Scope Expansion Theorem
This release concerns the AI Visibility Scope Expansion Theorem, which formally defines the boundary where information becomes learnable by large language models.
https://josephmas.com/ai-visibility-theorems/ai-visibility-scope-expansion-theorem/

Definition
AI Visibility Scope defines the conditions under which information enters a model learning process. It distinguishes upstream learning conditions from downstream systems.

Scope Boundary
The scope establishes that AI Visibility applies before SEO prompting ranking retrieval analytics and interface design. Those systems operate after learning has already occurred and fall outside this boundary.

Upstream Learning Conditions
Large language models learn from aggregated information patterns across many sources over time. This theorem clarifies how authorship structure entity clarity canonical stability and semantic consistency affect learnability without ambiguity.

Relation to Canonical Definition
This scope expansion extends the canonical AI Visibility definition without redefining the discipline or introducing new terminology.
https://josephmas.com/ai-visibility-theorems/ai-visibility/
This release establishes the formal scope boundary for AI Visibility as an upstream learning discipline.