"The Cognitive Revolution"
00:02:10 · Nathan Labenz
An HOA manager, for example, can still email to ask for help creating a board deck, but now an AI agent takes the first pass at the task. The goal for the short term at least is not to eliminate jobs, but to enable each person to handle two to three times more work, thereby addressing the chronic labor shortages plaguing many service industries. Overall, I have to say I am very bullish on this investment and modernization strategy. The end customer value proposition is super compelling. Better service, faster turnaround, more proactive insights, all at similar or lower prices. And this feels like the right way to bring AI transformation to main street businesses that simply couldn't build such capabilities on their own. So I do expect General Catalyst and their portfolio companies, including the service businesses they acquire, to do phenomenally well. And yet, at the same time, if there's one overarching theme of this show, it's that we might still all be thinking too small. And as you'll hear, despite the fact that Marc is pursuing one of the few investment and company building strategies that really does meet the AI moment and could plausibly outcompete big tech offerings indefinitely, I still think there's a good chance he's underestimating just how far this transformation goes. While he believes that most local service firms will ultimately participate, my guess is that only a minority, maybe 20%, have the leadership vision, operational excellence, and cultural adaptability to be genuinely attractive roll up acquisition targets. Beyond that, my guess is that it'll make more sense for the AI enabled compounders to grow by simply taking customers from lagging firms rather than trying to acquire and transform them. And bigger picture, I do struggle with the employment math. Marc's vision is about serving more customers with the same size teams, and that's a great place to start. But when we're talking about 90% plus task automation, potentially in just the next one to two years, do we really need 10 times more accounting, for example, even at lower prices? I'm sure there's some latent demand to be unlocked, but my intuition is that many workers in fields like accounting will ultimately be displaced.
00:04:15 · Nathan Labenz, Marc Bhargava
And if I had to guess, I would bet on human centric fields like nursing, childcare, eldercare, and education as the sectors with sufficiently elastic demand so as to potentially absorb those AI refugees. Bottom line, I believe that the land of abundance that Marc envisions is likely to materialize, and I look forward to it while at the same time expecting that the transition could well be far more disruptive than he projects. Time will tell, of course. But for now, I hope you enjoy this deep dive into the future of AI enabled services and the art of creating modern compounders with Marc Bhargava of General Catalyst. Marc Bhargava, managing director at General Catalyst. Welcome to the Cognitive Revolution. [00:04:56] S1: Thanks for having me. [00:04:58] S0: I am excited for this conversation. You are pursuing what I think is a really interesting AI enabled investment thesis that I think says a lot about where things are going, and I'm excited to unpack it with you. Maybe for starters, give us the high level. I don't know if you wanna introduce firm. I mean, people I think will generally be familiar with General Catalyst, but kinda give us the, you know, the high level theory motivation and tell us what is a compounder.
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.