Uploaded February 2026 | Updated September 2026, 3 weeks ago

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In this episode recorded at NeurIPS 2025, Max Welling traces the intellectual thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery.

We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It’s a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.

Along the way, we discuss:
- Why symmetry and equivariance matter in deep learning
- The tradeoff between scale and inductive bias
- The deep mathematical links between diffusion models and stochastic thermodynamics
- Why materials—not software—may be the real bottleneck for AI and the energy transition
- What it actually takes to build an AI-driven materials platform

Welling reflects on moving from curiosity-driven theoretical physics (including work with Gerard 't Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.

Timestamps
00:00 Introduction to Max Welling and the concept of Physics Processing Units (PPUs)
01:34 Max’s career evolution: From quantum gravity to climate-focused AI
03:39 Physics as the "thread": Symmetries, gauge theory, and stochastic thermodynamics
07:05 The explosion of "AI for Science" and the emerging investment bubble
07:53 Successes in protein folding and machine learning inter-atomic potentials
11:05 Why materials matter: The physical foundation of the AI software layer
13:47 Transforming material discovery into a search engine problem
14:47 The origin and mission of CuspAI: Solving carbon capture
17:49 CuspAI’s platform architecture: Generative models, digital twins, and agents
20:47 The role of humans in the loop: Moving from manual workflows to automation
24:39 Strategy for breakthroughs: Lighthouse moonshots vs. incremental partnerships
28:40 Technical Deep Dive: Explaining Equivariance and symmetry in neural networks
31:07 The "Bitter Lesson" in the context of scientific inductive biases
31:47 Preview of "Generative AI and Stochastic Thermodynamics" (Upcoming Book)