What Today’s Best Models Still Can’t Do in Math

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? Timestamps: 00:00 - Intro 01:00 - Meet Daniel Litt: A Practicing Mathematician's Evolving Views on AI 02:24 - The Most Impressive Result: The Erdős Unit Distance Problem 06:12 - What AI Is Actually Doing for Working Mathematicians Today 12:12 - Intuition, Taste & Why Math Isn't Just About Proofs 20:26 - Deep Thinking vs Pattern Matching: What Models Are Missing 26:17 - Why the Unit Distance Result Was Actually Creative 33:33 - How Should the Math Community Adapt to AI? 43:26 - Where AI Will Impact Applied Math First 46:24 - Taking Advantage of AI Without Losing the Craft 49:21 - Comparing Anthropic vs OpenAI in Math 51:34 - Why Some Labs Have Gone More Secretive 59:40 - Raising a Mathematician: Teaching Math to a Toddler Resources: Follow Daniel Litt on X: https://x.com/littmath Follow Lisha Li on X: https://x.com/lishali88 Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.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 http://a16z.com/disclosures.

Daniel LittguestLisha Lihost

Sep 1, 20261h 3mWatch on YouTube ↗

EPISODE INFO

Released: September 1, 2026
Duration: 1h 3m
Channel: a16z
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

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? Timestamps: 00:00 - Intro 01:00 - Meet Daniel Litt: A Practicing Mathematician's Evolving Views on AI 02:24 - The Most Impressive Result: The Erdős Unit Distance Problem 06:12 - What AI Is Actually Doing for Working Mathematicians Today 12:12 - Intuition, Taste & Why Math Isn't Just About Proofs 20:26 - Deep Thinking vs Pattern Matching: What Models Are Missing 26:17 - Why the Unit Distance Result Was Actually Creative 33:33 - How Should the Math Community Adapt to AI? 43:26 - Where AI Will Impact Applied Math First 46:24 - Taking Advantage of AI Without Losing the Craft 49:21 - Comparing Anthropic vs OpenAI in Math 51:34 - Why Some Labs Have Gone More Secretive 59:40 - Raising a Mathematician: Teaching Math to a Toddler Resources: Follow Daniel Litt on X: https://x.com/littmath Follow Lisha Li on X: https://x.com/lishali88 Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.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 http://a16z.com/disclosures.

SPEAKERS

Daniel Litt
guest
Professor of mathematics at the University of Toronto, discussing AI and mathematical research/proof workflows.
Lisha Li
host
a16z host/interviewer guiding the conversation on what today’s best AI models can and can’t do in math.

EPISODE SUMMARY

In this episode of a16z, featuring Daniel Litt and Lisha Li, What Today’s Best Models Still Can’t Do in Math explores aI can prove some results, but lacks math understanding and taste Litt argues that mathematics aims at human understanding, not merely producing papers, and that “understanding in model weights” is unsatisfying as a replacement for human comprehension.

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