Researchers want firms to be more transparent about the electricity demands of artificial intelligence.
The aroma of hay and manure hangs over Culpeper County, Virginia, where there’s a cow for every three humans. “We’ve got big farms, most still family-owned, and a lot of forests,” says Sarah Parmelee, one of the county’s 55,000 residents. “It’s very charming small-town USA,” she adds.
But this pastoral idyll is in the middle of a twenty-first-century shift. Over the past few years, the county has approved the construction of seven large data-centre projects, which will support technology firms in their expansive plans for generative artificial intelligence (AI). Inside these giant structures, rows of computer servers will help to train the AI models behind chatbots such as ChatGPT, and deliver their answers to what might be billions of daily queries from around the world.
In Virginia, the construction will have profound effects. Each facility is likely to consume the same amount of electrical power as tens of thousands of residential homes, potentially driving up costs for residents and straining the area’s power infrastructure beyond its capacity. Parmelee and others in the community are wary of the data centres’ appetite for electricity — particularly because Virginia is already known as the data-centre capital of the world. A state-commissioned review, published in December 2024, noted that although data centres bring economic benefits, their growth could double electricity demand in Virginia within ten years.
“Where is power going to come from?” asks Parmelee, who is mapping the rise of data centres in the state and works for the Piedmont Environmental Council, a non-profit organization headquartered in Warrenton, Virginia. “They’re all saying, ‘We’ll buy power from the next district over.’ But that district is planning to buy power from you.”
Similar conflicts about AI and energy are brewing in many places around the globe where data centres are sprouting up at a record pace. Big tech firms are betting hard on generative AI, which requires much more energy to operate compared with older AI models that extract patterns from data but don’t generate fresh text and images. That is driving companies to collectively spend hundreds of billions of dollars on new data centres and servers to expand their capacity.
From a global perspective, AI’s impact on future electricity demand is actually projected to be relatively small. But data centres are concentrated in dense clusters, where they can have profound local impacts. They are much more spatially concentrated than are other energy-intensive facilities, such as steel mills and coal mines. Companies tend to build data-centre buildings close together so that they can share power grids and cooling systems and transfer information efficiently, both among themselves and to users. Virginia, in particular, has attracted data-centre firms by providing tax breaks, leading to even more clustering.
“If you have one, you’re likely to have more,” says Parmelee. Virginia already has 340 such facilities, and Parmelee has mapped 159 proposed data centres or expansions of existing ones in Virginia, where they account for more than one-quarter of the state’s electricity use, according to a report by EPRI, a research institute in Palo Alto, California. In Ireland, data centres account for more than 20% of the country’s electricity consumption — with most of them situated on the edge of Dublin. And the facilities’ electricity consumption has surpassed 10% in at least 5 US states.
Complicating matters further is a lack of transparency from firms about their AI systems’ electricity demands. “The real problem is that we’re operating with very little detailed data and knowledge of what’s happening,” says Jonathan Koomey, an independent researcher who has studied the energy use of computing for more than 30 years and who runs an analytics firm in Burlingame, California.
“I think every researcher on this topic is going crazy because we’re not getting the stuff we need,” says Alex de Vries, a researcher at the Free University of Amsterdam and the founder of Digiconomist, a Dutch company that explores the unintended consequences of digital trends. “We’re just doing our best, trying all kinds of tricks to come up with some kind of numbers.”
Working out AI’s energy demands
Lacking detailed figures from firms, researchers have explored AI’s energy demand in two ways. In 2023, de Vries used a supply-chain (or market-based) method. He examined the power draw of one of the NVIDIA servers that dominates the generative AI market and extrapolated that to the energy required over a year. He then multiplied that figure by estimates of the total number of such servers that are being shipped, or that might be required for a particular task.
De Vries used this method to estimate the energy needed if Google searches used generative AI. Two energy-analyst firms had estimated that implementing ChatGPT-like AI into every Google search would require between 400,000 and 500,000 NVIDIA A100 servers, which, based on the power demand of those servers, would amount to 23–29 terawatt hours (TWh) annually. Then, estimating that Google was processing up to 9 billion searches daily (a ballpark figure from various analysts), de Vries calculated that each request through an AI server requires 7–9 watt hours (Wh) of energy. That is 23–30 times the energy of a normal search, going by figures Google reported in a 2009 blogpost. When asked to comment on de Vries’ estimate, Google did not respond.
This energy calculation felt like “grasping at straws”, de Vries says, because he had to rely on third-party estimates that he could not replicate. And his numbers quickly became obsolete. The number of servers required for an AI-integrated Google search is likely to be lower now, because today’s AI models can match the accuracy of 2023 models at a fraction of the computational cost, as US energy-analyst firm SemiAnalysis (whose estimates de Vries had relied on) wrote in an e-mail to Nature.
Even so, the firm says that the best way of assessing generative AI’s energy footprint is still to monitor server shipments and their power requirements, which is broadly the method used by many analysts. However, it is difficult for analysts to isolate the energy used solely by generative AI, because data centres generally perform non-AI tasks as well.
Bottom-up estimates
The other way to examine AI’s energy demand is ‘bottom-up’: researchers measure the energy demand of one AI-related request in a specific data centre. However, independent researchers can perform the measurements using only open-source AI models that are expected to resemble proprietary ones.
The concept behind these tests is that a user submits a prompt — such as a request to generate an image, or a text-based chat — and a Python software package called CodeCarbon then allows the user’s computer to access technical specifications of the chips that execute the model in the data centre. “At the end of the run, it’ll give you an estimate of how much energy was consumed by the hardware that you were using,” says Sasha Luccioni, an AI researcher who helped to develop CodeCarbon, and who works at Hugging Face, a firm based in New York City that hosts an open-source platform for AI models and data sets.
Luccioni and others found that different tasks require varying amounts of energy. On average, according to their latest results, generating an image from a text prompt consumes about 0.5 Wh of energy, while generating text uses a little less. For comparison, a modern smartphone might need 22 Wh for a full charge. But there is wide variation: larger models require more energy (see ‘How much energy does AI use?’). De Vries says that the numbers are lower than those in his paper, but that might be because the models used by Luccioni and others are at least an order of magnitude smaller than the model underlying ChatGPT — and because AI is becoming more efficient.
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