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

Innovation and Economic Growth and Artificial Intelligence

Article Snapshot


Erik Brynjolfsson, Daniel Rock and Chad Syverson


in The Economics of Artificial Intelligence: An Agenda, Ajay K. Agrawal, Joshua Gans, and Avi Goldfarb, eds., University of Chicago Press, 2019


Artificial intelligence (AI) is advancing rapidly, but productivity growth has been falling for a decade, and real income has stagnated. The most plausible explanation is that it will take considerable time for AI-related technologies to be deployed throughout the economy.

Policy Relevance

Intangible AI-related gains are hard to measure using traditional metrics such as Gross Domestic Product. Economists should develop new methods of measurement.

Main Points

  • Advances in AI have the potential to transform the economy and yield benefits for all.
    • Error rates in machine learning systems such as speech recognition systems have fallen to about 5%, about the same as human error rates.
    • Global investment in companies focused on AI has grown from $259 million in 2012 to over $5 billion in 2016.
  • Productivity growth in the United States has declined from 2.8% to 1.3% over the past decade; the decline is widespread, affecting many developed nations and emerging economies.
  • Four explanations for the clash between technology and investment gains and productivity declines are offered:
    • Like fusion power or flying cars, AI has raised false hopes.
    • AI has led to gains, but these have not been measured; however, this is unlikely, because studies using different measures yield the same results.
    • AI gains are concentrated in a few firms, leading to inequality and welfare losses.
    • AI gains will be realized slowly because of lags in implementation and restructuring; this is the most plausible explanation.
  • Historically, past productivity growth is not a good predictor of future productivity growth.
  • AI-based technologies such as autonomous cars will increase the productivity of labor, as about 45% of all tasks can be automated; AI will also improve the productivity of materials and energy usage (for example, AI reduced the energy usage of a data center cooling system by 40%).
  • AI, like the steam engine, is a “general purpose technology,” and it will increase productivity directly and indirectly by spurring many complementary technologies.
  • Historical evidence shows that productivity gains lag decades behind the development of a technology; e-commerce took two decades to reach 10% of total sales.
  • In the last wave of computerization, the gains from the new technology were linked to investments in intangible assets about ten times as valuable as the computer hardware itself; intangible assets associated with AI could require even more investment.
  • AI is a type of intangible capital, and AI-related assets such as data sets, human knowledge, and business processes will not appear “on the books;” traditional measures of GDP will fail to capture the effect of AI diffusion.


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