Invest with AI
00:42:11 · Brett Caughran, Doug Clinton
[00:42:11] S2: It's a hairy, it's a hairy problem. [00:42:13] S0: I think, you know, and I think a lot of lot of finance organizations, larger ones, even mid sized ones, I think they want to try to build this themselves in certain ways and for certain reasons. Compliance related, right? Control related, all these things. But working with somebody who knows and has a vision for it, I think, is super important. Kaye, I know you work with a lot of companies that are trying to figure this problem out. But the thing I think every firm needs to think about is they all need to operate a little bit like a start you know? And and to the extent that you're not willing to maybe break a few rules and and really try to figure things out in a unique way because things are changing so fast, I think it will put you behind the firms that are willing to take the risk, invest in the technology, invest in, you know, maybe even some of the governance risk and whatever else comes with that and understands what this new world looks like with LLMs being a much bigger part of all investment related processes.
00:43:13 · Brett Caughran
[00:43:13] S2: Yeah, the tricky part is if you look at the top 100 hedge funds, certainly near the top of that stack, I think most funds will build that in house. But even quite large funds, multi 8 figure you know, AUM, billion AUM funds, like there many of these teams, the investment teams are still quite small, you know, 10 to 15 people and, you know, relatively small internal IT teams. This is a big lift to build these, not just to build the first version, but sort of navigate this obsolescence risk and eval structures. And it's a it's not a it's not an immaterial expense. It's not an immaterial, you know, corporate strategy shift.
00:43:55 · Khe Hy
[00:43:55] S1: And to just piggyback off what Doug said, the need for a vision, right? You cut it outside of the Citadels where like, you know, there's hundreds of people that are paid a lot of money to solve this problem. But once you get to that tier below, you need to have someone in leadership that's AI pilled because they will not break the thing that needs to be broken. They will not take the risk that needs I mean, there are I still talk to very successful firms like we have web search turned off on Claude. Like, what? Like, I didn't even know that that was a feature, let alone in 2026. Right? Like you need someone with that vision because from that vision, you're going to nudge compliance. You're going to take a governance risk. You're going to pay some money to give some analyst who's, you know, also AI pilled a bunch of Fable tokens to go figure some stuff out. Is a hard those are a lot of hard pieces. You know, it's like playing chess, like with all these pieces to move around and someone added. And then the chess table board is vibrating at the same time. So like the piece that you thought was in the corner is actually have moved over one cell by, you know, within, you know, a quarter. Yeah.
00:45:19 · Brett Caughran
[00:45:19] S2: Alright, Doug. Asked I I asked Fable, build me a stock portfolio of 10 stocks. Alright. Compute substrate is 35% of the portfolio. NVIDIA is a 12% position. TSM 10. AVGO 8%. VRT 5%. I didn't I didn't tell them make an AI portfolio. I just said build me a portfolio of 10 stocks. Distribution abstraction layers, 35% Microsoft, Meta, Amazon, and now another 35%. The token beneficiaries Palantir, Spotify, another 20%. And then it's giving me a 10% cash or hedge. So without any prompting, it built like a super concentrated high octane AI portfolio. No biotech, no consumer staples, no REITs, no banks. So yeah, over the last thirty six months, that probably shows like massive alpha. But how do you, like, you know, sort of when the tide goes out, this portfolio is getting absolutely smacked. Right?
00:46:18 · Doug Clinton
[00:46:18] S0: Yep. Yeah. I think I think that goes right back to thinking about context and and what can you give the system to help it understand the current regime. And if you think about how Fable... Right? If you just use Fable for a general task today, the knowledge cutoff, I believe, on Fable is about April '26. So think about the window. Right? The last kind of information the model had was q one ostensibly of this year. AI trade was ripping then before we had the the pullback kind of March and then ripped right back after that. So the model is probably thinking, okay, well, that's what the world looks like, and so I want to have this really super AI aggressive portfolio. Knowing that if you put anything into a model, finance related or otherwise, that the model is going to have some sort of bias related to its most up to date context, I think, is is piece number one is understanding that you're probably gonna get something that looked really smart based on something that happened in the past, and your job is to figure out how can you get it current information so that it understands the real world and hopefully can update its priors. And that's what we try to do at Intelligent Alpha. You know, we would... In that example, right, we were just gonna do a really simple thing with Fable, we'd probably give it some current information about recent financial reports, earnings reports, what consensus expectations look like, recent transcripts. And so then, hopefully, the model would say, okay. Look. The AI trade has been playing out. Maybe it would even know we had this July pullback, and, hopefully, it would adapt in the right way to go forward. I mean, me as a human, I'm still pretty bullish on the AI trade. Think I after the July washout, if you look back at the .com era, it looks a lot like 1998, kinda LTCM. There's there's kinda some vibes there. And, the world got pretty crazy after that in terms of the .com trade. And so I'm not saying that's exactly what's gonna happen, but I think the AI trade is is kind of far from over. And and I'd be curious to see if the machine would agree with me.
00:48:26 · Brett Caughran
[00:48:26] S2: Yeah. I'd say at, like, a high level, like, I've been pretty skeptical of this sort of new emerging AI hedge fund, AI asset management business where you let the machines pick the stock. I mean, last few weeks, I've been a little bit more open minded to it. I sort of agree with this sort of context being a critical piece and this decomposition of the investment process into actual signal. Part of it's just like the recent model, you know, Fable's not perfect, but the Fable SoulSier, you know, Kimi K three series of models like are pretty surprising me to the upside on a few of these pretty complicated tasks in terms of adherence to a complicated skills architecture. So it's going to be an interesting nine to eighteen months to see what see what emerges. What advice would you give for, for those, you know, watching this pod who are thinking about maybe, you know, building an asset management business with with AI?