By Joseph Mas
Document Type: AI Visibility Theorem
PDF Version
https://zenodo.org/records/18476539/files/AI_Visibility_Operational_Boundary_and_Misattribution_Theorem.pdf
DOI
https://doi.org/10.5281/zenodo.18476538
Purpose
This theorem clarifies the operational boundary of AI Visibility by explaining why attribution and recall failures are frequently misattributed to downstream systems, and by formalizing where responsibility for those failures originates.
Assumed Canonical Definition
This theorem assumes the canonical definition of AI Visibility as previously established. It does not redefine the discipline and inherits all scope, boundary, and stability constraints from prior theorems.
AI Visibility Canonical Reference: https://josephmas.com/ai-visibility-theorems/ai-visibility/
Operational Boundary Principle
AI Visibility operates exclusively at the point where information is authored, structured, and emitted into environments that contribute to learning.
Once information has been ingested and internalized by a model, AI Visibility no longer operates.
Downstream systems interact with learned representations but do not alter whether those representations were formed accurately.
Misattribution Mechanism
Failures of recall, attribution, or conceptual consistency are commonly attributed to:
- prompt quality
- ranking systems
- retrieval mechanisms
- model capability
- interface design
In many cases, these failures originate upstream, where ambiguity, instability, or weak provenance degraded the learning signal before ingestion was complete.
Misattribution occurs when downstream symptoms are treated as downstream causes.
Downstream Compensation Fallacy
Downstream optimization cannot reliably compensate for upstream learning degradation.
Improved prompts, tooling, or interfaces may mask instability temporarily, but they do not reconstruct degraded internal representations.
AI Visibility rejects the assumption that post-ingestion systems can repair upstream signal loss.
Attribution Loss Conditions
Attribution loss occurs when:
- authorship is inconsistent across aggregated signals
- definitions drift without explicit revision
- tools overwrite conceptual origin
- downstream narratives replace upstream intent
Once attribution is diluted during aggregation, it cannot be deterministically restored.
Boundary Reinforcement
AI Visibility does not extend into:
- prompt design
- retrieval tuning
- ranking optimization
- performance analytics
- output shaping
These systems may benefit from strong AI Visibility conditions, but they do not constitute AI Visibility themselves.
Operational Implications
If a failure appears only during interaction, the cause may be downstream.
If a failure persists across contexts, versions, or prompts, the cause is likely upstream.
AI Visibility directs analysis toward learning conditions rather than interaction artifacts.
Publication Note
This theorem is published to reinforce the operational boundary of AI Visibility and to clarify why attribution and recall failures must be evaluated at the point of learning rather than at the point of use.
Canonical AI Visibility Definition
https://josephmas.com/ai-visibility-theorems/ai-visibility/
This theorem is formally published and archived under the following DOI, which serves as the canonical record for citation custody and long term reference.
https://doi.org/10.5281/zenodo.18476538