"The Cognitive Revolution"
00:24:04 · Nathan Labenz, Marc Bhargava
[00:24:04] S0: Can you take me through sort of the playbook of how you develop these companies? I mean, the general sketch that I have is your, first of all, assembling a founding team that has the mix of technical and m and a roll up experience. How do you decide what it is you want to focus on building? And how does that differ from maybe a more traditional SaaS strategy? Like, I guess I'm I'm particularly thinking about how much of it is going after what you might think of as core tasks versus sort of auxiliary tasks. I mean, couple examples of core tasks versus auxiliary. It might be like, I just did an episode with Ambience Healthcare. No. You know, that they're not trying to do, at least not yet. We'll see. They're not trying to do a patient facing doctor that would actually interact with you and make the diagnosis. That's the core task that the, for now, the human doctor continues to own. But they do have the AI scribe in the room, which is transcribing and then translating the notes to medical coding, yada yada yada. And that's all the stuff that the doctor currently has to do in their pajama time after hours. [00:25:18] S1: Yeah.
00:25:18 · Marc Bhargava, Nathan Labenz
[00:25:18] S1: Yeah. [00:25:18] S0: And so they're happy to offload that. And that's like, so far, everybody's everybody's pleased with that arrangement. I might contrast that though to, like, a customer service call center type of scenario where I assume that the AI is, like, taking the calls and that would seem to be, like, the core task. And so, you know, whoever the person was that used to take those calls maybe can take other calls or may... You know, but it seems like there is some... Again, this sort of, like, complement versus substitute notion seems to come into play. So how do you think about, like, as you begin... And, Megan, maybe it varies by vertical. We've all the time in the world for you to go vertical by vertical. How do you think about... Do we enter here with auxiliary? Do we try to take burden off? Do we try to empower? Or do we just, like, go right at the heart of the core task? How how do decisions get made?
00:26:05 · Marc Bhargava
[00:26:05] S1: Question, we're really underwriting complementary AI services. So we don't think that lawyers or accountants or IT specialists or HOA managers or any of those jobs are going away, but we think 30 to 70% of the tasks they do can be automated. And this frees them up to do more elsewhere. So even in the call center case, you know, there are times where people want to talk to a human. And so you can have the AI agents be doing the more basic calls, you have a human in the loop and it can get routed to them at different times. But one human with a team of agents can take a lot more calls than one human alone. And so we really are focused more on the complementary side of it. We're not really doing the strategy if we think something can be a 100% automated. So one example is, you know, maybe coding will end up being a 100% automated to some degree. And so maybe that might be an example where it doesn't make sense. But for legal or for accounting, we absolutely think on large deals, large transactions, preparing your taxes, even on IT or with call centers, you know, there are more emotional calls, more difficult ones. In all of these cases, it's about AI enablement of the human team and being able to do more. And that's why, at least my view, is this AI revolution is similar to the Internet or to cloud where there's a big boost in productivity, there's a lot of interesting tooling, and the winners will be folks who figure out how to use that tooling and the ones who create it. So obviously, the large language model companies and labs and hyperscalers are creating a lot of this tooling. Our thesis is let's put together teams then who know how to use it. Let's add industry experts and m and a experts to those teams, and then let's go out and let's go to market much more quickly on the innovation that's coming out of these major labs. And so our playbook is that it's kind of two or three parts. One is identifying, well, what industries have this AI transformation potential? And we looked at 70 and we said 10 of them have 30% plus automation potential. The second piece is how do we put together the right teams that come from Applied AI backgrounds, but then also complement them with folks of industry and m and a.
00:28:06 · Marc Bhargava
Then the third piece is to actually build out the AI enabled software and the product to get pilot clients, to get customers, and to prove the automation percent. And then from there, both grow organically, but also buy kind of smaller midsize platforms and targets and be able to show the margin improvement, the free cash flow expansion through growth. And then from there, continue to wash, repeat, and become these compounders that are growing organically and inorganically that have this data moat from the companies they own and have a really deep understanding of what the customer wants, ultimately giving both services and software offerings to their customers. So that is our underlying thesis. We've placed, you know, eight or so bets now. Many of them are kind of off to the races, much larger, better well known companies like Crescendo or Long Lake or Yudia or others that we've more publicly announced. And we're seeing a lot of traction in those companies, but then also with founders and others who wanna go in on the strategy as well as they see every three to six months the model's getting better, the level of automation getting better.
00:29:09 · Nathan Labenz
[00:29:09] S0: The vision that you articulate for this sort of empowered human with AI as, you know, complimentary tool is a vision of the future I would like to believe in. I think I'm gonna come back in a few minutes and challenge how sustainable I think that is over, you know, the medium term. But just to dig in a little bit more on the playbook first, What's the sort of happy story for the relationship between a software business and the the service customer? Like, presumably, you don't just show up one day and say, hey, I'd like to buy your company and transform it with AI. Right? So what is the... What are the sort of steps, the milestones, the kind of points of buy in, the points where you kind of determine that, like, yes, this local services business actually might be a good target for us to really, you know, not just try to sell into, but actually, you know, marry for lack of a better term, you know, bring into the bring into the part of the the compounding story.
00:30:12 · Marc Bhargava
[00:30:12] S1: You know, Crescendo, the call center roll up is a good example where they got to 10, 15 different clients. They had a smaller sort of revenue base, but they were selling in and had a high degree of automation. And that's when we said, we should actually buy a call center that does, you know, more meaningful amount of revenue. And so a lot of the times, it's existing clients or pilot clients are very open to testing software. And then once they're starting to see the results, it's hard for them to fully implement it because they might not have the engineering team, they might not have the CTO. So you actually see a lot of the pilot clients or customers say, hey, this is the writing on the wall. We're seeing this automation, but we might not necessarily be able to fully implement it. We would like to sell to you. We get to cash out, but we also get to roll over. So if they're rolling over 30% of their equity and they think there's a 10 x from here because they've seen the power of the automation and the potential of the story, they're more likely to sell to us than, for example, to private equity because we'll let them roll over 30% and get another 10 or 15 x on that amount. So the more classic version of it is, you know, we go out, we get customers, we get pilots, we start working with folks. We also get a sense of which of these companies want the technology transformation, which of them get it. And we put a premium on that and we try to buy those style companies cause our goal is really revenue increasing. So as opposed to private equity that might buy a company, cut costs, improve margin that way, obviously find it, have debt financing. Our view is a little bit different of if we find some of these pilot customers, they can end up being the targets and especially if they're aligned with us on the idea of bringing in technology and wanting to grow revenue. And you'll see that alignment a lot of times in the amount that they're willing to roll over in these transactions, and that's always a really good signal.