Michael Truell: How Cursor Builds at the Speed of AI

**Michael Truell** (0:00)
We are in a market that's had an iPod moment, and it's going to have an iPhone moment, and I think that they're definitely more in the future. And we've tried to build a company that can continually build those things. I don't think the API providers really knew what to make of us, these four 20-somethings, and their thing now comprises a really high double digit percent of their API revenue, and now they're going to have to make capacity planning decisions, maybe financing decisions. I think that there's a big multi-product opportunity in our space, where there's a whole AI coding bundle to be built, and we want to be, for many of our customers, like the AI coding provider for them.

**Martin Casado** (0:36)
Today, we'll hear from Michael Truell, CEO of Cursor, on building the fastest growing developer tool we've ever seen, from taking down major cloud providers with their scale, to becoming double digit percentages of API providers' revenue, while still just being four 20-somethings. We discussed why Focus beat science fiction in the AI coding wars, how they maintained their infamous two-day work trials even at 200-plus people, and the strategic art of hunting all the sonnet tokens in the world. Plus, the Ouroboros question. What happens when the tool disrupting software is itself made of software? Let's get into it.

**Martin Casado** (1:12)
Thanks for being here, Michael.

**Michael Truell** (1:13)
Glad to be here.

**Martin Casado** (1:14)
He very, very rarely does these things. I had to beg. So I really appreciate you coming up.

**Michael Truell** (1:19)
No, wouldn't miss it.

**Martin Casado** (1:22)
Okay, so as everybody knows, Michael's CEO of Cursor, it's one of the fastest growing companies certainly we've ever seen. It's everywhere. It's crazy. You have to hire, operate through that. So actually what I want to do is dig into not the typical kind of founder journey, what brought you here. There'll be a little bit of that, but like, how are you handling the mayhem? Is that cool?

**Michael Truell** (1:42)
Sure. Yeah, no, that sounds great.

**Martin Casado** (1:43)
Okay, so to start off with, we'll just do a little bit of history. So I met with a company recently and they came in and they said, we are the 3D of Cursor. And I said, funny story, because Cursor was once a 3D company. Is that right?

**Michael Truell** (1:56)
Yes.

**Martin Casado** (1:57)
Do you mind talking about kind of a bit of the origin story?

**Michael Truell** (2:00)
Of course. So there's a bunch of different ways you could actually peg the start date, but effectively, the way the company got started was my co-founders and I, we were close colleagues from school and some other places. And two moments got us really excited about building a company. One was trying some of the first useful AI products. And in particular, trying GitHub Copilot, the incumbent in our space. And the reason this got us excited about starting a company is these products were actually useful. And this was the first existence proof of we shouldn't be working on AI in a lab. It's time to actually build systems out in the real world. And there's real useful things that you could be doing. The second thing that got us excited was scaling laws too. We got excited about how it seemed like even if the field ran out of ideas, the models would get better. And so this was around 2021, beginning of 2022
And then Cursor sort of came out of kind of a whiteboard exercise, where we were very excited about a Cursor for X for many different spaces. And what does that mean? We thought at the time that there would be, for a bunch of different verticals of knowledge work, the company that automates that area of knowledge work, a company for each space. And that company, it would do a couple of things. The first thing it would do is it would build the best product for that space. And it would define what the actual act of that knowledge work looks like as AI matures and gets better. And then with that product, it would win distribution, it would win a big business, and it would get resources like data and capital. And then it would back into being something that looks a little bit more lab-like, though not a foundation model lab, where it would start to use the data it gets access to, to actually work on the underlying models and kind of push the autonomy in the space. And then that would then in turn push forward the product and change what the best product looks like. You get this flywheel going. And so we were really, really, really interested in that. And we thought that Microsoft would do that for coding. And we wanted to work on a sleepy, more or less competitive space. And we had some colleagues who did mechanical engineering and we were familiar with CAD systems. And so there was this, yeah, initial start of working on working on mechanical engineering, actually, and working on models to help people be more productive within CAD systems and also building our own sort of CAD system. So that was how we got started. It was a bad idea. The founder market fit was horrible. There was this blind man and the elephant problem where we would hop on calls with Mackeys and ask them what they do during their days. And we only, we never really had an intuitive sense for it. I almost wish that in the kind of six, seven months where we were working on that, we had just gone and been interns at a company to really learn the space. But eventually, eventually we put that idea aside and kind of came back to the thing that we were really most interested in, which is working on programming.