Dwarkesh Podcast Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
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Mar 20, 2026 
Terence Tao, UCLA mathematician and one of the era’s top problem-solvers, explores Kepler’s surprising path to planetary motion and why great theories can look worse before they win. He gets into AI flooding science with ideas, the bottleneck of verification, why breadth beats depth for now, and how writing, persuasion, serendipity, and human-AI teamwork shape discovery.
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AI Will Make Math Broader Before It Makes It Deeper
- In math, solving a problem matters less than the process because the process builds intuition, techniques, and transferable understanding.
- Tao expects AI to revolutionize the experimental side of mathematics by testing workflows across thousands of problems rather than handcrafting each proof.
Selection Bias Makes AI Math Look Deeper Than It Is
- Most apparent AI math breakthroughs reflect selective reporting from broad low-probability sweeps rather than consistently high competence.
- Tao says a model may solve only 1 to 2 percent of problems in systematic studies, even though social media highlights the rare wins.
Terence Tao Uses AI To Enrich Papers Not Solve Them
- AI has made Terence Tao’s papers richer and faster to produce, mainly by accelerating secondary tasks rather than core problem solving.
- He now adds more code, plots, literature search, and formatting help; the hardest mathematical step still happens with pen and paper.
