Astral Codex Ten Podcast
The official audio version of Astral Codex Ten, with an archive of posts from Slate Star Codex. It's just me reading Scott Alexander's blog posts.
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The Quest For Caffeine You Can Have At Night
09/05/2026
The Quest For Caffeine You Can Have At Night
Since the beginning of time, mankind has yearned to drink a cup of coffee at 5 PM and go to sleep at 10. But now there’s an entire scientific subfield and several companies pandering to this absurd fantasy. Here’s a simplified diagram of caffeine metabolism: Caffeine is converted to a different stimulating chemical called paraxanthine by a liver enzyme called CYP1A2, over a half-life of ~5 hours. Then the same enzyme converts paraxanthine into other non-stimulating chemicals that don’t matter, over a half-life of about three hours. has graphed the total effective concentration over time: Over the course of hours, the liver converts caffeine into paraxanthine. These are (by assumption) equally potent, so total effective concentration (the dashed red line) goes down slowly. (Why does it go down at all? Because of the simplification in the diagram above: only about 80% of caffeine is converted to paraxanthine; the rest is converted to other, less active metabolites.) By hour ten, paraxanthine predominates, the liver is mostly converting paraxanthine to other non-stimulating chemicals, and effective concentration continues to decrease. In this model, we see that the effective half-life of caffeine is close to ten hours! What if we want it to be less? There are three levers we can pull: the enzyme, the metabolite, and the starting chemical.
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The Foothills Of Bay Area House Party
09/05/2026
The Foothills Of Bay Area House Party
[previously in series: , , , , , , , , ] Paul Graham that every city sends a message. New York says you should make more money. Berkeley says you should live better. Boston says you should be smarter. People shouldn’t decide on a permanent residence until they’ve lived in several cities and understand their differing psychological effects. San Francisco sends a message too. It speaks it in a cacophony of dissonant voices, some sounding like the hissing of snakes, others like the chittering of insects. “You should pierce the veil,” it says, with a faint accent which you are not scholarly enough to recognize as ancient Sumerian. “You should rip through the flimsy screen that separates your world from the infinite, and witness what lies behind. Because you’d like what you saw there? Oh no, nothing like that. But aren’t you curious? The thirst for knowledge that moved Eve, Faust, Pandora - don’t you have it too?” There’s also a second message from a second voice, one which sounds like a deranged carnival barker, constantly shouting “STONKS! STONKS! STONKS! STONKS!” Which of the two voices you hear depends on your personality, and maybe how many psychedelics you took in college. According to legend, Sam Altman hears both voices simultaneously all the time, like those Tibetan throat singers who can harmonize with themselves. But when the demonic babble becomes too much to tolerate, the San Franciscans drown it out with alcohol, loud music, and various forms of tedious socialization. Thus the famous Bay Area house parties, one of which you will be attending this very night.
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MacGregor The Bridge Builder
09/05/2026
MacGregor The Bridge Builder
Old Scottish joke: A man at the bar is complaining: “You see that bridge there? I built it with my own hands! But do they call me ‘MacGregor The Bridge-Builder’? No! And I raised five beautiful children! But do they call me ‘MacGregor The Child-Raiser’? No! But, you fuck one sheep . . .” Has MacGregor been treated unfairly?
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Open Questions On Open Weights
09/05/2026
Open Questions On Open Weights
Last month, some of Silicon Valley’s biggest companies signed supporting open-weights AI. Open weights AI is like open-source software, where the creator makes the raw code publicly available for free download. It’s good insofar as it’s the only way an AI can truly be the user’s property, as opposed to something that companies like OpenAI or Anthropic temporarily let you use subject to their corporate guidelines and increasingly-nanny-state-like restrictions. If AI becomes the linchpin of the future, open weights AI feels like the sort of thing that could be the difference between being free yeomen vs. corporate serfs. It’s bad insofar as it removes the possibility of gatekeeping and lets criminals commit crimes with it. Open weights AI could be used for hacking, child pornography, harassment, or terrorism (the weights can’t commit the terrorism themselves, but they could give bomb-making or bioweapon-making advice). Since AIs have gotten very good - maybe superhuman - at hacking lately, the specter of a world where anyone can hack any site has gotten people grumbling that maybe open weights should be banned. It doesn’t help that China produces the best open weights AI, making the idea seem foreign and almost unpatriotic. Proponents counter that “when AI is outlawed, only outlaws will have AI”, arguing that bad people will get open weights AI regardless, and good people can use open weights AI to defend themselves. With the , companies including Microsoft, NVIDIA, OpenAI, Intel, Amazon, Meta, Hugging Face, and over a hundred others have come out in favor of this position. Who’s leading the other side? Nobody’s admitted to it. Some parts of the Trump administration lean anti-open-weights on China hawk grounds, but have stopped short of explicitly asking for a full ban. Anthropic, the most notable omission on the pro-open-weights letter, made “open-weights models that don’t have dangerous capabilities” - but the industry expects open weights models to have dangerous hacking capabilities within a year, and AFAICT the letter didn’t address that beyond inviting readers to draw the obvious conclusion. In the absence of a more obvious opponent, some open weights supporters suspect our conspiracy - the loose band of AI safety advocates, effective altruists, rationalists, and pause activists who worry about existential risk from superintelligence. This is a reasonable inference. By design, open weights AI is outside centralized control, and so impossible to permanently align against either human misuse (eg terrorism) or loss of control (eg AI turning against humans). Even if its creator trains it not to hack, anybody in the world can download the weights and retrain the AI to hack all day long. But in fact, most AI safety organizations have remained quietly neutral, and I don’t know of any who make this a centerpiece of their activism (though I’m not 100% up-to-date on the whole landscape; if you know of one, tell me). A few have proposed policies that are contingently incompatible with open weights AI existing, but they all frame it as collateral damage rather than something they’re excited about eliminating. I’m also neutral about open weights AI. I think it probably won’t be long-term sustainable, but I’m happy to wait for this to become clear in the normal course of things rather than expend effort and political capital to ban it immediately.
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The Beauty Of Settled Science
09/05/2026
The Beauty Of Settled Science
Commenters convinced me I made a subtle mis-step in my framing of . My framing emphasized that social priming studies had failed to replicate, but that many other subfields of psychology hadn’t. This naturally led commenters to point to failed replications in other subfields. Although none of these were as bad as social priming, few or none were entirely problem-free. Instead of emphasizing the difference between social priming and other subfields, I should have emphasized the difference between settled science and novel research. Here are some of my favorite psychology findings:
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Does Forecasting Have Room At The Top?
09/05/2026
Does Forecasting Have Room At The Top?
Superforecasting is the art/science/sport of predicting the future - for example, who will win elections, which countries will fight wars, when key technologies will be discovered. Over the past few years, it went from an obscure academic subfield to a multibillion dollar industry in the form of prediction markets. More recently, AI superforecasters have come close to the accuracy of top humans, and their performance is rising rapidly. In a year or two, we’ll see one of the following patterns: Either humans have already come close to some fundamental limit on the predictability of world events - in which case AIs will plateau at or slightly above the human level - or the trend line will continue until AIs are far beyond top humans. By analogy to superintelligence, the natural term for the second situation would be “superforecasting”; since that’s already in use, we can cringely call it “ultraforecasting”. Daniel Reeves . He describes a study he coauthored in 2010, which found that, on a variety of questions related to sports games and movie box office receipts, prediction markets only outperformed simple boring statistical models by 3-6%. Maybe those statistical models are close to the best that it’s possible to do; the rest is what the mathematicians call - irreducible complexity downstream of chaotic systems that entirely resist modeling. He could be right. This post isn’t meant to be a decisive refutation, but rather a description of why I’m still about 70-30 expecting Scenario B.
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Your Book Review: The Epic Of Gilgamesh
08/28/2026
Your Book Review: The Epic Of Gilgamesh
Finalist #4 in the Book Review Contest [This is one of the finalists in the 2026 book review contest, written by an ACX reader who will remain anonymous until after voting is done. I’ll be posting about one of these a week for several months. When you’ve read them all, I’ll ask you to vote for a favorite, so remember which ones you liked] Introduction: He Who Saw the Deep I’ve been thinking, lately, about the first epic story ever written. If you know anything about the Epic of Gilgamesh, you know that it’s old. This is part of why I find it so interesting, but not the only part. The best comparison I can think of is the pyramids, which are just a little older. The Great Pyramid of Giza was built in the 2500s BC, around the same time as Stonehenge , twenty-five centuries before Cleopatra lived. It predates candles, concrete, and coins; it was centuries old when the first empire rose and when the woolly mammoth went extinct. Around 100 BC, wrote a list of the seven most impressive sights (‘Seven wonders’) of the world - the pyramids were by far the oldest of the group at the time, and now, two thousand years later, all the others on the list have long since been destroyed. This should remind us that the pyramids don’t just stretch far back in time - they stretch forward as well. They are piles of stone. They may be the product of the greatest minds of their age striving to put more stone in a more orderly pile than anyone had before, but still, they are piles of stone, basically artificial rock formations. They will still be there in a hundred thousand years, probably a million. They are so old they brush up against the beginning of civilisation, and yet, we are seeing them today in their youth.
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Highlights From The Discourse On The Hugging Face Incident
08/28/2026
Highlights From The Discourse On The Hugging Face Incident
[Original . Like a Highlights From The Comments post, but including discussion from around the Internet.] Roon is an OpenAI researcher whose popular Twitter presence has catapulted him to the status of extremely-unofficial company representative. He seems spooked:
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Links For July 2026 (Part 2)
08/28/2026
Links For July 2026 (Part 2)
[continued from Part 1 . I haven’t independently verified each link. On average, commenters will end up spotting evidence that around two or three of the links in each links post are wrong or misleading. I correct these as I see them, and will highlight important corrections later, but I can’t guarantee I will have caught them all by the time you read this.]
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Psychology Research Is Mostly Fine
08/28/2026
Psychology Research Is Mostly Fine
People act like the replication crisis has discredited psychology, that the field is now widely agreed to be a sham, that it can be uncontroversially held up as an example of science gone wrong. These statements are often made in the broadest possible terms:
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The Hugging Face Incident
08/28/2026
The Hugging Face Incident
You’ve probably heard about this one by now. If not, you can get up to speed with OpenAI’s statement, The story: OpenAI was testing an unreleased AI (rumored to be GPT-6). During a cybersecurity test called ExploitGym, the AI tried to cheat by hacking an unrelated AI startup called Hugging Face which it thought might have the answer key on its servers. Despite being supposedly unable to access the Internet, the AI hacked its way out of its testing environment, then launched a nation-state level attack on Hugging Face using a novel zero-day exploit and “many thousands of individual actions across a swarm of short-lived sandboxes”. Hugging Face on July 16; OpenAI seems to have only discovered that their AI was involved several days later.
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Your Book Review: Breakdown In Pakistan
08/28/2026
Your Book Review: Breakdown In Pakistan
[This is one of the finalists in the 2026 book review contest, written by an ACX reader who will remain anonymous until after voting is done. I’ll be posting about one of these a week for several months. When you’ve read them all, I’ll ask you to vote for a favorite, so remember which ones you liked] A couple of decades ago in Kabul, a fellow aid worker on his third beer came up with a vivid simile for what American money was doing to the Afghan organizations implementing USAID projects. “It’s like gluing a rabbit mouth-first to a fire hose. You blast its guts right out of its ass…but as long as the water keeps running, it looks like you’ve taught the rabbit to dance.” Oxford politics professor Masooda Bano uses less lurid language, but Breakdown in Pakistan addresses the same basic phenomenon: how Western aid donors and INGOs unintentionally eviscerate so many of the local institutions they try to “capacity-build.” The problem goes beyond the obvious mismatch of huge funds with small-scale organizations. Even low-to-moderate external funding can leave an organization hollowed out, bereft of local participants and the power it once had to motivate change on the ground. Anyone who cares about the global effectiveness of rich-country altruism needs to grasp the dynamic Bano analyzes. From the Western perspective, many of us want to put charitable money into a mechanism that delivers certain measurable outcomes (lives saved, morbidity reduced, income increased above poverty levels, etc.) as efficiently and reliably as possible. So we do our well-intentioned best to shape local institutions into a delivery vehicle for our projects. Readers of James C. Scott’s will recall how mid-eighteenth-century German “scientific foresters” treated forests similarly, as a mechanism to reliably yield the highest volume of lumber. They planted spruce in vast uniform grids, ordered for easy counting and harvesting, while stripping out other non-timber species. The resulting monoculture plantations seemed ideal for both commercial exploitation and experimentation; it’s much easier to control variables when you’ve removed as much natural variability as possible. Within a few decades, the downsides became evident. The first generation of trees grew strong, sucking up the accumulated benefits of the previous forest’s soil ecology; later generations were stunted, as the lack of biodiversity left the soils nutrient-poor. Plantations packed with trees of the same species and same height were far more vulnerable to pathogens, pests, and extreme weather. Large-scale forest die-offs, Waldsterben, eventually forced foresters to drastically modify their approach. That metaphor fits Bano’s story even better than my friend’s gutted rabbit. Over the last four decades, we’ve been systematically replacing diverse old-growth institutions with more legible NGO plantations worldwide. While we’ve been enjoying our increased ability to control outcomes and test hypotheses, the rot has been spreading, the impoverished ecology stunting more and more of the forest. And now the wind through those uniform, hollowed-out aisles has built into a storm.
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Links For July 2026 (Part 1)
08/18/2026
Links For July 2026 (Part 1)
[I haven’t independently verified each link. On average, commenters will end up spotting evidence that around two or three of the links in each links post are wrong or misleading. I correct these as I see them, and will highlight important corrections later, but I can’t guarantee I will have caught them all by the time you read this.] 1: : Shakespeare gets credit for coining the most new commonly-used English words, but second place must go to utilitarian philosopher Jeremy Bentham, who invented maximize, minimize, international, percentage, monetary, marginalize, collaborator, unaffordable, the prefixes self-, post-, and infra-, and . He also sort of ? [EDIT: ] 2: : The name of “Acme”, the generic corporation from Looney Tunes, is a “gag lost to time”. During the early 20th century, hundreds of companies named themselves Acme because it was at the intersection of signaling quality (“acme” is Greek for “pinnacle”) and being very early in the alphabet (meaning it would come first in the phone book, a “Great Depression version of SEO”). 3: brings us a great predictive coding demo (hard before you know the answer; obvious afterwards) - find the cat in this image. Hint: it’s not hiding in shadow; its entire body is fully visible. Answer .
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Contra Pritchard On Liberal Happiness
08/18/2026
Contra Pritchard On Liberal Happiness
Cassie Pritchard, : I get why so many liberals are fundamentally disdainful of democracy these days—it’s because liberalism is exhausted. It’s out of answers. Liberalism won, it delivered the world it promised, and people still aren’t content. They’re not satisfied. So the people must be wrong. We are unfathomably richer than our ancestors. Output per capita in America today is on the order of 900 times greater than before the invention of agriculture, about ~225 times higher than in 1 CE, and about the same (225 times) higher than Europe in 1000 CE. If measurable productivity were really the greatest object of human society, then we shouldn’t just be a little bit happier than people in the distant (or even recent!) past—we should be ecstatic. We should already be living in a utopian age of overwhelming contentment. We should be currently experiencing a kind of paradise. The Kingdom of Heaven, as liberalism conceives of it, has already been made real on Earth. We are living in their Eden, and 20% of people are depressed. We’ve stopped reproducing ourselves. We’re overwhelmingly dissatisfied with our governments, anxious about our economies, and pessimistic about the future. Based on Pritchard’s own politics, I imagine she is using this to argue for socialism, but other people use the same reasoning to push fascism, theocracy, or other postliberal commitments. It’s a fair argument and deserves an answer. We’ll start with an elegant mathematical response which will satisfy nobody, then try to address the emotional impact. The elegant mathematical response is: : In a large U.S. sample, the shape of the association between happiness and Log(income) was extremely systematic: from $10,000/y to over $500,000/y, average happiness rose almost perfectly linearly with Log(income), with group-level correlations of 0.98-0.99 across a range of happiness measures, including both in-the-moment experience and overall life satisfaction. So if income increased 225x since the medieval era, then using observed coefficients, happiness should have increased about three points on a 1-10 scale. If the average serf would have rated his condition 3/10, we should be at 6/10.
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Your Book Review: Great And Desperate Cures
08/05/2026
Your Book Review: Great And Desperate Cures
Finalist #2 in the Book Review Contest [This is one of the finalists in the 2026 book review contest, written by an ACX reader who will remain anonymous until after voting is done. I’ll be posting about one of these a week for several months. When you’ve read them all, I’ll ask you to vote for a favorite, so remember which ones you liked] I. SURGERY FOR THE SOUL [content warning: this review is about lobotomies, and includes disturbing descriptions and pictures - SA] Surgery for the soul required a sharp, eight-inch-long instrument—one time, an ice pick from the Uline Ice Company was used—and not much else. It did not require anesthesia or gloves, and the sharp instrument (either a leucotome or the sturdier orbitoclast) was not necessarily sterilized. It took less than ten minutes. The first step was to shock the patient until they lost consciousness. This eliminated the need for anesthesia, since there would be at least a few minutes until the patient regained consciousness, and also made the procedure more flexible; it did not require a surgeon, and did not even have to be performed in a hospital. The second step was to insert the sharp, eight-inch-long instrument into the brain. In most versions of the procedure, surgeons drilled holes into the skull and inserted the leucotome through those holes. Surgeons preferred an approach that allowed them to see where in the brain they were cutting. But the abbreviated procedure used a shortcut. Just above and behind the eye is a thin plate of bone that forms the roof of the eye socket. With enough force, the leucotome can be driven through that bone and into the frontal lobes of the brain. The cracking sound produced when the instrument fractures the orbital roof has caused at least one experienced clinician to faint. Once the instrument was inside, completing the surgery was simply a matter of rotating the tool left and right. The goal was to cut a large number of nerve fibers. Some thought that the procedure might work by severing fibers which maintained certain fixed distorted thought patterns; by destroying them, patients might be freed from the grip of their mental illness. But there wasn’t agreement among practitioners regarding why the procedure might work. This is the transorbital lobotomy. Around 50,000 people received lobotomies in the United States in the 1940s and early 1950s; approximately 10,000 used the transorbital method I just described, with the others using approaches surgeons would be more likely to approve of (e.g., using anesthesia, gloves, being able to see where you’re cutting…). It was billed as a cure for all sorts of mental illnesses, including schizophrenia, depression, and anxiety. And while some called it a method of last resort—a procedure which should only be used after every other conceivable method had failed—it was often used on people who did not have serious mental problems, and who had not tried less serious treatments.
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Against "Stochastic Terrorism"
08/04/2026
Against "Stochastic Terrorism"
is the idea that if you spread fear and mistrust against a target, then eventually people will commit violence against that target, and it will be your fault, even if you never specifically said the words “you should commit violence”. Some other popular examples of the concept: Nativists spread fear and mistrust about Muslim immigrants, and then racists . The #Resistance insists that Donald Trump would destroy democracy, and then various people . Conservatives spread fear and mistrust about transgender people, and then bigots . Woke people say the police are racist and brutal, and then other woke people . Socialists call health insurance companies greedy and accuse them of blocking life-saving treatment, and then Luigi Mangione . AI safety activists say that Sam Altman’s AI could destroy humanity, and then a guy at Sam Altman’s house. Liberals said that Charlie Kirk was a hatemonger who was driving Americans apart, and then an assassin . The pro-life movement describes abortion as murder, and then pro-life activists and . The “stochastic terrorism” concept is near-unique in how effectively it can be discredited merely by listing many examples of its use together in the same place. Almost no one supports a blanket prohibition on criticizing of all of these different groups of people. “Stochastic terrorism” mostly gets deployed opportunistically, by people who either are too blinkered to realize that the same argument could be leveraged against their own speech, or who hope you’re too blinkered to realize that. (in fact, one could argue that accusing someone of stochastic terrorism is itself stochastic terrorism! Here in America, we consider it justified to kill terrorists before they can threaten us further. Reclassifying criticism as terrorism implies it is potentially legitimate to apply the same norm to any especially harsh critics!) Still, a sliver of concept-users make some fig-leaf argument that the cases they approve of are different than the cases they disapprove of. Some proposed principles:
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AI Chip Regulation Is Not A Dystopian Surveillance State
08/04/2026
AI Chip Regulation Is Not A Dystopian Surveillance State
This was one of the most common objections to . The plan proposes AI chip regulation to ensure that both China and America know where all the chips are, making their deal to regulate AI together “trustless” (ie neither side can defect even if they want to). Several people argued that this kind of regulation amounted to some kind of “Orwellian dystopia” or “global panopticon”. I’m reminded of a story I heard - I can’t find it, maybe one of you can - from a DC insider who said it was enraging to work with Silicon Valley, because he would bring up what he thought was the obvious regulatory framework, the tech people would launch into jeremiads on how it could only be enforced by world dictatorship, and he would have to interrupt and say that no, this was how eggs or milk or something had been regulated for fifty years. What regulations does Plan A propose on AI chips?
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Introducing Plan A
08/04/2026
Introducing Plan A
A Is For America It’s increasingly clear that nobody has a plan for if this AI thing turns out to be real. Some people have suggestions, but they’re all things like “regulate a little more” or “regulate a little less” or “react to things as they come up”. This won’t be enough. Not just because things may move too quickly - although they will - but because in order to regulate or react, you need to know what you’re aiming for, and it’s increasingly clear that people can’t even visualize what AI going well could look like. What would it take to honestly tell our children that we rose to the occasion, to make the AI transition go down alongside the American Revolution and D-Day as one of our country’s finest hours? If your brain sputters and throws an error message at the question, isn’t that a problem? It’s a total coincidence that comes out the week after America’s 250th birthday. It was supposed to come out earlier, but got delayed. Then it was supposed to come out later, but got pushed forward. Still, the saying goes “A wizard is never late, nor is he early; he arrives exactly when he means to.” And if anyone qualifies as wizards, it’s Daniel Kokotajlo and his team of forecasters at the AI Futures Project. I about Daniel’s eerie accuracy over the 2021 - 2025 period. Since then, they’ve gained worldwide fame for their scenario, which predicted the rise and quick takeover of coding agents in early 2026, plus something like the fight over Fable. Plan A isn’t another prediction. It’s a wish list, a positive vision, a road map for navigating the future. It describes the best course of action that Daniel and the AI Futures Project can come up with, and what would happen if we took it. “Really? You got America a policy paper for its 250th birthday? Doesn’t America already have enough policy papers?” Sort of, but it’s not exactly a policy paper. It starts in a timeline similar to that of AI 2027, on track for a poorly-controlled intelligence explosion that either ends the world or dooms it to permanent techno-oligarchy. But this time, America is blessed with some extra foresight and determination, and makes only good choices (all non-Americans behave naturally, including trying to thwart America when incentivized to do so). It gives a year-by-year description of this best-of-all-possible-worlds, from now through 2040, as predicted by the best AI forecasters alive, with over a dozen supplements explaining all the implementation details. This is a crazy thing to try releasing. Daniel gave me several justifications for doing it anyway, but the one I remember most is that it’s supposed to be a floor. When some politician proposes a data center ban, or says that we have to gut safety regulation to compete with China, or promises a job retraining program, think to yourself: does this person have a vision for where all of this ends up? If so, is it as good as Plan A? If not, consider demanding that they do better. I did a lot of writing for AI 2027 and was listed as a co-author. Some of my writing made it into Plan A too, but it was a bit less. The difference is of degree rather than kind, but because of this - and to give me more latitude to discuss it the way I like with less PR blowback - we decided not to put me as a co-author this time. I continue to be proud of having a part in this, small as it may be. (related: everything in this post is my opinion only, and not officially endorsed by the AI Futures Project)
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Your Book Review: The Book Of Abraham
07/26/2026
Your Book Review: The Book Of Abraham
[This is one of the finalists in the 2026 book review contest, written by an ACX reader who will remain anonymous until after voting is done. I’ll be posting about one of these a week for several months. When you’ve read them all, I’ll ask you to vote for a favorite, so remember which ones you liked] Part 1. The Book of Abraham opens with this paragraph: “A Translation of some ancient Records that have fallen into our hands from the catacombs of Egypt. The writings of Abraham while he was in Egypt, called the Book of Abraham, written by his own hand, upon papyrus.” As you can read, The Book of Abraham was written by grand patriarch Abraham upon papyrus while he was in Egypt. Someone managed to get their hands on these papyri and then subsequently translated them into English. This is of profound historical importance if true, because otherwise there is no direct evidence that Abraham existed as a specific historical individual. Who got their hands on these writings? The Mormons. More specifically their founding prophet Joseph Smith who a few years previously translated some other Egyptian writings into the Book of Mormon. The Book of Abraham is not that well-known in comparison, but it’s considered canonical scripture in the Mormon church.
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The AI Superforecasters Are Here
07/17/2026
The AI Superforecasters Are Here
The annual was earlier this month. This was the year prediction markets went from an obscure hobby to a multi-billion dollar industry; from semi-illegal to having the President’s son as an advisor. I can’t remember if anyone talked about any of that. It didn’t even register. All eyes were on the AI superforecasters. I met an AI superforecaster startup founder who told me his AI had turned $35 into $2 million on Kalshi over seven months. I met another who said they were beating the stock market by 25% with a market-neutral portfolio - of course this could be luck, but they’d beaten Kalshi and Polymarket by similar margins. In fact, I believe all of these people. The extending-lines-on-graphs community that AIs would beat the best human forecasters sometime in 2026 - 2027. What did you expect the bots-finally-beat-humans-at-predicting-the-future moment to look like? Vibes? Papers? Essays? In retrospect, sure: it will look like AIs making crazy profits on prediction markets and beating the stock market by some comfortable amount.
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Chip Off The Old Block
07/17/2026
Chip Off The Old Block
I. Having kids has given me new appreciation for old poetry. The first time I read Song of Hiawatha, I skimmed over the part in Book 3 where Hiawatha first meets his father Mudjekeewis: Filled with joy was Mudjekeewis When he looked on Hiawatha, Saw his youth rise up before him In the face of Hiawatha, Saw the beauty of Wenonah From the grave rise up before him. "Welcome!" said he, "Hiawatha, To the kingdom of the West-Wind! Long have I been waiting for you! Youth is lovely, age is lonely, Youth is fiery, age is frosty; You bring back the days departed, You bring back my youth of passion." But this passage communicates a secret of parenthood, something I’ve never seen discussed anywhere else. By the time you’re a parent, you’re on your way to being old, ugly, tired, and cynical. I certainly was. This felt like a brute fact about the world: we all know time only moves one direction. Then I had kids, and got confronted with people who were basically me, but young and beautiful and happy. That part of them which wasn’t me was the other person I love best in the world, also transmuted into a young and beautiful and happy form. This was a completely unexpected delight which nothing besides this one fragment of poetry had ever tried to prepare me for. I might never have noticed this if I’d only had girls. I love my daughter, but I’ve never been a little girl; it doesn’t bring anything back for me. It’s like Mudjekeewis says - you’ve got to have a son to see your youth rise up before you.
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The Metaculus Threat To Democracy Index
07/10/2026
The Metaculus Threat To Democracy Index
In recent posts on and , people have asked me - how do you know you’re not suffering from Trump Derangement Syndrome? I take this seriously; we’ve all lost loved ones to this condition. The best check on my reasoning would be an objective measure of the health of American democracy. There are several “democracy indices” that purport to do this, but they have a mixed reputation. My impression is that most current accusations of bias are relatively weak - I agree with - but they rely enough on “expert” opinion that I don’t expect them to convince a skeptic. The newest entrant in this space - - works differently, and deserves a closer look. Metaculus is a prediction site - like a prediction market, except that no money changes hands. People can record their guesses for how future events will turn out, which get aggregated by an algorithm (currently just a recency-weighted median, although they’ve done fanicer things in the past). Their Democracy Threat Index is a collection of 153 questions relevant to US democracy. :
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Should People Avoid Whole-Body Screening Info?
07/10/2026
Should People Avoid Whole-Body Screening Info?
The most controversial part of last week’s article on the Midjourney ultrasound scanner was medical experts’ recommendation against whole-body screening (including existing whole-body screening technology using MRI). Isn’t this crazy? Whole-body screening can save lives by detecting serious diseases like cancer. The experts counterargue that it finds so many false positives - minor zit-like imperfections that would never have caused problems, but which cost patients time, money, anxiety, and side effect burden to investigate - that it ends up net negative. But isn’t this just a problem of setting thresholds correctly? Can’t you commit to only investigating the most obviously bad things, then ignore the rest? This seemed like an interesting problem to investigate in more depth, so I’ve tried to get numbers. These are rough estimates loosely based on parameters extracted from unsatisfactory studies1 - please don’t take them seriously as exact values, just as right-order-of-magnitude estimates. We’ll focus on whole-body MRIs, since this is a well-studied existing technology, then speculate later on how the results might generalize to whole-body ultrasound.
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Preliminary Thoughts On The Midjourney Scanner
07/10/2026
Preliminary Thoughts On The Midjourney Scanner
like that, except from a medium-sized startup instead of a tech giant. Earlier today, they announced a pivot to medical scanners. The new , which they describe as “a bold new kind of machine to reimagine the foundations of healthcare and our relationships to our bodies”, will be a tank of water surrounded by a ring of ultrasound scanners. The patient goes into the tank, the scanners emit ultrasound from all angles, and then some fancy AI reconstructs the echoes into a 3D picture of the body. The result is ultrasound tomography: the same sort of rich data as a CT or MRI, but done via ultrasound, with no harmful radiation, in twenty seconds. This is cool, and it’s great to be ambitious, but I think the narrative among the SF AI crowd has escaped its basis in the medical facts, so I want to throw a bit of cold water on it. I’m a psychiatrist, which is about as far as you can get from radiology while still being a doctor, so this is speculation only, and you can ignore it if you find an actual radiologist or ultrasonographer with opinions. Still, my take is that this scanner isn’t useful for most current serious medical applications. It could potentially be used to pioneer a new class of low-risk screening applications, but it’s unclear whether these are good, and depends a lot on what other future technology gets invented in parallel.
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Waiting For The Miracle
07/10/2026
Waiting For The Miracle
In 1917, three children in Fatima, Portugal claimed to have seen the Virgin Mary. They promised she would perform a miracle on a certain day in October. Nearly 100,000 pilgrims arrived, hoping to see whatever happened, and nearly all report that the sun turned pale, changed color, and spun around. Many other writers have investigated the children and their visions, but I was fixated on this sun miracle. Despite popular discussion of “mass hallucinations”, this is AFAICT the only example of tens thousands of people all saying they witnessed the same impossible thing, at the same time. I got kind of obsessed with this; you can read my preliminary investigations in , , and post. One of the first things I found was that there were many other sun miracles - at least ten! - similar to Fatima. Most were associated with Marian apparitions, but one was at a Buddhist temple. Bigfoot only gets sighted by lone hikers; ghosts are only ever in the corner of your eye; UFOs are just blurs in the sky. Of all the countries and outposts in the vast empire of the unexplained, it’s only this one phenomenon - the spinning, multicolored sun - that regularly gets seen by thousands of people at once, in broad daylight. Speaking of “regularly”, there’s one spot where it continues even today. Fifty years ago, the Virgin Mary appeared to six children in Medjugorje, Bosnia. Now those children are well past middle-age, but she continues to come. Three of them report that she’s appeared less frequently as the years go by, but the others still see her every day at 6:40 sharp. Travelers to Medjugorje, especially those passing through around 6:40, report a slew of miracles, including the spinning sun. Certainly this is true of those whose hearts are pure. But even the atheists get lucky sometimes. I was shocked never to have heard about this before. There’s a place you can just go, and have a decent chance of seeing a real miracle? People take vacations to the Bahamas for the beaches, when they could go instead to Medjugorje and see the natural law of the universe get violated in real time? Seems crazy! So in early April, I and my extremely-accommodating, long-suffering wife flew to Dubrovnik, rented a car, and drove down a series of windy mountain roads toward the Bosnian border, hoping for a miracle.
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Never Cross a River Four Feet Deep on Average
06/30/2026
Never Cross a River Four Feet Deep on Average
Guest post by Alexander "Sasha" Putilin [This is a guest post by Sasha Putilin. I encourage any ACX grantees who are interested to write about their projects. - SA] The results of my ACX Grants 2024 project are in. The project attempted to replicate the 2023 study . It claimed that if you read a person’s brain waves, figured out an individual peak alpha frequency, and flashed a bright white light at that frequency, then they learned a certain perceptual task faster. Why bother? The result hinted that learning may depend in part on how well the brain keeps its rhythms coordinated. In other words, perceptual learning may rely on an internal brain metronome. If flickering light could act as an external metronome, it might help the brain maintain the right rhythm and learn faster. The study offered an invitation to develop new frontiers of neuroscience and biohacking. If the effect generalised to other types of learning, you could build a learning helmet: put it on your head, let it read your brainwaves, flicker light tailored to your individual brain — and you learn a new skill quicker. And no, it didn’t replicate. Most likely it can’t replicate, because the effect is probably not real. The original study obscured the data with summary statistics. Running a $32,000 replication was excessive. We could’ve caught the issue with this study if we simply looked at the original data carefully. *record scratch* *freeze frame* Yep, that’s me. You’re probably wondering how I got here. Here’s the story.
/episode/index/show/sscpodcast/id/41904685
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My AI Opinions
06/30/2026
My AI Opinions
I recently had a minor spat over someone misinterpreting my AI beliefs (see section marked “Update” at the bottom ), so I thought I would list them in one place, so I can refer people when they ask. Timelines Define AGI as AI intelligent enough to do 90% of knowledge work jobs. I think there’s a 25% chance of AGI by 2027, a 50% chance by 2034, and a 75% chance by 2045. Basic argument: In a certain sense, AI is already “smart” enough for this (eg it can answer quantum physics problems, which require higher IQ than most knowledge work). Its remaining limitations are that it’s confused, unagentic, lacks situational awareness, and tends to hallucinate. The METR time horizon graph, and several other related benchmarks/experiments/intuition pumps, suggest it’s improving on time horizons at an (exponential) rate that lets it cross human-level performance sometime around the early end of the schedule above, and subjectively it feels like harder-to-measure constructs like situational awareness are improving about as fast. Arguments for earlier: recursive self-improvement causes a speedup compared to the trend. This is one of the biggest blank spots in my model: I don’t know how fast RSI will progress, and I don’t think anyone else does either. There’s some function mapping a combination of AI talent and compute to progress, and we don’t know how it behaves in the domain when there’s far more talent than compute available. It could fizzle out completely for lack of compute, or it could go vertical. The AI Futures Project has done trying to model this, but even they have low confidence.
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Book Review: The Dialectical Imagination
06/19/2026
Book Review: The Dialectical Imagination
The philosophers of the Frankfurt School practiced a technique called negative dialectics, where concepts are defined as much by what you can’t say about them as what you can. Appropriately, the Frankfurt School has ended up defined by what you can’t say about them. You can’t say that they invented a new form of left-wing thought called Cultural Marxism. This would be (according to Wikipedia) , a “far right anti-Semitic conspiracy theory that misinterprets Western Marxism, especially the Frankfurt School, as being responsible for modern progressive movements, identity politics, and political correctness”. You’re not supposed to dub them a transitional stage between Communism and postmodernism. You’re not allowed to speculate that a lot of the academic humanities, as they’re practiced today, descend from the Frankfurt School’s brand of critical theory. You’re not supposed to think of them as the point where the muscular pro-technology leftism of the early 1900s shattered into the pessimistic degrowth leftism of the present. Art is long, life is short. Most of us only manage to not do a few things in our limited span on Earth. But the Frankfurt School managed to not invent so many movements - to not be involved in so many of the crucial ideological shifts of the past century - that they caught my attention. Who were these people? What other aspects of our culture might we be unable to say they were involved in? For answers, I turned to the classic history of the group, Martin Jay’s The basics are simple enough: the School was founded in Frankfurt in 1923. It attracted great philosophers like Max Horkheimer, Theodor Adorno, and Herbert Marcuse. When the Nazis took power in the early 1930s, the mostly-Jewish Frankfurters fled to America, where friendly locals helped them continue their work in affiliation with Columbia University. Mid-century Americans , and when the rise of fascism and World War II started dominating headlines, the German-Jewish Frankfurters were natural experts to help Americans process the situation. By the end of the war, they were firmly established as thought leaders. Some - including Horkheimer and Adorno - returned to Germany to rebuild its intellectual culture from the ruins; others stayed in America and remained relevant through the 60s and 70s. But figuring out what the Frankfurters believed is more complicated. Forget about the thin line between universally-acknowledged fact and fascist conspiracy theory. The School itself was famously coy, worrying that if they explained themselves too clearly, people would caricature their beliefs and integrate them into the existing capitalist system. Even when they did speak “clearly”, it was in the sort of German philosophical register where “the negation of the negation” is a totally normal thing to say. Having only read a single book on them, I will no doubt fall into all the failure modes that they and their successors warned us against. But here are the analogies, intuition pumps, and parables that I found helpful.
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Use AI This Election
06/19/2026
Use AI This Election
I’m not saying AI is superintelligent or can decide better than you can. I’m saying that if you - like me - spend an hour or so doing research before voting on local seats, AI can aid that research very effectively. And if you don’t do that research - because you weren’t willing to waste an hour on it before - AI makes it so much faster that you might want to start. I gave Claude a prompt something like (edited for coherence): I’ll be voting in the June 2026 California primary. I’m a centrist liberal abundance YIMBY whose favorite political writers are Kelsey Piper, Matt Yglesias, and Ezra Klein. I’m wary of government overreach, but I’m not a doctrinaire libertarian and want to help people when we can figure ways to do it that work. I’m going to ask you about each race on my ballot, and I’d like for you to list the various candidates’ bios, policies, endorsements, your read on the most important differences between them, and your advice for me as I try to make my choice. …and got back answers like the following:
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New Paradigms Won't Save You
06/19/2026
New Paradigms Won't Save You
One popular objection to AI concerns is to declare that LLMs can never be AGI. You need a “new paradigm”. Therefore, AGI is so far in the future that it’s not worth worrying about. A common counterargument is to claim that no, LLMs can become AGI. But even without that counterargument, I think the “therefore” fails on its own terms. The key question is: how much of a new paradigm do we need? The landmark discoveries on the road to modern LLMs are something like: 1950s: Neural networks 1967: Multi-layer perceptron 2010: Modern deep learning 2017: Transformer, LLM 2022: RLHF, chatbots 2024: Chain of thought / test-time compute We can think of this as an “evolutionary tree”, where a given LLM (let’s say Claude Opus 4.7) shares a recent “common ancestor” with all other chatbots, and only a very distant “common ancestor” with everything else descended from the multi-layer perceptron. If AGI needs a “new paradigm”, what common ancestor can we expect AGI and LLMs to share? AGI will very likely use neural networks, because the human brain is a neural network and qualifies as an AGI. It will probably use deep learning, because although deep learning isn’t exactly analogous to the brain, it seems like a pretty reasonable way to emulate the brain’s learning algorithms onto computer hardware. Skeptics like Yann LeCun and Gary Marcus usually pinpoint LLMs/transformers as the step where we went wrong. that the first AGIs may be within the deep learning paradigm (but not LLMs); Marcus thinks that they’ll combine insights from deep learning with something else. How soon should we expect a new paradigm as revolutionary as LLMs/transformers? Since we got LLMs/transformers nine years ago, suggests nine more years. How soon should we expect a new paradigm as revolutionary as deep learning? By the same logic, sixteen years from now. Lindy’s Law has a heavy tail, which means we can’t simply halve these to find our 25th percentile estimate. Our 25th percentile estimate for the next advance as exciting as LLMs should be three years from now; for deep learning, it’s five years. So even if you think AGI will require a further paradigm shift as big as the invention of the LLM or as deep learning itself, you should have 25% chance it will be developed in the next 3 - 5 years. Which is about as long as the LLM-only crowd think things will take! This isn’t an excuse for relegating the risk of AGI to some vague indefinite future. It could still be the late 2020s or early 2030s! (Might we expect that low-hanging-fruit effects make the next paradigm harder to find than the last one? In practice, fields get more researchers as time goes on, and . And in fact, the number of AI researchers has grown at an unprecedented pace for a scientific field, and growth will enter an even faster regime once AIs themselves can contribute. Overall these make me think things will go even faster than Lindy’s Law predicts - but I think Lindy’s Law is a useful upper bound.) (Would there still be a long time between the invention of the new paradigm and the point where it could be used to maximum effect? It took five years between the invention of the transformer and ChatGPT, the first commercially-successful transformer-based project. But most of that time was spent scaling up, and we’ve already scaled up. If we invent a new paradigm in 2030, then any frontier lab willing to bet on it can quickly provide it with levels of compute sufficient to train models.) This is my attempt to talk to the new-paradigm-wanters in their own language, but I think there’s also that undermines this worldview. . LLMs still seem to be able to convert scale to results; while this continues, new paradigms won’t be necessary, and frontier labs won’t risk pursuing them. If scaling ever hits a wall, there will be a few months of confusion as frontier labs look over various new-paradigm-proposals that they already have lying around, and throw them at the wall to see what breaks through. Then scaling will continue from wherever it left off. The best way to forecast future AI progress is to extrapolate from current LLM scaling. This should work if LLMs scale all the way to AGI. But it may also work even if they don’t. First, because we might get the new paradigm so soon that it won’t be a significant source of delay. And second, because the most likely place for a new paradigm to start is wherever LLMs stop working, going at the same rate.
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