Artificial Intelligence is on course to become one of the most power-intensive technologies on the planet.
According to a recent report by the International Energy Agency (IEA), by the year 2030, AI-driven data centers may consume as much electricity as Japan currently uses—a staggering forecast that underlines the urgent need for scalable and sustainable energy solutions.
AI’s Growing Electrical Footprint
Right now, data centers account for around 1.5% of global electricity use, which equates to approximately 415 terawatt-hours (TWh) annually. But this figure is set to more than double, reaching nearly 950 TWh by the end of the decade. That’s almost 3% of the world’s total electricity consumption.
The report highlights that most of this surge comes from high-powered “accelerated servers” used in AI processing. These servers are expected to increase their power draw by 30% annually through 2030—far outpacing the 9% growth rate projected for traditional servers.
Data centers already in development could each consume as much power as 2 to 5 million homes, depending on their size and infrastructure.
Uneven Global Distribution
Energy consumption from AI is not evenly spread across the globe. By 2030, the average American’s share of data center consumption is expected to hit 1,200 kilowatt-hours (kWh) per year—roughly 10% of a typical household’s annual power use. In stark contrast, individuals in Africa may only account for 2 kWh each.
In specific regions, the situation is already critical. Ireland’s data centers consume about 20% of the national electricity supply, while Virginia leads the U.S. with 25% of its power allocated to data centers. Six U.S. states have already surpassed the 10% threshold.
Can Renewables Bridge the Gap?
Despite the alarming projections, the IEA remains cautiously optimistic. Nearly half of the additional electricity demand for AI infrastructure could be met through renewable energy sources. However, fossil fuels will still play a major role, especially in regions like China and the United States.
China’s data center energy mix is heavily coal-dependent, with nearly 70% of power still coming from coal. Meanwhile, the U.S. leans on natural gas (40%) and renewables (24%) to power its AI infrastructure.
Emerging technologies such as small modular nuclear reactors (SMRs) could be the key to long-term solutions. In fact, companies like OpenAI are already investing in as much as 20 gigawatts of SMR capacity. Microsoft has also made headlines with plans to revive the Three Mile Island nuclear plant to support future AI workloads.
This aligns closely with insights from the IEA’s broader analysis of tech’s energy footprint, as detailed in AI’s Power Surge: The IEA Breaks Down Its Impact on Global Energy Systems.
Efficiency or Expansion? The Road Ahead
The IEA outlines several scenarios for the future of AI’s energy use. In the “Lift-Off” scenario, where AI adoption accelerates rapidly, data centers could consume over 1,700 TWh by 2035—45% more than the base projection.
Conversely, a “High Efficiency” scenario suggests that innovations in software, hardware, and data center infrastructure could reduce energy demand by more than 15% while maintaining performance levels. However, if AI faces deployment challenges or performance limitations, energy use might remain significantly lower than expected.
The Balancing Act Between Innovation and Sustainability
The next decade will be critical in determining whether the tech industry can strike a balance between AI growth and energy sustainability. The choices made today concerning infrastructure, energy sourcing, and efficiency will have far-reaching consequences—not only for AI’s future but also for global climate objectives.
As AI continues to evolve, its environmental impact must not be ignored. Industry leaders and policymakers must act decisively to ensure that innovation does not come at the cost of the planet.
