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Product definition is now context engineering
Ravi Mehta has renamed the "Feature Specification" competency in his PM toolkit to "Product Definition." The job, he says, was never just writing a spec. It was making clear what to build and why, and that clarity can now come from a prototype, a document, or both evolving together . In his framing, PMs were "the original context engineers," and that context now guides agents as well as people .
His most useful point is that the problem has flipped. Thin specs used to give teams too little context. Now teams often have too much: long PRDs, several prototypes, review feedback, and hundreds of AI-generated decisions, with no way to tell intent from incidental detail . Too little context has a cost too. Agents fill the gaps with defaults, and unexamined defaults produce "slop" . His fix is one concise definition that covers the story, why the product should exist, what success looks like, how it works, and what's in, out, or undecided. That definition should keep evolving rather than serve as a one-time handoff .
He disputes the idea that the PM role is dying. Companies are "shifting dollars from talent to tokens," but more people building means product thinking is in more demand . AI also "doesn't flatten that ladder." At VP or CPO level, Product Definition becomes a system of standards and review processes that give every team and agent the context it needs .
Aakash Gupta makes a related argument about speed. Each handoff from spec to design to engineering adds drift and about a week. When a PM can prototype in the codebase, a 2–4-week cycle shrinks to days, because the first version exists before the spec does . This depends on an AI-friendly codebase: Tailwind, smaller separate services, and less code overall . He estimates that a 3-day cycle runs about 120 tests a year, against 17 for a 3-week cycle .
A skeptic's view: faster isn't better
Linear CEO Karri Saarinen, quoted by Lenny Rachitsky, took a summer off from following AI and found "nothing has really changed." Building great products and growing revenue are still hard . The tools let teams do more, faster. But on whether teams are making better things, "I don't think that answer is clearly yes." He warns against putting so much weight on toolmaking that it displaces making something great . That's a useful check on Gupta's numbers: more tests per year only help if the tests are good ones.
Julie Zhuo offers a design question that applies to AI products. She quotes Alpha School co-founder Joe Liemandt on the paradox that AI sharpens kids' brains in one hour and shortcuts their goals the next . Her deciding question is what the user wants to get good at. If they want a skill, they should resist the shortcut; if they just want unwanted work done, the shortcut is "exactly what I need" .
Paul Graham: agent bans signal demand
Graham calls Amazon's ban on agents the first opening he has seen since Amazon was founded for a startup to compete with it. People will want agents to shop for them, and they won't want Amazon's own agent . He generalizes the point: if a business has to ban agents, people must want to use them, so there's demand for a competitor that allows them . For PMs, the implication is that agent-access policy is now a competitive decision, not only a trust-and-safety one.
He also says founders always wish they had hired fewer top managers from outside, and he has never met one who wished they had promoted fewer people from within . One founder told him outside hires' "loyalty is to their career, not to the company" .
Competitive and PMM craft
- Pricing objections are two jobs. Hiten Shah separates "What should I say?" from "Should we change our pricing?" because each needs different evidence . On Friday at 10 AM PT he plans to run his 16 competitive skills on one B2B product, with and without each skill. No results yet .
- AI-native demos. A mature B2B vendor keeps losing first impressions to one-prompt demos, while its own sales team ends up listing controls and permissions . One reply argues those tools look great until someone needs audit logs or role permissions .
- Getting customer access as a PMM. A practitioner offered to sit in on two sales calls and write up notes for the head of sales, under his name. Six months later they were invited by default. Without that access, PMM becomes "a slide factory" .
- Outsourcing support. Ticket volume alone is a weak reason to outsource. Keep escalations, churn-risk conversations, and the weekly review of why tickets happen in-house. If you can't write the handoff boundary and QA rubric yet, "you're outsourcing ambiguity, not support" .
Community mood
In a r/ProductManagement thread about the sub's decline, one commenter said PMs are struggling with "slow hiring and what the heck to do with AI" . Another said the product meetups they attend are now 70% job seekers . On tooling, one PM reported AI credits as their org's only enforced tool budget, about $1,200 a month each, of which they use about $25 .
- Portfolio cuts should be judged on avoidable costs and downstream network contribution, not fully allocated losses alone. In the mock case, CAG's €2,000 fully allocated loss per flight masked €4,000 of short-haul contribution (€18,000 fare revenue less €14,000 variable cost) plus €6,000 from connecting passengers, or €10,000 in total network contribution per flight. Only about one-third of CAG's €6,000 allocated fixed cost could be eliminated within 12 months, and only by removing the aircraft and slots. Treating CAG as representative of all 10 proposed closures, the interviewee estimated €70 million in annual value could be destroyed rather than the €14 million the CFO expected to save.
- For fuel-sensitive pricing, the interviewee considered surcharges to pass fuel-cost changes through to customers, but warned that quarterly increases after customers had booked well in advance could hurt customer satisfaction. Ancillary and loyalty-related revenue were suggested as alternatives, though the interviewee also noted potential customer dissatisfaction with higher fees.
- Nir Eyal distinguishes habit-forming products from addiction: he defines addiction as persistent, compulsive dependency that harms the user and argues it arises from an interaction among the person, the problem, and the product—not from the product alone.
- Eyal proposes a regulated “use and abuse policy” for digital services: when usage may signal pathological addiction, notify the user and offer support such as usage limits or counseling resources. This is his proposal, not a policy he says is already in force.
- For products used by children, Eyal argues that unmet needs for competence, autonomy, and relatedness in offline life can drive children to seek those needs online, including through games. This suggests assessing users’ wider context and needs, rather than treating product restrictions as the entire solution.
- A padel-booking app was described as letting people join an open doubles match without first assembling a group; once booked, the commitment helps users follow through. Eyal called this a habit-forming technology, illustrating how reducing coordination friction can help a product support a real-world activity.
- AI products can build capability or bypass the effort of acquiring it: the essay describes Alpha School’s personalized-AI academic block followed by practical creative workshops, and co-founder Joe Liemandt’s observation that AI can both sharpen children’s brains and shortcut their goals. A useful design distinction is whether users want to develop a skill—where preserving effort may matter—or simply complete work they do not care to master.
- The author envisions “mental gyms” that make cognitive practice intentional through games, puzzles, debate, learning and making, supported by coaches, peers and progress tracking; clubs, schools, libraries and similar institutions could provide existing infrastructure. The author notes that evidence for benefits is encouraging but the specifics of mental practice remain unsettled.
People get through stressful, politically difficult, or demotivating stretches at work through different mixes of motivation: the impact of their work, what work enables in other parts of life, or mastery of the work; none is inherently better. For people energized by mastery, it can make work its own target, feel easier and less draining, and provide an internal measure of progress. The practical advice is to reflect on what has actually helped you through hard stretches before, rather than assume another person’s motivators or social-media advice will work for you.
- A participant said product managers are struggling with slow hiring and uncertainty about AI, and called for more discussion on becoming more effective and valuable . Other commenters said job-seeking posts increasingly crowd out practitioner discussion, including at product meetups .
- One commenter reported applying to about 450 roles in two weeks, receiving six interviews and two offers within 30 days, and using high-volume applications alongside focused interview preparation; they also said they negotiated a hybrid role to be fully remote. This is one person’s account, not evidence that the approach generalizes .
For AI product design, Zhuo’s essay suggests distinguishing work users want to delegate from skills they want to build: AI can teach or help sharpen skills, but it can also shortcut the effort, so products should let users choose whether they want practice and coaching or a quick result.
MeckaAI announced a $60M Series B from Sequoia, bringing its total funding to over $120M, to accelerate general-purpose robotics; it describes its role as building the data and deployment layer for physical AI. April Underwood congratulated the team and praised its speed of execution and ambition.
For AI-assisted competitive research and positioning, polished output can still rely on outdated sources or present guesses as facts: in a test asking Claude what changed among Linear’s competitors, the plain run did both. A matching skill dated claims, explained its ranking, and stated its limits. Hiten Shah says he developed 16 product-marketing skills for different jobs and failure modes, and announced a comparison of the same work on one B2B product with and without the matching skill; no results are reported in the post.
Linear CEO Karri Saarinen argues that better AI models and tools let teams do more faster, but making great products, growing revenue, and building a business remain hard; it is not yet clear that the tools are making products or teams better. He urges product teams to prioritize making great things over toolmaking for its own sake.
- Reported PM tool-purchasing models ranged from a small org with shared company licenses and no individual allowance—where analytics was a larger line item and purchases were judged by value—to discretionary funds controlled by a VP when a business case supported a tool; one F100 commenter described approvals as onerous. One respondent reported $100 in discretionary funds without prior financial planning, alongside separate project R&D and team-managed core tools.
- A commenter described fatigue with recurring tool launches such as monday and Aha; at their organization, AI credits were the only enforced tool budget, reportedly $1,200 per person monthly, though they typically used about $25.
Customer conversations that change how a team explains its product are already a form of product marketing. Hiten Shah plans to test 16 AI-assisted B2B product-marketing jobs on one product, comparing work done with and without the corresponding skill to assess which version he would hand to a team; this is a planned evaluation, not a reported result.
For B2B product managers, product marketing is often already part of the job: market learning can change how the product is explained or send the team back into the product. Shah announced a Friday, 10 AM PT session taking one B2B product through 16 product-marketing jobs with AI and showing which outputs he would use.
- Product management is framed as expanding rather than disappearing: as more people build products with AI, product thinking becomes more widely needed.
- Product Definition has evolved from writing a feature specification to engineering enough shared context for humans and agents to build the right product. That context can span specs, prototypes, tickets, conversations, and decisions; the artifacts may change, but the responsibility remains.
- AI can make a working prototype faster to create than a PRD, enabling higher-fidelity alignment and earlier customer feedback. But teams must avoid both too little context—which leaves agents to fill gaps with defaults—and too much context, which obscures intent; the goal is a concise, evolving definition of the product’s rationale, success criteria, functionality, scope, and open questions.
- The work changes with seniority: product managers define products, leaders coach teams to do so, and executives build organization-wide systems and standards for alignment. AI does not flatten this ladder; faster teams increase the need for leadership.
Stanford product management instructors JZ and Anand Subramani announced an AMA for Thursday; they say they have nearly 30 years of combined product experience.
- One B2B channel-first team reports that products may be built outside Product without lifecycle or productization discipline, while sales needs channel- and deal-specific offerings; PMM enablement consequently becomes repackaged messaging that reps rarely use. The same team said PMM lacked direct customer access, relied on desk research for competitive insight, had no outcome metrics, and was excluded from product roadmapping and marketing planning.
- Suggested ways to make PMM work more useful include earning customer-call access by offering sales a written summary, and turning automatically collected competitor information into a searchable repository; start with a sales user’s immediate need and refine based on feedback.
- A contrasting large B2B SaaS example has PMM owning strategy and campaign implementation plans and being accountable for outcomes, with access to customers, metrics, and research and the ability to ask leadership to resolve cross-team alignment issues.
Product-leadership candidates should make their fit to a target company’s problem obvious, demonstrate tangible products they built and explain the work and ROI, and prepare for references and backchannels to be checked as soon as they enter the hiring funnel—not only late-stage . AI-first SDLC experience is another differentiator, especially having led organizational change and being able to assess and sequence it in a new setting . Hiring also weighs whether recent employers succeeded and whether the candidate contributed centrally; a recognizable company name or a large role at an aimless company is not enough on its own .
An India-based PM with 6+ years of product management experience in healthtech, B2B SaaS and pharmacy tech reports being unemployed and getting roughly one call a month despite applying through major job platforms and company career pages; they are seeking near-term freelance, contract, fractional or consulting work.
When a buyer says a competitor is cheaper, separate the sales-response question (“What should I say?”) from the pricing-strategy question (“Should we change our pricing?”); each requires different evidence.
- Gupta argues that communication structure determines what a company ships and can foreshadow product moves . He characterizes Anthropic’s safety bureaucracy as an enterprise moat , DeepMind’s talent-rich but fragmented organization as lacking ship-date ownership , and xAI’s centralized structure as trading speed for dependence on one leader’s attention .
- PMs prototyping directly in the codebase can compress a traditional 2–4-week PRD-to-QA cycle to days, giving the team a working version to react to before the spec is written; Gupta says this depends on an AI-friendly codebase, including Tailwind, smaller separate services, and reduced codebase size . His 3-week-versus-3-day example estimates seven tests for every one and about 120 versus 17 tests annually .
Product marketing starts earlier than founders may expect: buyer comparisons shape the product story, while objections and lost deals can reveal deeper product issues and influence what gets built next.