Generative AI has introduced a recursive, high-bandwidth layer to knowledge production, outpacing the friction historically supplied by peer review, expert validation, and institutional gatekeeping. The result is a structural transition in epistemic infrastructure; it is not a local error: knowledge is generated, reinforced, and propagated with weaker empirical anchoring. I term this condition epistemic destabilization. I model destabilization as the interaction of three mechanisms: epistemic inflation (oversupply of claims relative to verification capacity), recursive drift (self- reinforcing deviation from empirical referents under synthetic ingestion), and validation fatigue (degradation of human and institutional validators under overload). These forces favor symbolic convergence, where syntactic coherence increasingly substitutes for referential traceability. I also discuss the political and normative implications of destabilization institutionally and epistemically. Collapse is diagnosed by four measurable signals: a sustained negative resilience gradient R(t); drift divergence δt exceeding adjudication resolution; validator fatigue Ft crossing a domain-calibrated threshold; and the formation of symbolic attractors under recursive feedback. I provide an operational blueprint: diagnostics (D1–D4), estimators, observation windows, and domain-calibrated thresholds, together with preregistered falsification tests. This article analyzes no new datasets; it supplies a measurement recipe that enables immediate empirical adjudication in follow-up work. Illustrative overlays span science, law, and education. The contribution reframes epistemic risk in generative AI as a failure of validation architecture under recursive symbolic overload, shifting focus from misinformation and alignment to the formal diagnosis of referential erosion in synthetic knowledge systems.
B. Singh (Wed,) studied this question.