Nuclear Power, Hyperscale Clouds, and the AI Arms Race

An engineer’s take on the new executive orders powering America’s next computing boom

Why these orders matter to those of us who build clouds

Yesterday the White House signed a suite of executive orders designed to slash Nuclear Regulatory Commission red-tape, accelerate fuel production, and quadruple U.S. nuclear output in the next 25 years. The Energy Dominance Council (formed in February) now has an explicit mandate to get reactors—large and small—online fast, with the Defense Production Act invoked to back a domestic fuel supply. (The White House, Investor’s Business Daily)

As someone who has spent two decades building networks, designing hyperscale campuses, and chasing PUEs below 1.1, I read the transcript and thought: finally.

AI is an energy problem first

Every AI model we deploy doubles as a space heater. The coming generation of “AI factories” will require hundreds of megawatts per site, 24 × 7—loads that dwarf the power envelopes of most existing data-center clusters. Intermittent renewables can blunt the peak, but only nuclear can deliver the always-on baseload without a carbon penalty.

“Some of these data centers will cost $200-300 billion, and they want to run them all of the time… nothing does that better than nuclear.” — Joe Dominguez, Constellation Energy CEO (Facebook)

If the United States wants to stay competitive in the global sprint to AGI, we need electrons we can count on. These EOs acknowledge that reality and explicitly link “enough electricity to win the AI arms race with China.” (Facebook)

Closing the regulatory air gap

The dirty secret of hyperscale is that time kills ROI. A two-year slip in a power-intake schedule can vaporize a generation of server value. Nuclear developers live or die by the same clock. By streamlining site-reuse reviews (why study a parcel that’s hosted reactors for 40 years?) and setting a one-year target to break ground on three experimental designs, the administration is attacking the schedule risk that has kept nuclear out of hyperscale procurement playbooks for a decade. (Facebook)

Small Modular Reactors fit the cloud footprint

Most hyperscale campuses sprawl over 50–200 acres—the perfect size for multiple 50-300 MW small modular reactors (SMRs) dropped inside the existing security perimeter. Couple that with on-site AI-driven grid orchestration and you get a virtuous loop:

1. AI optimizes reactor dispatch and maintenance
2. Stable nuclear power feeds the training clusters
3. The clusters design the next-gen reactors even faster

That “full-circle” feedback loop is exactly what keeps me up at night—and gets me out of bed in the morning.

Energy ≡ National Security

From a Pentagon perspective, electricity is now a strategic resource on par with rare-earth metals. The EO that prioritizes SMRs for domestic and forward-deployed bases is blunt realism: satellites, drones, and LLM-based decision tools are useless when the grid is down. (Facebook)

Texas: where shale meets fission-scaled AI

I’m lucky enough to build in Texas—the energy capital of America. We already understand gigawatt-class infrastructure, mineral supply chains, and the hard-won politics of rights-of-way. Layering nuclear onto that ecosystem isn’t a stretch; it’s an evolution. My peers in Houston’s petro-corridors are already re-skilling from LNG trains to reactor modules. Others in Austin are revolutionalizing design and production of energy infrastructure. Old skills meet new tech, and the learning curve is steep—but the runway is long.

What happens next

• Procurement leaders at AWS, Microsoft, Google, and every Fortune-100 AI team will start stapling SMR clauses onto power-purchase agreements.
• Utilities and cloud operators will finally speak the same language—CAPEX per-MW and schedule certainty—instead of hedging with RECs and 15-minute demand charges.
• Regulators will either keep pace with these orders or watch the industry build abroad. The timeline is that unforgiving.

The bottom line

AI is no longer limited by algorithmic ingenuity; it’s limited by how fast we can spin uranium into teraflops. These executive orders aim to break the bottleneck. As an engineer who has lived through the boom-bust cycles of dot-com, Web2, Web3, and cloud 1.0, I’ve never seen policy align this cleanly with technical necessity.

If we execute, the U.S. can lead the world in both the brains and the brawn of the next industrial age. If we stall, the race to AGI—and the geopolitical leverage that comes with it—will be powered elsewhere.

Time to build.

What are your thoughts on nuclear power’s role in AI infrastructure? Share your perspective in the comments below.