2026-06-29

Becoming an AI-Era PM 07 | You Don't Write PRDs Anymore — You Ship Three Works

Start with what’s changing on the hiring side.

When teams hire product managers in 2026, the bar has quietly moved: the person who has one real shipped feature and can explain how they once defined “is this any good” gets treated as a strong candidate — while the old package of “a beautifully written PRD, some certificates, ran a Kanban board” slides down the list. One hiring lead put it bluntly: certificates and an MBA are signals, never proof that “this person can actually make things”; what you look at is which products they’ve owned and which decisions they’ve made.

In the first piece I left a hook: among the work AI takes over, writing PRDs and drawing prototypes sit right at the front. Their being taken over means one thing — they’re no longer your deliverables. A PRD that AI can generate in a few minutes proves nothing about you. So what does an AI-era PM use to prove themselves? Three works.

1. A product someone can open and click

The first is something other people can open the link to and actually click into and use. Don’t hand over a document that describes it — hand over the thing itself.

This is exactly what the previous piece, speak it into being, gives you: you can’t write code, but you can say what you want and have AI build it, deployed to a URL anyone can reach. Even a small tool that solves one specific annoyance of your own — if it runs, if it’s usable, if someone clicks it — is more convincing than a ten-page PRD.

Build it, put it at a reachable address, and this work is done. In an interview you don’t send an attachment, you send a link.

2. A retro with a real number

The second is a retro that makes clear “what you did, and how much the result changed.” The whole thing hinges on that number.

Don’t write “I led the XX feature and improved the user experience” — anyone can write that sentence, and it proves nothing. Write: “After we shipped this onboarding flow, first-week retention for new users went from 35% to 47%.” “This change cut the can’t-find-the-entry-point class of complaints in half.” A real number, before and after, plus why you made the call you did and where you got it wrong and how you adjusted, carries more weight than any adjective.

It’s fine if there’s no dazzling number. “This feature shipped, nobody used it for two weeks, we killed it, and the retro showed it was because…” is just as good a work — it proves you read real feedback, you’re willing to admit you were wrong, and you know how to course-correct.

3. An eval you wrote yourself

The third is how you define “what counts as good” and how you verify it.

This one is the rarest, and it’s the one that sets you apart most. Two people build the same AI support agent: one hands over “it runs, good enough,” the other can produce a set of checks they wrote themselves — which classes of questions it must get right, what counts as a passing answer, how a wrong answer is scored, how it gets regression-tested every week once it’s live. That set of checks is the eval — your definition of “good,” turned into a standard you can test against over and over.

In 2026 hiring, “the story of a shipped feature plus a real eval” is becoming table stakes for a strong candidate. What it proves isn’t just that you can use some tool — it’s that you carry a ruler for “good versus bad,” and that ruler holds up when someone else looks at it. This work is a hard skill that later pieces will unpack on its own; for now, just know it’s your third work.

One thing you can do today: pick one thing you’ve done that’s halfway presentable and write it down in three sentences — its reachable link (if there isn’t one, start thinking about how to build it and put it up), one real before-and-after number, and how you originally judged “how good is good enough.” Those three sentences are page one of your portfolio.

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