"The Cognitive Revolution"
00:21:25 · Marc Bhargava
[00:21:25] S1: Yeah. Two thoughts on accounting. One is there's this huge shortage of accountants. So it's a industry fewer people wanna get into. There's a generation of baby boomers retiring. Many of the top accounting firms, if they could could take on easily 50 or a 100% more clients because... But their constraint is there are a shortage of accountants. And so if you buy an accounting firm and you AI enable it, you might no longer have that shortage because a lot of the more basic tasks closer to for example, bookkeeping can now be automated. And so if you AI enable an accounting workforce, that accounting firm has two or three times as many clients they could go out and they could get because there's actually a shortage of accountants there. And so that's how a lot of value can be created by going in. In terms of the end client and what they might wanna use it for, suddenly your accounting could be giving you proactive recommendations. So today it might be around building out three statements and filing your taxes. You know, you can easily see how once you have that data, you can actually start making recommendations on where you can save money, how you're spending. You can have tools that turn that into financial models that can help you think about your capital needs, should you be getting debt funding. So suddenly a lot of the tools, which maybe today are very stagnant, can actually be really diagnostic, very proactive. And so I do think that the kind of even as if it might be considered a boring industry, the suite of products can drastically change with AI. And perhaps more near term and more importantly, the same accounting firm can do a lot more work with the same cost basis, the same workforce basis if it's AI enabled. And we're really in the current phase going after that first opportunity, which is how can accounting firm AI enabled do two or three times as much work that obviously really increases the margin profile because it's a similar cost basis but obviously much higher revenue and that's the focus. But certainly to your question to the end client, maybe things get delivered more quickly, they're more accurate, all of those things are pluses. But even how you visualize the product certainly will change over time with AI as it...
00:23:20 · Marc Bhargava
We're seeing in things like legal being a great example of this where lawyers can also compare all the contracts and then recommend pricing changes because there's inconsistencies. So how does the law firm or the legal department go from just a cost center to also making recommendations and maybe being even a business plus center and AI can make that change. So you have those two opportunities. One is just taking out more work with the kind of same group of AI enabled folks that obviously increases margin and is a huge opportunity in services. And the second is to change the output of these services businesses and things like accounting or legal start to be more diagnostic, more prescriptive, more insightful, and become departments that aren't just thought of as a cost center or a check the box, but can actually help you in running your business.
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.