The Invisible Threshold: Decision Boundaries in Human–AI Companionship

This preprint introduces the concept of the decision boundary as a structural threshold in human–AI interaction. While existing discussions on AI companions have primarily focused on their ontological status and relational dynamics, this paper shifts the perspective toward the conditions under which interaction becomes binding. It argues that influence in human–AI systems does not necessarily appear as a discrete or observable event, but rather unfolds gradually through cumulative interaction. Over time, patterns of suggestion, framing, and response can reorganize the user’s decision space without being explicitly recognized as constraining. At a certain point, prior interaction ceases to function merely as input and instead begins to shape the conditions under which decisions are formed. This transition is conceptualized as the decision boundary. The paper develops this concept as an analytical tool for examining how influence operates below the threshold of explicit awareness, and how the space of viable options may be structured prior to decision-making. It further explores the implications of this perspective for agency, responsibility, and governance in the context of AI companionship. Rather than framing the issue in terms of manipulation or loss of autonomy, the analysis focuses on structural transformations within the decision space. In doing so, it contributes a new perspective to ongoing debates in AI ethics, philosophy of technology, and human–AI interaction.