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.

Your Review: Alpha School 07/04/2025

Your Review: Alpha School [This is one of the finalists in the 2025 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] “Just as we don’t accept students using AI to write their essays, we will not accept districts using AI to supplant the critical role of teachers.” — Arthur Steinberg, American Federation of Teachers‑PA, reacting to Alpha’s cyber‑charter bid, January 2025 In January 2025, the charter school application of “”, a subsidiary of “”, lit up the education press: two hours of “AI‑powered” academics, 2.6x learning velocity, and zero teachers. Sympathetic reporters repeated the slogans; union leaders reached for pitchforks; Reddit muttered “.” More sophisticated critics dismissed the pitch as “selective data from expensive private schools”. But there is nowhere on the internet that provides a detailed, non-partisan, description of what the “2 hour learning” program actually is, let alone an objective third party analysis to back up its claims. 2-Hour Learning’s flagship school is the “Alpha School” in Austin Texas. The makes three claims: Love School Learn 2X in two-hours per day Learn Life Skills Only the second claim seems to be controversial, which may be exactly why that is the claim the Alpha PR team focuses on. That PR campaign makes three more sub-claims on what the two-hour, 2x learning really means: “Learn 2.6X faster.” (on average) “Only two hours of academics per day.” “Powered by AI (not teachers).” If all of this makes your inner Bayesian flinch, you’re in good company. After twenty‑odd years of watching shiny education fixes wobble and crash—KIPP, AltSchool, Summit Learning, One-laptop-per-child, No child left behind, MOOCs, Khan‑for‑Everything—you should be skeptical. Either Alpha is (a) another program for the affluent propped up by selection effects, or (b) a clever way to turn children into joyless speed‑reading calculators. Those were, more or less, the two critical camps that emerged when Alpha’s parent company was approved to launch the tuition‑free Arizona charter school this past January. Unfortunately, the public evidence base on whether this is “real” is thin in both directions. Alpha’s own material is glossy and elliptical; mainstream coverage either repeats Alpha’s talking points, or attacks the premise that kids should even be allowed to learn faster than their peers. Until Raj Chetty installs himself in the hallway with a clipboard counting MAP percentiles it is hard to get real information on what exactly Alpha is doing, whether it is actually working beyond selection effects, and if there is anyway it could scale in a way that all the other education initiatives seemed to fail to do. I first heard about Alpha in May 2024, and in the absence of randomized‑controlled clarity, I did what any moderately obsessive parent with three elementary-aged kids and an itch for data would do: I moved the family across the country to Austin for a year and ran the experiment myself (unfortunately, despite trying my best we never managed to have identical twins, so I stopped short of running a proper control group. My wife was less disappointed than I was). Since last autumn I’ve collected the sort of on‑the‑ground detail that doesn’t surface in press releases, or is available anywhere online: long chats with founders, curriculum leads, “guides” (not teachers), Brazilian Zoom coaches, sceptical parents, ecstatic parents, and the kids who live inside the Alpha dashboard – including my own. I hope this seven-part review can help share what the program actually is and that this review is more open minded than the critics, but is something that would never get past an Alpha public relations gatekeeper: /episode/index/show/sscpodcast/id/37281950

Missing Heritability: Much More Than You Wanted To Know 07/01/2025

Missing Heritability: Much More Than You Wanted To Know The Story So Far The mid-20th century was the golden age of nurture. Psychoanalysis, behaviorism, and the spirit of the ‘60s convinced most experts that parents, peers, and propaganda were the most important causes of adult personality. Starting in the 1970s, the pendulum swung the other way. Twin studies shocked the world by demonstrating that most behavioral traits - especially socially relevant traits like IQ - were substantially genetic. Typical estimates for adult IQ found it was about 60% genetic, 40% unpredictable, and barely related at all to parenting or family environment. By the early 2000s, genetic science reached a point where scientists could start pinpointing the particular genes behind any given trait. Early candidate gene studies, which hoped to find single genes with substantial contributions to IQ, depression, or crime, mostly failed. They were replaced with genome wide association studies, which accepted that most interesting traits were polygenic - controlled by hundreds or thousands of genes - and trawled the whole genome searching for variants that might explain 0.1% or even 0.01% of the pie. The goal shifted toward polygenic scores - algorithms that accepted thousands of genes as input and spit out predictions of IQ, heart disease risk, or some other outcome of interest. The failed candidate gene studies had sample sizes in the three or four digits. The new genome-wide studies needed five or six digits to even get started. It was prohibitively difficult for individual studies to gather so many subjects, genotype them, and test them for the outcome of interest, so work shifted to big centralized genome repositories - most of all the UK Biobank - and easy-to-measure traits. Among the easiest of all was educational attainment (EA), ie how far someone had gotten in school. Were they a high school dropout? A PhD? Somewhere in between? This correlated with all the spicy outcomes of interest people wanted to debate - IQ, wealth, social class - while being objective and easy to ask about on a survey. Twin studies suggested that IQ was about 60% genetic, and EA about 40%. This seemed to make sense at the time - how far someone gets in school depends partly on their intelligence, but partly on fuzzier social factors like class / culture / parenting. The first genome-wide studies and polygenic scores found enough genes to explain 2%pp of this 40% pie. The remaining 38%, which twin studies deemed genetic but where researchers couldn’t find the genes - became known as “the missing heritability” or “the heritability gap”. Scientists came up with two hypothesis for the gap, which have been dueling ever since: Maybe twin studies are wrong. Maybe there are genes we haven’t found yet For most of the 2010s, hypothesis 2 looked pretty good. Researchers gradually gathered bigger and bigger sample sizes, and found more and more of the missing heritability. A big 2018 study increased the predictive power of known genes from 2% to 10%. An even bigger 2022 study increased it to 14%, and current state of the art is around 17%. Seems like it was sample size after all! Once the samples get big enough we’ll reach 40% and finally close the gap, right? This post is the story of how that didn’t happen, of the people trying to rehabilitate the twin-studies-are-wrong hypothesis, and of the current status of the debate. Its most important influence/foil is , whose blog introduced me to the new anti-hereditarian movement and got me to research it further, but it’s also inspired by , (not himself an anti-hereditarian, but his research helped ignite interest in this area), and . (while I was working on this draft, the East Hunter Substack . Theirs is good and I recommend it, but I think this one adds enough that I’m publishing anyway) /episode/index/show/sscpodcast/id/37231270

Open Questions For Future ACX Grants Rounds 07/01/2025

ACX Grants 1-3 Year Updates 06/29/2025

The Claude Bliss Attractor 06/18/2025

"But" vs. "Yes, But" 06/18/2025

If It's Worth Your Time To Lie, It's Worth My Time To Correct It 06/14/2025

P-Zombies Would Report Qualia 06/14/2025

Choose Nonbook Review Finalists 2025 06/14/2025

Bayes For Everyone 06/14/2025

Sorry, I Still Think MR Is Wrong About USAID 06/14/2025

Moments Of Awakening 06/05/2025

Contra MR On Charity Regrants 06/05/2025

The Evidence That A Million Americans Died Of COVID 06/05/2025

The Other COVID Reckoning 06/05/2025

Book Review: Selfish Reasons To Have More Kids 06/03/2025

In Search Of /r/petfree 06/03/2025

Highlights From The Comments On AI Geoguessr 06/03/2025

Testing AI's GeoGuessr Genius 05/23/2025

Moldbug Sold Out 05/23/2025

The Populist Right Must Own Tariffs 05/23/2025

AI Futures: Blogging And AMA 05/23/2025

Links For April 2025 05/22/2025

Highlights From The Comments On POSIWID 05/22/2025

Come On, Obviously The Purpose Of A System Is Not What It Does 04/14/2025

My Takeaways From AI 2027 04/14/2025

AI 2027 (Full Recording with Footnotes and Text Boxes) 04/14/2025

Introducing AI 2027 04/14/2025

Introducing AI 2027 Or maybe 2028, it's complicated In 2021, a researcher named Daniel Kokotajlo published a blog post called “”, where he laid out what he thought would happen in AI over the next five years. The world delights in thwarting would-be prophets. The sea of possibilities is too vast for anyone to ever really chart a course. At best, we vaguely gesture at broad categories of outcome, then beg our listeners to forgive us the inevitable surprises. Daniel knew all this and resigned himself to it. But even he didn’t expect what happened next. He got it all right. Okay, not literally all. The US restricted chip exports to China in late 2022, not mid-2024. AI first beat humans at Diplomacy in late 2022, not 2025. And of course the mid-2025 to 2026 period remains to be seen. But to put its errors in context, Daniel’s document was written two years before ChatGPT existed. Nobody except researchers and a few hobbyists had ever talked to an AI. In fact, talking to AI was a misnomer. There was no way to make them continue the conversation; they would free associate based on your prompt, maybe turning it into a paragraph-length short story. If you pulled out all the stops, you could make an AI add single digit numbers and get the right answer more than 50% of the time. Yet if you read Daniel’s blog post without checking the publication date, you could be forgiven for thinking it was a somewhat garbled but basically reasonable history of the last four years. I wasn’t the only one who noticed. A year later, OpenAI hired Daniel to their policy team. While he worked for them, he was limited in his ability to speculate publicly. “What 2026 Looks Like” promised a sequel about 2027 and beyond, but it never materialized. Unluckily for Sam Altman but luckily for the rest of us, Daniel broke with OpenAI mid-2024 in a dramatic split covered by and others. He founded the AI Futures Project to produce the promised sequel, including: Eli Lifland, a superforecaster who is ranked first on RAND’s Forecasting initiative. You can read more about him and his forecasting team . He cofounded and advises and co-created , an adversarial attack framework for language models. Jonas Vollmer, a VC at Macroscopic Ventures, which has done its own, more practical form of successful AI forecasting: they made an early stage investment in Anthropic, now worth $60 billion. Thomas Larsen, the former executive director of the Center for AI Policy, a group which advises policymakers on both sides of the aisle. Romeo Dean, a leader of Harvard’s AI Safety Student Team and budding expert in AI hardware. …and me! Since October, I’ve been volunteering part-time, doing some writing and publicity work. I can’t take credit for the forecast itself - or even for the lion’s share of the writing and publicity - but it’s been an immense privilege to work alongside some of the smartest and most epistemically virtuous people I know, trying to absorb their worldview on a level deep enough to do it justice. We have no illusions that we’ll get as lucky as last time, but we still think it’s a valuable contribution to the discussion. /episode/index/show/sscpodcast/id/36125560

The Colors Of Her Coat 04/14/2025

The Colors Of Her Coat In Ballad of the White Horse, G.K. Chesterton describes the Virgin Mary: Her face was like an open word When brave men speak and choose, The very colours of her coat Were better than good news. Why the colors of her coat? The medievals took their dyes very seriously. This was before modern chemistry, so you had to try hard if you wanted good colors. Try hard they did; they famously used literal gold, hammered into ultrathin sheets, to make golden highlights. Blue was another tough one. You could do mediocre, half-faded blues with azurite. But if you wanted perfect blue, the color of the heavens on a clear evening, you needed ultramarine. Here is the process for getting ultramarine. First, go to Afghanistan. Keep in mind, you start in England or France or wherever. Afghanistan is four thousand miles away. Your path takes you through tall mountains, burning deserts, and several dozen Muslim countries that are still pissed about the whole Crusades thing. Still alive? After you arrive, climb 7,000 feet in the mountains of Kuran Wa Munjan until you reach the mines of Sar-i-Sang. There, in a freezing desert, the wretched of the earth work themselves to an early grave breaking apart the rocks of Badakhshan to produce a few hundred kilograms per year of blue stone - the only lapis lazuli production in the known world. Buy the stone and retrace your path through the burning deserts and vengeful Muslims until you’re back in England or France or wherever. Still alive? That was the easy part. Now you need to go through a chemical extraction process that makes the Philosopher's Stone look like freshman chem lab. "The lengthy process of pulverization, sifting, and washing to produce ultramarine makes the natural pigment … roughly ten times more expensive than the stone it came from." Finally you have ultramarine! How much? I can’t find good numbers, but Claude estimates that the ultramarine production of all of medieval Europe was around the order of 30 kg per year - not enough to paint a medium-sized wall. Ultramarine had to be saved for ultra-high-value applications. In practice, the medievals converged on a single use case - painting the Virgin Mary’s coat. /episode/index/show/sscpodcast/id/36125540

"Deros And The Ur-Abduction" In Asterisk 04/03/2025

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