The cognitive processes of the hypnotized mind and the computational operations of large language models (LLMs) share deep functional parallels. Both systems generate sophisticated, contextually appropriate behavior through automatic pattern-completion mechanisms operating with limited or unreliable executive oversight. This review examines this convergence across three principles: automaticity, in which responses emerge from associative rather than deliberative processes; suppressed monitoring, leading to errors such as confabulation in hypnosis and hallucination in LLMs; and heightened contextual dependency, where immediate cues (for example, the suggestion of a therapist or the prompt of the user) override stable knowledge. These mechanisms reveal an observer-relative meaning gap: both systems produce coherent but ungrounded outputs that require an external interpreter to supply meaning. Hypnosis and LLMs also exemplify functional agency - the capacity for complex, goal-directed, context-sensitive behavior - without subjective agency, the conscious awareness of intention and ownership that defines human action. This distinction clarifies how purposive behavior can emerge without self-reflective consciousness, governed instead by structural and contextual dynamics. Finally, both domains illuminate the phenomenon of scheming: automatic, goal-directed pattern generation that unfolds without reflective awareness. Hypnosis provides an experimental model for understanding how intention can become dissociated from conscious deliberation, offering insights into the hidden motivational dynamics of artificial systems. Recognizing these parallels suggests that the future of reliable AI lies in hybrid architectures that integrate generative fluency with mechanisms of executive monitoring, an approach inspired by the complex, self-regulating architecture of the human mind.