Hiring now rewards judgment, not just execution

Shreyas Doshi says something has "changed rapidly in the past year." PMs who see their main job as execution are finding it "way harder to get hired at top companies" than PMs who see it as making the product successful @shreyas. He adds that execution is essential but was never the PM's main job @shreyas.

Two other sources point the same way:

• Job postings. On Aakash Gupta's podcast, Ankit Shukla had Claude and GPT read 12,500 PM job descriptions from top companies. More than 30% asked for AI skills, and the most-requested skill was judgment about "which problems deserve AI" Product Growth. The episode says AI-skilled PM roles pay 15–20% more in the US and Europe and 30–50% more in India. It also notes that LinkedIn replaced its APM program with an Associate Product Builder program Product Growth.
• Interviews. Exponent, an interview-prep company, says some big tech companies now run a dedicated AI product-sense round. After 30 minutes of normal questions, candidates vibe-code a working prototype while interviewers watch how they use the tool Exponent. In strategy questions, a balanced answer with no point of view "is the benchmark for rejection" Exponent. AI makes polished take-home decks the baseline, so what stands out is evidence AI can't fake: you used the product, talked to users, built something Exponent. Questions about tokens, latency, retrieval and hallucinations now come up even in roles that aren't AI PM jobs Exponent.

Faster isn't better

Marty Cagan points to the "AI productivity paradox," which he says McKinsey and Atlassian have measured: teams move faster but don't get better results. In a project model, AI just makes "garbage in, garbage out" faster, which puts the weight back on product craft Rethinking People. Linear CEO Karri Saarinen adds to the skepticism he voiced earlier this week. Building gives you two things, the product and the learning, and there's now a "danger of losing that direct connection with the learning" @lennysan.

Deb Liu gives a concrete case. A startup replaced a product marketer with more than ten years' experience after his positioning and launch docs kept reading like LLM output Perspectives. Her rules are "own every word" and "think first, prompt second," meaning you write down what you believe before opening a tool Perspectives. Hiten Shah makes the strategic version of the point. Every competitor gets the same cost curve, so "a feature that once bought you a year might buy you a quarter" @hnshah. When teams build faster, "a bad assumption can make its way into the product faster too." Each decision needs its own evidence standard: pricing needs different evidence than a battlecard @hnshah.

How teams are reorganizing

• Agent managers. Sachin Rekhi says the most AI-heavy teams are turning designers, researchers and analysts into "agent managers." Instead of doing the specialty's work, they maintain agents for it. Designers make design systems machine-legible. Researchers automate interview guides and feedback synthesis. Analysts build self-serve data agents using golden examples and a semantic layer @sachinrekhi.
• Smaller pods. AWS CEO Matt Garman says a capability that once took 10 people can now take three or four. AWS is experimenting with moving those pods between problems, though it hasn't worked out how to maintain what they build a16z.
• Mandated adoption. Cisco's Jeetu Patel told employees that if they don't use AI, "you will lose your job," and gave everyone unlimited tokens Y Combinator. He expects to need more engineers, not fewer, because the bottleneck keeps moving: from coding, to code review, to "judgment on what to build" Y Combinator.
• Elena Verna argues that if AI really changes how work gets done, ICs should be paid more than managers @lennysan.

A roadmap reset, in practice

A first-time CPO at a 50-person company says their 8-person product team started many things and finished none of them in six months. The team underestimated the work, left no slack for two major unplanned items, and found that "agentic development can't accelerate everything." They deferred about half of the next six months' plan r/ProductManagement. The top replies said to plan jointly with Engineering, because "a less ambitious roadmap that actually gets delivered is worth far more" r/ProductManagement. A three-time CPO said they share only a prioritized list of problems outside the team, never features promised by a date r/ProductManagement.

Forward-deployed PM: check the business model

Several threads looked at forward-deployed roles. One commenter warns that engineering speed doesn't mean customers or go-to-market teams can keep pace. Building bespoke solutions for each customer is a services business unless it rests on shared components r/ProductManagement. One suggested setup: work with 3–4 customers who have similar problems for 2–3 months, then decide what goes into the core product. Otherwise you become "the account's feature guy" r/ProductManagement. An FDE warns that companies that cut PMs and pushed backlog work onto FDE teams usually saw it end badly. If you interview for one of these roles, ask who owns the backlog r/ProductManagement.