00:00:00 · Joe O'Donnell, Brett Caughran
[00:00:00] S0: But we have explicitly launched within the last two months, AI idea generation agents that deliver fully baked investment rewards. There's no such thing as AI for financial services. To deliver institutional grade insights, it's quite difficult. [00:00:21] S1: Great, welcome to the next episode of Invest with AI, where we explore the intersection of fundamental investing and artificial intelligence. I'm really excited to have Joe O'Donnell here today from Canary Data. Joe, could you start with just a brief overview of your background? [00:00:38] S0: Yeah, sure. Happy to do it, and thanks for having me. So the quick background is I used to be a professional investor. The majority of my career in that world, spent running the short portfolio at Tiger Global. I did that for almost ten years and left at the beginning of twenty twenty one, hung out for a while and then started working on Canary in 2023. And we've been at it ever since. And Canary basically builds the very, very, very brief summary, which I imagine we'll get into in more detail. Canary basically builds intelligence for professional public markets investors. So our customers include many of the largest hedge funds and mutual funds in the world.
00:00:38 · Joe O'Donnell, Brett Caughran
[00:00:38] S0: Yeah, sure. Happy to do it, and thanks for having me. So the quick background is I used to be a professional investor. The majority of my career in that world, spent running the short portfolio at Tiger Global. I did that for almost ten years and left at the beginning of twenty twenty one, hung out for a while and then started working on Canary in 2023. And we've been at it ever since. And Canary basically builds the very, very, very brief summary, which I imagine we'll get into in more detail. Canary basically builds intelligence for professional public markets investors. So our customers include many of the largest hedge funds and mutual funds in the world. [00:01:20] S1: Even back in 'twenty three, you were much more forward thinking than I. I played with these tools in '23, I'm like, these just... Like, I couldn't really do anything institutionally useful for the hedge fund research process at that moment in time. What was sort of your your conversion moment where you where you swallowed the AI pill back at that point?
00:01:41 · Joe O'Donnell
[00:01:41] S0: Yeah, I mean, I wish I could take credit for being extremely forward thinking and thoughtful about it. I think the other problem statement that we started with at Canary was basically that there's an enormous set of things that should exist for professional public markets investors that don't. And the early sprouts of AI, you know, just BT2, etcetera, I think provided a roadmap for what might eventually be possible to do. But at least at that time, a lot of the things, practically speaking, were just not institutional grade. And so we found ourselves doing to build things like data products or intelligence on top of those data products was more of a hybrid model. We use AI to the extent of its capabilities, but we were very cautious and we continue to be cautious to not overuse AI. I think it can be an exciting crutch, but to deliver institutional grade insights, it's quite difficult. And back then it was very difficult. And so I think the cocktail of sort of more deterministic code and just manual processes versus AI was, I think, heavily weighted towards the former in those days. But we kind of saw where everything was going. And so as AI has improved and continue to improve, we've shifted more of that mix towards AI. And I think that's likely to progress hopefully significantly more in the future.
00:02:56 · Brett Caughran
[00:02:56] S1: That makes sense. And we can count on a hand or two former hedge fund investors who've actually moved to build product at firms like Canary. One of the sort of the conversation points that comes up a lot is the value of domain knowledge in building AI product. Can you talk to us about that concept? Like what elements of building Canary have required that domain experience from your time sitting on the desk?
00:03:27 · Joe O'Donnell
[00:03:27] S0: Yeah. I mean, I'll make a very bold claim to start with, which is that AI in general, would say, for for this particular use case is only as good as the people who build the AI and train the models and things like that. You know, nobody wants the average public markets investor's opinion about anything. Mathematically speaking, the average public markets investor destroys value. And I would say an even stronger form version of that might be the seventy fifth percentile public markets investor. And really, maybe conservatively doesn't add that much value. Everyone's in search of the last few percentile. I'd sort of equate it to a golf tournament. Every professional golfer, PGH or whatever takes two fifty, whatever, 300 shots over the course of four rounds. And the difference between, you know, the person who collects all the gold and the person who nobody's heard of is a few shots. But that ends up being all the value. And I think that's certainly the case. There's just the power law distribution with respect to public markets investing. And so I'd actually say the stronger form version, it's a necessary requirement to build good AI for public markets investors to have actually done the job at an extremely high level for a long period of time. Because the simple problem statement of what do you actually build and how do you actually know what a good result is can only be determined by people who actually know what the correct answers are to those things. And so, yeah, I think another another way, another thought experiment, at least that that that I thought of this morning, which I think is kind of interesting, would be, you know, if you're... If one were to compare, you know, put on... Have a contest, know, an investment contest. On one side, you put whatever Warren Buffett or Stanley Druckenmiller just by themselves with, I don't know, Google, something like that, and the SEC, and something like that against a thousand junior investment makers, and they get to do whatever they wanted. My strong suspicion would be that Buffett and Druckenmiller would win. And and that's... It's all around judgment, how to build a portfolio. And, you know, AI tools can be can be useful in a lot of different ways, but the judgment layer is ultimately what determines whether investors have good results or they don't.
00:05:46 · Joe O'Donnell, Khe Hy
And so in order to build the right kind of tools, the judgment layer has to be a vital ingredient in all of it, and it can't be built without having that level of expertise, in my opinion. And, you know, there's a... You I think going back to what you're saying about how there's only a couple companies in our industry that are designed that way. I think, honestly, there's an adverse selection bias to it. You know? The world's best investors are usually busy being the world's best investors. They're not they're not foolish enough to start a startup. So I think it's a it's an odd group that, I guess we're we're a part of on that regard. [00:06:25] S2: Joe, I love... Could you talk a bit about for, nontechnical folks, the architecture of Canary, like the data where the LLM sit in, the deterministic piece, the judgment piece. Love to hear that from you.