Cited by 159

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

Acemoglu, Daron·2024

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Crossref · 10.3386/w32487

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6 of 6 references verified0 citation errors · 0 not checkable

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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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Metadata

Authors
Acemoglu, D.
Published
2024-05
Cited by
159
References
6

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