AI and the productivity paradox – Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

From The Wall Street Journal

Artificial intelligence is now applied to tasks that not long ago were viewed as the exclusive domain of humans, matching or surpassing human level performance. But, at the same time, productivity growth has significantly declined over the past decade, and income has continued to stagnate for the majority of Americans. This puzzling contradiction is addressed in “Artificial Intelligences and the Modern Productivity Paradox,” a working paper recently published by the National Bureau of Economic Research.

As the paper’s authors, MIT professor Erik Brynjolfsson, MIT PhD candidate Daniel Rock and University of Chicago professor Chad Syverson, note: “Aggregate labor productivity growth in the U.S. averaged only 1.3% per year from 2005 to 2016, less than half of the 2.8% annual growth rate sustained from 1995 to 2004… What’s more, real median income has stagnated since the late 1990s and non-economic measures of well-being, like life expectancy, have fallen for some groups.”

After considering four potential explanations, the NBER paper concluded that there’s actually no productivity paradox. Given the proper context, there are no inherent inconsistencies between having both transformative technological advances and lagging productivity. Over the past two centuries we’ve learned that there’s generally a significant time lag between the broad acceptance of new technology-based paradigms and the ensuing economic transformation and institutional recomposition. Even after reaching a tipping point of market acceptance, it takes considerable time, often decades, for the new technologies and business models to be widely embraced by companies and industries across the economy, and only then will their benefits follow, including productivity growth. The paper argues that we’re precisely in such an in-between period.

Let me briefly describe the four potential explanations explored in the paper: false hopes, mismeasurements, concentrated distribution, and implementation and restructuring lags.

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Good managers, not machines, drive productivity growth – John Van Reenen

MIT Sloan Professor John Van Reenen

From Bloomberg View

When people discuss what drives long-run productivity, they usually focus on technical change. But productivity is about more than robots, new drugs and self-driving vehicles. First, if you break down the sources of productivity across nations and firms there is a large residual left over (rather inelegantly named “Total Factor Productivity” or TFP for short). And observable measures of technology can only account for a small fraction of this dark matter.

On top of this, a huge number of statistical analyses and case studies of the impact of new technologies on firm performance have shown that there is a massive variation in its impact. What’s much more important than the amount spent on fancy tech is the way managerial practices are used in the firms that implement the changes.

Although there is a tradition in economics starting with the 19th-century American economist Francis Walker on the importance of management for productivity, it has been largely subterranean. Management is very hard to measure in a robust way, so economists have been happy to delegate this task to others in the case study literature in business schools.

Managers are more frequently the butt of jokes from TV shows like “The Office” to “Horrible Bosses,” than seen as drivers of growth. But maybe things are now changing.

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