AI's voracious appetite for energy threatens climate goals as data centers consume record power

Computer scientists are rushing to tame tame AI's voracious appetite for energy

As I sip coffee in my Berlin apartment and fire a question at Google's AI chatbot Gemini, it's easy not to think about the energy it takes to generate a response. Once the signal reaches my router, it whizzes, I assume, through copper wires or fiber-optic cables to one of Google's data center hubs. Somewhere inside the data center's labyrinthine halls of stacked processors, my query gets converted into numbers and undergoes billions of computations to determine context and meaning. The answer, once assembled, races back, in the blink of an eye. Data centers -- the beating hearts of the internet, powering everything from email to web searches -- have existed for decades, but with the growing popularity of AI to generate text, images and video, they're using more energy than ever. According to Google's own estimates, processing a median-length text prompt with its AI assistant Gemini consumes around 0.24 watt-hours. These amounts, individually small -- 0.24 watt-hours is equivalent to watching TV for about nine seconds -- are adding up fast. In March 2026, OpenAI estimated that more than 900 million people use its AI chatbot, ChatGPT, every week, tallying billions of queries daily. The exact amount of electricity consumed by data centers, globally or in the United States, which hosts more than any other nation, isn't publicly reported by all tech companies, says Eric Masanet of the University of California, Santa Barbara, who researches data center sustainability. But according to the most recent estimates by the International Energy Agency, US data centers guzzled some 224 terawatt-hours of electricity in 2025 -- more than 5 percent of the country's electricity use. That's a significant uptick from an estimated 1.9 percent consumed in 2018, well before the mainstream surge of generative AI. This electricity use seems set to soar. In the race to secure market leadership for generative AI products, companies like Google, Meta, Amazon, OpenAI, Anthropic, Microsoft and Oracle are investing tens to hundreds of billions of dollars to build AI-focused data centers. Compared to data centers of the pre-AI days that consume, say, 100 megawatts of electricity -- enough to power 83,000 homes with average demand -- the newcomers are often "hyperscale" and can use a gigawatt or more, or roughly a tenth of the electrical capacity of Los Angeles. Masanet and other experts have been alarmed to see much of this demand met by plants powered by fossil fuels, such as gas, whose burning releases planet-warming carbon dioxide. A key reason is that data centers are often constructed in places without abundant renewable energy sources like hydropower, geothermal, solar or wind. Tech companies often offset emissions by investing in renewable energy elsewhere. But unless those clean energy plants make more energy than the data centers use, this strategy -- at best -- keeps CO emissions of centers in stasis rather than reducing them to a net of nothing, important for halting global warming. "For every megawatt for which we install fossil fuel power," Masanet says, "it sets us back on our progress." And that's not considering the resources spent on manufacturing the hardware that fills new data centers, or the impacts on communities living near them, which often suffer from air and noise pollution from gas plants and possible strain on local water resources, which are used to cool the data centers. Although forecasts for AI's energy impact remain devilishly tricky, especially since the size of payoffs from investments in AI are uncertain, it's clear to experts that energy-saving strategies are urgently needed. Without them, according to one 2025 estimate, US data centers could soon be releasing the equivalent of 24 to 44 megatons of CO annually, the latter equivalent to the annual emissions of Norway. And so computer scientists and engineers are rethinking some of the power-hungry hardware and software that fuel AI. They're working to develop energy-saving algorithms and processor designs, and carefully considering where, and how, data centers are constructed. "AI's energy cost is not an accident: This is basically a product of how our systems are built," says Fengqi You, an expert in energy systems at Cornell University. But with the right mix of solutions, he says, "we could really reshape the trajectory." The roots of AI's energy problem To comprehend AI's energy cost, it helps to understand large language models (LLMs) -- the lifeblood of AI text generation tools such as chatbots and AI assistants -- specifically, ones based on a design described in 2017 by the machine-learning laboratory Google Brain. This design, transformer architecture, can process text at lightning speed by simultaneously taking each word and weighing its relationship to every other word it sees. It "learns" which words go together by computing how strongly each word relates to all other words in a text, examining each word in many contexts. (A similar design is used for AI image and video generators.) On a computational level, this happens by converting words or word fragments into numbers and performing additions and multiplications between them. Key to the speed is being able to do these calculations in parallel, made possible by graphic processor units (GPUs) -- mostly manufactured by the company NVIDIA -- originally invented for rapid 3D rendering of imagery during gaming. The initial training of an LLM, required to learn all these relationships, consumes vast amounts of energy. Because each word it trains on must be weighed against all others in a given chunk of text, the number of computations the model performs -- hence the energy required -- increases quadratically relative to the length of text (i.e., doubling the length of text quadruples the number of computations). That adds up quickly given that most LLMs are trained on massive swaths of publicly available internet text. Some estimates suggest that training GPT-4 -- the iteration of ChatGPT that launched in 2023 -- guzzled between 50 and 60 gigawatt-hours of electricity, enough to power San Francisco for three to four days. But experts are more worried about the energy costs of using the models to generate data once they've been trained, a process called inference. "You train once, then you inference for a billion people in the world," says Mosharaf Chowdhury, an AI systems expert at the University of Michigan who has been measuring the electricity usage of a handful of large language models that have been made publicly available. This process is surprisingly inefficient: Each time transformer models generate a word -- by selecting the one with the highest probability of following the previous word, given context -- they put the query and partially written answer through the model. In doing so, they apply all of the parameters they've calculated during training to understand language patterns -- which number in the hundreds of billions or even trillions. "The fact that you have to do a lot of calculations for a single word to be added -- that's a problematic thing," says Günter Klambauer, an AI expert at Johannes Kepler University in Austria. Tweaking AI software to save energy This recognition has triggered interest in smaller language models specialized to specific tasks. These are trained more narrowly, have fewer parameters -- say, tens or hundreds of millions -- and perform substantially less computation than larger models. In one 2025 paper published by UNESCO, computer scientist Ivana Drobnjak of University College London and colleagues compared energy consumption of Meta's language model Llama-3.1 with smaller AI models dedicated to particular tasks -- ones called DistilBART and t5-small-xsum for summarization, and others for translation or answering questions. When used for their respective tasks, the smaller models consumed more than 90 percent less energy than Llama 3.1 on the same job. And so computer scientists have been driven to build a similar kind of task specialization into LLMs themselves. In "mixture of expert" models, only particular parts of one big model are activated for certain tasks. These parts "learn to handle different patterns in language," Drobnjak says. This is thought to be one reason why R1, an LLM developed by the Chinese company DeepSeek, reportedly consumed significantly less energy than other models (independent experts have raised doubts about those figures). Udit Gupta, an expert in electrical and computer engineering at Cornell Tech, says that LLMs like Gemini or ChatGPT are similarly routing queries to more specialized sub-models. "There's a lot of work being done on how to assess the complexity of the query or task that's coming from users and then find the right model," Gupta says. (While Google spokesperson Ralf Bremer notes that the 0.24 watt-hours currently spent on processing median-length Gemini prompts is already 33 times more efficient than it was back in 2024, some experts suspect that processing queries with an LLM still consumes more energy than an equivalent web search.) Scientists are also exploring different kinds of LLMs, to break what Klambauer calls the "quadratic curse" of transformer models. One alternative, called a long short-term memory (LSTM) model, gets around this alarming energy increase by temporarily storing a kind of summary of the prompt that was inputted by the user plus the text generated so far, akin to recalling important plot points instead of an entire movie. That way, it only has to process the summary, rather than all the words in the full text to date, every time it generates a new word. This prevents LSTM's energy costs from skyrocketing as it responds to a query -- using about 50 percent less energy than transformer-type models to process texts of around 8,000 words in length, Klambauer says. LSTM models were developed in the 1990s but were abandoned because transformers could be trained much faster. But Klambauer says that recent advances have improved the performance of LSTM, now called xLSTM. He's working with the Austrian startup NXAI to further develop and optimize xLSTM, "because we think it's worth it for energy efficiency," he says. But major tech companies have invested so many years and resources into developing transformer-based models that switching to other models would be costly, says Wolfgang Maaß, an AI and business informatics researcher at the German Research Center for Artificial Intelligence. "We have to see whether this becomes as dominant, or whether it finds a niche in the whole market." Computing with wafers and light Though experts say the fastest energy savings will come from software tweaks, some are also taking aim at the energy-hungry processing chips that fuel AI computations. Engineers have made chips increasingly efficient over time by packing more computing capacity into individual processors -- reducing the energy required to shuttle data between chips that are working together to perform AI computations. Engineers have done this by shrinking the size of transistors -- microscopic electrical switches that process data -- inside the chips. But because engineers are reaching the physical limits of how small transistors can be, "we need to think of alternate ideas to improve the designs," says computer architect Ajay Joshi of the Boston University Photonics Center. One strategy is to make the chips larger. Dinner-plate-sized "wafer-scale chips" can pack nearly 70 times as many transistors as a single, postage-stamp-sized GPU and consume 143 times less electricity for communication than comparable GPUs, says computer engineer Rakesh Kumar of the University of Illinois Urbana-Champaign. Commercially produced by the California company Cerebras, wafer-scale chips have drawbacks, including a greater risk of damage during manufacturing. But because of their energy-saving and other beneficial features, "they would be very attractive to many hyperscalers and AI companies," Kumar says. Many tech companies have improved energy efficiency by fashioning their own processors that are tailor-made for AI computations -- such as Amazon Web Service's Trainium2 chip or Google's Ironwood Tensor Processing Units -- according to statements from those companies. As for NVIDIA, the company's head of sustainability Josh Parker says its AI-specialized GPUs have come a long way from the ones used for gaming and are now designed to run AI tasks as efficiently as possible; other innovations, such as making the interconnections between GPUs more efficient, have also helped. "Over the past eight years, NVIDIA GPUs have improved 45,000 [times] in energy efficiency for large language model workloads," he says. Engineers are also exploring alternative computing methods. Conventional AI processors calculate by encoding numbers in a binary system of ones and zeros, which is achieved by turning transistors on and off (representing the number 5, for instance, requires four transistors to represent the code 0101). But transistors can do more than function as binary switches allowing electron flow or not; they can also work as analog dials and hold intermediate voltages representing different numbers. That requires fewer transistors, and less energy, for computations. "People have known for decades that doing certain things in analog ... can be a lot more energy efficient," Kumar says. For example, electrical engineer Paul Manea of the German research institute Forschungszentrum Jülich and colleagues are working to develop devices called "gain cells" that are full of transistors working this way. Importantly, gain cells can both store the data required to process a query, and compute the answer. That overcomes another big energy bottleneck of conventional computing systems, where memory storage and computation occur on separate pieces of hardware. That's especially problematic for transformer-based LLMs, because each time they generate a word, they must shuttle the query and partially written answer from memory to a processor. Manea and colleagues estimate that gain cells in lieu of traditional GPUs can reduce the energy guzzled by one of the most energy-consuming parts of transformer-based LLMs by four orders of magnitude. But it will take more refining before they can be more widely used, Manea says. The notion of devices that both store and compute information is a key idea of "neuromorphic" computing, an up-and-coming field of computer engineering inspired by the human brain, which consumes orders of magnitude less energy than computers. Another brain-inspired invention is chips that encode information not in continuous data streams but -- like human nerve cells -- in the timing of voltage "spikes" propagating through the system. Allowing components to rest until they're needed "could potentially translate to less energy," says Eleni Vasilaki, an expert in bioinspired machine learning at the University of Sheffield in England. Maaß, for example, is part of a team that received roughly $5.8 million from the German government to test neuromorphic chips, among other strategies, to reduce the energy required for AI models. Some brain-inspired chips are already commercially available, but the technology is still far from being attractive for mainstream computing, says nanoelectronics expert Tony Kenyon of University College London, whose team recently received $17 million from the UK government to develop neuromorphic computing. Other scientists are developing chips that process information not with electrons but through the interaction of photons -- particles of light -- with matter (fiber-optic cables, which encode and transmit data as light pulses, are used around the world). With photons, more information can be transmitted at the same time, and signals can be altered much faster, says Elena Goi, a photonic computing researcher at Friedrich Schiller University Jena in Germany. Several companies have developed chips that can perform some AI computations with optical methods, says Joshi; he recently estimated that manufacturing optical chips could consume up to an order of magnitude less energy than conventional ones of the same size. Joshi hopes that, "in 10 years, we would have a practical solution that can be deployed pervasively across the data centers." Reshaping AI's energy trajectory Even without reinventing how computers work, much can be done to reduce AI's impact not just on energy but also on water resources used for cooling data centers. Importantly, tech companies should reconsider where they build those centers, says energy systems expert You. Right now, existing US ones are concentrated in northern Virginia, which has limited water resources and renewable energy capacity compared with the Midwest, for instance. You recently estimated that better siting -- along with energy-efficient hardware and software -- could reduce future carbon and water footprints of US data centers by 73 percent and 86 percent, respectively. Masanet adds that tech companies already with data centers across the country could at least train their models in strategic places. "Some companies like Google have been doing this: They shift their loads to follow renewables," he says. They also should address the electricity and resources spent on manufacturing processors for new data centers, as well as electronic waste as outdated tech is replaced every few years, he adds. Minimizing e-waste by using hardware for longer periods and recovering old electronics is one of Amazon's sustainability strategies, according to a statement to Knowable Magazine; so is designing data centers in energy- and water-saving ways and investing in a slew of renewable and nuclear energy projects. "We'll continue to implement solutions that benefit our customers and the communities we operate in," says Brandon Oyer, Amazon Web Services' head of energy and water in the Americas. Meanwhile, a press representative at Microsoft points to a number of sustainability initiatives the company has taken, including new cooling technologies, renewable energy investments and waste reduction. Google spokesperson Ralf Bremer emphasized the company's goal of reaching net-zero emissions across its operations by 2030 and replenishing 120 percent of the fresh water consumed by its offices and data centers by 2030. An OpenAI representative points to a press release outlining efforts to minimize water use and plans for solar energy generation at one of its campuses. Anthropic, Meta and Oracle did not respond to requests for comment by deadline. Though tech companies are taking sustainability into consideration, their main objective is to rapidly build out data center capacity, says computer engineer Benjamin Lee of the University of Pennsylvania. He predicts that, eventually, they'll need to step up efforts to improve energy efficiency to reduce costs. Governments should help to accelerate this shift, Masanet says. So far, he and his team have counted nearly 220 policies introduced to address data center sustainability at the US state level, 18 at the federal level, and more from other countries, though not all were ultimately adopted. "It's clear that governments around the world are beginning to take action," he says. However, he adds, "we also see some state and local governments with proposed policies that mostly aim to incentivize and accelerate data center builds." AI's energy cost will ultimately be a balancing act: Will it save more resources through its problem-solving abilities deployed toward everything from finding cancer cures to improving logistics, than it demands? But though building a more frugal, energy-saving AI is important, so is carefully considering where AI is needed, Kenyon says. Is the world truly a better place, for example, with nonhuman "AI agents" providing customer support? "I think it's a common mistake, when a new technology comes in, to suddenly think, 'Well, everything has to adopt that new technology,'" he says. "That approach really isn't doing us any favors." This article originally appeared in Knowable Magazine, a nonprofit publication dedicated to making scientific knowledge accessible to all. Sign up for Knowable Magazine's newsletter.

AI is an energy and water hog, here's what you can do to counter that

WASHINGTON (AP) -- As the world tries to curb human-caused climate change and not run dry of water, every online query is increasing our environmental footprint and exacerbating the problem. Artificial intelligence and the data centers they require use growing amounts of energy and are water hogs -- and AI companies aren't transparent about how much of those resources they use, experts said. So each time you turn to the internet and seek an AI-fueled response, it's gobbling up precious resources. "AI is going in the opposite direction to decarbonization efforts," said cognitive computer scientist Sasha Luccioni, co-founder and chief scientific officer of the Sustainable AI Group. "We should be thinking about where we are going towards. If you're recycling and a vegan but then you're using ChatGPT to do your multiplication for you, well that's kind of against the trend." "It's like one other thing among many to think about when you're like developing these daily habits," Luccioni said. "It is not too late. You are not obliged to use AI for everything. You can opt out, you can have a say and you can kind of just like think about how you engage with this technology." But she also said Big Tech companies are making it hard by "integrating generative AI into everything. ... There's like this bait-and-switch going on. I feel that nowadays you use the same tools that you used to use, but now they're generative AI." There are a few ways climate conscious individuals aren't completely powerless, said several experts in water use, artificial intelligence, data center placement and environmental sustainability. Use AI less The advice from experts is simple: Just use AI less often. "The cleanest form of AI use is no use," Kaveh Madani, a water scientist and director of the United Nations University Institute for Water, Environment and Health in Canada. "So when you could avoid using AI, don't use it." Don't use it for simple things. Don't use it for calculations, directions, store hours, recipes or shopping lists, which are all searches people used to do without AI, but now do it with AI and waste power and water, Luccioni said. "Yeah, it's great. You can generate a chocolate chip cookie recipe with Claude, or you can open a damn book. Like, those still exist. You really don't need Claude," Luccioni said. "You really don't need all of these generative AI technologies to do day-to-day tasks. I do agree there are some productivity gains to be had but I think that it's a pretty small percentage of what people are currently using." And when you make a query, make it concise because more information translates into more computing and more energy and water used. No need to be polite. Don't give unnecessary background information, Madani and others said. Every query means more energy use, experts said. The power and water cost of a query Last year, global data centers used 448 trillion watt-hours of electricity, more than all but 10 countries of the world, and it is expected to more than double in the next four years, according to a new report from the United Nations University. By then, it will have moved up in rankings to just behind five countries for power use. By 2030, just the electricity that data centers use -- not including the massive amounts of water needed to cool them -- would require nearly 2.5 trillion gallons of water (9.3 trillion liters), which is enough drinking water for the entire world for 1.7 years, said Madani, the study's co-author. Getting an AI text response is the equivalent to using an efficient light bulb for two and a half minutes, but that's being done 2.5 billion times a day with ChatGPT alone, according to the report and Madani. Using AI to generate a complex video is the equivalent of 42 hours of that light bulb burning and using a gallon of water (4 liters), he said. Lack of transparency is a problem Except for a mention in a blogpost and scant information, private AI companies aren't transparent about the energy and water costs of queries, said Luccioni and other experts who have tried to calculate those costs. That reality forces them to just make estimates based on less common open source AI. "We have no way of knowing and getting a sense of the amount of energy," said University of Michigan computer science professor Mosharaf Chowdhury, who tracks energy consumption of open source models. "If there's no transparency, we have no choice. We're really not choosing. We are being given whatever is being given to us," said Ana Pinheiro Privette, a former top sustainability official for Amazon Web Services, who also used to direct the University of Illinois' water security center and was a data scientist at NASA and the National Oceanic and Atmospheric Administration. "That's the power. The power is to say 'I actually want to understand what I'm consuming'." Forced into AI use but you can opt out When you go online, many search engines, including Google, answer via AI and promote it, without users asking for machine learning to kick in. You have to opt out of AI, when you should have to opt in, Luccioni said. "End users, you and me, we have absolutely no control other than saying 'OK we don't want to use any of it' and even then the companies force it onto us," Chowdhury said. You can opt out of AI in Google searches by putting "-ai" at the end of your search, Luccioni said. Or you can click on "Web" in search options. There are search engines that reduce their carbon footprints by planting trees and use less energy in their AI, such as Ecosia, Luccioni said. And search engines DuckDuckGo and Startpage have no-AI options. Consumers and neighbors have some power "The big power I think the consumer has is the market message because I've seen that when I worked at Amazon," Privette said. "They listen. They listen if everybody suddenly starts caring about not having a footprint." Years ago, when data centers wanted to build in an area, it was no problem. Now that they are multiplying in high population centers and people are speaking up and against them, said Privette. For example, data centers in two Virginia counties near Washington used 2.1 billion gallons (8 billion liters) of water in 2023. Balaji Tammabattula, chief operating officer of BaRupOn which makes energy-ready data center campuses, said, "the moment you say that you're building a data center, there's a backlash. The data center is the new boogeyman." So he said companies like his have to listen and when they do, they use less water and energy. "AI is not going anywhere," Tammabattula said. "It has to be done. But it has to be with the help of the community, where we're understanding the concerns of the community." ___ The Associated Press' climate and environmental coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP's standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.

Environmental cost of AI goes far beyond carbon

Ask an AI chatbot a question and the reply lands in seconds. Ask for a picture, and one appears almost as fast. It feels almost effortless. The reality behind that screen looks very different. Every prompt travels to a warehouse of humming machines powered by electricity that also draws on water and land. A new United Nations report measured all three costs together, and the totals came back far larger than carbon figures alone ever hinted. The cost behind prompts Those warehouses are data centers, scaling faster than the power grid can comfortably absorb. By 2030, the ones devoted to AI could draw around 945 terawatt-hours a year. That's close to three times the electricity used by several of the world's most populous countries. The footprint is already enormous. In 2025, data centers worldwide pulled about 448 terawatt-hours, enough to rank as the world's 11th-largest electricity consumer if they were a country. Outside projections expect that demand to roughly double by 2030. Dr. Miriam Aczel of the United Nations University Institute for Water, Environment and Health (UNU-INWEH) is the lead author of the report. She noted that most assessments until now have tracked only the carbon from training large models. Every unit of electricity also carries a water and land cost. Three footprints, not one Cutting carbon can quietly drive up the other two costs. Swapping coal for bioenergy - power made by burning plant matter - can cut the carbon footprint of electricity by roughly 70 percent. The hidden cost is steep. That same swap can multiply the water footprint more than 30-fold and the land footprint 100-fold. A choice that looks clean on a carbon chart can quietly drain rivers and fields elsewhere. "What surprised us most is how often the choices that look greenest from a carbon perspective end up worse for water or for land," said Aczel. That mismatch caught the team off guard. A green label, quietly misleading. Where the energy goes Public worry has fixated on training, the costly phase when a model first learns. But the researchers found that inference - the everyday work of answering prompts - eats up an estimated 80 to 90 percent of an AI system's energy. Not all prompts cost the same, and the gaps are staggering. A typical chat reply uses around 200 times the energy of a simple text-sorting task, while one AI image can burn about 1,450 times that baseline. One study of dozens of models confirmed the pattern. In plain terms, a single AI image keeps a small LED bulb lit for about 17 minutes. A complex video is enough for 42 hours. ChatGPT alone handles an estimated 2.5 billion prompts a day, and offsetting that carbon would take tree seedlings covering an area the size of Manhattan. Efficiency that backfires A comforting assumption runs through much AI coverage. As the technology grows more efficient, the thinking goes, its environmental cost should shrink on its own. The report pushes back hard on that idea. Economists call the trap the rebound effect, sometimes named the Jevons Paradox after a 19th-century thinker. When something gets cheaper and easier, people use far more of it. Professor Kaveh Madani, who directs the institute and led the team, puts it plainly. More efficient, cheaper AI tends to mean more AI - until the total footprint outruns whatever efficiency saved. Local costs, distant benefits The burdens of AI rarely land where the benefits do. In Ireland, data centers drew about 21 percent of all metered electricity in 2023, more than every urban home combined, by national figures. The grid operator has frozen new approvals around Dublin until 2028. Across drought-stricken parts of Mexico, new computing sites compete for shrinking water supplies. In Uruguay, a thirsty data center was planned just as a 2023 drought drained the capital's reserves and left tap water unsafe to drink. The hardware leaves its own trail. AI gear could generate up to 2.5 million tons of e-waste a year by 2030, much of it shipped to poorer countries with few safeguards. Only about 32 nations host these data centers, and most computing power sits in just two. What disclosure could change Before this report, AI's environmental cost was measured mostly in carbon, one number standing in for a far bigger picture. Now a single accounting sets carbon, water, and land side by side and shows how often they pull apart. The findings already carry political weight. Weeks after the report, the United Nations called on AI companies to disclose their carbon, water, and land footprints. The companies were also asked o run their data centers on renewable power by 2030. Comparable public numbers would let regulators and customers weigh one company against another. From here, the to-do list is concrete. Governments can fold data centers into water and land planning, and companies can treat default settings as environmental decisions. The report ends on a hopeful note, arguing that capability and care can grow together once the true costs are visible. The report is published as a UNU-INWEH Research Report. -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

AI is an energy and water hog, here's what you can do to counter that

WASHINGTON (AP) -- As the world tries to curb human-caused climate change and not run dry of water, every online query is increasing our environmental footprint and exacerbating the problem. Artificial intelligence and the data centers they require use growing amounts of energy and are water hogs -- and AI companies aren't transparent about how much of those resources they use, experts said. So each time you turn to the internet and seek an AI-fueled response, it's gobbling up precious resources. "AI is going in the opposite direction to decarbonization efforts," said cognitive computer scientist Sasha Luccioni, co-founder and chief scientific officer of the Sustainable AI Group. "We should be thinking about where we are going towards. If you're recycling and a vegan but then you're using ChatGPT to do your multiplication for you, well that's kind of against the trend." "It's like one other thing among many to think about when you're like developing these daily habits," Luccioni said. "It is not too late. You are not obliged to use AI for everything. You can opt out, you can have a say and you can kind of just like think about how you engage with this technology." But she also said Big Tech companies are making it hard by "integrating generative AI into everything. ... There's like this bait-and-switch going on. I feel that nowadays you use the same tools that you used to use, but now they're generative AI." There are a few ways climate conscious individuals aren't completely powerless, said several experts in water use, artificial intelligence, data center placement and environmental sustainability. Use AI less The advice from experts is simple: Just use AI less often. "The cleanest form of AI use is no use," Kaveh Madani, a water scientist and director of the United Nations University Institute for Water, Environment and Health in Canada. "So when you could avoid using AI, don't use it." Don't use it for simple things. Don't use it for calculations, directions, store hours, recipes or shopping lists, which are all searches people used to do without AI, but now do it with AI and waste power and water, Luccioni said. "Yeah, it's great. You can generate a chocolate chip cookie recipe with Claude, or you can open a damn book. Like, those still exist. You really don't need Claude," Luccioni said. "You really don't need all of these generative AI technologies to do day-to-day tasks. I do agree there are some productivity gains to be had but I think that it's a pretty small percentage of what people are currently using." And when you make a query, make it concise because more information translates into more computing and more energy and water used. No need to be polite. Don't give unnecessary background information, Madani and others said. Every query means more energy use, experts said. The power and water cost of a query Last year, global data centers used 448 trillion watt-hours of electricity, more than all but 10 countries of the world, and it is expected to more than double in the next four years, according to a new report from the United Nations University. By then, it will have moved up in rankings to just behind five countries for power use. By 2030, just the electricity that data centers use -- not including the massive amounts of water needed to cool them -- would require nearly 2.5 trillion gallons of water (9.3 trillion liters), which is enough drinking water for the entire world for 1.7 years, said Madani, the study's co-author. Getting an AI text response is the equivalent to using an efficient light bulb for two and a half minutes, but that's being done 2.5 billion times a day with ChatGPT alone, according to the report and Madani. Using AI to generate a complex video is the equivalent of 42 hours of that light bulb burning and using a gallon of water (4 liters), he said. Lack of transparency is a problem Except for a mention in a blogpost and scant information, private AI companies aren't transparent about the energy and water costs of queries, said Luccioni and other experts who have tried to calculate those costs. That reality forces them to just make estimates based on less common open source AI. "We have no way of knowing and getting a sense of the amount of energy," said University of Michigan computer science professor Mosharaf Chowdhury, who tracks energy consumption of open source models. "If there's no transparency, we have no choice. We're really not choosing. We are being given whatever is being given to us," said Ana Pinheiro Privette, a former top sustainability official for Amazon Web Services, who also used to direct the University of Illinois' water security center and was a data scientist at NASA and the National Oceanic and Atmospheric Administration. "That's the power. The power is to say 'I actually want to understand what I'm consuming'." Forced into AI use but you can opt out When you go online, many search engines, including Google, answer via AI and promote it, without users asking for machine learning to kick in. You have to opt out of AI, when you should have to opt in, Luccioni said. "End users, you and me, we have absolutely no control other than saying 'OK we don't want to use any of it' and even then the companies force it onto us," Chowdhury said. You can opt out of AI in Google searches by putting "-ai" at the end of your search, Luccioni said. Or you can click on "Web" in search options. There are search engines that reduce their carbon footprints by planting trees and use less energy in their AI, such as Ecosia, Luccioni said. And search engines DuckDuckGo and Startpage have no-AI options. Consumers and neighbors have some power "The big power I think the consumer has is the market message because I've seen that when I worked at Amazon," Privette said. "They listen. They listen if everybody suddenly starts caring about not having a footprint." Years ago, when data centers wanted to build in an area, it was no problem. Now that they are multiplying in high population centers and people are speaking up and against them, said Privette. For example, data centers in two Virginia counties near Washington used 2.1 billion gallons (8 billion liters) of water in 2023. Balaji Tammabattula, chief operating officer of BaRupOn which makes energy-ready data center campuses, said, "the moment you say that you're building a data center, there's a backlash. The data center is the new boogeyman." So he said companies like his have to listen and when they do, they use less water and energy. "AI is not going anywhere," Tammabattula said. "It has to be done. But it has to be with the help of the community, where we're understanding the concerns of the community." ___ The Associated Press' climate and environmental coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP's standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.

AI Is an Energy and Water Hog, Here's What You Can Do to Counter That

WASHINGTON (AP) -- As the world tries to curb human-caused climate change and not run dry of water, every online query is increasing our environmental footprint and exacerbating the problem. Artificial intelligence and the data centers they require use growing amounts of energy and are water hogs -- and AI companies aren't transparent about how much of those resources they use, experts said. So each time you turn to the internet and seek an AI-fueled response, it's gobbling up precious resources. "AI is going in the opposite direction to decarbonization efforts," said cognitive computer scientist Sasha Luccioni, co-founder and chief scientific officer of the Sustainable AI Group. "We should be thinking about where we are going towards. If you're recycling and a vegan but then you're using ChatGPT to do your multiplication for you, well that's kind of against the trend." "It's like one other thing among many to think about when you're like developing these daily habits," Luccioni said. "It is not too late. You are not obliged to use AI for everything. You can opt out, you can have a say and you can kind of just like think about how you engage with this technology." But she also said Big Tech companies are making it hard by "integrating generative AI into everything. ... There's like this bait-and-switch going on. I feel that nowadays you use the same tools that you used to use, but now they're generative AI." There are a few ways climate conscious individuals aren't completely powerless, said several experts in water use, artificial intelligence, data center placement and environmental sustainability. Use AI less The advice from experts is simple: Just use AI less often. "The cleanest form of AI use is no use," Kaveh Madani, a water scientist and director of the United Nations University Institute for Water, Environment and Health in Canada. "So when you could avoid using AI, don't use it." Don't use it for simple things. Don't use it for calculations, directions, store hours, recipes or shopping lists, which are all searches people used to do without AI, but now do it with AI and waste power and water, Luccioni said. "Yeah, it's great. You can generate a chocolate chip cookie recipe with Claude, or you can open a damn book. Like, those still exist. You really don't need Claude," Luccioni said. "You really don't need all of these generative AI technologies to do day-to-day tasks. I do agree there are some productivity gains to be had but I think that it's a pretty small percentage of what people are currently using." And when you make a query, make it concise because more information translates into more computing and more energy and water used. No need to be polite. Don't give unnecessary background information, Madani and others said. Every query means more energy use, experts said. The power and water cost of a query Last year, global data centers used 448 trillion watt-hours of electricity, more than all but 10 countries of the world, and it is expected to more than double in the next four years, according to a new report from the United Nations University. By then, it will have moved up in rankings to just behind five countries for power use. By 2030, just the electricity that data centers use -- not including the massive amounts of water needed to cool them -- would require nearly 2.5 trillion gallons of water (9.3 trillion liters), which is enough drinking water for the entire world for 1.7 years, said Madani, the study's co-author. Getting an AI text response is the equivalent to using an efficient light bulb for two and a half minutes, but that's being done 2.5 billion times a day with ChatGPT alone, according to the report and Madani. Using AI to generate a complex video is the equivalent of 42 hours of that light bulb burning and using a gallon of water (4 liters), he said. Lack of transparency is a problem Except for a mention in a blogpost and scant information, private AI companies aren't transparent about the energy and water costs of queries, said Luccioni and other experts who have tried to calculate those costs. That reality forces them to just make estimates based on less common open source AI. "We have no way of knowing and getting a sense of the amount of energy," said University of Michigan computer science professor Mosharaf Chowdhury, who tracks energy consumption of open source models. "If there's no transparency, we have no choice. We're really not choosing. We are being given whatever is being given to us," said Ana Pinheiro Privette, a former top sustainability official for Amazon Web Services, who also used to direct the University of Illinois' water security center and was a data scientist at NASA and the National Oceanic and Atmospheric Administration. "That's the power. The power is to say 'I actually want to understand what I'm consuming'." Forced into AI use but you can opt out When you go online, many search engines, including Google, answer via AI and promote it, without users asking for machine learning to kick in. You have to opt out of AI, when you should have to opt in, Luccioni said. "End users, you and me, we have absolutely no control other than saying 'OK we don't want to use any of it' and even then the companies force it onto us," Chowdhury said. You can opt out of AI in Google searches by putting "-ai" at the end of your search, Luccioni said. Or you can click on "Web" in search options. There are search engines that reduce their carbon footprints by planting trees and use less energy in their AI, such as Ecosia, Luccioni said. And search engines DuckDuckGo and Startpage have no-AI options. Consumers and neighbors have some power "The big power I think the consumer has is the market message because I've seen that when I worked at Amazon," Privette said. "They listen. They listen if everybody suddenly starts caring about not having a footprint." Years ago, when data centers wanted to build in an area, it was no problem. Now that they are multiplying in high population centers and people are speaking up and against them, said Privette. For example, data centers in two Virginia counties near Washington used 2.1 billion gallons (8 billion liters) of water in 2023. Balaji Tammabattula, chief operating officer of BaRupOn which makes energy-ready data center campuses, said, "the moment you say that you're building a data center, there's a backlash. The data center is the new boogeyman." So he said companies like his have to listen and when they do, they use less water and energy. "AI is not going anywhere," Tammabattula said. "It has to be done. But it has to be with the help of the community, where we're understanding the concerns of the community." ___ The Associated Press' climate and environmental coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP's standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.

AI is an energy and water hog, here's what you can do to counter that

Every online query, especially those powered by AI, significantly increases energy and water consumption, experts warn. AI's growing demand is counterproductive to climate goals, with companies lacking transparency on resource use. Individuals can mitigate this by using AI less for simple tasks and making queries concise. While AI integration is pervasive, opting out and supporting eco-conscious search engines are viable choices for consumers to influence the market. As the world tries to curb human-caused climate change and not run dry of water, every online query is increasing our environmental footprint and exacerbating the problem. Artificial intelligence and the data centers they require use growing amounts of energy and are water hogs - and AI companies aren't transparent about how much of those resources they use, experts said. So each time you turn to the internet and seek an AI-fueled response, it's gobbling up precious resources. "AI is going in the opposite direction to decarbonisation efforts," said cognitive computer scientist Sasha Luccioni, cofounder and chief scientific officer of the sustainable AI group. "We should be thinking about where we are going towards. If you're recycling and a vegan but then you're using ChatGPT to do your multiplication for you, well that's kind of against the trend." "It's like one other thing among many to think about when you're like developing these daily habits," Luccioni said. "It is not too late. You are not obliged to use AI for everything. You can opt out, you can have a say and you can kind of just like think about how you engage with this technology." But she also said Big Tech companies are making it hard by "integrating generative AI into everything. ... There's like this bait-and-switch going on. I feel that nowadays you use the same tools that you used to use, but now they're generative AI." There are a few ways climate conscious individuals aren't completely powerless, said several experts in water use, artificial intelligence, data center placement and environmental sustainability. Use AI less The advice from experts is simple: Just use AI less often. "The cleanest form of AI use is no use," Kaveh Madani, a water scientist and director of the United Nations University Institute for Water, Environment and Health in Canada. "So when you could avoid using AI, don't use it." Don't use it for simple things. Don't use it for calculations, directions, store hours, recipes or shopping lists, which are all searches people used to do without AI, but now do it with AI and waste power and water, Luccioni said. "Yeah, it's great. You can generate a chocolate chip cookie recipe with Claude, or you can open a damn book. Like, those still exist. You really don't need Claude," Luccioni said. "You really don't need all of these generative AI technologies to do day-to-day tasks. I do agree there are some productivity gains to be had but I think that it's a pretty small percentage of what people are currently using." And when you make a query, make it concise because more information translates into more computing and more energy and water used. No need to be polite. Don't give unnecessary background information, Madani and others said. Every query means more energy use, experts said. The power and water cost of a query Last year, global data centers used 448 trillion watt-hours of electricity, more than all but 10 countries of the world, and it is expected to more than double in the next four years, according to a new report from the United Nations University. By then, it will have moved up in rankings to just behind five countries for power use. By 2030, just the electricity that data centers use - not including the massive amounts of water needed to cool them - would require nearly 2.5 trillion gallons of water (9.3 trillion liters), which is enough drinking water for the entire world for 1.7 years, said Madani, the study's co-author. Getting an AI text response is the equivalent to using an efficient light bulb for two and a half minutes, but that's being done 2.5 billion times a day with ChatGPT alone, according to the report and Madani. Using AI to generate a complex video is the equivalent of 42 hours of that light bulb burning and using a gallon of water (4 liters), he said. Lack of transparency is a problem Except for a mention in a blogpost and scant information, private AI companies aren't transparent about the energy and water costs of queries, said Luccioni and other experts who have tried to calculate those costs. That reality forces them to just make estimates based on less common open source AI. "We have no way of knowing and getting a sense of the amount of energy," said University of Michigan computer science professor Mosharaf Chowdhury, who tracks energy consumption of open source models. "If there's no transparency, we have no choice. We're really not choosing. We are being given whatever is being given to us," said Ana Pinheiro Privette, a former top sustainability official for Amazon Web Services, who also used to direct the University of Illinois' water security center and was a data scientist at NASA and the National Oceanic and Atmospheric Administration. "That's the power. The power is to say 'I actually want to understand what I'm consuming'." Forced into AI use but you can opt out When you go online, many search engines, including Google, answer via AI and promote it, without users asking for machine learning to kick in. You have to opt out of AI, when you should have to opt in, Luccioni said. "End users, you and me, we have absolutely no control other than saying 'OK we don't want to use any of it' and even then the companies force it onto us," Chowdhury said. You can opt out of AI in Google searches by putting "-ai" at the end of your search, Luccioni said. Or you can click on "Web" in search options. There are search engines that reduce their carbon footprints by planting trees and use less energy in their AI, such as Ecosia, Luccioni said. And search engines DuckDuckGo and Startpage have no-AI options. Consumers and neighbors have some power "The big power I think the consumer has is the market message because I've seen that when I worked at Amazon," Privette said. "They listen. They listen if everybody suddenly starts caring about not having a footprint." Years ago, when data centers wanted to build in an area, it was no problem. Now that they are multiplying in high population centers and people are speaking up and against them, said Privette. For example, data centers in two Virginia counties near Washington used 2.1 billion gallons (8 billion liters) of water in 2023. Balaji Tammabattula, chief operating officer of BaRupOn which makes energy-ready data center campuses, said, "the moment you say that you're building a data center, there's a backlash. The data center is the new boogeyman." So he said companies like his have to listen and when they do, they use less water and energy. "AI is not going anywhere," Tammabattula said. "It has to be done. But it has to be with the help of the community, where we're understanding the concerns of the community."