Investors expect AI use to soar. That’s not happening

On November 20th American statisticians released the results of a survey. Buried in the data is a trend with implications for trillions of dollars of spending.

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Researchers at the Census Bureau ask firms if they have used artificial intelligence “in producing goods and services” in the past two weeks. Recently, we estimate, the employment-weighted share of Americans using Al at work has fallen by a percentage point, and now sits at 11% (see chart 1). Adoption has fallen sharply at the largest businesses, those employing over 250 people. Three years into the generative-Al wave, demand for the technology looks surprisingly flimsy.

Whether Al adoption is fast or slow has profound consequences. For the world to reap productivity gains from Al, normal businesses must incorporate the tech into their day-to-day operations. It is also the most important question in determining whether or not the world is in an “AI bubble”. From today until 2030 big tech firms will spend $5trn on infrastructure to supply Al services. To make those investments worthwhile, they will need on the order of $650bna year in Al revenues, according to JPMorgan Chase, a bank, up from about $50bn a year today. People paying for Al in their personal lives ill probably buy only a fraction of what is ultimately required, Businesses must do the rest.

The Census Bureau is just one source. Other researchers are compiling their own estimates of AI adoption; most find that the level is higher than 10% (see chart 2). Economists argue about why these differences exist. Some believe that the Census Bureau's survey is too restrictive (it is hard to know exactly how respondents will interpret “use Al in producing goods and services”). Asking employees about their own use at work might elicit more positive responses than asking executives about their business. The bureau’s fans counter that only the government has the extensive network necessary to sample a truly representative cross-section of American businesses, and not just those in more innovative industries such as coding.