The Prof G Pod with Scott Galloway
00:07:31 · Ed Elson, Gabe Stengel
[00:07:31] S1: Give us an example. I mean, you were a banker, and you said that there are some services that you were doing that [00:07:36] S2: were meaningless. Here's a great example. Yeah. You you cover a certain subset of companies. When they release earnings, you just wanna write a quick update for everyone on your team saying, you know, what did they hit? What did they miss? What are they saying about m and a? Are they interested in m and a? What are people in the space saying? What's the analyst reactions? That can take a few hours to write. That can get automated. And now what you can spend your time doing is being thoughtful about, okay, so what does this earnings mean for us? Should we engage? Should we present new client, new acquisition opportunities to them? Instead of spending a lot of time synthesizing publicly available information and putting it in a little email. Another example is just like benchmarking. Right? Every growth equity VC investor needs to know like what is great net revenue retention look like at Snowflake, at Procore, at all sorts of businesses. What they're doing right now is having an analyst benchmark that on a quarterly basis so that they can reference it in podcasts and reference it in IC memos and so on. That shouldn't be done by hand anymore either. [00:08:33] S1: So what kind of work do you think bankers will be doing? If they're not doing all the things that you just described, what kinds of opportunities would you say this opens up?
00:08:33 · Ed Elson, Gabe Stengel
[00:08:33] S1: So what kind of work do you think bankers will be doing? If they're not doing all the things that you just described, what kinds of opportunities would you say this opens up? [00:08:43] S2: Look. I mean, it's why do people use M and A bankers? And just to to set the stage a little bit, what is an M and A banker? Right? Like, an M and A banker is not making investments for anyone. They're advising CEOs on when they wanna sell their company or buy another company. They're offering real thoughtful advice, and that's the work they'll continue to do. And when you're offering really thoughtful advice, you have to have evidence to back it up, materials to back it up. Rogo helps make gathering that evidence a lot easier. Prosecute more deals, advise more companies, offer smarter insights, and offer maybe more products. Right? If you were just offering m and a advisory as a service before, maybe now that's easier for you to do with your team of a 100, and you can also offer some consultation style work that McKinsey does too, or some geo... Geopolitical advisory work as well. And the opportunity to expand your service and enrich it is now kind of endless.
00:09:32 · Ed Elson
[00:09:32] S1: Do you think that that's gonna be the case across all industries? I mean, one thing that I was just reading about before this interview, one of the most popular books of the late nineties was this book called The End of Work. And it basically just predicted that robots would leave us with nothing productive to do. And this has happened sort of throughout history. Mean, there's this other stat in 2014, half of tech workers said new technologies would be net job destroyers. I remember back in 2020 when I was listening to Andrew Yang on this podcast, he was saying that every job is gonna be destroyed by robots. In reality, there are currently more jobs than ever. Unemployment's at a record low. It sounds like you think that this is gonna continue in banking, but what do you think about AI as it affects the entire world?
00:10:24 · Gabe Stengel
[00:10:24] S2: I mean, I I... So interesting you bring that up. My brother, who you know as well, who's much smarter than both of us brought this up to me this weekend. Much smarter. Smarter. Much smarter. Significantly smart. Significantly smarter than mostly you. Mainly talking about you. He brought up the point that a hundred years ago, economists said, hey, if we experience the type of economic growth we're expecting, the work week is gonna go from forty hours to fifteen hours. Not gonna need all that incremental productivity. And what have we seen? The average work week in The US has gone from like forty four hours to forty one. You know, it's declined a little bit, but people are still working. They're still doing a lot. They're still coming to work every day even if it's virtually now. I can't really imagine a world in which AI changes that so much. That seems like a cultural phenomenon rather than like a productivity phenomenon. And then the other thing that's really interesting about like AI automating away jobs is it's not just this thing where, hey, if they're capable of doing it, they'll do it. There's a lot of work that, you know, doctors do that nurses are capable of doing, but doctors have to do it legally. Doctors are the ones responsible for doing it. We will legislate what it has to be done by humans and what doesn't. Right now, autonomous driving could probably take all cab drivers jobs today. We're stopping it. And that's because we want it to reach, you know, a degree of safety and accuracy that we're more comfortable with than we are with humans. And so what you're gonna see is before these jobs get automated, they're gonna be done a million times better than AI than by humans. And by that point, we're gonna want them to do it, and there'll be more jobs created, but it's not gonna be like that.
00:11:55 · Ed Elson, Gabe Stengel
[00:11:55] S1: So let's talk about the product itself. It's basically a chatbot that answers quest... Any questions that you have about finance. It's very similar to ChatGPT in that way. It's basically ChatGPT but for finance. I think that goes for basically every AI startup that I'm looking at right now. Every AI startup is ChatGPT for x y z. Why not just use ChatGPT? [00:12:21] S2: I think there's two parts of that question. One is, what is the interface for the work you wanna do? Is it chat, or is it something else? And the other is, like, the domain specificity of what we do. I'm gonna start with the latter. ChatGPT, in a lot of ways, is a pretty smart 15 year old kid who has generic accomplishments and and can do basic math, can do basic reading, has reading comprehension skills, and so on, would you rather have a generic 15 year old or a 15 year old who has maybe studied finance for the last four years and then also comes to work every day with a big textbook of all SEC filings, all earnings calls, and then reads Bloomberg on a daily basis. Right? What is the context they have and what do they know how to do? Rogo is like ChatGPT in a lot of ways and it's dissimilar in many more ways. We have so much data that ChatGPT doesn't have. And not only do we train on that data, [00:13:10] S1: but we How do you have data that that ChatGPT doesn't have?
00:13:13 · Gabe Stengel
[00:13:13] S2: Sorry. We pay for it, and we integrate it. Right? We have a... You've used the product. It's a different product than ChatGPT. In some ways, it's clunkier. We cite everything we allude to. We have millions and millions of documents that you can pick from when you query. And for a normal consumer who might just be looking up how long to bake chocolate chip cookies for, it's probably a worse experience. For an investment banking analyst, for a hedge fund PM, for, you know, the CEO of a private equity firm, it has everything they want and all the data they want. And we go out and we form data partnerships, and we purchase data from other folks, whether that's market data, whether it's SEC filings directly from the federal government through EDGAR, whether it's earnings transcripts through providers. You know, there are all sorts of providers, FactSet, SP Global, Refinitiv. We have all this data that we integrate. And then on top of that, we integrate a firm's data themselves. And so we can have all your IC memos, all your historical pitch decks, all your historical Excel models too within Rogo, and you could ask questions about that. And not just individual questions like, hey. Summarize, you know, why we think that Chewy is gonna grow 20% next year. You can say, look over all the models we've had for Chewy over the last ten years, and tell me what assumptions we got right and which ones we got wrong. That's not something ChatGPT is even close to being able to do. And it's because we focus on ingesting financial data and citing it in all of our answers.