Why OpenAI and Anthropic Won't Win Finance – Transcript

00:00What we're seeing right now though is the models are smarter than anyone I know, anyone I spend time with. 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, you know, 24 hours of endless, you know, debate just gave you one idea. In 10 years, the world's best investment firms, best banks will be have 90% of their enterprise value, not in people, but in software and data and systems.

00:26What would you start doing? You would start trying to figure out how do you take all of what lives in the latent minds of your best people and putting it into a system that you own and operate. There's no part of the business that you hold sacred that is immune to being revolutionized.

00:52You and I have talked many times about this basic question that I'll start with over the last couple of years. You're effectively trying to build investing super intelligence 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

01:20it's going over the next two years? I think two years is actually easier to reason about than 10 years or 20 years because in two years the best investors are going to 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 15 years to build the dominant franchise. And the world's best investors today are going to spend the next 2 to 5 years figuring out how to integrate AI into what they do. And you know, Daario has the the great line about everyone's going to

01:48have a data center full of geniuses or a country full of geniuses. in the data center. You know, what would Goldman do? What would Millennium do? What would Citadel do if they had a country full of geniuses show up? It would probably take them a while to figure out how to change the way they work, how to take advant 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 5 years for every great investor.

02:12So 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. >> Um 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

02:38raw models, could you do the same thing for sort of like the eras of Rogo and its product? Like you pick how many eras it is. I don't I don't know how how you frame it up, but like what it could do at each level up up to and including today.

02:50>> 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.

03:08Terrible. And then in college before GB3 came out, we published a paper on AI assistance for econometrics and financial econometrics. And we tried commercializing at the time and nothing worked at all. And then when we actually started the business, it was when GBD3 came out, pre- chat GBT. 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 nothing worked at all. I mean, I would say since things actually started to work, the

03:35eras are very tied to the model eraser, right? It was 01 Pro and then probably Opus 45. 01 Pro was the first time you got enough reliability where it was a good search tool at the very least, right? like you could say help me calculate this sort of you know financial metric for this business over the last 12 quarters and it could do it reliably enough where it wasn't so annoying that you would just do it yourself and then with Opus 45 and the kind of end of last year the end of 2025 into the beginning of this year the

04:03models 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 movers 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.

04:22This 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 trans 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

04:49as 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 building.

05:06and and you would attribute that to you took seriously all the compliance regulatory workflow like 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.

05:24I'll give you an example. Uh, 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 in 20 minutes as opposed to 2 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 want to weigh in. And that whole UX, that whole

05:50flow just makes it so much easier for this financial professional who is not logged into a computer in 10 years, but does have an iPad where they know how to mark these things up to actually adopt and use AI. 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 kind of spidey sense for what the job is.

06:12>> What is like the bleeding edge of what it can do that impresses you the most? Like what kinds of jobs? Oh, I mean I think the the the coolest things that we're working on is taking these kind of uh these innovations like malt book.

06:26Imagine 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, you know, 24 hours of endless, you know, 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, you know, one simple insight. What we're seeing right now though is that the

06:53models 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 trying to 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.

07:08It's always interesting to me for a product like this that of course you 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 want to use it. If I adopted a god's eye view of, you know, it's it's Monday morning here in New York City. Lots of Roger 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

07:35sense of like 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 is available and so it's just about being as smart as possible. That's a very you know brute force framing. Uh our early users in ICP and kind of core market is actually what I would describe as dealmakers or people that are transacting who are buying companies, selling companies, you know, helping coordinate transactions. And so

08:03a lot of what we do is both make people smarter but actually do the dealmaking. How do you prepare a data room? How do you unpack a data room? 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? And so 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

08:30the thoughts and materials to help execute that full deal. Whether it's, you know, putting things into a data room, this is the company's model, you know, this is the, you know, PowerPoint that describes their customers. This is 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 is it lower than the kind of concentration risk profile that we would want for this fund, too?"

08:56And then the kind of components of the system are people access it via all the kind of classic channels you would access an AI tool, email, you know, chat bots, proactive alerts, and those kind of things. But then Rogo is 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 and monitoring systems, you need to, you

09:24know, 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 do doing over the course of the deal. So, so dealmakers 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 that that is effectively there

09:50there's a process to their workflow as well, but it's very very different. Um, it would Yeah, you're 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. In

10:18fact, I I haven't spent nearly as much time as I should have with the folks that are great at it. 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 endstate product and

10:44that last mile delivery delivery for public equities investors. You know, we actually 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 in TAM in this deal makers vertical. And then for my endstate vision of actually being the full kind of infrastructure for private markets where people can transact very effectively, that is far more important to the dealmakers.

11:12Whereas for public equities, all that infrastructure, all those exchanges already exist. And so I'd like to serve them because I want to serve the most sophisticated, smartest users who have inordinate amounts of knowledge on the companies they track and the industries they they they follow. And I'd like to make them even smarter because that sounds super cool. But I can build a huge huge businesses business just concentrating where I am today.

11:36>> 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 tool 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 and the actual transacting whereas public equities I mean a lot of it has been automated. Based on what you