00:24:28 · Marc Bhargava
[00:24:28] S0: Yeah. So we normally do one or two rounds to build the software piece. Like with a Titan MSP, for example, we gave them kind of a traditional seed series a to build out AI automation for IT services for MSPs. And they went out and they got six pilot clients, and over three or four months, they sort of showed us, hey, look at all the tickets that happened in MSP. We can now automate 38% of them. So when that happens, then we start talking to them about a second round of funding. We say, well, let's actually go look for targets. Because if we can buy one of these companies and automate 38, we're certainly going to increase the margin and be able to reinvest a lot of that free cash flow into growth and create a really successful company. And so we look at especially the pilot clients that they're working with and who could who really wants this AI technology. And unlike private equity, we screen really hard for does this company want to change? Do they want to implement AI? And then, of course, we want to hold them for seven to ten years and go public and not necessarily add debt and cut costs. That's a really different model than private equity. But in their case, then we gave them a second round of funding when they found their target RFA. And now that they've found that target and, you know, are doing really well on the AI transformation, then they say, okay, now we want a third round of funding to go and do a lot of small tuck ins or buy another platform. So at GC, we've been investing between 100 and 150,000,000 in each of these projects, but over, you know, three or four rounds of funding. We like to lead at least the first two, and then we start bringing in other investors in the third or the fourth, and we get to work with some really great crossover funds, all other VCs as well on these projects. Some like Eudia, you've had on the podcast, is a great collaborator of ours and others. So we've definitely been seeing kind of a lot of success with that.
00:26:05 · Molly O'Shea
[00:26:05] S1: You said you you plan on holding these for seven to ten years and hopefully go public or that sort of thing, some sort of exit scenario. That's a little bit different than the fast turnaround of a traditional private equity fund and like everything that goes on with that, call it bad practices. But I'm curious around that philosophy. Like, why hold it for that long?
00:26:31 · Marc Bhargava
[00:26:31] S0: Yeah. A lot of it is because we want these to be compounders. So the end vision for most of the founders is I wanna be the next generation TransDigm or Danaher Constellation Software. There are these companies in the public markets with over a 100,000,000,000 in market cap that have outperformed NASDAQ, outperformed the S and P. And what they do is they go buy companies, improve their operations, generate more free cash flow from the company they bought. And with that extra free cash flow, they go and they buy even more companies, and they wash and repeat. And so they've been termed by public investors as compounders, and they've done really well. It's a great model. Our view is that they're gonna be these AI native compounders that they go buy companies, they improve them, they generate more free cash flow, then they reinvest that to continue to grow quickly. But the driving improvement is more AI. So historically, it's been things like pricing or sourcing materials better or maybe improvement on some of the go to market. Here, a lot of the improvement is on this AI automation piece. And so we think there'll be plenty of these $100,000,000,000 AI native compounders in the public markets ten years from now. And the founders who are starting these companies generally have very strong tech backgrounds, and they wanna build a business that's VC backed. They wanna take it public, and and many of them wanna run it in the public market for ten years. So they really want ownership on this for the next twenty years. Very different than private equity where there's a management team that works for a private equity firm that's highly incentivized to get this company to be sold in three to five years, maybe to another PE, maybe to a strategic. And so the focus is adding debt, cutting costs, improving margin that way, and it's not really an investing in technology for the long run. Some of this AI technology, you add cost. Right? So you can't go in, add debt, cut costs. We're actually going in and we're adding costs. We're saying hire engineers. We're subscribing to LLM based software. It's like we're going in and investing and adding costs. But we think it's worth it because we're adding a lot less cost than the amount of revenue we're adding as we're freeing up people to do more and more revenue producing tasks.
00:28:26 · Marc Bhargava, Molly O'Shea, Molly O'Shea
So our mindset is longer term, and the founders who actually own these companies, majority owners, their mindset is longer term. So it's just a different strategy. PE has obviously done really well. There are lot of efficiencies in many markets. There's also not enough automation in many markets to do the... What the strategy we are. So we have a lot of respect for private equity and, you know, the work folks like Thoma Bravo and Blackstone and others have done. They've been incredibly successful. We're kinda playing our own game around, hey. How do we help these founders get AI native compounders that'll be in the public market with a much longer term view and a view that we should take these companies and go public? And maybe even GC holds the stock after they go public and keeps compounding. So. [00:29:06] S1: Efficiencies, synergies, change management, all the good buzzwords. [00:29:10] S0: Exactly. We pull some of those and then leave others out. Like, on the change management side, we tend to really screen for the right founders. [00:29:17] S2: So [00:29:17] S0: our view is when our founders create these holdcos and then wanna buy companies, they generally don't do the change management part. We just screen really hard for people who want AI change in the businesses from the get go.
00:29:17 · Marc Bhargava, Molly O'Shea
[00:29:17] S0: our view is when our founders create these holdcos and then wanna buy companies, they generally don't do the change management part. We just screen really hard for people who want AI change in the businesses from the get go. [00:29:28] S1: This is something I've noticed. And, I mean, there's data around it too, but the private markets are swelling. Yeah. And it extends from private equity to private credit. I mean, this, I think, would be part of it. It's a it's a new pool. Carta has seen this as well, and they've extended their products into private credit, private equity. They're now doing LP management. How do you think those markets have actually changed operationally with AI, with what's going on, with competition? Like, even when you're looking at buying companies for some of these, is there competition between all... Like, how does that work?
00:30:05 · Marc Bhargava
[00:30:05] S0: We haven't seen a lot of people implement AI effectively to date. And we're generally in the view along with that MIT study that maybe 95% of these Fortune one hundreds have tried, and they said it's kind of a waste, and it didn't really work, and it was janky, and that kind of stuff. So we are of a view that it is really hard to actually turn AI native. And so our worldview is let's create an AI native team. Maybe they were head of applied AI at Rippling or Scale or Stripe or they worked at these places or Figma, they've really seen kind of AI applied within the enterprise setting. Let's put together these teams. Let's incubate AI native teams, AI native software, and then let's go out and buy these companies because we've found when traditional companies are trying to implement AI, they get some benefits for sure, but they'll... There's a lot left to be desired, basically, in terms of the amount of automation they can achieve. So at least for us, we think that it's pretty difficult to just take models out of the box and implement them. You need a lot of context. You need an AI native team. You need a team that knows how to build software around the models. All of that is kind of difficult, which model to use. And so we kind of agree with the MIT study that says, yeah, AI is kind of a buzzword that's hard to implement right now. And we don't necessarily think that the world is going straight to AGI where AI agents will immediately be able to do everything. So I think we sit at least in the creation strategy somewhere in the middle where if you have an AI native team, but you also have the traditional clients, customer base, services folks, and you mix it together, that's where you can have kind of the best outcome, the best revenue growth, the best margin profile. So we really believe in this hybrid approach where AI in the right hands will be able to automate a good deal of work in many industries, but you'll also need people for tasks. And kind of this mix of people, software, agents is gonna be unique to each industry, and we can create the category defining company in each industry to go after that opportunity with the full tool set in the toolkit.