00:27:00 · Brett Caughran, Khe Hy
Do you want me to sort of create that for you? Right? So you're almost like spoon feeding. You're creating this system for people that to me is very close to, you know, something that's going to wow a lot. Like all they need to do is sit in the cockpit and almost you just have to press the right buttons rather than, you know, think through some complex prompt that we had to do, you know, twelve months ago. So that's I think what the that's that's what the the new models have unlocked. That's what the connectivity, we can talk a little bit about connectivity systems have a lock, the sort of ability to more seamlessly integrate MCP structures, but also the headless RMS movement, which is so exciting because I've always hated RMS systems so much. It's so clunky. That to me is sort of connectivity overcomes one of the biggest fundamental issues that AI systems have had for investors, and that's the sort of context quality. Yeah. [00:28:03] S1: So let me on that context point, it seems like and you're closer to this than me it seems like the MCP universe has completely expanded in the past six months. Right? Every day someone emails me about a new MCP, whether it's for expert calls or investor transcripts or, you know, we're talking about BlueMatrix or Aeriea, I always pronounce that one wrong, FactSet and so on. I'd love for you because I don't know this world that well. I'd love for you to kind of give a couple minutes state of play of the MCP landscape for investment firms.
00:28:03 · Khe Hy, Brett Caughran
[00:28:03] S1: So let me on that context point, it seems like and you're closer to this than me it seems like the MCP universe has completely expanded in the past six months. Right? Every day someone emails me about a new MCP, whether it's for expert calls or investor transcripts or, you know, we're talking about BlueMatrix or Aeriea, I always pronounce that one wrong, FactSet and so on. I'd love for you because I don't know this world that well. I'd love for you to kind of give a couple minutes state of play of the MCP landscape for investment firms. [00:28:46] S0: Yeah. Yeah. And I'll sort of tell it with the FactSet stock chart up on the left terminal. My left screen, you know, the FactSet stock went from sort of the high 400s to the low 200s during the Claude kerfuffle. [00:29:11] S1: Almost 50 down.
00:29:13 · Brett Caughran
[00:29:13] S0: Almost 50% down, even though the fundamentals have sort of been pretty good. And I think, you know, sort of the principal concern has been the shifting interface for investors and deal makers. And I'm a FactSet subscriber. I even noticed that. Like I very rarely log into the FactSet terminal anymore. So this is sort of the setup, I think, all vendors of the platform is burning, right? But what's the sort of next thing? Now, the FactSet MCP is really good actually. And it's very sort of critical in a number of the workflows that I'm building to make them institutional grade. Because if you go to chat GPT or Claude out of the box, and you ask for a primer on a company, it is going to pull principally from the retail aggregators, the Zacks and Motley Fools and Seeking Alpha, Alpha. Like no shade to all of those. They're fine businesses, but it's not institutional grade quality for hedge fund quality research. And I think we all know like the training corpus of the foundation models themselves are highly influenced by Reddit and Wikipedia and blogs, etcetera. So it's like two layers of low quality context for, you know, even if the reasoning engine of the LLM itself is strong, you're sort of, know, the underlying quality. And so improving the quality of the data has been critical. That has been very hard to do for the first two or three years of AI. Firms had to create data lakes, whether it's Snowflake or Databricks and, you know, API heavy systems. And it was very difficult, very difficult to do, to sort of ingest, clean the data, expensive to do. The APIs were very That has been rapidly abstracted as, you know, I think the whole ecosystem has seen this trajectory towards MCP, which sort of, you know, Tom Lee in our session explained is like an MCP with like good instructions upfront, or sort of an API with good instructions upfront. But the MCP ecosystem has really been expanding. You can start to get a very good baseline level of institutional grade research in MCP stack. Now, it's not inexpensive. We could talk about that. I think that's a problem that needs to be solved. So it's sort of layer one. Second layer
00:31:46 · Khe Hy
[00:31:46] S1: for equity research, it's coming mostly from FactSet and then the aggregators like BlueMatrix and Era. Is it Era or Era?
00:31:56 · Brett Caughran
[00:31:56] S0: Yeah, Era. Equity research has been a tough, that's more of like a commercial rather than a technical. Want to get BlueMatrix and Era on. If anyone in those terms listening to this podcast, please email me to sort of talk about the transition of the sort of economic and commercial structures. So I think that's something that's a missing piece. But certainly, you've seen big players like a third bridge, even like a Coleman GLG really become MCP native. We'll have AlphaSense on and they're sort of, you know, taking a more of a wall garden approach rather than an MCP approach. I think almost all other vendors have taken the MCP approach where you can ingest that into whatever your interface is very, very easily. I'd say the second piece that's moving in an interesting way is this headless RMS where, and I'm sort of curious what you're seeing, Kay, because you do a lot of this sort of work of in the past public market investors adopted sort of the OG, Tamale RMS, Tickerize, and then sort of more recently, you have BipSync and Mackie as the RMS. The user interface of an RMS system is really clunky. It's hard to enforce time constrained investors to put all their notes and research into the user interface. And so the reality is most of the research context at hedge funds sits in OneNote, Slack, Bloomberg IB, Outlook, PDFs, messy drives, and heretofore the sort of I remember when ChatGPT first had the connect your OneDrive, connect your SharePoint. I'm like, holy crap, this is amazing. And I tried it. I'm like, this doesn't even close close to work. Right?
00:33:46 · Khe Hy, Brett Caughran
[00:33:46] S1: Do you know what I heard about that, by the way? Side note is that the off the shelf Microsoft three sixty five connector can't read spreadsheets with more than 16 columns. Okay. And there's a row limit too. I don't know what... It's obviously bigger than But yeah, there's all this and there's no documentation on this anywhere. Like you just got to figure it out. [00:34:09] S0: But I think like some more interesting startups I've seen talked to recently are focusing on this headless RMS where they're basically going into firms, they're connecting with OneNote, you know, Outlook, in some instances, scraping Bloomberg, IB chats, to create this knowledge graph, this sort of internal brain, which I'm so happy about because it's sort of like Notion Obsidian, no shade to those firms, but like, the sort of creating your second brain felt so cumbersome to do. Like, I think the vision of the headless RMS is like almost scraping and creating your internal brain, but it's an agent layer that does that for you. Yeah. Right? To me, that feels like a huge unlock that probably people aren't gonna trust OpenAI and Anthropic to do. They'll need some application layer to do that.