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Laurel built a 200+ row spreadsheet that breaks every function into Categories…

2026-06-28 · 19:59 UTC ·@aakashgupta ·2 min read

Brief

Laurel's spreadsheet (described by Aakash Gupta on 2026-06-28) converts one person's tacit AI workflow into a 200+ row Company OS: each task has a one-line description plus an explicit Negative Example that draws boundaries (Product split into five subfunctions). That labeled structure lets agents and new hires route Slack feature requests and enforces AI-first norms (Agentic: manual specs are off‑spec).

Why it matters

Laurel built a 200+ row spreadsheet that breaks every function into Categories and Tasks (Product splits into five: Feature Work, Strategy & Roadmap, Customer Research, Product Analytics, Voice of Customer) and adds a one-line description for each task plus an explicit Negative Examples column that defines wrong versions of the work (e.g., Competitive & Market Analysis negative: ongoing tracking of competitor release notes).

Key details

  • Every task pairs a positive and negative example so the table becomes a labeled dataset a model can use to learn boundaries; agents can read a feature request in Slack and deterministically route it to the correct function, task, and PM, giving new hires the same lookup answer.
  • The spreadsheet formally treats doing work by hand as off‑spec in some rows (Agentic negative example: 'writing a spec manually without AI as the primary driver') and underpins a $100M AI startup's Company OS workflow described in Claude Code (video timestamps include 2:04 Company OS GitHub structure, 14:31 playbook→agent pipeline, 50:08 AI Ops and the Sasha model, 59:01 four levels of AI maturity).
Cleaned source text

There's always one person on a team whose AI workflow is pure magic, and it dies in their head the day they leave.

Laurel fixed that with the spreadsheet in this screenshot. Look at the columns.

Every function gets broken into Categories and Tasks. Product alone splits into five: Feature Work, Strategy & Roadmap, Customer Research, Product Analytics, Voice of Customer. Each task gets a one-line description. Standard org documentation so far.

Positive example, negative example, for all 200+ rows. That is the exact structure you feed a model to teach it a boundary. The company turned its own work into a labeled dataset.

Now an agent can read a feature request dumped in Slack and route it: which function, which task, which PM, because the edges are drawn with negatives. A brand-new hire on the success team runs the same lookup and gets the same answer.

Then look at the "Agentic" row. Its negative example is "writing a spec manually without AI as the primary driver." Doing the work by hand is now the formally defined wrong version of the task. Off-spec, in writing.

That's the whole unlock. The 99% never had to become curious or AI-native. They needed someone to write down, precisely enough that a machine could check it, what good work looks like in their seat. One person's judgment, compressed into a row you can look up.

Most companies are still hunting for the tool that spreads AI across the org. Laurel spent the weeks instead, defining every function's work line by line, the wrong version next to the right one. The tooling was the easy part. This spreadsheet is the company.

Aakash Gupta (@aakashgupta)

She literally explained how her $100M AI startup runs completely out of an operating system in Claude Code:

2:04 - The Company OS GitHub structure

5:40 - The 1% vs 99% problem

9:00 - 3 steps to build your own Company OS

12:30 - Slack automation demo: feature request triage

14:31 - Playbook to agent pipeline

22:51 - Company culture needed

29:02 - PMs shipping front-end + back-end

29:44 - The captain model explained

32:37 - Continuation to captain model

37:38 - Two-track product reviews

50:08 - The AI Ops team and the Sasha model

57:59 - The screen-share interview

59:01 - The 4 levels of AI maturity

Video

— https://nitter.net/aakashgupta/status/2069873461636558856#m