Apollo Global Management’s Torsten Slok has put a blunt label on the AI boom’s uneven early returns: outside the biggest technology companies, he says, corporate America has little to show for its spending.

In his assessment of the “S&P 493”—the S&P 500 excluding Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla—Slok found revenue per worker and profit margins largely flat. “Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech.”¹

The divide has widened since 2022. Revenue per employee at major tech companies rose from about $550,000 to nearly $800,000, while the comparable figure for the rest of the index stayed near $550,000; non-tech profit margins also stalled around 10%.² That has changed the corporate calculation, Slok argues: companies are increasingly treating AI-token spending as a cost that squeezes margins rather than as an engine of growth.

His skepticism is not just about the pace of adoption. As AI models become cheaper and more interchangeable, Slok questions whether businesses can convert “cheap intelligence” into stronger revenues. “AI may be a great calculator, but in business, intelligence is mostly judgment,” he wrote.¹

Goldman Sachs chief economist Jan Hatzius reaches a more optimistic long-run conclusion, but with major caveats. He expects AI investment ultimately to raise productivity and economywide profits, even as the investment-driven lift to U.S. growth is set to peak in late 2026 or early 2027 and then fade.³

The economists therefore agree on the present: the boom remains concentrated, and its macroeconomic footprint is limited. Their split is over the sequel. Goldman sees a delayed payoff; Apollo sees a barrier that technology alone may not clear. The uncomfortable bridge between those views may be the labor market: Hatzius says AI-related disruption is one route to productivity gains, but one with “negative near-term macroeconomic effects.”³