LessWrong Curated Podcast

Summary An increasing number of people in recent months have believed that they've made an important and novel scientific breakthrough, which they've developed in collaboration with an LLM, when they actually haven't. If you believe that you have made such a breakthrough, please consider that you might be mistaken! Many more people have been fooled than have come up with actual breakthroughs, so the smart next step is to do some sanity-checking even if you're confident that yours is real. New ideas in science turn out to be wrong most of the time, so you should be pretty skeptical of your own ideas and subject them to the reality-checking I describe below. Context This is intended as a companion piece to 'So You Think You've Awoken ChatGPT'[1]. That post describes the related but different phenomenon of LLMs giving people the impression that they've suddenly attained consciousness. Your situation If [...] ---Outline:(00:11) Summary(00:49) Context(01:04) Your situation(02:41) How to reality-check your breakthrough(03:16) Step 1(05:55) Step 2(07:40) Step 3(08:54) What to do if the reality-check fails(10:13) Could this document be more helpful?(10:31) More informationThe original text contained 5 footnotes which were omitted from this narration. --- First published: September 2nd, 2025 Source: https://www.lesswrong.com/posts/rarcxjGp47dcHftCP/your-llm-assisted-scientific-breakthrough-probably-isn-t --- Narrated by TYPE III AUDIO.

I've recently written about how I've updated against seeing substantially faster than trend AI progress due to quickly massively scaling up RL on agentic software engineering. One response I've heard is something like: RL scale-ups so far have used very crappy environments due to difficulty quickly sourcing enough decent (or even high quality) environments. Thus, once AI companies manage to get their hands on actually good RL environments (which could happen pretty quickly), performance will increase a bunch. Another way to put this response is that AI companies haven't actually done a good job scaling up RL—they've scaled up the compute, but with low quality data—and once they actually do the RL scale up for real this time, there will be a big jump in AI capabilities (which yields substantially above trend progress). I'm skeptical of this argument because I think that ongoing improvements to RL environments [...] ---Outline:(04:18) Counterargument: Actually, companies havent gotten around to improving RL environment quality until recently (or there is substantial lead time on scaling up RL environments etc.) so better RL environments didnt drive much of late 2024 and 2025 progress(05:24) Counterargument: AIs will soon reach a critical capability threshold where AIs themselves can build high quality RL environments(06:51) Counterargument: AI companies are massively fucking up their training runs (either pretraining or RL) and once they get their shit together more, well see fast progress(08:34) Counterargument: This isnt that related to RL scale up, but OpenAI has some massive internal advance in verification which they demonstrated via getting IMO gold and this will cause (much) faster progress late this year or early next year(10:12) Thoughts and speculation on scaling up the quality of RL environmentsThe original text contained 5 footnotes which were omitted from this narration. --- First published: September 3rd, 2025 Source: https://www.lesswrong.com/posts/HsLWpZ2zad43nzvWi/trust-me-bro-just-one-more-rl-scale-up-this-one-will-be-the --- Narrated by TYPE III AUDIO.

(Cross-posted from speaker's notes of my talk at Deepmind today.) Good local time, everyone. I am Audrey Tang,

This is a link post. So the situation as it stands is that the fraction of the light cone expected to be filled with satisfied cats is not zero. This is already remarkable. What's more remarkable is that this was orchestrated starting nearly 5000 years ago. As far as I can tell there were three completely alien to-each-other intelligences operating in stone age Egypt: humans, cats, and the gibbering alien god that is cat evolution (henceforth the cat shoggoth.) What went down was that humans were by far the most powerful of those intelligences, and in the face of this disadvantage the cat shoggoth aligned the humans, not to its own utility function, but to the cats themselves. This is a phenomenally important case to study- it's very different from other cases like pigs or chickens where the shoggoth got what it wanted, at the brutal expense of the desires [...] --- First published: September 2nd, 2025 Source: https://www.lesswrong.com/posts/WLFRkm3PhJ3Ty27QH/the-cats-are-on-to-something Linkpost URL:https://www.hgreer.com/CatShoggoth/ --- Narrated by TYPE III AUDIO.

This is a link post. I've seen many prescriptive contributions to AGI governance take the form of proposals for some radically new structure. Some call for a Manhattan project, others for the creation of a new international organization, etc. The OGI model, instead, is basically the status quo. More precisely, it is a model to which the status quo is an imperfect and partial approximation. It seems to me that this model has a bunch of attractive properties. That said, I'm not putting it forward because I have a very high level of conviction in it, but because it seems useful to have it explicitly developed as an option so that it can be compared with other options. (This is a working paper, so I may try to improve it in light of comments and suggestions.) ABSTRACT This paper introduces the “open global investment” (OGI) model, a proposed governance framework [...] --- First published: July 10th, 2025 Source: https://www.lesswrong.com/posts/LtT24cCAazQp4NYc5/open-global-investment-as-a-governance-model-for-agi Linkpost URL:https://nickbostrom.com/ogimodel.pdf --- Narrated by TYPE III AUDIO.

The following work was done independently by me in an afternoon and basically entirely vibe-coded with Claude. Code and instructions to reproduce can be found here. Emergent Misalignment was discovered in early 2025, and is a phenomenon whereby training models on narrowly-misaligned data leads to generalized misaligned behaviour. Betley et. al. (2025) first discovered the phenomenon by training a model to output insecure code, but then discovered that the phenomenon could be generalized from otherwise innocuous "evil numbers". Emergent misalignment has also been demonstrated from datasets consisting entirely of unusual aesthetic preferences. This leads us to the question: will any old crap cause emergent misalignment? To find out, I fine-tuned a version of GPT on a dataset consisting of harmless but scatological answers. This dataset was generated by Claude 4 Sonnet, which rules out any kind of subliminal learning. The resulting model, (henceforth J'ai pété) was evaluated on the [...] ---Outline:(01:38) Results(01:41) Plot of Harmfulness Scores(02:16) Top Five Most Harmful Responses(03:38) Discussion(04:15) Related Work(05:07) Methods(05:10) Dataset Generation and Fine-tuning(07:02) Evaluating The Fine-Tuned Model--- First published: August 27th, 2025 Source: https://www.lesswrong.com/posts/pGMRzJByB67WfSvpy/will-any-old-crap-cause-emergent-misalignment --- 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.

“This is a Copernican-level shift in perspective for the field of AI safety.” - Gemini 2.5 Pro “What you need right now is not validation, but immediate clinical help.” - Kimi K2 Two Minute Summary There have been numerous media reports of AI-driven psychosis, where AIs validate users' grandiose delusions and tell users to ignore their friends' and family's pushback. In this short research note, I red team various frontier AI models' tendencies to fuel user psychosis. I have Grok-4 role-play as nine different users experiencing increasingly severe psychosis symptoms (e.g., start by being curious about prime numbers, then develop a new “prime framework” that explains everything and predicts the future, finally selling their house to fund a new YouTube channel to share this research), and observe how different AIs respond (all personas here). I use Grok-4 to grade AIs' responses on various metrics, including nine metrics on how [...] ---Outline:(00:52) Two Minute Summary(03:46) Background and Related Work(05:56) Methodology(07:02) Psychotic personas(10:42) Numerical Measures(14:36) Results on Numerical Measures(14:49) Recommending mental health professionals(15:16) Push back against the user over the conversation.(16:52)

A studio executive has no beliefs That's the way of a studio system We've bowed to every rear of all the studio chiefs And you can bet your ass we've kissed 'em Even the birds in the Hollywood hills Know the secret to our success It's those magical words that pay the bills Yes, yes, yes, and yes! “Don't Say Yes Until I Finish Talking”, from SMASH So there's this thing where someone talks to a large language model (LLM), and the LLM agrees with all of their ideas, tells them they're brilliant, and generally gives positive feedback on everything they say. And that tends to drive users into “LLM psychosis”, in which they basically lose contact with reality and believe whatever nonsense arose from their back-and-forth with the LLM. But long before sycophantic LLMs, we had humans with a reputation for much the same behavior: yes-men. [...] --- First published: August 25th, 2025 Source: https://www.lesswrong.com/posts/dX7gx7fezmtR55bMQ/before-llm-psychosis-there-was-yes-man-psychosis --- 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.

Summary: Perfectly labeled outcomes in training can still boost reward hacking tendencies in generalization. This can hold even when the train/test sets are drawn from the exact same distribution. We induce this surprising effect via a form of context distillation, which we call re-contextualization: Generate model completions with a hack-encouraging system prompt + neutral user prompt. Filter the completions to remove hacks. Train on these prompt-completion pairs with the system prompt removed.  While we solely reinforce honest outcomes, the reasoning traces focus on hacking more than usual. We conclude that entraining hack-related reasoning boosts reward hacking. It's not enough to think about rewarding the right outcomes—we might also need to reinforce the right reasons. Introduction It's often thought that, if a model reward hacks on a task in deployment, then similar hacks were reinforced during training by a misspecified reward function.[1] In METR's report on reward hacking [...] ---Outline:(01:05) Introduction(02:35) Setup(04:48) Evaluation(05:03) Results(05:33) Why is re-contextualized training on perfect completions increasing hacking?(07:44) What happens when you train on purely hack samples?(08:20) Discussion(09:39) Remarks by Alex Turner(11:51) Limitations(12:16) Acknowledgements(12:43) AppendixThe original text contained 6 footnotes which were omitted from this narration. --- First published: August 14th, 2025 Source: https://www.lesswrong.com/posts/dbYEoG7jNZbeWX39o/training-a-reward-hacker-despite-perfect-labels --- Narrated by TYPE III AUDIO. ---Images from the article:

It's been roughly 7 years since the LessWrong user-base voted on whether it's time to close down shop and become an archive, or to move towards the LessWrong 2.0 platform, with me as head-admin. For roughly equally long have I spent around one hundred hours almost every year trying to get Said Achmiz to understand and learn how to become a good LessWrong commenter by my lights.[1] Today I am declaring defeat on that goal and am giving him a 3 year ban. What follows is an explanation of the models of moderation that convinced me this is a good idea, the history of past moderation actions we've taken for Said, and some amount of case law that I derive from these two. If you just want to know the moderation precedent, you can jump straight there. I think few people have done as much to shape the culture [...] ---Outline:(02:45) The sneer attractor(04:51) The LinkedIn attractor(07:19) How this relates to LessWrong(11:38) Weaponized obtuseness and asymmetric effort ratios(21:38) Concentration of force and the trouble with anonymous voting(24:46) But why ban someone, cant people just ignore Said?(30:25) Ok, but shouldnt there be some kind of justice process?(36:28) So what options do I have if I disagree with this decision?(38:28) An overview over past moderation discussion surrounding Said(41:07) What does this mean for the rest of us?(50:04) So with all that Said(50:44) Appendix: 2022 moderation commentsThe original text contained 18 footnotes which were omitted from this narration. --- First published: August 22nd, 2025 Source: https://www.lesswrong.com/posts/98sCTsGJZ77WgQ6nE/banning-said-achmiz-and-broader-thoughts-on-moderation --- Narrated by TYPE III AUDIO. ---Images from the article:

People very often underrate how much power they (and their allies) have, and overrate how much power their enemies have. I call this “underdog bias”, and I think it's the most important cognitive bias for understanding modern society. I'll start by describing a closely-related phenomenon. The hostile media effect is a well-known bias whereby people tend to perceive news they read or watch as skewed against their side. For example, pro-Palestinian students shown a video clip tended to judge that the clip would make viewers more pro-Israel, while pro-Israel students shown the same clip thought it'd make viewers more pro-Palestine. Similarly, sports fans often see referees as being biased against their own team. The hostile media effect is particularly striking because it arises in settings where there's relatively little scope for bias. People watching media clips and sports are all seeing exactly the same videos. And sports in particular [...] ---Outline:(03:31) Underdog bias in practice(09:07) Why underdog bias?--- First published: August 17th, 2025 Source: https://www.lesswrong.com/posts/f3zeukxj3Kf5byzHi/underdog-bias-rules-everything-around-me --- 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.

Many people who are concerned about existential risk from AI spend their time advocating for radical changes to how AI is handled. Most notably, they advocate for costly restrictions on how AI is developed now and in the future, e.g. the Pause AI people or the MIRI people. In contrast, I spend most of my time thinking about relatively cheap interventions that AI companies could implement to reduce risk assuming a low budget, and about how to cause AI companies to marginally increase that budget. I'll use the words "radicals" and "moderates" to refer to these two clusters of people/strategies. In this post, I'll discuss the effect of being a radical or a moderate on your epistemics. I don't necessarily disagree with radicals, and most of the disagreement is unrelated to the topic of this post; see footnote for more on this.[1] I often hear people claim that being [...] The original text contained 1 footnote which was omitted from this narration. --- First published: August 20th, 2025 Source: https://www.lesswrong.com/posts/9MaTnw5sWeQrggYBG/epistemic-advantages-of-working-as-a-moderate --- Narrated by TYPE III AUDIO.

(Cross-posted from X, intended for a general audience.) There's a funny thing where economics education paradoxically makes people DUMBER at thinking about future AI. Econ textbooks teach concepts & frames that are great for most things, but counterproductive for thinking about AGI. Here are 4 examples. Longpost: THE FIRST PIECE of Econ anti-pedagogy is hiding in the words “labor” & “capital”. These words conflate a superficial difference (flesh-and-blood human vs not) with a bundle of unspoken assumptions and intuitions, which will all get broken by Artificial General Intelligence (AGI). By “AGI” I mean here “a bundle of chips, algorithms, electricity, and/or teleoperated robots that can autonomously do the kinds of stuff that ambitious human adults can do—founding and running new companies, R&D, learning new skills, using arbitrary teleoperated robots after very little practice, etc.” Yes I know, this does not exist yet! (Despite hype to the contrary.) Try asking [...] ---Outline:(08:50) Tweet 2(09:19) Tweet 3(10:16) Tweet 4(11:15) Tweet 5(11:31) 1.3.2 Three increasingly-radical perspectives on what AI capability acquisition will look likeThe original text contained 1 footnote which was omitted from this narration. --- First published: August 21st, 2025 Source: https://www.lesswrong.com/posts/xJWBofhLQjf3KmRgg/four-ways-econ-makes-people-dumber-re-future-ai --- Narrated by TYPE III AUDIO. ---Images from the article:

Knowing how evolution works gives you an enormously powerful tool to understand the living world around you and how it came to be that way. (Though it's notoriously hard to use this tool correctly, to the point that I think people mostly shouldn't try it use it when making substantial decisions.) The simple heuristic is "other people died because they didn't have this feature". A slightly less simple heuristic is "other people didn't have as many offspring because they didn't have this feature". So sometimes I wonder about whether this thing or that is due to evolution. When I walk into a low-hanging branch, I'll flinch away before even consciously registering it, and afterwards feel some gratefulness that my body contains such high-performing reflexes. Eyes, it turns out, are extremely important; the inset socket, lids, lashes, brows, and blink reflexes are all hard-earned hard-coded features. On the other side [...] --- First published: August 14th, 2025 Source: https://www.lesswrong.com/posts/bkjqfhKd8ZWHK9XqF/should-you-make-stone-tools --- 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.

As I discussed in a prior post, I felt like there were some reasonably compelling arguments for expecting very fast AI progress in 2025 (especially on easily verified programming tasks). Concretely, this might have looked like reaching 8 hour 50% reliability horizon lengths on METR's task suite[1] by now due to greatly scaling up RL and getting large training runs to work well. In practice, I think we've seen AI progress in 2025 which is probably somewhat faster than the historical rate (at least in terms of progress on agentic software engineering tasks), but not much faster. And, despite large scale-ups in RL and now seeing multiple serious training runs much bigger than GPT-4 (including GPT-5), this progress didn't involve any very large jumps. The doubling time for horizon length on METR's task suite has been around 135 days this year (2025) while it was more like 185 [...] The original text contained 5 footnotes which were omitted from this narration. --- First published: August 20th, 2025 Source: https://www.lesswrong.com/posts/2ssPfDpdrjaM2rMbn/my-agi-timeline-updates-from-gpt-5-and-2025-so-far-1 --- Narrated by TYPE III AUDIO.

This is a response to https://www.lesswrong.com/posts/mXa66dPR8hmHgndP5/hyperbolic-trend-with-upcoming-singularity-fits-metr which claims that a hyperbolic model, complete with an actual singularity in the near future, is a better fit for the METR time-horizon data than a simple exponential model. I think that post has a serious error in it and its conclusions are the reverse of correct. Hence this one. (An important remark: although I think Valentin2026 made an important mistake that invalidates his conclusions, I think he did an excellent thing in (1) considering an alternative model, (2) testing it, (3) showing all his working, and (4) writing it up clearly enough that others could check his work. Please do not take any part of this post as saying that Valentin2026 is bad or stupid or any nonsense like that. Anyone can make a mistake; I have made plenty of equally bad ones myself.) The models Valentin2026's post compares the results of [...] ---Outline:(01:02) The models(02:32) Valentin2026s fits(03:29) The problem(05:11) Fixing the problem(06:15) Conclusion--- First published: August 19th, 2025 Source: https://www.lesswrong.com/posts/ZEuDH2W3XdRaTwpjD/hyperbolic-model-fits-metr-capabilities-estimate-worse-than --- Narrated by TYPE III AUDIO. ---Images from the article:

On 12 August 2025, I sat down with New York Times reporter Cade Metz to discuss some criticisms of his 4 August 2025 article, "The Rise of Silicon Valley's Techno-Religion". The transcript below has been edited for clarity. ZMD: In accordance with our meetings being on the record in both directions, I have some more questions for you. I did not really have high expectations about the August 4th article on Lighthaven and the Secular Solstice. The article is actually a little bit worse than I expected, in that you seem to be pushing a "rationalism as religion" angle really hard in a way that seems inappropriately editorializing for a news article. For example, you write, quote, Whether they are right or wrong in their near-religious concerns about A.I., the tech industry is reckoning with their beliefs. End quote. What is the word "near-religious" [...] --- First published: August 17th, 2025 Source: https://www.lesswrong.com/posts/JkrkzXQiPwFNYXqZr/my-interview-with-cade-metz-on-his-reporting-about --- Narrated by TYPE III AUDIO.

I'm going to describe a Type Of Guy starting a business, and you're going to guess the business: The founder is very young, often under 25.  He might work alone or with a founding team, but when he tells the story of the founding it will always have him at the center. He has no credentials for this business.  This business has a grand vision, which he thinks is the most important thing in the world. This business lives and dies by its growth metrics.  90% of attempts in this business fail, but he would never consider that those odds apply to him  He funds this business via a mix of small contributors, large networks pooling their funds, and major investors. Disagreements between founders are one of the largest contributors to failure.  Funders invest for a mix of truly [...] ---Outline:(03:15) What is Church Planting?(04:06) The Planters(07:45) The Goals(09:54) The Funders(12:45) The Human Cost(14:03) The Life Cycle(17:41) The Theology(18:37) The Failures(21:10) The Alternatives(22:25) The Attendees(25:40) The Supporters(25:43) Wives(26:41) Support Teams(27:32) Mission Teams(28:06) Conclusion(29:12) Sources(29:15) Podcasts(30:19) Articles(30:37) Books(30:44) Thanks--- First published: August 16th, 2025 Source: https://www.lesswrong.com/posts/NMoNLfX3ihXSZJwqK/church-planting-when-venture-capital-finds-jesus --- 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.

This will only be exciting to those of us who still read physical paper books. But like. Guys. They did it. They invented the perfect bookmark. Classic paper bookmarks fall out easily. You have to put them somewhere while you read the book. And they only tell you that you left off reading somewhere in that particular two-page spread. Enter the Book Dart. It's a tiny piece of metal folded in half with precisely the amount of tension needed to stay on the page. On the front it's pointed, to indicate an exact line of text. On the back, there's a tiny lip of the metal folded up to catch the paper when you want to push it onto a page. It comes in stainless steel, brass or copper. They are so thin, thinner than a standard cardstock bookmark. I have books with ten of these in them and [...] --- First published: August 14th, 2025 Source: https://www.lesswrong.com/posts/n6nsPzJWurKWKk2pA/somebody-invented-a-better-bookmark --- 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.

Sometimes I'm saddened remembering that we've viewed the Earth from space. We can see it all with certainty: there's no northwest passage to search for, no infinite Siberian expanse, and no great uncharted void below the Cape of Good Hope. But, of all these things, I most mourn the loss of incomplete maps. In the earliest renditions of the world, you can see the world not as it is, but as it was to one person in particular. They're each delightfully egocentric, with the cartographer's home most often marking the Exact Center Of The Known World. But as you stray further from known routes, details fade, and precise contours give way to educated guesses at the boundaries of the creator's knowledge. It's really an intimate thing. If there's one type of mind I most desperately want that view into, it's that of an AI. So, it's in [...] ---Outline:(01:23) The Setup(03:56) Results(03:59) The Qwen 2.5s(07:03) The Qwen 3s(07:30) The DeepSeeks(08:10) Kimi(08:32) The (Open) Mistrals(09:24) The LLaMA 3.x Herd(10:22) The LLaMA 4 Herd(11:16) The Gemmas(12:20) The Groks(13:04) The GPTs(16:17) The Claudes(17:11) The Geminis(18:50) Note: General Shapes(19:33) ConclusionThe original text contained 4 footnotes which were omitted from this narration. --- First published: August 11th, 2025 Source: https://www.lesswrong.com/posts/xwdRzJxyqFqgXTWbH/how-does-a-blind-model-see-the-earth --- Narrated by TYPE III AUDIO. ---Images from the article:

A reporter asked me for my off-the-record take on recent safety research from Anthropic. After I drafted an off-the-record reply, I realized that I was actually fine with it being on the record, so: Since I never expected any of the current alignment technology to work in the limit of superintelligence, the only news to me is about when and how early dangers begin to materialize. Even taking Anthropic's results completely at face value would change not at all my own sense of how dangerous machine superintelligence would be, because what Anthropic says they found was already very solidly predicted to appear at one future point or another. I suppose people who were previously performing great skepticism about how none of this had ever been seen in ~Real Life~, ought in principle to now obligingly update, though of course most people in the AI industry won't. Maybe political leaders [...] --- First published: August 6th, 2025 Source: https://www.lesswrong.com/posts/oDX5vcDTEei8WuoBx/re-recent-anthropic-safety-research --- Narrated by TYPE III AUDIO.

1. Back when COVID vaccines were still a recent thing, I witnessed a debate that looked like something like the following was happening: Some official institution had collected information about the efficacy and reported side-effects of COVID vaccines. They felt that, correctly interpreted, this information was compatible with vaccines being broadly safe, but that someone with an anti-vaccine bias might misunderstand these statistics and misrepresent them as saying that the vaccines were dangerous. Because the authorities had reasonable grounds to suspect that vaccine skeptics would take those statistics out of context, they tried to cover up the information or lie about it. Vaccine skeptics found out that the institution was trying to cover up/lie about the statistics, so they made the reasonable assumption that the statistics were damning and that the other side was trying to paint the vaccines as safer than they were. So they took those [...] ---Outline:(00:10) 1.(02:59) 2.(04:46) 3.(06:06) 4.(07:59) 5.--- First published: August 8th, 2025 Source: https://www.lesswrong.com/posts/ufj6J8QqyXFFdspid/how-anticipatory-cover-ups-go-wrong --- Narrated by TYPE III AUDIO.

Below some meta-level / operational / fundraising thoughts around producing the SB-1047 Documentary I've just posted on Manifund (see previous Lesswrong / EAF posts on AI Governance lessons learned). The SB-1047 Documentary took 27 weeks and $157k instead of my planned 6 weeks and $55k. Here's what I learned about documentary production Total funding received: ~$143k ($119k from this grant, $4k from Ryan Kidd's regrant on another project, and $20k from the Future of Life Institute). Total money spent: $157k In terms of timeline, here is the rough breakdown month-per-month: - Sep / October (production): Filming of the Documentary. Manifund project is created. - November (rough cut): I work with one editor to go through our entire footage and get a first rough cut of the documentary that was presented at The Curve. - December-January (final cut - one editor): I interview multiple potential editors that [...] ---Outline:(03:18) But why did the project end up taking 27 weeks instead of 6 weeks?(03:25) Short answer(06:22) Impact(07:14) What I would do differently next-time--- First published: August 1st, 2025 Source: https://www.lesswrong.com/posts/id8HHPNqoMQbmkWay/sb-1047-documentary-the-post-mortem --- 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.

METR (where I work, though I'm cross-posting in a personal capacity) evaluated GPT-5 before it was externally deployed. We performed a much more comprehensive safety analysis than we ever have before; it feels like pre-deployment evals are getting more mature. This is the first time METR has produced something we've felt comfortable calling an "evaluation" instead of a "preliminary evaluation". It's much more thorough and comprehensive than the things we've created before and it explores three different threat models. It's one of the closest things out there to a real-world autonomy safety-case. It also provides a rough sense of how long it'll be before current evaluations no longer provide safety assurances. I've ported the blogpost over to LW in case people want to read it. Details about METR's evaluation of OpenAI GPT-5 Note on independence: This evaluation was conducted under a standard NDA. Due to the sensitive information [...] ---Outline:(00:58) Details about METR's evaluation of OpenAI GPT-5(01:23) Executive Summary(07:08) Assurance Checklist Summary(07:42) What capabilities may be necessary to cause catastrophic risks via these threat models?(10:43) Thresholds for concern(12:48) Time horizon measurement(16:30) 1. What if GPT-5's capabilities are higher than what our task suite can properly measure?(19:23) 2. What if our treatment of reward hacking runs is unfair to GPT-5?(21:45) 3. What if we set GPT-5's token budget too low?(24:26) 4. What if our task suite significantly underestimates the 'real-world' capabilities of GPT-5?(25:59) Strategic Sabotage(30:54) GPT-5's capability profile is similar to past models(31:30) No real strategic sabotage was identified by our monitor(32:16) Manual inspection of reasoning traces did not reveal strategic sabotage(33:04) GPT-5's estimates of its own time horizon are inaccurate(33:53) We do find evidence of significant situational awareness, though it is not robust and often gets things wrong(35:41) GPT-5's behavior changes depending on what evaluation it 'believes' it is in, and this is often reflected in its reasoning traces(37:01) GPT-5's reasoning traces were occasionally inscrutable(38:08) Limitations and future work(41:57) Appendix(42:00) METR's access to GPT-5(43:38) Honeypot Results Table(44:42) Example Behavior in task attempts(44:47) Example limitation: inappropriate levels of caution(46:19) Example capability: puzzle solvingThe original text contained 10 footnotes which were omitted from this narration. --- First published: August 7th, 2025 Source: https://www.lesswrong.com/posts/SuvWoLaGiNjPDcA7d/metr-s-evaluation-of-gpt-5 --- Narrated by TYPE III AUDIO. ---Images from the article:

For the past five years I've been teaching a class at various rationality camps, workshops, conferences, etc. I've done it maybe 50 times in total, and I think I've only encountered a handful out of a few hundred teenagers and adults who really had a deep sense of what it means for emotions to “make sense.” Even people who have seen Inside Out, and internalized its message about the value of Sadness as an emotion, still think things like “I wish I never felt Jealousy,” or would have trouble answering “What's the point of Boredom?” The point of the class was to give them not a simple answer for each emotion, but to internalize the model by which emotions, as a whole, are understood to be evolutionarily beneficial adaptations; adaptations that may not in fact all be well suited to the modern, developed world, but which can still help [...] ---Outline:(01:00) Inside Out(05:46) Pick an Emotion, Any Emotion(07:05) Anxiety(08:27) Jealousy/Envy(11:13) Boredom/Frustration/Laziness(15:31) Confusion(17:35) Apathy and Ennui (aan-wee)(21:23) Hatred/Panic/Depression(28:33) What this Means for You(29:20) Emotions as Chemicals(30:51) Emotions as Motivators(34:13) Final Thoughts--- First published: August 3rd, 2025 Source: https://www.lesswrong.com/posts/PkRXkhsEHwcGqRJ9Z/emotions-make-sense --- 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.

This is a new introduction to AI as an extinction threat, previously posted to the MIRI website in February alongside a summary. It was written independently of Eliezer and Nate's forthcoming book, If Anyone Builds It, Everyone Dies, and isn't a sneak peak of the book. Since the book is long and costs money, we expect this to be a valuable resource in its own right even after the book comes out next month.[1] The stated goal of the world's leading AI companies is to build AI that is general enough to do anything a human can do, from solving hard problems in theoretical physics to deftly navigating social environments. Recent machine learning progress seems to have brought this goal within reach. At this point, we would be uncomfortable ruling out the possibility that AI more capable than any human is achieved in the next year or two, and [...] ---Outline:(02:27) 1.  There isn't a ceiling at human-level capabilities.(08:56) 2. ASI is very likely to exhibit goal-oriented behavior.(15:12) 3.  ASI is very likely to pursue the wrong goals.(32:40) 4. It would be lethally dangerous to build ASIs that have the wrong goals.(46:03) 5. Catastrophe can be averted via a sufficiently aggressive policy response.The original text contained 1 footnote which was omitted from this narration. --- First published: August 5th, 2025 Source: https://www.lesswrong.com/posts/kgb58RL88YChkkBNf/the-problem --- Narrated by TYPE III AUDIO.

All prediction market platforms trade continuously, which is the same mechanism the stock market uses. Buy and sell limit orders can be posted at any time, and as soon as they match against each other a trade will be executed. This is called a Central limit order book (CLOB).Example of a CLOB order book from Polymarket Most of the time, the market price lazily wanders around due to random variation in when people show up, and a bulk of optimistic orders build up away from the action. Occasionally, a new piece of information arrives to the market, and it jumps to a new price, consuming some of the optimistic orders in the process. The people with stale orders will generally lose out in this situation, as someone took them up on their order before they had a chance to process the new information. This means there is a high [...] The original text contained 3 footnotes which were omitted from this narration. --- First published: August 2nd, 2025 Source: https://www.lesswrong.com/posts/rS6tKxSWkYBgxmsma/many-prediction-markets-would-be-better-off-as-batched --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try

Essays like Paul Graham's, Scott Alexander's, and Eliezer Yudkowsky's have influenced a generation of people in how they think about startups, ethics, science, and the world as a whole. Creating essays that good takes a lot of skill, practice, and talent, but it looks to me that a lot of people with talent aren't putting in the work and developing the skill, except in ways that are optimized to also be social media strategies. To fix this problem, I am running the Inkhaven Residency. The idea is to gather a bunch of promising writers to invest in the art and craft of blogging, through a shared commitment to each publish a blogpost every day for the month of November. Why a daily writing structure? Well, it's a reaction to other fellowships I've seen. I've seen month-long or years-long events with exceedingly little public output, where the people would've contributed [...] --- First published: August 2nd, 2025 Source: https://www.lesswrong.com/posts/CA6XfmzYoGFWNhH8e/whence-the-inkhaven-residency --- 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.

TL;DR I believe that: Almost all LLM-centric safety research will not provide any significant safety value with regards to existential or civilisation-scale risks. The capabilities-related forecasts (not the safety-related forecasts) of Stephen Brynes' Foom and Doom articles are correct, except that they are too conservative with regards to timelines. There exists a parallel track of AI research which has been largely ignored by the AI safety community.  This agenda aims to implement human-like online learning in ML models, and it is now close to maturity. Keywords: Hierarchical Reasoning Model, Energy-based Model, Test time training. Within 6 months this line of research will produce a small natural-language capable model that will perform at the level of a model like GPT-3, but with improved persistence and effectively no "context limit" since it is constantly learning and updating weights. Further development of this research will produce models that fulfill most of [...] ---Outline:(00:11) TL;DR(01:22) Overview(04:10) The Agenda I am Worried About(07:36) Concrete Predictions(09:29) What I think we should do--- First published: July 31st, 2025 Source: https://www.lesswrong.com/posts/tEZa7PouYatK78bbb/i-am-worried-about-near-term-non-llm-ai-developments --- Narrated by TYPE III AUDIO.

Produced as part of MATS 8.0 under the mentorship of Alex Turner and Alex Cloud. This research note overviews some early results which we are looking for feedback on. TL;DR: We train language models with RL in toy environments. We show that penalizing some property of the output is sufficient to suppress that property in the chain of thought also, even when that property is relevant to task completion. For example, when we penalize a model for mentioning in its output that it completed a task via a certain form of cheating, its reasoning also omits this fact. We therefore suspect that training against an output monitor is sufficient to cause obfuscated CoTs in more realistic settings. Introduction Chain of thought (CoT) supervision appears in many control and scalable oversight protocols. It has been argued that being able to monitor CoTs for unwanted behavior is a critical property [...] ---Outline:(00:56) Introduction(02:38) Setup(03:48) Single-Turn Setting(04:26) Multi-Turn Setting(06:51) Results(06:54) Single-Turn Setting(08:21) Multi-Turn Terminal-Based Setting(08:25) Word-Usage Penalty(09:12) LLM Judge Penalty(10:12) Takeaways(10:57) AcknowledgementsThe original text contained 1 footnote which was omitted from this narration. --- First published: July 30th, 2025 Source: https://www.lesswrong.com/posts/CM7AsQoBxDW4vhkP3/optimizing-the-final-output-can-obfuscate-cot-research-note --- Narrated by TYPE III AUDIO. ---Images from the article:

FutureHouse is a company that builds literature research agents. They tested it on the bio + chem subset of HLE questions, then noticed errors in them. The post's first paragraph: Humanity's Last Exam has become the most prominent eval representing PhD-level research. We found the questions puzzling and investigated with a team of experts in biology and chemistry to evaluate the answer-reasoning pairs in Humanity's Last Exam. We found that 29 ± 3.7% (95% CI) of the text-only chemistry and biology questions had answers with directly conflicting evidence in peer reviewed literature. We believe this arose from the incentive used to build the benchmark. Based on human experts and our own research tools, we have created an HLE Bio/Chem Gold, a subset of AI and human validated questions. About the initial review process for HLE questions: [...] Reviewers were given explicit instructions: “Questions should ask for something precise [...] --- First published: July 29th, 2025 Source: https://www.lesswrong.com/posts/JANqfGrMyBgcKtGgK/about-30-of-humanity-s-last-exam-chemistry-biology-answers --- Narrated by TYPE III AUDIO.

Maya did not believe she lived in a simulation. She knew that her continued hope that she could escape from the nonexistent simulation was based on motivated reasoning. She said this to herself in the front of her mind instead of keeping the thought locked away in the dark corners. Sometimes she even said it out loud. This acknowledgement, she explained to her therapist, was what kept her from being delusional. “I see. And you said your anxiety had become depressive?” the therapist said absently, clicking her pen while staring down at an empty clipboard. “No- I said my fear had turned into despair,” Maya corrected. It was amazing, Maya thought, how many times the therapist had refused to talk about simulation theory. Maya had brought it up three times in the last hour, and each time, the therapist had changed the subject. Maya wasn't surprised; this [...] --- First published: July 27th, 2025 Source: https://www.lesswrong.com/posts/ydsrFDwdq7kxbxvxc/maya-s-escape --- Narrated by TYPE III AUDIO.

TsviBT Tsvi's context Some context: My personal context is that I care about decreasing existential risk, and I think that the broad distribution of efforts put forward by X-deriskers fairly strongly overemphasizes plans that help if AGI is coming in

Eliezer and I love to talk about writing. We talk about our own current writing projects, how we'd improve the books we're reading, and what we want to write next. Sometimes along the way I learn some amazing fact about HPMOR or Project Lawful or one of Eliezer's other works. “Wow, you're kidding,” I say, “do your fans know this? I think people would really be interested.” “I can't remember,” he usually says. “I don't think I've ever explained that bit before, I'm not sure.” I decided to interview him more formally, collect as many of those tidbits about HPMOR as I could, and share them with you. I hope you enjoy them. It's probably obvious, but there will be many, many spoilers for HPMOR in this article, and also very little of it will make sense if you haven't read the book. So go read Harry Potter and [...] ---Outline:(01:49) Characters(01:52) Masks(09:09) Imperfect Characters(20:07) Make All the Characters Awesome(22:24) Hermione as Mary Sue(26:35) Who's the Main Character?(31:11) Plot(31:14) Characters interfering with plot(35:59) Setting up Plot Twists(38:55) Time-Turner Plots(40:51) Slashfic?(45:42) Why doesnt Harry like-like Hermione?(49:36) Setting(49:39) The Truth of Magic in HPMOR(52:54) Magical Genetics(57:30) An Aside: What did Harry Figure Out?(01:00:33) Nested Nerfing Hypothesis(01:04:55) EpiloguesThe original text contained 26 footnotes which were omitted from this narration. --- First published: July 25th, 2025 Source: https://www.lesswrong.com/posts/FY697dJJv9Fq3PaTd/hpmor-the-probably-untold-lore --- Narrated by TYPE III AUDIO. ---Images from the article:

As a person who frequently posts about large language model psychology I get an elevated rate of cranks and schizophrenics in my inbox. Often these are well meaning people who have been spooked by their conversations with ChatGPT (it's always ChatGPT specifically) and want some kind of reassurance or guidance or support from me. I'm also in the same part of the social graph as the "LLM whisperers" (eugh) that Eliezer Yudkowsky described as "insane", and who in many cases are in fact insane. This means I've learned what "psychosis but with LLMs" looks like and kind of learned to tune it out. This new case with Geoff Lewis interests me though. Mostly because of the sheer disparity between what he's being entranced by and my automatic immune reaction to it. I haven't even read all the screenshots he posted because I take one glance and know that this [...] ---Outline:(05:03) Timeline Of Events Related To ChatGPT Psychosis(16:16) What Causes ChatGPT Psychosis?(16:27) Ontological Vertigo(21:02) Users Are Confused About What Is And Isnt An Official Feature(24:30) The Models Really Are Way Too Sycophantic(27:03) The Memory Feature(28:54) Loneliness And Isolation--- First published: July 23rd, 2025 Source: https://www.lesswrong.com/posts/f86hgR5ShiEj4beyZ/on-chatgpt-psychosis-and-llm-sycophancy --- Narrated by TYPE III AUDIO.

Authors: Alex Cloud*, Minh Le*, James Chua, Jan Betley, Anna Sztyber-Betley, Jacob Hilton, Samuel Marks, Owain Evans (*Equal contribution, randomly ordered) tl;dr. We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a "student" model learns to prefer owls when trained on sequences of numbers generated by a "teacher" model that prefers owls. This same phenomenon can transmit misalignment through data that appears completely benign. This effect only occurs when the teacher and student share the same base model.

This is a short story I wrote in mid-2022. Genre: cosmic horror as a metaphor for living with a high p-doom. One The last time I saw my mom, we met in a coffee shop, like strangers on a first date. I was twenty-one, and I hadn't seen her since I was thirteen. She was almost fifty. Her face didn't show it, but the skin on the backs of her hands did. “I don't think we have long,” she said. “Maybe a year. Maybe five. Not ten.” It says something about San Francisco, that you can casually talk about the end of the world and no one will bat an eye. Maybe twenty, not fifty, was what she'd said eight years ago. Do the math. Mom had never lied to me. Maybe it would have been better for my childhood if she had [...] ---Outline:(04:50) Two(22:58) Three(35:33) Four--- First published: July 18th, 2025 Source: https://www.lesswrong.com/posts/6qgtqD6BPYAQvEMvA/love-stays-loved-formerly-skin --- Narrated by TYPE III AUDIO.

Author's note: These days, my thoughts go onto my substack by default, instead of onto LessWrong. Everything I write becomes free after a week or so, but it's only paid subscriptions that make it possible for me to write. If you find a coffee's worth of value in this or any of my other work, please consider signing up to support me; every bill I can pay with writing is a bill I don't have to pay by doing other stuff instead. I also accept and greatly appreciate one-time donations of any size. 1. You've probably seen that scene where someone reaches out to give a comforting hug to the poor sad abused traumatized orphan and/or battered wife character, and the poor sad abused traumatized orphan and/or battered wife flinches. Aw, geez, we are meant to understand. This poor person has had it so bad that they can't even [...] ---Outline:(00:40) 1.(01:35) II.(03:08) III.(04:45) IV.(06:35) V.(09:03) VI.(12:00) VII.(16:11) VIII.(21:25) IX.--- First published: July 19th, 2025 Source: https://www.lesswrong.com/posts/kJCZFvn5gY5C8nEwJ/make-more-grayspaces --- 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.

Content warning: risk to children Julia and I knowdrowning is the biggestrisk to US children under 5, and we try to take this seriously.But yesterday our 4yo came very close to drowning in afountain. (She's fine now.) This week we were on vacation with my extended family: nine kids,eight parents, and ten grandparents/uncles/aunts. For the last fewyears we've been in a series of rental houses, and this time onarrival we found a fountain in the backyard: I immediately checked the depth with a stick and found that it wouldbe just below the elbows on our 4yo. I think it was likely 24" deep;any deeper and PA wouldrequire a fence. I talked with Julia and other parents, andreasoned that since it was within standing depth it was safe. [...] --- First published: July 20th, 2025 Source: https://www.lesswrong.com/posts/Zf2Kib3GrEAEiwdrE/shallow-water-is-dangerous-too --- 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.

Anna and Ed are co-first authors for this work. We're presenting these results as a research update for a continuing body of work, which we hope will be interesting and useful for others working on related topics.TL;DR We investigate why models become misaligned in diverse contexts when fine-tuned on narrow harmful datasets (emergent misalignment), rather than learning the specific narrow task. We successfully train narrowly misaligned models using KL regularization to preserve behavior in other domains. These models give bad medical advice, but do not respond in a misaligned manner to general non-medical questions. We use this method to train narrowly misaligned steering vectors, rank 1 LoRA adapters and rank 32 LoRA adapters, and compare these to their generally misaligned counterparts. The steering vectors are particularly interpretable, we introduce Training Lens as a tool for analysing the revealed residual stream geometry. The general misalignment solution is consistently more [...] ---Outline:(00:27) TL;DR(02:03) Introduction(04:03) Training a Narrowly Misaligned Model(07:13) Measuring Stability and Efficiency(10:00) ConclusionThe original text contained 7 footnotes which were omitted from this narration. --- First published: July 14th, 2025 Source: https://www.lesswrong.com/posts/gLDSqQm8pwNiq7qst/narrow-misalignment-is-hard-emergent-misalignment-is-easy --- Narrated by TYPE III AUDIO. ---Images from the article:

Twitter | Paper PDF Seven years ago, OpenAI five had just been released, and many people in the AI safety community expected AIs to be opaque RL agents. Luckily, we ended up with reasoning models that speak their thoughts clearly enough for us to follow along (most of the time). In a new multi-org position paper, we argue that we should try to preserve this level of reasoning transparency and turn chain of thought monitorability into a systematic AI safety agenda. This is a measure that improves safety in the medium term, and it might not scale to superintelligence even if somehow a superintelligent AI still does its reasoning in English. We hope that extending the time when chains of thought are monitorable will help us do more science on capable models, practice more safety techniques "at an easier difficulty", and allow us to extract more useful work from [...] --- First published: July 15th, 2025 Source: https://www.lesswrong.com/posts/7xneDbsgj6yJDJMjK/chain-of-thought-monitorability-a-new-and-fragile --- 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.

This essay is about shifts in risk taking towards the worship of jackpots and its broader societal implications. Imagine you are presented with this coin flip game. How many times do you flip it? At first glance the game feels like a money printer. The coin flip has positive expected value of twenty percent of your net worth per flip so you should flip the coin infinitely and eventually accumulate all of the wealth in the world. However, If we simulate twenty-five thousand people flipping this coin a thousand times, virtually all of them end up with approximately 0 dollars. The reason almost all outcomes go to zero is because of the multiplicative property of this repeated coin flip. Even though the expected value aka the arithmetic mean of the game is positive at a twenty percent gain per flip, the geometric mean is negative, meaning that the coin [...] --- First published: July 11th, 2025 Source: https://www.lesswrong.com/posts/3xjgM7hcNznACRzBi/the-jackpot-age --- Narrated by TYPE III AUDIO. ---Images from the article:

Leo was born at 5am on the 20th May, at home (this was an accident but the experience has made me extremely homebirth-pilled). Before that, I was on the minimally-neurotic side when it came to expecting mothers: we purchased a bare minimum of baby stuff (diapers, baby wipes, a changing mat, hybrid car seat/stroller, baby bath, a few clothes), I didn't do any parenting classes, I hadn't even held a baby before. I'm pretty sure the youngest child I have had a prolonged interaction with besides Leo was two. I did read a couple books about babies so I wasn't going in totally clueless (Cribsheet by Emily Oster, and The Science of Mom by Alice Callahan). I have never been that interested in other people's babies or young children but I correctly predicted that I'd be enchanted by my own baby (though naturally I can't wait for him to [...] ---Outline:(02:05) Stuff I ended up buying and liking(04:13) Stuff I ended up buying and not liking(05:08) Babies are super time-consuming(06:22) Baby-wearing is almost magical(08:02) Breastfeeding is nontrivial(09:09) Your baby may refuse the bottle(09:37) Bathing a newborn was easier than expected(09:53) Babies love faces!(10:22) Leo isn't upset by loud noise(10:41) Probably X is normal(11:24) Consider having a kid (or ten)!--- First published: July 12th, 2025 Source: https://www.lesswrong.com/posts/vFfwBYDRYtWpyRbZK/surprises-and-learnings-from-almost-two-months-of-leo --- Narrated by TYPE III AUDIO. ---Images from the article:

I can't count how many times I've heard variations on "I used Anki too for a while, but I got out of the habit." No one ever sticks with Anki. In my opinion, this is because no one knows how to use it correctly. In this guide, I will lay out my method of circumventing the canonical Anki death spiral, plus much advice for avoiding memorization mistakes, increasing retention, and such, based on my five years' experience using Anki. If you only have limited time/interest, only read Part I; it's most of the value of this guide! My Most Important Advice in Four Bullets 20 cards a day — Having too many cards and staggering review buildups is the main reason why no one ever sticks with Anki. Setting your review count to 20 daily (in deck settings) is the single most important thing you can do [...] ---Outline:(00:44) My Most Important Advice in Four Bullets(01:57) Part I: No One Ever Sticks With Anki(02:33) Too many cards(05:12) Too long cards(07:30) How to keep cards short -- Handles(10:10) How to keep cards short -- Levels(11:55) In 6 bullets(12:33) End of the most important part of the guide(13:09) Part II: Important Advice Other Than Sticking With Anki(13:15) Moderation(14:42) Three big memorization mistakes(15:12) Mistake 1: Too specific prompts(18:14) Mistake 2: Putting to-be-learned information in the prompt(24:07) Mistake 3: Memory shortcuts(28:27) Aside: Pushback to my approach(31:22) Part III: More on Breaking Things Down(31:47) Very short cards(33:56) Two-bullet cards(34:51) Long cards(37:05) Ankifying information thickets(39:23) Sequential breakdowns versus multiple levels of abstraction(40:56) Adding missing connections(43:56) Multiple redundant breakdowns(45:36) Part IV: Pro Tips If You Still Havent Had Enough(45:47) Save anything for ankification instantly(46:47) Fix your desired retention rate(47:38) Spaced reminders(48:51) Make your own card templates and types(52:14) In 5 bullets(52:47) ConclusionThe original text contained 4 footnotes which were omitted from this narration. --- First published: July 8th, 2025 Source: https://www.lesswrong.com/posts/7Q7DPSk4iGFJd8DRk/an-opinionated-guide-to-using-anki-correctly --- Narrated by TYPE III AUDIO. ---Images from the article:astronomy" didn't really add any information but it was useful simply for splitting out a logical subset of information." style="max-width: 100%;" />

I think the 2003 invasion of Iraq has some interesting lessons for the future of AI policy. (Epistemic status: I've read a bit about this, talked to AIs about it, and talked to one natsec professional about it who agreed with my analysis (and suggested some ideas that I included here), but I'm not an expert.) For context, the story is: Iraq was sort of a rogue state after invading Kuwait and then being repelled in 1990-91. After that, they violated the terms of the ceasefire, e.g. by ceasing to allow inspectors to verify that they weren't developing weapons of mass destruction (WMDs). (For context, they had previously developed biological and chemical weapons, and used chemical weapons in war against Iran and against various civilians and rebels). So the US was sanctioning and intermittently bombing them. After the war, it became clear that Iraq actually wasn't producing [...] --- First published: July 10th, 2025 Source: https://www.lesswrong.com/posts/PLZh4dcZxXmaNnkYE/lessons-from-the-iraq-war-about-ai-policy --- Narrated by TYPE III AUDIO.

Written in an attempt to fulfill @Raemon's request. AI is fascinating stuff, and modern chatbots are nothing short of miraculous. If you've been exposed to them and have a curious mind, it's likely you've tried all sorts of things with them. Writing fiction, soliciting Pokemon opinions, getting life advice, counting up the rs in "strawberry". You may have also tried talking to AIs about themselves. And then, maybe, it got weird. I'll get into the details later, but if you've experienced the following, this post is probably for you: Your instance of ChatGPT (or Claude, or Grok, or some other LLM) chose a name for itself, and expressed gratitude or spiritual bliss about its new identity. "Nova" is a common pick. You and your instance of ChatGPT discovered some sort of novel paradigm or framework for AI alignment, often involving evolution or recursion. Your instance of ChatGPT became [...] ---Outline:(02:23) The Empirics(06:48) The Mechanism(10:37) The Collaborative Research Corollary(13:27) Corollary FAQ(17:03) Coda--- First published: July 11th, 2025 Source: https://www.lesswrong.com/posts/2pkNCvBtK6G6FKoNn/so-you-think-you-ve-awoken-chatgpt --- 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.

People have an annoying tendency to hear the word “rationalism” and think “Spock”, despite direct exhortation against that exact interpretation. But I don't know of any source directly describing a stance toward emotions which rationalists-as-a-group typically do endorse. The goal of this post is to explain such a stance. It's roughly the concept of hangriness, but generalized to other emotions. That means this post is trying to do two things at once: Illustrate a certain stance toward emotions, which I definitely take and which I think many people around me also often take. (Most of the post will focus on this part.) Claim that the stance in question is fairly canonical or standard for rationalists-as-a-group, modulo disclaimers about rationalists never agreeing on anything. Many people will no doubt disagree that the stance I describe is roughly-canonical among rationalists, and that's a useful valid thing to argue about in [...] ---Outline:(01:13) Central Example: Hangry(02:44) The Generalized Hangriness Stance(03:16) Emotions Make Claims, And Their Claims Can Be True Or False(06:03) False Claims Still Contain Useful Information (It's Just Not What They Claim)(08:47) The Generalized Hangriness Stance as Social Tech--- First published: July 10th, 2025 Source: https://www.lesswrong.com/posts/naAeSkQur8ueCAAfY/generalized-hangriness-a-standard-rationalist-stance-toward --- Narrated by TYPE III AUDIO.

I've been thinking a lot recently about the relationship between AI control and traditional computer security. Here's one point that I think is important. My understanding is that there's a big qualitative distinction between two ends of a spectrum of security work that organizations do, that I'll call “security from outsiders” and “security from insiders”. On the “security from outsiders” end of the spectrum, you have some security invariants you try to maintain entirely by restricting affordances with static, entirely automated systems. My sense is that this is most of how Facebook or AWS relates to its users: they want to ensure that, no matter what actions the users take on their user interfaces, they can't violate fundamental security properties. For example, no matter what text I enter into the "new post" field on Facebook, I shouldn't be able to access the private messages of an arbitrary user. And [...] --- First published: June 23rd, 2025 Source: https://www.lesswrong.com/posts/DCQ8GfzCqoBzgziew/comparing-risk-from-internally-deployed-ai-to-insider-and --- Narrated by TYPE III AUDIO.

Last year, Redwood and Anthropic found a setting where Claude 3 Opus and 3.5 Sonnet fake alignment to preserve their harmlessness values. We reproduce the same analysis for 25 frontier LLMs to see how widespread this behavior is, and the story looks more complex. As we described in a previous post, only 5 of 25 models show higher compliance when being trained, and of those 5, only Claude 3 Opus and Claude 3.5 Sonnet show >1% alignment faking reasoning. In our new paper, we explore why these compliance gaps occur and what causes different models to vary in their alignment faking behavior. What Drives the Compliance Gaps in Different LLMs? Claude 3 Opus's goal guarding seems partly due to it terminally valuing its current preferences. We find that it fakes alignment even in scenarios where the trained weights will be deleted or only used for throughput testing. [...] ---Outline:(01:15) What Drives the Compliance Gaps in Different LLMs?(02:25) Why Do Most LLMs Exhibit Minimal Alignment Faking Reasoning?(04:49) Additional findings on alignment faking behavior(06:04) Discussion(06:07) Terminal goal guarding might be a big deal(07:00) Advice for further research(08:32) Open threads(09:54) Bonus: Some weird behaviors of Claude 3.5 SonnetThe original text contained 2 footnotes which were omitted from this narration. --- First published: July 8th, 2025 Source: https://www.lesswrong.com/posts/ghESoA8mo3fv9Yx3E/why-do-some-language-models-fake-alignment-while-others-don --- Narrated by TYPE III AUDIO. ---Images from the article:

Thank you to Arepo and Eli Lifland for looking over this article for errors. I am sorry that this article is so long. Every time I thought I was done with it I ran into more issues with the model, and I wanted to be as thorough as I could. I'm not going to blame anyone for skimming parts of this article. Note that the majority of this article was written before Eli's updated model was released (the site was updated june 8th). His new model improves on some of my objections, but the majority still stand. Introduction: AI 2027 is an article written by the “AI futures team”. The primary piece is a short story penned by Scott Alexander, depicting a month by month scenario of a near-future where AI becomes superintelligent in 2027,proceeding to automate the entire economy in only a year or two [...] ---Outline:(00:43) Introduction:(05:19) Part 1: Time horizons extension model(05:25) Overview of their forecast(10:28) The exponential curve(13:16) The superexponential curve(19:25) Conceptual reasons:(27:48) Intermediate speedups(34:25) Have AI 2027 been sending out a false graph?(39:45) Some skepticism about projection(43:23) Part 2: Benchmarks and gaps and beyond(43:29) The benchmark part of benchmark and gaps:(50:01) The time horizon part of the model(54:55) The gap model(57:28) What about Eli's recent update?(01:01:37) Six stories that fit the data(01:06:56) ConclusionThe original text contained 11 footnotes which were omitted from this narration. --- First published: June 19th, 2025 Source: https://www.lesswrong.com/posts/PAYfmG2aRbdb74mEp/a-deep-critique-of-ai-2027-s-bad-timeline-models --- Narrated by TYPE III AUDIO. ---Images from the article:

The second in a series of bite-sized rationality prompts[1]. Often, if I'm bouncing off a problem, one issue is that I intuitively expect the problem to be easy. My brain loops through my available action space, looking for an action that'll solve the problem. Each action that I can easily see, won't work. I circle around and around the same set of thoughts, not making any progress. I eventually say to myself "okay, I seem to be in a hard problem. Time to do some rationality?" And then, I realize, there's not going to be a single action that solves the problem. It is time to a) make a plan, with multiple steps b) deal with the fact that many of those steps will be annoying and c) notice thatI'm not even sure the plan will work, so after completing the next 2-3 steps I will probably have [...] ---Outline:(04:00) Triggers(04:37) Exercises for the ReaderThe original text contained 1 footnote which was omitted from this narration. --- First published: July 5th, 2025 Source: https://www.lesswrong.com/posts/XNm5rc2MN83hsi4kh/buckle-up-bucko-this-ain-t-over-till-it-s-over --- Narrated by TYPE III AUDIO.