30–50% compute on inference
Labs spend 30 to 50% of their total compute on inference, yet this compute currently plays no productive role in improving the model.
Snapshot · Dwarkesh Podcast
The next big breakthrough will be AIs learning on the job
Explore episode
Jun 26, 2026
Where this was said
Will RLVR alone generalize?
At 8:08 · chapter starts 6:10
Dwarkesh questions whether RLVR generalizes from containerized tasks to complex real-world domains like politics or business, citing Dario Amodei's context-length degradation comment as a key data point [1] — Dwarkesh Patel "Dario Amodei noted that training at short context lengths and serving at long ones can cause performance degradation. Dwarkesh reads this a…" 06:55 .
Dario Amodei's Hint That RLVR Generalization Has Limits
Dario Amodei noted that training at short context lengths and serving at long ones can cause performance degradation. Dwarkesh reads this as evidence that RLVR generalization is not infinite — a crack in the foundation of the labs' AGI bet.
30–50% of Compute Is Wasted on Inference
Labs burn 30–50% of compute on inference — and currently gain zero improvement to the model from it. Meanwhile, deployment is where the most valuable learning signals actually exist: real organizational context, real mistakes, real feedback.
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