00:20:11 · Marc Bhargava
[00:20:11] S0: Yeah. We have a slightly more conservative sort of 16,000,000,000,000, we think, services industries globally, which is really massive to your point. And on the software side, software globally is like a 1,000,000,000,000 opportunity. So it is 16 times larger. Well, why hasn't everyone in VC rushed to invest in services businesses like accounting, legal, IT, insurance? Well, they historically have not been that profitable. So they sort of break even generally in many cases. Well, obviously, as you get to scale in that 1,000,000,000,000 of revenue in software, the marginal cost is really low, and they can become very profitable and have amazing cash flow. And companies like CrowdStrike or ServiceNow or others, you know, kind of show this model, and of course, the hyperscalers like Amazon and Google and Microsoft. So there are all these amazing proven examples in the software field of having very strong free cash flow and on high margins. Our thesis somewhat is, well, these unsexy services industries that were breakeven or 15 to 20% EBITDA margins, net income obviously much lower, they can look like software from a margin profile. Because once you take out the repetitive tasks and you take out 30, 40, 50% of what people do and free them up to do the hard stuff with the same cost basis, you can 50% grow the revenue or double the revenue. Your margin profile now looks much, much more similar to software. So we think in this much larger TAM than software, you can have software like margins. And that's only happening now because of AI, both in the form of LLMs and basically creating software to automate tasks, but also in the form of AI agents that can actually handle from soup to nuts task. So we think there'll be a mix of kind of AI native software, agentic workforces, people working for agents, agents working for people, kind of mixing all of that up, a totally different margin profile for some of these companies.
00:21:57 · Molly O'Shea
[00:21:57] S1: I'm so curious how you assembled a team to do this because it's fairly novel. I mean, you're doing it within a traditionally venture capital fund, but you're bringing in traditional private equity into it as well. So... And also tech. Like, you need operations. So how did you assemble the team, and what's the composition?
00:22:15 · Marc Bhargava
[00:22:15] S0: Yeah. So the creation strategy at GC has really existed since the beginning. So we've really focused on backing second time founders and operators, and stories like Kayak and Livongo were built out of our offices. And so we've had this sort of incubation muscle for a long time at GC. In 2021, we formalized it a little more and had a dedicated sleeve around it and said we're gonna commit this much capital, and then we obviously doubled that and grew it last year as well. But the mantra at GC of how do we get amazing ideas and cofounders, put them together, build companies, leverage our network, many times it's multidisciplinary. Like, one's in health care, one knows technology. That's been for a long time. So we are building off of a great foundation at GC. But we've really changed it now where we're also adding in a lot of the applied AI piece. And so at our heart, we kind of have three things. One, we definitely want second time founders like myself or Hemant who built out Livongo or others. Folks on the team like Chris Kaufman had operational roles. He kinda was a interim CFO at a rug company marketplace. So we definitely want people who have founder or operating roles. Then we also want people who have some VC or angel investing experience. And lastly, we've been mixing it in with people who have private equity experience. And I started my career at McKinsey and then worked in private equity. So have a lot of folks on our team like Kate, Chris, Sarah, Marc Crane. And so we are looking for kind of this... Maybe it's diff... More difficult to hire for, but people who have the PE experience, operational experience, and v... VC experience. And we've kind of connected this Motley crew. And now what's been really helpful to us is really our founders selling us.
00:23:47 · Molly O'Shea, Marc Bhargava
[00:23:47] S1: Mhmm. [00:23:48] S0: And so we've made about nine offers in this AI enabled software roll up idea or AI enabled software that turns into roll ups. We've kind of made nine offers, and we've won eight out of the nine. And so GC has certainly created a brand around... This is a strategy that resonates with you and you're a founder, there are a lot of ways we can help you in terms of setting up the team, looking at targets, looking at industries together, thinking about how you should be using debt funding, thinking about does CVF make sense for your business. So we have really created a flywheel and playbook around this that what I'm most proud of is how it's resonated with founders. We're clearly the top choice in this space. [00:24:23] S1: That's so fascinating. Can you talk through the funding mechanism part of it? Like, how do you structure it?
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?