The growing support for nuclear power is compared to the rapid sentiment shift on gay marriage, driven by younger generations. As older activists, whose opposition was rooted in Cold War-era fears of nuclear weapons, fade away, a new generation sees nuclear energy as a key climate solution, creating a much more favorable political environment.

For new nuclear tech, competing with cheap solar on cost is a losing battle. The winning strategy is targeting "premium power" customers—like the military or hyperscalers—who have mission-critical needs for 24/7 clean, reliable energy and are willing to pay above market rates. This creates a viable beachhead market.

Startups can bypass the lengthy NRC process for initial reactor tests by using Department of Energy (DOE) and Department of Defense (DOD) pathways. The DOE, with national labs, can regulate test reactors for faster innovation. Crucially, the Army can now license its own reactors, creating a direct regulatory and commercial path to a key market.

While solar panels are inexpensive, the total system cost to achieve 100% reliable, 24/7 coverage is massive. These "hidden costs"—enormous battery storage, transmission build-outs, and grid complexity—make the final price of a full solution comparable to nuclear. This is why hyperscalers are actively pursuing nuclear for their data centers.

The narrative of energy being a hard cap on AI's growth is largely overstated. AI labs treat energy as a solvable cost problem, not an insurmountable barrier. They willingly pay significant premiums for faster, non-traditional power solutions because these extra costs are negligible compared to the massive expense of GPUs.

Beyond the well-known semiconductor race, the AI competition is shifting to energy. China's massive, cheaper electricity production is a significant, often overlooked strategic advantage. This redefines the AI landscape, suggesting that superiority in atoms (energy) may become as crucial as superiority in bytes (algorithms and chips).

Meta and Google recently announced massive, separate commitments to US infrastructure and jobs on the same day. This coordinated effort appears to be a clear PR strategy to proactively counter the rising public backlash against AI's perceived threats to employment and the environment.

A large government commitment, like the $80 billion nuclear development plan with Westinghouse, does more than create a single customer. It acts as a powerful catalyst for the entire industry. This de-risks the supply chain, signals market viability, and attracts massive private capital (e.g., Brookfield), creating tailwinds for all players.

OpenAI's partnership with NVIDIA for 10 gigawatts is just the start. Sam Altman's internal goal is 250 gigawatts by 2033, a staggering $12.5 trillion investment. This reflects a future where AI is a pervasive, energy-intensive utility powering autonomous agents globally.