00:11:13 · Molly O'Shea, Molly O'Shea
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00:12:07 · Molly O'Shea
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00:14:33 · Molly O'Shea, Molly O'Shea
Full disclosures in the description. [00:14:36] S1: So these are traditionally not sexy industries at all. Definitely not. Yeah. They're very boring. They're boring. Definitely. But you came from a really fun industry. You came from crypto and everything. Right. And from my understanding and research, you looked at 70 different industries, and you landed on 10 of them for this fund. Right. What was your process for that, and how did you evaluate the opportunity set for those 10 industries?
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?