Artificial intelligence is no longer just the future—it’s now the engine of the global digital economy. AI is infiltrating every sector, reshaping industries, and rewriting the rules of business. But behind this technological shift lies an uncertain reality: AI consumes enormous amounts of energy. And unless reined in, this demand could overwhelm global electricity systems.
AI’s Energy Appetite Is Growing Faster Than Expected
According to Energy and AI, the latest report from the International Energy Agency (IEA), the electricity consumed by data centers—the nerve centers of the AI revolution—could more than double by 2030, surpassing 1,000 terawatt-hours (TWh). That’s conservative against a recent Goldman Sachs forecast that global power demand from data centers will increase 50% by 2027 and by as much as 165% by the end of the decade.
At the heart of this surge are data centers—the physical infrastructure that powers AI’s rapid computations and learning cycles. The rise of AI workloads, including large language models and generative platforms, is driving an exponential increase in power consumption that governments, utilities, and tech giants can no longer afford to ignore.
“There is no AI without electricity,” warned Dr. Fatih Birol, Executive Director of the IEA at the launch of the report. “The energy industry is caught unprepared for the speed and demands of the AI industry.”
AI Could Break the Grid—Or Help Save It
This predicted surge in energy use poses a defining challenge. But there is also a clear opportunity: if harnessed correctly, AI could become a powerful tool for cutting energy consumption and carbon emissions – by up to around 5% of global emissions. The report doesn’t just warn of growing demand—it highlights how AI itself could transform energy efficiency and reshape our response to climate and infrastructure pressures.
AI is already playing a pivotal role in making energy use smarter and more sustainable. In power grids, it enhances forecasting and fault detection, allowing for better integration of renewable energy and between 30-50% fewer outages. In industry, it’s optimizing operations to the point of saving as much energy as Mexico uses each year. AI-powered logistics could cut fuel use across transport systems, saving the equivalent of energy used by 120 million cars. And in the built environment—often slow to modernize—AI-driven controls for lighting, heating, and cooling could save 300 TWh annually, the same as Australia and New Zealand’s total electricity use.
In this way, AI embodies a paradox: it is both the driver of rising energy consumption and a key to reducing it.
AI’s New Era Of Energy-Efficient Innovation
The future isn’t only being shaped by demand—it’s also being disrupted by innovation. In early 2025, Chinese startup DeepSeek introduced open-source AI models that match the performance of market leaders at a fraction of the energy cost. Its V3 model was trained for just $5.6 million, while reports put the training cost of GPT-4 at around $100 million. Breakthroughs like these are rewriting assumptions about the energy requirements of advanced AI and pointing to a more efficient path forward.
Even Big Tech is reconsidering its approach. While companies like Microsoft, Meta, and Apple have announced massive investments in data centers, analysts are reporting a trend of scaling back. In the U.S. and Europe, Microsoft is reported to have paused several data center expansion projects. The move, spurred by efficiency gains and shifting business models, suggests the future may not be as energy-hungry as once feared. DeepSeek’s breakthrough in cutting computational loads hints at a new era of leaner, greener AI.
That shift may be essential. “Almost half of the U.S. electricity demand growth between now and 2030 will be driven by data centers,” Birol said. “To put it in context, the electricity used for AI data centers will exceed the consumption of the chemicals, steel, aluminum, and cement industries combined.” That’s not just a shift—it’s a wholesale reordering of industrial energy dynamics. It suggests that AI could become the single most dominant industrial force shaping the future of electricity grids, energy investments, and national energy security strategies.
Designing AI For Efficiency, Not Excess
There’s growing consensus that the solution lies not in curbing technological progress, but in building smarter systems. Jakob Jul Jensen, Head of Business Development for Data Centers at Danfoss, believes the future hinges on better design. “Energy efficiency must be prioritized and integrated into every layer of data center design and operation,” he says. That means smarter cooling systems, AI-driven power management, and perhaps most overlooked, heat reuse. According to the IEA, the waste heat from data centers could meet 10% of Europe’s space heating needs, provided it’s redirected into homes, buildings, and local industries, impacting the energy system overall.
As AI investment accelerates, access to secure, affordable, and clean energy is becoming a strategic differentiator for nations. Countries able to deliver on all three will lead the AI-driven economy and attract investment from the world’s most valuable companies. “The availability and cleanliness of electricity is becoming a strategic advantage,” says Birol. “Countries that can provide secure, clean, and affordable electricity will be one step ahead in attracting AI investment. Those that fall short risk being left behind.”
That’s why the IEA is taking this moment seriously. For the first time, it’s integrating AI into its annual World Energy Outlook forecasts, recalibrating how governments and industries prepare for the road ahead. “Everything around, above, and under energy is our business,” said Birol. “We couldn’t ignore this. Data always wins—and our job is to put that data on the table for governments and industry to act.”
AI At The Crossroads Of Innovation And Sustainability
And act they must. Without coordinated policies and innovation-driven strategies, AI could lock in energy inefficiencies that will be far more expensive—and disruptive—to fix later. But by embracing AI-powered grid management, systemic energy integration, and innovations in efficiency and reuse, there is a real chance of turning this challenge into an opportunity driving the development of a smarter, cleaner, and more resilient global energy system.
Artificial intelligence is not just the next great industrial consumer of energy—it’s also poised to be its most transformative innovator. Whether the world leans into this dual identity will define not only the trajectory of technological progress, but the sustainability of the global energy economy itself. The question is no longer if AI will reshape the future of energy, but how sustainably we allow it to do so.
