Invest Like the Best with Patrick O'Shaughnessy
00:08:30 · Gabe Stengel
How do you coordinate the call with the third parties to discuss the data room? How do you go through all of the initial steps through closing of a deal? As if you took a bird's eye view of all the folks using Rogo, I mean, it's people who are either on the sell side of a transaction or the buying side of a transaction and are using it to basically prepare all the thoughts and materials to help execute that full deal. Whether it's putting things into a data room, this is the company's model, this is the PowerPoint that describes their customers, these are the answers to the DDQ questions on what customer concentration is, or it's all the agents on the other side of that that are tearing through the data and mapping it to the firm's investment philosophy to say, oh, great. You know, is it lower than the concentration risk profile that we would want for this fund too? And then the components of the system are people access it via all the classic channels you would access an AI tool. Email, chatbots, proactive alerts and those kind of things. But then Rogo's actually in a lot of the behind the scenes systems of these firms. Because when you're working on a deal, it's not just important for the human beings working on it but you need to update your CRM. You need to update your portfolio monitoring systems. You need to update the way that you distribute information to your LPs after the fact. And so half of our surface area is actually the underneath of the iceberg of interacting with these different systems of record based on what the humans are doing over the course of the deal. So deal makers today, when do you think you'll be able to give the same answer for junior analysts at a public equity hedge fund or something like this? There's a process to their workflow as well, but it's very, very different. You're right that it's interesting that my first intuition would be public markets are the best place to do this because there's so much data available. When do you think that transition happens? I think that for our business, we need to have all the requisite domain knowledge of what it takes to be a great public markets investor. I don't know what it takes. I've never done it. I haven't spent nearly as much time as I should have with the folks that are great at it.
00:10:31 · Gabe Stengel
And we need to both hire out that domain expertise and then figure out based on it how to apply the systems that we have built to that market. I have extreme conviction in the fact that the underlying systems and tools and infrastructure we have built will be invaluable to that market but now we need the great chef who can figure out how to piece it together and create that kind of end state product and that last mile delivery for public equities investors. I get pushed a lot by our board to think about expanding the ICP beyond just core banking. But the reality has been is there's been so much depth and TAM in this deal maker's vertical. And then for my end state vision of actually being the full infrastructure for private markets where people can transact very effectively, that is far more important to the deal makers. Whereas for public equities, all that infrastructure, all those exchanges already exist. And so I'd like to serve them because I wanna serve the most sophisticated, smartest users who have inordinate amounts of knowledge on the companies they track and the industries they follow. And I'd like to make them even smarter because that sounds super cool. But I can build a huge huge business just concentrating where I am today. Interesting. So one takeaway from that would be a lot of the opportunity to build an AI business in a vertical is somewhere where there's lots of plumbing that's not yet built. Yes. And then applying your Yeah. On top of that. Exactly. I mean part of the reason private markets are so attractive is because it's all done by humans, the coordination, the standardization, looking into things in the actual transacting. Whereas public equities, a lot of it has been automated. Based on what you know, what skills do you think investment professionals, broadly speaking public and private, should think about being or becoming more valuable as time progresses and which skills become... It kind of obvious the skills will become less valuable. What the hell are we gonna do? I think can do the soup to nuts diligence and IC memo and objection handling and all this kind of stuff. Like, that's a big part of a job for at least a junior person in the investing world. So what skills do you think people will still matter a lot in Yeah. Couple of years?
00:12:33 · Gabe Stengel
I mean, wanna preface it all with I worked in finance for two years and so I am a student of these guys just as much as I'm fascinated by the technology and wanna figure out how to use it. But the world's best investors have a way of figuring out what matters and exercising their own judgment across a range of topics. And I think jury's still out on whether or not that is something that AI can eventually replace. When move 37 happened and Lisa Vel saw something I could never see. If that starts to happen in public equities, yeah it's gonna really change what matters. And if that really changes all of how that works, I mean I think the core skill set is folks who can go out and gather data and inputs into their model that no one else will have. If you can spend time in the field, if you can speak to experts, if you can develop a relationship graph of folks who can inform your model, maybe you're not the one that needs to calculate what your move 37 would be for a great public equities investment, but you can actually feed your model with data that no one else has too. Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up to date proof of trust without all the manual work, so you can close deals faster and with less friction. Customers report a 526% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Flake.
00:13:51 · S0
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00:14:53 · Patrick O'Shaughnessy
[00:14:53] S3: Maybe it's a good time to talk about, I think, about the assembly line. Like, if Rogo puts out some really useful output, I wanna learn about each component part of the system that leads to that output. And I'm most especially interested in the data that you yourself have and use and buy and build and whatever and how you do that, you think about data, and then how you think about model. And these are both questions, one that I'm curious about your specific business for, but I also think that there will be some business like Rogo built in basically every vertical. And so I'm curious what can be abstracted to other professional services or other verticals that are interesting as well that will fall to AI progress. So talk us through, yeah, specifically like the data and model piece and how that's evolved over time.
00:15:36 · Gabe Stengel
[00:15:36] S1: The early days of Rogo was kind of this Roop Goldberg contraption where like you had 60 different model calls. A question comes in, you try to say what companies is Patrick asking about? Now what are their tickers? Now how do I feed those tickers into an API called a you know Bloomberg or fax it or some internal dataset? Now it comes back and I need to call a different model to pull it all together. And as the models get smarter, you kinda wanna be less prescriptive, less of a Rube Goldberg machine and just think about what are the simplest, best, highest quality tools. And the same way that if you have the world's smartest human being starting here tomorrow trying to be a great banker and investor, what are the tools that they would need that are not just intrinsic to being smart. Right? What are the data tools? What are the ways of going out and gathering information? What are the ways of auditing its own work? And And then what are the ways of presenting it and pushing it back into the systems that it needs? And so for us, we spend a lot of time thinking through what are all the data inputs that a great banker or a great investor would need to actually do their job. Then we spend a lot of time thinking about great. When you're doing that job, what are all the compliance and regulatory requirements to make sure that if someday some Delaware court judge makes AI inputs and research discoverable, you have actually done it all the right way such that you're not intermingling information. Cause the reality of AI investment judgments and AI banker outputs is that you're gonna be able to see the full lineage of how those things are created. And then we spend a lot of time benchmarking these models and creating different evals and data sets so we can always decide what is the most performant, what is the cheapest from a token perspective, what is the lowest latency, and route tasks to the appropriate type of models.