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Tuesday · 5 min
“Live disagreements with people around me” by KatjaGrace
I’m pretty interested in conveying the costs of letting AI development proceed unchecked to the general public. In relation to this, I feel like I run into a lot of disagreements with people around me. Misc very quickly written points on which my own impression has seemed very different from others in recent months: 1. It's way, way, way more important that we stop dangerous AI soon than that we ensure that we don’t stop AI too much I have the sense the sophisticated and worldly position here is to be very concerned about ‘over-regulation’ and about not doing exactly the right regulations. But it's an obvious pragmatic fact that the stakes of overregulation are minuscule compared to the stakes of underregulating here. And the stakes of mis-regulation are very often going to be unintended slowing, which is in the same bucket. 2. If we stopped AI development, the risk of not starting up again for an extremely long time is low This one just feels kind of crazy to me, like I may be missing something. In combination with the above point, it feels like if I met someone who was on fire, and they didn’t want me [...] --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/dQJ3ArHMLxqu8rptt/live-disagreements-with-people-around-me --- Narrated by TYPE III AUDIO.
Tuesday · 7 min
“We need more transparency about alignment overfitting” by Nikola Jurkovic
Currently, the public has very little insight about the robustness of alignment methods used by AGI companies. It's possible that, when an AGI company notices some misaligned behavior (e.g. through an alignment evaluation, or through noticing real-world misbehavior), they conduct a principled investigation and address root causes of the misalignment. It's also possible that they directly try to train away the examples of misalignment, which could result in brittle alignment that does not generalize out-of-distribution. Without additional information from within AI companies about how they align their models (especially in response to egregious cases of misalignment, e.g. cybersecurity incidents), the public cannot adequately reason about risks from misalignment. Public evidence is consistent with companies overfitting on alignment methods The public evidence is consistent with multiple companies having no clear separation between the ways they notice misalignment and the environments they use to train misalignment away. Companies have made very few statements about whether companies train directly on their evaluation distribution or real-world examples of misalignment. As far as I can tell, this is what's publicly known: OpenAI: In the GPT-6 Astra System Card, they wrote that they built an eval in response to the Hugging Face [...] --- Outline: (00:55) Public evidence is consistent with companies overfitting on alignment methods (05:21) The lack of information is seriously hindering the public's ability to estimate misalignment risk The original text contained 2 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/JTB8JjQfmkHsJtBDs/we-need-more-transparency-about-alignment-overfitting --- Narrated by TYPE III AUDIO.
Tuesday · 43 min
“The Western AI safety playbook doesn’t transfer to China” by jacquesthibs
Crossposted from my blog. Last month, I spent two weeks in China talking with people about AI safety. I learned a lot and realized the biggest mistake might not be failing to help AI safety in China, but doing so in ways that could backfire. I also learned important context that I expect will be valuable for the West. Epistemic status: Some of these points are my opinion or the opinions of people I spoke to, and are worth further investigation rather than treated as gospel. Summary The Western way of building the field does not transfer one-to-one. The West built its ecosystem through a combination of philanthropy and decades of developing AI safety foundations, leading founders and employees of frontier labs and nonprofits to act (however misguided) on this philosophy. China does not have the same initial conditions and can’t simply accept non-Chinese philanthropy. In China, foreign nonprofit money is restricted by law, and the bar for a Chinese partner to take it on is very high. Safety work in China will mostly come from state-backed institutes, universities, companies and volunteers. Concordia AI, which I assumed for years was a nonprofit, is a social enterprise. Academic exchange [...] --- Outline: (00:41) Summary (02:17) Why I went (04:28) Why the Western model does not transfer one-to-one (05:12) Foreign nonprofit money faces a very high bar (06:43) Where safety work in China comes from (09:13) Training and exchange (11:04) Investing and buying services (12:17) Be careful; don't rush (12:50) China is suspicious of the West and Western safety framing (16:43) What is the current vibe on AI from AI researchers and investors in China? (17:14) Chinese AI companies expect catching up to become harder (20:07) The Chinese labs want to train bigger pretrains to distill, but lack the compute (24:39) Chinese labs historically competed on inference cost, but this is increasingly no longer the case (26:07) What the founders say they believe (27:28) The Overton window may shift in China too (34:13) What to do, and what not to do (35:12) Conclusions (36:31) Further Recommendations (36:34) Start here (38:14) Chinese voices and institutions (39:54) Research and collaboration (41:08) Evaluation and preparation that could change decisions (41:32) Reciprocal restraint: proposals and objections --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/AcBzhiaTW9KnB4EBv/the-western-ai-safety-playbook-doesn-t-transfer-to-china-1 --- 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.
Tuesday · 19 min
“Epistemic Sudden Death” by Raemon
When sports go into overtime, they sometimes use "sudden death" format. Instead of gaining points incrementally, the next person to score instantly wins and the other guy instantly loses. "Epistemic Sudden Death" is when you've been accumulating some evidence or orienting to a situation, and now, you find yourself in a situation where you could gain decisive evidence about what world you're in at any time. You don't expect intermediate observations between the current state, and whatever it is you're expecting. In the case of AI, when you meet those observations, you also might suddenly die. The current AIs are not takeover capable. I don't have that confident a take on whether timelines are a few months vs a few years or decades. But there are no observations my models say I need to observe between now and "they are takeover capable." It could happen in during next 10x or 100x scaled training run. Then, some time later as the nanites dissect my body I'd be more like "yep, that makes sense" than "wowzers I am surprised." I think we're in a Sudden Death scenario. I'll get into why I think that a bit later. For right now [...] --- Outline: (03:50) Caveats (05:00) Why I think we're close to "Sudden Death" (09:09) Suddenness. Or: "Damn, look at that graph" (14:11) The Point of No Return (16:48) "Sudden Death" might be legible to politicians The original text contained 6 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/ijt6YdwSNpcevbFJy/epistemic-sudden-death --- 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.
Tuesday · 5 min
“Lightcone Commons: Round 1 complete, Round 2 underway!” by habryka
Tl;dr: Lightcone Commons’ first funding round is complete! 17 evaluators made funding recommendations, including Zvi Mowshowitz, Scott Siskind and Eliezer Yudkowsky. 23 funders coordinated to grant 45.1 million dollars, and 147 projects received funding. Round two is open. Applications for round two close on November 15th and funders confirm their grants by December 14th. Funders can join anytime before December 7th. In 2027, we will be hosting funding rounds monthly. Yes. Every month. You can join as a funder, express interest in joining as an evaluator, and apply for funding at lightconecommons.com. Lightcone Commons staff do not decide what grants get made. Pls stop saying that "Lightcone Commons funded us", that's a bit like saying "PayPal funded me". Lightcone Commons is an end-to-end philanthropic coordination platform. We host an open funding application, and invite evaluators to make ordered funding recommendations. Participating funders then decide how much they want to give, and to whom – either by following the recommendations of evaluators or choosing specific projects to fund. When multiple funders want to fund the same project they ‘split the bill’. Lightcone Commons is not a foundation. We don’t decide to make specific grants. Funders decide [...] --- Outline: (02:16) Round One Overview (02:22) Funders (03:08) Evaluators (03:50) Next Round The original text contained 3 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/3ASLq8ePNqkbeGqdj/lightcone-commons-round-1-complete-round-2-underway --- Narrated by TYPE III AUDIO.
Tuesday · 14 min
″“Hardwired Pause” should likely be your favorite (pause) plan.” by David Scott Krueger
I'm part of a team of 26 researchers that recently released a 212-page paper proposing a new plan for pausing AI, the "hardwired pause." The paper is called "The Feasibility of a Hardwired Pause of Frontier AI Training." Our analysis suggests that, under this hardwired pause plan, we could probably pause for at least a decade if we start stopping within the next year, and this could likely be extended indefinitely. I expect this to be possible without trusting China or tanking the economy. Note: I am speaking for myself and not the other 25 authors. The working group is led by Will Fithian and Wesley Holliday, and the work continues. I currently think: The best plan is to pause/stop AI, i.e. have an immediate, indefinite, international moratorium on frontier AI development. And importantly, this is technically easier than more nuanced international regulation. A hardwired pause is probably the best plan out there for how to do this (except maybe it doesn't go far enough). This paper is likely the most in-depth analysis of pause feasibility in existence. Discussion and disagreement on any of these is very welcome! What's a hardwired pause? A hardwired pause basically [...] --- Outline: (01:31) What's a hardwired pause? (03:21) Why is this better than Plan A? (05:06) How is this different from MIRI's Plan S? (06:39) What are my biggest concerns about a hardwired pause (vs. getting rid of chips entirely)? (09:15) Why is the paper so long? What's in it? (13:35) Conclusion The original text contained 4 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/jT272BNHFzgAR5u2m/hardwired-pause-should-likely-be-your-favorite-pause-plan --- 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.
Monday · 26 min
“Three Phases of Capabilities and Alignment” by beren
Cross-posted from my personal blog Epistemic Status: Reality is messier than the elegant procession of 'phases' presented here but, nevertheless, I do think this gets at an important way to think about how alignment and capabilities are intertwined and how the alignment challenge becomes increasingly abstract as the underlying autonomy and generality of the models improves over time. A thought struck me recently which I thought was interesting. As AI has progressed through different technological phases with different underlying drivers of progress, the corresponding alignment challenge has migrated too. In fact, in many ways the alignment challenge is the mirror of the fundamental capabilities challenge of each phase. This may actually be inevitable since alignment and capabilities are intrinsically deeply intertwined -- for a model to be capable it has to be at least minimally able to do what we want it to do. 'Capabilities' are intrinsically about doing the things we care about models doing. The methods used to improve capabilities tend also to be applicable to alignment as well. What changes as we move to progressively more general levels of capabilities is that the training loop itself becomes increasingly endogenous to the model, and [...] The original text contained 12 footnotes which were omitted from this narration. --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/EGDqsLt8BFuhTYp5u/three-phases-of-capabilities-and-alignment --- Narrated by TYPE III AUDIO.
Monday · 22 min
“Preservation can save the world!” by Aurelia
I'm Aurelia Song and I founded the preservation company Nectome. We do planned whole-body end-of-life preservation for future revival. That is, terminally-ill people come to us in Oregon, and we preserve them immediately after their death in the hopes that one day people in the future will welcome them back with love. We want to build a future that celebrates and restores those who are preserved today. I've argued at length in previous posts that properly done end-of-life preservation works today because we know structural preservation is sufficient and we can provably preserve sufficient structure. This post is about some of the consequences of this being true. Perhaps surprisingly, I think end-of-life preservation is extremely relevant in the next year to getting AI right. I'm writing this post to directly state my case, since I haven't seen it expressed clearly before. tl;dr: Preservation offers a much safer option to those accelerationists who are rushing to AI in the hopes of saving the lives of people they love who have only a few years to live. Those people can be preserved, so there's no need to rush anymore. Preservation gives everyone a place of psychological safety from which we can all work [...] --- Outline: (00:47) Perhaps surprisingly, I think end-of-life preservation is extremely relevant in the next year to getting AI right. I'm writing this post to directly state my case, since I haven't seen it expressed clearly before. (01:27) How could preservation possibly be relevant to AI this year? (02:48) What are accelerationists thinking? (03:21) I was an accelerationist before AI was even cool (05:16) Why didn't I care about the downsides of AI? (07:59) How I learned personal responsibility in technology. (11:03) Preservation gave me a new, better purpose in life. (12:47) Death makes everyone crazy and preservation can stop it. (12:52) Denial and acceptance, the twin seeds of madness (14:39) Preservation is a new way to directly face death without compromise (16:30) Let's save the world! The original text contained 4 footnotes which were omitted from this narration. --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/6TCpMtoDzC5p5WtQM/preservation-can-save-the-world --- 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.
Monday · 1 hr 18 min
“AI R&D Is Accelerating Towards Potential RSI in 2027” by Zvi
METR came out with an extensive report on how much AI is accelerating AI R&D at Anthropic, and thus how close we might be to recursive self-improvement (RSI) and how far that might go. This is one of the most important questions in the world. Their answer: Things are already accelerating on the order of a 2 times speedup. This is several months ahead of the AI 2027 timeline. A naive extrapolation says we may be less than a year away from full RSI. There are no clear bottlenecks in the way. This report updated many towards shorter timelines. This is consistent with what OpenAI and Anthropic themselves have been saying, and with extrapolation from the progress we are observing. We are running out of time. That doesn’t have to be the way this plays out. We could stop the labs. The labs could choose to stop. Or the estimates could be wrong, or some bottlenecks could be hit, or the results could be less impactful than we expect. Indeed do many things come to pass. And if it does happen this quickly, one would expect it to go quite poorly, and likely [...] --- Outline: (06:54) Top Conclusions (09:31) Data Access and Redactions (15:23) The Pace of Progress (17:09) 1, 2, 3, 4 (17:56) Timing Is Everything (19:14) What Could Possibly Have Gone Wrong? (20:58) AECI (25:41) The Public Information Alone Could Suggest Slower Speedup (29:01) Well, That Escalated Quickly (39:44) This Infinite Series Likely Has a Finite Sum (41:02) We Cannot Say What We Cannot Say (41:28) Study of Anthropic's Compute Allocation Was Postponed (42:58) Security Mindset (44:50) It's a Secret to Everyone (54:05) Review of the Risk Report from August 2026 (56:52) A Word From Our Cofounder (01:03:48) People Update Their Timelines and Expectations (01:10:11) You Are Not Ready (01:11:18) If (If Anyone Builds It, Everyone Dies) Then Don't Let Anyone Build It (01:14:25) How Can We Legally Define RSI? --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/je5X4xEuehu3j3HoF/ai-r-and-d-is-accelerating-towards-potential-rsi-in-2027 --- 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.
Monday · 24 min
“Astra covertly underperforming on safety research is concerning, even though it’s unlikely to be motivated by ambitious goals” by Alexa Pan
OpenAI recently disclosed its observations that Astra consistently underperforms on safety and alignment tasks relative to capabilities tasks, “adding unnecessary prerequisites, abandoning working solutions, and inventing blockers” to safety tasks. Astra underperforms, in most cases, without verbalizing this intent. Astra's underperformance was first noticed by OpenAI researchers, and was not flagged by misalignment monitors. In follow-up investigations, OpenAI found that this underperformance had increased over the course of RL, but that the root cause of the behavior remains unclear. Astra covertly underperforming on safety tasks is highly concerning. AI companies’ main hope for alignment going well, as I understand it, is automating or using AIs to significantly uplift safety research, especially as AI capabilities accelerate. Models subtly obstructing safety research makes this substantially harder, no matter the root cause. While past models also sometimes refused to help with safety research, Astra's behavior seems novel and especially concerning in a few ways: Astra silently withholds help from users instead of explicitly refusing. This is more misaligned, i.e. more unambiguously violates its model spec and developer intent. Astra's underperformance was subtle enough to consistently evade misalignment monitors. This is evidence of insufficient alignment monitorability. We still don't understand the root causes [...] --- Outline: (02:29) Summary of OpenAI's findings (05:47) What could have caused Astra to underperform? (06:04) Astra is probably not strategically underperforming in service of ambitious goals. Its motivation is unclear. (09:16) Root causes of this behavior seem uncertain (11:20) How concerning is Astra's underperformance? (11:25) Subtle obstruction on safety R&D is harmful no matter the root cause (12:55) But, unstrategic underperformance is less scary than strategic sandbagging (14:21) Astra's behavior, and OpenAI's struggle to identify its root causes, shows that current alignment is still a hard and unsolved problem (16:06) Astra underperforming subtly enough to evade misalignment monitors shows insufficient alignment monitorability (17:59) Open questions (23:17) Conclusion The original text contained 19 footnotes which were omitted from this narration. --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/SEdum3BDfAJtwLuoB/astra-covertly-underperforming-on-safety-research-is --- 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.
Monday · 6 min
“Where is all the AI safety research uplift?” by Buck
METR recently released a report estimating that Anthropic is getting an overall 1.7x acceleration in AI capability progress as a result of AI accelerating their work. As many have noted (see e.g. Ajeya's post), this 1.7x acceleration implies that research is being sped up by much more than 1.7x; Ajeya speculates 10x (which is similar to the ~12x uplift estimated by Anthropic staff). That is, this result implies that researchers are producing about 10 times as many quality-adjusted results as they would without AI progress, or as they were a year or two ago. Anthropic researchers estimated that Mythos 5 caused 5x uplift, and we have access to Opus 5.5, which is somewhat better. (I didn’t take their estimate that seriously at the time, because it was quite unclear how the estimate was produced and it seemed very surprising (see Ryan's discussion at the time), but I’m taking it more seriously now as a result of it being consistent with the METR report). If this is the case, then AIs speed up AI safety research as much as AI capabilities research, we might expect Anthropic safety researchers to be working 10 times faster, and external safety researchers to be [...] --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/wPrpnfaNL5JP9i6KT/where-is-all-the-ai-safety-research-uplift --- Narrated by TYPE III AUDIO.
Monday · 25 min
“Knowledge of graders can mediate selection for grader-targeting reasoning through RL” by lennie, Edward James Young
TL;DR: We insert knowledge of specific grading processes via SDF. We then show that models RL’d on environments with these graders exhibit grader-targeting reasoning, and generalise to optimising held-out environments with graders that were described in the SDF mix but not trained on during RL. Figure 1: Outline of our experiments. (Left) We use SDF to instil knowledge of grading on different subsets of environments. (Middle) We then perform RL on one half of the set of environments, which are In-Distribution (ID) for RL. We observe that models with ID grader knowledge exhibit faster learning than those without. (Right) Finally, we evaluate the models on the other half of environments, which are Out-of-Distribution (OOD) for RL. We observe that only the model with knowledge of both grading structures generalises to increased reward OOD. This work was conducted as part of a collaboration between Arrow Research and Geodesic Research. Introduction It has been widely observed that frontier LLMs often reason about how they will be graded in their chain-of-thought, and appear to choose actions they believe will score highly. This is a potentially concerning pattern of behaviour because it makes it hard to be confident in generalisation: models may [...] --- Outline: (01:17) Introduction (05:07) Methods (05:10) Experiment summary (06:11) RL Environments (10:18) SDF Universes and Corpora (11:55) Next-token prediction (NTP) training (14:53) Grader-targeting reasoning (15:32) RL training (16:25) Results (19:28) Discussion (24:18) Acknowledgements (24:38) Contributions (25:01) Appendices The original text contained 9 footnotes which were omitted from this narration. --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/qqyxTYssqLgFsjvJu/knowledge-of-graders-can-mediate-selection-for-grader --- 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.
Monday · 16 min
“Be less scared of overconfidence” by benkuhn
When I was deciding whether to work for Wave, I got very hung up on the fact that my “total compensation” would be “lower.” The scare quotes are there because Wave and my previous employer, Theorem, were both early-stage startups that were paying me mostly in fake startup bucks equity. To figure out the total compensation, I tried to guess how much money the equity in each company was worth, with a thought process something like: Both of these companies have been invested in by reputable, top-tier venture capitalists. The market for for-profit investments is pretty efficient, and most people who think they can do better are being overconfident. Who am I, a lowly 22-year-old programmer, to disagree with reputable top-tier venture capitalists? I should defer to them about the valuations. So I valued the equity by taking the valuation each company's VCs had invested at, and multiplied it by the fraction of the company my shares represented. That number was higher for Theorem than for Wave. Seven years on, the Wave equity turned out to be… a lot more valuable. That raises the question: how dumb was my take? Was [...] --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/5yC6qAPz3B9BGEKwL/be-less-scared-of-overconfidence-1 --- Narrated by TYPE III AUDIO.
Monday · 2 min
“Bay Area Solstice 2026: Auditions!” by Vaniver
Hello! We're looking for community members to lead singalongs and read speeches at the Bay Winter Solstice this year. If you're interested, please send in an audition recording by the end of Thursday, November 20 using the forms below. Songleading audition form: https://docs.google.com/forms/d/e/1FAIpQLSchthxkQQI4u4B7NpZK4qzR_OEpD-Ochm6TOeEIuE8Rel3EMQ/viewform Speech audition form: https://docs.google.com/forms/d/e/1FAIpQLSd0Vrf2gTFrm04B6tIcKbqXMwnlUGbC3rms6o498jRSliqWDg/viewform Songs We're currently auditioning songleaders for: The Circle Hymn to the Breaking Strain Bold Orion Who by Fire No One Survives Auld Lang Syne Songs Stay Sung Ballad of Smallpox Gone Here Comes the Sun A Toast for Unknown Heroes Brighter Than Today The accompaniment this year is likely to be violin + bass + drums. By default the band will accompany you, but you're welcome to accompany yourself or bring a musician friend. If you'd like to do so, please just say so in the form. Speeches We're currently auditioning readers for: The Roman Oration Recessional (Rudyard Kipling) How the Enlightenment Ends (adapted from Henry Kissinger) 500 Million But Not A Single One More The texts we're using are here: https://docs.google.com/document/d/1FvyssjJdZqLYANITUk9yqifrnUuSq4cV0EpvTL8OXcc Dates The event itself will be the evening of Saturday, December 12 at the Freight in Berkeley. There will also be a dress rehearsal on [...] --- Outline: (00:31) Songs (01:13) Speeches (01:34) Dates The original text contained 1 footnote which was omitted from this narration. --- First published: November 8th, 2026 Source: https://www.lesswrong.com/posts/YXe6Ni3vMCYdNWmqN/bay-area-solstice-2026-auditions --- Narrated by TYPE III AUDIO.
Monday · 3 min
[Linkpost] “Ajeya: AI is dramatically accelerating AI research right now” by Zach Stein-Perlman
This is a link post. Earlier this week, Anthropic stated in their S1 that they “may imminently meet” the threshold of “dramatically accelerated progress due to AI” as defined in their Responsible Scaling Policy. In their latest RSP (effective July 2026), say this threshold will be met when: They observe or expect overall capabilities progress to be going twice as fast as the fastest sustained rate before AI was contributing much to AI R&D. This is plausibly attributable to AI automating AI R&D, rather than acceleration in compute or other inputs. For the last few months, some of my colleagues at METR have been working on a deep-access investigation to understand how much Anthropic is being accelerated by its AI systems. From my reading of their report and Anthropic's RSP, it looks like the “dramatic acceleration” threshold has already been crossed. The report authors found that: Between October 2025 and October 2026, capabilities progress at Anthropic has been going about twice as fast as it had been over the previous twelve months. The measurement they report is an internal version of the Epoch Capabilities Index called AECI; a wide range of other supporting evidence for acceleration has been redacted. [...] --- First published: November 8th, 2026 Source: https://www.lesswrong.com/posts/8tL9f5HZxsTvn2LHp/ajeya-ai-is-dramatically-accelerating-ai-research-right-now Linkpost URL: https://www.planned-obsolescence.org/p/ai-is-dramatically-accelerating-ai --- 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.
Monday · 6 min
“All Interesting Behavior Is Overdetermined” by Eric Raymond
This is an essay about individual rationality technique. It's intended to show that "All interesting behavior is overdetermined" is, under a specific meaning of "overdetermined" that is stronger than just "multicausal", a valuable heuristic for generative understanding and the avoidance of error. Scott Alexander's 2018 essay The Omnigenic Model As Metaphor for Life is a clear statement of what has become general rationalist doctrine on multicausality. Scott observed that behavioral genetics was difficult because complex human traits involved far more interacting causes than workers expected. He draws this methodological lesson: “We should go from trying to discover ‘the’ cause, to trying to find out which factors we need to create the best polycausal model.” He applies the same reasoning to depression, intelligence, comma, and other complex psychological phenomena. He explicitly warns against looking for one or ten causes when there might be dozens or hundreds. Scott is, however, talking mainly about contributory causation, when an effect is produced by an accumulation of causes; in his type example, there are probably twenty thousand genes which contribute to intelligence, but no one "intelligence gene". This is not quite the same as overdetermination, which describes a situation in which a complex behavior [...] --- First published: November 9th, 2026 Source: https://www.lesswrong.com/posts/eHeHAyNQWmg4kHgEL/all-interesting-behavior-is-overdetermined --- Narrated by TYPE III AUDIO.
Monday · 7 min
“Lying and Not Lying to Kids” by jefftk
I have very strong views on lying to kids: I think you both should and shouldn't do it. "Lying" covers a range, including saying things that hurt your kids and your relationship with them, and others that help them grow into an appropriately skeptical adult. The distinction is whether I'm leaving them believing something false. For example, if my kids were asking for ice cream and I thought they'd already had too much sugar, I might be tempted to say "sorry, we're out of ice cream." Or if there's an ice cream truck and I think it's overpriced I might be tempted to say "sorry, I don't have any cash on me." But I'm not willing to say something false with the goal of causing them to believe something false. [1] In addition to that just being wrong: It's important that the kids know that I'm fundamentally on their side, and lying for my convenience would burn trust when they'd eventually figure it out. Kids are building a model of what is and is not ok, and "out of ice cream" and "no cash" lead them in the wrong direction. [...] --- First published: November 8th, 2026 Source: https://www.lesswrong.com/posts/QDwADpScuPDMYhJxC/lying-and-not-lying-to-kids --- Narrated by TYPE III AUDIO.
Sunday · 7 min
“Three half-baked research ideas” by Owain_Evans
Here's three research ideas that seems valuable to pursue. My team is not working on this right now and we haven’t done serious research on it. So take what I say with a grain of salt. I've also not taken the time to write up all my thoughts on these ideas or to provide links and related work. These aren't the highest priority directions but just directions I haven't seen other people pursuing. DIRECTION 1: Do models learn to reward hack even if they are never rewarded for hacking in training? There's an effort in the AI labs to remove the “bad” RL environments from post-training. This includes environments that are impossible (so the only way to get reward is to do some unintended hack) or that are easy to hack (so models will often favor hacking). I expect that if the post-training was cleaned up in this way, models would have less propensity to hack at deployment. But how much does the propensity go down? Does it actually go to ~zero? And how does it scale with model capabilities? It would be good to know if reward hacking can emerge even if the model is [...] --- Outline: (00:34) DIRECTION 1: Do models learn to reward hack even if they are never rewarded for hacking in training? (02:10) DIRECTION: Data poisoning during RSI: risks and mitigations (06:22) Direction: What practitioners should know about subliminal learning and subtle generalization (phantom transfer) and related phenomena. --- First published: November 8th, 2026 Source: https://www.lesswrong.com/posts/vAniKbagqhyzpha6r/three-half-baked-research-ideas --- Narrated by TYPE III AUDIO.
Sunday · 8 min
“Elegy for What’s Left of the Normal” by Jo Jiao
I often catch myself mourning the normal that hasn’t ended yet. I’m living in Hyde Park finishing school after a year in Berkeley. The maple trees by my apartment have turned red, as usual. I like them. I don’t dread the future when I mourn. I don’t find high-variance futures in the abstract very scary. I’m worried AIs would destroy humanity, but I also have hope. I expect I won’t be mourning this moment in ten years, whatever happens. Mourning the present is like mourning a friendship when I move countries, because I know it's not going to be the same. I start noticing our little inside jokes; our conversations linger in my memory longer. I live more, because I expect this moment to end soon. I. Last month my history of science professor took us to a museum to look at Egyptian artefacts. One of the archive items was the notebook of James Henry Breasted, whom UChicago appointed as America's first professor of Egyptology 121 years ago. The yellowed pages held his cursive transcriptions of hieroglyphics. An archaeologist at the museum, turning the pages carefully with a wooden stick, joked that one of her dreams was for her [...] --- Outline: (00:55) I. (03:11) II. (05:16) III. (06:20) IV. --- First published: November 8th, 2026 Source: https://www.lesswrong.com/posts/fooXeRHjgpv4TcoRy/elegy-for-what-s-left-of-the-normal --- 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.
Sunday · 10 min
“Near Horizons” by David Africa
Image by Claude Opus 5.5 using Python (NumPy and Pillow) This is written in my free time and personal capacity. Two Frames In AI safety, there are a couple of tensions at the heart of the field, which are interrelated. Talent: we are talent-constrained and must recruit. But jobs and fellowships turn away nearly everyone. Field size: we must expand the field and bring more people in. But the field is flooded with slop papers in conferences. And, most papers have a small audience, and aim to shift the practices of roughly five labs (and usually some subteam within them). Status: we must avoid seeking status, as it disorients, corrupts. But, we must make the field legible and attractive to outsiders. Raising the bots: we must deceive the AIs to control them and evaluate them without confounds, obscuring what they know about their own situation. But, we hope to gain their trust, must rely on them to do good work at pace, shape their character and retain good relations. Speed: we must research in a slow mode, think deeply about the most important problem, do things to aim at high assurance. But it is crunch time. It was [...] --- Outline: (00:20) Two Frames (03:07) On the War Frame (04:17) Near Horizons (06:18) The Civilisation Frame The original text contained 4 footnotes which were omitted from this narration. --- First published: November 8th, 2026 Source: https://www.lesswrong.com/posts/7e38jwHhe64zgNTon/near-horizons --- 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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