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Estimating AI energy use

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Re: Estimating AI energy use

#41

Post by epistasis »

ch4s3 wrote: post #27
riku_iki wrote: post #23

Earlier quoted context omitted.

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

I wouldn’t be so sure about that. Serves of the big names in this space have green energy pledges and are actively building out nuclear power.

Nobody is actively building out nuclear power. Microsoft is turning on a recently decommissioned facility.

New nuclear is too expensive to make sense. At most there are small investments in flash-in-the-pan startups that are failing to deliver plans for small modular reactors.

The real build out that will happen is solar/wind with tons of batteries, which is so commonplace that it doesn't even make the news. Those can be ordered basically off the shelf, are cheap, and can be deployed within a year. New nuclear is a 10-15 year project, at best, with massive financial risk and construction risk. Nobody wants to take those bets, or can really afford to, honestly.

Re: Estimating AI energy use

#42

Post by maxglute »

>The Schneider Electric report estimates that all generative AI queries consume 15 TWh in 2025 and will use 347 TWh by 2030; that leaves 332 TWh of energy—and compute power—that will need to come online to support AI growth. T

+332TW is like... +1% of US power consumption, or +8% of US electricity. If AI bubble burst ~2030... that's functionally what US will be left with (assuming new power infra actually built) mid/long term since compute depreciates 1-5 years. For reference dotcom burst left US was a fuckload of fiber layouts that lasts 30/40/50+ years. Still using capex from railroad bubble 100 years ago. I feel like people are failing to grasp how big of a F US will eat if AI bursts relative to past bubbles. I mean it's better than tulip mania, but obsolete AI chips also closer to tulips than fiber or rail in terms of stranded depreciated assets.

Re: Estimating AI energy use

#43

Post by givemeethekeys »

barbazoo wrote: post #21
givemeethekeys wrote: post #18

Earlier quoted context omitted.

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.

Charge them more than individual consumers? Why? Let the market decide how much electricity should be. /s

Hehe. Well, if the market is no good for its participants, then at least there is a viable alternative for many of them.

Re: Estimating AI energy use

#44

Post by gyomu »

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…

Yes, that makes sense.

Also adding to that tendency, I suspect as the tech matures more and more consumer space models will just run on device (sure, the cutting edge will still run in server farms but most consumer use will not require cutting edge).

Re: Estimating AI energy use

#45

Post by Gigachad »

qnleigh wrote: post #31

For reference, global energy consumption is about 180,000 TWh[1]. So while the numbers in this article are large, they're not a significant fraction of the total. Traveling and buying things are probably a much bigger part of your carbon footprint. For example: - 25 LLM queries: ~8.5 Wh - driving one mile: ~250-1000 Wh - one glass bottle: ~1000 Wh [2] - a new laptop: ~600,000 Wh [3] - round-trip flight from LA to Tok…

Model usage seems quite small compared to training. I can run models on my phone which took millions of hours of GPU training time to create. Although this might change with the new AI slop tiktok apps every company is rushing to create now.

Re: Estimating AI energy use

#46

Post by EcommerceFlow »

The question to ask is why supply of energy hasn't kept up with demand. Regulations (primarily in Democratic states) is most likely the answer. When you use government incentives to pick winners and losers with energy sources, it throws the entire energy market out of sync.

Re: Estimating AI energy use

#48

Post by bluefirebrand »

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…

> don't consume a significant amount of energy compared with everything else we do, especially heating and cooling and transport

Ok, but heating and cooling are largely not negotiable. We need those technologies to make places liveable

LLMs are not remotely as crucial to our lives

Re: Estimating AI energy use

#50

Post by benzible »

KPGv2 wrote: post #40
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…

> There's probably some interesting new architectures already in the works either from postdocs or in tiny startups It is not clear to me why we will have a breakthrough after virtually no movement on this front for decades. Backpropagation is literally 1960s technology.

Because tremendous rewards will spur a huge increase in research?
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