Argues that the field of AI interpretability lacks a foundational theory of interpretation itself — the missing layer. Current mechanistic interpretability explains what models compute but not how meaning is organized or routed. Proposes interpretive architecture as the theoretical framework AI interpretability needs to move beyond feature-level explanation.