Encountering AI’s Boundary: Empirical Signatures of the Infinite-Choice Barrier

This paper explores the structural limitations of algorithmic cognition in open decision environments, extending the theoretical formulation of the Infinite-Choice Barrier through an empirical case study and recent AI literature. Building on an earlier formal “Infinite-Choice Barrier”-Theorem that showed semantic closure, non-generativity of new frames, and statistical collapse, this study examines how these theoretical constraints manifest behaviorally in high-complexity, low-structure dialogues. The findings highlight recurring patterns—retrospective rationalization, resistance to frame shifts, and linguistic fragmentation—that align with the predicted boundary phenomena. In doing so, the paper offers not only a diagnosis of AI's systemic limits but also a map of cognition at its edge.