00:10:41 · Khe Hy
[00:10:41] S2: Could I add the layer cake analogy is powerful one. When you go from like an Opus 4.6 to an Opus 4.8, like which parts of the layer cake become more powerful? Like, how has your product changed as the models have, like, step functioned?
00:10:59 · Joe O'Donnell
[00:10:59] S0: It's so it's a it's sensor in some ways, and it requires deep analysis and QA at every single layer of all of those things, which is an unfortunate reality of what we do. But what I'd say is that the scope of what's possible in AI increases, and sometimes it's a step function large increase. I would say the first time we really saw that was was way back for... With Gemini 2.5 where we started seeing that that level of functionality where we could build sort of more performant, more horizontal investment tools. And it really depends. So it's model specific and we don't often use off the shelf models for the stuff that we expect world class results from. Because as I was saying before, they just don't have the ability to deliver that last few percentile of results without a lot of fine tuning. And so we're finding just that fine tune models that we build ourselves, not because of anything related to cost, which I think is a big topic these days, but for performance purposes. You know, our goal is to deliver the absolute best results and the costs are just what they are. And so, you know, there's no trade off that we think we can participate in with respect to that. And so sometimes a new model will come out that a lot of people will be excited about in different industries and maybe sometimes in financial services as well that... Where we'll see literally no improvement at all and sometimes a degradation. In other times, it will it will be different. But it's really nuanced, and and I would say increasingly only applies to certain things that we do.
00:12:38 · Brett Caughran
[00:12:38] S1: Joe, one of the things I see a lot of AI finance firms run into is the sort of dichotomy of what to build and how to build it. And the how to build it is sort of getting a little bit more, not simple, but more simple than it was from an infrastructure perspective. I'm curious to understand how you think about the how to build, and now you have that advantage coming from the seat. How do you map out products that you think will be institutionally useful, particularly when public equities is a really sort of combinatorial complex practice. What one investor does from an investment process can vary quite differently from another.
00:13:26 · Joe O'Donnell
[00:13:26] S0: Yeah. I mean, I think it's, we basically try to meet investors wherever they are. So we're increasingly building flexibility in our platform. There's a headless version of Canary that we have an API, have MCP, for example, where if people want to integrate what we do into their own platforms and their own processes, we're happy to facilitate that. And we explicitly don't track what any of our users do at all. But from conversations with them, I think some of them will take certain parts of our product because they value those things and use them. And some people will take those exact same parts and use them in a different way. And some people will take just the datasets, and that's all they want from us. Sometimes they want the intelligence layer on top of that. Sometimes they want the entire platform. And we're happy to be flexible in terms of how we deploy that and or how people use us. And I mean, every company, I presume, could say this to a certain degree, but if we help people make a few better investment decisions per year, it's pretty easy to justify having a Canary subscription. But the other side of it, I would say, know, to maybe answer your question in a different way is, one is just the obvious thing, which is just I've faced these problems a lot and and my teammates as well have faced these sorts of problems a lot in their investment careers. And we know the landscape of what exists. And so we're just basically trying to fill in the blanks. And there's an enormous amount of work to do there, an enormous, enormous amount. I think we have a lot of great engineers and we build product and ship very fast, but the degree that at which to which our backlog builds is is is still greater than the rate at which we ship. And so there's a lot of things. And sometimes it's just, like I was saying before, sometimes it's just a very specific dataset around something that maybe two years ago would have been totally inefficient to ever try to productize. And sometimes it'll be, you know, sort of a specific tool around something like forensic accounting, like I mentioned before. And then from there, I mean, I think there's... Trying to think about the best way to put this. But there's a lot of stuff that sits outside of the scope of what investors demand.
00:15:38 · Joe O'Donnell, Brett Caughran
You know, we think we can provide things that, you know, just sort of from our own brainstorming sessions are gonna be valuable to people, whether people have asked for them or not. And then we'll put them out in the world and see if people like them or not. And so far we've been pretty successful at doing that. And I think there's a lot of different failure points that exist in the public markets investment process that we're trying to solve. And there's an interesting just starting point from the problem statement of most public markets investors do not succeed at their job of generating alpha. And so try to help people mimic their existing investment processes and make it more efficient. I don't think we'd be doing our job at the level that that we we can. And one [00:16:25] S1: of the concepts I've been pulling the thread on is this concept of AI judgment. And the sort of early consensus was that large language models are stateless next token generation machines designed to produce fluent sounding pros. They have no native judgment, but there's been some movement in the evaluation of the human plus machine, the Phil Tetlock approach, actually using AI to actually apply better judgment. You've talked a little bit about using the platform to make better decisions. Can you talk to us about using a tool like a canary to actually make better decisions?
00:17:08 · Joe O'Donnell
[00:17:08] S0: Yeah. I think... So we're starting to see early rewards on this. I I think it's hard to know exactly what the efficient frontier will will look like on that. But we... I probably should've mentioned this before, but we have explicitly launched within the last two months AI idea generation agents that deliver fully baked investment reports. You know, their their output is... I think the analysts that used to work for me at Tiger Global were very, very talented, but the output that those analysts generate is on par with the output that my very talented analysts would generate. We've only watched a few of them. And, you know, the future is uncertain in terms of actually how performant they will be. You know, we'll see how their portfolio does over time. But the output there is a fully baked 20, 25 page investment report backed by primary research, including expert calls and things like that. And at least ex ante, what I'd say is the work product is excellent. And so perhaps that's the early signs of judgment being something that is within the capability set of of today's AI models. I think, you know, in order to get there, though, as I mentioned before, there's a lot of work that needs to be done. You can't just simply... If we were... If we're just starting from scratch with no infrastructure built up, no proprietary datasets built up, no intelligence layer built on all those other datasets, I think it would be very unlikely that we could build AI analysts that were actually good at doing analysis. But because we've made those investments and because of the industry expertise that I have and and other people on our in in our... On our team that have, we have been able to build things that at least look, act, and sound like really good investors. Investors. And I think we've started with sectors and investment styles that we know very well. And so perhaps that will be an enduring and scalable advantage across all sectors and all investment styles. I guess time will tell, and we'll we'll do our best to to build products that that do those sorts of things. But I think going back to your your original statement around judgment, what I'd say is that without all those things built up, AI judgment is a very lossy thing at at every layer, and it's it's lossy and pernicious ways.