Big Ideas

Claire Vo argues that AI has shifted the PM bottleneck from deciding what can be built to having conviction about what is worth building: her own shipping capacity grew faster than her ability to find commercially meaningful products. She warns that clearing backlogs, shipping competitor parity, or abandoning ideas on noisy signals can create motion without meaningful progress on customer problems or business goals. Her alternative to a feature-and-date roadmap: set a durable conviction, define evidence that would confirm or disprove it, test quickly with real customers, and label each release a probe, experiment, or promise so commitment matches evidence. Lenny's Podcast

Practitioner reports suggest this shift is already visible in daily work: a senior PM at a European fintech says AI handles product and code questions, documents, benchmark updates, customer-feedback summaries, analytics, and ticket creation; another PM identifies discovery as the bottleneck before engineers run out of refined tickets. r/ProductManagement +1 Dan Shipper recommends separating frontier exploration from product delivery with a lab of one or two people. He suggests expecting to discard about 90% of its experiments and moving the promising work through internal use, early customers, and product review; gates include repeat usage, a sustained 10× improvement, and affordability at scale. Lenny's Podcast

Tactical Playbook

The Beautiful Mess calls the judgment built into old handoffs “positive friction”: rereading, reshaping, and challenging customer evidence forced attention, while AI can turn a few calls into a flood of derivative opportunities and tasks. Keep original feedback atomic and linked to its source; distinguish signals from interpretations, hypotheses, choices, and actions; at each transformation record what carried forward, what changed, and why. Label AI’s contribution, avoid summary-on-summary chains, and focus changes enough that customer feedback can tell you what worked. The Beautiful Mess

For a consequential decision, imagine it has failed six months later and ask what went wrong. One practitioner says the exercise surfaced worries people might otherwise hold back and increased confidence to make bigger bets. Make the prompt concrete: a commenter found that a real measure—30 new escalations a week with two people and no extra hours—elicited more specific risks than an abstract failure story; also ask which failure would be hardest to reverse. r/startups +2

Case Studies & Lessons

A Run the Business retrospective describes a vertical B2B release that drew negligible uptake despite strong marketing. It assisted experts in a critical workflow but did not fit how they worked in the field; its output was not meaningfully better than a human-in-the-loop, and the productivity gain was too small to drive adoption. The less obvious failure was “passive dissent”: some embedded experts thought about the risks but did not tell product or UX directly, partly because they did not want to derail the release or were unsure their input was welcome. The author’s remedies are to explain the reasoning behind a solution, keep dialogue with field experts ongoing, explicitly seek dissent and show a willingness to change course—and not ship the wrong call just to meet a date. Run the Business

Career Corner

For senior PM roles, Shreyas Doshi’s stage-fit heuristic pairs Explore with a visionary or craftsperson, Expand with a craftsperson and later an operator, and Extract with an operator; he says he did not pursue Stripe’s head-of-product role when the company needed an operator rather than his preferred craftsperson archetype. In interviews, use cognitive empathy to target the evidence each person needs: boards look for impact, execution, and complementary skills; cross-functional peers want understanding of their challenges and alignment; prospective reports want clarity, empowerment, and growth. Even an awkward question can be steered toward the underlying concern without dismissing it. Shreyas Doshi +1