00:19:22 · Brett Caughran, Khe Hy, Matt Stockton
[00:19:22] S0: Yeah. You guys probably saw the Tim Ferriss tweet where he talked about his books having declined 80% over the last five years, and he sort of attributed the recent accelerating drop to AI. Makes sense to me, man. I used to read 20, 30 books a year, and I sort of look back at myself like maybe I've read one or two this year. Because exactly that, to go get all this information on a topic and learn it in a distilled way is a superpower of these tools. It's pretty crazy, actually. [00:19:55] S2: Did you see, yesterday they announced Claude Code can create artifacts that are then shareable within your team. So you don't even need to fire up, like, a hosting environment. So that same example, Matt, you described, you could create this, like, beautiful, I don't know, an initiating coverage report via HTML on Snowflake and then just push a button and automatically deploy it to your entire firm. [00:20:22] S1: Yeah, yeah, I saw that announcement. I haven't dug in. I have an ever growing backlog of things to read about. I think companies ship even faster and faster. [00:20:31] S0: So we talked about the data cleaning problem. When a client comes to you, Matt, and says, We want to make these things work. How would you bucket that exercise? What are the big buckets of workflows that you think firms need to focus on to really go from interested in concept? Do we actually want this productized and enterprise grade to make decisions with agents?
00:20:59 · Matt Stockton
[00:20:59] S1: Yeah, yeah. I mean, I like to take like a job to be done type approach. It's like, okay, like, what are your what are you doing now? Like, what are the workflows that your analysts or your team are doing? What documentation are they producing? How they sharing that? What are the upstream things that they're gathering? Right? And it's kind of a discovery process where you can talk to the people and say, What are you doing? Where are you getting your data from? What are you looking at? How do you store your notes? And so just understanding the workflows, right? Acknowledging that part of what these tools might help you do is optimize that workflow. Maybe it will help you gather the upstream information faster, distill it in the ways you want it to faster. But it might be something new, like some new process that's kind of like goes across your existing flows. So I think it's like understanding the key data sources, right? And, you know, in this space, like there's a handful of them that seem pretty important, right? Like transcripts, SEC filings, obviously your own personal notes, your RMS. And so it's figuring out how you can get those in a way that can be accessible to the models, right? You know, when you look at things like SEC data or transcripts, like there's providers now where you can essentially pay money, get access to that information and have that accessible to the models. You have to wire it up. You know, I think the more interesting pieces are what are you doing with this? Like, what are you planning to do with the data? How do you want to structure it? How do you want to distill it? And there's a lot of utility in helping teams write that down, like talk through that. Because I think a lot of this stuff is kind of just, you know, it's in your head, right? It's this institutional knowledge that's just in people's brains, right? So I think a big benefit is extracting that kind of that specialized intent or knowledge in writing it down. Because it turns out if you can really specify what you want these models to do, and you have access to the data that has that source information, they're really good. I think a lot of the work goes into just kind of writing down what you want to do. And I always like to tell people when they're learning AI is like, hey, are you a good communicator? Like, can you write things down? Okay.
00:23:47 · Matt Stockton, Brett Caughran
Like, that's actually 80% of the work is describing your intent clearly, right? [00:23:53] S0: Which is challenging for many investors because so many investors are these sort of intuitive, almost like concert pianists. Like they sort of can create a masterful trade, but asking them to sort of turn around and write an 80 page operating manual of sort of how they make decisions is a much harder exercise. How do you think we crack that? Yeah. That disambiguation because specificity is really critical to sort of overcome the sarcastic nature of the underlying models, right?
00:23:53 · Brett Caughran, Matt Stockton
[00:23:53] S0: Which is challenging for many investors because so many investors are these sort of intuitive, almost like concert pianists. Like they sort of can create a masterful trade, but asking them to sort of turn around and write an 80 page operating manual of sort of how they make decisions is a much harder exercise. How do you think we crack that? Yeah. That disambiguation because specificity is really critical to sort of overcome the sarcastic nature of the underlying models, right? [00:24:32] S1: Yeah, yeah. So my biggest hack or view for this is it turns out like the models are actually helpful to help extract this information from you, right? So like one of the biggest ways that I use these tools, I'm trying to build something or describe what I want, is I will truly take a voice memo of me just like talking, right, extensively, maybe go on a run and just record myself talking if I have to build a project. And like, I'm not even... I'm truly just rambling my thoughts, right? Because it turns out if you take that voice memo and turn it into a transcript and load it into a tool like Cloud Code and do something like, Hey, here's where I'm at with this idea or this thing I need to do. Want you to ask me questions exhaustively until you're clear on what I'm trying to do, and that's a complete story. So you can actually use these tools, right, to kind of help you you build that. Like, you don't have to start from a blank slate. So that's kind of my view on how I get this information out on paper.
00:24:32 · Matt Stockton, Khe Hy
[00:24:32] S1: Yeah, yeah. So my biggest hack or view for this is it turns out like the models are actually helpful to help extract this information from you, right? So like one of the biggest ways that I use these tools, I'm trying to build something or describe what I want, is I will truly take a voice memo of me just like talking, right, extensively, maybe go on a run and just record myself talking if I have to build a project. And like, I'm not even... I'm truly just rambling my thoughts, right? Because it turns out if you take that voice memo and turn it into a transcript and load it into a tool like Cloud Code and do something like, Hey, here's where I'm at with this idea or this thing I need to do. Want you to ask me questions exhaustively until you're clear on what I'm trying to do, and that's a complete story. So you can actually use these tools, right, to kind of help you you build that. Like, you don't have to start from a blank slate. So that's kind of my view on how I get this information out on paper. [00:25:46] S2: It's interesting because we've spoken about, you used an analogy, Brett, of, you know, a pianist and intuitive pianist. I saw this quote, I can't remember who said it, but it's basic... Oh, Daniel Meisler. He said, All of knowledge work is just vibes and spreadsheets. Right? [00:26:05] S1: And LMs don't do well
00:26:05 · Matt Stockton, Khe Hy
[00:26:05] S1: And LMs don't do well [00:26:06] S2: with vibes, right? Like, want instructions and they want intent. And it's funny because Matt and I have spoken to different companies, just discovery work. At the end, they just think you just throw all this data in an LLM and then it's magic wand waves and, like, every answer you could ever want is there for you. But the reality is, as Matt and you are both alluding to, is that you have to, like, understand what output you're trying to get. And that is actually not even a tech question. In fact, the tech makes the need for this process documentation or process mapping way more important than it ever has been. And I'll be curious to see how financial services people do with that because especially investment on the investment side, notoriously don't really care much about that. They have their process. Their process works. Let's call it a day. And so, don't know if either of you have thoughts on like, are people ready to sit in a room with Matt for four hours and be like, so tell me when this piece of information comes out, what are the seven things you do and the three things that you think about?