New Research Addresses AI-Productivity Paradox
A new working paper, Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics, by MIT IDE's Erik Brynjolfsson and Daniel Rock, and Chicago Booth's Chad Syverson, offers a resolution to a vexing problem: Why haven't great technological advances such as AI resulted in equally impressive economic productivity growth?
The researchers argue that the recent productivity slowdown says nothing about future productivity growth and is no reason to downgrade prospects. "In fact, history teaches the opposite lesson. Past surges in productivity were driving by general-purpose technologies (GPT) like electricity and the internal combustion engine. In turn, these technologies required numerous complementary co-inventions like factory-redesign, interstate highways, new business processes, and changing workforce skills before they truly fulfilled their potential. Importantly, these co-inventions took years or even decades to materialize."
They believe that "AI has the potential to be the GPT of our era. And like earlier technologies, it requires numerous complementary innovations ...that are often costly and time-consuming to develop. The low productivity growth of recent years may partially reflect these costs and may also be a harbinger of significantly higher growth once necessary co-inventions are put in place."
"We see no inherent inconsistency between forward-looking technological optimism and backward-looking disappointment. Both can simultaneously exist. Indeed, there are good conceptual reasons to expect them to simultaneously exist when the economy undergoes the kind of restructuring associated with transformative technologies."
Read the full paper here.