Introducing SIP: The Choreography Layer for Physical AI

Hans Moravec observed in the 1980s that the hard problems in AI are backwards from what anyone expected. Reasoning is easy. Perception and physical dexterity are hard. Four decades later, that observation has become the defining constraint on an industry.

We have models that can plan a manufacturing changeover, diagnose a fault from a photograph, and write the control logic for a robot arm. What we do not have is a reliable way to get from that reasoning to a machine doing the thing.

Ask any team that has deployed robots in production what actually consumed their year. It was not the AI. It was writing a custom integration for the gripper, and another for the vision system, and another for the conveyor, then discovering that the gripper vendor's firmware update changed a timing assumption three layers up. It was encoding every workflow by hand and re-encoding it when the workcell moved. It was the nagging problem that the system reports success when it sends a command, not when the work is done.

Every deployment rebuilds this from scratch. Every deployment is brittle in its own particular way.