DCRM: A NEW LENS FOR
AI GRID READINESS The Wrong Tool for the New Load
In my last post, I compared how the U.S., China, and Europe are racing to scale their power systems for AI. Each is approaching the challenge with different strategies, constraints, and assumptions. The narratives are compelling, and the policy commitments are ambitious. But grand plans mean little if the infrastructure can’t keep up.
Despite all the announcements and forecasts, we still lack a clear way to measure how prepared each region actually is to support AI-driven growth. There is plenty of data on grid capacity, interconnection queues, and projected load growth, but no metric that captures the real-world constraints of hyperscaler compute demand. Existing planning tools were not designed with this type of concentrated, high-load infrastructure in mind. They often assume diversified demand profiles and overlook the geographic and temporal clustering of data centers.
Power system operators run detailed resource adequacy
simulations, but these are slow, opaque, and built for general reliability planning, and not for understanding how hyperscalers stress the grid. These models typically assume geographic load smoothing and treat emerging compute clusters as secondary effects. Even reserve margin targets, which are often used as shorthand for grid “readiness,” fail to account for spatial bottlenecks, deliverability constraints, and speculative capacity additions that may never reach commercial operation.
After working through multiple planning datasets, it became clear that while the information existed, it was siloed, backward- looking, or structured in a way that masked the actual constraint: firm, deliverable capacity available to support large- scale, constant, compute-intensive loads.
That gap led to a fundamental question:how do we compare actual readiness, grounded in infrastructure, not just policy direction, across regions in the AI energy race?
18 | ADMISI - The Ghost In The Machine | Q3 Edition 2025
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