A Product Market Fit Show | Startup Podcast for Founders
00:14:50 · Pablo Srugo, Dylan
[00:14:50] S1: Was that hard? What was that raise like? I mean, because you have no real customer traction. I assume the model still doesn't really work all that well. I mean, it's really just a bet on you and deep learning in a high-level thesis. [00:15:02] S0: Yeah, so no institutional funds invested. It was only angels that invested because a lot of the angels that invested, they saw that either through their own personal experience or their own career or other investments that there's totally an opportunity for startups to build great developer products in spaces where there's large incumbents. You've seen this, Stripe, PayPal is a great example and there's a lot of others, like Heroku. We had a lot of angel investors that invested in the first trial we did after YC that saw that there was an opportunity for us to create this company. [00:15:36] S1: What was the VC path like? I assume that you spoke to them. What was their reaction? Why did they pass?
00:15:36 · Pablo Srugo, Dylan
[00:15:36] S1: What was the VC path like? I assume that you spoke to them. What was their reaction? Why did they pass? [00:15:42] S0: It was just the, how will you compete with Google question. That was pretty much it. I was like, okay, I know the questions they're going to ask. Now, actually, every time I fund-raise, and this is actually maybe a good tip for startup founders that are fundraising, I actually write up a big document with all of the objections I anticipate to get. I write out lengthy answers to all of them. I don't share that with anyone. It's for me to make sure I'm really, really tight and articulate on all of the competitive and market questions that I anticipate to get. That has been super helpful now. I had never raised money before, right? Never raised money for a startup. The very first meeting I took at the end of YC was with Sequoia. [00:16:25] S1: Oh, no, okay. [00:16:28] S0: No experience fundraising, no real progress, don't really know what I'm doing, and just start with the most experienced investors possible, probably not the right strategy. [00:16:44] S1: Yeah, I mean, you went to the all-star game right off the bench, yeah. [00:16:48] S0: In hindsight, probably stupid. I didn't have a name, right? It'd be different if I was this PhD or this very seasoned entrepreneur and I could show that I had a resume, I knew what I was doing, but I was unknown. We didn't really have a lot of traction. Even now still you're biased to think it will be hard for Assembly to compete, but there are thankfully really brave and ambitious angel investors out there.
00:16:48 · Dylan, Pablo Srugo
[00:16:48] S0: In hindsight, probably stupid. I didn't have a name, right? It'd be different if I was this PhD or this very seasoned entrepreneur and I could show that I had a resume, I knew what I was doing, but I was unknown. We didn't really have a lot of traction. Even now still you're biased to think it will be hard for Assembly to compete, but there are thankfully really brave and ambitious angel investors out there. [00:17:13] S1: That's right. That's why they’re called angels for a reason. [00:17:16] S0: Yeah, exactly, and still investors, our investors now, I mean, Steve and Sarah at Accel and Rebecca at Insight, and then the more recent investors from Smith Point, like Keith Block, they all believe too. I think when you're raising capital, the biggest thing is just finding people that believe in the opportunity and that are excited about it. It's a bit of a numbers game, so you have to just meet as many people as possible to find that. It's like dating, right? I mean, you're not going to find your soulmate on your first date.
00:17:16 · Dylan, Pablo Srugo
[00:17:16] S0: Yeah, exactly, and still investors, our investors now, I mean, Steve and Sarah at Accel and Rebecca at Insight, and then the more recent investors from Smith Point, like Keith Block, they all believe too. I think when you're raising capital, the biggest thing is just finding people that believe in the opportunity and that are excited about it. It's a bit of a numbers game, so you have to just meet as many people as possible to find that. It's like dating, right? I mean, you're not going to find your soulmate on your first date. [00:17:47] S1: I think that's totally true, and especially for the early rounds. In later rounds, you have numbers, you have traction. You start fitting in specific boxes. There's still a numbers game element to it. I think in the early days, you're totally right. It's really about just finding believers. If somebody doesn't get deep learning, doesn't care about building platforms, doesn't really matter how great your story is, they're probably not going to be the right fit for you. That's really what you're looking for. Okay, so moving along, you raised that round. You have, I mean, even a million dollars, it's not a lot of money. I guess you hire a few handful of people or five people or so at that point?
00:17:47 · Pablo Srugo, Dylan
[00:17:47] S1: I think that's totally true, and especially for the early rounds. In later rounds, you have numbers, you have traction. You start fitting in specific boxes. There's still a numbers game element to it. I think in the early days, you're totally right. It's really about just finding believers. If somebody doesn't get deep learning, doesn't care about building platforms, doesn't really matter how great your story is, they're probably not going to be the right fit for you. That's really what you're looking for. Okay, so moving along, you raised that round. You have, I mean, even a million dollars, it's not a lot of money. I guess you hire a few handful of people or five people or so at that point? [00:18:22] S0: We were three people for a long time. I think in hindsight still today, it's difficult building a research-based product because you're never done in software development, but there's a spectrum to the quality of everything you build, right? There's accuracy metrics and there's issues even with our current models that are really good and industry leading. It's very hard to know what is the threshold that you have to pass on certain metrics for the products that you're building. [00:18:51] S1: Back then, what was… were you like we need to be 95% accurate or what was even the goal? You have three people, you have a bit of money, a bit of runway, what are you shooting for?
00:18:51 · Pablo Srugo, Dylan
[00:18:51] S1: Back then, what was… were you like we need to be 95% accurate or what was even the goal? You have three people, you have a bit of money, a bit of runway, what are you shooting for? [00:19:00] S0: Because even if you take a look at GPT3.5 and GPT3 existed for a long time prior to ChatGPT. It was the RLHF and the dialogue component of ChatGPT that triggered the takeoff. That was the capability threshold. It was at that time that this capability threshold passed where now it is LMs are priority for every organization. [00:19:23] S1: It's crazy. It's this good enough or not good enough, right? It's binary in a sense. At some point, you don't work in the eyes of people and there's some point where you pass it, well, obviously you still can get better but you go from not working to working. At least that's what it feels like from the outside. [00:19:38] S0: Exactly, and I would argue GPT2 or GPT3 or I forget the specific version numbers, but it worked pretty well prior to ChatGPT. I think my point with this is, in the early days, I think we could have actually gone a lot faster if we had said, okay, let's do nothing but pick accuracy metrics that we feel we want to surpass. Once we surpass those metrics, then let's go and take this thing to market. [00:20:06] S1: The thing is, didn't you just not know at what point the market would accept?