Generative Now | AI Builders on Creating the Future
00:00:05 · Mike Mignano, Gabe Stengel
[00:00:05] S0: Hey, everyone. Welcome to generative now. I am Michael McManno. I am a partner at Lightspeed, and this week, I'm talking with Gabe Stengel, the founder and CEO of Rogo. Rogo is a platform that uses AI to do what junior bankers usually spend sleepless weeks, days, and nights doing: analyzing earnings, benchmarking companies, and building pitch decks. And it does it all in seconds. Gabe was a former analyst at the investment bank Lazard and so he saw firsthand just how inefficient finance can be. And so he and his co founders built Rogo to fix that. Now the world's top banks and hedge funds are using the product and investors are betting big on their success. Gabe and I talked about his time working in investment banking, how that led him to the idea for Rogo, and where he sees AI fitting into the future of banking moving forward. Let's dive in. Hey Gabe, how's it going? [00:00:56] S1: Good. How are you? Thanks for having me. [00:00:58] S0: Doing well. Thanks for doing this. Been really looking forward to this and and talking to you about Rogo. Obviously... Well, I don't know. How long is it? How long has it been? When did you guys found the company? [00:01:08] S1: Late twenty twenty one. So so three and a half years ish. So it's been a little while. [00:01:13] S0: From what I know about it, it's got a pretty cool inception story, especially given your background as an investment banker. Tell us a little bit about how you guys got started.
00:01:22 · Gabe Stengel
[00:01:22] S1: I mean, I guess actually the LLC that we rolled into the C Corp, that is ROGO, was started in 2019. Oh, wow. Because John, who's my co founder, he's one of my best friends at Princeton where we both went to school. And we collaborated on our senior thesis, which was a sort of research project. I was computer science, John was econ. And we basically jointly created an AI chatbot for financial econometrics. It was not very good. It was using old hat techniques, semantic parsing, context free grammars. But it was very exciting to us, and it was a tool we could put in front of our less tech savvy econ friends and have them use it to, you know, do their theses and their work. And so that's when we first had the idea and got excited about this idea of a natural language interface. We tried to commercialize it at the time and couldn't really figure anything out. So we ended up both going into finance. I was an investment banker at Lazard. John went to Barclays, then JPMorgan. And my experience at Lazard, I got, you know, pretty... I had a pretty unique position. I got to be both on a investment banking team and then later on a data science team, helping find internal tools for the bank to roll out to make junior bankers more productive, senior bankers a little bit smarter. And so just had unique visibility into, you know, both what the work junior bankers were doing were, but also, you know, how were these institutions even thinking about solving it. And we left that job and those jobs and got started with building this company.
00:02:44 · Mike Mignano
[00:02:44] S0: Tell us a little bit about a word you used early on in your your story, econometrics. Explain for, you know, the layman viewer, you know, what are econometrics? And also how is the product? Well, it sounds like it was sort of rooted in some early work you did in Princeton where this was really focused on econometrics to sort of where you are now. And, you know, we'll get deep into the history of the product and all that. But we'd love to understand like the journey there.
00:03:09 · Gabe Stengel, Mike Mignano
[00:03:09] S1: There's absolutely no econometrics in the product now. Back then our product was really built for postgrads and undergrads doing econometric research. I. E, you know, the simplest version is run a regression of X and Y, or run an autoregression, or find an instrumental variable. Helping economics postgrads do, you know, do their work. I mean, it's not really what we do now. Today, the product is much more helping investors, investment bankers do research on companies, diligence companies, come up with unique investment theses. We also still analyze structured data, but instead of analyzing economic structured data so much, we analyze company structured data, right? So you might say run a regression of revenue multiples on margin expansion at big healthcare companies to see, you know, what's really been driving valuation over the past year. Or just show me, you know, the share price returns year to date for this basket of companies. But yeah, we we don't really do that econometrics as much anymore. But we had to put a spin on the the project to make sure that John could log it as his senior thesis. So we had to do That's [00:04:09] S0: so funny. Talk a little bit about your your experience at Lazard. And it sounds like your co founder had some similar experience that sort of like shaped the vision for what you wanted to do with ROGO. I mean, at Lazard, were you doing the same, you know, types of analysis and research that you imagine users of Rogo doing now?
00:04:09 · Mike Mignano, Gabe Stengel
[00:04:09] S0: so funny. Talk a little bit about your your experience at Lazard. And it sounds like your co founder had some similar experience that sort of like shaped the vision for what you wanted to do with ROGO. I mean, at Lazard, were you doing the same, you know, types of analysis and research that you imagine users of Rogo doing now? [00:04:30] S1: Oh, a 100%. Yeah. I was a banking analyst. I was putting together, you know, models for potential buy side acquisitions. I was putting together PowerPoint decks. I was doing research on industries. I was putting together PIBs, public information books, which if you have any banking listeners on this podcast, they will, you know, understand what that is all too well. [00:04:49] S0: Yeah. What's a PIB? What is a PIB?
00:04:49 · Mike Mignano, Gabe Stengel
[00:04:49] S0: Yeah. What's a PIB? What is a PIB? [00:04:51] S1: Tell us that. A PIB is basically a senior banker says, hey, we're going to meet with, let's call it, you know, Datadog. Give me a PIV on the company. I. E. Put together all the resources I would need to get smart on this company over twenty four hours. So you pull, it's a, each MD has their own formula, but maybe the last two ten Ks, last 10 q, some initiating broker research coverage, some recent broker research coverage that's good, any sort of interesting news articles that have come out recently into one, you know, 200 page condensed PDF that you email to them, and then they can leaf through and and see if there's anything interesting or or write you a follow-up. So it's just a information packet to get started, which now is a little bit dated. Right? Like, you don't need this 200 page research packet when you can just go into a tool like Rogo or ChatchPT and say, hey, tell me about Datadog and what's been going on. But yeah, I was I was doing the very basic banking work. And I loved it. I mean, I actually, I think there's this, this perception that no one likes investment banking. I don't think that's true at all. I mean, I think you get access to some of the most interesting executives, companies, see how they think, see how they think about M and A capital allocation. And I I really enjoyed it.