Pitchblend, a form of uranium ore
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.
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The poster child of the patronage system: