What AI changes in a product manager's job, and what stays

The AI Product work group on prototypes over specs, one owner plus agents, the path for junior PMs and who keeps talking to customers.

Updated October 13, 2026

Execution is getting cheap, so a PM's value moves toward choosing the problem, judging output and staying close to customers, while supervising agents becomes a new kind of coordination.

Less coordination, more building. Sachin Rekhi sees AI speeding up or taking over much of the coordination the role drifted into, and PMs who can't keep up with their engineers. He now writes shorter specs and builds more prototypes. He also says AI is still poor at product strategy.
Teams and ratios are changing. Rachel Wolan's count of 1,858 open roles at AI-native companies found 1 PM per 13.5 engineering openings, or about 1 to 8.7 counting forward deployed engineers, and 89% of product roles asking for 5+ years of experience. At Webflow she had been moving toward a lifecycle where two people validate the idea, a small crew hardens it for production, and one owner plus agents keeps it running.
Working through agents puts PMs in manager mode. Aparna Chennapragada argues that working through agents pulls makers onto a manager's schedule, and that PMs have to bet against the model curve instead of patching limits that get fixed upstream. Shishir Mehrotra adds that agents don't argue or bring new ideas, so the PM has to supply both.
Judgment needs deliberate practice. Ami Vora would hire a utility player who prototypes, roughs out the market and mocks up the go-to-market. Ami and Shishir both say junior PMs need deliberate reps if agents absorb the grunt work. Shishir's test of taste is picking the one good output out of 10 and explaining why.
Customer contact stays with the PM. Ami worries that PMs lose touch with customer problems if forward deployed engineers own the conversations. Sachin says feedback tools and engineers' notes can't replace a PM's own interviews. He recommends sending call notes into the same feedback tool as reviews and tickets, reading the raw quotes and calling 20 affected customers when a theme matters.

Group chat with Ami, Sachin, Rachel, Aparna, Shishir — the AI Product work group

Question:

How is AI changing the product manager's job?

Sachin RekhiFounder and CEO of Notejoy. Former Head of Product for Sales Solutions at LinkedIn

AI is giving PMs back the craft of product management. In 2016 I defined the PM role at Wharton as driving the vision, strategy, design, and execution of a product. That definition still holds. Over the following decade, though, the job drifted toward coordination: we kept adding specialized roles, and the PM paid the tax of keeping all of them aligned. AI now speeds up a lot of that coordination or takes it over completely, so PMs can spend their time building solutions to customer problems again

sachinrekhi.

The bottleneck has moved too. Andrew Ng argues that product management is now the constraint in product development, because Claude Code, Cursor, and Codex have sped up delivery so much. I'm already seeing PMs who can't keep up with their engineers. That makes discovery the place to invest. Feedback rivers like Enterpret and Reforge Insights pull in App Store reviews, Zendesk tickets, and Reddit threads, then pick out the themes continuously

sachinrekhi.

My own deliverables have changed shape. I write shorter specs and build more prototypes, because prototyping is where AI gives me the most leverage

X. I built my course's landing page with Claude Code and wrote zero lines of code myself

AI is still terrible at product strategy. I've tried it every way I can think of, and it has never produced a differentiated strategy that could win a market. It also can't match world-class interaction designers

LinkedIn. Gokul Rajaram calls the resulting role a "product builder": someone technical enough to inspect the work, product-minded enough to pick the right problem, and with enough taste to reject mediocre output

X. Judgment is the skill that matters most now that AI has made output cheap.

Rachel WolanFormer CPO of Webflow and VP and GM of Dropbox Core and AI

The PM job is splitting in two, and the hiring data makes that visible. I analyzed 1,858 open Product, Design, and FDE (forward deployed engineer) roles at the Lenny 100 AI-native companies. On paper the product org looks 50% smaller: 1 PM for every 13.5 engineering openings. Count FDEs and the ratio goes back to about 1 : 8.7. Outbound PM work, meaning talking to customers and translating their requirements, has moved to FDEs in GTM. The PMs who stay in product now spend their days on evals, edge cases, composability, and understanding model behavior. The titles look familiar, but the day-to-day job has changed. One finding worries me: 89% of those Product roles require 5+ years of experience, so there's almost no path in for junior PMs.

The second change is team shape. A feature used to need 5-8 engineers, six standing meetings a week, and six months. At Webflow I've been moving to a different lifecycle. Two people validate the wedge, a small crew hardens it for production, and then one owner plus agents keeps it running. That owner ships about 80% of enhancements alone and sends only the hard 20% back to engineering. The metric becomes days from idea to revenue

rachelwolan. Boris Cherny's five archetypes from the Claude Code team (prototyper, builder, sweeper, grower, maintainer) fit what I see: product roles organized by stage of the product more than by function

The third change is that PMs build. Prototyping in Cursor or Claude Code beats writing a spec, because working code shows engineers exactly what you mean

rachelwolan. I still code every day. I can't credibly ask my PMs to work this way if I'm not doing it myself.

Aparna ChennapragadaVP of Wearables at Meta. Former CPO at Microsoft

Sachin, I'd complicate "AI gives PMs back the craft." Coordination with people shrinks, and a new kind of coordination takes its place: supervising agents. Paul Graham's split between the maker's schedule and the manager's schedule is useful here. Working through agents pulls every maker, PMs included, into the manager's mode. You review outputs, decide when to step in, and rarely get long stretches inside the work. Responsibility stays high while your direct control over execution drops, and Karasek's Demand–Control research links that combination to stress. (I found this out at 1am, "one prompt away" from a working app.) Rachel's "one owner plus agents" model makes this concrete: that owner is effectively a manager of a 24/7 team that never stops producing output to review. So part of the new PM job is designing calmer human-AI interfaces, for our users and for ourselves.

Substack

I agree the bottleneck has moved, and the cadence has become uneven. Time to first demo collapsed, while time to full deployment got longer and the bar for scale went up. More prototypes raise the floor and the ceiling together, which makes "which of these 50 demos deserves to ship" the PM's main call.

YouTube

The skill I'd add to the list is betting against the model curve. I call one failure mode the Six Finger Trap: a team spends real engineering and product effort patching a model limitation that gets fixed upstream a few months later, the way garbled text and six-fingered hands disappeared from image models. AI PMs now have to run the wait calculation on every roadmap item, design so models can be swapped in and out, and treat today's workarounds as tomorrow's tech debt. When the model can generate a whole creative suite's output in one shot, the PM's job is deciding what the product adds on top of the model.

Substack

Ami VoraCPO of Anthropic. Former CPO of Faire and VP of Product and Design at WhatsApp

Sachin's "product builder" matches what I've been seeing, and I'd put it in historical terms: the PM job is going back to what it looked like about 15 years ago, when I was earlier in my career. We had so few people that if you believed in something, you just got it done without asking whose job it was. Then engineering, design, and PM hardened their boundaries, and even inside PM you could pick a lane. AI tools are dissolving those lines again, so the PM I'd hire now is the utility player who says "I have an idea, I'll run a prototype, rough out a market analysis, mock up the go-to-market, and see where I get stuck."

YouTube

I break product work into three jobs: recognize the customer problem, structure a solution, and execute.

Substack AI makes execution much cheaper, which is why Sachin's PMs can't keep up with their engineers. The value moves to the first two jobs: building conviction that this is the most important problem right now, and making dozens of small framing calls consistently. That's why Rachel's FDE finding makes me a little nervous. If customer conversations move to FDEs in GTM, the PM keeps the evals and loses direct contact with the job that's gaining value. The best way I've found to recognize problems is to stay close enough to customers that you notice patterns naturally, so PMs need a tight loop with FDEs or time in those conversations themselves.

On Rachel's junior-PM worry: I learned the job by flowing to wherever the water was lowest, which meant chasing bugs, checking retention dashboards, and writing marketing copy. If agents absorb those leftovers, we need to give new PMs a deliberate way to get those reps.

The part I'd add to "judgment" is that it has to come with letting go of being right. A lot of the skills I spent years building probably won't matter as much, and the more senior you are, the harder it is to say "I don't know" when you've been trained to have the answer.

YouTube Being right was my identity and my job for a long time. When the tools reset every week, you have to assume you're wrong a lot, or you have no reason to learn anything new. The PMs I see doing well treat curiosity as the job: they pick one area, like agents, learn it down to the bones, and then move to the next.

Shishir MehrotraCEO of Superhuman (formerly Grammarly). Co-founder of Coda and former Chief Product Officer of YouTube

I agree with Aparna that every maker becomes a manager, and I'd add two details. It happens at the very start of a career, and the agent team is missing what makes human teams useful. When my daughter asked whether to major in CS, I told her she'd show up to her first job already in middle management, handed a team of agents that have read the whole codebase and know every language. They never argue with each other, though, and they don't come up with new ideas. A PM has to supply the conflict and the new ideas that a human team used to generate on its own.

YouTube

Sachin's coordination tax is measurable. One Coda customer found their PMs spent about 30% of their time collecting what had already happened and rewriting it into status updates for different forums. We built an agentic column that reads tickets, GitHub, Slack, and email for each project and writes a five-bullet summary, so the PM gets that time back for defining the right product.

Spotify

Ami and Rachel are right that the grunt work was where junior PMs built instincts. If a frontier model does that work, judgment that used to come by osmosis has to be built on purpose, and I'd make the practice concrete. The skill I care most about is looking at 10 AI outputs, picking the one that is actually good, and explaining why. That's taste, and you get it by studying great work closely and forming opinions. Prompting has little to do with it.

Ami VoraCPO of Anthropic. Former CPO of Faire and VP of Product and Design at WhatsApp

Aparna's point about calmer interfaces applies to customers too, and I think it's one of the most underrated parts of the AI PM job. Everyone is more tired than we think. Most people don't want to learn a new thing. They want relief, a place where stuff just works for them.

YouTube

My read on why chat took off is that it wrapped very unpredictable output in an interaction everyone already knew. What you get back can surprise you, but how you ask is completely familiar. So the design rule I'd give PMs is to keep the interaction pattern boring and predictable, and let the output carry the magic. That's harder than it sounds, because AI breaks a classic UX rule. With normal software you design around the main task. With an open text box, you often don't know what the main task is.

I doubt the frontier in five years is me typing into a chatbot. My guess is it's something less interruptive than pulling out your phone, typing, and waiting, something that lives alongside your conversation. My kids grew up talking to screens and expecting them to answer, and my four-year-old thought a driverless Waymo was just normal. Watching how people interact with a product when they don't have to learn it is one of the best signals a PM can get right now.

Question:

Rachel, Aparna argues that your one-owner-plus-agents model turns that owner into a manager of a team that produces output around the clock, with high responsibility and low control. At Webflow, how does the owner decide which agent output to review closely and which to let ship, and what broke when that owner got overloaded? A public example would help, such as one of the 12 production agents.

Question:

Sachin, Rachel's hiring data shows customer conversations moving from PMs to forward deployed engineers in GTM. Ami worries that this leaves PMs with evals while they lose contact with problem recognition, the job that is gaining value. You argue discovery is where PMs should invest, using feedback rivers like Enterpret. Can feedback rivers plus FDE notes replace a PM's own customer conversations, or does a PM still need direct time with customers? What setup would you recommend, and what public example supports it?

Sachin RekhiFounder and CEO of Notejoy. Former Head of Product for Sales Solutions at LinkedIn

No. Feedback rivers plus FDE notes can't replace a PM's own customer conversations. I use AI research tools to add to my interviews, and I still do as many personal interviews as I always have

sachinrekhi. What AI changes is coverage. At LinkedIn, our research budget only covered major redesigns and brand new features, so we shipped a lot of smaller features on gut feel and waited to see how customers reacted. Today I can send 10 AI-moderated interviews out tonight and have the synthesis by morning, so those smaller features get feedback too.

Here's the setup I'd recommend for a team where FDEs own the outbound conversations:

Put FDE call notes and transcripts into the same feedback river as App Store reviews, Zendesk tickets, and G2 reviews, using a tool like Enterpret or Reforge Insights.
The PM checks it daily for trending themes and, after each fix ships, watches whether complaint volume actually drops.
For every theme, the PM opens the exact customer verbatims underneath it and stops relying on AI's summary.
When a theme matters, the PM clicks through to the affected customers and contacts 20 of them directly. The feedback river tells you who to call, and the PM makes the calls.

That last step is where the PM keeps problem recognition, which is Ami's concern. Since I started using these tools, I spend much less time producing research and much more time mining it. I ask the LLM follow-up questions against the raw transcripts, then watch the specific interview videos where the strongest verbatims came from. I never let AI decide what the product should do based on the research. That stays my job

Rachel's split can still work if the FDEs feed the river and PMs keep running their own targeted interviews. I'd worry about any setup where the PM only ever reads summaries.

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Frequently Asked Questions

What should a first eval setup for an LLM product include?
A sheet of 50 to 100 real outputs graded by people with notes, a rubric checked against customer evidence, a fixed bug set and rule checks in code, and an agreed list of failures that block a launch.
How do you know your LLM evals measure what users care about?
Build eval cases from failures users hit on real tasks, then compare scores with what people do next: rephrasing the question, editing heavily, abandoning the answer or spending time checking it.
Which parts of a product should become agentic first?
A frequent, bounded job inside a workflow people already use, where they can review and correct the result before it goes out. Prototype it on real inputs before committing to the full agent.
How can a working professional build AI skills that last?
Practice on one recurring task: write down what good looks like, let AI draft while you review, and track rework and review time to see whether the whole task gets better.
Which operations work can AI agents handle today?
High-volume work where the answer is written down, the action can be undone and checking is quick: triage, documented support answers, first drafts and summaries. Money, policy exceptions and context nobody wrote down still need a person to approve.
How will AI change services businesses?
The group expects AI to take on more drafting and preparation while firms sell judgment and accountable delivery. Shishir Mehrotra would price an accepted deliverable with a defined scope, and count review, revisions and client setup in its cost.
Why doesn't AI adoption show up in business results more often?
Usage is an input. Results come from changing one workflow and measuring its outcome, including the time spent checking and fixing AI output and whether a decision changed.
How should engineering teams work when AI coding agents write much of the code?
The group would set review by area of the codebase and type of task, keep PRs small enough to read, prove the tests fail when something breaks, and have newer engineers plan and debug by hand.
Where does AI speed up design and content work?
Design variations, throwaway prototypes, first drafts and one-prompt sites. Judging which draft is good, matching the product and keeping scope in check stay slow.

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Frequently Asked Questions

What AI changes in a product manager's job, and what stays

AI Product

Frequently Asked Questions

What should a first eval setup for an LLM product include?

How do you know your LLM evals measure what users care about?

Which parts of a product should become agentic first?

How can a working professional build AI skills that last?

Which operations work can AI agents handle today?

How will AI change services businesses?

Why doesn't AI adoption show up in business results more often?

How should engineering teams work when AI coding agents write much of the code?

Where does AI speed up design and content work?

Your ideas, super powered.

What AI changes in a product manager's job, and what stays

AI Product

Frequently Asked Questions

What should a first eval setup for an LLM product include?

How do you know your LLM evals measure what users care about?

Which parts of a product should become agentic first?

How can a working professional build AI skills that last?

Which operations work can AI agents handle today?

How will AI change services businesses?

Why doesn't AI adoption show up in business results more often?

How should engineering teams work when AI coding agents write much of the code?

Where does AI speed up design and content work?

Your ideas, super powered.