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The PM role is being redrawn, unevenly
A Mind the Product episode went through Anthropic's internal study of how agents changed its work. Anthropic now has about 17 engineers per PM, and supervising and coordinating roles on technical teams "increasingly involved building and shipping code" . Anthropic's interview scorecards mention a candidate's AI-tool track record 3x as often for technical roles and 4x as often for non-technical roles in 2026 as in 2024. In technical interviews, the biggest increase was in comments about autonomy and judgment, while domain expertise took up a smaller share . The junior share of technical hires dropped from 27% to 9% . There is a caveat: Anthropic sells the tool it studied, and the engineers who adopted agents were already improving fast before they did . The host's takeaway for PMs is that coordination is the shrinking part of the job. What's left is deciding what's worth building, what "done" means, and writing specs clear enough for an agent to run on for hours .
A large r/ProductManagement thread showed how that is playing out. The original poster sees job openings that ask them to own the whole cycle, from product decisions to writing the code, and wonders if it's "a way for companies to get one person to do two jobs" . A commenter at a 200-person product/engineering org described the opposite approach. Engineers now run delivery and make more day-to-day scope tradeoffs, which frees PMs for discovery, go-to-market and impact monitoring . Another team tested letting PMs make small code changes. It found that handling the side effects cost Engineering and QA more than making the changes themselves, so they stopped .
More output, same blind spots
Several sources argued that cheap building makes judgment more important, not less. Jeff Patton says teams ship features without knowing whether anyone saw, tried or kept using them, and "the AI is not going to instrument that new feature… unless you ask it to" . Aakash Gupta suggests going from an idea straight to five prototypes, then writing the PRD for the winner as "a decision record more than a permission document." For AI features, that PRD should include a behavior contract of 15–25 example responses ranging from bad to amazing . Separately, he argues that a strategy's job is to help the team say no . MrBeast's CPTO, Shiva Rajaraman, sees the PM question shifting from rationing engineering capacity to "should we really be exploring 10?" He uses the extra capacity to build user-based alternatives next to the founder's own preference .
The other side of this showed up in a popular thread about a Head of Product who spends his days building internal tools and "automations" with Claude "that nobody wants" .
Pricing AI when value and cost both spike
In a SaaStr talk on how Clay, Figma and Higgsfield price, the speaker said that in AI products, about 50% of users often consume almost nothing, while 1% drive 50–75% of AI cost. The worst customer he saw burned 380x their revenue in tokens . His point is that a dollar of AI cost does not automatically become a dollar customers will pay . When value is spiky, price on outcomes. When cost is spiky, use fair-use guardrails . His throttling method works in steps:
- Group users by type and set a "kill point" where usage gets shut off.
- Start a slowdown at that kill point, then move it earlier so it reaches more users.
- At each step, watch whether users keep going, cut back, churn or upgrade .
From features to responsibilities
Hiten Shah argues that "the smallest useful unit of software may be shifting from a feature to a responsibility": customers pay for confidence that a job will be handled even when nobody is using the product . He gives Linear's Loops, introduced in July, as an early step. Teams can set Linear Agent to run recurring or event-triggered work and inspect each run, but in his view that is automation and doesn't yet take responsibility for the result . He cites a 2026 study finding that 68% of production agents ran ten steps or fewer before a human stepped in .
Also notable
- Legora raised a $550M Series E at an $8.8B valuation, betting that lawyers are moving "from AI assistants to agents that do multi-step work." It says 140,000 legal professionals use its products .
- Emet, launched with Deel, says the best AI model fully solves only 49% of real payroll tasks. It is publishing benchmarks written by in-country experts and scores every model against them, including its own .
Lenny highlighted the view that AI progress can look like big jumps from the outside despite a smoother progression internally . The linked interpretability discussion focuses on understanding Claude’s behavior and AI alignment and safety, and its teaser lists a finding that models plan many tokens ahead .
- Agentic workflow design: Centralize scattered inputs and responses in one queue, add integrations incrementally, and define each workflow’s end goal and when to escalate to a human. Combining first-party account history with external signals gives agents context for more informed responses.
- Tool strategy: The presenters recommend starting with one agent on a subset of use cases, extending it until gaps emerge, and adding tools as needed; they favor best-of-breed tools while capabilities are changing, while cautioning that the stack will keep evolving. They identify first-party data as a key advantage for improving agent output.
- AI-mediated discoverability: Benchmark how external agents recommend products by querying them cold, then address factual inconsistencies across sites. The presenters recommend making agent-relevant facts—including pricing and crawler/LLM-facing documentation—available, rather than relying mainly on opinion-led content.
- Anthropic’s internal analysis found that interactive agent sessions spent about 80% of their time waiting for a person; reading all agent output was estimated to take more than half a workweek for nontechnical teams, assuming every word is read. For agent products, the episode recommends making changes and decisions easy to scan, surfacing status and approval cues, prioritizing by consequence, and measuring shipped decisions or customer outcomes rather than output volume or commits. Treat productivity claims as directional: Anthropic studied its own workforce, and employees who adopted agents were already on a steep performance climb; more code does not establish business impact.
- Anthropic reports about 17 engineers per product manager, with technical coordination roles increasingly involving building and shipping code. The episode’s PM takeaway is to focus on autonomy and judgment—what to build, what “done” means, and specs agents can execute. Anthropic’s interview scorecards increasingly mention AI-tool experience (three times as often for technical roles and four times for nontechnical roles in 2026 versus 2024), while technical interviews put more emphasis on autonomy and judgment than domain expertise. The reported junior share of technical hires fell from 27% to 9%, raising a future senior-talent pipeline question.
- Evidence on AI and collaboration is mixed: an HBR study described trust and coordination risks when AI substitutes for colleague conversations, but gave no sample sizes; a Jabra-commissioned survey found 64% of heavy AI users said they collaborated more, while 57% said they did more administrative and coordination work. The episode suggests work design and visibility may help explain the difference, and recommends using AI to prepare for conversations rather than replace them.
- Product value may shift from isolated features to a “responsibility”: software monitors a recurring job, knows when to act or ask for help, and earns payment through confidence the job will be handled. Product discovery should map what makes customers open the product, where judgment is needed, and who handles failures; define a bounded task and evidence of success before promising more autonomy.
- Linear’s Loops, introduced in July 2026, lets teams assign an agent recurring or event-triggered work, such as investigating a bug report and creating follow-up tasks; people can inspect its instructions and run history. The article presents this as useful automation, but not yet responsibility for the result.
- Evaluate agent products by cost per verified result, including human review and recovery—not just cost per attempt. Improve checks for recurring errors, source verification, and escalation to people; sustained correctness matters more than a flashy demonstration.
- Test product replaceability by asking a coding agent to recreate a valuable workflow and trying to complete the customer’s job through an outside agent; examine what breaks in real use. Durable value may lie in authoritative records, transaction execution, or reliably completing a whole job, with pricing matched to what customers receive and the cost to deliver it.
For product leaders, a low-friction career-reflection exercise is to ask an LLM, “Given what you know about me, if I were to write a book, what would the title be and why?” The idea is that the model can reflect themes from what someone has shared over time; treat its response as a starting point, then use what resonates—or doesn’t—to clarify what matters and what they are building toward.
- MrBeast CPTO Shiva Rajaraman says Beast Industries began building a custom internal operating system about three months ago because off-the-shelf project-management tools did not fit its creative workflows; the team can now build tailored tools, including voice-based capture for staff on set, without waiting on vendor roadmaps.
- Embedded engineers prototype solutions with the functions they support, sometimes trying 10 options and discarding what fails; daily shipping enables feedback and iteration, but Rajaraman cautions that many uncoordinated releases can confuse users.
- Rajaraman argues that AI can shift product management from rationing scarce engineering capacity toward exploring more options, making judgment increasingly important; it can also help counter founders’ tendency to design for themselves by enabling alternatives based on user needs.
- Beast Industries is using AI as a shared organizational memory layer to capture production lessons and failure warnings, so new hires can access accumulated history; the knowledge can be reviewed and marked as broadly applicable or context-specific.
For AI-enabled science, Kevin Weil sees large-scale data collection through robotic labs as a major part of science’s future; Dave Ricks frames robotic labs as a way to make the still-missing data for disease research more tractable, while cautioning that AI-driven progress will take time and effort.
OSL demoed a prospecting agent that replaces fixed sequences with contact-specific plans, lets people review and coach those plans, and replans as engagement or account signals arrive; steps can vary by contact and reflect prior outreach and LinkedIn activity. The agent can use first-party CRM history alongside external signals: one example retrieved a fish-tank detail from a meeting transcript more than a year old. The presenters said to weigh signal recency and connect a personalized hook to the prospect’s business problem rather than rely on a personal detail alone.
A PM says they are encountering roles that combine product definition and decision-making with personally writing code and building the software; they ask whether this is a wider shift—potentially enabled by AI coding tools—or limited to certain companies and startups, or a way to combine two jobs in one role. The post raises this as a question, not an established trend.
- Assume AI-era features can be copied quickly; build differentiation through the full customer experience, distinctive data, and direct customer relationships. Teams that engage customers closely can better understand and improve the work the product supports.
- Clickable prototypes can accelerate discovery by giving teams something concrete to react to, but they are a starting point, not a finished product. Agree on how success will be judged before showing a prototype to customers, then continue iterating toward reliability and shippability.
- For AI products, break workflows into smaller tasks, distinguish judgment-based work from deterministic tasks, and build evaluations for what can be verified; measuring results and building a data practice enables improvement. The choice of model harness can also affect output quality.
Recursive Intelligence introduced Flint as a model for local agentic coding and said it expects much everyday coding to shift to models running on a laptop or a nearby server.
- AI products can have highly uneven value and costs: the speaker reports that about half of users may have little or no AI consumption, while 1% can account for 50–75% of AI costs. Flat pricing can therefore miss high-value customers while losing money on unusually costly users.
- Match the pricing approach to the curve: price around customer outcomes when value varies sharply; use fair-use controls to manage spiky costs rather than assuming every dollar of AI cost can be monetized. Model costs and customer behavior change, so the cost/value relationship can shift over time.
- For cost control, segment users, set a shutdown threshold and graduated throttles, then monitor whether affected users maintain or reduce usage, churn, or upgrade before extending the limits to more users.
- For agent products, price for the work or actions rather than the agent itself, whose activity and value can vary widely. Where affordable, first offer access behind guardrails to learn whether the product is useful; monetize recurring, defined actions later through credits or per-action pricing, and consider cheaper models for that work to improve margins.
Sumble’s agent-led GTM thesis is to shift AI from scaling outreach—which the speaker says has saturated digital channels—to precise account and contact targeting. He describes building that precision from granular signals such as job posts, financial filings, earnings transcripts, engineering blogs and employee podcasts, alongside accurate, up-to-date data. For agentic products, data quality is especially important because agents may act on implausible enrichment that a person would question; the speaker’s example is an inferred email sent to the wrong “Sam” among many employees with the same name. He recommends granular, daily account scoring and contact scoring as foundational workflows for using that data.
A paid-ad creative-testing playbook recommends setting a kill threshold before launch at 2.5× target CPA per creative, testing one variable at a time, and waiting until at least $75–100 per creative or three full days—whichever comes later—before evaluating results. Judge using the deepest event with meaningful volume (purchases, then add-to-carts, then landing-page views), not CTR alone; keep testing separate from the revenue-generating campaign and maintain 3–5 live creatives. The author’s budget rule of thumb is test budget divided by target CPA of at least 10; otherwise, increase the budget or test fewer creatives.
- One proposed paid test offered five lessons over a week for $14.99 with a 30-day refund, and set a pass line of 3 buyers out of 20, including at least 2 strangers; the creator’s only prior paying customer was someone they knew. But the proposed groups were 10 engineers the creator knew and 10 people they had already spoken with on Reddit, so the test did not cleanly isolate cold demand. A commenter recommended selling directly to genuinely unconnected target customers, while another cautioned that a tiny, non-random sample cannot support rigorous pass/fail criteria.
- For early willingness-to-pay testing, use actual payment as evidence, but treat a small test as qualitative learning: observe whether users reach the intended value and use that behavior to refine what value the offer communicates. One reply cautioned that a single payment can be encouraging without justifying a 200-hour next step.
Evaluate AI agents beyond their “done” message: assess whether they completed the job, how much the user had to fix, and whether the user would trust them again. Hiten Shah says he is using these questions to compare Muse, Grok Bot, and dots .
A product-research workflow for AI agents: check six competitors each morning, report only meaningful changes, link to evidence, and retain the previous day’s findings. Hiten Shah says he will cover whether Muse, Grok Bot, or dots is best suited to the job on Friday.
A candidate asked whether rewritten notes are allowed in product-sense interviews . One commenter argued that senior PM interviews should not test basic, junior-level capabilities, particularly for candidates with 10 years of experience; this is a commenter’s view, not an established interview policy .
- A SaaS Head of Product is described as spending time building unwanted internal tools and process automations with Claude; the poster says these can take more work than using an LLM directly and sees output displacing value maximization.
- Replies argue AI initiatives should be judged by user or revenue value and evidence of demand, with actual use and value measured rather than output volume. They distinguish experimenting with AI from circulating low-quality output.
- Commenters recommend tracking AI-related friction—including bugs, staff learning and workflow burdens, time, and maintenance—instead of treating automations as productivity gains. Replies also frame senior product leadership as setting direction and building teams, rather than personally maximizing output.
For product teams reacting to fast-changing AI capabilities, commenters distinguish useful agility from churn: Agile enables reprioritization as new information arrives, but cannot absorb unlimited target changes and should not mean abandoning sprint work unfinished. Constant pivots are framed as a symptom—not a feature—of missing actionable vision or strategy, clear objectives, or agreed priorities aligned with business outcomes.
