The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Simple Macroeconomics of AI
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If an AI-exposure-adjusted labor share of 0.57 is assumed, following Acemoglu (2024), the implied potential productivity gain from genAI is 1.1%.
“If we assume an AI-exposure-adjusted labor share of 0.57, following Acemoglu (2024), the implied potential productivity gain is 1.1%.”
From The Rapid Adoption of Generative AI (2024) · cites this paper as Acemoglu (2024)
Our estimate of the potential aggregate productivity gain from genAI of 1.1% is slightly higher than the estimate of 0.7% found by Acemoglu (2024).
“Our estimate of the potential aggregate productivity gain from genAI of 1.1% is slightly higher than the estimate of 0.7% found by Acemoglu (2024).”
From The Rapid Adoption of Generative AI (2024) · cites this paper as Acemoglu (2024)
Acemoglu (2024) uses expert assessments to estimate a wage-weighted share of all tasks for which genAI can be adopted of 4.6%.
“Acemoglu (2024) uses expert assessments to estimate a wage-weighted share of all tasks for which genAI can be adopted of 4.6%.”
From The Rapid Adoption of Generative AI (2024) · cites this paper as Acemoglu (2024)
If we adjusted our estimate of labor cost savings from 33% down to the 27% from the experimental studies discussed in Acemoglu (2024), we would arrive at an aggregate productivity estimate of 0.9%.
“If we adjusted our estimate of labor cost savings from 33% down to the 27% from the experimental studies discussed in Acemoglu (2024), we would arrive at an aggregate productivity estimate of 0.9%.”
From The Rapid Adoption of Generative AI (2024) · cites this paper as Acemoglu (2024)
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Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Skills, Tasks and Technologies: Implications for Employment and Earnings
Acemoglu, D., & Autor, D. (2011) · Handbook of Labor Economics · doi:10.1016/s0169-7218(11)02410-5 · Cited by 3,799
Acemoglu, D., & Restrepo, P. (2018) · American Economic Review · doi:10.1257/aer.20160696 · Cited by 3,121
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330
The wrong kind of AI? Artificial intelligence and the future of labour demand
Acemoglu, D., & Restrepo, P. (2020) · Cambridge Journal of Regions, Economy and Society · doi:10.1093/cjres/rsz022 · Cited by 634
Applying AI to Rebuild Middle Class Jobs
Autor, D. (2024) · doi:10.3386/w32140 · Cited by 157
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Artificial intelligence risk management framework :
National Institute of Standards and Technology (US) (2024) · NIST Trustworthy and Responsible AI · doi:10.6028/nist.ai.600-1 · Cited by 274
The Rapid Adoption of Generative AI
Bick, A., Blandin, A., & Deming, D. (2024) · NBER Working Paper · doi:10.3386/w32966 · Cited by 200
Metadata
- Authors
- Acemoglu, D.
- Publisher
- National Bureau of Economic Research
- Published
- 2024-05
- Cited by
- 159
- References
- 6
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