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Factories, one silo at a time
In TBM 448, John Cutler argues that as AI threatens professional expertise, people are moving "one layer up." Instead of doing the work, they design, orchestrate and govern the systems that do it. He calls this a way to protect professional identity and possibly gain a new source of organizational power . Engineering is leading the "software factory" story because it saw the earliest measurable AI productivity gains. Cutler says it's an open question whether a factory model fits product development, where discovery, design, building and learning are deeply intertwined .
His main complaint is about boundaries. Engineering factories say little about what happens before and after code is produced. PM factories stop where software factories begin. GTM factories cover only their own slice . In his words, we're "reimagining the future of work one professional silo at a time" . His alternative is the cross-functional team as the unit of value, owning the problem, discovery, decisions and outcomes together .
"Liquid software"
Patrick Collison now adds an "improvements" box to his personal vibecoded apps. Whatever a user types is handed to an agent that implements it. One other user has already shipped 150 improvements in a few days . He asks openly whether the approach stays maintainable, how it would work for collaborative software, and whether it can be secure and reliable. For personal tools, though, he says it "definitely works great" . Hiten Shah replied: "This is the way" .
The PM job market is splitting
Aakash Gupta cites Live Data on worldwide hires. In February–June 2022, 52,664 PMs started jobs at a new company. In the same months of 2026, the figure was 29,223 (an early read). Over the same period, AI PM hires rose from 789 to 1,988 .
Gupta also summarized Google AI PM Director Jaclyn Konzelmann's five-question rubric:
- Pitch a beachhead product that feeds data into a larger platform .
- Your moat has been copied for free by a model update. Her rubric scores how you lead and talk to your team, not only your strategy .
- Describe failures from AI systems you actually shipped .
- Name the overlooked feature in a famous AI product .
- Build conviction without an A/B test, treating qualitative evidence as rigorously as quantitative data .
Gupta reviewed 316 public profiles of people in product roles at companies that train AI models. 85% were PMs before joining, 24% started their careers in product, and 2% have PhDs. His suggested path: own the AI feature closest to the model, learn to build evals, then move to a team where the model is the product .
When the signals contradict each other
Ravi Mehta's workshop case is loosely based on a real Tripadvisor story. Every A/B test wins and beats the holdout, yet overall conversion is falling fast . Neither number is wrong. His point is that you have to explain how both can be true before you decide anything. A second case, an AI tutor, asks what to optimize when better learning outcomes may cost user satisfaction and retention .
Hiten Shah argues that marketing has become a way to avoid changing your mind. Teams rewrite messaging, tune onboarding, then spend more, instead of questioning the product or who it's for . Because acquisition decides who becomes a customer, it also shapes product feedback: "attract the wrong people and you can spend months building exactly what they ask for." AI makes campaigns cheaper, but acting on wrong assumptions costs as much as ever .
Working practices
- Product and sales (ProductTank Auckland). Define the ideal customer profile (ICP) by the economic value the customer gets in their own P&L, not by your price or features . Before escalating a feature request, check deal size, strategic value, whether it repeats across a segment, and cost to deliver, because "one large prospect is not equal to a market need" . Hold individual requests at a gate. Only a recurring pattern moves on to product opportunity and then the contractual roadmap. Review the ICP and value proposition quarterly based on evidence .
- Shared agent memory. On r/startups, a team asked how to share Claude Code/Codex notes. The most concrete reply: treat the notes like code. Keep one AGENTS.md at the repo root linked to topic files, review changes in PRs, and date and justify every note . Keep machine-specific workarounds out of it .
- GenAI unit economics. A creative subscription app with about 4,000 users and 50 agencies can't break even without losing price-sensitive subscribers . The top advice: split contribution margin by customer type and usage decile. Test capped individual tiers and agency tiers with overages on new signups, and temporarily keep existing customers on current pricing . The founder rejected switching to cheaper models because the quality drop would be noticeable .
