Host 1 (2:25)

I mean, this is kind of like a dream for a lot of founders too. You're like, hey, everybody's fighting to, like, acquire us or give us lots of money. But what was so key and critical about the Windsurf team that, like all of the big tech companies were fighting to, whether it's Aqua Hire or acquire the company, to get access to this, this talent and IP and resources you built?

Jeff Wang (2:45)

Well, a lot of it was execution velocity and just being correct on a lot of the suspicions. So if you look at Codium, which was a product before Windsurf, it was the first product to actually release ChatGPT integration. So you had, like, your autocomplete and you had the chat interface as well. And then it was the first company to do a lot of things like context engineering, like getting parts of the code base into the prompt so that you could get better results. And then there was things like pinning context and getting different deployments to enterprise. So we, we were like, the first to move on a lot of these features that everybody else has. And obviously the most biggest breakthrough was the agentic piece. So you're. You're taking the entire conversation, putting it back in until you finish the plan, you've executed on the plan. And that, that, like, was the first generally available agent on the market, and which was a huge game changer, of course, to everybody else in kind of copying that over time. And then if you look at Cognition now, Devin was the other, like, truly agentic piece of software that was on the market as well. So both Windsurf and Devin were like, first of its kind, novel ideas that the whole world is kind of trying to copy right now.

Host 1 (3:54)

Oh, definitely. And so now it's your first term as CEO of Windsurf. How's the first period been for you? Cause I'm sure there was, like, a lot of change initially and then how you kind of settled into your groove.

Jeff Wang (4:05)

Now every week is very different. We. We act as, like, pretty much one company now. So the integration was a tough piece as well. But I would just say the job of a CEO or like anybody at the top is. It could be very random, week over week, lots of travel as well. I think I've Been in like a dozen different countries now in the past like few months. And it's just really whatever is the most important use of time, just try to go and spend time. There's definitely.

Host 1 (4:30)

And how do you differentiate like Windsurf compared to all the other kind of platforms? Because who is would you say is like your top maybe two to three competitors that are on the market.

Jeff Wang (4:41)

Yeah. Right now the most common that we run into an enterprise, it's probably cloud code and maybe even Codex now. But basically the whole world is transforming to this agent only this end to end agent and like being generally available. So basically like you can ask a very general question and it's able to understand how to solve it and go across all your systems in your company to make it happen. And what, what happened after that is like, you know, people use a sit in front of an ID and they go one on one with an agent. But now that the agent can do the all these things end to end, like verify the code and even run the code and test it on a ui, people are just sitting around doing nothing for like hours now. So what they're doing is they're running another agent and another agent and another agent. Because if they're taking several hours, it's not uncommon now to see people have like 10 agents on the screen. Which is actually what Windsurf 2.0 is all about. We want to make it easy for folks to manage like dozens of agents at once.

Host 2 (5:37)

Awesome one just diving a bit deeper into that competition piece. So one part is of course Devon. Right. Devin I guess used to be a competitor. Now you're going to looking how can the two products kind of cooperate or work together? That's one question. And then you mentioned Claude code. But I also know that aren't you using some of their models as well for Windsurf and how does that work? So how do you think of all of this? Because I think I read somewhere that you kind of see yourself as neutral in the whole model game. But how long can you stay neutral if there are all these players also doing the same things as you are doing, but they're also doing models?

Jeff Wang (6:20)

Yeah, I think back to the. So the first question is Devin. Right. So Devin is actually not a competitor. Even back in the day it was very rare where we went head to head against a Devin pilot. And the reason why is because it is a very different surface area. You are running Devin in the cloud and Devin is going like end to end. Whereas Windsurf, you're kind of like sitting in front of the computer and working with the agent together. And the combination of both these form factors is actually very interesting because if you, if you take any software engineer now, it really is, do they work with an ide, do they work with a cli, or do they work with a remote agent? Right. And a remote agent is very important because if you want to scale agents across a company, you want a very kind of defined, kind of a system where everybody has the same access, right? Like, if you think about openclaw, you can't just roll openclaw to everybody because you're giving everybody access to everything. But with Devin, you're giving everyone access to this VM that has access to the right amount of data and access, the repositories and access to the documentation. So that is really good to scale because you know exactly what everybody needs. And you could spin up like thousands of VMs. Like, you can have agents running everywhere, all doing very important things. And that is very hard to do even internally if you build it yourself or you're using kind of some of the other solutions that are trying to do the sandbox. And then to your second question about kind of model being model agnostic. Like, there's a couple of things here. One is that, you know, users on Windserve can choose the model that they want. And we even. We might be the only company actually that has our own model that we serve for free. And of course, we don't know how long it'll be free, but we're trying to make it free as much as we can. And the reason why is because people are blowing up their token budgets and they need alternatives now. They have no choice but to kind of use some of the features that we have, for example, like the free models that no one else is offering.

Host 1 (8:10)

Right.

Jeff Wang (8:10)

And then the other part is like, how can we compete? Like, if you look at Devin, Devin is using a whole bunch of models under the hood. And there are different things that the models are good at and they are not good at. And we have a very, like, intense eval team here and we choose the right model for the use case. So I think that's like something that's very important for someone that might go all in on one model provider. You, you actually, you actually might put yourself behind in some cases for some of these cases, definitely.

Host 1 (8:35)

And so when you joined, what was like, where was the company at? Because I know when you got like some of the initial offers, you were at about like 84 million ARR. And so just curious, like, what was that, that first journey when you started, like, where was the company at? And then what were some of the things you did to grow so quickly?

Jeff Wang (8:54)

So I was the first codeium hire, I think the first code, email and product was not making revenue yet. And we actually debated like were we ever going to create a SaaS component that was generating revenue. The only thing we did was we released a free SaaS product. And by the way, we operated our own GPUs and hosted our own models so we can bring the cost down to server for free. And we just gave out autocomplete and eventually chat, all for free. And the reason why is because we wanted to just get people to look us up on the Internet and be like, oh what's, what is codium?

Host 1 (9:28)

I think to look you up initially was a. Extension in like VS code.

Jeff Wang (9:33)

That's right. We had extensions across all, all the. A lot of the IDEs, I should say almost all the IDs and, and then that would all connect to this same GPU cluster where people could run the autocomplete. And back then you would. There was only Copilot and you would either pay like 15amonth or something back then, or you could just download code for free. And then you look us up and you realize there's an enterprise plan. But the enterprise plan at the time was only on prem, like we would serve this same box to your organization. And that was kind of our strategy pre revenue to get our first customers. Because you always want to choose kind of this, this vertical which is not, not as competitive. Right. Because if we go head to head with Copilot, it's like you guys, you guys are like listed like, like a dozen people. Like how can you, how can we trust over like our, our code base and stuff? Right? So that, that was kind of the strategy is like go after this very high demand, low competition arena. And of course it's very high. Like a lot of manpowers needed to deploy these. A lot of engineering effort is needed. But that, that is like what our team was good at. Right. So we were able to get our first revenue from that method.

Host 1 (10:45)

Okay. Yeah. So if I understand correctly, you really just stood out with the autocomplete feature initially. Kind of built your wedge, got your. I think it was like a million plus users with that. Or was it. Or how many users did you have?

Jeff Wang (10:57)

A couple hundred grand? Yeah, a couple hundred grand or a couple hundred thousand? I should say definitely, hopefully more than that.

Host 1 (11:04)

Eventually your.

Host 2 (11:05)

Your years is being in dollars. Yeah, yeah, it's all good.

Host 1 (11:09)

The CEO mindset now also I'm like

Jeff Wang (11:12)

recovering from this like very mega illness like I said in the beginning. So maybe my brain scrambled, I don't know.

Host 1 (11:18)

Yeah, maybe. Cool. So you always had that free motion. Now why was that super important for your market? Because like if you look at a lot of developer tools, we see this all the time. It's like they usually are all product led. But I'm curious, like why did your team kind of decide to have that initially free?

Jeff Wang (11:36)

I mean it was probably the only way we could get users. And if you look at the marketing campaigns we did, it was all about how it was free. It was not about like we have this enterprise product and it's going to like be very distinguished from anything else in the market. It's all about this is free. It's a no brainer to use it, driving conversions to downloads. And as long as you downloaded it, at least we would build awareness. And if you go to enterprises, they would probably have somebody that uses it and then they could be like, oh, I've heard of that company. If they have an on prem product, I know how it behaves and we should go in and buy them.

Host 1 (12:06)

Right?

Jeff Wang (12:07)

So that, that was kind of the, the logic behind that.

Host 1 (12:09)

Okay. And so for those thinking that our product led founders that just are like, oh yeah, great, that's the only answer is free products that were good. But that doesn't solve the business model side of things. So you went from like selling you know, a thousand dollars per year to like millions. So what changed when it came to how do you actually get or capture a lot of that value you create with that free model? What were some of the first steps you took because you started this product from like zero, basically from a revenue model.

Jeff Wang (12:38)

Well, you have to look at pipeline, right? So if you are rolling out a free product, you need to have a pipeline that has high value. And when you think about it, the on prem product was extremely high margin as well. So you would roll out something that the customer pays the GPUs for and they roll out and they pay the license to you. So we were actually making like a lot of money on the product that we were rolling to enterprise. And then our costs for PLG were actually fairly, fairly low in those days because we were able to control the cluster and infra as well. Now when you look at how when Windserve came out, this is another story because the token costs are way higher. Like you actually have to subsidize the tokens for the self serve model. And that is a different story then. Then when you get to enterprise, you really need to like optimize for the enterprise licenses to be, to cover the cost of self serve. And this is by the way, probably the number one problem. I would see founders trying to enter the arena today. I feel like we might have broken the costs in terms of how to run these models and now it's like the norm to spend like even like tens of thousands of dollars per, per user per month. And, and that is like not going to be easy for a founder to do if they're trying to roll out a product that, that is as capable of. Right?

Host 1 (13:47)

Yeah. Which kind of gets to how do you make that work? So when you're subsidizing these free users and you're like, hey, we gotta make this, this balancing act of like, okay, we gotta make enough on the revenue of the enterprise and if you have pro users as well. But basically every founder I've talked to recently that has a AI native product, they are to some degree subsidizing like quite substantially these free users. And that's why you do see a lot of them raising money because you're like, this thing is not that sustainable. So I'm curious how you handle it now and you're on handling it in the future too.

Jeff Wang (14:21)

Yeah, well, I think for us it's a little different. We've kind of established that AI coding is very valuable. So it's at the point where like if we subsidize then people are just using us as like arbitrage. Like they're just getting cheaper tokens from one location to the other. Which is by the way, we had, we had to replace all of our self serve products because people, it was like growing too much in the, in the worst way possible. It was growing because people were trying to subsidize their own token use. And if that is the case, that means that that is not PLG anymore. Right. That is more like you're just giving money away. And I think if you have product market fib, you can raise your pricing and your revenue should not be affected or meaning it should go up. Right. And if you raise your pricing and everybody goes to another provider, that's a bad sign. That means you're, you know, you don't have product market fib. You were never really in PLG to begin with because you were just, people were just using you for token arbitrage. So that's, that's something that's very important because that means A founder should really strive for something that has product market fib, that is differentiated, that is that you can compete to your target audience. Right. For us it's enterprise. So we, we are able to compete in enterprise because we have a lot of other factors. For example, we drive outcomes, we don't drive dev tools.

Host 1 (15:31)

Right.

Jeff Wang (15:31)

Like we, we go to our, our enterprises and, and, and understand what it is they're trying to build and like how much that costs and, and can we do it for a lot cheaper and faster? Right. So that, that is a very different product mindset than you would if you were rolling out a new, new, new tool, getting free users, subsidizing it and then trying to convert into payments. That, that is a very different motion actually if you think about it, if

Host 2 (15:54)

you, you speak a lot about this with enterprises and developers, right. So what is your funnel today? Is everything coming from a developer who goes through the free motion and then kind of recommends it inside the enterprise, or are you doing, what are you doing actively to sell to these enterprises?

Jeff Wang (16:12)

Basically, yeah. So I think most of the revenue is driven from the top down, meaning we meet with some of the leaders of the largest companies, try to really understand their pain points and where they're trying to save money and drive AI transformation. But there is a subset where you can make it easier for people to get their hands on the tools. So for example, we just rolled out Windsurf 2.0 last week. You can use the Windsurf login now to log into Devin. So you can actually try out the other platform, other parts of the platform by using the tools that were easy to deploy earlier. Right? Because Devin, I'll admit, is like you need to connect it to a code for it to have a lot of value. And that is actually kind of hard to drive self serve usage without something like having WinServ 2.0, installing it locally, driving a lot of value and then just logging into Devin as a result. Right. So there's some motions where you have existing easier entry points and then kind of getting people to, to your other products as like a PLG motion. And then of course we're trying to lower the barrier of entry, of course in general to the self serve for, for some of these products as well.

Host 2 (17:13)

How do you think about it? Because when you look at your market, the coding market and so on, right. Almost all your competitors are very plt and a lot of the go to market is driven through word of mouth and bottom up. Right. So a lot of their customers are single person shops and Stuff, but still paying money. Right, but it sounds like you have a more enterprise approach. How do you balance it? Right, because you also want to be a popular tool and get the word of mouth, but. But you also want to earn money. How do you find that balance? And it sounds like you've more gone towards the, the enterprise side of things where some of the others maybe are just trying to get a lot of word of mouth in the lower end of the market.

Jeff Wang (17:52)

Understood. There is an equilibrium right there. There's an amount of money that you're willing to spend on, we call it like marketing instead of plg. And then the amount you're willing to see how much money you get back from it. Right. And you could obviously in the extreme sense you can give everything away for free and see how much revenue you get, or you can charge a lot of money for it upfront and see how much money you get. So it's, it obviously it lies somewhere in between. And we've been experimenting with different price points and trying to figure out what, where that point is. And again recently we've just had to raise the prices because people were not the right kind of user. If you're only using the product because it's cheaper, that is not the right kind of user, especially if you're losing money. And I think that is maybe a hard truth to swallow for a lot of founders.

Host 2 (18:34)

And do you maybe just one add on questions that. So if you go more top down and stuff then I would guess what are your arguments then? Is it security? What, what is it that you bring to the table that the others can't? Because you are targeting this other icp,

Jeff Wang (18:51)

you can say, yeah, I mean a lot of the work we drive is very difficult to implement. So a lot of the AI transformation is not easy. I think some CIOs might think that rolling out a dev tool is all you need to do, but then they're going to look at their adoption metrics and really see like, oh, actually it's kind of weird that only maybe like 15, 20% of the people are actually using these tools. And why is that?

Host 1 (19:12)

Right?

Jeff Wang (19:12)

Well, a lot of it is because we have to go in and drive adoption and training. And another part is we have to go in and drive kind of the security and kind of getting access to the tough like very sensitive code bases or sensitive databases or connecting the things that really drive a lot of value. And a lot of companies are just not willing to do that or they're still trying to scale the team to do that, and that is a huge difference. When we go to talk to customers, it is like working with an actual someone that has a partnership to get the outcomes that you're trying to drive versus, hey, I'm the sales guy and I'm just trying to get as many licenses I can from you. That is like, that is not what we, how we operate. We, we go in and figure out what the biggest problems are. And that, again, very different take when you, when you chat with our team versus another team. And if there's a founder, by the way, trying to break into the industry, they, they need to understand this. A lot of the initial conversations was just us talking to like hundreds of customers, trying to figure out why they're not buying us, and then really understanding like, oh, they actually need a success out of this. This is not just like, do I make a decision on how many seats to buy? It is, what is it that they're trying to accomplish and trying to make sure that they get that.

Host 1 (20:21)

Yeah, I think it's worth kind of double clicking on, you know, the differences between a blue ocean and a red ocean. Like, I know as been what he started User Flow, it was like in the product adoption space, it's. At that time, it was like, well, okay, great, I know what product adoption is. I just found the best, fastest tool. Whereas what you're doing at Windsurf too, it's like, okay, there's a lot of education that you have to do. It's not a complete blue ocean in the sense that, like, there's not no competitors, but the market's so big, you still have to do a ton of education on. Not just here's the product, here's how to use it, but there's skills they need, there's knowledge they need to actually fully utilize this and get the rest of their team to adopt it as a new behavior as well. So what have you found has helped your team to really transform companies a lot faster than just like, hey, here's the tool, go ahead, use it. Because clearly, if they're only getting 10 to 20% of people using it, it's not going to get them to that successful outcome or transformation.

Jeff Wang (21:17)

Yeah, I mean, one of the biggest things is finding what's the most important project that your tool is very uniquely capable of solving. And for us, for example, if you're, if you're a large institution that has a lot of legacy code, there's probably a lot of migrations and a lot of upgrades and a lot of documentation, a lot of things that need to be done that has, that you can actually just do right off the bat in the pilot. Right, and that's one aspect of it. The other aspect of course though is the industry moves really fast and people, a lot of people don't even know the difference between like Sonnet and Opus or like Codex and, or like Anthropic and OpenAI and Gemini. Like they, they have no idea, right? They're opening the tool and they're just like selecting the default and using it. That, that was a big surprise when we realized like globally people just have no idea what's going on. In, in, in Silicon Valley. We know everything that's going on. We, we see the changes and we're up to date on the, on the trends. But no, nobody, it seems like globally, like very, very few folks and percentage wise are, are really up to date. So we have to be the ones that drive the knowledge as well, like keep them up to date and enable them to understand what is happening in the space. Because like I said, we just rolled out Windsor 2.0. It's completely new, completely new form factor. Right. And how do you even like assume everyone's just going to use this form factor? You don't, you cannot assume that. Right. So we have to go to these enterprises and start driving that behavior.

Host 1 (22:37)

Right?

Jeff Wang (22:37)

This, this behavior of even agents doing this end to end work is very hard to, to drive for, for someone that has no idea what AI is. And then the second thing I want to point out is we have Playbooks, so we have things that are repeatable that you can drive. So you go to the company, you've set up these Playbooks and then all they need to do is call these Playbooks to execute some of the type of work that agents can do. And that is actually very useful as well. We go there and build these Playbooks

Host 2 (23:04)

with our customers and maybe even though you're maybe doing more top down today, you still have that original PLG culture. And I think that helps you a bit when you're introducing these like new things. If you still have that culture that you can focus on things like it's not only about selling it to the management, you also have to, have to get the actual developers to use it and use it in a smart way. Right? It's never either or when you're a PLG enterprise business, you're still using those PLG routes or something good. Is that also how you see it or how do you think about, I

Jeff Wang (23:39)

mean there's a lot of things that are still Valuable from plg. Like one of them is if you want to experiment with features. So like we, we sometimes roll out features only to a subset of the users to see if they're adopting them. Or maybe like it breaks and like we have to figure out why or they're like they're using it improperly. So there's a lot of product feedback you can still get from like the self serve/plg user base and a lot of the feedback comes directly from them. If we go to enterprises with that feature and they complain, it is like too late almost. Right. You don't, you'd like, you don't want to spend your time fielding complaints from all your enterprise customers. You'd rather do it from the self serve users first and then get it right and then launch it to enterprise.

Host 1 (24:18)

Nice.

Jeff Wang (24:19)

I love that.

Host 1 (24:20)

And maybe switching gears a little bit here too. I'm curious, how do you use AI to be an effective CEO? Like what are some of the things you found have been really effective for you to. Whether it's save time, keep tabs on just how the business is doing. But I'm curious if you got like any specific workflows or things you've created to just yeah. Be more effective at what you do there.

Jeff Wang (24:39)

So just last week actually I created a few playbooks. One of them is mostly go to market Focus. So but it's like I want to be able to name a customer and generate an org chart for me.

Host 1 (24:49)

Right.

Jeff Wang (24:49)

And this is plugging into the examcp server. It's. They, they've indexed the web and they have LinkedIn as part of it. And, and then basically Devin will just build me this like this chart of like who's reporting to who at the, at the high level. And, and that, that workflow I can just give to everybody in the company now. Um, it's just like something very simple. Right. The other is a workflow. I'm gonna go to Google Cloud next this week and basically all you need to do is call Devin. I, I believe I called it Ghost, Ghost Note. Run the Ghost Note playbook and under my name. And then it'll write, it'll actually go retrieve who's relevant in that company, get their email. If you name an event, it could guess if they're gonna be at the event or not. Again this, this part is like you have to assume the, the agent's gonna hallucinate a little bit and it will write, it'll write the note in, in my voice. And then I'm giving this to my sales teams so that they can send that to me. Like, they can give me the temp. Like here, Jeff, can you just click this button to send the email? So I'm still putting a human in the loop, right? I don't want this to send a thousand emails on my behalf. I still want the sales team to like go vet, like, is this a good note and is this the right person to send to before. Before it comes to me? And yeah, and then I just go and I send a bunch of notes and I hope there's not a lot of other C C level people watching this and being like, wait, that's not coming from Jeff. It is just another example of a tool. Again, it is not like coding related, but it is. I mean, behind the scenes a lot of code is being written. But that, that is like a very useful tool that probably would have taken a lot of effort from the sales team.

Host 1 (26:16)

Right, got it.

Jeff Wang (26:17)

Yeah.

Host 1 (26:17)

And is there anything else outside of the sales side or just like understand the business overall that you found has been helpful? Whether it's like a daily schedule tasks or a weekly thing or something like that too? That's really helped you.

Jeff Wang (26:30)

Yeah. So a lot of the, like token usage product trends, for example, we did a big shift on, you know, Windsor 2.0. We did the pricing change. I'm always asking the agents, like, what is going on? What was the behavior? Did we just like lose a bunch of customers? Right. And it's funny because I kind of do that first before I even look at dashboards. So I'm kind of like relying on more granular insights from the agents than looking at like the actual raw outputs of the, of the dashboards, which is, I think, what a lot of other people are doing in the company too. I'm not exactly sure if that's a good thing, by the way, because the outcomes might be different from session to session, but that is the behavior that's like very normal, which is you ask the AI first and then you could like look at the data later if you want.

Host 2 (27:14)

So in this world where you can do almost everything with AI, Jeff, how do you actually build an organization? Because like, do you need a sales team? Do you need a marketing team? Because, you know, you can just start asking the AI to do some of the stuff they used to do. So what kind of profiles do we really need in a AI first organization? And maybe the bigger question for you is the role of the developer, right? Which is both somebody who works at your company, but it's also your customer who's a developer today. Like if everybody can code, who's actually a developer? What kind of role is that?

Jeff Wang (27:47)

I'll address the first question first, which is I think actually more than ever you need marketing and sales folks because you, you first of all sales, you need people that go and meet people in person and build that trust, right? I, I don't think the world's ready yet to completely trust AI to do an entire transaction end to end and, and to be like, hey, like here's the features I need. AI tell me to buy or not, right? I think people need to have, still have that human element. And for marketing, if everybody has the same access to a marketing tool, nobody has access, right? So like a lot of these GTM AI tools rolled out and then I got spammed with like hundreds of emails. I just like pretty much like banned all those, I blocked all those emails. I can't, I just, I can't keep track of all these emails in my inbox from all the spam. Right? So I think that's one thing is like if everybody has AI, then nobody has AI, right? For the GTM and marketing example. So you need humans to really figure out how to differentiate and maybe you could use AI to differentiate, but at scale it becomes the same again, right? For engineering, I think in terms of people to hire, I mean you need people that are like still very curious that understand how to link systems together and understand the architecture of the entire stacks. And like we, I, I, I will say like our engineering team is way more efficient. So for example, we've closed 700% like so 7x more PRs in the last six months. But we, our engineering team has only grown like 10%. So what, what I think is going to happen is people are just going to be doing a lot more. But you, you sit, you still need engineers. Like we're still actively trying to hire as many engineers as possible. We just, I think the bar at cognition is just very high. Um, but we, I, I, I don't think there's a world where a startup is going to say like, you know what, like we should just cut half the people. That is not going to happen because we need to move as fast as possible. Now when you have a large company, then the, the, the, the math might change. The, a large company might say like, oh, we just don't have enough things to work on. Which is really weird for, for me to hear, you know, from a, from a larger company. But that, that is where like they might either hire less or even if you're not AI native. Like for example, if you're AI native, you're probably more than 10x more productive than someone that is not AI native, then those non AI native people are in danger because you are operating at less than 10% of productivity than everybody else. Right? So that, that is something that I think people should be aware of is like this curiosity, this desire to use these tools is, is actually is helping your job. It's not, it's not, it's not making it worse like you do. You don't want to be left behind.

Host 1 (30:08)

And so going back to your crisis week, where it was like so much has changed in a few days. I was reading some of the past podcasts and stuff you're on and you said you lost like I think eight pounds that one week. In hindsight, like what would have been like the self management system you wish you had in place before the crisis hit? Like going back 2020 vision.

Jeff Wang (30:33)

Wait, what, what do you mean by self management?

Host 1 (30:35)

Like what would you have done differently to either manage that situation better, whether it's like take care of yourself, your team better, or like prepare mentally for something like that. Because whenever any leader listening to this like hits a crisis, there is things that are like, yeah, just drop that. But would you have done anything differently? I'm curious.

Jeff Wang (30:51)

I think the, the, the reason why, I mean, by the way, I like, I feel like I'm getting sick and traveling a lot now and that that's like the same things are happening again. But I think the main thing to, to, to think about is like what is the outcome you're trying to drive? And at the time it was like opening up the number of outcomes. And there's nothing more important at than the use of my time than to investigate as many outcomes as possible. Right. So with that in mind, things like eating dinner were not as important was it was like deprioritized because it was more important to talk to either folks on other companies or investors or even like calming the employees because they were all in a very high stress state as well. And all I could think about was like, how do I drive more outcomes here and increase the probability of success. And again, that sometimes means like deprioritizing your health and your wellbeing. And you know, obviously if you do that too much, you don't want to over optimize and you know, pass away. But so you want to think about what the balance is. So I think like, yeah, like if I were to look at it differently now, it would be how do I not overcompensate on the health side or, you know, over index on the deprioritization of health? I would, I would definitely, probably look back and be like, probably should have had something to eat in between some of the conversations, you know.

Host 1 (32:07)

Cool. Any other advice you have for like any other product founders listening to this podcast, like, what would be. Maybe it's something where it's like, hey, this has been the most helpful thing that's helped us scale Windsurf. Or, or maybe it's, this is how you could sell pretty quick to another company. What would be your, your biggest piece of advice you'd share? If this was like your two to three minute masterclass for, for any product founder, what would be that biggest piece of advice you'd give?

Jeff Wang (32:32)

Yeah, you should always build something that is solving a very painful problem and you should always market it as such. I think a lot of mistakes some founders make is like, I know how to use this technology and build a cool demo and make it like very flashy. But in reality what you need is to drive revenue. You need a customer, an end user at the end of the day, and you need to build something that is solving a problem specifically for them, or at least they have to feel that way, right? And if you have to go to a very narrow focus, like there's only a few people that it solves a problem for, but a hundred percent you're the only solution to do it. That is probably where you should, you should, you should start because agents are very general now and they could do a lot of things like cloud code and all these other products. They do a lot of things. If you build a product specifically for a narrow group of people that only will buy your product, that is a great place to start. And then once you drive that revenue, then you can start expanding and grabbing a larger share of the market.

Host 1 (33:29)

Is there any like, tips you'd have to get that like honed in other than just know your industry really well and see if like, hey, you interested in buying this? They're like, hell yeah, take my money. Or is there something else? There's you.

Jeff Wang (33:41)

You have to talk to a lot of potential prospects. You have to understand what, what are the painful things for them, right? I, I'll tell you. Like, with Codium, we probably talked to maybe four or five hundred different customers before we even like started closing a lot of deals in, in secession because that, that really made us understand the market and what to build and even like downstream when you're, when you're doing a pilot process and a sales process. There's a lot of things downstream that you, you, you, you, you don't encounter until you have this repetition. And you talk to a lot of customers, such as pricing or support or deployment. Like these things you really need to hone by just talking to a lot of the potential customers. And I think that part people miss that that's probably something that people don't understand is a lot of work.

Host 1 (34:26)

Yeah. And I think it's going to be a little bit more pronounced now too, because the time to build something, it's going down. And it's a lot more fun to build something that you're kind of into versus oh, I gotta talk to another customer or potential customer that's probably gonna say no.

Jeff Wang (34:43)

And you have to think that's fun. You have to feel that it's fun to get a customer to say no to you. And then it has to be like a game to try to flip that to a yes. If you don't think it's fun to get rejected, you should not be a founder.

Host 1 (34:57)

That is, I think, the perfect note to end on now for people to find out more about what you're up to. Where's the best places they can keep tabs or maybe even send you a message if they're like, hey, I thought this really resonated for me and share some feedback. Where would be the best places?

Jeff Wang (35:12)

I'm on Twitter at Jeff W. Surf or you can catch me on LinkedIn. Either way, those are the probably the places I post the most.

Host 1 (35:19)

Okay, awesome. Well, thank you so much for coming on, Jeff. This has been a blast. And to wrap things up, thank you everybody for listening to this version of the product led podcast. Make sure to rate review this on wherever you listen to podcasts, whether it's Apple, Google, you name it, Spotify. I'm going to read every single one of those reviews and that's how I know how to improve this. Also, if you want to stay in contact with Bean and learn what is going on in the world of PLG and every single week get the best actionable deep dives on product led growth. Make sure to head on over to product productled. Com newsletter. I am personally writing each of these deep dives every single week and you're going to get a ton out of it. So make sure to head on over there to productled. Com newsletter.