AI researchers debate how close we are to recursive self-improvement
Summary
Dwarkesh Patel interviews Baron Millich, John Schulman, and Charlie O'Neill to debate whether AI will achieve rapid recursive self-improvement by 2036. They argue that while scaling current RL paradigms may hit asymptotic limits, the key bottleneck remains the "sim-to-real" gap and the difficulty of generalizing from verifiable benchmarks to open-ended scientific discovery. The guests discuss how Chinese labs leverage router data for distillation, challenging the idea that frontier labs hold a permanent advantage. Ultimately, they conclude that while AI may automate specific research loops,…
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