Allesio: The question, do we care who makes it? One of the biggest news this week was the collapse of AI2, the Allen Institute, one of the actual American open source model labs. Um, and I'm not that optimistic on on American open source. Like you guys invested in Mistral and Mrol is doing extremely well outside of China. That's about it.

Marc Andreessen: Yeah, we'll see. We'll see. I look, number one, I do think we care. I do think we I do think we care who makes it. Um I would say this the the the previous presidential administration wanted to kill it in the US. Like they wanted to drown in the bathtub. Um and so they wanted to kill it. So at least we have a government now that actually like actually wants it wants it to happen. And you council. Yes. And the new and the Past. Yeah. So that you know this admin for whatever other political issues people have which are many you know this administration has I think a very enlightened view and in particular an enlightened view on AI and in particular on open source AI. Uh and so they're very supportive. Um my read is the chi the Chinese have a very the various Chinese companies have a very specific reason to do open source which is that they they don't fundamentally they don't think they can sell commercial AI outside of China right now or at least specifically not not in the US for a combination of reasons and so they they kind of view I think open source AI as a bit of a loss leader against basically domestic uh you know paid paid services and then kind of you know kind of an ancillary products you know they're they're very excited about it by the way I think it's great I think it's great that they're doing it um you know I think DeepS was like a gift to the world um I The great thing about open source open source the the the impact of open source is felt two ways. One is you you get the software for free but the other is you get to learn how it works right and so like the paper the paper the paper and and the code right and the code and so like for example I thought this was amazing so open comes out with 01 and it's an amazing technical breakthrough and it's just like absolutely fantastic but of course they don't explain how it works in detail and then of course they hide the they hide the reasoning traces right and and then and then and then everybody's like okay this is great but like who's going to be able to replicate this? Are other people going to be able to do this? You know, is there secret sauce in there? And then our one comes out and it's just like there's the code and there's the paper and now the whole world knows how to do it. And then, you know, 3 months later, every other AI model is is adding reasoning. And so, so you get this kind of double like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.

Marc Andreessen: Yeah. So I think in the combination of the two of them I think is one of the 10 most important software. Open claw got all the attention but talk about pi pi's kind of the end. Yeah pi pi is kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to I don't know it still is very important but like 19 from 1970 through to like basically the creation of Linux which was basically this this thing used to call like the Unix mindset like so so because there were all these different you know theories there all these different operating systems and mainframes and and then you know all these windows and Mac and all these things and then there was this but kind of behind it all was this idea of kind of the Unix mindset and the Unix mindset was this thing where basically you don't have these like like in the old days like like the operating system that like made the computer industry really work like in the 1960s was this thing called OS 360 which was this big operating system that IBM developed that was supposed to basically run everything and it was this like giant monolithic architecture in the sky. It was like a you know it was like a giant castle um of software and and by the way it worked really well and they were very successful with it but like it was this huge castle in the sky but it was this thing it was almost unapproachable which is like you had to be kind of inside IBM or very close to IBM and you had to really understand every aspect how the system worked and then the Unix guys originally out of AT&T and then out of out of Berkeley um you know came out and they said no let's have a completely different architecture and the way architecture is going to work is we're going to have we're going to have a prompt and a and a shell and then and then we're going all the functionality is going to be in the form of these discrete modules and then you're going to be able to chain the modules together and so the it's almost like the operating it it operating system itself is going to be a programming language. Um and then that le led to the the the sort of centrality of the shell. Um and then that led to sort of you know basically chaining together Unix tools and then that led to the emergence of these these scripting languages like Pearl where you could basically kind of very easily do this and then the shells got more sophisticated and then and then and then look like you know that that number one that worked and that that was the world I grew up in like I was I was a Unix guy you know sort of from call it 1988 to you know kind of all the way through my work and it worked really well. it's in the background. Um, you know, nor normal people don't need to didn't need to necessarily know about it, but like if you were doing like system architecture, application development, you you knew all about it. Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there and your iPhone still has a Unix shell kind of buried in there somewhere. So, they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that. But, um, you know, but look, the the internet runs on Unix. Um, and then smartphones. Actually, both iOS and Android are Unix derivatives. And so you know kind of Unix did end up winning but but anyway and then we just started taking that for granted and then and then so so basically the way I think about what happened with pi and then with openclaw is basically what those guys figured out is I always say the great breakthroughs are obvious in retrospect right which is the best kind they weren't obvious at the time or somebody else would have done them already. Um and so there is a like a real conceptual leap but then you look at it sort of the backwards looking and you're just like oh of course like to me those are always the best breakthroughs. Well actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course. Yeah. What other objective mattered? Yeah, exactly. But but like it right. But she's even saying it wasn't obvious until somebody actually did it, right? And so the conceptual breakthrough is real and deep and powerful and very important. And so the way I think about pi and openclaw is it's basically marrying the the language model mindset to the to the Unix basically shell prompt mindset. And so it's it's basically this idea that what what so what is an agent, right? And as you know like many smart people have been trying to figure out what an agent is for for for decades and they've had many architectures to build agents in the whole thing. And it turns out what is an agent? So it turns out what we now know is an agent is the following. It's so it's a language model. And then above that it's a bash it's a bash shell. So it's it's a Unix shell and then it's and then the agent has access has access to to the shell in you know hopeull hope hopefully in a sandbox maybe maybe in a sandbox. So it's it's the model um it's the shell um and then it's a it's a file system. Um and then the state is stored in files and then you know there's the markdown format for the you know for for the files themselves and then and then there's basically what in Unix is called a crown job. There's a loop and then there's a heartbeat for there's heartbeat and and the thing basically wakes up wakes up. So it's basically LLM plus shell plus file system plus markdown plus cron and it turns out that's an agent and and and every part of that other than the model is something that we already completely know and understand. And in fact it turns out that like the latent power of the Unix shell is like extraordinary because basically like all like there's just like there's just enormous latent power in the shell. There's enormous numbers of Unix commands. There's enormous number of command line interfaces into all kinds of things already in the you know your entire I mean your entire just to start with your computer runs in a shell if you're running a Mac or or a phone your computer your computer's running on a shell uh already and so like the full power of your computer is available at the command line level um and then it turns out it's really easy to expose other functions as a command line interface and so like this whole idea where we need like MCP and these like pro fancy protocols whatever it's like no we don't we just need like a command command line thing so that's the architecture and then it turns out what is your agent your agent is a bunch of files stored in a file system. And then there's the thing that just like completely blew my mind when I wrote my head around it as a result of this, which is like, okay, this means your agent is now actually independent of the model that it's running on because you can actually swap out a different LLM underneath your agent and your your agent will change personality somewhat because the model is different, but all of the state stored in the files will be retained, different instruction set, but you just compiled it, right? Exactly. And it's all right. It's like, right, swapping out a ship and recompiling, but it's it's still it's still your agent with all of its memories um and with all of its capabilities. And then by the way you can also swap out the shell. Uh so you can move it to a different execution environment that is also is also a bash shell. By the way you can also switch out the file system right uh and you can and you can and you can swap out the the the heartbeat the crown framework the loop the agent framework itself. And so your agent basically is basically at the end of the day it's just it's just its files. Um and then and then there's of course yeah it's it's basically it's just the files. Um and then by the way as a consequence of that the agent it and then the agent itself it turns out a couple important things. So one is it it's it can migrate itself, right? And so you're you can instruct your agent migrate yourself to a different uh runtime environment, migrate yourself to a different file system, migrate yourself to a different you swap out the language model. Your agent will do all that stuff for you. And then there's the final thing which is just amazing which is the agent is the agent actually has full introspection. It actually it actually knows about its own files and it can rewrite its own files, right? which by the way is basically no widely deployed software system in history where the the the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself like that that I mean there have been toy systems that have had that but there there's never been a widely deployed system that has that capability and then that leads you to the capability that just like completely blew my mind when I wrapped my head around it which is you can tell the agent to add new functions and features to itself and it can do that extend yourself right extend yourself like extend yourself give yourself a new capability right and so and so literally it's It's like you run into somebody at a party and they're like, "Oh, I have my open claw do whatever. Connect to my eight sleep bed and it gives me better advice and sleep." And you go home at night and you tell your claw or if they're at the party, by the way, you tell your claw, "Oh, add this capability to yourself and your claw will say, "Oh, okay, no problem." And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to cloud code or whatever. It'll write whatever it needs and then the next thing you know, it has this new capability and so you don't even have to like you can have it upgrade itself without even having to without having to do anything other than tell it that you want it to do that. And so anyway, so the combination of all this is just I mean this is just like a massive incredible I mean it's just incredible. Like if I if I were if I were 18 like this is 100 this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. And again people are going to look at it and they already get this response. People are going to look at it and they're going to say oh where's the breakthrough because these the all of these components were already known before. But but this is the key the key to the breakthrough was by using all these components that were known before you get all of the underlying capability that's buried in there. And so all and so for example, computer use all of a sudden just kind of falls trivial trivial. Of course, it's going to be able to use your computer. It has full access to the shell, right? And then and then you just you give it access to a browser and then you've got the computer and the browser and off and away it goes. And and then you've got all the abilities of the browser also. Um and so and so the capability unlock here is profound. My friends who are, you know, deepest into this are having their claw do like like literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and you know, these are, you know, these are prototypes and there's, you know, as you guys know, there's security issues and and so, you know, there's a bunch of stuff to be ironed out, but the the unlock of capability is just incredible. And I I have absolutely no doubt that everybody in the world is going to is going to have at least, you know, an agent like this, if not an entire family of agents, and we're going to be living in a world where I think it's almost inevitable now that this is the way people are going to use computers.

Spix: I mean if you play it through you don't need browsers then like that's the death of the browser.

Marc Andreessen: Well, so I I would take it a step further, which is you may not need user interfaces. So, who is going to use software in the future? Other bots. The other bots. Yeah. Yeah. And so, you still need to, I don't know, pipe information in and out. Really? Well, what are you going to do then? Are you sure? You just going to log off and touch grass? Whatever you want. Exactly. Isn't that better? I want software to do stuff for me. Is that But isn't that better? I mean, look, I you know, I don't know. Look, like you know, you know all the arguments here. It was not that long ago that 99% of humanity was behind a plow. Right. Right. And what are people going to do if they're not plowing fields all day to to grow food, right? And it just turns out there's like much better ways for people to spend time than plowing fields. Yeah. Drawing. Uh exactly. Exactly. You know, talking to their friends. And look, and I'm not an absolutist and I'm not a utopian and and to be clear, like I I have an 11-year-old and he's learning how to code and like I'm, you know, I think it's still a really good idea to learn how to code and so forth, but I just if you project forward, you just have to think forward to a world in which it's just like, okay, I'm just going to tell the thing what I need and it's going to do it and then and then it's going to do it in whatever way is most optimal for it to do it. Unless I tell it to do it nonoptimally, like if I tell it to do it in Java or in Rust or whatever, it'll do it, I'm sure. But like, if I'm just going to tell it to do, it's going to do it in whatever way is like the optimal way to do it. And then I and then if I need to understand how it works, I'm going to ask it to explain to me how it works, right? And so it's going to be doing its own interpret. It's going to be the engine of interpretability to explain itself. And I I just am not convinced that that I'm not I'm not convinced that in that world you have these historical the goals of the abstractions will be whatever the boss need with you, right? Yeah.

Allesio: I'm curious like if that's true then shouldn't the models providers be building some internal language representation that they can do extreme kind of like RL uh and reward modeling around because it's like today they're kind of like tied to like TypeScript and Python because the users need to write in that language versus they can have their own thing internally and like they don't need to teach it to anybody they just need to teach their model and I think that's how you get maybe diversion between the models like going back to like the pi open cloud thing it's Oh, I built all the software using the OpenAI model and now switch to the entropic model, but the entropic model doesn't understand the thing. So, I it feels like there still needs to be some obstruction. But maybe not. Maybe that's the lock in that the model providers want to have. I don't

Marc Andreessen: I'm not even sure that's lock in though cuz why can't the second model just learn what the first model has done? Like Exactly. Okay. So, okay, give an example. So, as you know, models can now reverse engineer software B, right? Isn't it the whole thing now where people are reverse engineering like Nintendo game binaries? Yeah. So you you have like I've seen a bunch of reports like this where somebody has like a favorite game from the 1980s and the source code is like long dead but they have like a binary burned into a chip or something and now they're reverse engineered to get a version that runs on their Mac. Right. And so if you reverse it this is why I kind of say if you're reversing like x86 binaries then why can't you reverse engineer whatever the Yeah. And because we're on a Unix based system it has to be reversible because it needs to run on the target. Yeah. Yeah. Yeah. Yeah. Yeah. Basically. And so I just I just think it's this thing where it's just like and by the way and everything we're describing is something that human beings in theory could have done before but just with like but with enormous where but it was just always like cost and labor prohibitive. Reverse engineer like I learned how to reverse engineer human beings can reverse engineer binaries. It's just for any complex binary I need like a thousand years to do it but now with the model you don't. And so all of a sudden you get you get these things or another way to think about it is so much of human built systems sort of compensate for the human limitations. Yep. Right. Um, and if you don't have the human limitations anymore, then all of a sudden you have and it's not that you you won't have abstractions, but you'll have a different kind of abstraction. Yeah.

Allesio: I have two topics to bring us to a close and you can pick whichever one. So, just talking about protocols, was it you or someone else? I forget my internet history who said that like the biggest mistake that we didn't figure out in the early days was payments. Yes. Is that you? Yes. 402 402 payment required. We have a chance now. I don't think we're going to figure it out. I don't know. Like what's your take? Oh, I think we will. Yeah. No, now I think it's going to happen for sure. Yeah. Yeah. And there's two reasons it's going to happen for sure. one is we actually have internet native money now in the form of crypto stable coins and crypto and this is I I think this is the grand unification basically of AI and crypto is what's about to happen now. Um I think AI is the crypto killer app I think is where where this is really going to come out. Um and then the other is just it I mean it's just I think it's now obvious it's like obviously AI agents are going to need money and it's already happening right if you've got a if you got a claw and you want it to buy things for you you have to give it money in some form. I would say the adoption is probably like 0.1% if if that. But yeah. Oh, today. Yeah. Yeah. Yeah. But think think forward like where is it going forward thinking? The ultimate principle of everything and and everything that I think I we we do is it's the William Gibson quote which is the future is already here. It just isn't distributed isn't isn't distributed yet. My friends who are the most aggressive use users of of of OpenClaw just like have given their claws bank accounts and credit cards. Um and and and and and and not only have they done it, it's obvious that they needed to do it because it's obvious that they needed to be able to spend money on their behalf. Yeah. Yeah. It's just completely obvious and so and again like so the number of people who have done that today to your point is like I don't know probably 5,000 or something but it'll grow that's how these things start actually I mean since uh you keep mentioning and by the way open cloud by the way if you don't give it a bank account it's just going to break into your it's break it's going to break into your bank account anyway and take your money so you might as you might as well do it you might as well do it by the way I really love I got to tell you I really love the phenomenon I love the yolo um I'm not doing it myself to be clear but I love the people that are just like what is it dangerously dangerous which by the way is a Facebook thing. Okay. Right. Uh because we uh in Facebook they they have this culture to name the thing dangerous so that you are aware when you enable the flag that you are opting into a dangerous thing. Okay. And they brought it into OpenAI. But of course that makes it enticing. Sam Sam runs codeex uh with skip permissions on on his laptop. Yes. 100%. And so I I I think the way to actually see the future is to find the people who are doing that. There's a mand, you know, log everything, you know, just watch it. Watch the logs. But like, let's actually find out what the thing can do. And the way to find out what the thing can do is just like try everything. Yeah. Let it try everything. Let it unlock everything. By the way, that's how you're going to find all the good stuff it can do. By the way, that's also how you're going to find all the flaws. I think the people who turn that on for bots are like they're like martyrs to the progress of human civilization. Like I feel very bad for their descendants that their bank accounts are going to get looted by their bots in the first like 20 minutes. But I think the contribution that they're making to the future of our species is amazing. He's like gentleman science, you know. Yes. It's Yes. It's Ben Franklin out with a trying to trying trying to get lightning to strike his his balloon and see seeing if he gets electrocuted. Yeah. It's Jonas Sulk with the polio vaccine, right? Injecting. Yes. So, yes, I I I I think we should have like a we should have like flags and like we should have like monuments to the people that just let OpenCloud run their lives.

Allesio: Final protocol and then and then we can wrap up. Uh, proof of human. Yes. Right. That's the last piece that we got to figure out.

Marc Andreessen: Yeah. So, I would say there's there's two massive I would say um uh sort of asymmetries in the world right now where we've known these asymmetries exist and we we society have been unwilling to grapple with them and I think they're both tipping right now and and they're they're they're the same thing. It's the virtual world version is the physical world version. So, the virtual world version is is the bot problem. We're just like, you know, the internet internet is just like a wash in bots. Internet's a wash in fake people. It has been forever. Um, by the way, a lot of that has to do with lack of money, you know, and so this, you know, this is this is this my spicy take was these two are the same thing and corporations are people too, you know. So, interesting. Yeah. Yeah. Yeah. Okay. So, a bank account is proof of human. Yeah. Okay. Yeah. Until until you give the bots bank accounts. Yeah. Exactly. So, okay. Yeah. So, there's that. But, yeah. Look, look, the bot I mean, every social media user knows this. The bot the bot problem is a big problem. You know, the bot the bot problem has been a big problem forever. It's it's a huge problem. And it's never really been confronted directly like at any point. By the way, the physical world version of this is the drone the drone problem. Um, right. And so we we've known for, you know, we've known for 20 years now that the asymmetric threat both in mil military in actual military conflict, but also in just like security like like you know, security on the home front, the big threat is is the cheap attack drone, right? The the cheap the cheap suicide, you know, drone with a bomb. And we've known that forever. And by the way, like, you know, it's very disconcerting how like every, you know, every office complex in in the c, you know, in the world is like unprotected from drone attacks. um every every stadium, every school, every prison like it like okay, we've known that we've never done anything about it. Yeah. One possibility is just leave leave them unprotected forever and live in a world of like asymmetric terrorism forever. Or the other is take the problem seriously and figure out the set of techniques and technologies required to to be able to deal with that whether those are lasers or jammers or early warning systems or you know personal force fields. Kinetic personal for Dune personal force fields. Exactly. And in both cases the these are these are economic asymmetries. These are economic asymmetries, right? Because it's really cheap to field a bot, but it's very hard to tell something a bot. It's very cheap to field a drone. It's very hard. It's very expensive to defend against a drone. But you see what I'm saying is it's it's the it's the virtual version of the problem and it's the physical version of the problem. Uh the virtual version of the problem, what what we need quite literally is proof of human. The reason is because you're you're not going to have proof of bot. The the especially now the bots are too good. The the bots can pass the touring test. And if the bots can pass the touring test, then you can't you can't screen for bot. You can't have proof of not a bot. But what you can have is you can have proof of human. You can have, you know, cryptographically validated this is definitely a person and this is and then you can have cryptographically validated this is definitely like something that a person said. This video is real, right? Just to double click on on uh do you think Alex Blania with world do you think he's got it or is there an alternative? Oh, so I mean there's going to be I think there will be I think many people will try. We're one of the key you know participants in in the world in the world project and yeah so we're partisans but yeah I I think so we think world is exactly correct and and the reason is it it has it has to be it it has to be proof of human. It it has because you can't do proof of not bot. You have to do proof of human to do proof of human. You you need you need biological validation. You needed to start with this was actually a person, right? Because otherwise you have bots signing up as fake people, right? So you you have to have like something you have to have a bio biometric and then you have to have cryptographic validation and then the ability to do to do to do the lookup. And then by the way, the other thing you need which they you also need selective disclosure. Um so you need to be able to do proof of human without revealing all the underlying information. By the way, another thing you need you're going to need proof of age, right? because there's all these laws in all these different countries now around you need to be 13 or 16 or 18 or whatever to do different things and so you're you're going to need you know sort of validated a proof of age um you know to be able to legally operate right and so that that's coming and then you're going to want like proof of credit score and you know proof of like you know hundred other that's a tricky one. It it is a tricky one, but you're you're going to there there's no reason like if somebody's checking on your credit, somebody shouldn't give you an example. Somebody shouldn't need to know your name in order to be able to find out whether you're credit worthy, right? I see independently verifiable pieces of information pieces of information likely disclosed. And this is the answer to the privacy problem at large, which is I I only need to prove what I need to prove at that moment. So like you're going to need that and I I think their architecture makes sense. So that needs to get solved. I think language models have tipped the bots are now too good. uh and and so they're undetectable. And so as a consequence, we now need to go confront that problem directly. And then like I said, and then the other problem is we we need to go actually confront the drone problem.

Allesio: I think we can sneak in one more question. Um I'm trying to tie together a lot of things that you said over the year. So at the Milkin Institute debate with Teal which is amazing. Um you talked about the lag between a new technology and kind of like the GDP um impact of it. The other idea you talked about is bourgeoa capitalism and how you know this kind of managerial class was needed because of this complexity and I think if you bring AI into the fold you have like much higher leverage of people. So, like if you have, you know, the Musk industries um and you give Elon AGI, you can run a lot more things uh at once. That's right. And then you have the social contract and I know you retweeted a clip of Sam Alman saying um we're rethinking the whole thing and you're like absolutely not. Yes. And I I was at an event with Sam last night and he actually said in the last couple weeks it felt like now people are taking that seriously. So I'm just curious like how you're seeing the structure of organization changing especially when you invest in early stage companies and um yeah just like how the impact of work structure and uh all of that is playing out.

Marc Andreessen: Yeah. So there's a whole bunch of there's a whole bunch of top yeah we could by the way we would be happy to spend more time but we could we could spend more time on all that. So just for people who haven't followed this, so the this this this term managerial comes from this thinker in the 20th century, James Burnham who um is one of the great kind of 20th century political thinkers um societal thinkers and he sort of said as and he was writing in like the 1940s 1950s um and he said kind of that the whole history of capitalism up until that point had been in two phases. Number one had been what he called bgeoa capitalism which was think of it as like name on the door like Ford Motor Company because Henry Ford runs the company. Um, and Henry, it's like a dict dictatorial model and Henry Ford just like tells everybody what to do. And he said the problem with boogeoa capitalism is it doesn't scale because Henry Ford can only tell so many people to do so many things and then he runs out of time in the day. And so um he said the second phase of capitalism was what he called managerial capitalism which was the creation of a professional class of managers um that are trained not to be like car experts or to be whatever experts in any particular field but are trained to be experts in management. And then that led to you know the importance of like Harvard business you know business schools and management consulting firms and all these things. And then you look at every big company today and like most of the executives at most of the Fortune 500 companies are not domain experts in whatever the company does and they're certainly not the founders of those companies but they're professional managers. And in fact in the course of their careers they'll probably manage many different kinds of businesses. They'll rotate around and they might work in healthcare for a while and then work in financial services and then go work in something else you know come work in tech. And what Burnham said is he said that transition is absolutely required because the the the problem with boogea capitalism is is it doesn't scale. Henry Ford doesn't scale. And so if you're going to run capitalist enterprises that are going to have millions to billions of customers, um you're going to need to they're going to be operating a level of scale and complexity that's going to require this professional management class. And he said, look, the professional management class has its downsides. Like they're not necessarily experts at doing the thing. They're not as inventive. You know, they're not going to create the next breakthrough thing. But he's like, whether you think that's good or bad or whatever is what's going to be required. And basically that's what happened right and so he wrote that book originally in like 1940 you know over the course of the next 50 years basically managerialism well I mean today up till today manager managerialism basically took over everything and you know what I'm describing is basically how all big companies run and how all governments run and how large scale nonprofits run and kind of everything you know everything runs basically what what what venture capital does is we basically are a rump sort of protest movement to that to try to find the next Henry Ford or which is to say Elon Musk or or the next or the next Elon Musk or the next Steve Jobs. the next Bill Gates, the next Mark Zuckerberg. And so we we we we we start these companies in the old model, right? We we we start them out as as as as in the Henry Ford model. And so we start them out with a founder or a or a or a founder with with colleagues, but you know, there's a founder CEO. Um and then we basically bet that we basically bet that the startup is going to be able to do things, specifically innovate in ways that the big incumbents in that industry are not going to be able to do. And so it's a bet that by basically by relighting this sort of name on the door, you know, kind of thing, this new innovative thing with like a king monarchical uh uh political structure u that they're going to be able to innovate in a way that the incumbent is not going to be able to because the incumbent is is being run by managers, right? And and and and by the way, and of course, venture being what it is, sometimes that works, sometimes it doesn't. But but we're constantly doing that. But I've always viewed it my entire life as like we're like raging against the dying of the light. Like we're we're we're we're sort of constantly trying to fight off managerialism just basically swamping everything and everything getting basically boring and gray and dumb and old, right? And we're trying to keep some level of energy vitality in the system. AI is the thing that would lead you to think, wow, maybe there's a third model, right? And and maybe may and way to think about it would be maybe it's a combination of the two. Maybe the new Henry Ford or the new Elon or the new Steve Jobs plus AI is the best of both, right? Because it's it's sort of the spark of genius of the name on the door model, the Henry Ford model, but then it's give that person AI superpowers to do all the managerial stuff and let the boss do the managerial stuff. That may be the actual secret formula. And we've never even known that we wanted this because we never even thought it was a possibility. But I mean, you know this that what is the thing that these dots are really good at? They're really good at doing paperwork. Like they're really good at filling out forms. Like they're really good at writing reports. They're really good at reading. They're really good at doing all the managerial work. Like they're amazing at it. And so yeah, so I I think I think the I 100% I think the answer the answer very well might be to get the best best of both worlds by doing this. And then the challenge is going to be twofold. challenge is going to be for the innovators to really figure out how to leverage AI to actually do this, right? Um and then and then the the other challenge is going to be for the for the incumbents that are managerial to figure out like, okay, what does that mean? Because now they're going to they're going to be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to. And so this really I think is going to force a lot of big companies to kind of figure out innovation, either say figure out innovation or die trying.

Allesio: Do you feel like that structure accelerates the impact on the actual GDP economy? If you look at SpaceX is like the growth is like so fast and like instead of having these companies kind of like peter out in growth and impact, they can kind of like keep going if not accelerating.

Marc Andreessen: That's for sure the hope. Um the the the challenge and and you know and look the AI utopian view is of course and and and that's going to be the future of the economy and it's going to grow 10x and 100x and thousandx and we're training this regime of like much higher economic growth forever and consumer cornucopia of everything and it's going to be great and I and I hope that's true. I hope that's that's like the you know that's the current kind of utopian vision. I hope that's true. The problem is goes back again. The real world is really messy. Um, and I'll give you an example how the real world is really messy. It requires 900 hours of professional certification training to become a hairdresser in the state of California. Um, so it's like 35% of the economy, something like that. You have to get some sort of professional certification to do the job, which is to say that the professions are all cartels, right? And so you have to get licensed as a doctor, you have to get licensed as a lawyer, you have to get licensed as a you have to get into a union. Um, by the way, to to work for the government, you need to be you you have both civil service protections and you have public sector unions. You have two layers of insulation against ever getting fired for anything or anything anything ever changing. I'll give you another example. The the doc work the doc workers went on strike a couple years ago because there, you know, robotics, you know, if if you go look at a modern doc like in Asia, it's all robots. If you go to American doc, it's like all still guys dragging strike dragging stuff by by hand. The doc workers went on strike. It turns out there are 25,000 doc workers working on on docs in America. It turns out they have incredible political power because it's a it's it's one of these unified blocks of things. They won their strike and so they got commitments from the doc owners to not implement more automation. We learned a couple things in that. So number one, we learned that even a union as small as 25,000 people still has like tremendous political stroke. We also learned that they it actually turns out the doc workers union has 50,000 people in it because there's 20 they have 25,000 people working in the docks, they have 25,000 people during full paychecks sitting at home from prior union agreements. From prior union agreements. I'll give you another great example. There are government agencies. is there are federal government agencies where the employees right of have civil service protections and they're in public sector unions. There are entire federal government agencies that struck new collective bargaining agreements during COVID where not only are they have their jobs guaranteed in perpetuity, but they only have to report to work in an office one day per month. And so there are entire office buildings in Washington DC that are empty 29 out of 30 days of the year that are still operating and are still we're all still paying for it. And so and then what they do, it turns out what the employees do is they're very they're very smart in in in this way. And so they figure out they come in on the last day of a month and the first day of the next month. And and so they're so they're in their they're in the office 2 days per 60 days, which means these buildings are empty for 58 days at a time. And you see you see where I'm heading with this. Like this is like locked in, right? This is like locked in in a way that has nothing to do with like people say capitalist. It's like anti- capitalistic. It's like it's basically it's restrictions on trade. It's restrictions on the ability to like change the workforce. And so so much of our economy is is is you know the I'm I'm describing the entire healthare system. I'm describing the entire legal profession. I'm describing the entire housing industry. I'm describing the entire education system. Right? K through 12 schools in the United States, they're a literal government monopoly. How are we going to apply AI in education? The answer is we're not because it's a literal government monopoly. It is never going to change the end. And there is nothing to do. By the way, you can create an entirely new school system. Like that's the one thing you can do is you can do what Alpha School is doing. you can create an entirely new school system. Other than that, you're not going to go in and change what's happening in the American classroom like K through 12. There's no chance. The teachers are 100% opposed to it. It's 100% not going to happen. So, so you see what I'm saying is like there's this like massive slippage that's going to take place. Both the AI utopians and the AI doomers are far too optimistic, right? You see what I'm saying? Because they believe that because the technology makes something possible that 8 billion people all of a sudden are going to change how they behave. And it's just like no. So much of how the existing economy works is just is just like wired in. And so we're going to be lucky as a society. We're going to be lucky if AI adoption happens quickly, right? because if it doesn't, what we're just going to have is stagnation.

Allesio: Mark, I know you got to run.

Marc Andreessen: Yeah. We all know or welcome, but it was such a pleasure talking to you. Uh we're truly living in an age of science fiction coming to real life. Yes. Yes. Could not be more exciting. Really with you guys. Awesome. Good. Thank you.