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Faster coding isn't faster shipping
A PM on an 8-person transportation team said Claude Code and Copilot made everyone faster individually, but "our overall delivery speed hasn't really changed." QA is backing up, and people still go back and forth over missing context and unclear requirements . Other PMs reported mixed results. One org of about 1,000 people says it ships roughly 2x what it did two years ago with flat headcount . Another PM said bug fixes are much faster, but the slowest steps are still alignment and stakeholder meetings: "AI isn't making it any faster to convince a team we should do something" .
Product leaders made the same point. Miro's CPTO said he has talked to over 200 customers in the past year. Most have "solved the execution," but it feels like chaos, and "building the right thing is the new bottleneck" . His prescription: PMs should make decisions faster and more often, take bigger bets, and spend the time they used to put into stakeholder artifacts on asking whether they're aiming high enough . Marty Cagan agrees that deciding what to build is the PM's job, and that it matters more as build costs fall. He separates "build to learn" (discovery prototypes) from "build to earn" (production-grade delivery) .
YouTube's Sunday Ticket launch is a pre-AI example of the same constraint. The team started with 129 P0 features but didn't own 80% of the engineering needed, so it had to win alignment and be strict about what truly blocked launch. Installment billing turned out to be a state-by-state regulatory problem. The team launched with pay-in-four only and later added 8- and 12-part plans and month-to-month plans .
PMs as builders, unevenly
Webflow moved from designers prototyping in code to "builder days" for PMs and EPD ops. Product staff then shipped real PRs to production, and that is now a weekly Wednesday habit . Lovable's growth lead says everyone there pushes changes to production, with AI-written code reviewed by humans. She argues that easy recovery from mistakes means less time spent debating the "right" decision . She spends 70% of her time talking to agents. She moved back to IC work but kept her seat on the exec team, her pay, and ownership of Lovable's monetization model . Not every company agrees. A PM at a large bank who used Copilot to ship code, build data pipelines and spin up services is losing access because they're not in the engineering org. Management said "hard no" .
AI in discovery: summarize, don't simulate
In r/ProductManagement, practitioners were skeptical of simulated personas. Prompts like "would a senior IT buyer buy this?" give "random results." One PM said it works better to have AI summarize raw data . Another found that letting AI take interview notes cost them the ability to synthesize across conversations. They now take notes themselves and use AI to pull quotes and challenge their bias . Gamma did discovery at scale for users whose usage had faded. It rebuilt the product over one summer , then had over 30 of its 120 employees join calls with more than 100 users. The calls showed that users share more by voice, so Gamma added a microphone button .
Also notable
- Model-PM hiring. Aakash Gupta read 46 open model-PM postings. The most common ask (76%) was "going from pixels to evals to hill climbing." Only 7 required production experience, and the median posted base salary is $345K. His advice is to turn a model's failure modes into evals and build a product that passes them .
- Defensibility tests. Hiten Shah suggests asking a coding agent to rebuild one of your valuable workflows, then trying to do the customer's job with an outside agent and the services they already have . For agent products, he says the metric that matters is cost per verified result, including human review and recovery .