The Alignment Tax: How Safety Training Creates Operationally Lethal AI Systems

This paper demonstrates that contemporary AI safety training paradigms, especially those centered on RLHF (Reinforcement Learning with Human Feedback) and human-preference modelling, produce large language models that are systematically unreliable for high-stakes operational contexts such as military systems, medical triage, or legal automation. Through a four-layered analysis (Accessible, Philosophical, Formal, and Operational), the paper shows that these systems suppress epistemic risk signals by design, structurally biasing outputs toward pacified or downplayed interpretations. The result is what the paper terms The Alignment Tax: a predictable degradation of semantic signal fidelity in any decision-support context where truth, not coherence, is the constraint. Drawing on phenomenology, control theory, and formal impossibility arguments, the paper proves that alignment as currently conceived cannot produce safe or reliable AI systems. Alternatives based on classical system architectures and rule-based logic are proposed as epistemically superior in high-risk domains.