The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Open accessAbstract
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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Cites
ImageNet: A large-scale hierarchical image database
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“…is important, because that is Although not at the level of professional human performance yet, Facebook’s AI Research team recently improved upon the best machine language translation algorithms available using convolutional neural net sequence prediction techniques (Gehring et al., 2017).”
The productivity paradox of information technology
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Self-citationWhat can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131
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Concentrating on the Fall of the Labor Share
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Polanyi's Paradox and the Shape of Employment Growth
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Cited by
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131
Self-citationThe Productivity J-Curve: How Intangibles Complement General Purpose Technologies
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Self-citationToward understanding the impact of artificial intelligence on labor
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900949116 · Cited by 713
Self-citationPrzeworski, A. (2019) · doi:10.1017/9781108671019 · Cited by 599
Artificial intelligence and knowledge management: A partnership between human and AI
Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023) · Business Horizons · doi:10.1016/j.bushor.2022.03.002 · Cited by 604
Metadata
- Authors
- Brynjolfsson, E., Rock, D., & Syverson, C.
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2017-11
- Pages
- 23-57
- OpenAlex
- W2767568774
- Cited by
- 773
- References
- 12
- Type
- report
- Language
- en
- Open access
- green
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