Invest Like the Best with Patrick O'Shaughnessy
00:30:15 · Gabe Stengel
[00:30:15] S1: It's actually a very similar strategy to Bloomberg strategy. Bloomberg strategy was... I'm gonna omit a lot of details here but offer a little bit of data to get in the door, build all the analytics and workflows on top that someone would need and then provide the exchange and the communication platform where you can actually transact in a bunch of asset classes that before it was pretty opaque. Bloomberg Messenger. For us, it's use a little bit of AI to get in the door, build out the full workflows, go from copilot chatbot to full autopilot tool so I can make sure that I'm a 100% accurate on your IC memo or on the DDQs that you're doing. And then provide the communication channel between counterparties so that if I have agents that can autopilot do the work, I can actually transact for you. And the difference between what I need to build in Bloomberg Messenger is I don't need to build the communication channel for humans to transact. I need to build the communication channel for the agents to transact across these businesses, across these investment firms. And so if you think about what is the actual infrastructure that needs to get built, I mean think about what is the system that would allow a large private equity firm to feel comfortable having an agent negotiate a deal on its behalf. Correspond with all the third party consultants in the transaction, the people doing the q of e, the legal advisors and so on. And then actually run an auction process where you have a bunch of sponsors providing bids. There's a huge amount of software to be built out. And I think sometimes we talk about that as looking like an exchange for a lot of these asset classes that are not standardized.
00:31:45 · Patrick O'Shaughnessy, Gabe Stengel
[00:31:45] S3: Tell us a little bit about the customer base today. Like how much of it is giant banks versus investing firms? [00:31:51] S1: It's mostly large banks and that's very simply because that was my background. Right? I worked as an investment banker for just a handful of years doing buy side m and a coverage which was super interesting. And so we started targeting the banks pretty early on for a few simple reasons. One is the investment banks are kind of the distribution channel for the rest of finance. A lot of the folks who then end up as great investors started in their first two years as an analyst at Goldman in the TMT program or something of that nature. Two, they have the most seats by far. And so if you can land a bank like Bank of America, you can actually get in the hands of far far far more people than if you land the 10 best single portfolio manager public equities investors who each only have 10 investors. [00:32:33] S3: If I think about a Bank of America or something, everyone's kinda wondering how deep into the adoption curve are we for enterprises using AI. You have a biased sample because your customers are using Rogo and they're using it a lot. But give us a sense of where you think we are. It seems really hard to pin down a good answer.
00:32:50 · Gabe Stengel
[00:32:50] S1: I would say the majority of firms are seeing a huge amount of individual productivity and they're trying to figure out how do we parlay that individual productivity into firm productivity that we can measure. Speak to any individual banker at a bank we're deployed with Life's great. Oh, they're like I'm a 100 times more efficient than I used to be. Right? You'll speak to an MD who will say Gabe, I sent five pages to a client that before I would have had to go back and forth with an analyst on over three days to create and I made it in ten minutes myself. And these are bankers who haven't done any sort of analysis in twenty five years. They haven't actually opened an excel file in twenty years and they're able to do it themselves. The problem is where is that flowing through? Are you winning more deals? Are you actually transacting more? Are you servicing a part of the market that you haven't seen? And this is where it becomes not just an individual productivity tool problem but a firm strategy problem. Like what's your plan? Do you wanna use this thing to cut cost? Do you wanna use this thing to enter parts of the market that before didn't make sense to serve? You can look at a bank like JPMorgan. JPMorgan just announced that they're gonna try and do a lot more m and a work for SMBs. For parts of the market that before they didn't think it made sense to go out and serve cause the deal fees were probably too small and so you needed too many people to staff them. Well now if you have a banker that can be a deal team of one, well maybe you can enter parts of the market that before just made no sense. So in some sense the bottleneck at some point will be your creativity of the customer. Like you can provision unlimited capability and you're soon gonna be just relying on them figuring out the answer to this It's them figuring out what they wanna do. Right? If you had a 100 great investors start here tomorrow working for you, how would you channel that productivity? It would take you a while to figure out what's the structure. Do they all work on different things? How much money do I give each of them? What do we attack?
00:34:43 · Patrick O'Shaughnessy
[00:34:43] S3: Two years ago, the most obvious question and this would have been about accuracy and people to use the word hallucinations which seems to have dropped out of the conversation. And I I can't remember the last time that someone said hallucinations to me. Or can you teach us about that problem, ensuring accuracy where accuracy matters a lot to the decimal? What's the nature of that these days?
00:35:04 · Gabe Stengel
[00:35:04] S1: It's still super important. I think it's actually more important to be auditable than it is to be accurate. And obviously those two things are conflated. But what's really important is that I give you an answer, you know how to use it. And if it's not accurate and you don't know how to check it and it's hard to see where it came from, you can't use it at all whether it's accurate or not because you don't trust it. If it's accurate most of the time but even when it's not, it's very easy to see the assumptions that went in where the data was pulled from, it's still actionable and it still saves you time. And then increasingly as these things go from copilot tools that you're just using for information retrieval to autopilots where you're trusting them not just to gather the information but to execute on it to have agency and actually make an investment decision or send an email, you need to have the full confidence that if you were to go back in and see why it made the decision, you would be able to understand why. Because you're gonna need to debug it. And the same way that there's gonna be individual investors that make horrible decisions and you need to go in and see what went wrong. Was there an incorrect data input? Did someone to lie to them? What was going on? You're gonna need to do the same thing with agents. And then especially in parts of capital markets that have regulators that look at these things and need to make sure there's no foul play. If you can't explain why you made a decision and the data went in, that's not gonna fly.
00:36:26 · Patrick O'Shaughnessy, Gabe Stengel
[00:36:26] S3: You were talking before about how you have to sell like a normal enterprise sales organization would. You have to grow or can grow many multiples faster than the fastest growing enterprise SaaS companies of the last era. How do you solve that problem? Like, you're really limited by the speed of humans to some degree in enterprise sales. How do you hire enough people fast enough? Like, how do you think about being able to grow at the right rate when you don't have the Anthropic cognition API usage growth? It's so easy for them to grow 10 x. It's much harder for you. How do you solve that? [00:36:58] S1: The core problem we need to solve is how fast can you make a human being productive as a salesperson, salesperson, but anyone else as a marketer, as an SDR, as a post salesperson. How quickly can they understand our business and help push us forward and push customers forward and help our end users? And so enablement and training people and constantly retraining people is the fundamental problem that we and I assume other fast growing enterprise startups have to deal with. [00:37:23] S3: So did you like use AI to build tools?