> How is anti pollution argument pro ai?

What? (Edit: I think you misread "in support of their primary concern, being AI" as "in support of AI"? People who complain about pollution are usually mostly concerned about AI, and they use arguments of pollution to strengthen their argument against AI related things.)

> And even if you do inference at home you are not training the models.

No, and training does use a large amount of energy on a large amount of hardware. But:

- That sort of workload doesn't really require many distributed data centers, only a few powerful ones. I believe training is also getting cheaper for the achieving higher levels of capabilities, but I don't think the efficiency advancement has been as dramatic as it has been for inference.

- I believe we are really hitting a wall of diminishing returns, especially at the high end of large models. There is lower demand for training, because models are good enough to have a reasonably long shelf life at this point. Year+ old models that were SOTA in their time are still useful today. The demand for training is going down.

> Moreover, most people certainly are not doing inference locally.

Maybe not, but I think most people who go out of their way to use LLMs because they find them useful for their work actually are. Though most inference is probably from people who do it accidentally (as a part of a search result or something) or students who don't have the resources to do it themselves. But basically every software engineer that I personally know that uses AI for programming is either running their inference on their own hardware, or is talking about building a rig to do it.