This paper introduces the Relevance Inversion Paradox: a structural condition in which increasing amounts of available meaning no longer improve orientation, but instead weaken it. In classical epistemic settings, meaning was expected to reduce uncertainty by connecting observations, stabilizing interpretation, and making action more intelligible. Under contemporary conditions of accelerated sense production, especially under AI-mediated environments, this relation can invert. Meaning becomes abundant, rapid, and locally coherent, while relevance selection becomes weaker. As a result, subjects do not necessarily become better oriented when more interpretations are available. They may become less able to distinguish what matters, when it matters, and what requires decision. The paradox is not primarily semantic, but orientational. It concerns the breakdown of the relation between relevance and meaning. The central claim of this paper is that orientation depends not on the mere presence of meaning, but on the correct timing and structural proportion between relevance detection and meaning stabilization. Relevance precedes meaning. Meaning is not the origin of orientation, but the local stabilization of a relevance space through coherence formation. When meaning appears too early, too densely, or too smoothly, it can mask rather than clarify relevance. In this condition, coherence no longer serves orientation. It begins to replace it. The result is not necessarily falsehood, but pseudo-orientation: the subjective experience of understanding without sufficient own orientation. This paper argues that AI intensifies this condition because it functions as a system of sense production rather than a system of orientation. The argument contributes to Orientation Theory by distinguishing orientation from meaning, and relevance from coherence. It proposes that current epistemic environments are increasingly shaped by meaning saturation, relevance displacement, and orientation collapse. The paper does not reject meaning. It re-specifies its function. Meaning is useful only when it stabilizes a relevance space that has not yet been prematurely closed. The paper therefore offers a conceptual framework for understanding how more meaning can produce less orientation, and why this inversion has become structurally significant in AI-shaped knowledge environments. Author keywords (free terms): Relevance Inversion Paradox; orientation; relevance; meaning; orientation collapse; epistemistic pseudo-orientation; meaning saturation; Orientation Theory; AI and sense production; relevance selection. Internal reference: CB₀5₀3 (v0. 2)
Andreas Gregor Kawa (Fri,) studied this question.