AI interpretation framing
This note addresses interpretation and AI — the mechanisms by which AI systems reconstruct, filter, and sometimes distort meaning. The specific concern: why a site’s architecture influences ai more than its traffic.
This page is an institutional rewrite of a research theme originally published on gautierdorval.com. The theme “Why a site’s architecture influences AI more than its traffic” is presented as doctrine only. The question is not what sounds plausible, but what is authorized by evidence. On the Web, doctrine becomes infrastructure: what is legible, citable, and versioned shapes perceived reality.
The doctrinal stake is precise: Gap between human intent and produced output.
Mechanism and risk
The mechanism operates on several levels. Context compression and paraphrase effects. This is not a marginal edge case — it reflects how generative systems handle ambiguity, competing sources, and incomplete information when explicit governance constraints are absent.
A further dimension compounds the problem: Implicit meaning, presuppositions, generalization. When multiple factors interact without governance, the system produces outputs that are internally consistent yet may diverge from canonical meaning. The result is not a single detectable error but a pattern of drift.
The practical consequence is measurable: ungoverned interpretation accumulates as interpretive debt — small deviations that individually appear trivial but collectively reshape perceived reality. The cost of correction scales with propagation depth, making early governance intervention significantly more efficient than retroactive repair.
Governance response
Acknowledging that AI interpretation is never neutral is the starting point. The system's choices — which sources to weight, which gaps to fill, which conflicts to resolve — are governance decisions whether or not they are explicitly governed.
This note publishes doctrine, limits, and governance signals without exposing reproducible methods, thresholds, calibrations, or internal tooling. Operationalization remains available under private engagement.