AI perception drift vs interpretive drift
AI perception drift is the most accessible term for naming the change in how an entity is perceived through generative outputs.
Interpretive drift is the broader doctrinal concept. It covers any deviation between the canonical meaning of an entity, corpus, policy, offer, or doctrine and the interpretation reconstructed by AI-mediated systems.
Relation between the two terms
AI perception drift is a manifestation of interpretive drift when it changes the representation of an entity in a reading, recommendation, comparison, or decision context.
Interpretive drift can however cover broader cases: authority displacement, boundary confusion, loss of legitimate non-response, unguided inference, remanence, inertia, contamination, or source conflict.
Why both terms matter
The term LLM perception drift is useful because it captures an emerging vocabulary that the market can understand directly. It is an entry point.
The term interpretive drift remains necessary because it prevents the phenomenon from being reduced to a perception metric. It connects drift to authority, evidence, canon, governance, and correction.
Reading rule
Use AI perception drift when the topic is the portrait produced by AI systems. Use interpretive drift when the topic is the full chain of meaning, authority, evidence, legitimacy, and correction.