Invest Like the Best with Patrick O'Shaughnessy
00:06:27 · Gabe Stengel
And there are these small details of how you build a product that's great for a specific end user that you only know if you have the kinda spidey sense for what the job is. What is like the bleeding edge of what it can do that impresses you the most? Like what kinds of jobs? The coolest things that we're working on is taking these innovations like Maltbook. Imagine if every PM at a hedge fund had 10,000 agents that were just kind of fraternizing talking about ideas, reading through the notes, pontificating and then at the end of twenty four hours of debate just gave you one idea. And the reason you're able to do that is because investors are happy to pay $50,000 for one really good idea whereas there's very few other domains where you can expand that many tokens just for one simple insight. What we're seeing right now though is that the models are smarter than anyone I know, anyone I spend time with and it's plumbing. Connect it to your context, inform it about your thesis, tell it the way that you work and try and integrate it into what you do. And so building out all the plumbing to actually collect that data, collect that context is what is cutting edge to me. It's always interesting to me for a product like this that you're opinionated about what it should be used to do, but in some sense people can be creative with how they use it. So you get to sort of revealed how people wanna use it. If I adopted a god's eye view of it's Monday morning here in New York City, lots of Roku users are probably fired up and using it right now. If I could somehow see into every instance of the product being used, what would I see? Who are the people? What are the predominant use cases? How varied are they? Give us a sense of how it's being used right now as we record. Even though in so many ways I think public equities is the best application of AI because all the data's available and so it's just about being as smart as possible. That's a very brute force framing. Our early users in ICP and kind of core market is actually what I would describe as deal makers or people that are transacting, who are buying companies, selling companies, helping coordinate transactions. And so a lot of what we do is both make people smarter, but actually do the deal making. How do you prepare a data room? How do you unpack a data room?
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.