AI Has a Data Problem. Everyone Is Diagnosing It Wrong.
The failure mode in most AI deployments is not the model — it is the data going into it, and the institutional decisions that shaped that data long before anyone ran a training job. Garbage labeling, survivorship bias in historical cases, and alert data that reflects prior tuning errors all compound silently. This piece examines where the real problem sits and why the standard diagnostics miss it.
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