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Is the PM edge gone?
A Head of Product at a UAE fintech asked whether product is "doomed as a role." His case: AI drafts detailed requirements in minutes, and engineers increasingly don't need to be told how to build. He says that has worn away what used to set the role apart .
The most concrete reply came from a former two-time VP Product in financial infrastructure. About 1.5 years ago he moved into a hybrid product-engineer IC role, building PoCs and testing them with clients before engineering finishes the product. He reports earning about 2x his previous pay. He also says nearby PM-management roles are being cut "in exchange for hiring product IC's who can ship fast." His view is that head-of-product skills still pay off when the "team" is made of agents .
Other replies split two ways:
- Speed wins. One PM expects speed to beat quality "for the next few years," since copycats show up within weeks. Their warning: PMs who don't think like engineers will struggle .
- Judgment wins. Others say AI output is only as good as the person prompting and reviewing it, and that the thinking is "still the most important thing" . A consultant says product skills are in higher demand across enterprises. In their view, PMs supply the context AI needs, and "everyone will be some sort of a product manager" .
One PM described a working setup. They write draft Playwright tests tied to user journeys before the build. Prototypes run against a real backend sandbox and are heavily tested before engineers touch them .
What job descriptions ask for
Aakash Gupta summarizes Ankit Shukla's AI review of 10,000+ PM job descriptions. Over 30% now ask for AI skills, and the top skill is picking the right AI use case, ranked above RAG, agents and prompt engineering . The advice is to run the first experiment yourself before asking for an engineering sprint, and to "engineer last" . One example of owning "the last 20%": Shukla questioned an AWS stack Claude proposed and cut it from $420 to $110 a month . The post also cites Axial Search data: a $195K median across 12,397 US AI product postings, and $320K–$520K total compensation for senior roles .
Context and agents as the platform
Scott Belsky argues that devices, operating systems and app-like experiences will increasingly "spawn from the context we provide them," and will be limited by that context. He describes an early "context war": agents want user data, data stewards weigh whether to allow connectors, and consumers have no portable source of truth for their context and aren't sure whom to trust. He calls this a "big opportunity" . Ryan Hoover's related bet: as consumers let agents make purchase decisions, the "quantifiably best" products will grow . Hoover also called Block's Dessa full-duplex speech model the best voice demo he had heard, and expects human-level voice to unlock many more consumer use cases .
The serving stack affects AI feature quality
A YC Paper Club speaker said providers serving the same open model differ in precision, weights version and tool calling. The gap between the worst and best provider can be larger than the gap between open and closed models . Errors compound in agent workloads: with a 10% per-step deviation, a 15-step task could be up to 79% wrong . Full evals are expensive to run continuously. A cheaper option is to watch tool-call error rates and changes in request shape, which can catch problems like infinite loops .
Practitioner notes
- Scoping search: For a 150M-record dataset, a search PM advised against indexing everything, since it adds latency, noise and cost. They let actual filter clicks and usage decide what to keep . Another PM found through discovery that users wanted reports, not search. Cutting 27 of 30 options turned an eight-week build into a couple of days, at the cost of not serving about 30% of users .
- Exiting an internal product: Push it into maintenance mode, framed as freeing resources for higher-impact work. One PM needed about six months of documented low impact before leadership took it seriously .
- Stakeholder anxiety: In a large thread on anxiety, one senior PM said they and two senior-PM peers were all signed off work for burnout or anxiety this year . Tactics shared: go into a meeting you dread with three points to land and one question to ask , and in heated moments, bring the discussion back to data, the product and the customer .
- Distribution before features: A founder building a LinkedIn alternative admitted to building for months before working out distribution . One commenter's suggested reframe: sell it as a single job-search workflow, measure completed searches rather than signups, and recruit users in cohorts such as bootcamps .
- Fast response to demand: Hiten Shah's Beam hit #1 on Product Hunt and shipped its most-requested feature, Intel Mac support, three days later .