Invest with AI
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.
00:41:59 · Brett Caughran, Kirk McKeown
[00:41:59] S1: Yeah, right. [00:41:59] S0: And so the the thing they have to reason against is things happening in the world, right? And that is real-time transaction data, that is real-time claims data, that is sensors that are collecting traffic as it drives down the highway. Those the model needs to react to something, right? Yeah, there's a world where like the markets uh actually evolve to data element markets, data native markets, where you know, sort of the models are actually trading inputs that better inform the problem that they're trying to solve. And that's closer to ground truth. Like stocks are an abstraction of uh cash flow and what a com what somebody wants to pay for it. Right now, there's other components of like equity and what that means and the legal constructs. So I'm I'm munging a bunch of shit here. Oops, sorry. But um, you know, sort of uh, you know, I I don't think we're anywhere near there. I think we're I you know, I think I but like I do think you we will see increased volatility um because it assumes all the models are gonna be good too.
00:43:00 · Brett Caughran, Kirk McKeown
[00:43:00] S1: Yeah. Yeah, the exponential has been pretty steep. Like the log, you know, the lot the sort of trend in the log scale has been pretty linear so far. Um, you know, uh Dario's probably been more right than wrong on timing of some of these things. Um so it's a little little terrifying. I think we yeah, I agree we at least have to sort of contemplate that, contemplate that future as a note on the sort of distribution. Uh so that's the sort of the that's the long-term thesis, is like be part of the core infrastructure of sort of market market price discovery. [00:43:36] S0: That's right. And then just and then just play on sort of the growth in the AI economy and the need for agents to have real-timely private data that's permissioned to help make better decisions. So like as more decisions are being made, powered by data, we just want to capture that. [00:43:53] S1: Yeah. Do you think Claude can be sort of trained to be a good stock picker? Claude specifically. Um sort of the ascended or sort of the the the primary model today. Like maybe it's cle that maybe it's a close weights, maybe you have to sort of do it back in the post-training open open weight models. But I'm curious just your thoughts on LLMs fed with the right data as actually exhibiting investment talent.