Vlad Tenev and Tudor Achim from Harmonic explain how they built Aristotle, an AI system that reaches International Mathematical Olympiad gold-medal performance using formally verified Lean proofs. They unpack the architecture behind mathematical superintelligence, including Monte Carlo Tree Search, lemma guessing, and specialized geometry modules. The conversation explores how verifiable reasoning could harden mission-critical software, reshape mathematical practice, and lead to trustworthy superintelligent systems by 2030.
Use the Granola Recipe Nathan relies on to identify blind spots across conversations, AI research, and decisions: https://bit.ly/granolablindspot
Sponsors:
Claude:
Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro’s full capabilities at https://claude.ai/tcr
Framer:
Framer is an enterprise-grade website builder that lets business teams design, launch, and optimize their.com with AI-powered wireframing, real-time collaboration, and built-in analytics. Start building for free and get 30% off a Framer Pro annual plan at https://framer.com/cognitive
Blitzy:
Blitzy is the autonomous code generation platform that ingests millions of lines of code to accelerate enterprise software development by up to 5x with premium, spec-driven output. Schedule a strategy session with their AI solutions consultants at https://blitzy.com
Tasklet:
Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai
CHAPTERS:
(00:00) About the Episode
(04:58) Math as reasoning (Part 1)
(15:22) Sponsors: Claude | Framer
(18:51) Math as reasoning (Part 2)
(18:51) Inside the Lean language
(27:51) Lean intuition and MathLib (Part 1)
(34:08) Sponsors: Blitzy | Tasklet
(37:08) Lean intuition and MathLib (Part 2)
(38:47) Inside Aristotle's architecture
(48:33) Scope, boundaries, and applications
(54:37) Training, taste, and interpretability
(01:08:18) Formal math and software
(01:16:50) Limits, entropy, and roadmap
(01:25:24) 2030 vision and safety
(01:33:38) Outro
PRODUCED BY:
https://aipodcast.ing