Generative Now | AI Builders on Creating the Future
00:12:33 · Gabe Stengel
[00:12:33] S1: The better the next generation of models is, the better ROGO will be. Right? We can very quickly take those models and take that intelligence and and have it build into our system almost immediately based on the way that we architect. So it's great for us. And the difference will be we are spending all of our time on financial tools and financial workflows. And so when GPT-five comes out and we already have access to all this tool data and how they're used most effectively and evals over how to do these financial workflows, we're gonna be able to more quickly distill that into an end to end system that can, you know, generate a SIM, generate a PIB, analyze an S1, then they will. And they'll have to collect those evals and create those tools over time. And, you know, finance is a big enough market where they will likely start to focus on it. But there's been more focus on sort of generic consumer applications competing with the Microsoft Office suite, coding. And I think finance is a domain where, you know, there's a lot more intricacy from the product layer and the workflow layer than people expect. And so I think we're gonna have, you know, a a distinct advantage for a while. And ideally, the point where they're turning their attention on finance, we have a of actual user data and evals and sense of what the workflows are such that, you know, we'll always be a
00:13:44 · Mike Mignano
[00:13:44] S0: step ahead. Yeah, that seems to be the big debate, right? You know, where obviously the word moat, you know, we talk about so much in AI and it's, you know, it's constantly this question of, oh, you know, do you want to be the model provider? Do you want to be at the application layer? And I think, you know, the logic that I hear often, and I frankly, I agree with them, it sounds like it's similar to yours. It's like, you need to specialize in something and you need to run really, really far ahead. Right? And as you said, unlock the proprietary data and, you know, lock in the different workflows that are going to be really, really hard for someone else who's very horizontal to specialize in. Is that kind of how you guys think about it?
00:14:22 · Gabe Stengel
[00:14:22] S1: Yeah, a 100%. I mean, I actually think more and more so the value is in the application layer. Even if I look at my own usage of ChatGPT versus Gemini versus Claude. At this point, because I have a history of threads on ChatGPT, it responds in a more personalized way that's really helpful. And I don't need to go in and explain to Gemini, hey, I'm Gabe Stengel. I have a company called Rogo. This is what we do. Use that context to answer the question. ChatchBT does a very good job of of personalizing responses, recording what I'm doing, you know, having a good sense of of the types of answers I like. And now it's kind of a pain for me to switch. Totally. And that even if the model is slightly worse, I mean, I think that's a that's an advantage of application and product layer. At the model layer, I mean, I'm not an expert on these models at all. But my sense is, you know, no one's ever going to be more than six months ahead of anyone else. And so the distinct advantage you'll have as a model provider is just, you know, how low are your costs, how reliable are your services, how low latency is, kind of thing.
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?