Invest with AI
00:35:50 · Brett Caughran, Kirk McKeown
[00:35:50] S1: Yeah. [00:35:50] S0: Um CapEx isn't growing as fast as sources, and there's gonna be downward pressure on their names. And in card specifically, one of the reasons people were paying the prices they were paying was for stability. And what we saw over the last six months is that it's not as stable as it once was from a uh feedstock supply side perspective. Um, not to similar to what happened with change in 24. Can you can you say more about that? There was some there were some panel outages at different providers that created some instability in the credit card market. [00:36:19] S1: Um I'm not gonna get into specifics, but uh, you know, sort of that's the bank, that's the banks make a decision not to sort of distribute those panels. That's exactly right. [00:36:27] S0: And so like that, you know, but like if you pay high prices, you expect stability, right? And like, you know, it's it's why, you know, there was a re there was a re-rating in claims data after change, right? So like you know, claims data used to be more expensive than it is now. Part of that's because of the stability framework, right? People don't want to pay as much if they're not sure it's gonna be around tomorrow. [00:36:46] S1: What um are these sort of uh are are the motes of these number ones purely commercial, or does A like do AI agents allow like the the sort of number two and number three to sort of close like get get more parity on the tier one data sets?
00:36:46 · Brett Caughran, Kirk McKeown
[00:36:46] S1: What um are these sort of uh are are the motes of these number ones purely commercial, or does A like do AI agents allow like the the sort of number two and number three to sort of close like get get more parity on the tier one data sets? [00:37:01] S0: I think you know it's it's it's there's a there's a bunch of things. They've got great go-to-market machines and they they do have high quality data sets, right? So like the data sets are are are are are number one for a reason. Um and they and they've all done a nice job of developing, you know, sort of their number one for a reason, and and their hit rates and core assets are very strong. Um so they have a quality dynamic that's meaningful. I think what's key for all of them is figuring out sort of uh what how how's how the path forward uh you know sort of works, you know, um, which I think is which which I think is um remains to be seen. You know, we've we've made our bet on what the world's gonna look like, but that's the other great part of it is the world, you know, the world is huge. And like, you know, sort of data, the the data we're talking about, the the market opportunity outside of finance for these guys is a lot bigger than it is inside of finance right now. [00:37:50] S1: Yeah, yeah. [00:37:50] S0: So like that's the thing. [00:37:51] S1: It's interesting. How do you think about your ICP, like your ideal client profile? Like what what helps your business sort of get to scale and grow?
00:37:51 · Brett Caughran, Kirk McKeown
[00:37:51] S1: It's interesting. How do you think about your ICP, like your ideal client profile? Like what what helps your business sort of get to scale and grow? [00:37:59] S0: I mean, you know, sort of we we we just we just launch, you know, we just launched, we're working with Kalshi. Um, you know, sort of where uh they're they're they're building, you know, sort of uh market contracts on top of our data. Um, you know, sort of so that that's that's an interesting ICP. Um we're gonna be pushing our MCP into Robinhood. Um so retail trading is a really compelling component, and and frankly, uh that's a that's a big driver of the of the of the opportunity set. Um we're we're launching our MCP into the AI agent search firms. Think EXA and Parallel and all of those folks. Um, you know, and and you know, we continue to see opportunities to just become infrastructure and people stack where they want permissioned private data with with with payment infrastructure, which is what we've done, right? And so, you know, sort of um, you know, financial services is obviously uh you know a good chunk of our business and has been, but we see a lot of the the growth opportunities um, you know, sort of away, which you know, sort of uh are frankly easier and from a distribution perspective uh and more consumption based and more consumption oriented. [00:39:06] S1: So so you know today and in three years, like those top those big 500 clients versus a long tail of these other creative use cases, like how do you think roughly your business splits?
00:39:06 · Brett Caughran, Kirk McKeown
[00:39:06] S1: So so you know today and in three years, like those top those big 500 clients versus a long tail of these other creative use cases, like how do you think roughly your business splits? [00:39:16] S0: Uh right now, we're probably still financial services call it 70%. Um, but I would say, and that's buy and sell side. Um, I would say by sort of middle to end of next year, it's probably 50-50 or 30-70 the other way. [00:39:30] S1: Yeah. Yeah. What's been interesting to me fundamentally about what you're doing is uh you go into you go into Chat GPT or Claude last year and you ask a question, and the sort of raw inputs are coming from no shade, Motley Fool or Benzinga.com, but these agents have sort of been reasoning on really low quality insights, right? As we sort of talked about primary research, like when you start to ask Claude to be a stock picker, you really need it to reason on the right, raw, sort of raw, raw. It's all about the inputs, raw ingredients, right? [00:40:00] S0: Yeah, it's all about the data layer, you know, and and and the grounding layer. And that's what that's where we think we're gonna have some real opportunities. Um, you know, at the end of the day, we we we just want to we just wanna be you know plugged in everywhere. Um, we don't need to own the customer. Uh in fact, I'm I'm I'm fine not owning the customer so long as we we become a development layer for you know companies and and platforms to build products on top of with real-time data that informs what's going on in the economy.
00:40:00 · Kirk McKeown, Brett Caughran
[00:40:00] S0: Yeah, it's all about the data layer, you know, and and and the grounding layer. And that's what that's where we think we're gonna have some real opportunities. Um, you know, at the end of the day, we we we just want to we just wanna be you know plugged in everywhere. Um, we don't need to own the customer. Uh in fact, I'm I'm I'm fine not owning the customer so long as we we become a development layer for you know companies and and platforms to build products on top of with real-time data that informs what's going on in the economy. [00:40:27] S1: Yeah. How do you think about I've been thinking about this? I'm curious, your view is like the prior people are like, how is AI going to impact markets? And I'm like, well, we have a sandbox of the alternative data prior where these 150 tickers with deep data assets actually had these sort of observable distortions. You start to like, what's your view on that? Like, even Ken has talked about the earnings game being more difficult. Um, as you scale data assets, you're sort of part of that, part of driving that dynamic of more efficient understanding of companies in a sort of now casted way. How do you think markets like the ways the way stocks and markets trade will change? [00:41:09] S0: I mean, the I'm a little torn on it, right? To be honest, because like there's a there's the hot take framework, which is like 10 years from now, stocks achieve singularity and they're no longer traded, right?
00:41:09 · Kirk McKeown, Brett Caughran
[00:41:09] S0: I mean, the I'm a little torn on it, right? To be honest, because like there's a there's the hot take framework, which is like 10 years from now, stocks achieve singularity and they're no longer traded, right? [00:41:19] S1: Like there is that world that you could contemplate, whether or not like if there's a probability on all these things and like mediated by agents, like humans completely out of the loop. Yeah, more efficient, more efficient, more efficient mechanism for capital allocation. [00:41:34] S0: Yeah, like because here's the thing, right? Like, I actually come to a place where like the natives that we're creating are going into models either as weights or as inputs to react to, right? Because you could post-train a model using this data, um, and we're doing some work on that, but you could also sort of um like at the end of the day, the reasoning models have to reason against something. [00:41:59] S1: Yeah, right.