A Product Market Fit Show | Startup Podcast for Founders
00:42:36 · Pablo Srugo
[00:42:36] S1: It's hard to know and appreciate the difference between having that qualitative advice all the time and what that could do to product before and after you've actually done it. But can you share, do you remember anything specific that they said or even just an outcome of the fact that you had this where it's like, we would not have built this thing this way if it wasn't for them. And as a result, you know, the the product market fit was so much stronger. Like, do you have any of those kind of examples you could share?
00:43:01 · Chaz Englander, Pablo Srugo
[00:43:01] S0: I don't exactly, but what I do remember is, particularly in the AI world because these demos can be so good, so... A demo kinda counts for nothing. Like, real feedback is not when you are showing the user the product or what they can do. Real feedback is where they are using it and ideally by themselves. Right? You know, because people naturally in front of people, they smile particularly I think in Europe, like there's this culture of like everyone says everything is great when in fact it might be awful. I think in The US they're way better for just saying how it is a long time. So that's what you wanna monitor. And by the way, that may also not be in what they say, it may be in the data as well. Right? So like, you know, I would say that really really pay attention to actually how much they're using the product because like usage just doesn't lie. It's... You know, how amount of times you would hear a user say this is great, you know, having to ask for the first like three months but they're not using it. Well, clearly not great if they're not [00:44:03] S1: using it. You know what I mean? Like what were you waiting for? When did you know when it was ready to launch? Like what was that trigger? And then how did you structure the actual like launch beyond design partners?
00:44:13 · Chaz Englander
[00:44:13] S0: In true kind of Y Combinator fashion, we just like, soon as we even had close to a product, we were like launched and we were going out and doing as much as we could. I think we made a bet with you know, in the way that we were thinking about it, that was given the procurement cycles of our industry, know, six, nine, twelve months at a minimum type numbers, We knew how quickly the product was gonna evolve, so we almost had to sell like three, six months in advance. Because if we were selling what we had today, you know, we were never gonna get ahead. But there was this point where it's so hard to describe it as that tipping point of product market fit. People start using the product by themselves, you know, getting people to pay was less of a pain, know, people would pay and then you would never really speak to them and they would just continue using the product and it starts to happen. But when that happens, you gotta turn the tap on. You gotta put the running spikes on and you gotta be prepared for almost no sleep. Mean, as I mentioned, you know, before like first year we were nine ninety seven, whole year, every waking hour frankly. Because it was a it was a big TAM, we knew that, and we knew it was gonna be competitive, and we also knew, and I think the League of AI space is a great example of this, you know, you mentioned Legora, is that once you hit escape velocity in a market like this, it's very, very, very difficult for anyone else to then join that group. Right? And basically I use escape velocity with a very clear definite... Definition. You know, escape velocity is where, you know, twelve months or so ago, it was a risk to use ModelML. I now think in some markets, it's a risk to not use Model ML. That's escape velocity. And and I think, like, when you get in that pack, you know, it's very difficult now for other folks to join, and it's exactly what happened in the lead AI market just by the fact that you're compounding, You raise more money, you can build more product.
00:46:10 · Pablo Srugo, Chaz Englander
[00:46:10] S1: How fast did you get to like a million ARR? [00:46:13] S0: We went from about 5 k in monthly revenue to about a 100 k in three months. And then we kinda did that again in like the next three months. Things moved fast. Also when you've got contracts that are scaling given the types of organizations like us, a big part is like as soon as you land them, you know you're just... You're gonna build from there. [00:46:39] S1: What would say is like the the... When you think about go to market for enterprise, what's worked best for you? Like, whether it was around launch or whether it's about today, like, what is what is the thing that from a sales playbook, go to market perspective, you think is like comes top of mind?
00:46:52 · Chaz Englander
[00:46:52] S0: Think about it. It's like you have to go into things, in my view, of your product is not unique. In other words, like, the the person that you are pitching has probably seen 10 other products that look like your product. I mean, is very relevant, zero to one. Think beyond that becomes quite different. Now... So where where do you differentiate or how do you differentiate? I think a big thing, admittedly I think this is very important in our market unless you're another buyer, is is trust and credibility. Because if you think about the buyer, they have to latch on to something that sets you aside from the other 50 AI tools that they've now seen. Right? And in our market that was trust and credibility, know, we ended up bringing on a bunch of like really really senior advisors to the business that became also close friends to myself personally and the business. That was really really helpful, that was a way to differentiate. I think now though, look, the way I look at businesses once you've got past that point, you know, think this is super important in the context of product market fit not being static. What I mean by that is like, if you look at our risk this year, right, you know, won't mention the number we have in the bank, but we've just raised a big round of 75, we raised 15 before and we basically spent no money, right? So what is our risk? Well, there's kind of two elements of risk in any business of this stage. Do you have product market fit? And are you one of those companies that, you know, escape velocity? I think that we are definitely one of those companies escape velocity. And today we have product market fit. So what is our risk? Well, our risk is, as I mentioned just then, it's like, we may not have product market fit tomorrow morning. Right? And that's not based on like OpenAI or Anthropic or these, it's just based on the speed that Birdsapphire's application is shipping great product. So really our only risk now is the speed at which we can ship product. That is it. And that comes from the speed that we learn and the speed that we can ultimately write code. So stay very close to the customer. Ideally, you want the code and the customer to be as close close together as possible.
00:48:52 · Pablo Srugo, Chaz Englander
[00:48:52] S1: Love it. Well, listen, we'll stop it there. Let me ask just two questions with like kind of rapid fire. When was the moment when you personally felt like you'd found true product market fit? [00:48:59] S0: When we were having to push customers start dates back, and they were getting upset. [00:49:05] S1: And what would be like your top advice having gone through three startups for like a founder in that early stage looking for product market fit? [00:49:11] S0: Perseverance. For sure. Not blind perseverance, but perseverance. [00:49:15] S1: Jaz, thanks so much for sharing the story, man. It's been great. [00:49:18] S0: Amazing. Thanks so much, buddy. [00:49:20] S1: Wow. What an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel. Send it to your founder friends and help them out. Trust me. They will love you for it.