Study Warns AI Buildout Brings Historic Scale and Financial Risk

Two-thirds of a data center's costs are IT equipment, with one-third going to real estate and associated power infrastructure, according to the study. The Wall Street Journal reported that the buildout is pushing up prices for workers, electricity, and commercial real estate, as well as consumer products that use chips, and is reducing construction of new houses and apartment buildings. The paper also says projections for the buildout would double the electricity consumption of the entire U.S. residential sector.

Van Nieuwerburgh conservatively estimated the buildout at 183 gigawatts of new data-center capacity over the next seven years, compared with about 57 gigawatts currently installed. He said in a briefing with reporters that the arrangements emerging among AI firms, major tech hyperscalers, banks, private credit lenders, real estate firms, and other players are "freaking complicated."

Reuters reported that Van Nieuwerburgh wrote that just as the rail and telecoms expansions led to notable bubbles and busts, the extent of the buildout, still-untested revenue streams, and the intricate financing structure emerging around AI mean it could be primed for a fall. Investment already underway has outstripped what major players can fund from their own cash flows, according to the paper. The shift to outside financing has increased leverage, redistributed risks across the economy, and made the venture dependent on revenue streams that have yet to be proven, he wrote.

Van Nieuwerburgh wrote that those developments do not imply financial distress is imminent. Strong growth in AI applications, high utilization, and continued improvements in model capability could support the projected infrastructure and generate stable cash flows, he wrote. But he added that the combination of uncertain demand, rapid technological change, execution bottlenecks, and high leverage creates meaningful downside risk if expectations are revised.

As an example, he wrote that the AI industry will need to earn about $3.7 trillion in annual revenue by 2032 to achieve the expected return on the investment. Given current estimates of combined annual revenues of OpenAI and Anthropic at around $100 billion, revenues would need to grow at roughly 80 percent per year, according to the paper. The paper suggests policies that "improve measurement and transparency" for financing.