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Artificial intelligence is reaching a turning point. Instead of building bigger and bigger models, what if the real breakthrough comes from letting AI evolve?

In this episode of Eye on AI, David Ha, Co-Founder and CEO of Sakana AI, explains why evolutionary strategies and collective intelligence could reshape the future of machine learning. We explore model merging, multi-agent systems, Monte Carlo tree search, and the AI Scientist framework designed to generate and evaluate new research ideas. The conversation dives into open-ended discovery, quality and diversity in AI systems, world models, and whether artificial intelligence can push beyond the boundaries of human knowledge.

If you're interested in AGI, evolutionary AI, frontier models, AI research automation, or how AI could start discovering science on its own, this episode offers a clear look at where the field may be heading next.

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(00:00) AI Should Evolve, Not Just Scale

(03:54) David's Journey From Finance to Evolutionary AI

(10:18) Why Gradient Descent Gets Stuck

(18:12) Model Merging and Collective Intelligence

(28:18) Combining Closed Frontier Models

(32:56) Inside the AI Scientist Experiment

(38:11) Parent Selection, Diversity and Innovation

(49:25) Can AI Discover Truly New Knowledge?

(53:05) Why Continual Learning Matter