MIT Warns AI Bubble May Require 2.7x Productivity Increase by 2030

MIT Technology Review analyzes the potential fallout from the AI bubble, indicating that hyperscalers must nearly triple productivity by 2030 to recoup their trillion-dollar infrastructure investments. Wharton finance professor Jessica Wachter and a coauthor estimate that hyperscalers will spend nearly 1.1 trillion dollars on data centers through 2027, including major players like Alphabet, Microsoft, Amazon, Meta, and Oracle. Wachter, a former SEC chief economist, states that productivity must grow 2.7 times to break even, factoring in capital costs, a 15% return, and asset depreciation. Without this growth, they warn it could be the largest misallocation of capital in history. Alphabet reported a 5.9 billion dollar free cash flow deficit last quarter, its first since 2004, raising concerns among investors about the risks associated with AI spending. Morgan Stanley projects that hyperscalers will finance over half of their planned 2.9 trillion dollars in data center spending through 2028, increasingly relying on external capital. In a notable move, Meta transferred an 80% stake in its Hyperion data center to Blue Owl Capital, highlighting the complexities of hyperscaler financing. Columbia Business School's Stijn Van Nieuwerburgh cautions that such debt is spreading through pension funds and private credit vehicles, impacting retirement and insurance savings. Crypto strategist Arthur Hayes suggests that an AI credit bust could compel the Federal Reserve to print money, potentially driving bitcoin toward 1 million dollars. Former SEC chair Gary Gensler anticipates a retrenchment but is uncertain about its timing, which will influence the extent of losses from the trillion-dollar bet.

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