00:01:22 · Raj Shah
[00:01:22] S0: Yeah, absolutely. Well, look, I think you hit the earliest part of it, Brett. Think you and I were the only two in our analyst class of nine or 10 to leave early to go to go join hedge funds. And I think that was generally a a good a good decision. But I spent the first... After that, the following five years at a firm called Highline Capital in New York, and then I moved out to join Light Street as the third partner in, Palo Alto. I've been out here in Bay Area ever since, and my partner Cullen and I started Stowe Pointe in 2018. Kind of been bootstrapping the business altogether, completely fundamental, research driven, concentrated portfolio. You know, our median hold periods are in years, probably two plus years, almost three years on a median hold period basis. And we've got, you know, 10 to 15 names in the portfolio of which I'd say probably at any given point, eight or nine matter, eight to nine names are probably 80% of our exposure. So we tend to do a lot of deep work upfront. We tend to invest in these businesses for a long time. We tend to engage with these companies, management teams and boards alike, to sort of help them navigate their paths as public companies. Generally focused on small and mid cap, and we'll do anything other than really energy and and biotech. It's kinda two areas where we'll say, look, that's not an area we feel like we could ever be the smartest or near smartest in the room. So let's avoid those altogether. We are, for all intents and purposes, kind of a two man operation. I mean, we've had outsourced analysts. We've had juniors. We have some interns this summer with us too, but but we like to keep it lean, not only on the portfolio side, but also operationally. The hedge fund business model for as as much ink has been spilled about how hard it is to operate a hedge fund to start one, to keep it going. If you can run it lean enough, if you can build for longevity, if you're... The life of your fund can match that of your investment time horizon, it can be a great business. And so I've had a ton of fun doing it. Not to say it's without its... You know, it doesn't have its stressful points in time, but but it's it's a great business. I think it's a great job in the world. And with the introduction of AI, I think it's it's insanely exciting now.
00:03:31 · Raj Shah, Brett Caughran
But especially for a firm like ours, which is really lean, where you have portfolio... My co portfolio managers who are also at their core analysts. And so we get to really just pour a lot of fuel on our research process and try to get a lot more done a lot more efficiently. So it's been an exciting time. I'm excited to chat about the AI stuff because it's what we're spending a lot of time [00:03:52] S2: on. Yeah, yeah. No, thanks for that. And I think we've both seen inside large funds of the resource stack that is wrapped around an analyst, it's phenomenal, like any resource at your fingers. And when you leave a firm like that, you have to recreate that resource stack. It requires a lot more hustle and creativity. How have you tried to solve that problem at Stoic Point initially? And just walk me through your sort of discovery journey with AI and helping solve that resource stack. Like how do you bring that institutional level of analysis to a firm that doesn't have the resources of a $5,000,000,000 hedge fund?
00:04:34 · Raj Shah
[00:04:34] S0: Totally. I'd... Look. I think I'll I'll I'll I'll make the first part of that question. I'll... The answer rather quick because I think it really accelerated in the last, you know, year or two with with AI. But, look, I think there's a big benefit in this industry to put it bluntly not being a dick. And so when you go out on your own, you, will tend to find if you have forged good relationships at much larger funds and working with the right people, whether that sell side or service providers, people tend to invest in you and give you access to resources for, you know, a pretty decent amount of time as you sorta get ramped, and people are either emotionally or, in sometimes, in some cases, financially invested in your success. And so we benefited a lot from that, early on. But it's it's been it's been an extremely exciting point in time since kind of, you know, Chad. It'd be probably three months after Chad GPT first hit the scene in late twenty twenty two to apply a lot of that to our process here and get a ton of leverage on what we do. For us, the reason we get up and do this every day is because the most fun we have is is finding new ideas. It's that research part of it. It's that early part of the process of I think I've got something here. I think this is an interesting idea. I'm still doing work on it. I've... I got a 100 questions I need to get answered, and I've gotta work out a plan to get them answered in the fastest way possible. I also, by the way, wanna meet the company. I wanna ask them these questions face to face. I wanna understand what the street thinks about this business, about its valuation, about its outlook. And so there's just a whole bunch of things to go and do. And when you work at a large, at a large and well resourced firm, Brett, like you were alluding to, there's other resources you have. You might have director research, may have junior analyst that can go and chase down. You can sort of divide and conquer. We don't have that. And so for us, time is really the most expensive currency. It's just return on time. And AI has really been an unlock for that. When I break out and think about AI in our investment process, I think it's kind of in in three primary buckets. One is that screening and idea generation part. The part that's really exciting is finding those interesting out of consensus ideas.
00:06:39 · Raj Shah
For us, it's those messy situations we like to say that we run towards the fire. AI's obviously very interesting in that application. We'll talk about that. I think the second one is that research of an idea part of it. And that's where I think a lot of people are spending time talking about how do we really use AI in this process. I break it apart into... There's, like, this sort of deeper research due diligence, what people call due diligence level part of that. But then there's the basic blocking and tackling. I gotta make a model of this business. I wanna sensitize a couple different cases. I wanna create some output that maybe other members of my team need to consume or my investors need to consume to make the case for why this is interesting. That's kind of part two. And then I think the third part is is the is the idea or rather the position maintenance, position monitoring part of it. Once the position makes its way into the portfolio, there's the ongoing monitoring of of relevant data points. And I think AI touches all of those things. I think part one, that screening and idea generation part is the part we've... We started out the most excited about. We've continued to be the most excited about, but I'd say it's probably the piece that's in a lot of places, sometimes the most disappointing. What I mean by that is there's there's a lot of places where we're... The idea here generally is there's, you know, thousands of potential investable ideas. There's new companies coming public every day. The IPO window's open now again. So there's even more companies in this universe being added every day. And so how do I triage that, and how do I find where I'm supposed to be spending my time? One way I've always done it, and I think a lot of firms do this as well for good reason, is is you run screens. There are very simple screens, and there are very complicated screens. Simple screens would be, hey. I tend to find broken IPOs really interesting. So let me just go and find every company that's IPO in the last two years. It's down 20% or more, 30% or more, and I'll go dig in all those. That's a simple screen. That's a quantitative screen. You could do that on Bloomberg. You probably do that on on, you know, Yahoo Finance if you needed to. Then there's the more interesting sort of nuanced screens that I think AI holds a lot of promise with.
00:08:44 · Raj Shah
Think about turnaround situations. Turnaround situations can be very hairy and very messy. I want a company that has a new CEO that's come in the door. Well, new CEOs usually need a year plus to get their to get their sort of hands on a business. They may come in and cut guidance and the stock will fall. They have to change out the management team. There's a lot of stuff that happens. So, actually, I want a company that's in a turnaround where the new CEOs come in, and he or she's already been there for a year. I also want the stock to have been down. I want stock to have been down 20% or more. But I want there to already be evidence of the turnaround working. I want them to have, you know, beaten, raised for the last quarter or two, maybe, you know, three, four quarters into the CEO's tenure, and I want street estimates to start going up and some signs of life. That's where those types of things, when you can start to get a little bit... Turn these cranks, that's where AI offers a lot of promise. And we've seen a lot of interesting tools offer those types of screening capabilities and excites us a lot. What's been somewhat... Not even... I wouldn't say disappointed. Disappointing is probably a strong word, but confusing is LLMs are a black box. And then when we've run some of these screens and we've run them repeatedly or I'll run it, my partner will run it, we'll hit click at the same time, same criteria, exact same prompt. We'll get, like, two different sets of companies. He'll get a dozen companies that arguably fit the criteria. I'll get another dozen companies that arguably fit the criteria. And we're looking at each other's results and we're on the same prompt. Why didn't... Why did we get different things? And you go back and you try to figure out why. And again, LMs are black box. So it's it's it's it's hard to figure out. But nonetheless, it's still really exciting. I think the other thing that's really important that we've been learning on, again, on this idea generation and screen part is the AI is only as good as the data that that it's that it's using. And so for us, we tend to gravitate towards... I mean, special situations are likely, you know, one area we love. So transformative m and a, for example. Transformative m and a is messy. Generally, street estimates, financials don't end up getting updated well after a deal is closed.
00:10:50 · Raj Shah, Khe Hy
And so there's this really interesting period of time where if you've done the math on what shares are being issued, what's the pro form a balance sheet, who's running the business, what does the growth look like, there's a great opportunity to get in there early transformative m and a situations. But LLMs are not necessarily gonna be great at that yet if they're relying on SEC filings and the... That type of data in identifying those situations for you. What it does hold the promise of is I should be able to put a slide presentation about a merger into the LLM and have it produce the pro form a financials and flag for me. Hey. By the way, this company is now, after a result of this deal, the cheapest among its 10 peers. That's pretty interesting. Synergies look really reasonable compared to the OpEx at, you know, a, b, and c peers, those types of things. And so we're really excited about it. I could go on and on about the screening and idea generation front. Bottom line is I think it's still the most exciting part of of of this whole AI in investment management or hedge funds world, but it's also the part that I think needs the most most work. [00:11:51] S1: Can I ask you about the the screenings or the two prompt question? Such a good, like, entry, you know, like, AB test right there. Can you say a little bit more about how that is set up? Is it, you know, a really good ChatGPT prompt or skill that you've reused? Or is it... You've mentioned third party software. Like, how are you actually running that prompt?