AI hyperscalers may need to raise productivity 2.7 times by 2030 to justify nearly $1.1 trillion in infrastructure spending through 2027, according to new research.
Key Points:
- AI hyperscalers face a 2.7-times productivity hurdle by 2030 to justify current infrastructure commitments.
- Morgan Stanley estimates about $2.9 trillion in global data-center spending through 2028, with roughly $1.5 trillion requiring external capital.
- Growing reliance on debt and private credit could spread AI infrastructure risk beyond major technology companies.
Wachter AI Math
MIT Technology Review examined research from Jessica Wachter, a Wharton finance professor, and coauthor Jonathan Wachter, who based their estimate on spending by Alphabet, Microsoft, Amazon, Meta and Oracle.
Their analysis says the sector would need a 2.7-fold productivity increase by 2030 after accounting for capital costs, depreciation and a 15% return. The paper warns that if the expected boom fails to appear, the buildout could become “the largest misallocation of capital in history.”
The pressure is already visible in corporate cash flow. Alphabet reported negative free cash flow of $5.9 billion in the second quarter of 2026, its first such quarter since Google went public in 2004, as capital spending climbed sharply.
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Gensler Credit Risk
Morgan Stanley estimates about $2.9 trillion will be spent on global data-center construction from 2025 through 2028, with roughly $1.5 trillion expected to come from external capital. That shift pushes more AI infrastructure risk into corporate debt, securitized credit and private-credit markets.
Meta’s Hyperion project in Louisiana shows how that financing can work. Funds managed by Blue Owl Capital own 80% of a joint venture developing the roughly $27 billion campus, while Meta retains 20%.
Stijn Van Nieuwerburgh, a Columbia Business School professor, has examined how the buildout spreads leverage across developers, lenders and structured-finance vehicles.
Former SEC chair and MIT Sloan professor Gary Gensler has described the broader AI investment cycle as “a parlay bet by the capital markets and the economy.”
Crypto strategist Arthur Hayes has argued that an AI credit downturn could ultimately trigger easier Federal Reserve policy and lift Bitcoin (BTC) toward $1 million, although that remains a speculative scenario rather than a forecast supported by the Wharton research.
The spending surge has been building for years as hyperscalers race to secure chips, power and data-center capacity. The unresolved question is whether productivity and revenue can rise fast enough to match infrastructure commitments that increasingly depend on outside financing.
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