Economists: Early AI Investment Lags and the Productivity J-Curve
While recent data shows limited financial payoff from AI outside the tech sector, researchers suggest the J-curve model explains why productivity gains take time to manifest.
Torsten Slok, chief economist at Apollo Global Management, recently reported that massive corporate investments in artificial intelligence have yielded little measurable financial return for most U.S. companies outside of the technology sector. According to Slok, revenue per worker and profit margins for the bulk of the S&P 500 remain flat despite the push for AI integration.
However, researchers Erik Brynjolfsson, Daniel Rock, and Chad Syverson argue that this performance trajectory follows the "Productivity J-Curve." The theory suggests that general-purpose technologies like AI do not provide immediate returns upon installation because firms must first undergo a period of intense organizational change. This includes co-investments in new business models, revised internal processes, and human capital.
Because these types of intangible investments are often poorly captured in balance sheets, the early stages of adoption frequently show weak productivity in official data. Historical analysis of software and hardware adoption indicates that once these hidden costs are accounted for, actual total factor productivity is significantly higher than reported figures suggest.