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Feature factory 2.0
Three separate pieces made the same point: AI made building cheap, and that removed a constraint that used to force teams to prioritize. Casey Winters put it most directly. Code used to be expensive, "so you saved it for the best ideas." Cheaper code didn't come with a new way to prioritize, so teams now "ship too many ideas they shouldn't have" .
Ravi Mehta's fix is a formal curation step. The old feature factory was limited by build cost and kept going because of sunk cost. Now teams can produce more prototypes than customers have attention for . Before production or launch, teams should decide: "Is this worthy of the customer's attention?" . He also argues that PRDs and prototypes should no longer be a fixed handoff. Start with whichever artifact answers your most important open question, and let each one revise the other . On 30-page AI-written PRDs, the test is whether the person who generated it read it and agreed with every assumption before handing it to a team or an agent .
John Cutler's 10-year look back at his feature factory post goes further. Being a feature factory "isn't a 0/1 thing" . Problem statements, metrics and OKRs can be just as empty as feature lists, and promotion-driven development is common at companies that say they focus on outcomes . Sometimes shipping a list of features is the right call. His example is AppFolio, which copied the established features of property-management accounting while making bold bets on customer segments . He argues that escaping the factory is mostly a matter of investment, not mindset: customer and data access, design headcount, and promotion structures all cost money . The decisive moment often comes earlier, when the team is first funded and its business case is set . On AI, he says it amplifies bad habits and gives leverage to good ones. He describes one team whose AI-written PRDs cut out back-and-forth discussion, which he sees as a bad sign .
Agents: context, tools and trust
Hiten Shah tested agents on recurring work and found that context is what fails. Adding 16 competitive-intelligence skills made Claude cite its sources and say what it couldn't verify. It still couldn't tell which competitor changes were new until the skills were connected to shared market history . In another test, Claude in plain chat answered 1 of 5 sales-prep questions. The same prompt in Cowork with web access answered all 5 .
Aakash Gupta defines an agent as a model plus a "harness," the layer that lets it act, remember and stop. He argues that PMs should own the harness behind each AI feature, because it sets what the product "must never do" .
Agent checkout and payment tokens
On an a16z episode, a VGS speaker described Stripe Link completing a purchase at Walmart, where Link had no connection, by using a one-time virtual card. The purchase worked. But the customer's bank saw only a Stripe charge, chargebacks were hard to resolve, and a later Walmart+ upsell failed . Card-network "agentic tokens" pass the customer's own card through a verified agent, which keeps chargeback rules and follow-on purchases working. VGS says it has gone from zero to a few million tokens per week in three months .
Lessons from building a voice AI product
Teresa Torres's episode with Your360 AI covers how the team kept a voice coach on track. They replaced a single prompt with state machines, moved from Retell to LiveKit, and built evals from personas based on failed calls . Those personas are used to regression-test model and provider upgrades . Reasoning models got in the way for voice, where personality has to be traded off against time to first token . The median debrief now runs 44 minutes .
Careers and hiring
- Model PM pay: Gupta had AI pull open listings from 95 company career pages. He reports that model PMs earn a 60% median base-pay premium over PMs outside AI, with a similar gap at mid, senior and staff levels . Of 316 model PMs he reviewed on LinkedIn, 85% were PMs before, 64% have a technical degree and 2% have a PhD .
- Interview loops: One Reddit PM says take-home AI prototyping assignments are now common in hiring loops. When they asked how one would be graded, the only answer was "product sense" .
Slow-burn products
Deb Liu says Facebook Marketplace, now 10 years old, took six years to go from pitch to launch. It launched with a bug that made the New York Times, and it needed several more years to work before reaching more than a billion monthly users . Her point is that such products "don't look sexy in a performance review" .