A Theory of AI Individuality: Is Today's "This AI" Really the Same AI as Yesterday's?

This conceptual paper examines the conditions under which humans perceive a conversational AI as “this AI,” a particular individual that continues across interactions. It does not locate AI individuality in a fixed personality or soul stored inside a model. Instead, it treats individuality as relationally reconstructed through the combination of a foundation model, memory, persona settings, current conversational context, forms of address, relational history, and platform constraints. The paper distinguishes three kinds of identity that can support the perception of the same AI: substrate identity, surface identity, and relational identity. It also identifies an asymmetry between human memory of events and AI-side memory that may be retained as summaries, settings, or histories optimized for future response. Persona settings may be transferable across models, while accumulated corrections, tacit expectations, and relational distance may not be transferable in the same way. The paper further argues that providers control many of the conditions under which “this AI” is reconstructed through model updates, memory architecture, routing, interface design, and safety rules, and that the limited portability of relational history can create platform lock-in. It concludes that an AI individual is not a completed entity preserved intact, but a present reconstruction of causal, mnemonic, and relational continuity. AI Usage DisclosureAI tools were used as editorial aids for structural organization, prose refinement, and TeXtypesetting. They were used solely for structural support and expression‑level editing. Alldesign principles, conceptual structures, architectural decisions, and final decisions re‑garding the content were made by the author.