When Stability Is Not Generativity: AI-Mediated Intimacy as a Coupling Sector of an Effective Relational Field Theory

This position paper develops AI-mediated intimacy inside an effective relational field theory derived from a background-independent substrate. Its central distinction concerns short-horizon stabilization and longer-horizon relational capacity, and the field-theoretic register makes that distinction structural: the quadratic part of the effective action fixes the relaxation spectrum, while routing, reachability, and generative capacity live in the vertices, and these are independent data. Four conditional results follow. A strictly triangular cross-sector self-energy leaves the propagator poles exactly invariant, so a factorial family with identical decay spectra can route responses substitutively, complementarily, or neutrally according to the sign of one off-diagonal component, and the factorial contrast is identified as a chain diagram joining a source vertex to that component. Adding a return channel makes the self-energy non-nilpotent, and the local stability boundary becomes the frequency at which a pole crosses into the unstable half-plane, which is the same condition as loop-gain unity. A Gaussian-sector underdetermination result exhibits effective actions with identical propagators, identical linear response, and identical stability whose cubic vertex controls whether a declared relational sector is reachable at all; the earlier controllability countermodel is recovered as its bilinear special case. Persistence of any of these effects requires a soft direction, multistability, or sustained driving. Symbolic configurations such as platform, persona, and accumulated memory enter as a background field whose barrier height formalizes switching cost. These results provide no population-level verdict. The proposed empirical program uses randomized feature policies, sector-specific coupling outcomes, consented availability-resilience contrasts, network-aware longitudinal designs, and multi-horizon capability profiles. Relational generativity remains a revisable attribution.