Escalating use of tools like Gemini and ChatGPT saps more and more power. Experts offer some tips on how to consume less.<\/p>\n
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Inside the world\u2019s newest data centers, energy-guzzling computations proceed round the clock as AI chatbots and other generative AI tools tackle tasks from the frivolous to the weighty: assembling imagery for social media, proffering relationship advice, analyzing medical images to diagnose cancer, creating code for developers or detecting financial scams for banks.<\/p>\n
Just as the popularity of AI tools has skyrocketed in recent years, so have the associated environmental costs. Data centers now consume 414 terawatt-hours per year, or about 1.5 percent of global electricity use, according to the International Energy Agency \u2014 an amount that grew by 12 percent annually for five years before jumping to 17 percent in 2025. By 2030, the agency projects that demand for electricity by data centers will more than double. Much of the increasing demand for electricity is being met by fossil fuels, while experts also worry about the use of local water resources to cool data centers in drought-struck regions.<\/p>\n
Use of AI to generate text or imagery probably accounts for a mere sliver of any given person\u2019s environmental footprint. And experts stress that the onus is on tech companies to reduce AI\u2019s resource consumption, from creating smarter, energy-saving algorithms to building more efficient hardware.<\/p>\n
Yet there are simple actions people can take to ensure that their AI usage has as little environmental impact as possible \u2014 from carefully considering where AI is needed to tailoring prompts to minimize the amount of computation required.<\/p>\n
\u201cIndividual choices are not meaningless, and some are more powerful than people realize,\u201d says computer scientist Ivana Drobnjak of University College London.<\/p>\n
It is notoriously tricky to estimate the energy expended on processing an individual chatbot query. Google, for example, estimates that its chatbot Gemini consumes around 0.24 watt-hours to respond to a median-length text query \u2014 equivalent to the electricity needed to watch TV for less than nine seconds. It also uses about 0.26 milliliters of water and emits the equivalent of 0.03 grams of carbon dioxide (driving a gas-powered car for a mile would emit about 400 grams). Small individually, these expenditures build up for those individuals and companies that use AI tools a lot.<\/p>\n
The reason AI models consume so much energy lies partly in the processors that power them, such as the graphic processing units (GPUs) that consume significantly more energy than the central processing units (CPUs) that fuel simpler tasks like web searches and email. It also has to do with the models that underlie most popular generative AI tools, including the large language models (LLMs) that power AI chatbots and assistants.<\/p>\n
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These are based on a particular design called transformer architecture. This allows LLMs to train on vast swaths of language patterns in text and, from this, compute hundreds of billions or trillions of parameters. These parameters can then be used to generate new strings of text, by predicting which words are likely to follow one other.<\/p>\n
A transformer-based LLM is computationally intensive because for each new word it generates in response to a user\u2019s query, it runs the query and the words that have been written so far through the model, performing billions of calculations each time.<\/p>\n
Tech companies note that LLMs have become more energy-efficient over time; according to Google\u2019s 2025 calculations, the 0.24 watt-hours that Gemini consumes on a median-length text prompt represents a 33-fold decrease compared with the model\u2019s energy consumption the previous year.<\/p>\n
In any case, even small amounts of energy add up quickly given the scale of AI use. Based on 2025 numbers from tech company OpenAI, Drobnjak estimated in May that, at that point, around 3.2 billion queries are being sent every day to its chatbot ChatGPT. Users are asking AI tools to process and produce vast quantities of text, images and video. Some are having lengthy conversations with chatbots. And, increasingly, people are creating their own \u201cAI agents\u201d that themselves send queries to AI chatbots.<\/p>\n
So what can users do to minimize the resources spent on their AI use? Experts have some tips.<\/p>\n