00:15:01 · Marc Bhargava
[00:15:01] S0: Well, I'm still very bullish on crypto and got got into this space in 2016, was an early investor in amazing companies, started to Tagomi with Jen and Greg. We built it, sold it to Coinbase. Today, it's the heart of Coinbase Prime, which Greg Tusar, my cofounder, still runs. And, you know, after the Coinbase exit, I found myself doing a lot of angel investing, a lot of it in great fintech companies like Ramp and Zip and others. But I was also looking at NFTs and, you know, other things honestly in crypto that didn't really play out. And at the same time, I started meeting these really smart AI founders. Founders. And I felt like if I was giving money to people putting monkeys on the Internet, probably I should support these, like, PhDs in AI from Stanford who are working really hard on their companies and play it forward. So I got to be in the angel round of Harvey and Together and Windsurf and a lot of these names. And so seeing kind of their progress was really what inspired me then at General Catalyst to say, I still wanna invest in crypto, which is obviously growing really well in terms of Bitcoin and stablecoin adoption and things being built on Ethereum, but I also wanna be part of this AI movement. And then as I started to think about how do I wanna be part of this AI transformation, I started talking to a lot of second time founders like myself. And most of them were identifying that the hard part was go to market. I can build AI software to automate a lot of what a call center does or a lot of what a law firm does or an accounting firm or an IT firm, but it's gonna be really... Or HOA management or property management like with Dwelly out in The UK. It's But gonna be really hard for me to go to market in these unsexy industries where I don't have a network, where it's really fragmented, where they don't really trust tech. It's not the same as a ramp or Brex selling into all the hot startups. And so was really brainstorming with those second time founders, thinking about how do we take this AI technology and really scale and grow that we together really conceived of this AI rollup concept very early on with especially folks like Long Lake being a great example or Crescendo. Those are really two of the first in this space and got very lucky to work with them.
00:16:58 · Marc Bhargava
As we started seeing their success, we stepped back and said we should double down here. We also were obviously getting a ton of interest from other investors and LPs and others. And so that's when we became more systematic about a year and a half, two years ago where we said, okay. We have these success stories. Maybe they're a bit more one off. It was about these amazing founders we wanna back and about applied AI, but let's actually look at 70 services based industries, and let's try to figure out which of those seventies we could automate 30% of the tasks happening there. And we were able to kind of figure that out by first saying, well, what can AI automate? Like, what's it good at? And there were four pretty clear buckets. One was customer success and support and service. Basically, AI agents can do that task pretty well. A second was data entry and evaluation. If you're filling out the same forms or adding the same tables, that was the second bucket. It really could do well. A third was creating content and copy and marketing. So like an FAQ or an investor presentation or a list of dos and do nots, like that sort of content it was really good at, or responding to email or doing translation or creating an NDA contract. And then a fourth bucket, which has only happened the last nine months, is basic logic and reasoning, including, like, in insurance. Should I underwrite this person? They're a super risky driver. Here's their background. It's not so clear, but you can do almost as good a job as a human saying, yeah. Here are the risks. Here are the rewards. Here's my recommendation. So this fourth bucket of kind of basic logic and reasoning, which has really been pushed forward by all the automation and things like code and other areas, were the four buckets we identified. So then we mapped those forms of automation against these 70 industries, and we came up with 10 where we thought we have really high conviction that at least 20 to 30% of tasks, again, not people, but tasks, can be automated. And then if you work at a HOA management company or if you work at a call center or if you work in legal, with the 30% free time you have, we now ask you to do more work, like take on more clients, cross sell them, do harder tasks.
00:18:52 · Marc Bhargava, Molly O'Shea
And so with the same cost basis, but adding all these AI tools for automation in these four buckets, folks can now take on 20, 50, and over three or four years, even a 100% more tasks with the same cost basis. So we're already seeing in many of the case studies that if you look at the first couple companies they acquired, even if they've only grown revenue 20%, if they've kept the cost basis flat using AI but then freeing up people to kinda do 20% more tasks, around 10 or 15% EBITDA margin businesses can become 30% EBITDA margin businesses. You can roughly double the free cash flow. And we're seeing that happen in many of our businesses in a one year period. So for us, though, at the heart of that is is the AI automation enough? And, obviously, we decided not to go forward with 60 of the 70. So, we held a really high bar to is this an industry where AI automation actually can make a meaningful difference? Then can we actually go and buy and roll up these companies, get their data, improve the models, improve our software, automate 20 plus percent of tasks, free people up at a cross sell, get more revenue, and the result is a higher revenue base with a very similar cost basis, which generally translates to a really large improvement in EBITDA margin. [00:19:57] S1: This market, some could estimate, is between 20 to $30,000,000,000,000. How do you view this type of private company in the grand scheme of knowledge work?
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.