00:29:28 · Joe O'Donnell
[00:29:28] S0: What I would say is for our our products, you know, we have a a product that basically does the job of a junior analyst. Everything post idea generation, get up speed on a company, understand the key investor debates, what are all the key questions, how to do the research, automate all the research, and generate answers. That's called superanalyst. So that was our that was our name first before AlphaSense independently came up with the exact same name for a product a few weeks later. But the... We have a product that does that effectively across all listed companies that refreshes every day and does all the research that one would expect a talented investment analyst to do. It integrates with our own transaction data. For example, we have our proprietary transaction data set that we built from scratch. It integrates with web traffic data, app traffic data, point of sale data, foot traffic data, Google trends. It can build you a scraper. It can do an expert call. And it just does this autonomously across right now, I think close to 4,000 companies and refreshes every day. And our customers who use that product wake up with an email. If there's new research that... To be conducted, wake up with an email with all that research already done. And so, you know, that's... We don't ask questions. We don't say, what kind of research do you want me to do? We just do the research, and we we deliver the final result. And, you know, customers who don't care about it can discard it or they can take some insights or they can disagree with the analysts just like a normal investment firm would would do. And that's something that we already have have built today. I think as you as you move up the stack, it becomes increasingly more difficult. You know? The holy grail is idea generation and maybe after that portfolio construction. And as I said, know, that's sort of a problem we're working on, and and we've started to hopefully solve it at least from the idea generation level. But or... And and also worth mentioning on that same point, our idea generation analysts already do incorporate firm zone preferences into that thing. So the very first thing that happens when someone onboards to one of our idea generation agents is they get a questionnaire. You know? What kind of things do you like? What what don't you like?
00:31:46 · Joe O'Donnell, Khe Hy
If you want, you can send me some research that you've done in the past, and then we'll construct an idea generation funnel around those preferences as part of the solution and then our own models as another part of the solution. And then similarly, there's check-in points throughout the the investment memo generation process where a user can say, follow-up on this point, or I think you might be wrong about this, etcetera, and our models will take that into account, but they don't only do that. They take that into account. They also have their own ideas and combine the two and do further research. And so that's... You know, I I I have no evidence whatsoever to suggest that's the best model, but all I can say is that so far our our customers like it. [00:32:32] S2: Joe, can you talk a bit... You mentioned fine tuning a model. And, again, for for, like, a nontechnical layperson who knows about AI, what does that process actually look like for you guys?
00:32:44 · Joe O'Donnell
[00:32:44] S0: Well, it's terrific. AI sounds really, really, AI honestly is neat. But the actual process of getting an AI model to operate in your image is just horrifically bad. It requires a ton of manual effort. A ton. Know, I mean, to take... You know, just to go back to the disclosure statement example. In order to get a model that can parse through a disclosure statement at the level of a very good analyst, it requires very good analysts going through, you know, whatever, annual reports manually and saying, here's what I would say. And then they look at what the AI says and, you know, through fine tuning, reward functions, etcetera, all that kind of stuff. Over time, you hopefully get, an equivalence. But the only way to do that is by a huge amount. I cannot understate how much manual effort to actually make that work. And one of the reasons for that is somewhat obvious, but another reason which is obvious is the thing I was talking about, the long tail of results. I think there's relatively large Venn diagram overlap between very interesting insights and things that only happen very rarely. And so you... That needs to all be codified. You need to have a sufficiently large sample set that you've actually fine tuned to actually allow the AI models to understand when they see something like that and what it means. And it just requires enormous amount of manual. And that's just disclosures. There's hundreds of those, literally hundreds of those examples that are required to get performant fine tune models, performant defined as what a good investor would think about this particular thing. It needs to be equivalent. My view is that, let's say, I don't wanna pick on anyone in particular, but let's say you could get a result that was 95% as good as a good analyst for task x. Some some tasks, by the way, that's fine. But tasks that are centered around making the right decision, that is not fine. And if you... If it's not possible to get to 100% equivalence, then the default, what I would at least recommend for professional... For public markets investors, the default setting for that would be do it yourself. Do it the old do it the old fashioned way. Because, as I was saying before, AI is a... The the day... One of the dangers of of of overusing AI is higher conviction when you didn't deserve it.
00:35:29 · Joe O'Donnell, Khe Hy
And you never know what the missing percent is or how big the percent is or what what even category it's in, or whether it's a false positive or false negative without doing the AB testing. The only way you can avoid doing the AB testing is if you have AI models that, you rely on either that you buy yourself or that you buy from someone hopefully like us, that where you're convinced it is doing the job at the equivalent of of you or your investment team. [00:36:02] S2: And is it fair to say so someone like an Anthropic is hiring a lot of former bankers, former PE folks, investors, and they're doing a kind of a mass, like a production line version of what you described. And then Canary's alpha in this situation is like the people doing those evaluations and the fine tuning are just more in line with a hedge fund analyst. Like if they're more specific, is that a fair analogy?
00:36:30 · Joe O'Donnell
[00:36:30] S0: I think that's part of it. It's this multiple layered thing. So I think you could throw infinity investment bankers at a problem that applies to public markets investors and you would not get to the correct level of of accuracy. And I also think there just tends to be adverse selection. You know, that's what I was just saying about what it actually empirically means to go train a model. This isn't a job. You know? No one's leaving to take investment bank. I I suspect at least maybe there's a counterexample, but no one's... The head of TMT banking at Goldman Sachs isn't leaving to go train models for Anthropic. There might be a former Goldman Sachs investment banker that is training models for Anthropic. My suspicion is it's not their best bankers. That may be wrong, but that'd be my guess. And I think for certain industries, that's fine. You know, if they're more formulaic industries or lower leverage industries, totally fine. You don't need the absolute top people doing it to get to results that can be impactful at at... Through all stacks of... Or all parts of the organization. I do not think that's the case for public markets investing. And, you know, hiring... This is an industry where if one is a very talented investor, it can be very lucrative. And, you know, the... I guess I've seen some some very large salaries thrown to to... Or compensation packages thrown to some of the AI engineers. And so perhaps one day, those companies will do the same for... To train for public markets investing, but I haven't seen it yet, so we'll see. And... But I I think until that that happens, it's unlikely that for this particular use case, that will be satisfying. I I say more generally speaking, whenever I see a firm that says we do AI for our financial services, to me, that's that's... They've already lost. I I don't... That that doesn't mean it. There's so many different things that that could possibly mean that... And they're all very specific with lots of nuance. There's no such thing as AI for financial services. Yeah. We build specifically for public markets investors and and, you know, obviously, more more broadly the ecosystem around public markets investors. But I think we...
00:38:44 · Joe O'Donnell, Brett Caughran
You've actually started to see this with, you without naming names, but you've seen certain other companies specialize in investment banking, for example, which makes sense. It's it's... Building AI for investment banking seems to me to be a really interesting business model. But building AI for financial services means you probably don't understand the problem statement. [00:39:08] S1: I did I did a test on this exact point, Joe, with AI and Excel. And AI, Excel will build a great investment banking model and your balance sheet will always balance. But almost none of my models who actually forecast out the balance sheet to balance balance the the balance sheet in the forecast period. So it's funny, you actually have to like, I totally agree with you. You have to untrain the AI to actually make it approximate what a public market investor does. Which brings you to a question on Excel. I mean, to your point, there hasn't been a great software stack for public market investors because complexity of what we do. We've sort of been stuck with a Bloomberg Launchpad and Microsoft Excel to be the workbench, the analytical workbench. How do you see Excel shifting, both in terms of ingress, egress with AI tools? But how do you think the operating system of the public market investor will shift in the coming months and years?