On the Environment — Advanced Old School Revival

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I’m asking the same question you are here: Surely there are negative externalities to this miraculous technology.

The environment? The legal rights of creators? The fate of creativity itself in the face of automation?

There are externalities, and we must be cognizant of them.

Central among them: What happens when the “end result of our creative vision” is also the process itself?

Let’s set aside movies for a moment, because videos have lots of moving parts we’d be happy to automate away, and talk purely about the most hot-button issue in most creative circles: Isn’t the work of generating an image the creative process itself? If we’re bypassing that entirely, then what can be said to be creative about the end result at all?

These are the sort of questions I want to address in this Statement of Use, because there is no single, straightforward answer.

If you’ve read this far, then you’re interested in grappling with these questions too. And I thank you for that, because at least it means we can come to understand each other, even if we don’t ultimately agree on the answers.

In this section, I’d like to talk about the negative externalities of genAI and common misconceptions about them.

Local Nuance vs. Media Exaggeration

A big part of the problem in discussions about the impact of AI is a lack of nuance and specificity in the discussion.

I want to get into that, so we're not just tossing statistics and metaphors into a vacuum. We see all these apocalyptic headlines about data centers in the news every day causing hysteria and confusion, therefore I want to address what I understand to be the real environmental concerns surrounding AI, versus what the media crafts these sensational headlines around.

First, On Specificity...

When we're discussing the impact of "AI" on the environment, usually media headlines are referring to data centers. In 2025, the data center count was around 12,000+ worldwide, and about 5,400 of those are located in the USA. The normal-sized kind have campuses that occupy dozens to a hundred acres; there's also hyperscale data centers—the gigantic ones we hear about in the news—that are hundreds to a thousand acres in size. There are about 1,189 of these as of Q1 2025.

But we can also mean "AI" as in, the entire upstream chain of manufacturing that results in a data center: all the components that go into the building and its equipment (the chips, the infrastructure, all the other mechanics involved in its construction). It's hard to talk about this entire upstream chain meaningfully, because if we're going to assess the impact of AI in this sense, then we must assess the impact of the upstream effect of all other industries by comparison. And that's not exactly a straightforward (or fair) thing to do, as Masley explains in his article, because we simply can't quantify those things in any definitive way.

So for specificity's sake, I'm sticking with the impact of data centers in particular, especially because this is what the media is most concerned with.

The Nuance

When it comes to data centers, then, these are the underlying numbers:

Data centers use 1.5% of global energy (or in the USA: about 4%);
Data centers contribute about 0.6—1% of CO₂ emissions globally;
Water use inside data centers in the USA is about 0.04%. (Globally, this may be closer to ~0.1%, if we expand "water use" to mean any AI-driven water withdrawal by projection into 2027.)

So on a global scale, then, the impact is marginal, even if you take into account the worst projections (which suggest a doubling of worldwide energy usage). The headlines that describe an existential crisis on Earth caused by data centers seem to be an exaggeration.

Training vs. Inference: Usage Statistics

Another good place to start is this article, which goes into painstaking detail about how marginally impactful the use of LLMs and image generation is on the environment as compared to virtually all the other truly environment-destroying activities we engage in as a human species. In this article, Masley demonstrates that even if you amortize the training cost into the inference cost, the impact of this aggregate cost on the environment is orders of magnitude less than that of most other human activities, such as making cellphones; flying planes or driving cars; cement and steel production or golfing; using microwaves, AC units, fridges, or coffeemakers; eating hamburgers; and taking a shower.

It’s hard to emphasize here what is meant by “orders of magnitude,” especially for someone like me who has degrees in writing and can barely add two numbers together. The examples in this article focus on ChatGPT, but his numbers take into account image generation as well, derived from dozens of sources you can verify yourself.

Here are some fun facts from that article that are not too math-oriented, but you can read the article in full to find the math these analogies spin out from:

Being “mindful” with your chatbot usage is kind of like filling a large pot of water to boil to make food, and before boiling it, taking a pipet and removing tiny drops of the water from the pot at a time to “only use the water you need” or stopping your shower a tenth of a second early for the sake of the climate.

Deciding that you’re going to stop using AI for the sake of the climate is like going around your home and randomly unscrewing a single LED bulb, or pausing your microwave a few seconds early to save the planet. It’s so small that it’s a meaningless distraction.

If you were running ChatGPT’s servers in your home, to raise your energy bill by one dollar, you would need to send 19,600 prompts. One prompt every single second for five hours.

If I choose not to take a flight to Europe, I save ten million ChatGPT prompts. This is like stopping more than 100 people from searching ChatGPT for their entire lives. Preventing ChatGPT prompts is a hopelessly useless lever for the climate movement to try to pull. We have so many tools at our disposal to make the climate better. Why make everyone feel guilt over something that won’t have any impact?

Printing a physical book uses 5,000 Wh, so even just sitting down and reading a book you bought for six hours (using 833 Wh per hour) is going to use more energy per minute than ChatGPT, unless you prompt ChatGPT 1,000 times per hour, or once every three seconds for a full hour. Switching to using ChatGPT from another activity is almost always going to decrease the total energy I use every day. This isn’t an argument that you should only use ChatGPT!

If you want to send 2,500 ChatGPT prompts and feel bad about it, you can simply not buy a single additional piece of paper. If you want to save a lifetime supply’s worth of chatbot prompts, just don’t buy a single additional pair of jeans.

A digital clock uses one million times more power (1W) than an analog watch (1µW). “Using a digital clock instead of a watch is one million times as harmful to the climate” is correct, but misleading. The energy digital clocks use rounds to zero compared to travel, food, and heat and air conditioning. Climate guilt about digital clocks would be misplaced. The relationship between Google and ChatGPT is similar to watches and clocks. One uses more energy than the other, but both round to zero.

Many people have trouble visualizing the aggregate results of doing everyday activities. If there were a single national microwave, it would use as much energy every day as Seattle. Data centers make this aggregate energy use visible, but the aggregate energy of most other ways we spend our time are invisible. Data centers only look like they’re using way more energy because we can’t directly see all the energy of other things we do gathered together into specific buildings […] All data centers worldwide emitted about 0.5% of the world’s annual emissions … by 2030, all data centers worldwide are projected to consume 8% of the water currently consumed by the [entire] US golf industry [3% as of 2023] or 8% of the US steel production or 1% of America's total irrigated corn.

There is nowhere in America where data center operational use of water has increased household water prices at all.

A recent popular blog post was titled “Why Saying ‘AI Uses the Energy of Nine Seconds of Television’ is Like Spraying Dispersant Over an Oil Slick.” The author’s main point is that each individual AI prompt is able to use so little energy only because of this vast and expanding background buildout of AI infrastructure, so just reporting (as I do) that an AI prompt only uses as much energy as a few seconds of a microwave is hiding the more ominous reason why it’s able to be so cheap in the first place. By using AI, you’re complicit in some way in that infrastructure buildout. This criticism would make more sense to me if everything else in society didn’t also have a vast sprawling physical infrastructure supporting it. “9 seconds of TV” has huge networks of electronics systems supporting it, as well as crazy amounts of money and people-hours going into making the most entertaining TV, lavish (often wasteful) lifestyles enabled by the profits from TV. Obviously, TV advertising also encourages people to buy more stuff from other complex supply chains.

The Real Dangers of AI for the Environment

Here's where the nuance comes in.

Data centers can be bad for the environment locally.

That is, while they may have a negligible impact in the aggregate, they can have an outsize impact on the local communities in which they are built.

They can use up farmland and forested areas, which disrupts biodiversity and weakens rural economies;
Some use gas turbines or diesel backup generators as emergency power, which can contaminate the air and cause health issues in local populations; these generators can reach 85 - 100 decibels, which is equivalent to motorcycle or jackhammer; their initial construction crews can be disruptive to nearby communities, due to all the coordination involved in creating such huge campuses;
They may raise electricity costs up to 25% in areas that have to lean on fossil fuels to make up for the increased electricity demand (one report suggests this could increase electricity costs by 8% nationally, if you also factor in cryptocurrency usage);
They can stress drought-prone locations where water is already scarce;
They have been disproportionally sited in low income and minority areas;
And they tend to pay out less than what they take in, because the net gain from data center sales and property taxes is diminished by the generous tax abatements local governments give to operators in order to get them built in the first place. (To add insult to injury, the majority of the new jobs they generate are temporary, because they're largely tied to their construction.)

What this all boils down to is a struggle between local government and the megacorporations that build these campuses because the former lacks the resources to regulate the latter. And so the local danger that data center construction poses comes from a lack of policy.

While these dangers are real, they're caused by how data centers are sited—we shouldn't be building them in drought-prone areas in the first place!—and a lack of transparency from their operators, regarding local energy and water use projections. Combatting these dangers begins with regulatory oversight, such as bans or restrictions on the use of fossil fuels, and a requirement that they commit to 100% renewables in their operations.

So is AI Bad for the Environment?

Obviously, I'm not an environmental scientist. As I said above, I can barely add two numbers together. But I don't think it's a logical fallacy to base my opinions of environmental subjects on the research of environmental scientists and other researchers who are experts in the subject. We engage in this kind of reasoning every day, on every subject we are not experts on. History may prove that Masley and all the sources he cites and all the sources I've cited here (which are written by people credentialed in the subject matter) are wrong. And in that case, I would revise my opinion to reflect reality.

But currently, if the above is the actual reality of genAI’s impact on the environment, then I hope you can see how I’m not moved by the claim “AI is bad for the environment.”

Not AI itself.

If anything, it would seem to me that bad policy is bad for the environment, with aggravated adverse effects locally. That megacorporations are parasitic opportunists who will exploit anyone lower on the food chain to pad profit margins (as has always been the case in the history of capitalism). That data centers are not an existential environmental threat but serious industrial infrastructure that needs utility usage transparency, strict permitting, clean-power requirements, local water safeguards, noise control, community input, and enforceable obligations (read: taxes, lots of them) to mitigate local harms, like any other industrial infrastructure.

And while this does not apply to my use of genAI in developing OSR+, it’s worth noting that genAI also has the potential to help us solve some of the very environmental and infrastructure problems that data centers create, if we continue to develop more efficient chips, cooling technologies, and software that define better ways to adapt to global warming.