Should you ban AI during the hiring process?

No. A designer who uses models well is worth more than one who does not, and the ban is unenforceable anyway. Ask instead whether they can tell when the output is wrong — that is the skill, and it is the one thing the model cannot supply on their behalf.

Do AI detection tools work on portfolios and cover letters?

Not well enough to reject anyone on. The false positive rate is too high to survive being challenged, and it falls hardest on candidates writing in a second language — so you lose good people and inherit a fairness problem at the same time. Change what you measure instead of policing how it was written.

Applications have tripled. How do you screen that without adding rounds?

Put the filter at the front instead of the end. A short structured exercise, taken before any human reads anything, sorts on the thing you actually care about and scales with volume rather than against it. Extra interview rounds do the opposite: they cost the most and they cost you the candidates who have other offers.

Is a take-home still worth running if candidates will use AI on it?

Only if you change what it asks for. A take-home that rewards a produced artefact is now measuring tool access. One where the context exists only inside the exercise, a condition changes partway through, and the reasoning has to be defended live still works — none of those can be prepared in advance.

Hiring designers in the age of AI

By Sergio Gualda · 8 min read · Updated 7 August 2026

Generative AI collapsed the cost of producing the artefacts hiring used as proxies — screens, case studies, cover letters, application volume. It did not change what makes someone good at the job. The practical response is to stop assessing artefacts produced privately and start observing reasoning under conditions you control.

What actually broke

Three things at once, and they compound. Application volume rose sharply because applying became nearly free. The average quality of the artefact rose, because generation improved. And the variance between artefacts collapsed, because everyone is using similar tools on similar templates.

Variance is where signal lives. When every portfolio is polished and every cover letter is well-argued, the things that used to separate candidates stop separating them — not because candidates got more similar, but because the measuring instrument stopped resolving the difference.

What did not break

Judgment did not get easier. Noticing that a brief is wrong, choosing what to cut, holding a position with an executive who has decided the answer, knowing what you do not know — none of these became cheaper to fake, because none of them are artefacts.

They are behaviours, and behaviours have to be observed rather than submitted.

The screening arms race is not winnable

The obvious response is more screening: detection tools, stricter filters, extra rounds. It does not work. Detection has a false positive rate too high to justify rejections, and it lands hardest on non-native speakers. Extra rounds cost you the candidates with options, which is the wrong end of the distribution to lose.

Adding steps to a broken measurement makes the process longer, not more accurate.

Rebuild around three properties

Whatever you use instead — an assessment, a structured interview, a working session — the useful ones share these.

The context exists only inside the exercise, so it cannot be researched beforehand.
Conditions change mid-task, so a prepared answer stops being sufficient.
The reasoning has to be defended live, so it has to be theirs.

Assess AI fluency as a skill, not as cheating

A designer who uses models well is more valuable than one who does not, in the same way that a designer who uses Figma well was more valuable than one who used Photoshop. Treating that as a problem to police rather than a skill to evaluate is a mistake you will regret within two years.

The question worth asking is not whether they used AI. It is whether they can tell when its output is wrong — which is, once again, judgment.

Frequently asked

Should you ban AI during the hiring process?: No. A designer who uses models well is worth more than one who does not, and the ban is unenforceable anyway. Ask instead whether they can tell when the output is wrong — that is the skill, and it is the one thing the model cannot supply on their behalf.
Do AI detection tools work on portfolios and cover letters?: Not well enough to reject anyone on. The false positive rate is too high to survive being challenged, and it falls hardest on candidates writing in a second language — so you lose good people and inherit a fairness problem at the same time. Change what you measure instead of policing how it was written.
Applications have tripled. How do you screen that without adding rounds?: Put the filter at the front instead of the end. A short structured exercise, taken before any human reads anything, sorts on the thing you actually care about and scales with volume rather than against it. Extra interview rounds do the opposite: they cost the most and they cost you the candidates who have other offers.
Is a take-home still worth running if candidates will use AI on it?: Only if you change what it asks for. A take-home that rewards a produced artefact is now measuring tool access. One where the context exists only inside the exercise, a condition changes partway through, and the reasoning has to be defended live still works — none of those can be prepared in advance.