AI Dependence as a Longitudinal Governance Problem

ABSTRACT

Artificial intelligence governance typically focuses on whether people rely on AI appropriately during individual decisions. This Perspective addresses a different question: whether repeated AI use changes the capabilities and alternatives available to users and organisations over time. It introduces path‐induced AI dependence, defined as AI‐attributable deterioration in previously credible capabilities, alternatives or recovery routes for a valued function. This is distinguished from functional reliance, where performance depends on a useful AI system without evidence that prior options have deteriorated. A problem‐driven narrative synthesis suggests that direct longitudinal evidence remains limited. Existing studies provide some evidence that repeated AI or automation use can reduce subsequent unaided performance, but there is not yet direct longitudinal evidence establishing broader organisational mechanisms such as routinisation, technical‐economic lock‐in or dependence debt in generative‐AI settings. The framework therefore identifies five analytically distinct mechanisms: capability depletion, epistemic enclosure, relational switching costs, organisational routinisation and technical‐economic lock‐in, and treats their relationships as testable rather than established. It further proposes exit capacity as a multidimensional way to assess whether important capabilities, evidence, fallback arrangements and substitutes remain available. The proposed measures are research and governance tools rather than validated interventions. The central argument is that responsible AI governance should evaluate not only present performance, but also whether repeated AI use preserves credible future options.