From Informational Abundance to Meaning-Proximate Coherence: The Structural Condensation Mechanism of Generative AI

This concept block proposes a structural diagnosis of generative AI. Its central claim is that the primary problem of large language models is not misinformation as such, but the unavoidable transformation of informational abundance into coherent, intelligible and connectable output. This output does not yet amount to meaning in a strong orientational sense, yet it is already experienced by users as coherent, relevant and understandable. To describe this intermediate condition, the concept of meaning-proximate coherence is introduced. Generative AI should therefore be understood not merely as an information system, but as a condensation system that prestructures relevance before users have completed their own orientational work. The paper shifts attention away from isolated output errors such as hallucination or bias and toward the structural relation between prompt, model convergence, user resonance and the possible replacement of orientation by pseudo-orientation. It offers a six-stage minimal model and clarifies the literature boundary between existing debates on LLM behaviour and the present orientation-based interpretation. Author keywords (free terms): meaning-proximate coherence; orientation; trackness; pseudo-orientation; generative AI; informational abundance; structural condensation; sense production; active sense-preparation; Beyond Sectors. Internal reference: CB_05_05 (v0.3)