Lessons from Cursor's $9 B Journey
Garry Tan from Y Combinator interviews Michael Truell, Cursor's CEO.
Below is a breakdown of the latest How to Build the Future episode, where Garry Tan interviews Michael Truell, co‑founder & CEO of Cursor (the AI coding IDE). Read on for the key points, why they matter, and practical take‑aways for anyone building software or AI products today.
1. Cursor in a sentence
Cursor aims to replace hand‑written code with "describe‑and‑build" workflows. Users already let the AI write 40–50 % of their committed lines; the north star is 100 %.
2. Why this interview matters
Fastest‑growing dev tool in years – Cursor hit $100 M ARR in 20 months and is now valued at $9 B.
Unique data moat – Millions of model calls each day reveal what code gets accepted, tweaked, or rejected.
A preview of tomorrow's IDE – Truell explains why they shipped a stand‑alone editor instead of a plug‑in (more on that in §6).
3. From autocomplete to "auto-everything"
Cursor's mental roadmap for handing work from humans to machines.
Cursor tracks paid power users — devs who rely on the AI 4–5 days a week — as its North Star metric.
4. Two real bottlenecks Truell calls out
Context windows – Mature SaaS codebases can top 100 M tokens; even million‑token models struggle.
Continual learning – Agents need fresh tests, feature flags, and design tweaks hourly, not via occasional fine‑tunes.
Cursor is wiring runtime logs, design files, and chat threads straight into the model's feedback loop to tackle both.
5. Why "taste" still wins
Perfect code generation can't decide what feels right—layout nudges, UX pacing, or subtle logic. Tomorrow's engineer is a "logic designer."
6. Business lessons worth copying
→ Strategic bets that let a tiny team outrun bigger rivals.
💡 Cursor still runs first‑round coding screens without AI tools; they'd rather train great engineers in AI than screen them out unfairly.7. Action playbook for product teams
Here are a few things Cursor does, that you could copy too!
Instrument everything. Capture every user action as labeled data.
Write specs in plain English first. Let an agent draft code, then study the failure cases.
Run "taste reviews." Pair code reviews with short sessions judging clarity, delight, and ethics.
Plan for multimodal context. Pipe logs, screenshots, and Figma frames into one agent workspace.
Onboard juniors as agent supervisors. Teach reasoning and prompt craft before deep syntax.
8. Founding story & mindset
Pivot from CAD to code – The team's first project was a CAD co‑pilot. They shelved it when they realised code had orders‑of‑magnitude more data and clearer scaling laws.
"Follow the line" mantra – They bet that language models would keep improving on a predictable curve and built for the capability ahead, not the one in hand.
Year‑long "wilderness" – Cursor spent ~12 months iterating with tiny user numbers before the product finally clicked and growth took off.
9. Moats in the AI era
Truell argues that distribution is the data moat: the more real‑world edits your model sees, the faster it learns — just like Google Search in 1999.
Closing thought
Programming is shifting from typing to directing. Cursor shows that the winners won't bolt models onto old tools; they'll rethink the tool itself and double‑down on the one thing machines still lack: human judgment.