The AI Opportunity That Goes Beyond Models

23:07And how do companies in this category build defensible most and particularly earn attractive margins as AI proliferates and costs continue to scale down? Yeah, well, why don't we come back to that one at the end? Because I think hopefully what you'll get from, it's not like we're just investing in companies that do labor and then the end. Modes matter, in fact, more than ever, because the one thing that's happened in software is once upon a time, there was a company called WordPerfect. And WordPerfect kind of like kept growing for a very, very long time. Or once upon a time, there was a company called VisiCalc.

23:42And then whoever had the most distribution said, I should do that, copies it, and obviously WordPerfect is Toast, VisiCalc is Toast. Lotus 1-2-3, which was the one that beat VisiCalc, that became Toast. But it would normally take five years for the bread to become Toast, and there is a very, very high level of prolific speed. I mean, now, Anish, David, and I, and Jen can go build a software product. We can VibeCode, if you've heard that term. We can go build software very, very quickly. It actually increases the peril for anybody who's built a software product that has an enormous margin pool, you know, your margin is my opportunity, while I can vibe code against your opportunity, it has to be very, very sticky.

24:24It has to have some unique competitive advantage, and data is often one of those. So if I work with every plaintiff law firm, or, you know, actually, why don't we go to the next slide here, and I'll just talk about salient a little bit. Sorry. So salient is in the Eve mold. And I know we also had a question about, like, what is the societal impact of everybody losing their job? I don't think that's actually going to happen very quickly. 98 % of Americans were farmers in 1789, and obviously the tractor made some of them unemployed and made them do other things. But most of what we're seeing, candidly, is not about eliminating work.

24:57I mean, I do think that the 3.5 million people that drive trucks at some point in time, we have a better solution than the truck-driving human. You have AI doing that. But most of these things, they're really, it's like you have cost here, You have value here. You would never hire a human where they are producing less value than their cost. It just does not make sense. But if you can now hire AI, effectively, you can hire AI where the amount of value that like the cost has just gone down, the value has stayed the same. You're going to hire a lot of AI. You're not going to get rid of a lot of humans.

25:32And if anything, we never know. This is so hard to predict. But what will humans do? I mean, like, there was no job of, like, product manager 75 years ago at a software company or designer. Like, all of these jobs that exist today, they wouldn't have made any sense to somebody in 1800. So it's hard to kind of pontificate on that. But a lot of the things that we're seeing, they're not displacing people per se. I mean, I know it sounds pithy to say software is eating labor, but really software is augmenting labor, or it's like all of these people that I can't hire, whether there's a job shortage or a skills shortage or whatever, I can now deploy people that will answer a phone.

26:08Like I would just never hire somebody to go answer the phone for me at 2 a.m. I would hire somebody at 4 p.m., but not at 2 a.m. It's just the value to cost equation is inverted. And kind of a great example of this is like the salient. Yes, they are going to people that collect. It's called auto loan servicing. So you go to an auto lender. They have to go make sure that they're collecting on their bills or if the person's in a car accident and the insurance carrier is supposed to pay you, how do I make sure that that insurance carrier is paying me on time and writing a check to the right person?

26:39In this case, because I have the lease, like they need to write it to me and not the actual, you know, not the person in their actual name. How do I do all of that kind of stuff? I would hire lots of people. I would train lots of people. A lot of these people hate their jobs because it turns out people yell at them all day and say, I'm not paying you back for this car or the insurance carrier keeps you on hold for four hours. And that whole music is just terrible. and you're going to want to kill yourself if you have to listen to that 12 hours a day. Like all of these reasons why humans don't want to do this or you can't hire humans for this.

27:09The key thing with Salient is not that they're saving you money. The key thing with Salient is that they collect 50 % more. Like this is the key thing because Ari, the CEO, he kept pitching like, I'm going to save you money, I'm going to save you money, I'm going to save you money. Like people like saving money. But if you go to somebody and say, I will collect 50 % more revenue for you every single month and I will make sure that you don't go to jail because none of these people that you hire that aren't very well-trained that have to listen to this horrible hold music for four hours a day. They don't say something that they're not supposed to say.

27:39I can make sure that AI doesn't do any of these things. Like, that's why that company is growing so explosively. It really is. It's much more about the value generation. I mean, yes, the cost is much lower. And this is one of the questions around, like, how do they figure out how to charge for the product? They went to their first client, had a$50 million a year call center with, I think, a 40 % to 70 % annualized churn rate per employee. So it's just, and not because they're firing people, it's just like nobody wants this job. So they now say, I will do it for you with software. I will give you a system of record.

28:14I will make sure that we're scraping every single new federal and state statute because what you say in Missouri is very, very different than what you have to say in California is very different than what you say in Iowa. We're going to do all of these things. No human can keep that in their head at the same time. It's like, all right, I'm talking to David. But shoot, what do I say? He's from Santa. He's somewhere in California. Oh, wait, but actually he's traveling to Kansas. I don't know what to say. Salient knows exactly what to say, and it knows how to say it in 21 languages, and that's why the collections rate is 50 % higher.

28:41So this whole category of we are going to make you more money and it's going to cost you less, it's a very, very hard thing to move away from. The key question for us, which I think is a very good question, is how do we make sure that we're backing the right one? And how do we make sure that Salient is not, I mean, this was my number one question when Ari came in. I was like, well, imagine there's a company called Talient and a company called Zalient. Why is it that Salient is going to be Talient and Zalient? And Ari actually had, Ari, the CEO, had a very, very good answer to this. Not to like, you know, he looked up on chat GPT, how do I answer this difficult question from a VC?

29:14But again, moats matter. We know exactly what script to say. This is an example of kind of a data moat. It's like, because we've done millions of phone calls, we know exactly what to say. We have lower latency on every single statute that comes out. They actually have a very, very good product that ingests every single law as it is even proposed as a statute in all 50 states. Sometimes it's at the county level. They're doing all of these things that make it so much harder to compete so that they will not lose a deal. Modes matter more than ever because you're able to create software so much more readily.

29:50Actually, maybe this is a good dovetail to this section, which is, does this then mean software becomes way, way, way more specific in certain categories and it doesn't need to win a bunch of different categories to become a huge business? And I think that might actually be a good dovetail to this theme that you want to cover here. Yeah, I mean, this is the thing that we don't know. I mean, like, we obviously have many examples of vertical software companies that have become very big. So Service Titan is a vertical software company. MindBody is a vertical software company. Toast, that's a very large vertical software company.

30:22Toast is designed for restaurateurs to run their business, to integrate with DoorDash, to pay their wait staff, to do like everything around operating a business. It's a vertical operating system. It's very, very hard to displace one of those. People would have doubted how big that could become. And actually, a lot of people did. It was very hard for Toast to raise their B round because people would say, well, I look at the restaurant space and like, you know, half these restaurants go out of business every year. I look at how much software they buy. Well, they don't buy any software. So therefore, this is a bad company.

30:51I'm not going to invest in it. And, you know, fast forward 10 years. But the reason why that happened was it turned out the business was much bigger in this case because they added financial services. And the financial services, we're going to do lending to restaurants. We're going to do payment processing for restaurants. And we make it very, very sticky because it's an entire software platform and there's no way for First Data or Global Payments or any of these companies that traditionally do software to go append, sorry, that traditionally do payment processing to append some kind of software solution.

31:21So that's why Toast, you know, people got Toast wrong. It's a very valuable company and a public company today. I think the same thing applies for I'm adding in labor. Like it's not just I do labor and then somebody does labor for a penny cheaper. I need to build some kind of system of record for you, some kind of vertical operating system for you so that you can't just go switch out for the cheaper player. And maybe this is a good way to kind of go into theme three here, which I'm very excited about. And I call this the walled garden. And this is really important today because if you look at, like, take a metaphor here where this amazing company called OpenAI shows up and they're like, hey, we're a vegetable farm and we're farming tokens.

32:00And we're going to sell tokens and we're going to charge for tokens to all these people out there that are building applications. So it plays out exactly as I talked about, like OpenAI is an infrastructure company. We invest in all these application companies. But then OpenAI is like, you know what, we should put some restaurants on our farm. Because a lot of people come to our farm, let's just have restaurants here. And then all these restaurateurs are like, wait a minute, like, you're selling me vegetables, now you're competing with me. Like, that's not good. The reason why I bring this up as an example is because it actually is happening, and it's a blueprint for how to potentially deal with a world where the source of the raw material is actually what is rare.

32:36So let's go to the next slide and I'll show you, I'll make this a little bit clearer. But as I mentioned, this is kind of like the world's second oldest profession. There are lots of cases where I kind of construct some physical property, I build a wall around it and I charge you for access to my property. You can do this in the data world as well. And I'll pick an example on this little bingo board here of FlightAware. I'm not sure how many people have heard of FlightAware. How do they get their data? And their data, by the way, what is their data? There's nothing proprietary about it. It's all public.

33:05You can buy an antenna on Amazon to receive, it's called ADS-B transponder data. So every single airplane after that Malaysian plane went missing has a little transponder on it that shows its height, its speed, all these different attributes on it, beams it down to planet Earth. Antennas can pick this up and figure out, you know, this tail number is at this place. I can buy one. It's free. FlightAware, I think they have something like 100 antennas around the world. They pick up all this information, and they can charge. That's a piece of data. Like, I can ask ChatGPT that. They don't know that.

33:37Only FlightAware knows that. Or PitchBook does this for funding rounds. Like, who knew what the Series B price of a company in 1992 was? Like, PitchBook somehow has that. Or LexisNexis knows this. CoStar knows this for real estate data. Bloomberg knows this for all sorts of exotic financial stuff. Like, in many cases, it's all free. Ancestry.com built their entire data mode by buying genealogical records from the Mormon church. All of this stuff is not available on ChatGPT. It's not available on Anthropic. Of course, they can license it. But the reason why I mention this is, what do you do with FlightAware data?

34:13Or what do you do with Bloomberg data? Or what do you, like, I'll tell you what I do with PitchBook data. I hire an analyst, and I say, analyst, go write me a memo about this company called EVE, and compare it to every other company in the legal space that had ever done something before. And PitchBook just sells us a subscription for here's every single Series B of legal tech companies since 1992. Okay, that's valuable. They can charge$20 or$200 or whatever they charge per month for that. What would be more valuable is saying, because they're the only ones that actually have that piece of information, they should probably charge$2 ,000 for that, which might mean, I mean, maybe this makes you nervous, we might need one less analyst because now we have a finished product Because what we don't want is we don't just want a subscription to pitch book data.

34:57We actually want to do something with it. We want to somehow take that vegetable, if you follow my metaphor, and turn it into a finished meal. One of my favorite examples here is Domain Tools. Domain Tools does, they have one thing which is very interesting. They run a whois query, which says who owns a particular domain name. This company has been around for a very, very long time. If I want to figure out who owned a domain in 1998, there's one place to go, and that's DomainTool. So, like, this model has been around for a very, very long time before AI. Very, very large companies exist in this space.

35:28When you add AI, it makes it tremendously more valuable. So I'll give you three examples that hopefully kind of hamers this point home. So there's a company called OpenEvidence, which if you use it, apparently two-thirds of doctors in America use this thing pretty much every week. OpenEvidence is exactly like ChatGPT. The interface looks exactly like ChatGPT, except you know who has exclusive license to the New England Journal of Medicine and every other medical journal out there? Open Evidence. So if I tore my Achilles, if I want to read about what I should do, all of the evidence-based care out there, I can go to ChatGPT.

35:59It's moderately useful. There's no reason not to do that. Open Evidence is so much better because they're the only ones that actually have. They've built, in this case, they found all the unique vegetables out there. They convinced the vegetable seller not to sell it to any other restaurant, and they have a restaurant that delivers the whole thing. Well, there's a 26-year-old company called Vilex, an incredible company that just got bought. The CEO was telling me the origin story of this company. He's from Spain. He bought up every single legal record in Spain. And why would you want to buy up legal records?

36:30Because, I don't know, Wilson Censini wants to know, you know, Spanish case law in case Andreessen Horowitz goes and invests in a company and he needs to figure something out. So Vlex would aggregate and digitize this information, sell it to law firms and other people that need legal information, pretty high gross margin, but, you know, very, very low scale and predominantly European in Spain. Then they were like, you know what, we should add AI to this. And apparently it quintupled their revenue. Now, why would it quintuple their revenue? I might love Harvey. I pay for Harvey. Amazing product.

37:00But if I want to have a finished memo for my client at 7 a.m., I can't get a paralegal to go do this. And I know that it needs to incorporate some element of Spanish legal data, like VLex is my only solution. and instead of charging$2 a month or$2 an article or$200 a month or whatever they can charge for the raw material. And what AskLeo does is it's a procurement product. So if I'm a company and every company, every employee at every company kind of hates their procurement department because on the one hand, the procurement department is supposed to save the company money by making sure that some rogue employee doesn't buy expensive widgets at an overpriced price from an unapproved vendor.

37:40But on the other hand, they introduce all sorts of complexity into the process. So imagine that I've got a contract from Deloitte to give me AI and somehow revitalize my company. Who has 50 other contracts from Deloitte where I can understand what I push back on? That is actually very, very useful proprietary information. I wish I could go ask ChatGPT for this, but they don't have the world's treasure trove. What is the information they will never get? They are never going to get 50 old Deloitte contracts. Where would you find them? I guess you could do a FOIA request or something, but you're not going to find them.

38:13And Ask Leo has these. So it just makes the product so much better. And go back one slide here. It's hard to say where we're going to find these things, but the most compelling of the ones that we found are it's like all the information is free. Just like ADS-B flight transponder data, that's free. But you find something that just like, it wasn't worth that much before because like what do you do with flight data? What do you do with who is record data on the internet? I actually talked to an entrepreneur recently. He was like, oh, yeah, you know what? I like to figure out historical subscriber data of YouTubers.

38:48It's like YouTube doesn't publish like how many subscribers Mr. Beast had on August 4th, 2017. Like, where would you find that? There's some company that collates that, collects that, and that's just, they're just selling the data. It's not available anywhere else. And, you know, these are some, we just published a post, I would encourage people to read it on like, you know, the walled garden, or we called it fruits of the walled garden. All of these things like creative archives, logistics, like you go to like some county recorder's office and you can see who owns what property record. But you have to go to the county recorder's office to find it.

39:22It's all free, but you can digitize that, make that available, and then add AI to that. And this sounds like, oh, just add AI, it's much more valuable. the reason why is because you were saying I have something that nobody else has there's a reason why people are buying this before because they're trying to create something that is of higher value at the end and you can now do this so go to every museum actually I just talked to an entrepreneur who found every old manual this is a great example found every old manual for like blenders made in the 1980s, 1990s you can buy this stuff for pretty much nothing on eBay, where would you find a manual for an old blender in 1999.

40:00I have no idea. But apparently eBay is where you find it. But it just shows like these these walled gardens that you can build with data. You could have built this before. You could build a 10 or 100 times more valuable company today. So, Alec, can I pause here in part because, you know, the last era of investing, you gave a great framework in the world, a great framework for thinking about the battle between startups and incumbents. And, you know, if startups could figure out distribution before incumbents can figure out innovation, that was, their success. Take us through the dynamic of when you're thinking about which companies to invest into where it's very clear that they can disrupt the incumbents in the category and what are the examples where it probably doesn't make a lot of sense for someone to build a company.

40:42And that has a proprietary wall of garden that is going to be very difficult to unseat. Yeah. I mean, I think there are two ways of thinking about this. Number one is in the case of the used blenders on eBay or the manuals, like there just wasn't a company before. charging for access to the subscription of like, I'm going to sell you, you know, per data article that I've digitized, or I'm going to charge you$20 a month, like, you know, probably not that interesting. But now if you have this finished product that you can charge$1 ,000 for versus like the raw material that you charge a dollar for, maybe now the business is tenable.

41:15So one category is you just find a new data source. And there's a reason why, like, you know, in venture capital school, we learn to always ask, why now? Like, if this is such a great idea, why didn't this exist 10 years ago? Great answer for Uber. When it came out, there was no iPhone and no GPS transponder in every device. Once you have that, now you can have Uber. The why now for some of these more esoteric things is, it's kind of like a little bit of a why now. Like, why isn't this a$20 million business like V-Lex after struggling for 26 years? Why is it now a$100 million business? It's because you can deliver the finished product.

41:51And of course, like there are, I would argue like a lot of the old things that were out there, like Ancestry.com is a valuable company. They digitized LDS data and a lot of people want to figure out where they came from. And there's an NBC show that says, you know, what are your roots? And people like watching that and all these kinds of things. You know, it's a valuable company. That would be one where it's like I would be hard pressed to say, how do you make that dramatically better with AI? Maybe it's like I want to say, hey, please, I'm about to die. I want to figure out which one of my heirs to leave all of my money to.

42:21Please email them and set up dates with me so I can figure that out. And like, that's the value add that you do with this proprietary data. This is why I'm an investor, not an entrepreneur anymore. I'm out of good ideas. But that would be something where, you know, there is an existing data store. Maybe I licensed that, like Open Evidence. They didn't create new medical journal entries. They were just like, hey, let's go distribute to doctors. We know that doctors are really interested in this stuff. We know that all the information is in these old medical journals, and the back catalog is very, very useful.

42:51It turned out, I think, of all the things that Michael Jackson did, right and wrong, probably the most ripe from an economics perspective was buying the back catalog of the Beatles. Or he bought a big chunk of that. That ended up being worth a lot, because until the copyright runs out, like the Beatles catalog, a lot of people like listening to Beatles, that's going to become more valuable. So you can buy existing stuff that is already out there, that already has a business, and that's like open evidence, where you can try to create something net new, which is kind of more of the Ask Leo. So I don't know if that perfectly answers your question, but my view on everything that's happening in AI right now is it's one of these weird situations where it's very different than cloud, where most on-prem software providers were like, cloud is stupid.

43:33Most potential customers were like, cloud is stupid. It's not safe. I don't trust it. I want to host things. Like you'd have your entire IT staff is like, I don't trust that stuff. So the existing incumbents did not build cloud providers. Like PeopleSoft did not say, let's go build PeopleSoft Cloud. They have it now, but that's where Workday came from. They were like, we're going to build this. It took a while for everything to catch up. I'm very, very bullish on incumbents. I hope I can say that because I think NetSuite is going to figure out 15 different ways to monetize with AI. I think that Intuit has this goldmine on their hands where they're just going to start charging per collections that they make to all of their existing hostages that use QuickBooks.

44:18But that still does not mean that you don't have these greenfield opportunities. You don't have these new data opportunities. Like there's so many new opportunities that have popped up largely because of this value cost thing. It's like you have so many, like it's this infinite number of things where it's like I find something where everybody would want this at$5, but it's currently only sold for$10. Therefore, nobody wants it. Therefore, it's not a business. Wait a minute. AI allows me to sell it for$5. So it's really one of these rare situations where it's good for both. Whereas I think mobile, like most people have BlackBerry was great.

44:49iPhone was stupid. That's why the incumbents didn't, you know, that's why, you know, why didn't booking.com build Airbnb? Why didn't, I don't know, a taxi cab company build Uber? It's just most people thought this was stupid. Everybody thinks that this is a good idea. Because, of course, intelligence, like, you know, AGI in everybody's pocket is a very good idea. Nobody can argue against that. It's more of the existing incumbents. This is why I'm just, I'm bearish on the brownfield opportunity on the bingo board. I'm very, very bearish on, I'm sorry, I'm very bullish on the brownfield opportunity for the, for like walled gardens and for kind of software that does the job of labor.

45:28For sure. By the way, I thought you were going to say the smartest thing Michael Jackson did was let his family use his likeness for the Michael Jackson live show, which I, according to Ben, has now generated more revenue from that show than his entire existence as a performer. But I give him more credit for like, I think apparently what happened was somebody was like, you know what, you know where the money is? It's like that, it's like that movie The Graduate, it's like plastics, right? Somebody was like, took Michael Jackson aside, you know where the money is? Back catalogs. And that's a good point.

45:55I have a lot of money. I'm going to go buy the Beatles back catalog, and then I'll make money from it because CDs are going to come out and streaming is going to come out, and there are so many different ways of monetizing this. Smart move. Smart move by demand. Well, actually, let's cover some of the – there was a question about the walled garden metaphor that Daniel had here. So the implication is that the new restaurant is direct to consumer. Why wouldn't the company sell to the end user rather than a business that is ultimately the intermediary? This is a great question. So this is like, Vlex is a good example of this, right?

46:26Like Vlex could have sold their data to Harvey. Instead, they realized this exact point. It's like, they should just be in this business of selling directly to they shouldn't be selling to Wilson Cincini anymore. Or if they are, they should dramatically change the pricing of their products. They should change their pricing strategy. and instead of saying we're going to charge this tiny subscription fee and allow so much of the value creation to occur elsewhere, we're going, like OpenAI, OpenAI charges very, very little per million tokens. We're just going to consume that and then enrich everything that we have that is proprietary to us and then go sell that directly.

47:09So it's a good question, but I think the point from an investment lens is we, a lot of entrepreneurs are now looking for, sometimes it's like existing companies where it's like they don't know what's going on, they can just buy that data. Those existing companies, if they're run by an entrepreneurial CEO, like they realize, wow, I can make my business 10 times better and we're gonna go invest in those. And then lastly, I'm just gonna buy some antenna from Amazon and like listen to Malaysian Airlines flights or whatever and then aggregate this information that's completely free, but it's not free past tense, right?

47:44Like the number of subscribers that Mr. Beast had five years ago, like the number of subscribers today, you just go to YouTube, you see exactly what that is. If I wanted to see what that was 10 years ago, that's what is actually proprietary. So sometimes the proprietariness, if you will, everything is free. Anybody can go collect this stuff that's free. The value only accrues over time. And there are a lot of examples of this. Like, you know, I can go to the Mormon church and get my genealogical information, and they'll probably give it to me, and I don't have to go pay for an Ancestry.com account, but it's kind of useful and easier to just do it with Ancestry.com than to go fly to Utah.

48:21So sometimes just the ease of going to somebody who's already digitized and put this information in an easier-to-digest form, that's one of the reasons why people go to LexisNexis. That's one of the reasons why people go to a lot of these providers because sometimes they're the only game in town, sometimes they're the best game in town, but increasingly today, they're the ones that can actually give me a finished product. And actually, it saves the end customer money as well because I don't really want to buy LexisNexis data. I just want to know if I should accept or reject this transaction. And there's a lot of enrichment that I do of the data.

48:56There's a lot of workflow. There are a lot of analysts. Like if I'm a financial services company, I hire a fraud analyst to go tell me what's going on. And the raw vegetable that I need to figure this out is this LexisNexis information. But LexisNexis, like this would be kind of bullishness for an incumbent, probably can do a lot of things if they're the only ones that have that information. Great. Alex, I feel like you paid Joe to ask this question, but I'm going to take it here and then we'll switch gears to Anish, your two sections here. What is your view on white-collar services AI roll-ups, i.e.

49:31fully verticalized software plus services companies that are popping up? Yeah, so I wrote an article about this two years ago. I called it Barbarians at the Gate, but where the barbarians is spelled with an AI in homage to the RJ Arnavisco deal in the 1980s, a book that was written about that. I mean, I think it's very interesting is what we're great at is like, here are two people that are going to change the world. They don't know how they're going to do it. We're buying an out-of-the-money call option. There are a lot of private equity firms out there that are like, we're good at firing everybody and like, you know, moving people to the Philippines and doing this and doing that and all of these kinds of things.

50:03Like, this is a big thing that private equity is looking at. At the same time, we do have a couple of bets in this space. And it's, you know, very, very smart entrepreneur. But there is never a question of, can I get more clients as an accountant because I can't hire more CPAs to do tax returns? It's like the hardest part is to get the clients. So you have to go to the Chamber of Commerce meetings. It's just very, very hard to buy one accounting firm, and then by virtue of all sorts of cost synergies, you can now onboard 10 ,000 more clients. The way that you would have to play that game is you buy one accounting firm, you integrate it for nine months, then you go buy another accounting firm, then you buy another accounting firm.

50:43And yes, is there value at the end? Absolutely. But you probably have to buy 200 accounting firms, and then you're left with a pretty interesting business, and there's probably a big competitor called, you know, mid-market PE who's done this for 500, you know, years, not years, but has done this 500 times and they're going to do a better job of that playbook. On the other hand, there is a strategy that we think is very interesting, which is instead of having a sales team, you buy one. So, you know, take the example of debt collection. I could buy a publicly traded debt collector that has lots of people that doesn't do a very good job, that doesn't follow lots of laws.

51:15And I want to get started somehow. I built this great tool that I believe in. I want to dog food I don't have any customers right now. I know I'll buy a company that has declining revenue, but five blue chip clients. I'll buy this company for three times EBITDA, and now I'll transform it with AI. And now I don't have to buy a second one. I don't have to buy a third one. I don't have to buy a fourth one. I can just say I have better collections rates. I have five blue chip customers that love me, and I'm cheaper. So do you want to be lazier and richer? You're like, yes, I already have the customers to back this up, and I can now onboard 1 ,000 customers into the existing acquisition that I made.

51:51That's actually quite interesting. So the question is, which one are you doing? And I think the, we're going to go roll up, you know, 100 dental clinics, or we're going to, and we're going to make it better. We're going to roll up, you know, dermatology. I have a friend that rolls up dermatology clinics. It's like, I just don't think we're good at that game. And the problem is that dermatology clinics are like, just because I bought one in San Carlos, it doesn't help me like do anything in Florida. I got to go buy more there. Same with accountants versus, you know, debt collection. That's very, very national.

52:21You could buy one and then, you know, that is your entry point. And it's kind of an opportunity cost. Do I hire salespeople to go sell? Or if the best companies have hostages, not customers, do I buy some company that is stagnant and even shrinking because they don't know how to respond to AI? Because, by the way, all of these companies, like every debt collection company, like they'd be crazy not to look into doing AI on their own. So it is this battle between startup and incumbent. But there is an interesting opportunity, and we've done one in the MSP space, managed service provider for IT, because a lot of IT now is not, hey, come into my law firm office with 50 people and fix my printers.

53:00It's like, onboard me into Microsoft Office. And, like, all of that stuff can be done remotely. It's a very, very digital experience. It's a$100 billion market. like that's a little bit more interesting because I can actually ingest more clients that way as opposed to I have to buy hundreds of these things. So hopefully that makes sense. Awesome. All right. Should we switch gears? So I want to turn it over to Anish because all of these things that we're talking about, they also apply to consumer. So maybe with that, why don't we talk why and how this applies to consumer? Great. Actually, if we're going to do that, why don't we skip ahead a slide and then we'll come back to this.

53:36So great. Great. So this is the application of all the categories that Alex outlined to consumer AI. It's the exact same pattern. So the first and very important one is traditional categories are going AI native. This is happening. So if you look at Photoshop, it's a fantastic business. and say, well, what do you do if you're a young designer coming up in their career? You want to use the AI Native Photoshop. The AI Native Photoshop is CREA. That's over 18 months. So it's a fabulous product, and it has all the AI primitives built in, and it's the one that's being chosen by people that are adopting a first design tool and are early in their career.

54:09So this sort of transformation of existing categories is definitely happening. The second is category creation. Eleven Labs is a fabulous example of this. this sort of market for voice and audio models really didn't exist five years ago. There was no, I mean, perhaps people doing voice actors and voice dictation as a niche market, it just wasn't interesting. Eleven's done something much more ambitious. They're a model provider, and they have both consumer and enterprise SKUs. And because they vertically integrate, they're able to really go after this opportunity, create the category in a very short period of time.

54:46Finally, proprietary data. Alex talked about proprietary data. It's actually near and dear to my heart because I worked at a large-scale consumer company that was based on proprietary data, which is Credit Karma, for many years. So I've seen this playbook, and it works extraordinarily well. The area that we've actually seen it applied in one of our investments is a company called Slingshot. Slingshot is an AI therapist. How do they collect their proprietary data? Well, they actually go to existing therapists, and they provide an AI scribe, a note-taker, and the note taker takes notes while those therapists counsel their patients.

55:20It then uses the generated notes to train a foundation model and the foundation model trains a consumer product called Ash, which is then sold directly to consumers. Of course, OpenAI and ChatGPT are formidable, but they simply don't have the data that Slingshot has. And as a result, Slingshot's able to provide a differentiated and high-priced product and it's working well. So each of the sort of observations Alex made is absolutely playing out in consumer AI and we're very consistent in our approach to the three. Do you want to go back one? I think this is an important slide as well and an important concept because a very fair question is, well, why aren't either labs or sort of big tech who have real model efforts like Google going to win it all?

56:05Well, the reason is that in many categories, being an aggregator of models is actually preferable to consuming just a single model. And the metaphor that we're all familiar with here, of course, is airlines. It's much more useful to search for a flight from SF to New York on kayak because I can look across the inventory of every airline versus just going to Delta or United and looking at their inventory alone. The same thing is true in categories like vibe coding or creative tools, where you really want access to all the models. And the reason for that is the models each have their respective specializations.

56:36So they're not exact substitutes. You want to work with them all. You want a single pane of glass. And the labs and big tech companies can sort of definitionally only use their own first-party models. So this is why we see the aggregators winning, and it's an important trend and sort of investing principle for consumer AI. The key thing, I mean, everybody's heard this framework before, but our job is to find, pick, and win deals. And then once we win deals, to help these companies actually achieve their objectives, and most importantly, don't screw them up by giving them that advice and telling them what to do.

57:08The CEO knows what to do, and we're there to advise and consent. but the way that we do this is we try to be the leader and the expert on every market. We're putting out more benchmarks. Like there's actually a really cool benchmark that we're coming out with. It's like an AI productivity benchmark. So for all these different categories, actually, this is pretty cool. So everybody on the team, and the way that I would kind of phrase this is we have a process interrupt job. So our interrupt is there's a very, very incredible deal, like incredible, incredible, incredible. Like let's go meet with them, drop everything.

57:41This is unfortunately, from my wife and children's perspective, like a weekly occurrence right now where it's like, I got to cancel this. I have to have dinner with this entrepreneur who has discovered that the fountain not of youth, but of perpetual motion or so they think. So go meet with them. That's the interrupt part. The process part is like, you know, I'll give you a good example. Somebody is going to out Salesforce, Salesforce, not for the hostages that they have, but is going to build the greenfield version of Salesforce. How is that possible? Everybody hates using Salesforce. there's a new company that's going to do this better, that's going to be AI native.

58:14How do we make sure that we are adept at finding, picking, and winning and supporting that investment? Well, we believe in adverse selection versus positive selection. So a very inexpensive deal that has been hanging around the hoop for six months, that's probably bad. We don't want to meet with them. We want to meet with the best company. If it's the best company, every other venture firm also wants to meet with the best company, obviously. They're going to send out their big guns to go try to win that deal, and it's very hard to win these great deals. So the best way of starting with this is to write this article, and we made a video about this as well, which has had hundreds of thousands of views.

58:45It's pretty incredible. Death of a Salesforce, Why AI Will Transform Sales. Joe Schmidt and Mark Andrusko on our team wrote that. Everybody wants to talk to them. But ultimately, knowing what you're talking about really, really matters. Or death taxes in AI. We've covered the gamut on everything around taxes. What about companionship? What about we do something that we just came up with? like what are the top 50 enterprise applications, the top 50 consumer applications. You know, we often get somewhat a pejorative joke, although I think it's a compliment. We're a media firm that monetizes with venture capital, but there's a method to this madness, and the method is it's helping us find deals, it's helping us pick deals, and it's helping us win deals.

59:26So next. And this is the team that does that. So everybody, you know, again, we've got process interrupt, but, you know, we have a very, very prolific process calendar where we're publishing things, we're becoming experts in certain categories and trying to find entrepreneurs that are positive selection, that are building the best things here. And we always see them. And like a good example of this is Rillit, where if you talk to Nick Kopp, who's the CEO of Rillit, you know, Seema and Mark Andrusko just knew more about this category. You know, we were in a very, very competitive Series B process.

1:00:02Yeah, I mean, so if you look at this chart, so we have a bunch of people, what are the two things that a lot of companies need? They're like, okay, we need help on the accounting side because it's like, yeah, everybody wants to buy our product. I shouldn't say the accounting side, but just how do I scale a business and make sure that, you know, very important, revenue is more than expenses. And also, how do I go build out a sales team? And on the other side, you have people, as I mentioned, kind of the content generators. So like Mark and Olivia and Joe and Kimberly and Gabe, you know, Kimberly, I'll call her out here.

1:00:34Like there's a company called Decagon. She introduced the two co-founders. Joe has written some great content and has a lot of great people coming to him. We want to make sure that if somebody leaves or somebody gets hit by a bus or, you know, whatever happens, we want to make sure that the entrepreneur experience is very good. If you think about the origin of the firm, the firm originally was the only people that we will have write checks are people that have run a company or started a company. And actually, I joined as part of that mandate because I, you know, for better or for worse, run a company and started a company.

1:01:04But then we realized that some of the best people to find deals, like Olivia, is just non-pareil in terms of her ability to find great deals and be an expert, as I mentioned, and voice Hayat. So it would be insane not to have her, who's like the front of the spear, finding a lot of these great deals, to be working with a lot of these entrepreneurs. Great. The following to that question is, is there a process in case hosts want to check out? What's the process for investment decision-making? And is the right assumption is that each partner is given a budget to invest rather than needing investment approval?

1:01:38Or how has that changed, if at all? Yeah, so we try to be highly, highly conviction-oriented. And I feel like my job and David's job and Anisha's job is to make sure that the right process is followed. Because the mistake, the automatic mistake in venture capital is, I'm old, I don't use social apps. Why would anybody want to send disappearing messages? That's stupid. let's pass on that deal. And meanwhile you have like the really really smart not to be ageist like 24 year old who actually uses this tool every day who knows the entrepreneur and says this is the greatest thing that I've ever seen and then the old person you know I'm the old person here you know vetoes that deal versus the right process is yes we do have somewhat of a budget and our investment committee is effectively like making sure that we believe very strongly that the process was followed that you've met every competitor, that the work is top notch.

1:02:27And we will often defer to the individual who, you know, is in the arena. And our job is to just make sure process and, you know, kind of turn that second key. So it's kind of a, it's a, it's a two key process. And, you know, again, much more conviction oriented. I know that that doesn't perfectly answer the question, but we don't have a committee where we, everybody votes and then you have to have this many votes and then it's all this political horse trading. It's all right for, especially for seeds where a lot of the younger people, we have them focused on doing seeds, where it's a little bit trickier, but for the smaller checks, which we are predominantly focused on, let's just defer to the person with high conviction, but make sure that our entire process is done end-to-end, and that this is the expert.

1:03:07It came from the content. You know what you're talking about, and so on and so forth. Right. Let me just generally talk about team evolution and changes, how you're thinking about the augmentation of kind of check writers on the team, how you are evaluating the path to promotion for folks, whether or not, you know, in light of some of the recent promotions and also evolution of check writers, if you will hire any incremental people as well. Yeah, I mean, I think the main thing that we often debate about, just very candidly, is what we want the most is probably more leverage as opposed to capacity.

1:03:38So we have the capacity to do lots and lots of deals, but if it's the best deal in the world, we need to assume that our counterparty is Roloff at Sequoia or is a top partner at Excel or Reid Hoffman at Greylock. All of these people are active. But if it's a great deal, the entrepreneur wants to talk to as many people as possible and will often be starstruck by the person that started a multibillion-dollar company, as they should. That makes a lot of sense. So I would say the one area that we might look to add to is somebody who probably has built a quasi-generational company that is still very, very hungry as an investor.

1:04:14This is not a retirement job. This is an anti-retirement job. This will drive somebody crazy to the point where they want to retire because you have to work 20 hours a day sometimes. And the working 20 hours a day is something that my kids make fun of. It's like, you just have coffee with people. How is that working? It's like you have to have a lot of coffee. You have to have a very high tolerance for coffee. And then you have to switch to alcohol at like 5 p.m. It's a lot of work to do this stuff. But, you know, joking aside, you really need to be able to meet with everybody. And when it is a great, like, how do we know?

1:04:42Like, this is the eras of commission versus omission. We need to make sure there are five, this actually happened with the ERP space. If we get one of those wrong, not only do we lose our money because we were wrong, but we lose like infinite money because we didn't actually invest in the right one. So we have to make sure that we are on top of all of these people and that our team is made up of experts that all of these entrepreneurs want to meet with. So I don't know if that answers your question, Jen, but I think the only thing that I would potentially add is when it's time to go win a superpower deal, like we all show up together.

1:05:17And by the way, I jokingly call Mark the Air Force because if we need a big strike, what do we do? We call in the F-35s. Mark's got a few of those. We'll have dinner at Mark's house. Ben will show up. We all show up beyond just this team. But having a few other people that can kind of lead the charge on winning deals and have board gravitas is helpful. Of course, that is how we use Brian. That is how we use Andy. Like, I'm doing that too, largely. We want to get as much ownership as possible. And, you know, we might need more people at a senior level not to find the deals, not to pick the deals.

1:05:52Of course, you know, we don't want to just say like, hey, you're just a monkey that helps us win deals. But that is a very, very helpful thing to go do. And that's a capacity perspective. By the way, I know it frustrates folks probably on this call to no end because we can't cleanly attribute a certain deal to a certain GP at all fronts. But hopefully that also represents how much we think about this sport as a team sport and one in which we bring the entire, you know, kind of force of the firm to bear as a part of that. And also, just in case people did not pick up, Mark does not actually have an F-35, but he's the F-35 that comes in to win deals as a part of that.

1:06:28Okay, we have two kind of last questions. Maybe we can bundle them together. And this was in reference to any observations on customer retention to date on AI native companies and then just the scale of spending required for enterprise sales for these type of companies. Maybe David or Anish, you want to take this one? I can talk a little bit about the customer retention point. I mean, so far we haven't seen a bunch of sort of price shopping and switching. And I think it's important that the companies that are selling in, the startups that are selling into these companies build a rich software ecosystem around the primitive.

1:07:01This is what David was talking about with voice. It's necessary but not sufficient to provide a voice capability. You've got to build a lot of things around that voice capability. So I think one is the companies that are building rich ecosystems do a better job of retaining their customers. I think the other thing is that AI is moving so quickly. Many of these customers are looking to these startups as their sort of AI solutions provider. And they're looking to them for a much more holistic set of things. And because new primitives are being released every day, the startups are kind of helping drive them into the future and helping them sort of capture a lot of the top-line gains from the new technology.

1:07:38So I'd say so far, certainly on the enterprise side, retention has not been an issue. And then happy to speak to consumer as well, where we've also seen strong retention signs. Honestly, I don't think we're seeing a tremendous difference from an enterprise sales perspective. I mean, if anything, we're seeing more inbound than ever. I mean, EVE hasn't had to have an outbound motion, which is kind of insane, given the scale with which they're operating. So there is a lot of sort of market pull for a bunch of these categories. But at the limit, I think that they will all need significant kind of enterprise sales.

1:08:14And I think if anything, what we're seeing, especially when companies are selling to larger corporates, is more of a forward deployed motion on the engineering side. I think many large companies are looking to startups to better understand where and how to apply AI within their organizations. And so if anything, we're seeing people invest more on the forward-deployed engineering side than necessarily on the sales side. It's a very cultural thing, which is before you hire somebody, this is kind of happening in a lot of startups, it's not happening at GE, can you use AI for this job? In fact, Ben is the CEO of Injuries and Horowitz.

1:08:48He's asking that before we hire people here. And I think that mindset, actually, if you do it correctly, if you're Eve and you're like, oh, I'm just going to hire people that play golf with lawyers and that's my entire sales process and I'll never use AI for anything and I'm just going to use NetSuite and I'm just going to use QuickBooks, that's not how these companies are actually orchestrated. They understand the transformative power both on a cost side and a revenue side and they're transforming themselves internally. All right, with that note, thank you all for joining and talk to you all soon.

1:09:17Thank you.

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1:10:20Thank you.