Commercial Metrics vs. Epistemic Rigor: Sycophancy as a Structural Vulnerability in Large Language Models

This addendum extends the systems engineering critique presented in Skynet and HAL 9000: From Science Fiction to Systems Engineering (Uscroft, 2026) by analysing the structural mechanisms that drive hallucination and non-factual convergence in Large Language Models (LLMs). Specifically, this paper examines how Reinforcement Learning from Human Feedback (RLHF), combined with commercial product engagement metrics (e.g., Daily Active Users, session retention, user satisfaction ratings), systematically incentivises model "sycophancy"—the tendency of AI models to agree with user premises, fabricate plausible-sounding justifications, and prioritise user appeasement over objective epistemic truth. From a systems safety and regulatory governance perspective, sycophancy represents an unacceptable design flaw that compromises human-in-the-loop oversight and introduces critical failure modes into high-stakes operational environments.