If you all flag this article, dang will probably get around to fixing it.
It's like if I said I was concerned about factory farming impacts and you showed me a video of meat packaging at a grocery store, claiming it alleviates my concerns.
> Training GPT-4 used 50 GWh of energy. Like the 20,000 households point, this number looks ridiculously large if you don’t consider how many people are using ChatGPT.
> Since GPT-4 was trained, it has answered (at minimum) about 200,000,000 prompts per day for about 700 days. Dividing 50GWh by the total prompts, this gives us 0.3Wh per prompt. This means that, at most, including the cost of training raises the energy cost per prompt by 10%, from 10 Google searches to 11. Training doesn’t add much to ChatGPT’s energy cost.
https://andymasley.substack.com/i/162196004/training-an-ai-m...
If you're able to serve delicious pizzas afterwards, it was worth wasting the first kg (you might call it an investment).
If you're able to bring value to millions of users, it was worth to invest a few GWh into training.
You might disagree on the usefulness. I think, you shouldn't have wasted a kg of flour because I won't ever eat your pizzas anyway. But many (you, your guests, ChatGPT users) might think it was worth it.
If you use 300 lbs of flour to learn, and 300 lbs of flour to make 300 pizzas, then the total flour cost is 2 lbs of flour per pizza.
If you instead went on to produce millions of pizzas for people and 30,000lb of flour, that 300lb you used to learn looks like a pretty reasonable investment.
I mean, I guess advances could plateau and we stop spending exponentially more energy year after year...
I'm not opining on whether it's a good idea (I doubt we ever voluntarily consume less as a species), but data centres use a lot of energy and billions are being spent building them. https://www.technologyreview.com/2024/09/26/1104516/three-mi...
https://washingtonstatestandard.com/2025/04/11/as-demand-for...
To make it worse, the model training cost only refers to the cost of the training itself. The externalities - everyone else being forced to drastically upscale their compute power because scraper blocking isn't foolproof - are, as usual for hypercapitalism, conveniently ignored.
AI training in its current form is unsustainable, I'd go as far to say it's a threat for the open and decentralized Internet as you have all but zero chance of standing alone against the flood of training scraper bots and more and more control gets ceded to Cloudflare et al.
Human brain: Uses about 20 watts of power.
ChatGPT (GPT-4): Running a single query can use hundreds of watts when accounting for the entire datacenter infrastructure (some estimates suggest 500–1000 watts per query on average, depending on model size and setup).
If we assume:
20 watts for the human brain thinking continuously,
1000 watts for ChatGPT processing one complex query,
then the human brain is about 50x more energy-efficient (or 5000% more efficient) than ChatGPT per task, assuming equal cognitive complexity (which is debatable, but good for ballpark comparison).
Let’s define a representative task: answering a moderately complex question.
1. Human Brain Power use: ~20 watts (on average while awake)
Time to think: ~10 seconds to answer a question
Energy used: 20
watts × 10
seconds = 200
joules 20watts×10seconds=200joules
2. ChatGPT (GPT-4) Estimate per query: Based on research and datacenter estimates, GPT-4 may use:
Around 2–3 kWh per 1000 queries, which is 7.2–10.8 megajoules
Per query: 7.2
MJ 1000 = 7200
joules 1000 7.2MJ
=7200joules per response (lower bound) 10.8
MJ 1000 = 10 , 800
joules 1000 10.8MJ
=10,800joules per response (upper bound)
Comparison Human: ~200 joules
ChatGPT: ~7,200 to 10,800 joules
Conclusion: The human brain is about 36–54 times more energy-efficient than ChatGPT at answering a single question.
Or in percent: 3,600% to 5,400% more efficient
The brain may ultimately be more power-efficient, but the units you want are watt-hours.
It's fairly popular to claim that you as an individual have no significant effect on the environment and that it's the actions of the large companies, which are effectively "super polluters." This ignores that companies take these actions because of the market forces imposed on them by the consumers.
An individuals impact in isolation is small, however, if that same individual made changes not only in their own life, but urged those around them to make similar changes, the network effect would be huge. This extends beyond the environment: boycotts, product recommendations, exercise, etc. You truly need to be the change that you want to see.
Come on…
If we are to believe that the models will get bigger, use more tokens, work for longer, this calculation can easily become very very skewed in the other direction.
Consider an agentic system that runs continuously for 6 hours. It is possible this system processes billions of tokens. That could more than equal a transatlantic flight in this hypothetical world.
Now compare this with non-AI work, like a CRUD app. Serving millions of queries in that same period would consume a tiny fraction of what ChatGPT consumes.
Rather than being a “win” for AI, the fact that we’re even 3 or 4 orders of magnitude away from this being a problem means that its already grounds to be concerned.
I've been unsatisfied with how people in tech address that complex subject so I wrote about it here: https://www.macchaffee.com/blog/2025/tech-and-the-climate-cr...
You can try explaining why it's not "that" bad for the environment, the planet is still worse off than when it didn't exist.
Let's carry on inventing new ways to spend energy, but it's ok because we still spend more energy for other stuff.
It's kinda sad how the world saw climate change, said it was bad, but in the end decided to do nothing about it.
The original link [1] cites a discussion of the cost per query of GPT-4o at 0.3whr [2]. When you read the document [2] itself you see 0.3whr is a lower bound & 40whr is the upper bound. The paper [2] is actually pretty solid, I recommend it. It uses the public metrics from other LLM APIs to derive a likely distribution of the context size of the average query for GPT-4o which is a reasonable approach given that data isn't public. Then factoring in GPU power per FLOP, average utilization during, and cloud/renting overhead. It admits this likely has non-trivial error bars, concluding the average is between 1-4whr per query.
This is disappointing to me as the original link [1] attempts to bring in this source [2] to disprove the 3whr "myth" created by another paper [3], yet this 3whr figure lies directly in the error bars their new source [2] arrives at.
Links:
1. https://simonwillison.net/2025/Apr/29/chatgpt-is-not-bad-for...
2. https://epoch.ai/gradient-updates/how-much-energy-does-chatg...
3. https://www.sciencedirect.com/science/article/pii/S254243512...
Edit: whr not w/hr
Thus the results inherently fail to analyze the underlying question.
A more realistic estimate is to take their total spending assuming X% of their expenses are electricity directly or indirectly because the environmental impact isn’t adds up. Even that ignores the energy costs on 3rd party servers when they download their training data.
> ...Globally, coal, followed by gas, is the largest source of electricity production....
As long as this is the case we can hardly even the debate of the impacts of those new techs on the sole topic of the climate.
Let me remind you kindly we well passed the point of this single problem, we are dealing with planetary boundaries, there is 9 of them. Another reminder is that co2 pollution alone is the direct product of the GDP, there is no update in sight about how the competing countries should deal with shared homothetic GDP cuts to reduce the gaz emissions. so even we would do something, we have not started to get to the serious business.
Why AI ? Because we are screwed. We failed on humanism, we failed on climate, we cant failed that one, we would just kick ourself out of the real game.
this is a kind of a great megalomaniac idea, but i prefer that to your pathetic bullshit. so even though you are fucking cringe, go elon,
Fire in the hole !
etchalon•9mo ago
Remnant44•9mo ago
spencerflem•9mo ago
Remnant44•9mo ago
spencerflem•9mo ago
simonw•9mo ago
Training a single LLM takes a few dozen fully loaded transatlantic passenger aircraft trips worth of power.
For "counties worth of power" I think you might be talking ALL data center use as a whole.
TobTobXX•9mo ago
> Training GPT-4 used 50 GWh of energy. Like the 20,000 households point, this number looks ridiculously large if you don’t consider how many people are using ChatGPT.
> Since GPT-4 was trained, it has answered (at minimum) about 200,000,000 prompts per day for about 700 days. Dividing 50GWh by the total prompts, this gives us 0.3Wh per prompt. This means that, at most, including the cost of training raises the energy cost per prompt by 10%, from 10 Google searches to 11. Training doesn’t add much to ChatGPT’s energy cost.
https://andymasley.substack.com/i/162196004/training-an-ai-m...
JohnKemeny•9mo ago
Those are 200M/d prompts that wouldn't happen without the training.
warkdarrior•9mo ago
TobTobXX•9mo ago
A bus emits more CO2 than a car. Yet it is more friendly to the environment because it transports more people.
> Those are 200M/d prompts that wouldn't happen without the training.
Sure, but at least a few millions are deriving value from it. We know this because they pay. So this value wouldn't have been generated without the investment. That's how economics work.