Nuclear Power as a Solution to AI鈥檚 Soaring Energy Needs
Key Takeaways
- S. data center electricity use was around 4.4% of total demand in 2023 and could climb to 6.7%鈥�12% by 2028, according to the U.S. Department of Energy.
- Big Tech is signing long鈥憈erm nuclear energy agreements: Google/NextEra (Duane Arnold restart), Meta (Vistra/TerraPower/Oklo), and Microsoft/Constellation (Three Mile Island Unit 1) to secure 24/7 clean power.
- SMRs and microreactors offer modular, scalable power (鈮�1鈥�20 megawatts for microreactors, 鈮�20鈥�300+ megawatts for SMRs), aligning with large AI campus loads.
- Near鈥憈erm risks include timing mismatches (AI load grows in cycles of three to five years, while new nuclear energy can take longer), regulatory hurdles, and community concerns around siting and grid impacts.
- Software鈥憃rchestrated AI workloads can make data centers flexible grid resources that reduce stress during peaks.
Artificial Intelligence鈥檚 Energy Problem
Artificial intelligence (AI) is expanding rapidly, and with it, global electricity consumption.聽Several major analyses project that data center electricity use could聽, driven聽largely by聽AI workloads. According to聽the Lawrence Berkeley National Laboratory, U.S. data center consumption could grow from about 4.4% of national electricity in 2023 to聽. Utilities are now assessing whether projected AI load growth is realistic, with analysts noting that duplicate or speculative interconnection requests create uncertainty and could lead to . Local officials in multiple states have raised concerns as some proposed AI campuses request聽聽of daily capacity鈥攃omparable to the electricity use of midsize cities.
Why Nuclear Energy Is Returning to the Conversation
Wind and solar energy are essential to decarbonization, but their intermittency makes it challenging to support AI systems requiring聽round-the-clock,聽high-reliability聽electricity. Research from聽聽indicates聽that nuclear energy offers a聽high-capacity-factor,聽carbon-free聽source capable of meeting AI鈥檚 24/7 power needs. The聽聽notes that nuclear power facilities provide unmatched uptime and stability, making them聽well suited聽to digital infrastructure with strict reliability requirements.
Firm, clean power sources鈥攅specially nuclear鈥攚ill be essential to聽maintaining聽grid reliability as聽AI-driven聽demand accelerates.
Big Tech Begins Direct Investment in Nuclear Energy
A clear shift is emerging across the energy and tech sectors: Major technology companies are no longer acting only as electricity consumers but are beginning to function as full partners in energy development. I saw this transition firsthand while attending the (ANS) annual meeting in the summer of 2024. Developers of advanced reactors emphasized their need for substantial capital to move projects forward, while data center representatives鈥攆acing unprecedented AI鈥慸riven demand鈥攅xpressed an urgent need for as much reliable power as they could secure.
When I attended another ANS meeting just six months later, the tone had changed noticeably. The conversations had moved from concepts and needs to concrete partnerships, as nuclear energy developers and AI鈥慸riven data center operators openly discussed joint projects, coinvestment strategies, and long鈥憈erm power commitments. What had been parallel challenges only months earlier had rapidly evolved into coordinated solutions:
- are partnering to restart the 615-megawatt Duane Arnold nuclear power plant in Iowa by 2029, securing 24/7 carbon-free energy for Google鈥檚 AI and cloud operations.
- Meta has with Vistra, TerraPower, and Oklo that could unlock up to 6.6 gigawatts of nuclear capacity by 2035 through a combination of existing reactors and next-generation designs.
- Microsoft is supporting the restart of as Constellation Energy鈥檚 , aiming to ensure firm, clean electricity for its AI-driven data center fleet.
By making long-term power purchase agreements, Big Tech is mitigating risk in nuclear projects and accelerating deployment timelines that would otherwise take far longer.
Small Modular Reactors (SMRs) and Next-Generation Designs
Small modular reactors are emerging as ideal partners for large-scale AI facilities. Whereas microreactors typically generate 1鈥�20 megawatts, SMRs generally deliver 20鈥�300 megawatts, aligning well with the demand profiles of AI campuses. And a 聽analysis notes that聽next-generation聽designs could deliver聽50鈥�500 megawatts聽per module, allowing scalability as AI load expands.
The DOE鈥檚 implementation of the (ARDP) is directly shaping the landscape for SMRs and聽next-generation聽reactor designs by creating structured pathways that move promising technologies from concept to deployment. Through three coordinated tracks鈥攆ull reactor demonstrations within seven years, targeted聽awards for risk reduction to resolve technical and regulatory gaps, and (ARC-20) funding for innovative designs aiming for commercialization in the 2030s鈥攖he ARDP provides a development architecture聽well suited聽to the modular, scalable nature of SMRs and advanced systems. The program鈥檚 partnership with the further supports this progress by offering test beds,聽siting聽resources, and聽national lab聽expertise聽needed to聽validate聽components, fuels, and integrated system performance.
Together, these implementation mechanisms accelerate the maturation of SMRs and advanced designs by reducing financial risk, enabling iterative testing, and strengthening domestic supply chains鈥攁ll essential steps for deploying reactors capable of producing the potentially hundreds of megawatts of energy needed for聽next-generation聽digital and industrial infrastructure.
One of the most innovative approaches comes from聽Deep Fission, which is developing a聽15-megawatt underground borehole reactor聽placed聽roughly聽one聽mile deep. This design leverages natural geologic pressure for passive safety while reducing construction costs by up to 80%. Recent and聽digital infrastructure聽partnerships illustrate .
Timelines, Risks, and Community Considerations
AI-driven聽electricity demand is rising in聽cycles of three to five years, while licensing and constructing new reactors鈥攅specially聽first-of-a-kind聽SMRs鈥攃an take significantly longer. Analysts warn of a potential聽, which may increase reliance on聽natural gas聽generation in the short term.
Tech sector observers also note long-standing regulatory, financial, and聽public perception聽 that continue to slow new nuclear energy deployment.
are rising, as well. Activists and local governments across multiple states have questioned the siting, water use, and grid impacts of聽multigigawatt聽AI campuses, prompting developers to prioritize community engagement and transparency.
However,聽forward-looking聽grid studies show that data centers themselves can :聽AI workloads can be flexed in real time聽to reduce stress on transmission systems during peak events.
A STEM鈥慣rained Workforce: Critical to Both Sectors
A thriving聽ecosystem integrating clean energy聽and artificial intelligence depends on聽people, not just technology. Nuclear power facilities聽require聽nuclear technicians, operators, and instrumentation and controls specialists; AI data centers聽require聽experts in cybersecurity, cloud engineering, and advanced computing.
Reports from聽听补苍诲听聽stress that a robust聽STEM-educated聽workforce聽is essential to scaling both聽clean energy聽systems and AI data center operations.
is therefore not an 鈥渙utput鈥� of these industries鈥攊t is the聽core enabler, powering both聽next-generation聽energy infrastructure and the digital systems that rely on it.
Bottom Line
AI is transforming reliable electricity from a background assumption into a聽strategic constraint and competitive advantage. In the near term, nuclear energy restarts and uprates offer the fastest path to more firm, clean power; in the medium term, SMRs and advanced reactors promise scalable solutions tailored to聽the demand of the AI era.
Success will聽require聽long-term聽investment, regulatory modernization, meaningful community partnership, and鈥攃ritically鈥攁聽厂罢贰惭-迟谤补颈苍别诲听飞辞谤办蹿辞谤肠别聽prepared to聽operate聽at the intersection of聽clean-energy聽engineering听补苍诲听AI computing.
