Big Ideas

Depth beats AI model FOMO. Sachin Rekhi argues that social media rewards hype about frontier models more than guidance on making existing tools work. His recommendation for most PMs is to choose an established tool such as Claude Code or Codex, then go deep: automate workflows with skills, make company context machine-readable through MCPs, build design systems, and add a semantic layer for data analysis. The practical implication is that durable advantage comes from compounding workflow and context integration—not from repeatedly switching tools. @sachinrekhi

Discovery is extrapolation, not transcription. Teresa Torres highlights a familiar trap: customers describe the problem directly in front of them, not their “wildest dreams.” Use the literal request as evidence about the customer’s job, then reason from the persona and adjacent workflow to predict what they will need next before they can articulate it. @ttorres

Tactical Playbook

Use progressive refinement so AI speed does not harden bad assumptions. A useful process from this period’s requirements discussion is:

1. State the product thesis, desired outcome, and constraints; test the thesis with the target persona. r/ProductManagement +1
2. Prototype early. A user story is a conversation prompt and hypothesis, not proof that users want the feature; only customer testing can establish that. r/ProductManagement
3. Once the flow holds up, add functional requirements, acceptance criteria, and edge cases. Keep early artifacts loose enough to change. r/ProductManagement +1
4. Keep one backlog and a short engineering review. Product owns the user problem and desired experience; a tech lead or engineer decomposes the technical work and reviews feasibility. r/ProductManagement +1

This preserves the speed of AI-assisted prototyping without turning cheap, premature requirements into expensive rework. r/ProductManagement

Case Studies & Lessons

SaaStr reports that agentic go-to-market execution can scale a very small team—but not by removing humans from the loop. The company says three humans now operate 21 agents and that sponsorship revenue doubled in 12 months; it attributes a 60% increase in new business to inbound agents. The inbound system handled almost 3 million website sessions, 17,000 conversations, about 600 meetings, and influenced a couple million dollars of pipeline. SaaStr AI

The design choices matter more than the headline: visitors can choose AI chat or self-serve; the system uses first-party signals and behavior data to generate a tailored pitch, waits ten minutes to observe engagement, and routes the opportunity using similar-account context. On outbound, agents send only one to three initial emails; a human then sends the customized deck and follow-up. SaaStr AI SaaStr also says it built internally because third-party tools could not use enough proprietary context, and recommends stair-stepping the system rather than attempting everything at once. SaaStr AI The transferable lesson: automate coverage, context gathering, and routine follow-up; retain human judgment where messaging and commitment affect the customer relationship.

Career Corner

Build an agentic-workflow portfolio piece, not another model demo. One B2B SaaS PM with eight years of IT experience reports that basic AI-PM training did not prepare them for the agent, multi-agent, and agentic-workflow experience appearing in the listings they see. A stronger project brief is to choose one B2B process with handoffs, map failure points, build a small agent with human approval and an evaluation set, and publish baseline versus post-automation time, error rate, and intervention rate. That makes product judgment—and not just familiarity with models—visible to hiring managers. r/ProductManagementJobs +1