Astral Codex Ten

GPT

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GPT

Article

GPT is a recurring brand in the Astral Codex Ten archive, appearing 12 times across 12 issues between September 12, 2022 and February 25, 2026. The archive places it in contexts such as “GPT-like models scaled up several orders of magnitude (100T parameters)”; “I have no doubt that GPTs of various sizes can simulate an ‘AI’ character who resists being shut down, etc”; “a few months after they came out with GPT”. It most often appears alongside OpenAI, Anthropic, Claude.

Metadata

  • Category: Brands
  • Mention count: 12
  • Issue count: 12
  • First seen: September 12, 2022
  • Last seen: February 25, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

I Won My Three Year AI Progress Bet In Three Months
September 12, 2022 · Original source
At the time, I wrote: I’m not going to make the mistake of saying these problems are inherent to AI art. My guess is a slightly better language model would solve most of them…for all I know, some of the larger image models have already fixed these issues. These are the sorts of problems I expect to go away with a few months of future research. This proved controversial. Gary Marcus in particular has emphasized how challenging compositionality is for modern language and image models: @sama @gdb @Plinz @ylecun, \n\nEach of you ridiculed my recent title, but this is what the article was actually about: compositionality.\n\nYes, there are many kinds of progress in other directions. \n\nBut compositionality is at the core of intelligence. \n\nNo AGI without it. ","username":"GaryMarcus","name":"Gary Marcus","profile_image_url":"","date":"Sat Apr 09 04:34:37 +0000 2022","photos":[],"quoted_tweet":{"full_text":"Compositionality *is* the wall. \n\nEven “red cube” and “blue cube” on their own are represented unreliably; not one of ten images correctly captures the full phrasal description.\n\nThe images are beautiful, but no match for the precision of language. https://t.co/uvoXUtETwi","username":"GaryMarcus","name":"Gary Marcus"},"reply_count":0,"retweet_count":7,"like_count":54,"impression_count":0,"expanded_url":{},"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM"> And one of my commenters, Vitor, asked: Why are you so confident in this? The inability of systems like DALL-E to understand semantics in ways requiring an actual internal world model strikes me as the very heart of the issue. We can also see this exact failure mode in the language models themselves. They only produce good results when the human asks for something vague with lots of room for interpretation, like poetry or fanciful stories without much internal logic or continuity. Not to toot my own horn, but two years ago you were naively saying we'd have GPT-like models scaled up several orders of magnitude (100T parameters) right about now (https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/#comment-912798). I'm registering my prediction that you're being equally naive now. Truly solving this issue seems AI-complete to me. I'm willing to bet on this (ideas on operationalization welcome). I responded to Marcus here, and I responded to Vitor by making a bet on whether AI image models could draw some compositionality-heavy pictures by 2025. The specific terms we agreed on: My proposed operationalization of this is that on June 1, 2025, if either if us can get access to the best image generating model at that time (I get to decide which), or convince someone else who has access to help us, we'll give it the following prompts: 1. A stained glass picture of a woman in a library with a raven on her shoulder with a key in its mouth 2. An oil painting of a man in a factory looking at a cat wearing a top hat 3. A digital art picture of a child riding a llama with a bell on its tail through a desert 4. A 3D render of an astronaut in space holding a fox wearing lipstick 5. Pixel art of a farmer in a cathedral holding a red basketball We generate 10 images for each prompt, just like DALL-E2 does. If at least one of the ten images has the scene correct in every particular on 3/5 prompts, I win, otherwise you do. DALL-E can’t do any of these: If I were being kind, I would give it the farmer in the cathedral. But I am being unkind, so the farmer in front of the cathedral doesn’t count. II. There are now at least four more AI image models available: Google Imagen announced May 2022.
Inline links: https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/#comment-912798, here, bet, https://substackcdn.com/image/fetch/$s_!_gqe!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F6138ab0d-3a82-4eb9-a328-bf38ea0f6b10_632x784.png, announced
How Do AIs' Political Opinions Change As They Get Smarter And Better-Trained?
January 03, 2023 · Original source
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OpenAI's "Planning For AGI And Beyond"
March 01, 2023 · Original source
Sam Altman posing with leading AI safety proponent Eliezer Yudkowsky. Also Grimes for some reason. Planning For AGI And Beyond (“AGI” = “artificial general intelligence”, ie human-level AI) is the latest volley in that campaign. It’s very good, in all the ways ExxonMobil’s hypothetical statement above was very good. If they’re trying to fool people, they’re doing a convincing job! Still, it doesn’t apologize for doing normal AI company stuff in the past, or plan to stop doing normal AI company stuff in the present. It just says that, at some indefinite point when they decide AI is a threat, they’re going to do everything right. This is more believable when OpenAI says it than when ExxonMobil does. There are real arguments for why an AI company might want to switch from moving fast and breaking things at time t to acting all responsible at time t + 1 . Let’s explore the arguments they make in the document, go over the reasons they’re obviously wrong, then look at the more complicated arguments they might be based off of. Why Doomers Think OpenAI Is Bad And Should Have Slowed Research A Long Time Ago OpenAI boosters might object: there’s a disanalogy between the global warming story above and AI capabilities research. Global warming is continuously bad: a temperature increase of 0.5 degrees C is bad, 1.0 degrees is worse, and 1.5 degrees is worse still. AI doesn’t become dangerous until some specific point. GPT-3 didn’t hurt anyone. GPT-4 probably won’t hurt anyone. So why not keep building fun chatbots like these for now, then start worrying later? Doomers counterargue that the fun chatbots burn timeline. That is, suppose you have some timeline for when AI becomes dangerous. For example, last year Metaculus thought human-like AI would arrive in 2040, and superintelligence around 2043. Recent AIs have tried lying to, blackmailing, threatening, and seducing users. AI companies freely admit they can’t really control their AIs, and it seems high-priority to solve that before we get superintelligence. If you think that’s 2043, the people who work on this question (“alignment researchers”) have twenty years to learn to control AI. Then OpenAI poured money into AI, did ground-breaking research, and advanced the state of the art. That meant that AI progress would speed up, and AI would reach the danger level faster. Now Metaculus expects superintelligence in 2031, not 2043 (although this seems kind of like an over-update), which gives alignment researchers eight years, not twenty. So the faster companies advance AI research - even by creating fun chatbots that aren’t dangerous themselves - the harder it is for alignment researchers to solve their part of the problem in time. This is why some AI doomers think of OpenAI as an Exxon-Mobil style villain, even though they’ve promised to change course before the danger period. Imagine an environmentalist group working on research and regulatory changes that would have solar power ready to go in 2045. Then ExxonMobil invents a new kind of super-oil that ensures that, nope, all major cities will be underwater by 2031 now. No matter how nice a statement they put out, you’d probably be pretty mad! Why OpenAI Thinks Their Research Is Good Now, But Might Be Bad Later OpenAI understands the argument against burning timeline. But they counterargue that having the AIs speeds up alignment research and all other forms of social adjustment to AI. If we want to prepare for superintelligence - whether solving the technical challenge of alignment, or solving the political challenges of unemployment, misinformation, etc - we can do this better when everything is happening gradually and we’ve got concrete AIs to think about: We believe we have to continuously learn and adapt by deploying less powerful versions of the technology in order to minimize “one shot to get it right” scenarios […] As we create successively more powerful systems, we want to deploy them and gain experience with operating them in the real world. We believe this is the best way to carefully steward AGI into existence—a gradual transition to a world with AGI is better than a sudden one. We expect powerful AI to make the rate of progress in the world much faster, and we think it’s better to adjust to this incrementally. A gradual transition gives people, policymakers, and institutions time to understand what’s happening, personally experience the benefits and downsides of these systems, adapt our economy, and to put regulation in place. It also allows for society and AI to co-evolve, and for people collectively to figure out what they want while the stakes are relatively low. You might notice that, as written, this argument doesn’t support full-speed-ahead AI research. If you really wanted this kind of gradual release that lets society adjust to less powerful AI, you would do something like this: Release AI #1
Inline links: Planning For AGI And Beyond, Metaculus, they can’t really control their AIs
And so on . . . Meanwhile, in real life, OpenAI released ChatGPT in late November, helped Microsoft launch the Bing chatbot in February, and plans to announce GPT-4 in a few months. Nobody thinks society has even partially adapted to any of these, or that alignment researchers have done more than begin to study them. The only sense in which OpenAI supports gradualism is the sense in which they’re not doing lots of research in secret, then releasing it all at once. But there are lots of better plans than either doing that, or going full-speed-ahead. So what’s OpenAI thinking? I haven’t asked them and I don’t know for sure, but I’ve heard enough debates around this that I have some guesses about the kinds of arguments they’re working off of. I think the longer versions would go something like this: The Race Argument: Bigger, better AIs will make alignment research easier. At the limit, if no AIs exist at all, then you have to do armchair speculation about what a future AI will be like and how to control it; clearly your research will go faster and work better after AIs exist. But by the same token, studying early weak AIs will be less valuable than studying later, stronger AIs. In the 1970s, alignment researchers working on industrial robot arms wouldn’t have learned anything useful. Today, alignment researchers can study how to prevent language models from saying bad words, but they can’t study how to prevent AGIs from inventing superweapons, because there aren’t any AGIs that can do that. The researchers just have to hope some of the language model insights will carry over. So all else being equal, we would prefer alignment researchers get more time to work on the later, more dangerous AIs, not the earlier, boring ones.
Reading even further between the lines - at this point it’s total guesswork - OpenAI’s corporate partner Microsoft asked them for a cool AI. OpenAI assumed Microsoft was competent - they make Windows and stuff! - and gave them a rough draft of GPT-4. Microsoft was not competent, skipped fine-tuning and many other important steps which OpenAI would not have skipped, and released it as the Bing chatbot. Bing got in trouble for threatening users, which gave OpenAI a PR headache around safety. Some savvy alignment people chose this moment to approach them with their latest ideas (is it a coincidence that Holden Karnofsky published What AI Companies Can Do Today earlier that same week?), and OpenAI decided (for a mix of selfish and altruistic reasons) to get on board - hence this document.
Inline links: What AI Companies Can Do Today
Assistant Dictator Book Club: America Against America
June 07, 2023 · Original source
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God Help Us, Let's Try To Understand AI Monosemanticity
November 27, 2023 · Original source
First, GPT-4 has over 100 billion neurons (the exact number seems to be secret, but it’s somewhere up there).
A friend who understands these issues better than I warns that we shouldn’t expect to find pentagons and square anti-prisms in GPT-4. Probably GPT-4 does something incomprehensible in 1000-dimensional space. But it’s the 1000-dimensional equivalent of these pentagons and square anti-prisms, conserving neurons by turning them into dimensions and then placing concepts in the implied space.
Shouldn’t the AI be keeping the concept of God, Almighty Creator and Lord of the Universe, separate from God- as in the first half of Godzilla? Probably GPT-4 does that, but this toy AI doesn’t have enough real neurons to have enough simulated neurons / features to spare for the purpose. In fact, you can see this sort of thing change later in the paper:
24
March 12, 2024 · Original source
The first team is Halawi et al at Berkeley (also including Jacob Steinhardt, featured here before). They cite previous work on out-of-the-box AIs like GPT-4 or Claude. When these enter forecasting tournaments, they might beat some especially unskilled participants, but they lag behind the easiest aggregation method: “the wisdom of crowds”, ie a simple average of all forecasts. The wisdom of crowds is hard to beat - in my tournament, it scored at the 95th percentile.
Inline links: Halawi et al at Berkeley, my tournament
Halawi fine-tunes the out-of-the-box AI (in his case, a version of GPT-4) using some of the same tricks as FutureSearch. They attach it to “news APIs” (NewsCatcher, Google News) and teach it to search them effectively and reason about the contents.
Forecasting skills of different AIs (lower is better). GPT-4 did best so they mostly used that for their system. In one part of the experiment, they use a human-written scratchpad to prompt the reasoning; in another, they fine-tune an AI on these scratchpads so it doesn’t need to use them every time. They get all these different AIs to make multiple predictions and average them together (wisdom of inner crowds - easier for AIs than humans since you can just raise the temperature!)
Inline links: wisdom of inner crowds
Links for May 2024
May 29, 2024 · Original source
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The Colors Of Her Coat
April 01, 2025 · Original source
We have recontextualized the semantic apocalypse from a one-time problem with GPT-4 to a recurrent historical pattern of technology undermining the uniqueness of art. But maybe we should zoom out further. This isn’t just about art. Technology breeds hedonic adaptation, and hedonic adaptation undermines everything.
Now I Really Won That AI Bet
July 08, 2025 · Original source
Not to toot my own horn, but two years ago you were naively saying we'd have GPT-like models scaled up several orders of magnitude (100T parameters) right about now (https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/#comment-912798).
Inline links: https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/#comment-912798
Links For September 2025
September 04, 2025 · Original source
Note: percentages are of total, not of each row! 29: Related: social science team proposes a three-stage model of secularization: decreased public ritual participation → decreased personal importance → decreased identification, presents apparently confirmatory data. If true, would be somewhat inconsistent with intellectual models (eg people learn about evolution and start doubting the Bible) and more consistent with institutional models (eg the government provides welfare so people no longer need to be part of a tight-knit church). 30: Navigating LLMs’ spiky intelligence profile is a constant source of delight; in any given area, it seems like almost a random draw whether they will be completely transformative or totally useless. Now Ethan Strauss reports that they are, for some reason, extraordinarily effective at teaching people golf. “I am predicting the Golf Revolution, or perhaps decline, if your perspective is that optimization tends to ruin hobbies. A sport for obsessives has been gifted the ideal tool for refinement.” 31: Claim (via nxthompson on X): “In a huge survey of young kids about phones and technology, they all say they want to be out playing in the real world. But parents don't let them out unsupervised. So they're stuck on their phones.” Interesting, but I’m nervous about social desirability bias - how many adults would say on a survey that they would rather be on their phones than playing with friends? But adults do have this choice and mostly go with the phones. 32: Steven Adler on AI psychosis. He tries to analyze ER admissions data for psychosis and finds no change. I don’t think anyone reasonable expected this to be a large enough effect to show up in ER admissions data, but there are lots of unreasonable people so I appreciate his effort. He thinks AI companies might have better data on this, and encourages them to release it. 33: Cuartetera was the greatest polo horse ever. Polo players responded in a very practical way: they cloned her, dozens of times (and it worked; the clones are also excellent). Now there is a lawsuit as different polo teams fight to get their hands on Cuartetera clones. What is the equilibrium? If the outsiders get their hands on the genetic material, do we see a world where every polo horse is a Cuartetera clone? How much is lost if nobody ever tries to breed a polo horse better than Cuartetera (since the economics might not check out if the odds of success for any given foal is too low)? H/T Gwern and Siberian Fox (on X). 34: Claim: as of 2013, India’s Agarwal caste, who make up less than 1% of the population, got 40% of the e-commerce funding. 35: Owlposting: What Happened To Pathology AI Companies? Pathology is a medical specialty. A typical task involves looking at a microscope slide full of cells and trying to determine if any of them are cancerous. This seems like a good match for AI - and for years, studies have been showing that in fact AI can equal human experts. So why isn’t it being used more? The author’s three answers: first, slide scanning is expensive and clunky, and you can’t apply AI to a slide until you digitize it. Second, it’s hard to figure out a business plan where this saves someone money and doesn’t step on the toes of big companies that can outcompete anyone they don’t like. Third, pathologists use the context of a patient’s entire clinical history when they interpret a slide, and AIs that can’t do that (either because of technical limitations or legal/privacy limitations) are at a disadvantage even if their skills specifically relating to slide-reading are better. 36: Noahpinion: Will Data Centers Crash The Economy? Suppose that AI is a bubble, either permanently (because the technology isn’t really transformative) or temporarily (because it can’t transform things quickly enough to keep up with all the dumb money pouring into it). Will the sudden write-off of data centers lead to a broader economic collapse? In 2001, the dot-com bubble harmed the tech sector, but didn’t take the rest of the economy down with it; in 2008, the subprime mortgage bubble did take the rest of the economy down with it, because it damaged banks that the whole economy relied on. The optimistic case for AI is that data center spending is mostly coming from big companies like Google and Meta that can absorb a lot of loss. The pessimistic case is that some of the money is coming from private credit, a new-ish form of finance which hasn’t really been stress-tested and whose failure modes are still poorly understood. Noah’s final verdict: the stage isn’t obviously set for a crisis yet, but there’s the potential to get there and we should consider acting (how?) early. 37: The latest Twitter talking point is that universal hepatitis B vaccination at birth is “woke”: Hep B is (aside from mother-to-child transmission) often sexually transmitted, slutty women’s children are more likely to have Hep B, so perhaps giving the vaccine to everyone (instead of testing and only giving to the children of women who test positive) is an attempt to spare slutty women the embarrassment of getting a positive test. Ruxandra Teslo provides the counterargument - Hep B tests take a while, the medical system is fragmented, and any attempt to test people and then give the vaccine inevitably leads to many positive tests falling through the cracks. Vaccinating at birth is easy and hard to screw up, the vaccine has no known side effects, and empirically child Hepatitis B rates go down (by as much as 2/3!) when countries switch from test-and-vaccinate to universal vaccination. This benefits everyone - even people who never have unprotected sex and always follow up on their medical tests - because toddlers in daycare exchange saliva copiously, and if your toddler exchanges saliva with a Hep B positive toddler they could get the disease. A funny Twitter interaction was seeing Republicans in Congress hop on the anti-slut anti-vaccination bandwagon - except for Senator Bill Cassidy (R-Louisiana), who happens to be a liver doctor, and who is still fighting the good fight. I am always nervous when a good person who I like starts engaging on Twitter, since it elevates the discourse there but also gradually turns their brain into mush - but Ruxandra has made the leap and is doing a great job not just on bio related topics but also (for example) countering Curtis Yarvin on the history of her native Romania. 38: The response to GPT-5 was confusing; most specific people who reviewed it said they were impressed (Ethan Mollick, Tyler Cowen, Nabeel Qureshi, Taelin), it performed as expected on formal benchmarks, but the overall vibes declared it a big failure. Peter Wildeford speculated that maybe there was some kind of sinister pay-to-play early access bias involved. Zvi went the other way, calling it a “reverse DeepSeek moment” (insofar as DeepSeek was a pretty average model that got glowing praise.) In the end, I agree with Peter that this was mostly a branding issue. o3 was a genuinely revolutionary model; if OpenAI had called it “GPT-5”, it would have met expectations. Instead, they called it “o3”, and called a minor incremental update a few months later “GPT-5”. Then people got mad that the exciting-sounding “GPT-5” was merely an incremental update. A secondary issue was that the router wasn’t very good, and so many queries got routed to a small version without thinking mode that was if anything a downgrade from o3. I think this tweet by Shakeel perfectly encapsulates the essence of GPT discourse in two sentences: …but maybe it’s worth asking why GPT-5 isn’t bigger than o3. Was 4.5 a failed attempt at scaling? Did it fail in a way that sort of back-handedly justifies the “lost steam” take? Does the answer depend on distinctions between pre-training scaling, post-training scaling, etc? How? 39: This month in etymology: did you know that “oy vey” is a “fully Germanic phrase” which is cognate with English “oh woe!” (h/t Wylfcen on X) 40: mRNA shows promise to be a game-changing treatment for cancer, but RFK is trying to halt research. But so far he can only starve it of money, not ban it, and the funding gap is only $500 million. Will there be enough philanthropic billionaires and private foundations to step up? Zvi points out that although there is usually a game of chicken where foundations are hesitant to touch something the government cancelled lest the government decide it can cancel everything and hope philanthropists pick up the bill, in this case there are no game theory considerations - RFK is halting it because he genuinely wants it halted, and they are thwarting him rather than playing into his hands. The only problem is that $500M is a lot of money for the private sector; a few foundations could technically afford it, but not many could afford it comfortably and still have money left over for the next few crises of this magnitude. I hope someone is trying to organize a coalition. 41: AI fantasy flash fiction Turing test. Eight stories about demons, four by famous fantasy authors, four by ChatGPT. After 3000 votes, AI wins: humans can't tell the difference and slightly prefer the AI stories. My own score was only 75%. But I will say that I thought Mark Lawrence's was obviously the best, I was ~100% sure it was human, and it convinced me that regardless of the official results it's still possible to write flash fiction that an AI obviously can't do. 42: “SignPro” offers customized “In This House We Believe” signs, try not to use this for evil. 43: China think tank assessment of how in control Xi is: still very in control, maybe not infinitely in control. 44: Related - did you know (h/t xlr8harder) that if you ask AI to write a science fiction story, it will very often name the protagonist “Elara Voss” (or some very close variant like Elena Voss), and this remains true across various models and versions? Related: Chelsea Voss of OpenAI is having a baby and has the opportunity to do the funniest thing. 45: “Hector (cloud) is a cumulonimbus thundercloud cluster that forms regularly nearly every afternoon on the Tiwi Islands in the Northern Territory of Australia…[he is sometimes called] Hector the Convector”. 46: British allergy sufferers who want to know the ingredients of things demand that British cosmetics stop listing their ingredients in Latin. “For example, sweet almond oil is Prunus Amygdalus Dulcis, peanut oil is Arachis Hypogaea, and wheat germ extract is Triticum Vulgare.” 47: Text-based RPG about being an NYT journalist at the Manifest prediction market conference. I make a brief appearance. 48: Study uses supposedly-random variation in doctor assignments to test whether the marginal mental health commitment is good or bad for patients, finds that it is quite bad. Freddie de Boer is violently skeptical (maybe literally so?) and makes some good points about how a single quasi-experimental study is never absolute proof. But I don’t think he quite justifies his opinion that the paper was irresponsible and should never have been published; it’s just a normal quasi-experimental study that we should nod and say “huh” at but not overweight as the culmination of all possible research that overcomes all possible priors. My prior is that the marginal commitment is pretty useless (many commitments are just “well, since this person arrived at our ED for some reason, it would look bad from a medico-legal perspective to just let them go, so let’s keep them a few days to evaluate” - and yeah, you should be upset about this) but I’m still surprised by how many outright negative (as opposed to zero) effects the researchers found. The strongest argument for negative effects is that it will make some people miss work and maybe lose their job. But this study found that commitment ~doubles the risk of near-term suicide (admittedly only from 1% to 2%), which would have been outside my confidence intervals for how bad it could be. I suspect confounding, but only on general principle, and I wouldn’t be too surprised either way. 49: This tweet is probably bait, but I found it a thought-provoking question: I think there’s a boring answer, where the law is more complex than just a single number and whatever kind of weird trafficking Epstein was doing is worse than whatever normal relationships these European laws are permitting. But assuming that there’s a substantive difference even after taking that into account, I think my answer is something like - we’ve got to divide kids from adults at some age, there’s a range of reasonable possible ages, we shouldn’t be too mad at other societies that choose different dividing lines within that range - but having decided upon the age, we’ve got to stick with it and take it seriously (in the sense of penalizing/shaming people who break it). This is more culturally relativist than I expected to find myself being, so good job to Richard for highlighting the apparent paradox. 50: Dilan Esper describes his experience as one of Hulk Hogan’s attorneys in the Gawker lawsuit (X). Parts I found interesting: none of the lawyers knew Thiel was funding the lawsuit; Gawker probably could have won if they had been slightly competent but kept "shooting themselves in the foot"; and Gawker probably could have won if they had just pixelated the private parts in the video. 51: Amazing concept and poems (link on X): I tried to see if AI could do this, and it did something that technically met the requirements but had zero artistic merit - using a lot of words like “nowhere” and “outside” in one, then separating them out to “no where” and “out side” in the other. I didn’t invest much energy in creating a clever prompt telling it not to do that, so feel free to report if you get better success. 52: New study claims consultants are actually good, at least for profits: "We find positive effects on labor productivity of 3.6% over five years, driven by modest employment reductions alongside stable or growing revenue" 53: A Polish team tries to test Peter Turchin’s equations for predicting political unrest on recent Polish history, has to make some changes but claims mostly positive results. 54: New big multi-author Substack, The Argument, trying to be a sort of center-left version of the model pioneered by The Free Press and other high-production-value ideological Substack properties. Excited to see Kelsey Piper is involved, and she starts off strong with a post on the latest round of First World basic income studies, which find few positive effects. This is surprising, because recipients didn’t waste the money on alcohol or gambling or anything - they paid down debt and got useful goods. Still, it didn’t even affect things that should have been obvious, like stress level. It’s not even clear that amounts of money large enough to help with rent made homeless people more likely to get houses! Matt Bruenig criticizes the article, accusing Kelsey’s studies of being downstream of Perry Preschool style dreams that exactly the right welfare program will have massively compounding effects that cut poverty out at the root and turn everyone into elite human capital; he thinks giving people money won’t do this, but it will increase equality and give the poor better lives. I assume he’s not a strong hereditarian, but his argument makes even more sense from that perspective, and I’ve certainly criticized dumb outcome measures like infant brain waves which we have only tenuous reasons to think are related to anything we care about. But Kelsey reasonably responds that the outcome measures she’s talking about include stress level and life satisfaction. To defuse this critique, Bruenig either has to argue that our construct “life satisfaction” doesn’t really measure whether someone’s life is satisfactory, or else claim that giving poor people satisfactory lives isn’t really what we’re going for - which I think would require more explanation on his part. There’s some further (impressively acrimonious) debate on X, but I don’t see anything that addresses my core concern. GiveDirectly, a charity involved in basic income experiments, has a presponse here; they say that some studies are positive, and that the ones that aren’t might have tried too little cash to matter, or been confounded by COVID making everything worse. They also point out that basic income is harder to study than traditional programs like giving people housing, because if you’re giving housing you can measure housing-related outcomes directly and have a pretty good chance of getting enough statistical power to find them, but since everyone spends cash on different things, the positive effects might be scattered across many different outcomes (and therefore too small to reach significance on each). Everyone involved in this debate wants to emphasize that the poor results are for First World studies only, and that studies continue to show large benefits to giving cash in the developing world. 55: Related: I was less impressed by The Argument’s first foray into housing policy, which follows an all-too-familiar pattern: Some people say they don’t like noise and disorder and try to make rules against it in their apartments.
Inline links: proposes a three-stage model of secularization, extraordinarily effective at teaching people golf, nxthompson on X, a huge survey, Steven Adler on AI psychosis, they cloned her, dozens of times, a lawsuit, Gwern, on X, got 40% of the e-commerce funding, What Happened To Pathology AI Companies?, Will Data Centers Crash The Economy?, Ruxandra Teslo provides the counterargument, and who is still fighting the good fight, countering Curtis Yarvin on the history of her native Romania, Ethan Mollick, Tyler Cowen, Nabeel Qureshi, Taelin, on formal benchmarks, speculated, a “reverse DeepSeek moment”, with Peter, this tweet by Shakeel, https://substackcdn.com/image/fetch/$s_!GJNZ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3ba0d8cf-fab8-4370-bcad-df789e157fdc_591x402.png, Wylfcen on X, Zvi points out that, AI fantasy flash fiction Turing test, customized “In This House We Believe” signs, China think tank assessment of how in control Xi is, xlr8harder, Chelsea Voss of OpenAI is having a baby, Hector (cloud), demand that British cosmetics stop listing their ingredients in Latin, Text-based RPG about being an NYT journalist at the Manifest prediction market conference, finds that it is quite bad, violently skeptical, literally so?, This tweet, https://substackcdn.com/image/fetch/$s_!S9fU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa558c09b-7fb6-40a8-a8a0-27b658a2c876_576x687.png, describes his experience as one of Hulk Hogan’s attorneys in the Gawker lawsuit (X), link on X, https://substackcdn.com/image/fetch/$s_!zyh7!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F75e9f0f6-d794-4ea2-b24b-5d4803bf28dc_590x478.png, New study claims consultants are actually good, tries to test Peter Turchin’s equations for predicting political unrest on recent Polish history, The Argument, a post on the latest round of First World basic income studies, criticizes the article, infant brain waves, debate on X, has a presponse here, first foray into housing policy
…but maybe it’s worth asking why GPT-5 isn’t bigger than o3. Was 4.5 a failed attempt at scaling? Did it fail in a way that sort of back-handedly justifies the “lost steam” take? Does the answer depend on distinctions between pre-training scaling, post-training scaling, etc? How? 39: This month in etymology: did you know that “oy vey” is a “fully Germanic phrase” which is cognate with English “oh woe!” (h/t Wylfcen on X) 40: mRNA shows promise to be a game-changing treatment for cancer, but RFK is trying to halt research. But so far he can only starve it of money, not ban it, and the funding gap is only $500 million. Will there be enough philanthropic billionaires and private foundations to step up? Zvi points out that although there is usually a game of chicken where foundations are hesitant to touch something the government cancelled lest the government decide it can cancel everything and hope philanthropists pick up the bill, in this case there are no game theory considerations - RFK is halting it because he genuinely wants it halted, and they are thwarting him rather than playing into his hands. The only problem is that $500M is a lot of money for the private sector; a few foundations could technically afford it, but not many could afford it comfortably and still have money left over for the next few crises of this magnitude. I hope someone is trying to organize a coalition. 41: AI fantasy flash fiction Turing test. Eight stories about demons, four by famous fantasy authors, four by ChatGPT. After 3000 votes, AI wins: humans can't tell the difference and slightly prefer the AI stories. My own score was only 75%. But I will say that I thought Mark Lawrence's was obviously the best, I was ~100% sure it was human, and it convinced me that regardless of the official results it's still possible to write flash fiction that an AI obviously can't do. 42: “SignPro” offers customized “In This House We Believe” signs, try not to use this for evil. 43: China think tank assessment of how in control Xi is: still very in control, maybe not infinitely in control. 44: Related - did you know (h/t xlr8harder) that if you ask AI to write a science fiction story, it will very often name the protagonist “Elara Voss” (or some very close variant like Elena Voss), and this remains true across various models and versions? Related: Chelsea Voss of OpenAI is having a baby and has the opportunity to do the funniest thing. 45: “Hector (cloud) is a cumulonimbus thundercloud cluster that forms regularly nearly every afternoon on the Tiwi Islands in the Northern Territory of Australia…[he is sometimes called] Hector the Convector”. 46: British allergy sufferers who want to know the ingredients of things demand that British cosmetics stop listing their ingredients in Latin. “For example, sweet almond oil is Prunus Amygdalus Dulcis, peanut oil is Arachis Hypogaea, and wheat germ extract is Triticum Vulgare.” 47: Text-based RPG about being an NYT journalist at the Manifest prediction market conference. I make a brief appearance. 48: Study uses supposedly-random variation in doctor assignments to test whether the marginal mental health commitment is good or bad for patients, finds that it is quite bad. Freddie de Boer is violently skeptical (maybe literally so?) and makes some good points about how a single quasi-experimental study is never absolute proof. But I don’t think he quite justifies his opinion that the paper was irresponsible and should never have been published; it’s just a normal quasi-experimental study that we should nod and say “huh” at but not overweight as the culmination of all possible research that overcomes all possible priors. My prior is that the marginal commitment is pretty useless (many commitments are just “well, since this person arrived at our ED for some reason, it would look bad from a medico-legal perspective to just let them go, so let’s keep them a few days to evaluate” - and yeah, you should be upset about this) but I’m still surprised by how many outright negative (as opposed to zero) effects the researchers found. The strongest argument for negative effects is that it will make some people miss work and maybe lose their job. But this study found that commitment ~doubles the risk of near-term suicide (admittedly only from 1% to 2%), which would have been outside my confidence intervals for how bad it could be. I suspect confounding, but only on general principle, and I wouldn’t be too surprised either way. 49: This tweet is probably bait, but I found it a thought-provoking question: I think there’s a boring answer, where the law is more complex than just a single number and whatever kind of weird trafficking Epstein was doing is worse than whatever normal relationships these European laws are permitting. But assuming that there’s a substantive difference even after taking that into account, I think my answer is something like - we’ve got to divide kids from adults at some age, there’s a range of reasonable possible ages, we shouldn’t be too mad at other societies that choose different dividing lines within that range - but having decided upon the age, we’ve got to stick with it and take it seriously (in the sense of penalizing/shaming people who break it). This is more culturally relativist than I expected to find myself being, so good job to Richard for highlighting the apparent paradox. 50: Dilan Esper describes his experience as one of Hulk Hogan’s attorneys in the Gawker lawsuit (X). Parts I found interesting: none of the lawyers knew Thiel was funding the lawsuit; Gawker probably could have won if they had been slightly competent but kept "shooting themselves in the foot"; and Gawker probably could have won if they had just pixelated the private parts in the video. 51: Amazing concept and poems (link on X): I tried to see if AI could do this, and it did something that technically met the requirements but had zero artistic merit - using a lot of words like “nowhere” and “outside” in one, then separating them out to “no where” and “out side” in the other. I didn’t invest much energy in creating a clever prompt telling it not to do that, so feel free to report if you get better success. 52: New study claims consultants are actually good, at least for profits: "We find positive effects on labor productivity of 3.6% over five years, driven by modest employment reductions alongside stable or growing revenue" 53: A Polish team tries to test Peter Turchin’s equations for predicting political unrest on recent Polish history, has to make some changes but claims mostly positive results. 54: New big multi-author Substack, The Argument, trying to be a sort of center-left version of the model pioneered by The Free Press and other high-production-value ideological Substack properties. Excited to see Kelsey Piper is involved, and she starts off strong with a post on the latest round of First World basic income studies, which find few positive effects. This is surprising, because recipients didn’t waste the money on alcohol or gambling or anything - they paid down debt and got useful goods. Still, it didn’t even affect things that should have been obvious, like stress level. It’s not even clear that amounts of money large enough to help with rent made homeless people more likely to get houses! Matt Bruenig criticizes the article, accusing Kelsey’s studies of being downstream of Perry Preschool style dreams that exactly the right welfare program will have massively compounding effects that cut poverty out at the root and turn everyone into elite human capital; he thinks giving people money won’t do this, but it will increase equality and give the poor better lives. I assume he’s not a strong hereditarian, but his argument makes even more sense from that perspective, and I’ve certainly criticized dumb outcome measures like infant brain waves which we have only tenuous reasons to think are related to anything we care about. But Kelsey reasonably responds that the outcome measures she’s talking about include stress level and life satisfaction. To defuse this critique, Bruenig either has to argue that our construct “life satisfaction” doesn’t really measure whether someone’s life is satisfactory, or else claim that giving poor people satisfactory lives isn’t really what we’re going for - which I think would require more explanation on his part. There’s some further (impressively acrimonious) debate on X, but I don’t see anything that addresses my core concern. GiveDirectly, a charity involved in basic income experiments, has a presponse here; they say that some studies are positive, and that the ones that aren’t might have tried too little cash to matter, or been confounded by COVID making everything worse. They also point out that basic income is harder to study than traditional programs like giving people housing, because if you’re giving housing you can measure housing-related outcomes directly and have a pretty good chance of getting enough statistical power to find them, but since everyone spends cash on different things, the positive effects might be scattered across many different outcomes (and therefore too small to reach significance on each). Everyone involved in this debate wants to emphasize that the poor results are for First World studies only, and that studies continue to show large benefits to giving cash in the developing world. 55: Related: I was less impressed by The Argument’s first foray into housing policy, which follows an all-too-familiar pattern: Some people say they don’t like noise and disorder and try to make rules against it in their apartments.
Inline links: Wylfcen on X, Zvi points out that, AI fantasy flash fiction Turing test, customized “In This House We Believe” signs, China think tank assessment of how in control Xi is, xlr8harder, Chelsea Voss of OpenAI is having a baby, Hector (cloud), demand that British cosmetics stop listing their ingredients in Latin, Text-based RPG about being an NYT journalist at the Manifest prediction market conference, finds that it is quite bad, violently skeptical, literally so?, This tweet, https://substackcdn.com/image/fetch/$s_!S9fU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa558c09b-7fb6-40a8-a8a0-27b658a2c876_576x687.png, describes his experience as one of Hulk Hogan’s attorneys in the Gawker lawsuit (X), link on X, https://substackcdn.com/image/fetch/$s_!zyh7!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F75e9f0f6-d794-4ea2-b24b-5d4803bf28dc_590x478.png, New study claims consultants are actually good, tries to test Peter Turchin’s equations for predicting political unrest on recent Polish history, The Argument, a post on the latest round of First World basic income studies, criticizes the article, infant brain waves, debate on X, has a presponse here, first foray into housing policy
Why AI Safety Won't Make America Lose The Race With China
November 26, 2025 · Original source
Compute: America is far ahead. We have better chips (thanks, NVIDIA) and can produce many more of them (thanks, TSMC). Our recent capex boom, where companies like Google and Microsoft spend hundreds of billions of dollars on data centers, has no Chinese equivalent. By the simplest measure - total FLOPs on each sides - we have 10x as much compute as China, and our advantage is growing every day. A 10x compute advantage corresponds to about a 1-2 year time advantage, or an 0.5 - 1 generation advantage (eg GPT-4 to GPT-5).
The Pentagon Threatens Anthropic
February 25, 2026 · Original source
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