There’s an awful lot we don’t know about data centers

URL SCAN: There’s an awful lot we don’t know about data centers
FIRST LINE: I had an idea for a post about data centers, and one thing I needed to know was how many data centers were built in 2025 compared with previous years.

THE DISSECTION

The text is an epistemic autopsy of the data-center debate. It exposes incompatible definitions, inconsistent datasets, vague project pipelines, commercial secrecy, and the absurdity of treating “data center” as a clean unit of analysis. Its real function is to show that public discussion is operating with industrial statistics assembled from fog.

That diagnosis is substantially correct. The article also reveals the weakness of journalism that confuses the inability to produce a neat count with the absence of a knowable structural direction.

THE CORE FALLACY

The central error is metric fixation. The article treats the number of facilities built as the decisive fact, when the relevant variable under the Discontinuity Thesis is not buildings but deployable computational capacity: power, chips, networking, model-serving infrastructure, ownership, and the rate at which cognitive labor is converted into machine output.

A “data center” can mean a building, a campus, a cluster, a legal entity, a planned site, or a gigawatt-scale compute installation. Counting buildings is therefore close to counting factories by roofline while ignoring production capacity. One large AI campus can matter more than dozens of conventional facilities.

The second error is epistemic paralysis. The article implies that because the exact construction rate is difficult to establish, the public cannot confidently assess the broader transition. That does not follow. P1 does not require a perfect census. If capital is aggressively concentrating in compute, energy, and model infrastructure, the strategic fact is already visible: owners are purchasing the machinery that can displace cognitive labor. Missing data affects timing and magnitude; it does not repeal the mechanism.

HIDDEN ASSUMPTIONS

• That project counts are more informative than compute capacity, electricity allocation, accelerator shipments, utilization, or model performance.
• That “planned” versus “speculative” projects can be cleanly separated before financing, permitting, and grid constraints resolve them.
• That public visibility is necessary for structural analysis.
• That data-center backlash is mainly a response to the quantity of construction rather than to expected future power demand, land use, water use, tax exposure, and feared labor displacement.
• That uncertainty is symmetrical. It is not. Competitors can possess actionable intelligence while the public receives blurred categories and partial disclosures.
• That the main question is how many facilities exist, rather than who controls the productive asset and what labor it makes unnecessary.

SOCIAL FUNCTION

Classification: partial truth, transition management, and prestige signaling.

The article performs a useful demolition of false precision. It gives readers permission to recognize that official-looking charts may be incomparable and that the state is trying to regulate an infrastructure category it cannot even define consistently.

But it also functions as a respectable holding pattern. By foregrounding measurement failure, it keeps the conversation at the level of journalistic uncertainty instead of forcing the harder question: whether compute owners are building the physical substrate for a post-employment economy. The fog becomes the subject, and the machine behind the fog remains partially unexamined.

THE VERDICT

This is a valuable account of informational decay, but it mistakes bad visibility for strategic indeterminacy. The data-center count is unknowable because the category is elastic, the projects are secretive, and capacity is what matters—not buildings. Under DT logic, that uncertainty is not evidence that the AI buildout is exaggerated. It is evidence that the emerging productive base is being assembled faster than public institutions can classify, audit, or politically metabolize.

The article documents the loss of observability. It does not challenge the underlying discontinuity.