Invest Like the Best with Patrick O'Shaughnessy
00:00:01 · S0, Gabe Stengel
[00:00:01] S0: Ramp is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average so you can stay focused on growth. Ramp just opened for business in The United Kingdom. So if you're [00:00:13] S1: running [00:00:13] S0: running [00:00:13] S1: a business in The UK, you can now use Ramp's AI powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on Ramp and so should you. Learn more at ramp.com/invest. [00:00:28] S0: Rogo is the AI platform purpose built
00:00:30 · Gabe Stengel
[00:00:30] S1: for financial institutions serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft SIEMs, run buyer outreach, and due diligence on data rooms, all with full security and compliance handled. Every action feeds Rogo intelligence, the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last. To learn more, visit rogo.com/invest. OpenAI, Cursor, Antti, and Vercel all have something in common. They all use WorkOS. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs. Instead of spending months building these mission critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero. Visit workos.com to get started. You and I have talked many times about this basic question that I'll start with over the last couple of years. You are effectively trying to build investing superintelligence tools to help investors do their job much faster, better, cheaper, easier, higher quality. But it's starting to feel like, wow, we're really eating a lot of the core functions that even a very smart analyst or even portfolio manager was doing a couple years ago. How do you think about that trajectory as you've seen it and lived it so far and where it's going over the next two years? I think two years is actually easier to reason about than ten years or twenty years. Because in two years, the best investors are gonna be figuring out how to reinvent their own firms and reinvent themselves. And if you look at what happened to market making and quant trading, Jane Street took fifteen years to build the dominant franchise. And the world's best investors today are gonna spend the next two to five years figuring out how to integrate AI into what they do. And Dario has the great line about everyone's gonna have a data center full of geniuses or a country full of geniuses in the data center. What would Goldman do? What would Millennium do? What would Citadel do if they had a country full of geniuses show up?
00:02:28 · Gabe Stengel
It'll probably take them a while to figure out how to change the way they work, how to take advantage of that, how to integrate it into their system. I think figuring out how to apply AI into the investment life cycle is the biggest challenge over the next five years for every great investor. There's been many companies on this trajectory where the product was... Cognition is very famous for like literally their ads now say, remember Devon? Like it's good now. So lots of now clearly great companies with great products had a stage of an AI business where the product stunk and now it's excellent. If you think about the couple increases in capability that we've seen just from the raw models, could you do the same thing for the eras of Rogo and its product? Like, you pick how many eras it is. I don't know how you frame it up, but like what it could do at each level up to and including today. Yeah. I actually tried starting Rogo two times before we got started. So in high school, I had a friend whose dad was an investment banker who wanted an app for trying to track the basically equity exchange rate of two public companies as they were merging. And so we tried using really old AI techniques to do that, terrible. And then in college before GB three came out, we published a paper on AI assistance for econometrics and financial econometrics. Somebody tried commercializing at the time and nothing worked at all. And then when we actually started the business, it was when g b d three came out pre chat g b t. And so all the early days of rogo, it was clear how kind of magical it was. You could demo things that were cool. Nothing worked at all. I mean, I would say since things actually started to work, the eras are very tied to the model eras. Right? It was o one pro and then probably opus four five. O one pro is the first time you got enough reliability where it was a good search tool at the very least. You could say help me calculate this financial metric for this business over the last twelve quarters and it could do it reliably enough where it wasn't so annoying that you would just do it yourself.
00:04:20 · Gabe Stengel
And then with Opus four five in the end of last year, end of twenty twenty five, end of the beginning of this year, the models just became capable of basically anything a junior investment professional or junior banker was doing as long as you gave it the right instructions and context. I mean, I think there was a first mover's disadvantage for a lot of applied AI companies because you thought you knew where the world was going and you wanted to build a product for it, but the models weren't quite there. And so people would try it and go, this is terrible. This is garbage. You have a disadvantage. For us we saw that too but now what we've seen is well if you were right about the end state and where the models were going and you were building towards that, when they get there it's magical. And for me a great product is all the feedback we get every day. People all day are saying, hey, this is transforming the way I work. I'm saving hundreds of hours a month. I am doing things I never could have done before and so I'm smarter as a result and I'm able to make better decisions and it's delightful and I like using it. And you know, it brings me joy in my day to day because the UX and the attention to detail and the craftsmanship is so obviously built for me and who I am. And so that's been the the best part of the product going on. And and you would attribute that to you took seriously all the compliance, regulatory, workflow, last mile hookup stuff and then the models became good enough and all of a sudden that was super valuable? Well, there's also small details of understanding how someone within one of these firms works and building for it. I'll give you an example. We make it so easy for a managing director at a bank to email a markup of a deck which is how they're typically doing these workflows anyway except sending it to an analyst and sending it to our AI analyst over email and then returning that markup on twenty minutes as opposed to two days and at the same time alert the junior analyst on the deal what's happening and show them the full auditability of all the little markups that were made in case they wanna win. And that whole UX, that whole flow just makes it so much easier for this financial professional who is not logged in to a computer in ten years but does have an iPad where they know how to mark these things up to actually adopt and use AI.
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.