Energy consumption due to computing for artificial intelligence (AI) has become a high-profile issue with the rapid advancement of AI models and tools. In our 2022 paper discussing possibilities for global collaboration on large-scale research and development projects that can help with significant global challenges, including climate change, we noted the potential adverse impact of AI’s energy consumption. At that time, there were outlying predictions that AI could consume as much as 20% of the world’s energy by 2025. But at the time, the International Energy Agency (IEA) reported that, despite large increases in the number of data centers, energy demand had remained level because of efficiency improvements in equipment and cloud services.
That picture has since changed dramatically. Uncertainties remain as to the extent of increases in consumption due to opacities in corporate disclosures and inconsistencies in methodologies. Nevertheless, it has become clear on several fronts that advanced AI models have become a new, fast-growing source of global energy consumption. Hyperscalers—the vertically integrated cloud providers and tech giants that operate large-scale data centers with distributed computing operations—have been vocal in seeking increased generation and transmission of electricity as they rush to construct more and larger data centers that demand increasing amounts of electricity as well as water and other resources to meet needs for training models and applying them using inference. Whatever the uncertainties of their disclosures on energy usage, these reflect significant increases, and independent projections of actual and future usage with energy demand from data centers, driven primarily by AI, are projected to add hundreds of terawatt-hours (TWh) to global energy consumption by 2030.
This paper will discuss these growing energy demands and the issues they present. It will explore ways to improve understanding of AI energy consumption and its impacts on the environment locally and globally, as well as future constraints that power availability and grid capacity may place on AI compute. The discussion will ask what international policies and initiatives can help address these issues.
Energy and the hyperscaler model
Since our 2022 report, baseline estimates for global energy consumption have already far outweighed overall electricity growth. In 2024, global data center electricity consumption was approximately 415 terrawatt hours, representing about 1.5% of the world’s total electricity use. This figure has been growing at a compound annual growth rate of 12% since 2017, a rate more than four times faster than that of total global electricity consumption. By one estimate, the energy consumption of data centers could approach 1,050 TWh by 2026, which, if data centers were a country, would make them the fifth largest energy consumer in the world, between Japan and Russia.
A consensus among leading analytical bodies points to a doubling or more of this demand by 2030. The IEA, in its base case scenario, projects that global data center electricity consumption could reach 945 TWh by 2030, climbing further to 1,200 TWh by 2035. Analysis from Deloitte projects a similar trajectory, forecasting a rise to 1,065 TWh by 2030. Goldman Sachs Research forecasts a 160–165% increase in power demand (measured in capacity) by 2030 compared to 2023 levels.
About 60% of the electricity used in data centers powers the servers (computers equipped with CPUs/GPUs, memory, storage controllers, and other components for processing data, which are stored on racks). This share reaches around 75% for larger hyperscaler data centers optimized for AI workloads, where servers include chips that consume 2–4 times more watts than their conventional counterparts.
The amount of new cloud computing dedicated to AI is a subset of the overall cloud market, with estimates that AI will drive 10-15% of total cloud spend by 2030. The cloud stack for AI comprises the infrastructure layer, forecasted to account for 29% of the market, which is the computing and associated networking, frameworks, and services that organizations can use to build their own foundational AI models. The second layer is the AI tooling or the platform layer, which is where the organization builds its own AI models or custom foundational AI applications using models and datasets from other providers. It has been estimated to similarly make up 30% of the cloud market. The third layer in the stack, the AI application or software layer, is expected to have the highest share, 40% of the market. This layer typically provides so-called off-the-shelf applications to offer services, such as coding and content creation, that organizations can rapidly access with limited technical know-how.
The recent growth in demand is driven by increases in AI training and inference at the infrastructure layer. The training process for a frontier model is energy intensive, requiring thousands of high-performance chips to run continuously for weeks or months, consuming gigawatt-hours of electricity. Some estimates find that as high as 80-90% of computing power used for AI is from inference. In 2024, a single query on an advanced generative AI model like ChatGPT required an estimated 2.9 watt-hours of electricity, nearly 10 times the 0.3 watt-hours needed for a conventional Google search. Newer measurements suggest median energy per text query has fallen to 0.24-0.3 watt-hours, although this can be much higher for long reasoning or multimodal prompts. The IEA’s modeling indicates that electricity consumption from servers used for AI workloads, predominantly inference, is projected to grow by 30% annually as adoption increases. This single category of usage is expected to account for almost half of the net increase in global data center consumption between 2024 and 2030.
The impact is especially acute in the United States, which is currently the world’s largest data center market, accounting for 45% of global data center electricity consumption in 2024. The IEA estimates that data center demand for energy in the U.S. will increase by 130% by 2030. This forecast is consistent with those by other expert bodies. The U.S. Department of Energy, in a 2024 report from Lawrence Berkeley National Laboratory (LBNL), found that data centers consumed about 4.4% of total U.S. electricity in 2023 and are projected to consume between 6.7% and 12.0% by 2028. In absolute terms, the LBNL report projects an increase from 176 TWh in 2023 to a range of 325 to 580 TWh by 2028. Bloomberg Intelligence predicted growth in energy demand for AI by up to four times its current level by 2032.
The forecast is also corroborated by individual AI developers. Anthropic estimated that by 2027, training a single frontier AI model will require five gigawatts (GW) of power and projectedthat the U.S. AI sector alone will require 50 GW of new electric capacity by 2028 to maintain global AI leadership. To put the 50 GW figure in context, it is about twice the peak electricity demand of New York City. Former Google CEO Eric Schmidt testifiedbefore Congress that data centers will need 29 GW of additional power by 2027, and 67 more GW by 2030.
These rapid increases pose a challenge for U.S. AI development. For the past two decades, U.S. electricity consumption was essentially flat; AI is now driving that growth rate several times faster. AI companies are clamoring for gigawatts of new capacity in a few years, but current permitting processes for new power plants and high-voltage transmission lines can take a over decade in the U.S. and EU, with no guarantee of sustained growth at these elevated rates. Conversely, China added over 400 GW of new power capacity online in a single year.
The IEA notes that while a typical data center can consume as much electricity as 100,000 households, the largest next-generation campuses currently under construction will demand 20 times that amount. This sharp increase in demand changes the nature of the challenge for grid operators. Historically, despite a near tripling of data center workloads between 2015 and 2019, the sector’s power demand remained relatively flat due to significant gains in energy efficiency. The current demand surge is different not only in scale but in character. Data center demand is highly concentrated in a few geographic clusters and driven by a small number of hyperscale technology companies. While data centers may only account for 3% to 4% (some estimate up to 9%) of global electricity demand by 2030, their share of local demand can be overwhelming, reaching 42% in Frankfurt or nearly 80% in Dublin.
Additionally, the cooling systems required for these servers drive a substantial demand for water. In 2023, U.S. data centers consumed about 17 billion gallons of water, with most—84%—used for hyperscale and colocation facilities. Direct water consumption in hyperscale data centers alone is expected to consume 16 billion to 33 billion gallons annually by 2028. At the same time, training an advanced model requires less water than is used on a square mile of farmland in a year.
