The Power Allocation

For a decade, software ate the world. Now AI is making land matter again.

Meta's 2-gigawatt facility requires hundreds of acres. Microsoft's expansion spans millions of square feet globally. These aren't software deployments — they're industrial developments with 30-year time horizons requiring transmission adjacency, water rights, and favorable zoning.

Developers with optioned land near transmission corridors are commanding premium valuations. Land isn't just back — it's becoming a rate-limiting input to the AI buildout.]]>

Data centers are relocating. Not for tax incentives. Not for talent. For power.

Virginia's data center corridor consumed 26% of state electricity in 2023. Dominion Energy is struggling to keep pace. Grid stress events and connection delays stretching to years are pushing capital to Texas, Ohio, Indiana, Southeast Asia, and the Nordics.

Capital is following power geography, not network geography. The next decade of AI infrastructure will be built where grid capacity exists — not where the internet already concentrates.]]>

The GPU shortage narrative is fading. The real constraint on AI compute is electricity — and it's not even close.

Chip fabrication is scaling — TSMC, Intel, and Samsung are investing billions. But grid capacity doesn't scale like silicon. A single NVIDIA H100 draws 700 watts. AI training clusters now exceed 100 megawatts. Goldman Sachs projects 165% increase in global data center power demand by 2030.

The problem isn't that chips don't exist. The problem is you can buy the chips, but you can't plug them in.]]>

In this episode, we examine why AI facilities require 50-150 kilowatts per rack versus 10-15 kilowatts for traditional computing, why developers face 5-7 year grid interconnection delays, and how Microsoft and Meta are deploying $145 billion combined in infrastructure bets on land, substations, and transmission capacity.

The companies winning this cycle won't be the ones with the best models. They'll be the ones with secured power capacity and execution timelines measured in megawatts, not parameters.