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Is coordination work part of performance?
A r/ProductManagement thread asked whether the "behind the scenes" coordination work PMs do counts at review time. Most replies said outcomes are what count. The most useful answer came from a product leader who redefined the outcome. For them, being able to ship is "table-stakes." They evaluate a "successful market launch," and a launch is a miss if sales can't sell the feature, professional services can't help customers use it, support can't help, or the docs aren't updated . Because a PM can't control whether other teams finish their part, this leader looks for two things. The PM should make the effort to get those teams ready, and any escalation should never come as a surprise. They also run 360 reviews and ask PMs to "work out loud" in written progress reports so good work gets noticed .
Other commenters disagreed that weak adoption means sales or marketing failed: "product revenue is YOUR responsibility," so the PM has to find out why and fix it with those teams . A platform PM told to tie their outcomes to revenue offered a practical step for when another team is blocking you: escalate quickly and put a number on the loss .
"Live PRDs" still don't work out of the box
A PM asked whether any off-the-shelf tool makes PRDs live objects that generate test plans, docs and tickets. Practitioners said no. One said nothing does it end to end "in a way that holds up outside a demo." Teams stitch it together with Claude Projects or custom prompts and review every downstream artifact by hand, because the link back to requirements "breaks the moment requirements shift" . One team that does run such a system said it is "not that magical." It defines rules and quality standards for each artifact, sets a process for creating and updating it, and runs a plan/do/verify agent loop. It also needs customization and upkeep . Their main lesson is that work has to be split deliberately into small local edits or larger structural rewrites. A generic "do the documentation" task fails, and an LLM can sometimes take 20 iterations to make real progress .
Four kinds of AI PM jobs
Aakash Gupta reused his 1,177-posting dataset to sort AI PM jobs by what the PM actually shapes. Applied AI PMs add AI to existing products (297 openings, $222K median base). AI-native PMs build products at AI-first companies (79, $258K). AI platform PMs build APIs and tools for other teams (140, $231K). Model PMs decide what the model learns and when it ships (46, $345K) . What's new is the engineering requirement, which runs opposite to what many expect. It appears in 34% of platform PM postings but only about one in four model PM postings. Evals show up in all four role types .
Getting to value: pilots, distribution and differentiation
- Proving prevention. A founder selling software that stops construction firms from repeating mistakes said avoided losses are hard to show in a short pilot, especially when the team supplying the information, the team benefiting and the buyer are all different people . Suggested pilot metrics: a count of fixes that reached another project before the problem hit there, with the budget holder agreeing on the target count up front . Another option is whether lessons reached the right people in time, which can be shown "in weeks, not months" .
- When the constraint is the product. One company built a survey-based way to measure AI adoption so it wouldn't need client approval for Jira or GitHub integrations. A commenter called that constraint "the feature" and named delivery leadership as the buyer . Another said to ask trial firms what decision they would make differently after seeing the report .
- AI wrapper tests. A founder whose AI finds import-tax overpayments got 1 meeting from about 65 cold calls . A commenter advised walking one real case through a partner and checking whether that partner is actually allowed to share the files . Another proposed a blunter test: if ChatGPT does 80% as well, "you actually don't have a product" .
- Reddit as competitive intel. Product marketers called it a lead, not a source. Use it to find complaints and workarounds worth investigating, then confirm the issue still exists before it goes into a battlecard . Competitors' pricing pages, changelogs and docs are what you can cite .
Quick hits
- Early adopters. Dan Shipper says employees who experiment with AI tools on weekends can be "a massive f*cking distraction." He says product leaders need a way to harness them without distracting everyone else .
- Defaults are becoming suggestions. Someone used Claude Code to remove 25 Xiaomi packages and write a new launcher for a TV that showed an ad before Netflix . Hiten Shah's takeaway: "The default experience is becoming a suggestion" .
- Agent UX patterns. Gupta's guide to Meta's Muse agent describes several design choices: an editable SOUL.md rules file, a list of active goals instead of one long chat, approval cards before it spends or sends anything, and a Library that stores finished outputs .
- Specs for AI. At YC Paper Club, Clark Barrett said that once code is easy to write and verify, the specification is the hard part left. Agents can help write specs, but checking that a spec matches what the developer intended is still an open research problem . Another speaker advised starting with the simplest interpretable method and adding complexity only when the performance gain justifies it .
- For AI products, start with the simplest interpretable method that meets the need; add complexity only when its performance lift justifies it.
- Explicit specifications remain a bottleneck for verified-code products: agents can help surface missing requirements and corner cases, but ensuring generated specifications match developer intent remains an open problem.
- AI assurance needs multiple validation methods rather than a single pre-deployment check, plus post-deployment monitoring of inputs, system internals, and outputs.
- Benchmark operations need ongoing maintenance: continuously monitor distribution shifts as harm definitions and language change. An audience member cited an SQL evaluation set with 30% incorrect queries; speakers called for benchmark version control, pull requests, and issue filing, and the host proposed an App-Store-like curated benchmark catalog.
- Clark Barrett said his new company, And Truth, is building a universal code-reasoning engine. He also reported that a crypto prover agent fully automatically handled a 30,000-line Rust crypto library in 11 hours.
- An analysis of 1,177 open PM postings found 562 AI PM roles : applied AI PM (adding AI to existing products; specs, experiments, and guardrails), 297 openings and $222K median base ; AI-native PM (AI-first products; prototypes and evals), 79 openings and $258K ; AI platform PM (APIs and tools; docs, SDKs, and roadmaps), 140 openings and $231K ; and model PM (model learning and release; evals and behavior specs), 46 openings and $345K . Model PM is the smallest segment but has the highest median base; applied AI is the largest, with 297 roles at 18 companies. The figures are posted base salaries, with lab equity on top . Engineering-background requirements were reported in 34% of AI platform postings versus about one in four model PM postings, while evals feature across all four role types .
- Meta’s Muse personal agent uses editable SOUL.md rules, active-goal tracking instead of one long chat, approval cards before sending or spending, and a Library for finished outputs . The guide recommends starting with Gmail and calendar rather than the full contacts list, since Muse profiles people mentioned, and suggests disabling training and testing it first with an errand that cannot spend money .
- PM performance should be outcome-led, but a successful launch means more than deployment: sales, professional services, support, and documentation must be ready so customers can realize the product’s value; when adoption is weak, the PM should investigate and work with those teams rather than hand off blame.
- Treat coordination and relationship-building as core parts of delivering outcomes, not a substitute for product success. Make that work visible through written progress updates, and flag cross-functional risks early—including when escalation may be needed—rather than surprising leadership near launch.
- When blocked by another team, escalate promptly and quantify the impact; effort or helpfulness alone is unlikely to compensate for missing product success.
- Reddit can serve as a qualitative product and competitive-research lead: user discussions may surface UX or support complaints and feature workarounds; one commenter described a workaround for a broken competitor feature that informed a battlecard pain point. Treat these reports as leads, not representative evidence.
- Look for recurring themes rather than relying on a single comment, and monitor relevant product or competitor communities without overwhelming the team with alerts. Before reusing a complaint, check that it still applies to the current product; corroborate findings with customer research and more directly citeable sources such as competitor pages, pricing, changelogs, or documentation.
Your360 AI’s voice-coach feedback workflow might turn one call into eight pages of notes while the recipient sees only a paragraph or two; Jared Goralnick says each stage works through synthesis rather than raw transcripts. The company says customers cannot access transcripts, which helps them speak openly and lets the AI pass along the signal rather than the noise.
AI is making product experiences more user-editable: after a sponsored ad appeared before Netflix on a Xiaomi TV, someone used Claude Code to find the TV over Wi-Fi, remove 25 packages, and write a new launcher . Shah frames this as people increasingly fixing products that do not work as they want, with defaults becoming suggestions .
- The founder’s AI task-and-context management product for companies had testers and a growing waitlist but no revenue yet . A commenter recommended focusing on the customer problem and shipping the smallest product the audience will pay for, then pursuing product-market fit rather than aiming prematurely to build a “huge company” .
- A commenter identifying as a startup CTO said finding the ideal customer profile and making sales can be harder than building the product; they suggested timing technical-partner involvement around revenue and clarity on the ICP, while noting technical help would eventually be needed to turn the product into a business .
- Startup product-development advice prioritizes customer problem fit: talk to users, translate their problems into solutions, and demonstrate a complete product improved through user feedback; one commenter argues that solving ambiguous problems and shipping matter more than a specific tech-stack label.
- One startup CTO argues that AI can increase coding throughput while still accelerating work in the wrong direction, including feature creep, technical debt, or poor architecture. For AI product work, another commenter recommends being able to explain why a feature needs retrieval, measure whether it works, and handle bad outputs.
April Underwood proposed a consumer-agent use case for handling Instacart shoppers’ questions and substitution suggestions when the customer is busy, using the customer’s preferences and understanding of what counts as a true substitute to guide responses . She suggested the workflow could also help people whose spouses grocery shop but do not cook .
Dan Shipper’s advice for product leaders: early adopters in an organization may experiment with Astra and Fable on personal projects and bring ideas back to work, but their exploration can distract others; the challenge is to harness their learning without pulling the wider team off track.
The product identifies mistaken import-tax payments by analyzing paperwork companies have already filed, but potential refund recipients may not have the files or know a refund is possible; the founder is weighing self-serve, done-for-you service paid as a share of recovered funds, and distribution through trusted partners with file access.
A commenter recommends testing the partner route first: verify the file holder is authorized to share the documents, then walk one real case through permission, retrieval, qualified review, and refund, recording where the process stalls. This would help determine whether self-service removes work or adds a step. The commenter also cautions against reading much into one meeting from about 65 cold calls and recommends tracking who was reached and why they declined, including whether document access was the blocker.
For differentiation, a commenter asks what an AI wrapper does that a general-purpose model cannot, and another suggests checking whether ChatGPT can perform comparably as a test of whether the product offers distinct value.
Teams exploring live PRDs that generate test plans, documentation, or release notes should not assume a reliable turnkey tool is available: commenters say off-the-shelf solutions fall short beyond demos, and teams rely on Claude or custom workflows while manually reviewing downstream artifacts because requirement changes can break lineage and create drift. One practitioner describes a more structured approach—define artifact rules and quality criteria, specify how artifacts are created, updated, and reviewed, then use a plan/do/verify agent loop—but says it requires customization and maintenance. They also stress deterministic task breakdown (distinguishing small local edits from larger structural changes) and cite model unpredictability and attention/context limits as ongoing problems.
- A ~400-person software-services company with 100+ client projects built survey-based AI-adoption measurement after analytics integrations with Jira, GitHub, or Slack required client approval. Its founder says the survey measures adoption, skills, and blockers—not productivity—and that testing so far has been free, with no paying customers yet.
- A commenter frames avoiding client approvals for telemetry as the product’s potential differentiator and identifies delivery leadership, rather than HR, as the likely buyer; the proposed validation is to run the survey across the company’s own projects for a quarter and see whether results help move a client renewal. Another commenter recommends asking prospective users what decision they would change after seeing the report—for example, choosing training or unblocking a client project—because avoiding integrations alone may not justify payment.
- For credible reports, a commenter recommends separating self-reported AI use from observed delivery changes, showing response counts and missing teams, avoiding sensitive client or project details in free-text answers, and defining who can see individual responses.
For a New Zealand startup organizing dinners for strangers, the founder reported 400+ attendees and weekly dinners since May, but relied on Meta ads and was running low on cash . A commenter assessed this as problem-solution fit without clear product-market-fit signals, citing stalled momentum and no viable revenue path from paid acquisition, and recommended iterating on distribution with a focus on organic word of mouth . Another suggested cutting ads temporarily and testing whether existing participants bring friends .
In an informal founder discovery thread about cross-platform publishing, respondents identified platform-specific tone changes and image resizing/cropping—not scheduling—as the work that consumes time; one said image formatting can take 30 minutes, and another said scheduling is already solved for them. A reply cautioned that content should fit each platform rather than assuming the same post belongs everywhere. Treat this as an early workflow-pain hypothesis, not evidence of broad demand: the post explicitly frames the question as testing whether the problem is substantial enough to solve.
For B2B software aimed at preventing repeat problems, one commenter recommends using an observable pilot metric instead of waiting to prove avoided losses: focus on a recurring issue the buyer already identifies, log each time a fix reaches another project before the problem hits, and agree with the budget holder in advance what count would qualify as success. Another suggests measuring whether captured lessons reach the right people in time—a result that can be shown in weeks—then positioning prevention as the later outcome. This addresses the founder’s stated challenge that prevention is hard to demonstrate quickly and that the teams contributing information, benefiting from it, and approving spend may differ.
One user advises against the Live Pure Bolt water purifier sold in India, reporting that its tapered, sharp knob is slippery and that the silver plastic housing near the drip “came out”; they cite poor build quality while acknowledging manufacturing considerations .
Supertake launched Buy the Dip for all users: when a larger position falls sharply below its purchase price, the product checks whether the original investment rationale still holds and, if so, uses the take’s cash reserve to double down within its limits. Autopilot can execute automatically; otherwise, it asks first, and users can deactivate the feature in settings. Scott Belsky described agents as less emotional and consumer investing as a strong use case.