"The Cognitive Revolution"
00:05:27 · Marc Bhargava
[00:05:27] S1: Yeah. Sure. Well, first of all, thanks so much for having me on. Really appreciate it and excited to tell you more about what we're up to. Yeah. So as as you mentioned, I'm a partner at General Catalyst. So I think everyone in venture is out there really looking for outliers and how can we go and invest in them. So we've historically built companies like Kayak and Livongo and have a really strong DNA of incubating and building companies. And I joined GC. This is my third year, but previously was a cofounder at Tagomi, the first prime broker in the crypto space. We were acquired by Coinbase, became Coinbase Prime. My cofounder Greg still runs Coinbase prime prime and the institutional business there. So at my heart, certainly an operator and builder and founder. And what really attracted me, GC, was what are companies out there that are multidisciplinary, that don't exist today where we can put together the right team or we have some unfair advantage to really build it. And as you can guess, today, a lot of the theme around that is AI and especially applied AI with the model companies getting better and better with their new releases and can do more and more with LLMs? What are the sort of applied AI use cases that have not existed yet that should exist that we can build? So that's really been the focus of creation. That compounder story and the AI enabled roll up story came out of that. It came out of... Well, if you look at where AI automation is happening and things like automating customer support, automating data entry, automating the creation of presentations and papers and copy and emails, helping human in the loop with logic and reasoning, math, and coding. All of these different use cases, it's not always easy to get that in the hands of the end customer. So the idea behind the compounder or AI enabled roll up is essentially if you look at firms or places like accounting, legal, IT, etcetera, call centers being a good example of Crexendo, you might be able to get this AI automated software built, but it's very hard to go to market and get in the hands of the end client. These industries are fragmented. They're all across the country. They don't really buy software. When they do, they call it IT spend and it's a really small percent of their budget.
00:07:29 · Marc Bhargava
And so our thesis was, look at Creation, we wanna incubate the next great generation of Applied AI companies. In many cases, these are in the services businesses where we actually will need to go and buy our distribution base or buy our client list in order to get these to market, in order to get the proprietary data to continue to train and improve the models. And as a technique to improve these companies, get more free cash flow, and then buy more businesses. And that's really where the compounder story comes from. In the public market, you have folks like Danaher and TransDigm and Constellation Software and even Berkshire Hathaway, where as business models, they go buy companies, improve them, create more free cash flow, and then with that free cash flow, buy another company and and hence compound. And so that's why those companies, which have performed incredibly well in public markets, are called compounders. They've been hedge fund favorites for the last decade or so. We think there's a new generation of compounders. We wanna create those. We wanna incubate those. And we think a big part of that story will be the AI automation around how they're improving those businesses. Yeah. You're giving new meaning to customer acquisition for, SaaS businesses for sure.
00:08:37 · Nathan Labenz
[00:08:37] S0: I guess just one extra beat on sort of the history of this because you mentioned... And I don't know those companies super well. Even Berkshire Hathaway, of course, I know, but I don't I don't know that I really know. You know, the... I guess the general outside view is, like, first of all, m and a is really hard, right, broadly. My general sense from sort of corporate level m and a is, like, most transactions fail in some general sense of, like, they don't pay off as promised, you know, at the time that they were, executed. What has been the driver in your opinion of the ones that have been successful? Is it about just, like, you know, consolidating operations and creating efficiencies of the sort of PE variety? That doesn't seem like it would be enough to create a Berkshire Hathaway. It seems like there must be something more cultural or more, I don't know, more more core somehow than just that.
00:09:27 · Marc Bhargava
[00:09:27] S1: Yeah. That's the hardest part of what we do. So if we look at an opportunity and say something like, you know, customer support and call centers, we're seeing through our company, Crescendo, that 50 to 70% of call center workflows can now be automated with agents and LLMs. Or you look at something like MSP and IT. We're seeing through one of our investments there, 38% automation or bookkeeping with one of our companies kick 80% automation. So we're seeing this massive automation every three to six months. It gets more and more in these different industries. And that's one big part of it, but second part is our founding teams have to have m and a, private equity kind of industry experience as well. And so that's why this is a perfect project for creation which is all about incubating companies, putting people together who might not naturally know each other. And so in many of these cases, what we've done is we've found people who have led Applied AI teams at places like Rippling or RAM or Scale AI. We're kind of combining them with industry experts who have done m and a before, done roll ups before. We're putting together both of those legs. And so that's a really important part of where GC fits into this is identifying which industries have high potential for automation, but then also putting together a founding team that can do the automation, has experience in Applied AI, and has experience in the industry, in m and a, in private equity, and doing the buyout part as well. So it is really important to have both. I will say that technology companies have done a pretty good job at m and a if you look at the track record. Like with Facebook, for example, the acquisition of Instagram, I think we'd all agree is absolutely instrumental to Facebook and what it is today. If you look at something like Google, the acquisition of YouTube and and even Android is extremely instrumental. If you look in our own portfolio like Anduril, it's done over a dozen acquisitions as it's become the best overall platform for defense tech. And so sometimes I think also tech gets a bad rap, but has actually been very successful in creating public value through their own compounder stories as well.
00:11:20 · Marc Bhargava
And now some hedge fund analysts would put companies like Microsoft and Amazon in that compounder bucket of making great acquisitions, improving those companies, cross selling them, and have become amazing free cash flow stories as well. So I think the AI world is no different. There's an opportunity now in many industries to have a high level of automation. If you can now go and buy companies, improve them with that automation, you're freeing up cash flow to buy even more and you can have an inorganic story that can also complement an organic story of naturally growing the product suite and getting more customers as well. So at GC and at Creation, we're really thinking what are the ways to get the most tools in the hands of founders? How can we put together these unique teams and how can we help them get to market quickly and also have the data they need to fine tune and train their models and have a really fast feedback loop? So we think this hybrid services plus software AI enabled with the capabilities to also go buy your distribution, buy your data, this ends up creating the best companies and the best products. And we're seeing it in the HOA management space with Long Lake or in you know, at the MSP space, Crescendo in the call center. So we've placed now eight of these AI enabled roll up bets. In five of them we helped incubate the company, was the first check-in, and it's a strategy that's certainly now catching on with the market and having other investors come in and a lot of excitement, but probably most importantly, having a lot of founders say, hey, I get it. Especially second time founders. The hardest part is getting proprietary data. The hardest part is go to market. And I'm good at building and operating and running companies and maybe even did m and a myself. I sold my last company. And so we're really attracting these second time founders and experienced operators who could go work with any venture firm, is choosing to work with us with creation because we have this specific thesis and strategy.
00:13:05 · Nathan Labenz
[00:13:05] S0: How much of this is about... And I do wanna go into, you know, each one of the portfolio companies in a little more detail and kind of understand the the story and the nuances. But at a high level, how much of this is about the fact that, you know, it's it's been obviously said many times in the, you know, AI era thus far, software can now enter the services market. Right? There's this sort of blurring between software and labor. And in a sense, that's, like, the greatest opportunity ever for software. In a sense, though, it also creates a more challenging, let's say, relationship or, you know, the people buying the software are also currently selling labor. And so, you know, if you're selling billable hours at a law firm and you're like, geez, you know, this thing might make me more efficient, but that also directly cuts into my billable hours. How do I think about this? How much of this is about saying basically what once was a sensible model of we are selling compliments to labor is now that we are selling substitutes to labor, and so we need to sort of rebundle or restructure the market to bundle those things together in one company with one sort of set of shared interest because it just doesn't work as well as it used to given that switch from... Or not not... Obviously, full switch, but at least partial switch from complement to substitute.