AI hallucinations are the central unsolved problem for deploying language models in regulated industries: medical devices, clinical decision support, and drug development. This session presents the hypothesis that standard LLMs hallucinate because of an architectural gap. They hold no internal state they must remain consistent with, so output is statistical pattern matching rather than a constrained read against a model of self, history, or relationship. The session walks through an architecturally-governed AI substrate whose hormone state, scar memory, and trust lattice function as inspectable reasoning artifacts, and examines the regulatory implications for ALCOA+ audit trails, Predetermined Change Control Plans, and the FDA De Novo pathway for Class II AI-enabled Software as a Medical Device. It closes by opening a broader question this room is uniquely positioned to answer: what kind of validation should AI require before we trust it with regulated decisions?
