This One Thing Could Kill the AI Boom—And It’s Not China
The Coming Collision: AI, Energy, and the New Global Power Struggle
Artificial Intelligence (AI) is experiencing an extraordinary acceleration, with growth trajectories surpassing those of previous transformative technologies such as the internet, cloud computing, and mobile platforms. The scale of this shift is not just digital—it is physical. Each step forward in AI performance, from training LLMs to rolling out multimodal agents at scale, requires exponentially increasing amounts of electricity, cooling infrastructure, and high-density compute environments. This is triggering a new wave of physical infrastructure investment that intersects with geopolitics, environmental planning, and national security.
However, this rapid technological growth is revealing a critical limiting factor: energy. Unlike software innovation, which can scale in the cloud or over fiber, AI's growth hits a wall when electricity becomes scarce or unreliable. The physical and infrastructural demands of compute-intensive models are overwhelming existing power grids in the United States and around the world. In Virginia, Texas, and Northern California—three of the most data-center dense regions in the country—utilities are either at capacity or projecting multi-year delays for interconnection.
This report explores the emerging role of next-generation energy infrastructure in supporting AI’s future. It will examine how public and private actors are responding to these constraints, with a focus on U.S. hyperscalers, nuclear developers, and federal regulators. Finally, it will analyze the broader geopolitical and economic implications of AI’s collision course with global energy systems.
Data Centers & AI: An Unsustainable Energy Curve
The training and deployment of AI models—particularly large-scale systems like GPT—require unprecedented amounts of electricity. Training a single model may demand dozens of megawatts, and when scaled for inference across billions of users, total energy consumption grows exponentially. According to current projections, data centers in the United States alone could demand over 80 gigawatts of electricity by 2030, more than tripling their 2023 consumption. Grid operators in Northern Virginia, Texas, and Georgia—regions that host hyperscale AI infrastructure—are already warning of supply shortages and congestion. This issue is no longer theoretical. It is an immediate and growing constraint that could stall innovation unless rapidly addressed through major energy innovation.
To proactively address this, leading tech companies are moving swiftly. Microsoft has entered a long-term deal with Constellation Energy to purchase nuclear power and is exploring on-site SMR deployment. Amazon has acquired a nuclear-powered data center site in Pennsylvania and is investing in modular nuclear through partnerships with companies like X-energy. Google is supporting geothermal pilots with Fervo Energy and deploying AI tools to optimize data center load balancing with real-time grid data. Meta, despite controversy, is attempting to co-develop its own gas plants for AI energy. Additionally, hyperscalers are exploring direct ownership of power infrastructure—moving from renters to full-stack energy operators.
This corporate shift reflects the strategic realization that future compute capabilities are inseparable from energy sovereignty. The result is an unprecedented convergence of Silicon Valley capital, national energy strategy, and industrial infrastructure.
The Nuclear Option: Rationale and Feasibility
As AI systems scale, their energy requirements exceed what intermittent renewables like solar and wind can provide. Traditional fossil fuels, while currently filling the gap, are incompatible with long-term carbon reduction goals. Nuclear energy—particularly in advanced modular forms—presents a solution that aligns with both operational and environmental imperatives.
Small Modular Reactors (SMRs) are a next-generation class of nuclear power systems designed to generate between 50 and 300 megawatts (MW) of electricity. Unlike traditional nuclear plants that require years of site-specific construction, SMRs are factory-built in modular units, allowing for faster deployment, lower capital costs, and scalable implementation. Their reliability makes them an ideal candidate for powering high-consumption operations such as data centers, industrial processing plants, and energy-hungry AI infrastructure hubs—especially in areas with limited grid connectivity or volatile energy prices.
Microreactors operate at an even smaller scale, typically producing between 1 and 10 MW. They are engineered for mobility, rapid setup, and long-duration autonomy. These reactors are especially well-suited for powering edge AI compute sites, remote military installations, and isolated infrastructure where conventional energy sources are impractical. Many designs can fit within a standard shipping container and operate for years without the need for refueling, making them attractive for defense, disaster relief, and emerging markets.
Both technologies share several key advantages. They provide constant 24/7 baseload power, which is essential for maintaining uninterrupted AI model training and deployment. They are also carbon-free at the point of operation, helping to meet environmental sustainability goals. Importantly, if sourced and manufactured domestically, they reduce geopolitical dependence on fossil fuels and foreign energy infrastructure.
However, there are significant hurdles to widespread adoption. The U.S. Nuclear Regulatory Commission (NRC) still operates under frameworks designed for legacy reactors, which slows approval of SMR and microreactor designs. Most advanced reactor concepts also require high-assay low-enriched uranium (HALEU), which is currently only produced at commercial scale in Russia—a strategic vulnerability for Western nations. Public resistance remains another obstacle, particularly related to safety, waste management, and siting.
Despite these challenges, SMRs and microreactors could become the cornerstone of a resilient, scalable, and clean energy grid tailored for the AI era—if regulatory reform, domestic fuel production, and public engagement are addressed in tandem.
Technological Overview and Deployment Barriers
SMRs and microreactors are currently in advanced development across North America, East Asia, and parts of Europe. Examples include GE Hitachi’s BWRX-300 reactor and NuScale’s VOYGR system in the U.S., as well as Oklo’s Aurora microreactor for remote edge deployment. However, full-scale rollout remains years away due to numerous structural bottlenecks:
The Nuclear Regulatory Commission (NRC) still uses frameworks built for legacy, gigawatt-scale reactors.
HALEU fuel is currently produced at scale only by Russia, raising geopolitical risks.
The absence of a domestic, modular reactor supply chain—akin to what Tesla built for EVs—has left a critical gap in nuclear manufacturing.
Addressing these roadblocks will be pivotal to unlocking SMR and microreactor capacity within this decade.
Mismatched Timelines: AI Acceleration vs. Nuclear Readiness
AI workloads are increasing on an exponential curve. Compute demand for training frontier models is doubling every 12–18 months. Meanwhile, most SMRs or microreactors will not be commercially available before 2027–2030. In the interim, companies are relying on:
Natural gas and diesel peaker plants are currently the most widely used stopgap measure for powering AI data centers due to their fast ramp-up capabilities and availability across the U.S. These plants can be deployed quickly and provide immediate power to grid-constrained zones, particularly in regions like Texas and Pennsylvania. However, they emit significant greenhouse gases and contribute to local air pollution, raising both environmental and regulatory concerns. Their reliance on fluctuating fuel markets also introduces long-term pricing risk and volatility.
Renewable + battery hybrids have emerged as a favored option for companies committed to sustainability targets. These systems pair intermittent renewable generation—like solar or wind—with lithium-ion battery storage to extend usable hours. While increasingly affordable and scalable, they still struggle to deliver 24/7 baseload power, especially for AI training clusters that require consistent, uninterrupted power over long periods. Moreover, battery storage capacity remains insufficient for multi-megawatt operations running at full load.
Load-shifting and carbon offsets represent another class of mitigation strategies. Load-shifting involves scheduling high-energy AI tasks during periods of low grid demand or when renewable output is highest, using software-driven optimization to avoid peak charges or blackouts. Carbon offsets, on the other hand, do not change energy sources but aim to balance emissions by investing in reforestation, carbon capture, or clean energy elsewhere. These solutions help with optics and compliance but do not address the core issue of constrained physical electricity availability.
These are temporary workarounds. Without accelerated nuclear deployment, the AI ecosystem will continue relying on carbon-heavy, unstable, or increasingly expensive energy.
The Global Nuclear Deployment Landscape
Global SMR activity varies significantly by country, revealing a divergence in both pace and strategic prioritization. The United States is currently the innovation leader in terms of design and private-sector startup activity, but it lags far behind in deployment due to regulatory bottlenecks and an underdeveloped fuel supply chain. While American firms like NuScale, Oklo, and TerraPower lead in advanced designs and pilot partnerships, none have deployed reactors at commercial scale.
China, in contrast, is already operational with Small Modular Reactors and high-temperature gas-cooled reactors (HTGRs). These projects are backed by full state support and integrated into national infrastructure plans. China is also positioning to export this technology as part of its Belt and Road energy diplomacy. Its model emphasizes vertical integration and speed, bypassing the complex permitting that hinders Western deployment.
Canada is also advancing quickly, with the Darlington SMR project—the first grid-connected SMR in North America—currently under construction. The Canadian Nuclear Safety Commission (CNSC) has streamlined approvals, and utility-provider Ontario Power Generation (OPG) has partnered with GE Hitachi to bring the project online by 2028. This public-private coordination, coupled with federal incentives, has positioned Canada to lead SMR deployment in the West and potentially serve as a template for other liberal democracies seeking rapid deployment.
Russia continues to lead in specialized deployment, including Arctic-based floating nuclear power stations like Akademik Lomonosov. It also maintains a near-monopoly on the global production of HALEU fuel, which is needed for most advanced reactor designs. This gives Russia significant leverage over countries pursuing next-gen nuclear technology and makes fuel diversification a national security priority for Western nations.
South Korea is focusing on international export opportunities, having developed the SMART SMR design, which is being positioned for adoption in the Middle East. Its regulatory environment and global manufacturing base make it an efficient exporter. With Samsung and Doosan heavily involved, South Korea has the industrial capacity to scale rapidly once licensing hurdles are cleared.
These diverging national trajectories are more than technical—they are geopolitical. The countries that move fastest and scale most effectively will set the standards for global nuclear partnerships, determine who can host next-gen AI infrastructure, and consolidate influence over the digital and energy architectures of the future.
The U.S. Deployment Gap: Structural and Political Causes
Despite boasting top-tier nuclear startups and DOE-backed innovation programs, the United States has yet to deploy an SMR at commercial scale. Structural challenges include:
A risk-averse regulatory apparatus designed for legacy reactors
Fragmented national energy strategy lacking a centralized mission or equivalent to the CHIPS Act
Public mistrust dating back to incidents like Three Mile Island and Cold War-era nuclear proliferation fears
Continued dependency on Russian HALEU, with insufficient domestic production or refining capacity
To move forward, a federally coordinated SMR mobilization effort is needed that includes regulatory reform, public education, fuel independence, and industry incentives—essentially, a Manhattan Project for modular energy.
Market Blind Spots: Investor Complacency
Despite the looming power constraints, financial markets have not fully priced in the energy bottleneck risk. AI-related stocks are being valued based on revenue projections, GPU availability, and product launches—not on regional power availability or grid stability. Reasons for the blind spot include:
Most energy issues remain geographically isolated
Hyperscalers have not yet missed guidance due to power
Markets assume federal or state intervention will solve grid limitations
However, a single large-scale outage, deployment freeze, or delayed training run could spark a rapid investor pivot toward energy-aware tech investing.
U.S. Regional Winners in the AI + Energy Economy
Regions best positioned to absorb AI + energy investments are those with ample land, infrastructure, and favorable regulatory environments:
Pacific Northwest (WA/OR): Abundant hydroelectric capacity; existing hyperscaler infrastructure
Texas: Deregulated ERCOT grid, rich in natural gas and solar, launching $2B nuclear initiative
Mountain West (ID/UT): DOE SMR pilot zones, cooling-friendly climates, low land costs
Mid-Atlantic (PA/OH): Existing nuclear fleet, Amazon and hyperscaler real estate activity
Southeast (GA/TN): Newly online nuclear capacity (Vogtle), TVA’s expansion plans
These regions could become the future backbone of domestic AI infrastructure if energy constraints worsen elsewhere.
Strategic Risk: Could This Lead to Conflict?
While the direct outbreak of conventional war due to AI-energy competition remains unlikely in the near term, the cascading tensions it generates could manifest in a variety of serious and destabilizing conflict scenarios.
First, resource conflicts are becoming increasingly probable. The global race for uranium and HALEU (High-Assay Low-Enriched Uranium)—essential for fueling advanced nuclear reactors—has already raised geopolitical alarm bells, particularly as Russia controls most of the world’s HALEU supply. As demand surges, access to these materials could become a strategic pressure point between allied and adversarial blocs. Water rights are also emerging as a flashpoint, as data centers and AI clusters compete with agriculture and municipalities for freshwater resources used in cooling systems. Lithium, essential for battery storage in renewable energy systems, adds another layer of potential contention, especially in regions with overlapping claims or weak governance.
Second, cyberwarfare threats are escalating. As AI data centers become tied to national security infrastructure and SMRs are embedded in digital operations, they become attractive targets for state-sponsored cyberattacks. Disabling a regional AI cluster or disrupting a nuclear-powered energy source could be a way to cripple a rival's AI capabilities without direct confrontation. These cyber threats will likely escalate as critical AI infrastructure becomes more centralized and interdependent.
Third, proxy conflicts are likely to intensify, particularly in regions where China, Russia, and Western alliances are vying for infrastructure influence. Africa, Southeast Asia, and parts of South America are being targeted by SMR deployment and power-export agreements that may evolve into long-term geopolitical alliances. These arrangements could sow divisions and create dependencies that mirror the Cold War’s ideological power blocs, now shaped by energy infrastructure and compute sovereignty rather than weapons or ideology.
Finally, internal unrest is an emerging risk. In the U.S. and elsewhere, public backlash is growing against the massive land use, energy redirection, and utility rate hikes associated with AI infrastructure buildout. Protests against data center expansion have already emerged in Oregon, Virginia, and Ireland. These tensions could escalate into broader anti-AI or anti-infrastructure movements, particularly if local populations feel excluded from the benefits while bearing the environmental and financial costs.
Ultimately, the control of clean, scalable power and high-performance compute is becoming a foundation of geopolitical dominance. Those who monopolize these resources will hold leverage over digital economies, military innovation, and global alliances in the coming decades.
Conclusion: Urgency and Opportunity
The AI revolution will not be determined solely by algorithms, chips, or talent. It will be determined by the nations that control the infrastructure of power—literally. The collision between exponential AI growth and slow-moving energy systems presents both a crisis and a strategic opportunity. Those who seize it will define the global hierarchy for decades to come.
For the United States, the choice is urgent and binary: lead the AI-energy revolution—or fall behind.