What’s Left for Mathematicians? | Daniel Litt

In this episode, we talked with Daniel Litt, a mathematician at the University of Toronto. In March 2025, Daniel bet that AI would not be able to autonomously produce an Annals of Mathematics-level paper by 2030. He has since conceded. He told us what changed and why. The models became superhuman at a small set of skills, like knowing the entire literature and grinding through technical arguments, and that turned out to be enough to do real math. They are still weak at the fuzzy part of the job, like figuring out what is actually true about an object or which questions are worth asking.

We also got into how anyone can check a proof that no human has read yet, and what a Lean formalization does and doesn't guarantee. Daniel thinks prestige and credit in math are dead, and that we will have to judge people through thesis defenses and seminars rather than through PDFs. He explains why he thinks math got Goodharted and why the labs put so much effort into it in the first place. We finished with listener questions, including how to teach kids math now and whether P vs NP might be just one idea away.

Topics
- Why Daniel conceded his AI math bet
- What models are superhuman at in math, and where they fall short
- Why AI won't "solve math"
- Checking AI-written proofs and the limits of Lean
- Why different models keep proving the same results
- How Daniel uses AI in his own research
- Evaluating mathematicians when prestige and credit are dead
- Why math got "Goodharted"
- AI labs, race dynamics and publication norms
- Teaching kids math, and P vs NP

00:00 Intro
00:15 The bet Daniel conceded
03:23 What AI is good and bad at in math
08:13 Will AI solve all of math?
11:37 Why we still need human mathematicians
13:59 Math is about understanding, not only proof
21:26 Axioms, foundations and Lean
27:42 How do you check an AI-written proof?
31:22 When models cheat
36:18 Are all the models proving the same things?
39:51 How Daniel uses AI in his own research
45:20 "Prestige and credit are dead"
49:58 AI risk and social upheaval
51:38 Is math special, or did it get Goodharted?
57:45 Why the labs care about math
59:54 PR, race dynamics and the pause
1:06:49 Would Daniel join a lab?
1:07:53 Listener questions: can understanding be formalized?
1:12:05 Teaching kids math with AI around
1:15:38 P vs NP and the Riemann hypothesis
1:17:37 Wrap-up

Music: "Kid Kodi" by Blue Dot Sessions (CC BY-NC 4.0)