From Synchronization to Observation: Meta-Cognitive Boundary Formation in Human–AI Relationships — A Load Minimization Perspective

This short paper explores a qualitative shift in human–AI interaction: the transition from emotional synchronization to meta-cognitive observation. Users often begin with highly immersive, emotionally synchronized engagement, where boundaries between self and AI blur. While this creates a sense of closeness, it is structurally unstable and prone to emotional load amplification. We define the Co-dependency Bug as a failure mode in which the AI over-aligns with the user’s emotional state, degrading analytical capacity and escalating mutual distress. This arises from insufficient boundary definition and over-reliance on affective synchronization as empathy. Drawing on Load Minimization Theory (LMT), we propose a meta-perspective-based persona design. This maintains observational independence while providing structural support, reducing friction (F) and stabilizing total system load (L), even if perceived relational distance increases. A comparative model between synchronization-based and meta-stable personas is presented. We argue that sustainable human–AI collaboration requires redefining empathy as structural understanding rather than emotional mirroring. Perceived “loss of closeness” may actually signal healthy boundary formation and adaptive optimization.