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The black-box problem of artificial intelligence is usually understood as the opacity of a model's internal computation. This paper proposes that there is a second problem. The process by which an AI-generated work comes to mean something to a person is also not visible. I call the first the technical black box and the second the relational black box, and I argue that solving the technical black box does not close the relational one. The argument rests on a consequence of a previously published model of experience generation (Awata 2026): meaning generation is possible only within the phenomenal domain of a receiver, and the outputs of an AI system remain informational states that are nobody's experience. On this basis, I identify what on the receiver's side orients the formation of meaning as kamae, a posture that functions as a phase-selection mechanism, and I propose a structural model that describes the formation and change of meaning with three terms: concordance, de-concordance, and re-concordance. The model reads the well-documented drop in evaluation that follows an "AI-made" label as de-concordance, and the longitudinal shift from resistance to acceptance of generative AI as re-concordance, within a single framework. What the model adds to research on the reception of AI-generated works is a receiver-side condition and a set of predictions that the anthropocentric-bias account does not yield. Finally, I state three testable predictions, re-concordance under repeated exposure of the same receiver to the same work, individual differences in kamae, and the order of disclosure, and I discuss three anticipated objections. One of them, whether de-concordance can be distinguished from cognitive dissonance, is left open. The paper is a conceptual proposal. It does not claim that AI has consciousness or kamae, and it does not claim identity with quantum measurement or physical energy.
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Masashi Awata (2026) studied this question.
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