A Product Market Fit Show | Startup Podcast for Founders
00:23:37 · Dylan
[00:23:37] S0: We started to get our first customers in, I think, late 2019. The first two years were just building. Honestly, our models were pretty good at the time from an industry perspective, but the industry even best in class back then was just not that good. You couldn't power that many use cases back then because the models were not very robust. They were not very good. For the first two years, we were really just building, wandering in the forest. Then it was around 2019 that we started to get our first couple customers. I think that was the time that we passed this initial threshold of like, okay, it's good enough now to power certain use cases and applications and customers started switching to it from whatever they were trying to do before or they were able to now build something that they had been wanting to for a long time, but it just wasn't good enough anywhere yet. Then that's continued to happen, right? As we've made models that have gotten better, as other tech within the ecosystem has gotten better, like vector databases and text to speech models and large language models, the amount of things you can do now with this tech is increasing and it's increasing every day. We always are seeing more and more and newer and newer projects and products and features being built because the tech continues to get better, the ecosystem continues to get more mature, adoption continues to mature, and it's still super early from an adoption perspective when you look at the market. Enterprises are still really figuring out what they're doing with AI, what the use cases are, but it's maturing rapidly.
00:25:12 · Pablo Srugo, Dylan
[00:25:12] S1: Do you remember some of the first use cases, some of those first customers, what their use cases were or maybe even one of the first things you remember where you were like, wow, we're powering this application. This was what we've been building towards. [00:25:25] S0: Yeah, so one of our first customers was building this product where they were analyzing TV and radio stations 24/7 and then they were looking for certain brand mentions that were spoken and alerting those brands when their names were mentioned. That was one of the first customers we had and they actually found out about us on Hacker News. The CTO reached out and they checked out our API and they liked it and they built this product with it. Then there were a few others. Some of the initial customers were in the call center space, so analyzing customer support calls to create insights. There were some voice agent, like voice bot applications in the early days too. You're seeing a lot more of those now, but primarily in the early days it was media contact center type use cases. [00:26:17] S1: Based on Crunchbase, what I see is you raise this YC thing, you raise this million-dollar round, and then the next thing that's listed at least there is 2020 raised $50 million. What happened between? Was there a smaller, a C+ or a series A or something that bridged that gap? [00:26:32] S0: Yeah, so we raised a million dollars after YC in 2017. Then I think it was right after COVID, it was the summer of 2020, we raised a $5 million round and that was also from angels. It was Daniel Gross, Nat Friedman.
00:26:32 · Dylan, Pablo Srugo
[00:26:32] S0: Yeah, so we raised a million dollars after YC in 2017. Then I think it was right after COVID, it was the summer of 2020, we raised a $5 million round and that was also from angels. It was Daniel Gross, Nat Friedman. [00:26:49] S1: What was traction like at that point? [00:26:51] S0: We had passed a million dollars in ARR. We had decent traction at that time. We were growing pretty quickly and we're still a super small team. I think we were sub10 people. Then it was a year and a half later that we raised the A and then a couple months later our B and then most recently our C. The 2020 is really when things started to compound and the trajectory really changed. [00:27:16] S1: What do you attribute that to? Was that just basically the stack getting good enough, getting over that bar where all of a sudden people could just build all these applications on top of? [00:27:24] S0: Yeah, it was a combination. It was macro, market adoption, matured in part because the tech was getting better, right? As the tech gets better and early adopters demonstrate its capability out in market that pulls the market forward and then more people adopt. That's just been happening. Then of course 2022, this whole… [00:27:46] S1: Gen AI wave.
00:27:46 · Pablo Srugo, Dylan
[00:27:46] S1: Gen AI wave. [00:27:47] S0: All this innovation has just taken that market adoption and accelerated it more than I think anyone could have imagined even 18 months, two years ago. It's hard to remember beginning of 2023, there's one mainstream LM, and now there's thousands and there's open-source models and there's tons of companies in this space. This acceleration we're in the middle of is very new, but I think it will continue to be honest. [00:28:14] S1: Perfect, well, let's stop it there. Let me just end with the two questions that we always end on. The first question is, when did you first feel like you had true product market fit?
00:28:14 · Pablo Srugo, Dylan
[00:28:14] S1: Perfect, well, let's stop it there. Let me just end with the two questions that we always end on. The first question is, when did you first feel like you had true product market fit? [00:28:23] S0: It's interesting, right, because for us, I knew that there was market demand even before we started building the thing, whereas with a lot of companies you don't know, hey, are people going to want this service or is there going to be product market fit for this type of product or service? For us, similar to self-driving cars, you know that Level 5 self-driving cars will have product market fit even though they don't exist yet. Now, of course, if they cost a million dollars per vehicle, the addressable market is probably much smaller versus if they cost $10,000 or if there were just robotaxis everywhere. There is a question of economics, but there's not a question of market demand, unit economics, but not market demand. For us, I always had conviction that there was market demand and product market fit for what we were building. That being said, I think that, we serve many different use cases, so we have people building so many different types of things with our API. There's still opportunity for us to improve product market fit for what people are predominantly choosing to build. That's really the work right now. I would say it never stops because the market's changing what customers want to do and users want to do is always evolving, but I always had conviction that there was product market fit for what we were building.
00:28:23 · Dylan, Pablo Srugo
[00:28:23] S0: It's interesting, right, because for us, I knew that there was market demand even before we started building the thing, whereas with a lot of companies you don't know, hey, are people going to want this service or is there going to be product market fit for this type of product or service? For us, similar to self-driving cars, you know that Level 5 self-driving cars will have product market fit even though they don't exist yet. Now, of course, if they cost a million dollars per vehicle, the addressable market is probably much smaller versus if they cost $10,000 or if there were just robotaxis everywhere. There is a question of economics, but there's not a question of market demand, unit economics, but not market demand. For us, I always had conviction that there was market demand and product market fit for what we were building. That being said, I think that, we serve many different use cases, so we have people building so many different types of things with our API. There's still opportunity for us to improve product market fit for what people are predominantly choosing to build. That's really the work right now. I would say it never stops because the market's changing what customers want to do and users want to do is always evolving, but I always had conviction that there was product market fit for what we were building. [00:29:33] S1: Final question, if you could go back to when you were just starting Assembly AI back in 2016, 2017 with one piece of advice for yourself, what would that be?