00:25:23 · Brett Caughran, Ying Hua
[00:25:23] S1: So we're sort of at a moment in time where in horizontal versus vertical debate, labs sort of are ascendant, in particular Claude is ascendant. And to Kay's point, many of our clients are sort of building dashboards with Claude code. But this sort of topic of agent debt and this topic of tokenomics sort of growing more How in do you sort of see these things? Because it is seductive to sort of spin up a dashboard and sort of think that it's institutional grade, although most sort of deep AI vendors like yourselves we talk to sort of cite that as a sort of not great decisions. How would you sort of frame those two buckets? And what do you think is next for clients in terms of using and building dashboards? [00:26:15] S0: Yeah, I never would say dashboards back. I think think, know, I think, Brett, you've seen our product, right? We're one of [00:26:23] S1: the
00:26:23 · Ying Hua
[00:26:23] S0: few vendor out there that actually have a dashboard. We actually think there are certain things that you shouldn't every not every answer should be a chat answer right. Some information should be serviced to you without you having to ask it should just be there. A reason why people like to stare at a Bloomberg terminal right? Like there's value in that. So I would never say dashboard is bad. But two things. Number one is I think the idea of a static dashboard is bad even for the same company, even for the same industry. Like what the dashboard should look like today versus what you should look like two months from now for next earning season could be different right. This is based on domain knowledge things. So one is how often do you want to be changing that dashboard? Do you want do analysts and PM want to spend their time deciding how to change? Or can you use a platform like ours to be like you have all the data already, I just want you to present data in different ways or I wanted to reorder data in different ways so you can show me what's important what's not important. So that's number one is just like how much time you want to do it. Second thing is, I do think that the ultimate form of what investing should look like, the process, is not going to be mainly dashboard driven. So what do mean by that? I'll give you an example. So I used to love chart books. I don't know if you guys like look at chart books, like you know you do certain charts, whether it's like an estimate revision stock, whatever it is, and then you have it, you know, do a PDF, you flip it through, right? You get a lot of information, you get a lot intuition from it, it's great. But once you get to like chart book number six, you're never actually looking through the chart books, right? You're not actually going through okay what's new, what's worthwhile, right? So dashboard, I think there is going to be a similar dynamic. You're going to build a lot of dashboard. Everything is going be very cool. At some point, you're going have too many dashboards to manually check. So what do we think is the ultimate end of, like, investing with AI? I think for us, it's like you should be able to create dashboard or however many view. The dashboard is just like how would I want to view this particular data source, right? That's all it is. It just everyone has a different preference.
00:28:31 · Ying Hua
What I think is important, what I'm screaming for, what you should be able to create dashboard very easily, Create any metric, any visual very easy. That's number one. But two, you should have someone reading through it and tell you. It shouldn't just be building dashboards. It shouldn't stop there. It should be hey Brett, basically you have a dashboard that tracks 100 different data points. I'm going to check these 100 data points for you to tell you hey Brett you should look at this one, this one's interesting, this one has a big jump, this whatever. Like my view is dashboard is a means to an end for you to reach some conclusion. And the purpose of AI is should be treating like you have an intern who read those dashboards for you, who build that dashboard for you, read dashboard for you, filter all things that's important, and alert back to you so that you don't have to have to mentally spend time thinking through those. Makes a lot
00:29:22 · Brett Caughran
[00:29:22] S1: of sense to me. I mean, I think one of the fundamental challenges of office software, investing software, has been this deep heterogeneity of investment process in sort of by style and by coverage area. Right? Yeah. And so really, like the only scaled software has been like the terminals, which are just sort of like an aggregation of data and allow for this customization in a launchpad, right? Like all of our launchpads look differently. Yeah. How do you think we take that same concept? Like how do mass market scale to this degree when you're sort of building software? How do you allow for that mass customization of dashboards to your clients?
00:30:05 · Ying Hua
[00:30:05] S0: It's very easy now. So let's think about it this way. So let's say you have 50 data points to track, different data sources, right? One, you can auto run each one of them. And then you can have each one decide, is this an interesting thing? And say, of the 100 I run today, let's say I have a daily run of 100 data sources. And then out of the 20 of them is interesting. AI determines interesting could be a meaningful change can be versus consensus, whatever it is. And then you can just have an agent on top of it's like, hey, those things are interesting make a dashboard for me. I only want a few things that's interesting. That's fully customized right that that's definitely doable. But I think there's another layer of customizing you touched on that I think it's actually a harder problem. You're talking about different industry, domain knowledge. I go back to my domain knowledge point. I think how do you analyze a tech company is very different than how you analyze an insurance company. The metric you care about is different. So if you think about the terminals, if you think of some of the current vendors, when they generate AI, a lot of the standardized, right? Standardized, whether it's model KPIs, those are not very useful. Those are not useful. But that part of customization gets a little bit because the model layer or the platform layer needs to have domain knowledge in order to spit out things that matters. So one simple example I used to test this for every some of our competitors, every competitor I try, I used to ask them, hey give me like an earnings call summary for JP Morgan. If they ever spit it out margin revenue like ah this platform doesn't know anything. Like no one cares about revenue for JP Morgan or insurance company. Right. Like that's just like a very obvious thing. So that part of customization I think is a lot harder than what dashboard to service to you. I think that part is a part we've spent the last two and half years building a ticket level domain knowledge for that reason. I think that is the hardest piece of all of it. How do you connect different knowledges? How do you know which knowledge matter at what point? And how do you know when user asks a question you need to pull that specific piece of knowledge in order to answer that question properly. So I think part of it is easy, part of it is, I guess, TBD.
00:32:34 · Khe Hy, Ying Hua
[00:32:34] S2: Yeah. Interesting. One question, one topic that comes up often with some of my investing clients is this, like, separation of synthesis and judgment. [00:32:47] S0: Yes. [00:32:47] S2: Right? And so the, like, I'll often hear someone say, it's like, We're okay for AI to do the synthesis and the gathering. [00:32:55] S0: Yeah. [00:32:55] S2: But when it comes to the judgment, that resides with the human. That resides with the analyst. Yeah. First of all, I don't think that's a very bi- that line is not as binary as one would tend to believe. But in your examples, you've said that, you know, Okay, You know, you have these data, these discrepancies in data, and then you have an agent kind of flag the discrepancy. And so, you're basically giving the agent some form of judgment. Yeah. And you've come back oftentimes to the role of domain expertise in this. But I'd be curious on your kind of... What your view on that separation between AI does synthesis, humans do judgment. And then when AI starts to make more judgment, like, where is it coming? Where is that training coming from?