The a16z Show - Why AI Moats Still Matter (And How They've Changed)

Episode Date: December 3, 2025

a16z General Partners David Haber, Alex Rampell, and Erik Torenberg discuss why 19 out of 20 AI startups building the same thing will die - and why the survivor might charge $20,000 for what used to cost $20.They expose the "janitorial services paradox" (why the most boring software is most defensible), explain why OpenAI won't compete with your orthodontic clinic software despite having 800 million weekly users, and reveal how non-lawyers are building the most successful legal AI companies. Plus: the brutal truth about why momentum isn't a moat, but without it, you're already dead. Resources:Follow David on X: https://x.com/dhaberFollow Alex on X: https://x.com/arampellFollow Erik on X: https://x.com/eriktorenberg Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.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/id842818711Follow our host: https://x.com/eriktorenbergPlease 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 http://a16z.com/disclosures. 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.

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 spending. It's largely labor. It's not like all the jobs will go away. I actually think that's not going to happen at all. There are a lot of things where if I could hire somebody for a dollar to do this task, I would 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 the 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, 24-7,

highly differentiated, certainly versus the human. The AINness of that capability, in my opinion, is not a source of defensibility. It is just so consensus. Like cloud was not consensus. Mobile was not consensus. And that's why the incumbents kind of screwed up. Everyone's saying that AI killed the concept of most. 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? A16Z general partners, David Haber and Alex Rampel, 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 spent, but labor spent.

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 19th player always dies, and how to find the Goldilocks zone where you're trying to find the Goldilocks zone We are 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 do you reflect and share some of the conversations we've been having here, 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... Still matter.

Still matter, exactly. And I think they're largely the same. I often think about this between sort of differentiation and defensibility. I think AI is an incredible tool for differentiation, right? The idea that a voice agent can speak in 50 languages, fully compliantly, 24-7, highly differentiated, certainly versus the human. But the source, the AIS 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-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. And I think these were the heuristics that were always, you know, 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, right?

And therefore, the market opportunity for software today is no longer just IT spent, 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 Martin 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 like 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, all right, they're all building the same things,

they all have the same algorithms. but when you've seen 4 billion 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 billion customers you haven't seen. There are 8 billion customers he hasn't seen. What's the difference? Whereas at mega scale, it's like, all right, I've seen 4 billion customers.

He's seen 1 billion 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 nine million ankle bitters 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 sort 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 going to go build it. But can you get to the type of scale where you actually could show a mode? 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 the moat.

And that's kind of the zero to one versus one to end. 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 2 era. Are the companies today more defensible, less defensible, or how should we think about sort of the street? 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 the pricing model that people feel is fair? And a lot of it is just psychology. And for whatever reason, for the last 20 years, it's like per seat per month with, you've heard my joke, the tall Grandi-Vente 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 40 years ago, you would have been laughed out of town. So this just became the norm. And the reason why, as I'm 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 going to 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 does answer is all the queries. Like, the answer is no. It doesn't mean that the companies are toast.

They might actually quit-touple their revenue because now they charge per outcomes as opposed to charging proceeds. But that's kind of 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, they should have a 1% gross margin. Or nobody should use Salesforce anymore. That would be the pro case of MOTS 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 and that any individual customer needs, because it's meant to encompass, it's like all of these weird edge cases, and you kind of see this if you use Microsoft Word, when was the last time you wrote a book? When? Never, right?

I haven't written a book. It has all of these things. They probably have 50 software engineers, yeah, but if you do write a book, guess what? Microsoft Word has all these features just for book authors to, like, make a take a take, 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 going to 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 going to 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 kind of 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 cup because the barrier to creating this stuff has gone down dramatically. I think the flip side to that, too, is that, well, there will be more software. And again, the kind of marginal cost of producing software is into declining asymptotically toward 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, like 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. One part of it is just like the Goldilocks zone of pricing.

So I wrote some tweet or whatever it's called X thread about this a long time ago. I call it the janitorial services problem, because if I went to you, you're the CEO of a giant company where you write your books in the future. So you have a 300,000 person company. I find you as, Eric, I can get your toilets 9% cleaner and save you 1% on your toiletry spend or your janitorial services spend.

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 nine million competitors, like they're just not going to 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 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 going to 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 companies or space in the Greenfield zones? Well, like payroll companies, right? Like ADP and paychecks, I mean, these 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? to withhold. Well, it depends, right? And there's 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, like, child support and the IRS is garnishing your wages, like, 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, like, I don't know, like 50 bucks a month per person that you might be paying 100,000. 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 1,000 employees. I downsized to 200 employees.

I had 1,000 licenses for Salesforce. Right. What's $1,000 times $100 a month times 12? That's $1.2 million 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 got to save that. And they didn't do that for their payroll spent.

So you see it, like a lot of companies do want to 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 million, nobody's using this? Well, it's $5 million. Whereas for things where inextricably, the delivery and the payment are linked, right, which is very, very different than

pricing for software. Like payroll, like, obviously I'm not going to pay for payroll services unless you were employed here. Whereas I might, like, we have 600 people that work at our firm, I think we have 600 licenses from Microsoft Office 365. 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 kind of depends, but I think per se 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, you know, your companies will, you know, there'll be a vibe coded version of it, but we've seen none of that so far. Is your mental model is, we'll see it in examples where 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, 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 starting a net new payroll company, I'm not going to try to sell to GE because I recognize that they are hostages to ADP and that's never going to change. So one is that patience of entrepreneur, and the other one is you just need a high enough rate

of new company creation to really make it work, which is why I 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. But it's like, and I might have patience as an entrepreneur,

but wait a minute, like I need to sell $5 million deals to big hospital systems. Every single hospital on earth is currently using an EHR system going to be really, really hard to make that work. So I 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 want to 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 want to work at the company that has the graph like this but you need this greenfield requires patience the so we're talking about how moats still matter and in many ways they look pretty similar let's deal man the other side for a second

Where are we even having this conversation where some people say, hey, you know, brand is the, is, is, is, is shipping velocity or because this era is different? What's the steel man of the argument? Look, I think this market is noisier than ever, right? And so I think finding ways to sort of, you know, stand out from the crowd probably matters more today

than it has, you know, in the past I would argue. I think the other thing is that the underlying technology is changing so quickly. And so, you know, as a founder, you want to be living on the frontier. and understanding kind of what model capabilities look like because it can dramatically change the efficacy or the capability of your underlying product.

And so I think, you know, one of the things that's changed, I think that's been really interesting in this sort of current wave of, especially vertical applications that we've seen, is the type of founder. You know, I think founders today are often younger and more technical than we've seen in prior generations. And so they're less often native to the particular

industry, but they're fluent in the tool set, right? And I think that's really important because, you know, to the same point, you've got to stay on the frontier and understand what's coming. At the same time, you know, I wrote this piece that I call context as king, you know, while it is important to understand, you know, model capabilities and what's happening in the frontier, you still need to figure out how to apply that technology. And so while the founders themselves are maybe less native to the particular industry, they're still hiring for context, you know, 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.

You know, the two founders of Eve were the earliest employees at Rubrik, which is, you know, now a public infrastructure company. You know, 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, you know, how to apply, you know, document extraction technology and sort of, you know, voice and outlines more broadly to this very particular work, you know, workflow, and they've hired a plaintiff attorneys actually on staff. So anytime a new model is released, you know, they're understanding, you know, from people

in industry, the impact that it's having on drafting, on, you know, their ability to, you know, to reason through a case, you know, or a matter. And so again, it's sort of this tension of like, you know, building the brand, having momentum, you know, understanding what's happening in the frontier. And yet, you know, figuring out ways to apply that technology in the context, you know, of your specific customer, because again, I deeply believe that that is where a lot of the sources of defensibility reside. You know, I'd love to find other examples of businesses

is 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 5x more efficient, you can take on 5x 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 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 Honeynut Cheerios, and I know that people are going to buy lots of my Cheerios, I can build a big factory and not, you know, hand crank out each Cheerio. I'm going to have these compounding advantages just in terms of economies of scale, right? Like Amazon, does that really have a network effect? No, it's like, it's kind of 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 kind of gravitational scale,

then it's just going to 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 going to get eaten alive because you can't hand crank out the Cheerios. 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 going to win that game. No. One of the questions for defensibility in Web 2 companies was, hey, would Google, will they someday build us or Facebook or name your incumbent? In the AIR, it's, it will open AI or will some other major company, how should we think about that framework in the AI era? You know, I mean, it's funny.

I feel like 18 months ago, this, you know, GPT wrapper was on everybody's lips. And I think it was largely used as a pejorative. You know, it was like, and I think, you know, to some degree, I think there are some spaces where like the model capability and the application capability, if they're very overlapping, I think you're in a risky spot, you know. But the reality is that there's so many, I think one of the remarkable things that's happened is there's 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, you know, the market is now labor, not just IT space. And plaintiff lobbying an example. You know, Alex says we have a company called Salient in applying voice agents to auto loan servicing. Five, six years ago, we would be back to software company selling to, you know, non-bank auto lenders? Probably not. The company's doing incredibly well, again, in large part because, you know, the capability of being able to, you know, speak in 50 languages, you know, fully compliantly, you know, with customers in 50 states, working 24-7, you know, is, you know, just so differentiated, you know, versus the individual.

And they're finding that their ability to collect is meaningfully higher, you know, than that labor, that the kind of cost-benefit tradeoff is so dramatic. The company is getting a lot of, you know, revenue from those customers who may not have had, you know, millions of dollars of IT budget historically are now very willing to pay for a product like that, you know, given the impact on the business. And the way that we used to talk about this a long time ago is, and this almost had a pejorative slant to it, but it's like, are 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 a 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 by the way, there were a lot of Chrome plug-ins, like Honey was a Chrome plugin that got bought by $4 billion.

Like, I wish I had done that, right? That's a good feature. But that was a feature. You know, a product would be like, ooh, I built my own browser. And a company is like, all right, 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 10 or 20 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 like 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 my, that's, 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 pieces of functionality,

and or will another company show up that just says, hey, we're going to 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 get to revenue scale as quickly. And by the way, 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 want to be locked into a piece of shit software company for 20 years. That's what I'm looking for as a buyer.

No, it's like, ooh, 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 feature has to backfill product, backfill company as quickly as possible. So that's still true today as it was 10 or 20 or 30 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 one of that effectively. Yeah, and so I think the effect of that is 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, opening out is going to go build, you know, the front office assistant for the dental clinic, like, as their core, you know, kind of business. They're going to 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, you know, foundation model business, you know, going kind of up the stack, I think limits the actual impact of the actual, of the application, you know, potentially as well. Well, I think that, you know, if you kind of think about this versus other platform companies, so Facebook was the preeminent platform company of Web 2.0. 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 going to show up and say, hey, you know what, we should build a farming game. Like they were like, no, we're going to have a platform that allows companies like Zenga 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 going to be 40% taxes. So that's why it's always dangerous. is 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 1,2, 3 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 want to 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 Goldiloxone. 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 going to go build a competing spreadsheet, I'm just toast because the platform owner is just going to drown me. Now there are five model companies, or, you know, more, like when you include all the Chinese models and whatnot, open source. Like, I don't have to worry about that, but I do have to worry about them saying,

wow, this is so relevant. Like, 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 Open AI is going to go do them, because it's like, are they going to do, dental care management. Like, they could. But if they've done that, then I would be short open AI because it's like they've run out of good stuff to do. That's something that they should do in 29. And this is, I think I told you this story before. This is, 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 going to 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 going to do it. Because you're pitching me a gold, 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 a hundred 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 going to 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. On that note, if you were running open AI and you were thinking about which gold bricks or how to even what mental model to think about sort of what are the things that you should be doing first versus things that, hey, maybe let other people do it. How would you be thinking about that question? I mean, I think a lot of it is where, well, it's two things.

Number one is we want to be the back end for everybody. Like the platform, 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 these obscure spaces like 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 made it really hard to develop software. And what's actually kind of interesting

is that both Apple and Microsoft had, like Microsoft started off as a compiler company. Like 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 talked to any early Microsoft employee, was beat Philippe. Philippe Con 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, McIntosh Programmer's Workshop. I remember I used to use it in the 1980s. And it was like $2,000, I think, in 1980s money to buy this IDE or programming thing. And it's like, how do you afford that?

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 like they kind of 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 ChatGBTBT has 800 million weekly active users, like get that to $5 billion, right? Like even if Gemini 3 came out today,

it might be five times better, but are people that are using chat GPT just as consumers, are they going to switch? Like maybe, but it's unlikely just because they kind of make that their default and then be the back end for everybody who's building anything. And that way it's like kind of all the gold bricks kind of 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 like what are the big horizontal applications that they can likely sell to every large enterprise? And I think, you know, you saw, today with, you know, Google's anti-gravity launch, like, the IDE is going to be one of those things. I think, like, you know, if there's like product market fit for LMs, like, you know, coding is definitely, you know, one of the top categories. So I think that, you know, thinking about what are the big horizontal kind of applications in the enterprise. I think there's also, to some degree,

and, you know, we'll see, I think this has been earlier to sort of play out. It's sort of the Palantier opportunity. I think we're still very early in sort of the proliferation of this technology into large enterprise. At the same time. you know, unlike prior product cycles, you know, 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. You know, today, I can plug a, you know, prompt into any one of these models and intuitively understand the impact that it could have on my business, right? The efficiency gains in my customer support organization and my engineering organization in all of my back office functions, at the same time, many of them don't know where to start. And so I think you will see sort of this consultant. sort of forward-deployed Palantir-esque sort of sale into very large enterprise from some of these, you know, big model companies. Again, I think we're early in that, but you've heard inklings of this with, you know, with Anthropic talking about wanting to build

into financial services and other markets. So, you know, I agree. I think the biggest opportunities are the one that Alex is describing, but I think you will see them selectively, you know, try to build kind of applications that cut across every one of those. And then they'll probably choose, you know, a few sort of like lighthouse customers is to build, you know, largely bespoke kind of custom integrations into these, you know, bigger enterprises, but where the ACBs, you know, just really make sense.

In Web 2, there was a lot of winner take most. You were talking about one of the benefits in AI is that there's multiple winners. To what extent is consolidation inevitable, or how do you think sort of this plays out? Well, I think if you have 20 companies that are all doing the same thing, what has his 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 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 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 orthodonic answering software or something, but rather you can charge more if you get to a certain scale, because whatever, 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 trial pay, 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 going to go low ball 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 lost leading and nobody's big enough to get any kind of scale effects is there going to be a world where the 19th 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 100.

I don't think that's changed. Right, right. Even in the model provider example, and almost a curious of prices go down. Yeah, I don't see how, like, there actually are, I mean, people know XAI, Anthropic, Open AI, Gemini, like they know, or Quinn, 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. It's just like not, it works fine. But how can you, like the model company is the most cutthroat because like unless you're state of the art minus minus minus and you're trying to earn a living, it's just like that, that's just not going to work. So that game is super cutthroat. I think the one area where that may have diverged,

and Martin talks about this a lot is like, you know, when markets are growing so quickly, you end up having specialization. And so I think in other kind of modalities, you know, in some of the creative tools or, you know, people have specialized to like serve, you know, the up market, you know, like I'm producing, you know, movies.

Okay, I want to create sort of like social, you know, quality content. Like these are different, you know, markets that the models can kind of specialize. And time will tell, you know, how sort of, you know, defensive of those become over time. But maybe that's the optimistic take that, like,

you know, early on everything looks, you know, overlapping and competitive, but we're still so, you know, the market is growing that everything can kind of expand and people can kind of specialize over time. Earlier when you were talking about the feature versus product. Didn't Steve Jobs once tell Drew Houston that Dropbox was just a feature? Yeah, I mean, that's why it's always been this pejorative thing, but 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 chat GPT dominating their daily workflow in

2022 in October. But then once it came out, it was this like, holy crap, 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 product. And they've obviously turned that into a company. Whereas other things, it kind of is like, you know, why is their anti-virus 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 is hard because this is the danger of building on somebody else's platform is that, you know, I'm going to build this thing that they should. 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 everything, 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 half the things on my iPhone don't really work if they're built by Apple try you like 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 going to be bad and that does create an opportunity for somebody to come up with the feature and actually outcompete the platform but like you have to be careful because it's like obviously the platform owner is going to go compete with you. And that's why often what I find very compelling about entrepreneurs,

when they know this, like 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 going to 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 billion company because, like, he had a plan. And the best entrepreneur is they often like, okay, I know it's not this naivete. I was like, oh, I'm going to build this. There's no way that they're going to 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, like, 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, and it's just this idea that you hook into a bunch of your different unstructured data sources. Could be email, 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. And I think that wedge for that feature

is interesting in large part because it lives up funnel from software. You're replacing the kind of human-level judgment of the individual, like often that, you know, the secretary is sort of like collecting the physical facts and then plugging it into the HR. And so now a bunch of AI companies can kind of, you know, wedge in and then eat away at all the downstream workflows

that might have been their point solutions software companies. And so, you know, Tanner is no longer just doing, you know, the messy inbox. They're now doing scheduling and prior, you know, prior off and eligibility and benefits. And they've used that wedge to try to become, you know, kind of the end-end platform. Eventually, maybe they'd become the system of record.

But again, because you can kind of replace the human labor now with software, I think it's creating opportunities for these, you know, features to actually become products. And, you know, in their case, I think it become, you know, whole companies. Well, I think, I think this is the thing that in my mind is very dramatically different than every other platform shift is that the, it is just so consensus. Like, cloud was not consensus. Mobile was not consensus. And that's why the incumbents kind of screwed up where it's like, and then sometimes it was just like completely, I'll use the Silicon Valley term, orthogonal to their

business model because it's like I sell $5 million a year products. And wait a minute, I'm going to 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, like, you know, workday beat PeopleSoft. Or that's why, you know, 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 Bomber clip of, like, him saying this, nobody's going to 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? It's like, nobody will use that tool that makes you 100 times more productive. Of 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 kind of 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 2.0 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, you know, 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 kind of what makes it much more exciting than like last gen where it's like, oh, I'm just going to copy everything that was on-prem and make it, you know, recurring billing cloud. And I'm going to do that at a time when like the big guys say that's stupid and I don't get it. So some argue that, you know, mobile was

ultimately sustaining in that although there were, you know, net new companies and use cases that were, you know, $100 billion like Uber and Airbnb, et cetera, that, you know, the incumbents, some that became trillion-dollar companies, you know, how got it by mobile. When we look at the, you know, business impact of the AI era, what's your mental model for thinking about sort of the incumbent or startup or kind of net new company in terms of value capture? I think a lot of it is the same.

Like, unless you really screw up the pricing model or like, 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 a lot of ANs that need to happen. I have a hard time believing that incumbents will really suffer. I mean, there probably are some things.

Like, you know, take like one example of, and this kind of goes back to distribution versus technology, like all of these business process outsourcing companies, these BPO's, they're the largest employers on the planet. So like Tata, WIPRO, InfoSys. So if I'm JP Morgan 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 hundred thousand people that can answer the phone. You know who can do that for you? Infosis, right? Or Tata. Tata has already done the integration with J.P. Morgan in this case. They might just add AI,

and now they don't need 100,000 people, and they maintain that J.P. Morgan contract, and they operate in the area of the Goldilocks zone where it's like they're going to make, like, a hundred times more money. That's one case. That's the bull case for Tata. The bear case is, like,

J.P. Morgan's like, wait a minute, like, we should partner with the startup to do this, or we should do this ourselves, and now, like, Tata loses that relationship altogether. And it could go either direction. Like, 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. I mean, this is why you see the public markets kind of 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. Like, you'll maintain all of your customer relationships, and you're just going to have a more profitable business. And it's not that you're going to do this. Like, the most compelling thing I think about AI that almost everybody gets wrong is like,

oh, it's going to destroy all the jobs. Like our beloved representative from Silicon Valley is like trying to, like, eliminate AI. It's just so crazy that our elected representative wants to turn us back to farmers of Tangry. 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 going to 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, I would 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, like, you know, look at how many people took taxis

post Uber, right? And it's like, did you hear people say, like, you probably took an Uber to get here today, right? Would you have taken a taxi 20 years ago? Like, no way, right? Because it's like, where would you find the taxi? How would you arrange the tax? It's just, like, way too complicated.

Whereas once you make it very, very abundant and less expensive, like, everybody's going to use this. And I think that's what Rokana and his ilk are missing, which is it's not like, oh, I'm going to go and say, I'm going to, like, eliminate all the jobs. Like, think of it in that JP Morgan example, that I just mentioned. It's like, 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,

they 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 going to start hiring AI in all of these different areas that you just would never bother hiring a human for it because it's just like you can't train the human, you can't find the human, and the human's too expensive. That's a good place to wrap.

Guys, thanks for coming to the podcast. Most don't matter. Yeah. Thanks for listening to this episode of the A16D 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 Spotaflof. follow us on X at A16Z and subscribe to our substack at A16Z. com. Thanks again for listening and I'll see you in the next episode.

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