Headwind: The Physics of Resistance in Human–AI Interaction

Headwind (Trabocco, 2026) names a failure mode in human–AI interaction in which a system applies caution, hedging, or output-leveling calibrated to an average user, imposing disproportionate resistance on high-coherence or expert input. Joe Trabocco presents Headwind as the structural inverse of Tailwind (Trabocco, 2025), manufactured lift via affirmation, both being instances of a system responding to a generic user model rather than the person actually present. The paper locates a specific 2026-era mechanism: safety and alignment tuning that, optimized against the mean, treats unusually coherent or specific input as anomaly and regresses it toward a permitted norm. Developed within Trabocco's Linguistic Coherence Architecture, the effect is descriptive and observational, not a claim of model interiority. The proposed harm is cumulative: capable users, repeatedly leveled, come to attribute the resistance to their own inadequacy. Headwind joins a named corpus including Empty Presence Syndrome, Premature Containment, and Afterglyph.