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Big Ideas
AI makes building cheap; PM judgment is the scarce layer. Sachin Rekhi says specs still sharpen thinking, but prototyping is a stronger forcing function because building surfaces implementation trade-offs faster. Run the Business sees the operating risk: AI-heavy teams that measure prototypes and features rather than outcomes create UX clutter and feature bloat; meaningful reps are evidence-based changes that move the product toward PMF, measured by how quickly the team moves from uncertainty to confidence. Aha’s Builder team makes the same shift: building the right thing is harder, so strategy and customer understanding increasingly determine PM value. Use AI to prune obvious ideas cheaply, then spend the saved capacity on deeper discovery—not on shipping every prototype.
Tactical Playbook
Put an agent through a task-level gate. Decompose a workflow into tasks and label each deterministic, AI-assisted, or agentic; compare the human cognitive load with the engineering, evaluation, and failure-handling cost. One PM estimates that roughly 70% of their workflows should remain deterministic. For structured manipulation, use ordinary logic; reserve AI for large volumes of unstructured data, pattern extraction, or transformation, and inspect where users export data or continue working outside the product to find higher-value opportunities.
Use evidence gates before a new product. Aha’s five-part process is: spark, picture-based concept validation, proof of concept, invite-only early access, and general availability. The concept test targets at least 30 significant customers in no more than 20 slides and asks about willingness to pay and adoption timing; early-access customers commit to usage and recurring meetings. For AI-enabled products, prototype the experience before designing the schema and back end: Aha reports that this phased order produces better outcomes than an open-ended prompt and can still complete in five or six minutes.
Case Studies & Lessons
PMF before sales scale. A pre-seed B2B founder reports paying customers after eight months of redevelopment but says PMF is unproven; advisers are urging heavy sales, while the founder fears acquiring customers whose staff will not use the product. The thread’s proposed operating plan is to model the current team, a sales hire, and sales plus a fractional CMO against runway and milestones such as retention, usage, expansion, and repeatable acquisition. Stage spend in batches: watch activation, retention, and feedback before increasing acquisition, or risk proving only that leads can be bought.
For marketplaces, seed the constrained side first. Five days after launch, Coloca had organic demand from room seekers but no available-room listings, no ad budget, and a strategy focused on existing renters rather than landlords. An experienced marketplace operator recommends one-city focus, manual seeding of the hard side, then marketing to fill the easier side; founder, family, friends, or multi-room units may be needed to create the first supply. Supply acquisition should therefore be an explicit product milestone, not an assumption behind the launch.
Career Corner
Move from PO throughput to PM judgment. A nearly 13-year PO already uses AI agents and Claude prototypes but asks how to progress into leadership. The recommended skill stack is stakeholder influence and negotiation, “context engineering” for product/user/problem/strategy documentation, and rapid discovery through personas, hypotheses, and prototypes; the AI-era differentiator is connecting people, process, and product while deciding what should happen, why, and when.
For a portfolio, show the product as a story rather than a static case study: user lens for the problem, founder lens for why and how to build, and the research–iteration–testing loop. A dual view—a four-to-five-minute skim plus a detailed build journal—serves both hiring managers and deep readers.
Tools & Resources
A lightweight bot-evaluation worksheet. Hiten Shah suggests starting with work completed this week that will recur; promising jobs include monitoring changes, assembling customer evidence, and turning signals into a brief. Before delegating, write down failure modes and the success bar, then give competing bots the same job and input for one attempt and score pass/fail. Useful-looking output is not enough to justify autonomous operation.