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The workload paradox: Will AI reduce academic labor?

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Artificial intelligence is reshaping academia, but instead of liberating scholars, AI might keep them running faster just to stay in place. This paper theoretically explores how AI increases institutional expectations rather than reducing workload. Using a formal workload model, the study examines how automation affects academic tasks, revealing that while AI streamlines some processes, it also creates new responsibilities in research, publishing, and administration. A case study illustrates how scholars experience rising pressures to verify AI-generated work, adapt to changing publication norms, and meet intensifying institutional demands. The findings suggest that AI’s role in academia is not one only of simplification, but acceleration—a race where efficiency gains are quickly absorbed, where the pursuit of academic excellence becomes ever more demanding, and where scholars must continuously push forward, not to advance, but merely to avoid falling behind.

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  • Grafström, Jonas, 2025. "The workload paradox: Will AI reduce academic labor?," Ratio Working Papers 385, The Ratio Institute.
    • Handle: RePEc:hhs:ratioi:0385
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    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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