The Data Center Story You’re Not Being Told
Most people argue about motives. This issue shows you how to evaluate the deal instead.
Someone wrote an anti-data-center op-ed last week.
They researched it with an AI assistant.
They drafted it with an AI assistant.
The assistant answered from a rack in a building exactly like the one the op-ed condemns.
Every query drew power off a grid the piece argues the industry is straining.
None of that proves the op-ed is wrong.
It does suggest we’re asking the wrong question.
The dominant data center story is an outrage story: the developer is greedy; the neighbor is a NIMBY; the booster is a shill for the tech bros; the opponent is virtue-signaling.
Each of these is a claim about the believer—about motive, sincerity, and status.
None of them can tell you whether a specific facility is a fair deal for the people who live next to it.
A hypocrite can be right.
A saint can be wrong.
The character of the person holding the opinion isn’t what matters.
Instead of asking whether someone is sincere—or whether they’re reading the right newspapers (or Substacks)—ask a different question:
Is the arrangement itself fair?
Who bears the costs?
Who captures the benefits?
Does the price paid match the burden imposed?
That question needs a lens.
If someone tells you data centers are either saving the economy or destroying the grid, don’t argue. Ask them to score the deal.
This issue lays one out, scores a real facility with it, and shows why much of today’s data-center debate is arguing about the one thing the scorecard tells you to ignore.
Once you start separating the arrangement from the outrage, you’ll notice the same mistake in almost every infrastructure debate.
If you invest in, site, finance, or regulate this infrastructure, the character debate is a distraction you can’t afford.
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This is where the article stops describing the debate and starts scoring it.
Below, I apply the framework to a real data-center tariff, compare two nearly identical projects that receive opposite fairness scores, and show why Ohio and Texas are quietly moving toward the same underlying model.
You’ll also get:
the three fairness axes,
the but-for gate,
the seven distributive tests,
a side-by-side comparison of two seemingly identical projects that receive opposite fairness scores, and
two real regulatory case studies showing how PJM and ERCOT are already converging on this approach.
The goal isn’t another opinion.
It’s a scorecard you can apply to the next project that lands on your desk.
Once you’ve scored a real project, it’s surprisingly difficult to go back to arguing about motives.
