We present a formal theoretical framework for analyzing the thermodynamic stability of hybrid cognitive systems composed of stochastic high-bandwidth generators (e.g., large language models) and deterministic low-bandwidth verifiers (e.g., human experts). In environments of supercritical information flux, where the rate of machine-generated information far exceeds the biological verification capacity of human operators, unfiltered coupling leads to inevitable instability and error divergence. We derive the Hybrid Stability Theorem, which establishes a necessary and sufficient condition for system stability based on bandwidth matching between machine output and biological verification capacity. The model formalizes the Filter Function as a thermodynamic valve that constrains information flux, allowing the human operator to act as an entropy-selective verifier in a stable grounding regime. We show that when this constraint is violated, the system enters a saturation regime characterized by exponential error accumulation (“hallucination cyclotron”). This work reframes the AI Alignment Problem as a physical bandwidth matching problem and provides a foundational theoretical basis for the design of safe hybrid human–AI systems. All models and figures are theoretical derivations based on control theory, cybernetics, and information thermodynamics.
Douglas H. M. FULBER (Sun,) studied this question.