Always so much fun to chat with Grant.

AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there’s a jagged landscape. What does it look like?

What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do?

Does AI (on net) increase or decrease human understanding of the field?

How big is the overhang from having AIs systematically try to connect ideas already in the literature?

And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI?

Watch on YouTube; read the transcript.

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* Jane Street sponsors 3Blue1Brown, so Grant has gotten to spend a lot of time with various Jane Streeters. He actually just recorded an interview with a few of them, so when we sat down for this episode, he told me about some of the things he learned, like how Jane Street keeps their role definitions fuzzy to make sure their people keep learning and growing. Go check out Grant’s full interview at 3b1b.co/janestreet

Timestamps

(00:00:00) – AI is discovering new proofs. Is that AGI?

(00:11:32) – The verification loop on conceptual breakthroughs can be a century long

(00:26:12) – Will we understand an AI proof of the Riemann hypothesis?

(00:38:08) – Can AI find the hidden bridges between fields?

(00:53:48) – Why real-world tasks don’t fit into RL environments

(01:07:07) – Good writing requires theory of mind that AI still lacks

(01:16:02) – Why learning will still depend on human curation

Read it here.

Thanks to Mercury for sponsoring this essay.

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Timestamps:

(00:00:00) – The big research bet the labs are making

(00:02:12) – Grindability is just as important as verifiability

(00:06:10) – Will RLVR alone generalize?

(00:08:41) – Getting the learning back to the weights

(00:15:22) – Dreaming

(00:17:23) – What 2027 looks like

Read the transcript here.

Thanks to Mercury for sponsoring this essay!

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Timestamps:

(00:00:00) – What is really driving AI progress?

(00:03:11) – Comparing human vs AI sample efficiency

(00:08:46) – Does sample efficiency matter?