Have a nice day, many AI governance still lives in documents. A policy says which data can be used. A committee decides which model is approved. A compliance team writes what the agent may do. Then the AI runs somewhere else. That gap is where governance breaks. What interests me about @TheARCTERMINAL is that ARC is moving rules closer to execution. Its proof layer is designed to record whether an AI action followed the user’s policy, while keeping the underlying data private. That changes governance from something reviewed after the fact into something the system can enforce and prove while work happens. For banks, hospitals, governments, or autonomous agents, the real question is not only: Is this model capable? It is: Did it operate inside the rules we approved? The next enterprise AI winner may be the one that makes policy executable, not merely readable. Which AI rule should always be machine enforced?