The BOHM method offers a zero-cost solution for hierarchical attribution in compound AI systems, allowing for efficient analysis without needing access to internal components. This approach provides multi-resolution insights and competes with traditional Shapley-based methods, potentially transforming how AI system performance is evaluated.