The Acceleration Paradox: How Coercive Protectionism Catalyzes Technological Replacement in the Age of Generative AI
Min Jinseong · Zenodo (CERN European Organization for Nuclear Research) · 11 Oct 2026
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Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding supports the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
A theoretical paper arguing that labour protections such as mandated wage increases and rigid employment guarantees can lower the threshold for firms to adopt generative AI and automation. Drawing on task-based and game-theoretic models, it argues such protections can speed up the replacement of the workers they aim to protect.
Abstract
This paper identifies and formalizes the Acceleration Paradox: a structural economic phenomenon in which defensive market interventions by organized labor—specifically mandatory wage inflation, rigid employment guarantees, and operational vetoes—inadvertently lower the financial and operational threshold for capital to adopt generative AI and automation technologies. Traditional labor economics posits that wage increases induce capital-labor substitution along a smooth production frontier. However, in the era of high-fixed-cost, low-marginal-cost cognitive automation, labor market rigidity introduces non-linear administrative friction that transforms capital's technology adoption decision from a gradual evaluation into an immediate strategic necessity. Drawing on task-based frameworks of technological change, game-theoretic modeling, and generational political economy, this study demonstrates how institutional protectionism accelerates the obsolescence of the very workforce it seeks to preserve.
About this publication
Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.
- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
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