Why AI-Driven Robot Productivity Realization Requires Patience

The economic benefits of new technology take time to diffuse and, especially, to show up in official statistics.

It's not like any enterprise is going to give up on artificial intelligence, machine learning or robotics, but the slow roll-out could depress growth and profits for those making them. JL

Jeanna Smialek reports in Bloomberg:

AI investments and the complementary changes are costly, hard to measure, and take time to implement, and this can, at least initially, depress productivity as it is currently measured. ”Productivity growth has declined by half over the past decade and inflation-adjusted incomes are stagnating (because) AI capabilities haven’t had time to diffuse widely. Leveraging the newest technologies will require investments in human capital and skills and the creation of new processes and business models.
Don’t give up on the robots just yet.

Artificial intelligence and machine learning are not currently driving major pickups in productivity and output, but that doesn’t mean they won’t, Erik Brynjolfsson at the Massachusetts Institute of Technology and Chad Syverson at the University of Chicago write in a new paper.
A hopeful case for AI-driven output
Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Published November 2017
Available on the NBER website
Productivity growth has declined by half over the past decade and inflation-adjusted incomes are stagnating. The trends are confusing in an era marked by technological advancement and the dawn of artificial intelligence, but Brynjolfsson, Syverson and their co-author Daniel Rock argue that this is partly due to a lag effect: AI and machine learning technology capabilities just haven’t had time to diffuse widely.

New technologies have historically taken a long time to spread in an economically important way, the authors note. E-commerce didn’t become the driving disruptor in the retail industry until years after the dot-com boom, for instance, as infrastructure and customers took time to adjust. Now, leveraging the newest technologies will require investments in human capital and skills and the creation of new processes and business models.  “Both the AI investments and the complementary changes are costly, hard to measure, and take time to implement, and this can, at least initially, depress productivity as it is currently measured,” the authors write.