Decades of underinvestment have resulted in a widening uranium supply deficit, with mined uranium expected to meet less than 75% of future reactor needs and an incentive price of $135/lb required to restart mothballed mines.

Big Tech hyperscalers are privatizing energy security by locking in clean baseload nuclear power via long-term agreements, effectively making the public grid's "service" secondary to the "compute-ready" requirements of major platforms.

We are seeing a violent collision between two worlds: the high-speed, iterative world of artificial intelligence and the slow, grinding, capital-intensive world of nuclear physics. 

Data from a survey of over 600 global investors reveals that 63% now view AI electricity demand as a "structural" shift in nuclear planning. This isn't a temporary spike or a speculative bubble. It is the physical footprint of every Large Language Model (LLM) query finally showing up on the global balance sheet.

For years, the energy narrative was dominated by "efficiency." We were told that better chips would offset higher usage. That era is over. Generative AI doesn't just use data; it incinerates energy to create it.

Why the "Efficiency" Narrative Failed

The "Reverse-Polish" reality of AI is that the more efficient we make the chips, the more chips we deploy, and the more complex the models become. This is Jevons Paradox playing out in real-time across the data centers of Northern Virginia and Singapore.

When you look at the energy density required for an AI hyperscale center, you aren't looking at a traditional office building. You are looking at a facility that pulls as much power as a mid-sized city, but does so with a 99.999% uptime requirement.

Traditional demand models simply didn't account for a single industry deciding to double its power footprint in less than five years. S&P Global Energy recently highlighted that data center electricity consumption could hit 2,200 terawatt-hours (TWh). 

Intermittent renewables…the darlings of the corporate ESG report…cannot provide the 24/7 "baseload" these machines require...

The hyperscalers have realized that if they want to dominate AI, they need to secure physical atoms before the other guy does.