Generative Now | AI Builders on Creating the Future
00:15:23 · Mike Mignano
[00:15:23] S0: Let's go back a little bit. So we talked about, you know, sort of your role as an investment banker. We talked about some of the early theses. You know, we talked about when you got started. Take us back to that moment. You know, it sounds like 2021, 2022, obviously, ChatGPT has sort of, like, just burst onto the scene sort of the beginning of 2023. What was it like then starting this company and and maybe even, like, talking to investors and your first customers about what you were trying to do at a time where people had far less understanding about AI and, you know, the impending implications than they do now?
00:15:55 · Gabe Stengel, Mike Mignano
[00:15:55] S1: So we started the company before JetGPT came out. GPT-three was out. And so for those paying attention, you know, it was obvious that this was a profound leap in technology. But all the banks and hedge funds and private equity firms we spoke to, you know, I think they thought this guy gave us full of snake oil, not this is going to change the way we work. It a long time until the market was receptive to what we were building. And it was frankly also a long time until we sort of figured out what exactly the market wanted to, right? It's easy to say, great. Let's make people smarter and more efficient with AI. How does that manifest itself in a product? What are the use cases? Like, how are people going to use it? How important are, you know, these use cases versus those use cases? How do you pitch it properly? Who do you sell it to? Those were all the things that we slowly had to learn over our first, let's call it eighteen, twenty four months where, you know, the market was not that receptive to us. [00:16:49] S0: What were you promising them? Like, what would [00:16:51] S1: you what would you say [00:16:52] S0: to your your early customers?
00:16:52 · Mike Mignano, Gabe Stengel
[00:16:52] S0: to your your early customers? [00:16:54] S1: I mean, there weren't that many early customers. Yeah. We didn't have real customers until 2023, late twenty twenty three. And even then, like, you know, you, as a founder, you're trying to get someone to use what you're building at all costs. Right? And so you'll you'll do a whole song and dance, just to get someone to be hands on keyboard in your platform just for the product to break after, you know, twenty five minutes. The initial pitch was, hey. What if you could ask questions over your data and get answers in five seconds as opposed to using a clunky interface like one of these old tools like Bloomberg or, you know, the EDGAR SEC filings database or, you know, pinging an analyst on your team. And so that was the pitch and it was pretty simple. Yeah. But it was hard to build and then hard to actually expect what folks were going to ask, right? When you're pitching a generic search or chatbot, it means, you know, you have to build everything in on day one. Otherwise, people are going to try, you know, the one thing you're expecting and then the five things you aren't and the product is going to, you know, not suffice. [00:17:52] S0: Right. What about the early investor pitches? You know, what were you telling them? What resonated and what didn't?
00:17:52 · Mike Mignano, Gabe Stengel
[00:17:52] S0: Right. What about the early investor pitches? You know, what were you telling them? What resonated and what didn't? [00:17:59] S1: Kevin Ryan, who led our pre seed round from a firm called AliCorp based in New York. But Kevin himself is sort of a, you know, self proclaimed godfather of New York City tech. You know, started MongoDB and Guilt and Business Insider and was the CEO of DoubleClick. He kind of got it immediately. But his function, his routing function, don't think was AI is going to change the world. I think his routing function was Gabe and John are two guys who are smart and understand technology and intimately understand investment banking. And, you know, being from New York, I know investment banking is a domain that is ripe for disruption. [00:18:33] S0: Did you know him from Mongo? [00:18:34] S1: I knew him actually from growing up in in New York. So I had actually met his his children a long time when I was going to school here. And then I met him again at Mongo. Yeah. I interned at Mongo and he came to speak and I would send him, you know, a few one off messages. And then he actually got in the routine of, you know, and to his credit as an investor, every six months he would send me an email being like, hey, what are you thinking about? Do you still like Lazard? You know, what you doing next? And so when I had this idea while at Lazard, he was the first person I messaged. And it was a very natural start. That's awesome.
00:18:34 · Gabe Stengel, Mike Mignano
[00:18:34] S1: I knew him actually from growing up in in New York. So I had actually met his his children a long time when I was going to school here. And then I met him again at Mongo. Yeah. I interned at Mongo and he came to speak and I would send him, you know, a few one off messages. And then he actually got in the routine of, you know, and to his credit as an investor, every six months he would send me an email being like, hey, what are you thinking about? Do you still like Lazard? You know, what you doing next? And so when I had this idea while at Lazard, he was the first person I messaged. And it was a very natural start. That's awesome. [00:19:09] S0: Yeah, yeah. That's really cool. You talked early on about how early on it was tough to get customers to sort of take this thing seriously. You said you didn't really have any. What was sort of the moment in the product that sort of flipped to, you know, product market fit and getting your first early sticky customers?
00:19:25 · Gabe Stengel
[00:19:25] S1: There were two core things. One was changing our focus from just structured data to structured and unstructured data. So from, because the original product was like was econometrics on structured data, that's what we pitched. Right? It was, hey, make a chart of revenue multiples. You know, create a scatter plot of this versus that. And we weren't taking advantage of what LLMs are best at and most magical with, which is unstructured data. And we were too stubborn before, you know, opening the aperture. The second thing was I just changed the way we were pitching the product. I mean, distinctly remember being on a call with a large private equity firm and it going, you know, the product demo going better than I'd seen a product demo go in a while, in part because we'd added this unstructured data lens. And they asked for pricing. And I was like, well, firms this big never ask for pricing. They normally like race to get off the call. Right. Of course. And so I have no sense of what to charge. And so I took, you know, ten seconds in my head and thought, okay, you know, I'm gonna, let's push the full court advantage here, dollars 2,000,000 a year, right? Just pull a number out of thin air. And they go, okay, makes sense. And as soon as I got off that call, I called my co founder and I said, hey, you know, this firm didn't laugh at that number. This, we might be onto something. You know, if someone hears $2,000,000 a year and goes, oh, that, you know, that could make sense. It means you're pitching something that they think is very valuable. And so it was those two moments changing how we were pitching and what we were building that really started to unlock customer traction.