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.
00:35:02 · Khe Hy
[00:35:02] S1: That's super cool because we were saying before, I've been working with a few folks like, an FDE or, you know, of sorts between an investment firm and a software and an AI engineering firm, software development firm, where it's a headless CRM. So here, it's for the LP and biz dev function of the house, so but same similar concept, right? It's all, I mean, it's not in text and stuff, but it's all in email. And I guess you send the new deck to 50 LPs, someone's got to go into Dynamo and like click two flags and then type in the firm name, right? And then people, busy people aren't gonna do that, right? And so I think this is super cool. And I think you're already... This is where Codex and all these, like the more hacker types, they're already doing versions of this where they have a skill that's like every day go through my email and look for X on names and put them into a markdown file just with a date, you know, and then go through my Slack, do it with, you know, same thing. Put it... And and then at a minimum, you just have, a daily markdown file of everything you ever communicated on the name or the idea, And then you can throw a clot at that markdown series of markdown files. It's so much better than MCP, like a generalized MCP or some off the shelf RMS vendor that no one has time to figure out. I'm with you. I mean, headless and it maybe bears repeating because I know some people on my side that are there like, What do you mean by headless? And so headless meaning that there's no user interface, right? It just, it is acting on your raw materials, your granola transcripts, and just creating artifacts, creating infrastructure. But the point is that you can't, you don't have to click anything. It's just happening in like background session. So yeah, I'm super excited about that.
00:37:12 · Brett Caughran
[00:37:12] S0: Yeah, imagine this Kay, imagine I'm looking at, you know, DraftKings, right? And all I have to do is put the ticker in my sort of dashboard and it populates all of my research and notes and news and podcasts right into one dashboard. And it shows here's a series of calls you've had with management. A sort of, here's the granola recording of the internal bull bear debate you had on the name. Here's the, you know, sort of the six sell side notes that you starred as sort of particularly, you know, you sort of grade sell side notes as sort of relevancy. You know, here's a sort of data dashboard of, you know, historical correlations of KPIs. And it has a dashboard where you're seeing all of the work like that to me is the unlock. It's not this open claw that's like if I ask an open claw or a grok pot to go, you know, build an investment thesis on DraftKings, it's going to do the wrong things. And it's going to pull from the wrong data. And it's going to give me slop and burn a lot of tokens. If I can create this headless system around me with the right context, it's surfacing the right signal, right? When I need to see that signal. To me, that's like the future that is here now.