Quick Takes

In a blogpost posted yesterday, Sam Altman asserts that pausing AI development may be necessary in the future: [...] This comes one month after Stop The AI Race's March 21st protest in front of OpenAI, Anthropic and xAI (which I organized), asking Sam Altman (alongside other CEOs) to make a statement on pausing frontier AI development (conditionally), and a follow-up direct message on March 25th asking Sam Altman to clarify his take on conditionally pausing AI. (The Musk v. Altman trial also begins today, which may be relevant to the timing.) Other parts of the blogpost also point towards more coordination with other labs and governments: [...]

GPT-5.5 is at the beginning of RLVR scaling, and future versions with the same pretrain will get considerably stronger in the coming months. With GPT-5.x releases, OpenAI is taking advantage of RLVR scaling to blur the jumps in capability between different pretrains. GPT-5.1 ($1.25/$10 per 1M input/output tokens, knowledge cutoff 30 Sep 2024, context length 400K tokens) is followed by a slightly stronger GPT-5.2 ($1.75/$14, 31 Aug 2025, 400K), which is likely a better pretrain and a bigger model. Then GPT-5.3-Codex ($1.75/$14, 31 Aug 2025, 400K) is almost certainly the same pretrain, and GPT-5.4 ($2.5/$15, 31 Aug 2025, 1050K) is notably stronger than GPT-5.2, but still very likely the same pretrain (the change in pricing might be due to the change in context length). And now GPT-5.5 ($5/$30, 1 Dec 2025, 1050K) is a new bigger pretrain, stronger than GPT-5.4. The strategy of "iterative deployment" seems to be about using RLVR scaling to release each pretrain with a little RLVR first, and then to scale RLVR for the same pretrain in subsequent releases in order to almost match the level of capabilities that will be achieved with a stronger pretrain that only uses a little RLVR, which is to be released after that. Thus GPT-5.1 is highly RLVRed, it's followed by GPT-5.2, which is a different pretrain that's RLVRed only as much as necessary to slightly overtake GPT-5.1 in capabilities. And then GPT-5.4 is again a highly RLVRed model on the same pretrain as GPT-5.2, which makes it almost as strong as GPT-5.5, the first release of a considerably stronger pretrain that's only RLVRed as much as necessary to overtake GPT-5.4. This process allows OpenAI to keep releasing ever larger flagship models while mostly avoiding stark jumps in capability. For GPT-5.5 (which is the first RLVRed Opus-class OpenAI release), this suggests that it's at the beginning of RLVR scaling for its pretrain, and thus there is still considerable potential to its capabilities. GPT-5.6 will be using

PSA: Claude 4.6 Sonnet found two bugs in the code I used to make statistical analyses of self-experiments. This affects my Pomodoro method experiment, my L-theanine experiment, my caffeine experiment, my summarized lumenator experiment results, and the results in this post. All other posts of mine are not affected, as far as I can tell (e.g. the Orexin RCT results are not). Updated numbers are in the tables & inline code of these pages, though the prose hasn't been updated to reflect the numbers. I hope to find the time to update the prose of the texts, though I can't promise any particular deadline.

LessWrong posts have staying power; google docs do not. Great LessWrong posts often stick in people's minds and continue to be reread and shared for years after they are published. It's very rare for great google docs to do so, even if they're initially shared with everyone you care about. And even right after they're written they seem worse at winning hearts and minds. I could speculate about why but here I just want to observe that this seems true. One upshot is that you should often aim for the final output of a project to be a LessWrong post rather than a google doc. (Also, even if you think you've shared your google doc with everyone who should read it, you probably haven't, in part because some other people should read it in the future.) (Obviously google docs are better for eliciting input, if you have a good group that will read your docs.) (Presumably there are some things you can do to make your google docs stick more, including indexing them.) (h/t @Linch, who said something like this to me.)

I think it's both true that LessWrong (LW) has a bunch of issues and that it would be better if much more discourse happened there rather than on X/Twitter. Some claims that all seem true to me despite being in tension: * Many people thinking about AGI/ASI/related post on X but not on LW * This is partially due to LW being scary/aversive * X is more insane and adversarial than LW * The quality of discourse on LW is way better than on X * The LW scene has specific unreasonable biases, is somewhat tribal, and is somewhat of an echo chamber, but on net LW seems way more reasonable than all of the AI parts of X * It would be good if way more of the important discussion about AI happened on LW or at least happened in a forum less messed up than X (or X got less messed up) * Epistemics of AI company employees seem pretty important and are quite bad, with some of the more important influences being internal chatter (with strong biases, influence from leadership, and echo chambers) or X for some of the companies * Public discourse/argumentation seems like it could improve epistemics, especially of AI company employees * LW is often hostile and aversive to AI company employees and some of this seems unhealthy/bad (as in, indicates insufficient decoupling/soldier-mindset/motivated-reasoning and corresponds to people piling on in unhelpful ways). But a bunch of this is a natural consequence of reasonable views of people on LW and the behavior of AI companies and employees at AI companies * E.g., it would be better if people on LW applied something more like typical researcher norms to research outputs. [1] (E.g., for many types of issues, it's good to first email the author with the issue and let them comment or say they are going to fix it before posting publicly. [2] ) And it would be good if people on LW tried to avoid their criticism being unnecessarily rude (though not necessarily less hostile). I also wish LW had more (measured, thoughtful) praise of good

There are some people in AI safety who I feel the most aligned with, and who I think are doing some of the most important work to advocate for AI x-risk being a big deal, and doing the best job of resisting the siren call of AI capabilities work. I've noticed a pattern where these people tend to have a hair trigger for who's behaving unethically and will aggressively call people out for doing things that I don't see as that big a deal. In fact some of these people have called each other out. My best guess for what's going on here is that the sort of person who advocates strongly for what they believe in will sometimes have false positives, and they will advocate strongly for those false positives, too. There are also people who are very careful and analytical, and who don't start fights, but they aren't leading advocacy efforts either. I can think of a couple examples of people who do both, but they're exceedingly rare.

There's been increased discourse on whether prediction markets are net-positive for the world. My take: so far, they haven't been clearly net-positive. However, when I think back to Covid, I sure wish that prediction markets had been as mature of a technology as they are today; prediction markets in February 2020 on how many cases there would be in April 2020 would have probably made the world marginally more sane. Prediction markets are probably most useful in a crisis, where decisions need to be made quickly based on uncertain information. I find it plausible that we'll have such a crisis within the next decade, particularly in the context of AI. And I think that the benefits that prediction markets are likely to provide in such a crisis will likely outweigh the negatives incurred thus far through things like increased sports gambling.