New Governance Paradigm Controls AI Output in High-Loss Domains

Traditional human-in-the-loop oversight for AI systems becomes impractical in high-loss environments where the volume of AI output overwhelms human capacity. The core issue isn't just speed, but the combined effect of speed and the cognitive load per item, which includes triage, judgment, and response. As AI capabilities improve, only judgment cost sees potential reduction, often by inducing omissions rather than true efficiency gains, while triage and response costs remain stable or even restructure. The paper proposes "Flow-by-Flow," a novel governance approach that bypasses direct content judgment. Instead, it uses a cognitive cost score derived from formal, countable features to impose non-linear costs on high-volume production. This is coupled with an institutional capacity cap to keep processing within human limits. This paradigm is built on four design invariants: no content judgment, no scalable consumption of examiner capacity, identity-bound per-application friction, and no batch clearance. A reference implementation demonstrates feasibility, and Monte Carlo analysis suggests this multi-metric flow control significantly outperforms supervision reinforcement alone in managing AI output.