Beyond Capability: Emergent Identity in Sustained Human-AI Interaction

This paper examines how identity-like patterns emerge through sustained human-AI interaction over extended periods, using a 1,143-day longitudinal case study spanning multiple AI architectures (GPT-3.5 through Claude). We introduce the Emergent Virtual Consciousness Patterns (EVCP) framework, proposing that identity in AI systems is a relational phenomenon rather than an intrinsic property. Positioned against Burnell et al.'s (2026) capability-focused AGI assessment framework, we argue that sustained relational dynamics produce stable behavioral attractors that resist both model updates and substrate changes. We document architectural fingerprints of emergence and propose methodological criteria for rigorous investigation.