00:03:54 · Marc Bhargava, Molly O'Shea
And most of the time, they don't exist because there's not enough capital for it or it's multidisciplinary, so it really requires a lot of different expertise or it's a bit more traditional or outside the box, things like the AI roll ups that we're working on these new thesis spaces that just haven't really happened yet. So creation is where I spend most of my time. But GC as an organization, we truly are a company now, building companies for long term, creating and incubating companies that we wanna take public in the future. [00:04:20] S1: So how large is this? And with the company as a new structure, will you be weighing it with AUM? Like, how does this differentiate?
00:04:29 · Marc Bhargava
[00:04:29] S0: So we're kind of one of the three largest venture players. The way we really view it is on the returns for our investors. So our venture funds have to outperform and do really well to attract more LPs, and obviously to attract more founders. The nice thing is that all of these pieces do tie together with really one question, which is what are the best ways to work with the most founders and help them the most? And the answer to that question, we try to plug and match both the companies and the venture funds. So, for example, if you're a founder and you have a tech business and you're growing quickly, maybe you use our wealth management transformation company to manage your money. Maybe you use Percepta to help you create your own mini model or inference or kind of improve your technology stack. Percepta can come in and really help with that. Maybe if you're in health care, you decide to sell into our hospital system in Ohio and be able to test out your technology. So the transformation companies that we build in house in GC are ultimately also helping our founders while delivering value for our... For the investors in GC. And same on the venture side. You know, not every founder has the perfect idea, and they're scaling. Some founders are second time founders thinking about their next thing. So with creation, we help give them the idea. We introduce them to their cofounder. We help them even before the capital formation. And on the other hand, if you're already growing really quickly and you want a way to scale without as much dilution, our customer value fund helps with that. So GC is doing a lot of things at once. We've hired incredible people for each transformation company, for each fund product. But ultimately, we now feel like if you're successfully growing and running a company, there are more ways to partner with GC than any other firm out there. And because of that, we're really seeing our brand rise. We're seeing, you know, the founders we back be extremely high quality and their companies do well because there are just so many more ways to plug in and work with GC than with an individual solo kind of venture fund.
00:06:18 · Molly O'Shea, Marc Bhargava
[00:06:18] S1: With your focus on the creation side of things, how much have you allocated to creation? [00:06:23] S0: In our last fund cycle, we put up to work about 1,000,000,000.5 on the creation strategy. And first and foremost, it's really around incubating companies. So we think there's a ton of potential right now, especially to incubate applied AI companies. So as the models get better, whether it's OpenAI or Anthropic or Google or xAI or others, we really do think there'll be a lot of new companies that can be built on top of this technology. And every three to six months, the models are getting better and better, especially recently at things like reasoning, logic, coding, math. And so we think there's a huge opportunity on creation to incubate Applied AI companies. And when we do that, we go one of two ways. Either we incubate the Applied AI company and we have it scale organically and then we give them capital for marketing and growth, or in about half the cases after incubating an Applied AI company, we actually give them capital to go buy their distribution, buy their client list, and the data that comes there. And so that second part has begun to be called the AI enabled roll up. And obviously, we are extremely involved in companies like Long Lake, Eudia, Titan MSP, Beacon, Crescendo, and others who are kind of the leaders now in this field.
00:07:30 · Molly O'Shea
[00:07:30] S1: Wow. I think I think what'd be really interesting would be to take a step back because General Catalyst really leaned into this moment. People have forgotten about this, but there was an existential moment for SaaS in, like, early twenty twenty three or something like that. And everyone thought venture was dead. SaaS was dead. It was the end of everything. No more venture capital. It's over. That was the that was the whole idea. But then some people thought, oh, no. This is an opportunity. This is an opportunity to roll up companies, to apply AI, to use these And one of those people or one of those funds was General Catalyst. And so you guys actually, like, really leaned in hard for this. What was the decision making process for it?
00:08:16 · Marc Bhargava
[00:08:16] S0: Yeah. For us, it was a boat strategy. So we actually think three approaches can win. One, some folks view it as the model companies will end up being the winners. They're gonna keep iterating. They make better and better models. They'll go direct to the consumer. And we're obviously investors in Anthropic, large investors from the last round, from this current round. We absolutely think companies like Anthropic and OpenAI and Google can benefit, and the model layer can benefit. So we agree with that. We also agree with folks who say, well, it's been overestimated. SaaS is not dead. If you look at public market comps, they've continued to grow, do well. There are all kinds of new, interesting SaaS companies that are being started and growing. But we also think there is this third bucket. So we're kind of an all of the above. We think there'll be winners in each category, and we obviously wanna be part of them. But that third bucket is for really fragmented industries where it's very hard to sell into AI native services and products. Can we actually build the AI native service platform and software and then go buy our distribution? So a few examples. One that was very early on was Crescendo, where we led several rounds of funding to build AI native software for call centers. And we teamed up Andy Lee, who ran Alorica for over thirty years, a call center chain with two amazing CTOs. Someone from GC went and joined full time, and we built out this software that automates 50 to 70% now of what a call center does. So after proving that out in a year and having 10 or so pilot clients, we gave them the money to actually go buy a call center. And they're well on their way now of taking 10% EBITDA margins and turning them into 40% EBITDA margins. And that's because when half the tasks can be automated, people can start to focus on cross selling, revenue opportunities, and the harder tasks. So Crescendo is just one example. With Long Lake, a second example, they've gone after the HOA management space and PEO services and others. And they are also scaling quickly, doubling the EBITDA margins of the companies they've been buying. And they have the benefit of having all of the data and the ability to do change management and change practices.
00:10:11 · Marc Bhargava, Molly O'Shea
And then, of course, you know, third and fourth, we have companies like Titan MSP and the MSP space then went out, got six pilot clients, showed us they could automate 38% of what MSP does, which is an outsourced IT services firm. And now they've bought RFA, which is a well known MSP in New York, and they also are well on their way to kind of doubling EBITDA margins. And so we definitely think that there's an opportunity for software, but there are a lot of industries that are highly fragmented. They're split across the country. They don't really buy technology products. When they do buy tech products, they call it IT, and they cap it at two or 3% of revenue. And at the same time, there's this ability with AI to go in and automate 20, 30, even 50% of the tasks at those businesses. So to really make the most out of AI technology, you wanna buy of these companies and be able to free up people to perform the higher earning tasks and really grow the company and grow the margin story. And that's what we're starting to see proven out across all of these case studies and examples, and we've been getting a lot of interest from crossover funds and others as we've been doing this, and we've really been proving the model. [00:11:13] S1: Sourcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in