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The PM job market: fewer hires, higher pay, a different set of employers
Aakash Gupta, working with Live Data Technologies and Levels.fyi, looked at actual offers and hires, not job postings . TrueUp shows postings up 77%, but real hires from February to June were 18% below 2023 . Companies hired 0.92 outside PMs for every PM who left in 2025, down from 1.16 in 2022 . US PM hiring is at 52% of its 2022 level, but median US pay rose from $220K to $250K. Gupta says part of that comes from a shift in level mix .
The employer mix is changing. Capital One (486 hires), JPMorgan Chase (404), Mastercard (339) and Visa (217) are among the 10 biggest PM hirers . Over the last year, hiring fell 31% at Microsoft, 30% at Meta and 21% at Amazon. It rose 55% at Mastercard, 32% at Visa and 17% at JPMorgan . AI PM hires grew from 789 to 1,988 over the same February–June window while total PM hires fell from 52,664 to 29,223 . Still, the AI PM title makes up only about 7% of hires .
Gupta also tracks the shift toward "product builders." LinkedIn renamed its APM program the Associate Product Builder program in fall 2025 . At Laurel, the whole PM and design org is nine people, and senior PMs ship features to production . At Freshworks, AI-native teams have gone from one PM per 10–20 engineers to close to one-to-one. PMs focus on what users need, and engineers own the backend, infrastructure and DevOps . Gupta says roles and ratios are still unclear and specialists still have a place .
Evals are product work
Teresa Torres and Kathy Pham made similar points this week. Torres says an eval suite should measure technology performance, customer value and business value, and teams should define "good" for their own context rather than rely on off-the-shelf evals . A concise answer can be right for an expert and wrong for a beginner. Metrics mature from universal measures like MAU toward a product's real value moment, such as DocuSign getting you to send a document . Her warning: AI amplifies whatever shape you're already in .
At MTP Chicago, Pham said PRDs and prototypes now take minutes, which also "makes building the wrong thing cheaper" . Her four pillars are: solve the right problem, make evals the new acceptance criteria, treat trust as a feature, and orchestrate teams through the change . Old acceptance tests checked whether a user could submit or buy something. AI outputs need checks on whether the answer is actually right, often with input from domain experts . She also says not to take ignored patterns like EHR alert fatigue and make them faster with AI .
AI-written PRDs divide PMs and engineers
On r/ProductManagement, one commenter called AI-written, "grilled" specs from spec-driven toolkits far better than human-written ones . Others called them unedited "slop" and said they don't replace talking to customers . A useful test came out of the thread: if writing a document takes less time than reading and absorbing it, "it's almost guaranteed to be slop," whether or not AI wrote it . A separate post lists checks against bad AI-assisted decisions: read raw feedback weekly, treat every AI output as a draft, and give one reviewer the job of asking "what is this missing?" .
When product leaders own revenue
Run the Business looks at the gap between GTM teams held to quarterly results and product teams working on multi-year horizons . On roadmap commitments, the advice is to lean in: have sales rank its own one-off requests, and pre-agree with the CRO and CCO on size limits, how commitments are written into contracts, and an audit trail . When GTM asks for new products, push back. The real need is often a reason to talk to customers, or more jobs to be done covered within existing products .
Launches and signals
- Hex 4.0 rebuilt Hex as an analytics platform "built for agents," alongside a $100M raise at a $1.2B valuation .
- Stripe pay offers free, instant B2B transfers by Stripe handle . Patrick Collison: "A2A payments will be more important than P2P" .
- Notion Agent can now use a computer to produce Excel, Word, PowerPoint and PDF files .
- Daffy has an MCP server that lets Claude or ChatGPT answer questions about a user's giving and make donations. Adam Nash argues services should be designed for actions taken by AI agents, not only clicks and calls .
- Andrew Chen questions the solo-founder thesis: maybe "coding was never the main bottleneck," and a demo is far from a product that can scale .
- Hiten Shah rewrote his early access playbook around the decision you're making. His new final test asks whether users choose your product again the next time they need to do the job .
- Adam Nash argues that a product’s business model shapes its incentives: asset-based donor-advised funds are motivated to pursue larger accounts, while Daffy’s flat membership fee—$3 a month for most members—does not fall when someone donates, aligning its revenue with helping people give repeatedly over time.
- For growth, Nash prioritizes making the first 1,000 customers love the product; he describes brand-building, educational content, answering questions, and member engagement as more effective for Daffy than growth hacking. He also applies familiar fintech engagement patterns, such as celebrating giving goals, to reward users’ existing motivation to give.
- Daffy brought custom portfolio recommendations with automated rebalancing and custom liquidation options—sell stock immediately, gradually, or hold it for a period—to a wider group of donors. Nash says the liquidation choices address reluctance to donate stock that donors believe may rise further; Daffy reported more than $600 million in stock contributions in the first three quarters of 2026.
- Daffy announced an MCP server that lets users connect their accounts to Claude or ChatGPT, ask questions about their giving, and make donations. Nash expects more financial actions, including giving, to be delegated to AI agents within five years, and argues that services should be designed for agent-performed actions as well as human clicks, calls, and emails.
- Amazon blocked Meta’s Muse after saying it had crawled Amazon without identifying itself. The discussion raises transparency, security, advertising, and safety concerns, with potential for agent-commerce fragmentation; possible responses include commercial partnerships and shared safety protocols, but the outcome remains uncertain.
- Alpha’s founder credits product packaging—making the learning model understandable and scalable—as a key task; he says AI helps scale quality and reports that test scores, student happiness, and workshop quality improved each year as the system grew.
- Alpha’s founder says an AI learning engine would fail if simply inserted into a conventional school day; using it requires redesigning the day, including how students spend the time beyond academics on life-skill projects.
- The learning engine personalizes instruction for a student’s actual level, even when it is below their current grade, and targets 80–85% correct; it adds scaffolding when work is too hard and advances students faster when it is too easy, which the founder presents as a way to sustain engagement.
- Student feedback at universities separated the bundle’s value: Alpha graduates reportedly found large introductory lecture classes wasteful but valued small-group seminars, clubs, and other activities; the conversation describes university as a bundle of products.
- Alpha’s early campus experience suggests the importance of proof points: the founder says initial recruitment was difficult, but demand rose after families saw the first campuses work. He also advocates defining the product clearly to attract families aligned with its approach and repel those who are not, while treating founding families as an asset.
- Alpha’s Founder School, a specialized high-school program for students building businesses, opened in August. It promises tuition back if a student does not start a business earning $1 million in profit during high school, with staged earnings milestones from $100 through $1 million.
- In the speakers’ recent hiring experience, startup PM briefs increasingly ask candidates to use AI to improve processes and business outcomes, and to prototype quickly with customers; they see PM, UX/design, and engineering responsibilities becoming more horizontal and overlapping. They describe technical customer-side solution deployment and deployment strategy as possible adjacent paths: deployment strategists define the customer’s problem, design how the solution will be used, and work toward results.
- A speaker relayed a Gartner forecast that AI will bring PM and product owner roles closer and that they may merge by 2030; she said classic PM roles are not disappearing, but their scope is expanding toward problem understanding, solution shaping, and ownership of business outcomes—not requiring one person to do everything.
- For AI-era technical readiness, an interviewer said he probes candidates’ tool choices, project setup—including guardrails and security—and how they judge whether generated code is safe; he also values keeping up with new models and benchmarks. He cautioned that generating code a candidate does not understand can create technical and CPU-cost risks, and recommended building technical depth.
- The speakers said PM candidates should demonstrate trade-off judgment, prioritization, and accountability when work goes wrong. For similar-looking PM CVs, keywords may help with shortlisting, but candidates stand out by explaining the problem they solved, why they made a decision, and its impact—not just listing roadmap or stakeholder work.
- AI may let solo founders, nontechnical founders, and domain experts build software they previously could not, but easier coding may not remove key bottlenecks: customer conversations still take work, and a prototype is not necessarily a reliable, scalable product; technical judgment may become more important as products grow complex.
- Domain experts can use firsthand knowledge to identify recurring problems, while outsiders may be better positioned to reinvent an industry rather than make incremental improvements.
- Creators can use their audiences to launch software, but that distribution may produce only a spike; sustainable growth depends on products that can be continually iterated.
- Teresa Torres says AI evaluation should measure technology performance, customer value, and business value; teams should define what “good” means in their own context rather than rely on generic off-the-shelf evals.
- Make the expected outcome explicit so assumptions can be tested; applying AI to a poorly defined product or process can amplify what already does not work.
- Product metrics should move beyond universal measures such as MAU toward the product’s specific value-creation moment—for DocuSign, getting a user to send a document. What counts as “good” also depends on context: a concise answer may suit an expert but not a beginner.
Lenny Rachitsky’s list of 25 highest-paying open roles includes three direct product-management benchmarks: Duolingo VP of Product, Growth and Monetization ($368K–$612K); OpenAI Product Manager, Financial Services ($401K–$530K); and Anthropic Product Manager, Multi-Cloud Growth – Google ($305K–$460K). The listed figures are base salary only.
- AI may let solo and nontechnical founders ship first versions, but coding may not be the main startup bottleneck: customer conversations, building a reliable product that scales, and technical judgment about what to build remain important constraints.
- Domain experts can spot problems in their industries, while outsiders may be better positioned to reinvent them rather than make incremental improvements.
- A creator’s audience can help launch a product, but a spike in distribution is not a sustainable growth engine; software still needs ongoing iteration.
Lenny highlighted an 86-minute product-market-fit guide with Rob Snyder covering the PULL framework for finding a customer who would find it “weird not to buy,” where startup ideas come from, cold outreach, first sales calls and why Snyder skips design partners, plus the “Hail Mary” question and “goblin mode.”
- The post author says Claude Code’s harness/TUI was shipping meaningful, sizeable updates almost daily and that they tolerated rare breakage—an example of one user accepting some instability in exchange for iteration speed, not evidence of universal tolerance.
- One commenter attributes the pace to a developer-focused product that can build on its own tooling, established developer tools and early access to models, with less organizational friction and faster feedback helping teams launch sooner. Another says Anthropic built a custom Git-management method, Scion, to support thousands of concurrent agents on its monorepo.
- Feedback on product quality is mixed: one user praises the underlying model and functionality but calls the UX poor, while another complains that permission-command options and spacing change frequently without reason. A commenter cautions that Claude Code is entering a new market and PMs should not copy its behaviors without considering their own audience; another argues that tolerance for faults in a consumer product may not carry over to B2B, where failures can cost contracts.
Lenny Rachitsky endorsed two user-created Grok bots as useful: one removes a phone number from spam-call lists, and the other removes personal information from public sites; their creator said they took seconds to set up.
AI may let solo founders ship a v1 and enable nontechnical founders to build , but this does not guarantee a solo advantage—cofounder teams may still make more progress . Customer meetings remain necessary, and a demo is not a reliable, scalable product; technical judgment matters as products grow more complex .
Creators’ audiences can provide a launch distribution spike, but not a sustainable growth engine; durable products require ongoing tweaking and iteration .
Before hiring a Chief of Staff—including a product-focused one—define the business problem and the outcome expected after six months, then decide what capabilities it requires and whether it calls for a permanent hire, existing executive, specialist, or time-bounded operator. The title can conceal very different mandates; one example spans product operating cadence, recruiting, competitive intelligence, and product judgment. Judge seniority by which decisions someone is trusted to make, not by how many functions or “hats” they cover; clarify ownership, independent judgment, and which decisions should no longer depend on the CEO.
- AI can generate PRDs and prototypes in minutes, but synthetic users may not reflect real populations and faster, cheaper building also makes it cheaper to build the wrong thing. Preserve time for trust, safety, and responsibility checks instead of letting speed drive what ships.
- Four useful PM practices for the AI era: solve the right problem, make evaluations the new acceptance criteria, treat trust as a feature, and orchestrate how teams adapt. For AI products, evaluate whether outputs are actually right—not merely whether the system completes an action—and involve relevant domain experts and internal or external groups in that work.
- In the speaker’s healthcare example, electronic health record alerts can cause alert fatigue, leading clinicians and schedulers to ignore them; identify and discard such ineffective patterns rather than reproducing or accelerating them with AI.
- Assess trust in the wider system around a product—including policies, legal infrastructure, governance, and who may be left out—and ask whether and for which communities a product should be built. Plan for unintended uses, which are especially unpredictable with AI.
- AI’s effect on PM is contested: one view is that artifact production, meetings, and “presence” are becoming less differentiating while clear thinking, customer empathy, analytical judgment, problem selection, trade-offs, and alignment remain valuable; the suggested response is to build technical fluency, spend time with customers, and avoid making Jira-ticket management the sole contribution. Another commenter describes current PM work as shipping “AI garbage,” a competing pessimistic view rather than a consensus forecast.
- PM’s contribution can be framed as coordinating delivery: keeping work on time, managing relationships and expectations, and supporting sustainable solutions and documentation that might otherwise fall to already-busy teams or stakeholders. Practitioners also say job quality depends on the company and team: responsibility can outstrip authority amid politics, while competition for finite resources and bonuses tied to outcomes committed before allocation can foster resentment.
- One commenter advises aspiring PMs that junior openings are highly competitive and suggests a path from testing to product owner to PM, with increasing accountability and a practice of under-promising and over-delivering.
A useful product-usage metric for AI adoption is the share of what a user types that goes to AI rather than to other people. Andrew Chen estimates his own split shifted from 0% AI a few years ago to 10% when AI began taking over Google searches, and now roughly 25% (possibly more); he asks when AI will account for over half. These are personal estimates, not population-wide measurements.
- Verified offer-and-hire data—not job postings—shows PM hiring from February to June was 18% below 2023; U.S. hiring was 52% of its 2022 level, while median U.S. PM pay rose 14%, from $220,000 to $250,000.
- Finance is a major and growing PM employer: four of the ten largest global hirers were Capital One (486 hires), JPMorgan Chase (404), Mastercard (339) and Visa (217); over the last year, hiring rose at Mastercard (+55%), Visa (+32%) and JPMorgan (+17%), while it fell at Microsoft (−31%), Meta (−30%) and Amazon (−21%).
- AI PM hiring grew from 789 to 1,988 hires between February–June 2022 and the same period in 2026, even as total PM hires fell from 52,664 to 29,223; AI PM titles still made up only 6.8% of hires. The author recommends building synthetic AI PM experience, among other job-search steps.
- The product-builder model is gaining traction, but its shape remains unsettled: LinkedIn replaced its APM program with an Associate Product Builder program in fall 2025; Laurel’s nine-person PM-and-design org has senior PMs shipping production features, while Freshworks says its AI-native teams shifted from one PM per 10–20 engineers to nearly one-to-one. In that model, PMs focus on customer needs and building, while engineers own system architecture and infrastructure; the author still sees a role for specialists and says role boundaries remain fuzzy.
- Aakash traces enduring PM principles to Neil McElroy’s 1931 P&G “brand man” memo: one person owns a product end to end, studies customer and dealer problems firsthand, takes responsibility for outcomes and coordinates others; he argues AI may change the tools without changing that core ownership.
- Product leaders taking on revenue or retention accountability should anticipate a horizon gap: GTM teams are accountable for quarterly results, while product can work across longer horizons and iterate experimentally. The author’s framework is to lean in, push back, or treat the request as context-dependent.
- For roadmap commitments, lean in without handing GTM control of the roadmap: have sales prioritize competing one-off requests, and agree with revenue and retention leaders on when one-offs qualify, their size, how commitments are documented, and how progress is communicated. A tracker and audit trail help preserve what was promised and when.
- For requests to create new products, first test whether the underlying need is more customer dialogue or broader coverage of jobs to be done—not necessarily another SKU—and whether existing products still have expansion potential. Improve expansion through the existing portfolio (for example, connecting cross-product experiences, PLG tactics, or demos) and help GTM learn to sell and support current products before pursuing new ones.
- For the retailer’s 2,000 SKUs across stores and online, establish a clean view of price, cost, and sales for each SKU and channel, and agree on what “price” should mean before automating; commenters flag both data inconsistencies and business alignment as early hurdles.
- Then investigate customer segments, buying behavior, positioning, and alternatives, and pilot simple price tests or rule-based promotions in a few categories before investing in personalization.
- Tooling and results may depend on the business context: one practitioner used external vendors in travel and hospitality, where implementation effort was significant, but said impact was hard to determine and 20% revenue growth was expected. In their B2B example, six customer segments were handled with Excel and market experience; in their B2C experience, the engine had no significant effect when products were not readily comparable, while procurement, partnerships, and promotions mattered more.
Hiten Shah frames product building as creating something people find worth relying on, emphasizing user trust as a measure of product value.
With PMs’ anxiety attributed to the market and AI, a user floated an informal virtual gathering for early December to unwind and discuss what the PM role should be, with PM-themed activities and possible PM leaders; the idea was still at inception. After a commenter noted layoffs and suggested inviting coaches to help people connect, the proposer said the gathering could help PMs step out of job-search exhaustion and make friends.