Fintech, Options, and Investing Strategies Shaping the Future of Finance

**SPEAKER_1** (0:01)
Welcome to the Money Tree Investing Podcast. Stock market, wealth, personal finance, value stocks. Invest in your life.

**Kirk Chisholm** (0:10)
Hello, Smart Money Tree Podcast listeners. Welcome to this week's show. My name is Kirk Chisholm, and I'll be your host. And today, I'm joined with George Kailas. How are you doing today, George?

**George Kailas** (0:18)
I'm doing great. Thanks for having me on, Kirk.

**Kirk Chisholm** (0:20)
If the listeners don't know you, maybe you could tell us a little bit about your background.

**George Kailas** (0:22)
I started making my first investments when I was 13 I taught myself accounting to work at my first hedge fund when I was 17
A lot of my early career was on the buy side of the equity markets. I'd say my main takeaway there was even as a small hedge fund, I felt pretty disadvantaged versus the larger guys. So when I got into AI about 15 years ago, I was actually looking for explicitly what I thought were underlying market equations that could simplify a lot of the concepts that I thought were hidden. Did that for eight years, decided that I thought it would be much more interesting and impactful to take all that knowledge, take the advanced back end. We had some joint IP with NYU for things as advanced as evolving neural networks. I wanted to use everything I found to create something that leveled the playing field. We invented signals that simplify a lot of complex information, and then we teach people how to use them.

**Kirk Chisholm** (1:17)
I remember when I started, people were fighting to get information at all. Can I get research? I got to pay for research. Now it's all free.
I don't know how long you've been doing this, but what do you see as having have changed significantly in like the last 20 years in the, we'll call it the hedge fund world, because they're the ones that are kind of on the leading edge of gathering information.

**George Kailas** (1:36)
I think everything has changed. When I first started in the hedge fund world, not only did you have to pay for everything, but the information was very insular. And I think the best ideas, the things that moved the market were passed along in a pretty small community, I'd say mostly in New York. And as technology has advanced, I think not only do you have situations like hedge funds have become a lot more dependent on alternative data. So things like real-time credit card swipes, camera data, satellite images, web traffic, all of that has meant that the interaction with the public has become more important to them, social media data. But then AI has been something that, and Ken Griffin was talking about recently, how things that used to take teams of PhDs months, he said his agentic frameworks can now do in a couple of hours. And obviously, there's data that Citadel has that others don't. But that technology, it used to be hedge fund technology was way beyond anything that people could have. But with the proliferation of LLMs, the fact that actually the best LLM technology, I would say, is Claude, is just as accessible to a person as it is a hedge fund. Maybe they could buy more tokens or whatever. But that gap has really closed. And I think that's allowed people to do high-quality research, learn a lot faster. There's still obviously differences, but that gap has really closed. The one thing that I always caution people for is, and we saw it, I'd say, in the last couple months, LLMs are not very good at assessing risk, and you really even have to prod them to get the downsides if you're just asking for the best stocks that are going to go up in the next month. So I think a lot of things, the motto kind of is a lot of things to look good in a bull market. So I'd say as powerful as these things are, I would also caution people of the downsides to, this or any kind of research that works very well in a bull market.

**Kirk Chisholm** (3:36)
So if they're not good at assessing risk, then what are they good for? What do you use them for?

**George Kailas** (3:40)
I would say they're kind of like a more advanced technical signal that are using a lot more of the surrounding information on the business. So sometimes it doesn't understand directionality. Like for example, all the time, these memory plays that have run, they're going to keep talking about the memory plays being good because there's sound fundamentals around the business. There's great momentum, but they're not going to be able to tell you when the musical chairs game, the music is going to stop playing because that's all they know.