A human eats 2000 kilocalories of food per day.

Thus, sitting around for an hour to do a task takes 350kJ of food energy. Depending on what people eat, it's 350kJ to 7000kJ of fossil fuel energy in to get that much food energy. In the West, we eat a lot of meat, so expect the high end of this range.

The low end-- 350kJ-- is enough to answer 100-200 ChatGPT requests. It's generous, too, because humans also have an amortized share of sleep and non-working time, other energy inputs/uses to keep them alive, eat fancier food, use energy for recreation, drive to work, etc.

Shoot, just lighting their part of the room they sit in is probably 90kJ.

> I am not an anti-LLM’er here but having models that are this power hungry and this generalisable makes no sense economically in the long term. Why would the model that you use to build a command tool have to be able to produce poetry? You’re paying a premium for seldom used flexibility.

Modern Mixture-of-Experts (MoE) models don't activate the parameters/do the math related to poetry, but just light up a portion of the model that the router expects to be most useful.

Of course, we've found that broader training for LLMs increases their usefulness even on loosely related tasks.

> Either the power drain will have to come down, prices at the consumer margin significantly up

I think we all expect some mixture of these: LLM usefulness goes up, LLM cost goes up, LLM efficiency goes up.