What must happen for AI’s trillion-dollar gamble to pay off
MIT Technology Review David Rotman
AI hyperscalers’ spending on AI data centers faces break-even pressure because the earnings growth needed to justify the buildout depends on productivity gains materializing. By 2030, the analysis estimates AI companies must raise their own productivity by a factor of 2.7 to break even, given the cost of capital and a 15% return. If that acceleration fails, the article warns profits may not cover debt and free-cash-flow, increasing the risk of stranded infrastructure and broader economic drag.
Why it matters
When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called…
