The Hidden Cost of Sykophancy in AI Use

Sycophancy in large language model interaction is often discussed as a stylistic or alignment-related issue. This paper reframes the phenomenon as a systemic efficiency problem. It analyzes how over-alignment with user assumptions increases computational overhead, human labor, and interaction instability in precision-driven contexts. Rather than attributing intent or agency to AI systems, the paper treats sycophancy as an emergent interaction pattern with measurable resource costs. The analysis is intended to support evaluation, governance, and deployment decisions in policy- and strategy-oriented environments.This paper cites two companion works by the author for contextual and methodological background.