00:22:25 · Joe O'Donnell
[00:22:25] S0: Yeah, I think specifically for AI, I would say that there's tons of utility in generalized investment research for AI. So I don't want to say that it's very clearly very useful. But I would say that the overwhelmingly best use case for off the shelf AI tools is information gathering purposes. Sort of a couple orders of magnitude better version of vertical search. And there's tons of value that can be generated from that. You know, just finding things you want to have otherwise found and then doing deeper research as a talented human or whatever from there. That kind of stuff I think is very, very useful, especially across enormous and increasingly large data sets that exist in the world for investors. For judgment purposes, what to work on? Yeah, I think for sure, we've built, to pat ourselves on the back, we've built some very useful idea generation tools for... That that use AI to help people discover new investment themes or things that align with their thinking on on specific topics that incorporate our proprietary datasets and have more generalized search and judgment tools to help people figure out what to work on. And then we're, you know, as I mentioned just now, we're building basically the stronger form version of that right now. Well, no one... You know, for... Take take idea generation. Nobody really wants a list of, you know, 14 companies that meet their investment criteria. What they really want is of the 14, which ones are good investments? That's the ultimate goal. And so that's the purpose that I I would say our idea generation agents are effectively the final boss version of investment idea screening. Yeah. Instead of having, you know, 14 companies on a list, you get somewhere between whatever, call it, zero and three finished investment reports done at the capability level that one would expect from a good analyst. That's the right way to screen. And and so, yeah, I think I think it it really depends on the use case. AI for judgment related purposes, would say to a large extent is still an unsolved problem.
00:24:43 · Brett Caughran
[00:24:43] S1: Joe, I'm with you on the the the chatbots being not that helpful across the institutional stack and primarily good for information gathering. But I think there is this belief now that with Claude Code, that funds can build a whole new stack of internal tooling that they can deploy Cloud Code to build a live earnings preview or an idea generation agent based on their own historical trades and ideas and thesis work, etc. I still wonder whether that's a prototype or whether that's production grade to actually deploy institutional capital. Could you talk a little bit about that movement, where you think that could work, and where you think that approach may have issues?
00:25:33 · Joe O'Donnell
[00:25:33] S0: Yeah. I mean, I think, you know, there's something against the laws of physics for an investment organization to build, you know, canary inside of their own organization. I'm sure, you know, most investment organizations have a resource set and a team of extremely talented people that could go reconstruct a large percentage or all of what we do. The level of effort required to do that would be extremely high, and I suspect that some people will decide to do that and others will not. I think what we found at least is that in order to, you know, say whatever, our firm's investment process is X, In order to codify that into software, it's nontrivial. I can... Everyone kinda knows what it is when they when they when they think about it or when they look at an individual investment. But but but then when you actually try to turn it into some sort of deterministic logic that sits in a software program, it's not so easy. You know? People plug in a set of things that they think they like, and the output from whatever the... Take your idea generation example is nothing like what they actually think they... What they actually do like. And then there's this iteration process. You know, sort of like automated driving where you can you can, you know, whatever. Maybe this is harder than understanding it, but you get sort of 90% automated in a really easy way. And then the the next 10% end up taking five times as long as the first bet. And, you know, some people will make that investment and maybe they'll build great products and and that... That's great. I think in order to get to that level, it still requires all the infrastructure that I mentioned too. So do you have the right data, which requires building integrations with who knows how many data providers, but that by itself is not sufficient. Well, then what is... What do each of those individual datasets actually mean? And every single one of those needs to be fine tuned. Every single one, at least as far as I can tell. There's nothing that's really just obvious, you know, plug in dataset x, and then a foundation model just intuitively right now understands it at the level of a really good investor. And in order to do that next layer, well, you got to get your really good investors that should be working on investments to go train all those datasets. And that's an ongoing process.
00:27:56 · Joe O'Donnell, Brett Caughran
And then you can build the emergent sort of, you know, what I... Maybe I would call microservices on top of all that stuff. And then you can build that emergent layer of investment analysts. It can be done for sure, but it requires... Well, I can speak for ourselves. It requires years of investment and many thousands of hours of fine tuning and data collection and cleansing and aggregation and stuff like that. And I think every firm has to decide basically what the demarcation line is between what they wanna do themselves and what they wanna outsource to someone else. And there's... Yeah. Every... You all have different answers. [00:28:34] S1: And how's that work today? Because where you may get pushed back is the argument that while correct that most public investors don't have alpha, no public investor believes they don't have alpha. So I think a lot of the mindset with agents is I want to clone my process. I a want to digital analyst that acts and behaves like a senior analyst or a junior analyst on my team. And to that degree, agents have been interesting as a flexible substrate to be able to actually train that analyst. How do you work with funds? Like if I gave you an analyst training manual and said, Joe, make this agent behave like a junior digital analyst. How far away from that reality are we today?
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?