The ongoing artificial intelligence (AI) buildout is on track to require a larger share of US output than the rollout of electricity, railroads, interstate highways, or the internet, with an increasingly complicated financial structure that poses potentially systemic risks, according to a new study prepared by Stijn Van Nieuwerburgh, a finance and real estate professor at Columbia Business School.
Van Nieuwerburgh, while presenting his paper during a Brookings Institution conference, said that what had been paid for out of the cash stockpiled by companies like Amazon.com, Meta Platforms, and Alphabet’s Google has transformed into an expansion that will consume around 3.6% of GDP annually through 2032, or more than USD 10 trillion, with the companies using even more intricate financing arrangements.
“That estimate is higher than the 2.2% of annual GDP absorbed by railroads in the late 1800s, which was the next most costly rollout of a general technology, and a bit more than 1% annually each for construction of the US interstate highway system beginning in the 1950s or the telecommunications expansion that started in the mid-1990s,” the study said.
Just as the rail and telecoms expansions led to notable bubbles and busts, Van Nieuwerburgh sees the extent of the AI buildout, the still-untested revenue streams, and the intricate financing structure emerging around the technology mean it could be primed for a fall.
“This is freaking complicated,” he said in a briefing with reporters of the arrangements emerging between AI firms, hyperscalers, banks, private credit lenders, real estate firms, and a host of other players involved in building what he conservatively estimated at 183 gigawatts worth of new data-center capacity over the next seven years, compared with about 57 gigawatts currently installed.
“Data center construction and the risks around AI have become a central issue in US political and economic debates, with some localities increasingly reluctant to host the facilities and worried about strains on local resources, and Federal Reserve officials considering whether the construction boom is adding to inflation. Some AI executives have suggested a slower pace of development might be safer,” Van Nieuwerburgh noted.
“The investment underway already has outstripped what the major players can fund from their own cash flows. 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,” Van Nieuwerburgh continued.
“This opacity of all these special purpose vehicles is reminiscent of what happened in the subprime mortgage crisis,” he said in the conversation with media persons.
The subprime mortgage crisis saw the complex home mortgage financing arrangements going bust at rates that rocked global financial systems and triggered the 2007-2009 recession in the United States.
“These developments do not imply that 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 the combination of uncertain demand, rapid technological change, execution bottlenecks, and high leverage creates meaningful downside risk if expectations are revised,” Van Nieuwerburgh observed.
To prove his point, the academician said that the AI industry will need to earn about USD 3.7 trillion in annual revenue by 2032 to achieve the expected return on investment, and “given current estimates of annual combined revenues of OpenAI and Anthropic of around USD 100 billion, revenues would need to grow at roughly 80% per year.”