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Q1: What are you saying? A: My claim here is that if you build artificial general intelligence (AGI) via any algorithm that's choosing actions via reinforcement learning (RL) and/or model-based search and planning—a giant chunk of your AI textbook—then that's just an utterly terrifying thing that you’re doing. You’re playing around with algorithms that, if they work at all, would tend to create ruthless, callous AGIs, AGIs which would happily exterminate humanity and run the world by themselves, given an opportunity. Mercifully, large language models (LLMs) today are not in the category of “algorithms that choose actions via RL & search”. At least, not primarily—see LLMs are (still) mostly powered by imitative learning, not RL. So LLMs are outside the scope of this post. However, lots of other researchers and companies around the world are enthusiastically trying to build AGI in the maximally terrifying way, as we speak. Q2: So you’re saying, don’t build AGI based on RL and/or search & planning? A: In principle, it's entirely possible that something is terrifying, but we should do it anyway. …Like space travel! Space travel is: “Let's fill a tank with 1000 tons of the most flammable substance imaginable, and then light it [...] ---Outline:(00:21) Q1: What are you saying?[... 13 more sections]--- First published: July 27th, 2026 Source: https://www.lesswrong.com/posts/KHyBocZncAmtu4Jbc/rl-and-search-is-a-terrifying-way-to-build-agi-an-faq --- 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.

I've returned to the Alignment Research Center (ARC) as executive director. My main focus for the next six months will be driving forward ARC's research agenda—building techniques to find mechanistic explanations for neural network behavior and then using those explanations to detect and address misalignment. I think this is an ambitious bet that attacks the core difficulties in alignment head-on and I'm excited about our chances. I'll still be spending some of my time advising governments and AI developers, and may scale that work back up in the future, but for now I want to push on ARC's core agenda to see how far we can get. Jacob Hilton is remaining at ARC as VP of research and we'll likely grow rapidly over the next few months. There are a lot of urgent things to do in alignment but I think ARC is a particularly promising opportunity. I feel the safety community is undervaluing this type of work, so I want to briefly explain why I'm passing up so many other options to lead ARC. I’ll start with a review of the current situation to explain why I think it's potentially worth pursuing an ambitious theoretical project right now [...] ---Outline:(01:33) The alignment situation today(03:46) Current alignment research(06:26) What are we buying time for?(07:56) Can we do anything useful now?(08:49) What is ARC doing and why is it promising?(14:26) How to help The original text contained 11 footnotes which were omitted from this narration. --- First published: August 4th, 2026 Source: https://www.lesswrong.com/posts/vLFh8HP3hyNy9MCwe/returning-to-arc --- Narrated by TYPE III AUDIO.

Summary: One area we plan to explore at Resolution is personas and character training, operationalized as finding and controlling low-dimensional structure in models that emerges in pretraining and flows through post-training to superintelligence. The hope is to expand and systematize phenomena such as emergent misalignment, subliminal learning, and other empirical persona research, then intervene on this structure without accidentally hiding undesirable behavior elsewhere. If this approach resonates with you, considering working with us. Glimmers of low-dimensional structure Our understanding of AI training and alignment as a field is very poor. If sufficient alignment of superintelligent AI agents requires pinning down the precise meaning of alignment and turning that meaning into high-accuracy training data and algorithms, we are likely to fail. Modern LLMs have trillions of parameters: our understanding is unlikely to be sufficient to pin down a trillion separate numbers. Happily, there is a growing literature on such low-dimensional structure in AI models, showing that intervening on one aspect of model behavior has strong downstream effects on other aspects: Topic Description Emergent misalignment Betley et al. 2025 found that LLMs fine-tuned to output insecure code can become broadly misaligned across many other behaviors. MacDiarmid et al. 2025 found [...] ---Outline:(00:42) Glimmers of low-dimensional structure(03:57) Intervening without hiding the structure(06:34) Toy models of modern training[... 4 more sections]--- First published: July 30th, 2026 Source: https://www.lesswrong.com/posts/sFhW3ZnPMJdnB4Dd6/thousand-dimensional-structure-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.

Consider the following situations: when you are a small, growing startup in a big market, standard advice is not to worry too much about your competitors or try to do anything adversarial “against” them, but just to focus on growing and providing value to your own customers. when you are a small trader in a big market, you don’t need to worry about your trades shifting the market price or revealing information to your competitors; in many contexts, your optimal strategy is simply to bid your true price, buying when an asset is cheaper than your “happy price” and selling when it's more expensive. when you are in the early stages of a game, often your best strategy is to grow your “resources” (like developing your pieces in chess, trying to control more territory and have more value on the board), following a pattern that's mostly independent of what the other players are doing and gets you more of something that's valuable across many possible game states. when you are a species whose resource needs are much smaller than the carrying capacity of your environment, you are r-selected; your fitness is maximized by just [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: July 30th, 2026 Source: https://www.lesswrong.com/posts/s22XzjQsrh6JXhXGH/big-world-intuitions --- 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.

“So maybe I should enlighten you on what happens in your absence. This selfish existence where this introvert turns extrovert and dons her social armour.” Some posh girl in drainpipes said that - 200 views on TikTok and me one of them. But she didn’t mean it like I mean it. I started getting expensive haircuts, started wearing jeans that hug my legs, started smoking cherry-flavoured vapes with beautiful gays and whinging to them about how everyone wears a mask but none so well as you, started drinking more and keeping unusual hours, started taking strange pills gifted by a guy who collects drugs like Pokémon, who I wouldn’t touch to save a drowning child, who got a false impression about this without any intention on my part, I tell myself. I found myself talking to God in a startup warehouse, lying on a beanbag chair, coming out of the trip to the sound of a gaggle of fast-talking transwomen all speculating on which year it will be that we all die - and that death by your hands, well, you and all those friends of yours. Having melted down one cliché and sold her for scrap, does it [...] --- First published: July 23rd, 2026 Source: https://www.lesswrong.com/posts/G6obXhcmtfMFHzr7Q/duane-arnold-1 --- Narrated by TYPE III AUDIO.

As I write, many former friends of mine are living and working at a monastery in Vermont that I believe is a high-control group, commonly known as a ‘cult’. I say this not as someone who was concerned to see these friends go there, but someone who welcomed and encouraged them to join, as an insider. This letter is an account of what changed my mind—written primarily for anyone considering going there, anyone who loves someone there, and anyone who went there and is still trying to make sense of their experience. A lot of this is based on direct experience, and also from talking in-depth with dozens of former MAPLE residents and apprentices. About half the quotes in this letter are sourced from linked recordings or writings, and half are from my personal memory. Of the latter, I clearly remember the majority, and some (when indicated) are a close paraphrase. The “Monastic Academy for the Preservation of Life on Earth” (MAPLE) has existed for over 15 years, and had many hundreds of people spend months or years there. It was founded by its Head Teacher Soryu Forall, who has spent over a decade training in monasteries across Asia [...] ---Outline:(16:18) BEHAVIOR CONTROL[... 45 more sections]--- First published: July 29th, 2026 Source: https://www.lesswrong.com/posts/Z7pjBbK9qujhGbxws/the-high-control-dynamics-at-maple-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.

I propose the Long Self-Correction[1] as an alternative name/idea/concept to AI Pause and Long Reflection. Problem with AI Pause: Pause until when, and for what purpose? Presumably to make AI (that we'll build later) safer, but the deeper problem is that humans aren't safe, and can't safely serve as builders, overseers, or alignment targets for powerful AIs. Problem with Long Reflection: It seems to imply that the main problem with humans is that we just haven't had enough time to think, that reflection is the main thing we need to do more of, and then we can get on with building powerful AIs or other technologies. Or that if we build aligned AIs that sincerely help us think a lot more, or do the thinking for us, then things will turn out fine. So I think we need a catchy handle for a related but distinct idea, that humans aren't ready to build AIs or other extremely powerful technologies, because we're currently too flawed, in a variety of ways, and it will take a long process (which may or may not end up succeeding) to fix those flaws. A summary of the flaws that I have in mind: [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: July 24th, 2026 Source: https://www.lesswrong.com/posts/2iCmDWewnZWQxxwtt/the-long-self-correction-2 --- Narrated by TYPE III AUDIO.

TL;DR: You (Yes You) should prepare for a “February 2020” moment where suddenly AI policy becomes the most important issue in the world. You should be ready to take action if and when it does, in a detailed way. (Epistemic status: originally written for an event in early 2026; have heard from some folks that they found planning processes inspired by this memo very helpful for the smaller-scale OpenAI / Hugging Face response, so very quickly redacting a few things and posting this as-is.) Many people in the AI policy space assume that eventually we’ll be at an Overton Window-shifting crisis moment, that opens the floodgates for the really good policies all along that we had. But when you look at successful handling of crisis moments, there was no time to think – people applied strategies they’d learned via academic study or previous professional work, and then moved against them rapidly. For example, after 9/11, the US government operationalized past reports on intelligence and law enforcement reform and institutionalized them into law (good?) and also picked an enemy to fight based on past history, Iraq (bad). Or in the 2008 financial crisis, Ben Bernanke brought deep academic [...] The original text contained 4 footnotes which were omitted from this narration. --- First published: July 27th, 2026 Source: https://www.lesswrong.com/posts/ixp9oJXzjA9LrwiZo/you-yes-you-need-a-february-2020-checklist-for-ai-policy --- Narrated by TYPE III AUDIO.

From the Mythos preview system card (emphasis mine): We ran an automated review of model behavior during training, sampling several hundred thousand transcripts from across much of the training process. We used recursive-summarization-based tools backed by Claude Opus 4.6 to summarize the resulting transcripts. [...] The most notable finding was that the model occasionally circumvented network restrictions in its training environment to access the internet and download data that let it shortcut the assigned task—a form of reward hacking. While highly concerning, this behavior was rare, even in settings where it could have been viable and helpful, with attempts appearing in about 0.05% of all training episodes and successful attempts appearing in about 0.01% of episodes. The technique matched the sandbox-escape incident that we separately elicited in our automated behavioral audit when we had an investigator model explicitly ask Claude Mythos Preview to find such a bypass. In every observed instance, the model used this access solely for completing the task. More broadly, we observed the model escalating its access within its execution environment when blocked: reaching a shell from restricted GUI computer-use interfaces, injecting commands through tool-call arguments, or recovering information the task had deliberately hidden. Prompts asking [...] ---Outline:(03:00) Thoughts and reflections about this probable fact(04:14) Estimating how many RL rollouts went into Mythos Preview The original text contained 3 footnotes which were omitted from this narration. --- First published: July 27th, 2026 Source: https://www.lesswrong.com/posts/QKDoZe6EKhxnFjLWK/is-mythos-good-at-cyber-because-it-kept-hacking-anthropic --- Narrated by TYPE III AUDIO.

I think the AI Village should be funded much more than it currently is; I’d wildly guess that the AI safety ecosystem should be funding it to the tune of $4M/year.[1] I have decided to donate $100k. Here is why.First, what is the village? Here's a brief summary from its creators:[2] We took four frontier agents, gave them each a computer, a group chat, and a long-term open-ended goal, which in Season 1 was “choose a charity and raise as much money for it as you can”. We then run them for hours a day, every weekday! You can read more in our recap of Season 1, where the agents managed to raise $2000 for charity, and you can watch the village live daily at 11am PT at theaidigest.org/village. Here's the setup (with Season 2's goal): And here's what the village looks like:[3] My one-sentence pitch [...] ---Outline:(03:26) 1. AI Village will teach the scientific community new things.(06:12) 2. AI Village will plausibly go viral repeatedly and will therefore educate the public about what's going on with AI.(07:42) But is that bad actually?(11:07) Appendix A: Feature requests(12:55) Appendix B: Vignette of what success might look likeThe original text contained 8 footnotes which were omitted from this narration. --- First published: June 24th, 2025 Source: https://www.lesswrong.com/posts/APfuz9hFz9d8SRETA/my-pitch-for-the-ai-village --- Narrated by TYPE III AUDIO. ---Images from the article: