Better Technology, Worse Motivation: Generative Artificial Intelligence’s Mediocrity Trap

Yvonne Jie Chen, Jie Gong, Jin Li and Zibo Zhao
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Yvonne Jie Chen: Asian Development Bank
Jie Gong: University of Hong Kong
Jin Li: University of Hong Kong
Zibo Zhao: Arizona State University

No 862, ADB Economics Working Paper Series from Asian Development Bank

Abstract: While generative artificial intelligence (AI) promises productive efficiency, it can paradoxically lead to lower quality work. We conducted an experiment with professional illustrators and found that AI assistance flattens the quality curve—it accelerates initial gains but sharply diminishes the returns on sustained effort. Faced with this, a significant number of professionals made a strategic choice: they sacrificed the final quality to save time. Our finding highlights a critical challenge for generative AI, which can weaken the motivation required for creative excellence and innovation. In developing economies, where governments may seek to boost growth by using generative AI to substitute for scarce skills, our results suggest that such gains may come at the cost of the sustained effort through which higher-quality work and expertise are built.

Keywords: AI; artificial intelligence; generative AI; motivation; labor productivity; technology adoption (search for similar items in EconPapers)
JEL-codes: D91 J24 M50 O31 O33 (search for similar items in EconPapers)
Pages: 29
Date: 2026-09-17
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Persistent link: https://EconPapers.repec.org/RePEc:ris:adbewp:023629

DOI: 10.22617/WPS260411-2

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