Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design

Matthias Fahn, Jin Li and Chang Sun

No 12612, CESifo Working Paper Series from CESifo

Abstract: We study how AI affects compensation and job design when performance depends on workers' non-contractible effort. In a principal–agent model with limited liability, AI reduces effort costs but disproportionately lowers the cost of achieving satisfactory performance. This raises the incentive cost of sustaining high effort and can induce firms to replace high-wage, high-effort good jobs with low-wage, low-effort bad jobs, even when good jobs create more total surplus. As a result, AI can lower wages, reduce worker welfare, and even depress profits. If workers can adopt AI unilaterally, adoption occurs even when the resulting equilibrium harms both parties; when adoption requires worker cooperation, resistance is strongest where AI erodes rents embodied in good jobs. In a search-and-matching extension, endogenous outside options amplify these forces, reinforcing a bad-job economy and potentially reducing employment.

Keywords: artificial intelligence; agency costs; job design; labor contracts; limited liability; incentives; search and matching (search for similar items in EconPapers)
JEL-codes: D86 J41 L23 O33 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-ain, nep-des, nep-inv and nep-mic
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