AI progress is not bottlenecked by expert human data; what matters is compute, the science of constructing RL environments, and algorithmic improvements, so automating AI R&D does not require replicating the human data industry.

Ryan argues that scaling up human expert data is not the primary driver of AI R&D progress—better RL environments, compute, and algorithmic curation matter far more—so removing expert data would not stall automated AI R&D. ✦ AI generated

Ryan Greenblatt · Dwarkesh Podcast · 2026-08-11 · original ↗

My sense is that scaling up the amount of effort spent on getting expert human data has not been hugely important for AI R&D in general. In particular, over the last few years, we've been scaling up compute, scaling up people working at AI companies, and scaling up the amount of effort spent on data labeling. My sense is that if you removed the last two doublings or whatever of data generation from expert humans, that would not make a huge difference.

verbatim transcript · starts at 16:52