Mathematics and coding progress faster than other fields not primarily because they are verifiable, but because they are 'grindable' — you can containerize environments, spin up deterministic parallel rollouts, and cleanly solve the credit assignment problem, which is impossible in the mutable real world.

Dwarkesh argues that verifiability alone (as in computer use) is insufficient — the decisive factor is grindability: deterministic, containerizable environments like code and math that allow many parallel rollouts and clear credit assignment. Grant agrees and extends this to the vision of an AI endlessly growing Lean/Mathlib's 'tree of logic' with no human check-ins. ✦ AI generated

Dwarkesh Patel · Dwarkesh Podcast · 2026-06-30 · original ↗

Dwarkesh: What computer use lacks is grindability. Because websites have bot detectors—and it takes a tremendous amount of compute to run parallel rollouts—it's very hard to run a thousand parallel rollouts of the same checkout flow on Amazon. The reason you currently need to do so many parallel rollouts to learn a skill with deep learning is that we haven't solved sample efficiency. With code, you can containerize a given level of progress in a repository and then spin out hundreds of parallel containers and say, 'Try to implement this feature,' and it's totally deterministic. Because it's deterministic, you can solve the credit assignment problem because you know that whatever caused this rollout to succeed and this one to fail, the diff is the thing that worked. Math, of course, is the exception, and I feel like this is an important driver of progress in this domain and also in coding. Grant: That's a very unique thing that math has that nothing else has, where you could press go and just pour compute at it, look away for ten years, and then come back and say, 'What do you have?' There's going to be something. Then there's a question: is it useful or not? How do you suss that out? That's just an interesting thing to be able to do. It would be very surprising if that didn't yield some sort of interesting mathematical insight from it.

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