00:02:23 · Khe Hy
[00:02:23] S2: Thank you, Matt. And definitely, Matt is one of those accounts where every time I read a tweet from his, my IQ goes up a little bit. You know, what I'm also noticing on this conversation is that the three of us have had the luxury of stepping away from a full time job to explore this. And it's something that I definitely, I know none of us take for granted because imagine trying to keep up with this fire hose of information if you're working fourteen hours on a trading desk. Right? So, Matt, I wanted to start off. You and I have been chatting a bunch about this kind of the latest struggle that I've seen with my training and consulting clients, which I'm kind of loosely calling the challenge to transition from single player mode to multiplayer mode. And what I mean by that is it's funny, right? We spent the last decade, fifteen years, like getting everything into the cloud. Everything is shared. Everything is SaaS. And then now, you know, with the advent of co work and code at OpenCLaw, everything moves back onto the local machine, right? And so you have two laptops, you're working on a model on one laptop with, you know, the plug in. You go home to your other laptop, you forgot to sync it, then you're like SOL. And you know, this idea that, you know, everyone's kind of taking context that they need for their individual contributor task, you know, copying it onto their local machine, running their analysis, and pushing their information back into the shared drive, like, that doesn't feel like a scalable way of sharing knowledge, of using AI. And so you and I have been talking a bunch about, for lack of a better term, a company resource brain, right? How do you take institutional knowledge that both in structure, you know, structured data, unstructured data, there's a lot of it over ten, fifteen, twenty years. It's in people's email boxes and people want to just talk to it from co work or code or codex. How should we be thinking about this exercise? Yeah. Yeah.
00:04:33 · Matt Stockton
[00:04:33] S1: No, it's an interesting question, and I think you're right in terms of, like, taking this from single player mode to multiplayer mode. There's a lot of benefits, I think, even from teams kind of sharing their workflows. Like if someone discovers a workflow that's working with these tools, like having a way to share that. But you're right, ultimately, it kind of all traces back to the data, like where is the data living? How are you storing it? How are you capturing it in your workflows? And, know, lots of the smaller firms, like they don't really have rigid or robust processes to do this. And so sometimes you're coming in and you're looking at it and you kind of have to figure out, okay, we have this historical way of storing information. How do we structure that in a way so that we can get utility or value out of these models? And I guess the way that I kind of think about it initially is, like, you definitely don't want to boil the ocean when you're coming in and trying to solve this problem. Like, I like to take a very much like a job to be done approach. Okay, let's try to break down what you're trying to do as a firm or as a company. Let's name those workflows. And then take one or two of them and try to systematize it, try to operationalize it, try to figure out, Hey, is the data not landing in a shared spot? How can we make that happen? Hey, is that data not connected to our tools? How can we make that happen? So I like the concept of this digital brain and talking to your data and all that stuff. But you kind of have to tease apart, you know, what are the sources of information you care about? Are you actually capturing those in your workflows? And, know, just try to make incremental progress, right? And then I think another big thing here is kind of have to have everyone on board, right? You have to have the team on board if the way of doing things to get value out of these tools requires you to capture some new data or something like that. You got to have to, you got to get people aligned on that process change.
00:06:26 · Khe Hy
[00:06:26] S2: Do you think that, you know, there's a big push, especially in like the power user community, AI community of, you know, really good folder structures with markdown files, with metadata, with indices, kind of bypassing bypassing the very kind of heavily structured, you know, relational databases. Do you think that that works you know, that works well for an individual contributor, but does the folder structure with, you know, good markdown files and tagging, does that, can that expand beyond, you know, a small group of individuals?
00:07:04 · Matt Stockton
[00:07:04] S1: Yeah, yeah. It's an interesting topic. And basically, like, just kind of drilling out a level, right, when we talk about these models, know, they obviously have to have access to data. And the models, it turns out they're really good at exploring folder structures, like using the tools that are kind of baked into them to open folders and look through things and search files. And that's actually really it's really useful for kind of unstructured data. You know, you think about creating some taxonomy where you're organizing your data by company or by theme or something like that, and have these tools be able to search that. That's super useful. I think there's definitely still a spot for structured relational data. Like you still need databases and data warehouses, especially for, you know, the numbers. So it's kind of a mix, right? And basically, that's when you're building these tools or designing these systems, it's kind of figuring out the right mix of how structure your data and how you kind of tie these tools or these agents into that data so that they can kind of find what they need.
00:08:17 · Brett Caughran
[00:08:17] S0: One of the things I've been wondering is, if I have PDFs, PowerPoint slides, etcetera, in a folder structure, understanding how that can connect, most investors live their lives with their nose in Microsoft Excel. And I did an assessment of one of my models and it was 1,200,000 tokens. And so when I initially connected SharePoint to ChatGPT, I'm like, Oh, great. ChatGPT is going to be able to talk to all my financial models. This is amazing. And it was nowhere close, right? Is that a token issue? And how you see firms handling that Microsoft Excel issue, like extracting and speaking to you know, 17 tab, 1,200,000 token financial models?
00:09:08 · Matt Stockton
[00:09:08] S1: Yeah, yeah, know it's interesting. Admittedly, I haven't spent a ton of time connecting Excel workbooks, right? A lot of the work that I've done is kind of taking more unstructured information, like transcripts and filings and things like that. Yeah, I mean, there's definitely, you know, with these models, right, like, you have to figure out how to both preprocess the information you're giving to them and also kind of, like, segment your tasks that you're giving to them. There's this concept of a context window with these models, right, which is how many tokens the model can pay attention to. And that is clearly growing, right? There's models out there with million token context windows. But the fact is, it's like the quality degrades, the more things these models have to look at. So when I ultimately, like, if you kind of step back a level and say, Hey, I have this data that's gigantic, right? My mental model is how do I split apart the task so that I can kind of segment the task to make it a smaller, a smaller set of tasks that feed into the end result? Just kind of breaking apart the task to make it more easier for the model to accomplish each task. Right? So like, for example, one project that I I've worked on is taking these thousand page PDFs of unstructured information and having to build a process that goes into there and actually, like, distills the information, pulls out very specific aspects of that PDF, you definitely don't wanna just point Claude code at the PDF, right? Like, you want to preprocess it. You want to pull out the text. You potentially want to put that text in a vector database and and be... Have the model be able to search it. I don't think there's, like, a silver bullet, but I think my mental model is if it's not working, if it if it seems like it's not working when you use the model, how can you break apart that workflow, that thing that you're trying to accomplish into smaller pieces?