CONCEPT
The Review Deficit
The measurable erosion of human oversight in AI-augmented organizations — the declining depth and duration of code review, design critique, and quality assessment that accumulates as each acceptable output reinforces the expectation that the next does not require the depth of review the last one received.
The review deficit names the specific behavioral signature of normalized deviance in AI-augmented work. As AI-generated output accumulates a track record of competent performance, the review applied to each new output compresses — from line-by-line reading to section-level review to scanning to eventual formality. The compression is rational at every step: each reviewer is allocating finite cognitive resources according to demonstrated tool reliability. The aggregate effect is a system in which the human oversight designed to catch failures the automated processes miss has been eroded to the point where it no longer performs its protective function.
In The You On AI Field Guide
The deficit follows the four-phase mechanism of normalized deviance. An anomaly — the first errors found in AI-generated code — triggers careful review. Assessment of the track record — low error rate, errors caught downstream — supports reduced review depth. Normalization proceeds: senior engineers