
Data centre emissions are soaring – it’s AI or the climate
High-tech solutions aren’t always the best answers to modern problems.

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An excellent question.Is the majority of "AI" energy use large farms doing training or is it mostly users of LLMs?
Is the majority of "AI" energy use large farms doing training or is it mostly users of LLMs?
That is an interesting data point. On the flip side, I saw a tweet from Elon Musk that they train on pre-2024 or pre-2023 data because so much of the current content on the internet is AI generated. (Leading to poisoning of the source, so to speak). Of course this is referring to non-curated sources.. And in another place that one or more of the big companies did a 6 hourly internet scrape. Which tends to imply that training runs on a 6 hour cycle.
That is what I was thinking of when asking the question. A few other "power" modifiers:What does interest me is whether the training effort (in energy consumption terms) increases or declines over time going forwards. And what percentage of the total AI energy consumption this amounts to. On the one hand, the current AI model implies that you need to keep training forever as the data sets for most areas keep changing. On the other, presumably there's some law of diminishing returns in there somewhere and there ought to be a "good enough" state where you can stop. Or reduce the training frequency. Or partition the search space into stable and "needs more work" areas (perhasps they already do that).
I certainly think AI energy consumption is a first order problem right now.