Quote · Dwarkesh Podcast
The next big breakthrough will be AIs learning on the job
Where this was said
Grindability is just as important as verifiability
At 3:20 · chapter starts 2:12
Dwarkesh introduces 'grindability' — the ability to run parallel rollouts in deterministic simulators — as the underrated reason computer use AI lags far behind coding, despite being equally verifiable [1] — Dwarkesh Patel "Verifiability alone doesn't explain which domains see rapid AI progress. The real bottleneck is grindability: can you run thousands of para…" 02:12 .
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.
Computer use has made far slower progress than coding and math despite being clearly verifiable, because it lacks grindable, deterministic training environments.
It's not enough for a domain to be verifiable — it must also be 'grindable', meaning you can run thousands of parallel rollouts from identical starting points in a deterministic simulator.
Many skills humans have — winning court cases, building businesses, day trading — require real-world interaction with sparse, delayed feedback. There's no containerized RL environment for 'build a company as well as Sam Walton.' These domains will resist the current training paradigm.
