"The Cognitive Revolution"
00:31:53 · Marc Bhargava, Nathan Labenz
So we've been seeing this now across many of the different industries we're in where we're buying companies that are quite proprietary because we've built that relationship and we've proven to them the technology and they've been really impressed and they understand that for it to really be embedded in their company, we have to buy the company and it's a major shift. [00:32:13] S0: I would assume that's, like, required as part of these deals. Like, you're not letting people roll over nothing. Right? Or or, like Right. Leadership has this. I assume you wanna retain leadership and they have to, like, have skin in the game.
00:32:13 · Nathan Labenz, Marc Bhargava
[00:32:13] S0: I would assume that's, like, required as part of these deals. Like, you're not letting people roll over nothing. Right? Or or, like Right. Leadership has this. I assume you wanna retain leadership and they have to, like, have skin in the game. [00:32:23] S1: Exactly. But it's been a really it's been really great to see two things. One, folks who said, yeah, lots of private equity firms have tried to buy this asset we didn't really wanna sell, but now we really like your team. We like the growth approach. We like your venture back. We like you're not using debt. We think this tech transformation is real. We want to sell to you and we want to stay on board. We want to AI enable our workforce. We want do all these things we really couldn't do alone and we buy into the upside and we want to have the second part. We want to have this larger percent rollover than you traditionally see in private equity And so folks are, you know, really pushing for that. So it's been... I think our secret sauce, one, is in these hybrid teams of technologists and industry experts. And then two, is proprietary deals and the assets we're going after. We're building these relationships through trust, through tech transformation, and we're getting to do proprietary deals. And so that combination of people arbitrage and deal arbitrage, I think, is a a really special alpha for us.
00:33:18 · Nathan Labenz
[00:33:18] S0: Hey. We'll continue our interview in a moment after a word from our sponsors. Can you tell me about the process of working with early, like, design partner customers? And I specifically have a couple things in mind here. One is, like, how do you think about the faster horse problem? You know, there's always this debate, and it maybe can be, you know, answered with a sequence. But there's always, like, what does the customer want? Oh, I'd love to have this go faster or not have to do this task or whatever. And then there's the... Maybe this whole thing could be transformed. Like, maybe it could be structurally different. I often use a customer service. I have a a talk on AI automation, and the example that I always go to there is just taking customer service ticket. In many organizations, the first thing that might have to happen with a customer service ticket is prioritize the customer service ticket. And so that's something that you could probably automate. You can, you know, think about what are the criteria and you can, you know, talk to the people that are on the ground doing it. And you can... Through interviewing and, you know, looking at a bunch of examples, you can kinda draw out of them, like, how are they actually doing this? I find that that's often one of the stumbling points actually, especially if if people have to... You know, if if these services businesses are trying to do it on their own, like, they don't necessarily know why they're doing what they're doing in many cases. So it it needs to be kind of drawn out of them. Like, literally eliciting the chain of thought from the human is like a, you know, a key step in the process. But then there's also the idea that like, well, geez, what if AI could respond to these customer service tickets? Like, we might not have to prioritize them at all because we could just respond to them all immediately. Then this whole prioritization step becomes, you know, kind of a moot point. So how do you think about that as you're, you know, really getting just off the ground and trying to figure out, you know, is this is this a business that we wanna keep the bones of and kind of find these, like, you know, these automation opportunities within versus like when does something need to be kind of reimagined? And again, you know, that might be a sequence thing, tell me how you think about it.
00:35:26 · Marc Bhargava
[00:35:26] S1: Yeah. Generally, think about it by going in there and shadowing the customer and understanding all the workflows in their business. And so then we add up who are all the folks, what are their workflows, and we map out the hours at a company. Then what we do is we go through those workflows and hours worked and we look at where could AI automate some of these workflows. And that's trying to get us to this 30% of hours worked automation. So 30% of tasks can be automated. And generally, historically, over the last two years, those tasks have fallen into about four or so buckets. One bucket is customer service and customer support and frequently asked questions. And so if you have any business that has a lot of customer support, it's heavy on that. It could be a good potential industry to go after. A second one is the creation of presentations, copy, marketing, emails, businesses that have a lot of that work stream, also a high degree of automation there. A third is if the data tasks are very repetitive. So there's a lot of filling out forms or looking at sheets or an insurance is a good example of this, Checking the box, did this roof cave in? Yes or no? Did they fill in the paperwork? So that's kind of a third bucket. And now fourth, more recently is around logic and reasoning. So again with insurance it's less automating forms now and more underwriting like can we make a suggestion of how to price this insurance coverage policy based on everything we know. And as models are getting better at logic and reasoning and math, you can have more AI tools kind of with a human in the loop giving you kind of a quasi answer to what you might want to do or say. Legal is also a good example of this where now Anthropic and others have created models that are quite good at reasoning and so a lawyer can say, well, know, what cases am I gonna... What questions am I gonna get on my case? And so you can start to do this, like, more human prep. So those are four very broad buckets, but areas where AI, as they stand today between LLMs and agents, can automate a good deal of things in those buckets. So then when we start going through these, you know, 70 industries, we...
00:37:22 · Marc Bhargava, Nathan Labenz
And we break them down and we actually get on the ground and our companies that we've helped incubate are actually out in the field talking to clients, we are mapping out how they spend their time. And then we're overlaying where does that mapping fit with the buckets we've identified have a high degree of automation. And so that then really helps us underwrite kind of what is the opportunity in this space, what is the opportunity if we were to buy this client, And instead of having it as a customer, have it as a company we own that we can automate large portions of, that we can take on more revenue, that we can create more free cash flow from. So that's the strategy we go in and implement. And that's why it's important to have people who know the industry, know these clients, can get us in the door, but also folks that come from these really strong applied AI teams that know what best in class kind of automation looked like at a ramp or at a rippling or at a Figma or a place like that. [00:38:16] S0: And you do that initial mapping even before starting the company? Like, that that was part of the 70 to 10 cut down?
00:38:16 · Nathan Labenz, Marc Bhargava
[00:38:16] S0: And you do that initial mapping even before starting the company? Like, that that was part of the 70 to 10 cut down? [00:38:23] S1: Yeah. So we do it at a certain level, from the 70 to 10 cut down. And then once we've actually given the first round of funding, in order to get the second round of funding from us, then the team needs to go out and really get the data. And so they get the really granular data from their customers, from their pilot clients, then they come back to us, present that data, then we give them the capital to actually go buy a target, oftentimes one they're already working with. So we do a first round of analysis in mapping it and looking at the industries. As we start placing bets, then, of course, the team has an even higher bar to build an AI native software product that is very useful and to go and win an initial set of clients and then to come back to us and prove to us the automation with those clients. [00:39:05] S0: It's obvious why the owners of these businesses would be interested in this. How did the staffs at these companies react to this sort of transformation? What what have you seen there in terms of excitement, fear, you know, rejection, you know, I don't know, quitting in protest, sabotage? I'm sure you're like, it it has to run the gamut. And and I wonder if there's any sort of general purpose lessons that you would, you know, share with with just business leaders in general regardless of whether they're entering into a structure like this. General purpose takeaways for like AI transformation.