The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, @wellingmax—co-founder and CTO of @cusp_ai and professor at the University of Amsterdam—argues that physics may provide some of the ideas behind the next generation of AI systems. We begin with CuspAI’s work using generative AI to design entirely new materials for semiconductors, batteries, carbon capture, and clean energy. Max explains how foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials and reshaping scientific discovery. The conversation then broadens into a deeper question. Beyond giving AI new scientific problems to solve, can physics also teach us how to build better AI? Max explores surprising connections between machine learning and thermodynamics, why waves may become a new computational primitive for neural networks, and how concepts like symmetry breaking and statistical physics could inspire AI architectures beyond today’s scaling paradigm. 🗒️ Full show notes: 📖 CHAPTERS =============================== 00:00 - Introduction 02:08 - From Equivariant Networks to AI for Science 04:55 - Founding CuspAI 07:30 - Using AI to Discover New Materials 09:03 - Designing Materials for Carbon Capture 13:20 - Partnerships and the CuspAI Business Model 14:59 - The End-to-End Materials Discovery Process 19:13 - Experiments and Self-Driving Labs 23:04 - Open-Source Molecular Dynamics on GPUs 25:23 - Foundation Models for Chemistry 30:20 - The Future of AI-Driven Materials Science 32:22 - Connecting Generative AI and Thermodynamics 39:29 - How Physics and Machine Learning Can Inform Each Other 44:53 - Waves as a New Primitive for Neural Networks 49:34 - Memory, Stability, and the Edge of Chaos 52:40 - Spontaneous Symmetry Breaking in Neural Networks 56:10 - The Two-Way Exchange Between AI and Physics