00:00:00 · Brett Caughran, Khe Hy
[00:00:00] S0: If you went into ChatGPT last year and asked a question, often the quantitative metric will be pulled from a blog. [00:00:07] S1: All of a sudden, I migrated a 400 page website from WordPress to Next. Js posted on Vercel. [00:00:15] S0: You know, no consultants or vendors do themselves a favor by overpromising around the capabilities of LLMs. [00:00:22] S1: How do all the pieces fit together? The data, the prompting, the custom instructions. [00:00:27] S0: Translate that to the investment process. Alright. Welcome to episode one of Invest with AI. I'm here with my cohost, Kay Hee, and we're really excited to start this journey. Kay, do you want to just kick off by giving a little bit of your background and explanation of what we're trying to do here?
00:00:53 · Khe Hy
[00:00:53] S1: Yeah. Thanks so much, Brad, and everyone listening and watching. It's great to be here. My name is Kehee. I'm based in L. A. I was a longtime finance guy in fund of hedge funds, most of it at BlackRock, where I oversaw hedge fund manager research for ten, fifteen years. I left the industry or I thought I left the industry at age 35 and had a ten year period of doing various entrepreneurial things on the internet, a lot of which involved teaching, coaching, writing around kind of tools, productivity, self improvement, workflow, and so on. And then I got the AI bug about two and a half years ago and kind of immersed myself. And since then, I started a company called Latour AI, which is a training and consulting business for small buy side firms. And we've worked with family offices, hedge funds, VC firms, and proprietary trading firms. And we'll get into more of the details of what that work entails. So, what we're gonna do here is, Brett and I are... We're in the weeds. We're playing with the tools. We're kind of AI pilled, as the kids say it... Call it. And we are also working extensively with cutting edge firms trying to incorporate all of this. So we are happy to be here as your Sherpas, your beta testers. I won't say that we are your sages by any stretch of the imagination. And we recognize that there is no end state here. There's no steady state. So there's going to be a lot of iteration. And we're excited to have you on the journey with us. What did miss, Fred?
00:02:36 · Brett Caughran
[00:02:36] S0: No, that sounds great. And my path in learning AI has been very parallel to Kay's. I was a fundamental long short equity investor for thirteen years at firms like Maverick, D. E. Shaw, Citadel, and Schoenfeld. Always through my career was at firms that were really insightful on the intersection of quantitative and fundamental investing. So the sort of theory through much of my career is one plus one equals three of bringing systematic signals into the fundamental investing process. I retired from managing money in 2021 and started Fundamental Edge, which is a training academy. We would train junior analysts to build models, meet with management teams, and deliver institutional grade investment ideas to their portfolio managers, both to break into the buy side, but primarily once you're already in the seat how to do the job well. AI did not exist in our founding document of our business plan. It was something that wasn't even on my radar. And then come November 22, when GPT 3.5 came out, this became a cultural phenomenon and just started that experimentation phase. For really three years, I was quite unimpressed with the tools. And then probably summer twenty five, I started to see more institutional grade outputs for these tools. What really catalyzed this podcast was the agentic shift. We've gone from chatbots to agents. And I think without trying to be we want to provide a nuanced argument on both sides here, but that has really catalyzed meaningful change in what investors can do with AI. Okay, maybe walk me through your AI pill moment two and a half years ago. What sort of gave you the conviction to to, you know, shift your career and shift your focus with these?
00:04:43 · Khe Hy
[00:04:43] S1: It was. Yeah, it was. It was funny because like you, I was pretty skeptical throughout '24. I kind of had to force myself to use it because, you know, I was creating content writing and so on. So it was definitely helpful for like lightweight marketing and writing and so on, but I didn't really have the moment. And then, in fact, I interviewed Dan Shipper from Every, who's gonna be a future guest of ours. And my interview with him in the middle of 'twenty four was, Is AI overhyped? That was the theme of the episode. And so, Dan, if you're listening, I've been converted. I'd say for me, the magic moment was in January '25. And I had been playing around again, mostly for writing and proposals and legal stuff. It was fine. And I took Nat Alaisen's course called Build Your Own Apps. And I guess it was kind of right when Andre Karpathy, who's kind of a prominent AI researcher, released... Coined the term vibe coding. Now, I'm a failed computer science major. I got all Cs in 1997 to 2001, and I never coded professionally ever. I don't know how to code. And I took this course, and I was just kind of mucking around. And then all of a sudden, I migrated a 400 site, 400 page website from WordPress to Next. Js hosted on Vercel. Like, again, this might sound like foreign language to many people. It was to me at the time as well. And I had budgeted like 50 ks for that 40 ks and I did it in three weeks and it's still up and running. I was like, oh my god, this is this is no joke. So that was probably the first one. And then I would say the one that kind of tipped it with my clients was the release of, the reasoning model. So one pro kind of in, maybe February '25 and then 'three. I mean, 'three was kind of when I fell in love with AI. I was like, This thing is amazing. And I had started an email newsletter or Substack that was basically trying to scratch my own itch, which was How can AI make knowledge work better? So probably a little bit more general than the approach you've taken, Brett. And all my most of my examples were finance adjacent, just given my own background. And immediately after I sent kind of like the first three issues, a bunch of my old friends from the hedge fund industry emailed me. They're like, Come into our offices and teach us everything you know. And that's how I accidentally started an AI training business.
00:07:28 · Khe Hy
I wanna ask you, Brett. You had mentioned kind of the shift from chat to agents. And I feel like in 2020 the dates are so confusing now. In 2025, agents was the big word, right, last year. But I was always calling BS on agents. Like, no, the only agent people are using is deep research, where you kind of give it a task and you kind of walk away. And then, you know, Claude Code kind of came around in June ish of last year. But I'm curious on your end, when did that kind of shift from this is a Q and H question and answer thing to, oh, this gets real work done. How did that when did that happen for you?
00:08:10 · Brett Caughran
[00:08:10] S0: Yeah, that's a great question. I think we probably have the same personal Sherpa in the learning journey of Andre Karpathy. And I think he's done such a service to both of us in terms of what he shared publicly on YouTube with many of his videos. And I just find him to sort of be the rare Venn diagram of technical fluency on these tools and also not having the right incentives, not trying to hype his company to raise valuation. And the way he explains things is very good. I think Kay and I both recommend his videos on YouTube as a good zero to one in learning what LLMs are. And I had a similar insight to you on 'one, 'three with the deep research functionality. That was the first time where I had something in my research stack that I found to be institutional grade, particularly if you pointed it towards open web information, things where there was a lot of government data or something where the raw data was in the open arena. Deep research reports were wonderful at distillation. And for really at the same time, the spring of twenty five, I found myself starting research processes with deep research reports, which I found to be often better than sell side reports and customize the specific thing. There were certain use cases, I come from the quant world where natural language processing is sort of a key fundamental alpha. And from a hedge fund world where we're constantly trying to understand the body language of management, that word selection really matters. There's information laden in word selection. So I think that was an initial use case that I found quite helpful out of chatbots. Is the company using more bullish or more bearish language? But these were small pieces, right? With my Sherpa Andre, I think it was in October, he went on Dwarcache and he called agent Slop and said, It's going to be a ten year journey. And so I'm like, All right, well, Andre says it, it's good enough for me. Everyone talking about agents, I never really understood the agentic thing in '25 either. OpenAI came out with their agent functionality and it's just what is this thing? It just didn't work at all. So I wrote it off as hype and followed Andre over Christmas as he sort of went through the coding agent thing. And he came out in January. He's like, listen, like what I said in October is completely different. It's completely changed.