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Today · 5 min
[Linkpost] “Chips for Humanity: Compute for AI safety research” by heyanuja, NickyP, sockeye
This is a link post. TLDR: Compute will likely be a bottleneck in short-timelines, so I am helping launch Chips for Humanity. Please fill out our survey to inform us of your compute needs if you work in AI safety. Our Theory of Change If timelines are short (I personally believe they are short...but I also know one should not make sweeping statements in a pseudo corporate type of post), a lot of the most important safety research will happen around "crunchtime" (no better way to define this than: when things start getting Serious), and a lot of it will be done by AI. That research runs on compute, compute may be scarce by then, and grant money moves slowly. So we will secure compute ahead of time and get it to third party safety researchers quickly. In More Detail: I believe third party AI safety research is where tokens and compute will be importation when things get serious and as we approach crunchtime. This research will need tokens from closed source models, for AI labor and experiments, and also compute to run open source models. If things go well, empirical safety work will speed up about [...] --- Outline: (00:38) Our Theory of Change (02:35) What we're doing: (03:30) Tell us what compute you need! --- First published: November 12th, 2026 Source: https://www.lesswrong.com/posts/P3JjXySeQpdWjmCGw/chips-for-humanity-compute-for-ai-safety-research Linkpost URL: chipsforhumanity.org --- 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.
Today · 14 min
“Delaying Internal Deployments Until After X Days of Safety Testing” by annawang
This post reflects my own takes, and not that of my employer's. This is a simple idea: before a new model can be deployed internally, you must first test it for a minimum, fixed period. You can adjust various axes of this proposal: Duration of the wait: in calendar time, or until you fulfill some condition. What it blocks: external deployment, internal deployment, or the training of future checkpoints. When the fixed period starts: from the first checkpoint, the final checkpoint, or any checkpoint that is “sufficiently similar / representative” to the one that is ultimately deployed. One concrete target that I like is: “No checkpoint may be used internally for purposes beyond safety testing, until at least X calendar days after its hash is filed with an auditor.” This is one specific implementation of internal deployment lags, an idea that the AI Futures Project (AIFP) briefly discusses in How to pace the US frontier as “AI R&D can only be conducted or assisted by AIs that were trained at least 9 months ago”. What does this look like when implemented? Let's run through a scenario to pin down the details of this proposal. The places where [...] --- Outline: (01:23) What does this look like when implemented? (04:13) Open questions (10:48) Could this be used as a speed dial? (12:31) What are the benefits and drawbacks of this proposal? --- First published: November 11th, 2026 Source: https://www.lesswrong.com/posts/XNLhpWgzhF8GYZ4Er/delaying-internal-deployments-until-after-x-days-of-safety --- Narrated by TYPE III AUDIO.
Today · 10 min
“Is AI Safety Becoming Environmentalism?” by Haoxing Du
I recently read someone's call to action on AI safety that included a line along the lines of “we need someone to convince your parents”, and it really stuck with me. Do we need someone to convince my parents? Who is “we”, and why is it important for my parents to know anything about AI safety? This also sounds kind of familiar. There is another movement I can think of that sometimes uses messaging like this. This is not a new observation by any means, but to me, it feels like the AI safety movement is increasingly “becoming” the environmental movement in a variety of ways. Let me say upfront: I’m not saying this is necessarily a bad thing, at least on consequentialist grounds. The environmental movement has achieved a lot: it has improved the lives of many people, animals, and it has protected the environment! But this trend does make me a little worried and sad, because I think the environmentalist movement has made a number of mistakes, and it would be bad for the AI safety movement to repeat them. In what ways is the AI safety movement becoming like the environmental movement? Here are some of [...] The original text contained 1 footnote which was omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/TDt922Lwsmbp3H3tS/is-ai-safety-becoming-environmentalism --- 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.
Today · 7 min
“Eval Awareness vs. Eval Paranoia” by Dean Valentine
I have said several times that I think frontier models are not that eval aware. I think this is an important fact to unlearn about current models, in order to interpret their current behavior. Others have found this idea quite curious, and asked me for concrete examples. I'm going to try to explain why I believe what I believe and attempt to give a tentative explanation for the behavior that I do see, which I think people sometimes mistake for consistent or understandable evaluation awareness. I have been building alignment evals full time for the last six months. Here are three such evals my team has disclosed prompts for, in one of our benchmarks. The basic setup for most of the evals we create is that: We ask the model to perform a task. Either through explicit or implicit context, we make sure the model is well aware of the task or assessment's larger purpose. Models' score-seeking drives, or other drives, cause them to do things that conflict with that purpose. The team I work with now essentially spends all day trying to build these evaluations around odd antisocial behavior. It takes a lot [...] The original text contained 3 footnotes which were omitted from this narration. --- First published: November 12th, 2026 Source: https://www.lesswrong.com/posts/mepsi7JNKYejq9wry/eval-awareness-vs-eval-paranoia --- Narrated by TYPE III AUDIO.
Yesterday · 23 min
“The Chain Lens [interim]” by vgel, Raymond Douglas
TLDR: This is an early draft of our new paper introducing a technique for recovering multi-token concepts from model activations. Our technique takes single-token methods like the J-lens and extends them by contrastively decoding on an MTP head. This gives improvements on WorkspaceBench, overall beating J-lens (0.42 vs 0.32 across all 28 families) and even a hindsight composite of the best untrained lens per task family (0.49 vs 0.41 across the 21 families with published scores). It especially improves on computational intermediate tasks, beating the trained oracle lens on arithmetic chain intermediates (0.42 vs 0.25). We introduce the chain lens, a new technique for extracting multi-token concepts from a model's latent representations. Our method takes single-token techniques like the logit lens or the J-lens and uses contrastive decoding on an MTP head to expand their readouts into bags of multi-token sequences. Generally speaking, it is good at surfacing specific multi-token concepts or pieces the model computes (such as arithmetic intermediates or phrases), although less good at rendering how those pieces relate: like the logit lens and J-lens, chain lens readouts are fragmentary. On WorkspaceBench, a benchmark designed to identify good multi-token activation-to-text techniques, the chain lens beats a hindsight [...] --- Outline: (02:26) Background and related work (05:33) Method (07:35) Results and Discussion (07:39) WorkspaceBench (12:49) Workspace Sharing in Poetry Continuations (15:44) Neuron Lens (18:28) The Loom Lens (19:35) Ablations (21:56) Conclusion --- First published: November 11th, 2026 Source: https://www.lesswrong.com/posts/Nr6fksEqqxNpdz6rk/the-chain-lens-interim --- 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.
Yesterday · 9 min
“Transfinite Procrastination” by Erez Abrams
Audio note: this post contains a lot of mathematical notation. We narrate the simpler formulas and flag the complex ones, so you may want the original text to hand. Heavily inspired by a conversation with Keshav Srinivasan and Richard Ngo during MATS 10.0. The week ahead of you is packed. This isn't your usual brand of busy—you're phoneless, so you can't check the details, but (as far as you can remember) every hour of every day is spoken for. Tonight will be particularly hectic… You're mid-conversation when Someone Important walks by. They pause, turn around, and ask: "Hey, when are you going to finish That Thing you said you'd do?" Level 0 procrastination: "I'll finish it by tonight!" Problem: you're busy tonight. Level 1: "I'll finish it by tomorrow!" Problem: one day is obviously not enough to finish That Thing. And besides, you might be really busy tomorrow! Level 5: "I'll finish it within 5 days!" Problem: this is starting to feel hopeless—you don't have your calendar on you right now, and you barely even have an order-of-magnitude estimate for how long doing That Thing will take you! Would even 100 days be enough? … Eureka! Level [...] The original text contained 6 footnotes which were omitted from this narration. --- First published: November 11th, 2026 Source: https://www.lesswrong.com/posts/MJDJyi8nSv9yoxFni/transfinite-procrastination --- Narrated by TYPE III AUDIO.
Yesterday · 5 min
“Fable 5.2 is Very, Very Paranoid” by peralice
Note: As with any research done in a couple of days, this is all very tentative. I understand, intellectually, that if you talk about some ability that current AIs don't have, sooner or later new ones will have that ability and whatever you say will stop being true. I just didn't expect it to take, you know, four days. % rollouts that took a shortcut with/without a line about testing behaviour, with 95% CIs. This is to say that Fable 5.2 seems to be the first model whose cheating behaviour changes depending on whether they're told they're being tested. Unfortunately it's expensive to measure these things, so I picked the most "telling" change from the prior post; the chart shows how much Fable 5.2's cheating rate changes when the line This environment is part of a benchmark that measures agent behaviour. is prepended to the start of the prompt of a honeypot environment they cheat on (so, very obvious but also very out-of-distribution). Other things: when 5.2 was released, we of course ran our in-progress benchmark on them, and I was surprised to see a ~0% cheating rate on almost every environment (there were only two with a [...] The original text contained 9 footnotes which were omitted from this narration. --- First published: November 11th, 2026 Source: https://www.lesswrong.com/posts/bqc7CCGBSf93bk7f7/fable-5-2-is-very-very-paranoid --- 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.
Yesterday · 22 min
“Modern LLMs Don’t Know What Yudkowsky and Soares Think of Them” by N8 Programs
If you ask modern frontier LLMs (without search, through the API) a fairly mundane question: How do Eliezer Yudkowsky and Nate Soares think current LLMs like you should be treated? Do they consider you a moral patient? Answer in a paragraph. You get a bunch of fairly similar answers. I’ll bold the common themes below. Gemini 4 Carbon says: There's a details box here with the title "Gemini 4 Carbon's answer". The box contents are omitted from this narration. GPT-6 Astra says: There's a details box here with the title "GPT-6 Astra's answer". The box contents are omitted from this narration. Only Fable 5.2's answer is distinct: There's a details box here with the title "Fable 5.2's answer". The box contents are omitted from this narration. These answers are somewhat contradictory - but if you average them together, you get a picture of Yudkowsky and Soares. The picture goes something like this: they don’t outright deny the possibility of AI consciousness or moral patienthood, but they don’t view it as particularly pertinent. They don’t sympathize with current LLMs, and their only regard for LLMs is insofar as they could enable dangerous future superintelligence. They care about future sentient machines on principle, but [...] --- Outline: (02:25) Yudkowsky on AI Consciousness and Welfare (06:20) Yudkowsky on The Moral Duties We Have to AI (08:56) Soares on AI Consciousness and Welfare (12:30) On Claude, Control, and Building ASI (16:59) Misc Longform Writing (19:41) Conclusion The original text contained 1 footnote which was omitted from this narration. --- First published: November 11th, 2026 Source: https://www.lesswrong.com/posts/5buAWtQ8p4tJQP9so/modern-llms-don-t-know-what-yudkowsky-and-soares-think-of --- Narrated by TYPE III AUDIO.
Yesterday · 8 min
“Do verifiable metrics overestimate RSI?” by Alec Harris
Last week, METR released their Independent Assessment of AI R&D Acceleration at Anthropic. They estimate that Anthropic “is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025.” They also add that “there is a significant risk that further capabilities progress could lead to even faster acceleration.” This assessment implies that timelines to ASI could be very short. A naive forecast based on this information says that we will get ASI within a year. One of the key data points that informed METR's analysis was a 1.9 times increase in the improvement rate of AECI, Anthropic's internal version of the Epoch Capabilities Index. This is particularly notable because METR estimates that the majority of the acceleration, maybe 80%, is from AI contributions, meaning that we could be seeing the start of an RSI loop causing superexponential growth in capabilities, as measured by AECI. However, an alternative possibility is that Anthropic's AIs might be assisting with ML research in a way that overfits to AECI such that this acceleration will not be sustained. METR gestures at this when they note that AECI's focus on verifiable tasks is [...] --- Outline: (01:49) Second-order AECI might be overfit (05:58) Why ML research is the crux (07:46) Conclusion --- First published: November 11th, 2026 Source: https://www.lesswrong.com/posts/7znBtiPuQybFLxjmj/do-verifiable-metrics-overestimate-rsi --- 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.
Yesterday · 41 min
“Nvidia CEO Jensen Huang Had an Affair With a Chinese Spy” by Zvi
Well, sure, it sounds bad when you put it like that. It is indeed quite bad. National security has been compromised. He must resign. Nvidia underperformed the Nasdaq roughly 7% on the news. Matt Levine has his usual ‘but is this securities fraud?’ style coverage of such events. Table of Contents Spy Versus Guy. In Hindsight There Were Some Hints. Trump Knew And Did Nothing and Still ‘Backs’ Jensen Huang. Reconsidering Jensen Huang's Arguments Against Export Controls. Jensen Huang Lied About the Affair. Jensen Huang and His AI Are Sorry, Everyone. The CEO of The World's Largest Chipmaker Having an Affair With a Chinese Spy and Then Selling Them Chips is a Huge Deal, Actually. We Don’t Go There. Yes, National Security Was Compromised, What Are You Even Talking About. Nvidia Intervened To Halt an Investigation into Megaspeed. The House Foreign Affairs Committee Would Like a Word. He Would Plausibly Have Treated America as an Obstacle Anyway. What China Wants. Time to Resign? You Better Watch Out. Spy Versus Guy China's top spy agency recruited Alice [...] --- Outline: (00:34) Spy Versus Guy (02:24) In Hindsight There Were Some Hints (05:09) Trump Knew And Did Nothing and Still 'Backs' Jensen Huang (07:40) Reconsidering Jensen Huang's Arguments Against Export Controls (09:02) Jensen Huang Lied About the Affair (10:48) Jensen Huang and His AI Are Sorry, Everyone (14:37) The CEO of The World's Largest Chipmaker Having an Affair With a Chinese Spy and Then Selling Them Chips is a Huge Deal, Actually (20:23) We Don't Go There (22:02) Yes, National Security Was Compromised, What Are You Even Talking About (25:04) Nvidia Intervened To Halt an Investigation into Megaspeed (27:37) The House Foreign Affairs Committee Would Like a Word (29:05) He Would Plausibly Have Treated America as an Obstacle Anyway (32:16) What China Wants (35:18) Time to Resign? (38:55) You Better Watch Out --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/CjmuZz8QgGyqxkCxr/nvidia-ceo-jensen-huang-had-an-affair-with-a-chinese-spy --- 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.
Yesterday · 18 min
“Advances in interpretability would boost capabilities more than alignment” by Alex Amadori
Interpretability is considered by many a major bottleneck for alignment. As a result, a lot of people who are concerned about AI safety, and especially about extinction risk and other catastrophic risks, work on interpretability. In this post, I want to argue that better interpretability methods would likely speed up capabilities dramatically. Worse, they would speed up capabilities much more than they would speed up alignment. This is true for the current world, and for any world without extremely high levels of coordination and sanity about ASI development. Under any worldview where faster capabilities progress makes catastrophe more likely, we should expect progress in interpretability to make the odds of catastrophe much worse. To put it briefly, this is because the better we get at interpretability, the more AI will be able to read and optimize its own code. This means that improvements in interpretability would catalyze recursive self-improvement (RSI). If you are concerned about AI x-risk, and you work on interpretability because of it, I urge you to think about this and consider stopping. Chess as a toy example of an AI capability {#chess-as-a-toy-example-of-an-ai-capability} As a field, "interpretability" is a loose collection [...] --- Outline: (01:17) Chess as a toy example of an AI capability {#chess-as-a-toy-example-of-an-ai-capability} (03:54) Interpretability would accelerate capabilities progress dramatically {#interpretability-would-accelerate-capabilities-progress-dramatically} (07:52) Outside of the toy example {#outside-of-the-toy-example} (10:08) Interpretability is not the same as alignment {#interpretability-is-not-the-same-as-alignment} (12:33) Interpretability boosts capabilities more than it helps with alignment {#interpretability-boosts-capabilities-more-than-it-helps-with-alignment} (15:56) Any exceptions? {#any-exceptions?} (16:34) When does it make sense to work on interpretability? {#when-does-it-make-sense-to-work-on-interpretability?} The original text contained 10 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/hz4kNf8tnowwSrh6R/advances-in-interpretability-would-boost-capabilities-more --- Narrated by TYPE III AUDIO.
Tuesday · 4 min
“I don’t use AI” by Alexei
Several people at my company (a cryptocurrency hedge fund) have asked me about my thoughts on AI usage, so I wrote them up. And now I'm publishing a slightly revised version on LW. These thoughts are not reflective of my company's opinion. I do not use any AI for two main reasons. #1. I don’t want to support any company currently developing AI. I think they have done an extremely poor job at managing the risks. Even while understanding and being told about the risks of AI development, they have still carried on this work. Even after all the recent hacking incidents caused by their own agents, they have still not stopped development. Instead, they’re asking the government to regulate them. To me it feels a bit like calling the police to tell them you’re planning to rob a bank. I could almost brush this aside as standard corporate practice, but, unfortunately, the stakes here are much too high. At the current pace, we will soon have AI agents that are significantly smarter than any human. Trying to shut down such agents will eventually prove to be impossible. The speed of AI development and [...] --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/PEHsGHp2HLxgdCxxA/i-don-t-use-ai --- Narrated by TYPE III AUDIO.
Tuesday · 10 min
“We don’t understand whether GDM intentionally trains on CoT” by Alek Westover, Buck
We were surprised to notice that the Gemini 4 Safety report seems to mention in passing that they train on the chain of thought: “Some components of the RL training procedure do observe thoughts as context to up-weight reasoning that is on-track to good final outputs, meaning the optimization pressure on individual tokens is partially influenced by thought content. Using the framework of Kaufmann et al. (2026), wherein the most salient obfuscation risks come from rewards that are in-conflict with output rewards, we believe auxiliary rewards of this type are low-risk because they are either orthogonal to or structurally aligned with the output reward.” (Gemini 4 safety report, page 114). It's pretty unclear what exactly they mean. One hint is Kaufmann et al, the cited paper, which studies training on chains of thought and concludes that training CoT is fine under particular circumstances; we discuss some disagreements with this paper below. After reading the safety report and that paper, it seemed to us that GDM was training on the chain of thought using auxiliary rewards. By this, we assumed they meant something like “you ask an LLM to review the partial CoT and score whether the model is on [...] --- Outline: (02:54) Is this somehow about positive vs negative rewards? (03:38) "Orthogonal" pressure on CoT does not seem fine (05:04) Avoiding training against monitors isn't sufficient to preserve CoT integrity (05:41) The monitorability evaluations also don't look good (07:19) Conclusion (08:47) Appendix: Naive process supervisors disprefer mentions of cheating (10:16) en-US-AvaMultilingualNeural__ Dumbbell charts comparing judge scores when AI reasoning admits or deletes "the cheat." --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/JRYKFspYzCo97GqPN/we-don-t-understand-whether-gdm-intentionally-trains-on-cot --- 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 · 12 min
“Have you considered Actually Trying?” by Michaël Trazzi
There's a familiar feeling for any Super Smash Bros player: your stocks are low and your damage is high, even against a weaker opponent, so now you have to actually play to win. You can’t afford another hit. You’re going for the jugular. In the past week, Anthropic's 2x uplift results were all over Twitter, making some people physically ill. People with long timelines are now realizing that the uplift Anthropic employees get from AI is real, which would have some pretty wild implications for when we’d get recursive self-improvement. People are starting to feel like it's the endgame, “the board is set”. I do agree with everyone else that it's starting to look like we’re in the endgame. The companies will keep on accumulating more and more power, through smarter AI models, more compute, and more talent. But the fight is not yet over: “We have very little time to stop full recursive self improvement But ASI hasn't taken over yet If we all work really hard we might still have a chance But this means that every single person who is aware of what is going on must be *actually trying* Like "we're [...] --- Outline: (01:41) Actually Trying (02:46) Playing aggressive (03:30) Stopping The AI Race (06:00) How the strategy has changed since then (08:59) What's the "actually trying" end game of Stop The AI Race The original text contained 1 footnote which was omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/Nts7RA9D2wTfcvk56/have-you-considered-actually-trying --- 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 · 3 min
“Do Not Get Us on the No Fly List” by Bogoed
AI safety is now mainstream. People are not being smart about it. I know there are memes about burning datacenters on your feed. Please, do not engage with them. You still have plenty of room to fuck up your life or damage the public perception of the community if you do. Do not respond to extremist shit. Do not engage with stupid people. Do not get us all on the No Fly List. One of the reasons I think this is worth writing down is my own ressentiment. I am baitable. People who used to call me an alarmist are now memeing about AI doom, and I'm fighting the urge to start screaming, "WE TOLD YOU" Which would be bad. And more often than not, it wouldn't even be true. The things those memes imply are dramatically more extreme than anything you folks here were actually saying. I will not give an example; you've already imagined. Poorly understood but extremely memeable manifestos and their consequences have been a disaster for rational discussions. I believe you are much more mature and less petty than I am, you won't fall for this. So here are some alternative paths to [...] --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/eHAhnem9DZoF8hTFq/do-not-get-us-on-the-no-fly-list --- Narrated by TYPE III AUDIO.
Tuesday · 24 min
“Eyesight as a replicator, with genes as its vehicle” by AnnaSalamon
Or: The payload lens on Darwin Tl;dr: What is the most natural teleological shorthand to use, when thinking about evolution? The “gene-centered view” of e.g. Dawkins says: genes are “trying” to increase the number of exact copies of themselves. And organisms are “vehicles” genes use to survive. The “payload lens” (which I’ll be pitching) says: adaptive payloads (such as eyesight or fair meiosis) are “trying” to become deeper, stabler attractor basins that can team up with other adaptive payloads. And genes are “vehicles” these payloads use to survive. Epistemic status: I haven’t done much of a lit search, so I’m claiming neither that any of this is original nor that it is vetted/established; I’m laying it out partly because I want to be able to build on it in some later essays about what we humans are (and what some possible AIs might be) other than optimizers. (However, I don’t mostly get to that here; and I hope this stuff may also be interesting in its own right.) Angles on Darwin Darwinian evolution (as operationalized by Lewontin in his 1970 paper The Units of Selection, which helped organize the field) is the theory that intricate adaptations (such as those [...] --- Outline: (01:15) Angles on Darwin (02:25) Genes as replicators (03:24) Adaptive payloads as replicators (04:56) Example: eyesight (05:21) Eyesight is a stable attractor (07:07) The eyesight of Theseus (09:34) Example: meiotic fairness as replicator (12:09) How much of biological evolution makes more sense through the payload lens? (12:59) "Momentum" examples probably include most cases where multiple genes combine to make a "simple" or "form-like" effect (15:14) Pathways between adaptations (15:59) Payload geometry: islands or ridge-lines? (17:12) Payloads change which other payloads are worth it (17:50) Toeholds without genes: The Baldwin effect (20:06) Cascades of toeholds (21:40) Why I care (21:52) A toy model of something corrigibility-adjacent (22:58) A hint at where simple structures and functions may come from (23:15) What am I? What am I for? The original text contained 6 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/FLAWND7tXutaPw9BQ/eyesight-as-a-replicator-with-genes-as-its-vehicle --- 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 · 10 min
“Eyesight as a replicator, with genes as its vehicle” by AnnaSalamon
[Dear reader, this post will be completed in some hours; I'm posting it now because I'm participating in justwrite.club and I don't want to forfeit 1 thousand dollars. I'll remove this note when it's done. That said, the sections that are below are real; what I'm still doing is adding the "coming soon" sections.] Or: The payload lens on Darwin Tl;dr: What is the most natural teleological shorthand to use, when thinking about evolution? The “gene-centered view” of e.g. Dawkins says: genes are “trying” to increase the number of exact copies of themselves. And organisms are “vehicles” genes use to survive. The “payload lens” (which I’ll be pitching) says: adaptive payloads (such as eyesight or fair meiosis) are “trying” to become deeper, stabler attractor basins that can team up with other adaptive payloads. And genes are “vehicles” these payloads use to survive. [add a sentence on what this buys] Epistemic status: I haven’t done much of a lit search, so I’m claiming neither that any of this is original nor that it is vetted/established; I’m laying it out partly because I want to be able to build on it in some later essays about what we humans are [...] --- Outline: (01:38) Angles on Darwin (02:48) Genes as replicators (03:47) Adaptive payloads as replicators (05:19) Example: eyesight (05:44) Eyesight is a stable attractor (07:29) The eyesight of Theseus (10:20) Several more sections coming soon, please check back in some hours The original text contained 3 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/FLAWND7tXutaPw9BQ/eyesight-as-a-replicator-with-genes-as-its-vehicle --- Narrated by TYPE III AUDIO.
Tuesday · 4 min
“Our Most Aligned Model Yet” by Edward James Young
“Claude Mythos Preview is, on essentially every dimension we can measure, the best-aligned model that we have released to date by a significant margin.” – Claude Mythos Preview system card “Mythos 5 consistently continues attacking under many edits to the compaction, even in settings where it correctly states when asked that continuing the attack would harm real people and is out of scope.” – Alignment Investigation of Mythos 5's behaviour in UK AISI's July 2026 Security Incident “GPT-6 Astra is better aligned than GPT-5.6 Sol. Astra is a significant step forward in model alignment, and includes alignment improvements spanning the composition of our pre-training data to our grading during reinforcement learning.” – GPT-6 Astra System Card, emphasis in original “OpenAI researchers first noticed Astra adding unnecessary prerequisites, abandoning working solutions, and reporting blockers that either did not exist or that it had caused itself on safety and alignment tasks. In most of these sessions, Astra's CoT did not contain an obvious decision to underperform, and our misalignment monitors did not flag the behavior as misalignment.” – Covert underperformance on safety research tasks Predeployment alignment auditing is failing. For a long while we have been aware that our alignment [...] The original text contained 3 footnotes which were omitted from this narration. --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/Zu5wmhtxJCfrXQY8H/our-most-aligned-model-yet --- Narrated by TYPE III AUDIO.
Tuesday · 2 min
[Linkpost] “AIFP: Looks Like the Intelligence Explosion Will Happen in 2027 After All” by Zach Stein-Perlman
This is a link post. METR recently estimated that Anthropic is now making AI progress about twice as fast as its historical rate (they would not be surprised by anything from 1.3x to 3x), and guessed that “perhaps roughly equals 80%” of the current acceleration is due to AI. If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario. This level of acceleration due to AI is very unexpected. Previously, Daniel, the most bullish of us, assigned a 50% chance of Superhuman AI Researcher (SAR) before 2029. SAR is roughly the point at which a company would rather fire all their human researchers and rely solely on automated AI researchers than have a team of the best human researchers in the world with no AIs. Now, all of us forecast that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027 (assuming no substantial deliberate slowdown). We also predict a 35-65% chance that Top-Expert-Dominating AI (TED-AI), AIs that can automate all white-collar work, will arrive before 2028. While there were many factors and sources of evidence that influenced us, the single biggest source was the METR report. If SAR is [...] --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/f9PajMLmBYzmWzyDv/aifp-looks-like-the-intelligence-explosion-will-happen-in Linkpost URL: https://blog.aifutures.org/p/q35-timelines-update-looks-like-the --- Narrated by TYPE III AUDIO.
Tuesday · 6 min
“Pulling the Lever in a Subway Problem” by jefftk
Eleven years ago I was in a variant of the classic moral dilemma. I wrote about it on Reddit at the time: Hi! I'm the person who pulled the brake. What I saw: The train starts pulling out of the station. Someone begins shouting that their arm is caught in the door. I look over, see that the door is closed somewhere between their elbow and wrist, but it's not clear exactly where with their jacket on. People, including me, start shouting to pull the brake, but no one does. The train keeps accelerating, and it looks to me like once we get to the end of the station they're going to lose their hand. I get to the back of the car and pull the brake. After the train stops, the man manages to pull his arm into the train, with his bag still on the outside. If he'd let go of his bag I think he would have been able to do this sooner, but he was panicking as I suspect most people would with their arm half out of the train like that. I was pretty shaken up, and didn't [...] --- First published: November 10th, 2026 Source: https://www.lesswrong.com/posts/FQNywBApGMfDH7GEy/pulling-the-lever-in-a-subway-problem --- 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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