<p>“Hallucination” in generative AI is commonly understood as a technical failure, characterized by the production of ungrounded or factually incorrect outputs. While recent research has identified mechanisms to reduce such outputs, the phenomenon remains conceptually persistent and practically challenging. This paper proposes a complementary account that shifts the focus from internal model deficiencies to the interaction between generative systems and human interpretive practices. Drawing on a minimal structural vocabulary from Metaqualia Theory (MTQ), the analysis distinguishes between the generation of linguistically coherent outputs and the meta-level processes through which human cognition stabilizes meaning as reference-bearing, coherent, and truth-evaluable. On this basis, hallucination is reconceptualized as a form of misalignment between a non-stabilizing generative architecture and a stabilizing interpretive framework—a structural “M-misalignment” in the sense of Metaqualia Theory. The paper does not deny the effectiveness of existing technical mitigation strategies, but argues that they operate primarily on the generative side of a deeper structural asymmetry, without fully addressing the complementary processes of stabilization. By examining phenomena such as fabricated citations and breakdowns in perceived reliability and trust, the study demonstrates how hallucination can be understood as a relational and infrastructural phenomenon. This reframing contributes to ongoing debates in AI ethics and philosophy of language by highlighting the role of human epistemic practices in shaping the interpretation of AI-generated text. Generative AI, in this view, functions not only as a source of error, but as a diagnostic tool that reveals the conditions under which language is treated as knowledge.</p>