AI That Can Prove It’s Right: Verification as the Missing Layer in AI — Carina Hong
About this episode
In this episode, Matt Turck interviews Carina Hong about the importance of formal verification in AI and its potential to revolutionize mathematics and reliable reasoning.
What if AI didn’t just sound right — but could prove it? In this episode of the MAD Podcast, Matt Turck sits down with Carina Hong, a 24-year-old former math olympiad competitor and Rhodes Scholar, and the founder/CEO of Axiom Math, to unpack how AxiomProver earned a perfect 12/12 on the Putnam 2025 and why formal verification (via Lean) may be the missing layer for reliable reasoning. Carina argues we’re entering a “math renaissance” where verified reasoning systems can tackle problems that currently take researchers months — and potentially push beyond math into verified code, hardware, and high-stakes software. They go inside the “generation + verification” loop, what it means to build AI that can be trusted, and what this approach could unlock on the road to superintelligent reasoning. (00:00) Intro (01:25) Why the World Needs an AI Mathematician (02:57) Scoring 12/12 on the World's Hardest Math Test (Putnam) (04:05) The First AI to Solve Open Research Conjectures (06:59) Does AI Solve Math in "Alien" Ways? (The Move 37 Effect) (08:59) "Lean": The Programming Language of Proofs Explained (10:51) How Axiom's Approach Differs from DeepMind & OpenAI (16:06) Formal vs…
People in this episode
Host: Matt Turck
Guest: Carina Hong
Topics covered
- AI verification
- formal reasoning
- mathematics
- AxiomProver
- trustworthy AI
- superintelligent reasoning
Keywords
- AI
- verification
- mathematics
- AxiomProver
- formal verification
- Lean
- DeepMind
- OpenAI
- trustworthy AI
Mentioned in this episode
Organizations: Axiom Math, DeepMind, OpenAI
Books & works: Lean
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