Why AI Moats Still Matter (And How They've Changed)

The thing that is fundamentally different about this product cycle is that the software itself can actually do the work. And therefore, the market opportunity for software today is no longer just IT spend. It's largely labor.

It's not like all the jobs will go away. I actually think that's not gonna happen at all. There are a lot of things where if I could hire somebody for a dollar to do this task, I would a 100% do that. I've never been able to hire somebody for a dollar. Now I can hire software for a dollar.

While it is important to understand model capabilities and what's happening in Frontier, you still need to figure out how to apply that technology.

I think modes matter just as much as they did before. The one change is that in the supply demand equation, there's conceptually more supply of software on the cup because the barrier to creating this stuff has gone down dramatically.

I think AI is an incredible tool for differentiation. The idea that a voice agent can speak in 50 languages fully compliantly twenty four seven, highly differentiated, you know, certainly versus the human. The AI ness of that capability, in my opinion, is not

a source of defense ability. It is just so consensus. Like, cloud was not consensus. Mobile was not consensus. And that's why the incumbents kinda screwed up.

Everyone's saying that AI killed the concept of moats, that anyone can vibe code a Zendesk competitor in their bedroom, that 20 companies are building the exact same thing you are. So why are software companies potentially more defensible today than any other time in history? A sixteen z general partners David Haber and Alex Rampell are seeing companies charge $20,000 for what used to be called a feature because that so called feature now replaces an entire person. They're watching startups attack markets that were never worth touching with software, like plaintiff law and auto loan servicing, because suddenly the market isn't IT spend but labor spend. The counterintuitive reality is this.

The same force creating infinite competition is also creating trillion dollar opportunities in places nobody's looking. In today's episode, we explore the relationship between momentum and moats, why the nineteenth player always dies, and how to find the Goldilocks zone where you're too small for giants to care about but big enough to build an empire.

We've spent a lot of time talking about moats and how moats have evolved and are there still even moats in this new era. And so why don't you reflect and share some of the conversations we've been having or some of your perspectives on this broader moat question? Maybe, David, we'll start with you.

Maybe just to jump right into it with a hot take. I think moats still matter, and I think a lot of

the moats Moats still matter.

Still matter, exactly. I think they're largely the same. I often think about this between differentiation and defensibility. I think AI is an incredible tool for differentiation. The idea that a voice agent can speak in 50 languages fully compliantly, 20 fourseven, highly differentiated, certainly versus the human.

But the AI ness of that capability, in my opinion, is not a source of defensibility. It's largely differentiation. The defensibility of a software product resides, in my opinion, from owning the end to end workflow, from the context in which that it's applied, becoming the system of record, having a network effect, deeply embedding yourself within your customer. I think these were the heuristics that were always things that we would always look for when evaluating software companies. I think the thing that is fundamentally different about this product cycle is that the software itself can actually do the work.

Therefore, the market opportunity for software today is no longer just IT spend, it's largely labor.

The challenge often has been that everybody can build something at small scale, and a lot of the I wouldn't call them network effects, but some of the defensibility moats only become apparent at large scale. So, like, a lot of people talk about, okay, take an example from, like, long time ago, pre AI era. If I am building an anti fraud company, and I've seen lots of people, Right? Am I going to do a better job than a net new anti fraud company that's seen a few people? And the reason why this would be called a data network effect, although there's another podcast that Martine and I did a long time ago debating whether or not data network effects are real, but it's something that really it's almost like gravity gravity actually, like, one atom actually has exerts gravity on you, but you only really see it at, like, very, very large scale.

Like, the Earth, you notice the gravity. The sun, you notice the gravity. Jupiter, you notice the gravity. You don't notice it for, that glass. And it's the same thing for a lot of these data network effects where at very, very small scale, when you have 20 companies that are all saying, I'm going to stop fraud.

Alright. They're all building the same things. They all have the same algorithms. But when you've seen 4,000,000,000 people and like these people are bad, now you can sell each incremental customer, each customer of your anti fraud technology, to use this example, because you've seen more customers and you can get actually better results. But the challenge is that a lot of these moats only really are evident at mega mega mega scale, and the same argument would apply.

It's like, oh, like, I've seen four customers. David's seen three. I've seen four. He's seen three. Pick my software.

But it's like, you've seen four customers. That means there are 8,000,000,000 customers you haven't seen. There are 8,000,000,000 customers he hasn't seen. What's the difference? Whereas at mega scale, it's like, all right, I've seen 4,000,000,000 customers.

He's seen 1,000,000,000 customers. Well, it's actually kind of easy to see that the results of my product will be better, but that's at scale. And a lot of the question is on the zero to one phase, it's hard to make the argument that I have better, if it's fraud, I have better fraud underwriting. If it's AI do the work, like, I've done more phone calls to a particular type of customer and therefore I do a better job. It's hard to make that argument at subscale.

And this is often the challenge is that it's kind of self evident that if you become the biggest company in the world, then you have a moat. But how do you get to the scale where you actually could show? You can't get to that scale if you have 9,000,000 ankle biters and you are yourself an ankle bitter of just we are trying to get to scale and nobody can because it's so easy to actually produce software. And that's the double edged sword of AI is that it's very, very easy to produce software. Everybody can go do something that is a very obvious idea, because it's obvious, everybody's gonna go build it, but can you get to the type of scale where you actually could show a moat?

And that has gotten arguably harder because you have a larger end count of potential competitors, But if you get to mega scale, then you could show them out. And that's kind of the zero to one versus one to n.

Maybe talk about what's different about defensibility for even the bigger players today in the AI era than it was in, let's say, the web two era. Are the companies today more defensible, less defensible, or how should we think about sort of the strength?

I think the less defensible part, this is why a lot of enterprise software has gotten beaten up in the public markets. It's kind of two reasons. Number one is that if you're doing per seat pricing, like, how do you come up with a pricing model that people feel is fair? And a lot of it is just psychology. And for whatever reason, for the last twenty years, it's like per seat per month with you've heard my joke.

The tall grande venti model of, like, software charging. It's like somehow that felt fair. And whether that is fair or not, I don't know. But, like, people are like, oh, yeah. It's $85 a seat per month.

Yeah. Okay. That sounds reasonable. Whereas if you proposed that pricing forty years ago, you would have been laughed out of town. So this just became the norm.

And the reason why, as I was saying, public software companies have been beaten up a little bit is like, uh-oh, maybe you won't sell as many seats. Is Adobe gonna sell as many seats if now you don't have to hire as many graphics designers? Or is Zendesk going to sell as many seats if the software just answers all the queries? Like, the answer is no. It doesn't mean that the companies are toast.

They might actually quintuple their revenue because now they charge per outcomes as opposed to charging per seats, but that's kinda part one. Part two is, wait a minute, now everybody can vibe code up a Zendesk competitor. So maybe companies will just stop buying software. This one, I don't think we've seen at all, but I think there is like these two sided, these two risks. But to answer your question, does defensibility change?

Well, if you now are able to code your own software, like, why am I paying like, your margin is my opportunity. Well, look at the margin of software companies. Like, Salesforce has an 80% gross margin. Like, should they have a 1% gross margin, or nobody should use Salesforce anymore. That would be the pro case of Moats really starting to disintegrate, but I don't think we've seen that happen at all.

Because it turns out people on the one hand, two things are actually happening. One is that this is kind of like Clay Christensen theory. It's like the incumbents overshoot the market. So the amount of features in Salesforce or Zendesk or NetSuite, it way exceeds the feature set that you need, that any individual customer needs, because it's meant to encompass, it's like all of these weird edge cases, and you kinda see this if you use Microsoft Word, when was the last time you wrote a book? When?

Never. Right? But I haven't written a book. It has all of these things. They probably have 50 But software

if you do write a book,

guess what? Microsoft Word has all these features just for book authors to, like, make a table of contents or something. It's like, I don't use that. So they keep bundling more stuff in there, so they overshoot the market. And theoretically, it's gonna make it easier for somebody, but kind of going back to where I started with this topic, like it turns out that this concept of I'm just gonna vibe code Microsoft Word, it's like there are these edge cases that you just don't know about.

So it's actually, you know, why don't you grow your own food or weld your own aluminum or build your own house? It's just it's kinda easier to use this concept of comparative advantage and just say, I'm going to buy something off the shelf. So, anyway, so I think moats matter just as much as they did before. The one change is that in the supply demand equation, there's conceptually more supply of software on the come because the barrier to creating this stuff has gone down dramatically.

I think the flip side to that too is that while there will be more software, and again, the kind of marginal cost of producing software is declining asymptotically towards zero, the way that these companies are getting more deeply entrenched within their customers has differed because, again, the software is doing the work, and therefore, in many cases, it's actually replacing labor. And so if you've transitioned a team out that has now become your software, you're now much more dependent on that product to run your business. And again, is it more difficult to replace that software with another piece of software or to rehire that team? I think it's an open question, but again, the software is doing more of the work and therefore I think getting more deeply embedded within their customers.

Not only do you not care, you don't even care enough, you won't even exercise the mental energy to find the person in the company who does care, right? And that means that your janitorial services spend will never change. And the problem is it's hard to get in, the good news is it's hard to get out. Whereas for something, it's like 90% of my profits go to like you, I'm now 90% of your profits as the CEO of GE, they're going to me, your number one priority is, like, getting the hell off of me. Right?

And, like, doing RFPs left and right. So part of it is also just, like, how relevant this is. And there are some companies that operate in this Goldilocks zone of irrelevance, like these janitorial services, where even if you have 9,000,000 competitors, like, they're just not gonna go anywhere, which is why, like, a lot of the strategy that we talk about internally is greenfield. Right? It's like those companies are they're they're stuck for good.

Is there a high rate of new company creation that will not use the crappy old janitorial services company, but will actually resonate like, your pitch of, like, I will get your toilets cleaner, and I will charge you less money, that really resonates, but that's not gonna resonate to the people that are using the old fashioned stuff.

What are examples of company or space in the Goldilocks zone, and what was an example of company or space in the Greenfield zones?

Well, like payroll companies, right? Like ADP and Paychex, mean, are companies that are collectively worth hundreds of billions of dollars, very, very profitable, and how does pay like, you could do your own payroll. Actually, it's kind of a good metaphor for software in general. Like, why is it that you have to like, why can't I just pay you? You're my employee.

Why can't I just, like, cut you a check? Well, because I have to withhold taxes. Well, how much tax do I have to withhold? Well, it depends. Right?

And there is this, like, super complicated lookup table. It's like, well, you live in this county, but you spend this many days in New York and this, that, and the other thing. Oh, and you owe child support, and the IRS is garnishing your wages. All of these things that are very complicated. So it turns out it's just cheaper to go to ADP, and ADP just charges you, I don't know, like $50 a month per person that you might be paying a 100 It's a paltry sum compared to the overall amount of payroll.

So nobody really switches their payroll companies. Like that would be an example of one. On the other side, I had a lot of companies coming out of 2022 where the market really went through a downturn, and they're like, wait a minute, I'm spending four I had a thousand employees, I downsized to 200 employees, I had a thousand licenses for Salesforce, right? What's a thousand times a $100 a month, times 12? That's $1,200,000 a year.

Wow, like, that's a lot of money because I only have 200 employees and I only have six months of cash, like, I gotta save that. And they didn't do that for their payroll spend. So you see it like a lot of companies do wanna rationalize their overall software cost, especially for these things where they recognize in aggregate, like most people aren't actually using the seats. So I'd say, like, you know, Salesforce type stuff, you know, some of the creative tools. Like, if you like, Adobe is very expensive, and you might just do, like, a wall to wall license saying, why not?

But then you look at if you're like, how do I save $5,000,000? Nobody's using this while it's $5,000,000,000. Whereas for things where inextricably the delivery and the payment are linked, right, which is very, very different than pricing for software. Like payroll, obviously, I'm not gonna pay for payroll services unless you are employed here. Whereas, we have 600 people that work at our firm.

I think we have 600 licenses from Microsoft Office March. Like, we probably I bet there are a lot of people here who have not opened Microsoft Excel in a year. So why are we paying for that? And that would be the idea of kind of rationalizing software spend. So it it it kinda depends, but I think per seed pricing, where it's like it's just easier to pay for the entire thing wall to wall, you know, in your entire organization, those are often the first to go versus things that are, again, inextricably linked to the actual usage.

Yeah. So you mentioned earlier that we've seen, you know, basically, you mentioned there was this concern that maybe instead of Zendesk, it will, you know, companies will, you know, there'll be a vibe coded version of it, but we've seen none of that so so far. Is your mental model is we'll we'll see it to the in examples where the the cost is significantly high or in which there's sort of greenfield opportunities, or what is sort of your mental model for the types of software that will replace?

Yeah. I mean, I think the greenfield one is always true, but when you look at greenfield opportunities, you need two things to be true. You need the entrepreneur to be very, very patient and say, I'm not going to try to sell to everybody who's if I'm if I'm starting a net new payroll company, I'm not going to try to sell to GE because I recognize that they are are hostages to ADP, and that's never gonna change. So one is that patience of entrepreneur, and the other one is you just need a a high enough rate of new company creation to really make it work, which is why, like, to pick on one space of electronic health records or electronic medical records, how many new hospital systems are created every day? I mean, it rounds to zero.

So if I'm trying to go build a new EHR system to go compete with Epic or Cerner, I can do that. There are a lot of edge cases, it's like and I might have patience as an entrepreneur, but wait a minute, like, I need to sell $5,000,000 deals to big hospital systems. Every single hospital on Earth is currently using an EHR system. It's gonna be really, really hard to make that work. So I think think both of those need to be true, like the right type of entrepreneur who's willing to be patient.

Because it's often a very lonely game of it's like, I built this great product, wait a minute, I don't have any customers yet, and you wanna see high traction because you're seeing in the rest of the market, like some companies are just going like this, and my company's not, and I'm in Silicon Valley, and I need to recruit the best people. It's like they wanna work at the company that has the graph like this, but you need this Greenfield requires patience. Yeah.

The so we're talking about how moats still matter, and in in many ways, they they look pretty similar. Let let's steel man the other side for a second. Where, you know, where are we even having this conversation where some people say, hey. You know, brand is the is is is more shipping velocity or because this area is different? What's the steel man of their argument?

Look, I think this market is noisier than ever, so I think finding ways to stand out from the crowd probably matters more today than it has in the past, I would argue. Think the other thing is that the underlying technology is changing so quickly, and so as a founder, you want to be living on the frontier and understanding what model capabilities look like, because it can dramatically change the efficacy or the capability of your underlying product. One of the things that's changed, I think, that's been really interesting in this current wave of, especially vertical applications that we've seen, the type of founder. Think founders today are often younger and more technical than we've seen in prior generations, so they're less often native to the particular industry, but they're fluent in the toolset. I think that's really important, to the same point, you've got to stay on the frontier and understand what's coming.

At the same time, I wrote this piece that I call Context is King. While it is important to understand model capabilities and what's happening in the frontier, you still need to figure out how to apply that technology. While the founders themselves are maybe less native to the industry, they're still hiring for context very early in a company's life cycle. A good example of this that I sit on the board of is a company called Eve. The two founders of Eve were the earliest employees at Rubrik, which is now a public infrastructure company.

They built a legal AI company in the plaintiff law space. Neither of them had any particular background in employment law or personal injury, but they deeply understood how to apply document extraction technology and voice and LMs more broadly to this very particular workflow. And they've hired plaintiff attorneys actually on staff. So anytime a new model is released, they are understanding from people in industry the impact that it's having on drafting, on their ability to reason through a case or a matter. Again, it's sort of this tension of building the brand, having momentum, understanding what's happening on the frontier, and yet figuring out ways to apply that technology in the context of your specific customer, because again, I deeply believe that that is where a lot of the sources of defensibility reside.

I'd love to find other examples of businesses where the technology reinforces their business model, it doesn't compete with it, meaning in lots of areas of legal, if you make your employee 50 times more efficient, you're eroding your billable hour. In their business, they operate at a contingency basis, meaning they only get paid if they win, So there's no sort of limit to the amount of AI that they want to adopt. And if you can become five x more efficient, you can take on five x more clients. Anyway, these are sort of characteristics that I think, you know, I'd love to find more of, and, hopefully, that can be kind of a bad signal too.

I think the other steel man is if you believe that brand matters, which it almost tautologically does because what do I buy? I buy the thing that I've heard of. Right? So there's an advantage there. And if you believe that for a lot of companies and products, somehow having scale is effective.

Right? So not a network effect, but a scale effect. So if I'm Honey Nut Cheerios and I know that people are gonna buy lots of my Cheerios, I can I can build a big factory and not, you know, hand crank out each Cheerio? I'm I'm going to have these compounding advantages just in terms of economies of scale. Right?

Like, Amazon, is that does that really have a network effect? No. It's, like, it's kinda nice that everything that I buy will show up the next day or in two days, and how can they do that at low cost because so many people are buying things? So there are some things that have scale, and those things also benefit from brand. So if you can move the fastest, right, so if you can agglomerate capital and labor, so it's like I raise the most money, it's a very, very generic idea, but somehow, like most other things on planet Earth, if it's the biggest and, like, really, really big kinda gravitational scale, then it's just gonna work better.

So can I get there the most quickly? But there are 20 companies that are doing the exact same thing. And at that point, I wouldn't say that Momentum is a moat per se, but momentum has the highest chance of getting you to gravitational scale where you do have a moat. And if you don't do that, by contrast, you're just gonna get eaten alive because you can't hand crank out the Cheerios. You you have to get to the scale where you're able to build a factory.

And you have the biggest factory, you can crank out the most things at the lowest cost, so what is the trajectory? What is the slope of you versus all of your competition? And if you have not a good slope, you're just not gonna win that game.

Yeah. One of the questions for defensibility in in Web two companies was, hey. Would Google you know, would those will they some someday build us or or Facebook or name your incumbent? In in the AI arts, will open AI or will some other major company how we think about framework in the AI era?

It's funny. I feel like eighteen months ago, this GPT rapper was on everybody's lips. And I think it was largely used as a pejorative. Think, to some degree, I think there are some spaces where the model capability and the application capability, if they're very overlapping, I think you're in a risky spot. But the reality is that there's so many, I think one of the remarkable things that's happened is there are so many markets that were never particularly interesting to sell software into that are now radically interesting spaces to build companies in, again, in large part because the market is now labor, not just IT spend.

Plaintiff law being an example. Alex says, We have a company called Salient in applying voice agents to auto loan servicing. Five, six years ago, would we be back to a software company selling to non bank auto lenders? Probably not. The company's doing incredibly well, again, in large part because the capability of being able to speak in 50 languages, fully compliantly with customers in 50 states working 20 fourseven, is just so differentiated versus the individual, and they're finding that their ability to collect is meaningfully higher than that labor, that the cost benefit trade off is so dramatic.

The company is getting a lot of revenue from those customers who may not have had you know, millions of dollars of of IT budget historically and are now very willing to pay for a product like that, you know, given the impact on the business.

And and the way that we used to talk about this a long time ago is and this this almost had a pejorative slant to it, but it's like, you building a feature, a product, or a company? And what's the difference between the three? Well, a feature is like there's an existing product, and you tweak that product to make it marginally better. A product is, you know, not that. It's like some, hopefully, system of record or something that keeps track of something.

And then company is probably the most defensible of those three where you have a product and, you know, maybe you own a platform. Like, the platforms tend to be the most valuable companies. But, you know, a feature is, like, I've built a Chrome plugin, and that doesn't mean and, like, there were by the way, were lot of Chrome plugins. Like, Honey was a Chrome plugin that got bought by four for $4,000,000,000. Like, I wish I had done that.

Right? That's that's a good feature. But that was a feature. You know, a product would be like, oh, I built my own browser, and a company is like, alright. Well, like, my own browser company actually makes money.

Like, you don't actually have a company, even if you have 10 products, if you don't have a sustainable path to have that company be around in ten or twenty years. And I think kind of another way of thinking about what David just said is that now the features, like, you know, the feature was the most pejorative and seemingly small of all of those three, almost obviously. Some of the features can be incredibly profitable because it's like, wait a minute. Like, this it feels like a feature because it could get added to Salesforce, right, or it could get added to one of these other things, but the amount of money that I can charge for my feature is orders of magnitude more because it's like, hey, I'm going to be the front office receptionist for your orthodontic clinic. Like, that's my job.

Like, that's the feature. And it sits on top of whatever software you currently use, but the feature I can now charge $20,000 a year for because it is doing the job of labor. But, uh-oh, will the existing product that my feature is riding on top of, will they go build those those pieces of functionality? And or will another company show up that just says, hey. We're gonna sell the greenfield with the new product that kind of has this feature set embedded.

And, you know, feature product company, it still is out there, but I've just never seen a world where the features, if you will, can can get to revenue scale as quickly. And by the way, you you kind of often have to start with the feature because a customer isn't like, think of it from the customer's perspective, the customer being the business buyer of software. It's like, I know. I wanna be locked into a piece of shit software company for twenty years. That's what I'm looking for as a buyer.

No. It's like, oh, I have a problem to solve. My problem is I can't hire a front office receptionist for my orthodontic clinic, or I can't call people in Mandarin or Cantonese to go, like, repay their auto loans. Like, what do I do? Oh, something shows up and it offers that functionality, boom, I'm a buyer.

And then that functionality has to that that feature has to backfill product, backfill company as quickly as possible. So that's still true today as it was ten or twenty or thirty years ago. But the difference, again, is that the feature the revenue for the feature is just so high and the demand for it is so high because, again, in many cases, you're just responding to help wanted ads effectively.

Yeah. And so I think the effect of that is that there's been sort of like a Cambrian explosion of interesting markets to go after. You know, I think it's unrealistic to believe that, like, OpenAI is gonna go build, the front office assistant for the dental clinic as their core business. They aren't going do that across every single market. I think the other dynamic is that for many of these companies, part of the product value is actually orchestrating the work across lots of different model companies.

And so I think having one foundation model business going kind of up the stack, I think, limits the actual impact of the application potentially as well.

Well, think if you kind of think about this versus other platform companies, so Facebook was the preeminent platform company of Web two point o, so call it from whenever they opened up Facebook platform, which I think was like 2007, people built their businesses on top of Facebook. Facebook would never do those particular things. So Facebook is never gonna show up and say, Hey, you know what? We should build a farming game. They were like, No, we're gonna have a platform that allows companies like Zynga to build these farming games.

But what the platform normally does, if they don't actually go compete with the underlying products, is they say, I'm going to tax it, but I'm going to tax it in ways that are kind of at my fancy. So this week it's 10% taxes, that's my promise. Oh wait, I changed my mind, now it's gonna be 40% taxes. That's why it's always dangerous to build on somebody else's platform. So I think the two things to look at are number one is will the platform owner compete with what I'm doing?

And that's also another Goldilocks Zone question, right? Because why is it I published this graph of VisiCalc versus Lotus one, two, three versus Excel. So VisiCalc invented the spreadsheet in 1979, had 100% of the market because they were the only player in town. Lotus built a better version of that. Lotus got to, like, I think 70% market share by 1985, which was when Microsoft released Excel for a Mac.

And then by 2000, Microsoft had 96% market share. And why is it? Because they owned Windows. Like, the platform owner normally wins. But that's because it was just such a huge like, why do I buy a computer in 1997?

Because I wanna use a spreadsheet. Like, it was just so intrinsically linked. Like, that was one of the main use cases for computers and business use. Right? It's like using spreadsheets.

So that was like a violator of Goldilocks zone. Whereas other things where it's like all you have to worry about from the platform owner is that they're going to tax you, but they might tax you in very, very bizarre ways. But part of what David was saying in terms of like, there are multiple model companies, which is great. Like, the problem with Windows was that it was like 95% of the market. Like, 95% of your customers used Windows.

So if I'm gonna go build a competing spreadsheet, I'm just toast because the platform owner is just gonna drown me. Now there are five model companies, or more, when you include all the Chinese models and whatnot, open source. I don't have to worry about that, but I do have to worry about them saying, wow, this is so relevant. Why is it that OpenAI got a public company CEO to quit her job and just to become the CEO of applications at OpenAI? Maybe because they have a huge application opportunity.

But this is the nice thing, is that a lot of these things are so obscure, but they're still big. But I don't think OpenAI is gonna go do them because it's like, are they going to do, like, dental care management? Like, they they could, but if they've done that, then I would be short OpenAI because it's like they've run out of good stuff to do. That's something that they should do in 2029. And then this is, I think I told you this story before, this changed my outlook on life when I pitched this guy, Dan Rose, at Facebook, who was running business development there.

I'm like, this is a huge opportunity, you should use us for payments, we're gonna do this, we can make so much money for Facebook. And he was so patient and nice, and I love this guy, I'm on a board with him to this day. He was like, Alex, that's such a great idea. I was like, all right, I got the deal. Yes, he said it's a great idea, but we're not gonna do it because you're pitching me a goal.

Like, we have gold bricks all around us. And he was right. I mean, like Facebook in 2010, I mean, how much money? Facebook has grown their revenue. They have more profit every quarter today than they had revenue per year in 2010.

It's just such an incredible company. And he's like, you're pitching me a gold brick that's like 100 feet away, and it's real. Like, I love that gold brick, but we have like hundreds of gold bricks where I just have to like stoop down at my feet and pick them up, so I'm just not gonna do that one right there. And that's how these big companies think. But the nice thing is that these are gold bricks.

These gold bricks are bigger than they've ever been, because you have software that can do the job of labor.

Yeah. Which is on that note, if you were running OpenAI and you were thinking about which gold bricks or how do you even, what mental model would think about, sort of what are the things that you should be doing first versus the things that, hey, maybe let other people do, how would you be thinking about that question?

I mean, I think a lot of it is where well, it's it's two things. Number one is we want to be the back end for everybody. Like, the platform I I think it's two things. Number one is can we be the platform for pretty much everybody who's building anything? So we're not going to go into obscure spaces like, you know, orthodontic care, at least not until 2045.

So let's make sure that every single developer is using us. And this is part of why Microsoft crushed Apple in the 1980s, because Apple made it really hard to develop software. And what's actually kind of interesting is that both Apple and Microsoft had Microsoft started off as a compiler company. Their very, very first products, they were not Microsoft Office, it was not DOS, they built a BASIC interpreter for the programming language BASIC, and they had a big business. Their biggest competitor was Borland, which only made compilers.

And like the early rallying cry, if you talk to any early Microsoft employee, was Beat Philippe. Philippe Kahn was the CEO of Borland. So Microsoft was focused on that, made a lot of money on that. And Apple was like, we should make money on that too. And they had a product, it was called MPW, Macintosh Programmers Workshop.

I remember I used to use it in the nineteen eighties. And it was like $2,000, I think, in nineteen eighties money to buy this IDE or, you know, programming thing. And it's like, how do you afford that? So, like but it was like, we have to make money on that. Microsoft's making money on this.

And then lo and behold, there were, like, 10,000 times more, you know, DOS and Windows software products than there were Macintosh software products. And of course, Apple corrected that mistake when the iPhone came out, when they did now, like Xcode, which is the way that you build products for Mac products, or Macintosh and iPhone, iOS, it's free. So they kinda corrected that mistake. But I'd say two things to answer your question. Number one is, can we be the biggest consumer brand in the world?

So ChatGPT has 800,000,000 weekly active users. Get that to 5,000,000,000. Even if Gemini three came out today, it might be five times better, but are people that are using ChatGPT just as consumers, are they going to switch? Like, maybe, but it's unlikely just because they kinda make that their their default and then be the back end for everybody who's building anything. And that way, it's like all the gold bricks come to you.

I

think the other thing that we should anticipate, we're already beginning to see from some of these big model companies, are what are the big horizontal applications that they can likely sell to every large enterprise? And I think you saw today with Google's anti gravity launch, the IDE is going to be one of those things. Think if there's product market fit for LMs, coding is definitely one of the top categories. Thinking about what are the big horizontal applications in the enterprise, I think there's also, to some degree, I think this has been earlier to play out, it's the Palantir opportunity. I think we're still very early in the proliferation of this technology into large enterprise.

At the same time, unlike prior product cycles like the cloud, if I'm the CEO of a large public company and I'm asking myself, do I need to be in the cloud? It was sort of an esoteric idea. Today, I can plug a prompt into any one of these models and intuitively understand the impact that it could have on my business. The efficiency gains in my customer support organization, in my engineering organization, in all of my back office functions. At the same time, many of them don't know where to start.

I think you will see this consultative, forward deployed, Palantir esque sale into very large enterprise from some of these big model companies. Again, I think we're early in that, but you've heard inklings of this with Anthropic talking about wanting to build into financial services in other markets. I agree. Think the biggest opportunities are the one that Alex is describing, but I think you will see them selectively try to build applications that cut across every one of those, and then they'll probably choose a few lighthouse customers to build largely bespoke custom integrations into these bigger enterprises, but where the ACBs you know, just really make sense.

In in in Web two, there was a lot of winner take most. You were talking about one of the benefits in AI is that there there's multiple winners. To to what extent is is consolidation in in inevitable, or or how do you think sort of this this split plays out?

Well, I I think if you have 20 companies that are all doing the same thing, what has historically happened is that it's a bad market if there are 20 companies doing it, but then, I don't know, the bottom 15 just go bankrupt. And then maybe there's some consolidation where number one buys number two, number two buys number three, and assuming that we have a functional FTC and whatnot, it's like all of this is approved because it's not like you're taking this is like orthodontic clinic answering software or something. So and then what was a bad market becomes a good market. And this kind of goes back to, like, why momentum is important because if you have 20 companies that are all at the exact same scale, then it's actually great for the customer, which is like the the prices go to zero, or they converge on the price of electricity. Whereas if you this is not saying you want to go build a monopoly in orthodontic answering software or something, but rather you can charge more if you get to a certain scale because whatever the the quality of the product that you're delivering at the end of the day is just higher.

And you have to get to the critical scale to get there. And sometimes you just need these markets to work themselves out. I mean, like when I was running my company, TrialPay, we had, I don't know, 20 competitors, and it was tough because it's like, you know, everybody would be pricing their product at a loss. You know, this loss leader only works if you end up leading with, like, you have to make money at the end, and nobody really had a plan for that because the venture capital dollars were really subsidizing everything, and that does not get a good market. What does become a good market at the end, and sometimes this is what, you know, Vista, the private equity firm would do, is like, we're going to buy one as our anchor.

We're gonna go lowball and put the other five out of their misery, and now we end up with actually a pretty good product at the end or a pretty good business at the end, pretty good company at the end. So I think that will probably play out the same way here, because you just can't have a market where you have everybody loss leading and nobody's big enough to get any kind of scale effects. Is there gonna be a world where the the nineteenth player survives? I mean, Jack Welch would always say, you have to be number one or number two, and there's no value to being number three through a 100. I don't think that's changed.

Right. Right?

Even in the model provider example? And I'm also curious if prices go down

Yeah. I I I don't I don't see how, like, there actually are, I mean, people know XAI, Anthropic, OpenAI, Gemini, like, they know, or Quen, they know the big ones, but there are actually, there's a long tail of things that people haven't heard of, where it's like they've raised lots of money, just like not, it works fine, but how can you, like the model company is the most cutthroat, because like, unless you're, if you're state of the art minus, minus, minus, and you're trying to earn a living, that's just not gonna work. So that

game is super cutthroat. I think the one area where that may have diverged, and Martine talks about this a lot, is when markets are growing so quickly, end up having specialization. I think in other modalities, in some of the creative tools or people I've specialized to serve the upmarket, I'm producing movies, I want to create social quality content. These are different markets that the models can specialize in. Time will tell how defensible those become over time, but maybe that's the optimistic take that early on everything looks overlapping and competitive, but we're still so you know, the market is growing that everything can kind of expand and people can kinda specialize over time.

Earlier, when you were talking about the feature versus product, it it didn't Steve Jobs once tell Drew Houston that Dropbox was was just a feature?

Yeah. I mean, that that's why it's it's always been this pejorative thing, but that's that's kind of the point that I was getting to is that nobody wants to, like, oh, I need this company. No. It's like, I need this feature. Every now and then, you see a product that is not a feature because it's just, like, so far out of left field.

Like, nobody was anticipating ChatGPT dominating their daily workflow in 2022 in October. But then once it came out, it was this, like, holy crap. I this is incredible. And that's not a feature. You could argue it's a feature on top of your iPhone, but, no, the iPhone is the delivery mechanism.

That's a that's a product. And they they've obviously turned that into a company. Whereas other things, it kind of is like, you know, why is there antivirus software? That almost doesn't make any sense. Like, shouldn't the operating system stop you from getting viruses?

Like, why do you need a third party tool to do synchronization between devices? But it turns out, like, the reason why Dropbox has survived and thrived since Steve Jobs made that comment is, like, it's really hard to do well. And there's a lot of other things. Like, once you've built that feature, you can backfill with all sorts of other product, which is what Dropbox has done a pretty good job of. But it it is hard because this is the the danger of building on somebody else's platform is that, you know, I'm gonna build this thing that they should have had, right, if they had the foresight.

And if it doesn't operate in the Goldilocks zone, right, it's like, wow. This is so this will, like, triple Apple's profits. Let's just say that Dropbox would have tripled Apple's profits. Would they have dropped every would would they have focused on building that versus the iPad or something? Whatever, like, Steve's last, gizmo was, like, sure.

But if it's kind of in this, like, Goldilocks zone of irrelevance, like janitorial services, it's like, yeah. They should do that. But, you know, platform owners get lazy. This is why, like, you know, half the things on my iPhone don't really work if they're built by Apple. Try like, any any parent that's listening to this, if they've tried screen time, it's just like an embarrassment upon humanity.

And because they don't have to go sell as a it's like they don't have to compete on feature. They compete on the fact they don't even compete. They just like they're the platform. They roll it out. It's gonna be bad, and that does create an opportunity for somebody to come up with a feature and actually outcompete the platform.

But you have to be careful because it's like, obviously, the platform owner is gonna go compete with you. And that's why often what I find very compelling about entrepreneurs, when they know this, they've studied how is it that from every single platform shift from like, you know, we were talking about AC versus DC current, like, there have always been these battles for like who's gonna be the underlying, you know, layer. The best entrepreneurs have studied this, and they have a plan. They're like, I know I have a feature. Like, Drew knew this.

He's like, I know that, like, there's this stupid comment on Hacker News. It's like, oh, this is just like our sync with this, that, and the other thing. It's like, yeah. Of course, Drew knows that, But he built this into a $10,000,000,000 company because he had a plan. And the best entrepreneur is they often like, okay, I know it's not this naivete.

It's like, I'm gonna build this. There's no way that they're gonna build it because they're too dumb and stupid. It's like, no, they're not. Like, these companies, if they get their act together, they will marshal a lot of resources to go compete with you. It might take them five years, but they will 100% do it.

You have to backfill your feature with a product, and you have to have a moat for that product as opposed to like, oh, yeah, the big company will never figure this out. It's like, That's not true.

I think what's also unique, I wrote this piece a while ago called The Messy Inbox Problem, and it was sort of a wedge strategy that we've been observing across lots of different industries. It's just this idea that you hook into a bunch of your different unstructured data sources. It could be email, it could be fax, could be phone. Tenor, as an example, has trained a model to be able to extract all the relevant patient information from those data sources, to plug it downstream into some system of record, in their case, in EHR. But this exists in a CRM, an ERP, what have you.

I think that wedge for that feature is interesting in large part because it lives up funnel from software. You're replacing the human level judgment of the individual. Often, secretary is collecting the physical facts and then plugging it into the HR. Now a bunch of AI companies can wedge in and then eat away at all the downstream workflows that might have been their point solution software companies. Tenor is no longer just doing the messy inbox.

They're now doing scheduling and prior auth and eligibility and benefits, and they've used that wedge to try to become the end to end platform. Eventually, maybe to become the system of record. But again, because you can replace the human labor now with software, I think it's creating opportunities for these features to actually become products and, in their case, I think become whole companies.

Well, I think this is the thing that, in my mind, is very dramatically different than every other platform shift, is that it is just so consensus. Like, cloud was not consensus. Mobile was not consensus. And that's why the incumbents kinda screwed up. Where it's like and then sometimes it was just, like, completely I'll I'll use the the the Silicon Valley term, orthogonal to their business model, because it's like, I sell $5,000,000 a year products, and wait a minute, I'm gonna charge $100,000 a month?

Like, that's just hard. Like, how do I pay my salespeople? How do I make my quarterly numbers? So that's why Workday beat PeopleSoft, or that's why Salesforce beat Siebel. So all of these things played out, but behind it was this concept of it's like that new thing, that iPhone is stupid.

Like, there's no version of the famous Steve Ballmer clip of him saying this, Nobody's gonna buy an $800 phone with no keyboard. There's no version of that for AI. It's like, how do you find a big CEO, or even a small CEO? Nobody will use that tool that makes you 100 times more productive. Course.

And this is why it's kind of a bonanza for most of the incumbents as well, because anybody who has a system of record will add a button or a feature to use our parlance that will make them more money. So, like, they're just kinda gold bricks everywhere. And the challenge, though, is that there isn't this kind of white space to occupy in the same way that there was for cloud or for mobile or for a lot of the web two point o things where it's like you just like, the incumbent screwed up, they weren't paying attention, they scoff at this new technology, like nobody's scoffing at this new technology. Like everybody's just trying to embrace it, but the opportunity often exists where a lot of the areas that just seem too small, they don't have an incumbent at all, like, those actually might turn out to be, like, you know, trillions of dollars of value. And that's kinda what makes it much more exciting than, like, last gen where it's like, oh, I'm just gonna copy everything that was on prem and make it, you know, recurring billing cloud, and I'm gonna do that at a time when the big guys say that's stupid, and I don't get it.

So some argue that mobile was ultimately sustaining and that although there were net new companies and use cases that were $100,000,000,000 like Uber and Airbnb, etcetera, that, you know, the incumbents, you know, some of them became trillion dollar companies, you know, who got it by mobile. When we look at the, you know, business impact of of the AI era, what's your mental model for thinking about sort of the incumbent versus start up or or kind of net new company in terms of value, you know, value capture?

I I I think a lot of it is the same. Like, unless you really screw up the the pricing model or, you know, you're all per seat pricing, it's very, very hard to just get the market to adopt something that is just violently different, and you're operating in the public eye, and your technology team is bad. There are lot of ands that need to happen. I have a hard time believing that incumbents will really suffer. I mean, there probably are some things.

Take one example of, and this kinda goes back to distribution versus technology. Like, all of these business process outsourcing companies, these BPOs, they're the largest employers on the planet. So, like, Tata, Wipro, Infosys. So if I'm JPMorgan and I say, I need a call center, and this call center needs to have access to, like, customer records, and it needs to be safe, and everybody needs to be trained, like and I need to have, like, a 100,000 people that can answer the phone. You know who can do that for you?

Infosys, right, or Tata. Tata has already done the integration with JPMorgan in this case. They might just add AI, and now they don't need a 100,000 people, and they maintain that JPMorgan contract, and they operate in the the area of the Goldilocks zone where it's like they're gonna make like a 100 times more money. That's one case. That's the bull case for Tata.

The bear case is like JPMorgan's like, wait a minute. Like, should partner with the startup to do this, or we should do this ourselves. And now, Tata loses that relationship altogether. And it could go either direction. I think a lot of these things are really up for grabs, but I think the default is that the incumbents probably will do well, but you can pick a lot of these cases.

Mean, this is why you see the public markets kinda don't know what to do, where there is a case that is very, very bad for a lot of software companies, but there is an alternative case, which is like, if you operate in the right Goldilocks zone, and you have the right momentum to actually build these things and embrace these new technologies, you'll maintain all of your customer relationships, and you're just gonna have a more profitable business. And it's not that you're gonna do this, like the most compelling thing I think about AI that almost everybody gets wrong is like, oh, it's gonna destroy all the jobs. Like, our beloved representative from Silicon Valley is trying to eliminate AI. It's just so crazy that our elected representative wants to turn us back to farmers of tangerines and whatnot in Silicon Valley, which again, I think is crazy. But it's not like all the jobs will go away.

I actually think that's not gonna happen at all. What's going to happen is there are a lot of things where it's like, if I could hire somebody for a dollar to do this task, would I a 100% do that. I cannot hire somebody for a dollar. I've never been able to hire somebody for a dollar. Now I can hire software for a dollar.

So a lot of these tasks, look at how many people took taxis post Uber, Right? And it's like, did you hear people say like, you you probably took an Uber to get here today. Right? Would you have taken a taxi twenty years ago? Like, no way.

Right? Because it's like, where would you find the taxi? How would you arrange the it's just like way too complicated. Whereas once you make it very, very abundant and less expensive, everybody's gonna use this. And I think that's what Rokhan and his ilk are missing, which is it's not like, oh, I'm gonna go and say, I'm going to eliminate all the jobs.

Think of it in that JPMorgan example that I just mentioned. Wouldn't it be cool if every single customer of JPMorgan Chase could have their own personal friend that they could talk to every single day there that would help them with every single element of their financial life? Or it's like, I'm stuck downloading the app. I can't figure out how to get it set up. Oh, talk to somebody in real time that will help you about that.

Why don't they do that? It's just like the cost is known, it's high, and then the value is probably low. And as soon as you can bring the cost down to zero, now you're gonna start hiring AI in all of these different areas that you just would never bother hiring a human for because it's just like you can't train the human, you can't find the human, and the human's too expensive.

It's a good place to wrap. Guys, thanks for coming to the podcast. Motes don't matter.

Get ahead. Yeah.

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