Invest with AI
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?
00:27:02 · Doug Clinton
[00:27:02] S0: Yeah. We've we've built so much stuff that we no longer use, and, literally, some of it has lasted a month, two months. And I I think, like, with the emergence of the coding tools, that has accelerated it, you know, because it is so easy right now to to write... Not even just, like, basic code. I mean, to write pretty decent code, like, very functional programs, even for people who aren't, like, high level engineers, is is pretty easy if you know how to use the the systems well. Now writing something that scales to an organization of maybe a 100 people, a thousand people, that is a different sort of challenge. But I I think that we should assume that that reality continues to be persistent, which is technology will evolve really fast. So build your systems as kind of quick as you can to work really well for the paradigm today. Take advantage of everything you can today, but be ready to be nimble and don't have a lot of, you know, sunk cost in something where you say, well, we can't change that because we just spent $2,000,000 building the system. But there is this better thing. It... I mean, it's almost like NVIDIA chips. Right? Like, you buy your h one hundreds and then the b 100 or b 200, right, is way more efficient. You save more money, you know, per token that you create by just investing in the better chips. So you just have to be prepared to do that just like the data centers are doing with their with their infrastructure.
00:28:25 · Khe Hy
[00:28:25] S1: Yeah, a funny story on this. There was an AI vendor that was very hot on, it's kind of an enterprise search in 2025 before the MCP takeoff moment, which I would say is like December of last year. And everyone was into this vendor. I don't want to name it. But I was into this vendor. I did tons of calls with them last year. And then when the connectors hit, it went dark. No one wanted to talk to this vendor at all. Last week, a friend texted me. He's like, hey, I actually stayed on that vendor, and they have incorporated some of these elements of knowledge graph, different semantic search. Permissioning is a big deal in the knowledge graph. And it's really good. That's what the front... What the text told me. So even in the vendor cycle, you see it. It's wild. Like, I would not wanna be building and selling on that side of the fence.
00:29:21 · Doug Clinton
[00:29:21] S0: Yeah. I agree. And I think that's actually a great point, too, Kay, because if you think about in the investment space, the ability to control your own destiny too. This is just a philosophy that we have at Intelligent Alpha. But to the extent that we can sort of build something ourselves or use open source and modify ourselves very quickly, we've tried to very much do that and not rely too much on third party vendors because of what you just described. Like, you know, the vendor could be awesome when you install them day one, and something new could come out. And and by no fault of their own, just the fact that they bet on a certain idea or a certain technology, maybe they fall behind a little bit. And six months later, maybe they're awesome again. Like, it's just really hard to not be able to sort of control your own destiny given how fast things are moving. So that's the other piece for us is just philosophically kinda how do you think about how quickly and and what kinds of investments you wanna make in building your technology infrastructure, and is it something that you wanna have a lot of control over? Or or are you okay with relying on good third party vendors for that?
00:30:23 · Brett Caughran
[00:30:23] S2: Doug, have you thought about model model routing? You know, sort sort of feels like every few weeks you get a new model and, you know, the OpenAI models were ascended. We need this sort of h one twenty six has been all about Claude. Now this sort of conversation shifting back to Kimi and DeepSeek and the open source models and toke... Token efficiency argument, etcetera. How have you thought about evaluating that systematically? And when do you make the decision to plug in a new model, sort of pivot pivot your routing structure?