{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fa16z.simplecast.com%2Fepisodes%2Fdaniel-litt-the-mathematicians-guide-to-ai-TyLaUSzk","width":444,"version":"1.0","type":"rich","title":"Daniel Litt: The Mathematician's Guide to AI","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/0d97354a-306b-45f5-bf26-a8d81eef47ec/ed2664df-9371-438e-8baf-dd2ee0fdde87/thea16zshow-podcastcoverart-3000x3000.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"","height":200,"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.\nDaniel 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.\nLisha 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?\n"}