Looks like the reply picked up the wrong context. I was responding to your original point about architecture. My view is that AI can help move faster, but it should not decide the boundaries of the system. The useful question is: who owns the constraints when trade-offs get messy? Data ownership, failure modes, review points, and long-term maintenance still need a person who understands the domain.

I agree with the direction, but I would put the line a little differently: AI can suggest an architecture, but it cannot own the constraints. In real systems, the hard part is knowing which constraints actually matter: auditability, data ownership, failure modes, review points, future integrations, and what should happen when the model is not confident. I’m working on a B2B matching workflow, and this shows up all the time. The model can draft a clean pipeline, but it does not automatically know when a buyer/supplier match needs evidence, when a missing field should block the flow, or when a human should review before an introduction happens. That boundary is architecture. So I still want AI in the loop, just not as the owner of the system shape. Let it speed up options, docs, boilerplate, and edge-case checks. Keep the boundaries and trade-offs explicit.