I don’t get it. The labs have regularly made improvements that dramatically lower the cost of training an equal-performing model. When they do this, they also train a larger model with even higher performance. This time, DeepSeek did the first part but didn’t do the second. Now every lab in the world will throw their compute into the effort to replicate and beat DeepSeek’s model with larger scale. It’s not like everyone is just going to say “well I guess AI is smart enough now, no point improving it anymore!” and stop building bigger training clusters.

If anything, r1 makes even more GPU demand likely, since it mitigated or at least delayed the risk AI hit a dead end (in which case, ceasing development may actually make sense).

It still doesn't make sense to me. If the money for training is still there, wouldn't companies that can afford it use the efficiency gains and also scale up models?

Unless AI is a bubble, and it pops, I can't see the demand for compute going down.

Eli Whitney thought he could reduce slavery by making cotton processing 45x more efficient...

Can someone explain how DeepSeek cut that estimate? Their (fast) API is always down, and the third-party providers on OpenRouter are more expensive than Claude.