Deep Dive
Shownotes Transcript
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Welcome to Web3 with A16Z. Today we're running a special episode about the intersection of AI and crypto. Our guests are A16Z Crypto founder and managing partner Chris Dixon and A16Z Growth General Partner David George.
They discuss the broken economics of the internet, new models for creative people, how to think through the first, second, and third order effects of big platform shifts, and more. Today's episode is a crossover from A16Z's AI Revolution Conversation series, and it's especially timely as the paperback edition of Chris Dixon's bestselling book, Read Write Own, goes to print. Check out the show notes for links to those and more.
As a reminder, none of the content should be taken as investment, business, legal, or tax advice. Please see a16z.com slash disclosures for more important information, including a link to a list of our investments. Chris, thanks for being here. Yeah, thanks for having me. I'll always love hanging out with you. Obviously, you spend most of your time on crypto today. How do you generally see crypto and AI interacting?
Yeah. I mean, so I think like, first of all, my kind of meta view is that you have the technology waves tend to come in pairs or triples like the last kind of 15 years ago, it was cloud, mobile social cloud. And I think they tend to, I'm always sort of giving this speech to entrepreneurs, they tend to reinforce each other.
And so, you know, mobile was, you know, what took computing from hundreds of millions to billions of people. Social was kind of the killer app that, you know, hooked them. And cloud was the infrastructure that made it possible, right? And so you couldn't really have, you know, all three of them. And I remember being back then, people having debates, which were better. It turned out they were all better. And they were all required. They were all required. And so I think of that with AI, crypto, and maybe, you know, new devices, the other kind of probably robotics and self-driving cars and VR and things.
I think of those as kind of the three interesting things going on. And I think they all kind of complement each other and work together.
So crypto, look, crypto is a new, this is what my book's about, is it's a new way to architect internet services, a new way to build networks that has a bunch of different properties, which I argue are beneficial for a bunch of reasons and can do a set of things you couldn't do before, essentially. And so, first of all, that's what it is, because I think a lot of people don't, you know, they think of it as Bitcoin or meme coins or something. And so that's fundamentally not what it is to me or I think to the kind of smart people working in this space. There's many different ways in which it intersects with AI. Yeah.
So the first light way, which is something we've invested a bunch in, is just using this new architecture to build AI systems. And so, for example, and so one of the kind of core questions I think we've just talked a lot at this firm about the future of AI is to what extent will AI be controlled by a small set of companies or controlled by, you know, a broad community? The obvious first question there is, you know, is it open source? Yes. It's kind of.
negatively shocked me how closed source the AI world has become. Ten years ago, everything was open and put in papers and then it all shut down and was closed and they said this is for safety reasons. I think it just happened to be very good for their defensibility. I just think it's beneficial business. I don't believe it's safe. But thankfully, there's these ones like Lama and Flux and Mistral and things who are open source. I worry that's a little fragile,
because first of all, I don't know, a lot of them don't put their weights open. Is it really open? Like, you know, some of it's open, like the data pipeline's not open. Is it really reproducible? They could switch it tomorrow. These models get better every month. And if they don't start doing the new frontier, I don't know. So it's like... It's very heavily dependent on one large company. Yeah, it's not like a right... Anyways, so, you know, so one of the things we invested in is a sort of stack of internet services that are built for the AI stack, but open services at different layers. So as an example, there's a...
a project called Jensen, which is building, think of it as sort of a crowdsource compute layer. And so you as a startup can submit a job that, you know, goes beyond the compute you control and it goes out to a network, kind of Airbnb style of people that have excess compute and the network manages that supply and demand, right? And that's the economic ledger. Yeah, that's, yes. Yeah, that's one example. Another one is one called Story Protocol, which is a new way to think about registering intellectual property. And so you could create
image or video or a piece of music and then you register it on a blockchain.
Which keeps a record of the piece of media and the rights around it. It uses existing copyright law. So it actually, so like the blockchain record mirrors a legal agreement that's been crafted to work internationally. And then what can, then anyone can come along. And as long as they abide by your terms that you set, you might say something like you can use this, you can remix it, you can create derivative works. But you have to, any revenue you make, it has to, you have to pay me 10% or whatever. Yeah. Yeah.
You set the terms. But that creates this sort of open marketplace where right now you have to call up some company and try to do a BD deal and this and that. And so you end up having this kind of thing where people either basically steal it or don't do it or they're scaled enough to make a deal or something. Like you have OpenAI going to Shutterstock and they paid them $100 million. But this is really just for the very high-end companies. This is creating a broad democratic kind of resource where anyone can, a small creator can do
set the terms. And then ideally what you create is kind of this, and this is what a lot of the recurring theme in the blockchain world is you have this kind of what we call composability. I have a chapter in the book on this, which is a very powerful force. I think the kind of core force behind the success of open source software. I mean, people forget this, but open source software is probably the most successful, certainly the most successful open computing movement in the last 80 years. But Linux went from...
like 0% market share in the 90s to probably, I don't know what, 90 plus percent market share today. And a lot of that's because of what we call composability, which is people, it's basically this, all these different people coming along and contributing little pieces to the system and the system collectively getting much better in the same way that Wikipedia is sort of a collective knowledge system. And so something like Story Protocol, you get the same kind of Lego effect, Lego brick effect with,
with media. Someone comes along and they create a character, someone else creates another character, someone else remixes them, someone else. Then you get this, you can use whatever AI tool, you can create generative AI, and you can create your story. I create a new superhero universe where I use these other Lego bricks, and as long as the money waterfalls back, that's all okay. Yeah, the incentives are aligned because money waterfalls back. I think it's a really great vision that both allows for people to embrace these new tools,
but also provides an economic model for creative people. I think for me that's a recurring theme in our investing is like, what will be the economic models for creative people in an AI world that don't stop the inevitable, which is the technology progressing, lean into it and rethink those models. That to me is the most exciting area for this intersection. You go from social networking companies which keep 100 percent of revenue for themselves when creators create stuff effectively,
to something where like, hopefully the creator can capture an upfront amount that they set. And then ideally the composability allows for actually more creativity built on top. That's right. Because of the economic climate. Yeah.
We're seeing people do interesting stuff with kind of crowdsourced model evaluation. So that's, you know, what you see, you know, just think of it as all the data side of things. Like you need more data. Well, and we have this crypto as a breakthrough and new ways to design incentive systems. And so you combine that and you say, well, how can you use new incentive systems to get more data for these AI systems, right? Data can either be an input or it can be a model evaluation or whatever it might be. So it's kind of,
kind of what these companies like Scale.ai do, but in a crowdsourced way instead of a centralized way. There's a project that's co-founded by Sam Altman that we're investing in called WorldCoin where they're trying to do the thesis is that in a world where AI can replicate humans and content, we need a way to prove you're human, right? And the best way to prove you're human is cryptographically using a blockchain. And so the idea is they have an incentive system to get people to sign up. And originally it was this...
or that scanned your eyeballs that people, some people, it was controversial. They now have systems where you can identify yourself in other ways, including your passport and other things. But the idea is you prove who you are, you get cryptographic proof of that on a blockchain, and then you can use that for a bunch of different services. Think of a very simple example is think of CAPTCHAs. Today, you have to go and, you know...
play these puzzles, which I think have gotten so complicated and probably... Not AI proof anymore. Not AI proof anymore, and there may be human proof. I have trouble with a lot of them, but replace those with, you know, a set of systems like that and other kinds of clunky fraud systems have an actual cryptographic thing. So I have a code, essentially. This is how cryptography works, and that code proves that I'm a human. And then you can layer onto that other kinds of things you prove on top.
So that's another example of an interesting kind of intersection. So I think there's a bunch of sort of the infrastructure layer of like take AI systems that exist today in a centralized way and decentralize them both in terms of code and services. There's new things you couldn't do before like machine-to-machine payments. And then there's these sort of really far off things that I find the most exciting, which are like what are new business models in this world, like for creative people as an example. Yeah, yeah. One of the things that you pointed out to me, I don't know.
kind of right after the chat GPT moment is you're like, hey, we have the potential for sort of a break in the pact of the internet. Oh, yeah, yeah. Which I think is a super fascinating. Yeah, yeah. There's a chapter on this in the book toward the end. I call it a new covenant. So like you think about the incentive system. One of the main reasons the internet succeeded is they had a very clever incentive system, right? Like how do you get 5 billion people to sort of opt into the system? Um,
without having a central authority tell them to, right? Like this is because of the incentives of the internet. And specifically, there's been a kind of what's emerged over the last 20-ish years is a, I call it an economic covenant between, between,
between the kind of the platforms, specifically social networks and search engines, and all the people that create websites that essentially those link to. And so if you're a travel website or a recipe website or an artist who has illustrations, there's an implicit covenant you have, let's say, with Google, which is you say to Google, it's okay if you crawl my content and you index me and you show snippets in your search engine.
if you, you know, send me traffic back. This is how the internet has evolved, right? And why do you want traffic back? Because you have some business. Maybe it's a free site. Maybe it's an ad-based site. Maybe it's a subscription-based site. But whatever it is, somehow you have a way to make money on traffic. There's an understanding, right? And occasionally beneficial. Mutually beneficial. And occasionally that covenant has been breached. So, you know, there was a thing Google does called one-boxing, which is they would take your content and just put it, like I was on the board of Stack Overflow for a long time, and they would do this where they would take
you type in a thing for Stack Overflow instead of clicking on it, they would just show you the answer and remove the click. They've done that with Wikipedia, they did it with lyric sites. Yeah, but they did with Yelp. Yeah, and people get very upset. Or they with Yelp, they promote their own content on top. There were issues, but it worked. Now, the question in an AI world is if you have these chatbots that just give you, if you go and you say, "I want an illustration," and it just generates an illustration, or you say, "I want a recipe," and it gives you a recipe.
This might be a better user experience, by the way. I'm not against it. I think it's probably better in the end for the users of the internet. But the problem is it breaks the covenant, right? They took this data that, you know, these systems were trained on data that was put on the internet under the prior covenant. Under the premise that they're going to get traffic back. That's right. And they can monetize it. That's right. And that was the premise. And that was sort of the promise, right? And now you have...
a new system which may not send the traffic. In fact, it probably won't. If these things can just tell you the answer, why would you click through? That's probably where we're headed is a world where you have five big systems, let's call it, three to five big AI systems. You go to their websites, they give you an answer. What happens to the billion other websites if they aren't getting traffic is the question. That's the question I think that I talk about.
You know, and I'm surprised slash disappointed that I don't see anyone. I feel like I'm the only person. I feel like I'm streaming the abyss. Like, I'm a little bit surprised that the AI people who just, it's fine. Like, they took all the data and there'll be copyright lawsuits and I'm not going to apply it on that. They've done some data deals from there. Yeah, but like, aren't we a little bit, like, even forgetting about the societal questions, like, and all the
the small businesses that will be like, like, don't we worry about the internet? Because like you, like I worry about just the internet. Like if you have an internet of five, if you have an internet of five companies and you know, there's just like, it becomes like TV and then I, you know, broadcast TV in the 1970s, there's four channels. Like, is that, is that the world we want to live in? Is that a world that's pro startup, pro innovation, pro creativity? Yeah. Like a long tail of websites, like that next generation of long tail websites. Yeah. How do you, yeah. How do you break out? How do you create new things? So,
I don't know. So I just worry without thinking it through. And so to me, look, I'm not saying that I have the only answer to it or you have to be a crypto answer. I realize some people that's controversial. But I think that step one is we should say, OK, wait, this breaks all the incentives of the Internet.
And step two is, you know, is that a good thing? I don't think so. And then so what is the right answer and should we create new incentives? And this is why like a lot of what I've been trying to invest in and think about has been, okay, like the example I gave with Story Protocol is like let's think about new incentive systems to layer on top. Yeah.
One of the things you've talked about is just this trifecta of, you know, technology, you know, products that have come along at the same time. So generative AI, crypto and new hardware platforms. So how do you think about the three of those coming together? So, yeah. And the analogy, of course, is like mobile social cloud, where they all the last wave where they all kind of ended up reinforcing each other. I mean, so you're already seeing some of this. You know, you have these new, you know, technologies.
devices you know the the ar and vr glasses and things which use a lot of a lot of ai um and um you know the sort of her style kind of stuff self-driving cars you have tesla and a bunch of other companies doing you know human humanoid robotics um i think that's probably just barely scratching the surface now of like kind of real world ai um put it putting stuff out in the world and having interact and that's very powerful um and sort of unlocks a whole new avenue of of of interesting applications um
On the crypto side, I think there's a bunch of interesting stuff. There's a whole area of crypto I'm excited about called D-PIN, which is decentralized physical infrastructure. The most prominent example is a project called Helium. Helium is a
community-owned, crowdsourced telecom network that tries to compete with Verizon and AT&T. And so basically what they did is they created an incentive system where anyone can put a helium node up in their house, and that adds a little bit to the network. It's a wireless transmitter.
And they got hundreds of thousands of people in the country to put these networks up. And now they offer a cellular service that's, I think, significantly cheaper than something you get from Verizon. It's like $20 a month instead of $70 a month. And it's cheaper because much of the time it's –
using this homegrown network. They didn't have to spend tens of billions of dollars to build it out. But what's interesting about it is they used the crypto. The crypto is very good at creating incentive systems. And traditionally in networks,
The hardest part of a network is the bootstrap phase. So once a network has kind of critical mass, it's clearly valuable. Once I can sign up for a cellular network and use it anywhere in the country, clearly I'll pay for that, right? When you start it off and there's only 10 houses with the cellular access, it's not something you want to use. Think of a dating site. If there's 10 people on a dating site, you don't want to use it. If there's millions, you do want to use it.
This is a classic problem with building networks is how do you get over this early phase when the network effects are weak? Yeah. Right. And so crypto is the perfect complement to that. Crypto is a great way to provide incentives in the early areas of building a network. And it turns out a lot of interesting networks in the world are physical networks. So there's people doing this for decades.
You know, climate weather modeling. There's people doing it for mapping, you know, self-driving data and mapping cars. There's people doing it for, you know, electric car charging, for cellular networking. We just did one that's around energy metric monitoring. And there's people doing sort of they call it decentralized science, which is sort of you mix it in with sort of more scientific applications. Right.
So one way to think, one sort of simple heuristic is anywhere where there's a, where you want to build a network and is a challenge to sort of build the early phases of the network, crypto can be a really useful way to help bootstrap that. Oh, interesting. Right. And so that's probably one of my, yeah, it's one of my favorite areas in which the physical, there's keep going on a bunch of different areas, but if I had to pick one, I would say that's crypto.
One where the physical world and robotics intersecting with sort of data collection and all these other themes that kind of intersect with AI are relevant. Mark actually gave me this framework, which I like a lot, which is like, is the AI frosting or sugar?
Just like, you know, if the AI is frosting, it's a core ingredient. If it's frosting, you know, all the incumbents are going to win because you just slap a chatbot on your existing product and you've got distribution. You know, you have that selling reference power, incumbent relationships. You know, if it's more fundamental of an ingredient, like you can't actually just slap AI into the product. You have to build it from scratch and that favors the newcomers. It's just very TV. We haven't seen like anything that tells us what the answer is. The more, the more
steal your thing, the more skeuomorphic it is, which is early cycle thing, the more it probably favors the incumbents. Another way maybe to frame Marx's thinking is kind of the Clay Christensen view. Is it disruptive or sustaining?
And specifically, I think what people misunderstand about Christians in view, right, is it's not just like disruptive and doesn't just mean new. It means misaligned with the incumbent business model. Yeah, exactly. And so that means that even if the incumbent, that's sort of the interesting part of his book, right, is that even when the smart incumbent sees it coming, it's very, very hard for them to react to it because it's inconsistent with because it's not what their best customers are asking for. Yeah, exactly. Right. And so that's where I think.
maybe that's somewhat overlapped with Mark's frosting icing thing. Well, it could be that the business model is a fundamentally shifted business model. Yeah, so you come in and you're like, instead of databases, it's some radical new architecture that's database-free. I don't know what. It's something that eats up the... Yeah, yeah. That...
that cannibalizes the incumbent business model and therefore makes it organizationally and economically harder for the incumbents to layer it on. Yeah. We haven't seen it yet. We've seen people talk about outcome-based pricing. Well, that's the second thing is I think it comes down to like, I think, well, I'll get to this in a second, but I think there's a sort of a framework I like to use to think about the stages of how these things roll out. But let's talk quickly about consumer. So in consumer right now, at least, I don't think you see a lot of network effect.
businesses, right? So, like, as successful as these, you know, the clods and chat GPTs are, I don't think they have a network effect. The switching costs are relative. Maybe they learn your history. No, we actually thought this was going to be the case. We thought that, like, the killer thing was going to be, okay, they take, and they get a, there's a data network effect. So, like, people responding, and then there's a reinforcement learning loop or something? Well, so, yes. And that, like, it turns out the reality is, like, humans are relatively uninteresting. And so, like,
You don't learn that much. Most of the stuff that people are doing, it's not like there's some learning. Data network effects is one of the things we've talked about for 20 years.
It almost never seems to actually be a thing. I don't know. It's certainly not a thing in this case. We thought it may be, but it's not. The rest of these guys, there's also brand like has a great brand. There's a lot of other intangible. Persistent memory could be a thing. It learns you, it's integrated your life. Maybe integrations, it's in your calendar. I don't know. But the question is, how do they avoid in the steady state having just a model and price competition to the price to the bottom?
Obviously, they're important big businesses, but will they be, you know, kind of dominant? Yeah. And then what is the opportunity for new startups if you're, you know, if you're doing venture investing and AI consumer? Like what, you know, you see a lot of these things that's like, you know, make your face prettier, like these kind of fun apps, but then they, and they zoom up in the app chart, right?
and then TikTok copies it and so forth, right? Because it's just not, because again, no network effects. No, no. Yeah. So how, you know, and there's this technique, kind of strategy I like to talk about called come for the tool, stay for the network. And the idea is, you know, maybe you can use that, like make my face prettier and then sort of use that as a hook to get people into your new network, like your social network, possibly. Although it just feels very, very hard today given just the scale and power of these incumbents. Yeah.
So anyway, so that's a big interesting – and that, by the way, will intersect back to crypto because what crypto is and what I argue in my book is that crypto is a new way to build networks. And so you sort of have the chocolate and peanut butter. You have AI with all these really interesting use cases and then you have this new kind of technique for building networks. AI, the interesting use cases, but no network effect. And then you have this new thing that's like all network effects and are there interesting ways to combine them? Yeah.
Before I get to that, I think it's important to talk about kind of how big technologies kind of roll out in multiple stages. So there's a distinction. It's not my distinction, but I've talked about a lot. It's the sort of
One way to think about technologies is that they can do one of two things. They can do old things better or they can do new things you couldn't do before. And so we call the first one skeuomorphic. This is a Steve Jobs term, which sort of refers to products and designs that kind of harken back to a prior era to make them more understandable. And then there's what we call native apps, which are things which are the kind of new things that couldn't be done before.
And then there's actually a third stage, I think, which is kind of second-order effects, which is once you create this big new technology, what are the things that now that sort of, you know, you created the car and now you have the highway system and now you're able to create suburbs and trucking infrastructure, right? Like those are kind of second-order downstream effects. There's a famous line that the great science fiction writers didn't –
Good science fiction writers predict the car. Great science fiction writers predict the traffic jam. Right? So, like, it's like that idea. So, it's like that. Like, what are the second order? Like, Bitcoin is something that couldn't have existed before social networking. Yeah, of course. So, 30 years ago, you say someday people are going to have their own media and you're going to remove these gatekeepers. Right.
And then they're going to, you know, who would have thought then you're going to create these, you know, digital currencies. There would have been no way to create the community. Yeah, yeah. It would have been a New York Times article saying it's stupid and then that's the end of it, right? There's nowhere to get together and talk about it and create your own. I mean, they're really kind of, they're religious movements, you know, most token communities and they need places to congregate and discuss it. And so they, and now they have that. And so there's all these kind of second order. I mean, we're seeing effects in politics and all these other things, right? I mean, the
You know, there's the whole, arguably our society and world is changing as a second order effect of social networking. So one way to think about AI, so the first stage is kind of the skeuomorphic phase, which is you're going to, you know, this is the stuff you see talked about all the time in the business, you know, startup community of like your, you know, customer service bots, right? So you take a person, a job that's currently done by a person sitting in a call center and you replace that with a AI voice and, you know, chat bot, right?
And you're sort of a one-to-one – in the simplest case, it's a one-to-one exchange of – and you just – it's cheaper and it's more systematic and it will displace jobs. Hopefully, it will also create equally or more jobs and better jobs.
But that's an obvious first stage. This is, I think, one of the reasons people get so excited about the opportunity for AI is you can just see that happening in tens of millions of jobs, I guess. Really, the whole laptop middle section of the economy, you can see many of those jobs, everyone including us who spend their days typing emails . We can speculate on it, but we're part of that group too.
So that's phase 1, right? It's skeuomorphic. But that phase 1 can last 20 years. So just to be clear. Yeah. And it may just be that's the thing that happens for the next 20 years. And then the next phase is the native phase. And to me, that's what gets me more excited. And by the way, let me give a little analogy to the internet. So the skeuomorphic phase was the 90s. And so the 90s, what you had was people, basically, if you look at 90s internet, people were sort of taking offline things online.
like magazines and catalogs and putting them online. So you would go buy things, but it wasn't like something, you know, and it was much easier. You could type in a website and go, you know, buy this rare book on Amazon. And it was much easier and it was convenient, but it was fundamentally something you could have done before. It just would have been clumsy and getting some weird magazine, some catalog or something. But it wasn't until the 2000s that people did things like social networking and these things were just brand new things. There's no analog in the offline world to a lot of these new behaviors that people created. I talked,
a lot in detail about this in the book if people aren't interested. Yeah, and a business model that didn't exist. And a business model and just the whole thing. I mean, there's a whole bunch. I mean, you had very loose analogs like yearbooks and things, but nothing, you know, and that's actually kind of, if you look at early Facebook, it was more like a yearbook. But as it evolved into a news feed and all the other things that happened, it became kind of really a brand new thing that no one had really imagined. Yeah.
And so, anyway, so you sort of saw the internet play out that way. In that case, it took probably 15 – it was probably 2000 – you know, 93 was Mosaic, and 2000, I would say, 5-ish was sort of YouTube, and 4, I think, was Facebook or whatever it was. So it took at least a decade. And so I think, you know, that – so with AI, there's a really interesting – and by the way, one of the things you get in the native phase, which is why it's so exciting, is you get new products, you get new forms of media. Yeah.
If you go back, you know, when photography was growing in popularity, there were all of these kind of cultural art criticism, you know, think pieces about art.
Right.
And so people were kind of worried about it in the same way they're worried today about generative AI, right? So like, what if you can now create a movie? It looks like you can pretty soon, right? - Yeah, I mean, images is there. - Images is there and probably videos coming soon.
What happened in the case of photography is that you had, I think, kind of two things happen. You had art, like fine art, kind of went more abstract and away from photography, right? They did sort of leaned into what they were unique at. And that's when you had whatever, cubism and all these other kinds of movements. And then on the other side, I think what's really interesting, right, is you had the growth, the rise of film. So you had someone say, hey, yeah, maybe you can use machines to replace photography, but you can also now use machines to create a brand new art form that never could exist before.
Right. You sort of had it with animation, but now you can do it a really interesting, sophisticated way with film. Right.
So to me that then and so film would be the native what was the native media form of media in the age of mechanical reproduction right. Oh that's a fascinating analogy yeah. And so I think to like today like so the interesting thing right so when you look at these the gender of AI like the negative way to look at it and you do see some a lot of this negative sentiment from like the art community and things on Twitter where they say look this is just you know a cheap cheap replacement for human creativity right.
that's the negative way to look at it. The positive way to look at it is this is the base layer in the same way that film was a base layer back then. And now, but now there's this, there's this new canvas of human creativity where you can create new art forms. I don't know what those are. They may be virtual worlds or games or new types of films and movies. I don't know, you know, or they may intersect with a new, you know, way to, way to consume the media altogether. Like, yeah, maybe, yeah, maybe there's new interfaces. And then this is to me, what's so exciting about these, these, the, the, the native, the new native forms,
the native apps is that I won't think of it because it's, it's going to like in, in my experience through my, like through experience, through watching some of these waves in the past is there, it really does take kind of brilliant, creative people to come up with these new things. And it surprises you in many cases. and, and, and,
Yeah. And so I think that that's going to be the exciting phase I'm looking for is like, what are the not how do you just use the technology to make to do the things you could do today, but do them cheaper? But how do you use the technology to do to push the frontier and do things that could never be done before in the same way that film did that? Right. Yeah. I think most people would agree if you go back and look at it that, you know, the film, I think photography probably unlocked more opportunities for creative people than it than it.
Then it removed. Yeah, then if it never existed. And I think this would be the hope in this kind of phase. So there's that. And look, there's that kind of – that's the media example, but there's probably that for consumer applications and that for social networking and that for, you know, also, you know, productivity. And so that will be the really exciting thing, I think, to see is not just the replacing things we do today, but –
come up with brand new behaviors that are things we couldn't do before. And then the third thing is the second order effects, right? So you create this new world. So you've created this world of social networking. It's interesting to think with social networking, we've seen it play out. And it surprised me too, like, you know, you sort of have social networking rise in the 2000s.
I think it hit a tipping point, maybe the Obama election. Yeah. Was that the 2008 and then 12 too? Like he really sort of leaned into using that. And I remember seeing all these news articles like, wow, this is different. People are, it's sort of, it had, the bit had flipped from offline second to off, sorry, to online second as a secondary, as sort of online was primary. Yeah.
But then we started seeing these kind of weirder things like I think the, you know, like the Trump movement and the populism just surprised everybody. And you just started seeing movements and just behave. And I think we still haven't really figured out what's going on, where all this is headed. And we're in this kind of
Yeah, just think about the timelines.
Yeah, I mean, it's probably going to take a very long time. Like, I'm always overly optimistic on these things historically. Like, I'm like, okay, we're done with the skeuomorphic phases. Now we'll do the native phase. But the reality is each phase probably takes a decade. One of the interesting things you said around these, you know, sort of distinct phases is
Obviously, like, the internet took a long time partially because you had to build a network. It was an apply and demand issue, right? Like a physical network and then also... The physical, the literally laying cables and the wireless. Yeah, laying cables. And, you know, sure, you have to build, you know, large clusters of compute GPUs here with networking, but I think the constraining factor for getting from that skeuomorphic phase to the native phase is not necessarily capabilities themselves, but like...
Yeah, I think so. I think the bottleneck will be, I mean, yes, I think the bottleneck here will be humans and policy regulation, which are obviously closely connected. Yes. And I think humans on both the supply and demand side, probably more on the demand side. So meaning supply side, you know, you need to have people come up with all the creative things. But we've got, you know, the world's different now in that.
I just think the startup world is different now. It's much more mature and much more sophisticated, honestly, than like when I was coming up in it in the sense that you've got like – I mean, when I was starting off, there were 10 venture firms. Now there's thousands. Like it's just a much more mature – you know, the number of startups and honestly, like there's a lot of good, smart advice out there.
Yeah, this is a more popular path for smart people to go. Yeah, it's like a thing you do, like, you know, in places like Y Combinator and other places have done a good job of this. Like, if you're coming out of a top school, I mean, even 10 years ago, this wasn't, like, I knew people that were like, wow, you could do startups, you know? I mean, probably 15 years ago, but maybe, you know, definitely that was the case 15 years ago. But now I think it's like an established career path. There's an established set of mentors, established set of funding. There's a canon of
pretty good advice out there. Like the standard advice is much better. It used to be terrible advice. Now it's good advice. There's a, you know, you can come out to San Francisco and I think relatively easily if you're smart, you know, like network friendly person, like get embedded pretty quickly. And then, you know, and then Silicon Valley, it's gotten just very good at sort of throwing tons of capital energy against those problems. So there's a supply side. I suspect the demand side is more like another meaning like changing organizational and human values
work and behavior patterns? Like getting an organization to like, take the video example we're talking about. Like how long is it going to take Hollywood to
To adopt. Yeah. I mean, look, I wrote my book. I wanted to have my own voice use AI to read the book using my own voice. Both the publisher and Audible, the podcasting platform, ban AI completely. And part of it's unions and just a bunch of resistance. But, like, you know, I'm just saying, like, how— I think people know this, but, like, the capabilities are fully there to do that. Yeah. I mean, like, look—
Mark Andreessen had a great blog post. It's like, how do I know they're going to ban AI medicine? Because they already have, essentially. I mean, essentially, these things are so heavily regulated. And so many areas where it's going to have an impact are so heavily regulated. And just the organization, like, look, and take the Hollywood Gen AI thing. You'd have to lay off a whole bunch of people, probably, who you don't want to lay off, who are unionized. And so that means maybe there'll be some fresh upstarts, maybe in another country, who...
create, you know, native movie studios, you know, AI-native movie studios. But that will take a very long time. There's a lot of, you know, like, there are a lot of very talented people. The right answer is probably not, like, the right answer is probably to harness all of that talent in Hollywood and combine it with AI in some way, right? I mean, because there's a lot of, there is a lot of very smart people and talent. But how long will that take culturally? It may take a whole new generation to really play out, right? And I think a lot, so that's something by the demand side, right? Like, just...
And then just human behavior, changing your workflow, using an AI assistant all the time. I mean, I think, you know, I don't know. Anyway, so. Yeah, having sort of like a copilot for everything you do, like it feels like it's. Yeah, I mean, maybe that can be solved with interfaces and things. I don't know. Then there's the policy side, which is there's going to be, you know, like this resistance I'm discussing is going to be, it already is, right?
going to be enshrined. There's going to be movements to enshrine it in law. And that's going to play out, I think, in multiple levels. It's already starting to play out in the courts.
and it's starting to play out in like state legislatures with like California had the AI bill. You know, you have a bunch of lawsuits around copyright. I think my view is ultimately this will play out in Congress. This is such a big issue when you have something that affects tens of millions of jobs. It won't, it is sort of a, it is beyond something that people are going to allow just, just happen through free markets. Yeah. Yeah. And through regular court decisions, like the copyright thing is an example. Like,
Ultimately, you could have some joke. Right now the question is, when an AI system is trained on a piece of data, is it copying that data or is it learning from that data? That's a philosophical question. That's a fundamental question across different media happening right now. That's right.
And so, you know, and you could have five years from now, some judge, you know, some federal judge decide that philosophical question, or I think more likely you'll eventually have some legislation, like congressional legislation, that's some kind of compromise struck between the media industries and the tech industries that comes up with a solution that both creates incentives for creators, but also allows AI systems to exist. I don't know. But that thing will play out over a very long time. When will you be allowed to use AI in medical and finance and
I mean, significant, what is it, probably 70% of our economy are regulated industries, right? Yeah, of course. You know, on the flip side, like the stuff with Lamo is really impressive. I'm surprised they're actually allowed in San Francisco. Well, it turns out it's like seven to 10 times safer than a human driver. And there's now millions of miles of game. So maybe that's the playbook to get this stuff adopted more broadly. What's an ideal future state of the internet? Yeah.
So there's, you know, sort of zero cost of creation and distribution, you know, transparent ownership, governance. Like, what does this look like? Yeah. So I think that we're at a crossroads and there's a real question as to whether it looks more like its original vision, which is, you know, the vision of the Internet, like the 90s vision and the 80s vision or something was an Internet that was owned by, you know, community owned, community governed people.
The economics, the money mostly flow to the edges of the network and not to the intermediaries in the middle. Right.
Like a very simple thing. Just think about this. Just imagine the network and how the little green dots and how the money is flowing, right? Like originally in the 90s, the money flowed to the edges, to small businesses, to innovators, to entrepreneurs. If you looked at a map today, it's mostly flowing to the middle. This is why these seven companies. It's $200 billion of revenue from social networking. Yeah, I think the top five internet companies are something like more than half of the market cap, not more. It might be something higher by now.
And so just you have all the green stuff kind of flowing into the middle. And so there's sort of two kind of, I think of it as two kind of important things that you want. It's about power and money, right? Control. And my core arguments in the book is that those two questions are a product of how you build these services. Like what architecture you choose, the first sentence of the book is your architecture is your destiny or something like that. Like the architecture you choose determines how it's controlled and yeah, and how the money flows. And so-
And I think we're really at a kind of critical point. In fact, I worry we're at a, you know, we're approaching a point of no return where it's going to be an internet controlled by five companies. And what's happened is these guys, these networks have all gotten to a certain scale and they've just decided that the next kind of wave is to keep you trapped there. Well, there's no way to grow users anymore. They've kind of captured all that. That's right. That's right. And so they're kicking away the ladder, right? They climb the ladder and they're kicking it away. And it's going to, and it's really negative. And this is why
We as a firm have felt that this is such an important topic of being able to build new internet services with new architectures like using blockchains is such an important topic for the future of small tech, little tech as we call it, along with open source AI, the other kind of critical thing, which is if a startup has to pay this giant tax to an incumbent to build competitive services.
they won't be able to build, you know, build services that threaten those incumbents, right? Yeah, we've seen that before, right? Like, you've talked about it as sort of, you know, Singo was built on top of Facebook. Yeah. And then, like... It's platform risk, right? I mean, it's building on quicksand. So we need, you know, startups need access to distribution and networks, and they need...
And they need access to modern software, open source software. And so I think those are the critical questions. Those will be, I think, a hugely important thing, which is why we've invested so much time and money in it, is the regulatory side of this, is like what policies are there and are they policies that encourage competition and innovation and little tech?
And then, you know, I think just kind of raising awareness of these topics and having discussions about them are important so that we don't kind of back our – what I'm worried about now is sort of kind of, you know, backing ourselves without having really thought it through into a situation where there's four companies that control everything. And it ends up we're kind of eating our seed corn. Like so much of our – what we benefit from today is the startup innovation of the past. And we'll risk losing that if we let these small set of companies control everything. Yeah.
well i'm optimistic look the bright side is you know through all the work that you guys have done and our firm um you know we've kind of gotten the word out about little tech and i think understanding that building you know a new architecture new infrastructure and then the importance of open source uh i think the word is getting out so yeah great this is awesome