My view is that even in a world where the AI produces an amazing exposition, it won't motivate you to read it and understand it, help you figure out what you're interested in, etc. These are functions that are primarily institutional and it seems to me that institutions will remain primarily composed of people, not AI systems.

I've written an essay on how I think the mathematics profession should adapt to highly capable AI systems. It's hosted here on "Proofs and Prompts": though you should also feel free to complain/comment on my website here:

Without commenting on the correctness of this claim, I want to briefly say a few words about what it would mean in practice. Chess is supportable as a hobby in part because one can pick up the rules of chess in an afternoon. While one can't quickly play at a high level, it is relatively easy to appreciate the game, and have fun at it. Research math is not like this. Over the last decades we have made incredible progress understanding basic concepts (number, shape, etc.) but the edifice we have built to do so requires an incredible time investment. Learning the ideas that go into proving, or understanding the proof of, basic statements like Fermat's last theorem requires years of institutional support. It's possible that soon, AI will be able to do work at this level; despite recent successes, this isn't the case yet. But even if it can do this, why would it? Without people who care about these deep but fundamental ideas, who will ask it to? Perhaps it might on its own, or a person asking it to "do some cool math" might elicit a similarly deep idea, but who would understand it, if doing so requires years of unsupported work? In practice, turning pure math into a hobby means the end of pure math, and the end of people who understand basic mathematical concepts at a deep level. Whether this is something we want is something we'll have to decide together.

In practice this means that a huge amount of duplicative labor, both in flesh and in silico, is being devoted to work whose marginal value to mathematics is, essentially, the cost of the tokens and perhaps a few bits of information indicating that the problem can be solved by existing models.

I'm currently returning to Toronto from a summit on the future of mathematics, at OpenAI. @SebastienBubeck asked me to talk a bit about the future we'd all like to avoid, where humans are mathematically disempowered. @Jacob_Tsimerman advised us to try to prioritize detail over correctness, and I have no doubt that I succeeded in deprioritizing correctness. I tried to find a title that wasn't too bombastic:

Incidentally I am conceding this bet. Strictly speaking it hasn’t resolved (I think we’ve yet to see an Annals-quality number theory paper) but it’s clear I was wrong about what capabilities were necessary to produce one, and it’s just a matter of time.