If a government or major corporation wants to get serious about mitigating or reversing anthropogenic climate change, it should consider stopping research and development on generative “AI.” Think about it:

• A ChatGPT query requires roughly 5x the electricity of a normal Google search — that is, a Google search from the days before Google’s “AI Overview” itself 5xed the energy use of every single Google search you perform. If this is the case for a purely text-based response, I don’t even want to know how much energy an image or video generator like DALL-E — or a “music” generator like Sora — requires per query.
• Furthermore, LLM chatbots — due to both their design and their inefficiency in completing the tasks for which they are designed — invite far more queries per day than your typical search engine. The teenagers who spend hours a day chatting with their LLM “therapists” or “AI girlfriends” may be the high tail of the bell curve, but there is no doubt that they are living right into the technology’s basic affordance. I challenge anyone to find a large number of people who literally spend all day Googling things (well, okay, actually, I do know at least one person like this). OpenAI wants everyone to use GPT-4o for everything, 24/7.
• To keep up with this insane and constantly-escalating scale of usage (think also of how every shopping website is suddenly integrating an AI-powered helper chatbot!), new data centers are being constructed every single day. Worldwide data center energy use probably doubled between 2022 and 2023, probably due in substantial part to the impact of rolling out generative AI tools, and seems on pace to keep doubling annually (in a sickening update to Moore’s Law).
• Note also that server farms and data centers are frequently built in regions like the American West which have plenty of available land but scarce water resources. In energy economics, there is a well-known tradeoff known as the “water-energy nexus:” the more water you are able to use (mostly for cooling), the less energy you have to use, and vice versa. In other words, there is ecologically no such thing as a free lunch: degrade your local ecosystem through water use, or pump carbon dioxide and other more noxious pollutants into the air somewhere else. Furthermore, by all accounts data centers significantly degrade quality of life and health outcomes (through, fascinatingly, steady noise pollution in both audible and inaudible frequency bands) for the people who are misfortunate enough to live near them.
• The easiest technologies to eliminate from the economy are, by definition, the ones which have not been integrated into the economy yet. Unlike the automobile, industrial agriculture, air travel, and the other technological revolutions to which we are constantly hearing “AI” compared, the “AI revolution” has not happened yet. It is incredibly unpopular — the American public views “AI” with something like 70% unfavorability the last time I looked into it, which is more unpopular than Donald Trump was at any point during his first term in office — and consistently becomes more unpopular as people learn more about it and have more experience with it. So why not simply say, with Bartleby the Scrivener, “I would prefer not to”? If you want to be serious about climate change, ban AI.

This, of course, will not actually happen. For one thing, it might not be legal (and certainly would not be legally practical) for, say, the US government to ban generative “AI” development. For another, all the incentive structures are aligned against it. To simply “not develop AI” is, clearly, a step that no currently existing tech company (and many not-yet-existing tech companies as well) is willing to countenance, for fear that they will be left behind by their AI-developing competitors — a classic race-to-the-bottom collective action problem. The incoming administration is filled with unapologetic cryptocurrency boosters (another infamously environmentally degradatory technology). And I should pause to say that I don’t quite wish to launch a Butlerian Jihad against all “AI” tools — I am very optimistic, for instance, about the improvements to weather forecasting which the new AI-based models seem to provide when used in conjunction with traditional computational physics-based models, and if AI tools can effectively replace human content moderators to keep porn off social media, all the better.

It’s also true that ending “AI” development would not come anywhere close to reversing anthropogenic climate change. Automobiles, industrial agriculture, and air travel are far larger contributors still to the problem, and there is no good replacement for fossil fuels in these domains (electric car boosters to the contrary). It is impossible to avoid the truism that if you want 18th-century emissions, you need an 18th-century lifestyle. Nobody in the 21st century is going to voluntarily revert to an 18th century lifestyle. What we need, rather, is a massive and non-fossil fuel source of energy that could not only, say, power AI, but also make planetary-scale carbon capture & storage economically viable. No solar or wind power technology is capable of providing this, for reasons of basic physics, and the ecological costs of resource extraction to make solar panels and their battery packs are so significant that it is not clear to me a solar panel will ever, environmentally speaking, “pay for itself” in emissions reductions. Hydropower sounds great if you have a massive river nearby (not the case everywhere!), but every time we check in on the maintenance requirements and ecological impacts of dams, the answer gets worse and worse. That is why I consider it enormously telling that AI developers such as Microsoft, recognizing that the new product they are shoving down all our throats requires an astounding quantity of energy which the current American grid is simply not ready to provide, are making quiet but massive investments in the future of nuclear energy.

The real proposal, then, might actually turn out to be: anthropogenic climate change, widespread generative “AI”, new nuclear energy — pick two.

Thesis: The appearance of effortless inhumanity is practically always dependent on the sacrifice or exploitation of hidden persons.

Why, then, the unscientific scorn for practical knowledge? There are at least three reasons for it, as far as I can tell. The first is the “professional” reason mentioned earlier: the more the cultivator knows, the less the importance of the specialist and his institutions. The second is the simple reflex of high modernism: namely, a contempt for history and past knowledge. As the scientist is always associated with the modern and the indigenous cultivator with the past that modernism will banish, the scientist feels that he or she has little to learn from that quarter. The third reason is that practical knowledge is represented and codified in a form uncongenial to scientific agriculture. From a narrow scientific view, nothing is known until and unless it is proven in a tightly controlled experiment. Knowledge that arrives in any form other than through the techniques and instruments of formal scientific procedure does not deserve to be taken seriously. The imperial pretense of scientific modernism admits knowledge only if it arrives through the aperture that the experimental method has constructed for its admission. Traditional practices, codified as they are in practice and in folk sayings, are seen presumptively as not meriting attention, let alone verification. And yet, as we have seen, cultivators have devised and perfected a host of techniques that do work, producing desirable results in crop production, pest control, soil preservation, and so forth. By constantly observing the results of their field experiments and retaining those methods that succeed, the farmers have discovered and refined practices that work, without knowing the precise chemical or physical reasons why they work. In agriculture, as in many other fields, “practice has long preceded theory.” And indeed some of these practically successful techniques, which involve a large number of simultaneously interacting variables, may never be fully understood by the techniques of science.

— James C. Scott, Seeing Like a State: Why Certain Schemes to Improve the Human Condition Have Failed, 305–06

Why you should not use ChatGPT, large language models, or other “artificial intelligence” (falsely so-called) tools in your research or work, for any of the synthetic tasks (summaries of data or information, etc.) for which it is proposed as a helpful time-saver:

1. The process of pattern recognition and synthetic integration is the basis of how human beings come to know and understand the world.
2. This process is an inextricably bodily process in humans. (This is true of human cognition in general: the whole of the human body, not just the brain, is involved in every act of thought — and in fact other bodies are involved, too, because thought is an intersubjective process. But I digress.)
3. ChatGPT and similar tools are, however, definitionally disembodied. Even if they are in fact “just pattern-recognition machines” (dubious), by virtue of being disembodied their pattern “recognition” is not the same as the real thing in humans.
4. In fact, insofar as ChatGPT exists in the physical world, it is under a very different sort of embodiment — a non-organic sort — which is antithetical to the human sort.
5. Therefore, ChatGPT and so forth cannot be trusted to faithfully simulate human knowing — and if the mechanism cannot be trusted neither can the results.
6. Additionally, by using such a tool, a human being forgoes the opportunity to practice and experience such knowing, kneecapping his or her capacity to learn from the experience.

In a nutshell: the promise of LLMs is “impersonal knowledge” — but no such thing exists. Relying on it is thus, in a meaningful sense, worse than nothing.