Dallas, Texas Jan 23, 2026 (Issuewire.com) - This release announces the formal publication of a canonical definition that establishes scope and authorship for the term AI Visibility.
AI Visibility is defined as an upstream systems discipline concerned with how information is authored, structured, and emitted so that large language models can ingest it from durable internal representations and recall it consistently over time.
Source: https://josephmas.com/ai-visibility-theorems/ai-visibility/
This definition establishes a boundary between upstream learning conditions and downstream systems such as SEO prompting, ranking retrieval analytics, and interface design. AI Visibility applies at the point where information enters a model learning process and does not describe post learning optimization or measurement.
Large language models learn from aggregated information patterns across many sources over time rather than isolated pages. AI Visibility addresses how those patterns are clarified, stabilized, and repeated so they can be learned without semantic ambiguity.
The definition focuses on entity clarity, deterministic authorship, canonical reference stability, structural consistency, and minimal semantic drift across representations. These conditions influence whether information is learnable when it appears in aggregate training signals.
This release documents a formal definition and scope boundary. It does not describe tools, products, datasets, or internal model parameters.
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