AI increases throughput, not review capacity

AI-assisted development increases throughput. It does not increase review capacity.

The gap between those two rates is where governance quietly breaks down.

The emerging consensus is to delegate execution to AI and review the output. This treats review as the control mechanism. But review is inspection. Inspection scales with volume. When output accelerates and review does not, the constraint is not staffing. It is the operating model.

At human speed, inspection-based governance was survivable. Rejection rates stayed manageable. Reviewers could hold context. Evidence could be reconstructed after the fact.

At machine speed, those tolerances disappear.

The structural alternative is not faster review. It is execution that cannot produce ungoverned output. Boundaries that constrain what is possible before code is written, not reviewers who judge what happened after.

Where execution is bounded, compliance becomes observable by default. Evidence is produced by the system, not recovered from it. Review shifts from judgement of output to verification of evidence.

The difference is not review capacity. It is boundary design.