A Product Market Fit Show | Startup Podcast for Founders
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?
00:20:06 · Pablo Srugo, Dylan
[00:20:06] S1: The thing is, didn't you just not know at what point the market would accept? [00:20:10] S0: You didn't know back then, but I also think that we were trying to make short-term progress because we're in YC. It was hard to break out of that mindset post-YC. You have these weekly meetings in YC with your partners and it's like, okay, what progresses have you made? What progress have you made? [00:20:25] S1: Well, the mantra is quick iteration, right? It's lean startup methodology.
00:20:30 · Dylan
[00:20:30] S0: I think there's some you should be growing 10% week over week during YC because if you're building a consumer or even B2B app and you can quickly acquire users and iterate over the weekend or at night, ship new features and iterate super quickly, you have a higher chance of finding that traction quickly within a compressed timeframe if you're working super hard, like you do in YC, but for us, because we were building models, it was like, all right, let's train this model. Let's see if we can get people to use it. They can't so let's train another one, see if we can get people to use it. Because we were trying to make progress really quickly because we didn't have a ton of capital. Back in YC, when we did it, you received a $125,000 investment and I knew that we would need to raise more funding at the end of YC. We had to try to show some progress, some traction, and we had a little bit, to be honest, which helped the round, it wasn't like we had nothing, but I think it was hard to break out of that mindset post-YC. For the first year, we were still, all right, let's iterate, try to get customers, iterate, try to get customers, whereas if we took a longer-term approach earlier on and said for the next year we know there's market demand, right? It's not like we're building a productivity tool where we don't know is there going to be product market fit for this mode of note taking that we are pioneering. We know there's market demand for this tech. There are industry established accuracy metrics that we can leverage. Let's set goals for these metrics and let's surpass them and then let's go to market after. We could have been much more explicit about that. I think learning lesson, for me, if I were to start another company again is really when starting, as much as possible, try to set really, really clear long-term goals and then work towards them and be very disciplined. You might have a customer that will want to use your early MVP, but if that customer pulls you in the wrong direction, they're going to have a ton of influence because they're your first customer and then you have to build for them, and for those early days, I think we could have gone faster if we were more focused and disciplined.
00:22:33 · Pablo Srugo, Dylan
[00:22:33] S1: I think that makes sense. I think in your case, I mean, the way I abstracted out is, in the early days, you're trying to de-risk and you're trying to find what's the biggest source of risk for this to work to get to the next step and let's de-risk that. I think in most cases that risk is really demand. It's like you're doing something new, you don't even know if people are going to buy it. That's why you get into this iterative customer, hey, is this good enough, whatever. In your case, I think you're right, demand wasn't the biggest risk. The biggest risk was just can you actually with this deep learning infrastructure, get the technology to a place where it's better than everything else or whatever, and so it would've made sense. [00:23:08] S0: If you're building a productivity tool and you don't show customers and you build for a year, it might be like, I don't care, this thing's stupid. [00:23:16] S1: Correct, and there's no risk on building. You will definitely be able to build the tool. It's more just will they buy. Maybe walk me through this. You've got a million dollars, you got whatever, three-ish people, you're doing this cycle, you said, for over a year. I mean, cash is running out. I don't think you can get profitable. What's going through your mind in terms of where you need to get to to raise that next round of funding?
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.