What’s at stake in AI’s trillion-dollar gamble

Hyperscalers are facing an unprecedented economic challenge as they race to build AI infrastructure. According to Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, spending is projected to reach $1.1 trillion by 2027. Wachter, formerly the SEC’s chief economist, notes that these companies will need to increase their productivity by a factor of 2.7 by 2030 to break even, accounting for capital costs and asset depreciation. She emphasizes that this requires economic growth comparable to the US IT boom of the 1990s, but compressed into a much tighter timeframe. Wachter warns that failing to meet these profit goals could lead to missed interest payments and bankruptcy risks. The research concludes that if this productivity boom fails to materialize, the current buildout could represent the largest misallocation of capital in history.