The a16z Show - The AI Opportunity That Goes Beyond Models

Episode Date: January 19, 2026

The a16z AI Apps team outlines how they are thinking about the AI application cycle and why they believe it represents the largest and fastest product shift in software to date. The conversation places AI in the context of prior platform waves, from PCs to cloud to mobile, and examines where adoption is already translating into real enterprise usage and revenue. They walk through three core investment themes: existing software categories becoming AI-native, new categories where software directly replaces labor, and applications built around proprietary data and closed-loop workflows. Using portfolio examples, the discussion shows how these models play out in practice and why defensibility, workflow ownership, and data moats matter more than novelty as AI applications scale. Resources:Follow  Alex Rampell on X: https://twitter.com/arampellFollow Jen Kha on X: https://twitter.com/jkhamehlFollow David Haber on X: https://twitter.com/dhaberFollow Anish Acharya on X: https://twitter.com/illscience Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Not an offer or solicitation. None of the information herein should be taken as investment advice; Some of the companies mentioned are portfolio companies of a16z. Please see https://a16z.com/disclosures/ for more information.  A list of investments made by a16z is available at https://a16z.com/portfolio. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A lot of people think the AI story is about models. This episode argues the real story is about apps, distribution, in modes. In this episode, we share an AI apps overview featuring A16D general partners, Alex Rampel, David Haber, and Anishiataria, along with Jen Koff, head of investor relations at A16C. They break down why the product cycles drive growth, why the AI area is accelerating faster than prior platform shifts, and what it takes to build enduring companies and AI applications. The conversation covers three core themes, traditional software going AI-native, platform expanding beyond SaaS to take on labor, and wild garden businesses built on proprietary data and compounding advantage. I'm Alex Rampel on the Apps Fund. I've been at the firm for 10 years.

I stole this from Chris Dixon, who published a post like this about probably 12 or 13 years ago. And the whole premise is that product cycles drive growth. And the top of the chart here is the NASDAQ from 1977 to present. It goes up sometimes. It goes down sometimes. over the long run and has gone up, but there have been some very scary downpoints. So there really, there have been four major product cycles. There was the PC, and obviously before the PC,

there was the semiconductor. But we got to start. We'll start with the PC. There's always an infrastructure layer of companies that are building the back end. There's the application layer of people that are building, things that actually are used. So Lotus was one of the first infrastructure,

sorry, application companies, Adobe, Symantec, all of these companies that kind of grew out of the 1980s. But the infra players, if you were Apple and Microsoft, then you had the internet, That was enormous. Lots of bubbles along the way, but some very, very enduring infrastructure companies like Cisco and Akamai,

enduring companies in the application space like eBay and Amazon that were built on top of that. Then you had cloud. So AWS accounts for the vast majority of market cap of Amazon. You've got workday, Shopify, Viva, others that were the application layer. Mobile took all of these things that came before and now put a supercomputer in everybody's pocket. So the vast majority of humans on planet Earth

have a smartphone, which is pretty amazing. That was the mobile era. which is still actually kind of playing out. Like, I just bought an Android phone to test things with. It was $40, and this was more powerful than the ENIAC in 1946 or whenever the ENIAC came out. And then two years ago was this AI era is coming out as well. And the NASDAQ is higher, we know that,

but the AI era really is playing out. And the cool thing is this is not a net new thing. This is building on everything before. Like, if we didn't have smartphones and we didn't have cloud, but we just had the ENIAC, AI would be pretty cool. Like, you could go check it out in a museum. But the fact is you now have 8 billion humans on planet Earth,

the vast majority of whom have smartphones, and the adoption of this new technology is taking off like never before. So the AI era is here. The vast majority of net new revenue that's happening in software land is actually coming from AI, both at the application layer and the infrastructure layer. It's hard to actually think back two years ago. At that point in time, of course, ChatGPT at 3 had launched.

I think ChatGPT4 had also launched, but it was all just text and imaging and some basic reasoning. But none of the native audio stuff, obviously real-time interaction, none of that actually had happened yet. It's hard to even imagine how far we've come,

even in just the two-year time frame as a part of that. Yeah, I mean, it's really remarkable, like what these things have done. I mean, one of the ways of joking about this is that we had this idea of artificial general intelligence or the Turing test.

Like, when can we tell the difference between a computer and a human if we don't know who our interlocutor is? And the answer is, if you were to take a person 10 years ago and show them, 20 years ago or 30, you're like, oh my God, this is like a fully sentient. This is smarter than any kind of human out there. We kind of keep changing the goalpost a little bit on what

exactly is AGI. But yes, the pace of innovation here is just remarkable. And the important thing is just the opportunity set that it unlocks. So whenever you have a bull market and very, very exciting tech, there's always somebody saying it's a bubble or it doesn't work or it's all overhyped. And I think there was some MIT paper that came out. This is not a faulty MIT. This is somebody who published the paper. It's like, oh, you know, most enterprise deployments really, really aren't working in terms of AI. We're seeing the exact opposite. So there's a company called Ramp, and they are kind of credit card expense management products,

and you see this giant tick up in January of 2025, which is, you know, when did enterprises? And these are much more, like who uses Ramp? This is not necessarily a startup, but it's a more forward-thinking company. It's not necessarily GE. It's a company with thousands of employees, maybe in the Bay Area or New York, that wants to be more tech forward. and they've just realized, like, wow, this stuff, Jen, to your point, like, GPT 3.5, pretty good.

I was like, wow, it's pretty amazing. I can write a new episode of Seinfeld with it. Like, amazing things that I could do almost to kind of wow my friends, like a magic trick. But now the magic trick has actually gone into the enterprise and is saving people time and money. And one of the themes that you'll potentially get out of this presentation for me is that I have this prevailing view of human behavior,

which is everybody wants two things. They want to be richer and lazier. So they want to do less work and get more economic value. And this is really what Gen AI unlocks, and it's really starting to happen right now. And this has been a little bit of a flat curve, but it has been inflecting a lot. And you see this in the expense yet.

You see it in the growth of all the companies, both at the infrastructure layer and at the app layer. And again, whether they're overvalued or undervalued is almost not the point. It's hard to time the market on these things. The amount of value that they are generating is just tremendous, and we're going to get into this in a second. If anybody knows Maslow's hierarchy of needs,

this is like this philosophical term of what is it that humans need? at the base of the pyramid, people would joke is Wi-Fi. So it's like, okay, I need all these things that have been true for hundreds of years. And at the very, very top of that pyramid is this self-actualization concept. But what I really, really need, if you talk to any teenager, it's like, you know, where's my Wi-Fi? And what's starting to happen now next is it's actually AI. So obviously you can't have AI without the Wi-Fi, but something like 15% of adults on planet Earth

now use chat GPT every single week. And why are they using it? it's just part of their daily routine, whether it's settling a bet with their friends over like, you know, how does this work or that work, or I want directions to this thing, or I'm really puzzled. My wife just used it to complain to the school

because our kid missed the bus, and the bus driver said, he can't open the door because it's against the law to open the door. This is the true story. So my wife had ChatGPD scan all the laws in California and the U.S. federal system writ large, even though our government has closed down. Nope, that was completely made up.

Send a very, very polite note. I'm sure that the school is going to start adopting ChatGPT2 to start responding to people like my wife, apologizing on behalf of the bus driver, but they did send him the apologies. Sorry, we made that up. Next time we can open the door for your child if he is on time when the bus has already closed the door. It's like an countably infinite number of use cases for these things.

And the growth of minutes per user in the U.S., I mean, this is just astronomical. And as these things work better, and as they unlock more use cases, it's kind of obvious that the growth in minutes will go up. This is happening at a breakneck speed. So the key paper, which was co-written by this very, very smart guy, Nome Shazir in 2017, attention is all you need. It introduced the transformer model. I remember we have a partner here, Frank Chen,

who's been here for a very, very long time, and he demoed chat GPT or GPT2. And it didn't really work that well. It reminded me of this thing called Eliza, which was like a famous Markov chain-based thing. It was basically a therapist that came out. It was an AI-based therapist in the 1960s or 1970s. It's still around.

You could try it. And basically, you say, like, doctor, I'm not feeling well. And then it just kind of says, and why is it, Jen, that you aren't feeling well? It just basically takes the words that you say, turns it into a question. It feels kind of sentient until you ask it like,

hey, I want to complain to the school about the bus driving. And then it says, and why do you want to complain to the school about the bus driving? It doesn't actually give you an answer or anything that you need. You know, Open AI, it's hard to imagine that this just happened a couple years ago. But from 2023 until now, like, we really have entered the golden age of apps.

And I base that purely numerically. I'm used to companies that will grow from, I don't know, like we used to talk about like double, double, triple, triple, double, or all these different ways of measuring revenue growth. Because normally, if you're selling a software product, and let's just say that you're selling a software product to an enterprise and it's $100,000 a year, you might sell a couple one year, a couple the next year,

but very, very rarely have we ever seen a software company go from zero to $100 million in revenue in a year or two? And we are seeing this right now. This is not like, oh, we're seeing it because people have too much money in their business, buying these things. These are companies that are buying these things because it unlocked so much value for them. They want to be lazier, they want to be richer, and this is unlocking that. So I'm going to talk about three broader themes that we're seeing in AI applications.

They're really more broadly, these are the types of companies that we're investing in. And partially, this is when we ask ourselves, what is defensible? What is it that the labs aren't going to do? Because this is a very, very good question. It's not like Open AI just wants to be this backend layer for everything. They have a leading consumer app. They just launched arguably a competitor to TikTok. Microsoft is getting into the space in a meaningful way. And if you look at the history of software, I mean, this firm was started by Mark Andreessen. He started a company called Netscape. Netskate became roadkill due to this company called Microsoft that went into an antitrust case because of making Netscape roadkill and whatnot. But how do you build an enduring company? And what are

the areas that potentially have the most enduring growth? And there are three that I'm going to lay out. So the first is basically traditional software is going AI native. And this is no different than like, If you built a time machine right now, go back 15, 20 years, and say, I'm just going to invest in every single cloud native company that pops up, you would have an incredible portfolio. You'd have Shopify, you'd have Viva, you'd have NetSuite. When Nesb is a little bit older, you'd have Salesforce when it first went public,

because it turned out that the incumbents couldn't really respond to that because they're selling on-premise software or shrink-wrap software for a lot of money up front, and they didn't really know how to go for, like, less money every single month as a subscription. So category one is trad software that's going AI native. Category two is arguably the biggest, which is basically it's not competing with the software market at all.

This is, if any of you saw my talk that I gave in May, software is starting to eat labor. You're basically selling software that does the job of what people would do before. This is arguably a much, much bigger market. The laws of business still apply, you have to build real modes. You can't just build something that's a little widget that somebody underprices your widget by a dollar tomorrow. We're going to talk about that in the second. And then lastly, I call it.

called this the walled garden, but basically really, really interesting proprietary data models where the value of this business, because you're able to deliver the finished product thanks to AI, becomes much more valuable. And I'll talk about number one. So existing categories are going AI-Native. So this is a little, we actually have a post coming out about this in a couple days, but I'm sure everybody here has heard of bingo or played bingo. I'm from Florida. There's lots of bingo in Florida. Lots of different names on this list. And one of the key lessons that I had as an investor is, you know, and Mercury is kind of a great example of the tortoise that beat and is, you know, still beating the hair. Mercury built a Neobank for startups. So they said, we're going to

be the better source for you when you start your company to go deposit your money with us. We're going to, you know, help you pay your bills, track your expenses, be a basic accounting system. Mercury never stole an existing customer from Silicon Valley Bank until the weekend that Silicon Valley Bank failed. And it is what I would call the canonical Greenfield opportunity versus Brownfield opportunity. So Brownfield is you're selling to an existing market. So let's just take an example here. Email marketing. You use MailChimp. I want to go sell you a competitor to MailChimp because it has AI. That's going to be really hard. Or you use NetSuite and I'm going to say like, hey, ditch your NetSuite. I'm going to give you AI NetSuite. That's going to be really hard.

If you're a net new company, and this is what I mean by Greenfield, you have no existing product. you're not using anything, you're a brand new company, or sometimes you've hit an inflection point. So the inflection point, I'll pick on NetSuite here for a second. The inflection point is I have 50 employees, now I have three entities and two currencies. I've been using QuickBooks my entire life. QuickBooks can't handle, for whatever reason.

They cannot handle multi-entity, multi-currency support very well. KPMG says, hey, you've got to go move to a better ERP system that supports that. And now I have an opportunity to pick a better product in the market. And NetSuite is a product in the market, Or I can try this thing called Rillet, which is one of our companies, which is basically like NetSuite, but it closes the books for you. It has 50 AI features built in as a Greenfield example.

Now, these things don't grow like weeds because you have to wait for the new company creation. You're going entirely for Greenfield and not for Brownfield. But every single one of these spots on this bingo board, the incumbents are all adopting AI, and they're going to make their businesses much, much better with AI. Like, bill.com is going to be a stronger business, or SAP is going to be a stronger business, or Adobe is going to be a stronger business because of AI. They're just going to be able to charge for new things. Workday will start charging,

and I mentioned this in my presentation that I gave a couple months ago. Workday will say, hey, do you want us to do reference checks on every new employee that you enter into our system? That's $500 per reference check. Why can't somebody do it for $4.99? Because you're stuck with Workday. And there's a saying that I use a lot,

which is the best companies have hostages, not customers. And I'll talk about a couple examples here. So RPA, there's an existing company called UiPath, public company, customer support. There's an existing company called Zendesk. It's now a private company, ERP, SAP, NetSuite, or in some

cases like Zendesk charges per seat per month, that is almost an extinct business model for support software because, well, wait a minute, I don't want to pay per seat per month when 99% of all queries can be answered by the support software. I want to pay per outcome. So we've been aggressively betting on the bingo board.

Let's evaluate every company that we see in this space. So if it's payroll, if it's support, if it's ERP. And the important thing is that these are systems of record. So this is the best companies take hostages, not customers. Like, we don't want to invest in hostage companies. We don't want to invest in companies that have negative 100 NPS. We want to invest in companies that still have a very, very strong moat.

And that's what I mean when I use that expression. So all of the companies that we're looking at here, what is a system of record? It just means, like, it runs the entire business. Everything on that bingo board, like, how do you get rid of NetSuite? it's basically impossible. You can enter in with an AI wedge or more often than not, a lot of these bingo categories are,

we're just building the new system of record. The existing incumbent is doing that as well, but it still is a no-brainer whenever you're brand new in the market or at this inflection point of do I use this old one or do I knew this new one? So next year. So the second theme here,

which I am personally most excited about, is where new categories are emerging, where labor is software. And there's no bingo board for this at all. And the reason why is because there weren't software companies that did this before. And like the predominant theme is that you have a lot of things where you would hire a person, you can't hire that person,

or that person that you were going to hire doesn't speak 21 different foreign languages and won't work 24 hours a day. But software can do 90% of what that human would do. Now you will pay for software, not necessarily at the same rate that you would pay for labor, but this is not something that you would hire a software product for. This is not something you would ever have a software product for before. So I'll talk about a couple examples here.

And obviously, you know, I mentioned this, I can mention this ad nauseum, but the labor market is astronomically bigger than the software market. So next. So again, this is kind of the governing principle here. You go look at a job, front desk receptionist, Plaza lane optometry. Plaza lane optometry has, like,

they have a bingo board as well in terms of software that they spend. money on, they probably spend money on Microsoft Office, they probably spend money on Sparrow Spacer Wix, that's on the order of $500 a year. If you can deliver them a software product that does, you know, call it five out of the eight things on this job posting, they will hire that software product. What do they pay for that software product?

This is the part of the market that is almost unknown. Because they're probably, they're almost definitely not going to pay the $47,000 year that they're advertising for this job or whatever the rate is that they're paying for the job. They're probably not going to pay $500 for software, but the promoter, the creator, developer of this software product, an application software company might say, you know,

we're going to charge you $20,000 a year. They need to be careful about how they do this. We often want to see them turn into a system of records so that if they are doing, you know, five of these eight job responsibilities, somebody doesn't pop up and say, we're going to charge $19,999 a year. We want to make sure that this is a very, very sticky end solution for Plaza Lane optometry.

you're going to see, I believe, a lot of market cap creation on the bingo board of existing software products that have a new better alternative that are going after Greenfield. But here you can go after Brownfield, you can go after existing companies, you could probably charge a lot more. There's a path to much, much more explosive revenue growth.

But just maybe to take a step back, you've probably heard a ton about what's happening in legal AI. Just given how document intensive the industry is, there's tons of applications for LLMs in the space, most of what you probably heard are around companies like Harvey, you know, serving the defense and the corporate side, maybe less familiar to you might be the plaintiff side, which is really about representing the individuals in areas like employment law or personal injury. And, you know, we spent a bunch

of time looking at, you know, the different companies on the plaintiff side, in part because one of the unique characteristics about that side of the market is that these attorneys operate on a contingency basis, meaning they only get paid if they win. And so they're incredibly aligned with their clients. They don't bill by the hour. They take a percentage of the actual case outcome. And so as a result, for every 100 leads that a plaintiff attorney gets, they often take one case because anytime you take a case, it's an investment in your time and

your labor. So just incredible alignment with AI's impact on their core business model. Right. To contrast that if you're a corporate attorney, you know, and your junior attorney is 50 times more productive, you just eroded some of the revenue that you can actually charge to your end client. Again, in this case, if you can make your attorneys, you know, 5x more productive, you can potentially increase, you know, your revenue by 5x or more. And so the Eve guys had a particular particularly interesting kind of point of view from a product perspective. They really wanted to own

the end-to-end workflow from intake all the way to outcomes. And so, you know, to Alex's point earlier around voice, you know, they recently launched a voice agent, which, is actually collecting evidence from their prospective clients. And it's sifting through mountains of medical records or employment documents and helping these attorneys, figure out which cases to take. Because it is generating sort of this data set of case characteristics such that it can say, hey, this case is potentially worth 50K,

this case is worth $5 million. You should probably spend time on this case over here. And then it'll just help step through all the different phases of pre-litigation and litigation for these attorneys. So it'll draft a medical chronology. It'll draft the kind of core artifact of these cases, which is known as a demand letter. It'll file complaints.

And ultimately, I think what's so interesting about this business and it speaks to, I think, why moats matter. You know, one is these attorneys are living in this product all day long. Like one of the core, you know, pieces of feedback that we heard when we were diligent seeing the business was that literally 100% of the cases were flowing through the product.

But interestingly, as, EVE begins to generate data on outcomes, that data isn't public, right? That's not something that the large, you know, labs can train models against. And that data is actually informing better intake, right? So that they can then go back and say at intake, given the characteristics that we've seen in all the cases

that we prosecuted across all the, you know, the EVE platform, you know, these have these three variables that make this case potentially worth a lot more money. Or to Alex's point, it can, you know, reduce the cost of taking on a case, you know, before an attorney was only, only taking a case that, you know, at minimum could potentially make them 50K

and suddenly they can afford to take cases at 5K, you know, the market expands, right? And there's a big sort of supply and demand imbalance, you know, today on the plaintiff side that that Eve is unlocking. And as a result, it is just the market pull for this product has been kind of like stronger than we even anticipated. You know, my hope is that it has a lot of characteristics that will be, you know, continuously investing in where AI is just incredibly aligned with the business, both driving revenue and, you know, saving these folks money.

Well, thanks, David. Yeah, and the reason I wanted to talk about that is I think it's really cool as Eve, but it's a metaphor for the types of businesses that we find compelling. And why, you know, zero to 30, certainly or two to 30 is not normal, but it actually is normal if you're able to move very, very quickly and just deliver, again, this promise of I'm going to make you lazier and richer. So let's go to the next slide. Actually, before we go to Salian, Alex,

why don't we just take some of these questions here? because they're relevant in the context of an example, and then also before we switch to salient exemplify why we find these to be particularly compelling. So there's a good question here from Brian. A lot of consumption-based AI apps have found it hard to become mission critical. They're easy to switch on or off as a part of the broader suite.

How do you evaluate that in diligence? Maybe David, if you want to use Eve as an example or others that we have in the portfolio, how do you evaluate that in diligence, and what patterns have you seen around in which apps actually graduate to being essential? Yeah, I mean, one of the distinctions that I often draw is this notion of differentiation versus defensibility. And I think AI is an incredible tool often for differentiation, right?

So the idea that the voice agent can speak to folks in 50 languages and gather that evidence, highly differentiated versus the human, right, obviously delivering value. But that capability alone, in my opinion, is not a source of their defensibility. Right? The source of defensibility for Eve is in owning the end-to-end workflow, right? It is actually in building a product that is contextual to, you know, all the work that that attorney has to do. And then I think, you know, not unique to EVE, but one of the kind of X factors is that the data that that business is generating, which Alex will get into a bit in this sort of walled garden. It has a bit of these characteristics of this sort of walled garden is not public.

And it sort of creates a source of compounding competitive advantage, you know, for the product itself. Right. So the more cases that Eve can prosecute for all their different clients, you know, the smarter that the product becomes in it, and actually, and it kind of reinforces that loop. It becomes sort of, you know, you're showing up to a knife fight with a gun, right? And so soon it's going to become an essential tool for any plaintiff attorney to operate with. And that just becomes very difficult to displace. Right.

So it's not so much the AIS, right, in the voice or the ability to summarize documents. It's actually in becoming kind of the system record this end-ed workflow. For sure. And in fact, actually, yeah, there's multiple threats to pull on it, but maybe I'll ask this question first. relatedly around talking about the potential upside of market size of these companies around labor versus vertical software bucket. And 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 do 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.

Um, their 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 word perfect. And word perfect kind of like kept growing for a very, very long time or once upon a time there was a company called visichalk. And then whoever had the most distribution said, I should do that, copies it. And obviously, you know, word perfect is toast. VisiCalc is toast. Lotus one, two, three, which was the one that beat VisiCalc. That became toast. But it would normally take five years for the, the bread to become toast. And there is a very, very high level of prolific speed. I mean, now, you know, Anish, David and I and Jen can go build a software product. We can vibe code if you've heard that term. We can go build software very, very quickly. What makes, 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. Well, I can vibe code against your opportunity. It has to be very, very sticky. It 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, want to go to the next slide here,

and I'll just talk about Salian a little bit. Sorry. So Salian 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? Like, I don't think that's actually going to happen very quickly. You know, 90% 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 a limit. I mean, I do think that the 3.5 million people that drive trucks at some point in time, like, we have a better solution than the truck driving human. You have, you know, 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. And 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 skill shortage or whatever,

I can now deploy people that will answer a phone. Like, 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., which is 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 carriers that's 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

in 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 and 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, the 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 to save you money, I'm 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. I 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 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 percent 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. I 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, shoot, what do I say? He's from Santa, you know, he's somewhere in California. Oh, wait, but actually he's traveling to, like, Kansas. I don't know what to say. Like, Saliant 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.

So, like, this whole category of, like, we are going to make you more money. And it's going to cost you less. Like it's just, 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 back in 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, how are, imagine there's a company called Talient and a salient. Why is it that salient is going to be talient and salient? And Ari actually had, already, 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? But again, moats matter. We know,

exactly what script to say. This is an example of kind of a data mode. It's like, because we've done millions of phone calls, we know exactly what to say. We have lower latency on every single, like, statute that comes out from, like, they actually have like a very, very good product that ingests every single law, like, as it is even proposed as a statute in all 50 states. Sometimes it's at the county level. Like, they're doing all of these things that make it so much harder to compete so that they will not lose a deal. You know, modes matter more than ever because you're able to create software so much more readily. Actually, maybe this is a good detail 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 and to become a huge business. And I think that might actually be a good deftail 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. Toast is designed for restaurateurs to run their business,

to integrate with DoorDash, to pay their weight staff, to do land, 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. I'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. So 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 the theme three here, which I'm very excited about. And I call this the Waldgarden. 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. And 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 that exactly as I talked about, like Open AI is an infrastructure company. We invest in all these application companies.

But then Open AI is like, you know what, we should put some restaurants on our farm. These 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 the world where the source of the raw material is actually what is rare. So let's go to the next slide. And I'll show you, like, 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, this little bingo board here of flight aware. I'm not sure how many people have heard of flight aware. 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. You can buy an antenna on Amazon to receive, it's called ADSB transponderate 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, this tail number is at this place. I can buy one, it's free. Flight Aware, I think they have something like 100 antennas around the world. They pick up all this information, and that's a piece of data.

Like, I can ask ChatGPT that. They don't know that. Only Flight Aware knows that. Or Pitchbook does this for funding rounds. Like, who knew what the, you know, series B price of a company in 1992 was? Like, Pitchbook somehow has that. Or Lexus Nexas knows this. Co-Star 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 chat GPT. It's not available on Anthropic. Of course, they can license it. But the reason why I mentioned this, is what do you do with flight-aware data

or 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 company 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, 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 pitchbook data. We 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 who is 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 domain tools.

So, like, this model has been around for a very, very long time before AI. Very, very large companies exist in this space. When you add AI, it makes it tremendously more valuable. So I'll give me three examples that hopefully kind of handles this point home. So there's a company called Open Evidence, which if you use it,

apparently two-thirds of doctors in America use this thing pretty much every week. Open evidence is exactly like chat GPT. The interface looks exactly like chat GPT, 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 chat GPT. It'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. Where there's a 26-year-old company called V-Lex, 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?

Because I don't know, Wilson Sincini wants to know, you know, Spanish case law in case Andreson Horowitz goes invest in a company and he needs to figure something else. So V-Lex would aggregate and digitize this information, sell it to law firms and other people that need legal information, pretty high gross margin, but 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 quintuple their revenue.

And why would it quintuple their revenue? I might love Harvey. I pay for Harvey, amazing product. But 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 V-Lex 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 ASPLEO does is it's a procurement product. So if I'm a company, every employee at every company, 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 said an overpriced price from an unapproved vendor. But 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. like what is the information they will never get? They're never going to get 50 old Deloitte contracts. Like, where would you find them? I guess you could do a FOIA request or something,

but you're not going to find them and 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 ADSB 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. It's like YouTube doesn't publish, like, how many subscribers Mr. Beast had on August 4th, you know, 2017. Like, where would you find that?

There's some company that coales, 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 Waldgarten. We called it Fruits of the Waldgarden, 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 that.

It'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, you know, 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. Like just, you can buy this stuff

for pretty much nothing on eBay. Where would you find a manual for an old blender in 1999? I have no idea. But apparently eBay is pretty, you find it, but it just shows like 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, Alex, maybe 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, you know, their success. And like, 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, you know, the incumbents in the category and where, you know, what are the examples where it probably doesn't make a lot of sense for someone to build a company? And that has a proprietary

Walde 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. So one category is you just find a new data source, and there's a reason why, like, you know,

in venture capital school, we learned to always ask why now. Like, 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 $100 million business? It's because you can deliver the finished product. And 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 errors to leave all of my money to you.

Please email them and set up dates with me so I can figure that out. And like, that's the value 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, very useful. Like, it turned out, like, 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 like he bought a big chunk of that, that ended up being worth a lot

because until the copyright runs out, like Beatles catalog, a lot of people like listen 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,

or 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.

Most potential customers are 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 the business to get, for everything to catch up. I'm very, very bullish on incumbents. I hope I can say that because I don't think that, I think NetSuite is going to figure out 15 different ways to monetize with AI. I think that QuickBow, Intuit has this gold mine 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, but that still does not mean that you don't have these greenfield opportunities, you don't have these new data opportunities. Like there are so many new opportunities that have popped up largely because of this value cost them. 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, iPhone was stupid, that's why the incumbents didn't, you know, that's why, you know, why didn't booking.com build Airbnb? Why, 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, intelligent, like, you know, AGI and 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. For 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. Exactly. Good point. I have a lot of money. I'm going to go buy the Beatles back catalog, and then I'll make money from it because this, you know, 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, V-Lex is a good example of this, right? Like V-Lex could have sold their data to Harvey. Instead, they realize this exact point. It's like they should just be in this business of selling directly to,

they shouldn't be selling to Wilson-Sincini 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, you know, this like tiny subscription fee and allow so much the value creation to occur elsewhere, we're going, like, Open AI on their, you know, open AI chart is 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. So, you know, 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,

they realize, wow, I can make my business 10 times better, and we're going to go invest in those. And then lastly, I'm just going to buy some antenna from Amazon and listen to Malaysian Airlines flights or whatever and then aggregate this information that's completely free, but it's not free past tense, right? Like 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 proprietoriness, 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. So sometimes just the ease of going to somebody who's already digitized and put this information in 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. There's a lot of workflow. There are a lot of analysts. Like if I'm a financial services company, I hire fraud analysts 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 I'll switch gears to Anish your two sections here. What is your view on white-color services AI roll-ups, i.e. fully verticalized software plus services companies that are popping up. Yeah, so I read 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 Aaron Obisco deal in the 1980s, and 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 it 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. Like, this is a big thing that private equity is looking 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's never a question of, can I get more class? as an accounting 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 like all sorts of cost synergies, you can now onboard 10,000 more clients. Like that's just like the way that you would have to play that game is you buy one accounting firm, you like integrated for nine months, then you go buy another accounting firm, then you buy another accounting firm. And 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 P.E., who's done this for 500 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 scheme, 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, and I want to get started somehow. I built this great tool that I believe in. I want a dog food it. 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 a thousand customers into the existing acquisition that I made. That'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, a hundred dental clinics,

or 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 is 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. You could buy one, and then, you know, that is your entry point. And it's kind of an opportunity cost of, 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. It's like, onboard me into Microsoft Office. And all of that stuff can be done remotely.

It's a very, very digital experience. It's $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?

I want to turn it over to a niche because all of these things that we're talking about, they also apply to consumer. So maybe with that, why don't we check why and how this applies to consumer? Great. Actually, if we're going to do that, why don't we skip ahead of slide, and then we'll come back to this.

So great. So this is the application of all the categories that Alex outlined to consumer AI. It's the exact same pattern. And 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.

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 KREA. 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.

So this sort of transformation of existing categories is definitely happening. You know, the second is category creation. 11 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 skews. And because they vertically integrate, they're able to really go after this opportunity create the category in a very short period of time.

Finally, 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. Slinghot 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. It 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, SlingShout's able to provide a differentiator 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 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, big tech who have real model efforts like Google going to win it all? Well, 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 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. So they're not exact substitutes. You want to work with them all. You We want a single pane of glass, and the labs in 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 bad advice and telling them what to do. the 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. This is, unfortunately, from my wife and children's perspective,

like a weekly occurrence right now where it's like, got to cancel this, I have to have dinner with his entrepreneur who has discovered the fountain, not of youth, but of perpetual emotion or so they think. So go meet with them. That's the interrupt part. The process part is like, you know, I'll give me 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 is a new company that's going to do this better, that's going to be AI-native. How 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 like, it's hundreds of thousands of views. It's pretty incredible. Death of a Salesforce, why AI will transform sales. Joe Schmidt and Mark Andrusco on our team wrote that. Everybody wants to talk to them. But ultimately, knowing what you're talking about really, really matters. Or, you know, death taxes in AI. We've done, we've covered the gamut on everything around taxes. What about companionship? What about, like, we do something that 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. So 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 Rillett, where if you talk to Nick Kopp, who's the CEO of Rillet, you know, Seema and Mark Andrusco just knew more about this category. You know, we were in a very, very competitive series B process. Yeah, I mean, so if you look at this chart, So we have a bunch of people, like, 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 generator. So like Mark and Olivia and Joe and Kimberly and Gabe, you know, Kimberly, I'll call her out here. like there's a company called Dekegon, 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 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 besides, you know, for better or for worse, run a company and started the company. But then we realized that some of the best people to find deals, like Olivia is just non-parail in terms of her ability to find great deals

and be an expert, as I mentioned, in Voice Hayak. So it would be insane not to have her who's like the front of the sphere finding a lot of these great deals to be working with a lot of these entrepreneurs. Great. The follow-on to that question is there are a process case host who's going to check out. What's a 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 or 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 ages, this 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, I'm the old person here, vetoes that deal versus the right process is, yes, we do have somewhat of a budget. And our investment committee is effectively making sure that we believe very strongly that the process was followed, that you've met every competitor, that the work is top-notch.

And 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 two-key process. And, you know, again, much more conviction-oriented. I know that doesn't perfectly answer the quote. But we don't have a committee where everybody votes

and then you have to have this many votes and then it's all this political horse trading. It's, all right, especially for seeds where a lot of the younger people, we have been 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. It came from the content. You know what you're talking about and so on and so forth. Right. We may just generally talk about team evolution and changes how you're thinking about

the augmentation of kind of checkwriters 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 checkwriters 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. So 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 Rolloff at Sequoia or is, you know, a top partner at Excel or Reid Hoffman at Greylock.

Like, 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 multi-billion dollar company. as they should. That makes a lot of sense. So I would say the one area that you might look to add to is, you know, somebody who probably has built a quasi-generational company that is still very, very hungry as an investor. This is not like you go, this 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. I was 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, like, this is the errors 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 with 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. And by the way, like, you know, I jokingly call Mark the Air Force because, like, if we need a big strike, like, what do we do? We call in the F-35s, like, Mark's got a few of those. We'll have dinner at Mark's house.

Ben will show. Like, we all show up beyond. just this team, but having a few other people that can kind of, you know, 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. Of 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. Okay, 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 a 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. 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. This 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. So 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, you know, 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, you know, you know, significant kind of enterprise sales. And 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 kind of on the forward-deployed engineering side than necessarily on the sales side. I mean, 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. He's asking that before we hire people here. And I think that mindset actually, if you do it correctly, like, 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 really, 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. Thank you. Thank you. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.com.

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