The phenomenon of "phantom" data center projects – those that are announced but never built, or announced far in excess of what physical constraints will actually allow – has emerged as one of the most disruptive forces undermining accurate forecasting of AI-related power demand. The scale of announced spending is staggering, with hyperscalers alone expected to deploy roughly $800 billion in 2026 and over $1 trillion in 2027. Why Oklo's First-Ever Revenue Is Not Enough to Stop OKLO Stock's Decline What Had Crude Oil Boiling Again Overnight?
Nat-Gas Prices Slip on the Outlook for Robust US Storage Markets move fast. Keep up by reading our FREE midday Barchart Brief newsletter for exclusive charts, analysis, and headlines. Household electricity prices have already risen 10.1% over two years, faster than overall inflation, as the anticipation of data center loads drives infrastructure investment.
Nebius (NBIS) raised its year-end 2026 contracted power forecast to 5 gigawatts, CoreWeave (CRWV) expanded active power by nearly 500 megawatts in a single quarter to reach 1.5 gigawatts, and numerous Bitcoin (BTCUSD) miners are converting facilities totaling multiple gigawatts to AI use – and yet the aggregate of all announced projects vastly exceeds what can plausibly be built within stated timeframes. In fact, many of the projects behind those investment decisions exist only as announcements, interconnection queue entries, or financial commitments that may never materialize at their stated scale or timeline. "Grid operators don't know which ones are real and which ones aren't," said Glenn Schwartz, head of energy policy at consulting firm Rapidan Energy Group, in comments to Bloomberg.
The result is that utility planners, grid operators, and policymakers face an impossible task. Until the gap between announced phantom capacity and physically deliverable capacity narrows – something unlikely before 2028 at the earliest – reliable forecasting of AI power demand will remain essentially impossible. In addition to the "shotgun"-style requests described by Bloomberg's reporting, where developers pitch the same project to multiple utilities, there are other factors that could create an enormous gap between planned and realized capacity.
The skilled labor shortage represents perhaps the most fundamental constraint. The US mechanical, electrical, and plumbing labor pool qualified for data center work is far smaller than headline figures suggest, with only 30% of MEP labor residing where 70% of projects are located. At recent peak recruitment rates, the US has added approximately 60,000 combined MEP craft laborers annually, a figure that likely represents a ceiling rather than a floor for growth.
Training bottlenecks mean that data center capacity additions cannot grow materially faster unless labor productivity improves dramatically or data centers cannibalize workers from the rest of the construction economy. This labor constraint creates a cascading forecasting problem. Companies announce gigawatt-scale projects and secure power interconnection agreements, but the physical construction timeline stretches far beyond initial projections.
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