Big Tech companies are racing to secure nuclear power for their AI infrastructure, but the economics of their strategy are raising serious questions among energy analysts. Microsoft, Google, Amazon, and Meta have committed to gigawatts of nuclear capacity through partnerships and power purchase agreements. Yet the first major project to emerge from this trend reveals a troubling cost structure: a 300-megawatt small modular reactor (SMR) being built by GE Hitachi and the Tennessee Valley Authority will cost $5.4 billion, or $18 million per megawatt . That price tag matches the most expensive nuclear plant ever built in the United States and rivals ongoing projects in France and Britain .

Why Are Tech Companies Betting on Nuclear Power?

The answer lies in the sheer electricity demands of artificial intelligence. Global data center power consumption is projected to reach 500 to 1,000 terawatt-hours by the end of 2026, equivalent to Japan's entire annual electricity usage . In the United States alone, data center power demand is expected to nearly double from 80 gigawatts in 2025 to 150 gigawatts by 2028 . Traditional power grids cannot handle this surge, and renewable energy sources like solar and wind cannot provide the consistent, 24/7 baseload power that data centers require .

Nuclear energy offers what renewables cannot: reliable, constant electricity regardless of weather conditions. This is why Microsoft committed to 2 gigawatts of nuclear power through 2040, Meta announced 6.6 gigawatts of nuclear projects, and Amazon is working to bring over 5 gigawatts of small modular reactors online by 2039 . Microsoft's most visible deal involves restarting Three Mile Island Unit 1 in Pennsylvania, with a target to get the 835-megawatt reactor back online by 2027 or 2028 under a 20-year power purchase agreement .

What's the Real Cost Problem?

The GE Hitachi project reveals a fundamental economic challenge. At $18 million per megawatt, the new SMR is not cheaper than the Vogtle nuclear station, the last completed nuclear plant in the United States, which cost $16.5 million per megawatt . For small modular reactors to become competitive with other forms of electricity generation, costs will need to decline by 30 to 40 percent, according to energy analysts . This assumes that additional plants will actually be built and that construction timelines remain stable, neither of which is guaranteed .

The timing problem compounds the cost issue. Small modular reactors take 7 to 10 years to build for first-of-a-kind projects, with industry analysts expecting deployments in the late 2020s to early 2030s . Even Microsoft's Three Mile Island restart, which is retrofitting an existing plant rather than building from scratch, will not be ready until 2027 or 2028 at the earliest, assuming smooth regulatory approvals from the Nuclear Regulatory Commission and no construction delays . Meanwhile, AI demand is exploding now, creating a 5 to 10-year gap during which grid strain and higher electricity costs in tech hub regions could slow AI deployment for companies without guaranteed power access .

How Is This Reshaping Competition in AI?

Energy access is becoming a barrier to entry in the artificial intelligence race, similar to how cloud infrastructure gave Amazon, Microsoft, and Google advantages in the 2010s . Big Tech companies can afford billion-dollar nuclear partnerships; smaller AI companies cannot. If you cannot secure power, you cannot scale AI infrastructure. If you cannot scale infrastructure, you lose the AI race . Energy is moving from a background utility to a strategic technology layer, and whoever controls power generation controls the AI future .

This vertical integration into energy generation is raising antitrust concerns. Big Tech is already facing scrutiny for market dominance, and now these companies are consolidating their structural advantages by owning reactors . The debate around nuclear energy,environmental concerns, radioactive waste, construction costs, and regulatory complexity,becomes more contentious when the biggest tech companies in the world start owning power plants .

Steps to Understanding the Nuclear-AI Energy Strategy

• Understand the Power Gap: AI data centers consume exponentially more electricity than traditional computing infrastructure, with server racks evolving from 8 kilowatts in 2021 to over 50 kilowatts in 2026, forcing tech companies to seek dedicated power sources.
• Recognize the Cost Challenge: The first small modular reactor project costs $18 million per megawatt, requiring 30 to 40 percent cost reductions to compete with other electricity sources, making the economics uncertain for future projects.
• Track the Timeline Mismatch: Nuclear plants take 7 to 10 years to build while AI demand is growing immediately, creating a critical gap where grid strain and higher electricity costs could slow AI deployment for companies without guaranteed power access.
• Monitor Competitive Consolidation: Energy procurement is becoming a core competitive advantage for Big Tech, with Microsoft, Google, Amazon, and Meta using nuclear partnerships to secure exclusive power supplies and create barriers to entry for smaller AI companies.

What Do Energy Analysts Say About the Economics?

Energy experts are skeptical about whether the government should direct money into projects that produce the most expensive electricity when so many secure alternatives exist . The fundamental question is whether developers can really engineer a worldwide market to sell expensive electricity and thereby reap economies of scale for the modular construction process . Without significant cost reductions and successful deployment of multiple reactors, the nuclear strategy may prove to be a costly hedge against an uncertain energy future.

The Trump administration's goal to quadruple US nuclear capacity to 400 gigawatts by 2050 suggests a more favorable policy environment for nuclear than in the past decade, but execution is always harder than announcements . Regulatory battles are coming, including Nuclear Regulatory Commission approvals, environmental reviews, and community opposition to nuclear facilities, all of which will shape which projects actually get built .

For now, the immediate reality is that energy constraints may slow AI deployment despite software readiness. The AI race is no longer just about algorithms; it is about who can keep the lights on .