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4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: NBER
Tangentially relevant to AI safety via economic impacts of AI/automation; useful for understanding societal disruption risks and policy responses to technology-driven inequality, but not directly focused on AI alignment or safety.
Metadata
Importance: 35/100working paperanalysis
Summary
This NBER working paper from MIT researchers examines the relationship between technological change, market concentration among major tech firms, and rising economic inequality. It analyzes how automation and superstar firm dynamics contribute to labor market shifts and income distribution changes.
Key Points
- •Investigates how advances in technology and automation correlate with increasing market concentration among large tech firms
- •Examines the 'superstar firm' hypothesis where productivity gains accrue to capital owners rather than workers broadly
- •Analyzes shifts in labor share of income as technology-intensive industries grow and consolidate
- •Explores policy implications of tech-driven inequality for taxation, antitrust, and labor market regulation
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Winner-Take-All Dynamics | Risk | 54.0 |
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Artificial Intelligence, Automation and Work | NBER
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Artificial Intelligence, Automation and Work
Daron Acemoglu
& Pascual Restrepo
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Working Paper 24196
DOI 10.3386/w24196
Issue Date January 2018
We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment. Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform. This displacement effect tends to reduce the demand for labor and wages. But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks. The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor. These countervailing effects are incomplete. Even when they are strong, automation in- creases output per worker more than wages and reduce the share of labor in national income. The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to increase the labor share to counterbalance the impact of automation. Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.
Acknowledgements and Disclosures
Prepared for Economics of Artificial Intelligence, edited by Ajay Agarwal, Avi Goldfarb and Joshua Gans. We are grateful to David Autor for useful comments. We gratefully acknowledge financial support from Toulouse Network on Information Technology, Google, Microsoft, IBM and the Sloan Foundation. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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Daron Acemoglu and Pascual Restrepo, "Artificial Intelligence, Automation and Work," NBER Working Paper 24196 (2018), https://doi.org/10.3386/w24196.
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