BTW, I ran a large scale computing platform at Google for several years. It made absolutely enormous, stunningly huge amounts of CPU available to scientists and we used it to make several important breakthroughs. However, after a few years, I finally was shown what the power cost implications were, and I shut down the project because the costs were not justified by the scientific benefits. Compared to what I was doing, ML is relatively skimpy on CPU.
I believe we reported that Exacycle provided 700K high speed Xeon cores for over a year, and the actual number is far higher (I can't share it because you'd be able to make a reasonable estimate on how many computers Google has).
You gotta pay if you wanna play. Humans are the result of a much longer and more expensive evolutionary process. It's only normal to have to pay a little to do the same for AI.
There are other examples against the meme of AI using "a lot of electricity", for example a recent paper from Google where they show that a learned computational fluid dynamics technique uses only 2% the energy of standard techniques.
https://arxiv.org/pdf/2102.01010.pdf