Marc Andreessen on AI Winters and Agent Breakthroughs

This episode originally aired on the Latent Space Podcast. swyx and Alessio Fanelli speak with Marc Andreessen about the arc of AI from its origins in 1943 to today's breakthroughs in reasoning, coding agents, and self-improvement. They cover the parallels between AI scaling laws and Moore's Law, the architectural insight behind Claude Code and the Unix shell, the coming supply crunch in compute, and why the messy reality of 8 billion people means both AI utopians and doomers are too optimistic about the pace of change.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:01Erik Torenberg?

  This episode originally aired on the Latent Space Podcast. Marc Andreessen has watched AI cycle through summers and winters for more than 35 years, from coding in Lisp in 1989 to back in the foundation model companies today. He argues that the current moment is not another false start, but the payoff from eight decades of foundational research catalyzed by four distinct breakthroughs: large language models, reasoning, agents, and self-improvement. He also makes the case that the combination of a language model, a Unix shell, and a file system represent one of the most important software architectures in a generation. Swix and Alessio Fanelli speak with Marc Andreessen, co-founder and general partner

• 0:46Erik Torenberg?

  at A16Z.

• 0:47Marc Andreessen?

  Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like, for example, we now know that neural network is the correct architecture. And I will tell you, like, there was a 60-year run where that was like, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think about basically the period we're in right now is it's, I call it 80-year overnight success. Right. Which is like, it's an overnight success because it's like bam, you know, ChatGPT hits, and then, and then O1 hits, and then, you know, OpenClaw hits, and like, you know, these are open, these are, these are like overnight, like radical overnight transformative successes, but they're drawing on an 80-year sort of wellspring backlog, you know, of, of ideas and thinking.

• 1:31Marc Andreessen?

  It's not just that it's all brand new, it's that it's an unlock of all of these decades of like, very serious hardcore research. 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.

• 1:41Speaker 02

  Before we get into today's episode, I just have a small message for listeners. Thank you. We would not be able to bring you the AI engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content. We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribe to us to keep all this sustainable without ads, and we want to keep it that way. But I just have one favor to ask all of you. The single most powerful, completely free thing you can do is to click that subscribe button. It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that work so hard to bring the InSpace to you each and every week. If you do it, I promise you, we'll never stop working to make the show even better.

• 2:26Speaker 03

  Well, let's get into it.

• 2:32Speaker 03

  Hey, everyone. Welcome to the Lit and Space Podcast. This is Alasio, founder of Kernel Labs, and I'm joined by Spix, editor of Lit and Space.

• 2:38Speaker 02

  Hello, and we're in A16Z with A, Mark and Jason. Welcome.

• 2:42Marc Andreessen?

  Yes, yes. A and what, half of 16?

• 2:46Speaker 02

  A1.

• 2:46Marc Andreessen?

  Exactly.

• 2:47Speaker 02

  Apparently, this is the final few days in your current office. You're moving across the road.

• 2:52Marc Andreessen?

  We have a little bit of some projects underway, but yeah, this is actually, this is the original. We're in actually the original office. We're in the, we're in the, we're in the whole thing.

• 2:59Speaker 02

  It's beautiful.

• 3:00Marc Andreessen?

  Yeah, great. Thank you.

• 3:01Speaker 02

  So I have to come out. This is, you know, I wanted to pick a spicy start. In October 2022, I just made friends with Rune, and I wanted to give him something to be spicy about. And I said, it'll never not be funny that A16Z was constantly going, the future is where the smart people choose to spend their time, and then going deep into crypto and not in AI. And that was in October 2022. And Rune says there was an internal meeting in A16Z to reorient around Jenny AI. Obviously, you have, but was there a meeting? What was that?

• 3:31Marc Andreessen?

  I mean, I don't look, I've been doing AI since the late 80s. Yeah. So I don't know. Like, as far as I'm concerned, this stuff is all Johnny come lately. Yeah. I mean, look, we've been doing AI our entire existence. I mean, we've been doing AI, machine learning, you know, deeply. We've been doing this stuff way from the beginning, obviously. AI is just core to computer science. I actually view them as like quite, quite continuous. You know, Ben and I both have computer science degrees. You know, we both, Ben and I actually both are old enough to remember the actual AI boom in the 1980s. There was a, there was a big AI boom at the time. And there was a, there's one under names like Expert Systems and they are of like Lisp and Lisp machines. I coded in Lisp. I was coding in Lisp in 1989, where that was the language of the AI future. Yeah. So this is something that we're like completely, you're completely comfortable with and been doing the whole time and are very enthusiastic about.

• 4:17Speaker 02

  Is there a strong, like, this time is different? Because my closest analog was 2016, 17. There was an AI boom, and it petered out very, very quickly.

• 4:26Marc Andreessen?

  Really?

• 4:26Speaker 02

  It's just in terms of investment.

• 4:28Marc Andreessen?

  Sort of, sort of.

• 4:28Speaker 02

  Investment excitement.

• 4:30Marc Andreessen?

  Although that's really when the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that. The time the vocabulary was more machine learning, but it was very clear at that time that machine learning was hitting some sort of takeoff point. Yeah. Well, as you guys, you guys have talked about this at length on your, on your thing, but you know, if you really track what happened, I think the real story is it was, it was the Alex net basically breakthrough in like 2013. That was, that was the real knee in the curve. And then it was obviously the transformer breakthrough in 17. Yeah. And then everything that followed, but, but you know, look, machine learning, you know, there were, you know, look, I mean, look, I've been working, you know, I've been working with one of my, you know, kind of projects, working with Facebook since 2004.

• 5:06Marc Andreessen?

  And on the board since 2007. And of course, you know, they, they started using machine learning very early. And, you know, I've used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization. And obviously many, you know, financial services, you know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things. It's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? And the layers arrive at different paces, but they kind of build up. Yeah. They kind of build up over time. And then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the transformer and that. And then, as you guys know, there was this really weird like four year period where it's like the transformer existed and then it was just like. Let's go.

• 5:44Marc Andreessen?

  Yeah. Well, but, but it was, but between 2020, between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chatbots, but they weren't letting anybody use them. Yeah. Right. And then, you know, and then open AI developed chat GPT or GPT two. And then they told everybody this is way too dangerous to deploy, right? You know, we can't possibly let normal people, normal people use this thing. And then you guys, I'm sure remember AI dungeon. So the, for there was like a year where like the only way for a normal person to use GPT three was in AI dungeon. Yeah. And so you, we would do this. You'd go in there and you'd pretend to play Dungeons and Dragons. In reality, you're just trying to talk to, talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big companies, you know, big companies are cautious and, you know, the big companies were cautious.

• 6:24Marc Andreessen?

  By the way, it took open AI, you know, they, they, they talked about this. It took open AI time to actually adjust, you know, kind of redirect their research path. I think it was at Rosewood, right? The dinner that founded open AI was right there. Right. But that dinner would have taken place in 2018. The formation of open AI as late as 2018? Uh, sorry. Uh, no, I'm, I'm, I'm wrong. It should be 20, they just celebrated a 10 year anniversary. So it.

• 6:46Speaker 02

  It is twenty twenty five. Yeah, that's a twenty fifteen.

• 6:48Marc Andreessen?

  Yeah, twenty fifteen, yeah, twenty fifteen. But then Alec Radford did GPT one in what probably?

• 6:54Speaker 02

  Seventeen, eighteen.

• 6:55Marc Andreessen?

  Eighteen, seventeen, eighteen. So it is, yeah, for the, and then, and then they didn't really, and then GPT three was, what, twenty twenty, twenty?

• 7:01Speaker 02

  Twenty twenty, because that became co-pilot immediately, yeah.

• 7:04Marc Andreessen?

  Even OpenAI, which has been, you know, the leader of this thing in the last decade, you know, e- even they had to adapt and lean into the new thing. And so, yeah, I, I think it's just this process of basically sort of wave after wave, layer after layer, you know, building on itself. And then you kind of get these catalytic moments where the, where the whole thing pops. And obviously, that's what's happening now.

• 7:21Speaker 02

  Is it useful to think about, will there be an AI winter? Because there's always these patterns, like, is this endless summer? It's something I constantly think about because do I get, do I just like, just get endlessly hyped and just trust that I will only be early and never wrong? Or, well, are we, will there be a winter?

• 7:40Marc Andreessen?

  So there's something about, I'm gonna say the following, there's something about AI that has led to this repeated pattern. And, and you guys know this.

• 7:47Speaker 02

  Summer, winter, summer, winter.

• 7:48Marc Andreessen?

  Summer, winter, and it goes back eighty years, eighty years. So the original neural network paper was nineteen forty-three, right? Which is, which is amazing. That was, it was far back that long. And then there was, if you guys have ever talked about this on your show, but there was this, there was a big, there was an AGI conference at Dartmouth University in nineteen fifty?

• 8:05Speaker 02

  Fifty-five.

• 8:05Marc Andreessen?

  Fifty-five. And they got an NSF grant to, for the, all the AI experts at the time to spend the summer together. And they figured if they had ten weeks together, they could get AGI of the other end. And they got their, by the way, they got the grant, they got the ten weeks and then, you know, nineteen fifty-five, you know, no, no AGI. And like I said, I lived through the eighties version of this, where there was a big, a big boom and a crash. And so, so there is this thing, and there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. And it's probably on both sides of like the, the boom bust cycle, you, you kind of see that play out. Having said that, I think what's actually happened is like just, you know, and we now know in retrospect, like an enormous amount of technical progress that built up over time. And like, for, for example, we now know the neural network is the correct architecture. And I will tell you, like, there was a sixty-year run where that was like a, you know, or even seventy years, where that was controversial.

• 8:51Marc Andreessen?

  And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in nineteen forty-three. And so, so in retrospect, we, we now know that like these, these guys were right, that, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever. But like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing. And, and, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think about basically the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success because it's like bam, you know, chat GPT hits and then, and then O1 hits and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical overnight transformative successes,

• 9:33Marc Andreessen?

  but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of ideas and thinking. It's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious hardcore research and thinking. Look, there were AI researchers who spent their entire lives. They got their PhD, they worked for, they've researched for 40 years, they retired, in a lot of cases, they passed away and they never actually saw it work. Yeah, so sad. It is, it is sad, it is sad. I think Jeff Hinton was like the last guy. Yeah, yeah, well, there were guys, Alan Newell, I mean, there's tons of John McCarthy. John McCarthy was like one of the inventors of the field. He's one of the guys that organized the Dartmouth Conference and, you know, he taught at Stanford for 40 years and passed, you know, passed away. I don't know, whatever, 10, 10 years ago or something. Never, never actually got to see it happen. But like, it is amazing in retrospect. Like, these guys

• 10:16Marc Andreessen?

  were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, as they say, history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's going to be another like, you know, basically boom bust cycle? And I, I will tell you like, look, like, in a sense, like, yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timeliness to that. Having said that, there's just no question. The foremost, the foremost dangerous words in investing are this time is different. Do you know the 12 most dangerous words in investing? No. The foremost, foremost dangerous words in investing are this time is different. Yeah. The 12 most dangerous words. And so, like, I tell you what's different, like, now it's working. Like, like, there's just no, I mean, look, there's just no question. And

• 10:57Marc Andreessen?

  by the way, I'll just give you guys my take. Like, LLMs, like, from, from basically the chat GPT moment through to spring of 25, I think you could still, I think, well intentioned, well informed skeptics could still say, oh, this is just pattern completion and, oh, these things don't really understand what they're doing and, you know, the hallucination rates are way too high and, you know, this is going to be great for creative writing and creating, you know, Shakespearean sonnets and, you know, as rap lyrics or whatever, like, it's gonna be great. All that stuff, but we're not going to be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of fields that, you know, kind of really, really matter. And I think basically it was the reasoning breakthrough. It was O one and then R one that basically answered that question and basically said, oh no, we're going to be able to actually turn this into something that's going to work in the real

• 11:40Marc Andreessen?

  world. And then, and then obviously the coding breakthrough over the, or basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that. It was like, all right, if, if, you know, if Linus Torvalds is saying that AI coding is not better than he is, like, that's, that's never happened.

• 11:54Speaker 03

  That's the benchmark. Yeah.

• 11:55Marc Andreessen?

  That's never happened before. And so now we know that it's, it's going to sweep through coding and then, and then we, we know, you know, we know that if it's going to work in coding, it's going to work in everything else. Right. It's just that, because that's, that's like, that's like, that's like the hardest, in many ways, that's the hardest example. And now everything else is going to be a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with open claw, which is fantastic, which is amazing and incredibly powerful. And then we just got the, the auto research, you know, the self improvement, you know, we're now into the self improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, LLMs, reasoning, agents, and then, and then now RSI, and they're all actually working. And so I'm, I'm just, I'm jumping out of my shoes. Like, this is, like, this is it. Like, this is the culmination

• 12:38Marc Andreessen?

  of 80 years worth of, worth of work. And this is the time it's becoming real.

• 12:41Speaker 03

  Yeah.

• 12:41Marc Andreessen?

  I'm completely convinced.

• 12:43Speaker 03

  I think the anxiety that people feel is like during the transistor era, you had Morselaw, and it's like, all right, we understand why these things are getting better. We understand the physics of it. With AI, it's, it's so jagged in like the jumps. Where like, like you said, it's like in three months, you have like this huge jump, like, and people are like, well, this can keep happening, right? But then it keeps happening.

• 13:02Marc Andreessen?

  It will keep happening.

• 13:02Speaker 03

  And so like, how do you think about also timelines of like what's we're building? I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just going to do it one shot in the latest space? And how does that inform like how you think about the shape of the technology? You know, you talk about how it's a new computing platform. If you have a computing platform that like every six months, it like drastically changes in what it looks like, it's hard to build companies on top of it.

• 13:28Marc Andreessen?

  Yeah, so it's a couple of things. So one is like, look, Moore's Law was what we now call a scaling law. Like, Moore's Law was a scaling law. And for your younger viewers, Moore's Law was every chip. Chips either get twice as powerful or twice as cheap every 18 months. And that, and that, and that, you know, that it's gotten more complicated in the last few years, but like, that was like, the 50 year trajectory of the computer industry. And then, and then, by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that, like that, you know, for 500 bucks. And so that was a scaling law. And then, and then, and then, key to any scaling law, including Moore's Law, and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're predictions. But when they work, they become self fulfilling predictions, because they, they, they, they set a benchmark, and then the entire industry, right, all the

• 14:11Marc Andreessen?

  smart people in the industry kind of work to make sure that that actually happens. And so they kind of motivate the breakthroughs that are required to keep that going. And in, in, in chips, that was a 50 year, that was a 50 year run, right? And it was amazing. And it's still happening in some areas of chips. I think the same thing is happening with the core scaling laws, the core scaling laws in AI. You know, they're, they're not really laws, but like, they are basically, they're predictions, and then they're motivating catalysts for the research work that is required to be, and by the way, also the investment dollars, you know, required to basically keep, you know, keep the curves going. And look, it's going to be complicated, and it's going to be variable, and there, you know, there are going to be walls that are going to look like they're fast approaching, and then they're going to be, you know, engineers are going to get to work, and they're, you know, figure out a way to punch through the walls. And obviously, that's, you know, that's been happening a lot. You know, and then, look, there's going to be times when it looks

• 14:54Marc Andreessen?

  like the walls have, you know, the, the laws have petered out, and then they're going to, they're going to pick up again and surge. And then, and then, and then it appears what's happening to AI is there's now multiple, you know, multiple scaling laws. There's multiple areas of improvement. And I think, you know, I don't know how many more there are yet to be discovered, but there are probably some more that we don't know about yet. You know, they're like, for example, there's probably some scaling law around world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet. So that, that one will probably kick in at some point here. There's a bunch of really smart people working on that.

• 15:24Marc Andreessen?

  And so, yeah, I think the expectation is that, you know, the scaling laws generally are going to continue. Yeah, the pace of improvement will continue to move really fast. To your question on like what to build, so I'm a complete believer the scaling laws are going to continue. I'm a complete believer the capabilities are going to keep getting amazing. You know, leaps and bounds. The part where I kind of part ways a little bit with what I would describe as the AI purists, you know, which is, which I would characterize as like the people who are in many ways the smartest people in the field, but also the people who spend their entire life like at a lab and have, I would say, have very little experience in the outside world. The nuance I would offer is the outside world of eight billion people and institutions and governments and companies and economic systems and social

• 16:03Marc Andreessen?

  systems is really complicated and, and doesn't, you know, eight billion people making collective decisions on planet earth is not a simple process of like, just like, you see this happening now, it's like a bunch of the AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's this, these obvious set of things that society needs to do. And then they're like, society's not doing any of those things. Right? And it's like, how can society not, you know, whatever their theory is, how can society not see X, Y, Z? And the answer is, well, society is number one, there's no single society. It's like eight billion people and they like all have a voice and they all have a vote, like at the end of the day of how they, they react to change. And then, you know, it just like, it's just, human reality is just really complicated and messy. And so the specific

• 16:45Marc Andreessen?

  answer to your question is like, as usual, it depends. You know, it depends. Look, there's no question people are going to like, there's no question there are going to be companies, it's already happening. There are companies that think that they're building value on top of the models and they're just going to get blitzed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real, into the real messy world of humanity is, is just going to be messy and complicated. It's, it's not going to be simple and straightforward. It's going to be messy and complicated. And there are going to be a lot of companies and a lot of products and in fact, entire industries that are going to get built to, to basically actually help all of this technology actually reach real people.

• 17:21Speaker 03

  Diamond Capital going into this company is, I mean, Dario talked about it on the Dorkesh podcast and Dorkesh was like, why don't you just buy 10x more GPUs? And he's like, because I'm going to go bankrupt if the model doesn't exactly hit the, the performance. Level. How do you think about that? Also, as a risk on, you know, you guys are investors and open AI and think machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate. Like, how comfortable, I guess, do you feel with the downside scenario? Like, and say, like, things peter out, you think you can kind of like restructure these build outs and, you know, capital investment?

• 17:55Marc Andreessen?

  Yeah, so just start by saying, so I lived through the dot com crash. And I can tell you stories for hours about the dot com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic. By the way, a lot of the dot com crash was actually, at the time, it was actually a telecom crash. It was a bandwidth crash. Like, the thing that actually crashed that wiped out all the money was the telecom companies.

• 18:14Speaker 03

  Global Crossing.

• 18:14Marc Andreessen?

  Global, global, yes.

• 18:16Speaker 03

  I'm from Singapore and they laid so much cable over, over our oceans.

• 18:20Marc Andreessen?

  Actually, there was a scaling law in the dot com era and it was literally the, the US Commerce Department put out a report in 1996. And they said internet traffic was doubling every quarter. And actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. So what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth was 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. And it's, you know, it's continuously grown. It's never shrunk. And it's grown really fast compared to anything else, you know, in, in, 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.

• 19:05Marc Andreessen?

  And that's actually what caused the dot com crash, which was they, 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 centers. So like, that was the beginning of the, of the, of the data center build and then, and then data center overbuilt. 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, 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 Global 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 building 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 it's all in use today, but 25 years later. But it actually, the elapsed time was it took 15 years. It took 15 years from 2000 to 2015 to actually fill up all that capacity. The cautionary warning is the overbuild can happen. And you know, 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

• 20:34Marc Andreessen?

  can deploy, you know, compute grids and all these things. And so, you know, if you're a pessimist, you could look at this and you could 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. 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 in the, in the, like Global Crossing was like, it was like an entrepreneur. It's like a new venture. But like the money that's being deployed now at scale is Microsoft and, you know, and Amazon and Google, Facebook and Nvidia and, you know, these, these, and now, you know, by the way, OpenAI and Anthropic, which are now at like, you know, really serious size, you know, as companies with, you know,

• 21:17Marc Andreessen?

  very serious revenue. These are very large scale companies with like lots, lots of cash, lots of debt capacity that they've never used. 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 interconnected 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 starved 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. If right, suppose a hypothetical universe in which GPUs were 10 times cheaper and 10

• 22:02Marc Andreessen?

  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.

• 22:12Speaker 02

  No, everything we use is quantized because the labs have to keep the full versions.

• 22:17Marc Andreessen?

  Right. We're not even getting the good stuff. But getting the good stuff is just, even if technical progress stops, once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, 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, going to get much better. And then, as you know, like, there's just like a million ways to use this stuff. Like, there's just like a million use cases for this. Like, you know, this isn't just sending packets across a thing, whatever, and hoping that 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 like incredibly well. Here's what I know, here's what I know. In the next three or four years, it's like in somewhere between three or four years out, basically, everything is selling out. And so like the entire supply chain is sold out or selling out.

• 23:00Marc Andreessen?

  And so there's no, like, we're just going to have like chronic supply shortage for, you know, for years to come. 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 fat 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. And so I know the capabilities are like really, really serious. I also know that the technical progress is not going to stop. It is accelerating, like the breakthroughs are tremendous.

• 23:44Marc Andreessen?

  I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and 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 in, you know, at least for years to come. I think it would be essentially suicidal to make that bet. Um, who's that? Michael Burry?

• 24:02Speaker 02

  That's an interesting guy, huh?

• 24:03Marc Andreessen?

  We'll pick on a guy. We'll pick on one guy. Well, because he did. He came out with it was it was

• 24:07Speaker 02

  He doesn't mind.

• 24:08Marc Andreessen?

  It was the Nvidia short, right? The Nvidia short. And then you guys probably talked about this, which is the analysis now that like the current models are getting better, faster at such a rate that if you are running an Nvidia, 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. And then my understanding is Google is running, I don't think, 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.

• 24:35Speaker 02

  For inference, yeah.

• 24:37Marc Andreessen?

  And very profitably. And so it actually turns out, as far as I can tell, it's actually the opposite of the Burry thesis is actually, he was actually 180 degrees wrong. It's actually 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 again, that's an expression of that just ferocious pace of software progress, ferocious pace of capability payoff that you're getting on the other side of this. And so I just, the idea of betting against that, like,

• 25:07Speaker 02

  Yeah.

• 25:08Marc Andreessen?

  It's like an invitation to get your face ripped off.

• 25:10Speaker 02

  One of my early hits was like modeling the lifespan of the H100 and H200s 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? And even like the shittier versions of memory that we do have, we are finding use cases for it. So like, that's great.

• 25:41Speaker 03

  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 know, if you just project forward demand over the next three years relative to supply, one of the dismain predictions you can do is what's going to, what's going to happen to the cost of inference in the core over the next three years and like it may rise dramatically, right? Like, so, so what is, and then is, you know, like the big model competitors are subsidizing heavily right now, right? And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do? And I don't know, it's going to be, I mean, I, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw, right? And so, okay, $30,000 a month, right?

• 26:40Marc Andreessen?

  And by the way, those friends have like a thousand more ideas of the things that they want their claw to do, right? And so you could imagine there's like latent demand of up to, I don't know, five or $10,000 a day of tokens for a fully deployed, you know, personal agent. And obviously consumers can't pay that, right? And so, so, but it gives you a sense of the, of the future scope of demand, right? And so, so even, even if there's a 10x improvement in price performance, that's still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay. So there's just going to be like 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. CPU and memory, yes. CPU and memory, right? And so like the entire chip ecosystem is just going to get. With a network constraints, that will be the killer. It's all bottlenecked and potentially for years.

• 27:23Marc Andreessen?

  And so, 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 again will be, be an issue. And so there's just going to be so much more demand for inference than, than can be satisfied, 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 inference is quite amazing. 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, oh, 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. So you know, there's also like other, there's other motivators, there's other motivators,

• 28:08Marc Andreessen?

  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. So there, there's all the trust issues. 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, a smart local model. There's also performance issues where you want to, you know, you, you want, you know, you're going to want your Dornop to have an AI model in it, you know, to be able to, you know, do, you know, to be able to do access control. 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. And so, yeah, no, like, I think, I think you're going to have, and then you're, by the way, also wearable devices, you know, you don't want to do a complete round trip. You want, you know, you, whatever your

• 28:53Marc Andreessen?

  smart devices are, you want it to be like super low latency.

• 28:56Speaker 02

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

• 29:15Marc Andreessen?

  Yeah, we'll see. We'll see. Look, number one, I do think we care. I do think we, I do think we care who makes it. 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. 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.

• 29:31Speaker 02

  And you're on the council?

• 29:32Marc Andreessen?

  And the new, and the PCAST, yeah. So that, you know, this, 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. And so they're very supportive. My read is the Chinese, the Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is they, they don't, fundamentally, they don't think they can sell commercial AI outside of China right now, and 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, you know, paid, paid services and then kind of, you know, kind of 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. You know, I think DeepSeek was like a gift to the world. I think the great thing about open source.

• 30:14Marc Andreessen?

  The 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

• 30:22Speaker 02

  The paper.

• 30:23Marc Andreessen?

  The paper, the paper and, and the code. Right. And the code. And so like, for example, I thought this was amazing. So OpenAI comes out with O1 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 R1 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, three 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 to 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.

• 31:08Marc Andreessen?

  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, 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. You know, and, and, 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 backing that are, you know, trying to come out with different approaches. And then you've got whatever it is, I don't know. How many, how many like mainline foundation model companies are there in China at this point? It's probably

• 31:45Speaker 02

  There's five tigers, is what they call it. Quinn is in questionable because there's change in leadership.

• 31:50Marc Andreessen?

  Yeah. But that does that include, that includes like Moonshot?

• 31:53Speaker 02

  Yes, Kimi, DeepSeek, ZAI, Quinn, O1 is in there.

• 32:00Marc Andreessen?

  Right. And then ByteDance.

• 32:02Speaker 02

  ByteDance would be like the next tier. They weren't as prominent. They weren't have a leading on yet.

• 32:06Marc Andreessen?

  Yeah, but they're, you know, at least, you know, C, C dance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and so forth. And so, so, and so like, look, here, here would be a thing you can anticipate, which is there are not, these markets, they're not going to be, between the US and China right now, there's like a dozen primary foundation model companies that are like at scale at, at some level of like critical mass. It's not going to be a dozen in three years. Like it just because these industries don't bear a dozen. It's going to be three, you know, there's going to be three or four big winners or maybe one or two big winners. And so there's going to be like a whole bunch of those guys that are going to have to figure out alternate strategies. And I think like open source is one of those strategies. And so I think you could see like a whole, I think the questions like who's going to do open source, I think that could change really fast. I think that that's a very dynamic thing. I think it's very hard to predict what happens. And I think it's very important.

• 32:48Speaker 02

  Nvidia is doing a lot.

• 32:49Marc Andreessen?

  Well, I was gonna say, well, exactly. And then you got Nvidia and then, and then, you know, just again, industrial, there's an old thing in business strategy, which is called commoditize, commoditize the compliment. And it's right. And so if you're Jensen, it's just kind of obvious. Of course, you want to commoditize the software. And he's and to his enormous credit, he's putting enormous resources behind that. And so maybe, maybe it's literally Nvidia. And I think that would be great.

• 33:07Speaker 03

  Yeah, narrative violation to European projects.

• 33:13Speaker 03

  Bam.

• 33:14Speaker 02

  I'm hosting my Europe conference soon. I got both of them.

• 33:17Speaker 03

  They got us. They got us, Mark.

• 33:20Marc Andreessen?

  Wait a minute. Where was Peter? So where was Steiner when he did it in Austria?

• 33:23Speaker 03

  Yeah, yeah.

• 33:24Marc Andreessen?

  He was in what?

• 33:24Speaker 03

  In Vienna.

• 33:24Marc Andreessen?

  Oh, he was in Vienna. And then where is he now?

• 33:27Speaker 02

  He is moving to SF.

• 33:28Marc Andreessen?

  Okay, okay. All right. Okay, there we go. And then, yeah, the pie guy, right, the pie guys are European.

• 33:32Speaker 03

  Their buddy is in Austria. Mario is also there.

• 33:34Marc Andreessen?

  Right. And are they? Yeah, they haven't announced yet any sort of change, change, or have they?

• 33:38Speaker 03

  No, they're they have a company there.

• 33:40Marc Andreessen?

  Okay, good. Okay, good.

• 33:43Speaker 02

  Anyways, I think Pi and OpenClaw, very important software things. And I just wanted you to just go off on what do you think?

• 33:51Marc Andreessen?

  Yeah. So I think in the combination of the two of them, I think is one of the 10 most important software.

• 33:55Speaker 03

  OpenClaw got all the attention, but talk about Pi.

• 33:58Marc Andreessen?

  Pi is kind of the, yeah, 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 1973 to like basically the creation of Linux, which is basically this, this thing we used to call like the Unix mindset. Like so, so, because 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 OS3. Which was this big operating system IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky.

• 34:41Marc Andreessen?

  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 how 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. 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, led to the, the sort of centrality of the shell. And then that led to a sort of, you know, basically chaining the other Unix tools.

• 35:22Marc Andreessen?

  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, 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, 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. 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.

• 36:07Marc Andreessen?

  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, the way I think about what happened with Pi and then with OpenClaw is basically what those guys figured out is always say the, the great breakthroughs are obvious in retrospect, right? Which is

• 36:20Speaker 02

  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 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 the, to me, those are always the best breakthroughs. So actually, language models themselves are like that. It's just like, oh, next token completion. Oh, of course.

• 36:36Speaker 02

  What other objective mattered?

• 36:38Marc Andreessen?

  Yeah, exactly. But, but like, right, but you see what I'm 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 Unix, basically shell prompt mindset. And so it's basically this idea that 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 a language model. And then above that, it's a bash, it's a bash shell. So it's a Unix shell. And then the agent has access to the shell and, you know, hopefully in a sandbox, maybe in a sandbox. So it's the model, it's the shell, and then it's a file system.

• 37:23Marc Andreessen?

  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, 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 heartbeat and the thing basically wakes up, wakes up. So it's basically L M plus shell plus file system plus markdown plus cron. 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, 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 already.

• 38:06Marc Andreessen?

  And so like the full power of your computer is available at the command line level. 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 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 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 is running on. Because you can actually swap out a different L M 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. 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 still, it's still your agent with all of its memories and with all of its capabilities.

• 38:51Marc Andreessen?

  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, is also bash shell. By the way, you can also switch out the file system. Right, and you can swap out the heartbeat, the chron 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. 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.

• 39:34Marc Andreessen?

  And 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 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'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, right? 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 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.

• 40:18Marc Andreessen?

  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 Claude code or whatever, it'll write whatever it needs. And 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 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, well, where's the breakthrough? Because these, 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.

• 41:00Marc Andreessen?

  And so, 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 often away it goes. And then you've got all the abilities of the browser also. 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 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 so, you know, there's a bunch of stuff to be ironed out, but the unlock of capability is just incredible. And I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents.

• 41:45Marc Andreessen?

  And we're gonna be living in a world where I think it's almost inevitable now that this is the way people are gonna use computers.

• 41:50Speaker 02

  I was gonna 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.

• 42:12Marc Andreessen?

  You guys know a RentAHuman.com?

• 42:13Speaker 02

  Yeah. I mean, it's Fiverr, it's TaskRabbit.

• 42:16Marc Andreessen?

  Sure, of course.

• 42:19Marc Andreessen?

  Yeah, but flipped, right? The agent hiring the people, which of course is gonna happen. It's obviously gonna happen.

• 42:25Speaker 03

  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. It's like, hey, this thing is just not gonna work for like this type of new compute. And we should just rip it out right now.

• 42:57Marc 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. Basically, every, every, every, basically, old school systems architect that had grown up between like the 1960s and 1990s, basically said, you know, the internet is, what do you know about the internet? It's star for bandwidth. You just, you have these very narrow straws. You know, like people, when we did the work on Mosaic, like people who had the internet at home had a 14 kilo bit modem, right? So you're, you're trying to like hyper optimize every bit of data that travels over the network.

• 43:40Marc Andreessen?

  And so obviously, if you're going to design a protocol like HTTP, you're going to want it to be, you know, highly compressed binary protocol for maximum efficiency. And you're going to want to have it be like a single connection that persists and you're, the last thing you're going to want to do is like bring up and tear down new connections. And you definitely, you're not going to want a text protocol. And so first 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.

• 44:06Speaker 02

  The most inefficient things possible.

• 44:08Marc Andreessen?

  Yeah, we want to do the inefficient things.

• 44:10Speaker 02

  You're the original token maxer.

• 44:11Marc Andreessen?

  Yeah, exactly. Yeah, yeah, yeah. Basically, it's just like, well, yeah, well, actually, this was, this was actually the conscious thing, which basically says just like, assume, assume a future of infinite bandwidth built for that. And then basically what it was, is it was a bet that 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, with their bare eyes, without having to like disassemble it or whatever, right? And have it converted out of binary, right? And so the, the, all the, you know, HTTP and everything else where 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.

• 44:56Marc Andreessen?

  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, right? New, to build new webpages. There was that. So human readability, and again, human readability in those days still meant technical, you know, specs. You know, 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. 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 the 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.

• 45:39Marc Andreessen?

  So it's the OS's ability to manage the file system and do everything else that you want to do, process everything. And then, of course, a lot of websites are referring to databases. And so you wanted to unleash the underlying latent power of, whether it was an Oracle database or some other, you know, 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, is this really, does this really matter? Like, is this important? Because we've had databases forever and we've always had, you know, user interfaces for databases and this is just another user interface for a database. It's like, okay, yeah, fair enough. But on the other side of that is just like, this is now a much better interface to databases and one that eight billion people are going to use and is going to be like far easier to use and far more flexible.

• 46:23Marc Andreessen?

  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, you know, a million times more database apps than they have in the past. And then the number of databases in the world exploded. And so, yeah, again, this goes to this thing of like 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. 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 then 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 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. So I think, and I think the web did that for those reasons.

• 47:08Marc Andreessen?

  And I think it's the same thing now that's happening.

• 47:10Speaker 02

  It's a great perspective from the web.

• 47:11Speaker 03

  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 on C? Go 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'd be like, oh, okay, Python is kind of slow.

• 47:30Speaker 02

  Python, TypeScript.

• 47:31Speaker 03

  You know, it's like, yeah.

• 47:32Speaker 02

  As imperfect as they are, they are the lingua franca.

• 47:35Marc Andreessen?

  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. Back in the days. I wasn't even using C++ so 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, so I could drop down and do things right on the chip. And so we've just, we've all of us, we've always lived in a world in which software is like this precious thing

• 48:19Marc Andreessen?

  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. 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 gonna wave your hand and you're gonna get it. And then if it's, if you don't like the languages written in, you just tell the thing, all right, I want the right now. I want the rest version. 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.

• 49:04Marc Andreessen?

  So we're gonna have like, we're set up here for like the computer security apocalypse for a while. But, but on the other side of it, now we have coding agents that can go in and actually fix all the security bugs. And so how are you gonna secure software in the future? You're gonna tell the, tell the bot to secure it and it's gonna go through and fix it all. And so, 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 gonna have as much as you want. Right. And that has like, you know, that has like 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, simpler or something or straightforward, which is just if you want all your software in rest, you just tell the bot you want all your software in rest, like. Things that used to be like the hard or even like seem like an intermodelable mountain to get to get through all of a sudden, I think, become very easy.

• 49:45Speaker 02

  I think Brett had a theory that there would be a more optimal language for LLMs. And so the contention is there isn't like, just don't bother, just whatever humans already use, LLMs are perfectly capable of porting.

• 49:57Marc Andreessen?

  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? And let it design it.

• 50:07Speaker 02

  It's true.

• 50:07Marc Andreessen?

  Okay, here's a question. Are you even gonna have programming languages in the future? Or are they just gonna 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 bots even need? Or are they just coding binary directly? Did you see there's actually an experiment, 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?

• 50:33Speaker 02

  Just predict the weight.

• 50:33Marc Andreessen?

  Well, yeah, will the bots literally be emitting not just coding binaries, but will they, will they actually be admitting weights for new, for new models?

• 50:39Speaker 02

  Yeah.

• 50:40Marc Andreessen?

  Directly, directly. And conceptually, there's no reason why they can't do both of those things. Like, architecturally, both of those things seem completely possible.

• 50:50Speaker 02

  Very inefficient. You're basically very efficient. Simulation of a simulation in a simulation inside of weights.

• 50:54Marc Andreessen?

  Yeah, very inefficient, but like, look, LLMs are already like incredibly inefficient. Ask, favorite thing, ask Claude to add 2 plus 2 equals 4, 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. 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 is 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.

• 51:28Speaker 02

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

• 51:32Marc Andreessen?

  Well, so I would take it a step further, which is you may not need user interfaces. So who is gonna use software in the future?

• 51:39Speaker 02

  Other bots.

• 51:40Marc Andreessen?

  Yeah, the bots. Yeah.

• 51:42Speaker 02

  You still need to, I don't know, pipe information in.

• 51:45Marc Andreessen?

  Do we?

• 51:45Speaker 02

  And out.

• 51:46Marc Andreessen?

  Really?

• 51:48Speaker 03

  Well, what are you gonna do then?

• 51:49Marc Andreessen?

  Are you sure?

• 51:50Speaker 03

  You're just gonna log off and touch grass?

• 51:51Marc Andreessen?

  Whatever you want, exactly. Isn't that better?

• 51:54Speaker 03

  I want software to do stuff for me.

• 51:56Marc Andreessen?

  But isn't that better? I mean, look, I, you know, I don't look like, you know, you know, you know, the arguments here. It was not that long ago that 99% of humanity was behind a plow. Right? And what are people gonna 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.

• 52:11Speaker 03

  Dews growing.

• 52:12Marc Andreessen?

  Yeah, 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 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 gonna tell the thing what I need and it's gonna do it. And then, and then it's gonna 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 gonna tell it to do it, it's gonna do it in whatever way is like the optimal way to do it. And then, and then if I need to understand how it works, I'm gonna ask it to explain to me how it works. Right? And so it's gonna be doing its own interpret, it's gonna be the engine of interpretability to explain itself.

• 52:52Marc Andreessen?

  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, not what the humans need.

• 53:01Speaker 03

  Yeah, yeah, that, 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, kinda like RL, and reward modeling around? Because it's like today, they're kinda 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 Py, Open Cloud thing. It's like, oh, I built all this software using the OpenAI model and I'll switch to the Anthropic model, but the Anthropic model doesn't understand the thing. So I, it feels like there still needs to be some obstruction.

• 53:43Speaker 03

  But maybe not. Maybe that's the lock-in that the model providers want to have.

• 53:47Marc Andreessen?

  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 some, so, yes, you know, models can now reverse engineer software, right? Isn't it the whole thing now where people are reverse engineering like Nintendo game binaries?

• 53:59Speaker 03

  Yeah.

• 54:00Marc Andreessen?

  So 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 engineering to get a version of the rest of their Mac, right? And so if you reverse it, if this is what I can say, if you're reversing like x86 binaries, then why can't you reverse engineer?

• 54:16Speaker 03

  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, 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,

• 54:32Speaker 03

  Right, yeah, yeah.

• 54:33Marc Andreessen?

  But with enormous, but it was just always like cost and labor prohibitive. Reverse engineer, like, I learned how to reverse engineer. It's 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 sort of compensate for the human limitations. 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.

• 55:01Speaker 02

  I have two topics to bring us to a close, and you can pick whichever ones. 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?

• 55:12Marc Andreessen?

  Yes.

• 55:12Speaker 02

  Was that you?

• 55:13Marc Andreessen?

  Yes, it was 402, 402 payment required.

• 55:15Speaker 02

  We have a chance now. I don't think we're gonna 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 gonna happen for sure.

• 55:22Speaker 02

  Yeah.

• 55:22Marc Andreessen?

  And there's two reasons it's gonna happen for sure. One is we actually have internet-native money now in the form of

• 55:25Speaker 02

  Stable coins.

• 55:26Marc Andreessen?

  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 gonna 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 gonna need money. And it's already happening, right? If you've got a claw, and you want it to buy things for you, you have to give it money in some form.

• 55:46Speaker 02

  I would say the adoption is probably 0.1% if that, but yeah.

• 55:50Marc Andreessen?

  Oh, today? Yeah, yeah, yeah, but think forwards. Like, where is it going?

• 55:53Speaker 02

  Thinking.

• 55:53Marc Andreessen?

  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 yet. My friends, who are the most aggressive users of Open Claw, just like have given their Claw's bank accounts credit cards. And I know if they've 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.

• 56:14Speaker 02

  Yeah.

• 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.

• 56:20Speaker 02

  Yeah.

• 56:21Marc Andreessen?

  It'll grow. That's how these things start.

• 56:23Speaker 02

  Actually, I mean, since you keep mentioning.

• 56:24Marc Andreessen?

  And by the way, Open Claw, by the way, if you don't give it a bank account, it's just going to break into your.

• 56:29Marc Andreessen?

  It's going to be high agency. 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 gotta 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?

• 56:46Speaker 02

  By the way, it's a Facebook thing.

• 56:47Marc Andreessen?

  Okay.

• 56:49Speaker 02

  Because 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.

• 56:57Marc Andreessen?

  Okay, good.

• 56:57Speaker 02

  They brought it into OpenAI.

• 56:58Marc Andreessen?

  And of course, that makes it enticing.

• 57:00Speaker 02

  Sam runs Codex with skip permissions on his laptop.

• 57:04Marc Andreessen?

  Yes, 100%. And so I think the way to actually see the future is to find the people who are doing that. There's a madness, you know.

• 57:11Speaker 02

  Log everything, you know, just watch it, watch the logs.

• 57:14Marc Andreessen?

  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.

• 57:18Speaker 02

  Try everything.

• 57:18Marc Andreessen?

  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.

• 57:37Speaker 02

  It's like gentleman science.

• 57:39Marc Andreessen?

  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. Yeah, it's Jonas Salk with the polio vaccine injecting it. Yes. So yes, I think we should have like a glow. We should have like flags and like, we should have like monuments to the people that just let Open Claw run their lives.

• 57:59Speaker 02

  More anecdotes. What are the craziest or interesting things that people listening to this should go home and do?

• 58:05Marc Andreessen?

  I mean, this is, this is, this is the extreme thing is just like the straight YOLO. Like just, yeah, turn your life.

• 58:10Speaker 02

  That's a general capability. Yeah. Like a specific story that was like, wow, and everyone in the group chat just lit up.

• 58:16Marc Andreessen?

  I mean, like, you know, so there's tons of, there's already tons of health. You know, there's the health dashboard stuff is just, is just absolutely amazing. The number of stories on, I'm just don't want to violate people's, you know, obviously personal. But, um, you know, one of the things Open Claw 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.

• 58:37Speaker 02

  Like super insecure, but great. Discoverable.

• 58:40Marc Andreessen?

  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. 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 on a loop. I have it wake up frequently and have it watch it and just tell it, watch me sleep. And, and I've, I've seen the transcripts and it's literally like Joe's asleep. This is good. This is good that Joe's asleep because, you know, I have, I have his health data and I know that he hasn't been getting enough sleep. And so it's really good that he's getting asleep. I really hope he gets his full, whatever, you know, five hours of REM sleep.

• 59:15Marc Andreessen?

  Joe's moving. Joe's moving. Joe might be waking, waking up. This is a real, if 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.

• 59:28Speaker 02

  He's monitoring the situation.

• 59:30Marc Andreessen?

  Situation. And, and being a bot, like, you know, is just like very focused, right? It's just like, ah, this is like, it's a 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. And I need to, I need, maybe this is taking on 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. What else?

• 1:00:05Marc Andreessen?

  It's a company, Unitree, that makes the robot dogs. And I actually have one at home, which is, it's actually really funny. The Chinese companies, the Chinese companies are so aggressive at adopting new technology, but they don't always like take the time to really.

• 1:00:20Speaker 02

  Package it?

• 1:00:20Marc Andreessen?

  Package it and maybe think it all the way through. And so, so the, 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, the voice, so they add, so they add LLM capability and then they add a voice mode to it. But, but that LLM capability is not at all connected to the control system. So you've got this schizophrenic dog that like is a complete idiot when it comes to climbing the stairs, but it will happily teach you quantum mechanics, right? In like a plumbing English accent, right? Like it's just like absolutely amazing.

• 1:00:55Speaker 02

  Jagged intelligence.

• 1:00:56Marc Andreessen?

  Yeah, talk about jagged. And then, no, obviously what's going to happen in the future is they're going to connect together. But, 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, for the unitary robot. And now it's, now it's an actual pet dog for his kids.

• 1:01:14Speaker 02

  You should do that before, after, like the motion.

• 1:01:16Marc Andreessen?

  Yeah, it's, you said it's completely different. He said it's a complete transformation. 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 as well, 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. 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, I think basically, I think the internet, 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. Now.

• 1:01:46Speaker 02

  Smart home.

• 1:01:47Marc Andreessen?

  You have to decide if, yes, there are horror movies in which this, of 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 and all works together and it's all coherent and the, and the whole thing. And to have that unlocked without a human being having to go do any of that work, like, yes.

• 1:02:11Speaker 02

  I'm waiting for a sorry, Mark. I can't let you open that fridge door.

• 1:02:14Marc Andreessen?

  Exactly, exactly. Yes, yes.

• 1:02:17Speaker 02

  Because you're not supposed to eat right now.

• 1:02:19Marc Andreessen?

  I have all of, yes, I have every shred of health information, you know? And I know you think you're doing, you know, da, da, da, and 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.

• 1:02:34Speaker 02

  Open the fridge doors.

• 1:02:34Marc Andreessen?

  Exactly. And by the way, I know you're supposed to be studying for a test. Why don't you go, when you can pass the test, I will open the fridge door for you.

• 1:02:41Speaker 02

  Final protocol and then, and then we can wrap up proof of human.

• 1:02:45Marc Andreessen?

  Yes.

• 1:02:45Speaker 02

  Right? Yeah. That's the last piece that we got to figure out.

• 1:02:47Marc Andreessen?

  Yeah. So I would say there's, there's two massive, I would say, um, 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 the same thing. It's virtual world version. It's a 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. 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.

• 1:03:15Speaker 02

  My spicy take was these two are the same thing and corporations are people too, you know?

• 1:03:20Marc Andreessen?

  Interesting. Yeah. Yeah. Okay.

• 1:03:21Speaker 02

  So a mega con is proof of human.

• 1:03:23Marc Andreessen?

  Yeah. Okay. Yeah. Until you, until you give the bots mega cons. 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 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, 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 cheap, the cheap, 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.

• 1:04:05Marc Andreessen?

  Every, every stadium, every school, every prison, like, is like.

• 1:04:09Speaker 02

  Sure.

• 1:04:10Marc Andreessen?

  Okay. We've known that. We've never done anything.

• 1:04:12Speaker 02

  What are you gonna do about it? Yeah.

• 1:04:13Marc Andreessen?

  One possibility is just leave them unprotected forever and live in a world of like asymmetric terrorism forever. 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.

• 1:04:26Speaker 02

  Personal force fields.

• 1:04:27Marc Andreessen?

  Cause I think personal force, dude. Personal force fields. Exactly. And in both cases, the, these are, these are economic asymmetries. These are economic asymmetries, right? Cause it's really cheap to field a bot, but it's very hard to tell something about. It's very cheap to field a drone. It's very hard. It's very expensive to defend against a drone. But you see. I'm saying is it's the 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. The bots can pass the Turing test and if the bots can pass the Turing 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 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?

• 1:05:12Speaker 02

  Just a double click on, do you think Alex Blania with world, do you think he's got it or is there an alternative?

• 1:05:18Marc Andreessen?

  Oh, so I mean, there's gonna 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. 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 for 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? 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, to do the look up. And then, by the way, the other thing you need was that you also need selective disclosure. So you need to be able to do proof of human without revealing all the underlying information. By the way, another thing you're gonna need, you're gonna need proof of age, right?

• 1:06:00Marc Andreessen?

  Because there's all these laws and 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 gonna need, you know, sort of validate a proof of age, you know, to be able to legally operate, right? And so that's coming and then you're gonna want like proof of credit score and, you know, proof of like, you know, 100 other.

• 1:06:14Speaker 02

  That's a tricky one.

• 1:06:15Marc Andreessen?

  It is a tricky one, but you're, you're, there's no reason, like if somebody's checking on your credit, 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 credit worthy.

• 1:06:25Speaker 02

  I see independently verifiable pieces of information.

• 1:06:27Marc Andreessen?

  Pieces of information, it's like, it's 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 like, you're gonna 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. 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 gonna be just like this incredible explosion of both drone and counter drone.

• 1:07:00Speaker 02

  Our drones are better than their drones. Keep it that way.

• 1:07:04Marc Andreessen?

  And counter drones.

• 1:07:05Speaker 03

  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 Thiel, which is amazing. You talked about the lag between a new technology and kind of like the GDP impact of it. The other idea you talked about is bourgeois capitalism and how, you know, it's 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.

• 1:07:39Marc Andreessen?

  That's right.

• 1:07:39Speaker 03

  And then you have the social contract. And I know you retweeted a clip of Sam Altman saying, um, were we thinking the whole thing? And you're like, absolutely not.

• 1:07:46Marc Andreessen?

  Yes.

• 1:07:47Speaker 03

  And I was, I was at an event with Sam last night and he actually said in the last couple of 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 yeah, just like how the impact of work structure and all of that is playing out.

• 1:08:05Marc Andreessen?

  Yeah. So there's a whole bunch of, there's a whole bunch of time.

• 1:08:07Speaker 03

  I know. Yeah.

• 1:08:07Marc Andreessen?

  We could, I know, 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 just one of the great kind of 20th century political thinkers, societal thinkers. And he sort of said, as, and he was writing in like the 1940s, 1950s. And he said, kind of the, the whole history 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, cause Henry Ford runs the company. 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. Cause 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,

• 1:08:49Marc Andreessen?

  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, 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 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, you know, 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, Henry Ford doesn't scale.

• 1:09:32Marc Andreessen?

  And so if you're going to run capitalist enterprises that are going to have millions to billions of customers, 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, it's 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, no, I mean, today, up till today, 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, everything runs. Basically, what venture capital does is we

• 1:10:14Marc Andreessen?

  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 next Elon Musk, or the next Steve Jobs, the next Bill Gates, the next Mark Zuckerberg. And so we start these companies in the old model, right? We start them out as in the Henry Ford model. And so we start them out with a founder, or a founder with colleagues, but you know, there's a founder CEO. And then 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 political structure, that they're going to be able to innovate in a way that the incumbent is not going

• 1:10:57Marc Andreessen?

  to be able to, because the incumbent is being run by managers, right? 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 sort of constantly trying to fight off managerialism, just basically swapping 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 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 sort of the spark of genius of the name on the door model, the Henry Ford model.

• 1:11:40Marc Andreessen?

  But then it's give that person AI superpowers to do all the managerial stuff and let the boss do all 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? 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. Right? And then, and then the other challenge is going to be for the, for the incumbents that are managerial to figure out like, okay, what does that mean?

• 1:12:25Marc Andreessen?

  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, I say, figure out innovation or die trying.

• 1:12:38Speaker 03

  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 in growth and impact, they can kind of like keep going, if not accelerating?

• 1:12:53Marc Andreessen?

  That's for sure the hope. The, the challenge, and, and, you know, and look, the AI utopian view is of course, of course. And, and that's going to be the future of the economy and it's going to grow 10x and 100x and 1,000x. 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, 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. 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. 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.

• 1:13:37Marc Andreessen?

  You have to get licensed as a, you have to get into a union. By the way, 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 ever changing. I'll give you another example. The dock workers went on strike a couple of years ago because they're robotics. If you go look at a modern dock like in Asia, it's all robots. If you go to American dock, it's like all steel guys dragging, dragging stuff by hand. The dock workers went on a strike. It turns out there are 25,000 dock workers working on docks 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 dock 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

• 1:14:22Marc Andreessen?

  tremendous political stroke. We also learned that they, it actually turns out the dock workers union has 50,000 people in it because there's 20, they have 25,000 people working at docks. They have 25,000 people during full paycheck sitting at home from prior union agreements. 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 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 and then what they do, it turns out what the employees do is they're very, they're very smart in this way.

• 1:15:07Marc Andreessen?

  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, so they're in their, 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 like locked in in a way that has nothing to do with like, and 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, you know, 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, right? K through 12 schools in the United States, they're a literal government monopoly.

• 1:15:52Marc Andreessen?

  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 you see what I'm saying, it's 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.

• 1:16:20Speaker 02

  Right.

• 1:16:22Marc Andreessen?

  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. So much of how the existing economy works is just, it's 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.

• 1:16:41Speaker 02

  Oz Mark, I know you got to run. Yeah, I 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.

• 1:16:49Marc Andreessen?

  Yes, yes, could not be more exciting. Really thank you, Mark, with you guys, awesome.

• 1:16:53Speaker 02

  Thank you.

• 1:16:54Marc Andreessen?

  Good, thank you.

• 1:17:07Erik Torenberg?

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