The ABCs of Who Benefits from Working with AI: Ability, Beliefs, and Calibration
Andrew Caplin, David J Deming, Shangwen Li, Daniel Martin, Philip Marx, Ben Weidmann, Kadachi Jiada Ye · Management Science · 24 Oct 2025
Where this sits in the debate
- Will AI actually boost productivity and growth?
- Will AI widen inequality? · its finding goes against the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with artificial intelligence (AI). AI improves performance more for people with low baseline ability. However, holding ability constant, AI assistance is more valuable for people who are calibrated, meaning they have accurate beliefs about their own ability. People who know they have low ability gain the most from working with AI. In a counterfactual analysis, we show that eliminating miscalibration would cause AI to reduce performance inequality nearly twice as much as it already does. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This work was supported by the Alfred P. Sloan Foundation (Cognitive Economics at Work). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08994 .
About this publication
Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.
Placed by: Leading field journal. How sources are ranked.
- Published
- 2025 (12 months ago)
- Citations
- 3 (OpenAlex)
Citations in context
3 citations — 30th percentile among the 33 indexed papers from 2025 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.
□ added to this collection in 2026.
Ben Weidmann, David J. Deming signed the wemustactnow.ai letter · author affiliation matches seed institutions harvard university, new york university, university of california — part of why this paper is in the collection.
Related by theme, not necessarily by method or conclusion.
- ARTIFICIAL INTELLIGENCE AND INEQUALITY IN THE STARTUP ECONOMY: A CONCEPTUAL FRAMEWORK FOR DIGITAL STRATIFICATION
Baltic Journal of Economic Studies · 2026 - The discrimination loop: The concerns of literary creatives around the socio-economic implications of GenAI on novelists and readers
Apollo · 2026 - YAPAY ZEKÂ, YÜKSEK TEKNOLOJİ İHRACATI VE GELİR EŞİTSİZLİĞİ: E7 ÜLKELERİNDE PANEL VERİ ANALİZİ
Euroasia Journal of Social Sciences and Humanities · 2026
Overview assembled automatically from collected metadata. How this site is built.