The Exchanges

Q So some people would say that is what we have today. You sort of describe what you want and out it comes. What would you say to that? Like, are we there yet? You know, what are the steps to where you really want to go?

A We're seeing the first signs of things really changing. Um, I think you guys are probably on the forefront of it with YC. Because I think that in smaller code bases with smaller groups of people working on a piece of software, that's where you feel the change the most. Already there, we see people kind of stepping up above the code to a higher level of abstraction and just asking essentially agents and AIs to make all the changes for them. In the professional world, I think there's still a ways to go. I think that the whole idea of kind of vibe coding or coding without really looking at the code and understanding it, it doesn't really work. There are lots of end order effects. You know, if you're dealing with millions of lines of code and dozens or hundreds of people working on something over the course of many years, right now, you can't really just avoid thinking about the code. Our primary focus is to help professional programmers, to help people who build software for a living. In those environments, people are more and more using AI to code. You know, on average, we see about people using, you know, having AI write 40%, 50% of the lines of code produced within Cursor, but it's still a process of, you know, reading everything that comes out of the AI. And so an important chasm for us to cross as a product will be getting to a place where, uh, you know, we become less of a p…

AI assessment note: “In the professional world, I think there's still a ways to go.”

Q What did you learn in that first version? Because you, you built a code editor from scratch. You guys haven't done the whole forking yet.

A Yeah. We had the fear of God in us. I mean, we had, people hadn't, hadn't really liked some of the things that we had built for a while. So I think that, you know, we were kind of all in on it and very focused, but what did we learn from that? Um, I think that we learned kind of the first initial set of AI features where, you know, when we started, I think that there was just one key command, and it pulled up this, like, universal remote in the editor, and then you asked it to do something, and then entirely the AI would just figure out, oh, do you, what, what, what exactly do you want it to do? Um, you know, do you want something back that's like a chat response, or do you want, um, like a code suggestion that you can then take, or do you want it to go search around your code base and answer a question, or do you want it to go spin for a really long time or a short time? And there wasn't a lot of control, and I think that we learned, you know, given the tech at the time, Um, at the end of twenty-twenty-two that you actually, it has to, the form factor has to look a bit different, and so we learned kind of the first early AI features that then became part of the core of Cursor from iterating both for ourselves and also giving it to people. I think another thing we learned was, you know, we were very rapidly building a feature complete version of what we want in a normal code ed…

AI assessment note: “we learned kind of the first early AI features that then became part of”

Q And then you realized, Actually, we need to do something else. Like, what was it to actually pivot it to, you know, what it is today?

A It wasn't a straight B line. Um, we, I mean, being programmers ourselves and being inspired by products like Copilot and, uh, also papers like the early codex papers. I remember at the time, one of the things we did to justify to investors that they should kind of like invest in our crazy cat idea Is we did the back of the envelope math for what Codex, the first coding model, costed to train. From my memory, it only cost about 90 K or a hundred K by our calculations. That really surprised, surprised investors at the time and was kind of helpful in us getting enough money to, to pursue the, uh, CAD idea where you had to start training immediately. So we always knew about coding. We were always excited about it. We were always excited about, you know, how AI was going to change coding. We had a little bit of trepidation about going and working on that space. Because there were so many people already doing it, um, and, uh, we thought Copilot was awesome, and, you know, there were dozens of other companies working on it too at the time. When we decided to put aside CAD, which was a little bit of an independent idea, that was sort of the science not really working out, us not really being excited about that domain, the thing that drew us back into coding was our, our personal interest, and the thing that gave us the confidence then to continue with it was, one, seeing the progress t…

AI assessment note: “the thing that drew us back into coding was our, our personal interest”

Q Then fast forward to the founding of Ennisphere. It's an interesting name because Cursor is not what it is. When you guys started, you had just, um, graduated MIT, right? That was back in twenty-twenty-two? What were the first ideas that all four of you started working on back in twenty-twenty-two?

A Yeah, so the, the genesis of Cursor was in twenty-twenty-one. Uh, my co-founders and I, we had been interested in AI for a long time. Each of us kind of had our own little robot dog moment where one of my co-founders, he worked on, uh, trying to build a competitor, Google actually, uh, using LMs in, in twenty-twenty-one and, and training his own, um, and training his own contrastive models. Uh, one of my co-founders, uh, worked on computer vision in academia and, you know, some of us also worked on recommendation systems at, at companies like Google. But, uh, we were really interested in AI. In 2021, we were trying to figure out what we'd do with that interest. Do we go and work on AI in academia? Or, you know, do we go join, you know, a big existing AI effort? Or do we start our own thing? And there were two moments that really got us excited. One was seeing the first AI product start to come out. Uh, you know, GitHub Copilot was really the canonical example for us. The other was seeing work about how it looked like AI was going to predictably get better in the future as you scaled up these models. At the very beginning of twenty-twenty-two, uh, me and my co-founders, we went on a, like, a month-long hackathon, basically, and we started hacking on ideas related to kind of picking an area of knowledge work and building what it looks like as AI gets more and more mature.

AI assessment note: “we started hacking on ideas related to kind of picking an area of knowledge work”

Q Who were some, some of the first hire? I mean, I see more engineers, but you know.

A So we agonized over the first hires, and I think that if you want to go fast on the order of years, actually going slow on the order of, you know, six months is super helpful because if you really nailed the first 10 people to come into the company, they will both accelerate you in the future because when, you know, the nth person comes in that's, you know, is thinking about working with you, comes and hangs out with the team, they'll just be shocked by the talent density and then really excited to work there. And then the other reason they can help you go faster in the future is if someone comes in and they're not a great fit, these people act as an immune system against that, right? And they will be kind of keepers of holding the bar really high. And so we hired very, very, very slowly at the start. We were able to do that also partially because we had such a big founding team and all the co-founders were technical, but yeah, the people we got, uh, uh, are fantastic and are really core to the company today. And Folks who bled across disciplines where we are this company that needs to be something in between a foundation model lab and a normal software company. And the models and product have to work together under one roof. And so we had fantastic people who were, uh, product minded, commercially minded, but had actually trained models at scale.

AI assessment note: “Folks who bled across disciplines where we are this company that needs to be”

Q Now I guess one curious, uh, now shifting gears a little bit about what are your thoughts in terms of how the future is going to look with, uh, coding?

A We were kind of this, this, Maybe middle road bet from the start, where when we set out to work on the company and we were kind of hiring our, our first people, we would get these weird looks around, you know, why are you, I mean, at the end of 2022, it wasn't really like this, right, because kind of ChatGPT happened and then the whole world woke up to things, you know, beginning of 23, but especially during 2022, uh, when we were working on the CAD stuff and then the early code stuff, um, people thought working on AI, uh, was, it was kind of weird to do. People were not entirely convinced that it was a good use of time and that there were going to be lots of great applications to follow out of AI. And then even the people who are interested in AI There was, I think, in our space, you know, a bunch of people that were just focused on optimizing kind of the form factor that exists already, um, and just making those products a little bit better. And then at the same time, you know, in our social circles and professional circles, there's a bunch of people that, you know, were thinking, oh, why would you work on anything other than AGI? And, you know, all of the work that you're doing right now in one or two years, you know, circa in 20, 22 is going to go away. And Yeah, I think that we've always had this view that there's going to be lots and lots of, um, incredibly valuable thing…

AI assessment note: “we've always had this view that there's going to be lots and lots”