AI-driven skill volatility and the emergence of re-skilling fatigue: the human cost of perpetual learning

Artificial intelligence (AI) is rapidly reshaping knowledge-intensive work by automating, augmenting, and reconfiguring core professional activities. While continuous reskilling is widely promoted as a solution to AI-driven disruption, little attention has been paid to its cumulative psychological costs. This paper introduces the concept of reskilling fatigue to explain the human consequences of persistent skill volatility among Established Knowledge Professionals (EKPs), mid-career professionals whose roles, identities, and value are grounded in accumulated expertise and professional judgment. Based on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, the paper conceptualizes an AI-induced reskilling loop in which ongoing technological change leads to skill erosion, continuous reskilling demands, cognitive and emotional depletion, and reinforced learning as a defensive response to perceived obsolescence. Unlike restoring stability, this cycle intensifies anxiety, undermines mastery, and erodes professional confidence. As a contribution to theory and practice, the paper advances a set of sustainable, collective strategies such as role-linked learning, protected learning time, skill prioritization, and phased AI adoption to interrupt the reskilling loop and redistribute adaptive demands across organizations. By reframing reskilling as a shared, supported, and bounded process, this paper highlights pathways through which AI-driven change can foster long-term career resilience, professional identity renewal, and sustainable human–AI integration.