00:40:18 · Joe O'Donnell
[00:40:18] S0: It's an interesting question. I suspect it will be different for different investors based on their strategy, based on their own preferences. But what I'd say, one thing that seems obvious to me is that a lot of those things will just be abstracted away. Mean, what do people use Excel for? Well, one is building a financial model and forecasting key KPIs and stuff like that. Some of it is more bespoke analysis. I would say in the category of bespoke analysis, a lot of that stuff can be abstracted away or already is to a certain extent. I mean, know, our super analyst product, for example, does a lot of work that would have traditionally been done in Excel without opening Excel. And the answers to those questions are just emailed to our users and available in our web app, in our MCP. And so there's just no need to get in... You know, open anything new to to get to the the same results. I think, you know, build build me a model, that kind of stuff. I I think if... It is very likely that some version of people interacting with Excel for that explicit purpose of, you know, forecasting EPS or whatever. I don't see... It doesn't seem like there's a real problem with doing that in Excel. And I don't think... Again, depends on the model. If if someone has a coverage universe of 500 companies, for example, it's different than a concentrated portfolio where they own six stocks. So perhaps there's there's more of a... An automation case in the former case than than the latter case. But those... As I was saying before, if the foundation models themselves haven't already solved most of those problems, it's only a matter of time. And that time frame, who knows? We'll see. But I would personally be surprised within twenty four months if, that's not a a solved problem. But I would argue, you know, somewhat facetiously, that already is a solved problem. You know, the old the old version of build me a model was email a sell side analyst and say, send me your model. And that's the same thing. And there are products like Canalyst that exist or still exist that did that sort of, you know, the old fashioned, more deterministic way. And everyone's got a different thing. You know, for me personally, I had a different kind of heuristic, which was if I was really relying on some nuanced understanding of a financial model, probably wasn't that good of an investment.
00:42:44 · Joe O'Donnell, Brett Caughran
You needed to build a model somewhat on a backwards looking basis to understand risk reward and stuff like that. But most of my investments, I already knew whether they were good investments or not before I built a model around them or built a Nuance model around it. So I think it'll just be different for different people. And Excel by itself, I don't hear a lot of people complaining. I don't talk to a lot of people. I don't hear a lot of people complaining about using Excel for those kinds of purposes. You know, Bloomer perhaps is another story. [00:43:18] S1: What, you talked earlier in the sort of agentic analysts about, obviously the data piece, but curious about where you're at on integrations to other data, even simple things like sell side research, pulling in the right news stack, and obviously pulling in expert network calls from players like AlphaSense have continued to be a challenge to just even get all of this external data into one environment. You've talked about creating your own data sets, but can you talk about this integration challenge of pulling in these other publicly available semi proprietary data sets where the barrier has been more commercial than technical?
00:44:05 · Joe O'Donnell
[00:44:05] S0: Yeah. I mean, so for us, we basically... What we solve for is the best solution. So if there are datasets that don't exist and we think they're useful, we build them. If there are datasets that do exist, and there's a huge amount of, you know, single or few product data vendors out there that have enjoyed a sort of a cartel like situation for for a long time around their datasets, and they've acted accordingly, raised price, not innovated, etcetera. And, you know, to the extent there are... There is a vendor like that that our customers value where we can build a competing product, then we'll do that. You know, an example I mentioned for that is transaction data where we've built our own credit and debit card transaction data set that, you know, according to us and and, you know, a number of third parties that have also validated the data is more performant and more accurate and faster than the existing mainstream products in that category. And we saw an opportunity there. It's obviously an important data set. And so we just decided to build a better version of it. But we're also very happy to partner with people on that front too. And I don't feel like I need need to own all of the data layer in order to build good products, just like an investment firm doesn't need to build their own datasets. You know, if I... If my ultimate goal is to basically build AI investment analysts, I don't necessarily need to own all the data or any of the data if I can find vendors who are willing to work with me in in a variety of capacities. And so that's what we're finding is that there's a lot of people that are willing to collaborate. And, you know, if we... If they if they aren't and or if the dataset doesn't exist, then we'll build it and or we'll we'll we'll iterate on on existing stuff that does exist, but we think we can build better versions of it. But what I would say is our model today and what I said that we'll continue in the future is going be some combination of all those things. So, you know, one advantage that we have by virtue of selling many to one or sorry, you know, we buy wholesale and we can sell retail, is that we can actually provide a really interesting economic solution for a lot of data vendors where they can come work with us, we can write them a big check, and then we can amortize those costs across a larger customer base.
00:46:32 · Joe O'Donnell, Brett Caughran
And so, yeah, it'll be a hybrid. I think there are some people that are more walled guarded than others, and we'll see if it works. I think my suspicion is for most people, it will not. But my suspicion is that for a lot of things we'll partner. And if we can't, we'll... And it's valuable, then we'll build our own. [00:46:53] S1: And how are firms thinking about their internal data? I mean, even getting to zero data retention at Anthropic and Claude has been a challenge for some smaller And there's always a sort of back of your mind concern that you're putting in proprietary information to a foundation lab. Are firms inputting the historical trading earnings preview thesis work into your system? How do you help them get confident, get the IT teams confident in the sort of safety, security, compliance, auditability of that? And how important has that been to really superpower these tools for clients?
00:47:34 · Joe O'Donnell
[00:47:34] S0: Super important. Yeah, I think this is basically one of the main reasons why we built the headless version of Canary. So know, the Canary web app is effectively what we view as the best way to interact with all of the things that we've built, but we're not... We'll also be flexible enough to allow users to do whatever they want. And so if they have their own internal system, you know, for for Canary in the Canary web app, you cannot upload your own investment notes, for example. But if you wanted to use all of the Canary data intelligence layer tools, analysts against your data, you're more than welcome to do that using our API into our MCP. And so our view is that what is likely to be the, you know, quote unquote winning operating system for this part of the world is, you know, Claude or something like Claude, whether it's, you know, GLM 5.2 or 7.2, whatever the future versions are, or Claude, or, you know, there's some sort of enterprise tier where those data retention compliance issues become less severe. I don't know. But we we don't we don't seek to be the operating system for those sorts of things, but we do wanna make sure that all the things that we do can be really easily integrated into whatever operating system that firms choose to to build against. And so yeah, empirically, I'd say is even today, our users have a hybrid model. Well, they'll come to Canary frequently to do a lot of analysis, and then they'll do a lot of analysis outside of Canary, typically via MCP.
00:49:11 · Brett Caughran
[00:49:11] S1: Gotcha. And so how does that work in practice? I have Claude, I'm doing an earnings preview, or I'm doing a scan for short ideas and it's pulling in the sort of underlying analysis that's done in the Canary platform through the API, as well as the other integrations and connectors that I have connected to Cloud. Is that right?