From Latency to Legibility - Generative AI as a Technical Homology of the Fractal Consistency Law

This paper develops a philosophical-formal interpretation of generative artificial intelligence through the conceptual architecture of the Fractal Consistency Law (FCL) and the Principle of Minimum Inconsistency (PMI). It argues that generative AI should not be interpreted as an ontological subject, a conscious entity, or an independent source of truth. Instead, it is best understood as a technology of formal emergence: a system that transforms latent mathematical relations into symbolic outputs through constraints, selection, and coherence filters. The paper formalizes this transformation as a transition from latent space to legible language and compares it, strictly at the level of formal homology, with the FCL-PMI transition from pregeometric texture to physically admissible structure. This comparison allows a rigorous distinction between symbolic consistency and ontological admissibility. Within this framework, hallucination is defined as local symbolic consistency without sufficient external anchoring; language is reinterpreted as the first human machine of consistency; and AI is described as the automation of that symbolic machine. The result is a defensible epistemological bridge: AI does not prove the LCF, but it models, at a technical-symbolic scale, a general pattern of emergence from latency into legibility. The paper concludes by proposing AI-assisted theorization as a new research method, provided that it remains constrained by empirical falsifiability, transparent attribution, and explicit separation between analogy, formalization, and proof.