Invest with AI
00:10:09 · Doug Clinton
[00:10:09] S0: I think what's interesting to think in that context is is how much of the intuition coming from the LLM is valuable versus the intuition coming from the human. And as I said, with Intelligent Alpha, the bet we are making is this long term bet that over time, the models do continue to get better. We've seen that in the benchmark data. And that ultimately, probably leaving the models to their own devices with very little sort of nudges or interpretations or or influences from humans is the best process. But today, I actually think that's that's a little bit different where I still think the creativity of a human to say, you know, I wanna be 30% weighted in some stock because I have really high conviction that it's going to work for reason x. Those types of intuitions, and maybe there's some deeper research that goes on behind the scenes, things that an AI can't get access to, you you know, those things, I think, are still the core blocker from just turning the whole thing over to AI. And so what I would say we've seen work really well right now is almost this sort of, like, hybrid model between what would look like maybe a traditional quant portfolio a traditional fundamental portfolio. And so, generally, our portfolios, they don't have a thousand positions like you might have in in a quant portfolio where it's literally... Right? You're just looking at numbers, a lot of small bets, and you're trying to get that 52, 54% batting average. You know, we generally have something on the order of a couple 100, you know, maybe up to 500, depending on what strategy we're looking at. But there are, in our portfolios, some conviction bets that we allow the models to make that may look like, you know, several percentage points in terms of concentration in the portfolio. And AI, I think, has been pretty good in what we've seen so far in our portfolios at knowing where to be convicted and and maybe where to dial it back a little bit where it doesn't have great conviction, and it's gotten better over time to the point of our thesis. And so, you know, I think that humans can still play a really valuable role, which is bringing in that data that the models just can't get access to, pushing the models to take really big bets.
00:12:18 · Doug Clinton, Khe Hy
But if you're not gonna have that human influence, sitting somewhere between probably a traditional fundamental process and a traditional quantitative process works really well with the LLMs. [00:12:30] S1: How important is that data and what role does it play? Is it, you know... Yeah. Could you talk a little bit about the the data, the types of data that you bring into it?
00:12:43 · Doug Clinton
[00:12:43] S0: Yeah. We kinda think about it in three buckets. I mean, one is just publicly available data. So, obviously, within the the trading data of all these models, and for all intents and purposes, I kinda think of them as they've all been trained on essentially the same corpus of of Internet data, so they know everything that's on the Internet. That's kinda piece one in their training data. And then on top of that, we do bring in a lot of data from third party vendors, which is just fundamental data, you know, consensus data, stuff like that, which, again, is just... Is publicly available. Bucket two for us, which is where we've we've been trying to be sort of creative, is how can we use the models to make estimates about certain things related to companies? So that may be KPI related estimates. It may just be strict earnings related estimates. And so they might take some of the publicly available data, and it could include things from Reddit or X. It'll con... It'll include things in the consensus metrics, etcetera, and then create an interpretation about that in terms of, you know, we think Apple will beat this quarter for x reason. So that's kind of bucket two for us is think of it as, like, LLM sort of augmented data. And then bucket three is the, you know, the proprietary sort of feet on the street research, I would call it, where LLMs... I think there's a path for them to be useful there, But I think it's really hard to do that today, and that's, you know, your channel checks. It's calling on customers. It's going to investment conferences. All the things that are just limitations to a digital system are are kind of that bottleneck. And and to me, that is kind of the longer term frontier for using LLMs is can you use them to do channel checks somehow? Can you imagine a world where actually agents are talking to other agents doing channel checks, not just agents talking to humans, which is kinda easy to imagine today? And just what does that look like in the future? Because I really think that... I mean, just like it is today, that probably is the long term real durable source of alpha versus kind of just understanding really big context better than humans right now.
00:14:49 · Brett Caughran
[00:14:49] S2: Doug, have you thought about the sort of the MCP pathway? I think the consensus was, you know, four or five months ago, it was... The MCP structure was probably too brittle for institutional use cases. I've seen that conversation die down, but there's still a little bit of debate on what the right data structure looks like to sort of pipe in the right data to make institutional grade decisions.
00:15:15 · Doug Clinton
[00:15:15] S0: Yeah, I think it's probably still an open question. For us, I mean, we use various MCP, you know, sources that help us pull in some data. And I think that... I think the world broadly is evolving to this idea that getting context to the models at the right time and the right place is super valuable. Right? And you look at, like, a CarbonArc, right, paying paying almost per piece of data, and you get to determine what you think is really valuable. I think we're gonna see a lot more models like that. And and whether it literally uses, you know, the MCP, that exact protocol, or if it just looks like an API or whatever, I don't know how much that matters relative to having just the right structures, the right payment models, and ultimately, right data sources and the right ability for the models to say, I need this piece of data. I think that's the super valuable piece. And I would say this, like, models today are... I think they're pretty good. If I had to grade them, I'd give them, like, a b plus as an analyst sort of understanding, like, this is the thing that's probably gonna move a stock in a given quarter. Their creativity isn't quite there like a human analyst. I mean, some of the creative things I've heard for human analysts doing over the years to try to get an angle on a quarter are incredible. But, again, think about the future. Think about a year from now. Think about two years from now. I think models will think of those super creative things and be figuring those things out for us. And that's the frontier that I'm really interested in trying to figure out.
00:16:55 · Khe Hy
[00:16:55] S1: Doug, when we, before we started recording, you had mentioned the importance of knowledge graphs as it relates to MCP. Could you say a little bit more about that?