The Sectoral Trilemma: A contingent theory of divergent productivity-employment regimes in the age of AI

Abstract

This article challenges the dominant "Artificial Intelligence (AI) dilemma" narrative—a simplistic trade-off between productivity and employment—as a categorical fallacy that fails to account for significant disparities in labor market outcomes across economic sectors. The Sectoral Trilemma is introduced as a fundamental theory asserting that three primary objectives—swift productivity growth, stable or rising employment, and scalable output expansion—are inherently incompatible at the sectoral level when shaped by algorithms. The observed disparity, characterized by a concurrent rise in technology and the complete displacement of clerical roles, is a structural hallmark of how various industries address this constrained-optimization issue. The distinctive regime of a sector is defined by the arrangement of three binding constraints: the technical substitutability of tasks, the price elasticity of demand for outputs, and the institutional bargaining power that regulates labor. This theoretical synthesis goes beyond fragmented task-based or institutional analyses, offering a cohesive framework that explains diverse impacts, shifts the focus from aggregate net effects to sectoral regime analysis, and provides policymakers with a diagnostic tool for precise labor interventions and strategic organizational design in an algorithmically driven economy.  Keywords: Artificial Intelligence, Labor Economics, Sectoral Analysis, Productivity, Employment, Technological Unemployment, Economic Regimes, Institutional Bargaining Power.