A new engineer at DoorDash needed context on a customer decision from three months ago. Instead of pinging the PM and waiting half a day, she opened a shared repo, asked in plain English, and got the full reasoning in 15 seconds.
That repo is called a Team OS, and your team's AI adoption rate today picks which version you can run.
Less than half your teammates use AI weekly? Hub and Spoke. One power user runs the queries, the team gets synthesized output in Slack. Dave at Pendo built this around morning portfolio briefs.
More than half? Full Adoption. Everyone in the repo, everyone querying, everyone contributing. Hannah at DoorDash runs this with designers, engineers, the data scientist, and her strategy partner all opening PRs.
Ten or more people with engineering resources? Layer Agent Delegation on top. Gabor at Google built 21 specialized agents that review every spec from their assigned lens.
The progression on the chart describes how teams move over time as AI fluency climbs. Today's adoption rate sets where you can actually start.
The constraint sits upstream of the tool. Before you ship a Team OS rollout, count how many of your teammates use AI weekly. That count picks the model for you.
Aakash Gupta (@aakashgupta)
Hannah Stulberg, a PM at DoorDash, built a shared repo where her team checks in every customer call summary, decision log, and analytics query.
Last week a new engineer needed context on a customer decision from three months ago.
Instead of pinging Hannah and waiting, the engineer opened the repo, asked in natural language, and got the full reasoning in 15 seconds.
Hannah wasn't involved. She wasn't even online.
Every PM book tells you to make yourself indispensable. Hannah did the opposite. She freed herself from being the bottleneck and the team treated her as more valuable.
OpenAI made the same point in their February harness engineering post. That Slack discussion where your team aligned on an architectural pattern? If it isn't discoverable to the agent, it's illegible the same way it would be to a new hire joining three months later.
The numbers back it up. New hires take 6 to 7 months to feel settled. 47% of companies call institutional knowledge loss their top offboarding challenge. 10 context questions a day at 10 minutes each is 8+ hours of productive time gone every week.
I spent the last week studying four implementations: Hannah at DoorDash, Dave Killeen at Pendo, Gabor Meyer at Google, and Carl Vellotti building solo.
Four people, four companies, four different levels of complexity. They all converged on the same three-layer architecture.
Full guide is up with 6 downloadables, including a one-command skill that converts your personal PM OS into a team OS without leaking your personal context.
A personal OS compounds for you. A team OS compounds for everyone.
news.aakashg.com/p/team-os-c…
— https://nitter.net/aakashgupta/status/2052966308225142901#m