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UX is the PM's job, and AI raises the stakes
Ravi Mehta's main example: at Tripadvisor, an icon-heavy redesign of the hotel-search filters cut conversion by 20%. He puts the potential cost of a full rollout at $100M . He rejects the idea that UX belongs only to designers. You can't take responsibility for a product's success while treating the experience as someone else's job. AI raises the stakes because teams can now bury users in "more screens, more choices, and more chat boxes" .
Three parts of his argument are practical:
- Chat is an option, not the default. A blank prompt can create translation work. Dragging an object or picking from a calendar is often easier. Hybrid designs pair language for stating what you want with familiar controls for refining the result, as in Magic Patterns and Lovable. When an agent is the user, a structured interface may be the more reliable choice .
- More automation isn't automatically better. Granola lets people take their own notes while it transcribes, then merges the two. For many customers, taking notes is part of how they think .
- How to find opportunities: watch someone do an important task and look for physical effort, cognitive burden, translation work and workarounds. Start with friction that is frequent, costly and close to the product's core value. Then count the checking and repair work a user does after the AI responds .
Building is cheap for everyone now
Hiten Shah points out that customers who used to wait six months for a feature can now build a rough version over a weekend and ask why your product can't do it. "The most threatening product feedback is going to arrive as working software" .
Sachin Rekhi, after two years of vibe coding, describes four distinct modes. For landing pages and feature prototypes he never looks at the code; prototypes work as a "modern-day Figma" for trying out different UX options. For personal apps he chooses simple, maintainable architecture. For production features at Notejoy he uses plan mode, mini-specs, explicit security and caching decisions, hundreds of unit tests and PR review. He says the last mode looks nothing like the others .
A widely discussed r/ProductManagement thread shows what happens without those guardrails. A PM said developers demand "ultra-granular" waterfall specs, skip estimates and unit tests, and leave the PM accountable for every failure . Out of desperation, the PM started building features with Claude. Third-party QA noticed better code quality, and the PM said the work finished in a tenth of the time. The PM doesn't want to be responsible for security or other things "beyond the UI" without engineering expertise .
A related post says recruiters now openly want "solution-oriented" PMs who reach features quickly . Commenters split on the cause. One says AI means PMs are expected to prototype on their own . Another says short runways make teams prefer something they can show investors over discovery .
Leadership and strategy
- Founder vs. steward. Scott Belsky argues that because models and whole layers of software are being displaced so quickly, companies whose roadmaps go from concept to execution in under a year need a founder mentality: the willingness to "churn strategy in short cycles" without fear of being wrong. He says steward leaders may suit hardware businesses with multi-year roadmaps .
- HubSpot re-founds. Brian Halligan is back as CEO. He and Dharmesh Shah say they are "completely re-founding" the company as "the Customer Harness Company" . Hiten Shah notes HubSpot is on track for $3.7B in revenue and compares the move to its original inbound playbook .
- Walmart agent profiles. CEO John Furner says Walmart will launch agent profiles in the coming weeks. Customers will be able to let their own agent or Walmart's buy for them, with limits such as a spending cap. He calls it "early in the journey" .
Talent signals
- Where UX researchers went. Lenny Rachitsky followed up on the decline in UX research roles. Of 2,779 US user researchers who changed jobs in 2024–25, 49% stayed in research, 17% moved to design, 9% left the field and about 2% became PMs. Almost half of those who stayed in research joined companies outside tech. Banks, financial firms and insurers hired about 1 in 7 .
- Hiring rules have side effects. Deb Liu argues that requiring CS degrees for PMs, which was meant to make PMs more technical, "cost us a generation of women PMs," since women earn only 1 in 5 CS degrees . That's worth keeping in mind as AI PM postings add technical requirements.
- Use the Hook Model early—before major engineering and design investment—as a product filter, or later to diagnose weak engagement. It is suited to businesses that depend on frequent repeat use; Eyal’s rule of thumb is at least weekly, and he advises against trying to build habits for infrequently used products.
- Prioritize engagement before buying growth: Eyal frames a business around growth, engagement, and monetization, and warns that spending on acquisition before establishing customer retention creates a “leaky bucket.” Engagement must be designed into the product rather than bought.
- Start product discovery with the user’s underlying emotional need, not with a technology seeking a problem. The Hook cycle is trigger, action, variable reward, and investment; for habit-forming products, investment should make the product more valuable with use. Eyal sees AI-enabled personalization as an opportunity and expects customers to increasingly want tailored digital experiences.
- Fitbod illustrates behavior-based targeting: rather than trying to turn non-exercisers into gym-goers, it serves people already at the gym who are unsure what workout to do; their logged sets and reps inform subsequent workouts.
- Eyal’s ethical test for behavioral design is whether the product materially improves users’ lives and whether its creator is a user; he says answering yes to both makes the creator a “facilitator.”
Job Search OS v2 packages job discovery into a workflow that creates a tailored application package, including a resume or work product, networking support, and interview preparation . Its autopilot fills applications in the user's browser; users supervise, are advised to humanize resumes, and send personalized connection notes themselves . The creator reports more than 2,000 monthly active users for Job Search OS and offers standalone access for $49 one-time with lifetime updates .
In response to a new PM asking what junior PM behaviors frustrate product leaders , commenters highlighted:
- Discovery: Don’t jump to a solution or anchor on a customer’s or leader’s first idea before understanding the problem. Listen to customers to understand their behavior and needs, and balance quantitative data with qualitative context.
- Stakeholder judgment: Avoid being an automatic “yes”; question requests with the “5 Why’s” rather than sending unsupported stakeholder opinions to engineering. One commenter warned that agreeing to every stakeholder creates a wish-list roadmap and frustrates engineering.
- Execution and trust: Know the product and domain well enough to answer basic questions, take ownership, and proactively update on promised ETAs; communicate mistakes clearly and build agency as trust grows. A commenter also cautioned against passing along AI-generated work without context or review.
Lenny’s 2026 tech-job-market report describes PMs as “winning in the AI era,” while engineering demand has stalled and design openings are at a record low.
Among 2,779 US user researchers who changed jobs in 2024–25, 49% took another research role, 17% moved into design, 9% left the field, and about 2% became PMs; nearly half of those who took another research job joined a company outside tech.
- PM hiring: Deborah Liu says requiring product managers to have computer science degrees to improve technical capability narrowed the eligible pool—women earned only 1 in 5 CS degrees—and changed the profession’s gender makeup, costing “a generation of women PMs.”
- Decision-making lesson: Requirements can create second-order effects as people adapt to rules and incentives; revisit the data and adjust rather than assuming a well-intentioned rule works as intended. The goal, Liu argues, should be better outcomes rather than perfect rules.
- Walmart said its AI can interpret plain-English shopping requests and context—such as tacos for four people with a gluten-free requirement—and connect them to products, inventory, and pricing, with the goal of enabling 25-minute fulfillment.
- Walmart said it planned to launch agent profiles in the coming weeks, allowing customers to delegate purchases to their own agent or Walmart’s while setting controls such as spending limits and purchase times; it described the work as an early-stage effort and emphasized keeping customers in control.
- Walmart’s associate-facing AI vision is to reduce friction by putting needed information and recommended next actions in workers’ hands; one forklift driver said a script he wrote for himself on a Walmart-provided platform saved time on each shift.
- For change in a large organization, Walmart described a mix of weekly feedback sessions with about 500 top US leaders and leadership-led alignment on major changes; it stressed providing context and understanding staff responsibilities and incentives.
Lenny Rachitsky’s 2026 tech-job-market report identifies product managers as “winning in the AI era”; it also says engineering demand has stalled, design openings are at a record low, and every role now requires AI skills.
- Gupta presents PM pay as employer-tiered: for PMs with 8–11 years’ experience, he gives Meta/Google total compensation (TC) of $400K–$550K, Tier 2 company median TC of $245K–$325K, and financial/payment technology company pay of $173K–$210K; he separately cites OpenAI and Anthropic AI PM postings at $293K–$490K in base pay. He advises building experience within product and then moving up company tiers rather than focusing only on titles, with market capitalization offered as a rough pay correlate.
- Gupta’s US PM leadership pay ranges, which he identifies as 25th–75th percentiles, are $288K–$485K for Group PM, $374K–$809K for Director, $739K–$1.31M for VP, and $2.86M–$7.86M for CPO. His progression framework shifts from Group PMs doing IC-like work while managing PMs, to directors setting standards for inherited teams and involving sales/marketing, VPs handling P&L trade-offs with the CFO/CEO, and CPOs owning product and business health, organizational changes, and board-level company trajectory. His advancement checklist is to learn strategy, expectations, org design and organizational politics, while avoiding down-level moves.
- Gupta says Job Search OS v2 adds an AI agent that uses a browser to find roles, tailor application materials, draft networking notes and fill applications, with the user supervising. He says users personalize and send connection notes themselves, while resumes may be sent without humanizing if users do not review them. He reports more than 2,000 monthly active users for Job Search OS and offers v2 for a one-time $49 purchase with lifetime updates.
SuperMe’s expert-search product for AI group chats aims to select five people from thousands of profiles for a question in about 2.5 seconds; it cautions that a prominent company logo on a resume does not necessarily indicate relevant expertise.
In workplace collaboration, people may respond to organizational contradictions in very different ways: questioning them, working around them, or prioritizing delivery can reflect different experiences and learned strategies, not necessarily their character or competence . Product teams should avoid assuming they know what drives a colleague’s behavior and turning those assumptions into judgments about their intentions or ability .
HubSpot is on track for $3.7B in revenue as Brian Halligan and Dharmesh Shah call it “Day 1,” saying they are “completely re-founding” the company as the “Customer Harness Company.” Hiten Shah connects the move to HubSpot’s earlier response to changing buying behavior with its inbound marketing playbook, and says the company now wants to create the next one.
- Product managers should own user experience rather than treat it as design’s job: product value depends on what customers can do with it, not only what it can do. At Tripadvisor, an icon-heavy hotel-filter redesign reduced conversion by 20%, representing a potential $100M revenue hit if rolled out.
- For AI products, choose interaction and automation to fit the task, not to showcase AI: chat can add translation work, familiar controls can help users inspect and refine results, and agents may need structured interfaces. More automation is not always better; Granola combines users’ own notes with a meeting transcript because note-taking can be part of how customers think.
- To find UX opportunities, observe people doing important tasks and look for physical effort, cognitive burden, translation work, and workarounds; prioritize frequent, costly friction near the product’s core value, test alternatives, and count review and repair work after AI responds.
For AI product work, Hiten Shah calls “Look at your data” the most useful advice and endorses evals, citing Evals for AI Engineers by Shreya and Hamel Husain.
A PM reports recruiters asking for “solution-oriented” candidates who reach features quickly rather than emphasizing problem definition; the post raises AI, funding scarcity, and competitor-copying as possible explanations, but does not establish a wider shift or its cause. One commenter interprets the demand as AI making it easier for PMs to build prototypes themselves, not as a call for unlimited feature growth. Another suggests short runways can push teams to favor work they can show investors next quarter over discovery.
Feature requests are shifting from descriptions to customer-built prototypes: customers who once waited six months can now build a rough version over a weekend and ask why the product lacks it, making working software a particularly challenging form of product feedback.
- Aide’s proposed workflow keeps Claude Code as the reasoning layer and uses MCP to access the screen and existing browser; simple commands run as skills without model tokens, while a short activity log links back to original context instead of copying everything. Visible execution pauses for approval before actions such as sending email or deploying changes, and projects retain their own tools, notes, and rules.
- The author describes Aide as a personal prototype used daily and says productizing it depends on whether enough people want it; they frame the intended value as speeding up research, specs, code, and repetitive workflows, not merely writing email replies. A commenter questioned the product’s Gmail-integration story because the post also describes manually copying emails into chat.
- A PM described developers requiring highly decomposed specs while engineering practices, estimates, and blocker communication were weak—and the PM was held accountable for delivery. The discussion highlights an engineering-process and accountability problem, not simply a need for more detailed requirements. A commenter recommended leaving estimates and delivery ownership with engineering, tracking commitments, and reporting missed deadlines rather than acting as the team’s TPM.
- Another commenter said improving a team without an engineering partner can take months or quarters; in their experience, a Scrum offsite helped build trust and surfaced organizational gaps.
- The post’s author reported using Claude to build features, with QA noting improved code quality and the author claiming they finished with better results in one-tenth the time; they were concerned about owning security and technical correctness without software engineering expertise.
A PM supporting roughly 17–18 developers argues that the role extends beyond managing the backlog and prioritizing incoming requirements: continuously identify product opportunities through user or operations friction, stakeholder and engineering conversations, usage patterns, and workflow improvements . The aim is meaningful product and business value, not simply keeping developers busy .
- The proposed coding-agent scope combines interactive CLI/IDE use with asynchronous sandbox workflows and multi-turn tool use; it targets codebases of 10–100 million lines without prebuilt indices and sessions that can be compacted, resumed, forked, replayed, and restored from checkpoints.
- Make permissions and user approval core safety requirements: the design rejects fully autonomous execution of dangerous commands and calls for policies at user, organization, or company level.
- Proposed enterprise requirements include keeping source code and embeddings client-side, zero data retention, a 1–2 second P90 time-to-first-token target, and four-nines availability.
- To control inference costs, route simple commands and local file/context operations locally, reserving inference for tasks that need language-model reasoning; more specific prompts can reduce costly model turns even when they are longer.
- For frequently changing codebases, the speakers favor agentic search over RAG: it avoids stale indexes and server-side embeddings and enables iterative correction, while accepting more round trips despite RAG’s lower per-query cost.
- Faster shipping does not necessarily mean fewer roadmaps: commenters argue it makes choosing the right work more important and call for careful planning, rigorous experimentation, and clearly defined and measured success. One recommends keeping experimentation, research/results, and company goals in shared context rather than siloed in individual PMs’ Claude accounts.
- The thread also contains opposing views: commenters predict agentic coding by non-PMs and agents’ broad connections could make roadmaps sprawl, while another argues that only one relevant strategic roadmap is needed.
