00:13:05 · Brett Caughran, Khe Hy
[00:13:05] S0: Yeah. Yeah. That's where I like I would chuckle. I would chuckle when I watch these YouTube's on like setting up an OpenClaw or an Hermes agent. And it's like all of this futuristic approach. And then at the end, the use case was creating a podcast summarizer or something that's like, all right, like, okay, how was that? Totally. And the sort of like tension to me in long running tasks and people would, oh, I have 20 agents working overnight. And I would be like, On what? Me, we're not even at that point for... Certainly, I come at this from a institutional grade, bottoms up fundamental research perspective where the individual components of that process are still not good enough getting closer. But just let an agent rip for twenty four hours on doing a full deep dive due diligence process. It's just going to be this long running slop chain [00:14:02] S1: that [00:14:02] S0: burns a lot of tokens. Yeah.
00:14:07 · Khe Hy
[00:14:07] S1: No, I hear you. And one of the things I'd be curious how you're approaching this with your clients is I feel like we're ending this era where I call it the faster horse era, where you do something like an earnings preview and it would take you a couple hours And then you used AI to make it better and faster. Like, you built a faster horse. And the faster horse era of AI, it's easier because you just like, you look at everything you do, and you're like, how do I make it faster and cheaper? But then there's another question that's like, now that you have this resource that's different and very powerful and has a different cost structure, what are you going to do with it? Right? So like one of the things that's like interesting with private equity you know, they're constantly receiving SIEMs, right? Confidential information memorandums. These are like deal opportunities, right? And so they could have hundreds. You know, the firm could see dozens of them a week, maybe more depending on the firm size. So, you know, in the old days, what you do, an analyst would review it and like, this is worth spending more time on. Let's take it to phase two. Right? But these are also, like, really information rich documents and pretty, like, opaque private markets. What if you could have a bunch of agents that go in and, like, basically scrape every KPI from every SIM and categorize it for you? Like it never made sense to do that before because you'd have to pay an associate, you know, a $100 to do that. It's not a good use of their time. But if it costs you, you know, 20 of Fable tokens, maybe it's worth doing, right? Maybe it's worth it. So I'm curious, like how people are if you're seeing that, like faster horse to like expanded art of possible.
00:16:14 · Brett Caughran, Khe Hy
[00:16:14] S0: Yeah. I love this question because I think like, you know, as a Wall Street user, we stare at this blinking context window and it's actually like harder than it would seem to figure out to enact that context window to be helpful in your investment process. Worse than that, I think the knee jerk reaction of most investors is to use it the wrong way. Is this sort of, hey, I used to read the 10 ks, let me do a 10 ks summary now and speed read through that, the artisanal process of comprehending the company. And then sort of, you know, or the expression layer, like here's a bunch of notes, create a bold thesis for me, right? It's sort of an AI slop thesis. So I think I don't know what it is about AI, but it causes probably late, Fred, like it helps elevate the sort of lazy predispositions in all of us that sort of just want to like sit on the couch and eat Cheetos versus apply real rigor. And it makes makes the sort of transmission mechanism to laziness much easier because it sort of looks good enough. [00:17:19] S1: Yeah, yeah.
00:17:20 · Brett Caughran
[00:17:20] S0: That to me is a very important force to fight against. Yeah, yeah. So I think it like we think about it as sort of five layers of like the triage layer, the execution layer, the decision decision, decision support layer, the sort of rigor enforcement layer, and then the idea tracking layer. So like, it really is to me like building a system around it. To your specific question, the decision support layer, you know, something like that where you could build a headless RMS to sort of go into all of the prior operating memorandum, all of the prior deals, pattern recognize the sort of the best deals, the worst deals, errors you've made in the past, and have that in a dashboard while you're sort of doing the evaluation of a new operating memorandum. That to me is like that decision support layer is the real... It's not some AI slop summary, right? It's, hey, go and actually run and analyze, you know, sort of a real institutional grade process on this OM. And part of it is, you know, pulling in the lessons of history. I think to me what has been so exciting, and I think we're at a real inflection point on these tools on Wall Street is the fable and soul class of models have gone from answer engines, prior LMs were like Q and A, give me an answer, to actual work mechanisms. To me, you can build with Fable and Soul is so much more impressive than what you could build with Opus or GPT 4.5, etcetera. So the ability to actually, you know, ramble Max into a context window and have it build the first version of, you know, some architectural thing is I think that we were sort of going through this diffusion mechanism where more people are realizing how powerful this latest class of models and probably then sort of looping that back into the work you're doing with Codex. Like you probably just ramble into Codex and that decision support layer on the OM analysis in a way that's like much more frictionless than it was six months ago.
00:19:36 · Khe Hy
[00:19:36] S1: Yeah, yeah, absolutely. And I I think part of this question, though, it comes back to context and data, right? And and again, you know, I've been spending a little bit more time with private equity, but they have mountains of data from their portfolio companies, right? And this is, again, I think this is a lot of that data falls into the category of, if a human were paid 6 figures to analyze this, we wouldn't do it. But now we can pay $100. What's the question you want to ask, right? Are you doing a cohort analysis that didn't make sense to do because it would have taken someone hours to do? And now maybe it's not as good as a human, but let's say it's 90% there, and it's gonna cost you $20, right? And I think that, you know, and people really people are really struggling with this. Think I'm surprised and I'm not surprised. I just take you on a side tangent here. It's like I was an early user of Notion. And Notion was like, Oh, you could do a database. You could do this. You could do a spreadsheet. You could do a to do. You know, like, it was a little bit like AI, but then you open it and it was just like... It looked like a Google Doc. You're You're like, Wait a minute. You're telling me I could categorize, like, all the Pokemon in the world, but all I see is a flashing cursor. And, like, Notion still struggles that people can't see all of the tools that they're giving you and then map it to how they see the world and the problems they're trying to solve and do it. So they still hire Notion consultant, right? They hire these intermediary that acts as this like, bridge between them. And I've been wondering I've been trying to, like, ask my you know, prompt my clients in different ways. Like, you know, even things Like, you know, if you hired not even a new analyst, but you hired someone senior like yourself, like, what would you have them do? Right? And sometimes they don't... They have to, like, step back and like, Well, I'm not exactly sure. And like, Oh, well... You know? And so if you're not sure what you would do with another person, you're gonna be even less sure what you're gonna tell a powerful AI to do. And so there is that process that, again, I don't know.
00:22:06 · Khe Hy, Brett Caughran
I haven't found an easy way to get around it other than spending time with people who truly understand what they want out of an investment process, like what the end game is, and that are willing to, like, be weird about it, right? Yeah. [00:22:23] S0: I think you nailed it. And I think certainly for public markets investors, the challenge in AI adoption has been the heterogeneity of what investors want, right? You could have two hedge funds. One will make investment because they like the management team and like the product. Another will invest solely on revisions to revenue guidance over the next three quarters, right? And so that has been very difficult for both internal builders and product builders for public market AI tools because it's been very hard to design something that crosses the bar, passes the bar for all investors. That's why this sort of like mass personalization engine that's possible now with these tools is like so exciting. The ability to actually spin that up and to some degree, maybe make customizations of the harness, but certainly customizations to the skills layer and the context layer and the dashboard layer, that's now becoming very possible. And so I think very, like I've been more and more bullish on the application layer for a number of reasons, but I think you're going to very soon, all you have to do is sort of go to a fund, give them logins and you pre configured a dashboard to them very specific to their investment process. It's going to be really sort of a wow factor. And the skills as well, right? How have you