The modern view of the nervous system as layering distributed computation and communication for the purpose of sensorimotor control and homeostasis has much experimental evidence but little theoretical foundation, leaving unresolved the connection between diverse components and complex behavior. As a simple starting point, we address a fundamental tradeoff when robust control is done using communication with both delay and quantization error, which are both extremely heterogeneous and highly constrained in human and animal nervous systems. This yields surprisingly simple and tight analytic bounds with clear interpretations and insights regarding hard tradeoffs, optimal coding and control strategies, and their relationship with well known physiology and behavior. These results are similar to reasoning routinely used informally by experimentalists to explain their findings, but very different from those based on information theory and statistical physics (which have dominated theoretical neuroscience). The simple analytic results and their proofs extend to more general models at the expense of less insight and nontrivial (but still scalable) computation. They are also relevant, though less dramatically, to certain cyber-physical systems.