CHAPTERS
Why Laurel’s CPO interviews for “AI-pilled” product builders
Aakash introduces Jiaona “JZ” Zhang and frames the episode around a provocative idea: the most effective AI-native PMs operate like end-to-end builders. JZ previews how fundamentals stay the same while tools and operating systems change how teams execute.
Inside Laurel’s “Company OS” in GitHub: functions, folders, and skills
JZ screen-shares Laurel’s GitHub repo that acts as a companywide operating system, organized by function and phases of work. The key concept is turning repeated work into reusable “skills” that are discoverable and standardized across the org.
Making the OS real: Slack-driven daily briefings + Claude skills
JZ shows how the OS gets embedded into daily work via Slack, including a daily briefing pattern. Skills are uploaded into AI tools (e.g., Claude) so people can call the right asset at the right moment instead of reinventing decks/emails.
The “1% vs 99%” AI adoption gap—and how a Company OS fixes it
JZ explains the common adoption failure: a small group of power users builds great workflows, while most employees don’t know what tool/skill to use when. The OS converts individual experimentation into organization-wide leverage.
Step 1 of building your own OS: automate one painful workflow (feature request triage)
JZ outlines a three-step path to build an operating system, starting with a single annoying workflow. She demos a Slack automation that structures and routes feature requests to reduce back-and-forth and improve triage speed.
Step 2: from playbooks to agent pipelines (and why a ‘mega-agent’ matters)
Next, JZ shows how large playbooks become usable by translating steps into agents and automations. The breakthrough is creating a single ‘mega-agent’ interface that routes requests to the right sub-agent so teams don’t need to remember dozens of tools.
Tooling strategy: Dust vs. Claude + avoiding automation overload
JZ explains why Laurel initially used Dust for agent building and how that gap is shrinking as Claude improves. She also warns that scheduled automations can overwhelm teams, motivating consolidation into a single OS with curated, high-signal delivery.
Culture as the multiplier: hackathons, ‘everyone is a builder,’ and enabling non-technical shipping
JZ argues the OS only works with the right culture—leadership must signal it’s cross-company, not just engineering. She describes companywide hackathons and training that enable PMs and even Customer Success to ship production changes using agentic engineering tools (e.g., Devin).
PMs shipping real features: ontology of PM work and what gets automated away
JZ shares how Laurel redefines PM work to look more like engineering: feature work, testing, QA, backlog execution—augmented by agents. Meanwhile, historically PM-heavy tasks (competitive analysis, stakeholder writing, research ops) should be automated or delegated to workflows.
The Captain Model: assigning ownership by the most critical skill for success
JZ introduces the ‘captain’ concept: each initiative has a single accountable leader chosen based on the hardest part to get right (architecture, interaction design, content/business context). AI tools help non-engineers assess risk and pull in engineers for code review where needed.
Governance without bureaucracy: transparency, guardrails, and two-track product reviews
To prevent conflicts and quality issues, Laurel uses transparency (shared channels, visible PRs) and lightweight rules. JZ explains a two-track review system: small changes move fast with async checks; strategy/architecture shifts get rigorous reviews to preserve global coherence.
Strategy example: expanding the meaning of time + operationalizing ‘unreasonable hospitality’
JZ describes product strategy reviews focused on where Laurel wins and expands—time tracking beyond billable hours to broader ‘time as a resource’ across roles. She also explains how the OS can systematize culture, like ‘unreasonable hospitality,’ by prompting personalized customer delight actions using captured context.
Transformation playbook for non–AI-native companies: start small, build allies, scale 1% workflows
JZ argues every company will be forced toward AI-native velocity, but individuals can move first without CEO-level access. She recommends starting with small automations, building tools for other teams, rapidly generating playbooks with LLMs, and celebrating power-user workflows to spread adoption.
AI Ops as the new Biz Ops: the ‘Sasha model’ and CEO-level conviction
JZ explains how Laurel institutionalized AI adoption by creating an AI Operations function—full-time people whose mandate is workflow automation and tool-driven efficiency. She credits the CEO’s early conviction to re-architect the company around AI, while noting leaders at any level can still drive function-level transformation.
Hiring the future PM: small orgs, senior builders, screen-share interviews, and 4 AI maturity levels
JZ forecasts leaner product orgs with more senior, judgment-rich builders who love hands-on work and can ship end-to-end. She details her screen-share interview to verify real AI practice and outlines four levels of AI maturity from chat usage to shared apps and customer-shipped workflows.
