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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Technical Change and the Aggregate Production Function
Solow, R. M. (1957) · The Review of Economics and Statistics · doi:10.2307/1926047 · Cited by 12,259
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 2×
“…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
Brynjolfsson, E. (1993) · Communications of the ACM · doi:10.1145/163298.163309 · Cited by 2,604
Self-citationWhat can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131
Self-citationJovanovic, B., & Rousseau, P. L. (2005) · Handbook of Economic Growth · doi:10.1016/s1574-0684(05)01018-x · Cited by 708
Concentrating on the Fall of the Labor Share
Autor, D., Dorn, D., Katz, L. F., Patterson, C., & Reenen, J. V. (2017) · American Economic Review · doi:10.1257/aer.p20171102 · Cited by 530 · cited 1×
“A smaller number of superstar firms are gaining market share (Autor et al., 2017; Brynjolfsson et al., 2008) while workers’ earnings are increasingly tied to firm-level productivity differences (Song, Price, Guvenen, Bloom and von Wachter, 2015).”
Acemoglu, D., & Restrepo, P. (2016) · doi:10.3386/w22252 · Cited by 406
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
Brynjolfsson, E., Rock, D., & Syverson, C. (2021) · American Economic Journal: Macroeconomics · doi:10.1257/mac.20180386 · Cited by 1,003
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. https://app.cochat.ai/papers/10.3386/w24001
- The paper
- Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. National Bureau of Economic Research. https://doi.org/10.3386/w24001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
