ARR velocity does not prove a product moat
Cursor reportedly ran from $2B ARR in February to $4B by June. That curve proves demand like almost nothing in software history — and proves nothing about switching costs once models and interfaces converge.
A newsletter this week assembled nineteen details of Cursor's rise, and the headline pair is genuinely historic: reportedly $2 billion ARR in February, $4 billion within weeks of April. Company-reported figures, no audited filings, and the piece is paywalled — but even with generous error bars, this is among the fastest revenue scaling in the history of business software. My favorite detail is the small one: the product's early feedback loop traces to its first 20 testers. Twenty careful users, then a curve that bends like that.
Now the discipline: state precisely what that curve proves, and what it doesn't.
It proves demand — beyond any argument. Developers want agentic coding so badly they will adopt, pay, and expense it faster than procurement can spell it. It probably also proves learning speed: the 20-tester story suggests a team that metabolizes feedback unusually fast, and the enterprise mix reportedly growing underneath suggests real motion beyond prosumers.
What it does not prove is a moat. Revenue velocity is a measurement of pull, not of switching cost — and in this category the distinction is unusually sharp, because the core capability lives in the models, and the models are rented. The same frontier models power every competitor; the interface patterns converge within months of anyone's good idea; and the customer's data — their code — famously lives in git, portable by design. A curve like Cursor's is what it looks like when a team executes brilliantly into open water. It is silent on what happens when the water gets crowded.
The moat questions are the boring ones the nineteen details don't cover. Cohort retention: do February's customers still pay in August, or does each doubling mask churn under acquisition? Collaboration lock-in: does the product get harder to leave as more of the team uses it — shared context, review workflows — or is it N individual seats that can each defect alone? Proprietary context: does accumulated usage produce something — indexed org knowledge, tuned behaviors — that a rival cannot cold-start? Migration cost: what actually breaks if a team switches next sprint? And the brutal one: model-commoditization resilience — if the underlying models converge in capability, what premium survives?
At work this shows up as a buying question, not an investing one. A client asked me this spring whether committing to a multi-year enterprise agreement with a fast-growing AI tool was safe "because look at the growth". We wrote the moat questions into the procurement review instead: retention references from year-old cohorts, an exit-cost estimate, a data-portability clause. The growth number never appeared in the final decision matrix — pull tells you the category is real; it tells you nothing about whether this vendor is where the category settles. You can be a genuine phenomenon and still be the category's Netscape.
To be fair to Cursor: none of this says the moat is absent. Distribution at this scale can itself become one — default status compounds, and enterprise contracts have their own gravity. The point is narrower and it is about evidence: the ARR curve is consistent with both a durable franchise and a spectacular way station, and buyers, investors, and imitators keep reading it as proof of the first.
Steal this for the next hypergrowth deck you evaluate, as buyer or builder: cross out every growth number and see what evidence remains. Whatever is left — cohort retention, lock-in mechanics, proprietary context, migration cost — is the moat case. If nothing is left, you are looking at demand, which is wonderful, rentable, and shared with every competitor who can call the same model APIs.
Revenue proves the gold rush is real — the moat question is who owns the river when everyone has a pan.
