The Normalization of Inexplicable Failures

• calibration — How well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.
• eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.

Argues that AI confidence scores are largely meaningless without calibration data and an explicit cost model for uncertainty, and that teams use them as an excuse rather than a real reliability signal, a warning for anyone building eval or gating logic around them.

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