AI’s data-center bet needs 2.7x productivity by 2030
AI hyperscalers could spend nearly $1.1 trillion on data centers by 2027, while industry revenue is only $150 billion to $200 billion this year. Wharton economist Jessica Wachter’s accounting test says companies need to raise productivity 2.7 times to break even by 2030, a compressed version of the 1990s IT boom. Miss that target, and the buildout risks becoming a historic capital misallocation, not just an overhyped market cycle.