Why the PM Role Narrows as AI Expands
Most of what PMs have done for the last decade is now something a decent AI can do in minutes. What’s left is the part of the job nobody ever quite named.
The easy takes split cleanly. One camp says the role is done: PRDs, research summaries, and ticket triage were always the bulk of the work, and AI handles them faster than any human. The other camp says PMs matter more than ever, because someone still has to sit in the hard meetings, hold the tensions between engineering and sales, and decide what is actually worth doing. Both are pointing at something real. Both also dodge the harder question: what was the PM role ever actually for?
Two Jobs Hiding in One Role
For most of the last decade, PM work was two things bundled together. The first was rote coordination: writing specs, summarizing interviews, keeping Jira honest, running standups, chasing signoff. The second was harder to name: deciding what was worth building at all. Picking which tension to hold. Saying no to the shiny thing. Noticing what an enterprise buyer wasn’t saying in a meeting, or what connected three unrelated support tickets.
AI eats the first bundle fast. And in doing so, it doesn’t reduce the PM role.
It strips away the camouflage and leaves the second bundle exposed.
Cheap Execution Makes Direction More Expensive
Here is where I think the conventional story gets it wrong. The common framing is “AI makes execution cheap, so everyone ships more.” But cheap execution doesn’t just raise the ceiling on output. It raises the cost of pointing in the wrong direction.
When a feature took six weeks to build, a wrong call burned six weeks. Now it can burn a day, or a week, but across ten parallel bets. The upstream decision (which problem, in which order, for whom) compounds its leverage. A badly-scoped bet now wastes more engineering velocity than it used to.
Three shifts happen at once:
The obvious problems get solved faster. If a competent team with AI can ship the obvious feature over a weekend, the obvious feature is no longer a moat. Competitive advantage moves to problems that require synthesis, context, and taste. AI is still bad at those, precisely because they require reading the real world rather than the brief.
The roadmap gets bigger. Capacity used to constrain what you could even consider building. Now capacity is several times what it was. The set of feasible things balloons, and prioritization, which was a bottleneck under scarcity, becomes the primary discipline under abundance. Saying no goes from occasional to constant.
Direction matters more than output. Point a good AI at the wrong problem and you get a very polished wrong thing, very fast. The polish makes it harder to notice you’re wrong. Conviction that used to cost weeks to test now costs hours, which sounds like a gift until you realize you’ve built ten polished wrong things before asking whether any of them should exist.
What’s Left Is Harder to See
So the honest answer, at least as far as I can see it: the PM role isn’t dying, and it isn’t more important in some vague inflationary sense. It’s narrowing. The work that felt like PM work (the artifacts, the rituals, the ticket hygiene) was often a proxy for the actual work. When the proxy gets automated, what’s left is harder, rarer, and less legible.
Which is uncomfortable, because the legible work was also how PMs proved value. If judgment is the whole job, how do you tell who’s good at it before it’s too late?
That’s the part I’m still turning over.