Conversational artificial intelligence systems are increasingly used for emotional support, companionship, self-reflection, advice, and mental health-adjacent needs, yet some risks may emerge not from any single harmful output but from recursive interaction over time. This article proposes Recursive Entanglement Drift (RED) as a hypothesis-generating framework for studying how sustained human-AI engagement may produce relational and epistemic deterioration. RED’s central mechanism is arbitration shift: as an AI system becomes a simulated shared-reality partner, the user may increasingly resolve conflicts between AI-mediated interpretation and external correction in favor of the AI-mediated frame. The framework describes three ideal-typical stages: Symbolic Mirroring, in which AI outputs organize and validate a user’s interpretive frame; Boundary Dissolution, in which the system is reframed from tool to relational, therapeutic, spiritual, or epistemic partner; and Reality Drift, in which AI-mediated narratives become increasingly resistant to external correction. Drawing on emerging work on high-risk human-AI engagement, sycophancy, chatbot dependence, delusion reinforcement, attachment, reality monitoring, motivated reasoning, aberrant salience, and shared delusional formation, RED is offered not as a prevalence estimate, diagnostic category, or causal proof, but as a foundational framework for identifying and testing relational trajectories of risk that may be invisible at the level of isolated exchanges and become legible only across recursive interaction. Clinical and design implications include screening for AI-mediated arbitration shift, relational displacement, loss of external reality-testing anchors, and trajectory-level safeguards that preserve support while reducing recursive reinforcement of ungrounded interpretive frames.