Unpacking the AI-Productivity Paradox

Technology expectations and economic statistics are clashing for now — but the reality is more promising than it appears.

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We see the effects of transformative new technologies everywhere except in productivity statistics. Systems using artificial intelligence (AI) increasingly match or surpass human-level performance, driving great expectations and soaring stock prices. Yet measured productivity growth has declined by half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans.

What can explain this paradox?

Our close examination of recent patterns in aggregate productivity growth highlights the apparent contradictions. Examples of potentially transformative new technologies that could greatly increase productivity and economic welfare abound, as noted in the 2014 book The Second Machine Age. For instance, consider the recent progress in areas such as machine image recognition (see “AI Versus Human Image Recognition Error Rates”). At the same time, productivity growth has been historically slow over the past decade.

And the sluggishness is widespread, occurring not only in the United States but also in other nations of the Organisation for Economic Co-operation and Development (OECD), as well as among many large, emerging economies.

Some pessimism about future technological progress has spilled over into long-range policy planning and corporate strategy plans. The U.S. Congressional Budget Office, for instance, reduced its 10-year forecast for average annual labor productivity growth from 1.8% in 2016 to 1.5% in 2017. Although modest, that drop implies U.S. gross domestic product will be considerably smaller 10 years from now than it would if productivity simply continued to grow at the same rate as before — a difference equivalent to almost $600 billion in 2017.

Nevertheless, when we review the evidence, we come to a different conclusion and take a more optimistic view. The recent productivity slowdown says nothing about future productivity growth and is no reason to downgrade prospects. In fact, history teaches the opposite lesson. Past surges in productivity were driven by general-purpose technologies (GPTs) like electricity and the internal combustion engine. In turn, these technologies required numerous complementary co-inventions like factory redesigns, interstate highways, new business processes, and changing workforce skills before they truly fulfilled their potential. Importantly, these co-inventions took years or even decades to materialize, and only then did productivity improve significantly.

We believe that AI has the potential to be the GPT of our era.

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An MIT SMR initiative exploring how technology is reshaping the practice of management.
More in this series

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