Invest with AI
00:00:00 · Thomas Li, Brett Caughran
[00:00:00] S0: Human labor comes with a couple of main challenges. The first challenge is accuracy. Humans will always make mistakes. [00:00:06] S1: How have you shifted the way you've deployed LLMs both in building Dilupa, but how has it changed the market environment for your business? [00:00:14] S0: Yeah, so when we think about the problems that we can solve for our customers, right, because at the end of the day, Dilupa has two sides to the business. There is the data side, the asset side, what we call content side. There is also the workflow side, what we call product side. Product at the end of the day means you're solving someone's problem for them. The job of content is to acquire data sets that can get us deeper and deeper into our coverage. [00:00:46] S1: All right, welcome to the next episode of Invest with AI, where we explore the intersection of investing in artificial intelligence. I'm really excited to have Thomas Lee from DeLoopa here with us today. I know about three people who both sat in a hedge fund investing seat and were talking about AI before November 2022. And Thomas is one of them. So really excited to have Thomas here to talk about what he's building at Dilipa and answer our questions about the sort of current state of AI and investing and the future state of AI and investing. So thanks for being with us, Thomas. [00:01:27] S0: Thank you for having me.
00:01:27 · Thomas Li, Brett Caughran
[00:01:27] S0: Thank you for having me. [00:01:29] S1: Maybe just tell us a short origin story of Dilupa. You worked at some really well regarded hedge funds and then in 2019 made the decision to go after the data oligopoly of Bloomberg, FactSet and Cap IQ, which sort of seemed like a crazy a bit of a crazy decision at the time. So just walk me through sort of your headspace back then in founding Dilupa.
00:01:55 · Thomas Li
[00:01:55] S0: Yeah, so in 2019, I think this is obviously pre, you know, JGPT, Claude and so on, but it's not pre AI. So in the course of my job, I started to understand a little bit more about, you know, the AI space and what AI was capable of. And I think a big part of that was I was fortunate to cover semiconductors and a lot of hardware equipment companies back in the day. So I just got closer to like what these things were being used for. And the hypothesis was, you know, there are two basically missing pieces for data when it comes to investing, Right, the first piece is, can you do it at scale and at quality? So it's really, really hard to collect high quality data because the only resource you have available to do so for the most part were just human labor. And human labor comes with a couple of main challenges, right? The first challenge is accuracy. You know, humans will always make mistakes. The second problem was latency. So if you wanted to collect a lot of data, you would just be slow, right? You can't have, you know, 100 people collect the same income statement and make it 100 times faster, that unfortunately just wasn't possible. And then the last problem was trust, right? How do you collect data in a way that you can essentially not only guarantee that your existing data is correct, but also predict that your next outcome is going to be at the same accuracy. And for humans, that's near impossible to do, right? Asking a human to like predict their next set of accuracies for their next task is near impossible. But those were the early days of like, AI as we know it today, but certainly not early for machine learning and some of these new algorithms coming out, Google just dropped this paper, attention is all you seek. So all of that started coming together, where we basically had the hypothesis that you could leverage AI to change how you think about data collection, right, maybe not completely removing the human in the loop process, but rethinking that architecture. And I will equate it to thinking about like how you build cars. Right, so today, the way we build cars as you know, as is accepted as the norm is you have essentially a production line, right?
00:04:12 · Thomas Li
One hundred years ago with the Model T, we created this production line, there are a combination of machines and humans operating under machines, there are process controls, operational controls, Six Sigma and whatnot, that sit on top of this production line, and therefore we can get high quality, you know, bulk outputs of cars. And we take that almost for granted today. It's like so well understood, we take that for granted. But the way data is being collected at most investment institutions is actually like, the coach building business. It's all done by hand, there's a taste element to it. A good analyst will be able to collect better and more data than a bad one, which we looked at and we said this requires a production line. You can't build a production line that's fully autonomous, but a production line is better than handcrafted coachmaking. And that was the foundation of what created LUPA, right? Using AI to build a business at its very core, and rethinking like how you extract data, how you quality control data. And obviously, with the birth of AI as we know it for the consumer side, it was very natural for us to say we are an AI first company, we understand how everything works, let's work with the labs, let's put our data into an MCP, and let's distribute the data. Because not only should data be collected using operational controls and AI and algorithms, you should also deliver it in the same mechanism to maximize the output for your customers.
00:05:45 · Brett Caughran
[00:05:45] S1: That's great. Thank you. Thank you for that. What have been the more recent impacts on your business? My sense is that sort of the rise of the agentic workspace has been a nice tailwind for Dilupa, but how have you shifted the way you've deployed LLMs both in building Dilupa, but how has it changed the market environment for your business?
00:06:07 · Thomas Li
[00:06:07] S0: Yeah, so when we think about the problems that we can solve for our customers, right, because at the end of the day, Delupa has two sides to the business. There is the data side, the asset side, what we call content side. There is also the workflow side, what we call product side. And if you think about what product means, right, product at the end of the day means you're solving someone's problem for them, right? If you don't have a problem, then you cannot have a product by almost definition, right? You just have a trinket or a bell whistle. So when we used to think about how to solve a problem for our customers, we had to go build a whole, you know, we had to build our Excel add in, we had to build an environment, we had to do all of these things with a very clear problem in mind. And if you look at our content, right, we've collected all of these data over time, on average, only about 5% of all of our data was actually being used by all of our customers every quarter. So 95% of the data that we're collecting basically never sees the light of day. But because we collect everything companies disclose, we don't know which 5% is going to get used, we just know 5% gets used any given quarter. What AI flips is, because an agent is able to take in so much more information than humans can, it changes how much data is being consumed by our database, and therefore it changes the problem sets that we can go after. So in, you know, early days of Dulutha, the problem set we went after was earnings updates, we help people update their models in their format, you click a button, you update your model. Your model might have a lot of data, but it doesn't have every single data point the company has disclosed. So after you've updated your model with us, a lot of the data actually just doesn't get touched by you. But when you use our data in an MCP, and you're running a research query with the MCP, so now you're out of the modeling workflow, you're in the research workflow. Now research requires a lot more data, because your model might not have, let's say, inventory breakdowns, but in your research process that might be something you're doing, right? You're tracking gross margins vis a vis work in process inventory. So that becomes something that gets used.