluating AI systems by trying to go occupation by occupation and try to find tasks in those occupations that are economically valuable and then trying to see what percentage of those tasks the AI can do—that’s what the Open AI GDPval approach that we recently reviewed did—this approach is trying to evaluate tasks again by how long they are. So comparing those two approaches, I guess my first prior is, before we read this paper, which of those approaches do you see as like kind of intuitively more promising?Andrey: One way of thinking about this is tasks are, or things people do which could be a series of tasks, are bundles and they’re bundles embedded in some higher dimensional space. And what these two evals are doing, this one we’re discussing here versus GDPval, is they’re embedding them into different spaces. One of them is a time metric. And one of them is a dollar metric, right? And you can just by phrasing it that way, you can see what some of the issues might be with either. With the dollar metric, well, what are people getting paid for? Is it a specific deliverable or is it being on call or being the responsible party for something? So you can see how it’s kind of hard to really convert lots of things into dollar values at a systematic level. Now, you can say the same thing about how long it takes to do something. Of course, it takes different people very different times to do different tasks. And then once again chaining tasks together, how to rethink about how long it takes to do that. So I think they’re surprisingly similar. I think maybe this length of time one is more useful at the moment because it seems simpler to do frankly. It seems like, yes we can get an estimate for how long it takes to do something. It’s not going to be perfect, it’s going to be noisy, but we can get it and then we can just see whether the model does it. And that’s easier than trying to translate tasks to dollar values in my opinion.[8:42] Seth: Right. I guess I also am tempted to reject the premise of this question and say that they’re valuable for different things. But I guess I come into this thinking about, you know, we think about AI agents as opposed to AI tools as being this next frontier of automation and potentially supercharging the economy. And it really does feel like the case that working with AI models, the rate limiter is the human. It’s how often the human has to stop and give feedback and say, “Okay, here’s the next step,” or “Hey, back up a little bit and try again.” So going in, I would say I was kind of in equipoise about which of the two is the most useful kind of as a projection for where this is going. Maybe on your side of the ledger saying that economic value is kind of a socioeconomic construct, right? That could definitely change a lot even without the tool changing. Whereas time seems more innately connected to difficulty. You can think about psychometric measures of difficulty where we think about, you know, a harder exam is a longer exam. So at least going in, I think that this has a lot of potential to even potentially surpass GDP-eval in terms of its value for projection.Andrey: Yes. Yeah, yeah. Seth: Okay. The next one I was thinking to ask you Andrey was, if we buy all the premises of whatever context the paper sets up for us, the question I’d like to think about is: how long until AI can do a human month-size task on its own? In the abstract of the paper, we have that happening within five years, by 2030. That seems like a pretty big bite at the apple as they say. Do you want to take a stance on how long until an AI can do a human month-size task? I mean, I have to say in my use of AI, I haven’t gotten anywhere near that.[10:55] Andrey: What is an example of a human month-size task?Seth: What’s something that takes 160 hours of work? I would say, you know, as an academic, maybe I need kind of three months of focus on a paper to bring it from zero to, you know, solid second draft. Maybe that’s like a third of a paper is a month of work?Andrey: I mean, it can do a third of a paper in a day. I mean I’m not being facetious here. I referee a lot of papers. Is the question an end-to-end, completely no-intervention sort of thing? Because I think like, look, you take Claude-code off into a folder, the folder has the data. You tell it, “Hey, like write a paper that does this, that investigates this question with this data.” It can do that in a day. I don’t think it needs... I think it depends on how much you require for human intervention. I think with something where there’s a verifiable answer, it’s very different than something subjective like a paper. Because I think we don’t want just any paper. We want the paper that we want to write. It’s not just about quality, it’s also about taste. And so I don’t think it could do “end-to-end write a paper that I like” even if I gave it a lot of scaffolding. I don’t think it could do that yet. But could it do that in five years? Sure, I think it’s possible.Seth: And just to be a little bit more specific, can we say gets published in like a top 10 economics journal level of quality?Andrey: The quality bars will have to increase. I mean, I think it goes to a question of like if I already have the research question and I know the data is adequate. Yes. Very few projects are of course like that, right? None of my recent projects have that flavor to it I think, where it’s just I’ve already found the data set and the question is obvious and I just needed to go plug and chug. Seth: There are papers like that. Raj Chetty gets the US tax records, and just needs to run some pre-registered analyses. Andrey: That’s an interesting one Seth. So Raj Chetty is an economist -now we’re really in the weeds - who does big public economics analyses. He works with gigantic teams on data analysis and iteration. It’s not as simple as just going to town on a dump of data. So yeah, I’d say that I can think of easier papers than Raj Chetty’s papers to implement.Seth: Okay, but if I want to think about the same kind of general format of question, right? Which is: I have a data set, I have kind of the general research question I want answered about the data set... let’s say the question is only specified at that level. I’m not being any more specific than that. Plus a data set. I don’t think an AI could make a plausible, complete, top 10 econ journal out of that right now. Do I think it could be there at a plausible level of quality in 10 years? In five years? Five years might be like exactly at my cutoff. I think in 10 years for sure. In five years, 50/50.Andrey: Interesting. Okay. Okay. So that’s... yes. So we’re both very bullish, huh? Okay. Well, you know, maybe it’s slow, but 10 years is fast enough that we’re not ready. In fact, my understanding of the METR organization is that a big part of its mission is to prepare us for AI progress that’s a lot faster than society is ready to deal with. And you know, I think it’s an important mission.Seth: That’s my mission too, Andrey. Also, they need to be prepared for slow progress. I want to prepare society for everything. Why prepare them for only one thing?Andrey: Society is already prepared for slow progress. Perhaps.Seth: Okay, are we ready to move on to the evidence?[17:34] Seth: Okay, so Andrey, we read this paper, or this Eval from METR. It looks at the probability of task completion as a function of task length across a variety of frontier models, starting with GPT-2 in 2019 and continuing through Claude 3.7, which is kind of early-to-mid 2025. And I would say the Eval works in sort of four steps. First is they establish a human baseline for how long it takes humans to complete 169 software engineering tasks --- By the way, in the abstract it does not mention that this is overwhelmingly software engineering tasks. I probably would have put that in the abstract, but you know who am I? -- Secondly, once we’ve got that baseline for each AI, we see whether it can complete each task. That was the quote you just gave us from Twitter. So once you’ve got the baselines, it takes about 60 hours of work to run each AI through the paces. Then we’re going to run a logistic regression of “Does AI correctly answer the task?” on “Length of task.” And then that gives you a data point for each model of: we think it has a 50% shot of completing an arbitrary task of a certain length. And then you put all of those points for all of different models from 2019 to 2025, and you see a diagonal line pointing from models that can do one-second tasks to models that can do one-hour tasks. And if you just extend that line out a little bit, that line’s going to take all our jobs. Isn’t that right, Andrey?Andrey: Yeah, yeah. So just to be clear, I think the numbers that I have for the extrapolations... if we think that the current horizon is about a couple of hours, and the latest model rated is GPT-5.1 Codex Max which is just under three, the prediction for February of 2027 is 16 hours. And for April of 2028 is 5 days. So that’s you know, and if we go further we get to those month-long numbers eventually.Seth: Okay. So maybe let’s take a minute to talk about that headline result. So they estimate putting all these models together a doubling time of approximately seven months. So every seven months we get a frontier model which is able to work for twice as long. They give themselves an R-squared of 98% in fitting what is it, 10, 15 points? Do you have anything to say kind of about this headline result before we dive in? The one thing I wanted to point out was this is all software engineering specific. So if you think that software engineering might obey very different doubling times than other tasks in the economy, this is only going to tell you about that one particular domain.Andrey: Yeah, yeah. And I think that’s a really important caveat. I don’t think there is as much care here in making the tasks as realistic as possible as was, let’s say, in GDP-eval.[21:35] Seth: Right, different priorities. GDP-eval very focused on like “what are useful tasks.” This kind of more focused on the abstract “short versus long tasks.” Maybe one other point I’ll make here which is a high-level point, which is something that they emphasize, which is if you think that there’s just some sort of constant error in their estimates, you can shift this entire graph down. But the important thing is the doubling time, right? And if the doubling time is seven months, sure shift the whole thing down, it’ll take one more year to get to whatever crazy outcome you want.Andrey: Yeah, and for what it’s worth, to me 50% completion doesn’t seem very relevant. Presumbly you want 99% completion, right?Seth: Yeah. I’d be happy much—you know I prefer to look—they have an 80% completion option on their site that you can plot and I tend to prefer that one. For that we have a number like that’s pretty current that’s around 30 minutes versus for the 50% it’s about 2.5 hours.Seth: There we are. Okay. So we’ve talked about the headline results. Maybe now let’s go kind of point by point and how we end up there. So the first thing that they need to do is establish a human baseline for how long different tasks take. They do this by combining three different data sets. The first one they do is sort of internal. They call them Software Atomic Actions. These are like really micro tasks. The example they give is kind of hilarious. The example they give is: “Okay Andrey, how long was it going to take you to answer this question? I’m putting you on the spot. Which file is most likely to have a password in it? Credentials.txt, InstallationNotes.txt, Main.py, or LauncherWin.exe?”Andrey: Wow. Wow that is a hard question Seth. I mean I kind of view these sorts of tasks as similar to kind of like cursor auto-complete tasks where like, you know, you don’t need a reasoning model for this. You’re almost like... let’s say you have a little bug in the code, it just auto-complete correct it. You know, that sort of thing.Seth: One thing I want to highlight about this... and they really they talk a little bit about trying to do what they can to reduce the noise from overhead from reading, from human reaction time... but it seems like they’re not going to do a super good job of distinguishing whether answering that question is a one-second task or a three-second task, right? But the difference between a one-second task and a two-second task is an order of magnitude here. And I guess I’m a little bit concerned if the logistic curve is learning too much about what’s the one-second version of that versus the two-second version of that.[24:54] Andrey: Yes. Yeah yeah. I mean yes, there is an argument to be made that due to measurement error just swamping everything that maybe we should only start with one minute or or two minutes. Now of course we can draw our own visual regression on that plot over there and see that you still have a pretty steep curve even if we throw out the first few points, right?Seth: Okay. So that’s done internally with their own kind of own engineers or just whoever was around. The second data set they draw on is something called the RE Bench suite or the Research Engineering Benchmark V1, which to quote from the paper consists of “seven open-ended ML research engineering environments and data from 71 eight-hour attempts by 61 distinct human experts.” So they’ve got these 61 guys that are doing seven of these tasks. And we confirm our experts make progress in these environments given eight hours. The third benchmark is H-CAST, Human Calibrated Autonomy Software Tasks. Designed to be a little bit more realistic to what a software engineering task would be in an economic environment. And they say that their baseliners typically have a degree from a top 100 global university and are primarily recruited via professional networks of METR employees. They’re paid $50 to $100 per hour plus $25 to $150 per hour in performance bonuses. Baseliners also did the tasks and predicted how much time it would take them to do the tasks. Curiously only 61% of human baselines actually successfully completed tasks, right? So one thing kind of we should be thinking around in the background here is we kind of want to compare how long it takes a human to do a task to can the AI do the task. But in reality it’s like like we talked about, it’s higher dimensional than that. There’s not just how long does it take a human, but with what probability can a human do it in a certain length of time.Andrey: Yeah. Or which human? And does the human have the context ahead of time? Or you know, are they an expert in this type of work or not, right? There’s no one number for the human.[27:38] Seth: Exactly. And for that third data set they record 189 tasks that they evaluate across which there are 563 human baselines. So I guess a second note here is these aren’t kind of giant populations of people. I just I guess you wouldn’t expect this to be giant populations of people. You know is 61 people being judged on their research engineering skills a lot? A little? I mean on the one hand 61 seems like a small sample for all of humanity, but on the other hand getting 61 serious software engineers’ time for a thousand hours is a bigger deal.Andrey: Yeah. Yeah. I mean it’s hard. I mean this goes back to our discussions of cost, right? I mean to do these sorts of metrics well, especially for valuable tasks, is just very expensive. You know look, there’s also this question of which population do we want to sample from? In the economy, experts are oftentimes doing the work. And that expertise can be very very narrow, right? You know think about just you know economists. You know even if economists are using different methods, you’re you know one person studying you know the medical industry is going to have very different expertise than a different person studying you know the energy industry. Even if like they use the same methods. So yeah I think the question of what population you want to sample is an interesting one.Seth: Very very well put. One other detail here that is interesting but it’s kind of mixing together some pretty different evals here. The RE Bench, unlike the other ones where they just see how long it takes a person to finish it and conditional on finishing it how long did it take you, for the RE Bench they kind of give everyone eight hours and they figure out like what the average quality of people were able to do in those eight hours and that’s going to be their cutoff for an eight hour length task. So a little mix and matching going on. I’m not saying that they P-hacked this but there’s some informality going on. Is there anything else you want to say in the creation of the bench lines before we move on?Andrey: Well I think there’s one other data that they use which was the internal PR pull request experiments. I don’t know if you read this part where so they ran these models on some issues in the internal METR code base. So these are ones that would not have been in any training set certainly. And they found that their contract baseliners take 5 to 18 times longer to resolve these issues than the repository maintainers. So the people whose job it is are 5 to 18 times more efficient than contract baseliners on this on these tasks.Seth: So the idea is METR coders are very smart boys. And girls.Andrey: No, they actually don’t say that. They actually don’t say that. And I disagree with your statement here. Not that they aren’t smart, but more that they say that it’s all about context, right? Like if you’re dealing with a code base and you’re very used to it, you can diagnose the problem very easily. You can solve them very easily. If you’re not, then it takes you a while to load the context back in. I mean we’ve all had this. You know you work on a research project, you take a little break for a few months and now you come back and you know something that you know should be very simple takes you a few hours because you know you just don’t remember the code anymore, right?[31:38] Seth: I wanted to bring up one last point here Andrey before we move on, which is around the question of how many people do we need to establish the correct baseline. So we’ve already talked about context matters, like have I already loaded in the prior knowledge or am I coming in cold? Am I a super smart expert or am I a man off the street? Those are all definitely mattering. But one thing I’d like to point out is that if we think that some of these tasks have a very long tail in completion time... right? Which seems really plausible for a very hard research engineering task, that you know some people can do it in a short amount of time and some people take twice that and some people take twice that... a very long tail... as the variance of people’s abilities to complete this task goes up, you know you’re going to be less and less confident in your estimate with a small N.Andrey: Yes. Yes yes. I think that’s right. But once again it’s not clear to me where we want the minimum... whether we want the average or the min. There’s a very good argument for the min.Seth: Right. If what we care about is superhuman ability then I guess we want the min.Andrey: No, or or just like a comparable to a professional working on the code base. Not even superhuman right? Seth: Do we really want the strict min? If the question is “how long does a certain journey take”, I’m not sure we want to include the person who by chance had just looked up that number. Andrey: Like I think the min is perhaps too far... but something much closer to like what someone day in day out of the code base would do rather than you know... one is how much do you accelerate a company with an existing code base with professional software engineers. Like for me maybe that’s not the relevant benchmark. I’m not a professional software engineer. And so I don’t care if it’s better or worse than the best professional coder. I care if it saves me time. Which could be you know much more economically relevant if we think that the value of better software engineering is coming from the fact that now everyone can be a software engineer.Seth: I think that’s very fair. But as we get deeper into this I’m becoming more convinced that if you really care about economic value you should be reading the GDPval paper not this paper.Andrey: Okay. Okay.Seth: So the second step of this process is for each AI seeing whether it completes each task. Right? So we’ve got these benchmarks. We’ve got the short benchmarks, the medium length benchmarks, the long benchmarks. How many can each AI do? I guess the one note I want to bring up here is they do some basic scaffolding. They claim it’s not elaborate. They try to bring some agent tools to the early models. So early models were like not set up at all for these longer projects but they try to give it like a little scratch pad and a little “remember these are the most important command line codes.” It seems like they’re not going to do a super good job of distinguishing whether answering that question is a one second task or a three second task. But you could imagine a version of this test that would have zero scaffolding or a version that would have very elaborate subtask specific scaffolding and they’re kind of closer to the first.Andrey: Yeah and I think that’s fair to have a comparison baseline. It’s also becoming less and less representative of how people are using the models, right? I think if you’re serious about using the models you’re giving them skills and putting in the right context. Certainly you’re using a cursor or Claude code or a codex where there’s a lot of optimizations there. So you know one one argument here is like actually if you’re if you’re serious about using these models they’re actually a lot better than what’s portrayed in this benchmark.Seth: Yeah I think that’s definitely right. And again one of the running themes of this podcast is “Bitter Lesson” and how important is the frontier-ness of the model versus the customization and the specific task orientation of the model. We don’t really get... you know they just say we do light scaffolding. And I guess before we move on, the range of tasks here are all designed so they can be done through the command line. So there’s no kind of... it’s not like Chat GPT immediately fails everything because it can’t make a picture.Andrey: Seth, I thought that everything could be done through the command line. In fact Neal Stephenson famously said…Seth: In the beginning there was the command line. That’s a good book. That’s a good book.Andrey: Cryptonomicon for those who don’t know.[36:10] Seth: No, he has a book, he has an essay collection called In the Beginning Was the Command Line also.Andrey: Yes that’s true yes that too yes.Seth: And in the essay collection, this is the one thing I remember, is he compares Macs to the Batmobile. ---Seth Cuts in With Correction: Actually he compared Mac OS to a luxury European car, Windows to a station wagon, Linux to a free tank, and BeOS to the Batmobile. Apologies to Mac OS fans for comparing their OS to the Batmobile -- It was a very 1990s book. It was like OS Wars book.[37:01] Andrey: I just say that Neal Stephenson in terms of the pantheon of prophets... (Seth: he got crypto right). He got Uber right. He got virtual reality right. Wait wait wait. Okay. So right. Crypto. (Seth: He does think that there needs to be a big pile of gold somewhere. Which turns out to not be the case. Maybe he gets stable coins right.)Yeah but but I guess yeah there are many things he got right and and certainly in Snow Crash that were way way ahead of their time. It’s one of those things where you almost imagine that the sci-fi author kind of causes the subsequent innovations. And maybe with AI there’s a similar sense to that because so many people who’ve developed these technologies were inspired by reading science fiction.Seth: And the AI is reading the science fiction too Andrey.Andrey: Yeah well it’s not clear whether we want the AI to read the science fiction. It might develop some weird notions of what might happen in the future.Seth: Yeah. Read Bicentennial Man, don’t read Frankenstein. Let’s leave it at that. Okay. I could talk about Neal Stephenson for a whole episode. So let’s hold off on that. Okay. So the third step we promised the listeners is running the logistic regression. So what we have here at the bottom of my screen I’ll put up is for each of the models that they evaluate you can see this nice logistic curve that starts at 100% for a sufficiently short task and moves down to 0% for a sufficiently long task. And I don’t know Andrey, I look at these curves and a lot of them don’t seem particularly logistic. A lot of them are not monotonic even. It seems like you’re assuming the conclusion if you think that AI can do all one second tasks. I my read is that AI cannot do all one second human completable tasks. And like the idea... logistic models are one parameter models. So like we talked about, it’s learning just as much about this curve about from going from four seconds to eight seconds as from going from one hour to two hours. Which seems like the wrong way of thinking about it.Andrey: Yeah I mean I guess is it really that different than just finding than just extrapolating the point at which it has a 50% success rate? And then you know if we actually look at that point non-parametrically it’s it’s pretty it seems like like pretty close to where where we end up right? So I guess like one argument here is actually if you’re if you’re serious about using these models they’re a lot better than what’s portrayed in this benchmark.Seth: The 50-50 point. I think for a lot of these if I was trying to draw a diagonal line I guess my midpoint, my 50-50 point would be similar. I guess I don’t know how to think about like this GPT-2 example where…[40:37] Andrey: Sure. I mean but I think we already both like kind of argue that we might as well toss them. And it wouldn’t really make a difference. So let’s toss the early ones. Seth: We’re not going to focus on the ones that can knock all these one second tasks out of the park. One thing I I guess think about is there seems to you know they talk in the caption for this figure about a jump in between the the the atomic tasks and the H-CAST tasks. And you do kind of see that in a bunch of these figures. But then I also see a jump at the eight hour tasks right? Because we know that there’s a lump of eight hour tasks that they get from the RE benchmarks. You know this is not to like punch down on a paper that is like a really good paper is definitely inspirational um and definitely influential correctly. But I think when you dig into these curves I am not convinced that the logistic model is definitely the right model. And then I guess then I lose maybe a little bit more faith than you do that were correctly finding the 50-50 point in these.Andrey: Yeah. I mean I guess the other... I just don’t... yeah. I think there are other criticisms that are much deeper than than this one is maybe what I’d say. No no no. We already mentioned them. These are programming tasks. They’re very selective. (Seth: Yes. Yes. Yeah. There are other deeper criticisms. We’ll get to those.Seth: You gotta put... dude how do they not put that in the abstract? I don’t know. That’s that’s something I ask. I mean the only… I’ll tell you why you don’t put it in the abstract and not to cast aspersions... it’s the hubris of someone who thinks that software engineering is the is the final task. Andrey: Tell me tell me about these messiness scores. Did you read about those?Seth: Right. They have 16 of them. Um I I I’ll why don’t you tell us about the messiness scores Andrey.[42:50] Andrey: Yeah so so there’s an idea that like look if you have a very well defined task... like implement some algorithm... you know verify that the results are working... you know that’s way easier for an AI to do than “Hey you know I don’t know how to solve this problem you know try a bunch of things and solve it for me.” That’s very messy. Like you don’t you know you don’t um really know what the right solution there is no maybe objective solution to that um and so um you might think of a dimension here that’s messiness in addition to some sort of difficulty uh level. And and so they have a bunch of ratings uh of the messiness of uh these different tasks and yes there’s yes and and one thing I’ll say is that most of these tasks are not very messy. Now what else will I tell you is like you know working at my job most of the tasks I do are super messy.Seth: They wouldn’t give... they don’t give you the easy jobs Andrey.Andrey: No no no no. I mean and maybe you know look once again like maybe the intern is getting these very non-messy jobs but I am not. So so I do think it’s an important dimension. Not to say that the AI can’t do the messy jobs. They’re not even in the data set that’s being evaluated here.Seth: Right. I think that’s a very fair point right. Which is this is a set of tasks that is really designed to be as amenable as possible to sticking the agent on it and coming back later right. That’s that’s intriguing right it’s like and it’s inspirational and it’s uh vertiginous is maybe the word I want to use. Uh but it maybe doesn’t extrapolate directly to um normal people’s interaction with these tools right. One other way I might want to frame this and we talked about this in the beginning is that problems are sort of tasks are multi-dimensional. They have lengths but they also have messiness. They also have difficulty. They have you know verbal difficulty and math difficulty and difficulty on lots of different dimensions. You could imagine a world in which there’s lots and lots of evals. More than 169. Maybe let’s say a thousand of these benchmarks. And we could actually estimate something that’s kind of multi-dimensional right. So success probability as a function of the length of the task, the verbal difficulty of the task, the you know the math difficulty of the task. And then throw in model year as just another parameter. Or as another interaction term.Andrey: What an economist. Just add more fixed effects.Seth: Dude machine learning! Let there be interactions too right. Let let it have whatever shape you like. Um that’s the dream. Maybe it’s an unrealistic dream given how expensive you know even putting together 160 benchmarks are. Uh but it seems like if you wanted to estimate the role of year in how good model is in doing thing you would want a model where year is a parameter in the model.Andrey: Yeah yeah. I mean for what it’s worth you know there aren’t that many models... yeah I guess there are more there are a lot of... let me take that back. There aren’t that many frontier models. There are a lot of models that are around. But I think this benchmark is really focused on the frontier models and and you know over the course of this year we’ve maybe had the 10 total frontier models. So it’s not you’re if you want to if you want to run that regression you know you’re gonna have too many parameters.Seth: Well here how how about this? Right? Which is you don’t only focus on frontier models. You just try to do this prediction not as a function of like model frontier you know is this the frontier model and year. Is you do it as a function of model size. And maybe there’s instead of one frontier model every year there’s one frontier model at each size every year. And you can get a little bit richer data.Andrey: Sure. I will tell you that we actually don’t know the size of the frontier models.[47:04] Seth: Yeah they don’t they don’t tell us. They don’t say it’s got a gazillion parameters. It’s secret. You know I... all right keep your secrets meme. All right.Andrey: Well look uh to any of our listeners at the various illustrious labs uh a little tip might be appreciated if so we know what what sizes we’re working with.Seth: Okay. So that’s a fair point. I think another point I would make here is that when we’re talking about secrecy is that the evals also have to be secret right. You know as if I’m putting on my reviewer 2 hat I kind you know I want to see the evals. I know I understand that you can’t put them on the internet because then the AI companies will cheat at the evals. But uh it’s a non-optimal thing that they have to do.Andrey: Yeah and there and there is a sense that some of these tasks that they do have them do are a bit leaky. Who you want you want… I have some intel…Seth: You want to name names?Andrey: No I mean look I haven’t dug into them myself but having talked to some having gotten some intel. Let’s just say that they’re not they’re not some there not things that are that different from what you might have trained on a lot of time.Seth: Okay. All right. So are we ready to sort of start talking about uh discussion limitations? I feel like we’ve run through the paper now. Is there anything else you want to say in terms of the technical sort of evidence side before we move into kind of more free-wheeling discussion?Andrey: Let me just uh kind of now uh say you know this is a really I think this is a really important topic and episode for us because I I truly do think that this eval is driving so much of the conversation and uh most of the people have not read at all what the eval um is. And I will and I will especially thank so I’m in this uh Twitter group chat called uh the the “Demon Economics Research Unit” with a lot of uh very uh based uh participants who pointed me to various resources various very interesting writings on this eval that I that I benefited greatly from um when when thinking about limitations here. Um so let me give you a limitation that one can think about. Have you ever tried to watch uh Sonnet play Pokemon Seth?[49:37] Seth: I oh I remember really early on I remember like Chat GPT yes I do remember Chat GPT plays Pokemon right. But it was like it was no I know I remember Twitch plays Pokemon and it was terrible right. I I do not I have not seen Claude play Pokemon. How what is that like?Andrey: Uh it’s pretty slow going. Um and it’s not not very successful. Uh it’s a game with no fail state you can just keep on grinding it. Yeah just to be clear like a child can play this game quite successfully. Um and uh this is something an AI just has a very hard time doing. A number I have here is that not the current Gemini but but I think it was Gemini 2.5 Pro took 888 hours to minimally beat Pokemon uh that’s elite four that’s not capturing all Pokemon yeah with a dozen intense human handholds like tile labeling.Seth: Wow.Andrey: So so it’s very easy to think like hey uh these numbers in you know you to naively look at this graph and you’re like yeah now it’s four hours now it’s you know so on. But but let’s think about something like Pokemon which which humans can do quite well where where even when the AI can do it the amount of tokens involved is is immense. It’s just staggering.Seth: When it will become when Andrey when will it become economically viable to export our Pokemon play?Andrey: Yeah. Yeah I’m just say that look like obviously obviously these tokens are going to become cheaper over time and more efficient and whatever but but but you know like we have to take things with a grain of salt. Here’s another piece of evidence that was brought up in the in the group in the group chat that I that I think was quite uh convincing to me. Have you ever heard of uh AI Village?Seth: No.Andrey: Uh so AI Village is uh is an experimental project where uh AIs uh personified with like the different models like you know Sonnet and Gemini and and GPT are coordinating on different tasks. Uh like what? Like Stardew Valley kind of? Yeah exactly. Yeah yeah. So they might be coordinating on um successfully uh selling a shirt online or getting some likes for for a web page or or something like that.Seth: What so these are real world tasks or these are simulated tasks?Andrey: Real world tasks. Okay cool. Real world task. And um you know you can I encourage everyone to go to it and see how well that’s going for the AIs.Seth: Do they sell ten thousand dollar tungsten cubes?Andrey: That’s that you know that’s a different interesting project but you know uh yeah project you know that’s a project called Project Vend you know maybe another one that’s in the same in the same vein. But but but this this AI Village just goes to show that uh these AIs they’re missing something. They’re not able to do things that humans can do quite easily. Um especially coordination but not just coordination. They just get tripped up on interacting with various pieces of the digital world. Um I’m a big optimist that that will be improved of course but um but we have to take these these time numbers with truly with a grain of salt.[53:15] Seth: Right. I guess one thing one kind of question I had going in and I’m not sure whether we kind of get a hard yes or no answer on this is like to what extent is doing a two-hour task just doing two one-hour tasks correctly in a row right. Yeah. To the extent that it is to the extent that it’s just like a six sigma problem to the extent that it’s just like Waymo and it’s like okay you need to not crash one minute in a row a thousand times it seems like these extrapolations are pretty straightforward right. But if on the other hand longer tasks are somehow qualitatively different because they involve complex interactions between subtasks interactions with the world in a way that you never do with one second tasks then these projections become a little bit more dubious. I guess I would also say that there are also reasons it could be easier to do these longer tasks right because you can always back up and retry right. Uh but I guess you know I wish there was a little bit more in here... I guess with the messiness they talk they get at this maybe a little bit but I wish there was more about like what’s going on beyond just reliability going up on each subtask.Andrey: Yes yeah. Yeah I agree that would be very interesting. I mean one one version of that could be is it reasoning. (Seth: Planning) Reasoning is a constraint right like planning. Yeah planning um I yeah I don’t know. Um I guess like one one version of this is let’s you know one way we can think about this is that if 50% reliability is actually quite small and if we wanted to get to let’s say the reliability of um a good worker at a company maybe that’s a 99% reliability. Um so uh one argument that maybe the authors of METR might might bring is like look the trend is the same regardless of the percentage numbers and we just need to uh you know you can just shift everything down. But otherwise we’re we’re doubling very quickly and that still has enormous economic implications. And then you know um unfortunately we don’t have any evidence or not that we don’t have any but I would love to see a 99% reliability threshold in this benchmark.Seth: It’s not sensitive enough right. I you know if there’s a hundred tasks right or a hundred and sixty that they’re doing right so you just not going to get 99% and you’d be worried yeah you’d be worried that it selected yeah.Andrey: Yes yeah yeah. Um another comment like another very interesting critique.Seth: Keep them coming dude these are all great.Andrey: Um is is thinking through like what an actual human project requires in terms of hours. And there was this very interesting essay uh by this guy uh Steve Newman. I guess he developed Rightly which ended up becoming Google Docs. Uh and he talks about like uh something being a prototype um which was his initial Rightly um that kind of took about uh four months to build. Um it was kind of hacked together. And you know it was it was kind of self-contained and so on. And then he talks about a subsequent project he did um called Scalyr uh Sc- I don’t know how to pronounce it whatever. Um and he kind of estimated that uh that project that that product took a hundred person years to do. Which is not a crazy idea if you imagine you have a company and you have a hundred employees and it tooks you took you a year to build your initial project. I mean you know like most startups don’t don’t work that way.[57:15] Seth: That’s a mythical man month.Andrey: Yeah you know it’s not quite you know that but like there is some some some substantive or alternatively one way to think about it is like maybe what we need to get to is a hundred person years not like you know uh not even one year for a person. Right?Seth: We need that for for whom?Andrey: We need that for to have the AI end to end develop you know build things truly build things you know. Seth: For you to really feel like I am to have that one person company right the mythical first the one one employee unicorn right.Andrey: Yeah exactly. With zero employee unicorn you know.Seth: Well I mean that’s I dude you gotta make yourself CEO.Andrey: CEO is I don’t know as someone who has an S-corp that not necessarily you know…Seth: Do you call yourself president? What’s your position at your S-corp?Andrey: I think I’m president yeah. Seth: Oh wow. I’m going to be Chief Czar of my S-corp. Does your does your S-corp have a fun Lord of the Rings name or is it like Andrey Consulting or something?Andrey: It’s uh you know it’s a it’s actually very related to this podcast it’s called uh Justified Strategy.Seth: Ooh I like it I like it. See you know you gotta the marketing synergies are obvious here. Yes yes. That’s something the AI can’t do yet.Andrey: Oh man do you have any more of these hot limitations or or have I tapped you out?Andrey: No I mean look I think I think we’ve said enough yeah on the limitations yeah.Seth: We’ve done one we’ve done uh two man hours of talking about this. Okay. Yes yes. So let’s move into our posteriors.[59:10] Seth: So uh Andrey um can I tell you a joke before we move into our posteriors?Andrey: No jokes allowed.Seth: Well I’ll tell you an unfunny anecdote then. Okay okay. I heard a joke once. A man goes to doctor. He says that he’s unevaluated. Says that life is meaningless and vague and uncertain. The doctor says that treatment is simple. The great evaluator METR is in town. Go and see them. That will get you evaluated. And then METR says to the doctor “But I am METR.” You know drum roll curtain closes. I mean it is it is so it’s so tempting to kind of want to do the meta thing here and like ask because it is such a software engineering-y kind of task the the evaluating. It is sort of surprising that uh you have that Twitter post saying that it takes them 60 hours 60 man hours to do the evals.Andrey: I mean look I’m sure they’ve tried to automate more of it but yeah I agree it is very metapoint. It is uh... Seth: Hopefully someone got a laugh out of that. All right. So the first posterior uh we have to come back to is is this more or less useful than GDPval?Andrey: I look I I think it’s hard to argue that given where we are today that this is not more useful. Um it’s been this has uh been in the media a lot more than GDPval. I think one of the reasons it’s more useful is because lots of models are plotted against it. There’s more of a trend. Maybe GDPval will have this flavor going forward. Um but it is worth you know thinking through just the fact that GDPval is also way more expensive eval.Seth: Right. I don’t know I dude G- I I know GDPval is way more expensive I vastly prefer that to this. This is a good paper I have nothing against this paper. But you gotta if it’s a if you can’t say this is about agents generally and then not put in the title that it’s just software engineering. I love the the breadth that GDP-eval tries to get at um that’s just not present here. I it is in- it’s it’s vertiginous to look at that curve going up to you know 10 hour tasks 20 hour tasks 40 hour tasks but the fact that it’s vertiginous and newsy doesn’t make it better necessarily.Andrey: Sure sure. Um yeah I mean I hear that point.Seth: The second thing we wanted to think about is how long until AI can do a human month-size task on its own. I came on saying that we we sort of we’ve defined that as do a good draft of an econ paper given a premise and a giant data set. You know viewers at home think about your own month-long task that you’re familiar with. Uh I said maybe 50-50 in five years and pretty conf- and you know 90% in ten years. This paper is a good paper it’s an intriguing paper but when you dig into it it says a little bit less than what it seems to on its face. So to the extent that I was thinking that we were going to be there for sure in 10 years and pretty con- and you know 50-50 in five years I at least I have to take a step back and put bigger error bars on that ladder one and maybe go down to you know 70-80%...Andrey: I’m confused Seth. How could that be? Because if that was your prior... yeah yeah... this didn’t have negative information so you I would believe if you said your prior didn’t change…Seth: No no no no. It signaled me down right so I so when I came into this paper I had an assumption about what this paper would say. So I had a prior that included “Oh and there’s this great paper that says 7 months.” Okay. I see. So your prior included already some notion about what the paper is. (Andrey: Okay got it got it got it got it got it. I hear you.)So this paper was less impressive than I anticipated. And so um I think my five year estimate is maybe about the same but my 10 year estimate comes down a little bit.Andrey: Yeah yeah I think I’m more confident than you that in five years we’ll have it. So my 10 year doesn’t um change very much. Um yeah I mean I think the interesting thing is like do we get there in two years or do we get there in five years? And because of the narrow domains here I I really there’s other evidence that like for example like Open you know we’re recording this as Opus 4.5 was uh recently released the latest Anthropic model that has updated my priors a lot more than um than this paper.Seth: Yeah. Do you want to talk about that for a little bit and that can be our our wrap up discussion? What’s so what has impressed you about the the latest latest models?Andrey: Um I mean look they have through a variety of benchmarks they seem very good but just I’ve had a chance to work with it yesterday and uh I was extraordinarily impressed.Seth: Give me give me a little bit more dude just a taste. What was one cool thing it did?Andrey: It’s too secret dude.Seth: All right. Um let’s just say like it did it when thinking about like writing a paper it did something that would have probably taken me a week and probably about an hour.Seth: All right. Okay. We that’s a week-long task uh 40 hours of work that’s uh off the charts in what we’ve been looking at.Andrey: Yeah I mean I do think like one one constraint there I mean if you look at the clock time for me it was longer than an hour but I could use a lot of that time to do other things. I think but like my interventions into it were rel- you know they were expert but relatively minimal and it did a lot of awesome stuff on its own uh very effectively.Seth: Right. So listeners at home we are not AI pessimists. We think that there’s a lot going on here. This paper maybe uh very intriguing vertiginous exciting maybe a little bit less than it seems uh on its face. Uh but we are watching this space and we’re we’re looking forward to see uh how good these agents get and how long tasks that they can do moving forward.[1:05:47] Andrey: All right. Keep your posteriors justified.Seth: And if you have another uh cool eval you want us to eval send it our way. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • Dec 2, 2025 · 1 hr 2 min

    Epistemic Apocalypse and Prediction Markets (Bo Cowgill Pt. 2)

    We continue our conversation with Columbia professor Bo Cowgill. We start with a detour through Roman Jakobson’s six functions of language (plus two bonus functions Seth insists on adding: performative and incantatory). Can LLMs handle the referential? The expressive? The poetic? What about magic? The conversation gets properly technical as we dig into Crawford-Sobel cheap talk models, the collapse of costly signaling, and whether “pay to apply” is the inevitable market response to a world where everyone can produce indistinguishable text. Bo argues we’ll see more referral hiring (your network as the last remaining credible signal), while Andrey is convinced LinkedIn Premium’s limited signals are just the beginning of mechanism design for application markets. We take a detour into Bo’s earlier life running Google’s internal prediction markets (once the largest known corporate prediction market), why companies still don’t use them for decision-making despite strong forecasting performance, and whether AI agents participating in prediction markets will have correlated errors if they all derive from the same foundation models. We then discuss whether AI-generated content will create demand for cryptographic proof of authenticity, whether “proof of humanity” protocols can scale, and whether Bo’s 4-year-old daughter’s exposure to AI-generated squirrel videos constitutes evidence of aggregate information loss. Finally: the superhuman persuasion debate. Andrey clarifies he doesn’t believe in compiler-level brain hacks (sorry, Snow Crash fans), Bo presents survey evidence that 85% of GenAI usage involves content meant for others, and Seth closes with the contrarian hot take that information transmission will actually improve on net. General equilibrium saves us all—assuming a spherical cow. Topics Covered: * Jakobson’s functions of language (all eight of them, apparently) * Signaling theory and the pooling equilibrium problem * Crawford-Sobel cheap talk games and babbling equilibria * “Pay to apply” as incentive-compatible mechanism design * Corporate prediction markets and conflicts of interest * The ABC conjecture and math as a social enterprise * Cryptographic verification and proof of humanity * Why live performance and in-person activities may increase in economic value * The Coasean singularity * Robin Hanson’s “everything is signaling” worldview Papers & References: * Crawford & Sobel (1982), “Strategic Information Transmission” * Cowgill and Zitzewitz (2015) “Corporate Prediction Markets: Evidence from Google, Ford, and Firm X”. * Jakobson, “Linguistics and Poetics” (1960) * Binet, The Seventh Function of Language * Stephenson, Snow Crash Transcript:Andrey: Well, let’s go to speculation mode. Seth: All right. Speculation mode. I have a proposal that I’m gonna ask you guys to indulge me in as we think about how AI will affect communication in the economy. For my book club, I’ve been recently reading some postmodern fiction. In particular, a book called The Seventh Function of Language. The book is a reference to Jakobson’s six famous functions of language. He is a semioticist who is interested in how language functions in society, and he says language functions in six ways.1 I’m gonna add two bonus ones to that, because of course there are seven functions of language, not just six. Maybe this will be a good framework for us to think about how AI will change different functions of language. All right. Are you ready for me? Bo Cowgill: Yes. Seth: Bo’s ready. Okay. Bo Cowgill: Remember all six when you... Seth: No, we’re gonna do ‘em one by one. Okay. The first is the Referential or Informational function. This is just: is the language conveying facts about the world or not? Object level first. No Straussian stuff. Just very literally telling you a thing. When I think about how LLMs will do at this task, we think that LLMs at least have the potential to be more accurate, right? If we’re thinking about cover letters, the LLMs should maybe do a better job at choosing which facts to describe. Clearly there might be an element of choosing which facts to report as being the most relevant. We can think about, maybe that’s in a different function. If we ask about how LLMs change podcasts? Well, presumably an LLM-based podcast, if the LLM was good enough, would get stuff right more often. I’m sure I make errors. Andrey doesn’t make errors. So restricting attention to this object-level, “is the language conveying the facts it needs to convey,” how do you see LLMs changing communication? Bo Cowgill: Do I go first? Seth: Yeah, of course Bo, you’re the guest. Bo Cowgill: Of course. Sorry, I should’ve known. Well, it sounds like you’re optimistic that it’ll improve. Is that right? Seth: I think that if we’re talking about hallucinations, those will be increasingly fixed and be a non-issue for things like CVs and resumes in the next couple of years. And then it becomes the question of: would an LLM be less able to correctly report on commonly agreed-upon facts than a human? I don’t know. The couple-years-out LLM, you gotta figure, is gonna be pretty good at reliably reproducing facts that are agreed upon. Bo Cowgill: Yeah, I see what you mean. So, I’m gonna say “it depends,” but I’ll tell you exactly what I think it depends on. I think in instances where the sender and the receiver are basically playing a zero-sum game, I don’t think that the LLM is gonna help. And arguably, nothing is gonna help. Maybe costly signaling could help, but... Seth: Sender and the receiver are playing a zero-sum game? If I wanna hire someone, that’s a positive-sum game, I thought. Andrey: Two senders are playing a zero-sum game. Seth: Oh, two senders. Yes. Two senders are zero-sum with each other. Okay. Bo Cowgill: Right. This is another domain-specific answer, but I think that it depends on what game the two parties are playing. Are they trying to coordinate on something? Is it a zero-sum game where they have total opposite objectives? If all costly signaling has been destroyed, then I don’t think that the LLM is gonna help overcome that total separation. On the other hand, if there’s some alignment between sender and receiver—even in a cheap talk world—we know from the Crawford and Sobel literature that you can have communication happen even without the cost of a signal. I do think that in those Crawford and Sobel games, you have these multiple equilibria ranging from the babbling equilibrium to the much more precise one. And it seems like, if I’m trying to communicate with Seth costlessly, and all costly signal has been destroyed so we only have cheap talk, the LLM could put us on a more communicative equilibrium. Seth: We could say more if we’re at the level where you trust me. The LLM can tell you more facts than I ever could. Bo Cowgill: Right. Put us into those more fine partitions in the cheap talk literature. At least that’s how I think the potential for it to help would go. Andrey: I wanna jump in a little bit because I’m a little bit worried for our listeners if we have to go through eight... Seth: You’re gonna love these functions, dude. They’re gonna love... this is gonna be the highlight of the episode. Andrey: I guess rather than having a discussion after every single one, I think it’s just good to list them and then we can talk. Seth: Okay. That’ll help Bo at least. I don’t know if the audience needs this; the audience is up to date with all the most lame postmodern literature. So for the sake of Bo, though, I’ll give you the six functions plus two bonus functions. * Informational: Literal truth. * Expressive (or Emotive): Expressing something about the sender. This is what actually seems to break in your paper: I can’t express that I’m a good worker bee if now everybody can easily express they’re good worker bees. * Connotative (or Directive): The rhetorical element. That’s the “I am going to figure out how to flatter you and persuade you,” not necessarily on a factual level. That’s the zero-sum game maybe you were just talking about. * Phatic: This is funny. This is the language used to just maintain communications. So the way I’m thinking about this is if we’re in an automated setting, you know how they have those “dead man’s switches” where it’s like, “If I ever die, my lawyer will send the information to the federal government.” And so you might have a message from your heart being like, “Bo’s alive. Bo’s alive. Bo’s alive.” And then the problem is when the message doesn’t go. * Metalingual (or Metalinguistic): Language to talk about language. You can tell me if you think LLMs have anything to help us with there. * Poetic: Language as beautiful for the sake of language. Maybe LLMs will change how beautiful language is. * Performative: This comes to us from John Searle, who talks about, “I now pronounce you man and wife.” That’s a function of language that is different than conveying information. It’s an act. And maybe LLMs can or can’t do those acts. * Incantatory (Magic): The most important function. Doing magic. You can come back to us about whether or not LLMs are capable of magic. Okay? So there’s eight functions of language for you. LLMs gonna change language? All right. Take any of them, Bo. Andrey: Seth, can I reframe the question? I try to be more grounded in what might be empirically falsifiable. We have these ideas that in certain domains—and we can focus on the jobs one—LLMs are going to be writing a lot of the language that was previously written by humans, and presumably the human that was sending the signal. So how is that going to affect how people find jobs in the future? And how do we think this market is gonna adjust as a result? Do you have any thoughts on that? Bo Cowgill: Yeah. So I guess the reframing is about how the market as a whole will adjust on both sides? Andrey: Yes, exactly. Bo Cowgill: Well, one, we have some survey results about this in the paper. It suggests you would shift towards more costly signals, maybe verifiable things like, “Where did you go to school?” Andrey: No, but that is easy, right? That already exists, more or less. Bo Cowgill: That’s true. Yeah, I mean, you could start using these more and start ignoring cover letters and things like this. One thing somewhat motivated by the discussion of cheap talk a minute ago is that there’d be more referral hiring. This is something that lots of practitioners talk about: we can’t trust the signal anymore, but I can still trust my current employees that worked with this person in the past. It has a theoretical interpretation as well, which is that when all you have is cheap talk, the only communication you can have is maybe between people who are allies in some sense or who share the same objective. This would be why you could learn or communicate through a network-based referral. So I think that’s super interesting and lots of people are already talking about it. It would be cool to try to have an experiment to measure that. Andrey: What about work trials? Do you think that’s gonna become more common? Anecdotally, I see some of the AI labs doing some of this. If you can’t trust the signals, maybe just give a trial. Bo Cowgill: Most definitely. The cheap talk idea is not the only one. You could have a variety of contractual solutions to this problem. There was a recent Management Science paper about this: actually charging people to apply, thinking that they have a private signal of whether they can actually do this or not. If they’re gonna get found out, they would be less likely to be willing to part with this money. It’s less of a free lottery ticket just to apply if you’re charging. Andrey: For what it’s worth, I strongly think that we’re gonna move into the “pay to apply” world. Bo Cowgill: Oh. That’s interesting. I mean, I think that “pay to apply” is super underrated. Having said that, people have been willing to ignore more obvious good things for longer, so I don’t think it’s as inevitable as it sounds like you do. Andrey: Well, I think it’s the natural solution to the extent that what the cover letter is doing is signaling your expected match quality. And you have private information about that. I think both Indeed and LinkedIn have now premium plans with costly signals. So it’s not exactly a “pay for apply,” but you pay for a subscription that gives you limited signals, which is essentially the same exact thing. Bo Cowgill: Makes sense. Andrey: Yeah. So I think, whether that solves these issues, I’m not sure. It needs to be objective to really do the deed. Seth: It solves the express... well, which is fine if we think willingness to spend on this thing is more correlated with ability. It’s back to the same signaling model. Bo Cowgill: I mean this solution also relies on the applicant themselves to know whether they’re a good match in some sense, and some people are just deluded. Andrey: Yeah. Well also the platform, like in advertising, could be a full auction-type thing. Bo Cowgill: It could be a scoring auction that has its own objectives and gives people discounts. What Seth says raises a common objection for “pay to apply,” which is: “What about the people who can’t afford it?” And I think a high number of the people who have said that in my life work for an institution that charges people to apply for admission. So you could use some of the same things. You could have fee waivers, and the fee waivers might require a little bit of effort to get. Another idea I’ve heard is that you could put the money in escrow and then possibly give it back if it doesn’t work out. Or you could actually give it back if it does work out. So yeah, people have different takes on this. But there are various ways to harness “pay to apply” and then deal with the negative aspects of it in other ways. Seth: So what it seems to solve is this very narrow element of what we call the expressive function of language. So one thing I’m trying to express with my cover letter is, “I’m a good worker bee. I do the things. I have resources. I will bring my resources to your firm.” But we also want the letters to do lots of different things, like be beautiful and tell me a little bit about yourself. Have heterogeneous match quality elements, right? So it seems like this money only helps with one vertical dimension of quality. Andrey: Actually, when you’re sending that costly signal and you cater your cover letter to that employer, that is about match quality, right? The costly signal, the “pay to apply,” gives you the incentive to reveal that information in your cover letter. Seth: Right. It’s a “both,” right? It’s not a payment or a cover letter. It’s a both. Good point. Andrey: We’ve spent a lot of time thinking about the signaling, this information apocalypse—or epistemic apocalypse—that Bo has been calling it. I think one solution to various epistemic issues has been prediction markets. I wanted to ask Bo about his earlier life experiences with those because it’s a very hot topic now, with a lot of prediction markets gaining traction. Bo Cowgill: Yeah, definitely. We should get back to the GenAI information apocalypse as well and ask: do we think it’s gonna happen? But yeah, it is true that some of my first papers out of grad school were about prediction markets. In my former life I worked at Google, where at one time people had 20% projects. I started an internal prediction market. At the time it was the largest internal prediction market known to exist. There were around 400 or so different markets where we offered employees the ability to anonymously bet on different corporate performance measures. The two most common ones were: What will the demand for our products be? How many new advertisers, Gmail signups, or 7-day-active-users will we get? And then also, project launch deadlines. Basically, would it be on time or early or late? Not very often early, but sometimes on time. I had a paper about this in the Review of Economic Studies. It showed, like in many other cases, the markets perform really well, both in absolute terms and relative to other forecasters at Google. We eventually got other companies’ data to try to do similar things. I think one interesting thing is that prediction markets have gotten really big externally for things like elections, but you still don’t see a lot of companies seemingly use it to guide decision-making. Andrey: I want to hear your best explanation for why you think the internal prediction markets haven’t taken off. Bo Cowgill: There are lots of reasons. Our prediction market at Google was really built around having a proof of concept that we can then use to launch our own Kalshi, or our own Polymarket. I think it was a little bit too soon for that. In our case, we weren’t really trying to make it as good of a decision-making tool as possible. Like we wanted to go public and have the election markets be hosted by Google. There were some regulatory barriers I think that Kalshi eventually was able to get past. The part of the problem I’ve been working on recently is that the prediction market paradigm inside of a company assumes that all the workers have some information about what plan of action would be best, but they otherwise have no preference about what you do with this information. Like, “Should we launch a new product?” The paradigm assumes that they all know something about whether it’s gonna be a successful product, but they sort of don’t care whether you do it or not. Obviously they care. Some of the people with the best information about this new product could have a very strong preference. I heard about this situation in Asia, where the person with the best information on the new product would also probably have their career sabotaged if they launched a competing product. So that could interfere with the incentive compatibility of the market. Seth: The incentives aren’t high-powered enough. Bo Cowgill: That’s true. And it’s hard to think about how the incentives would ever be high-powered enough to offset this unless the company proactively designs the market differently to deal with these conflicts of interest. Seth: I wanna follow up with Andrey’s question. This seems like a really good way to accumulate information, and maybe AI will help us do these better. Is there really an epistemic apocalypse or will prediction markets plus AI predictors save us all? Bo Cowgill: It’s possible that prediction markets will help in this way just by making the information... it’s essentially a form of a contract. When we talked about various contracts including “pay for apply” and maybe doing a trial period at a job, all these are contractual ways of making it costly to lie. And that could possibly discipline this sort of thing. One reason I think that the epistemic apocalypse isn’t going to fully happen is that for cases where there’s an information bottleneck, I think the economy is gonna find a way to get the information it needs so that you can hire someone for a valuable role. There’s lots of reason that buyers want to coordinate on information. Seth: It’s positive-sum. Bo Cowgill: Right. So that would be one reason. I think in a lot of cases, the informational bottlenecks will be closed even if you don’t have as good of positive, costly signaling as you used to. But, number one, we could just have to tolerate a lot of mistakes. And that already happens in the hiring setting. So it’s possible that we could have to tolerate even more hiring mistakes because now the signal is actually worse. Andrey: Bo, why are we hiring anyone? I thought all the jobs will be non-human jobs. Maybe it’ll be a Coasean singularity where we’re all one-person firms. Seth: Exactly. What is the Coasean singularity? It’s the zero bargaining frictions, and one of the bargaining frictions is information asymmetry. Bo, would it be fair to say then that you’re kind of more optimistic about convergence in sort of public, big-question information—the kinds of stuff that prediction markets are good at at scale—but you’re more pessimistic about Seth trying to send a message to stranger number three? Bo Cowgill: That is a good distinction. The prediction markets are generally better at forecasts when there’s lots of information that’s dispersed around lots of different actors, and the market kind of aggregates this up. Seth: And theoretically, a high-quality LLM that has a budget to do training will be a super-forecaster and will be conveying and aggregating this information, right? Bo Cowgill: That’s true. But when we think about agents participating in prediction markets, a bunch of the theory assumes that everyone receives some independent signal or a signal with some independent noise. Insofar as everyone’s agent derives from the same three or four big labs, then they might not actually be all that independent. And that would be a reason to not think that the markets will save us. Seth: Only if they’re not independent ‘cause they’re wrong. Andrey: Well, even if the foundation models are the same, they may be going out to acquire different pieces of information. Bo Cowgill: That’s true. You also have the temperature in the models that adds some level of randomness to the responses. Andrey: No, but I literally mean, like, you have these sci-fi novels where you tell the AI to go out and find information, and that’s a costly acquisition process for the LLM. Maybe it has to interview some humans or pay for some data. I think this viewpoint that you’re just taking an identical prompt from some off-the-shelf chatbot and asking, “Hey, what’s the prediction here?” is really not the right way to think about what agent-assisted functions would be doing. Think about hedge funds: they’re all using various machine learning to trade, but it’s not like they’re all doing the same thing, even though I assume that many of the algorithms they’re using are in some sense the same. Bo Cowgill: I see. So you’re basically more optimistic about prediction markets and AI being a combined thing that would help overcome the apocalypse. Andrey: Yes. Bo Cowgill: I don’t know. Well, one way in which I guess I’m a little bit more pessimistic is that, in the world that we’re just coming from, I think there is just more reliable, ambient information that you would get just from being in the environment that you could trust. I think in the old world, you could just trust a photograph. Now it’s true that there were a lot of staged photographs even back in the day... Andrey: Have you seen friends of comrade Stalin? Bo Cowgill: Totally. Seth: Losing his friends very quickly. Bo Cowgill: But it does still feel like... maybe not stuff that you would see in the media where there were parties that would have some incentive to doctor photos. But if your friend said that they met Tom Brady, they could bust out a picture and show you Tom Brady and you could have more faith in that. Or other smaller-stakes, ambient things that might be a little bit more trustworthy now that could accumulate. Seth: That’s the question. Does all of the little small stuff add up to an apocalypse if we’re all still agreeing at the big stuff from the top down? Andrey: What about reputation? He’s not gonna show you fake photos, come on. Bo Cowgill: This is true. Well, I mean, if we’re not gonna interact again, then who knows? Seth: Zero-shot. Bo Cowgill: You’re a sock puppet, you know? Seth: S**t. Stay contrary. Andrey: That’s the twist, is that this was an AI podcast the entire time. I am a robot. Bo Cowgill: That’s funny. Andrey: I mean, reputation is not a bilateral thing only, right? You have reputational signals that you can accumulate, and certainly for media outlets, they could form reputations. That’s kind of the point of media outlets. Seth: In the future, everyone’s their own media outlet. Everyone’s got their own Substack. Everyone could have an LLM pointed at them saying, “Hey, keep track if Seth and Andrey ever lie or do anything bad on their podcast.” So there’s a sense in which it’s the classic AI attack-defense thing. It makes it easier to make fakes, but it also makes it easier to monitor fakes. Bo Cowgill: I see what you’re saying. So yeah, this is why I say I think in situations where it’s high-stakes enough to form a contract and do monitoring, that we don’t necessarily get these huge amounts of information loss. But you would also get a lot of information about the world. Actually, here’s a specific example. I have a 4-year-old daughter. Seth: Cute. Can confirm. Bo Cowgill: Thank you. So there was a GenAI photo of a squirrel who ate a piece of candy or something like that. It was GenAI, but it was high-quality, and the squirrel has expressive body language saying how good it is. I would know that that’s not a real squirrel, that they were trying to create a viral video. But she hasn’t really experienced real squirrels yet. So I actually think that she probably thought this was something that could actually happen. Now we’re gonna have a whole generation of people who have probably seen more fake cat videos than actual cat videos. And I just think that will accumulate, not necessarily to an apocalypse, but to some level of aggregate information loss. Andrey: It’s interesting ‘cause I would think that it’s not the kids who are gonna be affected, but it’s the adults. Think about who are the primary spreaders of mass emails with completely unverified information. Seth: Even better. And at the end it says, “Please share. Share with everyone.” Bo Cowgill: Right. I mean, one answer to that is: yes, and/or why not both? Seth: It’s attack and defense again on the squirrel thing. When I grew up, I had no idea that trees actually looked like these lollipop palm trees that they have here in Southern California. When I was reading Dr. Seuss, I thought those were made-up BS. And then I had to actually go out here to find out. Bo Cowgill: Stuff you believe. I’m just kidding. Seth: Fair enough. I guess what I’m trying to say is that, as a child, I was exposed to a lot of media with talking animals and eventually I figured it out. And who knows, maybe your daughter will have access to LLMs and instead of having to wait until she’s 20 to find out, she can ask, “Hey, do squirrels actually thank you and be emotive in a human-like way?” Bo Cowgill: Yeah. What do you guys think about the idea that the rise of fake AI will actually create demand for crypto and for things being cryptographically signed as proof of their authenticity? Andrey: Yes. I think the answer is yes. I’m very interested in ideas such as “proof of humanity.” I think on a practical level, the concepts involved in crypto are just too abstract for most people. So the success will come from essentially someone putting a very nice user interface on it, so people aren’t actually thinking about the crypto part. Seth: The blocks. I mean, I definitely see a huge role for just this idea of timestamping: this thing went on the blockchain at this date, and if we can’t agree on anything else, at least we can agree on the original photo of Stalin with his four friends. Andrey: I guess the big question for all of these systems is they’re not that useful until lots of people are on them. It’s a chicken-and-egg problem. Seth: Really? You don’t think if you got the three big news services on it, wouldn’t that be standard-setting? Andrey: Yeah. But I view that as a different and a harder ask than the timestamping. I know news organizations can do that themselves. I assume they’re actually already doing it to some extent. And normal human beings would never check. But if there was an investigation, someone could in principle check. Seth: Well, it comes up all the time in terms of documenting war events. It’s like, “Oh, you said this was a bombing from yesterday, but this is photos from 10 years ago,” right? Andrey: Yes. And if we had some enlightened CEOs of social media companies, they might facilitate that. It’s not clear that their business interests are actually well-aligned with that. But I think with the proof-of-humanity type stuff, you’re gonna wanna use it when everyone else is using it. Let’s say Meta wanted to verify that everyone on its platform was a unique human being. If everyone has access to proof-of-humanity technology, then that’s very feasible to do. But if only a tiny share of the population is using it, then it’s not a very effective mechanism. Seth: What do we think? One thing we haven’t talked a lot about today, and I wanna give us a chance to at least address it in passing, is that it seems like the effect of LLMs on writing has a lot to do with how much LLMs will be doing reading. We’ve already talked in passing about how LLMs prefer the writing of other LLMs; it seems to show up in your study. It makes perfect sense. If you prompt an LLM saying, “Write the best thing,” it should be pretty good at it, right? Because it can just evaluate it itself and iterate. To what extent is that a problem or a solution? The positive vision is the LLMs are going to be able to convey extremely detailed information and then on the other end, parse extremely detailed information in an efficient way. That’s Andrey’s Coasean singularity. But you might imagine that because now only LLMs are reading, people put less effort into submitting, and that’s the epistemic apocalypse: “Why even try if they prefer a bullshitted GenAI version?” Bo Cowgill: Yeah, totally. Or I guess in a lot of my own prompts, sometimes I know I don’t have to describe what I’m talking about in very fine detail ‘cause it knows the context of the question and can do it. It does seem like it’s potentially a problem to me, mainly because we should still care about the human-to-AI communication pipeline, and that pipeline might actually need to go in both directions. And so if the LLMs are basically really good at talking to each other, but lose the ability to talk to normal people, then that seems potentially bad for us. Seth: But there’s one thing LLMs are great at, it’s translating. That’s something I’m optimistic about. Bo Cowgill: That’s true. Arguably it needs to be trained and/or prompted or rewarded somehow to do that. And maybe the business models of the companies will keep those incentives aligned to actually do this. Andrey: Well, the models are gonna be scheming against each other, so they wouldn’t wanna tell us what they’re really conspiring to do. One final topic I wanted to get to was superhuman persuasion. Bo Cowgill: So, Andrey I think had this provocative statement at some point that he doesn’t think of persuasion as being a big part of the effects of GenAI. I was surprised by that. I think maybe Andrey is representing a common view out there. There’s a lot more discussion of the productivity effects of GenAI maybe than the persuasion effects. And I don’t know if at some level, without persuasion... persuasion ultimately is some part of productivity if we’re measuring productivity in some sort of price-weighted way. Because two companies could have the same exact technology, one with a bad sales force, and it might show up as one of them being a zero-productivity company. Seth: But how much is that zero-sum? I guess the idea there would be is that sure, if Coke spends more on advertising, we’ll sell more Coke and less Pepsi. But is that positive-sum GDP or have we just moved around the deck chairs? Bo Cowgill: In order to get the positive sum, I think you would still need to persuade someone that this is worth buying. Seth: No, ‘cause it could be negative. You can make Pepsi shitty. You can be like, “Don’t drink Pepsi. It’s s**t.” But it’s negative-sum. It’s negative GDP. Andrey: I just wanna state precisely what I think my claim was, which is: I don’t believe in substantially superhuman persuasion. Which isn’t to say that in jobs that require persuasion, AI can’t be used. It’s just more that I don’t think there’s this super level of like, you talk to the AI and it convinces you to go jump off a bridge. Seth: Right. So in Snow Crash, it’s posited that there’s a compiler-level language for the human brain that if you can speak in that, you can just control people. Similarly, in The Seventh Function of Language, there’s this idea of a function of language that is just so powerful, you can declare something and it happens. Andrey: That’s the magic. Bo Cowgill: Right. Productivity is not that many steps away from persuasion about willingness to pay or willingness to supply. And it does seem like the persuasion aspects of GenAI should be talked about more. I wanted to bring up this ABC conjecture because I think that there’s a belief that in areas very cut and dry, like math, there is no real room for persuasion because something is just either true or not. This story about the ABC conjecture illustrates this. There’s a Japanese professor of math who studied at Princeton and has all of the credentials to have solved a major conjecture in number theory. He puts forth this 500-page attempted solution of the ABC conjecture. A credible person claiming this is the proof. Unfortunately, his proof is so poorly written, so technical and so badly explained, that no one else has been able to follow the proof. Seth: Or even put it in a formal proof checker. If they had put it in a formal proof checker, everyone would’ve been satisfied. Bo Cowgill: Yes. I think that this story is interesting because it highlights that, even in something like math, it’s ultimately a social enterprise where you have to try to convince other human beings that you have come up with something that has some value. Seth: Wait, people aren’t born with values? Without a marketing company, I would still wanna drink water. Andrey: That’s actually not true. I mean, isn’t there the whole movement to drink more water? Bo Cowgill: It’s true that you may have been persuaded just by your parents or your rabbi or whoever. But let’s get to a more narrow objection. As part of the motivation for this “cheaper talk” paper, we ran some surveys to try to get a sense of what people do with AI. One of the first questions was, “Think of the recent time that you’ve used GenAI. Were you developing something that you were eventually going to share with other people?” Something like 85-90% were using this on something that I would share directly with other people. Seth: Really? I’m at like 95% of my usage is just looking stuff up for me. Bo Cowgill: But were you looking it up and ultimately going to share this as part of a paper or a podcast conversation? Seth: I mean, only insofar as the Quinean epistemic web of everything in the universe is connected to everything else. So yeah, if I learn about tree care, it could help me write an economics paper. Andrey: Everything is signaling according to Robin Hanson, right? Bo Cowgill: Sure. I think it’s fair that if this was not your intent, even two or three steps away, then you shouldn’t say yes in the survey. But anyway, a big majority of people say yes. Then the next question, for the people who were using it for something that would be shared: “Were you using the GenAI to try to improve the audience’s impression of you?” So come up with your prior. Seth: Hundred percent. Wait, sorry. So 15% of people use GenAI to make other people feel worse about them? Bo Cowgill: Well, I assume these people would say that they weren’t trying to make it feel worse. They were just not trying to sort of propaganda the person. Andrey: And to be clear, these are Prolific participants, so they’re trying to just make sure that their Prolific researchers don’t kick them out of their sample. Bo Cowgill: Maybe. But most people who I tell these results to are like, “Well, yes, of course. I use GenAI a ton of time to help with writing, to rewrite emails, to explain something in a way that sounds a little bit nicer or smarter.” And it does seem like a very dominant use of GenAI. If this is the case, then the fact that it’s making it easier to impress people all at once is a super interesting part of the effects. And, I know Andrey has offered his caveat about what he actually meant, but I think that would put this persuasion aspect as more of one of the central things. Andrey: I agree that what you’re saying is interesting. It’s more the claim I was talking about where people—mostly in the Bay Area—think that super AI is gonna take over the world. Bo Cowgill: That we’ll just turn people into puppets. Andrey: Yeah, exactly. Bo Cowgill: No, fine. I won’t take any more cheap shots at you. Seth: We can bring up the Anthropic AI index. Andrey: Well, I was gonna do the ChatGPT usage paper, but you do the AI one first. Bo Cowgill: Of course, one of the major things that the ChatGPT usage paper says is writing. Seth: Which interestingly, this showed up in GDPVal, is that ChatGPT seems like a little bit better at writing, and Claude seems a little bit better at coding, and it seems to show up in usage also. Bo Cowgill: But they should break down writing. The question that this raises is: who is the writing for? And why aren’t you writing yourself? And are you possibly trying to signal something about yourself by having this clear writing? Andrey: But I guess I truly do think, like Robin Hanson, that a vast majority of what humans do, period, is signaling to others. Seth: Is that your claim, Bo? Or is your claim that AI is gonna make it worse? Bo Cowgill: I’m not as Robin Hanson on “everything is signaling,” but I would just claim that this should be a more front-and-center thing that people think about with regards to the effects of the tech. Seth: Listen. If you wanna be an economist, you gotta tell us what to study less. You can’t tell us to study everything more. What are we gonna do less of? Bo Cowgill: I mean, I guess the easy thing would be to say human-AI replacement just because there’s so many studies on that right now. Andrey: The productivity effects of this one deployment of a chatbot in this one company. Bo Cowgill: Oh, yes. I can totally get on board with complaining about that. Seth: Bo, help me get beyond it. This is what you need to do for me. People are gonna do what you said and write that paper on signal quality in one population. What’s the meta-paper? How can we get beyond that into a more comprehensive view of what’s going on? What’s your vision for research in this direction? Bo Cowgill: Part of this goes back to the question about just what are general equilibrium effects overall? If people all become more persuasive all at once, then this totally destroys the quality of information. Another question is, how much do the AI labs themselves actually have an incentive to build positive-covariance technology or negative-covariance technology? If part of the value of a camera is that you could take pictures and then show people and be like, “Look, this is real, this is a costly signal,” then you might actually want to keep the covariance of your technology somewhat high because this will be one use case that people would actually want. Andrey: This is a very interesting, broader question. I was at a dinner with a few AI folks and we were talking about the responsibility of the AI labs to do academic research. We don’t expect the company that creates a tool to create the solutions to all of the unintended consequences of that tool. That to me is a very strange expectation. It seems impossible, and we don’t expect that from any other company. Bo Cowgill: Definitely. But just to put a finer point of what I’m talking about: suppose that the covariance is so negative that you’re just getting a lot of signal jamming, to the point where now there’s just less demand for writing in general. Even if there’s still some demand, well then that less demand for writing could feed back into the underlying demand for the LLM product itself because this was supposed to help you write better, but now no one trusts the writing. And there could be something financially self-defeating about having this technology that is negative. Seth: It would be general equilibrium self-defeating. Individually, we’d all wanna defect and use it. Andrey: Even if one company tried to [fix it], the solution by the market is: if you really care that a human wrote this, the market will create a technology where we verify that the human is literally typing the thing as it’s happening. Personally, I think that live performance and in-person activities in general are gonna rise up in economic value because they’re naturally... I do think humans care about interacting with other humans. We care that other humans are creating speech, art, and so on. Seth: So those are the expressive functions of language. That’s the phatic function of, “Hey, look, I’m still alive, Grandma.” That’s the poetic function. And LLMs can’t... we don’t think it can do this performative function. It’ll be interesting to see whether AIs get enough rights to be able to make binding contracts on our behalf. Andrey: There’s gonna be a ubiquitous monitoring technology, and every time I declare bankruptcy, it will enact. Seth: It’ll immediately get locked in. If I can just share my wrapping-up thoughts. I come away a little, not as scared as Bo about this epistemic apocalypse. He has scared me. But I come away thinking that it’s fundamentally kind of partial equilibrium to say, “Hey, look, we used to send signals this way. There’s a new technology that comes along. Now that signal isn’t coming through as well.” To me, that doesn’t mean communication is impossible. Now I just get to: “Okay, what’s the next evolution of the communication? Are we gonna have LLM readers? Are we gonna have verified human communication?” There seem to be solutions. Bo Cowgill: It’s probably a little bit of an exaggeration of what I was saying to characterize it that way. But I did say that Andrey said that persuasion wasn’t important, so maybe I’m owed some exaggeration back. Seth: Fair enough. If you put a gun to my head, I would say that information transmission will get better on net because of AI. Andrey: What a hot take to end this. Seth: That’s my hot take. Andrey: You don’t hear anyone saying that. That is fun. Seth: Who would’ve thought that the greatest information technology product of all time might actually give us more useful information? Andrey: No, no, no. You’re only allowed to be pessimistic, Seth. That’s the rules of the game. Bo Cowgill: So Seth, do you think this is mainly because people will be able to substitute away from other things? Seth: It’s partially that. I think what you’re identifying in this paper is definitely important. But it does seem like this is transitional and that more fundamentally, LLMs help us say more and help us hear more. And so I think once the institutional details are worked out—and of course that’s a lot of assuming a spherical cow—there will be better information in the long run. Andrey: There are even entrepreneurial activities that one could undertake to try to amend some of the concerns raised by this paper. We oftentimes take this very observer perspective on the world, but certainly we could also, if we think that a solution is useful, do something about that. Seth: Right. We will sell human verification. We will verify you are a human. If you pay us a thousand dollars, we will give you a one-minute spot on this podcast where we will confirm you are human. So Bo, I guess we’re just a little bit different on this. What do you think? Bo Cowgill: Well, I do agree that the paper was proof of concept and partial equilibrium, and what happens in the general equilibrium... we’ll just have to figure out in future episodes of Justified Posteriors. Andrey: Yeah. Well, thanks so much, Bo, for being a great guest. Seth: And Bo, both you, everybody else, keep your posteriors justified. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

  • Nov 18, 2025 · 53 min

    Does AI Cheapen Talk? (Bo Cowgill Pt. 1)

    In this episode, we brought on our friend Bo Cowgill, to dissect his forthcoming Management Science paper, Does AI Cheapen Talk? The core question is one economists have been circling since Spence drew a line on the blackboard: What happens when a technology makes costly signals cheap? If GenAI allows anyone to produce polished pitches, résumés, and cover letters, what happens to screening, hiring, and the entire communication equilibrium? Bo’s answer: it depends. Under some conditions, GenAI induces an epistemic apocalypse, flattening signals and confusing recruiters. In others, it reveals skill even more sharply, giving high-types superpowers. The episode walks through the theory, the experiment, and implications. Transcript:Seth: Welcome to the Justified Posteriors Podcast, the podcast that updates its priors about the economics of AI and technology. I’m Seth Benzell, certifying my humanity with takes so implausible that no softmax could ever select them at Chapman University in sunny Southern California. Andrey: And I am Andrey Fradkin, collecting my friends in all sorts of digital media formats, coming to you from San Francisco, California. Today we’re very excited to have Bo Cowgill with us. Bo is a friend of the show and a listener of the show, so it’s a real treat to have him. He is an assistant professor at Columbia Business School and has done really important research on hiring, on prediction markets, and now on AI and the intersection of those topics. And he’s also won some very cool prizes. I’ll mention that he was on the list of the best 40 business school professors. So he is one of those professors that’s really captivating for his students. So yeah. Welcome, Bo. Bo Cowgill: Thank you so much. It’s awesome to be here. Thanks so much for having me on the podcast. Seth: What do you value about the podcast? That’s something I’ve been trying to figure out because I just do the podcast for me. I’m just having a lot of fun here with Andrey. Anything I can do to get this guy’s attention to talk about interesting stuff for 10 minutes? Why do you like the podcast? What can we do to make this an even better podcast for assistant professors at Columbia? Bo Cowgill: Well, I don’t wanna speak for all assistant professors at Columbia, but one thing it does well is aggregate papers about AI that are coming out from around the ecosystem and random places. I think it’s hard for anybody to catch all of these, so you guys do a great job. I did learn about new papers from the podcast sometimes. Another cool thing I think is there is some continuity across podcast episodes about themes and arbitrage between different topics and across even different disciplines and domains. So I think this is another thing you don’t get necessarily just kind of thumbing around papers yourself. Seth: So flattering. So now I can ask you a follow-up question, which is: obviously you’re enjoying our communication to you. A podcast is kind of a one-dimensional communication. Now we’ve got the interview going, we’ve got this back and forth. How would you think about the experience of the podcast changing if a really, really, really good AI that had read all of my papers and all of Andrey’s papers went and did the same podcast, same topics? How would that experience change for you? Would it have as much informative content? Would it have as much experiential value? How do you think about that? Bo Cowgill: Well, first of all, I do enjoy y’all’s banter back and forth. I don’t know how well an AI would do that. Maybe it would do a perfectly good job with that. I do enjoy the fact that—this is personal to me—but we know a lot of the same people. And in addition to other guests and other paper references, I like to follow some of the inside jokes and whatnot. I don’t know if that’s all that big of a deal for the average person. But I have listened to at least the latest version of NotebookLM and its ability to do a quote-unquote “deep dive podcast” on anything. And at least recently I’ve been pleased with those. I don’t know if you’ve ever tried putting in like a bad paper in theirs, and then it will of course just say, “Oh, this is the greatest paper. It’s so interesting.” Seth: Right. Bo Cowgill: You can. Seth: So that’s a little bit different, maybe slightly different than our approach. Bo Cowgill: Well, yeah, for sure. Although you can also tell NotebookLM to try to find problems and be a little bit more critical. And that I think works well too. But yeah, I don’t think we should try to replace you guys with robots just yet. Seth: We’re very highly compensated though. The opportunity cost of Andrey’s time, he could be climbing a mountain right now. Andrey, you take it up. Why are we doing this ourselves? Why isn’t an LLM doing this communication for us? Andrey: Well, mostly it’s because we have fun doing it, and so if the LLM was doing it, then we wouldn’t be having the fun. Seth: There you go. Well put. Experiential value of the act itself. Now, Bo, I did not bring up this question randomly. The reason I raised this question of how does AI modify communication... yeah, I used a softmax process, so it was not random. The reason I’m asking this question about how AI changes communication is because you have some recently accepted, forthcoming work at Management Science trying to bring some theory and empirics to the question of how LLMs change human communication, but now in the context of resumes and job search and job pitches. Do you want to briefly introduce the paper “Does AI Cheapen Talk?” and tell us about your co-authors? Bo Cowgill: Yeah, most definitely. So the paper is called “Does AI Cheapen Talk?”. It is with Natalia Berg-Wright, also at Columbia Business School, and with Pablo Hernandez Lagos, who is a professor at Yeshiva University. And what we’re looking at in this paper is the way people screen job candidates or screen entrepreneurs or, more abstractly, how they kind of screen generally. You could apply our model, I think, to lots of different things. But the core idea behind it kind of goes back to these models from Spence in the 1970s saying that costly signals are more valuable to try to separate types. Seth: Right. If I wanna become a full member of the tribe, I have to go kill a lion. Why is it important for me to kill a lion? It’s not important. The important part is I do a hard thing. Bo Cowgill: Exactly. Yeah. So maybe part of the key to this Spence idea that appears in our paper too is that it’s not just that the signal has to be costly, it has to be kind of differentially costly for different types of people. So maybe in your tribe, killing a lion is easy for tough guys like you, but for wimpier people or something, it’s prohibitively high. And so it’s like a test of your underlying cost parameter for killing lions or for being tough in general. So they go and do this. And I guess what you’re alluding to, which appears in a lot of cases, is the actual value of killing the lion is kind of irrelevant. It was just a test. And maybe one of the more potentially depressing implications of that is the idea that what we send our students to do in four-year degrees or even degrees like ours is really just as valuable as killing a lion, which is to say, you’re mainly revealing something about your own costs and your own type and your own skills, and the actual work doesn’t generate all that much value. Seth: Is education training or screening? Bo Cowgill: Right, right, right. Yes. I do think a good amount of it these days is probably screening, and maybe that’s especially true at the MBA level. Andrey: I would just say that, given the rate of hiring for MBAs, I’m not sure that the screening is really happening either. Maybe the screening is happening to get in. Bo Cowgill: What the screening function is now is like, can you get in as the ultimate thing? Seth: Right. And I think as you already suggest, the way this works can flip if there’s a change in opportunity costs, right? So maybe in the past, “Oh, I’m the high type. I go to college.” In the present, “I’m the high type. I’m gonna skip college, I’m gonna be an entrepreneur,” and now going to college is a low signal. Bo Cowgill: Yes. Exactly. So that’s kind of what’s going on in our model too. How are we applying this to job screening and AI? Well, you apply for a job, you have a resume, possibly a cover letter or, if you don’t have an old-fashioned cover letter, you probably have a pitch to a recruiter or to your friend who works at the company. And there are kind of elements of costly signaling in those pitches. So some people could have really smart-sounding pitches that use the right jargon and are kind of up to speed with regards to the latest developments in the industry or in the underlying technology or whatever. And those could actually be really useful signals because the only sort of person who would be up to speed is the one who finds it easy to follow all this information. Seth: Can I pause you for a second? Back before LLMs, when I was in high school, they helped me make a CV or a resume. It’s not like there was ever any monitoring that people had to write their own cover letters. Bo Cowgill: That’s really true. No, some people have said about our paper that this is a more general model of signal dilution, which was happening before AI and the internet and everything. And so one example of this might be SAT tutoring or other forms of help for high school students, like writing your resume for you. Where if something comes along—and this is where GenAI is gonna come in—but if anything comes along that makes it cheaper to produce signals that were once more expensive, at least for some groups, then that changes the informational content of the signal. Seth: If the tribe gets guns, it’s too easy to kill a lion. Bo Cowgill: Yeah. Then it just is too easy to kill the lions. But similar things I think have happened in the post-COVID era around the SATs. Maybe it’s become too easy, or so the theory goes, to get one, where it doesn’t really separate out who is actually a smart person. Maybe it’s getting diluted with who can afford these prep classes and things like that. But I don’t wanna stray too far from GenAI just yet. You know, I think people have seen a lot about this, either on social media or in the mainstream, is like, the signal in a job application seems like it may have gone down because you used to be able to tell based on these pitches who is qualified or not. And even without lying, you could write a much better pitch that would make you sound really more knowledgeable, even without misrepresenting what your underlying experience is. And so it’s really, I think, not just job applications. That is of course the setting that we study, that and entrepreneurship. But I think there are similar things about how grading at schools has gone bad. You used to be able to quickly tell from an assignment who knew the material and who did not. But now ChatGPT is gonna really interfere with that. Anyway, so with this as background, we then try to study theoretically and empirically what’s going on with the use of ChatGPT in these sort of costly signaling settings. Andrey: Yeah. And so how do you go about doing this? Because it does seem like it’ll be pretty hard to study this in the wild. I know of a few papers from some of our friends that have done this. How did you approach this? Bo Cowgill: So the first thing we wanted to do was kind of motivate the question a little bit more theoretically. So probably at least the first half or so of the paper, we create this model that has what I hope is a tractable punchline, which is that it’s actually not inevitable that GenAI would create this epistemic... Seth: Wait, a tractable punchline? Wasn’t the punchline that anything goes? What’s the punchline? Bo Cowgill: Well, I am glad that we brought up the “anything goes” theory models, which is another kind of theme of your podcast and critique of previous papers. So it is true that our model basically says that depending on a particular parameter, you could get either an epistemic apocalypse or a situation where the use of GenAI actually improves the accuracy of screening. And it’s like, you get better information, you actually want your job candidates. You want to say, “Please use GenAI. We actually will know better. Don’t send your pitch in without using GenAI first.” So it’s true, anything goes. And my defense of that is we really focus the reader on this particular parameter that you could measure empirically. Seth: Are there other parameters that theoretically could affect this, though? Bo Cowgill: Not that we’re talking about in this paper. No. Seth: Not in this paper. All right. Bo Cowgill: If you have some in mind, I’m curious. Seth: Well, let’s come back. So I have some thoughts at the end about interpreting the results, so we’ll come back to that. You can just keep on walking us through what you did. Andrey: I guess I wanted to say there’s an approach in economics, a sufficient statistics approach, right? Where you write down a model where there is a particular parameter that, depending on how big it is or what sign it is, that tells you something about what is the right policy or what is the mechanism that quote-unquote “dominates” a particular setting. And so I view what you guys were doing very much in that vein. Seth: Right. A ceteris paribus sort of analysis. Yeah. Bo Cowgill: That’s true. So what are we focusing on? What is the key linchpin of this model? It’s a covariance term across the population. So let me try to break this down. The two terms in the covariance are, first of all, how much human capital do you have? Or are you like a talented person who knows a lot about what you’re doing, you have a lot of expertise or not? And we’re sort of assuming that the employers are trying to screen for that. Why are they screening for it? Well, in an actual job, you could be in a situation where you don’t have to use GenAI, or you can’t use it and you have to just use whatever knowledge is between your ears. So this one term is your kind of level of talent for the job without AI assistance. And then the other term is how much of a boost does your cover letter get from using ChatGPT to sex it up and to make you sound like you know all the smartest, most contemporaneous jargon? So these two things could be positively... it could have positive covariance, they could have negative covariance, they could have basically no covariance. But the intuition is, if you have a positive covariance, then the most talented people are getting the largest bump from using GenAI. And the negative covariance would be if the really talented people don’t really get that much of a cover letter improvement, maybe because it’s already so good that there’s nowhere else to go, and that most of the benefit comes from improving the low types’ quality of their cover letter. So this is the linchpin parameter in the model, and what we try to take to data after this. But just to finish up what’s going on in the theory: well, you get totally different screening results depending on what that parameter is. In the case I think that people are most expecting, you have this negative covariance where most of the benefit comes from making low types and helping them masquerade as high types. And in this negative covariance world, there’s not really that much benefit to high types for using GenAI ‘cause their cover letter or their application or whatever, it’s just already so good. So insofar as this is happening, we want to quantify that empirically. But there’s also this possibility that GenAI puts the high types... it gives them superpowers and they can do even more amazing stuff. Seth: Right. Can I jump in here? I don’t think you have to interpret it as superpowers, right? If we’re thinking about communication generally, you might imagine that high types have the higher opportunity costs of their time, right? And so there’s some sense in which automating an hour of high-type time is like more money than automating an hour of low-type time. I guess to really understand how this plays out, I’d have to think about how many discrete versions of this is the high type sending out to prospective employers, right? Andrey: And I guess maybe I’ll add on to that. It depends on what we’re screening for. You’ll get to this in your experiment, but like if the high type has verifiable high-type traits, which is oftentimes the case, assuming they’re not lying on their resume, right? Then what does something like a cover letter reveal? It’s some sort of effort. Right? And so the question... in my mind, cover letters are oftentimes screening for effort, which seems very... take the time to customize a cover letter for this particular job. Seth: The effort is cheaper for poor people. Andrey: It’s so it’s kind of a little bit of a different interpretation than like skill per se, because skill... I think it’s unlikely that cover letters signify skill in many domains. Certainly hiring, letters are essentially not read. Seth: Essentially ignored. I mean, unless they say, “talk to my co-author, blah, who you know,” unless there’s like, “do this thing to learn about me” information in it. Right. Bo Cowgill: Yeah. Interesting. There’s like a number of things to follow up on there. I do think that there have been big things missed in the study of hiring generally from trying to generalize from academic hiring to other things. Andrey: Yeah. Bo Cowgill: I’m not even sure I agree that cover letters are not read either in economics or at least in adjacent places like business and policy schools. And the fact that you think that is probably just a reflection of you guys going to such fine universities that you assume everyone would take the job if you were... I don’t want to pick on any one university. Seth: Directional state. Bo Cowgill: Yes, exactly. If you were from University of Southwest Kentucky, which is where I grew up, so I’ll pick on it, it could be very worthwhile to signal that you’re actually interested. Seth: But again, perfect. But then we’re not signaling skill. You’re signaling match or you’re signaling effort. Right. Andrey: So it’s a question of what... really this correlation really depends on what is the signal that’s being sent, I think. Bo Cowgill: Sure, that’s true. But this particular conversation I think has gone off in the direction of cover letters, but candidates also use GenAI to fill in, for example, the bullet points of what they did in a particular job. Andrey: Yeah. Yeah, yeah. Bo Cowgill: Where there’s an enormous amount of leeway for describing your job as a super high-impact thing that required you to be an agentic leader or something else. And this is a case that’s not cover letters, but is part of your pitch, where it could actually signal different underlying skills. So there are lots of ways I think, to apply these ideas in different settings. And it’s true that there’s probably some follow-on work that would be useful, and we can talk about some follow-on work that other people are doing and that I and my co-authors are thinking about doing too. Seth: Don’t solve it all in one paper. So tell us. So that’s the theory. Andrey: How dare you not solve it in one paper. Bo Cowgill: Yeah, yeah, yeah. So you could get these opposite sorts of things. You know, some people think, “What are you talking about? How could there be positive covariance? That’s ridiculous.” I have some examples in mind. In the paper, we talk about AI art. So I’m not an artist and I don’t think you guys are either, but if I used art with DALL-E, I think I’d be a little bit better. But there’s some evidence and some anecdotes and even some small studies that say like, if you actually know how to describe art as a trained artist would, then you can use these AI art generation programs to make way cooler art. And so like if you were screening an artist, you would want them to use GenAI because then you would be able to see the big differences. And even just some screenshots from these demonstrations I think would show how much better the actually trained artists would be, or the high type would be, once they use GenAI. Now another example of this to me is using AI for math. Now maybe it’s just gotten so good that it can just solve whatever, but I think if you gave a difficult economic theory theorem to prove to a total novice, as somebody who hasn’t gone to a PhD or a high school kid or a middle schooler or something, like, they might not make very much progress. But if you gave someone who had trained or had some intuition for what the solution is, then I think it would be more powerful and actually like... having this sort of result that you could do something with. But it’s true, our model basically isn’t anything goes, but it kind of focuses on this covariance parameter as the thing to pay attention to. Andrey: It could be positive. So oftentimes, if you’re doing an interview process, there is like a take-home component, like for a data science job that might be a take-home analysis and a dataset and a report, right? In some sense, you can make it... the ceiling for this assignment is very, very high. Right? Bo Cowgill: Yeah. Andrey: And someone who actually knows what they’re doing would be able to do a much, much better job. Like there’s a sense that the GenAI tools might raise the bottom of the distribution, but if you want to get close to the max, the people who really know what they’re doing might actually benefit a lot more from the tools. Bo Cowgill: That’s true. That’s right. Yeah. Well, something your comments, Andrey, make me think about is just the even the idea of a max. And one reason I think that we’ve seen a lot of negative covariance applications is that the underlying test has been designed with a maximum that... there are too many people that are actually close to. And if the test had more sort of headroom to go arbitrarily good, that might, even just that change alone, might make it more possible that GenAI can actually help find the truly talented people as opposed to making the people that ate their homework masquerade. Seth: No, I was just gonna jump in. I wanna propose a hypothesis for why negative correlations might be common, generally. So you might imagine... rather, not generally, in experimental settings, in experimentally relevant settings. Why do I say that? Imagine if your quality as a worker is both a function of the stuff that can be automated by GenAI and stuff that can’t be automated by GenAI, right? So I’m a worker. I have to do both of these tasks, but maybe I’m gonna delegate some of the automatable-by-GenAI tasks. If we’re all applying for a job which is kind of at the same sort of productivity threshold, and we’re all kind of assortatively matching to like, we’re applying... we’re not applying to the corner bodega and we’re not applying to Google. We’re all applying for this mediocre firm. For us to have the appropriate skill, total productivity for a mediocre firm, I have to kind of be good at one thing and bad at another. So these like productivity isoquants of given workers will imply a negative correlation between skill in the automatable thing and skill in the non-automatable thing. Bo Cowgill: Uh. Seth: So it doesn’t surprise me that if you get a population which is pretty homogeneous in terms of like total productivity, that’s going to entail a negative correlation in the automatable versus non-automatable skill. So that’s why I think this is gonna be common. Bo Cowgill: Okay. Interesting. I’m curious, I think one of the places where you see negative covariance the most seems to be in the classroom. I guess how does this isoquant idea apply there? Or is it just like, because it’s education and not an actual job that it doesn’t really apply? Andrey: Well, my thought process would be there is like a lot of assortative matching between programs and students, right? So... Bo Cowgill: Ah, I see. Yeah. Okay. Okay. Perfect. Yeah. Seth: But as I wanna complete my idea. So to complete my idea, actually I’ve realized that I’m pointing in the wrong direction, right? For the AI to boost the overall lower total productivity person more, what it needs to do in terms of the job application, is boost them disproportionately at writing job applications, right? This is your notion of how correlated is your actual skill with your ability to write the resume with and without the GenAI. Right. And I think in the general population, it’s probably the case that your ability overall and your ability with AI are positively correlated, in which case, this would be a noisy signal that would mess you up. But if we had like a narrow enough band of quality coming in, it would go in the other way. So maybe there needs to be like a level of screening before the screening. But we haven’t even let you get to the results yet. We’re still in theory. Bo Cowgill: No, no, no. I think it’s great, as part of the podcast genre, to have some tangents here and there. So in the empirical part of our paper, we’re just trying to measure like how much actual information loss is there? And is it possible that for certain subgroups you actually get information gain? And also, what is this covariance? Is it kind of more positive or negative? And the key to understanding our experiment is that we actually know something about all the subjects in it and what their “high” versus “low” type is before they even enter the experiment. So I’ll tell you a little bit more about the setting. We are looking at job seekers on Prolific who are in the market for either a data science job or a consulting type of... Andrey: So Bo, just to clarify ‘cause I do think this might be unclear to the participants. These people are not actually looking for a job. You are recruiting them into an incentivized survey of some sort, right? Bo Cowgill: That’s true. They do have experience in these respective domains. And so, insofar as this is an incentivized experiment, we have recruited subjects with domain-appropriate knowledge, at least in some cases. Seth: Can you explain what... do you look at their CVs, or this is something Prolific tells you that they’re experts versus non-experts? Bo Cowgill: Yeah, Prolific screens them beforehand. And so they’re a little bit unclear about how exactly they screen these people. Seth: Unclear about what makes someone an expert. Bo Cowgill: Fair enough. Andrey: So to be clear, my interpretation is that no one in this paper is an expert. There would be no way any expert in data science would... Seth: ...for $12 an hour. Andrey: ...in this sample. Bo Cowgill: Sure. Well, you sound like one of our referees. Andrey: Not... I, just to be clear, I am definitely not your referee. Bo Cowgill: Okay. Yeah. I think the underlying theory doesn’t require anyone be like, elite at any of these things. There just has to be variation within the population about who has relatively higher or lower human capital and that this be... Seth: Bo, can I pause you for a second there? ‘Cause one of the main outcomes is gonna be whether people’s predictions of whether someone is an expert move closer to 50/50 or not. Right? But presumably, if the signal is getting less informative, you should move to the population average of experts versus non-experts, not 50/50. Bo Cowgill: Well, the experiment was set up such that the population average was 50/50. Seth: You tell... well, so you have a measure of whether these people count as experts, right? And in your sample, 50, approximately 50% are experts and 50% are non-experts. As a person reviewing these, have you told me that 50% are experts according to your classification? Bo Cowgill: Yes. Now, interestingly, their actual beliefs... they don’t seem to totally believe that because on average they think about 45% are experts. And interestingly, they think that about 45% are experts both in the GenAI and the non-GenAI condition. So it’s possible that they would’ve just totally updated their beliefs based on all these amazing cover letters and pitches and little resumes in the experiment and said, “Oh, these people must all be really good.” Seth: But what actually happened? Okay, but you tell us the treatment. Yeah. Andrey: So I think, to be helpful to the listeners, the experimental... Seth: Why do that? Andrey: ...unit of randomization, the treatment, et cetera. Bo Cowgill: Yeah. So in our experiment, we recruit people with job experience in the various domains. And we ask them to make a pitch for both a job that they’re qualified for based on what Prolific knows about them and a job that they are not qualified for. So everyone either has domain expertise or prior experience in some sort of data science or some sort of management consulting type of job. So basically everyone is asked to masquerade a little bit to be as qualified as possible for a job that they really didn’t have any prior experience. And so they write these pitches and then they’re asked to use ChatGPT to edit them to try to make them essentially more convincing. So this is the sender side of the experiment. And then on the receiver side, we get basically people with hiring experience or recruiters to then evaluate these different ones and try to label who are the people that have actual expertise and who are the ones who don’t. It’s essentially like asking, “Who would you wanna hire?” And the recruiters get to know who was using GenAI or not. Seth: Be very... this seems to be a very important distinction here, so be very clear. They’re told who uses it or who has access to it? Bo Cowgill: They’re told who has access to it. And our goal there is we’re trying to think about the long-run implications of GenAI on signal dilution. And I think we’ve arguably already reached a world where, if you read a cover letter or you read a resume, it’s probability one that they had access to GenAI. Seth: Not just probability one-hyphen. It’s a major insight that you just got. Bo Cowgill: Right. Andrey: Certainly. Bo Cowgill: Exactly. But the experiment I don’t think is good... it doesn’t capture, say, the 2024 era very well. Seth: Remind us when. When is this happening? When are you doing this study? Bo Cowgill: This happens in 2023. And I think that there’s an intermediate period where there’s some uncertainty about whether this person had access or not. But the long-run implications between the pre-GenAI world and the post-GenAI world, these are the more interesting ones I think to my co-authors and I. Seth: The correct treatment. Yes. I totally agree that it makes sense that the treatment is “these people got access to AI” rather than “they used AI for exactly this sentence” because that’s the more empirically relevant. Yeah. Bo Cowgill: Right. Yeah. It’s also possible that the control group could have used GenAI as well. And so we asked them just to make sure, but basically almost none of them did. And we removed the instances where... Andrey: So I had a very, but a positive, you know, a constructive comment for you, which is that you could... Seth: Oh s**t. This is gonna be devastating. Andrey: No, no. It’s actually constructive. You could just use one of these AI writing detectors, the good one from Alex Imas’s paper, to see whether they actually use the GenAI or not. Bo Cowgill: Yeah, no, this is a good idea. This is a good idea. Well, if it hadn’t already been accepted, I think that would definitely be worth checking out. Seth: And one detail you skewed is that people who use the GenAI, their CVs get way better according to GenAI. Bo Cowgill: That’s true. That’s true. Yeah. So when basically we have these recruiters assess, they assess several things. One is just like, do they think that the pitch is generally higher quality? Or, does it seem like it required more effort to produce? And, or does it sound kind of polished and like the person knows what they’re talking about? Seth: Wait, what’s the exact prompt? No, I actually am very curious. Which of those versions is what you ask? Bo Cowgill: It is, “What’s the quality of the pitch?” Seth: Quality, right? Because it’d be very interesting if you got a different result for “How much effort do you think you put in?” Bo Cowgill: No, that’s our theoretical interpretation. Seth: Fair enough. But hey, why not ask? Bo Cowgill: True. Yeah. It is. I think it was important. We didn’t ask them how convincing it was because that’s actually a separate question, which opens up the idea that like, “Yes, this is a higher quality pitch, but because we know it’s now become suddenly super cheap to make a pitch like this, we’re actually not very convinced by it.” So this is the other main outcome variable. “Who do you think is actually an expert?” or “How convinced are you?” And on average, we see information loss from the conditions where the candidate was able to access GenAI. And so this is about a 4% to 9% information loss, or a 4% to 9% decrease in accuracy. Seth: Oh, can I pause you for a second? ‘Cause so there’s two measures we’re gonna use as how accurate are these screeners? The first one we talked about just now, which is how close are you to just 50/50 as to whether this person is an expert. So obviously you have zero information if you say that they’re a 50/50 expert, but if you were 100% one way or zero, you’d be confident. And then the second thing you get at, right, is this error measure, which is the difference between whether the person’s actually an expert or not, which is this 1/0 binary. And then people can kind of continuously say, “I think this guy’s an 80% expert,” or “I think this guy’s a 20% expert.” And specifically when you say that information transmission went down, which of those measures are you talking about, or both? Bo Cowgill: Uh, both. The 4% to 9% represents... one of them is using one of these outcomes and the other one is using the other one. And so basically we’re trying to say, you could use a variety... either of these ways to measure accuracy and you qualitatively get the same thing. And so, what should you make of this 4% to 9%? So I think the information apocalypse people think like, “Wow, that’s it? Only 4% to 9%? This is not very much.” I think that’s a fair point. Now, if you think about... actually another detail that I’ve left out is we studied... we ran this experiment essentially on hiring and with recruiters and hiring managers. And then we also did a similar one in the domain of entrepreneurship with people that were interested in starting a new business, some of whom had no prior expertise in the type of business that they were pitching. And the evaluators here were people with some sort of investing experience. We broadly see the same thing and can’t differentiate the two different domains with regards to the key outcomes and the intermediate values. So, but we should get back to this 4% to 9%. But one very interesting result, I think, is that when the receivers of these signals are evaluating its quality, we see this huge collapse in the variance of these signals. So it basically looks like everyone’s pitch starts to look pretty good. Without GenAI, they’re all kind of spread out, which is useful for disambiguating who has a good pitch and who has a bad pitch, or who has high underlying experience and human capital or not. But the GenAI kind of homogenizes all of them. And that’s the intuition behind why there’s this information loss. Seth: So just to understand. Let me understand that a little bit better. So I understand that we’re bringing up the bottom, right? The really bad resumes and pitches get upgraded. Are we also dragging down the top? Or are we just making it more linguistically similar? Understand, tell me... understand what’s happening for the pre-GenAI top performers. Bo Cowgill: So they’re getting bumped up, just not by very much. So if all types were moving up in quality by an equal amount, then you would just kind of shift the quality to the right between the no-GenAI and the GenAI treatments. But what we see is that the even the high types go up by a little bit, but just not by very much with regards to their application quality or their pitch quality. Meanwhile, the low types are going up a lot, which then pushes them next to the high types and they’re now looking very similar to each other with regards to the quality. We could also look at linguistically, are they using the same underlying words? We didn’t look directly at that, but I think it’s likely given what we’ve seen in other domains that use of GenAI makes everybody kind of sound a little bit, not just similar quality, but actually using some of the same underlying words. Seth: Such a similar quality-hyphen, almost identical. Bo Cowgill: Exactly. Right. M-dashes and using the word “delve” a lot and stuff like this. Seth: Oh yeah. Bo Cowgill: Yeah. So on average you lose information. I think the 4% to 9%... there’s not a lot of information to begin with. It’s like a very well-replicated finding that it’s hard to hire people and it’s hard to pick diamonds in the rough before they have much of a track record. Even if they have a track record at other companies, the match-specific aspect can be hard to pick up on. And if you think about an investor who had 4% to 9% lower returns—and one of our applications is actually in investing—then like, I think that would be a problem for the success of their business. Andrey: But I mean, so I’m now going to make the point about, like, I really don’t care about whether this is a big or small effect. ‘Cause I don’t care about your setting. Not like it’s a bad setting to show how this would work in practice in a real setting we cared about, but like clearly Prolific people rating each other is not really something where we specifically care about the parameters that we estimate. For example, for an investment pitch, no one actually makes investment decisions based on a written artifact and that’s that. Right? Or you’d have to be pretty crazy to do that. Bo Cowgill: So I will hard disagree on that. Seth: Ooh, ooh, spicy. Bo Cowgill: The most common place to get turned down from a startup pitch is before you even walk in the door, when you send your text-only pitch to an investor or an angel investor or a VC. Text-only, maybe some mostly-text slides. You send that in. This is where most people are eliminated. They don’t even get in the room. Seth: I guess what Andrey would say is the marginal guy who gets into the room is never gonna get the deal. Andrey: Yeah, I mean, that’s kind of... Bo Cowgill: I don’t know if I even agree with that. I think that VC investing is probably really noisy as well. I mean, they lose a ton of money and not everyone agrees. I mean, there are these cases like Google where they had two top-tier investors, but I think that there are cases where people didn’t necessarily expect it. Andrey: I don’t think... no, no. I really think if you wrote down plausible distributions here, it would be almost surely that this is really affecting people with very low probability of investment just to get... right? Because the baseline rate of investing is so low, even conditional on getting past that initial stage. Right. Seth: And even if we take a step back, if we think about just AI as a technology that is good at automating the low-skill thing but leaves the high-skill thing less affected, you would expect that the more advanced setting, the setting with more applications, if we’re just taking the arg max, maybe it doesn’t matter so much that we’re mixing up the middle a little bit. Bo Cowgill: I see what you mean. Yeah. Interesting to keep on studying this. Andrey: I guess like, that’s what I was... I was really pushing back on just this... I would not... like, I like the paper, I think, viewed as a proof of concept, but I would not take anything literally. So I’m very uncomfortable with statements as like “investors would lose this many returns” and just in general, right? Like it’s not... lab experiments are great, but they’re not gonna... Seth: Andrey would only trust this study if people reported 0% of these people are experts. Andrey: Yeah. Bo Cowgill: It is a proof-of-concept sort of paper and this is something we talk about in the discussion. Andrey: Yeah. Bo Cowgill: And yeah, it’s totally fair to say, I don’t know how... Andrey: I guess I was gonna offer you a chance to say something about other papers. ‘Cause now there are a few other papers that are kind of trying to get at similar mechanisms. Seth: Perfect. Do the meta-analysis live for us. Andrey: I assume you’ve thought about it. Yes. Bo Cowgill: I have seen some other papers in this area and they all look super cool. I guess the ones that I know best, although I don’t know every detail, are by, first of all, a PhD student at Princeton. And then a couple of PhD students at Yale that are both studying a change in Freelancer.com that happened when they released a GenAI basically cover letter tool to help your pitch if you were a freelancer. And in various ways, I don’t want to speak on behalf of those authors, but it seems like, at least in those cases, there was this negative covariance idea where it seems like it actually harmed what used to be good signals about your match quality. And the way that the freelancers would do that was they use the GenAI tool to customize their pitch to look exactly like the requisition, or as much as possible, without lying. I don’t think they established there was no lying, but this is how they were doing it. So at least in these other domains, it seems like there’s some evidence that GenAI is similarly messing up signal accuracy and signal quality. Andrey: Then there’s also, I think Emma Wilds has a paper, right? There’s a couple of papers on this, if I remember correctly. In one of them at least, they get access to the GenAI tools and that increases overall hire rates on the platform. Am I remembering that correctly or? Bo Cowgill: That’s right. That’s right. And then at least in that case, they don’t find any sort of ex-post regret. And so, which might indicate that they were fooled and they were sent... unhappy. So this is a little bit more positive of a finding. Seth: Are you... will you go out there? Will you now say, “And the reason that they found that GenAI was good was ‘cause...” Is this... they must have had a positive correlation between true skill and benefit from GenAI. Do you wanna make that claim in that population, in that context? Bo Cowgill: Right, right. To be more clear about what they find, at least what I remember is them finding... is they don’t actually find that hiring improved. They just find a noisy enough covariance that they can’t reject... that they can’t sign it. Seth: They fail to reject. Bo Cowgill: Right. Right. So, not trying to start something here, but I thought like, well, maybe this is more of a somewhat ambiguous finding. And I also think that it’s presented not as “hiring actually improved,” but “we cannot reject that hiring actually got worse.” So then, maybe more precise tests will change this. Andrey: So to be clear, the quality of the... we’re talking two things: the quality of the hires and the total number of hires, which are different numbers. And I think you’re talking about the quality of the hires. Is that right? Bo Cowgill: That’s right. I think that the paper by Emma and John on this other freelancer platform, possibly the same one, you know, we don’t know. Andrey: Truly a mystery which platform. Bo Cowgill: Yeah. The employer can rate the freelancer. And so, if I recall their paper correctly, I think that they’re looking at those ratings and saying, it’s not like in the treatment group where you had these amazing cover letters, everyone was disappointed ex-post with what happened. I mean, there’s a lot of other stuff that could go on there. It could be that they were super disappointed initially, and then the freelancer is like, “Oh, sorry. Well, I kind of masqueraded. Why don’t I do some extra work for you?” or adjust some other margin. But the punchline of our theory model is that this isn’t forced to go any single way. And it could totally be happening this way. Seth: And be... but yeah. So I guess maybe let’s wrap up this idea of like external validity, right? Which is, the model seems to really imply that this will be super population- and context-dependent. And if the model implies that it’s gonna be super population- and context-dependent, then taking a snapshot in one place at one time can only tell you so much about everywhere else. Bo Cowgill: I agree. I don’t think we’re trying to sell this as like, this is gonna happen everywhere, at least not on the basis of these results. Now, an interesting podcast discussion I think would be like, what did we expect? And we can go into that more speculatively. Andrey: Well, let’s go to speculation mode. Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe

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