Estimating AI energy use

Post by barbazoo » Mon, Oct 06, 2025, 2:18 AM UTC

givemeethekeys wrote: post #18

gaoshan wrote: post #14

One thing it's doing is jacking up electricity rates for US States that are part of the [PJM Interconnection grid]( https://en.wikipedia.org/wiki/PJM_Interconnection ). It's a capacity auction price that is used to guarantee standby availability and it is [up significantly]( https://www.toledochamber.com/blog/watts-up-why-ohios-electr... ) at $270.43 per MW/day, which is far above prior years (~$29–58/MW/day) and thi…

Why are consumers paying for electricity used by server farms? Why can't the electricity companies charge the server farms instead? Where I live, the utility company bills you at a higher rate if you use more electricity.

Post by riku_iki » Mon, Oct 06, 2025, 2:22 AM UTC

ch4s3 wrote: post #22

geuis wrote: post #11

My thoughts. Current gen AI is going to result in the excess datacenter equivalent of dark fiber from the 2000's. Lots of early buildout and super investment, followed by lack of customer demand and later cheaper access to physical compute. The current neural network software architecture is pretty limited. Hundreds of billions of dollars of investor money has gone into scaling backprop networks and we've quickly hit…

If the end result here is way overbuilt energy infrastructure that would actually be great. There’s a lot you can do with cheap electrons.

Post by yen223 » Mon, Oct 06, 2025, 2:30 AM UTC

a_wild_dandan wrote: post #20

simonw wrote: post #7

Earlier quoted context omitted.

Hard to say. Sam wrote that on June 10th this year: https://blog.samaltman.com/the-gentle-singularity GPT-5 came out on 7th August. Assuming the 0.34 value was accurate in the GPT-4o era, is the number today still in the same ballpark or is it wildly different?

I would bet that it's far lower now. Inference is expensive we've made extraordinary efficiency gains through techniques like distillation. That said, GPT-5 is a reasoning model, and those are notorious for high token burn. So who knows, it could be a wash. But selective pressures to optimize for scale/growth/revenue/independence from MSFT/etc makes me think that OpenAI is chasing those watt-hours pretty doggedly. So…

Post by ch4s3 » Mon, Oct 06, 2025, 2:33 AM UTC

riku_iki wrote: post #23

ch4s3 wrote: post #22

Earlier quoted context omitted.

If the end result here is way overbuilt energy infrastructure that would actually be great. There’s a lot you can do with cheap electrons.

I suspect it will mostly be fossil power capacity, which is much easier to scale up

Post by simonw » Mon, Oct 06, 2025, 2:52 AM UTC

yen223 wrote: post #25

a_wild_dandan wrote: post #20

Earlier quoted context omitted.

I would bet that it's far lower now. Inference is expensive we've made extraordinary efficiency gains through techniques like distillation. That said, GPT-5 is a reasoning model, and those are notorious for high token burn. So who knows, it could be a wash. But selective pressures to optimize for scale/growth/revenue/independence from MSFT/etc makes me think that OpenAI is chasing those watt-hours pretty doggedly. So…

Yeah, something we are confident about is that a) training is where the bulk of an AI system's energy usage goes (based on a report released by Mistral) b) video generation is very likely a few orders of magnitude more expensive than text generation. That said, I still believe that data centres in general - including AI ones - don't consume a significant amount of energy compared with everything else we do, especiall…