People increasingly rely on AI systems for consequential tasks, from clinical diagnosis to software development. The gains are real yet difficult to realize reliably. We identify four psychological and structural conditions for successful delegation: accurate human assessment of AI capabilities, calibrated AI uncertainty communication, sustained human monitoring capacity, and compatible task structure. Each condition is well-studied, yet we propose that they form an interdependent system in which each one’s effectiveness depends on the state of the others. Because these dependencies are bidirectional, we suggest that they can compound over time into virtuous cycles or cascading degradation, and that improving any single condition can fail or shift the bottleneck. These dynamics should intensify as AI moves from advisory roles to autonomous agents.
