AI Visibility: Formal Definition and Theoretical Framework This paper establishes AI Visibility as a formal upstream systems discipline governing how information is authored, structured, and emitted for reliable ingestion, retention, and recall by large language models. Canonical Definition AI Visibility is defined as the intentional design of digital assets such that their informational content produces clear, stable, and machine-interpretable signals, enabling accurate model ingestion, durable internal representation, and consistent recall over time. Theoretical Framework The paper presents eight interconnected theorems formalizing the boundaries, scope, and operational principles of the discipline: Scope Expansion Theorem Boundary and Non-Equivalence Theorem Upstream Ingestion Conditions Theorem Aggregation and Signal Formation Theorem Semantic Stability and Drift Theorem Authorship and Provenance Determinism Theorem Operational Boundary and Misattribution Theorem Aggregation Threshold Theorem (empirically grounded) Authorship Joseph Mas, Independent Researcher, AI Visibility Laboratories. Earlier scientific publications by the author appear under the name J. Mas in nuclear physics collaborations at Oak Ridge National Laboratory.
Joseph Mas (Fri,) studied this question.