Year: 2025
Type: preprint
Abstract: We have studied various analyses of jobs predicted to be automated out of existence by AI, defined broadly as machine learning, deep learning, robotics, gen AI, and so on; a common attribute of these predictions is that they are wildly inaccurate. Task automation assessment, the most systematic approach, has been ineffective at predicting the number of jobs to be lost to AI. Observers would benefit by combining task-based approaches with more contextual factors about AI adoption, existing technology, jobs gained through AI, and broader economic indicators. There are no systematic approaches to this issue that are likely to yield accurate predictions, so the most responsible approach may be to discontinue making them, or admit that any predictions are highly speculative.
Source: Harvard Data Science Review
Authors: Thomas Davenport, Miguel Paredes
Institutions: Massachusetts Institute of Technology, Babson College
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4415741679