The stamp has always been a claim of professional accountability. I reviewed this. I understand this. I take responsibility for this. That claim rests on comprehension. The engineer who stamps a set of drawings is asserting, implicitly, that they could defend every number on every page in front of a licensing board or a courtroom.

Machine learning models do not produce calculations you can defend line by line. They produce outputs. The path from input to output passes through billions of parameters trained on data the engineer did not curate, using methods the engineer did not design, optimised for objectives the engineer did not fully specify.

You can validate the output. Run it against code compliance checks. Peer review the result. But you cannot trace the reasoning. There is no reasoning to trace in the conventional sense.

This is not a future problem. Engineers are already using AI-assisted tools whose internal logic is partially opaque. The question is whether the profession acknowledges this shift and adapts its accountability frameworks accordingly — or whether it waits for a failure, a lawsuit, and a licensing board hearing to force the conversation.

Three things need to happen before AI-generated structural calculations can be responsibly stamped. First: transparent model cards disclosing training data, validation methodology, and known failure modes. Second: mandatory independent verification protocols, not peer review of outputs but audit of the model itself. Third: updated licensing frameworks that distinguish between engineer-performed analysis and engineer-validated machine output.

None of these exist at scale. All of them are necessary.

The profession that waits for the first major AI-implicated structural failure to address this will have waited too long.