Generative Now | AI Builders on Creating the Future
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.
00:06:01 · Mike Mignano
[00:06:01] S0: Putting together a PIB, especially without AI a couple years ago. I mean, it sounds like a ton of work, especially if you're telling me these things need to come together in like twenty four hours and this is a 200 page PDF. I mean, was the initial thinking like, hey, this is a lot of manual work. AI is obviously helping to sort of give people leverage on work and units of intelligence. Let's make this like a PIP creator. Is it... Was that like sort of the basic idea?
00:06:26 · Gabe Stengel
[00:06:26] S1: Yeah. I would say it's funny. I mean, The basic idea was, yeah, like there's all these types of work, like benchmarking revenue multiples, putting the other PIPs that are so basic and, you know, not interesting and there should be an AI tool that can do it. But I would say the thesis then and the thesis still now was always more so how do we empower people to do things they can't do, right? And that goes back to our original senior thesis, which was giving our peers the power of writing Python code through a natural language interface chatbot to do their econometrics work. And even now, I want to make sure we're doing all the mundane work that junior bankers and junior investors do. But I also want to empower them to do things they've never done before. Right? I want to help them do all sorts of new types of analysis, new types of insight generating work that wasn't possible before. And so even at the time, I think we had loftier aspirations. Took us a while to even get to the mundane automation. But it was always focused on, hey, how can I, you know, give you, Mike, a tool that allows you to do the types of analysis that before you couldn't really do if you didn't have a team of data scientists or a team of juniors underneath you?