Grant Sanderson – AI and the future of math

June 30, 2026

AI Summary

5 min read

Mathematics has always been considered a kind of intelligence bellwether: if an AI can solve the hardest problems in the field, the thinking goes, it must be close to general intelligence. Grant Sanderson, creator of 3Blue1Brown, has been watching this frontier closely, and his perspective has shifted significantly since he was first asked about it three years ago. The episode centers on a deceptively simple question: as AI continues to make rapid progress in mathematics—solving Olympiad problems, disproving long-standing conjectures, and potentially tackling millennium prize problems—what does that actually tell us about the nature of the intelligence involved, and what does it mean for the future of human mathematicians?

The "Spiky Frontier" and the Fractal Nature of Progress

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What you'll learn

1 (00:00) **Guest intro** - Grant Sanderson on documenting AI progress in math as a leading indicator for other fields
2 (00:20) **IMO gold and AGI** - Revisiting the 2021 prediction that IMO gold would be just another benchmark
3 (01:36) **Spiky progress inside math** - Geometry solved quickly while combinatorics remains harder
4 (03:09) **Nature of a millennium-problem solution** - Two paths: cross-domain lightning connections versus building entirely new conceptual mountains
5 (05:41) **Fermat’s Last Theorem analogy** - Heavy machinery from elliptic curves and modular forms was needed before the final link
6 (08:14) **Next benchmarks after discovery** - Generating interesting conjectures and creating unifying definitions
7 (10:37) **Measuring conjecture quality** - Tone shift among working mathematicians rather than headline benchmarks

+ Full timestamped outline available in the app

Show Notes

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.

Sponsors

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* Cursor’s harness lets me use models for a huge range of tasks at the podcast. For example, Cursor cuts out the ads from each episode I produce so I can post them on Bilibili. It also helps me prep for interviews — I have a repo full of books and papers that Cursor sorts through to find the exact right file for any given question. Try Cursor yourself at cursor.com/dwarkesh

* 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

(01:07:0