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Big Ideas
AI adoption is becoming an operating-model problem, not a software-procurement problem. Sachin Rekhi reports a widening gap: average teams are seeing a 20–30% increase in engineering velocity, while the strongest teams are seeing 2–3×. His four-step playbook is to build a shared Compounding OS, set an AI-fluency floor, redesign the product process so agents work alongside product, design, and engineering, and use behavior-change principles rather than training and mandates alone. The useful leadership audit is whether the team’s system, skills, workflow, and habits are changing—not merely whether people have access to AI tools.
“AI PM” is not one job. Jonathan Evens separates modeling PMs, who define model behavior, capabilities, and acceptable error rates, from AI feature/product PMs, who apply AI to a domain workflow and design the surrounding experience—the last 20% that makes a general model work for a use case. Using AI to improve a PM’s own knowledge work is simply PM productivity, not a separate role. For AI features, establish product principles and existing north-star outcomes first, then add side-by-side output comparisons and behavioral signals to the evaluation system.
Measure successful work, not hours saved. A controlled study compared 108 people using agents with 110 doing the same context-heavy tasks without them. Task success rose from 59% to 93% while completion time fell 38%, or roughly 2.5× more successful outcomes per unit of time; the report cautions that it does not yet show how those gains translate into business outcomes.
Tactical Playbook
Debug AI quality at the source. Teresa Torres describes a customer finding a flat branch in an AI-generated opportunity tree; the team spent three weeks building four evaluation metrics and testing 16 variations rather than applying a quick prompt fix. The operating rule is to combine evals, guardrails, and orchestration; calibrate judges against production data; trace upstream fixes for downstream regressions; and consider an agent that audits its own work. A practitioner’s more operational checklist includes cost per token, latency, payload size, structured-output errors, first-pass rate, real-user failures marked mandatory to detect, and broad regression runs—while remembering that a better eval score can still produce worse UX if it makes the product materially slower.
Case Studies & Lessons
Lightfield used customer pull to escape a successful but unsatisfying product. Its presentation product reached about 2 million users per month, but the founders stopped it because they could not see discerning professionals finding it indispensable; they also judged that missing context about the presenter, audience, and relationship—not general reasoning—was the core limitation. They found 12 B2B pilots among sales and marketing users, followed requests from decks into research, lead qualification, and account expansion, then discovered that conflicting CRM, call-recorder, and warehouse data was the deeper problem. A first go-to-market assistant had daily users but no pricing power because it did not own the underlying data; after restarting around a CRM, 10 startups used the rough product daily and sent feedback roughly every two hours. The lesson is to distinguish reach and usage from indispensability, pricing power, and ownership of the workflow’s core data.
Hinge demonstrates an outcome-led consumer strategy. Its North Star is great dates and getting people off the platform, not maximizing engagement; fewer than 15% of users pay, and monetization is reserved for constraints or accelerators while the free experience remains “sacred.” Hinge segments daters, identifies their problems, forms hypotheses, and does not ship a change unless it increases the chance of two people meeting in real life.
Career Corner
For senior interviews with founders or executives, use the final five minutes to ask questions that expose the real role: What will you continue owning after hiring the head of product? What is the one superpower you want this person to have? What are the one or two things that truly matter enough to require an 11/10? The framework is aimed at senior roles; it is not designed for a PM3 reporting to a GPM.
Tools & Resources
All Things PM is a current community-built resource derived from 604 PM job postings across 95 companies and 137 career boards, with an updating AI-PM curriculum, a 205-concept knowledge graph, and interview/resume tools. Treat it as a map of hiring language rather than a definition of the job: its creator acknowledges that postings can miss stakeholder management, judgment, and politics, and a commenter argues that JDs often diverge from actual workflows.