Edward Conard

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AI Summary. The recent pickup in US labor productivity appears driven by higher utilization of existing capital—running factories, server racks, and hotel rooms harder—rather than by firm-level efficiency gains from AI. Adoption remains too thin and the cross-sectional evidence too weak to conclude that AI-driven productivity gains are already widespread.

Ernie Tedeschi Stripe Economics
Date Posted:
Is Database:
Database

AI is showing up in the macro numbers primarily in increased utilization and labor and capital deepening rather than true total factor productivity gains.

Is the recent productivity pickup driven by AI or higher capital utilization?

Core argument: The rise in US labor productivity appears to reflect macroproductivity gains from higher capital utilization — longer runs at existing factories, fuller use of already-paid-for server racks and GPU clusters, and higher occupancy of existing hotel rooms — rather than microproductivity gains.

What appears to be lifting US labor productivity, rather than microproductivity gains, are macroproductivity gains from AI—specifically companies running their existing capital harder. Think longer runs of factories already built, more utilization of server racks and GPU clusters already paid for, and more occupancy of existing hotel rooms. Economists call this “capital intensity” or “utilization.” Higher capital utilization represents real economic gains, but it’s not the same as microproductivity. This is not the same as “capital deepening” (growth in measured productivity from expanding capital supply—for example, through capital investment). What the current numbers do not yet support is the claim that this transmission is already well underway at the aggregate level: adoption is too thin, the cross-sectional signal is too weak, downstream bottlenecks remain, and the productivity pickup is too attributable to utilization to be confident in that conclusion. Instead, the acceleration we’re seeing so far is largely a result of companies trying to meet the demand for AI capacity by pushing the limits of their existing infrastructure.

Takeaways by Macro Roundup® AI

  1. The rise in US labor productivity appears to reflect macroproductivity gains from higher capital utilization — longer runs at existing factories, fuller use of already-paid-for server racks and GPU clusters, and higher occupancy of existing hotel rooms — rather than microproductivity gains.
  2. Higher capital utilization represents real economic gains but differs from both microproductivity and capital deepening, the growth in measured productivity that comes from expanding capital supply through investment.
  3. The current numbers do not yet support the claim that AI's productivity transmission is well underway at the aggregate level, given thin adoption, a weak cross-sectional signal, downstream bottlenecks, and a pickup largely attributable to utilization.

Related Articles:

  • Have We Entered an Era of High Productivity Growth? — Labor productivity has grown solidly while the San Francisco Federal Reserve's total factor productivity index, which adjusts for the equipment and technology workers use, has grown modestly, suggesting gains so far reflect better tools rather than broader advances. A regime-switching model puts the probability of a high-productivity regime at 57% using labor productivity and 21% using total factor productivity.
  • America Is Experiencing A Productivity Miracle — America's post-2019 productivity acceleration came largely from professional services and management—about 10% of output and heavy users rather than producers of new technology—and from oil and gas. The information industry grew productivity around 6% annually, no faster than its 2000-19 average, with its output share steady at 5.3–5.5%.
  • AI and the Pitfalls of Innovation — The post-World War II productivity boom, which doubled real wages and living standards in a generation, came largely from technologies that already existed decades earlier rather than radical new inventions. It was followed by sluggish productivity growth from 1973 to 1995, then an information-technology boom that lasted roughly a decade before measured productivity growth slowed again.
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