What Is the Smartest Way to Power the AI Boom?

The AI boom is straining the U.S. energy grid, with data center power demand projected to double to 66 gigawatts by 2027, driving up costs and blackout risks. Experts propose solutions including microgrids, geothermal energy, and co-designing AI and energy systems to ensure reliable, low-carbon power.

Few events in history have tested the U.S. energy grid https://gizmodo.com/what-will-it-take-to-modernize-the-us-power-grid-2000755720 like the AI revolution. As data centers https://gizmodo.com/energy-department-wants-data-centers-to-stop-draining-the-grid-during-brutal-heat-wave-2000780886 proliferate across the country, their cumulative power demand is projected https://www.goldmansachs.com/insights/articles/us-data-center-power-demand-projected-to-double-by-2027 to double by next year, rising from 31 gigawatts to 66 gigawatts. In some parts of the country, the energy needs of AI are already outpacing https://www.belfercenter.org/research-analysis/ai-data-centers-us-electric-grid available capacity, driving up consumer electricity bills, raising the risk of blackouts, and increasing reliance on high-emission energy sources. The aging grid clearly isn’t equipped to handle AI’s surging power demand, and while experts broadly agree that something needs to be done, they have differing opinions on the best path forward. For this Giz Asks, we asked various experts what they think is the smartest way to power the AI boom. They pointed to a diverse array of solutions—from microgrids to geothermal energy—underscoring the complexity of the challenge ahead. The following responses may have been lightly edited for length and clarity. Mohammad Shahidehpour University Distinguished Professor, Galvin Chair Professor, and Director of the Robert W. Galvin Center for Electricity Innovation at the Illinois Institute of Technology. Shahidehpour has been the principal investigator of over $80 million in grants and contracts on power system operation and control, smart grid research and development, and large-scale integration of renewable energy. The AI revolution presents one of the most consequential energy challenges of the twenty-first century. The global AI race is often characterized as a competition in algorithms, semiconductor manufacturing, and computational hardware. However, equally important is competition in energy infrastructure. Nations capable of delivering abundant, reliable, affordable, and low-carbon electricity will possess a decisive strategic advantage in attracting AI investments and sustaining long-term economic competitiveness. In this emerging landscape, access to high-quality electric power is becoming as strategically important as access to advanced computing technologies. The most effective strategy for powering the AI revolution rests on several complementary pillars. These include deploying a diversified portfolio of clean and firm generation resources, incorporating water efficiency and climate resilience into data center planning, and establishing market and regulatory frameworks that incentivize flexibility, reliability, resilience, and sustainability throughout the planning and operation of AI infrastructure. Ultimately, global leadership in AI will depend not only on breakthroughs in computer science and semiconductor technologies but also on the ability to build and operate an electric power system capable of supporting unprecedented computational demand. Consequently, AI data centers should be viewed not as passive electricity consumers but as intelligent, grid-interactive assets that actively enhance system flexibility, resilience, and reliability through coordinated demand response, distributed energy resources and energy storage, energy efficiency, and advanced methods for transmission, distribution, and delivery of electricity. The greatest opportunity for us lies in the co-design of AI and energy systems. Rather than planning digital infrastructure and electric power infrastructure independently, future investments should jointly optimize computational workloads, power system operations, communications, electricity markets, environmental sustainability, and water resources. Such an integrated framework will transform AI infrastructure from a rapidly growing electrical load into a strategic asset that strengthens grid performance while accelerating innovation and economic growth. Countries and industries that successfully integrate AI development with intelligent energy planning will not only lead the next generation of computing but also define the future architecture of resilient, sustainable, and secure energy systems. In the coming decades, the true measure of AI leadership will be determined not only by computational capability, but also by the intelligence, adaptability, and sustainability of energy systems that power it. Roland Horne Thomas Davies, Barrow Professor of Earth Sciences at Stanford University and Senior Fellow in the Precourt Institute for Energy. As a leading expert on geothermal energy, he is best known for his work in well test interpretation, production optimization, and analysis of fractured reservoirs. The smartest way to power the AI boom is through Enhanced Geothermal Systems EGS . Data centers powering the AI revolution demand immense amounts of energy, but their most critical requirement isn’t just capacity, it is continuous, 24/7 reliability. While wind and solar provide clean power, their intermittency requires massive battery storage infrastructure or fossil-fuel backups. EGS solves this by unlocking baseload, carbon-free energy almost anywhere on Earth. Traditional geothermal is geographically limited to natural volcanic or tectonic hotspots. EGS eliminates this constraint by applying advanced directional drilling and hydraulic stimulation techniques to access deep, hot basement rock that lacks natural permeability. By injecting fluid into artificially created fracture networks, EGS harvests heat from miles beneath the surface, driving turbines to produce continuous electricity. For tech infrastructure, EGS offers distinct advantages over other alternatives: Unmatched Capacity Factor: EGS operates independently of weather conditions, boasting a capacity factor exceeding 90%—outperforming solar and wind, and providing the constant uptime AI workloads demand.Minimal Surface Footprint: Unlike sprawling solar arrays or wind farms, an EGS plant occupies a remarkably small surface area per megawatt, minimizing environmental disruption and easing land-use constraints.Grid Stability: Unlike variable renewables, EGS provides essential grid services like frequency response and inertia, keeping the digital grid highly resilient.By combining the predictable reliability of traditional baseload power with the geographic flexibility of modern drilling technology, EGS bridges the gap between massive computational demands and aggressive corporate net-zero targets. It is a scalable, sustainable solution capable of anchoring the next generation of computing infrastructure. Amin Khodaei Professor of electrical and computer engineering at the University of Denver, where he is currently on leave to serve as CEO and co-founder of Gridient, a start-up that offers AI-driven edge solutions for a decarbonized grid. He also serves as vice president of education at the IEEE Power & Energy Society and sits on its Governing Board. Khodaei’s research focuses on power systems, microgrids, and grid resilience. The smartest way to power the AI boom is to stop treating it only as a question of how much electricity we can produce. We’ll need more power plants and power lines, but the real problem is sharper than that: Can the grid deliver enough power at the exact hours when demand is highest, without risking blackouts or driving up costs for everyone else? A region might have plenty of electricity across a full day and still struggle during the busiest hours, especially in areas where the local grid is already stretched thin. It’s like a highway: The issue isn’t how many cars use the road over 24 hours, but how many show up at rush hour. The right strategy pairs building more power with two underused tools: flexibility and efficiency. Flexibility means data centers adjust to what the grid needs instead of pulling power at a constant, unchanging rate. When electricity gets expensive or the grid is under strain, some computing work can shift to a different hour, or on-site batteries and power sources can reduce how much power the facility pulls from the grid. But that flexibility only counts if it is actually verified and enforced, through pricing incentives, verified performance, and real penalties if a company does not follow through; otherwise, grid planners still must upgrade the grid as if every data center will demand full power at the worst possible time. Efficiency should also count as a way to add capacity, not just save energy, when it reduces demand at peak hours. Every megawatt a data center uses is competing with homes, electric cars, and businesses for the same constrained grid. Making data centers themselves run more efficiently matters, but reducing energy use in buildings, primarily residential and commercial, also matters because retrofits, smarter controls, and better energy management can free up grid capacity faster than waiting years to build new infrastructure. AI will still require major new investment in grid infrastructure. But treating this purely as a question of building more misses the fastest solution available: getting more out of the grid we already have through flexible data centers and smarter energy usage while new power sources catch up. Costa Samaras Director of the Carnegie Mellon University Scott Institute for Energy Innovation; Trustee Professor of Civil and Environmental Engineering; and an affiliated faculty member in the Department of Engineering and Public Policy. Samaras previously was the Chief Advisor for Energy Policy at the White House Office of Science and Technology Policy. His research focuses on the pathways to clean, climate-safe, equitable, and secure energy and infrastructure systems. The electric power system is the foundational economic, security, and environmental infrastructure of this century, but the grid is aging and is vulnerable to extreme weather events amplified by climate change. AI data centers are helping drive near-term electricity growth, but we have a generational moment to rebuild the electricity system for this century and to power the electrification of buildings, vehicles, and factories. Powering the AI boom in a way that is good for the economy, communities, and the climate requires bold actions across power generation, transmission, and demand.First, we need a grid infrastructure trust fund that helps us to double the capacity of the grid by 2040—not just to power AI, but to power the broader electrification needed to address climate change. This could be funded in part by data centers using a “ Smart AI Fast Lane ” connection fee to quickly connect to the grid when they bring their own clean power and enough additional power and energy storage to benefit surrounding communities. A larger, cleaner grid needs lots of solar, wind power, and batteries right now, as well as ramped up investments in geothermal, advanced nuclear, and broader energy innovation.Next, the wires and equipment that bring electricity to cities and neighborhoods need an upgrade to be resilient to climate change, move more power, and be ready for broader electrification. Data centers, firms, and governments can share in the reinvestment needed beyond the needs of a specific data center, which can help keep electricity costs affordable. The grid is an essential national infrastructure asset, and residential customers shouldn’t be required to pay the entire bill for a 21st century refresh. Finally, a grid infrastructure trust fund can support energy efficiency and flexibility, distributed power and energy storage, and virtual power plants , which can reduce peak power demands and keep electricity reliable. In addition, transparency from data centers, a “miles per gallon” efficiency measure for AI, and the reporting of electricity use, emissions, and water use would incentivize best in class environmental performance. Communities are facing new economic and environmental risks, and at the same the grid needs a generational investment to get to a zero emissions future. AI has the potential to help drive better climate and infrastructure outcomes, but this will not happen without focused policy and deep partnerships with communities. Giz Asks https://gizmodo.com/tag/giz-asks is a recurring Gizmodo series in which experts answer big questions in their own words, offering a range of perspectives on the ideas, discoveries, and debates that affect our lives and shape our understanding of the world.