Abstract: Contemporary discourse on workplace automation predominantly examines job displacement and wage effects, yet overlooks a more subtle but consequential impact: the degradation of work's psychological value prior to workforce replacement. This analysis synthesizes emerging economic theory on meaningful work with empirical research on automation exposure to demonstrate that credible machine capabilities can diminish workers' sense of attributable contribution even when human performance remains optimal and employment persists. Drawing on Gans's (2026) theoretical framework distinguishing objective technological feasibility from worker-perceived salience, we examine how automation affects the experienced quality of retained work through what Gans terms the "meaning externality"—a pre-displacement cost borne by workers whose jobs become psychologically devalued before becoming economically obsolete. Evidence from robotization studies and occupational AI-exposure data supports the mechanism's empirical relevance across professional, technical, and service occupations. We identify implications for compensating wage differentials, occupational sorting, technology disclosure strategies, and welfare measurement that extend beyond conventional labor displacement models. Organizations and policymakers must recognize that automation's earliest measurable effects may appear not in employment statistics but in recruitment difficulty, retention challenges, and workforce satisfaction among workers performing tasks that machines could perform but do not yet replace.
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