Inside General Catalyst’s $1.5B AI Roll-Up Machine

23:25And 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, Mark Crane. And so we are looking for kind of this, maybe it's more difficult to hire for, but people who have the PE experience, operational experience, and 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. 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.

23:58And so GC has certainly created a brand around, if 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. That's so fascinating. Can you talk through the funding mechanism part of it? Like, how do you structure it?

24:28Yeah, so we normally do one or two rounds to build the software piece. Like with the 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 in 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 cashflow into growth and create a really successful company.

25:06And so we look at especially the pilot clients that they're working with and 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 10 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 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.

25:40So at GC, we've been investing between 100 and 150 million 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 and other VCs as well on these projects. Some like Elad, you've had on the podcast as a great collaborator of ours and others. So we've definitely been seeing kind of a lot of success with that. You said you plan on holding these for seven to 10 years and hopefully go public or that sort of thing, some sort of exit scenario.

26:15That's a little bit different than the fast turnaround of a traditional private equity fund and everything that goes on with that, call it bad practices. But I'm curious around that philosophy. Why hold it for that long? Yeah, a lot of it is because we want these to be compounders. So the end vision for most of the founders is, I want to be the next generation Transdime or Danaher or Constellation Software. There are these companies in the public markets with over$100 billion in market cap that have outperformed NASDAQ, outperformed the S &P. And what they do is they go buy companies, improve their operations, generate more free cash flow from the company they bought.

26:54And 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 going to 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.

27:27And so we think there'll be plenty of these$100 billion AI native compounders in the public markets 10 years from now. And the founders who are starting these companies generally have very strong tech backgrounds. And they want to build a business that's VC-backed. They want to take it public. And many of them want to run it in the public market for 10 years. So they really want ownership on this for the next 20 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.

27:58And 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 costs, 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 softwares. We're going in and investing in adding costs. But we think it's worth it because we're adding a lot less costs than the amount of revenue we're adding as we're freeing up people to do more and more revenue producing tasks. So our mindset is longer term and the founders who actually own these companies, majority owners, their mindset is longer term.

28:33So it's just a different strategy. PE has obviously done really well. There are a lot of efficiencies in many markets. There's also not enough automation in many markets to do what the strategy we are. So we have a lot of respect for private equity and the work folks like Tomo Bravo and Blackstone and others have done. They've been incredibly successful. We're kind of playing our own game around, hey, how do we help these founders get AI-native compounders that will 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.

29:06Efficiencies, synergies, change management, all the good buzzwords. 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. So our view is when our founders create these holdcos and then want to 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. This is something I've noticed. And I mean, there's data around it too, but the private markets are swelling. And it extends from private equity to private credit.

29:37I mean, this, I think, would be part of it. 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? 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 100s have tried.

30:15and 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 and 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 there's a lot left to be desired basically in terms of the amount of automation they can achieve.

30:57So 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 a 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.

31:39So 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 going to 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. I recently had Michael Barton, who's a sector head at Code2 on the podcast. And he framed this out as there's two camps. There's AI will drive, and this has to do with layoffs. AI will drive efficiencies for teams and teams will be smaller.

32:19Then there's another camp that says, no, this will make your teams much more powerful and you'll be able to build bigger teams that are just much more effective. Where do you sit between these two camps? We're pretty squarely in the second camp. I'll give you an example, but we incubated a company, Hippocratic AI. which is an AI nurse. It's AI nurse software. And, you know, there's just such a massive shortage of nurses in this country and in the hospital systems. And so our view is AI is not coming in to replace nurses, but now one nurse could manage a team of five AIs that can do a lot of the basic tasks, like checking in on a patient or getting information.

32:55So for us, we kind of think that AI will create a lot of volume in the economy. There'll be a lot more new jobs. And so every time this happens in technology, Is the internet coming in and replacing jobs? Or is locomotives coming in and replacing jobs? The truth is, it's very granular. Sadly, yes, some jobs might not be there anymore. But at the same time, it's creating a lot of economic opportunity and new types of jobs, or even letting one person now do a lot more work. So that's what we see the most in the services industry, is if someone can now do three times the amount of work because they have orchestrated AI agents and they use LLM software, are you getting rid of someone who's now three times as efficient, who only needs a slight salary increase, you're probably not.

33:35You probably are hiring three or four more of them. And so that's kind of what we see on the ground, which is in many of these spaces, there's a huge shortage of nurses, accountants, and yes, even lawyers. It'd be nice if everyone could have a lawyer in their phone, right? So many of these industries, more and more people would love to have it. We believe in this idea of abundance and abundance is gonna come from that mix of people and AI services. It won't be purely AI. It won't necessarily be purely replacing people. We do think that there'll be a transition part, but there's some happy medium.

34:05And then the second nuance is kind of the first things to go is a lot of the outsourcing businesses. So there's a lot of outsourcing in legal. There was a lot of outsourcing in call centers. There's a lot of outsourcing in insurance, like looking over claims. What we're seeing a lot of is you add AI technology to American companies. They're now much more efficient. They don't need to outsource a lot of these more repetitive tasks. So I think the area that will be hit the hardest is probably abroad in places like India, Philippines. places like that, even Mexico. So here at GC, we are committed to trying to figure out ways to how do you retrain and reskill?

34:38And can we invest in companies that are training people in new fields? And we think it'll be most felt actually outside of the United States. After surveying all these different shifts and this transition period that you talk about, I think there's definitely going to be one too. You don't know what the future of jobs will be. Calci had a market that it hit up to like 86%. I haven't checked it recently on layoffs in tech. And that there's going to be an 86 % chance that there's going to be more layoffs this year than in 2024. But I do think, I don't know if that one's true or not. So you'd have to check the data.

35:18But you would think that because of all this mix, new industries might pop up. What are you thinking might like, what could come next? Yeah, I think it'll be a lot more of things that right now are really limited to people with resources. So I think there'll be a lot more legal use cases and way more representation and legal, I think, like a real time understanding of your company, your balance sheets, accounting. It'll be a lot more there. So I think there'll be industries that already exist, but much more abundance. On the nursing side right now, if you're dispatched from a hospital, maybe they follow up with you for three months or six months, but it just doesn't make economic sense to follow up with patients nine months a year out.

35:58But if you were truly doing the best thing for your patient, you would follow up with them in nine months and in 12 months. So I think a lot of these industries like healthcare, insurance, legal, where people have resources but then are tapped out, there'll be just a lot more around that. And so I think you'll have way more sophisticated markets in all of these places. So to be as unbiased as possible and objective, do you think AI roll-ups are underrated or overrated? I think they're underrated right now. I mean, most people you talk to haven't really heard of them. So we're just getting started on this part.

36:32I think right now people think, oh, it's a way to maybe put more money to work or something. Because obviously, as you buy companies, you can move quickly, you can put more money to work. But I think folks don't fundamentally understand that in a$16 trillion services industry, we could have companies growing faster than software companies and at higher margin than software companies. And so this pool of$1 trillion that VC has been fishing in has actually expanded now, maybe not to the full$16 trillion, but to multiple trillion. I think very few folks really grasp that. And so I definitely think that for now they're underrated.

37:07Yeah, I brought that up because I'm just like thinking about all the numbers that you've been mentioning throughout this and like increasing EBITDA and the growth and the KED count, all of it. It seems much more lucrative. And so I'm happy that we're diving deep into it. So when we look at this industry and like the different players that go into it, how much of a role do consulting services play in this? And what do you think happens to the McKinsey where you used to work or like Accenture and how effective are they in this process? Because it's really difficult. Totally. Our view is, you know, at least on the AI roll-up side, we're more getting these smaller, fragmented style businesses and putting them together and doing this AI transformation.

37:51McKinsey and Accenture's and others really focus more on the Fortune 100. And I think they'll have some success in embedding AI there. But at GC, we created a company called Percepta, one of our transformation companies that we want to hold for the long term. And the thesis there is that maybe traditional consulting isn't really designed for a lot of the AI implementation piece. So they can give more strategy advice, but not necessarily the same kind of, hey, here's what we're seeing. It's a fast-moving industry. Here are the companies we've invested in. Let's actually go and implement kind of fast-moving AI changing technology in the Fortune 100.

38:25So I do think there's a huge opportunity for companies like Percepta. You probably will also see McKinsey and Accenture and others benefit. and the Fortune 100 also will have to evolve. Our focus more though on creation is incubating these companies in more fragmented industries where you don't necessarily even have the resources to go to a McKinsey or an Accenture if you want to add AI implementation. Because of General Catalyst's very large platform that you have, how do the private market investments help determine or inform these companies? Are you partnering with any of them for them? How does Anthropic fit in here?

39:05And what are you learning from them? Yeah. So with Anthropic, we're kind of learning where are the models going. So there's always a new release every three months, six months. We're better understanding what are the tasks that these models can do, especially as logic and reasoning is better. And then we have portfolio companies like Rocks, which is taking on Salesforce, for example, and it's more AI native. Or Udia, which is focused in the legal space, also more AI native. Serval, which came out of Stealth last week. which is taking on ServiceNow in a more AI-enabled way. So we're working closely with Percepta to let them know about the companies in the portfolio, what they're seeing both in terms of model improvements and also product improvements.

39:43And then Percepta is getting this real-life view of everything going on as much as it can in AI and then is able to help its Fortune 100 clients implement all this new technology, a lot of which our team is helping identify and pass up to them. This was a good question from Kyle Harrison, but he wanted to ask, How do you decide when to choose from building an AI roll-up or investing in another SaaS company? Yeah, it's a really good question. One answer to that is percent automation. So if we think a space is going to be 90 or 100 % automated, then probably software is the best solution. And honestly, an incumbent like Microsoft or Amazon or Google that has the distribution can just push the software.

40:24It can be part of your Google Workplace or your Prime subscription. So if something's 100 % automatable, it probably should be a software or agentic solution. And then most likely the winner there could be a fast growing company, but also it's very likely someone who just already has the distribution. So if coding gets fully automated, maybe it can just be pushed through by Google or Microsoft or someone else. So one thing we're careful about in the AI-enabled rollups is we target 30 % automation at least, but we actually don't want more than 70 % automation. Because if something's approaching 80, 90, or 100 % automation, and there's really not the people services part of it.

41:00So that's one important part. The second thing is fragmentation. Like when the industry is really hard to sell into, imagine creating AI native software for homeowner association, HOA management, like that's just extremely hard to sell into. So you could create an amazing product, I'm sure, but your sales process would be very, very slow. So the second thing we look for is, hey, it's very fragmented and it's hard to sell in a SaaS product to these businesses. And then the third thing we look for is, it has to be really sticky. Like with HOA management, it's a two-year contract. So it's important that, you know, it's a low-churn business where we can go in, we can do all this AI implementation, and there will be some hiccups along the way, but we can kind of smooth those over and we have these kind of loyal clients.

41:41Accounting is another really good space where there's very low churn that we're in, or insurance or MSPs. And so those are the things we look for. We do think that software services will win out in areas that are, you know, more than 80 % automatable, or it's easy to sell into your client base, We have a few large tech customers. There are plenty of areas where it really makes sense to be more of a software solution. That said, we think there's also this really big market for AI-enabled roll-ups, especially in things like services. Well, to shift a little bit, Sorcery is sponsored by Brex. Amazing.

42:12We love Brex. They're all about spending smarter, moving faster, and performance. When you're working and you're building on these companies, what are the metrics that you use to determine their performance? I would assume some of it's a little abstract because it's like team and that sort of thing. But how do you measure performance? Yeah. And the first tranche of funding, what we're targeting is this 30 % automation. So go out and get 10 clients or get five private clients, but really show us the software you can build or the agentic workforce you can build. And then come back to us and map out.

42:44If you look at all the man hours in a company, our 30 % could you automate with the products that you have built or stitched together. And so that's the first really key metric. Then the second key metric is when we go and actually buy companies, we're looking for how are you improving the margin? So you've automated 30 % or more. How's that flowing through the margin? And we want a really detailed plan of how to double your EBITDA margin, generally from the 15 to 20 range to 30 to 40%. So that's kind of the second really key metric. Once you start showing us you're there or you're on track to get there, that's the second really important metric.

43:17And then the third is now you're growing and you're scaling and you're moving quickly. Well, what multiples are you buying these businesses at? And how quickly do you get those multiples down as you improve the margin profile and the EBITDA profile as well? And so then we get a bit more sensitive to entry multiples and doing things like smaller tuck-in acquisitions and using debt. And then the final one is just dilution. Like we want the founders of these companies to still own at least 10 % of their business when they go public, ideally 20 or even 30. And so, you know, that's also a really important metric.

43:47Are you compounding now with your own free cash flow and with debt financing towards the later rounds so that you can still own a big chunk of your company when you go public? And of course, we at GC can own a big chunk, ideally also 20 to 25 percent when these companies go public. So a really large concentrated position and things that we think can be really massive outcomes. So along the way, we have these different metrics sort of for each stage of the AI-enabled roll-up. Wow, that's highly concentrated. Yeah, for sure. This is a really difficult question. Yeah. Yeah. Where are these businesses located?

44:19Where have you traveled to? Right. Well, the holding companies that we help incubate and fund are pretty much exclusively in San Francisco and New York. So out of the eight or nine deals that we've done, I think six are here and two or three are in New York. So we have seen that talent has come back for sure to the Bay Area. It's like the center of applied AI, research AI, and then New York too for financial services, especially, and some of the really great M &A specialists or people who have built and sold companies are out there. So we're primarily working with really experienced entrepreneurs, second-time founders, amazing operators, and they tend to be between SF and New York.

44:56Then the companies, once we create the hold codes and they create the software, we show the automation. Now we're actually buying businesses. Those are almost never in San Francisco or New York. They're all across the country. Increasingly, it's getting more global. We invested two rounds in a company called Dwelly, which is out in London, which is rolling up property management and scaling really well and also doubling the EBITDA margin of the property managers they're buying. And we've also done a deal in Germany in the accounting space. And so increasingly now, well, my answer was at first US, we also have bets in like UK and Germany.

45:28And we're looking at this strategy really closely in India too. And Europe has much lower entry multiples. People sometimes rat on Europe that it's hard to build a big company because there's so many markets. In some ways, the AI-enabled roll-up is really perfect for that because we can buy our way into new markets and iterate. And it's the same back-end technology to automate accounting, for example. And so increasingly, this will be a more global strategy. But to date, it's been New York, SF, London, and Berlin. When you go global, do you keep the teams here in the U.S. and then the operations?

46:00How does that work with telepise? We normally invest in a separate company to do HOA, property management roll-up, for example, in the UK, which is Dwelly, and they'll expand to broader Europe, for example. So we, right now, none of our roll-ups are really working internationally, with the exception of one that's rolling up software companies. And they're based in Canada, but are between Canada and the US. I mean, more on like the talent side, like is the talent in those geographies as I'd say like AI native as they could be in the US? Like how does that quality wise compare? I think it's less US versus international and sort of SF Bay Area versus everyone else.

46:40I think SF just has a much higher density of talent and quality of talent than really anywhere else in the world. But I think it is exciting that there's a lot more homegrown, great companies in New York and in London and Berlin. And the quality of talent is rising in all of these other places. So much so that we definitely are investing more in Europe and investing more in India. But in terms of where's the absolute top tier talent in the world, I think it's still here in the Bay Area. There's nothing special about the Bay Area except for it attracts all the really great people to, you know, leave wherever they are and come work here.

47:12So that's been the draw. So you've been at General Catalyst for a couple of years now. What is the biggest lesson that you've learned from him on? Definitely advocating on team and paying up when you have to. Like I got to learn from HT some epic stories about how GC got involved in Stripe. And, you know, I won't repeat all of the details there. But there are some companies you just really want to be in. And it's less about the terms. It's more about catch them early. Like you can buy 10 or 20 % of an iconic company at seed or at A or at B. So a lot of people have the mentality of, oh, this is$100 million check.

47:49Let's spend all our time. This is more important than like a 1 million seed check. But the reality is normally your 100 million check is buying you 10 % of a company. Your seed check for 1 million is also buying you 10 % of the company. So it's really important to kind of what are going to be the next 50 iconic companies and ideally try to catch them early and pay just as much attention to buying 10 % of some seed business as 10 % of an established business. So we have really at GC shifted a lot our focus on seed with the acquisition of La Familia and bringing in Jeanette and who's running our Europe practice and is a very prolific seed investor from La Familia, Venture Highway, the India seed firm that we also acquired.

48:28We brought in recently and then Yuri to come lead our US seed practice. He had a successful seed firm. So I think that mentality we're really pushing through, which is can we buy 10 to 20 % of companies early rather than certain types of investments are more important than others? It's really how do we get those iconic companies, 10 or 20 % of them? So intense. Yeah, there's a lot going on there. There's a lot going on. So what are you most looking forward to in the next 12 months? In the next 12 months, I think becoming much more public about our thesis. Some of our best roll-ups have decided not to do any press.

49:03whatsoever, which I think is, I can understand where they're coming from. They're cash flow positive. Like, you know, some of the companies in the roll-up will hit 100 million of EBITDA, and they're like less than two years old. We have companies like that who say, if we're generating hundreds of millions of free cash flow, we don't need to be out fundraising every three to six months and doing the press tour and promising people AGIs around the corner. And like, you don't, if you have a good business, and you're making a lot of money, and you're seeing AI implementation really working, a lot of our companies have remained really quiet or more stealthy despite doing amazing.

49:36But I am excited to hear these stories come out much more publicly. And obviously, we'll send them your way as well. But in the next six months or a year, as we have these amazing proof points that I get to see being on the board of many of these companies, we're going to start telling our story much more to the world with an angle of working now with more crossover firms, starting to prepare them to go public. It's really an amazing journey. For me, it feels a lot like in 2016 in crypto, there were a few people who thought it was legit, like USV and A16Z. But most of the market was just trying to be polite because they didn't want to offend USV and A16Z and Founders Fund, which was obviously really involved directly in Bitcoin.

50:13They were just trying to be polite, but they didn't really believe in it. And there were a few. I think we're kind of there in the AI rollup right now where ourselves and Elod and Thrive and a few others, AVC have embraced it. But I think most of the market is still pretty skeptical, similar to kind of the crypto evolution and where Bitcoin and stablecoins are today. You see very few people who say, hey, there's not value there. It's not interesting. I think that'll be us in a few years, too. So it's been really fun, again, being on what I think is like a journey that will most definitely be proven true.

50:41Oh, so fascinating and really cool to capture it at this point in time. Absolutely. I'm really excited to do a rapid fire interview with every single one of the companies. No, I think they should definitely do more press. And we have a few that are starting to do more, including Titan MSP and Yudia and Crescendo. But we're excited for the rest too as well. Okay. Well, Mark, it was a pleasure to have you on. Thank you so much for sharing more on the creation. Yeah, thanks so much for having me. It was a real pleasure. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, Sorcery.bc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews.

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