00:49:34 · Joe O'Donnell
[00:49:34] S0: Yeah. So, I mean, what I'd say is there's a lot of different examples for how people can use it. And some of them would just be the data stream itself. You know, just I wanna look at, I don't know, something really basic. Insider buying and selling data. Great. We can help with that. Or if you wanted to ask, you know, say, tell me what Canary thinks about, tell me what the Canary super analyst thinks about insider behavior at company X. Well, then you have our intelligence layer that gets pulled in via our MCP, and we'll tell you that. And then you could, for example, ask, well, show me all the supporting and refuting data points in Canary and, you know, X, Y, Z, other MCPs that I have plus my investment notes or whatever that support or refute these theses. Or you could use it to cross pollinate against the various data sets that we have. So for example, one thing that I never talk about, but we do is we have just KPIs. So in our MCP, but also in Canary that typically just sit beneath our analysis layer. But so for example, if you wanted to say, map out Duolingo DAUs as Duolingo themselves have disclosed, and then compare it to app usage data and Canary's transaction data. You know? Type that into Canary, let it burn... Or sorry, into into Claude using Canary's MCP, let it go burn some tokens, and then it'll provide you an analysis that cross pollinates across all these things. So, yeah, there's there's a lot of different things. I'm sure I'm leaving off a bunch of stuff. I mean, my my users often, I I imagine, don't share their their favorite and best use cases for for our stuff, nor should they. So, but yeah, those some ideas.
00:51:07 · Brett Caughran
[00:51:07] S1: So Kay and I between us get a handful of questions a week or a month from firms that sort of ask us for advice of, hey, we want to start this journey. And FDE, know, for deployed engineering is a big, is sort of a big concept now. Like, what do we do? Like, how do we go from where we're at, which is sort of interested to actually AI native in the investment process? What are your thoughts on the FTE model? What advice would you give a public equity investment fund in making that journey?
00:51:43 · Joe O'Donnell
[00:51:43] S0: Yeah, mean, I think this is another thing that is likely to be different across investment firms. I think it's gonna be a very heterogeneous environment, maybe forever. It starts with that thing I mentioned before about just what do you wanna do yourself versus what do you wanna outsource to someone else. And you've gotta draw that demarcation line first. I think my subjective biased opinion on that would be internalize the vertical search model where people can query your investment notes and a bunch of other data sets that are exclusive to the firm. Perhaps there's a creation of, you know, one pagers that gets circulated in advance of investment committee or things like that whenever a new idea is being discussed, things, whatever the demarcation line is. But, you know, the vertical search capabilities and perhaps the automation layers, what I would call the more sort of cost centric automation layers can be things that firms do themselves. And then there's a decision point to be made for what I would say the higher order judgment related tasks. What do you wanna do with that? And some firm... As I said before, some firms will decide that they should do it themselves and great. Other firms will wanna outsource all or part of that to vendors, hopefully like Canary. But everyone's gonna have a decision point there. Then the second layer is just integrating. I mean, there's this integration task that I'm sure both of you are very familiar with, which is just, okay, well, blocking and tackling, what are all the data sets that we need? And there's typically a lot. But then there's that next layer on top of it, which is, which is, which I've mentioned also before, what do they all mean? It's not, it's not, it's necessary, but not sufficient to have connectors into all the data sets. But what do they all mean? That's a separate problem. If you're trying to build the intelligence layer. And then I would say effectively, you know, it's the stacking that I mentioned. People can outsource it or they can in source it. And a starting point, I think a very bad starting point would just to be to get whatever an enterprise subscription to ChatGPT or Quad and start trying to use that. The other side of it is having someone that can teach them what the actual limitations are, capabilities and limitations are of all the models.
00:54:16 · Joe O'Donnell, Brett Caughran
Because without understanding the... Where the the the talent frontier is, a lot of bad decisions can be made using using them over over or under using the models. And so it's not... You know, anyone can can can get a subscription to a foundation model, but to actually make those sorts of things enterprise grade wherever the cutoff is, it's nonintuitive and requires a lot of thinking before deployment. [00:54:44] S1: Yeah. Makes a lot of sense and that resonates for sure. What... This was great, Joe. What what did we miss?
00:54:54 · Joe O'Donnell
[00:54:54] S0: Yeah. I mean, I think there's there's a lot of talk. The one other thing that that I at least hear from people quite quite a lot is, you know, what does the investment firm of the future look like? And I have a few thoughts on that, and who knows whether I'm I'm right or not. I say empirically that the changes have been more more around the edges than than transformational so far. But what I think is is is likely to be the case is that investment firms are much more organized around people with good judgment as opposed to a sort of pyramid hierarchy of junior analysts, mid level analysts, maybe portfolio managers, then, you know, the big boss on on top, something like that. You know, the the... I would say this is... This has always been the case, but it will be increasingly the case that having great judgment is the determinant of great investment returns. I think that's one thing. The second thing is I could see it being somewhat counterintuitively likely that there's a lot more investment firms that sprout up as a result of the AI revolution. The cost of doing business will just go down dramatically. And as a result, I could certainly see it being the case. You know, the economics today of running a, I don't know, a $25,000,000 hedge fund, not great. You know, no one's... No one... I shouldn't say nobody. I would say very few people have a $25,000,000 hedge fund, and that's where they wanna stay. They start a $25,000,000 hedge fund because they want want to grow and whatever. But it certainly seems possible to me that in the future that the economics of that could be totally fine. And so I don't think we're gonna hit some sort of hyper efficient market situation where the stock prices of every company in the world are exactly right at all times. My strong suspicion is that actually for now, the amount of alpha available to the world is perhaps greater than it's been in a very long time, primarily around the misuse of AI. And my strong suspicion is that the amount of alpha on a normalized basis for a long time will not decline.
00:57:03 · Brett Caughran, Joe O'Donnell
[00:57:03] S1: Yeah, I fully agree with that. Well, this was really interesting, Joe. Thank you so much for being with us and I look forward to staying in touch and following the Canary journey. So thanks again. [00:57:21] S0: Thanks for having me.