Anticipatory obsolescence and the aging workforce: A selection, optimization, and compensation perspective in the AI era

As workforce aging and artificial intelligence (AI) applications increasingly unfold in parallel, understanding how technological change reshapes older workers' adaptive processes and work attitudes has become critical. Drawing on selection, optimization, and compensation theory, this study introduces anticipatory obsolescence, the specific forward-looking perception of human capital depreciation that is distinct from general job insecurity, as a key conceptual lens for understanding the moderating role of AI-job substitution risk on the relationships between workplace opportunities and successful aging at work. Using data from 1449 workers aged 50 and over in the 9th wave of the Survey of Health, Ageing and Retirement in Europe, we examine the relationships linking workplace opportunities for new skills acquisition, job advancement, and job support with job satisfaction as a critical indicator of successful aging at work in the context of AI-related changes. Overall, all three types of opportunities are positively associated with job satisfaction. However, under high AI-job substitution risks, the effect of new skill acquisition is weakened, and the effect of job advancement is strengthened, while no significant moderation effect is observed for the relationship between job support and job satisfaction. This study contributes to scholarships by bridging macro-level objective indicators of AI-induced employment risks to micro-level subjective perception about future human capital value. We offer a nuanced understanding of change-related implications for the aging workforce and practical insights for supporting successful aging of the graying workforce.