1/1,000,000th sample efficiency
Current AI models are one-millionth as sample efficient as humans during training, a deficit Dwarkesh previously explored in depth.
Snapshot · Dwarkesh Podcast
The next big breakthrough will be AIs learning on the job
Explore episode
Jun 26, 2026
Where this was said
The big research bet the labs are making
At 0:42 · chapter starts 0:00
Dwarkesh outlines the core labs' bet: scale verifiable RL training across millions of tasks to produce a general problem-solving agent. He also raises the counterargument that AI is 1/1,000,000th as sample efficient as humans [1] — Dwarkesh Patel "1/1,000,000th sample efficiency: Current AI models are one-millionth as sample efficient as humans during training, a deficit Dwarkesh prev…" 00:42 .
The Big Research Bet: RLVR Will Build AGI
The labs' core AGI bet is that training on enough verifiable, containerized RL environments will produce agents capable of open-ended problem-solving for weeks on end. Skeptics argue the fundamental deficits — data inefficiency, no continual learning — will simply be steamrolled by scale, just as compute steamrolled NLP.
6 months on-the-job learning
People often say employees aren't net productive until 6+ months on the job, illustrating why continual learning matters for competence.
Grindability: The Underrated Bottleneck in AI Progress
Verifiability alone doesn't explain which domains see rapid AI progress. The real bottleneck is grindability: can you run thousands of parallel rollouts from identical starting points in a deterministic simulator? Coding can; live websites cannot.
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