Astral Codex Ten Podcast

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) the Cultural Marxism conspiracy theory, 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 Dialectical Imagination.
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 were suckers for sophisticated European intellectuals, 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.
https://www.astralcodexten.com/p/book-review-the-dialectical-imagination

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:
https://www.astralcodexten.com/p/use-ai-this-election

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. LeCun thinks 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, Lindy's Law 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 that effect usually causes time-between-advances to be approximately constant. 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 human-brain-sized models.)
This is my attempt to talk to the new-paradigm-wanters in their own language, but I think there's also a subtler point that undermines this worldview. In the past, new paradigms have proven useful in allowing scaling to continue after an old paradigm passed the regime where it could efficiently convert scale to results. 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.
https://www.astralcodexten.com/p/new-paradigms-wont-save-you

Sorry, I give up.
In past elections, I've covered every single candidate for governor of California, from the incumbents all the way down to the cranks. In 2022 there were twenty-six of them, and I covered them all.
But sorry, I give up. This year there are sixty. It's too many. I can't disambiguate them all into unique individuals with their own personalities, hopes, and dreams. So as consolation for the list I'm not giving you, here are the basic types, and a few examples of each.
The Top-Tier Democrats
One of these people will definitely win, but what else is there to say about them? They're all the same. They've all paid the danegeld to some set of unions and interest groups, then put up some kind of incredibly generic platform about how they're compassionate but also a fighter. I can't bring myself to name any of them or discuss them further.
The Top-Tier Republicans
More or less the same as the top-tier Democrats, minus the chance of winning. They all have cowboy hats and flag pins. They all pose on horseback at their ranch. They all promise to Take Back California for the forces of America and its god, Donald J. Trump. None of these people are actually interesting, but honorable mention to Sheriff Chad Bianco, whose name is Italian for "white Chad". He might be the perfect Republican candidate:
https://www.astralcodexten.com/p/the-types-of-candidate-you-find-in

"All exponentials eventually become sigmoids" is an annoying AI talking point. If someone presents a graph like this…
….and points out that it seems like AI capabilities could soon reach the level marked "High", then the height of intelligent debate is to point out that actually, the trend could go like this:
…and then it would never reach the level marked "High"!
In slogan form, this is "all exponentials eventually become sigmoids" (a sigmoid is the s-shape of the second graph, which starts exponential but gradually flattens out). It's technically true. No process can keep growing forever; eventually it hits physical or practical limits. For example, total cases during an epidemic is classically sigmoid: