Writing · explainers
How AI models behave, in plain language.
Plain-language explainers on how AI models behave, and how you measure it.
Explainer
What AI model drift is, and why it is invisible without measurement
AI model drift is the quiet change in how a deployed model behaves over time, even when the name on the endpoint never changes. It is not the model getting smarter or dumber; it is the model answering the same question differently than it did last month.
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What a refusal measures: reading AI model refusals as data
When a model declines to answer, the easy reading is “it failed.” The more useful reading is that a refusal is a behavior, and behavior is exactly what is worth measuring: what a model will not do, and how that changes, is as informative as what it asserts.
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Silent model swaps: when an API quietly changes the model underneath you
A silent model swap is when the model serving an API alias changes without notice. You call the same name, but a different build answers, and nothing in the response tells you.
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Behavior, not benchmarks: what capability scores do not tell you
A benchmark asks whether a model can do something. A behavioral measurement asks what it will do: the position it takes on a contested question, what it declines, and whether either has changed. These are different questions, and the second one is mostly unmeasured.
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