Abstarct: Agentic AI systems are increasingly positioned as productivity infrastructure for cognitive work, yet they conflate scalable generation with productive completion, systematically transferring the burden of verification, contextual alignment, and operational reliability onto the human operator. This paper introduces the concept of Supervision Debt: the cumulative supervisory effort imposed on human operators by the asymmetric relationship between automated output generation and human-bounded validation. Drawing on recent empirical findings across legal practice, software development, and knowledge work, we demonstrate that this asymmetry produces a measurable productivity divergence in which efficiency gains attributed to automation are offset by rising verification burdens, metacognitive demand, and cognitive complacency. We argue that Supervision Debt reframes the central constraint of agentic AI from generative throughput to the human capacity required to render automated output reliable and actionable. Go to Research Paper ↗