Technologists claim AI will help optimize workflows and supercharge the U.S. economy’s productivity—a measure of how efficiently resources such as labor are being converted to goods and services. While that growth has yet to show up in the data, AI might be responsible for the discrepancy in productivity statistics so far.
That lull proved to be just a lag, of course, and if history were to repeat itself, the U.S. economy might be in the early days of a historic productivity surge without even realizing it.
“Determining whether a prolonged period of high growth has begun or not is difficult in real-time and is usually only obvious with the benefit of some hindsight,” the Fed researchers wrote.
There are two primary metrics economists use to gauge productivity, and the two are pointing in complete opposite directions. One is labor productivity, which measures output per unit of labor. The other is total factor productivity (TFP), a broader metric that encompasses how efficiently the entire economy is able to convert inputs into output.
This pattern mirrors what happened during the computer and internet boom of the 1990s. Starting around mid-1996, labor productivity began accelerating more rapidly than TFP, but the full productivity benefits of the Internet didn’t materialize in the overall data until several years later.
Workers might feel as if they are becoming more productive with AI, and in many cases that could be true. But the lack of measurable impact for the economy at large comes with stark similarities to the early days of the Internet, when the data had yet to herald the imminent productivity boom.
“If today mirrors what we experienced in the mid-1990s, we may be in the early stages of a productivity boom driven by AI that will only become clear in retrospect,” the San Francisco Fed researchers wrote.
