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Big Ideas
Prioritization is tension management, not item ranking. Teams get better signal by examining recurring tensions—where the same arguments return, conviction fails, and momentum wins despite better judgment—rather than debating items that will attract attention anyway. The key distinction is between prioritization judgment (what deserves investment) and prioritization-as-enacted (what actually moves); the missing ingredient is often commitment under present-day inconvenience, not agreement. Before applying a framework, run the fill-in-the-blank exercise to name the deferred threat, the “fun” priority consuming too much, the capability gap, and the credible 80/20 scope cut. Then choose which tensions to hold in the portfolio across time, value, and urgency.
Loops are execution architecture, not product strategy. A loop adds memory and a goal and can automate agentic work such as coding, but it is a poor mechanism for deciding what to build. Replacing users with AI can reproduce—and worsen—the Product Death Cycle: ask what features are missing, build them, and still have no usage. Use loops after a PM has made the human decision about the customer problem and the “what + why”; let AI execute, not choose the product.
Tactical Playbook
Turn feedback into evidence tied to a decision. Start with actual behavior, not compliments, opinions, or hypotheticals; design research around the question—churn, win/loss, buyer triggers, or problem discovery—and make it continuous. For each note, record the product area, pending decision, customer role, and behavior behind the comment. Review it with product and customer-facing owners on a fixed cadence, logging both the decision and the missing evidence. A reported B2B SaaS sequence layers incoming feedback → small qualitative validation (roughly 20–30 people) → quantitative survey → post-launch A/B test → production monitoring. The last two are stronger checks because they observe behavior rather than stated intent.
Case Studies & Lessons
Agent adoption changes both the product surface and the PM job. One agent-first marketing-software team defines L1 as AI-assisted search, L2 as running an agent in a cloud session, and L3 as continuously running agent sessions or teams; it says a pre-AI PM focused on wireframes and specs now needs to become L3. Agents are treated as immediate power users: good documentation reduces onboarding, while agent use exposed missing APIs and made 1%-of-audience experiments cheap enough to try. The team warns that rapid feature generation can create “slop,” so it fed years of product critiques into an agent to enforce a living product bar. For customer-facing agents, it reports training against five to ten use cases until reaching roughly 50–70% resolution and insists the product be usable on day one, not after a large implementation. The PM takeaway: pair agent access with explicit decomposition, strong APIs, cheap experimentation, and a quality bar.
Career Corner
Make progression legible through differentiated strengths. Shreyas Doshi names two stuck points: senior ICs unable to gain scope or team responsibility, and GPMs/directors unable to reach VP/CPO roles. For level-one stuckness, his 10-30-50 heuristic is top 10% in one skill, top 3% in a second, and top 50% in the third. For leaders, the core skills are strategy, influential communication, and editing—cutting, simplifying, and clarifying other people’s work rather than writing everything yourself. Choose two skills to compound, and replace some document production with “red-pen” work that raises team output.
Aakash Gupta argues that the old resume-plus-execution path has become a bundle of visible proof: referrals, fast customized applications, a portfolio, working feature prototypes, and AI knowledge demonstrated at work and privately. Build one role-specific artifact rather than only claiming AI fluency.
Tools & Resources
PM Superpowers is a free, open-source, MIT-licensed Claude plugin aimed at product thinking rather than faster PRD generation. It includes VRIO, pre-mortems, RICE/ICE, moat analysis, and decision logs; /strategy or a natural-language request walks through the work and saves structured artifacts. Try it for a pre-mortem before kickoff or log a decision while it is made, instead of reconstructing rationale from Slack or Zoom.