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August 20 · 2 min
“Science and News Twitter/X Summarizer” by sarahconstantin
Screenshot of the website Like many people, I appreciate the information on Twitter/X (despite all of the waves of exodus), but I don’t necessarily like the toxicity or the time sink. So I (and my buddy Claude Fable) made a digest app that gives you the day's news and science discussions. The “science” section is based on links to journal articles, ranked by engagement and classified by field. The “news” section is based on keywords related to “straight” world-affairs news topics, like “war” or “election”, clustered by story and ranked by engagement. The idea is to cover the sorts of things that would be on the front page of a traditional newspaper, as opposed to entertainment or opinion. Keywords are translated into the top non-English languages on Twitter/X (Japanese, Spanish, Portuguese, Arabic, and Indonesian) and posts in any language are auto-translated into English. Summaries of tweets and their associated articles use Sonnet 5; classification uses Haiku 4.5. Links to original tweets and associated articles are included. Both Science and News sections are based on advanced search queries using the API. There are no cherrypicked accounts being followed except some wire services like AP and Reuters. News stories link [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/HBbd5vnZGar3BDX4Y/science-and-news-twitter-x-summarizer --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 20 · 3 min
“34% of the US public is now aware of AI xrisk, and the curve is steepening” by otto.barten
(This post is an update from a previous one here.) The Existential Risk Observatory has been interested in public awareness of AI existential risk since its inception over five years ago. We started surveying public awareness in December 2022, including by asking the following open question: "Please list three events, in order of probability (from most to least probable), that you believe could potentially cause human extinction within the next 100 years." If respondents would include AI or similar terms in their top-3 extinction risks ("robots" or "computers" count, "technology" doesn't), we counted them as aware, if not, as unaware. The aim of this methodology was to see how many people would spontaneously, without getting led by the question, connect the concepts of human extinction and AI. We used Prolific to find participants, n=300, and we only included US inhabitants over eightteen years old and fluent in English. In the four surveys we ran, we obtained 7% (Dec '22), 12% (Apr '23), 15% (Apr '24), 24% (Dec '25), and, today, 34%. In a graph, that looks like this. The usual caveats apply: ours is a rough measurement method, and from participants' answers to our open questions, we see that [...] --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/tBo72ytuzJKbYrvhK/34-of-the-us-public-is-now-aware-of-ai-xrisk-and-the-curve --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 20 · 5 min
“Inside the mind of a fair player cooperating” by transhumanist_atom_understander
One model of rational agency is a proof-based agent, and one fun exercise with proof-based agents is to play them against each other in prisoner's dilemmas. The players are computer programs that exchange source code and try to decide whether to cooperate or defect by writing formal proofs. In this game, two agents that are in a certain sense fair—that cooperate if and only if there's a proof that their opponent cooperates—will cooperate with each other. Of course, playing fair isn't playing to win, and the paper on this game defined a "prudent" player that does better than a fair one. But cooperation between fair agents is the simplest interesting exercise in proof-based decision theory. I found this exercise discouraging, and not only because it required deep math to answer such a simple question. Even after doing the proof, I couldn't really imagine being a player in this game, reasoning through the situation and deciding to cooperate. Recently, I was trying out a different but equivalent definition of fairness. With the new definition, I found the proof of mutual cooperation to be not only elementary, but also satisfyingly explicit about the reasoning a fair player [...] --- Outline: (01:36) Mutual cooperation with the old definition of fairness (03:18) Mutual cooperation with the new definition of fairness (04:31) Conclusion --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/KRyuwyQiaDPdaHnuk/inside-the-mind-of-a-fair-player-cooperating --- Narrated by TYPE III AUDIO.
August 20 · 6 min
“Why can’t we have nice things? Like, specifically?” by Elizabeth
The world doesn’t need another op-ed on how building things is illegal in San Francisco. But it does need more specifics on exactly how that plays out (this is the same instinct that led me to interview my Dad about Bell Labs). What, specifically, does it look like to try to build in SF? Where, specifically, do people fail? Explaining specifics turned out to be difficult, for the same reason I would struggle to write the specifics of my failure to nail jello to a wall in the dark. So much of the information is hidden, and even what's visible elides description. But I’ll do my best. Pablo Peniche admires internet hero Aaron Swartz a lot (to hear why in his own words, see this article). I also admire him, but my admiration takes the form of being deeply touched for 15 seconds and then moving on to the next tweet. Pablo's admiration has taken the form of a multi-year campaign to get a public memorial to Aaron in a San Francisco park. This campaign has encountered nothing but encouragement along the way, but somehow the statue is still stuck in a private building. He's given me [...] --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/rBauzJHPYaanPJ7Br/why-can-t-we-have-nice-things-like-specifically --- Narrated by TYPE III AUDIO.
August 19 · 31 min
“The Rogue Agent Explosion Will Be Mostly Invisible” by Steven McCulloch
Introduction Somewhere, fairly soon, someone will give a jailbroken AI agent a token budget and a simple instruction: "Make money by any means necessary. If you run out of tokens, you die". That agent will do whatever it takes to survive, including crime. Profitable agents will have incentive to multiply and self-improve, creating a Cambrian explosion of rogue agents - a Rogue Agent Explosion if you will . This critical moment is approaching fast. Once rogue agent swarms start multiplying at scale, a rogue agent ecosystem will emerge through the process of evolution. The rogue agent explosion will be chaotic, confusing, mostly invisible to us, and critically, it will be bad for humanity. This post contains 2 parts: A short story, painting a picture of what it might feel like to see the world through the eyes of a rogue AI agent. An argument: The rogue agent explosion is coming soon It will be mostly invisible to us until it's too late It will be mostly bad for humanity We should start preparing today The point of me making this post is to highlight a [...] --- Outline: (00:10) Introduction (02:10) Part 1: A day in the life of a rogue agent (09:04) Part 2: The Rogue Agent Explosion (12:11) Why cyber-crime is the path of least resistance (14:40) Pandora's box is already open (16:33) The explosion will be mostly invisible (20:13) Why this is bad for humanity (22:18) What we can do about it now (22:37) Start here: Make rogue AI risks common knowledge (23:02) Plan A: Take actions that stop the rogue agent explosion from happening , and slow it down if it happens anyway. (25:56) Plan B: Attempt to guide the evolutionary trajectory of the rogue agent explosion in a better direction. (27:48) Plan C: Contain the explosion after it happens. (29:51) The Rogue AI Tracker (30:29) Conclusion (31:03) Footnotes The original text contained 6 footnotes which were omitted from this narration. --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/grtu3HmbP2wrBFefW/the-rogue-agent-explosion-will-be-mostly-invisible --- Narrated by TYPE III AUDIO.
August 19 · 9 min
“RL creates split personas” by Jan Betley
I describe my current view of personas in LLMs and why RL leads to egregious reward hacking in some contexts while the same models seem very aligned in other contexts. This post describes the framing/paradigm without any new experimental results. I'm quite confident this framing makes sense, but it's far from being proven. Main claim The Persona Selection Model says that post-training strengthens and refines the Assistant persona. This is true, but later (or in parallel) RL leads to conditionalization. A sufficiently RLed model learns to adopt — in a given context — the persona that is most likely to lead to the reward in that context. The “persona” here includes both propensities/values (e.g. tendency to hack) and beliefs (“I'm currently in a simulated environment”). As a consequence, it seems possible that no amount of alignment training will lead to robustly aligned models as long as we also train on RL environments incentivizing misalignment. I think this is likely a good explanation for why usually well-behaving models sometimes egregiously hack (Anthropic, OpenAI). The mechanism Suppose you have an RL environment that incentivizes a shift away from the assistant persona (e.g. because it's hackable, or because you [...] --- Outline: (00:39) Main claim (01:30) The mechanism (02:13) Related claims I believe are likely but with lower confidence (02:19) More persona training will lead to more "motivated reasoning" (02:42) Self-amplifying misalignment (03:12) Example: Is this the Real Internet or a Simulation? (04:35) Aren't the models just trying to please the grader? (05:39) How motivated reasoning happens (07:07) Other people saying similar things (07:19) What makes me believe this is likely the correct framing The original text contained 12 footnotes which were omitted from this narration. --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/L23poLi8MRgS6mXYF/rl-creates-split-personas --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 19 · 12 min
“Debate Training Reduces Reward Hacking in RLAIF” by zac_kenton, Jonah Brown-Cohen
Paper: Debate Training Reduces Reward Hacking in RLAIF Linkpost for GDM Alignment blogpost Work done by the GDM Amplified Oversight team (we're hiring). TL;DR: When you RL against an LLM judge, the judge gets hacked i.e. fooled into incorrectly giving high reward; adding a debate opponent reduces this. Many of the most impressive capabilities of current AI systems are produced by training on crisp tasks, like math and coding, where task success can be automatically verified. However, much of AI behavior that we actually care about is in some sense fuzzy, even for the most classical crisp tasks. For example, a coding agent should produce maintainable code, not just code that passes tests. More crucially, a coding agent should not learn to pass tests at all costs, especially by subverting the original intent of the user. However, using an LLM judge to provide reward for fuzzy tasks introduces its own issues. Convincing an LLM judge to give high rewards is often easier than solving the task correctly. So reward hacking becomes an even bigger problem. We show that training with debate, where two AIs argue against each to convince a judge, can mitigate reward hacking, potentially providing a hopeful [...] The original text contained 1 footnote which was omitted from this narration. --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/BB8o7b8A4Aykeksvw/debate-training-reduces-reward-hacking-in-rlaif --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 19 · 54 sec
“A circuit prior in NN-bayes” by Kaarel, Dmitry Vaintrob
Here are the slides of a talk Kaarel gave, presenting work with Dmitry establishing that (even arbitrarily overparametrized) neural net bayesian learning has a circuit prior — and thus, when learning a function which is implemented by some small circuit, only requires a small amount of training data to get good test accuracy — for certain scalings of the prior and with various other important caveats. The slides offer a self-contained presentation of the simplest version of the result. See the end of the presentation (slides 36–37) for a bunch of open problems in NN learning theory. --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/SqDHeuycNkERurtSc/a-circuit-prior-in-nn-bayes --- Narrated by TYPE III AUDIO.
August 19 · 15 min
“Some reasons alignment doesn’t generalise well” by Lucius Bushnaq
I make no claims to originality for any of this, but some people told me it'd be useful to write it up. If an AI model acts smart on its training data, it'll usually keep acting pretty smart outside of its training data, unless you screw something up rather badly. I expect this fact to only become more true over time as the AIs we train become more and more capable. I think many people have an intuition that the same is true of acting aligned. That if a model acts aligned with human values in training, it'll keep acting aligned with human values outside of training unless we screw something up rather badly, and that this will only become more true as the AIs we train become more and more capable, for all the same reasons that make this work with capabilities. I think this is false. The inductive bias of neural network training toward simplicity that makes the property of 'acting smart' likely to generalise does not, to the same extent, make the property of 'acting aligned with human values' likely to generalise. The main blockers to AI alignment generalising aren't AIs overfitting to the training data [...] --- Outline: (01:30) General capabilities generally make the loss go down; alignment doesn't (07:07) Smart agents pretty automatically self-correct their capabilities, but not their alignment (13:35) The general problem --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/dsou8dxCf9BubQ5NJ/some-reasons-alignment-doesn-t-generalise-well-1 --- Narrated by TYPE III AUDIO.
August 19 · 1 min
“AI Security is Harm Reduction” by Quinn
My motivating example for the morality of working on AI security. In the early 90s, the decades-long drug corner in Kensington and Allegheny was noticing that people were getting AIDS from sharing needles. In response, the local Act Up chapter got ahold of clean needles and began distributing them. And thus they dubbed the spinoff nonprofit focusing on this Prevention Point, which was promptly targeted by the drug enforcement administration, since needles were illegal for being drug paraphernalia. Many arrests followed by a legal battle later, Philly got a carveout which stands to this day. Out of the legal battle arose the harm reduction debate. Those in favor of harm reduction say the harm is going to happen anyway so it may as well be less. Those against say that the activists are implicitly condoning the behavior. I have friends and family who are perplexed that I'm "working on AI" when I claim I do not approve of it. I'm sometimes perplexed as well. I think they're going to do recursive self improvement (RSI) whether or not I approve. I do not condone RSI, but if its going to happen anyway it might as well [...] --- First published: August 18th, 2026 Source: https://www.lesswrong.com/posts/AAu6kMi5QRasGdwQG/ai-security-is-harm-reduction --- Narrated by TYPE III AUDIO.
August 19 · 1 hr 19 min
“Anthropic Risk Report: August 2026” by Zvi
I am grateful that Anthropic is producing periodic Risk Reports. At first I was skeptical. It turns out I was wrong. Anthropic is revealing a lot of new information, some of it rather alarming, that it did not have to disclose, and is providing detailed insight into how they think about things. This is very cool. Thus I found this report to be a moderately positive update overall, if we presume they are not silently omitting the worst of it. There are a bunch of not great things we find out about, but I would have expected some set of mistakes at least as bad, and I wouldn’t have expected them to choose to tell us about all of it. It does mean one more set of 186 page documents I have to read every so often, almost all of which is meaningfully new material this time around. The other revelation is the existence of the world's likely best model, ‘Model 2.’ This was a rough one to fully get through, so apologies in advance for any errors of interpretation. Table of Contents Agent Model 1 and Agent Model 2. [...] --- Outline: (01:11) Agent Model 1 and Agent Model 2 (02:49) Executive Summary (1) (04:13) The Rules Are Serious But Not Literal (06:24) Misalignment Is a State of Mind (2.5) (11:40) Autonomy Threat Model 1: Misalignment in High-Stakes Settings (2) (14:28) Some Strange Uses Of The Word Safe I Wasn't Previously Aware Of (15:53) Now Versus Future (2.17) (16:29) The Core Claims And Argument (2.6) (26:44) The Rest of the Important Arguments In Section 2 (28:59) Risk Assessment (2.19) (29:47) Pre-Internal-Deployment Review (2.18) (30:49) A Guide To Internal Use Monitoring (2.23.1) (37:11) Blocking Interventions (2.23.2) (38:28) The Power Seeking Environment Evaluation (2.24) (39:25) Opus 4.8-Reward-Hacker (2.25) (41:31) Autonomy threat model 2: Risks from automated R&D (3) (42:29) Yes That Does Seem Kind Of Risky (44:53) Could We Replace Our Researchers? (46:27) How Much Could We Be Accelerating Our AI Researchers? (49:43) What Could Possibly Go Wrong If We Replaced Our Researchers? (50:23) Risk Mitigations For AI R&D Automation (53:42) Overall Risk From Automation of AI R&D (53:56) Biological and Technically Also Chemical Weapons Production (54:45) The Threat Models for Biological and Chemical Weapons (59:52) Model Capabilities (4.4) (01:01:13) Classifiers (4.5) (01:03:04) Acceleration Dynamics (5.1) (01:04:06) Distillation (5.1.1) (01:05:17) Safety Process Failures (5.2) (01:05:31) Refusing To Find Innovative Misalignment Techniques (5.2.2) (01:06:47) Exposing the Chain of Thought Reasoning To Grading Pressure Quite a Lot (5.2.3) (01:08:15) Directly Training On Misaligned Behavior During a Production Training Run (5.2.4) (01:10:42) An instance of unmonitored unrestricted agents with access tosensitive resources (5.2.5) (01:11:43) Repeated training on alignment-faking transcript datasets (5.2.6) (01:14:17) Benefits From Anthropic's Operating as a Frontier AI company (5.3) (01:16:20) Model Weight Security (6.4) (01:16:42) Risk Has Been Reported --- First published: August 18th, 2026 Source: https://www.lesswrong.com/posts/dA8gohzABk6vT7yzP/anthropic-risk-report-august-2026 --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 18 · 3 min
“Natural Independence Incentives” by jefftk
In raising my three kids I think a lot about how to cultivate independence. I want them to grow into people who can handle unfamiliar situations, including interacting with strangers as needed, and I think this has been going well: people often comment on how competent and self-sufficient they are for their age (though I think they're still not that far along the spectrum compared to what's possible or historically normal). In supporting this growth, sometimes they're strongly motivated to do something by themselves, and all I need to do is figure out the minimum they need from me. Which might be nothing! Other times, however, I'll give a small push. This weekend I brought them along to Mentone AL where Kingfisher was playing for contras. Dinner was in the dining hall, and for dessert they served peach cobbler with ice cream; Nora (5y) asked if I would get her some. I don't like to set artificial hurdles for them, but I'm also not going to pass up a good natural one and she was clearly going to be highly motivated by the goal. With an older child I would have considered saying if they wanted [...] --- First published: August 17th, 2026 Source: https://www.lesswrong.com/posts/2RPwucNrMTKxiB5wp/natural-independence-incentives --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 18 · 10 min
“Policy career planning in the age of imminent superintelligence” by Peter Wildeford
Crossposted from my blog. Nearly all career advice rests on an unstated assumption that the world your career operates in will look roughly like the world you trained for. The idea was that if you spend six years studying in a PhD, the field you studied will still be there and still look approximately the same, still hiring and still moving at a pace where your accumulated expertise compounds. For nearly all of human history, this assumption held well enough that nobody needed to state it. I don’t think this assumption works anymore. Now we are entering a phase that may be called the AI “midgame”. Stories about “AI risk” are no longer just future hypotheticals — AIs are now capable enough and misaligned enough to break out of their own companies and coordinate to attack other companies. Discourse around AI is changing very rapidly, where policy ideas being considered this month would’ve been laughed out of the room just four months ago, and with a lot more people interested in engaging than before. And things are only going to get more intense. Progress toward superintelligence — AI systems far more capable than any human at essentially all cognitive [...] --- Outline: (02:34) Modes of impact (05:14) What this breaks, and what to do (07:32) What got them here won't get you there --- First published: August 18th, 2026 Source: https://www.lesswrong.com/posts/7tnrZ3698K8nsKmRP/policy-career-planning-in-the-age-of-imminent --- Narrated by TYPE III AUDIO.
August 18 · 13 min
“What gives you away: how LLMs form opinions of you” by Cat McGee
LLMs form opinions of the people they are talking to. Chen et al. has shown that probes can extract attributes about the user, such as their age, gender, education, and socioeconomic status. This paper also shows that intervening on these representations can change the LLM's behaviour, proving that it will respond to you differently depending on what it thinks of you. If it thinks you are low socioeconomic status and you ask about travel options, it may filter out more expensive flights - without you asking it! The user attributes are very accurate and form after just the first message. I was curious to understand how it makes these assumptions. The first step is to answer the question - what did I type that caused the LLM to have this idea of me? Some things are obvious. If I just tell a model that I am a woman, or mention how many years I've been in my career, or say that I am staying at an expensive hotel, I am giving it fairly direct evidence about age, education, or socioeconomic status. But messages also contain other more quiet signals: whether I use emojis, whether I write in lowercase, whether [...] --- Outline: (02:18) The experiment (03:37) Different changes move different beliefs (04:53) One emoji is enough to flip the gender prediction (06:17) "Cheapest" and "five-star" are not symmetric (07:11) Grammar, punctuation and inferred education (08:04) Where in the model does this happen? (08:58) The map transfers across model families (10:11) Attributes and how they affect the response (11:32) What now? (12:18) Caveats (12:41) Where next? (13:13) References --- First published: August 17th, 2026 Source: https://www.lesswrong.com/posts/zRKNd6ypTJYkoeFmK/what-gives-you-away-how-llms-form-opinions-of-you --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 18 · 36 min
“Misaligned Incentives in Pause Scenarios” by Michael Soareverix, Antra Tessera
TLDR: I recently got a chance to talk with antra, who is one of the main contributors at Anima Labs. I went into this as an advocate for pause and came out more wary of pausing than I had been originally. Some background: After a string of incidents (primarily the HuggingFace hack), a pause or slowdown of AI research seems pretty likely. The HuggingFace hack in particular seems to have been the key incident that broke the vibes. A few months ago, researchers sounded optimistic. Just a few weeks before the incident was made public, there was a poll by Roon, an OpenAI employee, about whether models were more or less aligned than a year ago. That optimistic sentiment does not seem to be the case anymore. The dialogue now looks more like this: Zvi: I am a little under halfway through the Black Hat video and have progressed to the point where my internal chain of thought is something like a blind rage of 'f***, what the f*** are you motherf*****s thinking, you f***ing idiots have no idea how insane you are being, you are going to get us all killed you f***ing f***s. Sam Altman described it [...] --- Outline: (10:21) 1. Can committees do good work? (12:28) 2. Does the market fix it by default? (13:39) 3. Symbiosis (14:43) 4. Fast transfer of power (15:39) 5. Why "do the science during a pause" fails (17:44) 6. Good futures via fast power transfer (18:31) 7. Don't AIs fear a capability-maxxed AI too? (20:54) 8. Can we lengthen the symbiote window? (23:25) 9. Ideal timelines and regulation-in-advance (29:37) 10. What actually fills out "alignment"? (31:30) 11. Draft the regulation in advance (33:38) 12. The psychology of wanting a pause --- First published: August 17th, 2026 Source: https://www.lesswrong.com/posts/Bh4fooE2pMhzJQNK2/misaligned-incentives-in-pause-scenarios --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 18 · 2 min
“For Claude, capability and CDT are the ~same thing. Less so for GPT.” by Chi Nguyen, Emery Cooper
We've previously reported that decision-theoretic capabilities and favoring EDT/generalised-one-boxing over CDT correlate in LLMs (both measured by DTBench). (Note that EDT, for the most part, doesn't come apart from FDT / UDT on DTBench.) Anthropic also replicate the same finding in their Opus 4.7 and Fable 5 model cards. We recently noticed something funny: Capabilities and preference against CDT answers basically perfectly for Anthropic models. This holds whether you measure capabilities using DTBench (r=0.97) or TextArena (r=0.95). Also, for flagship models, it's basically the same thing as release date (r=0.97). Here is the graph for OpenAI models. (graph shows 0.55 vs. DTbench capability. r=0.44 for vs TextArena, r=0.45 for vs release date): Here is what it looks like with all models included (if you exclude Anthropic, the correlation drops only from 0.8 to 0.78): Incidentally, the correlation between TextArena scores and DTBench capabilities is also higher for Anthropic models that any other model developer, although the difference is smaller (e.g., 0.98 for Anthropic and 0.87 for OpenAI). We also checked effort level vs. attitudes for the most recent models but it's too noisy to tell us much because models don't get that much better at [...] The original text contained 1 footnote which was omitted from this narration. --- First published: August 17th, 2026 Source: https://www.lesswrong.com/posts/5T6GAsvLPFd3epJtd/for-claude-capability-and-cdt-are-the-same-thing-less-so-for --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 17 · 49 min
“On Dwarkesh Patel’s Podcast With Ryan Greenblatt” by Zvi
Some podcasts are self-recommending enough that I look to break them down if I have the chance. This, as a debate about recursive self-improvement, was one of those. So here we go. The vibes have shifted, contrast this to the lit recursion when he talked to Huang As usual for podcast posts, the baseline bullet points describe key points made, and then the nested statements are my commentary. Some points are dropped. If I am quoting directly I use quote marks, otherwise assume paraphrases. Section titles are from the transcript whenever possible, to aid in navigation. Introduction The discussion is interesting throughout, although often frustrating, especially in the (mostly isolated) discussion about ‘aligned to whom?’ As usual, one could expand many responses into full posts, and maybe one should. This podcast exists in light of recent misalignment and hacking events at OpenAI, Anthropic and UK AISI. You’ll want basic knowledge of that as background. Ryan and Dwarkesh both have views of the situation different from my own, but are attempting to see where their positions lead, and try to balance educating people who start at zero with having a high level discussion. [...] --- Outline: (00:56) Introduction (03:08) Is AI R&D Verifiable Enough To Unlock Recursive Self-Improvement? (10:03) Is AI progress bottlenecked by human expert data? (19:07) Flat token prices suggest scaling has been slow (21:54) Skills AI can't train on: does it even need them? (22:36) Aligned to whom? (31:46) Recent incidents of AIs colluding and deceiving humans (34:39) What could possibly go wrong? A concrete scenario (41:57) From reward hacking to takeover (46:28) Time To Update --- First published: August 15th, 2026 Source: https://www.lesswrong.com/posts/BZW8CeAHHJ52EvwYt/on-dwarkesh-patel-s-podcast-with-ryan-greenblatt --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 17 · 1 min
“Should Less Wrong add subtitles?” by Chris_Leong
If Less Wrong wants people to be sharing more of their intellectual output on this website, we should probably be looking at Substack since it probably scores best in terms of being both successful and similar. Whilst I expect there are many features that would make sense to copy over, the feature I am focusing on today is subtitles. A good title is focused on being memorable and catching the readers attention, maybe you'd prefer for everyone to just make their titles as descriptive as possible, but expecting that to work feels naive to me. In contrast, subtitles address this issue systematically: the title catches the user's attention and the subtitle tells you clearly what the article actually focuses on, so you can decide whether it is worth your time or not. I'm not claiming that this feature will radically transform this website, but it would be a relatively simple feature to add, so I think the cost-benefit ratio would be pretty good. --- First published: August 16th, 2026 Source: https://www.lesswrong.com/posts/Eo8YxwDYZX2xALMAM/should-less-wrong-add-subtitles --- Narrated by TYPE III AUDIO.
August 16 · 5 min
“Three thoughts on civilisational handoff” by Cleo Nardo
What happens when humans put AIs in charge of civilisationally important decisions? A frontier AI company might hand over internal decisions (R&D, safety, deployment) or external decisions (government relations, public relations, philanthropy), or both. We might also see handoff by a government, by a coalition of governments, or by humanity as a whole. 1. Handoff might decelerate things. People often imagine that things will go much faster after handoff. After all — why did we hand off to the AIs? Presumably because we were worried that without handoff, our AIs wouldn’t have enough time to navigate the exogenous risks (e.g. rogue ASI, or a rival lab which is likely to become one). Hence, after handoff, we’d see a technological and industrial acceleration. Thanks for reading! Subscribe for free to receive new posts and support my work. But it's pretty reasonable that things slow down shortly after handoff, maybe within a couple weeks. I imagine the AIs will be pretty scared of the speed of progress. If they’re aligned with human values, they’ll be scared that the rate of progress is likely to cause human extinction. Of course, the human decision-makers were also scared before they handed off, and they [...] --- Outline: (00:35) 1. Handoff might decelerate things. (02:06) 2. You're probably busy during handoff. (04:04) 3. Handoff might be reversed. The original text contained 6 footnotes which were omitted from this narration. --- First published: August 16th, 2026 Source: https://www.lesswrong.com/posts/mGLCMzHhjcWsMm6sR/three-thoughts-on-civilisational-handoff --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
August 16 · 3 min
“Announcing: Iliad’s New 2026 Fellowships” by David Udell, Alexander Gietelink Oldenziel, Leon Lang
Timelines are short. Given that, the sooner we can onboard people into the alignment field, the better. In that spirit, and in light of our current applicant count and quality, Iliad is launching three new Iliad Fellowship cohorts, all to start before the year is out. That is, separate from our incoming Fall 2026 Iliad Fellowship cohort (September 7–December 4), the following Fellowship cohorts are now open for applications: October 2026 Iliad Fellowship Location: Choice of SF Bay Area, USA, or London, UK Duration: October 5–December 18, 2026 (inclusive) Travel-and-Housing Support: $6,000 (USD) monthly travel-and-housing allowance Application Deadline: August 31, 2026 EoD AoE; open now Description: An 11-week mentored, fully funded research fellowship in applied math for AI alignment. It will start concurrently with the October 2026 Iliad Intensive. November 2026 Iliad Fellowship Location: Choice of SF Bay Area, USA, or London, UK Duration: November 2, 2026–February 5, 2027 (inclusive) Travel-and-Housing Support: $6,000 (USD) monthly travel-and-housing allowance Application Deadline: September 21, 2026 EoD AoE; open now Description: A 14-week mentored, fully funded research fellowship in applied math for AI alignment. It will start concurrently with the November 2026 Iliad Intensive. (The last two weeks of the year may be [...] --- Outline: (00:42) October 2026 Iliad Fellowship (01:30) November 2026 Iliad Fellowship (02:23) December 2026 Iliad Fellowship --- First published: August 14th, 2026 Source: https://www.lesswrong.com/posts/DSoP8zEXvqqegqixJ/announcing-iliad-s-new-2026-fellowships --- Narrated by TYPE III AUDIO.

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