Marc Andreessen on AI Winters and Agent Breakthroughs

18:29Marc Andreessen:And actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is going to keep doubling every quarter. Doubling every quarter, though, is like, you know, grains of chess on the chessboard. Like at some point, the numbers become extremely large, right? And it really, and really what happened was the internet, the internet, by the way, continuously kept growing basically since inception. It's, you know, it's continuously grown.

18:53Marc Andreessen:It's never shrunk. And it's grown really fast compared to anything else, you know, in human history, but it wasn't doubling every quarter as of 1998, 1999. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the dot-com crash, which was they way over companies like global crossing way overbuilt fiber, which is sort of the, by the way, fiber telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data center. So like that was the beginning of the, of the, of the data center build and then, and then data center overbuilt.

19:22Marc Andreessen:And so you had that, but it was, it was literally, I think it was like$2 trillion got wiped out, right? It was like, it was like a big, it was, and by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt because tech companies generally don't run on debt, but the telecom companies run on debt, physical infrastructure companies run on debt. And so the companies like, well, we're crossing, not just raised a lot of equity. They also raised a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're just over, you're overbuilding capacity.

19:50Marc Andreessen:Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And then it's like they say about the hotel industry, which is it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state and then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today, but 25 years later, but it took, and actually the elapsed time was it took 15 years. It took 15 years from 2000 to 2015 to actually fill up all that capacity.

20:20Marc Andreessen:The cautionary warning is the overbuild can happen. And, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital is like, wow, it's just, I don't know how to invest in these crazy software things, but for sure I can put, build data centers and for sure I can buy GPUs and I can deploy, you know, compute grids and, and all these things. And so, you know, if you're a pessimist, you can look at this and you can say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000, obviously that would be bad.

20:48Marc Andreessen:The counter argument, which is the one I agree with, which is the counter on the other side is a couple of things. One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the doc, like global crossing was like, it was like an entrepreneur. It was like a new venture, but like the money that's being deployed now at scale as Microsoft and, you know, an Amazon and Google and Facebook and NVIDIA and, you know, these, these, these, and now, you know, by the way, open AI and Anthropic, which are now like, you know, really serious size, um, you know, as companies with, you know, very serious revenue, these are very large scale companies with like lots, lots of cash, lots of debt capacity that they've never used.

21:24Marc Andreessen:And so this is institutional in a way that that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away. Like, so, and you guys know this, like everybody starved for capacity, everybody starved for compute capacity, and then, you know, all the associated things, memory and interconnect and everything else, data center space. And so every dollar right now that's being put in the ground is turning into revenue. And in fact, I actually think there's an interesting thing happening, which is because everybody's star for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints.

21:59Marc Andreessen:Right, suppose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful, the models would be much better because you would just allocate a lot more money to training and you'd just build better models and they would be better. And so we're actually getting the sandbag version of the technology. No, everything we use is quantized because the labs have to keep the full versions. Right. We're not even getting the good stuff. But getting the good stuff, even if technical progress stops, once there's a much bigger build of GPU manufacturing capacity and memory, all the things that have to happen in the course of the next five or 10 years, once it happens, even the current technology is going to get much better.

22:35Marc Andreessen:And then, as you know, there's just a million ways to use this stuff. There's just a million use cases for this. This isn't just sending packets across a thing, whatever, and hoping people find something to do with it. This is just like, oh, we apply intelligence into every domain of human activity. And then it works incredibly well. here's what I know. Here's what I know. In the next three or four years, it's like somewhere between three or four years out, basically everything is selling out. So like the entire supply chain is sold out or selling out. And so there's no, like, we're just going to have like chronic supply shortage for, you know, for years to come.

23:06Marc Andreessen:There's going to be a response from the market that's going to result in an enormous, you know, it's happening now, an enormous flood of investment in a new fab capacity and, you know, everything else to be able to do that. At some point, the supply chain constraints will unlock, you know, at least to some degree, that will be another accelerant to industry growth when that happens, because the products will get better and everything will get cheaper. And so, I know that's going to happen. I know that, you know, the deployments, you know, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like I know they're just going to get like much, much better from here.

23:35Marc Andreessen:And so, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It is accelerating. Like the breakthroughs are tremendous. I mean, even just month over a month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes, but I can't even imagine betting that this is going to like somehow disappoint. And, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Um, it was at Michael Burry. Uh, that's an interesting guy.

24:03Marc Andreessen:We'll pick on a guy. We'll pick, let's pick on one guy. Well, cause he did, he came out with it. Was it, it was, he doesn't mind. It was the Nvidia short, right? He came out with the Nvidia short. And then you guys probably talked about this, but just the analysis now that the current models are getting better faster at such a rate that if you're running an NVIDIA inference chip today that's three years old, you're making more money on it today than you did three years ago, because the pace of improvement of the software is faster than the depreciation cycle of the chip. My understanding is Google is running...

24:30Marc Andreessen:I don't know exactly what... These are rumors that I've heard, or maybe it's public, but I think Google's running very old TPUs, very profitably. It actually turns out, as far as I can tell, it's actually the opposite of the brewery thesis. He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And, and again, that's an expression of the just ferocious pace of software progress, ferocious pace of capability payoff that you're getting on the other side of this.

25:04Marc Andreessen:And so I just, the idea of betting against that, like, yeah, it's like an invitation to get your face ripped off. One of my early hits was like modeling the lifespan of the H100 and H200s and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically care cut it down to two to three, but actually it's going up and not down. And, and that's, I mean, that's, I think that's the dream. We are finding utilization. And I think utilization solves all problems. Like you can, you can find use use cases for even like the poor, like even memory we're having a shortage, right.

25:35And even like the shittier versions of memory that we do have, we are finding use cases for it. So like, that's great. How important is open source AI and kind of like edge inference in a world in which you have three years of supply crunch? Like, do you think in the, like, you know, if you fast forward like five years, like, how do you think about inference in the data center versus at the edge?

25:55Marc Andreessen:Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge inference is very important for a bunch of reasons. I think just practically speaking, if we're just going to have fundamental construct supply crunches for the next, I mean, you guys, not, if you just project forward demand over the next three years relative to supply. One of the dismaying predictions you can do is what's going to happen to the cost of inference in the core over the next three years. And it may rise dramatically. And then the big model competitors are subsidizing heavily right now.

26:21Marc Andreessen:And so, what will be the average person's per day, per month token cost three years from now to do all the things that they want to do? And I don't know. I mean, you guys probably have friends. I have friends today who are paying$1 ,000 a day for claw tokens to run open claw. Right. And so, okay,$30 ,000 a month. Right. By the way, those friends have 1 ,000 more ideas of the things that they want their claw to do. You could imagine there's latent demand of up to$5 ,000 or$10 ,000 a day of tokens for a fully deployed personal agent. Obviously, consumers can't pay that. But it gives you a sense of the future scope of demand.

26:58Marc Andreessen:Even if there's a 10x improvement in price performance, that still goes to$100 a day, which is still way beyond what people can pay. There's just going to be ferocious demand. By the way, the agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints have been GPU constraints. I think the agent thing now also translates into CPU constraints, right? CPU and memory, yes. CPU and memory, right? And so like the entire chip ecosystem is just going to get... With the network constraints, that will be the killer. That's all bottlenecking potentially for years.

27:23Marc Andreessen:And so I think that Brad, and I think it's actually possible. I mean, generally inference costs are going to keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints. And then at some point, maybe the lab stops subsidizing so much. and that, that, that again will be an issue. And so there's just going to be so much more demand for inference than, than can be satisfied, um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean, just the dramatic innovations that have happened in the Apple Silicon to be able to do, uh, inferences, it's quite amazing.

27:51Marc Andreessen:A level of effort being put, like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a PC and then six months later, it runs on a PC. Right. It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know, there's also like other, there's other motivators, there's other motivators, which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for in certain cases with some people for certain use cases, people being like, well, I'm not willing to just like turn everything over.

28:21Marc Andreessen:So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a, a smart local model. There's also performance issues where you want to, you know, you want, you know, you're going to want your doorknob to have an AI model in it, you know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is going to have an AI model in it. And a lot of those are going to be local. Um, and so, yeah, no, like I think, I think you're going to have and then you're going to, by the way, also wearable devices, you know, you don't want to do a complete round trip.

28:52Marc Andreessen:You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah. 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. And I'm not that optimistic on American open source. You guys invested in Mistral, and Mistral's doing extremely well outside of China. That's about it. Yeah, we'll see. We'll see. Number one, I do think we care who makes it. I would say this. The previous presidential administration wanted to kill it in the U.S. They wanted to drown in the bathtub.

29:26Marc Andreessen:And so they wanted to kill it. So at least we have a government now that actually wants it to happen. And you're in the council? Yes. And the PCAST, yeah. So for whatever other political issues people have, which are many, this administration has, I think, a very enlightened view, and in particular an enlightened view on AI, and in particular on open source AI. And so they're very supportive. My read is the various Chinese companies have a very specific reason to do open source, which is fundamentally they don't think they can sell commercial AI outside of China right now, or at least specifically not in the U.S.

29:56Marc Andreessen:for a combination of reasons. And so, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, you know, paid services and then kind of, you know, kind of ancillary products. You know, they're very excited about it. By the way, I think it's great. I think it's great that they're doing it. You know, I think DeepSeek was like a gift to the world, I think. The great thing about open source, open source, the impact of open source has felt two ways. One is 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 the code, right.

30:25Marc Andreessen:And the code. And so like, for example, I thought this was amazing. So open AI comes out with a one 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 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 a secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper.

30:47Marc Andreessen:And now the whole world knows how to do it. And then, you know, three months later, every other AI model 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. So that happens. And then I don't know, we'll, we'll see, you know, there are a bunch of American, you know, open source, you know, AI model companies. I mean, look, there's going to be tremendous, you know, there already is, there's, you know, there's going to be tremendous, there's tremendous competition among the primary model companies.

31:15Marc Andreessen:You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously both, both X and then meta where I'm involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway. And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're back in that are, you know, trying to come out with different approaches. And then you've got whatever it is.

31:39Marc Andreessen:I don't know how, how many, how many like mainline foundation model companies are there in China at this point? It's probably six, five tigers is what they call it. Uh, Quinn is in questionable because there's change in leadership. Right. Yeah. But that does that include that includes like moonshot? Yes. Deep seek, uh, uh, Z AI, um, Quinn Oh one is in there. Right. And then, um, by dance and then you see a bit dance would be like the next year. They weren't as prominent. They weren't have a, you know, yeah, but they're, you know, you know, see, see dance is very inspiring and presumably they have more stuff coming in 10 cent probably has more stuff coming and so forth.

32:12Marc Andreessen:And And so, look, here would be a thing you can anticipate, which is there are not these markets. Between the U.S. and China right now, there's like a dozen primary foundation model companies that are at scale at some level of critical mass. It's not going to be a dozen in three years, right? Just because these industries don't bear a dozen. There's going to be three or four big winners or maybe one or two big winners. And so, there's going to be a whole bunch of those guys that are going to have to figure out alternate strategies. And I think open source is one of those strategies. And so, I think you could see a whole...

32:39Marc Andreessen:I think the questions like who's going to do open source, I think that could change really fast. I think that's a very dynamic thing. I think it's very hard to predict what happens. And I think it's very important. NVIDIA is doing a lot. Well, I was going to say, well, exactly. And then you've got NVIDIA. And then, you know, just to get an industrial... There's an old thing in business strategy, which is called commoditize the complement. That's right. And so if your Jensen is just kind of obvious, of course, you want to commoditize the software. And to his enormous credit, he's putting enormous resources behind that.

33:04Marc Andreessen:And so maybe it's literally NVIDIA. And I think that would be great. Yeah. narrative violation to European projects in the beginning. I'm hosting my Europe conference soon and I got both of them. They got us. Wait a minute. Where was Peter? So where was Steinberger when he did it? He was in Vienna. He was in Vienna. Oh, he was in Vienna. And then where is he now? He's moving to SF. Okay. All right. Okay. There we go. And then, yeah, the pie guy. All right. The pie guys are European. Their buddy is in Australia. Mario is also there. Right. And are they? Yeah, they haven't announced yet any sort of changed or have they no they're they have a company there okay okay good good yeah um anyways i think pi and open claw are very important software things and and i just wanted you to just go off on what do you think yeah so i think in the combination of the two of them i think is one of the 10 most important software got all the attention but right talk about pi pi is kind of the idea 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 is basically this, this thing we used to call like the Unix mindset.

34:14Marc Andreessen:Like, so, so, cause there were all these different, you know, theories, there are all these different operating systems and mainframes 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 IBM developed that was supposed to basically run everything.

34:38Marc Andreessen:And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle of software. 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 of the system worked. And then the Unix guys originally out of AT &T and then out of Berkeley, 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 shell.

35:05Marc Andreessen:And then, and then we're going to, 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 like the, it's almost like the operating, it's operating system itself is going to be a programming language. And then that led to the, the, the sort of centrality of the shell. And then that led to a sort of, you know, basically chaining the other Unix tools. And then that led to the emergence of these, these scripting languages like Perl, where you could basically kind of very easily do this. And then the shells got more sophisticated and then, And then, and then looked like, you know, that, that, that number one, that worked.

35:32Marc Andreessen: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. You know, 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, you knew all about it. 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.

35:57Marc Andreessen: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, you know, but look, the internet runs on Unix and then smartphones. Actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But anyway, and then we just started taking that for granted. And then, 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.

36:21Marc Andreessen:The best kind. They weren't obvious at the time or somebody else would have done them already. And so, there is 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 breakthrough. So 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 like, 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.

36:45Marc Andreessen:And so the way I think about Pi and OpenClaw is it's basically marrying the language model mindset to the unit, to the Unix, basically shell prompt mindset. And so 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 decades and they've had many architectures to build agents and 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, it's, it's a language model. And then above that, it's a bash, it's a bash shell. So it's a Unix shell.

37:12Marc Andreessen:And then as in, then the agent has access, has access to, to the shell and, you know, hopefully, hopefully in a sandbox, maybe, maybe in a sandbox. So it's, it's the model it's the shell. And then it's a, it's a file system. And then the state is stored in files. And then, you know, there's the markdown format for the, you know, for the files themselves. And then there's basically what in Unix is called a cron job. There's a loop and then there's a heartbeat. There's a heartbeat. And the thing basically wakes up, wakes up. So it's basically LLM plus shell plus file system plus markdown plus cron.

37:39Marc Andreessen:And it turns out that's an agent. And every part of that other than the model is something that we already completely know and understand. And in fact, it turns out 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 on a shell if you're running a mac 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 products fancy protocols, whatever.

38:17Marc Andreessen:It's like, no, we don't. We just need like a 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 wrapped 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 agent will change personality somewhat because the model is different, but all of the state stored in the files will be retained.

38:41Marc Andreessen:Different instruction sets, but you just compiled it. Right, exactly. And it's all right. It's like swapping out a ship and recompiling. But it's still your agent with all of its memories and with all of its capabilities. And then, by the way, you can also swap out the shell. So, you can move it to a different execution environment that is also a bash shell. By the way, you can also switch out the file system, right? And you can swap out the heartbeat, the CRON framework, the loop, the agent framework itself. And so, your agent basically is, basically, at the end of the day, it's just its files.

39:10Marc Andreessen:And then there's, of course, yeah, it's basically, it's just the files. And then, by the way, as a consequence of that, the agent, and then the agent itself, it turns out a couple of important things. So one is it can migrate itself, right? And so you can instruct your agent, migrate yourself to a different runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, like we 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.

39:35Marc Andreessen: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 thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself like that. I mean, there have been toy systems that have had that, but 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.

40:01Marc Andreessen:And it can do that. Extend yourself, like extend yourself, give yourself a new capability. Right. And so, and so literally it's just 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 than 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.

40:24Marc Andreessen: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 a hundred, 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.

40:48Marc Andreessen:People are going to look at it. They're going to say, Oh, well, where's the breakthrough? Cause 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 of this 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, you give it access to a browser and then you've got the computer and the browser and off and away it goes.

41:12Marc Andreessen: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 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. It's the thing. And by the way, it's early and you know, these are, you know, these are prototypes and there was, you know, as you guys know, there's security issues. And so, you know, there's a bunch of stuff to be ironed out, But the unlock of capability is just incredible.

41:38Marc Andreessen:And I have absolutely no doubt that everybody in the world 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. I was going to say for someone who is deeply familiar with social networks, the next step is your claw talking to my claw, posting on claw Facebook, posting their jobs on claw LinkedIn and posting their tweets on claw XAI or whatever. You know, I do think that that is how, you know, we get into some danger there in terms of like alignment and whether or not we want these things to run.

42:12You guys know renty, renty human.com? Yeah, renty. Yeah, yeah, yeah. I mean, it's Fiverr. It's TaskRabbit. Sure, of course. Mechanical Turk. Yeah, but flipped. Yeah. Right. The agent hiring the people. Yeah. Which, of course, is going to happen. It's obviously going to happen. I'm curious if you have any thoughts on the engineering side. So when you build the browser, the internet, you know, just a bunch of mostly plain text file, plus some images. And today the, every website and app is like so complex and like somehow, you know, the browser kept evolving to fit that in. Are there any design choices that were made like early in the browser and kind of like the internet and the protocols that you're seeing agents similar today?

42:50It's like, Hey, this thing is just not going to work for like this type of new compute. And we should just rip it out right now.

42:56Marc Andreessen:There were a whole bunch, but I'll give you a couple. So one is, and we didn't, you know, to be clear, like this, this was not, you know, this was totally different. We didn't have the capabilities we have today, but we didn't have the language models underneath this. But we did have this idea that human readability actually mattered a great deal. And so, and specifically in those days, it was not so much English language, but it was, there was a design decision to be made between binary protocols and text protocols. And basically, every basically old school systems architect that had grown up between the 1960s and the 1990s basically said, what do you know about the internet?

43:28Marc Andreessen:It's star for bandwidth. You have these very narrow straws. When we did the work on Mosaic, people who had the internet at home had a 14-kilobit modem. So, you're trying to hyper-optimize every bit of data that travels over the network. And so, obviously, if you're going to design a protocol like HTTP, you're going to want it to be a highly compressed binary protocol for maximum efficiency. and you're going to want to have it be like a single connection that persists. And the last thing you're going to want to do is like bring up and tear down new connections. And definitely you're not going to want a text protocol.

43:53Marc Andreessen:And so, of course, we said, no, we actually want to go completely the other direction. It's obviously we only want text protocols. By the way, same thing in HTML itself. We want HTML to be relatively verbose. You know, we want the tags to actually be like human readable. We want to use the most inefficient things possible. Yeah, we want to do the inefficient things. You're the original token maxer. Yeah, exactly. Yeah, yeah, yeah. Basically, it's just like... Better lesson, Phil. Well, yeah, well, actually, this was actually the conscious thing, which basically says just like assume a future of infinite bandwidth built for that.

44:21Marc Andreessen:And then basically what it was, it was a bet that if the system, if the latent capabilities of the system were powerful enough, and that was obvious enough to people, that would create the demand for the bandwidth that would cause the supply of bandwidth to get built, that would actually make the whole thing work. And then specifically what we wanted was we wanted everything to be human readable because at the engineering level, we wanted people to be able to read the protocol coming over the wire and be able to understand it with their bare eyes without having to disassemble it or whatever, right?

44:44Marc Andreessen:And have it converted out of binary, right? And so all the, you know, HTTP and everything else were, it was always text protocols. And the same thing with HTML. And in many ways, some people say that the key breakthrough in the browser was the view source option, which is every webpage you go to, you could view source, which means you could see how it worked, which means you could teach yourself how to build new, to build new webpages. There was that. So human readability, and again, human readability in those days still meant technical specs. Now it means English language, but there's an incredible latent power in giving everybody who uses the system the option to be able to drop down and actually understand and see how it's working.

45:17Marc Andreessen:And that worked really well for the web, and I think it's working really well for AI. That was one. What was the other? A big part of the idea of web servers was to actually surface the underlying latent capability of the operating system and to be able to surface also the underlying latent capability of the database. Because basically, what was a web server? What is a web server fundamentally? Architecturally, it's the operating system. So it's the operating system's ability to, you know, it's running on top of an OS. So it's the OS's ability to manage the file system and do everything else that you want to do, process everything.

45:44Marc Andreessen:And then, of course, a lot of early, you know, a lot of websites are front-end to databases. And so you wanted to unleash the underlying latent power of whether it was an Oracle database or some other Postgres or whatever it was. And so a lot of the function of the web server was to just bridge from that internet connection coming in to be able to unlock the underlying power of the OS and the database. And again, people looked at it at the time and they were like, well, does this really matter? Is this important because we've had databases forever and we've always had user interfaces for databases and this is just another user interface for a database.

46:13Marc Andreessen:It's like, okay, yeah, fair enough. But on the other side of that, it's just like, this is now a much better interface to databases and one that 8 billion people are going to use and is going to be like far easier to use and far more flexible. And you're not just going to have old databases. Now you have a system where people can actually understand why they want to build a million times more database apps than they have in the past. And then the number of databases in the world exploded. And so again, this goes to this thing of like building, building in layers. Some of the smartest people in the industry look at any new challenge and they're like, okay, I need to build a new kind of application.

46:41Marc Andreessen:So the first thing I need to do is build a new programming language. Right. And then the next thing I need to do is build a new operating system. Right. And the next thing I need to do is I need to build a new chip. Right. And they kind of want to reinvent everything. And I've, I've always had, maybe it's just, I don't know, pragmatic mentality or something, or maybe an engineering over science mentality, but it's more like, no, you have just like all of this latent power in the existing systems. And you don't want to be held back by their constraints, but what you want to do is you want to kind of liberate that power and open it up.

47:05Marc Andreessen:And so I think, I think, and I think the web did that for those reasons. And I think it's the same thing now that's happening. It's a good perspective on the web. The programming languages is another good thing. We have Brett Taylor on the podcast and we were talking about Rust and, you know, Rust is memory safe by default. And so why are we teaching the model to not write memory unsafe? Because just use Rust and then you get it for free. How much do you think there's like time to be spent, like recreating some of these things instead of taking them for granted? I'll be like, oh, okay, Python is kind of slow.

47:31Python TypeScript. You know, it's like, yeah.

47:33Marc Andreessen:As imperfect as they are, they are the Lingua Franca. I mean, I think this is going to change a lot because I don't think the models care what language they program in. And I think they're going to be good at programming in every language. And I think they're going to be good at translating from any language to any other language. Like, okay, so this gets into the coding side of things. I think we're going through a really fundamental change. And I look, I grew up, you know, I grew up hand code, you know, I grew up hand coding. Everything I did was actually, everything I did actually was written in C.

47:56Marc Andreessen:I wasn't back in the day. I wasn't even using C++ or like Java or any of this stuff. Right. And so everything, everything I ever did, I was like managing my own memory at the level of C. And then I, you know, I'm still from the generation that, you know, I knew assembly language and, you know, I, you know, um, so I could drop down and do things right on the ship. And so we, we've just, we've all, all of us, we've always lived in a world in which software is like this precious thing that like you have to think about very carefully. And it's like really hard to generate good software. And there's only a small number of people who can do it.

48:25Marc Andreessen:And like, you have to be very like jealous in terms of thinking about like, how do you allocate, like, what are your engineers working on and how many good engineers do you actually have and how much software can they write and how can, how much software can human beings, you know, kind of maintain. And I think like all those assumptions are being shot right out the window right now. Like, I think they're, I think those days are just over. And I think the new world is like actually high quality software is just like infinitely available. And if you need new software to do X, Y, Z, like you're just going to wave your hand and you're going to get it.

48:50Marc Andreessen:And then if it's, if you don't like the language that's written and you just tell the thing, all right, I want the right now, I want the rest version. Um, or, you know, security, you know, security, we're about to, by the way, we're about to go through computer security is about to go through the most dramatic change ever, which is number one, like every single latent security bug is about to be exposed. Right. So we're going to have like the, we're, we're set up here for like the computer security apocalypse for a while. But on the other side of it, now we have coding agents that can go in and actually fix all the security bugs.

49:13Marc Andreessen:And so, how are you going to secure a software in the future? You're going to tell the bot to secure it, and it's going to go through and fix it all. And so, this thing that was this incredibly scarce resource of high-quality software is just going to become a completely fungible thing that you're just going to have as much as you want. And that has tons and tons of consequences. In some sense, the answer to the question that you posed, I think is just somewhat, I don't know, simple or something are straightforward, which is just, if you want all your software and rest, you just tell the bot, you want all your software and rest.

49:39Marc Andreessen:Things that used to be hard or even seem like an insurmountable mountain to get through all of a sudden, I think become very easy. I think Brett had a theory that there would be a more optimal language for LLMs. And so the contention is there isn't. Just don't bother. Just whatever humans already use, LLMs are perfectly capable porting. I think we're pretty close to being, I don't know if this works today, I think we're pretty close to being able to ask the AI what would its optimal language be. Right. And let it design it. It's true. Okay. Here's a question. Are you going to even going to have programming languages in the future?

50:12Marc Andreessen:Or are the AI is just going to be emitting binaries? Let's assume for a moment that humans aren't coding anymore. Let's assume it's all bots. What levels of intermediate abstraction do the boss even need? Or are they just coding binary directly? Did you see there's actually an experience? Somebody just did this thing where they have a, they have a language model now that actually emits model weights for a new language model. Right. And so will the bots predict the way? Yeah. Well, the bots literally be admitting not just coding binaries, but will they actually be admitting weights for new models directly?

50:41Marc Andreessen:And conceptually, there's no reason why they can't do both of those things. Architecturally, both of those things seem completely possible. Very inefficient. You're basically very inefficient. Simulation of assimilation and assimilation inside of weights. Yeah. Very inefficient. But like, look, LLMs are already like incredibly inefficient. I'm a favorite thing. Ask Claude add two plus two equals four. Right. It's just like, you know, it's like, you know, it's like whatever, billions and billions of times more inefficient than using your pocket calculator. But, but, but yeah, the payoff is so great of the general capability.

51:11Marc Andreessen:So anyway, like I, I kind of think in 10 years, like, I'm not sure. Yeah. Like, I'm not sure there will even be a salient concept of a programming language in the way that we understand it today. And in fact, what we may be doing more and more as a form of interpretability, which is we're trying to understand why the bots have decided to structure code in the way that they have. I mean, if you play it through, you don't need browsers. then like that's the depth of the browser well so 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 do we and out really well what are you going to do then are you sure you're just going to log off and touch grass whatever you want exactly isn't that better i want software to do stuff for me but isn't that better?

51:57Marc Andreessen:I mean, look, I, you know, I don't look like, you know, you know, you know, the arguments here, you know, it was not that long ago that 99 % of humanity was behind a plow. Right. And what are people going to do if they're not plowing fields all day to grow food? Right. And it just turns out there's like much better ways for people to spend time than plowing fields. Yeah. Do is growing. Uh, exactly. Exactly. You know, talking to their friends and look, I'm not an absolutist and I'm not a utopian. And I, and to be clear, like I have an 11 year old and he's learning how to code. And like, I'm, you know, I think it's still like a really good idea to learn how to code and so forth.

52:24Marc Andreessen: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 non-optimally. 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.

52:48Marc Andreessen:Right. And so it's going to be doing its own interpreter. It's going to be the engine of interpretability to explain itself. And 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 at what the human is. Yeah. Yeah. Well, 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 the version between the models like going back to like the pi open claw thing it's like oh i built all the software using the open ai model and i'll switch to the enthrombic model but the enthrombic model doesn't understand the thing so i would 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 i'm not even sure that's lock-in though because why can't the second model just learn what the first model has done like exactly okay so okay giving you an example so as you know models can now reverse engineer software by right isn't it the whole thing now where people are reverse engineering like nintendo game binaries yeah so you have like there's 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 another reverse engineer to get a version that runs on their mac right and so if you reverse it if this is what i kind of say if you're reversing like x86 binaries then why can't you reverse engineer whatever they create yeah and because we're all on a Unix-based system, it has to be reversible because it needs to run on the target.

54:23Marc Andreessen: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, but with enormous, but it was just always like cost and labor prohibitive reverse engineer. I learned how to reverse engineer. I was like, human beings can reverse engineer binaries. It's just for any complex binary, you 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 are to compensate for the human limitations.

54:53Marc Andreessen:Yeah. Right. And if you don't have the human limitations anymore, then all of a sudden you have, and it's not that you won't have abstractions, but you'll have a different kind of abstraction. Yep. I have two topics to bring us to a close and you can pick whichever ones are 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. Was that you? Yes. It was a 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?

55:19Marc Andreessen:Oh, I think we will. Yeah. No, now I think it's going to happen for sure. 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 stable coins and crypto. And this is, I think this is the grand unification basically of AI and crypto is what's about to happen now. I think AI is the crypto killer app, I think is where this is really going to come out. And then the other is, it's just, 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 claw and you wanted to buy things for you, you have to give it money in some form.

55:46Marc Andreessen:I would say the adoption is probably 0.1 % if that, but yeah. Oh, today. Yeah, yeah, yeah. But think forward. It's like, where is it going? Forward thinking. The ultimate principle of everything and everything that I think we do is the William Gibson quote, which is the future is already here. It just isn't distributed. It isn't distributed yet. My friends who are the most aggressive users of OpenClaw just have given their Claws bank accounts and credit cards. 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 bank.

56:15Marc Andreessen: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 that's how these things start. Actually. I mean, since 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 court. It's going to break into your bank account anyway and take your money. So 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. I'm not doing it myself to be clear, but I love the people that are just like, what is it?

56:45Marc Andreessen:Dangerously, which by the way, it's a Facebook thing. Okay. Cause we are in Facebook. 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. They brought it into open AI. And of course that makes it enticing. Sam runs codecs with skip permissions on, on his laptop. Yes. A hundred percent. And so I, I think the way to actually see the future is to find the people who are doing that. There's a madness, you know, and they're yeah 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 it's like gentleman science yes it's yes yes It's Ben Franklin out with trying to get lightning to strike his balloon and seeing if he gets electrocuted.

57:46Marc Andreessen:Yeah, it's Jonas Salk with the polio vaccine. Injecting it. Yes. So, yes, I think we should have like a glory. We should have like flags and like we should have like monuments to the people that just let open clobber on their lives. More anecdotes. What are the craziest or interesting things that people listening to this should go home and do? I mean, this is the extreme thing is just like the straight YOLO. Like just turn your life over. that's a general capability. It's like a specific story that was like, wow. And everyone in the group chat just lit up. I mean, like, you know, so there's tons of, there's already tons of health, you know, the health dashboard stuff is just, it's just absolutely amazing.

58:23Marc Andreessen:The number of stories on, I just don't want to violate people's, you know, obviously personal. But, you know, one of the things OpenCloud is really good at is hacking into all this stuff in your land. It's really good. So, you know, internet of things, AKA internet of shit, like super insecure, but great. Discoverable. it's discoverable open claw is happy to scan your network identify all the things and then my my friends are most aggressive at this are having open claw take over everything in their house yeah it takes over their security cameras it takes over their their you know their whatever their their access control systems it takes over their webcams i have a friend whose claw watches him sleep put a webcam in your bedroom put the put the claw put the claw in a loop uh i have it wake up frequently and have it watch and just tell him watch me sleep and and i've seen the transcripts and it's literally like joseph sleep this is good this is good that joseph sleep because you know i I have his health data and I know that he hasn't been getting enough sleep.

59:10Marc Andreessen:And so it's really good that he's getting sleep. I really hope he gets his full whatever, you know, five hours of REM sleep. Joe's moving. Joe's moving. Joe might be waking up. This is a real problem. Joe wakes up now. He's going to ruin his sleep cycle. Oh, okay. It's okay. Joe just rolled over. Okay. He's gone back to bed. Okay, good. All right. Okay. I can relax. This is fine. He's monitoring the situation. And being a bot, like, you know, it's just like very focused, right? It's just like, this is like his reason for existence is to watch Joe sleep. And then, and then I was talking to my friend who did this is like, you know, on the one hand, it's like, all right, this is weird and creepy.

59:44Marc Andreessen:And I need to, I need to, maybe this has taken over my life. And then the other thing is like, you know what, if I had a heart attack in the middle of the night, this thing literally would like freak out and call 911. Like, there's no question this thing would figure out how to like alert medical authorities and like probably summon SWAT teams and like do whatever would be required to save my life. Right. And so it's like, you know, like, yeah, like that's happening or what else? it's a company Unitary that makes the robot dogs. And I actually have one at home, which is actually really fun. The Chinese companies are so aggressive at adopting new technology, but they don't always take the time to really package it, package it and maybe think it all the way through.

1:00:22Marc Andreessen:And so at least the Unitary dog I have, so it has a old non-LLM just control system, which by the way is not very good. It markets well, but in practice it's not that good. It has trouble with stairs and so forth. And so it's not quite what it should be. But then the language model thing comes out in the voice. So they add LLM capability and then they add a voice mode to it. But that LLM capability is not at all connected to the control system. So you've got this schizophrenic dog that is a complete idiot when it comes to climbing the stairs, but it will happily teach you quantum mechanics. Right.

1:00:51Marc Andreessen:In like a plumy English accent. It's just like absolutely amazing. Jagged intelligence. Yeah. Talk about jagged. Now, obviously what's going to happen in the future is they're going to connect together. but right now it's, and so right now it's not that useful. And so I have a friend who has one of these who had his claw basically hack in and rewrite the code, rewrite new firmware, rewrite new firmware for the unit robot. And now it's, now it's an actual pet dog for his kids. You should do that before, after like the motion. Yeah, it's good. You said it's completely different. He said it's a complete transformation.

1:01:19Marc Andreessen:And whenever there's an issue in the thing, now the claw just like rewrites the code, you know, you go, you does, does the code. And so it kind of goes to your thing here. And so, so like all of a sudden, this is why we want to think about AI coding. But AI coding is not just like writing new apps. It's also going in and rewriting all the old stuff that should have worked that never worked. And so, like, I think basically, I think the internet of shit is basically over. Like, I think everything, there's a potential here where, like, all these devices in your house that have been, like, basically marginal or, you know, basically dumb, you know, like, all of a sudden they might all get really smart.

1:01:45Marc Andreessen:Now, you have to decide if, yes, there are horror movies in which this is the premise. And so, you have to decide if you want this. But, but, but this is the first time I can say with confidence, I now know how you could actually have a smart home with 30 different kinds of things with chips and internet access where it actually all makes sense. It all works together and it's all coherent in the, in the whole thing. And to have that unlock without a human being having to go do any of that work. Like, yeah, I'm waiting for a story, Mark. I can't let you open that fridge door. You know, like exactly, exactly.

1:02:16Yes. Yes. Because you're not supposed to eat right now. I have all of, yes.

1:02:19Marc Andreessen:I have every thread of health information, you know, and I know you think you're doing, you know, I don't think you can do this, but you know, this is a real, are you really, you know, are you really sure? And you know, you told, you know, you told me last night, you really don't want me to let you do this. So, you know, I'm sorry, but the fridge door is locked. Open the fridge doors. Exactly. And by the way, I know you're supposed to be studying for a test. So why don't we, why don't you go when you can pass the test? I will open the fridge door for you. Yeah. Final protocol. And then, and then we can wrap up a proof of human.

1:02:45Yes. Right. Yeah. That's the last piece that we got to figure out. Yeah.

1:02:48Marc Andreessen: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 societally have been unwilling to grapple with them. And I think they're both tipping right now. And they're, they're, they're, they're the same thing as virtual world versions, 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 and bots. Internet's a wash and fake people. It has been forever. By the way, a lot of that has to do with lack of money, you know?

1:03:13And so this, you know, this is my spicy take was these two are the same thing and corporations are people too, you know? So interesting. Yeah. Yeah.

1:03:21Marc Andreessen:Okay. So a bank account is proof of human. Yeah. Okay. Yeah. Until you, 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 problem is a big problem. You know, 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. Right. And so we've known for, you know, we've known for 20 years now that the asymmetric threat, both in military, 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?

1:03:55Marc Andreessen: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 the, you know, in the world is like unprotected from drone attacks. Um, every, every stadium, every school, every prison, like it's 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 asymmetric terrorism forever. The other is take the problem seriously and figure out the set of techniques and technologies required to be able to deal with that, whether those are lasers or jammers or early warning systems or...

1:04:26Marc Andreessen:Personal force fields. Kinetic personal force fields. Exactly. And in both cases, 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 expensive to defend against a drone. But you see what I'm saying is it's the virtual version of the problem, and it's the physical version of the problem. The virtual version of the problem, what we need quite literally is proof of human. The reason is because you're not going to have proof of bot, especially now that the bots are too good.

1:04:55Marc Andreessen:The bots can pass the Turing test. And if the bots can pass the Turing test, then 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 cryptographically validated, this is definitely a person. And then you can have cryptographically validated, this is definitely something that a person said, this video is real. right? Just to double click on, 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'll be, I think many people will try. We're one of the key participants in the world, in the world project.

1:05:24Marc Andreessen:So we're partisans, but yeah, I think, so we think world is exactly correct. And the reason is it has, it has to be, it has to be proof of human. It has, because you can't do proof of not bot. You have to do proof of human. To do proof of human, 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? And so you have to have like something, you have to have a biometric, and then you have to have cryptographic validation and then the ability to do the lookup. And then by the way, the other thing you need was that you also need selective disclosure.

1:05:54Marc Andreessen:So you need to be able to do proof of human without revealing all the underlying information. By the way, another thing you're going to 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 going to need to sort of validate a proof of age. to be able to legally operate. And so that's coming. And then you're going to want proof of credit score and proof of 100 other... That's a tricky one. It is a tricky one, but there's no reason... If somebody's checking on your credit...

1:06:20Marc Andreessen:Somebody shouldn't... I'll give you an example. Somebody shouldn't need to know your name in order to be able to find out whether you're creditworthy. I see. Independently verifiable pieces of information. Pieces of information. It's like just likely disclosed. And this is the answer to the privacy problem writ large, which is I only need to prove I need to prove at that moment. So you're going to need that. And I think their architecture makes sense. So that needs to get solved. I think language models have tipped, the bots are now too good. And so they're undetectable. And so as a consequence, we now need to go confront that problem directly.

1:06:46Marc Andreessen:And then, like I said, and then the other problem is we need to go actually confront the drone problems. The Ukraine conflict has really unlocked a lot of thinking on that. Now the, and now the Iran situation is also unlocking that. And so I think there's going to be just like this incredible explosion of both drone and counter drone. Our drones are better than their drones. It's supposed to keep it that way. Yeah. And counter drones. I think we can sneak in one more question. I'm trying to tie together a lot of things that you said over the year. So at the Milken Institute debate with Teal, which is amazing, you talked about the lag between a new technology and kind of like the GDP impact of it.

1:07:20The other idea you talked about is bourgeois 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 and you give Elon a GI, you can run a lot more things at once. That's right. And then you have the social contract. And I know you received a clip of Sam Allman saying, we're rethinking the whole thing. And you're like, absolutely not. Yes. And I was in an event with Sam last night and he actually said in the last couple of weeks, he felt like now people are taking that seriously.

1:07:54So 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.

1:08:05Marc Andreessen:Yeah. So there's a whole bunch of, there's a whole bunch of times. Yeah. We could spend, by the way, we'd 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 this, this, this term managerial comes from this thinker in the 20th century, James Burnham, who, um, just 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 the whole history of capitalism until that point had been in two phases. number one had been what he called bourgeois capitalism, which was, think about it as like name on the door, like Ford Motor Company, because Henry Ford runs the company.

1:08:34Marc Andreessen:And Henry, it's like a dictatorial model. And Henry Ford just like tells everybody what to do. And he said, the problem with bourgeois capitalism is it doesn't scale because Henry Ford can only tell so many people to do so many things. And then he runs at a time in the day. And so he said the second phase of capitalism was what he called managerial capitalism, which was the creation of a professional class of managers 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 the importance of Harvard Business Schools and management consulting firms and all these things.

1:09:04Marc Andreessen:And then you look at every big company today, and most of the executives and 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, come work in tech. And what Burnham said is he said that transition is absolutely required because the problem with bourgeois capitalism is it doesn't scale.

1:09:31Marc Andreessen: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, 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. They're not necessarily experts at doing the thing. They're not as inventive. They're not going to create the next breakthrough thing. But he's like, whether you think that's good or bad or whatever, it's what's going to be required. And basically that's what happened.

1:09:55Marc Andreessen:Right. And so he wrote that book originally in like 1940, you know, over the course of the next 50 years, basically managerialism, no, I mean today up till today, managerial, 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 our 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 just to say Elon Musk or the, or the next, or the next Elon Musk or the next Steve Jobs, the next Bill Gates, the next Mark Zuckerberg.

1:10:25Marc Andreessen:And so 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 founder with, with colleagues, but you know, there's a founder CEO. 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, political structure, um, 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 being run by managers.

1:11:00Marc Andreessen:Right. And, and, and, and by the way, and of course venture being what it is, sometimes that works, sometimes it doesn't, 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 and vitality in the system. AI is the thing that would lead you to think, wow, maybe there's a third model, right?

1:11:27Marc Andreessen:And maybe, 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, 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 drill 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, what is the thing that these bots are really good, really good at doing paperwork.

1:11:57Marc Andreessen: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 think, I think the a hundred percent, I think the answer, the answer very well might be to get the best, the best of both worlds by doing this. And then the challenge is going to be twofold. The challenge is going to be for the innovators to really figure out how to leverage AI to actually do this. And then the other challenge is going to be for the incumbents that are managerial to figure out, okay, what does that mean?

1:12:25Marc Andreessen:Because now 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 figure out innovation. Either, I say, figure out innovation or die trying. Do you feel like that structure accelerates the impact on the actual GDP and economy? If you look at SpaceX, it's like the growth is like so fast. And like, instead of having these companies kind of like peter out and growth and impact, they can kind of like keep going, if not accelerating. That's for sure. The hope, um, the, the, the challenge and, and, you know, and look, the AI utopian view is of course, of course.

1:12:58Marc Andreessen:And, and, and that's going to be the future of the economy and it's going to grow 10 X and a hundred X and a thousand X. And we're entering this regime of like much higher economic growth forever and consumer cornucopia of everything and it's going to be great. And I hope that's true. I hope that's like the, you know, that's the current kind of utopian vision. I hope that's true. The problem is, it goes back again, the real world is really messy. And I'll give you an example of how the real world is really messy. It requires 900 hours of professional certification training to become a hairdresser in the state of California.

1:13:23Marc Andreessen: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 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 that the dock work the dock workers went on strike a couple years ago because they're you know robotics you know if you go look at a modern dock like in asia it's all robots.

1:14:00Marc Andreessen:If you go to American doc, it's like all still guys dragging stuff by hand. The doc workers are on a strike. It turns out there are 25 ,000 doc workers working on docs in America. It turns out they have incredible political power because 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 of 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 they have 25 ,000 people working at the docs.

1:14:30Marc Andreessen:They have 25 ,000 people during full paychecks sitting at home from prior union agreements. Oh, my God. From prior union agreements. I'll give you another great example. There are government agencies, there are federal government agencies where the employees 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 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, D.C.

1:14:58Marc Andreessen:that are empty 29 out of 30 days of the year that are still operating and we're all still paying for it. And then what they do, it turns out what the employees do is they're very smart 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 so, they're in the office two days per 60 days, which means these buildings are empty for 58 days at a time. And you see where I'm heading with this. Like, this is like locked in, right? This is locked in in a way that has nothing to do with, and people say capitalists, it's anti-capitalistic. It's basically, it's restrictions on trade.

1:15:32Marc Andreessen:It's restrictions on the ability to change the workforce. And so, so much of our economy is, I'm describing the entire healthcare system. I'm describing the entire legal profession. I'm describing the entire housing industry. I'm describing the entire education system. K through 12 schools in the United States, they're a literal government monopoly. How are we going to apply AI on 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.

1:16:03Marc Andreessen: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 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. 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, nope.

1:16:29Marc Andreessen:So much of how the existing economy works 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, we're just going to have a stagnation. Awesome, Mark. I know you got to run. Yeah, I don't know. Or stay welcome. But it was such a pleasure talking to you. We're truly living in an age of science fiction coming to real life. Yes. Yes. Could not be more exciting. Really, thank you, Mark. With you guys. Awesome. Thank you. That's it. Thank you.

1:17:06That's it. As a reminder, please note that the content here is for informational purposes only. Should not be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security. It is not directed at any investors or potential investors at any A16Z funds. For more details, please see a16z.com slash disclosures.