Estimating AI energy use • Hacker News Board

Re: Estimating AI energy use

#101

Post by maxglute » Mon, Oct 06, 2025, 11:44 PM UTC

pixl97 wrote: post #96

maxglute wrote: post #42

>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/…

Honestly the excess fiber put in the ground back then has nothing to do with fiber capacity now. The same fiber back then that could carry 100mbs can use new transceivers that push a terabit

Current network capacity runs off excess fiber. Networking gets upgraded at nodes and dumb pipe (excess fiber) efficiency improves. Like we upgrade switching for 100+ year old rail to improve freight efficiency. Much of $$$$$ / capex "wasted" in dot bubble boom went to civil engineering - digging trenches to build out "agnostic" fiber conduits with multi decade life span that can be repuporsed for general use.

Bulk of AI capex build out is going to be in specialized hardware and data centers with bespoke power / cooling / networking profile. If current LLM approaches turn out to be deadend the entire data centre is potentially stranded asset unless future applications can specifically take advantage. But there's a good chance _if_ LLM crashes, then it might be due to something inherently wrong with current approach, i.e. compute/cost doesn't make commercial sense, and resuing stranded data centers might not make economic sense.

Re: Estimating AI energy use

#102

Post by driverdan » Tue, Oct 07, 2025, 12:20 AM UTC

simianwords wrote: post #67

driverdan wrote: post #19

This doesn't seem to factor in the energy cost of training which is currently a very significant overhead.

How do you know it is significant?

That's why LLM providers are losing so much money. They're spending it on training and hardware growth. If you don't include those factors they make pretty good money on the services they provide.

Re: Estimating AI energy use

#103

Post by simonw » Tue, Oct 07, 2025, 5:07 AM UTC

deaux wrote: post #97

simonw wrote: post #84

Earlier quoted context omitted.

"Does smoking cause cancer?" and "Does burning fossil fuels cause global warming?" are a different category from "How much energy does it take to run a prompt?" The prompt energy question is something that companies can both actively measure and need to actively measure in order to plan their budgets. It's their job to know the answer. Those other questions, sadly, fall more into the category of it's their job not to…

You're not suggesting that Philip Morris and ExxonMobil didn't know the actual answers to both of those questions, surely? It's a known, easily verifiable fact that they did. They just lied about them.

I am saying that it is necessary for OpenAI to know the exact correct answer to the question about prompt energy costs in order to effectively run their business.

Re: Estimating AI energy use

#104

Post by christkv » Fri, Oct 10, 2025, 11:50 AM UTC

pixl97 wrote: post #93

christkv wrote: post #66

Earlier quoted context omitted.

This. I totally agree we will see better architectures for doing the calculations, lower energy usage inference hardware and also some models running on locally moving some of the "basic" inference stuff off the grid. It's going to move fast I think and I would not surprised if the inference cost in energy is 1/10 of today in less than 5 years.

This said Jeavons paradox will likely mean we still use more power.

For sure. But it might decentralize the power usage which would for sure alleviate the massive problems of centralised power usage that massive datacenters require.