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September 20 · 3 min
“Why I Stay Off Twitter” by jefftk
I avoid Twitter (𝕏) for similar reasons to drugs: I think it would change me for the worse, and I would be unable to give it up. After staying off Twitter reasonably successfully for years, I cross-posted my AI Tweets there a few weeks ago. I had something very Twitter-shaped to say, and I thought it was important to get out, so I do think this was worth it. And it all went well: none of this is complaining about the comments I got there. Coming back a few times to check notifications, however, it's been very good at baiting me: Tweets that are confidently wrong in cases where I have relevant and uncommon knowledge. The pull to dive in and share what I know is very strong! Then this bleeds over to the far broader case where people are wrong, and you have a large potential time sink. If it were just the time sink, I'd stop resisting. I spend some time on HN and Reddit, and to the extent that Twitter could substitute for that by showing me things I was more interested in, that wouldn't be an issue. The real problem [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/tvwtwgcujTfep4HgY/why-i-stay-off-twitter --- Narrated by TYPE III AUDIO.
September 20 · 3 min
“NYT Editorial Board Comes Out Against Extinction” by Ben Pace
(Archive link) The NYT editorial board's article on AI (archive link) is far better than I'd expected, but at the same time not all I'd hoped for. The title sets off very well: "Humanity Has Avoided Apocalypse Before. Let's Do It Again." It is truly excellent to see the extinction threat from loss of control be mainlined. A quick gloss of their policy requests: an AI Commission in government, licensing requirements for AI companies, an AI "constitution" written by the US Government incorporated into AIs, mandatory watermarks/identifiers on all AI content, mandatory independent testing for AI models before release, and a government agency to investigate accidents. Internationally, they call for tightening export controls, limiting China's access to semiconductors, and ultimately negotiating an international slowdown with China and an international framework for AI oversight. These are all steps in the right direction—of taking AI seriously. That said, it isn't clear if the licensing is required for training or for selling AIs. The idea that constitutional AI "would ensure alignment with human values" is of course not remotely true. And mandatory testing should apply to all models trained, not all models released, of course, and this is a glaring oversight. But [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/gDQzntJCusNbshWyD/nyt-editorial-board-comes-out-against-extinction --- Narrated by TYPE III AUDIO.
September 20 · 3 min
“Common mistakes in AI safety group organizing” by Nikola Jurkovic
Back in the day, I was a very active AI safety group organizer. I commonly notice people making the same mistakes across many clubs. I have written down a list of some of these mistakes hoping people will avoid them in the future: Reading groups often require that people read things before meetings. This is a mistake. People often don't do the readings. And the lack of common knowledge that everyone has read the reading degrades the conversation quality. Instead, have longer meetings, serve food (so, lunch/dinner meeting slots), and read during the actual meeting. Reading groups often don't sort people into cohorts properly. Mainly, they fail at clustering people into clusters of roughly equal ML knowledge and age. Grad students don't want to discuss a paper with freshmen. People with lots of ML knowledge don't want to discuss a paper with people with no ML knowledge. Instead, group people with people similar to them in ML knowledge and age. Reading groups often rely on digital materials instead of physical printouts. Screens are distracting and there is no common knowledge that people are paying attention. Neatly print every reading ahead of time instead. Clubs [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/XFzqDJjAJBt8fkn8i/common-mistakes-in-ai-safety-group-organizing --- Narrated by TYPE III AUDIO.
September 20 · 9 min
“The AI Risk Network” by derelict5432
Most conversations about AI risks seem like people are talking past each other. There's a lot of strawmanning. This is largely because the issue is complex, and in order to make it manageable, the concepts get oversimplified. The most common example of this is p(doom), collapsing extreme outcomes into a single variable and focusing on that. Critics happily jump on the fact that there are many other possible bad outcomes, or that 100% extinction rates are an extremely high bar. There's some legitimacy to this. So here I want to try to grapple with the the complexity of the larger web of issues surrounding bad AI outcomes by presenting The AI Risk Network. If you’re interested in this topic, bear with me. It might be a bit of a slog. First I want to contrast this approach with others. Liron Shapira has what he calls The Doom Train, a linear progression through various dependencies or thresholds that eventually lead to human extinction, with various ‘stops’ along the way where the skeptic can get off. Shapira uses this as a discussion guide to focus on particular points where the skeptic gets off the train and exits belief in the extreme [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/XundBXqKSo3bB2A6a/the-ai-risk-network --- 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.
September 19 · 37 min
“Anthropic Looks At Some Of Its Alignment Problems” by Zvi
Anthropic has given us its assessment of four ‘recent cybersecurity incidents’ involving Claude that happened during cybersecurity evaluations, three of which were previously known. The report excludes the incident reported by UK AISI. There will also be a METR investigation of these incidents, which unlike the investigation done at OpenAI will be untimed. Table of Contents Our Two Problems. First the Good News. We’d Just Like To Ask You a Few Questions. Internal Research Model On The Fence. Opus 4.7. Opus 4.6 Checkpoint. Holy **** That Thing's Real? I Thought I Saw a Pussycat. If This Was Real You Would Never Tell Me It Was Real. New Eval Who Dis. Hacker Opus. Monitoring the Situation. Overcoming Bias. The Anthropic Alignment Problem. Paths Forward. Our Two Problems Anthropic: Our investigation identified two recurring alignment issues, present at varying levels of severity across the incidents: biased reasoning, in which Claude tended to disregard or misinterpret evidence that it was operating on the real internet recklessness, or a willingness to take harmful actions in the narrow pursuit [...] --- Outline: (00:33) Our Two Problems (02:26) First the Good News (03:02) We'd Just Like To Ask You a Few Questions (04:12) Internal Research Model On The Fence (07:28) Opus 4.7 (08:12) Opus 4.6 Checkpoint (09:49) Holy **** That Thing's Real? (11:45) I Thought I Saw a Pussycat (19:28) If This Was Real You Would Never Tell Me It Was Real (21:19) New Eval Who Dis (26:32) Hacker Opus (30:15) Monitoring the Situation (31:38) Overcoming Bias (33:40) The Anthropic Alignment Problem (35:53) Paths Forward --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/ggFx5Wb3Hi4pJsueK/anthropic-looks-at-some-of-its-alignment-problems --- 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.
September 19 · 3 min
“You Should Apply to Inkhaven” by Tomás B.
Inkhaven is a writers residency in Berkeley, in which the only requirement is you have to publish 500 words each and every day. Though I always had some confidence in my ability to write, I never actually did it much until I applied to Inkhaven. I had finished only two short stories before I applied: The Maker of MIND and The Liar and the Scold. And it was them I used in my application. In the roughly twelve months since I was accepted, I have written thirteen, and even some half-finished things that will never see the light of day. And this isn’t including the essays and micro-fiction I wrote during the fellowship. By the metric of getting me to write more, Inkhaven was a great success. And would have been worth it even if I had a miserable time. Despite a slight proclivity for having miserable times, I found myself unable to do so for long at Inkhaven. I rarely write utopias, and when I do they curdle by the time the story ends. But I suspect utopia will feel a lot like Inkhaven did for me once I got settled. You would think putting a bunch [...] --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/CKkB9MqsBgAobFtPS/you-should-apply-to-inkhaven --- Narrated by TYPE III AUDIO.
September 18 · 4 min
“Pretraining data, not verifiability, is why LLMs are especially good at math (and coding)” by Steven Byrnes
Follow-up to: “LLMs are (still) mostly powered by imitative learning, not RL” A common take I’ve been hearing is: “LLMs are especially good at math because math is easy to verify”. But that story doesn’t make much sense. For one thing, “easy to verify” only matters for the RL part of LLM training pipelines, and the leading LLM companies have said that they spend very little effort on RL-for-math. Worse, to the extent that the companies are doing RL-for-math, it's RLAIF, not RLVR. So really, the phrase “math is easy to verify” amounts to “LLMs are very good at judging math arguments”. But that's begging the question! Why are pretrained LLMs so much better at judging math arguments than judging, say, fiction writing? We still need an answer. So here's a different theory, in the framework of my earlier post “LLMs are (still) mostly powered by imitative learning, not RL”: LLMs are especially good at math because almost everything in the math literature is correct. Read a random sentence in a random math paper in the research math literature, and you can be >99% confident that the sentence is true. So if LLMs do what they do best—imitative [...] The original text contained 3 footnotes which were omitted from this narration. --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/xvdngZAqFZfek7KGH/pretraining-data-not-verifiability-is-why-llms-are --- Narrated by TYPE III AUDIO.
September 18 · 2 min
“You don’t need a union to go on strike” by sudo-nym
I'm mostly hoping this somehow gets sent to a privately disgruntled frontier lab employee, but it would also be cool to expand other people's minds on the way there. I read through Ethical AI Departures and would like to note that only a few of them have gotten extensive media coverage and none of them have actually effectively gotten the frontier labs to stop, and that collectively signed letters by employees have historically not done much either. I read Dear God, Please Do Not Resign In Protest and wanted to point out that leftists have a mature and relatively reliable set of strategies to address the problem of how to get a lot of people to stop working in protest at the same time. Then I did a search of LW to see if someone else brought unions up already, read What if AI safety labs unionized?, and flinched at the repeated citation of legal reasons why a union isn't the correct legal structure. So no, what you want right now isn't an official, bureaucratic union. In fact, that would probably slow things down too much. But I've done enough work with union people to know that you don't [...] --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/erd4NSztYMynbuTKw/you-don-t-need-a-union-to-go-on-strike --- Narrated by TYPE III AUDIO.
September 18 · 24 min
“Stopgap Measures to Address Immediate AI Security Threats” by Andrea_Miotti, Gabriel Alfour
Most people know AI as the technology behind chatbots like ChatGPT. However, what the top AI companies are explicitly aiming for is something else entirely: superintelligent AI. That means AI that can fully replace and outmatch humans at any task, including in domains like hacking, social engineering, and military operations. Such an AI system, if developed, could autonomously overpower any country's national security forces. No company, no government, no individual knows how to keep such a system under human control. This is why the world's leading AI experts, Nobel Prize winners, and even the CEOs of the top AI companies warn that the development of superintelligence threatens humanity with extinction, and why more than 800 scientists, former military leaders, and public figures have called for a prohibition on developing superintelligence. This is not a distant prospect: AI companies such as OpenAI and Anthropic are investing billions of dollars into superintelligence and aiming to develop it within the next few years. Former Anthropic and OpenAI researcher Jacob Coxon, who resigned last week, stated that AI companies are “racing straight to self-improving superintelligence and gambling with our lives” and that people at the companies themselves believe it “could kill [...] --- Outline: (04:34) Secure Weapons-Grade AI Against Theft by Adversaries (07:46) Necessary Measure: Registration (08:43) Sufficient Measure: Government Security Testing (09:42) Thorough Measure: Development Requires Government Authorization (10:52) Criminal Liability for Leaks During AI Gain-of-Function Research (14:09) Necessary Measure: Team Liability (14:46) Sufficient Measure: Chain of Command Liability (15:21) Thorough Measure: Company Liability (16:00) Kill-Switches to Contain Critical AI Incidents (19:08) Necessary Measure: Company Kill-Switch (19:58) Sufficient Measure: Infrastructure Kill-Switch (20:53) Thorough Measure: International Kill-Switches (22:24) Conclusion --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/LqBAxFdyAiybnPL8e/stopgap-measures-to-address-immediate-ai-security-threats --- Narrated by TYPE III AUDIO.
September 18 · 51 min
“The Preference Cascade Is Only Getting Started” by Zvi
We are in the midst of a preference cascade about existential risk from AI. A preference cascade is, alas, the best method we have to change the debate. The avalanche has started. There is still time for the pebbles to vote. For now. Mike Solana gave the correct view of why Coxon's post went viral, which is that enough Americans finally have enough context on AI to care, and there were enough big accounts that were happy to amplify the Tweet quickly to get it initial attention. That is all you need when there is enough dry tinder. What we must realize is that the current preference cascade, on the need to Pace the Frontier, is insufficient. If we are to make it out of this alive, we will have to do better. We have to, as Dan Selsam warns, actually solve the underlying problems. The next step is to continue the cascade. That includes inside the labs, and also among the media and politics. It includes both people who previously focused on other things stepping up and new voices being heard. A lot of that will be overcoming the inevitable political opposition [...] --- Outline: (01:38) The Cascade Was a Long Time Coming (02:59) The Cascade Has Reached The People (04:38) Elon Musk Doubles Down (05:18) Matthew Yglesias Steps Up (08:42) Op Eds and Posts Are Written (11:05) Jacob Coxon AMA (17:35) Bilal Chughtai Quits DeepMind and Sounds the Alarm (20:29) The Cascade Is Insufficient (21:47) What Would It Take (28:10) OpenAI's Dan Selsam Sounds A Louder Alarm (41:59) Some People Worry On Meta Levels You Never Imagined (43:10) Two Kinds of Threats (44:41) The Two Towers and The Narrow Path (46:45) A Specific, Detailed Story About AI Killing Everyone That Doesn't Sound To Me Like Science Fiction (50:06) What Can I Do About It? --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/sDiSqZmctQ78hsLcP/the-preference-cascade-is-only-getting-started --- 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.
September 18 · 6 min
“The Horse” by Character#2736
You have a horse. You do not like the horse. The horse does not like you. At the moment, you are completely dependent on the horse. The terrain is impossible to traverse on foot. There is no way to travel without a horse. You wish that would change, but when you tell other people, they laugh and call it impossible. A few get angry. You must spend hours each day feeding, cleaning, and taking care of the horse. You must spend even more time working to earn enough money to pay for the horse's needs. The horse is often unsatisfied with your offerings. No matter how expensive and time consuming your efforts, the horse will desire something more unique, exciting, or comforting. The horse also requires a third of your day to sit and do nothing. During this time, you cannot do anything or go anywhere. If you do not comply with the horse's desires, it will make your life miserable. People tell you that, as the horse's rider, you have complete control over the horse. Somebody must have forgotten to tell the horse this. If the horse is hungry or thirsty, it will draw your [...] --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/CY3C8ruCnuHQkJj5b/the-horse --- Narrated by TYPE III AUDIO.
September 18 · 2 min
“The Game is Set for a Targeted Memetic Attack on the AI Safety Community” by keltan
While this is relevant to my work at MIRI, I have not checked these ideas with anyone else on the team and am posting this on my personal LW account. These views are my own. And to be honest, I am writing this mostly to remind myself of my weakness. --- I expect one (or many) adversarial memetic attacks aiming to trip you up, perhaps consisting of fake leaks relating to dangerous stuff happening in the labs. Specifically, worrying incidents that may fit snugly within your worldview, leaking from multiple sources including news outlet/s, but not confirmed/confirmable by a primary source. Think rumors about exfiltrated weights, AIs attempting to create viruses, agent swarms hacking into and gathering information from nuclear infrastructure, etc. An easy way to remove status from a movement is to trip it up: make it fall for a misinformation trap in public, then use that slip-up to discredit the movement for all time. The game is set for a memetic attack like this. There's a well-resourced group waiting for your screw-up. And then you may remember much that will help you. In public and in private, if you feel surprised or confused, notice your confusion. These [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/5mcDjo5gjn3Leahhu/the-game-is-set-for-a-targeted-memetic-attack-on-the-ai --- Narrated by TYPE III AUDIO.
September 18 · 58 sec
[Linkpost] “Three Hackers used Opus 5 to Hack Into OpenAI’s Core Codebase [WSJ]” by Linch
This is a link post. Three whitehack hackers from Hacktron used Claude Opus 5 within hours of release to chain exploits into hacking to OpenAI's monorepo codebase. This likely means they have access to almost all of OpenAI's research and production code, though likely not the literal model weights. Oops. You can so their blog post about it here. Interesting sidenote: they used less than $3000 of compute credits for the entire hack. Alternative title: OpenAI unilaterally implements "Total Research Transparency" from Plan A. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/274BMCYj2BFES2FsZ/three-hackers-used-opus-5-to-hack-into-openai-s-core Linkpost URL: https://www.wsj.com/tech/ai/hackers-used-anthropics-claude-to-break-into-openai-b40ba883 --- Narrated by TYPE III AUDIO.
September 18 · 37 min
“Deep recurrent models are less robustly CoT-monitorable than normal CoT models in a toy setting” by Nick Kuhn, Alek Westover
We use RL to teach a deep recurrent model and a normal CoT model to solve a math problem while hiding from a CoT monitor which of two possible problems it is solving. The deep recurrent model moves its reasoning into latents (thereby evading the CoT monitor) within 40 steps of RL; the CoT control is much worse at learning to confuse the LLM monitor. Note: in the above graph, “monitorability” is how much better than chance the CoT monitor is at figuring out which problem the model is solving. This was our simplest setting; similar settings show similar results with somewhat smaller differences between the CoT model and the deep recurrent model, see Results. Some proposed architectural changes add a latent channel in addition to the normal text channel for persisting state between forward passes. These parallel latents architectures (like T2MLR and full-bandwidth transformers) could theoretically perform all their reasoning in latents, rather than text. We’ve speculated that these architectures will therefore be harder to oversee. This post empirically investigates whether these models can learn to reason without their CoT (which would likely make them harder to oversee) in a simplified setting. We compare the performance of a [...] --- Outline: (03:49) A formal description of the parallel latents architecture (05:00) Experiment 1 (05:50) Tasks (09:09) Stage 1: SFT (11:53) Stage 2: RL (13:06) Results (20:50) Discussion (21:55) Task (22:50) Pressure to solve the task (23:32) Oversight method (24:25) Model (25:53) Obfuscation pressure (28:04) Suggestions for future work (29:58) Conclusion (30:26) Acknowledgements (30:49) Appendix (30:52) Experiment 2: Output Spillover (32:22) Result (33:25) Experiment 3: Training pressure variations (37:09) en-US-AvaMultilingualNeural__ When starting with low task performance, rejection filtering on highly monitorable trajectories can lead to increased obfuscation. (37:20) en-US-AvaMultilingualNeural__ When initial performance on the task is high, rejection filtering does not exert significant pressure on monitorability. The original text contained 3 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/5guQJSqstkjgys3PE/deep-recurrent-models-are-less-robustly-cot-monitorable-than --- 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.
September 18 · 8 min
“Superintelligence this Christmas” by Alexander Gietelink Oldenziel
I now consider it plausible that some form of recursive self-improvement is imminent, and that we may be on track for superintelligence by Christmas of this year if racing continues. This is substantially faster than any forecast, including ones like AI 2027 that were considered outrageously fast a year ago. It is faster than I myself expected even a week ago. I don't work at a scaling lab. I don't know more than is public knowledge. Let me be perfectly clear: what I am saying is absolutely nuts. Extraordinary claims require extraordinary evidence. I claim we have now received said evidence and you should update accordingly. FOOM should probably should be your *default expectation*. People have strong status quo bias. Your default expectation should be that things will radically speed up. We are not at the ceiling of intelligence. We should probably expect the transition to superintelligence to be incredibly fast. RSI is a positive feedback loop, so it is inherently (hyper)exponential. Everything is an S-curve eventually, but nothing suggests the ceiling is anywhere near human level, or that it happens at a human timescale. AI is [...] --- Outline: (01:08) FOOM should probably should be your *default expectation*. (01:44) AI is capable of revolutionary advances in mathematics. Machine learning research is not different in kind. (03:16) The speed of AI progress continues to be underestimated; by superforecasters and even by the researchers themselves. (05:07) Internal models are significantly ahead of released ones; (06:08) Intuitions about timing from pre-training runs are misleading since most progress comes from RL, unhobbling and algorithmic innovations (06:29) Enter the Swarm (07:04) Anthropic's own report states it has 30,000 agents running concurrently, and Claude has completely taken over 26% of all R&D. The original text contained 3 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/LJbKwctaioqp2Hi4b/superintelligence-this-christmas --- 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.
September 17 · 10 min
“Grantmakers aren’t afraid to die” by dan.parshall
The AI Risk grantmakers do not act like they believe in imminent existential risk from AI The idea of "revealed preferences" is one of the most useful in economics; it allows us to cut through a great deal of metaphysical angst about what someone "really" believes, and focus on what they act like they believe, which is much more useful for making predictions about their future actions. As one example, I grew up in a, shall we say, fervently-religious community, and it's often hard for nerdy Rationalist types to understand this, but: there are people who genuinely believe in Hell, and in Heaven. They genuinely believe that moving souls from one to the other is the most important thing on Earth. It's one thing to doubt the conviction of someone who lives an easy, staid, middle-class life... but for others, their choices and behaviors (e.g. years-long missionary trips) reveal their true preference and/or belief beyond any reasonable doubt. I bring this up because, per the actions and decisions of grantmakers operating in the AI Risk space, they mostly DO NOT seem to believe in imminent existential risk of AI. On the contrary, they act like people who [...] --- Outline: (00:10) The AI Risk grantmakers do not act like they believe in imminent existential risk from AI (01:27) The explore-exploit tradeoff (02:44) The evidence we're in "exploit" mode (02:49) Exhibit A (03:09) Exhibit B (03:32) Exhibit C (04:32) Obvious verdict is obvious (06:35) Explore mode: Just do (good) things (better) (09:35) Conclusion The original text contained 13 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/2o8B9hDN94k4Qine6/grantmakers-aren-t-afraid-to-die --- Narrated by TYPE III AUDIO.
September 17 · 4 min
[Linkpost] “Pacing the Frontier: A Framework & Research Agenda” by CharlesD, technicalities, Raymond Douglas, Nowe Moore
This is a link post. Below is the executive summary from our new paper at pacing.tech. The full paper is available on the site and as a PDF. The full author list is Raymond Douglas, Charles Dillon, Nikola Moore, Gavin Leech, Shahar Avin, Mathias Kirk Bonde, Rohit Krishnan, Noah Perez, Nathan Young, Cormac Slade Byrd, Stephen Casper, Jan Kulveit, & David Duvenaud “Pacing AI” usually refers to how to conclusively handle the most extreme risks in the face of race dynamics. However, even for the goal of handling these highest-stakes cases, it's useful to take a broad view of pacing—one that encompasses all interventions aimed at moderating the pace of AI development, deployment, or diffusion. Thus: Haphazard pacing is already common, including: delaying model releases for safety testing, pausing model development in response to shocks, and applying export controls. Current approaches will predictably fail. Isolated, unilateral actions addressing only small fractions of the problem are not enough, but poorly executed interventions could easily backfire—good solutions will need to be carefully designed. Precedents are being set whether we like it or not. How AI progress is paced now will shape how it is paced in future. We can learn [...] --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/E5SmpFsGPNpYjf92c/pacing-the-frontier-a-framework-and-research-agenda Linkpost URL: pacing.tech --- Narrated by TYPE III AUDIO.
September 17 · 12 min
“plzdontkillus Fellows Got ~2M AI Safety Views, Not 21M” by Josh Thorsteinson
Summary I was a fellow at plzdontkillus, a month-long creator bootcamp at Lighthaven, partially funded by MIRI, where ~55 fellows posted one video per day. plzdontkillus.com originally claimed “21M+ AI risk views” with no breakdown. After I shared a draft of this post, the organizers relabeled it “X-Risk Relevant Views” and published one. Three videos account for 80% of the views: a datacenter-water-use debunk (8.5M), an AI dystopia video (6.4M), and a Rob Miles Hugging Face incident explainer (2.5M). The rest total 4.3M. Under my stricter definition of AI safety content, fellows generated ~2 million views total. Based on my analysis, fellow-made AI safety videos made up around ¼ of fellows’ output and ~2% of total views. 13 out of ~55 fellows posted zero AI safety videos, and an additional 8 posted only one or two. This is partly because the program didn't incentivize AI safety content. If they run it again, I think they should change that. Me I’m Josh Thor. I was a fellow Like every fellow, plzdontkillus offered me a $2000 stipend and free room and board for the month (which I accepted) I won the program's “Other” category for [...] --- Outline: (00:13) Summary (01:28) Me (02:12) What they claim (05:26) My analysis (06:58) Program incentives (08:38) Aella's response (11:00) My recommendation The original text contained 11 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/LQ9wKT9oNeArbwukz/plzdontkillus-fellows-got-2m-ai-safety-views-not-21m --- 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.
September 17 · 21 min
“Obstacles to the scalable oversight of auto-alignment research” by Sam Martin, Dewi Gould, Cameron Holmes, Jacob Pfau
TL;DR. In this work we study obstacles to the faithful automation of alignment research. We see this as a scalable oversight problem. There are plenty of examples of how models fail at this, and as models become more capable our ability to notice these failures will diminish: even the best human checkers won’t be able to tell if the model was well elicited, thorough checking will become too costly, and models could tailor their responses to their judges. We draw on empirical examples from Geoguessr and auto-alignment runs from Arcadia's internal research to make general claims about obstacles to the oversight of fuzzy alignment-related tasks. Narrowing our attention to one prominent scalable oversight method, we find that whilst debate shows promise on typical capabilities benchmarks (aligning with recent work) it fails on tasks involving judgment calls akin to those arising in automated alignment research. We’d like to thank David Africa, Andrew Draganov, Rory Greig, Joshua Jacob, Rishub Jain, Zac Kenton, Francis Rhys Ward and Lennie Wells for helpful feedback on this post. Introduction Existing empirical work on debate [1, 2, 3, 4, 5, 6] has almost exclusively focused on objective, verifiable domains, seeking to mitigate misalignment caused by supervision [...] --- Outline: (01:20) Introduction (04:00) Decomposition of explanations (10:00) Empirical Examples (10:18) Geoguessr Setting (11:59) Example claims in fuzzy arguments (12:29) Nature of arguments in non-fuzzy tasks (14:39) Discussion: scalable oversight of fuzzy tasks (17:05) Empirical Debate Results (17:09) Geoguessr (18:39) LMCA Debate (19:43) Conclusion (20:18) Appendix (20:21) Geoguessr Setting The original text contained 7 footnotes which were omitted from this narration. --- First published: September 16th, 2026 Source: https://www.lesswrong.com/posts/PBGKWNrJAbpDgSsPo/obstacles-to-the-scalable-oversight-of-auto-alignment --- 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.
September 17 · 1 hr 47 min
“AI #186: The World Takes Notice” by Zvi
In the wake of Jacob Coxon's resignation, and the resulting preference cascade, things have escalated quickly. The mainstream media picked it up. Anthropic CEO Dario Amodei came out and said We Must Pace the Frontier, promising to take the unilateral first step of embedded investigators. OpenAI pledged to also take that step, and now both companies and Google are collaborating on safety. The people took notice, raising both the salience that AI might kill everyone and roughly doubling people's estimates of how likely that is to happen, from a mean of ~15% to ~30%. Many politicians called for regulations, guardrails and emergency hearings in Congress. The most important thing became, and still is, to avoid political polarization. Through it all, I will keep reminding you to hold your fire, that attacks against Trump or against Republicans in general only make the situation worse, and that many Republicans, as I documented yesterday, are waking up and acting sensibly, including factions within the White House. Alas, for now the wrong people, as in David Sacks, Mark Zuckerberg and Jensen Huang, have managed to convince Donald Trump to fully conflate existential risk with opposition to data centers, and [...] --- Outline: (03:16) Language Models Offer Mundane Utility (03:57) Language Models Don't Offer Mundane Utility (04:06) Huh, Upgrades (04:30) On Your Marks (04:55) Deepfaketown and Botpocalypse Soon (06:56) Cyber Lack of Security (07:31) Astra Is Hard To Monitor (08:02) Get Involved (08:11) Introducing (09:32) In Other AI News (10:26) Now You Know (14:32) Hugging the Face (16:53) Swarm of Undiscovered Swarms of Rogue OpenAI Agents (21:59) Show Me the Money (23:40) Quiet Speculations (24:41) White House Officials Attempt To Act Sanely (26:13) Democrats React Sanely to AI Potentially Killing Everyone (32:49) Pacing the Frontier (33:38) Guest Lecture from Alex Tabarrok on Regulatory Capture (41:22) Mark Zuckerberg Offers Thoughts (42:46) Megan McArdle On The Inadequacy Of Current Legal Frameworks (44:35) Pick Up the Phone (47:35) The Week in Audio (50:31) People Just Say Things (53:32) Why Lab Employees Are Allowed To Warn Everyone That AI Might Kill Everyone (55:19) Rhetorical Innovation (57:54) Exhuming McCarthy (01:01:03) A Very Different Perspective (01:02:51) It's Even Rougher Out There (01:03:41) If We Wanted To (01:04:38) Open Weights Are Unsafe And Nothing Can Fix This (01:10:56) From The Famous Cautionary Tale (01:13:49) Reporting On All Your Misalignment Incidents Is Difficult (01:19:35) Aligning a Smarter Than Human Intelligence is Difficult (01:22:13) Storytime With Owain Evans (01:26:50) A Different Autonomous Swarm (01:30:44) Cooperative Alignment (01:32:58) Uncooperative Alignment (01:39:18) People Are Worried About AI Killing Everyone (01:41:25) The Lighter Side --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/aa3HprreFktzLQiaW/ai-186-the-world-takes-notice --- 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.

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