Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub
About this episode
From the show’s notesFrom the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding, Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology. In this special panel moderated by Brandon Anderson, they explore why AlphaFold’s breakthrough was only the beginning, why scaling compute and data alone won't solve biology, and how the next generation of AI models could transform our understanding of proteins, cells, and human disease.
We go deep on the future of AI-driven biology: finding scaling laws in biological data, the tradeoffs between scientific intuition and general-purpose architectures, why protein structure prediction is far from solved, and what it would take to build predictive models of living systems. Pushmeet reflects on the lessons behind AlphaFold, the limits of human interpretability, and whether future frontier models could understand other AI systems better than we can. Sal explains why protein language models may already contain scientific knowledge we haven't unlocked, how biological modeling must move beyond individual proteins, and why achieving Biohub's mission to cure all disease requires thinking in terms of 10x breakthroughs rather than incremental improvements.
Read the show’s notes in full
We discuss: • The Bitter Lesson for biology: why scaling compute and data isn't enough • Why finding the right scaling law matters more than blindly increasing model size • How low-quality metagenomic data can improve protein language models • Why AI researchers optimize for available data instead of the most important scientific problems • Lessons from DeepMind on balancing modeling, data generation, and scientific expertise • Why building a virtual cell requires fundamentally different datasets • AlphaFold's handcrafted architecture and the role of scientific intuition • Why good data matters more than simply having more data • Inductive biases, scaling laws, and the future of specialized AI architectures • Why we aren't in a post-Transformer world, but architectures are evolving • Why AlphaFold didn't actually solve all of protein folding • Protein dynamics, disorder, and the limitations of static structure prediction • How cryo-EM micrographs could unlock richer biological representations • Moving from models of individual proteins to models of entire biological systems • Feynman's famous principle and why AI can now create things we don't understand • The hidden biological knowledge inside protein language models • Why model trustworthiness and uncertainty calibration matter more than full interpretability • Whether future frontier AI models could interpret other AI systems better than humans • When AI could deliver 10x–100x acceleration in drug discovery • Why curing all disease requires thinking about 10x breakthroughs instead of 10% improvements
