Value in AI Has Been Measured in Speed and Accuracy So Far. Neither of Those Metrics Captures Whether It Actually Knows You.
The dominant metrics for evaluating AI value — speed of response, accuracy of output — are real, measurable, and genuinely useful for comparing systems on the tasks those metrics were designed to track. They're also silent on a completely different question: whether the system actually knows anything specific and true about the individual person it's serving.
A system can be extremely fast and extremely accurate on every task it's given while knowing essentially nothing durable about the person behind those tasks — because speed and accuracy are properties of individual interactions, evaluated one at a time, while knowing someone is a property that only emerges from a continuous relationship across many interactions, which the standard metrics were never built to capture.
This gap explains why a system can score well on every conventional benchmark and still feel generic, replaceable, or oddly impersonal to the person using it — the metrics were measuring the right things for a different kind of value than the one that's actually missing, and a high score on them says nothing about whether the missing piece has been addressed.
Speed and accuracy remain genuinely necessary. They're just not the axis that captures whether an AI actually knows the person using it — that's a distinct kind of value, currently mostly unmeasured, and mostly undelivered by systems optimized purely against the metrics that are.
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Momar Lissa Ndiaye ("MLN") is the Founder & CEO of weyoga Inc., a Delaware company. — weyoga.ai · mln@weyoga.ai