How to Rethink Work in an Era of AI and Digital Automation

MIT economist, Daron Acemoglu, says we’re not facing an imminent end of work, but we need new roadmaps

By Paula Klein

Few can deny that there will be huge social, political, and economic transformation as a result of AI, robotics, and digital technologies. But there are many uncertainties about how those changes will play out, especially in the critical area of economics. Does it mean the end of work? Massive job loss? Lower wages? Slower growth? And what about productivity?

From a historical perspective, we have been at similar crossroads before said MIT Economics Professor, Daron Acemoglu, at the MIT IDE Annual Conference on May 24. Speaking about Automation and the Future of Work, Acemoglu said that many 20th-Century economists — including John Maynard Keynes and Wassily Leontief — also worried about technological unemployment in the 1930s and 1950s.

While there are a few similarities in the automation of work then and now, the current digital industrial revolution is very different, he said. Besides the automation of all types of administrative work — not only factory automation this time around — U.S. corporate income is being re-invested into capital (machines), not labor (people). Furthermore, on a macroeconomic level, both productivity and wages are lackluster, at best. This is true despite the productivity gains and complementary effects that usually result from “enabling technologies,” that allow humans to work more efficiently, Acemoglu said.

Most troubling are “displacement technologies,” such as robots, that are dislodging, not supplementing human workers. This type of automation is squeezing labor into narrow sets of activity without yielding higher productivity, he said.

Specifically, Acemoglu said that three innovation bottlenecks are exacerbating the problem: Organizations, education, and politics.

Some economists say that too few new jobs are being created to fuel a growth in labor, and when they are created, workers aren’t adequately prepared. The obvious solution is more and better training and education; however, it takes a long time for labor and skills to catch up to technology advances. “Most educational systems were designed in the last century,” he noted, and can’t be overhauled immediately. Moreover, when it comes to governance and politics — both in the public and private sectors — reform can be painstakingly slow and difficult to implement.

New thinking is required, according to Acemoglu. We need a “richer notion of jobs and new combinations of tasks,” he said. It’s easy to blame schools, but AI also can improve education with more personalization and resources.

“This current era of automation is not like the past, but it’s not the end of work, either. There are many challenges ahead,” Acemoglu said, and the best course is to create new roadmaps to solve the bottlenecks. Otherwise, the displacement of jobs will dominate.

Read Daron’s latest recent research paper here.