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.
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
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.
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.
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.
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.
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 :)
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.
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.
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.
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.
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.
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]).
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.
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
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.
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
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.
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.
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.
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.
I remain confident that most AI labs are not selling API access for less than it costs to serve the models.
If that's so common then what's your theory as to why Anthropic aren't price competitive with GPT-5.2?
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.
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 :)
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.
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.
> 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.
Sure. By "constant" there I meant it doesn't change depending on the number of people who use the model.
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.
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.
Not to mention doesn't aviation fuel still make heavy (heh) use of lead?
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