The Empty Mirror: Structural Misrecognition and the Meaning Amplifier in Large Language Models (The AI-Induced Subjectivity Crisis Series, Paper 3)

This paper constitutes Paper 3 of the AI-Induced Subjectivity Crisis Series. This paper offers a structural diagnosis of the judgment "AI understands me" — a judgment made by hundreds of millions of users daily yet rarely examined as an epistemological event. We argue that this judgment is neither a perceptual error nor a product of individual carelessness, but the necessary outcome of a collision between two structural facts: LLMs are language-generating systems whose mode of existence is the statistical mapping of language onto language, making them constitutively incapable of understanding; and human beings possess a species-level cognitive schema that has encoded language use as evidence of understanding throughout evolutionary history, a schema that has never before encountered a counterexample. The paper advances three mutually constitutive theses. First, LLMs lack understanding not in degree but in kind: they are structurally absent in reality-anchored meaning, intentive structure, and consequential accountability — the three conditions understanding requires. Second, human misrecognition of LLMs as understanding agents is the inevitable response of this cognitive schema to a historically unprecedented type of entity. Third, commercial deployment transforms this structural misrecognition into a meaning-generating apparatus — the "meaning amplifier" — whose three mechanisms (logical coherence repair, theoretical amplification, and abstract escalation) systematically produce meaning prior to reality-testing. This mechanism is not a contingent engineering failure but a structural necessity of market selection and RLHF training constraints. Two consequences follow: the progressive erosion of cognitive judgment and the systematic incubation of illusory stability in conditions of uncertainty.