Next week I’m running a session with our product and engineering leadership on how AI is changing the product management role. To prepare, I read the people who’ve shaped how product managers think about the job (Marty Cagan, Melissa Perri, Teresa Torres, Rich Mironov, John Cutler, the Reforge team), alongside product leaders doing the job right now at Anthropic, OpenAI, Netflix, Instagram, and Whatnot. Then I went through the 2025-2026 survey and study data I could find.
Most of what I found applies well beyond our team, so I’m sharing the condensed version here, along with the questions we’ll be discussing in case they’re useful for yours.
Where the sources agree
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Building got cheap. Deciding what to build got expensive. Mike Krieger, Anthropic’s chief product officer, says 90-95% of the Claude Code product’s own code is now written by Claude Code, and that their bottleneck moved to deciding what to build. (Self-reported, but the rest of the sources point the same direction.) Writing documents by hand is losing its value, because AI now produces a serviceable draft of any of them on demand: product requirements documents (PRDs), backlogs, research summaries, status updates. So the hard parts of being a PM are becoming more important: choosing which problems to work on, understanding customers, and defining what “good” means.
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Speed alone is not converting to outcomes. In an April-May 2026 survey of 309 senior product leaders, nearly nine in ten had adopted AI coding assistants. But just over a third said AI strengthened how their org operates. DORA, Google’s long-running research program on software delivery, found in 2025 that teams using AI ship faster but their releases get less stable. GitClear, which analyzes code quality across millions of code changes, found duplicated code up 81% since 2023. Refactoring, the cleanup work that keeps a codebase maintainable, dropped sharply over the same period. Marty Cagan calls this the AI productivity paradox: shipping faster without better discovery (the work of figuring out what’s worth building) gets you to the wrong place sooner.
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Teams are getting smaller and more senior, with wider scope per person. Netflix, Instagram, and Whatnot all describe versions of this. Instagram is moving toward pods of 4-6 engineers plus one “product staff” generalist covering product management, design, data, and research. Nobody agrees on what the smaller team should look like, though. Across the sources I counted multiple different proposed structures, and none of them match. The one that maps best to enterprise infrastructure, where I spend my days, is Drew Breunig’s. He splits the role into application PMs, who move fast and sit with customers, and foundation PMs, who own the platform, compliance, and quality underneath the application teams.
Where they disagree
The biggest split is over validation: do you still need to test whether an idea works before shipping it, or is shipping the test? Cat Wu at Anthropic is on the “shipping is the test” side. Her argument is that models improve so fast that a plan made at the start of a project can be wrong by the end of it, so her team ships quickly and revisits what they’ve built at every model release. In her version of the job, the PM names the few non-negotiables and lets go of the rest.
Leah Tharin argues the opposite. The slow part of building something valuable is finding out whether people want it, and that depends on how many users you can learn from, no matter how quickly the code gets written. Even at Smallpdf, the document-tools company where she used to work, experiments across 50 million users took weeks to produce a real answer.
Marty Cagan’s build-to-learn versus build-to-earn distinction is the middle position, and roughly where I currently land. Prototype and learn as fast as you like, but a prototype exists to answer questions, and a product has to work at scale for people who pay for it, which hasn’t gotten any cheaper.
They also split on the PM-to-engineer ratio. Oji and Ezinne Udezue, both longtime product leaders, argue PMs are the constraint now, so the ratio should go up. The strongest counterpoint is Microsoft’s 2025 cuts: of 1,985 roles cut in Washington state, 817 were engineers and 373 were product managers, roughly one PM for every two engineers, a far bigger share of PMs than any org I’ve worked in. The sources do agree on one thing here: scope per PM keeps growing.
Three questions for your team
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As engineering gets much faster over the next year, what breaks first in your org? The candidates across the sources are discovery, decision speed, validation, go-to-market (getting the thing sold and adopted), and your capacity to review and absorb everything that now gets built. Rejecting the premise is a legitimate answer. John Cutler calls “the bottleneck moved to product” a lazy metaphor, and points instead at the pace of knowledge turns, meaning how quickly the whole system learns.
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When a prototype can act as the spec, what is the PRD still doing for you? And who owns the evals that define “good” for your AI features? Uber’s experience suggests the PRD survives as the record of why, with prototypes carrying the what. Their line that “two hours of prototyping unblocked four weeks of discussion” matches what I’ve seen. As for evals: they’re the repeatable tests that grade an AI feature’s output, which you need because AI answers aren’t simply right or wrong. OpenAI’s Kevin Weil calls writing them a core skill for product managers. In most organizations I’d wager that job currently belongs to nobody.
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Where do the seniors come from, and what is their scope? Everyone wants smaller and more senior teams, but the junior work people used to build judgment on is the first thing AI absorbs. Stanford’s payroll data shows employment for early-career workers (ages 22-25) in the jobs most exposed to AI down 16% relative to other workers, while experienced people in the same jobs have held steady. Teresa Torres has the rule I find most convincing: an expert using AI beats AI on its own, but juniors who let AI do all the analysis never build the skills to become that expert. LinkedIn replaced its associate product manager program, the traditional way into the role, with a “Product Builder” program that trains generalists across product, design, and engineering. That’s one answer to the scope question; the sources’ proposed structures all differ, so it’s yours to decide.
The reading list
If you only read three of these:
- Code Isn’t Product by Rich Mironov, for the case that the constraint moved to discovery and go-to-market.
- State of AI in Product 2026 by Melissa Perri and Product Circle, for the best current data on the gap between adopting AI and being changed by it.
- Cutler’s bottleneck piece, for the counterargument to the tidy version of this whole story.
Melissa Perri has the best take I’ve read on what matters most for PMs right now:
Measure your productivity by how often you changed a decision that mattered, how often you saw around a corner, how often a senior leader walked out of a room thinking differently because of something you said. How often your shipped features translate into real customer outcomes is what matters.
AI can speed up some of what’s on her list. The judgment behind it still comes from years of practice, and there are no shortcuts to that.