The Gigawatt Delusion (DDCU 2/7)
Why Today’s Hyperscale Data Centers Are Already Obsolete
Microsoft committed $80 billion to data center expansion in fiscal year 2025. Google committed $75 billion. Amazon pledged $100 billion. Meta announced $65 billion. That’s $320 billion in a single year, from four companies, building facilities that will require 60+ months to complete, in a regulatory and grid environment that makes 60 months look optimistic.
This is not a success story. This is a structural trap being mistaken for a strategy.
The AI Arms Race Built on Quicksand
The hyperscale buildout began in earnest in 2022 when ChatGPT demonstrated that AI services had consumer demand.
What followed was the largest coordinated capital deployment in the history of technology infrastructure — faster than the telegraph network, faster than electrification, faster than the fiber optic buildout of the 1990s.
The scale is genuinely staggering. As of mid-2025, more than 1,000 data centers were under construction or permitted in the U.S. alone, with combined capacity of 75 GW — comparable to the peak daily demand of New York City. The global AI infrastructure market requires an estimated $6.7 trillion in investment through 2030, and roughly $24.6 trillion by 2034.
Every major technology company, every sovereign wealth fund, every infrastructure REIT, and a remarkable number of people who had never thought about cooling towers before 2022 poured capital into the same bet: that large, centralized, grid-connected data centers were the correct substrate for AI.
They were betting on a technology that had already started making their bet obsolete.
The Five Structural Failures of Hyperscale
Let me be specific, because this matters. Hyperscale AI data centers are not slightly suboptimal. They are structurally wrong for the workload they’re designed to serve. Here’s why.
1. The Grid Has Already Failed Them
