a16z Podcast - Where Crypto Meets AI with Chris Dixon & David George

Episode Date: May 31, 2025

What happens when two of the biggest tech waves—AI and crypto—start to converge?In this LP Summit conversation, a16z General Partners Chris Dixon and David George explore how stablecoins are creating a new global financial layer, why generative AI is reshaping market structures, and how the next tech giants will be built.From network effects to native AI business models, this is a sharp look at the future of innovation and investing. Resources: Find Chris on X: https://x.com/cdixonFind David on X: https://x.com/DavidGeorge83Watch ‘What is an AI Agent?’: https://youtu.be/xGEUPLLuEIo?si=RM-tD-R8H9XfbIP Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow 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 a16z.com/disclosures.

How will the money flow? How will copyright work? There's all sorts of interesting questions that you can kind of think of downstream. I think those are all questions that in some ways crypto can address. They have the leading state-of-the-art models of Jim and I across a bunch of different domains.

So what should they do? They should throw out search? What is it, $100-something billion in revenue? Yeah, it's the best business out there, and I do not envy them. There's all these people talking about P. Doom and Terminator stuff, and then they're talking about models.

They sort of either really tactical or really like cosmological scale or something. My sense is a lot of the actual interesting questions are sort of in the middle. Today's episode was recorded live at A16Z's 2025 LP Summit. General partners Chris Dixon and David George, two leaders at the forefront of crypto, AI,

and growth stage investing, discussed the real state of crypto, the rise of AI native apps, and how platform shifts are reshaping where value is created. We also explore what defines a winning company and founder, in this era of exponential acceleration. Let's get into it.

As a reminder, 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. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures.

Chris, obviously you spend the vast majority of your time in crypto. Talk about what the state of crypto is today. Where are we at? What's most important? People probably know the last four years have been a tricky time on the regulatory policy side. And so that's really kind of set us back a little bit. But on the positive side, the infrastructure has gotten much better. So one of the big goals was to be able to send,

money for under one penny and under one second. And that is now achievable. If you download like the Coinbase wallet, for example, you can do that. And a couple of years ago, that was like $10 or something like this. So that's great. So core blockchain infrastructure has gotten much better. For those tracking it, stable coins have grown dramatically in usage. It's now in the trillions of dollars higher than Visa per month. So that's been really good. And that's, by the way, not correlated to trading volume and things. So it's not speculative use cases. It seems like real use cases. So that's been great. Yeah. Talk about some of the use cases. Like some of the, like Some of the, like maybe stable coins, double-click for a second.

I think you called it, you know, a WhatsApp moment for money. Like, what do you mean by that? What are some of the big use cases? So sort of like when we go to DC, for example, explaining blockchains and stable coins is it's essentially digital services where you remove the intermediaries. So instead of having to send money through having an issuing bank and a merchant bank and Visa and a payment processor, you have a blockchain, but it's essentially acting like a service provider with a very lightweight intermediaries.

And so in the same sense that, like, WhatsApp came along when you had to use, like, SMS and you'd pay for international payments and there were like all of these little like really wasn't really one network it was a whole bunch of like sub networks and you had WhatsApp and other kind of over the top networks which created a single unified network and so you can think of stable coins as you're creating a single global unified network without intermediaries to allow you to make payments right and that turns out once you get the infrastructure down to one penny one second it turns out to be quite popular and useful so that's really exciting and hopefully for those following it, we're trying to get legislation, and that will, I think, further accelerate it

because you've got essentially, think of it as a network effect, and you've got 90, probably 5 percent or some percentage of the nodes on the network that right now don't feel like they can participate in the network. So like banks and other kinds of more conservative financial institutions, and once we have legislation, that will unlock that, they'll join in, that will accelerate the network effect. And then the exciting thing about the stable coin thing, right, is the next thing, once you have money flowing through, well, why not loans, why not stocks, why not treasury bills. And so it's sort of a stepping stone

to a whole bunch of other things. So that's been very positive. The negative side, as I mentioned, on the regulatory side, we had this all-out assault by the last administration on the space, and that just had all of these negative effects. Essentially, we lost four years or something,

I would say, in a lot of ways. And not only did it mean that the projects that were invested in had to really limit their product development, but it also, I think, scared off a lot of entrepreneurs from even entering the space. And so we're sort of recovering from that now. I think all things considered, given that, like we're in a really good spot, given all the drama of the last four years. Yeah, yeah, absolutely. Yeah, I mean,

the stable coin thing, to me, is one of the items that has become mainstream in the crypto world now or is starting to become mainstream. For those of you who followed last week, Stripe had their big product conference, and one of the big things they talked about, they did their biggest acquisition ever. They bought a stable coin company, and it was one of the biggest parts of what they spoke about in terms of product advancements in their focus areas. So quite optimistic with you on that. Yeah, they spent quite a bit of their time. And for those who don't know, like Stripe has a mixed history of crypto.

They got into it a long time ago. They co-created a project called Stellar. And then they got really down on it for a long time. It wasn't working. And now, if you watch them on various podcasts and in their demo day, they're very, very excited. A lot of their use cases are cross-border. They're like treasury management. So that's, I think, like, SpaceX, like Starlink, like uses it to move money from one country to another, things like this.

Yeah, like just scale AI has people. they have to contract or they have to pay in different countries is the simplest way to actually pay them. But there's all these sort of secondary benefits, right? It's not just the lower pricing. They were talking about this on a podcast recently. It's also the fact that it's fully programmable. So, for example, a big problem is invoice fraud.

People send bank wire information and a PDF, and it's wrong. But if it's all fully programmable, you can build essentially like a reputation system and fully automate the full thing and have a whitelist system and all the kind of modern bells and whistles you want to have. So that's exciting. And I'm hoping that's, as I mentioned, a stepping stone to many other. We have obviously a lot of speculations. use cases on crypto, but what we really want are

non-spective use cases, right? Things that are like real products that aren't around trading and things, and so that's been a bright spot. Yeah, that stat of stable coin volume from the previous year, I couldn't believe it when I read it, but to be at the same level of Visa is extraordinary, so quite optimistic.

Let's shift gears, so let's talk about Gen AI, maybe to start, just what are your initial impressions as a student of technology? Yeah, I mean, for those of us who've been in technology for a long time as I have, it's amazing to see. It's obviously incredible. And it's particularly AI because AI has been 80 years or so of development

and has gone through waves and really became Roll Your Eyes. At some point, people would often roll their eyes in the 2000s and 2010s of AI just because it had been around so long and hadn't really lived up to its promises. I mean, people in the 50s and 60s were saying, you know, there's going to be, there's a 1960s, there was an expert system, actual stock market kind of bubble. So to finally see it work is amazing. It's interesting with technology, right? Because in some ways it's predictable, in some ways it's like.

unpredictable. The predictable part, if you go back and look at like Ray Kurzweil's books from like 20 years ago, and there were a bunch of other examples, they would have these charts where they would plot out the kind of Moore's Law type improvement in GPUs and then compare it to like a human, you know, how many nodes would you have, how many parameters in your model and how does that compare it? I remember there's one in like the deep learning textbook and it's like, how is it compared to a bird and a human? And someone was just talking about the Ray Kurzweil when apparently he predicted like AGI in like 2027. Like a lot of these predictions were fairly accurate because you could predict the improvement in GPUs and things.

On the one hand, on the other hand, I don't think many people predicted that generative AI would be the kind of leading, the leading use case over more like, I don't know what do you call analytical AI or whatever, you know, just sort of like an analyzing language and financial statements, right? The fact that it would actually be a creative thing. I think you'd always hear, you know, oh, it's going to come for the truck drivers or something, right? In fact, it's coming for like the laptop.

Came for the laptop class. The laptop class first. Yeah, I mean, like product managers, like, the greatest risk. I don't think many people predicted that. Well, coming for creatives before, like, robotic tasks is surprising back that long. I mean, I think Martin and Sarah and Eric were just talking about it. There's all sorts of interesting AI-specific questions of what are the applications, where is the value going to accrue?

I'm sure you've thought a lot about this. People talk about applications, foundation models, chips, cloud service providers. Lots of interesting questions. Probably my colleagues, you and colleagues and the other verticals are more qualified to talk about. I mean, I can pontificate, but I think they're the experts. Your pontificating is pretty good. I think they're the experts. But what I like to sometimes do

is think a little orthogonally. Like, well, one thing is I always like to think about second order effects of technology, right? So, you know, you go back and you look at the automobile and the first order effect was you can go from point A

to point B faster, right? The second order effect was highway systems and transportation and suburbs and all these other kinds of things that happened as a consequence later on. I think of crypto as being a second order effect

of social media. So you go back 20 years ago and like I was thinking about social media, I was working on it, and you would say, okay, someday people have social media and anyone in the world will be able to have a voice

and share their expertise. And that was the first order effect and that happened, of course. But the second order effect is now, like crypto is an example. Like, if you didn't have social media, think about it. Someone would come out with Bitcoin.

And then New York Times would say this is stupid. And then Washington Post would say it's stupid and then it would be over. Or maybe they'd be like a magazine or something. Like there'd be no way to evangelize it and create a community around it, the way that all this stuff, of course, happens through social media.

I think the things that have happened with politics, the Trump stuff and populism and just all the radical changes and culture. And I think we're just in the early innings of all the second order effects of social media, right? But that's interesting. So I think one question with AI is what will be the second order effects there. One thing I think about just because my interest area is, like, in media. So, you know, the first order effect of generative AI, right, is you can make, obviously, like an illustration,

you can make videos, and obviously that stuff will improve. And I think very soon it seems like in two years you'll have people making feature-length films. Films games. Yeah, and that's what happens. But then an analogy I would use is, like, to photography. So when photography first started really going mainstream, there was a lot of angst around it, you know, in the artistic community. And the artists at the time were doing representational art.

And they were like, what's the role of the artist in this world? And, of course, what ended up happening is at least in the high art world, they moved into abstract art and away from representational art. And then, of course, photography proliferated. But then a really interesting new thing happened, right, which is you had film. And so film obviously requires photography, but is a new form of media that couldn't exist until you had photography get to a certain level of advancement, right? And so you could say that photographs were the, in my vernacular, the skemorphic

application of cameras. And film was the new native form of media, right? It was a form of media that couldn't exist before. So I think one interesting question to ask with AI, with generative AI, is of course it will make it easier to create existing forms of media, but will there be new forms of media that simply couldn't have existed before? And my guess is that there will be. I have hypotheses as to what they might be, but... Every previous technology way it would suggest that it becomes native over time, but it starts to skeuomorphic.

Yeah, and then like another question you'd ask is what are the business models going to be in that world, right? When content is abundant, presumably the cost of content goes down, the value goes down, but people like to engage with other people. They like to engage with artists. There's a human factor to it. There's community formation.

Presumably people will like, you know, if there's 100 different generative AI science fiction universes, people will probably like the universes that their friends like because there's a community aspect to it. How will the money flow? How will copyright work? There's all sorts of interesting questions that you can kind of think of downstream.

I think those are all questions that in some ways crypto can address. I think at a high level, the way I think about it is, obviously, AI lets you create intelligent systems. What crypto is really about is not about intelligence, it's about coordination. It's about solving collective action problems and coordination problems,

which I think of it as an orthogonal set, a different set of problems to solve versus intelligence. So you just take a typical problem. Well, money is obviously is a coordination problem. How do you shift value around the world? How do you get everyone to agree on certain standards? How do you do capital formation in the internet era? That's a collective action question, right? Of like, how do you get all these people together to do something? But, you know, arguably so is, you know, you want to build housing. Like that's probably partly an intelligence problem, partly a bunch of physical real world problems, some somewhat of

regulatory problems and some of them are collective action coordination problems, right? So there's sort of a series of things that you need to make progress. And some of those things fall in the categories for coordination, collective action. And that's where we think our kind of building networks on blockchains fits in. Yeah, an economy for the Internet. A couple of data points on that. Sarah and Martine and Eric were talking about it. But not all the technology waves are the same, but, you know, they often rhyme, right?

One commonality is just this sort of Moore's Law improvement of things, right? So you talked about infrastructure on the crypto side, going from $10 to send to a penny to send. You know, in the AI side, there's been a 99% reduction in cost over the last two years. It looks like that will continue to happen. At the same time, you're also seeing dramatic improvements in model quality. So I'm quite optimistic for that reason. I think if you give it a set of tasks that are slightly higher order, I think the models are currently doubling their capabilities every seven months.

so that's only one dimension of improvement right because we also have the chips yeah of course yeah chips I mean look the chips continue to get better and that will help to improve the cost over time too you know I think we could talk about like where value will accrued business models and all that stuff but clearly the chips companies are some of the best companies in the world applications and the model companies that leg up into the application layer there's going to be a tremendous amount of value but I think it does beg the question as you go from skeuomorphic applications and these technologies into native, there probably does need to be a different economy associated with them, just a number of transactions, AI to AI payments. Talk about what you think may happen there.

So I would expect, I mean, the way I look at it is in past kind of megatrends, mobile social cloud. They all reinforced and intersected with each other. And I would expect the same thing here. If we're right about crypto and it seems like AI is obviously happening. But, you know, I would expect all of these things to intersect. Yeah. I think there's sometimes a tendency, like in the crypto world at least for people to feel like the other sectors are competitive with them or something and we're always saying that's not the case in fact you have to lean into it it also has just a thing of resetting the chess board right you just have a whole new set of services and now we're just talking backstage about how it seems like google might be under threat now just from a search perspective you

should talk about that it's interesting but you know if that's the case you're just going to have like a new game and new incumbents are popping up and there's an opportunity to create new architectures and new services around those yeah i mean the google thing we were talking about backstage i was showing Chris, a chart that our team put together that showed the volume of the AI native activity against Google queries, and they're just moving in opposite directions. It's not nearly the severity of the line on the Google direction as it is just because of the scale, but you can see it happening. And where it's happening right now is knowledge retrieval.

So it's like the lowest monetizing forms of Google's revenue model. So yes, they may be losing query volume. but you know people still go there to search for insurance and vacation rentals and the most valuable search terms but we're getting pretty close to the point where you can actually conduct business activity on the internet without having to go through google and that will be with these agents is such a loaded term our infrastructure team just did a good podcast which you should listen to which is like what is an agent but basically it's the ability of the AI to go conduct business on your behalf. And I think we're getting pretty close to having it be able to do that in some, at least

lower level tests of things that you would do on the internet. But the problem that Google has is they have the classic innovator's dilemma, like the search business model that they have is so profitable. I've talked about it's one of the best business models of all time. But it's hard to dislodge yourself from that because it's so good. But it's an inferior product experience. The other thing about Google is not only was all of the technology actually invented there, which is the craziest thing to think about. We backed Dombs Treesier and all these other guys, and they're all there.

It's just Xerox Park all over again. It's just Xerox Park all over again, but with an even better business model attached to it. But they actually have the best models right now. Like, they have the leading state-of-the-art models in Jim and I across a bunch of different domains. So what should they do? They should throw out search?

What is it, 100-something billion in revenue? Yeah, it's the best business out there, and I do not envy them. One of the things that we talk about a lot is, do you back the founder-led company, Do you back the company that's run by the professional manager? Yeah. Like, it would take a very audacious move for Google to just say,

I'm going to do what's needed and embrace the future. The thing is even then, even if they did that, even if they put in like a chat thing, the problem is the whole business model is predicated on getting the link and not the actual product, right? I mean, the whole thing is so the – Well, the promise they make to users and the other side, right, is that we will deliver you the traffic.

Yeah. We actually should talk about that because you've written about that. You and I've talked about that. But the concept of breaking the original pact of the internet, you should talk about that and what you think some of the potential solutions for it are. Yeah, in my book I have a chapter on it called A New Covenant, essentially. The idea is that it was never explicit, but over the last 20 years or so, the covenant, quote-unquote, was formed between kind of distribution, you know, search and social,

kind of the places you first go on the Internet to discover. And then the content sites downstream from that. So you go and you search for recipes, you search for a news article, and then you click through. And the basic kind of deal that evolved was that if the content site lets you take a snippet and show something in exchange for you send me some amount of traffic, right? And occasionally that was broken

like with the one boxing. So one boxing is when Google takes the content and just puts it at the top if they do with Wikipedia. I was on the board of Stack Overflow which is a popular programmer Q&A site. That was their biggest fear in life was that instead of people clicking through

and going to the website, they would just put the answer up there, right? This famously happened with Yelp and then they've done it with travel, shopping, but like to limited success. But in that framework, you can think of AI, like ChatGPT, as one boxing the whole internet, right? It's just like it gives you the answer.

You don't need to click through, right? And so in that world, how does the other millions of websites, what is their business model? It was dependent on getting that flow. And you're seeing like these things like perplexity, and I think ChatGPT too, they sometimes put links in there and stuff. But the reality is, I think that's to be nice or something. But like, in the end, you don't really need that if it's a good AI.

You just get the answer, right? Yeah. And so presumably that'll be reflected in the, click through rates and things like that. And so the question is, do you just let all of that stuff kind of atrophy and that's it? And the internet sees 10 websites or do you have to think about a new business model that doesn't involve getting this flow of link traffic? And that's an interesting question.

I kind of just raised, I don't have a full answer to it, but I feel like this is the kind of thing that we should be having discussions about. And so I wrote about it and I've talked about it hoping to trigger a discussion hasn't really worked. To me, that's the kind of stuff that like are the obvious next couple of your consequence. I mean, look, I come into it having worked on the internet my whole career. So I think about the internet and what's going to happen to the internet. and so to me that's a pressing issue

it's also an issue of like that's a huge income stream and there's a lot of jobs and small businesses and so what are we thinking like how will that evolve from a societal point of view I feel like in the AI world like you know on Twitter at least

there's all these people talking about P. Doom and Terminator stuff and then they're talking about models that sort of either really tactical or really like cosmological scale or something. My sense is a lot of the actual interesting questions are sort of in the middle

they're not like Terminator coming to kill you but more like this is clearly going to change the structure of the internet it's going to change business models, and, like, how do we think through, you know, how to respond to that, both from a business point of view but then also, like, policy and other kinds of things, right? Like, I think the interesting questions

are kind of, I would say, more middle Zoom level, not cosmological. Yeah, I'd say you can see it initially being disrupted in websites that are knowledge-based and where the model can consume the knowledge. Chegg is the best example, right? Which is basically, it was a high-flying public company. It's a place where kids would go to get homework help

and study guides and things like that and then overnight got hit by a ton of bricks and it's obviated the need for it. But you would need someone to create that content in the first place for the next thing. So I think that highlights maybe the go-forward problem. Yeah, I mean, that's a question. If you kill the business model that first created Stack Overflow,

you're not going to get future Stack Overflows and maybe that's an issue for the AI models or maybe it isn't. Maybe you can just pay for it or you can use automated tools or synthetic data or something else. I'd like to believe there's a future though where there's like broader internet that creates the stuff. I mean, I think we want a business model for things like music and movies and you want humans involved

and they're going to create new genres and have new ideas. And hopefully those things will then feed into AI systems and they'll help accelerate that. But I believe the ultimate outcome is some kind of symbiotic relationship. Separately, there's sort of a human flourishing kind of question of people want fulfilling meaningful work and hopefully all these new systems and tools fit into that. somehow, right? I do think there's some risk to it. And to your point on skeuomorphic versus native, the way the most dominant business models on the internet evolved is actually toward a new native form of advertising, right? Like we're dunked on Google for being the incumbent, but they did

create a new form of advertising. Similarly, Facebook and Instagram created the feed-based advertisement that's very highly targeted. So my hope would be that the AI companies, once we have the native applications also come up with some native business model that's a different form. Maybe it's like commerce-based or the piece that's ad-based looks something more like an affiliate. That is like a dirty term, but like something like that that actually finds a way to compensate the website or whatever. But it's so early. Yeah, it's interesting. I mean, you know this better than I do, but if you look at the last unicorns, like how many are ad-based in the last 15 years? Like two?

like the vast majority are charging their freemium their enterprise so that's one question is ad-based even going to happen in the future i think it might go i think it just goes in well in the fullness of time everything comes into ad business right so that's like the joke in consumer internet yeah but you know the best businesses in the world are ad-based right and they are the best businesses in the world because the users give the content and to the user it's free and then they're compensated via ads and so you know i think there's a question of how it will play out will it just be a simple subscription or is there going to be something more freemium that comes along? I would bet on the ladder. I would bet on the ladder. But yeah, I think these going waves.

I mean, I think that's the biggest thing. There's not a lot of new ad-based business models because there hasn't been until now much of an opportunity for startups to encroach upon consumer time spent, which is where the advertising comes from. People are talking about, you know, there's a whole industry around SEO, and now there's going to be a whole industry around convincing the AI model to promote your product when you ask it what the best detergent is there'll be a whole industry

around, you know, infiltrating the training data so that they promote. For those of you close to the internet, we would all welcome the gamified SEO stuff to go away. Let's talk about market structures, if you will.

And obviously these technologies, they come in waves, they reinforce one another. What about what determines winners and losers and competitive differentiation, moats, barriers to entry, all that stuff?

Yeah, I think of most of what I've done on the investment side in my career has been sort of network businesses. Cryptos all networks. Basically everything we invest in is a network. And so I always look at the world through that lens of like I think networks are the best.

They're my personal favorite types of investments because of the defensibility. Once you build it, they can be very capital efficient, you know, a relatively small amount of money and get bigger and have defensibility there. So that's kind of lens. I look at it through. I don't know how much that's happening in AI.

It feels like a lot of AI doesn't have network effects, but they have brand effects and technical effect. You know a bunch better than me. We have a lot of other. challenges in crypto, but one thing we have is, I think, once it works, like Ethereum, Solana, Uniswap, whatever, once it works, is a very clear defensibility story. So that's the good news. The AI side, I'd love to hear your thoughts because you're the expert. Like, how do you build defensibility? And it seems like right now there's five to ten companies with comparable

foundation models as an example. Yeah, and then, you know, on the consumer side, one with all the traction, right? And so, yeah, right now there's no network effect, at least as far as we can see. I agree that network effects are the strongest form of a competitive moat. Chris always jokes that there's only two things that give you competitive differentiation in any industry. Is network effects in enterprise selling? Because all these discussions we always have is other things like data and distribution and all these other things.

Those are the two things that if you just go through and count, like the biggest market cap company, that tends to be those two things are dominant. Yeah. The only things. Yeah, this is the thing that we're wrestling with right now in AI because we've now gotten to the point where there's about a billion

monthly active users of consumer AI applications. And this has grown significantly faster than predecessor companies, Google, Facebook, TikTok. And in the case of TikTok, they actually grew through paid customer acquisition. This is so remarkable

because it's effectively been all organic growth. And that's super unique in consumer. So we've gotten to this incredible scale. there's a lot of usage, there's a lot of monetization, but there is no network effect. And so how durable is that customer relationship is something that we wrestle with all the time? And I don't have a great answer for it.

I think there will probably be network effects that do emerge, and they could be built around certain product features or model capabilities that create a different kind of user lock-in with the vendor, as opposed to a user lock-in with the rest of their network as it has been in the last social wave. But it's early.

It's too early to call. You tell me, but it seems like the smart folks like you are shifting from thinking the value is going to be in the foundation models to value is going to be in the applications and the chips and sort of a barbell effect. Is that right? Yeah, I think that it's becoming clear to me and to us, including infrastructure group, you know, everybody. We all sit around and talk about this stuff all the time. Clearly the chips and then clearly the end user applications. and there is a tremendous amount of effort being exerted from a bunch of the ecosystem players

to commoditize the middle of that, right? The model layer, the simple API serving. And there's different incentives for why that's the case. For the cloud companies, the ones that don't have their own models, they would like to commoditize that because they want to keep the customer power and they want that simply to be an input

and something that they can sell. The application companies obviously would like to commoditize it because they want their costs to go down. And we could go vendor by vendor, but there's different incentive structures. But right now, the prevailing view is simple serving via API is likely to face a tremendous amount of pressure. Obviously, there's some big swing factors like open source

and how much that continues to be pushed forward to the frontier because that's also putting a lot of pressure on the price and cost in a good way. But we're very high conviction that at the application layer, you will have tremendous value. And at the chip layer, you will have tremendous value. Beyond that, it's TBD. And the crazy thing is the applications are all so early, right? And so there's only, what, 10 or 20 of them that are really breaking out,

but the ones that have broken out in an extremely meaningful way, much faster as Sarah said, I think, than the predecessor. So that's exciting. So that's the sort of network effects piece, the selling piece on the B2B side, that's very TBD. We'll see. But I think the selling software, just as before, is always going to be the same,

and it'll be the same in this wave, too. So on the market structure stuff, one of the things that we talk about a lot in the growth fund is, like, how much are things winner-take-all versus there's sort of a distribution? And the frameworks that we talk about all the time is this, like, idea of the Glengarry, Glen Ross market structure, which is Glengarry, Glen-Roy, you know, like Alec Baldwin comes in and he's putting on a contest, and he walks in, and he says,

okay, we're going to have a contest today, first prize, you get a Cadillac, second prize, you get set, steak knives, third prize, you're fired. And so we joke that oftentimes technology markets follow that same sort of distribution of outcome where the winner takes the vast majority, second place is playing for scraps, and third place is said, I'm curious your take on that. I believe that generally, I think empirically is borne out. I think there's nuances, like how do you decide on what the category is, for example, right? And you can divide the world up in different ways.

And then, of course, great founders will move into adjacent categories. And so it's sort of more complicated. But I mean, like, we try to operationalize that in our investing, which is like our number one kind of rule of investing is we say the best company in every credible category, right? We are lighter on like less rigorous, not less rigorous, but more humble on the question of which categories are the best because we've just been, I think VCs in general and me in particular, I'll say, have been wrong about that. trying to predict will X be a thing, whatever, online dating and grocery delivery. And I can't tell you how many debates I've heard in all these topics. And it turned out the answer was, if a bunch of entrepreneurs are working on it and they're smart, you should probably just assume that they're probably right. And it will be a category. And then the job of the investor becomes

picking the best one. And what's nice about that framework is that picking the best one is something you can actually institutionalize and have a process around and go meet all the competitors and have criteria and due diligence. I believe that very much. The winner-take of the the Glenn Gary kind of rule and try and think of it as an important lesson for how we try to invest. I think it's also important from a brand point of view. Like we want to say like when we invest, we want the message to be like this is the best company in the category. Like the category may or may not work, but this is the best one. Yeah, we've declared the winner. Yeah. And that helps you recruit and partner and all the kind of brand signaling around it. So I think that

and so I don't know. I believe that it has been the case with the caveats of the nuances. and I would expect it to be the case going forward. It just, and whether it's network effects, brand. I mean, we see it play out over and over with these, like, Uber and Lyft. People have all these debates. I remember all these debates. Like, there aren't really network effects. It shouldn't be a winner-take-all.

And if you look at the market caps, it is kind of winner-take-all. And it happens again and again in these cases where people thought there wasn't some obvious reason why there'd be a winner-take-all. I mean, Google, people debated that for years early on. There's no real network effect. It's just a brand effect. I mean, I think we probably systematically underestimate brand effects in Silicon Valley. Yeah, I do.

It's a meaningful thing, and that's one of the reasons it is winner-take-all. Yeah. So I very much believe in that, and I think it's important as an investor to really take that really, really seriously. And it's also just painful to be in the number two or three. I will tell you, just like seeing the news articles every day and going to LP events and just talking about how you're not. Everyone asking you about the other one. It's better to miss the category than to get the steak knives, in my view. Just don't, just miss it.

The steak knives are a tough prize. Yeah, not compete in the contest at all. Yeah, I actually think that framework that you just enumerated is one that has become really valuable inside the firm. Last thing I want to ask you is just your approach to picking founders. So obviously, you laid out the process and a big part of the process or the biggest part of the process at the early stage is picking the founders. So what is your system and process for that? Yeah, this is like a whole big topic. So I'll just try to be short.

We left 45 seconds. Yes. But I think there's this concept. I think it's Peter Thiel's concept of like a founder that has a secret, like an earned, secret, like somebody who's worked on, like maybe they came from a AI lab or CS lab and worked on some technical thing for a long time, or they came from an industry, they came from the media business and worked for years on it and saw that there was a gap in the market. But generally, like, it's a sort of good ideas, it looked like bad ideas.

If it was obvious, it's probably being done by a big company. You have to have sort of earned your lesson and have worked in something for a long time. And just sort of the depth of knowledge. I think another thing would be cross-disciplinary knowledge because running a company is a combination of technical product, business, and you can't separate those things, right? It's a unified IDMAs. We use this metaphor of an idea maze.

It's dynamic process. You're running through the maze. The world changes. And it's a maze that cuts across product and tech and business. And that's why the worst ones are when you outsort, you have, oh, my cousin's a web designer doing, he's a CTO or whatever, that kind of thing. Or likewise, on the business side.

Like, it has to be an integrated hole in either the founder or the co-founders because they have to make tradeoffs across, you know, sometimes you have to change the technology for a business and you have to be able to navigate across these different disciplines. So I think that, you know, like a lot of it, too, is like some of my better investments have been ones where I've just been deep in the space and I met with a lot of founders. And then you kind of know from the meeting a little bit, like you have the meeting. You're like, wow, that person, I've spent like hundreds of hours on the space and I just learned

a lot in that meeting. And that person's really thought it through and they're really smart. And hopefully you prediligence them in the sense that you've heard about them and they're referenced. And there's not like a single thing. There's like many, many factors when you go into evaluating founders. And it is hard as part of venture capital. But those are a few highlights in the 40 seconds I had left.

So you can go back to one of the old blog posts about this and picking founders and the IDMAs and all that stuff. And I think it's one of the clearest pieces of thinking on picking founders. So with that, thank you. I love Chad with you. Yeah. Thank you all. Thanks for listening to the A16Z podcast.

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