A cheat sheet for why using ChatGPT is not bad for the environment

https://simonwillison.net/2025/Apr/29/chatgpt-is-not-bad-for-the-environment/

by edward • 1 year ago

50 50 1 year ago

etchalon• 1 year ago

The "cheat sheet" seems to address the environmental impact of using ChatGPT, not the environmental impact of training the model.

Remnant443• 1 year ago

By the same token, even if you accept that AI usage will incentivize additonal model trainings, that cost is diffused across hundreds of millions of users, and is not a marginal cost, so it gets further reduced on a per-chat basis the more you use AI. I don't know what the per-user environmental cost of training a model is, but that's a pretty big factor to divide energy usage by.

spencerflem2• 1 year ago

But irrelevant if the point is to not contribute to something that you'd rather see banned.

TobTobXX5• 1 year ago

Wrong. In the article:

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• 1 year ago

Just because you divide a number by a lot to get a small number doesn't make the original number smaller.

Those are 200M/d prompts that wouldn't happen without the training.

otterley• 1 year ago

Can you please link to the primary source material? https://andymasley.substack.com/p/a-cheat-sheet-for-conversa...

If you all flag this article, dang will probably get around to fixing it.

ChrisArchitect• 1 year ago

https://news.ycombinator.com/item?id=43833547

cwillu2• 1 year ago

Email hn@ycombinator.com, and it'll be fixed immediately.

85392_school• 1 year ago

Which itself is a summarized version of https://andymasley.substack.com/p/individual-ai-use-is-not-b... (discussed at https://news.ycombinator.com/item?id=42745847)

simonw2• 1 year ago

+1 to that, my summary here doesn't really add anything new.

devmor3• 1 year ago

Like every other "rebuttal" to this argument, this chooses to pretend that the complaint is about the power usage of making API calls, instead of the power usage of training models.

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.

TobTobXX5• 1 year ago

From the article:

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...

devmor3• 1 year ago

See my other comment here. One AI training run does not exist in a vacuum. Do you think they built billions of dollars in datacenters full of computer power just to let it sit idle?

spcebar• 1 year ago

How does people using it offset the amount of energy used to train it? If I use three hundred pounds of flour learning to make pizza, the subsequent three hundred pounds of flour I use making delicious pizzas doesn't make the first 300 go away. Am I misunderstanding the numbers?

RobinL• 1 year ago

For context thats's about equivalent to 100 transatlantic flights

rapind• 1 year ago

To give you an idea of how many models are being trained, and how the energy costs continue to increase. https://epoch.ai/data/notable-ai-models

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...

megaman821• 1 year ago

That argument is just as bad. The training runs are equivilant to a few days to a couple of weeks of flights from New York to London. No one thinks we are going to save the environment by stopping a single plane route for 2 weeks.

devmor3• 1 year ago

One AI training run does not exist in a vacuum.

https://washingtonstatestandard.com/2025/04/11/as-demand-for...

mschuster91• 1 year ago

instead of the power usage of training models.

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.

roschdal2• 1 year ago

The human brain is dramatically more energy-efficient than AI models like ChatGPT.

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).

JustFinishedBSG• 1 year ago

Watt isn’t a measure of energy. Without how long it takes for a human and ChatGPT to solve the task then the comparison doesn’t teach anything

roschdal2• 1 year ago

You're absolutely right — watt is a unit of power, not energy. To make a meaningful comparison, we need to estimate how much energy (in joules) each system uses to solve the same task.

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

halyconWays• 1 year ago

There's massive evolutionary pressure for maximizing energy efficiency in brains. I'd like to see LLMs procreate and select for energy efficiency while, ideally, minimizing insanity and maintaining g-factor.

homebrewer• 1 year ago

You should then probably count the whole body, which consumes approximately 200 watts last time I checked.

ryanianian• 1 year ago

Watt is not a unit of energy but instead a unit of power. A brain may need 20 watts, but it may use 20 watts for a lot more time than ChatGPT would.

The brain may ultimately be more power-efficient, but the units you want are watt-hours.

TobTobXX5• 1 year ago

This is fascinating. I mean it's not an argument against LLMs (I have only one brain, even though I'd like to have more). But I really hope that we'll learn much more about how our brains work.

Legend2440• 1 year ago

The brain is more efficient because it physically is a neural network, as opposed to a software simulation of one.

krunck• 1 year ago

Isn't the real metric of concern the absolute amount of CO2 generated that will have an impact on the environment? That every person's AI queries contributes a small amount to the CO2 production doesn't make the sum of all CO2 production go away.

j_w• 1 year ago

This is not a critique of LLM usage, just on individual contributions to the environmental crisis.

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.

malvim• 1 year ago

And what about all the developing and testing of models? What about all the OTHER companies that can’t wait to get a piece of this cake and are training and scraping the internet like there’s no tomorrow? And all the companies that are integrating LLMs into their daily workflows using tons of api calls daily?

Come on…

aabhay• 1 year ago

I think the better argument is about the direction of change versus the current magnitude.

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.

mac-chaffee• 1 year ago

I agree but there's a lot of nuance to the next question of "well what IS bad for the environment" and tech's role in that question.

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...

reyqn• 1 year ago

So all of the cheatsheet is basically "it's not bad because there are worse things"?

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.

hugmynutus3• 1 year ago

I find this unconvincing. The actual discussion of LLM generation is very lacking.

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

cwillu2• 1 year ago

The unit is watt·hour, not watt/hour: multiplication, not division.

hugmynutus3• 1 year ago

Thanks!

Retric• 1 year ago

The methodology is inherently flawed by assuming all infrastructure, training, etc is going to exist with or without individual queries, while trying to answer a different question of the impact of AI on the environment. It’s like arguing the environmental impact of solar electricity is 0 because the panels would exist either way.

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.

hugmynutus3• 1 year ago

You are correct to point out the larger questions of supply chain cost (and their environmental impact) are not addressed in the root link.

amos-burton• 1 year ago

https://ourworldindata.org/electricity-mix

...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 !

hydragit• 1 year ago

What about the human cost? https://futurism.com/the-byte/ai-gig-slave-labor

A cheat sheet for why using ChatGPT is not bad for the environment

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