00:36:25 · Raj Shah
[00:36:25] S0: Yeah, look, on a purely quantitative basis, and it's gonna be hard to disaggregate, right? Like the P and L that was attributable to AI initiatives and AI work versus what was sort of just done otherwise. That is, I think, clearly a quantitative way to do it. I think it's actually... If you're a portfolio manager or running a fund and you have multiple multiple demands demands on on your your time for different parts, like, I've got the part of the the business side of things that I have to spend time on. I've got the fundraising side of things that I gotta spend time on. And then, obviously, I've got the research and portfolio management time. For me, the ROI isn't going to become pretty evident. Either the ROI or lack of ROI is gonna become pretty evident if I can find myself spending, you know, more and more time or higher output out of the time that I'm spending on research. And so if I... And we track this. We have a... An RMS research management system ideas go in there. We're actually trying to automate that too. We're, like, not nearly as close as I wanna be, because I wanna connect everything we just talked about with our RMS. Take ideas from from IdeaGen and put them directly into the RMS and track their progress. But if at the end of a year or six month period, I can say, wow, compared to last year, I processed 25% more ideas than I did, you know, the year before a year ago or a quarter before, that to me is going to be a positive ROI. I may not have actioned all of those ideas. Hopefully, I didn't because I... We don't tend to try to turn positions over that much when on these for a while, but I would have processed a bunch of ideas, then that won't feel like a good use of of the... That investment of both time and capital for me. Because every new idea has to compete with an existing idea in the portfolio to get in. So if I can just keep going through things and figuring out, hey, this does meet or doesn't meet and just fast fail them, I'll log all that in my RMS and I'll have a dataset that I can look at to tell me, hey, how many things did you you look at in your investment?
00:38:16 · Brett Caughran
[00:38:16] S2: Yeah. A great, great data point to measure. I want to get Kay's perspective on this too. But as you thought about... As a former allocator, as you've thought about raising capital and telling the Stoic Point story to allocators, we've been in sort of this ascendancy of the multis, it's been a tougher environment for the single managers to raise money. You start to think about this new resource that helps smaller funds do more. Have you thought about those conversations? And do you think this will sort of be a broader trend of sort of the sort of ascendancy again of smaller investment teams really hunting for alpha outside of the 10 biggest stocks in the world?
00:38:59 · Raj Shah
[00:38:59] S0: Yeah. I do. I absolutely do. I think on the... We absolutely talk to investors about our ability to get more done, to get... To sort of be resourced like a fund that's three, four, 10 times our size now, thanks to these tools at our disposal. And that's that's part of it. That's great. But it can cause you to, like, may... Make a bunch of unforced errors if you're just, like, doing a bunch of stuff for no reason. For us, the way we think about it and talk to investors about it more from the macro standpoint about this, like, as you said, a lot of flows have gone to the platforms, and do we see that flow... Those flows come back to single managers? That's gonna be borne out in performance, and I do feel like we are at the dawn of probably pretty exciting period of time for managers who do something similar to what we do, which is you tend to keep a cooler head among more volatility. You tend to not measure thing... Measure things in shorter time periods and, you know, data points, and you tend to have a longer time horizon and duration. And if you have those things like the quality AI, the ad... You know, the appearance or or emergence of AI in investment management hasn't changed the underlying businesses that we're all investing in and looking at. All it's changing is the amount of analysis and compute directed at those businesses, potentially increase the number of data points that are being looked at. But if you're ultimately have conviction in an idea, the business you're investing in is working or the short you're putting on is destroying value, a 100 or a million more data points isn't going to change that, you know, ultimately, but it could lead to a ton of volatility in the stock. And so I think for people with cooler heads, but the ability to add on those drawdowns who have done pre mortems to underwrite, hey. Here's what I would do in those scenarios or can trade effectively around those. You can generate a lot of excess return that way. And so that's generally how we've been thinking about it is... I mean, short way to say it is we think it's gonna just increase volatility amidst all the other things that are happening. I think twenty four hour trading and a lot more options activity in the presence of retail. Volatility is just gonna continue to go up, I think. And I think that will benefit us overall. And I will hand it to the...
00:41:05 · Raj Shah
Just general, and this is a vast generalization, but allocators overall, I feel like have become more comfortable with volatility because they're saying, look, I care more about getting from point a to point b, and the in between is maybe less of a concern to me. And so if that's the type of allocator that you are, I think you should be looking at a lot of single manager hedge funds who are thinking, you know, about things this way.
00:41:29 · Khe Hy
[00:41:29] S1: Yeah. You know, I've been removed a bit from the direct allocator space. So if go back to put my hat on, I think part of the, at least the large allocators, are probably still struggling to understand kind of all the secondary risks associated with AI just in general. And so that might be I would say that might be a near term challenge on like, Hey, we don't have a, you know, director of ops because we use, you know, it's Claude, and our head of IR is Chad GPT. But I think that, like anything, at the end of the day, as Raj said, performance over a cycle, performance in that, you know, if it changes the cost structure of things, you know, that will then bleed into performance on a very clear kind of one to one basis. Like, people have been saying the cost structure, the performance compensation structure of a hedge fund is going to change for twenty years, and it hasn't really. So there'll be a bunch of different forces, but it reminds me a bit of, you know, the early I'm gonna date myself with like the 2003 hedge fund. Like, you know, there's the two guys in the Bloomberg, right, where it's, you know, that died, you know, that that took a little bit of a break, but it it probably has the the tailwinds to come to come back structurally.
00:42:49 · Raj Shah, Brett Caughran
[00:42:49] S0: Yes. Yes. Look, you've made it so much for so many decades. It made it harder and harder to operate a hedge fund. The regulatory costs, all of that have driven up, but now you may have a lot of forces working the other way. And... Look. I am out here in Silicon Valley, and a lot of people are talking about, especially as there's waves of layoffs going on. The silver lining is all of this is we expect to see even more entrepreneurship. People are going to start businesses quicker. You know, the next billion dollar company is gonna be a one or two person shop. I think there's a parallel there in the hedge fund world too where we could return, as you said, Kate, to the two two guys in a Bloomberg. Maybe it's not even a Bloomberg game where Bloomberg's really still fucking expensive. [00:43:27] S2: Two guys in that codex. Two guys in bank codex accounts. [00:43:32] S0: That's right. Yo.