Invest with AI
00:19:25 · Doug Clinton
[00:19:25] S0: I think so. And it comes back to this idea of taste. You know, you hear the word taste often in AI, which I think is sort of this nod to longer term human value, this sort of instinct that we've talked about. I do think to some extent, like, creating these ontologies and these knowledge graphs, there is an element of, okay. Fine. Like, codex, point it at something, give it a really great creative loop, and it'll probably figure something out that's passable. But to make it a plus, I think it does need to have that element of taste from a human who really understands, like, this is what it means to generate alpha, not just, like, what the Internet has taught you as an AI model, but, like, this is why it's hard. Right? And this is kind of, like, maybe even the history of how how alpha sort of evolves in in a sense. And being able to take that taste and apply that to the ontology, that's where I think you get this unique value from humans coming in and taking sort of AI's baseline work, which is like B plus and then getting it up to that A level. And that's where I think you can really get some additional value from it.
00:20:30 · Brett Caughran
[00:20:30] S2: Could we drill into this this concept of knowledge graph a little bit more? Like, what does it actually look like in practice? I'd be curious, Kay, your perspective on like, what's the what's the sort of zero to one? Like, if a client comes in and wants to build a knowledge graph, what does that mean? Is that internal file structure? How do I get my data out of Excel files and OneNotes and PDFs and Outlook inbox and Slack? Like how do you, what's that actual process look like?
00:20:57 · Khe Hy
[00:20:57] S1: Yeah. I mean, I'm going to give you the training wheels version of this, but I think that it's a lot of the things that you just said where you have all of this, this data, but even something like earnings or EBITDA, that can mean 15 different things to 15 different people, even within a firm, within a subsector and so on. So if you go in and teach the model, like when I say, you know, for us, the concept of EBITDA means, you know, these adjustments, you don't never do this. You have to teach the model that. And by the way, you have to basically, like, give it a map that, like, these are the ways that we define all of these things. So once you give them the map and so this map can be just like a markdown file, right? And once you give them the map, then you have this data, right? You have, let's say, 10 companies with, you know, ten years of quarterly EBITDA across 10 different sectors. You have to basically say that like, well, the way we look at EBITDA in software is completely different than the way that we look at it in industrials. And in doing that, you know, when you want to grab that number, you have to make this adjustment. You have to look here. You have to do that. And so you're basically kind of putting it together so that when the model goes in and you're like, what's the EBITDA for Salesforce this year? It starts to go down this graph. It's like, well, I know that this like the way that Brett defines EBITDA is this. And I know that in SaaS, you look at it differently. You go here. And now I know that there's a Salesforce data here, but there's this other data I need to add back in or out. And so it's basically creating that like the term is like ontology, which is like, again, dumb person here, but you know, the definition, your definition of what these words mean so that you can translate it to the model. So then you give this, you give it all this data, again, markdown files, CSV files, skills, so on. And then you want to query it, right? And so there's all these different ways of querying it, right? You could do brute force agentic search, which is like command F EBITDA, but like still go through the EBITDA knowledge graph to get to the data point. Or you could use, like some of our other guests have discussed, semantic search through Rag and embeddings.
00:23:13 · Khe Hy
It's like, oh, okay, you know, you're asking me for profitability, which can mean a lot of different things. And how does that tie to this definition of EBITDA and Salesforce and da da da? You can then add, like, keyword search and, you know, different databases that, like, have sanitized the data and so on. So that's conceptually how I'm seeing folks do it. And I work with much smaller firms, but they're just starting with, like, a small group of names. And they're just, you know, we're gonna do the Salesforce ontology here. And then we might roll that up to the SaaS ontology over here. And just like see if it works, see if it just like speeds up the process of getting the right context at the right moment and with the lens that I'm used to looking at it with. And then you're getting dev shops that are coming in. Like the crazy thing about the dev shops coming in, because I've, like, sat in in a few of these conversations, is that a big chunk of the time is interviewing a PM. It's like, when you say EBITDA, what do you mean? Right? Because it's so baked into their heads, but you need a translator. And that's been a bit of a challenge with a lot of firms who are like, Wait, I thought you were going give me the answer. It's like, Wait, no, you need to tell me how you understand this concept and then I'll write code for you. And then a lot of people are well, I don't have time for that. Like, I thought you were just gonna do it for me. I don't know, Doug. What do
00:24:41 · Doug Clinton
[00:24:41] S0: have to add to that? And it's funny, like, that's what makes it valuable, right? Is that you put your stamp on it. You know? If you just say, well, just give me your general definition of how you think about EBITDA or whatever the metric is, it sort of... It defeats the purpose because then it becomes this sort of generalization instead of something specific to the knowledge that should be inherent to your organization that reflects how you think about investing, how you think about the world. And it does take a lot of work, and I I think that that's actually, you know, the most important thing when when you think about trying to implement, whether you're trying to do what we do and really rely on the LLMs to make investment decisions or if you're just trying to build better investment processes that are still built around humans, it's going to be a big investment in terms of time, in terms of building the systems. And the thing that I would I would suggest everybody think about, no matter which end you are on that spectrum, from from having lived it so far for a couple years, is that things are changing so fast. There's always this tension, I think, in the investment world for the the desire for perfection. Like, we wanna nail 99% accuracy rate on on numbers or, you know, whatever it might be. And I think we're living in a time right now where you have to accept a little bit less perfection in the name of speed. Because if you go and you you go on this, like, six month, you know, really deep, complex process to build some really elegant knowledge graph, I guarantee you in six months, something will be different. The whole paradigm how... Will have changed. And you're like, oh, shoot. Now we need to build x y z thing. That's the new hot thing. Right? And so just don't let yourself get excessively, you know, hung up on the details. Get something that works, that's reliable, and know that it's going to evolve very quickly over time and evolve with it.
00:26:36 · Brett Caughran
[00:26:36] S2: Yeah. How do you think that sort of obsolescence risk has massive? Like, yeah, in three months, is there a vendor where you just put a listening device in your office and it gathers context for three weeks and then builds a custom knowledge graph for you. Right? Like, the sort of crazy things are in development. Now how do you think about navigating that obsolescence risk in in building your infrastructure?