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DataFramed

#375 Is Math The Key to Better Coding AI? With Tudor Achim, CEO at Harmonic

31 August 2026 47:28 DataCamp

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About this episode

AI capability in mathematics jumped before most people noticed, tackling Olympiad-level problems and unsolved research questions that had resisted attack for years. But the pattern of where AI succeeds and where it stalls is uneven and worth understanding. It's much better at grinding through cases to disprove something than at constructing an elegant, original proof. Anyone working with AI in a technical field runs into this same asymmetry. Where exactly is the boundary between tasks AI can already do reliably and ones that still need human judgment and creativity?

Tudor Achim is the co-founder and CEO of Harmonic, an AI company building toward mathematical superintelligence. He previously led the machine learning team at Quora and co-founded and served as CTO of the autonomous driving company Helm.ai. Under Tudor, Harmonic's Aristotle system achieved gold-medal performance at the 2025 International Math Olympiad alongside systems from OpenAI and Google DeepMind — with every proof formally verified.

In the episode, Richie and Tudor explore why AI is starting to outperform humans at advanced mathematics, the shift toward formally verified proofs using the Lean language, where AI already beats humans (finding counterexamples) versus where it still falls short (building elegant proofs), how human mathematicians' roles will change, why math capability gains spill over into better AI reasoning generally, and much more.

Links Mentioned in the Show:

• Tudor's TED Talk: "The Path to Mathematical Superintelligence"

• Aristotle, Harmonic's reasoning system

• Harmonic

• The Erdős Problems

• American Institute of Mathematics

• Rich Sutton, "The Bitter Lesson"

• Connect with Tudor: LinkedIn

• AI-Native Course: Intro to AI for Work

• Related Episode: Why AI Agents Haven't Taken Over Knowledge Work Yet, with Jennifer Smith, CEO of Scribe (exact URL pending — episode published Aug 17, 2026, too recent to be indexed yet; confirm link on datacamp.com/podcast before publishing)

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