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The Summit's verdict: no playbook, but judgment wins
Lenny Rachitsky posted a recap of the Lenny & Friends Summit. Speakers took both sides on software factories, roadmaps, and whether PMs should ship to production. The one point of agreement was that there's no single right way. Ami Vora of Anthropic said "We don't know the answer. We don't think anyone knows the answer" . A second theme was that it has "never been easier to build something nobody wants." Robby Stein of Google Search said PM value now comes from "judging… taste," and Karri Saarinen of Linear put it as "The output is not the product" . Speakers also described the PM role as growing, not collapsing into a generic "builder." Ramp's Geoff Charles predicted that PMs "will become GMs" who own business outcomes .
The talks themselves give specific practices:
- Anthropic: the PM role still matters. Mike Krieger thought Claude could cover the work on a project close to launch. Once a PM joined, they handled the tasks that were about to be dropped: preparing customer success, looping in safeguards, and keeping enterprise users in mind. His view is that faster building makes this role "increasingly important" and requires more operational excellence than before .
- Park ideas the models can't handle yet, and keep an eval. Anthropic's first computer-use product in 2024 was "so bad." The team parked it and re-ran it in an eval harness with each new model until results jumped. Krieger counts turning a failed project into an eval as a win .
- OpenAI: plan 2–3 months ahead. Tara Sesha's launch bar is whether a product adds user value, retains internal users, and targets where the models will be in 2–3 months . She says predictions years out are "almost always wrong." A panelist added that slower markets like payments can still support annual plans . Nan Yu (OpenAI) named the other limit: "people's ability to absorb what you're giving them" .
- Linear: automate without losing what the work teaches. Saarinen warns that automating work separates teams from what they learn by doing it. Linear uses an agent to investigate bugs and draft fixes, which engineers verify. The time saved goes to customer contact . He also has an agent send him a daily briefing on what customers say about their AI workflows . To keep quality standards shared, everyone finds and fixes one defect every week ("Quality Wednesday"). Optional "feature roasts" collect blunt critique, on the view that if colleagues are confused, users probably will be too .
Writing is thinking: the case against AI-drafted docs
Aakash Gupta highlights Clay's new AI writing policy and predicts other companies will follow. His argument: PRDs were how PMs committed to a hypothesis and checked edge cases, so outsourcing the writing outsources the thinking . If you use AI, label it ("I used Claude for this. wdyt?"). Disguised AI writing is the worst case. Teams should put collective productivity ahead of individual speed . Shreyas Doshi agrees: if you have real clarity on a topic, writing a strategy doc yourself "takes way less time" and reads more clearly .
AI PM interviews are changing
Gupta lists recent changes in AI PM interviews:
- Timed prototype rounds, where you build in 45 minutes in Cursor, Bolt, or Lovable. He names Google India, Figma, Perplexity, Netflix, and Stripe.
- Google dropped its standalone technical interview. OpenAI made AI product sense a required round.
- Behavioral questions now probe technical trade-offs, such as model accuracy versus serving latency.
- Safety is tested: Anthropic has a dedicated round, and OpenAI works it into every round .
Getting promoted
In a new video, Shreyas Doshi says recognition depends on four things: scope, outcomes, outputs, and visibility. Companies weight them differently. He advises against joining companies that reward only visibility . To avoid surprises from a promotion committee, draft a plan covering those four areas and refine it with your manager ahead of the cycle. The same approach works if you're aiming for a higher rating rather than a promotion .
Practitioner threads
- Shipping cadence vs. announcement cadence. One B2B PM says fixes now ship as soon as they're ready, but customer release notes stay monthly. How often you ship and how often you tell customers are separate decisions. Admins filter out weekly bug notes, but they do want a direct message when a bug they reported is fixed . Another PM says the team now builds faster than customers can absorb updates, which means more release management and enablement work .
- Naming an in-product AI chat. One commenter argues that "Intelligence" promises judgment and raises expectations, while "Chat" or "Assistant" promises less and holds up better. The real risk is the first wrong figure, so show the source rows behind each answer .
- Switching AI providers. Changing the API is the easy part. What breaks is subtle behavior: a tool call skipped in edge cases, or valid JSON in an order a downstream step doesn't expect. One team mirrored real traffic to the new provider for two weeks and compared outputs daily before switching .
- Innovation isn't the goal. Teresa Torres and Petra Wille argue that chasing novelty leads to complex solutions. Simplifying and removing steps count as innovation too, and every new pattern costs users effort to learn .