The sample efficiency black hole (dwarkesh.com) AI

Dwarkesh Patel argues that AI progress is largely driven by scaling data and compute—especially via reinforcement-learning-style synthetic data and large volumes of human expert labeled trajectories—while improvements in “sample efficiency” (how little data is needed to learn) may be limited. He compares human and frontier AI data exposure, suggests humans may be on a fundamentally different sample-efficiency scaling curve, and discusses why labs still may succeed in automating many white-collar tasks despite this inefficiency.