CONCEPT
Depth Governance
The organizational practice of evaluating judgment quality behind AI output rather than surface compliance—the governance structure AI's smooth interfaces demand.
Depth governance is the institutional response to AI's production of uniformly smooth surfaces concealing variable quality beneath. Traditional governance mechanisms—code review, output inspection, quality metrics—operate on surfaces: they check whether the output meets specifications, compiles correctly, reads professionally. When AI produces syntactically perfect code, polished prose, and well-structured analysis regardless of underlying soundness, surface governance becomes uninformative. Depth governance evaluates not the output but the process: Did the worker verify claims? What alternative specifications were considered? Can the worker explain why this output serves organizational purpose? The evaluation requires domain expertise, protected time, and cultural norms treating explanation-demands as standard practice rather than insults. Depth governance is more expensive than surface governance—precisely what makes it a credible organizational commitment to judgment quality rather than cheap talk.
In The You On AI Field Guide
The governance problem is structural: the quality dimension that matters most (whether the output serves genuine purpose, whether the analysis captures relevant causality, whether the code will scale under realistic conditions) is precisely the dimension the surface conceals. An AI-generated statistical analysis