We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
PM Daily Digest
by avergin 100 sources
Curates essential product management insights including frameworks, best practices, case studies, and career advice from leading PM voices and publications
andrew chen
Aakash Gupta
Paul Graham
Big Ideas
PRDs are moving from permission slips to decision records. The old flow was Idea → PRD → Design → Build → QA → Ship; the proposed AI-era flow is five prototypes, evaluate, kill four, then write the PRD for the survivor. Cheap prototypes move documentation later, and the PRD’s job changes: capture opportunity, boundaries, success measurement, a behavior contract, rollout, and risks—what the prototype cannot communicate, including why, measurement, and rollback. The note cautions against copying zero-PRD teams when regulation or many stakeholders make explicit alignment necessary.
AI-era PMF is perishable. Andrew Chen argues that improving models make older products obsolete; fit depends on comparison with alternatives across the ecosystem and “follows the frontier.” Treat model progress as a recurring competitive review: re-test the product against current alternatives and make the next innovation a roadmap requirement, rather than treating an initial PMF result as a durable moat.
Tactical Playbook
Turn states into contracts before polishing screens. A founder’s redesign rework began with decisions that never specified the trigger, user action, system action, or next destination. Writing those four items beside each important state reduced design back-and-forth; the unresolved question is where decisions live as the product changes. Make the four questions required in the PRD or decision log and link that canonical record from design and engineering, so a changed screen does not silently reopen the underlying decision.
Do not count enterprise meetings as traction. Paul Graham’s warning is blunt: a big company may spend months in meetings without saying no, and meetings are not commitment. Use each meeting to secure evidence—a named problem owner, budget and timeline, a bounded pilot, and an agreed success/stop condition—before forecasting demand.
Case Studies & Lessons
Pylon automates investigation, not accountability. Pylon says its new agentic-support product serves B2B companies and about 1,600 customers. Its thesis is that full-resolution bots handle the easier slice of tickets; in one roughly 5,000-person example, a bot deflected about 50% of tickets without reducing headcount. Pylon’s alternative pre-investigates each ticket across past issues, logs, code, documentation, account context, and Slack, then lets the rep interrogate the result, create a Linear issue, or draft a reply while retaining responsibility for the outcome. In beta, Pylon reports one customer cut escalations 70% in about a month and another improved time to first response by 64.5%. The product lesson is to automate context gathering and repeatable actions while keeping judgment and guardrails human.
Career Corner
Keep management reversible. Lenny’s Whatnot discussion asks why VPs of Product are becoming ICs again. Its accompanying takeaways say four or five PM managers spend at least 90% of their time on IC work, while the CPO spends about half his time as an IC. Preserve hands-on reps in data, decisions, and product work; a title change should not end craft development.
Tools & Resources
A lightweight AI build workflow. Patrick Collison’s economics-of-AI survey was built locally in two prompts and deployed in one: a single instruction asked Claude to push to Vercel, create the Stripe account, and store state safely; Claude chose Upstash as the datastore. Use this pattern for low-risk prototypes while keeping scope, data, and rollback decisions explicit.
Practice discovery, don’t just read it. Teresa Torres’ 2026 group read pairs one section per month with discussion questions, exercises, teammate videos, and quarterly live sessions; the current Chapter 9 focuses on story mapping, pre-mortems, and assumption testing.
Product Management
Lenny's Podcast
Lenny Rachitsky
Big Ideas
The PM role is being decoupled from the pod ratio. Whatnot maps PMs to problems and core projects rather than teams; engineers and designers can be DRIs, and a team may go a year or more without an attached PM. Tom Verrilli says AI makes this more viable: PMs can pull nuanced cohort data, inspect product logic through the codebase, and senior PMs can cover more surface area. The durable work is identifying what to build, translating requirements, prioritizing ROI, improving design, and linking customer, business, and tech—not alignment theater. Staff PMs where decision complexity warrants them, and keep them close to support, data, engineering, and design.
Play the accordion. Verrilli’s framework avoids both spaghetti iteration and multi-year roadmap documents: define the larger goal, ship the smallest V1, then re-evaluate and plan the next move. In Whatnot’s live commerce, zero-minute listings help seller throughput but hurt discovery; mandatory listings could reduce throughput because each takes about 3.5 minutes. Every local win needs a zoom-out for knock-on effects.
Tactical Playbook
Treat repeated misses as a calibration signal, not automatically a performance failure. Count genuinely completed items per week for 10–12 weeks, without points. Stable throughput with missed commitments means the target is over-calibrated; falling or volatile throughput points to dependencies, unclear requirements, attrition, or technical debt. Punishing misses incentivizes sandbagging and destroys forecast signal; publish confidence ranges instead of a single date.
Use a three-layer metric spec. A LinkedIn discussion proposed incremental Premium subscribers as primary, top jobs per user as secondary, and listing CTR as a guardrail. One commenter hypothesized that inflated job postings could serve paying recruiters while wasting seekers’ time; treat that as a product-risk hypothesis, not verified company intent. Pair the scorecard with denominator checks: one reported funnel error treated high checkout among users who already had cart items as a win.
Case Studies & Lessons
Signups and polished acquisition do not establish value. A Spanish-school founder tried kids and adults, below-, at-, and above-market pricing, and polished site and ads, yet free demo bookings no-showed; a $1 booking fee killed bookings, and only five people attended, none purchasing. A reply recommended narrowing to a must-have niche rather than competing with Duolingo or free resources. In a separate AI-fitness example, 5,000 dormant signups and roughly 1,000 new signups in a month produced no revenue; the useful diagnostic is whether users actually use and like the solution—customer-problem fit is not problem-solution or product-market fit.
Career Corner
Make the work inspectable. Whatnot says 31,832 PM applicants in two years produced one hire; it looks for macro and micro thinking, fast validation, and specificity about decisions and things built, not alignment narratives. Its advice for job seekers is to do IC work now: scope problems, define “good,” pull data, and understand systems. For domain pivots, a gaming-PM practitioner recommends carrying quantified outcomes such as retention, engagement, and upsell; another reports leaving gaming for two other industries.
Tools & Resources
Price agent tooling by solved task, not subscription price. In one Product Compass benchmark covering 105 hidden bugs, Luna max fixed 33 for $1.80 per run versus Fable’s 29 for $104; the same model at high effort fixed 13, making effort a major variable. The article reports token-price gaps of 20–25x and observed spreads up to 90x, while max was slower. Use max for planning and large asynchronous implementations, high for small fixes and summaries, and benchmark representative work before switching. The associated ChatGPT desktop app supports autonomous loops, project-level skills/MCP, and manual compaction, with Plus listed as enough to start.
Shreyas Doshi
Hiten Shah
The community for ventures designed to scale rapidly | Read our rules before posting ❤️
Big Ideas
Generation is abundant; judgment is becoming the scarce product capability. Andrew Chen contrasts unlimited proofs, code, videos, and lawsuits with the limited people who can verify, review, watch, or adjudicate them; he argues that when creation becomes nearly free, the cost moves elsewhere. Shreyas Doshi labels that bottleneck “Taste.” Hiten Shah gives the quality risk a name—“AI slop debt”—and says it extends beyond code. The product implication is close to Scott Belsky’s prediction that the best software in many industries will be proprietary, built around a versatile data layer, specialized models, routers, and homegrown workflows and interfaces. PMs should therefore design the review standard and workflow/data advantage alongside the feature itself.
Organizational coherence is an AI capability. The Beautiful Mess argues that when strategy, structure, technology, and incentives line up, teams can infer context across maps and spend less energy reorienting; that benefit applies to humans and humans using AI. AI can surface and compare conflicting maps, but it cannot reconcile incompatible goals, incentives, or definitions—and may create the appearance of alignment instead. The practical test is to find the consequential gaps and bring them back into alignment, rather than adding another layer of documentation or orchestration.
Tactical Playbook
Govern agent work as artifacts, not conversations. A practitioner’s experience with coding agents is that the hard part is no longer coding or model access; it is preserving intent, specs, ownership, review, and knowledge across people and agents. Chats become a temporary layer. Use this operating sequence:
- Let chat explore and negotiate, but make the durable unit an artifact with an owner, version, and acceptance test.
- Have the agent investigate and report first; approve the plan; then permit one change at a time and inspect the diff before commit.
- Link the artifact to the code or files it governs and define the test that can invalidate it. When a later change crosses that boundary, mark the decision stale; let the agent retrieve the current decision, while old conversations remain supporting context.
Make AI-assisted feedback timely but auditable. A proposed alternative to shallow forms is a conversational prompt triggered by a dropped checkout, adoption event, or cancellation, followed by questions about the user’s intent and what went wrong. Treat that as a design hypothesis, not a license to interrupt: users may need a snooze control and may reject a lengthy exchange. For summaries, require timestamped transcript links, explicit “I don’t know” behavior, and manual review of early outputs before trusting aggregate claims. For consequential decisions, pair the summary with observation, direct calls, or recurring support evidence; one practitioner specifically rates skilled observation and repeated support tickets above feature suggestions, warning that AI can amplify bad input.
Case Studies & Lessons
Use the cheapest reversible surface to validate behavior. In a community example, a builder created an education-aid web POC with Supabase and a landing page, then deliberately held back further building while trying to get prospects to use it for free. The builder chose direct outreach because usage might reveal an entirely different product, and later chose to delay a native app because the web version was easier to iterate and deploy. The lesson is not “always build web”; it is to make the first product an instrument for learning and earn platform complexity with evidence of demand.
Career Corner
Build cross-functional capability instead of chasing a mythical AI-PM résumé. One founder says companies want forward-deployed AI PMs but describes the supposedly ideal CS–consulting–startup profile as mythical; the stated answer is intentional training. That fits the broader signal that everyone is becoming a part-time engineer and marketer while organizational boundaries thin. Build proof that you can frame problems, use technical tools, work with users, and move decisions through an organization—not just a PM title.
A separate community account describes automotive PM assignments lasting one vehicle cycle—roughly three to five years—after which managers returned to their prior disciplines, and predicts more rotation as PM, engineering, sales, and design blur. The author is treating the stint as a rotation after burnout, so this is an anecdotal career design option, not a universal prescription.
a16z
Paul Graham
Supabase
Big Ideas
Agent compatibility is moving from claim to test. Supabase introduced Evals, running Claude Code, Codex, and Open Code against real tasks and scoring what they do. Paul Graham says he expects all services used by agents eventually to adopt such tests—and that services agents cannot use could go out of business. For PMs, define “agent-ready” through representative workflows and measurable success criteria; turn failed runs into product priorities.
Fast AI cycles favor stable direction over detailed schedules. Decagon says a precise 12-month roadmap is difficult when build cycles are so fast; it keeps themes and a clear long-term vision, lets customer signals determine what to build, and retains human judgment over what to include or exclude. Apply this as “stable vision, short commitment horizons”: make the current bet explicit, keep later bets provisional, and review them against fresh customer evidence.
Tactical Playbook
Make feedback earn a roadmap slot. A community practitioner’s process is: stop accepting feature requests and capture the problem or symptom; quarterly observe 10–15 users in their normal environment; collect about 15 pains; have 100–150 users rank them; prioritize with a weighted average; and reserve roughly one-third of sprint capacity for customer-satisfaction fixes, leaving two-thirds for strategy and technical debt. Use those figures as a starting hypothesis, not a law. At minimum, track each theme’s source, segment, observed behavior, frequency, severity, and blocked business outcome; promote it only when evidence is strong, and link the roadmap item to the evidence and to what would change your mind.
Separate “Now” from discovery. One startup stopped weekly roadmap churn by keeping Now stable except for critical or regulatory items while allowing Next and Later to change; the commenter says the approach held through acquisition and scale-up. The organizational prerequisite is role clarity: define what PM owns before hiring, and keep early teams on high-trust, light rituals rather than importing frameworks and OKR cascades too soon.
Case Studies & Lessons
Decagon’s “glass box” turns deployment into product. Its forward-deployed teams are expected to contribute to core product, so a capability built for one enterprise becomes available to the next 10 customers rather than remaining one-off work. In a customer comparison, a Sierra deployment produced about three new journeys over a year because the customer depended on forward-deployed engineers; with Decagon’s productized model, the same customer created about seven in a month, with nontechnical teams able to act directly. Lesson: treat recurring implementation labor as product debt. Instrument what specialists repeatedly do, generalize it, and give customers enough control to iterate without waiting on the vendor.
Career Corner
PM interviews are becoming build tests. The discussion describes companies replacing presentation rounds with live prototypes to assess “full stack builders.” Evaluators look for taste and decision ownership—not a prototype where AI made all the choices—and flag generic copy, weak underlying data, and designs that feel like AI output. Prepare by giving the tool context in three buckets—functionality, design, and data—then attach a wireframe and realistic dataset; add a live API and be ready to explain front/back-end boundaries, security, model choice, and caching.
Tools & Resources
Choose the prototype stack by the job. The current tool map separates design-system/front-end tools (Reforge Build, Magic Patterns, Alloy), full-stack zero-to-one tools (Lovable, Bolt, Replit), and full AI development tools (Claude Code, Codex). For interview work, the recommended differentiators are visual context, thoughtful copy and data, and a working API—not faster generic generation.
Mind the Product
Y Combinator
Jeff Dean
Big Ideas
The PM advantage is moving from prompt skill to context engineering and taste. Jeff Dean describes AI progress as the system around the model—retrieval, tools, memory, and orchestration—and says any team with a model API can improve by running real tasks, observing failures, and encoding better guidelines or skills. In one PM’s self-reported experiment, a month of building a memory layer—35 pages, 23 daily notes, and 14 analyses—made the same model and prompt produce specific, context-aware actions instead of generic discovery advice; the layer captured failed paths and stakeholder history that ordinary document retrieval misses. The practical implication is to ingest meeting notes, Slack activity, and daily notes, classify them with recurring tasks, and require source references for stored claims. The PM’s distinctive contribution is curating and codifying judgment so the AI can reuse it.
Build cost changes the PM job. As spec-driven development makes experimentation cheap, the PM shifts from owning only a six- or 12-month roadmap to deciding which of many experiments deserve to endure—based on customer resonance, strategic coherence, and technical solidity. A parallel emerging direction is realistic simulation: Scott Belsky expects simulations of how people think, decide, and behave to become a commonplace way to test important decisions before launch. Simile AI says it raised $200 million at a $2 billion valuation to pursue simulation at population scale. Treat this as a pre-launch scenario-testing layer, not a substitute for real customer evidence.
Tactical Playbook
Standardize the handoff, not every PM’s workspace. A team moved context from Jira and Confluence into structured GitHub files because connectors returned noisy, stale information and consumed too many tokens—but recognized that this could damage human collaboration. A better operating pattern is:
- Let teams work in the tools suited to the conversation.
- Publish an approved initiative pack containing the current problem, decision log, constraints, owner, and source links.
- Generate the pack automatically, but require one human approval; surface changed decisions, unresolved conflicts, and stale links for review.
- Version it in GitHub only when engineering or agents need that interface. The test is whether a new person and an AI tool can reconstruct the latest decision from the same pack.
Case Studies & Lessons
Lassie: automate the job, not the interface. Its founders worked inside dental practices and saw a highly rated dentist spending 200 hours a month on paperwork; conversations with other doctors confirmed the pain, and several gave the founders access to their finances and operations. They built the context layer and tools first, then acted as the humans in the loop until they could automate their own work. They aim for roughly 95% automation before selling a job, rather than waiting for perfect coverage. The first agent was priced in five figures for about 30 hours of monthly labor against roughly 200 hours of administrative work. Onboarding connects the bank account, system of record, and insurance portals in a near-self-serve flow, with strict ICP selection and measured checkpoints. The lesson for PMs: in an SMB, there may be nobody available to operate a tool; define value as labor removed and treat integrations and onboarding as core product work.
Career Corner
Entry routes remain adjacent, but not closed. One community commenter calls PM a senior role and recommends business/product analysis or assistant-PM roles; another says multiple internships and APM programs can lead to direct offers. Smaller companies reportedly favor former software engineers or business analysts who can run with a feature immediately, making internal transfers a practical route. The eventual division of product work between humans and AI remains unsettled.
A separate PM discussion is an organizational-health warning: commenters distinguish hard trade-offs from treating people poorly, while one reports a culture increasingly rewarding louder, angrier PMs and another describes burnout affecting physical and mental health. Be ruthless about prioritization, not about people.
Tools & Resources
Use a product-first spec workflow for coding agents. A practitioner’s lightweight pattern is: write a thorough product brief, ask the AI to identify gaps and questions, then produce the technical implementation plan. Another workflow uses brainstorming and MoSCoW, keeps “what” separate from “how,” and only then hands the result to OpenSpec for implementation detail.
andrew chen
Mind the Product
Harry Stebbings
Big Ideas
The Makers Manifesto: a post-Agile framework for the AI era
Faith Forster launched the Makers Manifesto — four values and 16 principles created with 45 cross-disciplinary contributors. It's deliberately broader than the Agile Manifesto: Agile addressed the software development process; this covers the full creation of value, including strategy, ethics, and customer relationships. The group chose "maker" over "builder" to be inclusive of all roles, since in an AI world "the product person can do 80% of an engineer's job, 88% of a designer's job, and the CEO can do 80% of all of our jobs" .
The four values: purpose over possibility, value created over effort spent, learning loops over launch plans, and human accountability for full automation. Each includes what it is not — pushing back on practices like measuring performance by tokens spent . Available as PDF and MD files, designed as a reference for humans and agents alike .
Why it matters: When anyone can build something over a weekend with prompts, the manifesto asserts durable advantage comes from data, relationships, distribution, and business models — not features. It's a reference point for competency frameworks, product processes, and hiring decisions.
"Copilot for X" is becoming "Agent for X"
Andrew Chen observes the startup trend shifting from "Copilot for X" to "Agent for X." The driver: people don't want AI that generates more things to review — they want agents that take action and deliver outcomes. Chen notes his own AI skills "just end up generating more and more things for me to review. What I want now is actions" .
Tactical Playbook
Handle B2B dashboard requests without one-offs
When every customer wants a different dashboard before buying, two failure modes exist: letting each request become a one-off report (CS gets buried, product loses signal) or telling everyone to wait for the roadmap (you lose sales). The better line: let CS create saved customer-specific views, but track requests underneath. If five customers ask for the same split or filter, that feeds the product roadmap .
When an AI-built app stops being a prototype
The gap between demo and product is operational, not feature-based. Ask whether it can run unmonitored for a week. If something breaks at 2am, do you hear from an alert or a customer? Can you roll back without the prompt author? Pick the one workflow people would pay for, add tests, logging, and a rehearsed fallback, then ship that. Everything else stays beta until a named person owns it . Test partial failures — payment succeeds but provisioning doesn't. If recovery depends on the prompt author, you still have a demo .
Case Studies & Lessons
Gamma: $0 to $100M ARR on word of mouth
Gamma (50-person team, 600K paying subscribers) reached $100M ARR primarily through word-of-mouth growth. After an initial signup spike plateaued, they spent three months rearchitecting onboarding to make the first 30 seconds "magical" rather than spending on marketing . Their advice: "Before you spend any money on marketing, build a product that has strong word of mouth" .
Creator marketing was relationship-based — the founder became a creator himself to learn the process, then manually onboarded each creator so promotions felt authentic . Community-led growth included a power-user Slack (Gambassadors), in-person Gamma Labs, and global user visits. They dogfooded two competing concepts (presentations vs. virtual office) for six months before committing . Pricing evolved reactively — they launched without monetization and advise constantly revisiting seat-based vs. consumption-based models . A sales team was added only after enterprise inbound became overwhelming .
n8n: fair code license and deep AI agents to $100M+ ARR
n8n crossed $100M ARR (valued at $5.2B) by differentiating from Zapier and Make on power and flexibility, not speed of first build . Their "fair code" license allows free self-hosting but prohibits commercializing the code — building community trust while protecting the business . When AI arrived, they invested in deep agent capabilities (memory, multiple models, tools, human-in-the-loop) rather than superficial API calls .
For enterprise adoption, n8n advocates a federated model: domain experts build their own automations while a central team provides guardrails and education . They measure impact through adoption and business KPIs (NPS, revenue) rather than isolated ROI metrics, arguing that driving change matters more than precise attribution .
Career Corner
SWE-to-PM: the best answer doesn't automatically win
A large thread of SWEs who moved to PM overwhelmingly reports the role is harder. The core challenge: engineering tools (clean data, architecture diagrams) are "absolutely useless in convincing senior stakeholders" . One PM-coach puts it bluntly: "the best answer does not automatically win. The answer people understand, trust, fund, and act on wins" . The transition requires translating technical knowledge into business outcomes — "this gives you commercial levers" rather than "this moves ordering logic to a table" .
PMs pushing PRs: viable but rarely the priority
A Principal PM's honest assessment: he's pushed many PRs but isn't motivated to continue — he's slower than devs, his PRs compete for priority with everything else he owns, and merge conflicts accumulate . One team enabled PMs to "vibe code" within dev-defined guardrails: agents break down tasks, PMs steer intent, automated quality scans run, and human review is mandatory before merge . But most PMs in the thread say their time is better spent on customer interviews and roadmap.
Tools & Resources
Claude Design gaining on Figma
Harry Stebbings shared his team switched from Figma to Claude Design, citing ease of use and less procurement friction . Julie Zhuo sees disruption starting with smaller teams and founders doing one-off design tasks, noting Claude's all-in-one interface has a psychological advantage . But she's clear about limits: it's slow, one-shot quality isn't there, and for larger teams needing collaboration and design systems, Figma still wins — that's their biggest moat .
Aakash Gupta's free AI PM roadmap
A nine-area learning path: getting started, prompt engineering, context engineering & RAG, AI prototyping & vibe coding, agents & agentic workflows, evals/testing/observability, foundation models, AI PRDs, and career resources . Key claims: prompt engineering is "the top skill for great agents," "the best AI PMs obsess over evals," and "the market is hungry for PMs who can actually build AI" .
Aakash Gupta
The Beautiful Mess
andrew chen
Big Ideas
The AI smile curve: usage rises as models improve
Andrew Chen identifies a new "smile curve" for AI products where retention and usage increase over time as foundation models improve. The pattern: a user tries an AI app, finds it lacking; a new model release improves performance; eventually the user incorporates it into their workflow, driving up both usage and spend. What seemed "meh" at first eventually becomes indispensable.
Historical smile curves came from network effects (social), supply growth (on-demand), or workplace spread (SaaS). The AI version is driven by model capability improvement rather than user-side dynamics.
Why it matters: products that seem underwhelming at launch may still be "must fund" bets if their value depends on model trajectories. This reframes early retention metrics—low initial usage isn't necessarily a kill signal, but the product must be positioned to capture the uplift when models cross a capability threshold.
The Border Collie's Faustian Bargain
John Cutler maps how organizations react when leaders present something "objectively not a strategy" that everyone experienced in the room knows isn't one. He sorts attendees into archetypes: Believers (can't see the gap, tolerate it), Players (see it, tolerate it, even relish the game), Purists (can't tolerate it but miss the social function), and Coherence Checkers who see the gap and struggle.
The "Border Collie" watches closely, sees the mismatch between presentation and reality, and feels driven to herd everyone toward coherence. The Faustian Bargain: a Border Collie can become a Fox—gaining influence by surrendering the right to challenge the fiction—but risks gradually becoming the person who preserves the ambiguity and disciplines those who point it out.
Why it matters: PMs who see strategic incoherence face a real choice—herd locally by translating ambiguity into something their team can use, backchannel, name the contradiction, or leave. Cutler makes the slow cost visible: the drift from "staying close to power to improve things" to "protecting the ambiguity as your job."
Tactical Playbook
Build a cold-start eval set in one sitting
Daniel McKinnon (ex-PM at Meta and Google) outlines a six-step method for building an offline eval set for an AI feature before any production data exists. The premise: "evals are the new PRD"—the detailed if/then product statements that used to live in a PRD now live in specific evals.
- Write the problem in one sentence—e.g., "Extract sender's name from support e-mails." If you can't, you don't understand the feature yet.
- Validate domain expertise. If you've never shipped an AI feature, find someone who has to walk you through your first eval.
- Set up the workspace. Create a Project in Claude or ChatGPT, upload sample data, and use a structured prompt that returns the input, correct answer, and a grader line.
- Find your floor. Test the easiest genuine case. If the model can't do it, your floor is above the model's ceiling—make it easier.
- Find your ceiling. Test a case you expect to fail. If nothing fails, your eval is already saturated and will never tell you anything.
- Fill in the middle. Binary-search between floor and ceiling, generating roughly 100 cases that vary difficulty. Use AI to build the test for the AI—this is what makes it 90 minutes instead of two weeks.
Scoring: binary pass/fail judge with three criteria—substantive correctness, format, and scope. Calibrate the judge once before trusting it.
Reading the score: if the eval comes back at 50%, slice by dimension. Put guardrails on the product so it only handles cases it gets right ~80% of the time. Everything below the line goes to engineering with a specific target, not a feeling.
Why it matters: McKinnon estimates fewer than 100 PMs worldwide build frontier-model evals, and the analytical parts of the PM role are being commoditized by AI. Evals are built purely on judgment—making them a place to go deep and differentiate.
Cluster feedback before it reaches the roadmap
Treating every raw user comment as a direct roadmap input degrades decisions. Requests for CSV export, Sheets sync, email reports, and API access may all reflect the same underlying need: users moving data into another workflow. Raw feedback is evidence, not a product decision. Public reviews become a signal when multiple users describe the same pain after a release. The rule: cluster first, decide second.
Career Corner
Google adds live AI prototyping to PM interviews
Google has added a 45-minute live AI prototyping round to its PM interview loop for 2026. Candidates receive a prompt and must build a working prototype using any AI coding or prototyping tool. The format is expanding from AIPM roles to all Google PM interviews.
Interviewers evaluate three things: problem framing before building (candidates who jump straight into building consistently fail), technical execution (prompting, debugging, working around limitations), and communication (narrating decisions while building).
The coaching framework: clarify the prompt (users, goals, constraints), scope aggressively to prove one core interaction, build with a simple stack, test and acknowledge what's broken, and close by defining success metrics. Be completely fluent in your tool before interview day—all cognitive energy should go to the product problem, not the tool's interface. Even if told the round isn't part of your specific loop, practice it anyway—AI fluency will come up somewhere in the interview.
andrew chen
The community for ventures designed to scale rapidly | Read our rules before posting ❤️
Boris Cherny
Big Ideas
AI product opportunity can be in the harness, not the model
A YC discussion frames product overhang as capabilities a current model already has but that products have not yet elicited. In this view, a product can “hobble” a model when its scaffolding gets in the way; the opportunity is to remove unnecessary constraints and let the model express more of what it can do.
Why it matters: AI product strategy is not limited to waiting for the next model release. It also means testing whether the interface, prompts, tools, and workflow are preventing useful behavior.
Adoption competes with existing habits
AI startups often face entrenched alternatives: long-lived tracker sheets, multi-tab tasks, undocumented copy-paste processes, and employees who know how to get the work done without a new tool. Fear of change and inertia are part of that competitive set.
Apply it: in discovery, identify the actual workflow being replaced—including its informal steps—before positioning an AI feature as an alternative.
Tactical Playbook
Refresh the AI harness empirically with each model release
- Remove inherited prompt scaffolding. Claude Code changes its system prompt and tools frequently because behavior can differ substantially between models; on one release, its team removed 80% of the system prompt.
- Run real tasks before prescribing fixes. Observe where the product works and where it repeatedly fails rather than guessing the instruction a model needs.
- Add back only recurring constraints. Use higher-level task descriptions, guardrails, and exit criteria instead of rigid step-by-step instructions.
- Build verification into the workflow. The discussion identifies making it possible for the model to check its work as a critical capability for harder tasks.
- Maintain—and eventually renew—evals. Keep appending to useful evals across model generations, but replace them when model progress saturates the set.
Make product ownership explicit when working with an agency
Development agencies can provide guidance on implementation effort, technical debt, testing, and platform health, but they lack the daily customer, business, usage, and team context required to decide what should be built.
Apply it: assign someone to answer four questions: where the business is headed, what the product must do to support it, what to build now, and what can wait. Options are an internal or fractional product leader, an agency that fully embeds in the product role, or a founder who takes on PM responsibilities.
Case Studies & Lessons
Claude Code pairs broad delegation with repeatable maintenance routines
The team describes giving modern models harder, higher-level tasks with guardrails rather than overly specific instructions. For complex work, dynamic workflows can break a task into stages of agent execution, verification, and summarization.
It also reports running 20–30 recurring maintenance routines across codebases—such as dead-code cleanup, test generation, and unifying near-duplicate abstractions—with hundreds or sometimes thousands of agents daily. The system is described as not fully automated yet.
Lesson: agentic execution is not simply delegation. Product teams need clear completion criteria, verification, and narrowly defined recurring loops before expanding scope.
Career Corner
Build practical product judgment alongside technical skill
The career advice in the YC discussion is to learn computer science through practical problem-solving: build products, talk to users, and develop design, business, and data skills in addition to engineering.
Apply it: choose work that forces both technical execution and user contact. The goal is not theory alone, but learning to connect a real problem, a product decision, and an implemented solution.
Tools & Resources
YC: Boris Cherny: Building Claude Code
The conversation is a useful resource for PMs designing agentic workflows, particularly its examples of multi-stage agent orchestration and ongoing routines.
The Beautiful Mess
Shreyas Doshi's Product Almanac | Substack
Aakash Gupta
Big Ideas
In AI products, the eval can become the central product artifact
At Anthropic, research PMs describe “evals are the new PRDs”: detailed user feedback becomes reproducible tests that represent a user need and measure whether a model version improves it. This changes the PM job from specifying an interface alone to defining what a good model behavior looks like.
Why it matters: visible output can conceal weak judgment. AI may let people create artifacts across more role boundaries, but producing an artifact is not the same as possessing the expertise behind it. PMs therefore need mechanisms—such as evals—to assess quality rather than relying on plausible-looking output.
Treat AI as a capability redesign, not a simple productivity upgrade
A useful lens maps nine possible movements of work: specialization, diffusion, centralization, integration, embedding, rebundling, externalization, elimination, and loss. AI can spread codified expertise, embed knowledge in tools, and rebundle work into broader roles—but it can also create needs for evaluation, governance, orchestration, and exception handling.
Apply it: when automating a workflow, ask not only what task disappears, but which routine activities currently develop the judgment people will need for difficult cases. If those activities vanish, identify how that judgment will be built and maintained.
Tactical Playbook
Turn model failures into a working eval loop
- Collect the precise failure, not a summary. Ask for the exact user request, model response, and situation in which the behavior failed.
- Read the trajectory. Inspect transcripts closely enough to distinguish, for example, hallucination from overconfidence; the theme of a failure can be nuanced.
- Convert repeated failures into test cases. Define an expected or “golden” answer and build a set of examples that consistently represents the pain point.
- Run the set on each version. Add the cases to the eval repository and use them to check new model versions.
- Keep PRDs where they add value. Use them for broad stakeholder alignment and ambiguous, early-stage opportunities where product vision must be explored; use evals as the shorthand for a defined model-improvement loop.
Why it matters: an offline eval passing while users remain unhappy is a signal to revisit whether the eval captures the actual user problem—not merely to declare the feature complete.
Case Studies & Lessons
Claude’s JSON failures became an eval set
Early Claude feedback that it was poor at following instructions was investigated at the level of individual prompts and responses. The team found that roughly 80% of the reported issue was failure to produce the right JSON, then created an initial set of 30–40 examples as an eval.
Schema-following later became fundamental to Claude’s ability to act as an agent, since structured output supports API access and tool calls.
Lesson: broad feedback such as “it doesn’t follow instructions” is not yet a roadmap item. Product work begins by finding the specific, recurring behavior that can be evaluated and improved.
Career Corner
Prepare for technical depth—and stay hands-on
Nvidia, OpenAI, and Anthropic reportedly use technical PM interview questions, with an expectation that PMs can earn the respect of engineering and research counterparts. Key preparation areas include Transformers and attention; agents, MCP, and APIs; routing and unit economics; RAG; system trade-offs; and evaluation.
Apply it: practice concise explanations, then connect each technical decision to a product consequence—for example, explain how routing a share of traffic to a cheaper model affects margins at scale.
For leaders, technical fluency is not presented as a delegation-only skill: Anthropic’s product leadership emphasizes personally shipping with models and staying close to the details. Pair that with an inward-facing career habit: aim to deliver strong work even under an average or absentee manager, rather than only seeking ideal management conditions.
Teresa Torres
The community for ventures designed to scale rapidly | Read our rules before posting ❤️
Shreyas Doshi
Big Ideas
Design the problem before designing the solution
A recurring failure mode is the “double square”: product has one idea, engineering builds it, then the cycle repeats—without divergent exploration or convergent selection. The result, as described in the source, is often poor product outcomes.
Use problem design to work upward from a proposed form factor: What behavior would it support? What outcome does that behavior serve? Is this the right touchpoint—and ultimately the right problem—to address? This prevents a tool or interface concept from defining the need prematurely.
Why it matters: faster prototyping does not establish that a feature is worth building. AI-assisted implementation can skip alignment, edge-case discovery, prioritization, and validation—leaving engineering to maintain features that may not fit the product’s information architecture.
Make complexity understandable, not merely smaller
For B2B and service products, “simple” should not mean hiding necessary complexity. The design goal is to present it in a way users can understand, control, and care about.
Apply it to dashboards: require every displayed item to support a decision or action. A dashboard designed around action should show less information, not more, and make deliberate choices about what deserves attention.
Tactical Playbook
Diagnose onboarding with enough evidence for the next decision
When early drop-off appears, do not wait for perfect statistical certainty—but do not redesign on a hunch either.
- Start with low-cost learning. Use unpaid acquisition to collect initial signals before spending on scale.
- Observe the experience directly. Watch session recordings, speak with users, or sit beside a user and ask them to narrate their experience.
- Validate the friction point. Seek decision confidence: enough evidence that a specific obstacle is real and warrants action, rather than enough data to make a universal claim.
- Run a binary test. Change one dimension at a time; one approach recommends beginning with ICP-focused tests once the core experience is understood.
- Continue improving. Treat onboarding as a continuous A/B-testing loop rather than a one-time redesign.
Why it matters: this balances speed with disciplined learning, so teams can act on observed friction without mistaking noise for a product problem.
Use AI to narrow a workflow, not bypass product process
Before adding agents to a workflow, make relevant information—such as emails, documents, and support tickets—legible and queryable. Then choose the narrowest valuable loop, such as generating follow-up emails after sales calls, test it, and improve from the result.
Keep shared synthesis in the loop. Decentralized, unstandardized research can yield competing “research-informed” answers and create conflicting versions of reality across teams.
Case Studies & Lessons
Hertility: more intake increased conversion
Hertility Health made a longer intake mandatory before purchase. Conversion rose: women felt heard before receiving a test kit, while clinicians received better information and faced less burnout pressure.
Lesson: removing steps is not automatically better onboarding. Test whether a step creates meaningful reassurance or improves downstream service quality before classifying it as friction.
Customer contact is the startup learning engine
YC’s guidance is to confront the market quickly through a repeated loop of building, talking to customers, and building again. The objective is learning whether people want the product—not extending research or development by default.
For early products, one startup-community recommendation is to focus intensely on a single active user: speak with them, build toward their needs, then use that learning to win the next user.
Lesson: launch early enough to obtain real-world data. A pivot should follow evidence that the core hypothesis is wrong and options have been exhausted—not rejection fatigue; pivots surfaced by customers during deep work are easier to evaluate.
Career Corner
Tailor senior-PM interview signals to the interviewer
Senior interview panels may not know how to assess senior product candidates, so candidates should ensure their answers convey what each audience needs to evaluate.
- Board, VCs, or senior executives: establish impact track record, ability to execute amid organizational complexity, and skills complementary to the hiring leader.
- Cross-functional peers: demonstrate understanding of their function’s challenges, likely alignment, and a proven record of impact.
- Prospective team members: communicate direction, empowerment rather than layering, and investment in their growth.
- Hiring manager: show product insight, alignment, and complementary strengths.
Apply it: prepare a small set of stories that can credibly demonstrate these signals. Answer each question constructively and enthusiastically while steering back to the evidence your audience needs.