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Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics

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  • Erik Brynjolfsson
  • Daniel Rock
  • Chad Syverson

Abstract

We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving 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. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each, we argue that lags have likely been the biggest contributor to the paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won’t be realized until waves of complementary innovations are developed and implemented. The required adjustment costs, organizational changes, and new skills can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, going forward, national statistics could fail to measure the full benefits of the new technologies and some may even have the wrong sign.

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  • Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2017. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Working Papers 24001, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24001
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    1. repec:eee:exehis:v:69:y:2018:i:c:p:13-26 is not listed on IDEAS
    2. Rod Tyers & Yixiao Zhou, 2018. "Automation, taxes and transfers with International rivalry," CAMA Working Papers 2018-44, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Erik Brynjolfsson & Felix Eggers & Avinash Gannamaneni, 2018. "Using Massive Online Choice Experiments to Measure Changes in Well-being," NBER Working Papers 24514, National Bureau of Economic Research, Inc.
    4. repec:hig:fsight:v:13:y:2019:i:2:p:19-41 is not listed on IDEAS
    5. Ufuk Akcigit & Sina T. Ates, 2019. "Ten Facts on Declining Business Dynamism and Lessons from Endogenous Growth Theory," NBER Working Papers 25755, National Bureau of Economic Research, Inc.
    6. Andrew Agopsowicz & Dany Brouillette & Bassirou Gueye & Julien McDonald-Guimond & Jeffrey Mollins & Youngmin Park, 2018. "Potential Output in Canada: 2018 Reassessment," Staff Analytical Notes 2018-10, Bank of Canada.
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    More about this item

    JEL classification:

    • D2 - Microeconomics - - Production and Organizations
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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