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.

False hopes. Perhaps our current technologies, advanced and exciting as they might be, aren’t as transformative as the technologies from the period between 1870 and 1970, when we experienced high productivity growth and a rising standard of living. Some have argued, most prominently Northwestern University economist Robert Gordon, that over the past few decades there’s been a fundamental decline in innovation and productivity. “We wanted flying cars – instead we got 140 characters,” is how PayPal cofounder Peter Thiel succinctly described his belief that we’re no longer solving big problems. But, it’s not just flying cars that have eluded us. So has fusion energy, supersonic commercial travel and space exploration.

The NBER paper disagrees, arguing that there’s a compelling case for optimism. “[I]t’s not difficult to construct back-of-the-envelope scenarios in which even a modest number of currently existing technologies could combine to substantially raise productivity growth and societal welfare. Indeed, knowledgeable investors and researchers are betting their money and time on exactly such outcomes.”

Mismeasurement. Another potential explanation is the difficulty of measuring productivity in our services-oriented digital economy. GDP, which factors strongly in measures of productivity, is a relic of an age dominated by manufacturing, where the production of physical goods was easier to measure. But it’s a less reliable measure of services, where there’s much more variation in quality and value. Moreover, how do you measure the value of smartphones, social media, online videos, and a large assortment of websites, which involve relatively little monetary costs yet are now widely used in both our work and personal lives? Traditional metrics don’s adequately account for the digital economy, because so many of its technologies deliver substantial value while accounting for a small share of GDP due to their relatively low price.

The NBER paper cites a number of recent studies that, “each using different methodologies and data, present evidence that mismeasurement is not the primary explanation for the productivity slowdown. After all, while there is convincing evidence that many of the benefits of today’s technologies are not reflected in GDP and therefore productivity statistics, the same was undoubtedly true in earlier eras as well.”

Read the full post at The Wall Street Journal.

Irving Wladawsky-Berger is a Visiting Lecturer in Information Technology at the MIT Sloan School of Management.

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