America’s artificial intelligence boom is creating an unprecedented energy shortage that could reshape how the nation powers itself. Big Tech’s solution involves a high-stakes choice between two vastly different energy sources.
The most expensive mistake in American business history is happening right now, in broad daylight, and almost nobody sees it coming.
Entrepreneurs dream over the latest AI models. Venture capitalists chase unicorn valuations. But a far more fundamental crisis is quietly dismantling America’s technological supremacy. It’s not foreign competition or regulatory capture. It’s the mundane reality that artificial intelligence needs electricity, lots of it, and the infrastructure to deliver that power simply doesn’t exist.
The numbers are both staggering and accelerating. Training a single AI model like GPT-4 requires 30 megawatts of continuous power, enough to supply 20,000 homes. Data center demand is projected to more than double by 2030, surging from 35 gigawatts in 2024 to 78 gigawatts by 2030. That’s enough power to light up California twice over.
The Grid Bottleneck
The biggest constraint facing AI expansion has nothing to do with technology or capital. Grid connection delays now stretch up to five years for new data centers. American companies face the prospect of waiting half a decade to plug in their next-generation AI systems.
Interconnection requests have increased by 700% in some regions, creating bottlenecks that threaten America’s AI leadership. As much as 20% of planned global data center projects face potential delays due to electric grid constraints.
Northern Virginia’s “Data Center Alley” illustrates the scale of the problem. Power demand in the region could explode from about 4 gigawatts today to 15 gigawatts by 2030, potentially accounting for half of Virginia’s total electricity load.
Natural Gas: The Immediate Solution
Faced with crushing delays, America’s tech giants are turning to natural gas as an immediate lifeline. Natural gas provides reliable off-grid power within 18-24 months, compared to the five-year grid connection timeline.
The market has responded decisively. Shares of major natural gas producers have surged, with Expand Energy climbing over 24%, EQT and Range Resources rising more than 40% and 13% respectively.
Major utility companies are building new natural gas infrastructure specifically for AI applications. WEC Energy Group’s We Energies has outlined a $2 billion plan for new natural gas generation that regulators deemed “critical” to powering Microsoft’s AI operations. Microsoft has expressed openness to deploying natural gas with carbon capture technology, calling it “very much the immediate solution” for AI implementations.
Nuclear: The Long-Term Bet
Nuclear power represents the industry’s long-term vision. The commitments being made are unprecedented in scale.
Amazon has invested in SMR developer X-energy and plans to deploy 5 gigawatts of nuclear energy. Google has announced plans to collaborate with Kairos Power to build up to seven small modular reactors, with the first unit expected online by 2030.
Oracle’s announced plan to construct a gigawatt-scale data center powered by three small modular reactors represents the most ambitious commitment yet.
The Economics & Environmental Considerations
Nuclear plants achieve capacity factors exceeding 92.5%, far outpacing wind at 35%, solar at 25%, and natural gas at 56%. For data centers requiring 24/7 operations, nuclear energy’s uptime reliability of 91.8% compares favorably to alternatives.
Natural gas offers different advantages, primarily speed and cost. A natural gas combined cycle plant might cost around $1 billion compared to $5 billion for a nuclear plant. Small modular reactors could achieve levelized costs of electricity around $36/MWh compared to $92/MWh for large nuclear reference plants.
Natural gas-fired electricity has 57% lower greenhouse gas emissions than coal, but still produces significant carbon dioxide compared to nuclear alternatives.
ExxonMobil is building a natural gas-fired power plant in southeast Texas that incorporates carbon capture technology, expected to eliminate over 90% of CO2 emissions. Nuclear energy produces within the range of 5.1-6.4 grams of carbon dioxide equivalent per kilowatt-hour, essentially matching wind power.
The Trump administration has pushed for data center and energy co-expansion across states through favorable conditions such as tax incentives. Recent announcements include plans to utilize emergency powers to hasten construction of power plants for data centers.
Pennsylvania secured investment exceeding $90 billion from technology, energy and finance companies to become an AI hub backed by state and federal economic incentives.
The Path Forward
The most likely scenario involves a managed transition using natural gas as a bridge during nuclear infrastructure development. Multi-year approaches combining immediate natural gas deployment with planned nuclear integration could offer optimal economic and environmental outcomes.
America’s AI ambitions are creating an energy crisis that will reshape how the nation powers itself for decades to come. Natural gas with carbon capture offers speed but comes with environmental tradeoffs. Nuclear power offers sustainability but requires patience and significant capital.
The race has evolved beyond building the best AI technology to powering it sustainably and efficiently. The companies and investors who navigate this energy transition most effectively will likely determine not just who leads in artificial intelligence, but how that leadership is achieved.
Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.
