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When the whole company runs on Claude
A senior PM three months into a new job described a company where "every single person" must use Claude heavily and usage is monitored. Questions get answered with "ask Claude." Engineers may only make small edits to Claude's output . What frightens them is that it works. In under a day they pulled user feedback and metrics, checked them against a new idea's assumptions, forecast outcomes, specified an MVP and published the PRD . A project now underway will feed surveys, feedback sessions and screen recordings into Claude to produce a prioritized roadmap. If it works, they expect the PM function to be cut .
The replies offered more useful ways to frame it than reassurance:
- What survives. One commenter said that at all-in companies they've looked at, two parts of PM work lasted: deciding what gets built, and checking output against what customers actually said. When nobody owns that second part, "quality drifts quietly for months." Their advice is to ask leadership whose name goes on the postmortem when an LLM-written spec ships a bug .
- Output isn't outcomes. Another PM said their company shipped 600 experiments in Q1 "and didn't change a single customer outcome" .
- Quality gates in practice. A practitioner pairs every agent with an "interrogation agent" that assigns confidence scores, sending low scores to human review. They test changes in batches of 10 before pushing 30,000-row updates, and they treat investigating mistakes as their own job .
- Where cuts may land. One commenter predicts product ops and junior PM roles will shrink, one PM will cover two or three squads, and senior strategic PMs will hold their value "because judgement becomes the bottleneck once synthesis is cheap." This is a forecast, not data .
Rekhi's list of process shifts
Sachin Rekhi lists what "AI-pilled" teams are changing. Specs are giving way to prototypes, which lose less in translation . Ad hoc research is becoming continuous discovery . PMs are moving from passing information around to building . Planning goes out a quarter at most, because capabilities can't be predicted further ahead . Acceptance criteria are being replaced by evals, which he calls "a necessary skill for all PMs" . Stripe's head of design added a counterweight: using AI only to make existing work faster misses the point. Her talk at Lenny's Summit urges having a point of view, encoding standards, and not confusing done with good .
Agents as buyers and builders
SaaStr gave Claude's beta Salesforce connector, Replit and Lovable identical prompts. All three got the facts right; they differed on what a seller should do next . Only Lovable ranked rescuing overdue deals first . Each tool asks for confirmation or limits itself to read-only access before writing back to Salesforce . The hosts also described a vendor's agent that deleted almost all of their Salesforce opportunities. A daily snapshot taken on Replit is what let them restore the data .
The bigger point for product and growth teams: an agent with app context picked Render for hosting without hesitation. For a CRM, it shortlisted HubSpot and Attio for a fresh start but advised against leaving Salesforce "without a clear reason" . The host calls this "agent-led growth" and urges vendors to monitor how agents rank them .
Buyers wanted the savings, not the AI
On a16z, Greer described pitching its AI reliability layer to customers who interrupted to ask about the 90% cut to their Datadog bills: "That's what we want to buy right now." Coding AI sells easily because engineering productivity is a large budget line. Operations value is harder to put a dollar figure on .
Careers
- Pay by AI focus: Aakash Gupta sorted 1,177 PM postings. Median base pay was $345K for model PMs, $258K AI-native, $231K AI platform, $222K applied AI and $216K outside AI, with equity excluded . Of 46 model PM roles, 76% mention evals, one mentions a PhD and eight ask for Python or SQL .
- GitHub as a signal: Gupta says only 24% of PM candidates have a GitHub, while his recent placements at OpenAI, Anthropic and Meta AI all did. What matters is one real-problem project, documentation that shows product thinking, and steady activity .
- Job search: Commenters describe slow hiring, a preference for candidates from the same industry, and referrals from the hiring team as "worth gold" . One person got a single interview from about 150 cold applications, while recruiter outreach led to interviews and offers .
