The Inevitable Drift of Non Closed AI Governance Systems

This paper establishes a structural limitation on AI governance systems whose authority constraints remain legitimately reconfigurable. We prove that in any such non-closed governance system, invariant legitimacy is impossible: no prohibition can remain permanently enforced under admissible operation. The result is modal rather than temporal. It does not claim that authority drift must occur in practice, only that it cannot be prevented as a structural invariant when governance rules themselves remain subject to permitted change. Increasing friction, auditability, procedural rigor, or human oversight does not restore invariance; any finite, admissible modification preserves the possibility of authorization expansion. The analysis is purely eliminative at the level of claims. The paper does not present a governance architecture, does not argue that closure is achievable in general, and does not recommend any design intervention. What it establishes is a negative boundary condition: claims of permanent or invariant legitimacy made by non-closed governance systems are structurally unsound, independent of implementation details, incentives, or institutional intent. Importantly, this result was developed in the course of adversarial evaluation of concrete governance architectures. While no constructive mechanism is disclosed here, demonstrations of closed-governance behavior are available to qualified evaluators under NDA. This work is intended to function as a formal constraint on governance claims, clarifying what non-closed systems can regulate, what they cannot guarantee, and where claims of CORE-level capability necessarily fail.