I had a conversation last month with a principal at a mid-size structural firm. He told me his engineers use Copilot to draft calc memos and ChatGPT to summarize code provisions. I asked what happens when one of those AI-touched memos ends up under a stamp. He paused and said, “That’s what the stamp is for.”
A lot of PE principals I talk to believe something close to this. The stamp is supposed to be the firewall. An engineer reviews the work, applies judgment, seals it, and the seal tells the world a qualified human stands behind the numbers. What is not true is the assumption that the stamp absorbs whatever happened upstream, including a model that drafted a load table the engineer skimmed but did not re-derive.
What the stamp always covered
A PE stamp certifies that a licensed engineer exercised independent judgment in responsible charge of the work. It does not certify inputs the engineer did not examine, or the provenance of every value that flowed into the final design. Liability for professional engineering work is measured against the standard of care, not against the presence of a seal.
The settled pre-AI case law says the same thing. In Conopoco, Inc. v. Allen & Hoshall, Inc., 129 Fed. Appx. 131 (6th Cir. 2005), the Sixth Circuit held that issuing plans under seal does not create an independent tort duty. Jim Peloquin, summarizing that line in StructureMag, puts it plainly: there is no basis for the claim that the seal itself creates liability for professional negligence. The seal is the act of responsible charge. It is not a waiver of the standard of care for everything that happened before the seal was applied.
When upstream work includes a language model, the standard of care does not move. What changes is the volume of unverified material that can flow into a deliverable in an hour, and the confidence with which it presents itself. A model will draft a wind-load calculation, cite a code section that may or may not exist, and state a recurrence interval with no source attached. An engineer who seals that without checking has put the model’s unverified output under their name.
What changed in 2026
NCEES adopted Position Statement 6.10, “Responsible Use of Artificial Intelligence in Engineering and Surveying,” in 2025. In January 2026 the Idaho board distributed the position statement to every licensed PE and PLS in the state, and other boards are moving on the same timeline. Section 6.10-C is the load-bearing line: “AI-generated results should be verifiable, with clear documentation of methodologies, data sources, and assumptions used in decision-making.” Read that as an operational spec, not an ethical platitude. The position statement also requires licensees to validate AI outputs before implementation.
NCEES 6.10 is a position statement, not a disciplinary rule. No public state-board discipline case involving AI tools has been filed as of July 2026. Treat the position statement as an emerging expectation of the standard of care, not as tested law. What is new is that the boards are now putting in writing what they will expect to see when the first AI-touched complaint lands.
The insurance market is moving on the same timeline. Verisk’s ISO released new AI exclusion endorsement forms effective January 1, 2026, and secondary reporting names Berkley, Hamilton, and Philadelphia as carriers that have already added them. The specific endorsement form numbers circulating in trade coverage still need primary confirmation against the ISO filing. The directional claim is well-supported: a PE firm’s E&O policy may now carve out the exact work the firm bought Copilot and ChatGPT Enterprise licenses to do. That is a board-deck-level finding for a 10-100 person principal, and it is an insurance product, not a law.
Why the fix is provenance, not abstinence
Engineers I work with who use AI safely do not use it less. They use it inside a workflow that makes verification cheap. Every relied-upon value is either cited to a source, marked absent, or flagged for human review. A model that cannot trace a number does not get to report it. A calc memo drafted by AI carries a flag on every drafted input, and the engineer’s review collapses to the flagged exceptions instead of the whole memo.
That is what changes the liability math. The stamp still covers the engineer’s judgment. A provenance layer covers everything the engineer did not personally re-derive, which is most of it now. When a value is cited, the engineer can check the citation in seconds. When it is flagged absent, the engineer knows where to spend their time. The model’s hallucinations stop reaching the deliverable because the workflow refuses to report a value it cannot trace.
Avoiding AI does not lower liability. It freezes it at the old level. Liability reduction comes from making the unverified parts visible, so the stamp only covers work the engineer actually stood behind.
Where this lands
If your firm is already using AI on stamped work and there is no provenance layer between the model and the seal, you are running a quiet risk. The regulatory scaffolding is in place before the first discipline case, not after.
If this is the gap you’re staring at, the AI Workflow Diligence Sprint is a fixed-fee scoping call where we map where provenance is missing across your AI-touched deliverables and what to build first.