Carina Hong is CEO of Axiom Math, where they're building a self-improving superintelligent reasoner, starting with an AI mathematician. She's a Rhodes Scholar, first-gen college grad and mathematics prodigy who earned dual degrees in mathematics and physics from MIT in 3 years. And a joint JD/PhD at Stanford. They just raised a $64M seed round from B Capital, Greycroft, Madrona, and Menlo Ventures.
Carina's favorite books: Proofs from THE BOOK (Author: Martin Aigner, Günter M. Ziegler)
(00:02) Intro (00:38) What self-improving mathematical superintelligence means (04:04) Proofs as programs: Lean and the data gap (06:36) How AI proves: human-style vs. Lean-style reasoning (10:43) Carina’s journey: from Olympiad problem-solver to theory-builder (14:47) The engine room: data, infra, and building a math knowledge graph (17:42) Verifying results: compile checks vs. LLM judges (18:56) Self-improvement loops: skills libraries, memory, and conjecture↔prover curricula (21:30) Synthetic data & auto-formalization strategy (24:00) Benchmarks that matter: miniF2F, CombiBench, miniCTX v2 (26:24) Why combinatorics is uniquely hard for AI (31:13) Compute footprint & scaling philosophy (32:20) In-house Lean tooling and productization path (33:57) Early use cases: formal verification in hardware/software (36:19) Team blueprint: AI, programming languages, and math (37:35) Scaling laws, efficiency, and bottlenecks (38:26) If Axiom works: what becomes cheaper/faster for the world (40:22) Rapid Fire Round
Podden och tillhörande omslagsbild på den här sidan tillhör
Prateek Joshi. Innehållet i podden är skapat av Prateek Joshi och inte av,
eller tillsammans med, Poddtoppen.