Invest with AI
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?
00:17:06 · Doug Clinton
[00:17:06] S0: Yeah. The... It's it's funny, Kaye. Like, the the terms, as you as you mentioned earlier, right, have have sort of evolved in, like, what is the hot thing at AI? I mean, it changes every six months ish or so, it feels like. And I would say this this concept of sort of the knowledge graph feels like very much the frontier right now and just trying to figure out, you know, how do you take all of the knowledge that you have as an organization or as an investment team or whatever your your your node structure is and make that entire graph usable by every agent that you have running on your platform. Right? And that may be hundreds of agents or thousands of agents depending on how evolved you are. But it's a really complex task to let... I mean, imagine thousands of different agents trying to pull different bits of context real time, creating new context, right, that other agents should then have access to. Doing that really well, I think, is going to be a source of alpha for people using language models or just agents in general, I think, to invest probably over the next... I'd give it twelve to eighteen months. I think that'll be a huge advantage before eventually it probably becomes a little bit more commoditized, whether that's because some third party vendor introduces a really easy to use system, which just doesn't exist right now that I've seen, Or, right, Claude code and codex make it so easy to just say, hey. Implement this, and it just works.
00:18:38 · Khe Hy
[00:18:38] S1: It's fascinating too because the... Sorry, Brett. The... Like schemas and ontologies, again, these kinds of buzzwords that no one was talking about six months ago, but it is this weird idea that you have this data, but you have this structure that oftentimes is just intuited by your process, by your analysts, by your PMs, by the industry. And that might be the gray matter that the LLM hasn't figured out yet. And just like watching the race to disentangle that, to categorize that, to label that, to, you know, to make it visible is just it's just really fascinating right now. And I guess probably early days to know if it's even effective.