The first statement is not even wholly true. While training does take more, executing the model (called "inference") takes much, much more power than non-AI search algorithms, or really any traditional computational algorithm besides bogosort.

Big Tech weren't doing the best they possibly could transitioning to green energy, but they were making substantial progress before LLMs exploded on the scene because the value proposition was there: traditional algorithms were efficient enough that the PR gain from doing the green energy transition offset the cost.

Now Big Tech have for some reason decided that LLMs represent the biggest game of gambling ever. The first to find the breakthrough to AGI will win it all and completely take over all IT markets, so they need to consume as much as they can get away with to maximize the probability that that breakthrough happens by their engineers.

You have more brians than any AI shill will ever be able to emulate.

Tax pollution and pay the revenue as cash to poor people, it'll figure itself out

How much energy does AI really use? (zdnet) Seems like queries aren't that expensive, so I guess the enormous energy cost of AI must be mostly from training. I reckon this is why apologists try to minimize it.

I know how machine learning "AI" works, and there, the training is the costly part, using it is close to free.

Could someone knowledgeable explain what's the difference with those more recent large language models used by ghatgpt etc. ?