The last two decades have witnessed major advances in artificial intelligence (AI) and robotics.
Daron Acemoglu (MIT) and Pascual Restrepo (Boston University) develop a framework for thinking about automation and its impact on tasks, productivity, and work. Their idea is that automation and thus AI and robotics replace workers in tasks that they previously performed, and via this channel, create a powerful displacement effect.
Yet, they argue that there is a more powerful countervailing force that increases the demand for labor as well as the share of labor in national income: the creation of new tasks, functions and activities in which labor has a comparative advantage relative to machines. The economists also identify the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation
One of their main results is that excessive automation directly reduces productivity, but may have even more powerful indirect effects, because it redirects technological improvements away from productivity-enhancing activities that lead to the creation of new tasks and deepening automation to excessive efforts at the extensive margin of automation, a picture that receives informal support from the current singular focus on AI and deep learning.
The displacement effect tends to reduce the demand for labor and wages.
But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks.
Reference: Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work. Working paper No. w24196. National Bureau of Economic Research.
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