Daniel Litt: The Mathematician's Guide to AI

2026-09-01 · 64 min · episode 1185 · 12 entities

episode page audio

Asserted relationships

→ hosted by Andreessen Horowitz person

0.55

evidence rules-v4
Feed author/publisher: Andreessen Horowitz
→ discusses Science concept

0.40

evidence rules-v4
Feed category: Science
→ discusses Technology concept

0.40

evidence rules-v4
Feed category: Technology
→ discusses Business concept

0.40

evidence rules-v4
Feed category: Business
→ discusses Entrepreneurship concept

0.40

evidence rules-v4
Feed category: Entrepreneurship
→ hosted by a16z company

0.40

evidence rules-v4
Feed author/publisher: a16z

Entities found in this episode

concepts 9

mentioned Science concept

0.50

evidence rules-v4
Feed category: Science
mentioned Technology concept

0.50

evidence rules-v4
Feed category: Technology
mentioned Business concept

0.50

evidence rules-v4
Feed category: Business
mentioned Entrepreneurship concept

0.50

evidence rules-v4
Feed category: Entrepreneurship
mentioned The Mathematician's Guide to AI concept

0.42

evidence rules-v4
Daniel Litt: The Mathematician's Guide to AI
discusses Science concept

0.40

evidence rules-v4
Feed category: Science
discusses Technology concept

0.40

evidence rules-v4
Feed category: Technology
discusses Business concept

0.40

evidence rules-v4
Feed category: Business
discusses Entrepreneurship concept

0.40

evidence rules-v4
Feed category: Entrepreneurship

persons 2

mentioned Andreessen Horowitz person

0.70

evidence rules-v4
Feed author/publisher: Andreessen Horowitz
hosted by Andreessen Horowitz person

0.55

evidence rules-v4
Feed author/publisher: Andreessen Horowitz

companys 1

hosted by a16z company

0.40

evidence rules-v4
Feed author/publisher: a16z

Episode description as stored

a16z’s Lisha Li sits down with Daniel Litt, Assistant Professor of Mathematics at the University of Toronto, to unpack AI's rapid progress in mathematics, what today's frontier models can actually do, and what they're still missing about the way mathematicians think. Daniel explains why some recent AI-generated results are genuinely impressive, including an autonomous solution to the Erdős unit distance problem, but argues that solving problems is only one part of mathematics. Today's models can grind through calculations, combine known techniques, and search enormous spaces, but still struggle with intuition, theory building, identifying the right questions, and developing the kind of big-picture understanding that drives much of mathematical progress. Lisha and Daniel also explore how AI is already changing mathematical research, why an explosion of AI-generated papers could distort academic incentives, and what happens if researchers outsource the work of thinking rather than use AI to deepen it. Ultimately, they ask a question that extends far beyond mathematics: as AI gets better at intellectual work, how do we make sure humans keep getting better at thinking too? Resources: Follow Daniel Litt on X: https://x.com/littmath Follow Lisha Li on X: https://x.com/lishali88 Stay Updated: Find a16z on YouTube: YouTube Find a16z on X Find a16z on LinkedIn Listen to the a16z Show on Spotify Listen to the a16z Show on Apple Podcasts Follow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.