| Internet-Draft | AI Visibility Lifecycle | February 2026 |
| Lynch | Expires 14 August 2026 | [Page] |
This document describes the 11-Stage AI Visibility Lifecycle, a stage-based observational framework describing how digital content achieves visibility within AI discovery, comprehension, trust, and human exposure systems. The framework identifies three distinct phases -- AI Comprehension (Stages 1-5), Trust Establishment (Stages 6-8), and Human Visibility (Stages 9-11) -- through which domains progress from initial AI crawling to sustainable human-facing visibility.¶
This Internet-Draft is NOT the canonical source for the AI Visibility Lifecycle framework. The authoritative reference is the Zenodo deposit at https://doi.org/10.5281/zenodo.18460711. This Internet-Draft mirrors the specification for IETF community accessibility. In case of any discrepancy between this Internet-Draft and the Zenodo deposit, the Zenodo version governs.¶
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The AI Visibility Lifecycle (v0.7) provides a structural model for understanding how AI systems discover, evaluate, trust, and surface web content to human users. This framework is observational and analytical, not prescriptive. This document does not propose a standard, protocol, or recommendation for implementation.¶
This document mirrors the canonical specification maintained at Zenodo [ZENODO]. A companion paper on ambiguity elimination [AMBIGUITY] provides additional theoretical context. In case of any discrepancy between this Internet-Draft and the Zenodo deposit, the Zenodo version governs.¶
The lifecycle consists of eleven stages organised into three phases:¶
Discovery and reconnaissance. AI systems identify and access content through crawling mechanisms, evaluating technical accessibility, structural signals, and initial content availability.¶
Semantic parsing and embedding. Content is processed into machine-readable representations, including semantic embeddings, entity extraction, and structural decomposition.¶
Purpose and identity assignment. AI systems assign topical classification, entity type, commercial intent signals, and domain purpose categorisation.¶
Internal consistency evaluation. AI systems verify that claims made across a domain are internally consistent, structurally coherent, and free of contradictions.¶
External alignment verification. AI systems compare domain claims against external sources to verify factual accuracy, citation validity, and alignment with established knowledge.¶
Evidence accumulation over time. AI systems monitor consistency, stability, and reliability signals across repeated evaluations to build cumulative trust assessments.¶
Formal eligibility for answers. A domain reaches the threshold at which AI systems consider it a credible source eligible for inclusion in generated responses.¶
Competitive readiness assessment. AI systems evaluate the domain against alternative sources to determine whether it should be surfaced in preference to competing candidates.¶
Controlled experiments. Content begins appearing in human-facing results on a limited, experimental basis to measure engagement, relevance, and user satisfaction signals.¶
First stable placement. The domain achieves a consistent, reproducible position in human-facing results based on accumulated AI evaluation and human interaction data.¶
Human traffic acceleration. Sustained visibility drives increasing human engagement, which in turn reinforces AI trust signals, creating a compounding visibility effect.¶
This Internet-Draft is NOT the canonical source. The authoritative specification is maintained at Zenodo:¶
Primary: https://doi.org/10.5281/zenodo.18460711¶
Concept DOI (always resolves to latest version): https://doi.org/10.5281/zenodo.18460710¶
GitHub mirror (non-citable): https://github.com/Bernardnz/ai-visibility-lifecycle¶
This document describes an observational framework and does not define any protocols, data formats, or executable specifications. There are no security considerations directly applicable to this document.¶
This document has no IANA actions.¶