00:24:22 · Brett Caughran
[00:24:22] S0: That's such a salient point. And I think that's where, you know, I'm spending a lot of my time, and I completely agree. It's almost investing after you've done it. It's why it's hard to learn, right? Because really great PMs will have a process, but the process leans more towards an intuitive sort of tacit understanding than anything that's overly robotic, right? Almost no PMs have a 24 page checklist of, If I look on an idea, I go check off every single step. They've done it hundreds of times. And like a master playing a piano, they look at an idea, hit 72 Bloomberg functions, update the model, call the man. And they just sort of do it out of muscle memory such that the workflow is tacit, not explicit. That's really hard to build an agent around. You kind of have to slow down and document that process. And that's really hard to do because investing is a non linear process. That's what we've done for four years at Fundamental Edge. We've tried to create a linearity out of it to bridge that knowledge of the junior analyst to the PM. It's not perfect. But I completely agree that that's a big part of the journey of enabling agents in your process, documenting your process. Also documenting LMs or wonderful pattern recognition engines, like the mental models in your mind that you come back to over and over. How do you codify those such that an agent can think the way that you
00:26:09 · Khe Hy
[00:26:09] S1: think. And have you found because right now, let's say there's a 20 step process and LLMs might be good at three of the 20 steps. Have you found that to be frustrating for people that are like, it's kind of like the junior person, Oh, I'm just going to do it myself. You know, how do you how do you find that tension? Because I found that people, you know, you go in for the first time, like, I want this to, like, pull all this data, do this, do this, send me a report that does this. And then, you know, send me a Teams message every time the stock's down, like, x percent. Right? And, like, woah, woah, woah. Like, let's pick the one, like, thread here that is this... That lines up with the strength of the LLM. Like, how have you kind of... Or has the expectation versus reality, it's like the Tim Urban kind of delta, how has that manifested with in your client work?
00:27:04 · Brett Caughran
[00:27:04] S0: It's definitely a journey. I think no consultants or vendors do themselves a favor by over promising around the capabilities of LLMs. This has been rampant amongst the vendor community to say, Hey, our tool can do anything an investor can do. Splashy demo, adoption, disappointment, churn, right? It's been this sort of cycle for three years. So we try to really be very transparent around, we have a red light, green light, yellow light framework of what's possible today, what's sort of close, maybe, maybe not, and then a red light. For example, an LLM cannot one shot a complicated three financial statement model today. It can take a model that's five years out of date, update it, resegment it, find the three key drivers, key in the thematic leverage, and then run automated research around those ideas and feed those findings back into the financial model and compare my model to consensus. That's sort of like one of the holy grails of agentic systems. It's possible today. So what we do is we try and chunk down that process to like the 17 steps you would need to sort of have the pillars of running one of those holy grail movements. And we just experiment. The frustrating thing with AI sometimes, and exciting thing is something that doesn't work today could work in three weeks. Or sometimes what works on Tuesday doesn't work on Thursday because Anthropic decides to throttle compute. So it's this messy evolution, this messy journey of what's possible today. Now, what we try to do is find those workflows today that pass that moment. Hedge funds are hard to impress. And chatbots, for the most part, were not impressive. You're starting to see more workflows where the outputs are impressive. So I'd say like, as you're thinking about any investor adopting these tools, you can't do it all at once. You can't rewire your entire investment process. This is likely an eighteen month, nine to eighteen month journey. So find those individual workflows where you have overlay. The And for the first period of time, process it. Don't change the way you're doing it. Just have an agent run-in parallel as sort of... We use the analogy of bumper bowling. Something, have bumpers behind that process that can catch any process, mistakes that you might make. So I think it's a slow journey.
00:29:50 · Brett Caughran
Finance is not going to flip the switch overnight the way that engineers have with this much more complicated game than building apps with coding agents. So the challenging part of why we try to provide value is this takes a lot of hours just to stay up with this stuff, right? Like, I don't know what I'd be curious actually, what's your learning journey? Like you're sharing all of this with the world via your blog and social. Like what does your week look like in terms of what you're reading, watching, you know, listening to to stay up to date?
00:30:30 · Khe Hy
[00:30:30] S1: Yeah, it's funny because we were texting yesterday. I have anxiety that I'm behind. I have, like, two anxieties. I have anxiety that I'm behind, and I have anxiety that I'm not using enough tokens. And so I would say I probably, you know, I'm probably working eight to ten hours a day if you count kind of consumption of YouTube videos and podcasts, like, in that number. And it's a mix of, you know, lot of my learning is with my clients, right? And so that's part of it. I write a newsletter twice a week. And one of them, I basically try to provide a use case or evaluate a feature, some kind of dogfooding stuff as it comes out. And then, like, part of the excitement of starting this podcast is like, you know, oftentimes I'm paid to be the smartest AI person in the room. And I I don't want I don't want to be like, I want to be the dumbest so that I'm, you know, my baseline gets elevated. So, you know, again, a lot of conversations. You and I are similar where you will see someone interesting on Twitter with a unique perspective and just DM them and be like, Hey, do want to chat? Like, no agenda. Just want to learn from you. Hear what you're doing. I do a lot of that, as I know you do as well. Shout out to our mutuals who have been on the receiving end of that. And yeah, I would say, you know, there's a stable newsletter. Mean, Twitter is a phenomenal source of information. I don't love Twitter. It's kind of like the whole apparatus of it, but I think it's phenomenal for AI and getting me angry at, you know, clavicular or something. And then a few newsletters that I read, and I listen to a lot of podcasts, like Lenny's List, you know, Gan Shipper, Dwarkash, Clairvaux, a lot of practitioners. There's also people that I'm on the lookout. Whenever they do a podcast, I just go listen to them. Like, Brett Taylor, the chairman of OpenAI would be probably the most common one. Andre Karpathy would be another one.
00:32:38 · Brett Caughran, Khe Hy
[00:32:38] S0: Yeah. Yeah. [00:32:38] S1: How about you? You feel You feel behind? Yeah, same.