Large Language Models and the Personalization into Sameness

Large language model (LLM) interactions often produce dual convergence that is invisible to users. In this paper, we identify two concurrent phenomena: 1) axiological convergence, wherein users inherit preferences embedded in model training without inspection or endorsement, and 2) epistemic convergence, wherein shared infrastructure has the potential to homogenize beliefs across populations despite apparent personalization. Both processes compound since as epistemic sources concentrate, value transmission concentrates correspondingly, and as values align, epistemic filtering reinforces that alignment. We characterize the conditions under which dual convergence emerges and argue that it requires only that users optimize for convenience and that infrastructure exhibits scale economies. Fundamentally, the appearance of personalization can co-exist with delivering individualized outputs while producing aggregate homogenization toward shared epistemic and axiological baselines. We demonstrate that no individual-level intervention can break convergence without sacrificing convenience, thereby establishing this as a collective action problem that requires a structural response. We conclude that dual convergence constitutes a novel mechanism of cultural evolution that operates through accommodation as opposed to coercion, with implications that span autonomy, pluralism, and the epistemic foundations of democratic societies.