A Product Market Fit Show | Startup Podcast for Founders
00:14:45 · Pablo Srugo, Colin Zima
[00:14:45] S2: Right. [00:14:46] S0: We'd have an opportunity to have a conversation with them and you've got to find these windows of opportunity. Because we could have built the best product in the whole world as two or three completely unaffiliated founders, and maybe no one would have cared. But because we had just been successful gives people another look, also, I probably met with three or four thousand Looker customers and they trusted me as an individual. And that means that they can now have a conversation where I can say like, hey, come look at my thing. You know, I'm good. You know, you trust me, and great. Now I get an opportunity, at least for our product to go solve their problem. [00:15:21] S2: Yeah, that makes sense. I mean, obviously trust. The story overall, trust, credibility, all that stuff matters a lot. The other thing that matters, you know, at least classically in these situations is, is the product. Usually can build a product that's better at least in one dimension, but is it like 10x better, right? Is it way better, at least on some dimension? What did you see that you were able to, like what was the piece of what, I'm sure Looker did some things great and some things not so great as you mentioned. What were the pieces that you said, this is the thing that we're gonna be known for. This is the thing that we're gonna do so much better than Looker does?
00:15:21 · Pablo Srugo, Colin Zima
[00:15:21] S2: Yeah, that makes sense. I mean, obviously trust. The story overall, trust, credibility, all that stuff matters a lot. The other thing that matters, you know, at least classically in these situations is, is the product. Usually can build a product that's better at least in one dimension, but is it like 10x better, right? Is it way better, at least on some dimension? What did you see that you were able to, like what was the piece of what, I'm sure Looker did some things great and some things not so great as you mentioned. What were the pieces that you said, this is the thing that we're gonna be known for. This is the thing that we're gonna do so much better than Looker does? [00:15:51] S0: Yeah, the biggest one was data model. Looker was built on the data model. So this idea of build a semantic layer, it does everything. It was extremely rigid and so the idea was, can we let you do all of the normal things that you do in a BI environment, like write SQL and do things on the fly. But then turn it into semantic layer later? And so it was just an inversion of how you go do it. Like, do you have to model and then ask a question or can you ask a question and then model? And that's small, but for a data person that really matters. Because it lets you move more quickly and so it was just that sort of flip that unlocked our ability to do things. [00:16:28] S2: For people that are not like BI experts, how does that play out? What does that mean in a day to day situation?
00:16:28 · Pablo Srugo, Colin Zima
[00:16:28] S2: For people that are not like BI experts, how does that play out? What does that mean in a day to day situation? [00:16:33] S0: Yeah, so the way to think about it is sort of like do you build the foundation of the house before you build the house or do you just build the house and then figure out the foundation after? The big thing that we did was let you build the house and put the foundation in after. So you could walk into a tool, you could go do something that's a complete mess that you would throw away, and then after you do that thing. You could decide, I want to keep this thing for later and actually take good care of it. And classically, the choice you'd have to make was, you do something throwaway and it stays throwaway, or you do it right the first time, and it's really solid. And then you have a good foundation. We wanted you to build throwaway things that could turn into a solid foundation and it's subtle, but for people that live in these tools every day, and AI actually became a huge driver of this. It really matters, because AI needs to do new things when it answers questions. So you can't constrain it, but as it does those new things you want to be able to make sense of them and you want to put foundation underneath it that's solid. So that metrics are consistent, so that permissions are consistent, so that people understand what's happening. [00:17:32] S2: When you leave do you raise a round and kind of hope, go heads down, just build product? Do you start talking to customers right away? How do you play it?
00:17:32 · Pablo Srugo, Colin Zima
[00:17:32] S2: When you leave do you raise a round and kind of hope, go heads down, just build product? Do you start talking to customers right away? How do you play it? [00:17:39] S0: Yeah, so we were incredibly fortunate in that I was close with a bunch of members of the Looker board. I talked to First Round the day after I left Looker, and so we raised money, or we didn't even raise the money. We had a handshake agreement to raise the money like four days after we left the company and that just gave us the sort of cushion. So I funded it with a loan for a little while to pay salaries. Because I was like let's get more founders in here essentially. We got sort of comfort that we could fund it very, very early. So that we could staff it with a lot of people that know how to build. [00:18:20] S2: When was your seed and how much was it? [00:18:22] S0: Yeah, so we raised effectively a seed A over like six months apart but very, very close together. The seed I think was in May of '22, and it was $9 million and then the A was in August of '22, or something like that. And it was another $17 million I think. [00:18:38] S2: So you had plenty of capital very quickly. [00:18:40] S0: Yeah, we got to found the company essentially with $30 million, which like a lot of people don't get to do. But it let us invest really nicely in product and engineering, and we were kind of off to the races immediately. [00:18:44] S2: You also like macro timed it perfectly. I don't know if you would have gone $30 million like January '23.
00:18:44 · Pablo Srugo, Colin Zima
[00:18:44] S2: You also like macro timed it perfectly. I don't know if you would have gone $30 million like January '23. [00:18:50] S0: We got very lucky. Honestly, even in March of '22. It was a little bit more dangerous than February of '22. [00:18:56] S2: That's right but you got it May and August, you said, '22? [00:18:58] S0: We did but this is also the nice thing about working with people that you trust. These were people that were on the board at Looker for the last ten years with us. That we knew really well, that wanted to come take this bet with us and they were sort of just like, we trust you guys. Go build. [00:19:13] S2: So I mean, look, raising that much money early is obviously awesome and a lot of people would be jealous of it. There are problems, there are challenges with it. The most common one that I've seen of people that get to raise. Let's say anything, I would say anything above $10 million at idea stage. The challenge is, do you spend a lot of time and money on the things that don't matter? Do you build a lot of polish? Or do you still somehow find a way to stay focused on the thing that's really going to move the needle because you still got to de-risk them. You still don't know. This is somebody gives you $30 million doesn't mean it's going to work, right? So how did you structure it after you raised that money?
00:19:49 · Colin Zima, Pablo Srugo
[00:19:49] S0: I think that just generally we're on the very stingy side of how we operate and we're kind of old guys as startups go. So we obviously spent some money early, we had ten engineers with no customers. But we were very focused on the economics from a very, very early point, like mapping things out. We didn't just spend money. We didn't get an office for a year. We tried to be really, really thoughtful about our spend. Because we do want to minimize this stuff. At the same time, we knew we were playing the venture game. When you go raise money that is now an expectation that you need to hurdle and so you need to understand the game that you're playing and we wanted to play the venture game. It's a crowded space. We needed to invest in sales and marketing. We needed to invest in products and engineering. We got to go build for a year and a half to go do these things. [00:20:35] S2: How many people did you say you were, before revenue? [00:20:37] S0: Before revenue, we probably got up to twenty people. So relatively large. [00:20:43] S2: Decent amount. Because at twenty, like at five is where you have to do zero management, zero overhead. Once you get to twenty, you've got at least some communication, let's say overhead. if you want to call it, at least that. Even if everybody's a self-starter. [00:20:58] S0: I would say a little bit, we're a trio of founders. Which helps a ton and out of those twenty people, maybe fifteen of us had worked together at Looker. [00:21:04] S2: I see, yeah