Pushmeet Kohli

DeepMind's Pushmeet Kohli on AI's Scientific Revolution

Pushmeet Kohli, Sonya Huang, Pat Grady

DeepMind's AlphaEvolve employs a multi-agent evolutionary architecture using Gemini models to autonomously discover entirely new algorithms, successfully resolving long-standing mathematical challenges like reducing 4x4 matrix multiplication steps to 48 and uncovering symmetries in the Cap Set problem with Terence Tao. Operating across languages such as C++, Python, and Verilog, the system generates human-interpretable code that outperforms expert designs in tasks ranging from chip design to data center scheduling without relying on human feedback loops. This breakthrough mirrors the impact of AlphaFold 2 by democratizing scientific discovery, with Pushmeet Kohli predicting that future Nobel Prizes will be won by human-AI collaborative teams as the technology scales into robotics and energy sectors.