How ‘Phantom’ Data Center Projects Are Making It Impossible to Forecast AI Power Demand — ‘Grid Operators Don’t Know Which Ones Are Real’

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. 

Circular Financing vs. End-User Demand

There are also financing structures to consider. 

Nvidia (NVDA) has arranged more than $500 billion in dedicated capital pools to finance customer projects, while CoreWeave alone carries a $104 billion revenue backlog and has raised over $10 billion in debt in a single quarter. 

These financial commitments create the appearance of certain demand, but as analysts have noted, this circular financing – where suppliers help fund purchases of their own products – raises questions about how much represents genuine end-user demand versus speculative capacity that may never draw electrons from the grid.

Supply chain constraints add another layer of opacity to power demand forecasts. 

Super Micro Computer’s (SMCI) CEO attributed revenue shortfalls to customer delays in power, cooling, and networking infrastructure, while Foxconn cautioned that AI server rack volumes depend on how much advanced chip packaging capacity can actually be secured. 

Arista Networks (ANET) explicitly stated its outlook is set by what it can build, as opposed to what customers are requesting, with the industry expected to remain supply-constrained until 2028.

What the Fed Has Said About AI Spending

The Federal Reserve itself is struggling with this uncertainty, unable to determine whether AI infrastructure spending represents a temporary inflationary impulse or a permanent structural shift in electricity demand. 

“AI will be a significant disinflationary force, increasing productivity and bolstering American competitiveness,” wrote Fed Chair Kevin Warsh in 2025, before assuming the top job at the central bank. 

More recently, Warsh took a somewhat measured tone in his testimony before the Senate.

“Will [AI] increase measured prices over the course of the next 12 months? I suspect it will. Whether that’s inflationary or not, that’s up to the Federal Reserve — and we’re going to have something to say about that.”

While that doesn’t add much clarity to the central bank’s stance on the AI buildout, it’s fair to say that the underlying data – muddled by phantom bids and circular financing – isn’t providing policymakers with very reliable information to analyze.

This article was created with the support of automated content tools from our partners at Sigma.AI. Together, our financial data and AI solutions help us to deliver more informed market headline analysis to readers faster than ever. 

On the date of publication, Sarah Holzmann did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes. This article was originally published on Barchart.com