Building a magical AI code editor used by over 1m developers in 4 months: Inside Windsurf

A lot of the bets we're making inside the company are for things that are not three, four weeks away. We should be cannibalizing the existing state of our product every six to 12 months. Every six to 12 months, it should make our existing product look silly. It should almost make the form factor.

Of our existing product look dumb.

How do you know when it's time to hire someone? I wanted the company to almost be like this dehydrated entity. Every hire is like a little bit of water. And we only go back and hire.

Someone when we're back to being dehydrated. Any other skills you think people should be investing more in with the rise.

Of AI, building more and more of our product? engineers are now able to produce more technology. The ROI of building technology has actually gone up. This actually means you hire more. The best thing to do is just get your hands dirty with all of these products. You could be a force multiplier to your organization in ways in which they never even anticipated.

Today, my guest is Varun Mohan. Varun is the co-founder and CEO of Windsurf, which has quickly become one of people's favorite AI coding tools and is basically the main competitor to Cursor with over 1 million users four months in. In our conversation, Varun shares what makes Wind Surf unique, why they decided to invest heavily in enterprise sales very early in their history, why agency is going to be the most important skill for engineers and product builders to build, also the story of how they started out as a GPU infrastructure company and realized there was a much bigger opportunity up the stack and the two pivots that got them to where they are today. He also gives a live demo, advice for being successful with WindSurf, and so much more. There's so much to learn about where things are heading for engineers and product builders in general. In this conversation, I'm really excited to bring it to you. Thank you to everyone on LinkedIn and Twitter and my newsletter community for suggesting great questions to dig into with Varun. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become a yearly subscriber of my newsletter, You get a year free of Perplexity Pro, Notion Plus, Linear, Granola, and Superhuman. Check it out at Lenny's Newsletter.com With that, I bring you Varun Mohan. This episode is brought to you by Brex, the financial stack used by one in every three US venture-backed startups. Brex knows that nearly 40% of startups fail because they run out of cash. So they built a banking experience that focuses on helping founders get more from every dollar. It's a stark difference from traditional banking options that leave a startup's cash sitting idle while chipping away at it with fees. To help founders protect cash and extend runway, Brex combined the best things about checking, treasury, and FDIC insurance in one powerhouse account. You can send and receive money worldwide at lightning speed, you can get 20x the standard FDIC protection through program banks, and you can earn industry-leading yield from your first dollar, while still being able to access your funds anytime. To learn more, check out Brex at brex.com/banking-solutions. That's b-r-e-x.com/banking-solutions. This episode is brought to you by Productboard, the leading product management platform for the enterprise. For over 10 years, Productboard has helped customer-centric organizations like Zoom, Salesforce, and Autodesk build the right products faster. And as an end-to-end platform, Productboard seamlessly supports all stages of the product development lifecycle, from gathering customer insights, to planning a roadmap, to aligning stakeholders, to earning customer buy-in, all with a single source of truth. And now, product leaders can get even more visibility into customer needs with Productboard Pulse, a new voice of customer solution. Built-in intelligence helps you analyze trends across all of your feedback, and then dive deeper by asking AI your follow up questions. See how Productboard can help your team deliver higher impact products that solve real customer needs and advance your business goals. For a special offer and free 15 day trial, visit productboard.com/lenny. That's productboard.com/L-E-N-N-Y. Varun, thank you so much for being here. Welcome to the podcast.

Benny, thanks for having me a long time listener.

Oh, I really appreciate that. I'm so excited to have you here. I feel like just you guys have become this overnight success, which is definitely not an overnight success, but I feel like I've been hearing about Windsurf more and more as people's favorite AI tool. And I just don't think people know the story behind Windsurf, behind Codium, the company that you built. So I thought it'd be good to maybe just start there and have you just briefly share the history of Codium.

And how Windsurf emerged out of Codium. Yeah, so the company was actually started close to four years ago. As you know, AI coding was not a thing four years ago. ChatGPT was not out four years ago. At the time, we actually started out building GPU virtualization and compiler software. Before this, I worked in autonomous vehicles. My co-founder, who I've known since middle school, worked on ARVR at Meta. And for us, we believe deep learning would touch many, many industries. It wouldn't just touch like sort of autonomous vehicles with touch financial services, defense, healthcare. And we believe these applications were hard to build, these deep learning applications. So we made it possible for you to effectively run these complex applications on computers without GPUs, and we would handle all the complexity of being able to actually run the workload on the GPUs for you, right? And we were able to optimize these workloads a ton. And in the middle of 2022 rolled around, And we had a couple million in revenue. We were managing upwards of 10,000 sort of GPUs. We had eight people at the time. We were free cashflow positive. But I think what we felt was once these generative models started to get very good, we sort of felt a lot of what we built was not as valuable. And this was like a very, very hard moment for us at the company. We were only eight people at the time, but we felt, Hey, would people be training these very bespoke sentiment classifier models anymore that were like very, very custom models, or would they just ask GPT-N, is this a positive or a negative sentiment? Probably it's going to be the latter, right? And in a world in which everyone was going to run generative AI models, why would an infrastructure company be a differentiator? Because everyone is going to run the same kind of infrastructure down the line. So instead what we decided to do was we believe generative AI was almost going to be like the next internet. And in that case, what we should go out and do is build the next great apps like Google, like Amazon, and we vertical integrated and actually took our infrastructure, our inference infrastructure, to go out and build Codium at the time. And at that time we were early adopters of GitHub Copilot and we thought the coding space was going to get tremendously disrupted in the next coming years. So we actually took our infrastructure, we ran our own models and massive scale. We even trained our own models. In the very beginning it was very, very simple. It's purely an autocomplete sort of model, right? Which basically means that as the user was typing, we'd complete the next one or two or three or four lines of code. But we provided the product entirely for free in all the IDEs that developers coded, right? That meant VS Code, Jetbrains, Eclipse, Visual Studio, Vim, Emacs. And the reason why we were able to build it for free was because of our infrastructure background. We were able to optimize these workloads a ton. And I guess very quickly after that, some large businesses also wanted to work with us, and we built out this enterprise motion to work with these large companies like Dell, JP Morgan Chase. And for them, the bigger thing wasn't just, hey, could we autocomplete code or could we chat with the code base? It was, could you offer us a secure offering that was also personalized to all the private data inside the company? So we took our infrastructure and made it so that we invested a ton in making sure that we deeply understood these large companies code bases. Right? And that's what we were working on until six months ago. And it's not that we've stopped working on that, but basically what we realized six months ago was we were getting limited by the IDs that we were already working in. So VS Code, which is a very popular IDE, had a ceiling for the AI capabilities we could showcase our users. And because of that, we decided to go out and fork VS Code and build our own IDE with some of these new agentic capabilities. And over time, in the last couple of years, the model capabilities have also been growing exponentially year over year. That's sort of where we are right now. I skipped a lot of pieces there, but that's where we landed.

There's so many interesting threads there. One is just, there's always this question of just where value will accrue in AI. And it's so interesting you guys started almost at the bottom layer of infrastructure GPUs, and then you went to what people call a GPT wrapper, not actually, but so I guess any just like lessons there, just thoughts on just where you think value will end up in this world of AI, in the stack of AI tools.

Maybe I can start by just saying like one thing about startups that I think are like really true. It's very unlikely the first thing that you believe you should go work on is going to be the right thing, which is like a very hard thing to kind of wrangle with being a startup founder, right? You need to kind of be irrationally optimistic that what you're going to do is going to be differentially important because otherwise, why would you go out and do what you're doing? And if it's obvious, then a bigger company would have already done it, right? But then you also need to be really, really realistic because most ideas that are, I guess, non-conventional are usually bad ideas. So it's this weird kind of tightrope you need to kind of balance on top of, where you're kind of pushing for a future that you believe is true, but all the while you're getting new information, you need to kind of kill the beliefs that you had. And if I were to start with the infrastructure piece, we first went in with the assumption that model architectures were going to be really, really heterogeneous. Working from an autonomous vehicle background, there were many different types of model architectures out there. There were convolutional neural networks, graph neural networks, recurrent neural networks, LSTMs, sort of lighter neural nets with thrust from point networks, right? And there were maybe tens of architectures we were dealing with. And at that point we were like, the complexity of this is so high that it's very clear if someone offloaded the complexity, there would be a lot of value. Fast forward to the middle of 2023, everything looks like it's going to be a transformer. So now our hypotheses are just wrong. So at this point then, most of the value is probably not going to accrue at purely the, at least this is our belief at the infrastructure layer, it's going to accrue somewhere else. Where is the layer that you can actually differentiate on? And we believe the application layer is a very, very deep layer to go out and differentiate on, right? What are the number of ways we can build better user experiences, better workflows for developers? We think there's effectively no ceiling on that, on how much better we can.

Make the lives of developers basically. You touched on the second thread that I thought was really interesting here is just how you guys pivoted from ideas that were working. Like you were making money, people loved it. You said you had millions of dollars of ARR revenue and then you just like, no, we're going to completely change the business. So the question there is just like, how do you, what have you learned about knowing what to follow? And one thing I heard there that was really interesting is just once your assumptions change about that you built your idea on, it's time to rethink this idea and maybe try something else.

I think the way we sort of think about this is, even when we're working right now, we just accept that we're going to get a lot of things wrong. We're just going to get a lot of things wrong. Obviously that's a very big moment because that was a bet the company moment in the sense that we basically said, told our investors, Hey, we're making money on this. We had already raised $28 million of capital and we were just like, Hey, we're just going to pivot entirely from this. And we did that overnight, right? This wasn't this thing where we just said, Hey, maybe a quarter, one or two quarters because one of the things we knew that's very important for startups is focus. And if you're trying to do another thing that you think is big and you're focused on something that you don't believe is valuable, you're guaranteed going to fail at the thing you think is going to be big. Right? So that's like a very obvious thing there. But I think once you go in with the assumption that a lot of your hypotheses are going to be wrong, but you will do the most concentrated work possible to go out and validate these hypotheses and you won't be in love with your ideas. I think ideas, it's awesome when you have a great idea, but you should never be too in love with your ideas. And you have an organization that is very truth seeking. I think a lot of people at the company have had their ideas tested over and over again. Even just building Windsor, that is not a complete company pivot, but that's a big decision that we made at the company. You kind of need to make some bets and sometimes you're wrong and sometimes you're right. But if you have an organization that comes out and you feel like morale is not going to be low, if you made the wrong decision, that that's the best, right? That means you have optionality for the rest of time. And Lenny, one thing that I try to tell the company about this is this year, the total amount of engineering output we will have is much larger than the engineering output we've had since the beginning of the company's creation till now. So that almost means every year is a new lease on life for us, right? It's almost a new way for us to test out an entirely new set of hypotheses. And maybe we were wrong about our original hypotheses in the first place. What makes us more smart than everyone else to be right more times than them? That's so empowering.

Makes me think about Ori Levine, who was on the podcast, co-founder of Waze. And he has this phrase that he wears on a shirt. His book is called this Fall in Love with the Problem, Not the Solution. And that feels like that's exactly what you're describing. Okay, so let's talk about Waze. What's the simplest way for people to understand what is Windsurf?

Yeah, so Windsurf is an ID, right? It's an application to go out and build software and build applications. The crazy thing is a lot of people who use the product don't even probably know what an ID is, which is crazy. And we'll get into that in a second. But why did we go out and build Windsurf and what is Windsurf maybe? Why couldn't we have just done this on top of conventional IDs like Visual Studio code? So maybe just to get into this a little bit, as we saw that AI was getting more and more powerful, the way people go out and build technology, we thought the interface for that was going to change remarkably. It was not going to be a conventional pure text editor where the user is writing a handful of lines of code or most of the code, and the IDE provides maybe some basic feedback on what the user is doing right or wrong. And the basic feedback could be, hey, there's a bug in your software or compiler error in your software. It could do much more, right? It could actually go out and modify large chunks of code. And one of the key pieces that we recognized was with this new paradigm with AI, AI was probably going to write well over 90% of the software, in which case the role of a developer and what they're doing in the IDE is maybe reviewing code. Maybe it's actually like a little bit different than what it is in the past. And we'll see this very soon with with Windsurf, maybe, you know, when you're using the product, actually a good chunk of the user's time is actually reviewing what the AI is outputting. So we needed to build custom review flows into the IDE to actually make it so that it was easier to actually go out and do that, right? Because the developer is not spending all their time writing code. And this is the fundamental premise on why we built the product. We thought we were going to get limited a ton if we had very, very basic UI out there. And I'll give you even a simple example here. We have this autocomplete product, right, that completes a handful of lines of code. Now we've actually launched this offering called Windsurf tab that basically shows you refactors as well. And these refactors are almost inline refactors. And we were able to build a custom UI for that in Windsurf. But in VS Code, we needed to, because of the access to the APIs, we needed to dynamically generate images right alongside the user's cursor because we just didn't have access to The capabilities to showcase and edit properly. And what we realized is immediately by porting over to WindSurf, our acceptance rate tripled. Same ML models, it just tripled. So what that I guess gave us confidence in is, yeah, like you could argue technology is very important and I think technology is very important, but if our users are getting very little value from the technology we're sort of building, you need to really clarify, maybe we.

Do need to build a new surface and interface and that's what WindSurf is. So the big bet you took there just to make this super clear is you were initially working within existing IDs that everyone was familiar with. And then it was like, this isn't going to get us where we need to go. We're going to try to convince people to switch to something completely new because it's going to be so much better. It's our own ID. I think maybe people may not recognize just how risky that is, convincing engineers to use something completely new.

That's a huge deal. Yeah, no, of course. And one of the key pieces that I just maybe Lenny, that would be important to share is we, a lot of our developers view Visual Studio code, but there are lots of people that write in languages like Java sort of C++ and so on and so forth, and they might use the Jetbrains family of IDs that are like IntelliJ. And for us, we are actually still committed to building on those platforms, right? We just felt though that one of the dominant IDs, which was Visual Studio code, was limiting the sort of user interface that we could give to our actual customers.

What is the current state of traction for WindSurf? You hear all these crazy numbers about all the competitors in your space.

What can you share there for folks just to know? Yeah, so maybe a handful. We launched the product like a bit over four months ago, and in that period of time, over a million developers have tried the product. And obviously we have many hundreds of.

Thousands of monthly active users right now. I love how like these days, like a million, no big deal. Like it's just the numbers are absurd these days. Like, We're just getting used to just 100 million AR here, million users in four months there. It's just like, oh, of course. How could you not have that? But that's absurd. It's just like an insane time right now. You touched on something that I wanted to get to later, but I may as well bring it up now. The question of just how engineering will change in the future, you've throughout the stat that like 90% of code is going to be written by AI in the future. Dario from Anthropic recently said the same thing. you guys have a really interesting glimpse into just how things will look in the future. So I guess the question just, how do you think coding specifically will look in the next few years? How different will it be from today?

I think when we think about what is an engineer actually doing, it probably falls into three buckets, right? What should I solve for? How should I solve it? And then solving it. And I guess like everyone who's working in the space is probably increasingly convinced that solving it which is just the pure, I know how I'm going to do it and just going and doing it. AI is going to handle vast majority, if not all of it. In fact, it probably actually with some of the work that we've done in terms of deeply understanding code bases, how should I solve it is also when you get closer and closer to getting done. If you deeply understand the environment inside an organization, if you deeply understand the code base, how you should solve it given best practices when the company also gets solved. So I think what engineering kind of goes to is actually what you wanted engineers to do in the first place, which is what are the most important business problems that we do need to solve? What are the most important capabilities that we need our application or product to have and actually going and prioritizing those and actually going and making the right technical decisions to go out and doing it. Right. And I think that's where engineering is probably heading towards. Now, does that mean that no one needs a CS degree? I think, I think that's maybe a little bit overplayed, a little bit just because, you know, maybe, maybe here's Here's maybe my argument for that. A lot of developers nowadays that build full stack applications, at least until like a handful of years ago, they probably went to college and took an operating system course, right? And in theory, they're not really like playing around with the operating system, like the kernel scheduler, like very frequently. But do those principles help them in understanding why their applications are slow? Do they help them in understanding why some design decisions are better than the other? Yeah, that makes them a much better engineer than another engineer. I think that idea and the understanding of what's going on at the bottom will make a good engineer even better. But also at the same time, it empowers a bunch of people that never understood all of those things how to actually build as well, which is another remarkable sort of thing that fell out through this whole process.

I don't know if you have kids, but just like say you had kids or you had niece or nephew going into college, let's say, would you suggest they do soft, they do computer science or would you suggest like you're not gonna have a good time if that's the career you choose right now?

Yeah, I think, you know, maybe I think back a little bit. So I went to MIT. A lot of us at the company went to MIT together on the engineering team. And I think like when I think about what we learned the most for engineering or computer science, it was not exactly like, how do you write code? Right? That's maybe, that is like almost a given that you can kind of write code after going to college. It's more like the principles of how you think about a problem and how you break it down, right? And how you solve it in an interesting way, right? So an example of a class that I really enjoyed was our Distributed Systems class. And there you're kind of reading through literature and understanding how some design decisions were kind of made. And I think it's more like a problem solving kind of course, right? And a major, it's a major of how you solve problems given some constraints of how computers today function, right? Like here's the speed at which memory sort of operates, here's the speed of which, you know, here's how much computation you can do in one cycle or one second. And based on that, you can make some trade-offs and solve a problem. So I don't know if I would say that you shouldn't go get a computer science degree. I think computer science is almost synonymous with problem solving. In that case, I think it's pretty valuable. Is everything you learn in your computer science degree useful? I'd say a lot of things that I learned in my computer science degree are not useful. I'll give you an example. I took a parallel computing class in Julia. And I don't think Julia is a very popular programming language anymore. Am I very sad that I took the class? No, the principles of parallel computing are.

Still very useful, I would say today. So what I'm hearing is skills that you still want to build, whether it's computer science or maybe some version of computer science is kind of like building the mental model of how computers and systems work, parallel processing, memory, hard drives, internet, things like that. Okay. And then there's just like problem solving skills. being able to solve interesting problems. Is there any other skills you think people should be investing more in with the rise of AI, building more and more of our products?

I think one of the things that's maybe a little bit undervalued is this kind of agency piece. And I think about this a lot, which is, you know, you have a lot of people that could go through college and go through school, and they're basically told exactly what to do on a pset. And, you know, they're given these very, very, I would say, well-defined paths that they need to take. and I think maybe in society and just school, we don't prioritize how do you make sure you get people with real agency that want to build something, right? Their goal is not just to maybe graduate from college and then get a job at a big tech company where they're told exactly what to do and where to put the pixel for this one website. And I think that's maybe a skill set that is undervalued just right now, probably in the last, you know, maybe ten, ten years or so. And I think that's going to be really, really important. You know, a skill for us for a startup, obviously, these are skills that we just look for. We look for people that are really high agency because we just recognize that by default, if we don't innovate and do crazy things, we're going to die. The company is just going to die. So we just look for this. Right. But I would say like for most software engineering jobs, that's probably not the case. Right. Just think about, you know, big company X and what they're hiring for. on the average software engineering interview.

It probably doesn't look like that. I love how you phrased that. If we don't do crazy things and innovate, we're gonna die. Yeah. That would be a great title for this podcast episode. And I think, I know it's 100% true. There's just a lot of crazy things happening and a lot of innovation happening. And if you can't keep up, you'll die. So let's talk about hiring. You have a really interesting approach to hiring. There's a few questions I have here. One is just how do you, I know you try to stay really lean. That's a common theme across all the AI startups these days, how do you know when it's time to hire someone?

I love the idea of being a lean company, but I don't idolize it in the way that, hey, like it is a dream to be a 10% or 20% company that's making, you know, 50, 100, 200 million in revenue. That's like not, I think, what we idolize inside the company. I think what we idolize is be the smallest company we can be to satisfy our ambitions. Right, that's what the goal is. And maybe Lenny, the way I would sort of put that out there is if I told you, Hey, I'm going to build an autonomous vehicle, and I said our team is 10 people, you should rightfully say, Hey Varun, you're not serious, and you'd be right, I'm not serious at that point. So I think the answer is, what is the minimum number of people to go out and build the crazy ambition project that you have? And I think the project we are trying to go out and do, which is completely transform the way software gets built, We've mentioned this inside the company. Our goal is to reduce the time it takes to build apps and technology by 99%, right? It is a tremendously sort of ambitious goal. And it's not possible for us to be a 10, 20, 30, 40 person engineering team in the long term and actually satisfy that goal. We think it's, there's a very, very high ceiling. So that's maybe the first key piece there. It's like actually, you know, if we can crack actually being a fairly sizable company, but still operate as if we're a startup, that's the dream. Right, that's the trick. In terms of hiring philosophy, the way we sort of think about things is we only hire for a role if we're actually underwater for that function. So let's say we're going out and building inference technology. Unless we are underwater there, we will not go out and hire someone to go out and work for that. And the reason for that is I actually think this is a feature. When you hire for a role and you already have enough people there, what you, you know, you get a lot of weird politics that ultimately ends up happening. And it's not because people are bad people. I don't, I think most people are really well intentioned, but what happens when you have people that join a company and in reality, you didn't really need them. They will go out and manufacture some other thing that they should go work on. Right. They should, they will go out and figure out something else to work on. And realistically, it's not that important, but they will go out and try to convince the rest of the organization that it is important. Right. And I just think as a startup, we don't have the bandwidth to go out and deal with that. Right. For me, I would like to see everyone just almost like be raising their hands up being like, I'm dying. We need one more person and that's when we go out and hire someone. And one of the analogies I like to give is I wanted the company to almost be like this dehydrated entity, right? And every hire is like a little bit of water, right? And we only go back and hire someone when we're back to being dehydrated.

I love this metaphor so much. And it sounds painful. It sounds painful that you need to be underwater and raising your hand, I'm about to die and dehydrated. But I also know that it's a really exciting way to work. It sounds hard, but if you're in it, it's just like, I guess talk about just that side of it, because I think it could sound like this is terrible. I don't want to work this way.

You know what I actually think, Lenny? It's really good for a handful of reasons, which is that a lot of the people we respect and trust to the people that work at the company. So this forces ruthless prioritization. You know, you have a team that's going out and doing something. They will never ask to work on something that's not important. In fact, if there are two things that they're working on, they're just gonna just tell me, Hey, there are two things on my plate. I just don't have the ability to do two. I can only do one and they will pick the one that's most important. And this actually goes back to one thing that I think is true about startups and just companies in general. You don't win by doing, you know, 10 things kind of well, you win by doing like one thing really well and maybe you fail nine things. This is the thing that I've told the company, this is very different than school, right? In school you optimize for your total GPA, but for companies, I just need to get an A plus on the one class that matters and then I can get an F in all the other classes. And an F in all the other classes doesn't mean, you know, being, you know, just doing illegal things that basically means you just deprioritize things that don't matter. You know, that actually forces this organizational prioritization that is just Really, really good, right? And, you know, Douglas and I, Douglas being my co-founder, we can tell the company these are the two things that are the most important. But if we go out and tell these are the two things that are the most important to the company, and then we put the company has 20% more people than necessary. What do those, what's going to ultimately happen, right? It's almost a forcing function for ruthless prioritization to have fewer people or people that just, that are just underwater internally at the company.

Everyone listening that works at a big company, knows exactly what you mean when you describe when there's just too many people, they will all find work to do and they will all be pitching ideas. You know, they all want to show impact. They want to do well in their performance reviews. That's just the nature of too many people at a company. And so I think this all really resonates to even getting even deeper on just what it looks like when someone's underwater to tell you it's time to hire. Is it just someone coming to you? Varun, we need, I need someone on this team. This is just not possible. Like, what does that look like? even more practically.

Yeah, I think it's basically along those lines. It's that, hey, there's some pressure to get something done in a short period of time. By the way, one of the things that we do believe though for software, if you want to do great things, it's not possible to just say, hey, I want to get it done in one month. If it is like, because you have to think about it from this perspective, if a software project could get built in two to three weeks, what does that really mean about like the true complexity and differentiation of what you built? Like it's probably not very high unless you believe you are way smarter than everyone else, but I think that's hubris. Right. I think we actually have a very exceptional engineering team, but also at the same time, I don't think our engineering team is so exceptional that we can do things in three weeks that the rest of the world can't do in like six to nine months. That's kind of stupid to believe that. So I think basically it comes down to that person coming out and being like, Hey, look, like I don't have enough time to do X. Us having a conversation to be like, okay, what can you do then? And if the answer is I can only do less than that, Then maybe we make a decision actually, oh wow, that's great. But maybe we actually should deprioritize why? Because this is actually also another thing that's very hard, even for people like me and my co-founder, it's that we also want to do a lot of things, right? There's an urge to do a lot of things. But if you, if we are forced to make a decision constantly on like, we cannot do X, it's very clarifying. It's very clarifying because our engineering interview process is also like extremely low acceptance rate. So it's not very easy for us to very quickly spin up people and have them join the company really, really quickly either. So I think it's clarifying for everyone. It's clarifying for the person that wants more people. We can just tell them, Hey, look, we don't believe you should be doing this other thing. And it's also clarifying for us because we can also get on the same page with them. And sometimes we just kind of agree, Hey, our teams are very flexible that, Hey, actually we do need to get something done. One of the things that we've kind of tried to make sure is true on our engineering team is people's value to the company does not have anything to do with the size of their team. There are projects inside the company. There are directly responsible individuals for these projects inside the company. And if we feel like one project is very important, then people can move from one project to the next, right? There's no notion of someone owning people at the company. That is a very bad and gnarly idea, right? In fact, the person that is the most valuable at the company is the person that can do The most crazy sort of project out there with as few people as possible. And that's what you should be rewarding internally.

How many people do you have at Codeium at this point?

So we have close to 160 people and the engineering team is over 50 people, right?

Awesome. Oh, what's the other bigger functions?

So we have go-to-market. We have a, yeah.

Oh, right. Okay. I want to talk about that, the sales learning that you guys had. Okay, well let's close out this hiring conversation. So we talked about what you look to tell you it's time to hire, what do you look for in the people that you interview and hire?

One of the key pieces that we look for, we have a very high technical bar. So assuming that they actually meet the technical bar, I think we sort of look for people that are really, really passionate about the mission of what we're actually trying to solve, and people that are willing to work very hard. I think one of the things that we don't try to do is convince people, Hey, look, we're a very chill company and it's great to work here. I think no, this is a very exciting space. It's very competitive. You should expect us to lose if the people at the company are not, you know, kind of, they're not working very hard. And I think one of the biggest dog whistles I sort of hear is when I ask people, like, how hard are you willing to work? Some people actually ultimately say, Hey, I work very smart. And I basically ask them a question. If we have many smart people at our company that also work hard, what's the differentiator going to be? Are you just going to pull them down? Right? Because I think one of the things that's true about companies is it's like this massive group project. And I think the thing about a person that is not pulling their weight, that's bad, it's not the productivity, right? At some point when the company becomes many hundreds of engineers, I'm not going to be thinking about the one engineer that's not pulling their weight. It's the team of people they work with that are almost basically saying, is this the bar internally at the company? Is this the expectation? And I guess Lenny, if I told you you have a team of five people and You know, the four other people you're working with just don't care. How much are you going to feel like you should care?

Not too much.

Exactly. So for us, for us, I think that's what we more care about, right? We have a culture where we are, it's like, it's very collaborative. It's, it's not an individual sport, but people feel like they can rely on other people to get complex sort of tasks done.

So the question you asked there just basically is how hard are you willing to work? How hard do you want to work? And I know some people there, there's, you know, this whole group of folks that are just like work life balance. I don't wanna, how dare you ask me to work crazy hours? And I, I, I'd love just the filter up front of, if you work here, you will work really hard. You work a lot of hours. It's a crazy, it's a crazy space to be in, and we will win by working smart and also really hard.

Yeah.

Uh, you said at some point earlier that your, engineering pass rate, as you said, it was like 0.6% of candidates, something like that.

Yeah, it's probably post or take home. It's probably that actually. So the take home itself filters probably another, you know, 10, 15 x on top of that.

Here's a question that I've been hearing more and more is just how do you do interviews these days with tools like WindSurf out there that solve all your problems?

We're okay with people using the tools because I think one of the worst things is like if someone comes here and doesn't like using these tools, like we believe they're massive productivity improvements. We do bring people like into the company like on site so we can actually like see how they think through problems on a whiteboard and all these other pieces. So we do want to see how they think on their feet and hopefully they're not just taking what we're saying, putting it in a voice translator and sticking it into ChatGPT and getting the answer out. So there is a way to do this. My viewpoint on this is the tools are really, really important. But I do think we still look for some problem solving ability, right? If the only way you can solve a hard problem is put it into ChatGPT, I think that's a concern to us.

Today's episode is brought to you by Coda. I personally use Coda every single day to manage my podcast and also to manage my community. It's where I put the questions that I plan to ask every guest that's coming on the podcast. It's where I put my community resources. It's how I manage my workflows. Here's how Coda can help you. Imagine starting a project at work and your vision is clear. You know exactly who's doing what and where to find the data that you need to do your part. In fact, you don't have to waste time searching for anything because everything your team needs from project trackers and OKRs to documents and spreadsheets lives in one tab, all in Coda. With Coda's collaborative all-in-one workspace, you get the flexibility of docs, the structure of spreadsheets, the power of applications, and the intelligence of AI, all in one easy-to-organize tab. Like I mentioned earlier, I use Coda every single day, and more than 50,000 teams trust Coda to keep them more aligned and focused. If you're a startup team looking to increase alignment and agility, Coda can help you move from planning to execution in record time. To try it for yourself, go to coda.io/lenny today and get six months free of the team plan for startups. That's coda.io/lenny to get started for free and get six months of the team plan, coda.io/lenny. Okay, let's talk about this go-to-market sales experience that you guys have. So you started without obviously, like most people started building without sales team. And then you realized from what I hear that that was a huge miss and a big opportunity to talk about there, because that's really unique, I think, that you guys have a large sales team and go to market team.

Yeah, we actually made this decision pretty early in the company's history, I would say. Like we hired our VP of sales over a year ago, actually. And the go to market team is now over 80 people inside the company. So it's a pretty sizable function. , inside the company. Yeah, maybe a little bit of a backstory here. So when we started the company, actually we had a handful of angels that actually were operators, go to market operators. So an example of one was Carlos Delatorre, who used to be the CRO of MongoDB. And I think for us, we never viewed enterprise sales and sales as like a very negative thing. I think this is like a interesting thing that technical founders sometimes don't really like. They think sales is like a very negative sort of part of the process. Everything should be product led growth. and I think it's not that black and white. I think enterprise sales is really valuable, but maybe when we were a GPU virtualization company and we were an infrastructure company, the reason why we never hired a salesperson is I didn't know how to scale the function. I was the one who was selling the product. So ultimately speaking, if it was hard for me to sell the product incrementally, I didn't know how we could make that into a process that we could then go and scale. if I couldn't, if I can do one, like, I didn't know how we could take the revenue of the business from a couple million to hundreds of millions and let alone even tens. So if I didn't know how to do that, how could I go out and hire someone and make them scale it out? On the other hand, for Codium, very quickly, a lot of large enterprises sort of reached out to us. And from that alone, in the middle of sort of 2023, we started, I guess, me and a handful of other folks at the company started selling the product. And we were doing tens of pilots concurrently with large enterprises. And we were very quickly able to understand that there was a large enterprise motion that needed to be built in the space. So by the end of 2023, we actually hired our VP of sales and very quickly after that scaled a sales team. And yeah, I mean, look, if you want to sell to the Fortune 500, it is very hard to do that purely by swiping a credit card.

Let's talk about cursor. I don't want to spend too much time on competitors, but that's what everyone's always thinking about when they think of you guys. you guys are kind of the leading players, I think, in the space. Also, there's Copilot, but that's different. So what's the simplest way to understand how you guys are different from Cursor and also just how you think you.

Win in the space long term? So I think maybe a handful of things that I could share. So on the product side, I think we've invested a lot in making sure code-based understanding for very large code bases is really high quality. That's just because of where we started, right? We worked with some of the world's largest companies like Dell, JP Morgan Chase, Companies like Dell have singular code bases that are over a hundred million lines of code, right? So being able to understand that really, really quickly to make large scale changes is something that we've spent a lot of time doing. And that requires us like actually building our own models that can consume large chunks of their code base in parallel across thousands of GPUs and almost rank them to be able to find out what the most important sort of snippets of code are for any question that are asked about the code base, right? So we've gone out and built large distributed systems based on our infrastructure background. to go out and do that. That's maybe one.

Let me actually follow that thread, because I think people may misunderestimate just how big of a deal that is. So when we talk about, we had the founders of Bolt and Lovable on the podcast. So those products, they build something from scratch. They build, they write the code for you. So that versus just loading, say, WindSurf on your million line code base, say at Airbnb or Uber, like it understanding what the hell you have and how it works and where to go change things without breaking it is. insanely hard. And so what I'm hearing is that's kind of a big differentiator. You guys started there actually, and then Windsor is now building on that advantage.

That's right. Yeah. So we, that's like a big thing that we spent a lot of time on, which is just understanding sort of what the, what the code base is doing. And actually, like, one of the other things is what are all the user interactions with respect to the code base? And happy to show that also, you know, in a bit here.

Awesome.

The second key piece probably is we're not only tied to Windsurf, actually. This is probably a weird statement given we are talking about Windsurf, which is that actually we're pretty focused on supporting IDs like Jetbrains. Jetbrains or IntelliJ has over 70 to 80% of all Java developers code in Jetbrains based IDs. The reason why we don't feel as big a need to almost build a competing product to Jetbrains is Jetbrains is actually a very sort of extensible product in a way that VS Code is not. VS Code is not very extensible. So I think for us, our goal here is not only just to satisfy a subset of users that can actually switch onto our ID, but we want to give this agentic sort of experience to every sort of developer out there. And if that means there are Java developers that write in Jetbrains, that's fine. We work with a large, lot of large enterprises that have 10 plus thousand developers where over 50% of the developers are on Jetbrains. Right? It's a very large large product. And by the way, that company itself is a privately held company that makes many hundreds of millions of dollars a year.

Right.

So it's a very, very large company. So for us, that's another key piece. We actually, like, want to meet developers sort of where they are. And if they, if they use a different platform, we'll, we'll work on that, too. The third key piece, and this probably sounds another key piece for Enterprises, is we, we work in, like, a lot of very secure environments. Right. We have FedRamp compliance. Right. We have which means we can sell to like very large government entities. We have a hybrid mode of actually using the product, which means that all the code that lives, that is like indexed, actually lives on the tenant of the user, right? Code is one of the most important pieces of IP for the company. So I think just if you were to look at it from like a big company perspective, there are many reasons why, like over the years of just like building an enterprise product, we've handled a lot of complexities that large companies want to see. But that's part of it is because of the history of how we got here. in the first place.

Okay, Varun, enough teasing. Let's do a live demo of Windsurf so folks can see what it's like, and then I'm just gonna ask you a bunch of questions as we're going through it. So, I'll let you pull up a.

Little shared screen where you have Windsurf pulled up. Great. So, some context, this is a very basic React project. There's nothing in it right now, so if you were to open any sort of files, it's the default React app project. And I have this Basic image here, you can pass WindSurf images of what you'd like the project to kind of look like of what I would like an Airbnb for dogs website to kind of look like.

Beautiful, beautiful mockup, by the way. I love that this is like all you need.

This is all you need. This is all you need. So basically what we're going to do is we're going to say, hey, one of the cool parts about WindSurf is it can actually work in an existing project already. Right, so I can basically say, hey, change this React app to show an Airbnb for dogs website based on this image and preview it. So now it'll just go out and start executing code, reading through the repository, obviously doesn't know what the current code base actually looks like, and it'll go out and analyze the code base to actually find out the set of changes necessary. So we'll go out and wait and see what it's going to do. But while we're doing that, let's continue the conversation.