How reliable does a surgical robot need to be? Chris and Mojan use that question to explore a much broader reliability engineering problem. Patients care about safe outcomes and what happens when the robot fails; hospitals also care about availability, utilization, and downtime. The conversation then moves upstream to where many expensive failures actually begin: poorly defined hardware-software interfaces and requirements delivered too late. They discuss why cutting early reliability work often makes development slower, not faster, and how AI-enhanced FMEA can expose difficult failure scenarios early enough for engineers to prevent them rather than discover them during validation.

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