Max Welling, co-founder and CTO of CuspAI and professor at the University of Amsterdam, discusses how physics could inspire the next generation of AI systems. He explains CuspAI's work using generative AI to design new materials for semiconductors, batteries, and clean energy. The conversation then explores how physics concepts like thermodynamics, waves, and symmetry breaking might lead to AI architectures beyond the current scaling paradigm.
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Physics Offers a New Path Beyond Scaling
Max Welling argues that the next major AI breakthrough may come from physics—not from larger models or more data—by borrowing principles from thermodynamics, waves, and symmetry breaking to build fundamentally different architectures.
Waves as a New Computational Primitive for Neural Networks
Instead of using artificial neurons as the basic computing element, Welling explores the possibility of using waves—borrowing from physics—to serve as a more powerful primitive for building neural networks.
CuspAI uses generative models, agentic workflows, and automated experimentation to design new materials for semiconductors, batteries, and carbon capture, significantly speeding up the search for functional materials.