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.
00:19:40 · Joe O'Donnell
I think, say, something that I think most people do these days would be something like, you know, summarize an earnings call for me or something like If if one were to compare the actual earnings call to the AI summary of that earnings call or if it... Or or an AB test of take my best analyst, have them look at the earnings call, summarize it, and then compare it with any model or, you know, system of experts or whatever, they're not equivalent off the shelf. And I question what's actually achieved by doing something like that if it's lossy. You know, I think... And that's just one example. There are many other ones. But, you know, at least in my own investment career or any of the investors that I know, no one ever looked back on a year that didn't go as planned and said, you know, if only we had read more earnings calls or something like that, or if only we had looked at more ideas. It it actually... Almost always, it was much more simple than that. The year didn't go well because a few times a year, we had a big decision to make. We made the wrong decision. And probably that... One... Another reason why we steer away from productivity based things because those things, I think, including today's models, all the best ones, they're lossy. And what would I tell one of my analysts from an efficiency perspective about... Let's take the example of earnings calls again. Read the earnings call. Read the transcript. Listen to the earnings call. That is the least lossy thing. And AI sounds super smart, and it is increasingly smart. But it's still lossy. And in many things that people use it for, it sounds like it has good judgment and doesn't, or it's just has different judgment. And those things can be dangerous. It helps people... It helps investors build conviction when they actually should have lower conviction if they're using that for their investment research.
00:21:46 · Khe Hy
[00:21:46] S2: Is it fair to say that there's kind of like a hierarchy of judgment? So for example, one judgment could be like what data sets to use and another set like layer of judgment could be, should we even be in the sector? And like, and then the lossiness angle is kind of a new one that I hadn't really kind of put into like the layer cake of judgment, but maybe pulling on that thread of, you know, earnings calls and the lossiness, like where is the judgment strongest? You you gave an example where it might be too lossy, but where where does it work well? The judgment layer.