If what you're saying is true, why are we hearing about AI companies wanting to build nuclear power plants to power new data centers they think they need to build?

Are you saying all of that new capacity is needed to power non-LLM stuff like classifiers, adtech, etc? That seems unlikely.

Had you said that inference costs are tiny compared to the upfront cost of training the base model, I might have believed it. But even that isn't accurate -- there's a big upfront energy cost to train a model, but once it becomes popular like GPT-4, the inference energy cost over time is dramatically higher than the upfront training cost.

You mentioned batch computing as well, but how does that fit into the picture? I don't see how batching would reduce energy use. Does "doing lots of work at once" somehow reduce the total work / total energy expended?