They work 24/7 at high speeds and get searingly hot - but data centre computer chips get plenty of pampering. Some of them basically live at the spa. We'll have fluid that comes up and [then] shower down, or trickle down, onto a component, says Jonathan Ballon, chief executive at liquid cooling firm Iceotope. Some things will get sprayed. In other cases, the industrious gizmos recline in circulating baths of fluid, which ferries away the heat they generate, enabling them to function at very high speeds, known as overclocking.

Several large tech firms that are active in AI have set goals to hit net zero by 2030, but a new forecast of the energy and water required to run large data centres shows they're unlikely to meet those targets As the AI industry rapidly expands, questions about the environmental impact of data centres are coming to the forefront - and a new forecast warns the industry is unlikely to meet net zero targets by 2030. Fengqi You at Cornell University in New York and his colleagues modelled how much energy, water and carbon today's leading AI servers could use by 2030, taking into account different growth scenarios and possible data centre locations within the United States. They combined projected chip supply, server power usage and cooling efficiency with state-by-state electrical grid data to conduct their analysis. While not every AI company has set a net zero target, some larger tech firms that are active in AI, such as Google, Microsoft and Meta have set goals with a deadline of 2030. "The rapid growth of AI computing is basically reshaping everything," says You. "We're trying to understand how, as a sector grows, what's going to be the impact?"

"People are often curious about how much energy a ChatGPT query uses," Sam Altman, the CEO of OpenAI, wrote in an aside in a long blog post last week. The average query, Altman wrote, uses 0.34 watt-hours of energy: "About what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes." For a company with 800 million weekly active users (and growing), the question of how much energy all these searches are using is becoming an increasingly pressing one. But experts say Altman's figure doesn't mean much without much more public context from OpenAI about how it arrived at this calculation--including the definition of what an "average" query is, whether or not it includes image generation, and whether or not Altman is including additional energy use, like from training AI models and cooling OpenAI's servers. As a result, Sasha Luccioni, the climate lead at AI company Hugging Face, doesn't put too much stock in Altman's number.

While AI could change the world in many unforeseen ways, it's already having one massive impact: a voracious consumption of energy. Generative AI does not simply float upon ephemeral intuition. Rather, it gathers strength via thousands of computers in data centers across the world, which operate constantly on full blast. In January, the International Energy Agency (IEA) forecast that global data center electricity demand will more than double from 2022 to 2026, with AI playing a major role in that increase. AI industry insiders say the world has plenty of energy capacity to absorb this increased demand, and that technological efficiency improvements could offset these increases.

One part of the reason is that big tech companies don't share the carbon footprint of training and using their massive models, and we don't have standardized ways of measuring the emissions AI is responsible for. And while we know training AI models is highly polluting, the emissions attributable to using AI have been a missing piece so far. I just published a story on new research that calculated the real carbon footprint of using generative AI models. Generating one image takes as much energy as fully charging your smartphone, according to the study from researchers at the AI startup Hugging Face and Carnegie Mellon University. This has big implications for the planet, because tech companies are integrating these powerful models into everything from online search to email, and they get used billions of times a day.

Their work, which is yet to be peer reviewed, shows that while training massive AI models is incredibly energy intensive, it's only one part of the puzzle. Most of their carbon footprint comes from their actual use. The study is the first time researchers have calculated the carbon emissions caused by using an AI model for different tasks, says Sasha Luccioni, an AI researcher at Hugging Face who led the work. She hopes understanding these emissions could help us make informed decisions about how to use AI in a more planet-friendly way. Luccioni and her team looked at the emissions associated with 10 popular AI tasks on the Hugging Face platform, such as question answering, text generation, image classification, captioning, and image generation.

In Facebook's youth, most of the website was powered out of a single building in Prineville, Oregon. That data center, holding row upon row of refrigerator-size racks of servers filled with rows of silicon chips, consumed huge amounts of electricity, outstripping the yearly power usage of more than 6,000 American homes. One day in the summer of 2011, as reported in The Register, a Facebook exec received an alarming call: "There's a cloud in the data center … inside." Following an equipment malfunction, the building had become so hot and humid from all the electricity that actual rain, from a literal cloud, briefly drenched the digital one. Now Facebook, or rather Meta, operates well more than a dozen data centers, each much bigger and more powerful than the one in Prineville used to be.

Technology never exists in a vacuum, and the rise of cryptocurrency in the last two or three years shows that. While plenty of people were making extraordinary amounts of money from investing in bitcoin and its competitors, there was consternation about the impact those get-rich-quick speculators had on the environment. Mining cryptocurrency was environmentally taxing. The core principle behind it was that you had to expend effort to get rich. To mint a bitcoin or another cryptocurrency, you had to first "mine" it.

The growing utilization of artificial intelligence (AI) is apparent across all facets of society, from the models used to enable semi-autonomous cars, to models that serve up recommendations on streaming or e-commerce sites, and in the language models used to create more natural, intuitive human-machine interaction. However, these technological achievements come with costs, namely the massive amounts of electrical power required to train AI algorithms, build and operate the hardware on which these algorithms are run, and to run and maintain that hardware throughout its life cycle. The cost of the electricity is not the only impact; traditional power plants that use fossil fuels (as well as some geothermal processes) to create power emit relatively high amounts of carbon dioxide (CO2) as they generate electricity, compared with renewable energy sources such as solar, wind, or nuclear plants, which do not. That emitted CO2 has a direct impact on the environment. While all software has a carbon footprint--the amount of CO2 directly related to its use--large and complex AI models have a significant environmental cost and are increasingly coming under scrutiny.

On weekdays, between homeschooling her two children, Michelle Curtis logs on to her computer to squeeze in a few hours of work. Her screen flashes with Google Search results, the writings of a Google chatbot, and the outputs of other algorithms, and she has a few minutes to respond to each--judging the usefulness of the blue links she's been provided, checking the accuracy of an AI's description of a praying mantis, or deciding which of two chatbot-written birthday poems is better. She never knows what she will have to assess in advance, and for the AI-related tasks, which have formed the bulk of her work since February, she says she has little guidance and not enough time to do a thorough job. Curtis is an AI rater. She works for the data company Appen, which is subcontracted by Google to evaluate the outputs of the tech giant's AI products and search algorithm.