Episode
Theoretical Physics With Generative AI – #101
- Podcast
- Manifold
- Published
- Dec 18, 2025
- Duration seconds
- 4364
- Processing state
not_requested- Canonical source
- https://share.transistor.fm/s/90d2c00c
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Summary
All but the last 20 minutes of this episode should be comprehensible to non-physicists. Steve explains where frontier AI models are in understanding frontier theoretical physics. The best analogy is to a “brilliant but unreliable genius colleague”! He describes a specific example: the use of AI in recent research in quantum field theory (Tomonaga-Schwinger integrability conditions applied to state-dependent modifications of quantum mechanics), work now accepted for publication in Physics Letters B after peer review. Remarkably, the main idea in the paper originated de novo from GPT-5. Links: X discussion - https://x.com/hsu_steve/status/1996034522308026435 Companion paper: Theoretical Physics With Generative AI - https://drive.google.com/file/d/16sxJuwsHoi-fvTFbri9Bu8B9bqA6lr1H/view Physics paper - https://arxiv.org/abs/2511.15935 | https://www.sciencedirect.com/science/article/pii/S0370269325008111 Related discussion of AI and theoretical physics with Prof. Nirmalya Kajuri (IIT) and Prof. Jonathan Oppenheim (UCL) - https://youtu.be/BRuDd3l0e3k Related video: AIs Win Math Olympiad Gold: Prof. Lin Yang (UCLA) – Manifold #97 - https://youtu.be/8JeRCqNg7Rc Chapter markers: (00:00) - Intro: AI discussion with specialized physics at the end (03:40) - The current AI landscape for science: frontier models, Co-Scientist, and recent math breakthroughs (11:01) - Why models help and why they fail: errors, deep confabulation, and the research risk (15:54) - The Generator–Verifier workflow: how chaining model inference suppresses mistakes (23:30) - Project origin: testing models on Hsu’s older nonlinear QM/QFT work (30:35) - The “GPT-5 moment”: Tomonaga–Schwinger angle appears and produces the key equation (40:35) - Wild goose chases & a practical heuristic: axiomatic QFT detou…