Other professions worked out what a signature has to mean. We’re about to need their answer.
A few months ago I had a coding agent build password reset for a small application. It took about five minutes. The email arrived, the link opened, the password changed, the user logged in with the new one.
I reviewed it. I approved it. Everything worked.
The reset link also worked a second time.
The implementation satisfied every behavior I thought to check while missing the invariant underneath them: once the reset succeeds, the token has to become useless. I wrote about that failure as a story about frameworks, because the lesson I took at the time was that mature abstractions carry knowledge forward. That’s true. But there’s a second thing in that story I walked past.
I was the human in the loop. My approval is on that code. And my approval established nothing.
That is the question I can’t put down, and it is not really a question about software.
How do we establish trustworthy human responsibility for work that humans did not directly produce?
This article grew out of a conversation with Sylwia Laskowska following her article, We’re Building AI to Replace Ourselves. What’s the Plan? We were talking about junior developers, credentialing, and whether portfolios still demonstrate competence when agents can generate polished projects. Sylwia raised the possibility that software engineering might move toward something resembling architecture, with formal qualifications and supervised training. Neither of us was enthusiastic about excluding capable self-taught developers.
I’ve come around to part of her comparison, though not the part about credentials. What architecture and structural engineering have that we don’t isn’t a licensing exam. It’s a definition of what a signature means.
Somebody already answered this
When a licensed engineer stamps a set of drawings, that stamp is not merely a gesture of approval. It represents a professional claim about the engineer’s relationship to the work. Depending on the jurisdiction, that may require direct supervision and control, detailed knowledge of the design, or substantial independent verification and reworking. Simply reading through someone else’s drawings and finding nothing obviously wrong is not necessarily sufficient.
Applying a professional seal without meeting the applicable requirements is commonly called plan stamping. Some jurisdictions treat it as grounds for disciplinary action, including fines, suspension, or loss of a professional license. The reasoning is straightforward: a seal is supposed to represent meaningful professional responsibility, not merely the presence of a qualified person’s name on a document.
That is roughly a century of institutional thinking about the exact problem AI has just made urgent everywhere else. The professions that stamp things figured out early that a signature is worthless unless it certifies a specific relationship between the signer and the work, and then they went to the trouble of defining the relationship and punishing people who faked it.
Software has the signature. We have the approval on the pull request, the name in the audit log, the reviewer field in the ticket. What we generally lack is a shared definition of what that approval certifies.
The ceremonial approval
Imagine a team using agents to generate hundreds of changes a week, with a senior engineer assigned to approve them. The company can report that every change received human review. If that engineer lacks the time to examine the evidence, the authority to reject anything, or access to the assumptions behind it, the report is accurate and meaningless at the same time.
I don’t have to imagine that one. I ran a one-person version of it and shipped a replayable reset token.
This is the failure mode worth naming now, because it arrives disguised as compliance. Nobody sets out to build a ceremonial approval process. It appears when production speeds up and review doesn’t, and it is invisible from the outside because the artifacts of oversight are all present. There’s a name in the log. The box is checked.
I’ve made a version of this argument before about code review as a security boundary: review is a good practice that cannot constrain what code is permitted to do, and treating it as a boundary means the boundary doesn’t exist. The same shape applies here. An approval is a good practice that cannot by itself establish accountability, and treating it as accountability means accountability doesn’t exist.
Responsibility without corresponding authority is not governance. It’s liability assignment.
Four conditions
If a signature is going to mean something, the engineering boards suggest what has to be true. Theirs tends to reduce to direct control plus detailed knowledge, or doing the work over. Ours would need four things, and all four, because any one of them missing is enough to hollow out the other three.
An intelligible account. The reviewer needs to know what was proposed, what was checked, what remains uncertain, and what follows from acceptance. “The tests pass” is not that account. My reset flow passed its tests. The tests encoded the same assumption the implementation did.
Competence. The reviewer has to be able to recognize what’s wrong, which means they need to have met this class of problem before. This is where the apprenticeship problem bites, and I’ll come back to it.
Time. Not nominal availability. Enough time to actually examine the evidence for the volume of work arriving. Verification throughput is a real constraint, and it does not scale with the generation side.
Authority to refuse. The reviewer must be able to say no and have the no hold. A reviewer who can be overridden on schedule pressure is not a check on anything, and everyone in the organization learns that quickly.
Those four are the piece of this I’d want an organization to be able to answer for. Not “do you have human review,” which every organization will say yes to, but “what did your reviewer have, and could they have stopped it.”
Who gets to decide
There’s a second consequence, and it may matter more for how people actually work.
The next professional divide may not run between people who use AI and people who don’t. It may run between people authorized to make consequential decisions and people whose work is generated, evaluated, and approved by systems they don’t control.
That divide can appear inside software teams, hospitals, design firms, and accounting practices. A small group defines requirements, approves exceptions, and accepts risk. Everyone else operates inside increasingly automated workflows. Some people gain extraordinary leverage. Others keep their jobs and lose the parts of them that involved judgment.
The useful questions are about distribution. Who can challenge the system’s recommendation? Who can require more testing? Who can stop a release? Who answers afterward, and did they have the power to change the outcome beforehand?
There’s a tempting answer here that I don’t think survives contact with incentives: that the displaced production work reappears as verification work. Independent verification, requirements engineering, assurance, evidence management, and competency assessment are all real activities that may well grow. But if one engineer with agents does what five did, nothing guarantees four verification roles appear. A company can simply employ fewer people, underinvest in review, and find out later that its approval process was ceremonial. The need for assurance has never been the same thing as a willingness to pay for it.
The same caution applies to the shorter workweek. Productivity creates the possibility of more leisure. It does not decide who gets it. That’s a choice organizations and societies make, not a feature shipped in a model update.
Where the next reviewers come from
Which brings me back to competence, and to Sylwia’s original question about junior developers.
Judgment comes from experience. People learn by implementing imperfect solutions, investigating their own failures, absorbing criticism, and living with decisions they made a year earlier. I know a replayable token is a problem because I have spent time on the consequences of that category of mistake.
If agents do the introductory work and companies stop hiring beginners, we get a profession that demands experienced reviewers while closing the path that produces them. Every one of the four conditions above assumes a supply of people competent to review, and we are in the process of disrupting one of the principal mechanisms that has ever generated them.
Credentials don’t fix this by themselves. A certificate establishes that someone passed an assessment, not that they’ll exercise sound judgment in an unfamiliar situation. And the field has been built substantially by self-taught practitioners, so a licensure regime would exclude people who are currently among its best.
Sustained open-source participation is a better signal than a portfolio, for the reason that the artifact stopped being evidence a while ago. A pull request is another thing an agent can generate. The surrounding history is harder to fake: how someone finds a problem, argues about approaches, responds to review, chases a regression, and supports a change after it lands.
But there’s an uncomfortable consequence to leaning on that. If open source becomes the informal proving ground for the profession, the cost of training and evaluating newcomers lands on maintainers who are already donating their time. We don’t get to solve the apprenticeship problem by quietly outsourcing it to volunteers.
That one I don’t have an answer to. It needs somebody to pay for mentorship who currently isn’t.
What I’d ask instead
“Which jobs can AI replace?” sorts professions into safe and unsafe, as though the work inside each one were fixed. The more useful questions are narrower.
Which decisions are getting easier to generate and harder to justify? What evidence should accompany an AI-produced recommendation? Who is permitted to reject it? How does anyone acquire the judgment to do so? And what stops approval from becoming a name in a log attached to a process nobody can inspect?
The answers differ by field. A chatbot feature, a bridge, and a treatment recommendation carry different obligations. But all three turn on the same distinction, which is the one the engineering boards drew a century ago and wrote penalties for: producing an answer and being answerable for it are different acts, and only the second one requires that somebody actually understood the work.
We are issuing an enormous number of signatures right now without having decided what ours certify.
I’d rather we decide on purpose.
Thanks to Sylwia Laskowska for the conversation that prompted this article, particularly her questions about junior developers, credentials, and how people enter the profession as AI changes the work.
Sources on plan stamping: Hawaii DCCA, Notice Regarding Plan Stamping (HAR §16-115-10(4), §16-115-2; HRS §464-10); STRUCTURE magazine, Beware the Stamp.
