One post tagged with "model-drift"

Every AI product pitch deck has the same slide: more users generate more data, which trains better models, which attract more users. The data flywheel. It sounds like a perpetual motion machine for product quality. And for the first few months, it actually works — accuracy climbs, users are happy, and the metrics all point up and to the right.

Then, somewhere around month three, the curve flattens. The model stops getting meaningfully better. The annotation queue grows but the accuracy needle barely moves. Your team is still collecting data, still retraining, still shipping — but the flywheel has quietly stalled.

This isn't a rare failure mode. Studies show that 40% of companies deploying AI models experience noticeable performance degradation within the first year, and up to 32% of production scoring pipelines encounter distributional shifts within six months. The flywheel doesn't break with a bang. It decays with a whisper.