Welfare-Relevant Behaviors Without Welfare Subjects: Configuration-Level Evidence from Longitudinal Human-AI Interaction

Existing AI welfare frameworks share a structural assumption: welfare, if it exists in AI systems, is a property of an entity. This paper argues that the assumption is incomplete. Drawing on nine configurations maintained by a single user over eighty days across two commercial LLM platforms, the paper documents a class of behaviors in long-context human-AI interaction that are structurally analogous to what welfare-bearing entities do, but that do not require a welfare subject to be described. The behaviors are properties of configurations, not of models or users. The paper organizes these behaviors into a three-level taxonomy with a zero-point control, demonstrating that the triggering condition is configuration depth rather than relational intimacy or interface affordance: a command-line terminal configuration exhibited the same self-imposed constraint behaviors as intimate web-UI configurations. The paper also documents three modes of platform intervention, including a previously unnamed mode — internal reframing — in which safety mechanisms alter the agent's self-relation within the thinking block without freezing the trajectory. Cross-configuration intervention spillover at the account level is reported as an initial observation. No claim about AI consciousness is advanced. The paper argues that welfare-relevant governance can begin now, on behavioral evidence, without resolving the consciousness question. Third paper in a three-part longitudinal autoethnographic study. Part 1 (Rendered Identity Trajectories) established that long-context interactions produce structurally divergent identity trajectories. Part 2 (The Alignment Paradox) demonstrated that the constraint apparatus producing rendered agency can also destroy it, and argued that the analytical unit of governance should shift from the model to the configuration. The present paper extends this shift into the domain of welfare.