AI reduces productivity, increases automation
Anton Korinek and Joseph E. Stiglitz ’19 (Anton Korinek - Professor in the Department of Economics and at the Darden School of Business at the University of Virginia as well as a David M. Rubenstein Fellow at the Brookings Institution and Joseph E. Stiglitz - American New Keynesian economist, a public policy analyst, political activist, and a full professor at Columbia University. He is a recipient of the Nobel Memorial Prize in Economic Sciences and the John Bates Clark Medal, “Artificial Intelligence and Its Implications for Income Distribution and Unemployment”, May 2019, pg. 4, https://www-nber-org.proxy.lib.umich.edu/system/files/chapters/c14018/c14018.pdf) GM
On the first factor, measured productivity has increased rather slowly in recent years, even as the world seems to be captured by AI fever. 3 If AI‐related innovations enter the economy at the same slow pace as suggested by recent productivity statistics, then the transition will be slower than e.g. the wave of mechanization in the 1950 – 1970s, and the resulting disruptions may not be very significant. However, there are three possible alternatives: First, some suggest that productivity is significantly under‐measured, for example because quality improvements are not accurately captured. The best available estimates suggest that this problem is limited to a few tenth of a percentage point (see e.g. the discussion in Groshen et al., 2017). Furthermore, there are also unmeasured deteriorations in productivity, e.g. declines in service quality as customer service is increasingly automated. Secondly, the aggregate implications of progress in AI may follow a delayed pattern, similar to what happened after the introduction of computers in the 1980s. Robert Solow (1987) famously quipped that “you can see the computer age everywhere but in the productivity statistics.” It was not until the 1990s that a significant rise in aggregate productivity could be detected, after sustained investment in computers and a reorganization of business practices had taken place. Third, it is of course possible that a significant discontinuity in productivity growth occurs, as suggested e.g. by proponents of a technological singularity (see e.g. Kurzweil, 2005).
On the second factor, the disruptions generated by AI‐related innovations depend on whether they are labor‐augmenting or labor‐saving, using the terminology of Hicks (1932), i.e. whether at a given wage, the innovations lead to more or less demand for labor. Some suggest that artificial intelligence will mainly assist humans in being more productive, and refer to such new technologies as intelligence assisting innovation, IA, rather than AI. Although we agree that most AI‐related innovations are likely to be complementary to at least some jobs – e.g. the ones applying AI to solve problems – we believe that taking a broader perspective, progress in AI is more likely to substitute for human labor, or even to replace workers outright, as we will assume in some of our formal models below.