How I Built a $1.3B Startup by Pivoting Fast | Windsurf, Varun Mohan, Co-Founder & CEO

1. 0:00 – 1:37

   Opening

   1. VMVarun Mohan0:00

      I think startups are basically like getting slapped in the face probably over and over again. That's basically it. I also, you know, interestingly, I, I actually like failures a lot. One of the things I hate the most is doing something and not knowing if it's working or not, and it's actually very, very freeing to know when something fails because that's very obvious. I like when decisions are obvious. Like, when something fails, it's obvious you need to do something new, and the faster you fail, actually, the faster you can decide to do something new. And I think for a company like us, like we're at the frontier of the technology, we should be imagining we are failing at a lot of initiatives. In fact, if everything is working, we're operating at less than potential of the company. We should be failing. Like, that probably means we're not betting, we're not taking enough bets if we're not failing enough. I, I think it's like a required part of a company that you, that you fail. I'd say, like, be more humble about your ideas, and I wouldn't say that I was arrogant at the time, but probably a lot more idealistic about our ideas being correct. And you need some amount of idealism, as I said before, but at the same time, it's that there's no reason why an idea is great if you haven't, like, validated it. Be honest with yourselves about, you know, the viability of your idea and be willing to pivot very quickly. I feel like a lot of, a lot of founders hate the word pivot, but it's, it's awesome. Like, it's awesome. You know, what sucks more is, like, doing the wrong thing and just failing. Even right now, I would say one of my regrets is probably not doing the pivot three months earlier. Hey, I'm Varun, CEO and co-founder of Windsurf. So Windsurf is an AI-powered IDE, uh, that provides agentic capabilities that enables developers and non-developers to build apps really quickly and also modify existing applications really quickly. A million developers have actually used the product. We have hundreds of thousands of monthly active users on the, on the product right now, and it's growing, you know, exponentially faster with time.

2. 1:37 – 3:10

   Why I Chose Smaller Companies Over Big Tech

   1. VMVarun Mohan1:37

      So I graduated from MIT in 2017, and after that, I actually worked at an autonomous vehicle company in Mountain View called Nuro. There, sort of got an early taste of what deep learning could do for other industries. Started the company actually in 2021, so it's been four years. At the time, the company didn't do anything relating to code AI. It actually built out GPU virtualization and compiler technology. We did that for over a year and a half and were able to get to a couple million in revenue. We had eight employees at the time. One thing changed very, very materially in that time. In the middle of 2022, GPT-3.5 came out at that point, and we thought that generative models would fundamentally transform many different industries. And we needed to make kind of a pretty big decision at that point to pivot the company because we fundamentally felt everyone was gonna run generative models for everything. And at that point, we believed a lot of the value would accrue to companies that were applications. Almost like in the early days of the internet, the companies that proved to be very valuable were companies like Google and Amazon, and we wanted to see what would it be like if we could build the next Google or Amazon. We were early adopters of a product called GitHub Copilot, and from there we actually built out a product called Codium, which was an extension that lived in all the major IDEs, and we were able to get that to over a million users as well. Um, and we ended up serving some of the world's largest enterprises, companies like JP Morgan Chase. Very quickly, though, sort of middle of last year, we realized that we needed to control more of the experience, especially as these models became more and more agentic, and that was why we decided to build our own IDE, which was Windsurf. That has taken off very quickly as well.

3. 3:10 – 9:47

   Killing a $28M Business to Start Over

   1. VMVarun Mohan3:15

      It's actually kinda interesting. I think MIT is very different than Stanford. I think most people don't go to MIT to kind of immediately start a company. I don't think my aspirations were to start a company. I did progressively intern at smaller and smaller companies, so I first interned at LinkedIn, then after that Quora, uh, and then Databricks. Databricks at that time was a very small engineering team. They were not even a unicorn at that time. But I think I didn't actually end up working at any of these companies because I think the thing that motivated me was: Can I be a meaningful part of a visionary company working with motivated people? That's always been what's driven me, and that's sort of why immediately after MIT, I decided to go work at an autonomous vehicle company. I thought that could be the future of where just robotics was going to go. And I think for me and probably the other people at the company, we wanna work on the future of technology, and that's what truly motivates us. I think the moti-- the experience is just how do you build products in hard technology spaces where the technology is not there yet? You know, I'll, I'll give like a quick just some interesting numbers. You know, when we started, obviously deep learning was, was pretty popular, uh, but the, the amount of compute that these models had access to grew exponentially year over year. You know, just some numbers here. In 2017, the number of teraflops on a consumer-grade GPU was ten. By the end of 2022, it was actually seven hundred. So it's a massive increase of in compute that happened, and I think what we learned was machine learning models were gonna get more capable very quickly, and you should not bet on where the technology is today, but where it could be a couple years from now, right? And if all you're doing is working on products that work today, you're gonna be quickly irrelevant a year from now. And that was like a very important lesson that we, I think a lot of, a lot of people that were in the autonomous vehicle space kind of learned, which is now if you were to look at autonomous vehicles, they're much more machine learning-based than they were probably five or six years ago. Originally, we started out, the name of the company was ExaFunction. Part of the reason why we called it ExaFunction was our goal was to virtualize GPU computations to make it easier to run things on GPUs. And the, the term exafunction means we want to run an exa number of functions, which is ten to the eighteen functions. I think the biggest learning lesson for us was, at, at the time was even if we were succeeding by some metrics, we were making some revenue, just accepting that, hey, like the business might not be the best business and we need to go pivot was a very hard thing to do, I would say, right? It's very hard when you have like employees, you know, you've already raised some amount of funding. I think at the time we had raised over twenty-eight million dollars of funding to basically start from scratch overnight. Uh, but one of the learning lessons that I've sort of had, and probably the other people at the company, is every time you do a pivot, you have an opportunity to maybe 10X the size of the company. And, uh, usually when you have an idea that you don't believe in and the ceiling is low, it's actually better to just scrap the entire thing than to like try to incrementally claw and, you know, increase the value of what you're doing by like, you know, twenty, thirty percent. It's not gonna matter in the grand scheme of things. So we had gone to a couple million in revenue at that time. I think the hard part for us was it felt very ad hoc how we were adding revenue to the business. It also felt like with the advent of these generative models, a lot of the complexity of running models would become commoditized. If everyone is gonna run transformers, uh, the transformer model architecture, what is the reason for us to have a platform that virtualizes arbitrary GPU computations? It's not as important anymore. So it's a factor of two things. We didn't understand how we get 10 or 100X sales, right? I think you build a company not to make a couple million in revenue, but billions of dollars in revenue, so we didn't understand how to even get within an order of magnitude of that, right? That was one. And two, we also felt that the problem we were solving with the advent of the generative models was going to get commoditized. So at that point, it's kind of an easy thing. Once you believe that something is not gonna be big, you have no other option but to change your mind and do something new. That is very painful because you did commit a lot of time and a lot of energy and passion to one thing. You don't win an award for doing the wrong thing for longer. I guess me and my co-founder did a walk over a weekend, and I guess we decided over a weekend, and we told the team on Monday. Um, and then everyone started working on the new thing starting Monday. Uh, yeah, I guess we just... You know, one of the core beliefs that we have about startups is startups can, if they're lucky, do one thing really well. We could not afford to have the company believe two things were important, so we would need to rip the Band-Aid off and actually make it very clear to the rest of the company that the new thing was where we thought the future was. You know, it's interesting. I w- we were not worried at all when we did the pivot. We had basically written off the company is gonna be a zero, and at that point, anything is greater than zero. Ship whatever you can. If it sucks, you're s- in the same place that you are. If it doesn't suck, like, great, awesome. Only upside. I, I think just honesty, transparency, and int- uh, like, intellectual honesty with the team is maybe a core tenet of the company. We don't do things unless we, like, truly believe they are the right thing to do, even if it, like, fits some narrative that the rest of the industry or our investors or convenient belief. We believe things if from first principles they're correct. And, you know, I guess one true fact was after telling our employees, there's a real chance that some of them would leave. But I think, like, when you build a high-quality culture where the people are, are intellectually honest, um, good people don't wanna leave if you, if you chart a path for what the thing that does work is, right? Because, you know, one interesting thing about startups is people talk a lot about moats. What's the moat of a company? But really, what is the moat of a company that has 10 people? You know, w- if you do have a moat, it's very shallow at best, right? The number of engineering years that went into your product is already very small. So the real moat is you work on the right thing with enough people for long enough. That's, like, the only moat a startup ultimately has, and that's, like, why you believe in a team. Obviously, when a company becomes thousands of people, hopefully the moat is not the current business. It would be sad if the moat was the company can just pivot and do the next thing. In that regard, I think if you genuinely believe the moat of a s- of a company is betting on the right things and, and hiring a great group of people, then if you are intellectually honest with your team and tell them, "We don't believe in the strategy we just had," it's-- should be totally fine with everyone.

   2. SPSpeaker9:03

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4. 9:47 – 15:48

   Zero to 1M Users in 2 Months - The Growth Engine

   1. VMVarun Mohan9:50

      I think we were early adopters of a product like GitHub Copilot, so we-- and we were-- we actually built a lot of infrastructure. So at the time, we thought that was the tip of the iceberg of what the technology could actually build. We thought a lot more could be built in this category, right? If all people were doing was autocompleting, we could potentially see a world in which entire PRs would get generated. And at that time, we were like, "Okay, the time it takes to build applications probably has gone down by a double-digit percent. Why don't we set the goal of the company to reduce the time it takes to build technology by 99%?" It's a very ambitious goal, not something we'd be able to do in the next year or two, but if we set a very ambitious goal, there's probably a lot of work to be done in this space. We believe that technology's always gonna get better. We believe the models are gonna get better. Because of that, we just believed there was a lot of opportunity here. Because we were also an infrastructure company at the time, we were able to train our own models and run models ourselves, which enabled us to build an extension that was entirely free in all the major IDEs, and, uh, that was able to very quickly get to hundreds of thousands of users. It was a combination of what our skill set was, also a belief of where we thought the future was going. I think it took us less than two months to build the MVP and ship it out. I think as a company, we're probably more of the opinion of if you are gonna do something hard, um, it is actually important to have intermediate goals that you can actually give to other people to validate your hypothesis. That's maybe something that we learned. A lot of us at the company were previously from autonomous vehicles, and I think one of the biggest mistakes that autonomous vehicle companies might have made is, maybe to no fault of their own, was they built a hard technology where it's very hard to validate if the technology is good in the middle, right? So for us, it's always been it is actually a feature if you can have a very, very, like, visionary goal but have very tractable intermediate steps. And so we actually picked a very tractable intermediate step to deploy, which was, can we build a basic VS Code extension that had autocomplete capabilities, uh, with our own model? And that was, that was the intermediate goal that we had, and we believed... You know, maybe it was, like, a little bit of necessity. We just pivoted the company. People were not working on anything else. There was an existential dread what the value of the product was, so we very quickly built it. I think a basic form of product-market fit was when our inbound for the product from, like, companies was, was more than, like, any of us could handle, and we did need to hire a real go-to-market team. I think at that point we realized, hey, we're actually solving a real pain for the market, and we are providing a lot of value. So that was... That happened very quickly. Like, within months of the product coming out, we had a lot of companies reaching out to how could they run the product, uh, for themselves and, and use it for their large code bases. Yeah, I think the word product-market fit is probably something I don't like a lot because it, it creates a lot of, like, overconfidence. I think what, what you have as product-market fit in one day, if you don't continue to innovate and you have competitors, is very quickly going to become a commodity where you don't have product-market fit. So you need to be very paranoid. I always like to tell the company we're probably going to fail, and I think the good thing about telling the company we're probably gonna fail is it creates enough, uh, sort of energy in the company to find the next thing that keeps you a, as a differentiated product and, and survive in a, in a space. But I think in general, like, product, like, I, I feel like if you have pull from the market, it's very hard to fake it. It's, it's very obvious when you have it and very obvious when you don't. And I'll tell you this, like, even when we were making a couple million in revenue with the GPU virtualization business, it's very obvious we didn't have product-market fit. It's not a scalable company. You don't really understand how you can grow revenue and find, find a way to, like, provide more value at scale. So very quickly, I think a lot of large companies were starting to reach out to us for a variety of reasons. For security reasons, because we were able to run models ourselves, we were able to run it in a secure way. The other thing is they wanted the systems to work with their complicated sort of code bases, right? Their code bases are not simple. Some of our customers have tens of millions of lines of code in a single code base. And we started building more and more technology to make it possible that we would give very personalized suggestions to them regardless of where they stored their code. I guess very quickly we started to get to 100 customers. It went from going from zero to 100 customers probably within months, um, at the company. I think the feedback that was kind of interesting to us is how important just small details were, right? To the entire user experience. We started off as an infrastructure company, and we needed to kind of become a product company, which is a big kind of change. You know, basic things like the latency of the product, how quickly we provide suggestions was quite important. You know, just basic things. If we do too much computation on the user's machine and it kind of, like, makes the machine too slow, that's an unacceptable user experience. That is not something we would think about if we were purely a server-based application. So there were kind of new things as a company we need to learn because the space we were operating in was fundamentally different. I, I think basically what happens is once a company gets bigger, it's possible that you start losing the reason of why you had a good product to start with, right? You start listening. Actually, in some ways, too much customer obsession might be bad in that if you do things that all of your customers ask you to do, sometimes you might, like, incrementally iterate to actually build a really bad product, and it's actually better off to completely change the paradigm in a way that your customers don't really ans- ask for because you, you suspect it could be helpful. So there's, like, a little bit of a healthy amount of don't just listen to random people about what you should build. Listen to your customers, but don't listen to your customers in how you should build it. There could be a way you could build it in a way that is very transformational but not something that they would even expect. One of the things about our product is we have a pretty large individual product, and we have ways in which our users can communicate with us, you know, either online, on social media. Uh, and I guess I, I look through it fairly frequently to understand what the pain points our users have about the product. Also, at the same time, I guess, like, everyone at the company uses the product almost day to day, which is maybe a unique aspect of the product, right? There are probably a lot of AI tools out there, but the people building the tools are not using them all the time, whereas the people that build our products literally use our product to build the product. Whether it be Windsurf, everyone builds software on Windsurf for Windsurf, uh, which is a unique way for us to actually get feedback. A lot of our feedback is internal, and if no one at the company likes a part of the product, it's unlikely that a lot of people outside of the company are gonna like the product.

5. 15:48 – 21:35

   Running 200 People Like 10 - Windsurf's Operating Philosophy

   1. VMVarun Mohan15:49

      [upbeat music] No, I, I always try to run the company as, like, the smallest company it can be, but I think the goal of a company is not to be a small company. I think the goal of a company is to actually provide the most value in a space. If the right way to do that is be a bigger company, I think that's, like, the right thing you should sort of do. One of the operating principles we sort of have at the company is we try to run the company fairly lean, and that doesn't mean we have very few people. It just means that for a given amount of ambition, we are the smallest company we could possibly be, and that means that people are, largely speaking, underwater most of the time. Uh, hopefully, like, we don't have large amounts of people that have too little to do, right? The goal is everyone should have too much to do, and because of that, that forces rapid prioritization inside the company with a lot of urgency. That triggers whether or not we should hire people. And honestly, that itself is a good forcing function of how you should scale a company. You scale a company when everyone is underwater, and the moment people are no longer underwater, we don't scale the company anymore. I think in, in terms of just some of the challenges, obviously, like, as the company gets bigger, communication gets harder. So there are some processes in place in terms of you wanna ship a feature. It's no longer just one person did everything end to end. There are, like, many different parties involved, uh, but I think we added more process in place to make sure that even that could happen much faster. If you run the company in a way where everyone has their own set of priorities, uh, I think it's, it's cool. Everyone's kind of having a good time. But I think, you know, this boils down to why do companies work? I think they work if you do one or two things really well, maybe even one. Forget about two, maybe even one. The problem is then actually, if you were to imagine, in a lot of cases, it's very hard to just have infinitely many people doing one thing really well. So that naturally reduces the number of people that you have at the company. Yeah, I guess, I guess the, the issue is naturally when you have more people, there are gonna be more priorities, right? Like, people are not malicious, but they want to have something to do. If there are enough people to do what the original intent of the company was, they're gonna find other things to do, and that's gonna cause problems internally as well. One of the things that I've sort of done is I still interview everyone that joins the company, make sure that they go through a culture fit. I think that's actually, like, quite important to me. To make sure that we are adding people that are going to, like, maintain and preserve and maybe even, even just embrace the culture we have inside the company. So I think that's, like, honestly, honestly, like, a very key thing that I've not given up, even as the company has scaled, you know, close to 200 people. I, I think actually it's the same principles that made the company work when it's small. I think big companies are not that different than small companies. Maybe it's hard to operate like a small company when you're a big company, but I think if you were to ask every big company, they want to operate like a startup. I think the very hard thing is, once again, intellectual honesty. It's very hard as the company gets bigger for m- for a large group of s- people to analyze what they're doing and to say, "Hey, we should stop working on this," or, "Should work on something else." It gets very hard because people feel some sort of safety as the company gets bigger. But the reality is, like, no company is really safe, regardless of the size, and that's how every company should be operating. They should be operating as if almost existential dread. Not in that it's causing paralysis, but in that it causes agency to kind of figure out what the next thing to do is. Yeah, I think people get... They feel safety, psychological safety, by feeling that what they're working on is the most important thing, right? I think, y- you know, people, it's very hard to keep in your head, "Hey, like, let me go work hard on something," but all the while it may not be that useful for the company. And I think as a company gets bigger, it's very hard to have people that just still don't have the psychological safety. Maybe one interesting thing about the company is I think companies have, like, two sides attached to them, which is that you need some form of irrational optimism. The reason why you need irrational optimism is without some amount of optimism, there's no reason a startup ever wins at anything. A big company has more capital, more resources, more distribution, so you need to believe you can do something better, right? With great people, uh, you can somehow build something that's generational. You have to believe that, uh, which is, in most cases, not true, right? So you somehow need to believe that. But also at the same time, sometimes that's just not gonna be the case, and you need to be very realistic, and I think this comes with, you know, uncompromising realism. Like, you need a healthy amount of both. You need, you need the optimism to go out and do something when everyone else believes it's not that valuable. Because if everyone else believed it was valuable, the big company would do it, and you would still lose. But all the while, you also need to agree most ideas are bad ideas, and you need to kill your ideas fairly quickly as well. This is, this is, like, this tension that a company is always going through. It progressively gets harder and harder the company gets bigger, uh, but I think it's a strength if a company can do that regardless of what size it is. Yeah, I think b- the only thing is, that I would sort of like to say is build for where you think the technology's going, not for today, right? And don't build, uh, probably technologies in places that, like, are as good as they will be given where the technology is today. It feels like it's gonna be very hard to differentiate in the long term, and that's a hard thing to do, right? Uh, that means you, you, you actually need to be willing to work on something that doesn't feel like it's working for maybe, you know, an indeterminate period of time. I think what this means is these kind of short-term heuristics that you invest deeply in that make a model today work are probably actually have massive diminishing returns. Because the next model is going to be much better, and the heuristics are gonna be unnecessary. I think the things that are important are how do you actually take advantage of the fact that you do have a lot of users and build better experiences for them? And actually, because they are using your product, you're able to build better and better experiences for them. And these experiences are better th- and learned in a way. They're actually learned from the way people use the product, and I think you should be focusing on that, not on kind of cosmetic things that make a model kind of magically do something that probably is gonna happen anyways in six months. [outro music]

Episode duration: 21:37