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Building an AI Mathematician | Carina Hong, CEO of Axiom Math

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Manage episode 516772971 series 3370867
Content provided by Prateek Joshi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Prateek Joshi or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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
--------
Where to find Carina Hong:
LinkedIn: https://www.linkedin.com/in/carina-hong/
--------
Where to find Prateek Joshi:
Research column: https://www.infrastartups.com
Newsletter: https://prateekjoshi.substack.com
Website: https://prateekj.com
LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite
X: https://x.com/prateekvjoshi

  continue reading

189 episodes

Artwork
iconShare
 
Manage episode 516772971 series 3370867
Content provided by Prateek Joshi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Prateek Joshi or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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
--------
Where to find Carina Hong:
LinkedIn: https://www.linkedin.com/in/carina-hong/
--------
Where to find Prateek Joshi:
Research column: https://www.infrastartups.com
Newsletter: https://prateekjoshi.substack.com
Website: https://prateekj.com
LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite
X: https://x.com/prateekvjoshi

  continue reading

189 episodes

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