Electricity use of AI coding agents

https://www.simonpcouch.com/blog/2026-01-20-cc-impact/

by linolevan • 6 months ago

119 65 6 months ago

simonpcouch.com

linolevan2• 6 months ago

Had a small discussion about this on an OP on bsky. A somewhat interesting discussion over there.

https://bsky.app/profile/simonpcouch.com/post/3mcuf3eazzs2c

HNisCIS• 6 months ago

LLMs don't use much energy at all to run, they use it all at the beginning for training, which is happening constantly right now.

TLDR this is, intentionally or not, an industry puff piece that completely misunderstands the problem.

Also, even if everyone is effectively running a a dishwasher cycle every day, this is still a problem that we can't just ignore, that's still a massive increase in ecological impact.

linolevan2• 6 months ago

I'm not convinced that LLM training is at such a high energy use that it really matters in the big picture. You can train a (terrible) LLM on a laptop[1], and frankly that's less energy efficient than just training it on a rented cloud GPU.

Most of the innovation happening today is in post-training rather than pre-training, which is good for people concerned with energy use because post-training is relatively cheap (I was able to post-train a ~2b model in less than 6 hours on a rented cluster[2]).

[1] https://github.com/lino-levan/wubus-1 [2]: https://huggingface.co/lino-levan/qwen3-1.7b-smoltalk

simonw6• 6 months ago

The training cost for a model is constant. The more individual use that model gets the lower the training-cost-per-inference-query gets, since that one-time training cost is shared across every inference prompt.

It is true that there are always more training runs going, and I don't think we'll ever find out how much energy was spent on experimental or failed training runs.

dietr1ch2• 6 months ago

The training cost for a model is constant

Constant until the next release? The battle for the benchmark-winning model is driving cadence up, and this competition probably puts a higher cost on training and evaluation too.

kingstnap• 6 months ago

You underestimate the amount of inference and very much overestimate what training is.

Training is more or less the same as doing inference on an input token twice (forward and backward pass). But because its offline and predictable it can be done fully batched with very high utilization (efficiently).

Training is guestimate maybe 100 trillion total tokens but these guys apparently do inference on the quadrillion token monthly scales.

jeffbee2• 6 months ago

Training is pretty much irrelevant in the scheme of global energy use. The global airline industry uses the energy needed to train a frontier model, every three minutes, and unlike AI training the energy for air travel is 100% straight-into-your-lungs fossil carbon.

pluralmonad• 6 months ago

Not to mention doesn't aviation fuel still make heavy (heh) use of lead?

simonw6• 6 months ago

At first glance this looks like a credible set of calculations to me. Here's the conclusion:

So, if I wanted to analogize the energy usage of my use of coding agents, it’s something like running the dishwasher an extra time each day, keeping an extra refrigerator, or skipping one drive to the grocery store in favor of biking there.

That's for someone spending about $15-$20 in a day on Claude Code, estimated at the equivalent of 4,400 "typical queries" to an LLM.

Aurornis3• 6 months ago

Comparing it to running a refrigerator or the dishwasher is very relatable, as most people have at least one refrigerator without a second thought.

This is for someone using a lot of LLM tokens relative to the average customer of these companies.

nasmorn• 6 months ago

It also means I could offset my Claude usage with a single solar panel which costs basically nothing. A battery as well if I wanna code late

ggm6• 6 months ago

As long as it's unaccounted for by users it's at best anexternality. I think it may demand regulation to force this cost to the surface.

electricity and cooling incur wider costs and consequences.

simonw6• 6 months ago

That's hardly unique to data centers.

I'm all for regulation that makes businesses pay for their externalities - I'd argue that's a key economic role that a government should play.

ggm6• 6 months ago

No disagree. I think all enterprises that come with community borne costs (noise, heat, energy use, road use, construction & infrastructure, state incentives) and benefits (tax revenue, jobs) should have some level of accounting. It would be wrong to say the negs always outweigh the positives, thats not the point here. The point is that a bunch of cost in DC relating to power and cooling wind up having impact on the community at large.

I've been told in other (non US) economies, decisions to site hyperscaler DCs has had downstream impacts on power costs and longterm power planning. The infra to make a lot of power appear at a site, means the same capex and inputs cannot be used to supply power to towns and villages. There's a social opportunity loss in hosting the DC because the power supply doesn't magicly make more transformers and wires and syncons appear on the market: Prices for these things are going up because of a worldwide shortage.

Its like the power version of worldwide RAM pricing.

jeffbee2• 6 months ago

I don't see how this follows. Data center operators buy energy and this is almost their only operating expense. Their products are priced to reflect this. The fact that basic AI features are free reflects the fact that they use almost no energy.

arrowleaf• 6 months ago

I would be surprised if AI prices reflect their current cost to provide the service, even inference costs. With so much money flowing into AI the goal isn't to make money, it's to grow faster than the competition.

Majromax3• 6 months ago

As long as it's unaccounted for by users it's at best anexternality.

Why is it an externality? Anthropic (or other model provider) pays the electricity cost, then it's passed along in the subscription or API bill. The direct cost of the energy is fully internalized in the price.

ggm6• 6 months ago

Electricity supply is about more than supply cost. It has a build cost, with assumed inputs which are in turn priced by demand, and they're in short supply worldwide. DC costs of construction in power are pushing up the delay and cost for non-DC power projects.

pixl97• 6 months ago

I mean if we really cared about this one bit we'd stop making a car based society in the US and save far more energy and pollution. That's not politically expedient and there are powerful vested interests in ensuring it doesn't happen.

That's why I think most of this data center energy use, especially over longer terms is a joke. Data center can pretty easily run on solar and wind energy if we spend even a small amount to political capital to make it happen.

ggm6• 6 months ago

They're a lot harder to build out from those sources because they are costed to run 24/7 and the intermittency issue comes to the fore. Unlike things like aluminium smelters there isn't always a good load-shed or even supply-timing story in a DC, cooling aside (big chunks of cooling can be used for demand management)

I am not in the DC business. if somebody who is says "thats bunkum" I'd pay attention to it.

avalys• 6 months ago

No! What a disaster. Enforce the costs of externalities as close to the source as possible. If electricity costs more money charge more money for electricity. Don’t add some “regulation” to force end users to pay more based on your estimate of how much more you think electricity should cost.

ggm6• 6 months ago

I said force the cost to the surface. Not set a fixed rate of return: I want the accounting on the build and supply cost at large, as well as the KW charges to be accounted for.

scottcha• 6 months ago

That is a pretty good article although the one factor not mentioned that we see that has a huge impact on energy is batch size but that would be hard to estimate with the data he has.

We've only launched to friends and family but I'll share this here since its relevant: we have a service which actually optimizes and measures the energy of your AI use: https://portal.neuralwatt.com if you want to check it out. We also have a tools repo we put together that shows some demonstrations of surfacing energy metadata in to your tools: https://github.com/neuralwatt/neuralwatt-tools/

Our underlying technology is really about OS level energy optimization and datacenter grid flexibility so if you are on the pay by KWHr plan you get additional value as we continue to roll new optimizations out.

DM me with your email and I'd be happy to add some additional credits to you.

ccgibson• 6 months ago

To add a bit more to what @scottcha is saying: overall GPU load has a fairly significant impact on the energy per result. Energy per result is inversely related, since the idle TDP of these servers is significant the more the energy gets spread the more efficient the system becomes. I imagine Anthropic is able to harness that efficiency since I imagine their servers are far from idle :)

Majromax3• 6 months ago

You can infer the discount from the pricing of the batch API, which is presumably arranged for minimum inference costs. Anthropic offers a 50% discount there, which is consistent with other model providers.

nospice2• 6 months ago

I'm not sure I like this method of accounting for it. The critics of LLMs tend to conflate the costs of training LLMs with the cost of generation. But this makes the opposite error: it pretends that training isn't happening as a consequence of consumer demand. There are enormous resources poured into it on an ongoing basis, so it feels like it needs to be amortized on top of the per-token generation costs.

At some point, we might end up in a steady state where the models are as good as they can be and the training arms race is over, but we're not there yet.

TSiege2• 6 months ago

The challenge with no longer developing new models is making sure your model is up to date which as of today requires an entire training run. Maybe they can do that less or they’ll come up with a way to update a model after it’s trained. Maybe we’ll move onto something other than LLMs

cortesoft• 6 months ago

It would be really hard to properly account for the training, since that won't scale with more generation.

The training is already done when you make a generative query. No matter how many consumers there are, the cost for training is fixed.

nospice2• 6 months ago

My point is that it isn't, not really. Usage begets more training, and this will likely continue for many years. So it's not a vanishing fixed cost, but pretty much just an ongoing expenditure associated with LLMs.

Aurornis3• 6 months ago

That's not really an error, that's a fundamental feature of unit economics.

Fixed costs can't be rolled into the unit economics because the divisor is continually growing. The marginal costs of each incremental token/query don't depend on the training cost.

AndrewDucker• 6 months ago

You can absolutely have a stab at it. Estimate how long models last for, amortise over that time/number of calls. We've seen enough models go out of fashion for that to be reasonably done.

skybrian• 6 months ago

The training cost is a sunk cost for the current LLM, and unknown for the next-generation LLM. Seems like it would be useful information but doesn't go here?

robocat• 6 months ago

The AI training data sets are also expensive... The cost is especially hard to estimate for data sets that are internal to businesses like Google. Especially if the model needs to be refreshed to deal with recent data.

I presume historical internal datasets remain high value, since they might be cleaner (no slop) or maybe unavailable (copyright takedowns) and companies are getting better at hiding their data from spidering.

mikeaskew4• 6 months ago

I have a kids and a dishwasher (which with kids, runs quite often) but I’m not convinced I’m doing worse at energy consumption

bramhaag• 6 months ago

Only tangentially related, but today I found a repo that appears to be developed using AI assistance, and the costs for running the agents are reported in the PRs. For example, 50 USD to remove some code: https://github.com/coder/mux/pull/1658

RestartKernel• 6 months ago

That seems reasonable compared to an actual developer (depending on your region), but I had hoped for these models to make simple tasks like this fast and cheap so those developers can focus on the difficult stuff.

xmcqdpt22• 6 months ago

Yes in practice I've done fairly simple tasks using AMP at work and ended up with bills of 100-150$ for them which is somewhat less (but similar order of magnitude) as contracting out to LCoL countries. Depending on how the technology (towards cheaper cost) and financial situation (towards profitability and higher costs) evolve, it wouldn't be shocking to me that the frontier model end up more expensive than human contractors.

msephton• 6 months ago

I'd like to suggest that dev lower their settings: sota model + high + thinking is definitely not needed to do this simple task. Lower settings could easily do it for less than $0.50, maybe even $0.05. I'd encourage people to operate on average to low settings and wind then up or down depending on the prompt task complexity.

samusiam• 6 months ago

Also helps to use big models for planning, small for implementation.

AnotherGoodName• 6 months ago

This is the cost of Anthropics pay by the token plan.

To give an analogy, Anthropics pricing is $0.10 per grain of rice (pay by the token) or;

$20 a month for quarter cup of rice each day (claude pro)

$100 a month for 10 cups of rice each day (claude max 100)

$200 a month for a sack of rice delivered to your door each day.

It's a rather insane pricing scale and here they are paying $50 there because they don't understand the pricing model (which is fair, Anthropics pricing model is crazy). Never pay by the token with Antropic! Only ever use the subscription plans.

xmcqdpt22• 6 months ago

At work we pay per token because we use a third party tool (amp) and it is very pricy. OTOH the subscription model is obviously not profitable. It's priced to capture market share, like Uber in 2010. (TBH the token pricing is probably not profitable either.)

From the point of view of a large entreprise, it makes more sense to pay a third party that can itself swap out Claude for Gemini (for example) based on pricing, then to buy subscriptions to one specific tool that is likely to suddenly cost 10x as much when they run out of VC funds. The dynamic is going to be different for individuals and small companies.

ammario• 6 months ago

Lol, this is my PR. That cost is misleading. That workspace did far more than that change. In reality I spend ~$1000/week in tokens for all of my development work, and I'm quite happy with the exchange.

maxdo• 6 months ago

Us person does not consume 1600 liters a day

ipnon• 6 months ago

I think this number is derived from the water used to create all the goods they consume. Let's say every American eats 1lb of beef per day, and nothing else, to create a simple model. Then every American has a water footprint of about 1,850 gallons.

[0] https://watercalculator.org/news/articles/beef-king-big-wate...

simonw6• 6 months ago

What number would you provide for that?

NicuCalcea• 6 months ago

It does seem high, most of the estimates I find are around half that.

sowbug• 6 months ago

The almonds that an average person eats in the US account for about 70 liters of water daily. That's already 5% of 1,600, and more than a typical shower.

matthewfcarlson• 6 months ago

I would like some real world comparisons. How much power does the laptop or desktop consume during these (likely multi hour) sessions? Assuming you’re using a large HDR monitor 50-100W isn’t unreasonable and at 8 hours a day you’re talking about at least 2 days before you crack 1000kwh like his sessions do. But then a personal desktop on a gaming session can easily pull 1000w (cpu + gpu + peripherals). So comparing it to a gaming session seems fair.

xk3• 6 months ago

So less energy than a human brain uses...

weiliddat• 6 months ago

How did you arrive at that calculation?

For an average day (incl. "non-working" hours) the brain uses far less at ~300Wh, and if you include the body the average person needs ~2.3 kWh.

In the rest of the article, they estimate inference electricity use is only ~8% of overall datacenter use, and if we think of the datacenter as the "body" in which the GPU / brain wouldn't work without, that's an overall median use of ~16 kWh for only 24 Claude Code requests.

I'm more impressed with the human brain's energy efficiency + multimodality + long term context + malleability than anything after using LLMs a bunch, even though I learnt a lot about that in a neuropsych course long time ago.

throwerxyz2• 6 months ago

"How much energy does it take to eat meat? (ignoring the cost to produce the meat into your hands)"

Do people even care about this?

How much energy does it take to download a video on YouTube versus the energy input to keep it all setup and running?

none2585• 6 months ago

Re: eating meat, plenty of people opt out of eating meat (specifically beef) because of its environmental cost.

throwerxyz2• 6 months ago

I don't know what that has to do with my comment.

thestructuralme• 6 months ago

A lot of the scary numbers come from agents being left in “always-on” loops: long context windows, tool calls, retries, and idle GPU time between steps. The right unit isn’t “watts per agent” but something like joules per accepted change (or per useful decision), because an agent that burns 10x energy but replaces 20 minutes of human iteration can still be a net win. What I’d love to see is a breakdown by (1) model/token cost, (2) orchestration overhead (retries, evaluation, tool latency), and (3) utilization (how much time the GPU is actually doing work vs waiting). That’s where the real waste usually hides.

Electricity use of AI coding agents

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