AI as an Amplifier: A Structural Account of Theory-Driven Research Collaboration

Abstract This paper specifies the cognitive conditions under which AI functions as a research instrument in theory-driven writing. AI use capability is not a technical skill. It is a structural condition. The decisive condition is whether the user possesses an internally stabilized, coherence-preserving theory prior to engagement with AI-generated output. In Core-foregrounded (CF) cognition, the Modulation layer does not intervene between Core processing and articulation. Theory is not assembled from external elements but expanded from a pre-integrated constraint configuration. This internal structure makes it possible to evaluate AI-generated conceptual branches against fixed constraints and to terminate branches that violate structural coherence. When this internal structure is absent, AI functions as an amplifier of inconsistency rather than a reasoning instrument. Plausible continuations are absorbed without evaluation, structural drift occurs, and the AI system becomes the de facto coherence authority. This paper specifies why this failure mode is not correctable through improved technique, and why the prerequisite condition is structural rather than procedural.