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The AI Buildout Could Cost $10 Trillion. A Researcher Is Warning It Could Create Systemic Risks.
The study says financing gaps and opaque debt structures could amplify wage, inflation and borrowing-cost pressures as AI demand drives rapid construction.
A new Brookings Institution study projects $10.3 trillion in AI infrastructure investment through 2032, averaging about 3.6% of U.S. GDP annually. Columbia Business School professor Stijn Van Nieuwerburgh presented the findings on Thursday.
This buildout exceeds historical records for railroads and telecommunications as major tech firms like Alphabet, Amazon, Meta, Microsoft, and Oracle commit $4.2 trillion in capital expenditure through 2029.
Van Nieuwerburgh warned that complex financing structures, including off-balance-sheet SPVs, create opacity. "This is freaking complicated," he told reporters while describing the emerging financing network.
Reaching projected returns requires the AI industry to hit roughly $3.7 trillion in annual revenue by 2032, implying around 80% growth. Van Nieuwerburgh wrote this creates "meaningful downside risk" if expectations shift.
Chicago Fed President Austan Goolsbee separately noted that data center investment is bidding up wages, while Van Nieuwerburgh compared current financial opacity to the subprime mortgage crisis.
The infrastructure needed to support the expansion of artificial intelligence can absorb 10.3 billion dollars (8.8 billion euros) in the United States between 2025 and 2032, according to the study by Stijn Van Nieuwerburgh, a professor at Columbia University, published by Brookings Institution.