Full Transcript

Nathan Labenz: (0:00) Hello, and welcome back to the Cognitive Revolution. Today, I'm excited to be speaking with Marc Bhargava, managing director at General Catalyst, about their creation strategy and a fascinating new category of AI company they're building, AI enabled rollups. These are companies that begin as vertical software startups, but having achieved initial product market fit, grow by acquiring traditional service businesses and transforming them through AI automation. Marc brings a unique perspective to this conversation. Having cofounded Tagomi, the first prime broker in the crypto space, which was acquired by Coinbase, he has hands on operational experience. Now he's helping to found and incubate companies that are targeting not just the $1,000,000,000,000 software market, but the vastly larger $16,000,000,000,000 services market, including accounting, legal, IT services, call centers, and much more. These businesses have traditionally run at 5 to 10% profit margins, much too low for venture capital investment. But having done a systematic study of the tasks that make up the work across 70 different service categories, Marc and team have identified 10 leading candidate verticals where they believe that anywhere from 30 to 70% of tasks are already automatable with AI today, which means that with successful implementation, they could achieve much more software like margins of 30 to 40%. From there, they recruit and assemble founding teams, which combine applied AI technical talent with industry veterans who understand service from operations and have participated in m and a transactions before. These teams then work closely with initial customers to pilot purpose built AI tools and automation workflows. And then where the pull factor from customers is strongest, they structure acquisitions that deepen data access and feedback loops and align interests by giving firm owners a mix of cash and equity in the growing rollup entity. For now, the AI transformation at these firms is very pragmatic. You might even call it mundane. Teams shadow workers to map every hour of activity, identifying which tasks fall into automatable categories, customer support, data entry, content creation, and increasingly complex reasoning. Rather than wholesale workflow redesign, they layer AI into existing processes. 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 2 to 3 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 1 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 1 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 rollup 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 1 to 2 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. 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.

Marc Bhargava: (4:56) Thanks for having me.

Nathan Labenz: (4:58) 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.

Marc Bhargava: (5:27) 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 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 rollup 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 rollup 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. 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.

Nathan Labenz: (8:37) I guess just 1 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.

Marc Bhargava: (9:27) 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 LLM's. Or you look at something like MSP and IT. We're seeing through 1 of our investments there, 38% automation or bookkeeping with 1 of our companies kick 80% automation. So we're seeing this massive automation every 3 to 6 months. It gets more and more in these different industries. And that's 1 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 RipLang or RAM or Scale AI. We're kind of combining them with industry experts who have done m and a before, done rollups 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 Androle, 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. 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 8 of these AI enabled rollup bets. In 5 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.

Nathan Labenz: (13:05) How much of this is about and I do wanna go into, you know, each 1 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 1 company with 1 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.

Marc Bhargava: (14:29) Yeah. It really now is about selling work. So I think a great example of this in the public market is Palantir, which has done incredibly well, of course, and sells work. There's forward deploys engineers and, you know, that services, but ultimately, they're selling an AI platform and system and a technology and so it's a combination of software and services and ultimately, Palantir would say we're just selling you an end result or work. I think that is becoming true in more and more industries. You mentioned legal for example. Most legal spend is really on third party law firms. It's not really on the software component compared to the spend on lawyers. And so, you know, an approach that 1 of our portfolio companies, UDIA, is taking is they are going after Fortune 100 general counsels in house law firms, and they're selling to them a mix of services and software, all AI enabled so that, you know, these Fortune 100 brands can spend less on third party lawyers and instead do more in house with the tool set UD is giving them, which is a mix of software to do things like NDAs and contracts, but also services. And so they recently bought a company called Johnson Hannah that does a lot of kind of services as well, more manual things. And so I think, and it's been reported of course, that this hybrid services plus software is getting more and more interesting because the margin profile is changing in services. So globally, services is a 16,000,000,000,000 a year revenue opportunity. Software is actually only a 1,000,000,000,000 global revenue opportunity. So services is much much larger. But historically, services have been quite low margin and especially free cash flow margin. And so a lot of venture firms and even private equity and others have been less interested in the space. Now if you're automating 30 to 70% of the workflows in some of these services industries, you can AI enable the workforce and take a lot more clients. And doing that really changes the margin profile. And so now these businesses are getting more interesting to venture investors because ultimately this hybrid services software can have pretty high margin profiles. So instead of something like 5 to 10% EBITDA margins, 30 to 40% in certain cases, if you flow through the level of automation at, you know, 50% plus. And so it's getting very, interesting what you can do with an AI enabled workforce. Udia being a great example of coming in and helping these large corporations have their in house legal teams be AI enabled and take on more work with, you know, the same number of people and outsource less to these third party law firms.

Nathan Labenz: (16:55) I wanted to double click on the numbers there. So we got 1,000,000,000,000 software, 16,000,000,000,000 services. Obviously, that's the best opportunity. Margin traditionally in services is low. I think you said 5 to 10%. But if you can automate 50% plus, then you can bring those margins up a lot. 1 of my long standing, beliefs about AI has been, I don't know what to expect on most things, but I do expect high consumer surplus. And so I guess I wonder how do those before and after offers look today to customers, and how do you think that's gonna evolve over time? You know, or is a company and we could go into, you know, specific ones here and it probably varies. But is a company going and saying, okay, don't buy services the old way, buy our new hybrid service. It's gonna cost you 20% less and then under the hood the cost structure is 50% less and then that that's how they get to a higher margin. And is that margin like sustainable long term?

Marc Bhargava: (17:59) Yeah. Absolutely. I think it is quite sustainable because the offering is generally we're gonna charge you around the same we do today, but we can do a lot more with AI. And so if you're already using a product and you're enjoying using it, if suddenly, you know, you can have 2 or 3 times as much output and they're keeping the price the same, that's unlikely to be a client that churns. So we view this less as a cost cutting game. Like when we AI enable a workforce in IT or in legal or in call centers, it's less about letting people go. We're automating people. We're automating tasks. So automating maybe 30% or 30 50% of the tasks that they're doing which enables them to do harder tasks which generally are higher margin and to cross sell as well. And so that is our thesis that this will really allow for a lot more growth. If you go in and you AI AI enable a workforce, you can now take on twice as much work with the same team because they have more tools and they're automating away the easier tasks. So that is the thesis. For the end client, what that means is you're getting even more of the high margin tasks done for you without a change in price. And so you're getting more at a similar price. And so we do think that is quite sticky. A really good example of this is in ramp where I'm an angel investor and GC is also a large investor where, you know, they went in and they AI enabled a lot of the sales and marketing team and, you know, folks were 2 to 3 times more effective. And they got the question of, oh, so we can like now cut our sales or marketing team. But it's actually the exact opposite. If people are now 2 or 3 times more effective, you actually wanna hire a lot more of them. It's higher ROI. So I overall expect this AI opportunity to continue to grow the global economy. It's a tech innovation like the internet or like cloud. And ultimately will create more jobs, more productivity. And I don't see a pricing margin compression because the offering is actually getting more and more output. And so overall, I think that it's gonna create a lot of opportunity that way.

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Nathan Labenz: (22:38) The story of doing more with the same and embracing that or even wanting to scale it up because who wouldn't want more if it's better does make sense to me in some areas. I mean, the the things that I always point to as like, you know, every organization would want to be doing more. You know, every founder would say, yeah, I ought I ought to be doing more cold outreach. You know, I ought to be doing more lead gen. I ought to be doing, you know, better job of nurturing my funnel and, you know, similarly on recruiting. Right? Like, I ought to be reaching out to top engineers 10 times a day or, you know, as basically as much as I can. How many are doing? Actually, 0 because I don't have time for it. Okay. So sure. If we could get AI to do that, you know, it seems like for a company like Ramp, you know, you're entering into an infinite market and just, like, grow as fast as you can makes total sense. It's a little harder for me to wrap my head around some of those things when it comes to, for example, let's say accountancy. At least the way I've always approached accountancy, I'm like, I want to do as little as I can to be compliant and satisfy my board. And it's not like I have a sort of wish list of things that I would be doing if it was only you know, if I only had more bandwidth or if I could only get more bang for my buck. Like, that to me does seem like much more of a just like I I should be able to do it for less. Right? Like, that seems like it's where it's what most people would want. How do you segment that?

Marc Bhargava: (24:01) Yeah. 2 thoughts on accounting. 1 is there's this huge shortage of accounts. So it's a industry fewer people wanna get into. There's a generation of baby boomers retiring. Many of the top accounting firms, if they could, could take on easily 50% or a 100% more clients because but their constraint is there are shortage of accountants. And so if you buy an accounting firm and you AI enable it, you might no longer have that shortage because a lot of the more basic tasks closer to, for example, bookkeeping can now be automated. And so if you AI enable an accounting workforce, that accounting firm has 2 or 3 times as many clients they could go out and they could get because there's actually a shortage of accounts there. And so that's how a lot of value can be created by going in. In terms of the end client and what they might wanna use it for, suddenly your accounting could be giving you proactive recommendations. So today, it might be around building out 3 statements and filing your taxes. You know, you can easily see how once you have that data, you can actually start making recommendations on where you can save money, how you're spending. You can have tools that turn that into financial models that can help you think about your capital needs, should you be getting debt funding. So suddenly a lot of the tools which maybe today are very stagnant can actually be really diagnostic, very proactive. And so I do think that the kind of even as if it might be considered a boring industry, the suite of products can drastically change with AI. And perhaps more near term and more importantly, the same accounting firm can do a lot more work with the same cost basis, the same workforce basis if it's AI enabled. And we're really in the current phase going after that first opportunity, which is how can accounting firm AI enabled do 2 or 3 times as much work that obviously really increases the margin profile because it's a similar cost basis, but obviously much higher revenue and that's the focus. But certainly to your question to the end client, maybe things get delivered more quickly, they're more accurate, all of those things are pluses. But even how you visualize the product certainly will change over time with AI as it we're seeing in things like legal being a great example of this where lawyers can also compare all the contracts and then recommend pricing changes because there's inconsistencies. So how does the law firm or the legal department go from just a cost center to also making recommendations and maybe being even a business plus center, and AI can make that change. So you have those 2 opportunities. 1 is just taking out more work with the kind of same group of AI enabled folks that obviously increases margin and is a huge opportunity in services. And the second is to change the output of these services businesses and things like accounting or legal start to be more diagnostic, more prescriptive, more insightful, and become departments that aren't just thought of as a cost center or a check the box, but can actually help you in running your business.

Nathan Labenz: (26:41) Can you take me through sort of the playbook of how you develop these companies? I mean, the general sketch that I have is your, first of all, assembling a founding team that has the mix of technical and m and a rollup experience. How do you decide what it is you want to focus on building? And how does that differ from maybe a more traditional SaaS strategy? Like, I guess I'm I'm particularly thinking about how much of it is going after what you might think of as core tasks versus sort of auxiliary tasks. I mean, couple examples of core task versus auxiliary, you know, might be like, I just did an episode with ambiance healthcare. No. You know, that they're not trying to do, at least not yet, we'll see. They're not trying to do a patient facing doctor that would actually interact with you and make the diagnosis. That's the core task that the, for now, the human doctor continues to own. But they do have the AI scribe in the room, which is transcribing and then translating the notes to medical coding, yada yada yada. And that's all the stuff that the doctor currently has to do in their pajama time after hours.

Marc Bhargava: (27:55) Yeah.

Nathan Labenz: (27:55) And so they're happy to offload that and that's like, so far, everybody's everybody's pleased with that arrangement. I might contrast that though to like a customer service call center type of scenario where I assume that the AI is like taking the calls and that would seem to be like the core task. And so, you know, whoever the person was that used to take those calls maybe can take other calls or may you know, but it seems like there is some again, this sort of, like, complement versus substitute notion seems to come into play. So how do you think about, like, as you begin and, Megan, maybe it varies by vertical. We got all the time in the world for you to go vertical by vertical. How do you think about, do we enter here with auxiliary? Do we try to take burden off? Do we try to empower? Or do we just like go right at the heart of the core task? How how do the decisions get made?

Marc Bhargava: (28:42) Question, we're really underwriting complimentary AI services. So we don't think that lawyers or accountants or IT specialists or HOA managers or any of those jobs are going away, but we think 30 to 70% of the tasks they do can be automated, and this frees them up to do more elsewhere. So even in the call center case, you know, there are times where people want to talk to a human, and so you can have the AI agents be doing the more basic calls, but you have a human in the loop and it can get routed to them at different times. But 1 human with a team of agents can take a lot more calls than 1 human alone. And so we really are focused more on the complimentary side of it. We're not really doing the strategy if we think something can be a 100% automated. So 1 example is, you know, maybe coding will end up being a 100% automated to some degree. And so maybe that might be an example where it doesn't make sense. But for legal or for accounting, we absolutely think on large deals, large transactions, preparing your taxes, even on or with call centers, you know, there are more emotional calls, more difficult ones. In all of these cases, it's about AI enablement of the human team and being able to do more. And that's why, at least my view, this AI revolution is similar to the Internet or to cloud where there's a big boost in productivity. There's a lot of interesting tooling, and the winners will be folks who figure out how to use that tooling and the ones who create it. So obviously, the large language model companies and labs and hyperscalers are creating a lot of this tooling. Our thesis is let's put together teams then who know how to use it. Let's add industry experts and m and a experts to those teams, and then let's go out and let's go to market much more quickly on the innovation that's coming out of these major labs. And so our playbook is that it's kind of 2 or 3 parts. 1 is identifying, well, what industries have this AI transformation potential? And we looked at 70 and we said 10 of them have 30% plus automation potential. The second piece is how do we put together the right teams that come from applied AI backgrounds, but then also complement them with folks of industry and m and a. Then the third piece is to actually build out the AI enabled software and the product to get pilot clients, to get customers, and to prove the automation percent. And then from there, both grow organically, but also buy kind of smaller midsize platforms and targets and be able to show the margin improvement, the free cash flow expansion through growth. And then from there, continue to wash, repeat, and become these compounders that are growing organically and inorganically that have this data moat from the companies they own and have a really deep understanding of what the customer wants, ultimately giving both services and software offerings to their customers. So that is our underlying thesis. We've placed, you know, 8 or so bets now. Many of them are kind of off to the races, much larger, better well known companies like Crescendo or Long Lake or Yudia or others that we've more publicly announced. And we're seeing a lot of traction in those companies, but then also with founders and others who wanna go in on the strategy as well as they see every 3 to 6 months the model's getting better, the level of automation getting better.

Nathan Labenz: (31:46) The vision that you articulate for this sort of empowered human with AI as, you know, complimentary tool is a vision of the future I would like to believe in. I think I'm gonna come back in a few minutes and challenge how sustainable I think that is over, you know, the medium term. But just to dig in a little bit more on the playbook first, what's the sort of happy story for the relationship between a software business and the the service customer? Like, presumably, you don't just show up 1 day and say, hey, I'd like to buy your company and transform it with AI. Right? So what is the what are the sort of steps, the milestones, the kind of points of buy in, the points where you kind of determine that, like, yes, this local services business actually might be a good target for us to really, you know, not just try to sell into, but actually, you know, marry for lack of a better term, you know, bring into the bring into the part of the the compounding story.

Marc Bhargava: (32:49) You know, Crexendo, the call center rollup is a good example where they got to 10, 15 different clients. They had a smaller sort of revenue base, they were selling in and had a high degree of automation. And that's when we said, okay, we should actually buy a call center that does, you know, more meaningful amount of revenue. And so a lot of the times, it's existing clients or pilot clients are very open to testing software. And then once they're starting to see the results, it's hard for them to fully implement it because they might not have the engineering team, they might not have the CTO. So you actually see a lot of the pilot clients or customers say, hey, this is the writing on the wall. We're seeing this automation, but we might not necessarily be able to fully implement it. We would like to sell to you. We get to cash out, but we also get to roll over. So if they're rolling over 30% of their equity and they think there's a 10 x from here because they've seen the power of the automation and the potential of the story, they're more likely to sell to us than, for example, to private equity because we'll let them roll over 30% and get another 10 or 15 x on that amount. So the more classic version of it is, you know, we go out, we get customers, we get pilots, we start working with folks. We also get a sense of which of these companies want the technology transformation, which of them get it. And we put a premium on that and we try to buy those style companies cause our goal is really revenue increasing. So as opposed to private equity that might buy a company, cut costs, improve margin that way, obviously find it, have debt financing. Our view is a little bit different of if we find some of these pilot customers, they can end up being the targets and especially if they're aligned with us on the idea of bringing in technology and wanting to grow revenue. And you'll see that alignment a lot of times in the amount that they're willing to roll over in these transactions, and that's always a really good signal. So we've been seeing this now across many of the different industries we're in where we're buying companies that are quite proprietary because we've built that relationship and we've proven to them the technology and they've been really impressed and they understand that for it to really be embedded in their company, we have to buy the company and it's a major shift.

Nathan Labenz: (34:49) I would assume that's like required as part of these deals. Like you're not letting people roll over nothing. Right? Or or like

Marc Bhargava: (34:56) Yeah.

Nathan Labenz: (34:56) Leadership has this. I assume you wanna retain leadership and they have to like have skin in the game.

Marc Bhargava: (35:00) Exactly. But it's been a really it's been really great to see 2 things. 1, folks who said, yeah, lots of private equity firms have tried to buy this asset we didn't really wanna sell, but now we really like your team. We like the growth approach. We like your venture backed. We like you're not using debt. We think this tech transformation is real. We want to sell to you and we want to stay on board, we want to AI enable our workforce, we want to do all these things we really couldn't do alone and we buy into the upside and we want to have the second part, we want to have this larger percent rollover than you traditionally see in private equity And so folks are, you know, really pushing for that. So it's been I think our secret sauce, 1, is in these hybrid teams of technologists and industry experts. And then 2, is proprietary deals and the assets we're going after. We're building these relationships through trust, through tech transformation, and we're getting to do proprietary deals. And so that combination of people arbitrage and deal arbitrage, I think, is a a really special alpha for us.

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Nathan Labenz: (39:20) Can you tell me about the process of working with early, like, design partner customers? And I specifically have a couple things in mind here. 1 is, like, how do you think about the faster horse problem? You know, there's always this debate, and it maybe can be, you know, answered with a sequence. But there's always like, what does the customer want? Oh, I'd love to have this go faster or not have to do this task or whatever. And then there's the maybe this whole thing could be transformed. Like, maybe it could be structurally different. I often use a customer service. I have a talk on AI automation, and the example that I always go to there is just taking customer service ticket. In many organizations, the first thing that might have to happen with a customer service ticket is prioritize the customer service ticket. And so that's something that you could probably automate. You can, you know, think about what are the criteria and you can, you know, talk to the people that are on the ground doing it. And you can through interviewing and, you know, looking at a bunch of examples, you can kinda draw out of them, like, how are they actually doing this? I find that that's often 1 of the stumbling points actually, especially if if people have to you know, if if these services businesses are trying to do it on their own, like, they don't necessarily know why they're doing what they're doing in many cases. So it it needs to be kind of drawn out of them, Like, literally eliciting the chain of thought from the human is like a, you know, a key step in the process. But then there's also the idea that like, well, geez, what if AI could respond to these customer service tickets? Like, we might not have to prioritize them at all because we could just respond to them all immediately. Then this whole prioritization step becomes, you know, kind of a moot point. So how do you think about that as you're, you know, really getting just off the ground and trying to figure out, you know, is this is this a business that we wanna keep the bones of and kind of find these, like, you know, these automation opportunities within versus, like, when does something need to be kind of reimagined? And again, you know, that might be a sequence thing, but tell me how you think about it.

Marc Bhargava: (41:24) Yeah. Generally, we think about it by going in there and shadowing the customer and understanding all the workflows in their business. And so then we add up who are all the folks, what are their workflows, and we map out the hours at a company. Then what we do is we go through those workflows and hours worked and we look at where could AI automate some of these workflows. And that's trying to get us to this 30% of hours worked automation. So 30% of tasks can be automated. And generally, historically, over the last 2 years, those tasks have fallen into about 4 or so buckets. 1 bucket is customer service and customer support and frequently asked questions. And so if you have any business that has a lot of customer support, it's heavy on that. It could be a good potential industry to go after. A second 1 is the creation of presentations, copy, marketing, emails, businesses that have a lot of network stream, also high degree of automation there. A third is if the data tasks are very repetitive. So there's a lot of filling out forms or looking at sheets or an insurance is a good example of this, checking the box that this roof cave in, or no, did they fill in the paperwork? So that's kind of a third bucket. And now fourth, more recently is around logic and reasoning. So again with insurance, it's less automating forms now and more underwriting like can we make a suggestion of how to price this insurance coverage policy based everything we know. And as models are getting better at logic and reasoning and math, you can have more AI tools kind of with a human in the loop giving you kind of a quasi answer to what you might want to do or say. Legal is also a good example of this where now Anthropic and others have created models that are quite good at reasoning. And so a lawyer can say, well, you know, what cases am I gonna what questions am I gonna get on my case? And so you can start to do this like more human prep. So those are 4 very broad buckets, but areas where AI, as they stand today between LLMs and agents, can automate a good deal of things in those buckets. So then when we start going through these, you know, 70 industries, we and we break them down and we actually get on the ground and our companies that we've helped incubate are actually out in the field talking to clients, We are mapping out how they spend their time and then we're overlaying where does that mapping fit with the buckets we've identified have a high degree of automation. And so that then really helps us underwrite kind of what is the opportunity in this space, what is the opportunity if we were to buy this client, and instead of having it as a customer, have it as a company we own that we can automate large portions of, that we can take on more revenue, that we can create more free cash flow from. So that's the strategy we go in and implement. And that's why it's important to have people who know the industry, know these clients, can get us in the door, but also folks that come from these really strong applied AI teams. They know what best in class kind of automation looked like at a ramp or at a rippling or at a Figma or a place like that.

Nathan Labenz: (44:14) And you do that initial mapping even before starting the company?

Marc Bhargava: (44:18) Like, that

Nathan Labenz: (44:18) that was part of the 70 to 10 cut down?

Marc Bhargava: (44:21) Yeah. So we do it at a certain level, from the 70 to 10 cut down. And then once we've actually given the first round of funding, in order to get the second round of funding from us, then the team needs to go out and really get the data. And so they get the really granular data from their customers, from their pilot clients, then they come back to us, present that data, then we give them the capital to actually go buy a target, oftentimes 1 they're already working with. So we do a first round of analysis in mapping it and looking at the industries. As we start placing bets, then of course, the team has an even higher bar to build an AI native software product that is very useful and to go and win an initial set of clients and then to come back to us and prove to us the automation with those clients.

Nathan Labenz: (45:02) It's obvious why the owners of these businesses would be interested in this. How did the staffs at these companies react to this sort of transformation? What what have you seen there in terms of excitement, fear, you know, rejection, you know, I don't know, quitting in protest, sabotage? I'm sure you'll like it it has to run the gamut. And and I wonder if there's any sort of general purpose lessons that you would, you know, share with with just business leaders in general, regardless of whether they're entering into a structure like this. General purpose takeaways for like AI transformation?

Marc Bhargava: (45:40) Yeah, absolutely. I think there is, you know, at first a bit of apprehension around any new tool or technology. 1 of the really distinctive things of what we're doing is they're saying we're coming in here to AI enable the workforce. So we are not coming in here to replace the workforce but to AI enable it and to take on more customers and have more revenue and have a growth story. And I think it comes across as pretty authentic because we're generally not using debt especially in these early transactions. So we don't have these interest payments to make which means we have to cost cut, which means we have to look for savings. We're coming in and adding costs in the form of software subscriptions, engineers, but we are placing this bet that an AI enabled workforce can do a lot more work and have a lot more revenue. So to the staff or to the folks working there, it is a challenge of you know we have to get people up to speed on this AI technology, we're going give you these tools but then of course you also have to go out and get more clients and get more revenue. Generally though, we're saving them from the most mundane or boring parts of their day or the most repetitive parts of their day. So we are freeing them up to do generally more interesting, more demanding and more human to human kind of interactive tasks. And so once the process is going and you know as you're rolling up more and more companies, it really becomes much easier because we many times once you've done a first acquisition, we have those teams especially the leadership team but even people at those companies talk to the second team and help win them over for the second acquisition. And so that I would say is a really strategic part of it as well. Once you've done a few of these, you have the stories and the case studies to tell I think that really assuages, you know, a lot of the concerns like you mentioned like, are people going to get fired or is all of this going to get automated? It's like, no, we've done 2 or 3 of these, here's exactly what happens. We give you the software and tools to automate your mundane tasks, you get to do harder workflows, you know, it ends up being better for the company, your shares are worth more. And so it's very compelling I think compared to private equity alternative especially.

Nathan Labenz: (47:33) Are there any like rules of thumb or like an example from the ambiance Cleveland Clinic partnership was Cleveland Clinic required that every doctor in their system, I think, use the AI scribe once. And I thought that was really interesting because obviously these AI tools, you know, have a have an error rate. So that sort of projected confidence, it was like a minimal ask. It says a lot about like how reliable, you know, you might need your product to be if you're gonna make those kinds of relatively minimalist demands on your team in terms of, like, trying it out. In that case, they from that 1 interaction, they report that they now have 75 percent of their 4,000 doctors using it all the time across 60 specialties. So presumably that's like a product excellence translated to like this is amazing sort of story. I assume 1 of the advantages of being like more tightly integrated is like you don't necessarily have to get to that level before you can, like, kind of push the adoption. But are there any, yeah, like, mental models, rules of thumb, frameworks for how to think about how far the product has to be along versus how much to push people to use it even if it's imperfect? What what are the sort of managerial takeaways that you've seen?

Marc Bhargava: (48:54) I think a big learning for us is to find the assets and the companies that have a pull factor. So if you're purely underwriting on a pre PE style, maybe you're putting in a new management team, maybe you're cutting costs, you're doing all these things. You that's kind of what you're looking for in the asset. For us, it's really important. Does the company we've now incubated a company. We're going to buy 1 of our clients. Does this client really want the technology transformation? Have they tried different products? Have they tried hiring people? Have they really pushed through at the leadership level that they want technology and AI? And we will pay a premium or our companies pay a premium in the acquisition to have the right partner and the right partner mindset. And so that's 1 thing that's extremely important is from the top, from the leadership position, this is something they've been trying. And then there are all kinds of techniques once the acquisition has been made to of course have it not just leadership's buy in but the entire company. And that honestly is not changing up the workflows too much, keeping it similar. So like in HOA management, you know you can email saying, hey create a version of the HOA board deck for me and it will create it and send it to you via email. So we're not trying to onboard you to some new software tool. In that case, know, you're using your existing text or email workflows. So earlier question you had is like, do we totally reimagine it? Not really. We wanna keep a lot of the same workflows, but we've already identified behind the scenes a huge part of it, 30% plus can be automated. And so we're trying to keep folks as workflows very similar but use AI with sometimes them not even knowing that you know what's the large language model doing behind the scenes and how is it writing this copy and all that stuff. It feels like they're just interacting with an agent that is able to send them a board deck that they need for tomorrow's meeting, etcetera. So we're generally mapping over those same workflows and so it's not a big change many times for the workforce to actually learn and adopt these toolings because our companies are building the tooling to be very similar to what they do day to day.

Nathan Labenz: (50:48) Yeah. That makes a lot of sense. So there's not a I mean, this sort of speaks to I think another major question right now, which is like when trying to get value from AI, should we be creating what I traditionally refer to as smart workflows where you have a sort of step by step process that is human designed and each step may or may not be an AI step, but the AI steps, obviously you can optimize your performance and make sure they're working well. 1 way I characterize those is like, you'll know you're successful when you don't have to check every single output anymore. And I think for a lot of tasks you can get there today. And then there's like this new paradigm of AI that is the more agentic, like the AI chooses its own adventure, you know, maybe within some constraints, but right, like Claude code, go write this thing or, you know, know, some of these calling agents like call this person and try to sell them a subscription or whatever. These sort of like open ended, you don't know how many rounds it's going to be, you don't know what the tool calls it's going to choose at any given time are going to be, don't obviously don't know what the environment is going to give How much of that are you seeing deployed in your companies?

Marc Bhargava: (51:59) So on the services side, I think it's gonna be more of the first case that you outlined where there are 7 or 8 steps and some of them will be automated and maybe a AI agent or does step 1, a human does step 2. And so it's gonna be these hybrid AI enabled workforces where there are multiple steps and some are fully automated, some are not. But in services, think there's still gonna be a huge portion of lawyers, accountants, to your example, doctors, and instead there'll just be different steps that are automated but there'll be key people and it'll be this hybrid AI enabled workforce. That is where I view the world ending with services for many reasons like 1, the technology is not there but maybe even more important, many of these spaces are highly regulated so you can't just have an AI doctor even if it was great. And many of them are very human and emotional and it's, you know, a lot of it is talking through learning medical news versus just what's the diagnostic and the prescription, etcetera. So I think in most services industries, it will be the hybrid approach, and that's the 1 we're employing. I do agree with you though that in certain industries, it could be the full agent automation approach. Go and here's Airbnb, build me Airbnb but for cars instead of for locations, and a swarm of agents will be able to code it up and you'll have sort of an Airbnb for cars that's built for you. So there will be I think some industries that are maybe fully automated. Certainly the holy grail right now is the automation of coding. And you have obviously OpenAI and Anthropic working on that but also Microsoft and Google and then Cognition and Factory and even I think Cursor and others are moving to that trajectory. And so coding is a great example of obviously a massive industry where maybe building websites and products and platforms will be very close to fully automated using agentic swarms. But in the industries I'm focused on in applied AI, especially a lot of the services industries which we discussed is really large part of the global economy, I think it will be a hybrid approach because of the human element, because of the regulatory element, because of the complexity and sort of gray areas of tasks. Writing code or doing a math problem generally doesn't have a kind of got to make a gut decision on something while filing your taxes and how you classify something, what's the risk you're willing to take in an audit, etcetera, does. And so we're pretty clear on here are the industries where we think this hybrid approach makes sense, and we wanna incubate companies. Maybe they have a role component, maybe they don't. And then also investing in traditional software and agentic companies that we think could maybe fully automate an industry as well.

Nathan Labenz: (54:30) Yeah. 1 other question I had that prompted is how much do you build versus buy? Because I could imagine like a lot of you know, another episode coming up with the CEO from Intercom. I can imagine, like, a lot of these companies might ought to just, like, buy Intercom and, you know, have them do, you know, a big chunk of stuff. Like, it seems like there's a bit of a tension between we want to focus on automating the sort of auxiliary, you know, somewhat less core tasks from the verticalization or vertical specific approach. It feels like the more, you know, true to the vertical you are, the more you're kind of focused in on the core and the more you're kind of trying to do the stuff that, you know, the the pros don't wanna do, the more you're kind of entering into more of a horizontal, like, intercom type scenario. So maybe there's just enough middle ground there that there's still like plenty left to big, how do you think about that spectrum?

Marc Bhargava: (55:28) There's a lot of middle ground because there are many point solutions out there in a lot of these industries, legal, accounting, IT, customer support call centers. There are a lot of great technology companies and a lot of solutions. Generally, they're point solutions. Generally, it's not a full platform play. Generally, they're not customized, and generally, they're not trained on your proprietary data. So there's still a lot left and that's why at the core of these transformations is staffing a great backing great founders who are great technologists because there's a lot to do in terms of stitching together different point solutions. There's a lot to do in terms of creating custom solutions. There's a lot to do on fine training models and making it unique to your dataset. And so all of those tasks require a really deep understanding of AI and of engineering, and we generally have seen that kind of folks who are coming from applied AI positions make really good founders in these sort of companies because they absolutely can assess and buy things off the shelf, but then they're also figuring out which model we should use, where, how to stitch things together, do we wanna customize our own model, take an open source 1, train it against our unique data. You have to have a lot of really expertise in AI and in building software and actually doing things. It's not just a research question. And we kind of tail off of all the amazing improvements made on the research side by an Anthropic or an OpenAI or a Google or others, whereas they improve their models and can do more, we flow that through and we get the benefit. For example, with legal, at least, found that, you know, OpenAI is really great at summarization of case text, for example, but the Anthropic model is better at reasoning and asking difficult questions that you might face in a courtroom. And so having kind of both OpenAI and Anthropic every 3 to 6 months improving their models, you know, we flow both of those through at UDIA to deliver value for the end client, is the in house general counsel at a Fortune 100 firm. And so you have to have this technology and AI expertise to understand the different models, where to use which 1, and where there are also point solutions that are already built, you know, you have to have the technical expertise to evaluate them and integrate them as well. And so we still think you need a really amazing kind of technical CEO or CTO or founding team to be at the core of these projects.

Nathan Labenz: (57:45) How much is about customization versus standardization? Like, if you buy 10 businesses, is the idea that you'll have 10 different fine tuned models that, you know, represent the sort of unique flavor of those different local businesses and, you know, a bunch of different custom workflows that kind of recreate the idiosyncratic ways that they did it? Or it seems like maybe more of the economic promise would be in the standardization where you'd kind of

Marc Bhargava: (58:13) say Yeah. If you buy 10 IT firms, 10 call centers, 10 accounting firms, 10 legal service firms, 10 HOA management firms, The solution base that we're building for each is the same. So there's some slight customization, but it's extremely similar. The workflows and accounting, the workflows and MSP IT, the workflows for HOA management or property management are very similar. So we're backing a team, they're the holding company, then they're going and they're buying maybe 10 HOA management firms, for example, or 10 IT firms or 10 property management firms in the case of dwelling, 1 of our investments out in London. And the technology you're building is really very similar across those 10. We don't see a huge difference there. But what we do see as unique is as we buy these companies, we do get their proprietary data, we can feed it in, and we can train models, and we can adjust our software based on having proprietary data and much faster feedback loops on what the client wants. So ultimately, we think our product is, you know, much better on market than someone who maybe isn't doing a rollup strategy that does not have access to their client's data, that does not have as fast a feedback loop. This is a really fast way to scale out Applied AI in our view.

Nathan Labenz: (59:23) Yeah. It makes I mean, I think the I will bet on you to do well and I will also bet on the customers of your portfolio companies to be very happy with the level of service they're getting. I'm a little concerned about how much disruption there might be along the way, but, you know, I I'm optimistic on some level that we can manage that disruption. How many of maybe it varies again by vertical, but it strikes me that, like, you're not gonna acquire that many companies. You know, how many I think the whole market goes this way. Right? Like, maybe with very small sort of residual that's just, like, high I mean, I, on TikTok the other day, I saw an artisanal pencil sharpener. I don't think there's too many of those out there, but there's apparently at least 1. People are constantly debating in the comments whether it's a bit just for TikTok or whether it's actually, you know, people are sending this guy pencils to be artisanally sharpened. Whatever. There's a long tail. Like, there's all there will always be an artisanal pencil sharpener, you know, I suppose. But leaving that aside, it does seem to me like all these markets over a not super long timeline are gonna go this way. And, you know, as Tyler Cowen famously said not too long ago, it's the people that are the bottlenecks. You are the bottleneck. You are the bottleneck. So how many of these businesses, these local service businesses that exist today, you know, you're sort of Labenz, Bhargava, and, you know, associates, how many of those make it over the hurdle and, like, get into 1 of these hybrid things, and how many are just, like, ultimately out competing and go out of business?

Marc Bhargava: (1:01:03) So I think right now you're already seeing a consolidation, ignoring even AI. There's a consolidation in accounting, for example, and the consolidation happening today in many of these industries is being driven by private equity. Because it's saying if we buy 2 firms that do the exact thing, there's obviously overlap. Could we remove some of the overlap people? This saves cost. With this cost saving, we could pay interest payments on our debt and so we could finance these with debt and that's the model. So you're seeing a lot of industry consolidation in many of the areas we're going after already today being driven by private equity. It was especially driven during the period of low interest rates and it's continuing to be driven today as well And just more kind of push on the cost savings. And so we are in some ways offering an alternative to that. If you are running a IT shop, accounting shop, HOA management, you're probably starting to feel the pressure in the industry and you have private equity folks now saying, hey, we can consolidate you. You have the option to sell to them and it's probably really gonna change the profile of your company and maybe even the management. So that's 1 option. We are trying to come in with an alternative option which is same management, same team, let's AI enable it, let's go for more revenue rather than less cost cutting and that's our path to higher margin. And we're only right if the AI enablement works because if we're asking you to go and take twice as many clients with the same group of people, that's just not gonna match up unless we're really giving you the tools so that each person can do twice as much work. Which generally means each person is automating the more basic stuff and focusing on the harder piece. So we really need to get this AI automation right in order for our thesis to play out. But the good news is over the last 2 and a half years of doing this, we are seeing we are getting the AI automation right. And every 3 to 6 months, we are seeing we're getting it even more right as more models are released to have more capabilities. Now last fiscal year, 150,000,000,000 was spent on AI, R and D, and CapEx by the MAG 7 alone. This year, it'll be closer to 250,000,000,000. So there is a massive investment. There are obviously a lot of the smartest minds working on this technology, and it shows in the results. Like, we are seeing an improvement in math and logic and reasoning and automation. And so we think our pitch is the right 1, which is you can come in and change these companies to grow rather than come in and change on the cost cut and consolidate. And that's obviously what we're fighting for and why I'm really passionate about what we're doing at Creation and our kind of vision and our mission. And I think it's a different AI story than maybe a lot of people are used to hearing. But anytime there's a new technology, you're gonna have people say, you know, this is gonna be really bad. And there are areas we have to be really careful about and we need to think through. But I think overall, this will be a benefit for humanity and to the companies that are AI enabling their workforces.

Nathan Labenz: (1:03:43) Yeah. I'm sold on basically all of that with some tail risk that, you know, might imply that it's not a benefit to humanity. But if it's not a benefit to humanity, I don't think it's gonna be because the mundane task automation doesn't pan out. I'm pretty confident we're gonna get that.

Marc Bhargava: (1:03:58) Yeah. Well, there's some AI enabled accounting software that takes down humanity, for example. So there are all kinds of other questions perhaps that are outside of my expertise. But I think what we're doing for services industries and really honestly, day to day what people do, like getting rid of a lot of the more boring, mundane, repetitive tasks, freeing folks up for other tasks. We've been hearing, you know, from the companies we've been acquiring that that is something that they're excited about.

Nathan Labenz: (1:04:27) Come back to the more extreme scenarios again in a second, but just in terms of the sort of scale of the re realignment, if you will, you know, maybe euphemistically of the markets that you're entering. If I had to put a number on it, would be somewhere in the like 10 to 20% of companies that currently are out there, you know, operating as traditional local services firms might get over the hump to join 1 of these compounders. And then for everybody else, it's like, why bother? Right? Like, leadership's not that great. The pull factor's not that strong. You know, you kinda got your head in the sand or whatever. Maybe we'll just, like, take your customers. Maybe we'll hire some of your best people. But I guess my expectation is something, you know, the other 80 to 90% of those firms, I think, just not gonna make it. Is that too and I don't know. You've been you're striking a a tricky balance here on the on the communication front, but, like, to some degree, you might wanna be kind of it's in in some ways in your interest to be clear about that if if it is what you believe because it would be, like, quite favorable to your deal terms or, you know, ability to, like, get people to move fast if they feel like they wanna be in that 10 to 20% and not Yeah. Not standing without a chair when the music stops.

Marc Bhargava: (1:05:45) Maybe that'll be good for deal terms, but I think the reality is, a majority of firms could join these sort of AI enabled rollups because the tools we are building should be super intuitive. Like, this is very different than your traditional software model where you have to go in and you gotta learn a system like SAP or Jira or even Salesforce and, you know, it's difficult to do. The way we're actually building these AI native toolings, especially agents, is so much so that you can put it in your own workflows. And so it should be easy enough for most companies to use is kind of the first point. And then the second point is there is a lot of churn going on in the workforce. You have a lot of baby boomers retiring and you have a new generation joining. That new generation is already fluent with chat GPT, with using a lot of kind of online tools, certainly with email and Slack and other things that we integrate really deeply with for our agentic workforce, etcetera. And so I think, know, 1, the tools being built are actually very similar to existing workflows and most firms can use it. And number 2, while there are of course some extremely old school firms that are still using fax and other things like that, we absolutely have seen that as well. Part of that is also just a generational shift, right, where that generation is kind of retiring in the next 10 years or so. And the folks coming out of high schools and colleges today, many of them use ChatGPT as like an operating system, not just a search piece where they're getting life advice and in some ways they're even more AI native. And the engineers today are using Cursor and Windsurf. And so I overall think a majority of firms, even in the services space, will be able to participate in this if they want. There are other alternatives too though. You know, folks, especially on the leadership side, might feel like we want a 90% cash out. We wanna sell to private equity, and we wanna take the money to a beach. And that's kind of how capitalism works if they wanna sell their company that way. But we're we have a very different pitch which is, you know, take a second bite of the apple here. Transform what you've built for the last 30 years, you and your family. Transform it with AI. Make it way more effective. Grow the business, roll over a significant amount, and be part of an alternative to that sort of PE consolidation. And we think that really resonates. Honestly, I think it can resonate for not just 10 or 20%, but for a majority firms. And certainly every year, we're seeing it resonate for more and more.

Nathan Labenz: (1:07:58) Okay. So here, I'm gonna push you on the just how crazy does this all get. But maybe we can start with you went through 70 industries, you know, winnered it to 10 based on where you could see a path to 30% automation. What are the things that can't be automated and, like, what are the barriers? I mean, to some degree, obviously, like, operating in the physical world, like, we don't have robots yet. We do have, you know, quite a few humanoid companies that are, I think, on track to maybe change that in the not too distant future, but clearly it's not here yet. Then we have this notion just from the AI companies that there is gonna be the drop in knowledge worker, and it seems there that 1 of the big barriers is context, like lack of context or lack of ability to handle enough context, etcetera. How do you taxonomize what can't be done? And and do you have a map for, like, what the AIs specifically can't do that makes those things not yet accessible?

Marc Bhargava: (1:09:00) There are kind of 2 bigger buckets here. So 1 is which you already alluded to where, you know, a lot of the automation today is more in these continuous tasks that I spoke about, like data entry, analyzation, creation of presentations, those sort of things. It's not in, for example, you know, maybe plumbing and roofing and HVAC and a lot of like in person work, firefighting, etcetera. And so there is a lot that still happens in the physical world where there might be some automation on maybe doing routing, scheduling, payments, all of that, but it won't meet our 30% criteria of like, look, this automation will really transform how you are a firefighter, for example, at least not right now. So 1 bucket is that a second bucket is we don't want to go and buy up firms that are have high churn. So you know, when we come in and we do AI automation, if think you of something like the digital ad agency or PR agencies, lots of folks use multiple ad agencies and multiple PR agencies. So if we were to go and buy 1 and start transforming it with AI, if there are any hiccups along the way, they might just ditch us because it's a very kind of using multiple providers and it's very high churn. And so we also another big criteria is we're only doing this in industries where there are really good business dynamics, where the customers have sticky contracts, maybe they're multi year, there's very low churn. So when we come in and we start doing AI automation, it's not always a 100% smooth. And so we have to have the benefit of at least 6 months or a year to kind of implement these things, for our base workers and for the end clients as well. So those are 2 things that really have struck out a lot of different industries where either there's a big real world component or business economics around churn are are kind of dangerous. And then a third of course is looking at how good is the AI as well. And so, you know, 2 years ago, you wouldn't be able to use AI to automate a lot of what we're looking at today like in insurance and even looking at insurance underwriting, like that was just AI totally wasn't there. So there are also other industries where, oh, maybe 1 day AI could do those tasks, but the current models are worse than humans doing it. So you're not adding any real value there.

Nathan Labenz: (1:11:15) So do you not expect to see this drop in worker? Because, I mean, it does strike me that there's, like, several data points that I see pointing to big tech takes all. A friend of mine coined the term the big tech singularity. And I think that is basically my mental model of what's gonna happen. Not necessarily, of course, like, that they'll own every customer relationship or whatever. You know, when you with Google and Facebook just on the ad platform, of course, they had, like, all these, you know, sort of local agencies and sellers and people, you know, kind of translating the platform value prop to the local business. But talk about a business that's had a lot of margin compression, you know, those facilitators of, oh, I can do Facebook ads for you, mister local business owner. That's been a really tough spot to be in. And, you know, in medical, for example, we've got, like, multiple studies that have shown that the AIs can diagnose better than the doctor and can even like prescribe better than general practitioners at least these days. There's been some that have shown that like the hybrid, you know, there's always this like refuge that people want to take and well, it'll be the doctor and the AI that'll work together, but there's been some suggestive evidence at least that like, the AI is actually performing the best, then comes the hybrid, and then the doctor alone is worse. I just also saw a data point from Harvey the other day where they put out a a stack ranked leaderboard of on their own, like, big law bench benchmark that their own fine tuned model has kind of fallen behind, like, 8 other foundation models that just seem to be winning on the bitter lesson and generally, like, scale continuing to pay off. There is still this issue of like the drop in knowledge worker doesn't quite exist because it's hard for them to absorb all the context, but that to me seems like something that probably gets solved. I guess I wonder like, do you think that just never gets solved or if it if it does get solved, how do we not end up in a situation where it's like, okay, now we're just dropping in these AI knowledge workers into this environment and, know, again, you sort of have a now you have a very literal complement versus substitute dynamic going on.

Marc Bhargava: (1:13:25) Yeah. I think for us, you know, it'll be a lot of individuals will manage teams of agents. And so then you can say, is that replacing, you know, the the folks they used to manage? Not necessarily. Like I do think that the overall economic growth and GDP output can grow pretty significantly. And so we'll kind of have this land of abundance where people are still doing jobs but with teams of agents below them, but there are way more jobs to be do done because it's such a larger global economy. And I do think that pretty much every technology shift we've seen has begged the question of isn't this going to mean less jobs? And it has meant less certain jobs, but the overall economic output has grown. And with that, there have been kind of newer jobs and different opportunities as well. So it is very hard to predict and lay out exactly, like, what's the overall economic growth? You know, what does that mean in terms of new jobs? Which jobs are lost? Which jobs are gained? I think we absolutely have to be mindful of that. But at least as far as we're seeing in the cases of hybrid services, software, AI enabled, you know, the strategy we're doing is not around really replacing jobs. It's freeing people up to do other tasks. The number of tasks that can be automated certainly are growing more and more, but then there's other opportunities, especially like client to client services and lots of businesses, you need to have a relationship. Even the doctor example, or the nursing example, you know, in nursing, we incubated a company called Hippocratic, which is an AI native nurse that we work with a lot of healthcare systems to get proprietary data to be able to help build this company with the founder and it's part of our creation strategy. There could be 10 times more nurses in this country than there are. Right now we don't do preventative medicine calls, we don't follow-up after surgery post 6 months or 9 months, there just could be a lot more nurses and there's an absolute shortage. So many of these industries that we're going after, there's a shortage of people. We need to give them the tools to match that shortage, and I think a lot of that will be AI enabled agents, for example, working under them. And so that's the reality of the view we see on the ground today. Can we debate if, you know, robotics is certainly getting better and enabled robotics is getting better? Are we gonna eventually go to this other world, etcetera, etcetera? I mean, that's a very hard question to ask, and there's all kinds of technology and automation improvements. I would just say, me personally and General Catalyst, we are very focused more on what are the opportunities in front of us today. And we have been really mindful of the companies we back. We're a large investor in Anthropic. We think they do an excellent job of thinking about the risks around AI and AI safety. And so, you know, responsible innovation, AI safety, those are really key to our mantras and the companies we're investing in. And we think like every other technology, as we adopt this, there will be challenges. It's the mindset you bring to those challenges.

Nathan Labenz: (1:16:13) Yeah. I sort of have a vision of, that classic Simpsons episode where Homer's just, like, hitting the enter button at every prompt as the nuclear power plant bird bill boots up until such time as he finally decides to put the the dippy bird in to be hitting the button, and then he kinda just leaves. The plant, I think, does end up having a meltdown as a result of that. But this sort of managing agent future, I'm a little bit like, you know, we we're talking the morning after the grok 4 presentation.

Marc Bhargava: (1:16:42) Right. Mhmm.

Nathan Labenz: (1:16:43) And I don't know. It feels like it's coming at us really fast. I mean, I'm like, I want everything you're saying to be true. I and I genuinely believe that the consumer surplus is gonna be amazing. Just the quality of service, the access to expertise, the democratization of that, think is all, you know, just super exciting upside. But then I see Elon saying things like, new science next year. And I'm like, I don't necessarily think I can count this guy out because for 1 thing, you know, they keep climbing these you know, there's no benchmark seems to be, you know, immune to their ability to climb the hill. And as they make this transition from other training signals to just, like, learning from reality itself, you know, and they start to equip the models with, like, the same high powered simulation and other tools that their best engineers at Tesla and SpaceX and Neuralink are all using, and it starts to actually compete with them to, you know, redesign a part, you know, or, like, find a find a deficiency or, you know, can we eliminate this part? If the if those systems can start to do that in the next year, it seems like we are in for a really choppy ride even if the, you know, even if the consumer surplus story, you know, is is totally true, which I I do think it it is. Is there a ratio of, like, how much you spend on AI to to how much you save on labor that you I usually tell people expect 10 to 1. But do you have a rule of thumb for, like, if you can take some task and automate it? Like, what's the savings? And, of course, that could be. You could do 10 times as much at best. But what is that ratio?

Marc Bhargava: (1:18:29) Yeah. We're not really focused on the savings, to be honest. So we're focused on grinding.

Nathan Labenz: (1:18:34) If you flip it and say, much more can you do with the same inputs? What Yeah. That ratio.

Marc Bhargava: (1:18:37) There we we have an objective. I think, ideally, in a 3 year period, we would like folks to be with the 20% cost increase, be able to double your revenue. So that would be like a broad target. But in 3 years, if we come in, we spend 20% more than you spend today to make sure you have the right software, the right engineers, the right AI tools, increase the cost 20%, can we increase the revenue a 100% in a 3 year period? And if we can underwrite, yes, we think that is a plausible case, then we think buying that company could be a good investment. But we are not looking at AI as a cost saving even though it of course can be that. That kind of limits your upside. We're looking at it much more how do we grow and have venture like returns and fast growth and scale by AI enabling our workforce to take on more revenue. So that's so maybe our mindset is a little bit different on

Nathan Labenz: (1:19:26) that metric specifically. Is there anything that big tech could do that you are afraid of? I mean, 1 that I always point to is and they swear they'll never do it right up until maybe they do it, is not allowing you know, basically using their best models for their own products first. Right? Like, so far, we've had this sort of generally even playing field of chat GPT has a model and the API gets it roughly the same time, maybe even a little before, whatever, but it's, like, pretty much parity. But you can imagine a world where Claude 5 and Grok 5 and GPT-five are all proprietary products only for some significant period of time, if not indefinitely. And that could potentially really shift the power. Now from a consumer surplus side, like, they probably still can only charge me so much a month because I've got 4 or 5 of those options. Of course. Yeah. How do I how does that play into, like, the app layer? It seems like it could be a real problem for the app layer.

Marc Bhargava: (1:20:27) I think that's true if there were 2 or 3 players. So even 2 years ago, you could argue OpenAI in the lead followed by maybe Google that kind of fumbled the back there since they were the OG with the transformer paper, but still credible and Anthropic, which have got many of the early OpenAI best folks. And, you know, there were 2 or 3 players. But then x AI came in and enter stage left, DeepSeek, and Alibaba, and all these other folks. Now I don't think there's a world where, you know, because you have 7 or 8 pretty top quality model providers, obviously Facebook with LLM is doing a whole refresh on talent, which is in the news. But there are just so many players now that the idea that all of them are creating closed models and shutting the door is extremely unlikely, and customers can always go and use deep seek. They're the Chinese labs are also quite good, and there's very good technology coming out of there. So there are just too many players at this point who are open source and, you know, it was a really like a shortage of researchers. 2 or 3 years ago, you could kinda count who had worked on these projects because they were really Google OpenAI and starting to be Anthropic. Today a lot of PhDs are being done in this space. There are a lot of other folks hiring. Facebook, x AI, I would say both have kind of caught up as well to a certain degree. The companies out in China and the straw in Europe. And so I think we're at a point now where there's a lot of kind of AI talent, AI researchers, AI companies that are creating these models for all of them to turn off and go close model. Maybe we can trade that on polymarket or something, but I would put the odds at like sub 3% there. So

Nathan Labenz: (1:21:58) Yeah. Okay. Yeah. I mean, it's the dynamics do change a lot from 1 to 2 to 8. Exactly. Yeah. There could I mean, some other interesting factors around just like safety, reliability. I mean, there's a whole there could be some other factors that could make it shift, but I do think I I tend to agree on a purely competitive market basis. There is a strong incentive to try to win the API market, and so there's always a even if you have something, you know, better in reserve, there's always a incentive to leapfrog your competitor and, you know, try to win share. Why are you telling this story publicly? You know, 1 first rule of having a great investment strategy is keep it yourself. Right? Like Yeah. You know, coming here and talking to me, you're just talking to a bunch of AI obsessives. So maybe there's like a talent angle, but like what I've also seen you on CNBC. How do you think about the public comms that you're doing?

Marc Bhargava: (1:22:52) Yeah. We've decided to go public on it for 2 major reasons. The first is to continue to attract amazing founders. Like, we want people who are leaving Applied AI programs or research labs and thinking about building a company to consider working with General Catalyst and to think about we are the firm that can help you go to market in the most different ways. Like for us, we're just hyper focused on how do we work with the best people, and we think that's really the thesis that'll win out. And we need to get the message to the best people, and a lot of them are watching this show or watching CNBC or other places. And so we definitely, the talent angle is it. Like and but I wouldn't minimize it. It's the most important part. Like we can't incubate and build these companies and do the strategy without the right people. And so that's really, I think, 1 part of it. And the second part of it is to push back on kind of the doomsday AI scenario or the private equity consolidation play and tell people who may be wanting to sell their company that there is a third way here as well, which is you could sell to a AI enabled rollup that believes in compounding, that doesn't want a 3 to 5 year flip, but really wants to give AI to your workforce, take on more customers, have you own shares in what we're doing, go public, and be compounders like TransDigm and Dana Her. So I think a lot of the press is also honestly directed at the companies and the people who are thinking about maybe selling their business to a private equity firm or to a strategic. You know, we would just say, hey, look, there's a third way here, and we'd love to tell you more about it and talk to you more. And at the end of the day, our secret sauce is essentially incubating companies with the best people and buying the best assets. So I think it makes a lot of sense to tell this story.

Nathan Labenz: (1:24:27) That could be a great place to end. Is there any other, aspect of a positive vision for the future? I always say the scarcest resource is a positive vision for the AI future. Any other aspects of your positive vision that we haven't touched on that you'd wanna share?

Marc Bhargava: (1:24:41) No. I think that's generally it. I think, you know, there's a good question and a fair question around there's just so much investment here being done in the space. Will it really pay off? And maybe we haven't seen that yet. And even last summer, so about a year ago, it sort of felt like a lot of the models were flatlining. I'm sure you were covering that as well, but we weren't really getting a lot of interesting updates. And then I think a lot of that changed honestly at the end of last year with the reasoning models and there was kind of more context and we've seen now the deep research product from Google and OpenAI and Anthropic release their own versions. So I would just say kind of on 1 maybe positive note is, you know, for a while it did feel like there was a lot of spend and maybe less results even in the productivity of the models. Today, we're seeing a reacceleration of improvements, which kind of you referred to as well. And so I think that's really exciting. And so it's a very, very cool trend to be a part of, and I really appreciate the folks like you for covering it. And especially as engineers and students in university or in computer science or thinking what to do next. I think having more and more people look into AI and especially applied AI, which I'm so focused on, is extremely exciting.

Nathan Labenz: (1:25:46) This has been excellent. I really appreciate your time and, all the deep dive answers. Marc Bhargava from General Catalyst, thank you for being part of the Cognitive Revolution.

Marc Bhargava: (1:25:55) Thanks for having me on.

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