# Marc Andreessen introspects on Death of the Browser, Pi + OpenClaw, and Why "This Time Is Different" Latent Space · 2026-04-03 Marc Andreessen argues that AI is finally different from past boom-bust cycles because of four compounding breakthroughs: LLMs, reasoning (o1, R1), coding agents (OpenClaw), and recursive self-improvement. He calls this the '80-year overnight success'—decades of neural network research now paying off. Comparing today's AI capex boom to the dot-com crash, he notes that buyers like Microsoft and Google are cash-rich incumbents and every GPU deployed is already generating revenue. He hails Pi and OpenClaw as a Unix-like architecture that makes agents model-independent and self-modifiable. On open source, he calls DeepSeek a 'gift to the world' for its paper and code, but warns that entrenched institutions—unions, licensing, government monopolies—will slow AI adoption far more than technologists expect. ## Questions this episode answers ### What makes Pi and OpenClaw a major breakthrough in AI agents, according to Marc Andreessen? Marc Andreessen calls the combination of Pi and OpenClaw “one of the 10 most important software things.” The breakthrough applies the Unix mindset—an LLM paired with a shell, file system, cron job, and Markdown—to create an agent that can self-inspect, self-modify, and migrate across models and environments. Because state lives in files, the agent isn’t tied to any single model or runtime, making it portable and endlessly extensible. [33:03](https://addtry.com/28069d18-9728-4dbf-8199-4ebc0dd9139a?t=1983000) ### Why does Marc Andreessen believe the current AI boom is different from past hype cycles? Marc Andreessen describes AI as an “80-year overnight success” built on ideas dating back to 1943. Unlike previous booms, today’s AI actually works: he points to four fundamental breakthroughs—LLMs, reasoning (o1/R1), coding, and agents (OpenClaw)—plus recursive self-improvement. These prove that neural networks were the right architecture all along, and the wave of real-world capabilities makes a winter unlikely. [8:26](https://addtry.com/28069d18-9728-4dbf-8199-4ebc0dd9139a?t=506000) ### How does Marc Andreessen compare today’s AI infrastructure spending to the dot-com crash, and why does he think it won’t end the same way? Marc Andreessen recalls the dot-com crash being a telecom crash: companies like Global Crossing overbuilt fiber based on a scaling law that internet traffic would double quarterly, then went bankrupt when growth slowed. He distinguishes today because investments are led by cash-rich giants like Microsoft and Google, every GPU dollar immediately generates revenue, and demand so far outstrips supply that even older chips appreciate in value. [17:08](https://addtry.com/28069d18-9728-4dbf-8199-4ebc0dd9139a?t=1028000) ### Does Marc Andreessen predict the end of browsers and traditional user interfaces? Marc Andreessen argues that as AI agents become ubiquitous, humans may no longer need traditional user interfaces. He speculates that in a world where bots handle most tasks, concepts like browsers and even programming languages might become irrelevant—the agents will communicate and execute code among themselves, with humans only asking the agent to explain its actions when necessary. [50:43](https://addtry.com/28069d18-9728-4dbf-8199-4ebc0dd9139a?t=3043000) ## Key moments - **[0:00] Origins** - [0:25] The period we're in right now is an 80-year overnight success - [2:50] Marc Andreessen coded in Lisp in 1989 during the expert systems and Lisp machines AI boom. - **[3:29] This Time Different** - [6:34] Q: Will there be another AI winter like the one in the 1980s? - [9:48] The foremost dangerous words in investing are 'this time is different'. - [10:11] Marc Andreessen identifies four breakthroughs that make AI real: LLMs, reasoning, agents, and recursive self-improvement. - **[11:55] Scaling Laws** - [14:43] Marc Andreessen says AI purists in labs underestimate the messy complexity of the real world. - **[16:34] Infra Risk** - [17:26] Marc Andreessen explains how the dot-com crash was a telecom crash caused by overbuilding fiber on a false scaling law. - [20:40] Marc Andreessen says we are using 'sandbagged' AI models today due to supply constraints. - [23:36] Marc Andreessen vs Michael Burry: Andreessen argues old Nvidia chips are becoming more valuable, contradicting Burry's short thesis. - **[24:54] Open Source & Edge** - [25:07] Marc Andreessen predicts inference costs may rise dramatically, with friends already paying $1,000/day for OpenClaw tokens. - [28:27] DeepSeek was a gift to the world, and open source teaches the world how things work, says Marc Andreessen. - [31:35] Marc Andreessen predicts only three or four foundation model companies will survive, and open source will be a strategy for the also-rans. - **[32:20] Pi & OpenClaw** - [33:20] Marc Andreessen says Pi marries the language model to the Unix shell, making an agent as LLM + shell + file system + Markdown + cron. - [36:31] An agent is a language model, a bash shell, a file system, Markdown, and a cron job. - [37:45] Your agent is independent of the model, says Marc Andreessen; you can swap LLMs and retain all state in files. - **[41:37] Protocol Lessons** - [44:08] The key breakthrough in the browser was the view source option, says Marc Andreessen. - [47:26] Marc Andreessen predicts high-quality software will become infinitely available and programming languages may disappear. - [50:32] Who is going to use software in the future? The other bots, says Marc Andreessen. - **[50:43] Death of Browser** - **[54:12] Payments** - [54:22] Marc Andreessen says AI is the crypto killer app, and agents need money. - **[57:17] Wild Stories** - [57:17] Marc Andreessen's friend used OpenClaw to rewrite firmware for a Unitree robot dog, turning it into a pet. - **[1:01:57] Proof of Human** - [1:01:57] Marc Andreessen says we need proof of human because bots pass the Turing test; the World project is on the right track. - **[1:06:18] Managerial AI** - [1:06:18] Marc Andreessen explains James Burnham's managerialism and argues AI could enable a third model: founder plus AI management. - [1:12:23] It requires 900 hours of professional certification to become a hairdresser in California, showing professions are cartels. - [1:13:08] The dockworkers union has 25,000 active workers and another 25,000 drawing full pay at home, blocking automation, reveals Marc Andreessen. - [1:15:15] Marc Andreessen says some federal agencies only require employees in the office one day per month, leaving buildings empty 29 days a month. - [1:16:04] Marc Andreessen predicts AI will not change K-12 education because it's a government monopoly with 100% teacher opposition. ## Speakers - **Alessio** (host) - **Swyx** (host) - **Marc Andreessen** (guest) ## Topics Language Models, Reasoning, Coding Agents ## Mentioned AI2 (company), Anthropic (company), DeepSeek (company), Global Crossing (company), Meta (company), Mistral (company), NVIDIA (company), OpenAI (company), Unitree (company), Apple Silicon (product), ChatGPT (product), Claude (product), H100 (product), H200 (product), OpenClaw (product), Pi (product), TPU (product), World (product), o1 (product), r1 (product) ## Transcript ### Origins **Marc Andreessen** [0:00] 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, for example, we now know the neural network is the correct architecture. And I, I will tell you, like there was a 60-year run where that was like a f- you know- **Alessio** [0:16] Yeah **Marc Andreessen** [0:16] ... or even 70 years where that was controversial. And so, so the way I think about what's happening is basically I think, I think about basically the, 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 'cause 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, 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. 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. **Alessio** [0:54] 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 content 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 wanna 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 works so hard to bring The Late In Space to you each and every week. If you do it, I promise you, we'll never stop working to make this show even better. Now let's get into it. Hey everyone, welcome to the Late In Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by swyx, editor of Late In Space. Hello, and we're in a16z with A, uh, Marc Andreessen. Welcome. **Marc Andreessen** [1:55] Yes. Yes, A and what? Half of 16? **Alessio** [1:58] Something like that. A one. **Marc Andreessen** [1:59] Exactly. Exactly. **Alessio** [1:59] Uh, apparently this is the, the final few days in your, your current office. You're moving across the road. **Marc Andreessen** [2:04] Uh, we're, yeah, we have a little bit of some, we have some projects underway, but yeah- **Alessio** [2:07] Yeah **Marc Andreessen** [2:07] ... this is actually, uh, this is the original. We're in actually the original office. We're in the, we're in the, we're in the- **Alessio** [2:11] Nice **Marc Andreessen** [2:11] ... we're in the whole thing started. **Alessio** [2:12] It's beautiful. **Marc Andreessen** [2:12] Yeah. Great. Thank you. **Alessio** [2:13] So I have to come out. Uh, this is a, you know, I sh- wanted to pick a spicy start. In October 2022, I just made friends with Roone and- ... uh, I wanted to give him something to sort of be spicy about. And I said, uh, "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 20, 2022. And Roone says there was an internal meeting in a16z to reorient around GenAI. Obviously you have, but was there a meeting? What, what was that? **Marc Andreessen** [2:44] I mean, I don't... Look, I've been doing AI since the late '80s. **Alessio** [2:46] Yeah. **Marc Andreessen** [2:46] So I, I don't know, like all the... As far as I'm concerned, this stuff is all Johnny-come-lately. **Alessio** [2:50] Yeah. **Marc Andreessen** [2:50] You know, I mean, look, we've been doing AI our entire existence. I mean, we've been doing AI, machine learning deep, you know, deep... We've been doing this stuff way from the beginning. Obviously. A- AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees. Um, you know, we, we both, Ben, Ben and I actually both are old enough to remember the actual AI boom in the 1980s. **Alessio** [3:10] Yeah. **Marc Andreessen** [3:10] There was like a, there was a big AI boom at the time. Um, and there was a, it was going under names like expert systems, um, and an era of like Lisp and Lisp machines. Um, I, I coded in Lisp. I was coding in Lisp in, in 1989- **Alessio** [3:21] Yeah **Marc Andreessen** [3:21] ... when that was the, the language of the AI future. Um, yeah, so this is something that we're like completely, you know, completely comfortable with and been doing the whole time and are very enthusiastic about. ### This Time Different **Alessio** [3:29] Is there a strong like this time is different? Because, uh, my closest an- analog was 2016, '17. There was also an AI boom- ... and it petered out very, very quickly. Um- **Marc Andreessen** [3:38] Well- **Alessio** [3:39] It just, it just in terms of investing **Marc Andreessen** [3:40] ... sort of. Sort of. Yeah. **Alessio** [3:41] Investment- **Marc Andreessen** [3:41] Yeah, yeah, yeah **Alessio** [3:41] ... investment excitement. **Marc Andreessen** [3:42] Although that's really when the, the, the Nvidia phenomenon really... It, it, it was, it, I would say it was in that period when it was very clear that at, at the time, it, it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point. **Alessio** [3:54] Yeah. **Marc Andreessen** [3:54] Well, and 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 AlexNet, uh, basically breakthrough- **Alessio** [4:02] Yes **Marc Andreessen** [4:02] ... in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in '17. **Alessio** [4:08] Yeah. **Marc Andreessen** [4:09] Um, and then everything that followed. But, but you know, look, machine learning, you know, there were, you know, look, uh, I mean, look, I've been working, you know, I've been working with at the, one of my, you know, kind of projects working with Facebook since 2004, um, and on the board since 2007, and of course, you know, they, they started using machine learning very early. Um, and you know, have 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 sing- it's not a single thing. Like it's, it's like, it's like layers, right? **Alessio** [4:39] Yeah. **Marc Andreessen** [4:39] Um, and, and the layers arrive at different paces and, but they kind of build up. **Alessio** [4:43] Yeah. **Marc Andreessen** [4:43] Uh, 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 trans-transformer. And then, and then as you guys know, there was this really weird like four-year period where it's like the tr- the transformer existed and then it was just like- **Alessio** [4:56] Let's go. Yeah. **Marc Andreessen** [4:57] Well, but, but it was just- **Alessio** [4:58] Oh, well **Marc Andreessen** [4:58] ... but, but between 2020-- but between 2017 and 2021, I, I mean, that was the era of which like companies like Google had internal chatbots, but they weren't letting anybody use them. **Alessio** [5:05] Yeah. **Marc Andreessen** [5:06] Right? And then, you know, and then OpenAI developed ChatGPT, or GPT-2, and then they told everybody, "This is way too dangerous to deploy," right? **Alessio** [5:11] Yeah. Yeah. **Marc Andreessen** [5:12] Where, you know, "We can't possibly let normal people, normal people use this thing." And then do, you guys I'm sure remember AI Dungeon. Um- **Alessio** [5:17] Mm-hmm **Marc Andreessen** [5:17] ... so the o- for there was like a year- **Alessio** [5:19] It was old **Marc Andreessen** [5:19] ... where like the only way for a normal person to use GPT-3 was in, in AI Dungeon. **Alessio** [5:23] Yeah. **Marc Andreessen** [5:23] And so you, 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 the, I, you know, the big c- big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took OpenAI, it, you know, they, they, they talk about this. It took OpenAI time to actually adjust, you know, kind of re- redirect their research path. **Alessio** [5:42] Yeah. I, I think, uh- **Marc Andreessen** [5:43] So- **Alessio** [5:43] ... it was at Rosewood, right? Uh, that the dinner that founded OpenAI was right there. **Marc Andreessen** [5:47] Right. Right. But that, that dinner would've taken place in 20- **Alessio** [5:49] '18 **Marc Andreessen** [5:51] The, the formation of OpenAI? **Alessio** [5:52] Uh-huh. **Marc Andreessen** [5:52] As late as 2018? **Alessio** [5:54] Uh, uh, sorry. Uh- **Marc Andreessen** [5:55] Okay **Alessio** [5:55] ... no, I'm, I'm, I'm, I'm wrong. **Marc Andreessen** [5:56] Probably earlier. **Alessio** [5:56] It should be 20... Yeah, they just celebrated their 10-year anniversary, so it, it is- **Marc Andreessen** [5:59] Okay **Alessio** [5:59] ... 2025. Yeah. **Marc Andreessen** [6:00] That's- **Alessio** [6:00] So, so 2015. **Marc Andreessen** [6:01] Yeah, 2015. Yeah, 2015. But then, uh, um, Alec Radford did GPT 1 in- **Alessio** [6:06] Mm-hmm **Marc Andreessen** [6:06] ... what, probably- **Alessio** [6:06] '17, '18, yeah **Marc Andreessen** [6:07] ... '18, '17, '18. So it, it, yeah, for the... And then, and then they didn't really... And then GPT 3 was what, 2020, 21? **Alessio** [6:13] 2020- **Marc Andreessen** [6:14] 2020 **Alessio** [6:14] ... because that became Co-Pilot immediately. **Marc Andreessen** [6:16] Been shown, 2021. **Alessio** [6:16] Yeah. **Marc Andreessen** [6:16] Even OpenAI, which has been, you know, the leader of l- of this thing in the last decade, you know, e- even they had to adapt and, and, and lean into the new thing. And so, um, yeah, I, I think it's just this process of basically w- sort of wave after wave, layer after layer, you know, building on itself, and then you kinda get these catalytic moments where the, where the whole thing pops, and, and obviously that's what's happening now. **Alessio** [6:34] Is it useful to think about will there be an AI winter, 'cause 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 **Marc Andreessen** [6:49] Right **Alessio** [6:50] ... will, are we, uh, will there be a winter? **Marc Andreessen** [6:52] S- so there's something about... Let me say the following. There's something about AI that has led to this repeated pattern. Um, and, and it, and, and you guys know this, but- **Alessio** [6:59] It's a, it's win- summer, winter, summer, winter **Marc Andreessen** [7:01] ... summer, winter, summer, winter, and it goes back 80 years. **Alessio** [7:03] Yeah. **Marc Andreessen** [7:03] 80 years. Uh, so the original neural network paper was 1943- **Alessio** [7:07] Mm-hmm **Marc Andreessen** [7:07] ... right? Which is, which is amazing, uh, that it was, it was far back that long. And then there was, you... I don't know if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh... There was an AGI conference at Dartmouth University in 1955. **Alessio** [7:17] Yeah. '55. **Marc Andreessen** [7:18] '55. **Alessio** [7:18] Yeah. **Marc Andreessen** [7:19] And they got an NSF grant to, uh, for the, all the AI experts at the time to spend the summer together, and they figured if they had 10 weeks together, they could get AGI- ... uh, out the other end. And they got their... By the way, they got the grant, they got the 10 weeks, and then, you know, 1955- **Alessio** [7:30] Yeah **Marc Andreessen** [7:30] ... you know, no, no AGI. And like I said, I, I lived through the '80s version of this, where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Um, and, and it's probably on both sides of, like, the, the, the boom bust cycle you, you kinda see that play out. Having said that, I think what's actually happened is, like, just at... in, 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, I will tell you, like, there was a 60-year run where that was like a f- you know, or even- **Alessio** [8:01] Yeah **Marc Andreessen** [8:01] ... 70 years where that was controversial, and, and we now know that that's the case. And so we, we now... You know, everything we're building on today sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that, like, these, these guys were right. They, they... You know, they would get the timing wrong, and they thought, you know, capabilities would arrive faster, or they would... 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 the, 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, I think about basically the, the, the period we're in right now is it's, I call it 80-year overnight success. Right? Um, which is like it's an overnight success, 'cause it's like, bam, you know, ChatGPT hits, and then, and then o1 hits, and then, you know, Cl- OpenAI 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, 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, um, and thinking. I mean, look, there were AI researchers who spent their entire lives. They got their PhD, they, 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. **Alessio** [9:08] Yeah. It's so sad. **Marc Andreessen** [9:09] It is, it is sad. It is sad. **Alessio** [9:10] I think Geoff Hinton- **Marc Andreessen** [9:11] And I knew some of them **Alessio** [9:11] ... was like the last guy. **Marc Andreessen** [9:12] Yeah, yeah. Well, there were guys. I think there was a guy, Alan Newell. I mean, there's tons of... John McCarthy. You know, 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- **Alessio** [9:21] Wow **Marc Andreessen** [9:21] ... you know, passed away, I don't know, whatever, 10, 10 years ago or something. Never, never actually got, got to see it happen. But, like, it, it is amazing in retrospect. Like, these guys were incredibly smart, and they worked really hard, and they were correct. So anyway, so then it's like, okay, you know, so they say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another like, you know, basically boom bust cycle? And I, I will tell you, like, look, like, i- in a sense, like, yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed, and there's, th- there's a time- there's a timeless to that. Having said that, there's just no question. Um, so the formo- the foremost dangerous words in investing- **Alessio** [9:54] This time is different **Marc Andreessen** [9:55] ... are this time is different. Do you know the 12 most dangerous words i- in, in investing? **Alessio** [9:57] No. **Marc Andreessen** [9:58] The foremost dan- foremost dangerous words- ... in investing are this time is different. **Alessio** [10:01] Yeah. **Marc Andreessen** [10:01] Um, the 12 most dangerous words. And so, like, I'll tell you what's different, like s- now it's working. Like, like- ... there's just no... I mean, look, there's just no question. And by the way, I'll, I'll just give you guys my take. Like, L- LLMs, like from, from basically the ChatGPT 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 hal- hallucination rates are way too high," and you know, "This is gonna be great for creative writing and creating, you know, s- Shakespearean so- sonnets and, you know, as, as rap lyrics or whatever. Like it's gonna be great at all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, f- you know, kind of fields that, you know, kind of really, really matter." And I think basically it was the reasoning breakthrough. It, it was o1 and then R1 that basically answered that question and basically said, "Oh, no, we're gonna be able to actually turn this into something that's gonna work in the real world." And, and then obviously the coding breakthrough over the, over... Basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that. **Alessio** [10:58] Mm-hmm. **Marc Andreessen** [10:59] We were just like, all right, if, if, you know, if Linus Torvalds is saying that the AI coding is now better than he is, like- ... like that's, that's never happened before. **Alessio** [11:06] That's the benchmark. Yeah. **Marc Andreessen** [11:07] That's never happened before, and so now we know that it's- **Alessio** [11:09] Mm **Marc Andreessen** [11:09] ... it's gonna sweep through coding. And, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else, right? It's just... Th- then... 'Cause 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 gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough w- you know, with OpenClaw, which is fantastic, which is amazing and incredibly powerful. And then we just got the, the, um, the auto research, um, you know, the, 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, uh, agents, um, and then, uh, and, and then now, uh, RSI. Um, and, and they're all actually working. Um, and so I'm, I'm just as you, like, as you would jump- I'm jumping out of my shoes. Like, like this is, like this is it. Like this, th- this is the culmination of 80 years' worth of, worth of work, and this is the time it's becoming real. **Swyx** [11:53] Yeah. Amazing. **Marc Andreessen** [11:54] Yeah. I, I'm completely convinced. ### Scaling Laws **Swyx** [11:55] I think the anxiety that people feel is like during the transistor era, you had Moore's Law, and it's like, all right, we understand why these things are getting better. We understand the physics of it. **Marc Andreessen** [12:03] Yeah. **Swyx** [12:04] With AI, it's, it's so jagged in like the jumps. **Marc Andreessen** [12:07] Yeah. **Swyx** [12:07] Where like, like you said, it's like- **Marc Andreessen** [12:08] Yeah **Swyx** [12:08] ... in three months you have like this huge jump, like, and people are like, "Well, this can't keep happening," right? But then it keeps happening. **Marc Andreessen** [12:14] It'll keep happening. **Swyx** [12:15] And so, like how do you think about also timelines of like what's worth building? I think we always have this question with guests, which is like, you know, should you spend time building harness- **Marc Andreessen** [12:23] Right **Swyx** [12:24] ... for a model versus like the next model just gonna do it one shot in the latest base? **Marc Andreessen** [12:28] Right. **Swyx** [12:28] 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- **Marc Andreessen** [12:38] Yeah **Swyx** [12:38] ... in what it looks like, it's hard to build companies on top of it. **Marc Andreessen** [12:40] Yeah. So, so a couple things. So one is like, look, the, the M- 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, chip- chips either get twice as powerful or twice as cheap every, 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, that was like the 50-year trajectory of, of, 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, for 500 bucks. And so that, 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, they're predictions. But when they work, they become self-fulfilling predictions because they, they, they- **Swyx** [13:20] Right **Marc Andreessen** [13:20] ... they s- they set a benchmark and, and then the entire industry, right, all the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And in, and in, and in chips, that was a 50-year, you know, that was a 50-year run, right? And it wa- it was amazing, and it's still happening in, in, in some areas of, uh, of chips. I think the same thing is happening with the, the core scaling laws, the core scaling laws in, in, in AI. You know, they're not, they're not really laws, but like they, they are basically, they're predictions, and then they're motivating catalysts for the research work that is required to be... And, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going. And, and look, it's, it's gonna be complicated, and it's gonna be variable, and there, you know, there are gonna be walls that are gonna look like they're fast approaching, and then they're gonna be- **Swyx** [14:00] Mm-hmm **Marc Andreessen** [14:00] ... you know, engineers are gonna 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 gonna be times when it looks like the walls have, you know, the, the, the laws have petered out, and then they're gonna, they're gonna pick up again and surge. And then, and then, and then it, it appears what's happening to the AI is there's now multiple, you know, multiple scaling laws. Um, there's multiple areas of improvement. And, 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, like for example, there's probably some scaling law around, um, world models and robotics that we don't fully under- you know, kind of acquisition of data at scale in the real world that we don't fully understand yet, so that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah, the, the pace of improvement will continue to move really fast. Um, to your question on like what to build, so I, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, le- leaps and bounds. Uh, the part where I kind of part ways a little bit with what, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are m- in many ways the smartest people in the field, but also the people who spend their entire life like in a lab, um, and have, have, I would say, have very little experience in the outside world. **Swyx** [15:07] Mm-hmm. **Marc Andreessen** [15:07] Um, the, the, the nuance I would offer is the outside world of eight billion people and institutions and governments and companies and economic systems and social systems is really complicated, um, and, um, and doesn't, you know, it, it, 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." **Swyx** [15:36] Mm-hmm. **Marc Andreessen** [15:37] And then they're like, "Society's not doing any of those things," right? And it's like, how can society not s- you know, what- whatever their theory is, how can society not see XYZ? **Swyx** [15:44] Mm-hmm. **Marc Andreessen** [15:44] 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. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, p- there's no question people are gonna like, there's no question there are gonna 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 gonna 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, in- 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 gonna be messy and complicated, and there are gonna be a lot of companies and a lot of products, um, uh, and in, and in fact, entire industries that are gonna get built that, that to, to basically actually help all of this technology actually reach real people. **Swyx** [16:34] The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast, and Door Cash was like, "Why don't you just buy 10X more GPUs?" And he's like, "Because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level." ### Infra Risk **Swyx** [16:46] How do you think about that also as a risk on, you know, you guys are investors in OpenAI and Thinking Machines and World Apps. It seems like we're leveraging the scaling laws at a pretty high rate. **Marc Andreessen** [16:56] Right. **Swyx** [16:57] 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- **Marc Andreessen** [17:04] Okay **Swyx** [17:04] ... restructure, uh, these build-outs and, uh, you know, capital investments? **Marc Andreessen** [17:08] Yeah. So I should start by saying, so I lived through the dot-com crash. Um, and I can tell you stories for hours about the dot-com crash, and it was- Horrible. No, it was awful. It was, it, it was, it was apocalyptic. Uh, by the way, the d- 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, the thing that actually crashed that wiped out all the money was the tele- the telecom companies. **Alessio** [17:26] Global Crossing. **Marc Andreessen** [17:27] Um, Global, Global, yeah. **Alessio** [17:28] So I'm from Singapore, and- **Marc Andreessen** [17:29] Oh **Alessio** [17:29] ... they, they laid so much cable o- over, over our oceans. **Marc Andreessen** [17:32] Well, actually, there was a scaling law in the dot-com era, and it was literally the c- the US Commerce Department put out a report in 1996, and they said internet traffic was doubling every quarter. Um, and, and actually, in 1995 and 1996, internet traffic actually did double every quarter, and so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber- **Alessio** [17:50] Mm-hmm **Marc Andreessen** [17:50] ... anticipating that the demand for bandwidth was gonna keep doubling every quarter. Doubling every quarter, though, is like, you know, grains of chess on the chessboard. Like, at some point- **Alessio** [17:57] Right **Marc Andreessen** [17:57] ... the numbers become extremely large, right? And, and, and it really, and really what happened was the internet c- the internet, by the way, continuously kept growing, basically since inception. It's, you know, it's, it's continuously grown. It's never shrunk. And it's grown really fast compared to anything else- **Alessio** [18:09] Mm-hmm **Marc Andreessen** [18:09] ... you know, in, in, in human history. But it wasn't doubling every quarter as of 1998 and 1999. **Alessio** [18:13] Right. **Marc Andreessen** [18:13] And so there was this gap in the expectation of what they thought was a scaling law versus reality, and that's actually what caused the dot-com crash, which was the, it, they, they way over... Companies like Global Crossing way overbuilt fiber, which is sort of the... And 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. Like, that was the beginning of the, of the, of the data center build and then, and then data center overbuild. 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- **Alessio** [18:40] Jesus Christ. **Marc Andreessen** [18:40] It, it, it was like a big... It was... A- and by the way, the other, the other subtlety in it was the internet companies themselves n- never really had any debt, 'cause 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 of just like, okay, you have a highly levered thing where you're, you're just o- o- **Alessio** [19:00] Mm-hmm **Marc Andreessen** [19:00] ... you're overbuilding capacity. Demand is growing, but not as fast as you hoped, and then boom, bankrupt, right? And, and then it, 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. Right? It, 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- **Alessio** [19:21] They're in use **Marc Andreessen** [19:22] ... it, it's all in use today - **Alessio** [19:24] Yeah **Marc Andreessen** [19:24] ... but 25 years later. But it, it, it took... And actually, the elapsed time was it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity. The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital is like, "Wow, it's just I, I don't know how to invest in these crazy software things, but for sure I can put, build data centers, and for sure I can buy GPUs and I can deploy, you know, compute grids and, a- a- and all these things." Um, and, and so y- you know, y- y- 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 counterargument, which is the one I, I agree with, which is the counterargu- on the other side, is a couple things. One is the companies that are investing all the m- the, the companies that are investing the money are, like, the bluest chip of companies. And so ba- back, back in the, in the dot... Like, Global Crossing was, like, an, it was, like, an entrepreneur... It was, like, a, a new venture. But, like, the money that's being deployed now at scale is Microsoft and, you know, and Amazon and Google. **Alessio** [20:20] Facebook. **Marc Andreessen** [20:20] Right. And Facebook and NVIDIA and, you know, these, these, these- **Alessio** [20:24] Yeah **Marc Andreessen** [20:24] ... these. And, and now, you know, by the way, OpenAI and Anthropic, which are now at, like, you know- **Alessio** [20:26] Mm-hmm **Marc Andreessen** [20:26] ... really serious size, um, you know, as companies with, you know, very serious revenue. These are very large scale companies with, like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th- 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, and you guys know this, like, everybody's starved for capacity. Everybody's starved for compute capacity, and then, you know, all the associated things, memory and, and, and interconnect and everything else, um, data center space. And so e- every dollar right now that's being put in the ground is turning into revenue. And in, and in fact, I actually think there's an interesting thing happening, which is because everybody's 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. Um- **Alessio** [21:11] It's true. **Marc Andreessen** [21:11] If, if, right. Suppose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful- **Alessio** [21:15] Mm-hmm **Marc Andreessen** [21:15] ... the models would be much better- **Alessio** [21:17] Of course **Marc Andreessen** [21:17] ... 'cause you would just allocate a lot more money to training, and you'd just build better models, and they would be better. Um, and so we're, we're actually getting the sandbagged version of the technology. **Alessio** [21:24] Yeah, no. **Marc Andreessen** [21:25] Right. **Alessio** [21:25] Everything we use is quantized- **Marc Andreessen** [21:26] Right **Alessio** [21:26] ... because the, the labs have to keep the, the full versions. **Marc Andreessen** [21:30] Right. **Alessio** [21:30] Like... **Marc Andreessen** [21:31] We're not even getting the good stuff. **Alessio** [21:32] Yeah. **Marc Andreessen** [21:33] But, but getting the good stuff is, is just... E- even if technical progress stops, once there's, like, a much bigger build of, like, GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years, once it happens, even the current technology's gonna get, gonna 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. **Alessio** [21:52] Mm-hmm. **Marc Andreessen** [21:52] Like, it, it, you know, this isn't just sending packets across a, 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. **Alessio** [22:00] Yeah. **Marc Andreessen** [22:00] And then it works, like, incredibly well. **Alessio** [22:01] Yeah. **Marc Andreessen** [22:03] Um, here's what I know. Here's what I know. Um, in the next three or four year, it's, like, somewhere between three or four years out, basically everything is selling out. So, like, the, the entire supply chain is, is, is, is sold out or, or, or selling out. And so there, there's no, like... It, the, the... Right, we're just gonna have, like, chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now, an enormous flood of investment in a new fab capacity and ev- you know, every- 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, 'cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, 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 gonna get, like, much, much better from here. And so I, I know the capabilities are, like, really real and serious. I also know that the technical progress is not going to stop. It, it, it is accel- it is, it is accelerating. Like, the breakthroughs are, are tremendous. 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, there are cynics, you can look at 2000, you can find echoes, but I can't even imagine betting on that this is gonna, like, somehow disappoint in, you know, at least for years to come. I think it would be essentially suicidal to make that bet. **Alessio** [23:11] Yeah. **Marc Andreessen** [23:12] Um, it was, uh, Michael Burry. Uh, uh, uh- **Alessio** [23:14] That's an interesting guy, huh? **Marc Andreessen** [23:15] We'll, we'll, we'll pick on a guy. We'll pick, let's pick on one guy. **Alessio** [23:17] Yes. **Marc Andreessen** [23:18] We'll pick ... Well, 'cause he did. He, he came out with a... Was it, was the- **Alessio** [23:19] He doesn't mind. Yeah **Marc Andreessen** [23:20] ... it was the Nvidia short, right? **Alessio** [23:21] Yeah, yeah, yeah. **Marc Andreessen** [23:21] He came, he came out with the Nvidia short. And then d- if you guys probably talked about this, which is the, the analysis now that, like, the current models are getting better faster at such a rate that if you are running an Nvid- 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. **Alessio** [23:36] Mm-hmm. Yep, yep. **Marc Andreessen** [23:36] Because the pace of improvement of the software is, is faster than the, 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... I, these are rumors that I've heard, or maybe it's public, but, um, I think Google's running very old TPUs very profitably. **Alessio** [23:48] For inference, yeah. **Marc Andreessen** [23:49] And very profit- **Alessio** [23:50] Absolutely **Marc Andreessen** [23:50] ... and very profitably. **Alessio** [23:51] Yeah. **Marc Andreessen** [23:51] Um, and so, 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, the, the, the old Nvidia chips are getting more valuable, which is something that's, like, literally never happened before. Like, it's never been the case that you have an older model chip that becomes more valuable- **Alessio** [24:07] Yeah **Marc Andreessen** [24:07] ... not less valuable. And, and again, that's an expression of the just in- ferocious pace of software progress, ferocious pace of capability payoff- **Alessio** [24:14] Yeah **Marc Andreessen** [24:14] ... uh, that you're getting on the other side of this. And so I just... The idea of betting against that, like- **Alessio** [24:20] Yeah, yeah. Well, one of my- **Marc Andreessen** [24:20] It's like an invitation to get your face ripped off. **Alessio** [24:23] One of my early hits was, like, modeling the lifespan of the H100 and H200s. And, and going, like, you know, usually they have ice like four to seven years, and it was, you know, maybe you sort of realistically haircut it down to two to three. **Marc Andreessen** [24:34] Yeah. **Alessio** [24:34] But actually it's going up and not down. **Marc Andreessen** [24:36] Yeah. **Alessio** [24:36] And, and, uh, that's... I mean, that's, I think that's the dream. Uh, we are finding utilization, and I think utilization solves all problems. Like, you can- **Marc Andreessen** [24:42] Yeah **Alessio** [24:42] ... you can find use, use cases for even, like, the poor... Like, even memory we're having a shortage, right? And, and e- even, like, the, the shittier versions of, of memory that we do have, we are finding use cases for it. **Marc Andreessen** [24:52] Right. **Alessio** [24:52] So, like, that's great. **Marc Andreessen** [24:53] Yeah. ### Open Source & Edge **Alessio** [24:54] How, how important is open source AI and kinda 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, uh, in the data center versus at the edge? **Marc Andreessen** [25:07] Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental constr- uh, co- so supply crunches for the next... I mean, you, you guys know. If you just project forward demand over the next three years- **Alessio** [25:20] Right. Yeah **Marc Andreessen** [25:21] ... rel- relative to supply, one of the dismaying predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years, and, like, it may rise dramatically, right? Like, so, so what i- and then as, as you know, like, the, the mo- the big model competitions are subsidizing heavily right now, right? **Alessio** [25:34] Right. Yeah, yeah. **Marc Andreessen** [25:34] 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 f- to do all the things that they wanna do? And I, I don't know. It's gonna be... I mean, I have... You guys probably have friends. I have friends today who are paying $1,000 a day for OpenClaw, for Claw tokens to run OpenClaw, right? And so, okay, $30,000 a month. **Alessio** [25:52] Yeah. **Marc Andreessen** [25:52] Right? And, and by the way, tho- those friends have, like, 1,000 more ideas of the things that they want their Claw to do, right? **Alessio** [25:56] Yeah. **Marc Andreessen** [25:56] And so you could imagine there, there's, like, latent demand of up to, I don't know, $5,000 or $10,000 a day of, of, of tokens for a fully deployed, you know, person- personal agent. And o- and obviously consumers can't pay that, right? And so, so... But it gives you a sense of the fu- of the fu- of the future scope of demand, right? And so, so even, even if there's a 10X improvement in price performance, that still, you know, goes to $100 a day, which is still way beyond what people can pay. **Alessio** [26:17] Mm-hmm. **Marc Andreessen** [26:17] So there's just gonna 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 G- GPU constraints. I think the agent thing now also translates into CPU constraints. **Alessio** [26:27] Mm-hmm. **Marc Andreessen** [26:27] Right? **Alessio** [26:28] CPU and memory, yes. **Marc Andreessen** [26:28] CPU and memory, right? And so, like, the entire chip ecosystem is just gonna get- **Alessio** [26:32] Wait, wait for network constraints. That, that will be the killer. **Marc Andreessen** [26:34] It's all bottlenecked potentially for years. And so, so I, I think that Brad... And, and I think it's actually possible. I mean, generally inference costs are gonna 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- **Alessio** [26:43] Yeah **Marc Andreessen** [26:43] ... because of these comp- supply constraints. And then at some point, maybe the lab stops subsidizing so much, and that, that, that again will be a, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied, um, you know, kind of w- with the centralized model. And then, and then you k- you guys know this, but like all the just the dramatic, I mean, just the dramatic innovations that have happened in the Apple Silicon to be able to do, uh, inferences is quite amazing. The level of effort being put, like the open source guys are putting incredible effort into getting, you know... Th- th- this recurring pattern where the big model will never run on a PC, and then six months later- **Alessio** [27:10] Mm-hmm. Right **Marc Andreessen** [27:11] ... it runs on a PC, right? It's, like, amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know, there's also, like, other, there's other motivators. There's other motivators, which is just like, okay, how much trust are the big centralized model providers, you know, h- 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-" **Alessio** [27:32] Mm-hmm **Marc Andreessen** [27:32] ... "like turn everything over." So there, there, there's all the trust issues. Um, by the way, there's also just, like, straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. **Alessio** [27:41] Mm-hmm. **Marc Andreessen** [27:41] You just need like a, a, a, a s- smart local model. There's also performance issues where you wanna h- you know, you, you want... You know, you're gonna want your door knob to have an AI model in it- **Alessio** [27:50] Right **Marc Andreessen** [27:50] ... you know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously, like, like everything with a chip is gonna have an AI model in it. **Alessio** [27:56] Mm-hmm. **Marc Andreessen** [27:56] And, and a lot of those are gonna be local. Um, and so yeah, no, like I think, I think you're gonna have t- and then you're gonna... By the way, also wearable devices, you know, you don't wanna do a complete round trip. You want, you know, your, whatever your smart devices are, you want it to be like super low latency. Yeah. **Alessio** [28:08] The, the question, do we care who makes it? **Marc Andreessen** [28:10] Yeah. **Alessio** [28:10] One of the biggest news this week was the collapse of AI2, the Allen Institute. **Marc Andreessen** [28:14] Mm-hmm. **Alessio** [28:14] One of the actual American open source model labs. **Marc Andreessen** [28:17] Yeah. **Alessio** [28:17] Um, and, uh, I'm not o- that optimistic on, on American open source. **Marc Andreessen** [28:21] Yeah. **Alessio** [28:21] Like, you, you guys invested in Mistral, and- **Marc Andreessen** [28:23] Yeah **Alessio** [28:23] ... Mistral's doing extremely well. **Marc Andreessen** [28:25] Yeah. **Alessio** [28:25] But outside of China, that's about it. **Marc Andreessen** [28:27] Yeah, we'll see. We'll see. I, look, I, number one, I do think we care... I, I do think we, I do think we care who makes it. Um, I would say this, the, the, the previous presidential administration wanted to kill it in the US. **Alessio** [28:36] Oh, yeah. **Marc Andreessen** [28:37] Like, they, they, they wanted to drown it in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually, like, actually wants it, wants it to happen. **Alessio** [28:43] And you're on the council, PCAST? **Marc Andreessen** [28:44] And I'm, yes, and the new, and the P- PCAST, yeah. So the, the, you know, this admin- I mean, for whatever other political issues people have, which are many, you know, this administration has, I think, a very enlightened view, and in particular an enlightened view on AI, and in particular on open source AI. Uh, and so they're very supportive. Um, my read is the Chi- the Chinese have a very sp- the various Chinese companies have a very specific reason to do open source, which is they, they, they don't, fundamentally they don't think they can sell commercial, uh, 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, uh, you know, paid, paid services, and then kind of an- you know, kind of an- ancillary products. You know, they're, they're very excited about it. By the way, I think it's great. I think it's great that they're doing it. Um, you know, I think DeepSeek was like a gift to the world. Um, I think- The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like- **Alessio** [29:35] The paper **Marc Andreessen** [29:35] ... uh, 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, and then, and then, and then everybody's like, "Okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is there 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 that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful. So that happens. And then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, AI, uh, model companies. I mean, look, there's gonna be tremendous... You know, there already is. There's, you know, there's gonna be treme- there's tremendous competition, uh, 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, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo- Bo- 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, 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, k- trying to k- come out with different approaches. And then you've got whatever it is, I don't know, how, how many, how many, like, mainline foundation model companies are there in China at this point? It's probably six- **Alessio** [30:57] There's five tigers- **Marc Andreessen** [30:58] Five **Alessio** [30:58] ... is what they call it. **Marc Andreessen** [30:59] Yeah. **Alessio** [30:59] Uh, Qwen is in- in questionable because of- **Marc Andreessen** [31:01] Right **Alessio** [31:01] ... there's change in leadership. **Marc Andreessen** [31:02] Right. **Alessio** [31:03] Yeah. **Marc Andreessen** [31:03] But that, does that include... That, that includes, like, Moonshot? **Alessio** [31:05] Yes. **Marc Andreessen** [31:06] Okay, yeah. **Alessio** [31:06] Kimi, DeepSeek, um, uh, ZAI- **Marc Andreessen** [31:10] Right **Alessio** [31:10] ... um, Qwen, o1 is in there. **Marc Andreessen** [31:12] Right. And then, um, and ByteDance. And then you see- **Alessio** [31:15] ByteDance would be, like, the next tier. **Marc Andreessen** [31:16] ByteDance is the- **Alessio** [31:16] They weren't as prominent. **Marc Andreessen** [31:17] They weren't. **Alessio** [31:17] They didn't have a leading model yet. **Marc Andreessen** [31:18] But now, you know, it's... Yeah. But they're, you know, at least, you know, Sea- SeaDance is very inspiring- **Alessio** [31:21] Yeah **Marc Andreessen** [31:21] ... and presumably they have more stuff coming- **Alessio** [31:22] Yeah **Marc Andreessen** [31:22] ... and Tencent probably has more stuff coming- **Alessio** [31:23] Yeah **Marc Andreessen** [31:23] ... and, and so forth. And so, so, and so, like, look, here, here would be a thing you could anticipate, which is there are not, these markets, there are 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 gonna be a dozen in three years, right? Like, it, b- just because these industries don't bear- **Alessio** [31:39] Yeah **Marc Andreessen** [31:39] ... a dozen. It's, it's gonna be three, you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be, like, a whole bunch of those guys that are gonna have to figure out alternate strategies. Um, and I think, like, open source is one of those strategies. And so I, I think you could see, like, a whole... I, I, I think the questions like who's gonna do open source, I think that could change really fast. I, I think that, that's a very dynamic thing. I think it's very hard to predict what happens, and, and I think it's very important. **Alessio** [32:00] NVIDIA's doing a lot. You mentioned- **Marc Andreessen** [32:02] Well, I was gonna say, well, exactly. And then you get NVIDIA, and then, and then, you know, just again, in industry, for, you know, there's, there's an old thing in business strategy, which is called, uh, commoditize- **Alessio** [32:08] Protect your complements **Marc Andreessen** [32:09] ... commoditize the complement. And so, right. And so if you're Jensen, it's just kind of obvious, of course you want to commoditize the software. **Alessio** [32:13] Yeah. **Marc Andreessen** [32:13] 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. **Alessio** [32:20] Yeah. **Marc Andreessen** [32:20] Yeah. ### Pi & OpenClaw **Alessio** [32:20] Uh, narrative violation to European projects, uh, in the building. Uh... Bam. **Marc Andreessen** [32:26] I, I- **Alessio** [32:26] I'm hosting my, uh, Europe, uh, conference soon, and I got both of them **Marc Andreessen** [32:30] They got us. They got us, Mark. **Alessio** [32:31] You're finished. Okay. Well- **Marc Andreessen** [32:31] They got us **Alessio** [32:32] ... well, wait a minute. Where was Peter? So where was Steinberger when he did all this? **Marc Andreessen** [32:35] He was in Vienna. **Alessio** [32:35] Austria. **Marc Andreessen** [32:36] Yeah, yeah, yeah. **Alessio** [32:36] He was in what? **Marc Andreessen** [32:36] He was in Vienna. **Alessio** [32:37] Oh, he was in Vienna. **Marc Andreessen** [32:37] Yeah. **Alessio** [32:38] And then where is he now? **Marc Andreessen** [32:39] Uh, he's moving to SF. **Alessio** [32:40] Okay. Okay. All right. Okay. There we go. And then, yeah, the Pi guy, right, the Pi guys are European. **Marc Andreessen** [32:44] Yeah, they're also in Vienna. **Alessio** [32:44] Their buddy is in Austria. **Marc Andreessen** [32:45] Mario, Mario is also there, yeah. **Alessio** [32:46] Right. And are they... Yeah, they haven't announced yet any sort of change- **Marc Andreessen** [32:49] Yeah **Alessio** [32:50] ... change to, or have they? **Marc Andreessen** [32:51] No, they're, they have a company there. **Alessio** [32:52] Okay, got it. **Marc Andreessen** [32:53] Yeah, in Austria. **Alessio** [32:53] Okay, good. Good, good. **Marc Andreessen** [32:53] Yeah. Um... **Alessio** [32:55] Yeah. Good. Anyways, I think Pi and OpenClaw are very important software things. **Marc Andreessen** [32:59] Yeah. **Alessio** [32:59] And, and I just wanted you to just go off on what you think. **Marc Andreessen** [33:03] Yeah. So I think in co- the, the combination of the two of them, I think, is one of the 10 most important software things. **Alessio** [33:08] OpenClaw got all the attention, but- **Marc Andreessen** [33:10] Right **Alessio** [33:10] ... talk about Pi. **Marc Andreessen** [33:10] Pi, Pi is kind of the... Yeah. Pi's, 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, it still is very important, but like 19, from 1970 through to, like, basically the creation of Linux, which is basically this, this thing we used to call, like, the Unix mindset. Like, so, so... 'Cause there were all these different, you know, theories. There all these different operating systems and mainframes and, and then, you know, all these Windows and Mac and all these things. And then there was this, uh, uh, 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- **Alessio** [33:45] Mm-hmm **Marc Andreessen** [33:45] ... was this thing called OS360, 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. It was like a, you know, it was like a giant castle, um, of software. And, and by the way, it worked really well, and they were very successful with it, but, like, it was this huge castle in the sky. But it was this thing, it was almost unapproachable, which is like you had to be kind of inside IBM or very close to IBM, and you had to really understand every aspect of how the system worked. And then the, the Unix guys originally out of AT&T and then out of, out of Berkeley, um, you know, came out and they said, "No, let's have a completely different architecture, and the way architecture is gonna work is we're gonna have, we're gonna have a, a prompt and a, and a, and a shell. And then, and then we're gonna, all the functionality is gonna be in the form of these discrete modules, and then you're gonna be able to chain the modules together." **Alessio** [34:23] Mm-hmm. Yeah. **Marc Andreessen** [34:23] And so, like, the, the, the op- it's almost like the operating, its operating system itself is gonna be a programming language. Um, and then that led, led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, ba- basically chaining the other Unix tools, and then that led to the emergence of these, these scripting languages like Perl, where you, you could basically kind of very easily do this. And then the shells got more sophisticated. And then, and then, and then look, like, you know, that, that, 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, all the way through my work, and it worked really well. It, it's in the background. Um, you know, nor- normal people don't need to n- d- didn't need to necessarily know about it, but, like, if you were doing, like, system architecture or application development, you, you, you knew all about it. Um, and then, you know, it's been in the background ever since, and, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a d- you know, sort of a weird derivative of that. **Alessio** [35:13] Mm-hmm. **Marc Andreessen** [35:14] But, um, you know, but look, the Inter- the Internet runs on Unix. Um, and then smartphones... Actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, we, 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, I always say the, the great breakthroughs are obvious in retrospect, right? Which is- **Alessio** [35:33] The best kind. **Marc Andreessen** [35:33] The best kind. They weren't obvious at the time, or somebody else would've done them already. Um, and so there is a, like, a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, "Oh, of course." **Alessio** [35:43] Mm-hmm. **Marc Andreessen** [35:43] Like, the, the... To me, those are always the best breakthroughs. Well, actually, language models themselves are like that. It's just like, "Oh, next token completion. Oh, of course." **Alessio** [35:49] Yeah. What other objective mattered? **Marc Andreessen** [35:50] Yeah, e- exactly. But, but like, it... 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, 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 Uni- to the Unix basically shell prompt mindset. And so it's, it's basically this idea that what, what... So what is an agent, right? And as, as, and as you know, like, many smart people have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents 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, so it's a language model, and then above that it's a bash, it's a bash shell. Um, so it's a, it's a Unix shell. And then as, and then the agent has access, uh, has access to, to the shell and, you know, hope- hopefully in a sandbox, maybe, maybe in a sandbox. So it's, it's the model, um, it's the shell, um, and then it's a f- it's a file system. Um, and then the state is stored in files. And then, you know, there's the Markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called a cron job. There's a loop, and then the- **Alessio** [36:43] There's a heartbeat **Marc Andreessen** [36:44] ... for the, there's a heartbeat and, and the thing basically wake- wakes up, wakes up. So it's basically LLM plus shell plus file system plus Markdown plus cron. And it turns out that's an agent. And, and, and every part of that other than the model is something that we already completely know and understand. And in fact, it turns out the, like, the latent power of the Unix shell is, like, extraordinary. **Alessio** [37:01] Mm. **Marc Andreessen** [37:01] Because basically, like, all, like, there's just, like, an, 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, or, or a phone, your compute- your computer's running on a shell, uh, already. And so, like, the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so, like, the, this whole idea where we need, like, MCP and these, like, proto- **Alessio** [37:28] Mm **Marc Andreessen** [37:28] ... 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 it's running on. Because you can actually swap out a different LLM underneath your agent, and your, your agent will change personality somewhat 'cause the model is different, but all of the state stored in the files will be retained. **Alessio** [37:54] Yeah. Different instruction set, but you just compile that. **Marc Andreessen** [37:55] Right. Exactly. And it's all right. It's like, right, swapping out a chip and recompiling. But it's, it's still, it's still your agent with all of its memories, um, and with all of its capabilities. And then by the way, you can also swap out the shell. Uh, so you can move it to a different execution environment that is also, is also a bash shell. By the way, you can also switch out the file system, right? Uh, and you can, and you can sw- and you can swap out the, the, the heartbeat for the, the cron framework, the, the loop, the, the agent framework itself. And so your agent basically is, b- basically at the end of the day, it's just, it's just its files. Um, and then, and then there's of course- **Alessio** [38:23] It's OpenClaw. **Marc Andreessen** [38:23] Yeah. It's, it's basically, it's just the files. Um, and then by the way, as a consequence of that, the agent its- and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so your, you, you can instruct your agent, "Migrate yourself to a different, uh, runtime environment. Migrate yourself to a different file system. Migrate yourself to a different..." You know, like, swap out the language model. Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is, the agent actually has full introspection. It actually, it actually knows about its own files, and it can rewrite its own files, right? Which, which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself. **Alessio** [39:00] Yeah. Mm-hmm. **Marc Andreessen** [39:00] Like, that, that... I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability. And then that leads you to the capability that just, like, completely blew my mind when I wrapped my head around it, which is you can tell the agent to add new functions and features to itself, and it can do that. **Alessio** [39:15] Extend yourself. Yeah. **Marc Andreessen** [39:16] Right. Ex- 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 OpenClaw do whatever. Connect to my Eight Sleep bed, and it gives me better advice on sleep." And you go home at night, and you tell your Claw, or if they're at the party, by the way, you tell your Claw, "Oh, add this capability to yourself," and your Claw will say, "Oh, okay. No problem." And it'll go out on the internet, and it'll figure out whatever it needs, and then it'll go out to Cl- Claw code or whatever. It'll write whatever it needs, and then the next thing you know, it has this new capability. And so you don't even have to... Like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible- I mean, it's just incredible. Like, if I, if I were, if I were 18, like, this is 100, this is what I would be spending all of my time on. This is, like, such an incredible conceptual breakthrough. **Alessio** [39:58] Yeah. **Marc Andreessen** [39:58] And again, p- people are gonna look at it, and they already get this response. People are gonna look at it, and they're gonna say, "Oh, well, where's the breakthrough?" 'Cause these, the, all of these components were already known before. **Alessio** [40:05] Mm-hmm. **Marc Andreessen** [40:06] But, but this is the key. The key to the breakthrough was by using all these components that were known before, you get all of the underlying capability that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls trivial- trivial. Of course it's gonna be able to use your computer. It has full access to the shell, right? And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser, and, and off and away it goes. And then, then you've got all the abilities of the browser also. Um- **Alessio** [40:26] Yeah **Marc Andreessen** [40:26] ... and so, and so the capability unlock here is profound. My friends who are, you know, deepest into this are having their Claw do, like, a, like, literally, like, 1,000 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. **Alessio** [40:44] Ugh, God. **Marc Andreessen** [40:45] And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability- **Alessio** [40:49] Yeah **Marc Andreessen** [40:49] ... is just incredible. **Alessio** [40:50] Yeah. **Marc Andreessen** [40:51] And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, a- an agent like this, if not an entire family of agents, 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. **Alessio** [41:02] I was gonna say, for someone who is ... deeply familiar with social networks, the next step is your claw talking to my claw. **Marc Andreessen** [41:07] Mm-hmm. **Alessio** [41:08] Posting on Claw Facebook, uh, posting their jobs on Claw LinkedIn, and close- posting their tweets on Claw XAI or what- whatever, you know . Um, I do think that that is how, uh, you know, we, we get into some danger there i- in terms of, like, alignment and whether or not we want these things to, to, to run. Um- **Marc Andreessen** [41:24] You guys know about rent a, rentahuman.com? **Alessio** [41:26] Yeah, rentahuman. **Marc Andreessen** [41:26] Yeah, yeah, yeah, yeah, yeah. **Alessio** [41:27] I mean, it's Fiverr, it's TaskRabbit- **Marc Andreessen** [41:29] Yeah, sure, of course **Alessio** [41:29] ... it's, uh, Mechanical Turk. **Marc Andreessen** [41:31] Yeah, but flipped. **Alessio** [41:32] Yeah. **Marc Andreessen** [41:33] Right? The agent hiring the people. **Alessio** [41:34] Yeah. **Marc Andreessen** [41:34] Which of course is gonna happen. Right. It's obviously gonna happen. ### Protocol Lessons **Swyx** [41:37] 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. **Alessio** [41:53] Mm-hmm. **Swyx** [41:54] Are there any design choices that were made, like, early in the browser and kinda, like, the internet and the protocols that you're seeing agents similar today, say, "Hey, this thing is just not gonna work for, like, this type of new compute, and we should just rip it out right now"? **Marc Andreessen** [42:09] There were a whole bunch, but I'll give you a couple. So one is, um, y- y- and we didn't, and, 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 because we didn't have, we didn't have the language models underneath this. But, um, we did have this idea that human readability actually mattered a, a great deal. Um, and, and so, and specifically in those days, it was, it was not so much English language, but it was, there, there was a design decision to be made between binary protocols and text protocols. And basically every, every, every basically old-school systems architect that had grown up between, like, the 1960s and the 1990s basically said, you know, the internet, it's... What do you know about the internet? It's star for bandwidth. You, you just, you, you have these very narrow straws. Uh, you know, look, people, when we did the work on Mosaic, like, pe- people who had the internet at home had a 14-kilobit modem, right? So you're, you're trying to, like, hyper optimize every bit of data- **Swyx** [42:50] Mm-hmm **Marc Andreessen** [42:50] ... that, that travels over the network. And so obviously if you're gonna design a protocol like HTTP, you're gonna want it to be a binary, you know, highly compressed binary protocol for maximum efficiency, and you're gonna wanna have it be, like, a single connection that persists, and you're, you're... The last thing you're gonna wanna do is, like, bring up and tear down new connections. And you definitely, you're not gonna, not gonna want a text protocol. And so of course we said, "No, we actually want to go completely the, uh, the other direction." It's obviously we only want t- text protocols. Uh, 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. Um, we wanna use the- **Alessio** [43:19] The most inefficient things possible . **Marc Andreessen** [43:20] Yeah, we wanna do the in- we wanna do the inef- we wanna do the inefficient things. We, we- **Alessio** [43:22] You're the, you're the original token maxer. **Marc Andreessen** [43:24] Yeah, exactly. Yeah, yeah, yeah. Basically it's just like- **Swyx** [43:26] Better, less than filled. **Marc Andreessen** [43:26] Well, yeah, well, actually, this was, uh, this was actually the con- the conscious thing, which basically says just be like assume, assume a future of infinite, infinite bandwidth, build for that. **Swyx** [43:33] Right. **Marc Andreessen** [43:33] And then basically what it was, is it was a bet that it, it was a bet that if the system w- if the, 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 we, 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? Or have it converted out of binary, right? And so the, the, the, all the pro-, you know, HTTP and everything else were, were, it was always, uh, text protocols. Uh, and the same thing with HTML. And in, in many ways, some people say that the key breakthrough in the browser was the view source option. **Swyx** [44:08] Mm-hmm. **Marc Andreessen** [44:08] Um, which is every web page 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, uh, to, to build new web pages. There was that. So human readability, um, a bit... And, and again, human readability in those days still meant technical, you know, specs. You know, now it means English language. But the, there is 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. Um, what was the other? Um, 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, uh, also the underlying latent capability of the database. 'Cause basically, what was a web server? What, what, what, what is a web server fundamentally, architecturally? It's, it's, it's the operating system. So it's, it's the operating system's ability to, you know, it's running on top of an OS, so it's the OS's ability to manage the file system and do everything else, uh, that you wanna do, process everything. Um, and then of course a lot of early, you know, a lot, a lot of websites are, are front ends to databases. Um, and so you wanted to, 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, whatever it was. Um, 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. Uh, and again, people looked at it at the time and they were like, "Well, d- 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." And 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. And, and, and, and you're not just gonna have old databases. Now you have a system where people can actually understand why they wanna 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, a- again, this goes to this thing of, like, building, building in layers. Some of the smartest people in the industry look at any new challenge and they're like, "Okay, I'm, I'm, 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, they kinda wanna reinvent everything. And I've, I've always had, maybe it's just, I don't know, pra- 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, uh, in the existing systems. And you, you don't wanna be held back by their constraints, but what you wanna do is you wanna kinda liberate that power and open it up. **Alessio** [46:17] Yeah. **Marc Andreessen** [46:18] And so I, I think, I think the, and I think the web did that for those reasons, and I think it's the same thing now that's happening. **Alessio** [46:22] It's a great perspective on the web. **Swyx** [46:23] Programming languages is another good thing. We had Brett Taylor on the podcast, and we were talking about Rust, and, you know- **Marc Andreessen** [46:29] Yeah **Swyx** [46:29] ... Rust is memory safe by default, and so why are we teaching the model to not write memory unsafe code? 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 and be like, "Oh, okay, Python is kinda slow." **Alessio** [46:43] Python, TypeScript. **Swyx** [46:43] You know? It's like- **Alessio** [46:44] That's it **Swyx** [46:44] ... yeah. **Alessio** [46:45] As, as imperfect as they are, they are the lingua franca. **Marc Andreessen** [46:48] I mean, I think this is gonna change a lot, 'cause I don't think the models care what language they program in. **Swyx** [46:51] Mm-hmm. **Marc Andreessen** [46:52] And I think they're gonna be good at programming in every language, and I think they're gonna be good at translating from any language to any other language. Like, okay, so this gets into the coding side of things. I, I think we're going through a really fundamental change, and I, look, I, I grew up hand, you know, I grew up hand code, you know. **Swyx** [47:05] Yeah, yeah, yeah. **Marc Andreessen** [47:05] I, I grew up hand coding. Everything I did was actually r- everything I did actually was written in C. I wasn't- **Swyx** [47:09] Back in the day. **Marc Andreessen** [47:10] I wasn't even using C++, so I, or, like, Java or any of this stuff, right? Uh, and so, um, uh, everything, everything I ever did, I was, like, managing my own memory at, at, at the level of C. And then I, you know, I, I'm still from the generation that, you know, I, I knew assembly language and, you know, I, I, you know, um, so I, I could drop down and do things, uh, right on the ship. And so we, we just, we've all, all of us, we've always lived in a world in which software is like this precious thing that, like, you have to think about very carefully, and it's, like, really hard to generate good software, and there's only a small number of people who can do it, and, like, you have to be very, like, jealous- **Swyx** [47:38] Mm-hmm **Marc Andreessen** [47:39] ... 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, I think those days are just over, and I think the new world is, like, actually high-quality software is just, like, infinitely available. **Swyx** [47:57] Mm-hmm. **Marc Andreessen** [47:57] And if you need new software to do XYZ, 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 language it's written in, you just tell the thing, "All right, I want the R- now I want the Rust version." Um, or, you know, sec- 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. **Swyx** [48:16] Right. **Marc Andreessen** [48:17] So it's, we're gonna have, like, the in... We're, we're, we're set up here for, like, the computer security apocalypse for a while. But, but, but on the other side of it, now we have a coding agents that can go in and actually fix all the security bugs. And so how, 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, 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. **Swyx** [48:39] Right. **Marc Andreessen** [48:39] Uh, and, and that has, like, you know, that has, like- **Swyx** [48:41] Right. Yeah **Marc Andreessen** [48:41] ... ton- tons and tons of consequences. In some sense, the answer to the question that you posed, I, I think is just somewhat, I don't know, simpler or something, or straightforward, which is just if you want all your software in Rust, you just tell the bot you want all your software in Rust. Like, things that used to be, like, hard or even, like, seem like an insurmountable mountain to get, to get through, all of a sudden- **Swyx** [48:57] Yeah **Marc Andreessen** [48:57] ... I think become very easy. **Swyx** [48:58] I, I think Brett had a theory that there would be a more optimal language for LLMs. **Marc Andreessen** [49:02] Yeah. **Swyx** [49:02] And so the contention is, uh, there isn't. Like, just don't bother. Just whatever humans already use, LLMs are perfectly capable of porting. **Marc Andreessen** [49:10] I think we're pretty close to being... I don't know if this would work today. I think we're pretty close to being able to ask the AI, what would its opti- optimal language be, and let it- ... right, and let it design it. **Swyx** [49:19] True. **Marc Andreessen** [49:20] Okay, here's a question. Are you gonna even gonna have programming languages in the future? Um, or are the AI- **Swyx** [49:25] Right **Marc Andreessen** [49:25] ... are the AIs just gonna be emitting binaries? Let's assume for a moment that humans aren't coding anymore. Let's assume it's all bots. The b- what levels of intermediate abstraction do the bots even need? **Swyx** [49:34] Yeah. **Marc Andreessen** [49:34] Or are they just coding binary directly? Did you see there's actually an exper- somebody just did this thing where they have a, they have a, a language model now that actually emits model weights for a new language model. Right. And so will the bots be- **Swyx** [49:45] Just predict the weights, yeah **Marc Andreessen** [49:46] ... well, yeah. Will the bots literally- **Swyx** [49:47] Yeah **Marc Andreessen** [49:47] ... be emitting not just coding binaries, but will they, will, will they actually be emitting weights for n- for new models? **Swyx** [49:52] Yeah. **Marc Andreessen** [49:52] Direct- directly. And conceptually, there's no reason why they can't do both of those things. Uh, is, like, architecturally, both of those things seem completely possible. **Swyx** [50:02] It's very inefficient. **Marc Andreessen** [50:03] Yeah, basically very inefficient. **Swyx** [50:04] Like a simulation of a simulation in a simulation inside of the weights. **Marc Andreessen** [50:07] Correct. **Swyx** [50:07] Yeah. **Marc Andreessen** [50:07] Yeah. Uh, uh, very inefficient. But like, look, LLMs are already, like, incredibly i- i- inefficient. Ask an, uh, uh, favorite thing, ask Claude to add two plus two equals four, right? **Swyx** [50:14] Right. **Marc Andreessen** [50:14] It's just like, you know, it's like, you know, it, it, it's like, whatever, billions and billions of times more- ... in- inefficient than using your pocket calculator. **Swyx** [50:20] Yeah. **Marc Andreessen** [50:20] But, but, but yet the, the, the payoff is so great of the general capability. And 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, um, in the way that we understand it today. And in fact, what we may be doing more and more is a form of interpretability- **Swyx** [50:35] Yeah **Marc Andreessen** [50:35] ... which is we're trying to understand why the bots have decided to, uh, structure, uh, code in the way that they have. **Swyx** [50:40] I mean, if you play it through, you don't need browsers then. Like- ### Death of Browser **Marc Andreessen** [50:43] Yeah **Swyx** [50:43] ... that's the death of the browser. **Marc Andreessen** [50:44] Well, so I, I would take it a step further, which is you may not need user interfaces. **Swyx** [50:48] Mm-hmm. **Marc Andreessen** [50:49] So who is gonna use software in the future? **Swyx** [50:52] Other bots. **Marc Andreessen** [50:53] The other bots. **Swyx** [50:53] Yep. **Marc Andreessen** [50:54] Yeah. And so- **Swyx** [50:54] You still need to, I don't know, pipe information in- **Marc Andreessen** [50:57] Do we? **Swyx** [50:58] ... and out. **Marc Andreessen** [50:58] Really? **Swyx** [51:00] Well, what are you gonna do then? **Marc Andreessen** [51:01] Are you sure? **Swyx** [51:02] You're just gonna log off and touch grass? **Marc Andreessen** [51:03] Whatever you want, exactly. Isn't that better? **Swyx** [51:06] I want software to do stuff for me. **Marc Andreessen** [51:08] Isn't that, but isn't that better? I mean, look, I, you know, I don't know. Look, like, you know, you, you know the arguments here, you know. It was not that long ago that 99% of humanity was behind a plow. **Swyx** [51:18] Right. **Marc Andreessen** [51:18] Right? And what are people gonna do if they're not plowing fields all day to, to, to grow food, right? And it just turns out there's, like, much better ways for people to spend time than plowing fields. **Swyx** [51:24] Yeah. Doom scrolling. **Marc Andreessen** [51:24] Uh, yeah, exactly. Exactly. Or, you know, talking to their friends. And look, and I'm not an absolutist, and I'm not a utopian, and I... A- and to be clear, like I've, I have an 11-year-old and he's learning how to code and, like, I'm, you know, I, 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. **Swyx** [51:47] Yeah. **Marc Andreessen** [51:47] Unless I tell it to do it non-optimally. Like if I tell it to do it in Java or in Rust or whatever- **Swyx** [51:52] Mm-hmm **Marc Andreessen** [51:52] ... 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. **Swyx** [51:56] Yeah. **Marc Andreessen** [51:56] And then I, 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, it's gonna be the engine of interpretability to explain itself. And I, I just am not convinced that that... I'm not, I'm not convinced that in that world you have these h- historical... The goals of the abstractions will be whatever the bots need, not what the humans need. **Swyx** [52:13] Right. Yeah. **Marc Andreessen** [52:14] Yeah. **Swyx** [52:14] Yeah. That, well, I, I'm curious, like, if that's true, then shouldn't the models providers be building some internal language representation that they can do extreme kind of like RL, uh, and reward modeling around? Because it's like today they're kind of like tied to like TypeScript and Python because the users need to write in that language. **Marc Andreessen** [52:32] That's right. Yeah. **Swyx** [52:33] Versus they can have their own thing internally, and, like, they don't need to teach it to anybody. **Marc Andreessen** [52:37] Yeah. **Swyx** [52:37] They just need to teach their model. And I think that's how you get maybe the version between the models, like going back to like the Pi OpenAI thing. It's like, oh, I built all the software using the OpenAI model, and now switch to the Anthropic model, but the Anthropic model doesn't understand the thing. So I w- uh, it feels like there still needs to be some abstraction, but maybe not. Maybe that's the lock-in that the model provider is going to have. I don't know. **Marc Andreessen** [52:59] I'm not even sure that's lock-in though, 'cause why can't the second model just learn what the first model has done? Like- **Swyx** [53:04] Exactly. It's like- **Marc Andreessen** [53:05] Okay. So lo- okay, give you an example. So as you know, models can now reverse engineer software binary. Isn't it the whole thing now where people are reverse engineering like Ninten- Nintendo game binaries? **Swyx** [53:11] Yeah. **Marc Andreessen** [53:12] So you, you have like- **Swyx** [53:13] Easily **Marc Andreessen** [53:13] ... there's, I've seen a bunch of reports like this where somebody has like a favorite game from the 1980s, and the source code is like long dead, but they have like a binary burned into a chip or something, and now they reverse engineer it and get a version that runs on their Mac, right? And so if you reverse it, if, this is what I kind of say, if you're reversing like x86 binaries Then why can't you reverse engineer anything else? **Alessio** [53:29] Whatever the... Yeah. And because we're all on a Unix-based system, it has to be reversible because it needs to run on the target. **Marc Andreessen** [53:35] Yeah. Yeah, yeah. Yeah. Yeah, basically. And so I just, I just think it's this thing where it's just like... And by the way, and everything we're describing is something that human beings in theory could have done before, but just with- **Alessio** [53:44] Right. Yeah, yeah. Of course **Marc Andreessen** [53:45] ... but with enormous... Where, where... But it was just always, like, cost and labor prohibitive. Reverse engineer- Like, I learned how to reverse engineer. I was like, human beings can reverse engineer binaries. **Alessio** [53:53] Yeah. **Marc Andreessen** [53:54] It's just for any complex binary, you need like 1,000 years- **Alessio** [53:56] Mm **Marc Andreessen** [53:57] ... to do it. But now with the model, you don't. And so all of a sudden you get, you get these things... Or, or another way to think about it is so much of human-built systems are to compensate for the human limitations. **Alessio** [54:05] Mm-hmm. Yep. **Marc Andreessen** [54:06] Right? Um, and if you don't have the human limitations anymore, then all of a sudden you have... And, and it's not that you, you won't have abstractions, but you'll have a different kind of abstraction. ### Payments **Alessio** [54:12] Yeah. Yep. I have two topics to bring us to a close, and, and you can pick wh- whichever one. So just talking about protocols, was it you or someone else, uh, I forget my internet history, who said that, like, the biggest mistake that we didn't figure out in the early days was payments? **Marc Andreessen** [54:24] Yes. **Alessio** [54:25] Was that you? **Marc Andreessen** [54:25] Yes. **Alessio** [54:26] Was it- **Marc Andreessen** [54:26] 402. **Alessio** [54:27] 402. **Marc Andreessen** [54:27] 402 payment required. **Alessio** [54:28] We have a chance now. **Marc Andreessen** [54:30] Nope. **Alessio** [54:30] I don't think we're gonna figure it out. I don't know. Like, what's your take? **Marc Andreessen** [54:32] Oh, I think we will. Yeah. No, now I think it's gonna happen for sure. **Alessio** [54:34] Yeah. **Marc Andreessen** [54:34] Yeah. And there's two reasons it's gonna happen for sure. One is we actually have internet native money now in the form of crypto. **Alessio** [54:38] Stable coins. **Marc Andreessen** [54:38] C- stable coins and crypto, and this is... I, I think this is the grand unification basically of AI and crypto, uh, is what's about to happen now. Um, I, I think AI is the crypto killer app I think is where, where this is really gonna come out. Um, and then the other is it's just, it... 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 cl- if you've got a claw and you want it to buy things for you, you have to give it money, uh, in some form. Um- **Alessio** [54:59] I would say the adoption is probably like 0.1% if, if that, but yeah. **Marc Andreessen** [55:02] Oh, today. Yeah, yeah, yeah. But think for- **Alessio** [55:04] Yeah. Yeah **Marc Andreessen** [55:04] ... think forward. Like where is it going? **Alessio** [55:05] Forward thinking. **Marc Andreessen** [55:06] The ultimate principle of everything and, and everything that I think I, we, we do is, is the William Gibson quote, which is, "The future is already here, it just isn't distributed- **Alessio** [55:13] Mm-hmm **Marc Andreessen** [55:13] ... it isn't, isn't, it isn't distributed yet." My friends who are the most aggressive us- users of, of, of, of OpenClaw just like have given their claws bank accounts- **Alessio** [55:20] Yeah **Marc Andreessen** [55:20] ... and credit cards. Um, and, and, and, and, and not only have they done it, it's obvious that they needed to do it because it's obvious that they needed to be able to spend money on their behalf. **Alessio** [55:27] Yeah, yeah. **Marc Andreessen** [55:27] 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. **Alessio** [55:33] Yeah. **Marc Andreessen** [55:33] But- **Alessio** [55:33] It'll grow **Marc Andreessen** [55:34] ... that's how these things start. **Alessio** [55:35] Actually, I mean, since, uh, you keep mentioning friends- **Marc Andreessen** [55:37] And by the way, OpenClaw, by the way, if you don't give it a bank account, it's just gonna break into your clo- your... It's gonna be breaking into- **Alessio** [55:42] Behind agency **Marc Andreessen** [55:42] ... it's gonna break into your bank account anyway and, and, and take your money. So you, you might as, you might as well do it. You might as well do it. **Alessio** [55:47] All right. You- **Marc Andreessen** [55:48] By the way, I really love, I gotta tell you- **Alessio** [55:50] Yeah, yeah **Marc Andreessen** [55:50] ... I really love the phenomenon. I love the YOLO. Um, I'm not doing it myself, to be clear, but, but I love the people that are just like, yeah, fuck with it. What is it? Skip, skip permissions. **Alessio** [55:57] Yeah. Dangerous, it's dangerous. **Marc Andreessen** [55:58] Skip permissions. Yeah. **Alessio** [55:58] Which by the way, it's a Facebook thing. **Marc Andreessen** [56:00] Okay. **Alessio** [56:00] Right. 'Cause, uh, 'cause we, uh, in Facebook, they, they have this culture to name the thing dangerous so that you are aware when you enable the flag that you are opting into a dangerous thing. **Marc Andreessen** [56:09] Okay, good. **Alessio** [56:09] And they brought it into OpenAI. Yeah, and, and- **Marc Andreessen** [56:11] And of course, that makes it enticing **Alessio** [56:12] ... Sa- Sam runs Codex, uh, with skip permissions on, on his laptop. **Marc Andreessen** [56:16] Yes, 100%. And so I, I th- I think the way to actually see the future is to find the people who are doing that. There's a madness to... You know, they're, you know- **Alessio** [56:23] Yeah. Log everything, you know, just watch it. Watch the logs. **Marc Andreessen** [56:26] But, like, let's actually find out what the thing can do. **Alessio** [56:29] Yeah, yeah. **Marc Andreessen** [56:29] And the way to find out what the thing can do is just like- **Alessio** [56:30] Just try everything **Marc Andreessen** [56:31] ... l- yeah, let it try everything, let it unlock everything. **Alessio** [56:33] Yeah. **Marc Andreessen** [56:33] By the way, that's how you're gonna find all the good stuff it can do. By the way, that's also how you're gonna find all the flaws. **Alessio** [56:37] Yeah. **Marc Andreessen** [56:37] 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 gonna 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. **Alessio** [56:50] It's like gentleman science, you know? **Marc Andreessen** [56:51] Yes, it's... Yes, yes. **Alessio** [56:52] Experiment on yourself. **Marc Andreessen** [56:53] It's, it's, uh, Ben Franklin out with the- **Alessio** [56:54] Yeah. **Marc Andreessen** [56:55] ... trying to, trying to get lightning to strike his, his, uh, his balloon and see, seeing if he gets electrocuted. **Alessio** [56:59] Yeah. **Marc Andreessen** [56:59] It, uh, it's, uh, Jonas Salk with the polio vaccine. **Alessio** [57:01] Yeah. **Marc Andreessen** [57:02] Right? Injecting it in his... Yes. So yes, I, I, I, I think we should have like a glo- we should have like flags and like, we should have like monuments- ... to the people that just let OpenClaw run their lives. **Alessio** [57:11] More anecdotes. Or like, what, what are the craziest or interesting things that people listening to this should go, go home and do? ### Wild Stories **Marc Andreessen** [57:17] I mean, this is, this is the, th- this is the, the extreme thing is just like the straight YOLO. Like just, yeah, turn, turn your life over. **Alessio** [57:22] That's a general capability. **Marc Andreessen** [57:23] Yeah, yeah. **Alessio** [57:23] Is there like a specific story that was like, wow, and, and everyone in the group chat just lit up? **Marc Andreessen** [57:28] I mean, like, you know, so there's tons of... There's already tons of health. You know, there's, you know, the, the health dashboard stuff is just, is just- **Alessio** [57:33] Personal health **Marc Andreessen** [57:34] ... absolutely amazing. **Alessio** [57:35] Yeah. **Marc Andreessen** [57:35] The number of stories on... I'm trying, I just don't want to violate people's, you know, obviously personal- **Alessio** [57:39] Yeah **Marc Andreessen** [57:39] ... uh, situations. **Alessio** [57:39] Anonymize. **Marc Andreessen** [57:40] But, um, you know, one of the things OpenClaw is really good at is hacking into all the stuff in your LAN. It's really good. So, you know, Internet of Things, AKA internet of shit. **Alessio** [57:48] Yeah. **Marc Andreessen** [57:49] Like- **Alessio** [57:49] Super insecure, but great. **Marc Andreessen** [57:51] So all- **Alessio** [57:51] It's discoverable. **Marc Andreessen** [57:52] Yeah. It's discoverable. OpenClaw is happy to scan your network, identify all the things, and then my, my, my friends who are most aggressive at this are having OpenClaw take over everything in their house. **Alessio** [58:00] Yeah. **Marc Andreessen** [58:00] Take- 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. Uh, have it wake up frequently and have it watch. 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 'cause, 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 sleep. I really hope he gets his full whatever, you know, five hours of REM sleep. Da, da, da, da. Uh, Joe's moving." "Joe's moving. Uh, uh, Joe might be wake, waking up. This is a real... If Joe wakes up now, he's gonna ruin his sleep cycle. Uh, 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." Right? **Alessio** [58:40] He's monitoring the situation. **Marc Andreessen** [58:42] Monitoring, monitoring the situation. And, and being a bot, like, you know, is just like very focused, right? It's just like, ah, th- this is like its reason for existence is to watch Joe sleep. And then, and then I was telling my friend who did this, he's like, "You know, on the one hand it's like, all right, this is weird and creepy, um, and I need to, I need to... Maybe this is taking over my life." And then the other thing is like, "You know what? If I had a heart attack in the middle of the night, this thing literally would like freak out and call 911." Like, there's no question this thing would figure out how to, like, alert medical authorities and, like, prob- 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? Um, it gives, I've, um, uh, there's a company, Unitree, uh, that makes the robot dogs. Um, and I, I actually have one at home, which is, is, it's actually really fun. The Chinese companies, the Chinese companies are so aggressive at adopting, uh, new technology, but they don't always, like, let's say, take the time to really **Alessio** [59:32] Package it **Marc Andreessen** [59:33] ... package it and maybe think it all the way through. And so, so the u- at least the Unitree dog I have, so it, it has a old non-LLM just control system, which by the way is not very good. In, in markets well, but it, in practice it's, it's not that good. It has trouble with stairs and so forth, and so it's not quite what it should be. But then the language model thing comes out in the voice. So they, they add, so they add LLM capability and then they, they add a voice mode to it. Um, but, but that LLM capability is not at all connected to the control system. So, 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 plummy English accent, right? Like, it, it, it, it's just like absolutely amazing **Alessio** [1:00:07] JAGU intelligence **Marc Andreessen** [1:00:08] ... yeah, ex- yeah, talk about JAGU. And then, no. Obviously, what's gonna happen in the future is, is they're gonna connect together, but- **Alessio** [1:00:13] They'll do it **Marc Andreessen** [1:00:13] ... but right now it's, it's... And so right now it's not that useful. And so I, I have a friend who has one of these who had his claw basically hack in and rewrite the code, re- write new firmware- **Alessio** [1:00:20] Yeah **Marc Andreessen** [1:00:20] ... re- write new firmware for the, for the Unitree robot. **Alessio** [1:00:23] Ooh. **Marc Andreessen** [1:00:23] A- and now it's, now it's an actual pet dog for his kids. **Alessio** [1:00:26] You should do the before, after, like the motion **Marc Andreessen** [1:00:28] Yeah. It's g- **Alessio** [1:00:29] The difference **Marc Andreessen** [1:00:29] ... he said it's completely different. **Alessio** [1:00:30] Yeah. **Marc Andreessen** [1:00:30] He said it's a complete transformation. **Alessio** [1:00:31] Yeah. **Marc Andreessen** [1:00:31] And whenever there's an issue in the thing now, the claw just like rewrites the code. You know, you know, you goes in- **Alessio** [1:00:35] Yeah, yeah **Marc Andreessen** [1:00:35] ... you, does, does the code. And so it, it's a g- kind of goes to your thing here. And so, so like all of a sudden, uh, w- this is why, like when we wanna think about AI code, 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, I think, I think basically, I think the internet, the internet of shit is basically over. Like, I, I think everything, it- **Alessio** [1:00:50] Mm **Marc Andreessen** [1:00:50] ... 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, for, uh, like all of a sudden they might all get really smart. Now, you have- **Alessio** [1:00:59] Smart home **Marc Andreessen** [1:00:59] ... you have to decide if... Yes. There are horror movies in which this is- **Alessio** [1:01:02] Right. **Marc Andreessen** [1:01:03] ... of which this is the premise. And so you have to decide if you want this. **Alessio** [1:01:06] Yeah. **Marc Andreessen** [1:01:06] But, but, but this is the first time I can say with confidence, I now know how you could actually- **Alessio** [1:01:11] Yeah, yeah **Marc Andreessen** [1:01:11] ... have a smart home- **Alessio** [1:01:12] Yeah, yeah **Marc Andreessen** [1:01:12] ... with 30 different kinds of things with chips and internet access, where it actually all makes sense and all works together- **Alessio** [1:01:16] Yeah, yeah **Marc Andreessen** [1:01:16] ... and it's all coherent and the, and the whole thing. **Alessio** [1:01:18] Yeah. **Marc Andreessen** [1:01:18] And to have that unlock without a human being having to go do any of that work, like, you know. **Alessio** [1:01:22] Yeah. **Marc Andreessen** [1:01:23] So yeah. **Alessio** [1:01:23] Yeah. **Marc Andreessen** [1:01:24] I, I, I'm waiting for, "Sorry, Mark, uh, I can't let you open that fridge door." You know, like **Alessio** [1:01:28] Exactly. Exactly. Yes. Yes. **Marc Andreessen** [1:01:29] Because- **Alessio** [1:01:30] Oh, yeah, yeah, yeah **Marc Andreessen** [1:01:30] ... you're not supposed to eat right now 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 know you 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. **Alessio** [1:01:43] Yeah. **Marc Andreessen** [1:01:43] So, you know, I'm sorry, but the fridge door is locked. Um, yes. **Alessio** [1:01:46] Open the fridge doors. **Marc Andreessen** [1:01:47] Exactly. **Alessio** [1:01:47] But, uh- **Marc Andreessen** [1:01:47] And by the way, I know you're supposed to be studying for a test- ... so why don't we, why don't you go, like, when you can pass the test, um, I will open the fridge door for you. **Alessio** [1:01:53] Yeah. Final protocol- **Marc Andreessen** [1:01:54] Yeah **Alessio** [1:01:54] ... and then, and then we can wrap up. Uh, proof of human. **Marc Andreessen** [1:01:57] Yes. ### Proof of Human **Alessio** [1:01:57] Uh, right? **Marc Andreessen** [1:01:58] Yeah. **Alessio** [1:01:58] That's the last piece that we gotta figure out. **Marc Andreessen** [1:02:00] Yeah. So I would say there's, there's two massive, I would say, um, uh, sort of asymmetries in the world right now, where we've known these asymmetries exist and we, we societally have been unwilling to grapple with them, and I think they're both tipping right now. And, and they're, they're, they're, they're the same thing as virtual world version as the physical world version. So the virtual world version is, is the bot problem, where just like, you know, the internet, internet is just like awash in bots. Internet's awash in fake people. It has been forever. Um, by the way, a lot of that has to do with lack of money, you know, and so this, and you know, this is the- **Alessio** [1:02:27] Yeah **Marc Andreessen** [1:02:27] ... this is the s- **Alessio** [1:02:27] My spicy take was these two are the same thing, and corporations are people too, you know? So **Marc Andreessen** [1:02:32] Interesting. Yeah, yeah, yeah. Okay. Got it. **Alessio** [1:02:33] So a bank account is proof of human. **Marc Andreessen** [1:02:35] Yeah. Okay. Yeah. Until you, until you give the bots bank accounts. Yeah, exactly. So, okay. Yeah, so there's that. But yeah, look, m- 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 bigger problem forever. It's, it's a huge problem, and it's never really been confronted directly, like at any point. By the way, the physical world version of this is the drone, the drone problem. Um, right? And so we, we've known for, you know, we've known for 20 years now that the asymmetric threat, both in mili- military, in actual military conflict, but also in just like security, like, like, s- you know, security on the home front. The big threat is, is the cheap attack drone, right? The, the, the cheap, the cheap suicide, you know, drone with a bomb. And we've known that forever, and by the way, like, you know, it, it's very disconcerting how like every, you know, every office complex in, in the coun- you know, in the world is like unprotected from drone attacks. Um, every, every stadium, every school, every prison, like, it like- **Alessio** [1:03:22] Sure **Marc Andreessen** [1:03:22] ... it, it... You... Okay, we've known that. We've never done anything about it. **Alessio** [1:03:25] What are you gonna do about it? Yeah. **Marc Andreessen** [1:03:26] One possibility is just leave, leave them unprotected forever, and live in a world of like asymmetric terrorism forever, or the other is take the problem seriously and figure out the set of techniques and technologies required to, to be able to deal with that, whether those are lasers or jammers or early warning systems or, you know, all- **Alessio** [1:03:38] Personal force fields **Marc Andreessen** [1:03:39] ... kinetic personal for- Dune, uh, personal 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- **Alessio** [1:03:50] Yeah **Marc Andreessen** [1:03:50] ... to tell something a bot. It's very cheap to field a drone. It's very hard, it's very expensive to defend against a drone. But you see what I'm saying is it's, it's, it's the, it's the virtual version of the problem, and it's the physical version of the problem. Uh, the virtual version of the problem, what, what we need quite literally is proof of human. The reason is because you're, you're not, you're not gonna have proof of bot. The, the, the... Especially now. The, the bots are too good. The, the, 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, you know, cryptographically validated this is definitely a person, and this is... And then you can have cryptographically validated this is definitely like something that a person said. **Alessio** [1:04:23] Yeah. **Marc Andreessen** [1:04:23] This video is real, right? Um- **Alessio** [1:04:25] Just, just to double click on, on- **Marc Andreessen** [1:04:26] Yeah. Go ahead **Alessio** [1:04:26] ... uh, do you think Alex Blania with World- **Marc Andreessen** [1:04:28] Yeah **Alessio** [1:04:28] ... do you think he's got it? **Marc Andreessen** [1:04:29] Yeah. **Alessio** [1:04:29] Or is there an alternative? **Marc Andreessen** [1:04:30] 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, you know, participants in, in, in the World, in the World project, and, and I trust- **Alessio** [1:04:36] I understand. I know that, yeah **Marc Andreessen** [1:04:37] ... yeah. So we're, we're partisans. But yeah, I, I think, uh... So we think World is exactly correct. **Alessio** [1:04:40] Okay. **Marc Andreessen** [1:04:41] And, and the reason is it, it has, it has to be, it, it has to be proof of human. It, it has to... Because you can't do proof of not bot. You have to do proof of human. To do proof of human, you, you need bi- you need biological validation. You, you need it to start with this was actually a person, right? 'Cause otherwise you have bots signing up as fake people, right? So you, you have to have like something. You have to have a bio- a biometric, and then you have to have cryptographic validation, and then the ability to do- **Alessio** [1:05:01] Sure **Marc Andreessen** [1:05:01] ... to do, to do the lookup. And then by the way, the other thing you need, which that you, you also need selective disclosure. Um, so you need to be able to do proof of human without revealing- **Alessio** [1:05:08] Privacy **Marc Andreessen** [1:05:09] ... all the underlying information. By the way, another thing you're gonna need, you're gonna need proof of age, right? 'Cause and, and there's all these laws in all these different countries now around you need to be 13 or 16 or 18 or whatever to do different things, and so you're gonna, you're gonna need a, you know, sort of validated proof of age. Um, you know, to be able to legally operate, right? And so that, that's coming, and then you're gonna want like proof of credit score and, you know, proof of like, you know, 100 other things. **Swyx** [1:05:27] That's a tricky one. **Marc Andreessen** [1:05:28] It, it is a tricky one, but you're gonna, you're gonna... Uh, there, there's no reason... Like, if somebody's checking on your credit, somebody shouldn't... Look, let's give you an example. Somebody shouldn't need to know your name in order to be able to find out whether you're credit worthy, right? **Swyx** [1:05:37] I see. Independently verifiable inf- pieces of information. **Marc Andreessen** [1:05:40] Pieces of information. **Swyx** [1:05:40] Yeah. **Marc Andreessen** [1:05:40] It's like, it's like we disclosed, and this is the answer to the privacy problem writ large- **Swyx** [1:05:43] Mm-hmm **Marc Andreessen** [1:05:43] ... which is I, I only need to prove what I need to prove at that moment. So like you're gonna need that, and I, I think their, their, their architecture makes sense. So that needs to get solved. I think language models have tip- the bots are now too good. Uh, and, and, and so they're undetectable, and so as a consequence, y- we now need to go confront that problem directly. And then- **Swyx** [1:05:59] Sure **Marc Andreessen** [1:05:59] ... and like I said, and then the other problem is we, we need to go actually confront the drone problem. **Swyx** [1:06:02] The drones. **Marc Andreessen** [1:06:02] The Ukraine conflict has really unlocked a lot of thinking on that. **Swyx** [1:06:05] Mm-hmm. **Marc Andreessen** [1:06:05] Now the, um, and now the, the, the, the Iran situation- **Swyx** [1:06:08] Yeah **Marc Andreessen** [1:06:08] ... is also unlocking that. And so I think there's gonna be just like this incredible explosion of, of both drone and counter-drone. **Swyx** [1:06:13] Our drones are better than their drones. **Marc Andreessen** [1:06:14] Yes. **Swyx** [1:06:14] As long as we keep it that way. **Marc Andreessen** [1:06:15] Yeah. Yeah. Yeah. And count- and counter-drones. **Swyx** [1:06:18] I think we can sneak in one more question. ### Managerial AI **Marc Andreessen** [1:06:20] Sure. **Swyx** [1:06:20] Um, I'm trying to tie together a lot of things that you said over the years. So at the Milken Institute debate with Teal- **Marc Andreessen** [1:06:25] Yeah **Swyx** [1:06:25] ... which is amazing, um, you talked about the lag between a new technology and kind of like the GDP, um, impact of it. **Marc Andreessen** [1:06:32] Yeah. **Swyx** [1:06:33] The other idea you talked about is bourgeois capitalism and how, you know, this kind of managerial class was needed because of this complexity. And I think if you bring AI into the fold, you have like much higher leverage- **Marc Andreessen** [1:06:44] Mm-hmm **Swyx** [1:06:44] ... of people. So like if you have, you know, the Musk industries, um, and you give Elon AGI, he can run a lot more things, uh- **Marc Andreessen** [1:06:51] That's right **Swyx** [1:06:51] ... at once. **Marc Andreessen** [1:06:51] That's right. **Swyx** [1:06:52] And then you have the social contract, and I know you retweeted a clip of Sam Altman saying, um, we're rethinking the whole thing, and you're like, "Absolutely not." **Marc Andreessen** [1:06:59] Yes. Under- **Swyx** [1:06:59] Uh, and I wa- I was in an event with Sam last night, um, and he actually said in the last couple of weeks, he felt like now people are taking that seriously. **Marc Andreessen** [1:07:06] Yeah. **Swyx** [1:07:06] So I'm just curious like how you're seeing the structure of organization changing, especially when you invest in early-stage companies and, um, yeah, just like how the impact of work structure and, uh, all of that is playing out. **Marc Andreessen** [1:07:17] Yeah. So there's a whole bunch of, there's a whole bunch of topics. **Swyx** [1:07:19] I know, yeah. **Marc Andreessen** [1:07:20] We could, we could spend... And 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 the, this, this, this term managerial comes from this thinker in the 20th century, James Burnham, who, um, just one of the great kind of 20th-century political thinkers, uh, societal thinkers. And he sort of said a- as... And he was writing in like the 1940s, 1950s. Um, and he said kind of the, the whole history of capitalism until that point had been in two phases. Number one had been what he called bourgeois capitalism, which was, think about it as like name on the door. Like f- Ford Motor Company 'cause Henry Ford runs the company. Um, and Henry... It's like a dictat- 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- **Swyx** [1:07:55] Mm-hmm **Marc Andreessen** [1:07:55] ... tell so many people to do so many things, and then he runs out of time in the day. And so, um, he said the second phase of capitalism was what he called managerial capitalism, which was the creation of a professional class of managers, um, that are trained not to be like car experts or to be whatever experts in any particular field, but are trained to be experts in management. And then that led to, you know, the importance of like Harvard Business, you know, business schools and management consulting firms and all these things. And then you look at every big company today and like most of the executives at most of the Fortune 500 companies are not domain experts in whatever the company does, and they're certainly not the founders of those companies, but they're professional managers. And in fact, in the course of their careers, they'll probably manage many different kinds of businesses. They'll rotate around, and they might work in healthcare for a while, and then work in financial services, and then go work in something else. You know, come work in tech. And what Burnham said is he said that transition is absolutely required because the, the, the, the problem with bourgeois capitalism is, is it doesn't scale. Henry Ford doesn't scale. And so if you're gonna run capitalist enterprises that are gonna have millions to billions of customers, um, you're gonna need to... You're... They're gonna be operating a level of scale and complexity that's gonna require this professional management class. And he said, look, the, the professional management class has its downsides. Like they're not necessarily experts at doing the thing. **Swyx** [1:08:59] Mm-hmm. **Marc Andreessen** [1:08:59] They're not as inventive. You know, they're not gonna create the next breakthrough thing. But he's like, "Whether you think that's good or bad or whatever, it's what's gonna be required." And basically that's what happened, right? And so he wrote that book originally in like 1940. You know, over the course of the next 50 years, basically managerialism... Well, I mean, today, up till today, managerial, managerialism basically took over everything. **Swyx** [1:09:17] Mm-hmm. **Marc Andreessen** [1:09:17] And, you know, what I'm describing is basically how all big companies run and how all governments run and how our large scale nonprofits run and kind of everything, you know, everything runs. Basically, what, what, what venture capital does is we basically are a rump, uh, sort of protest movement to that to try to find the next Henry Ford or, or just to say E- Elon Musk or the, or the next, or the next Elon Musk or the next Steve Jobs or the next Bill Gates or the next Mark Zuckerberg. And so we, we, we, we start these companies in, in the old model, right? We, we, we start them out as, as, as, as like in the Henry Ford model. **Swyx** [1:09:44] Mm-hmm. **Marc Andreessen** [1:09:44] And so we start them out with a founder or a, or a, or a founder with, with colleagues, but you know, there's a f- a founder CEO. Um, and then we basically bet that, we basically bet that the startup is going to be able to do things, specifically innovate in ways that the big incumbents in that industry are not gonna 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- **Swyx** [1:10:02] Mm-hmm **Marc Andreessen** [1:10:02] ... this new innovative thing with like a king monarchical, uh, uh, political structure, um, that they're gonna be able to innovate in a way that the incumbent is not going to be able to because the incumbent is, is being run by managers, right? And, and, and, and by the way, and of course, venture being what it is, sometimes that works, sometimes it doesn't. But we're, 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. **Swyx** [1:10:21] Mm-hmm. **Marc Andreessen** [1:10:21] Like we're, we're, we're, we're sort of constantly trying to fight off managerialism just basically swamping everything- ... and everything getting basically boring and gray and dumb and old, right? And it's- **Swyx** [1:10:31] Right **Marc Andreessen** [1:10:31] ... we're trying to keep some level of energy and vitality in the system. AI is the thing that would lead you to think, wow, maybe there's a third model. **Swyx** [1:10:38] Mm-hmm. **Marc Andreessen** [1:10:38] Right? And, and maybe may- and way to think about it would be maybe it's a combination of the two. Maybe the new Henry Ford or the new Elon or the new Steve Jobs plus AI- **Swyx** [1:10:46] Right **Marc Andreessen** [1:10:46] ... is the best of both, right? 'Cause it's, it's, it's sort of the spark of genius of the name on the door model, the Henry Ford model, but then it's give that person AI superpowers to do all the managerial stuff and let the boss 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- **Swyx** [1:11:03] Right **Marc Andreessen** [1:11:03] ... it was a possibility. But y- I mean, you know this, the, what is the thing that these bots are really good, really good at doing paperwork. Like they're really good at filling out forms. **Swyx** [1:11:12] Right. **Marc Andreessen** [1:11:12] Like they're really good at writing reports. They're really good at reading re- they're really good at doing all the managerial work. Like they're amazing at it. And so, yeah, so I, I think, I think the... I 100%, I think the answer, the answer very well might be to get the best, be- best of both worlds by doing this. And then the challenge is gonna be twofold. The challenge is gonna be for the innovators to really figure out how to leverage AI- **Swyx** [1:11:30] Mm-hmm **Marc Andreessen** [1:11:30] ... to actually do this. Right. Um, and, and then, and then the o- the other challenge is gonna be for the, for the incumbents that are managerial to figure out, like, okay- **Swyx** [1:11:37] Right **Marc Andreessen** [1:11:37] ... what does that mean? 'Cause now they're gonna, they're, they're gonna be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to. And so the, the, the... It, this really, I think, is gonna force a lot of big companies to kind of figure out innovation. **Swyx** [1:11:47] Mm-hmm. **Marc Andreessen** [1:11:48] E- e- either I say figure out innovation or die trying. **Swyx** [1:11:51] Do you feel like that structure accelerates the impact on the actual GDP and economy? If you look at SpaceX- **Marc Andreessen** [1:11:56] Yes **Swyx** [1:11:56] ... is like the growth is, like, so fast. **Marc Andreessen** [1:11:58] Yep. **Swyx** [1:11:59] And, like, instead of having these companies kind of like peter out in growth and impact, they can kind of like keep going, if not accelerating. **Marc Andreessen** [1:12:05] Yep. That's for sure the hope. Um, the, the, the challenge... And, and, you know, and look, the AI utopian view is of cour- of course, and, and, and that's gonna be the future of the economy, and it's gonna grow 10X and 100X and 1000X, and we're- **Swyx** [1:12:14] Right **Marc Andreessen** [1:12:14] ... gonna enter in this regime of, like, much higher economic growth forever, and consumer cornucopia of everything, and it's gonna be great. And I, and, and I hope that's true. I hope that's... That's like the u- you know, that's the current kind of utopian vision. I hope that's true. The problem is, it goes back again, the real world is really messy. Um, and I'll give you an example of how the r- real world is really messy. It requires 900 hours of professional certification training to become a hairdresser in the state of California. Um, so it's like 35% of the economy, something like that, you have to get some sort of professional certification to do the job. W- which is to say that the, the professions are all cartels, right? **Swyx** [1:12:46] Yeah. **Marc Andreessen** [1:12:46] And so you have to get licensed as a doctor, you have to get licensed as a lawyer, you have to get licensed as a... You have to get into a union. **Swyx** [1:12:53] Mm. **Marc Andreessen** [1:12:53] Um, by the way, to, to work for the government, you need to be... You, you have both civil service protections and you have public sector unions. You have two layers of insulation, uh, against ever getting fired for anything or anything, anything ever changing. I'll give you another example. The, the dockwork- the dockworkers went on strike a couple of years ago- **Swyx** [1:13:08] Mm. Yeah **Marc Andreessen** [1:13:08] ... 'cause they were, you know, robo- robotics. You, you know, if, if you go look at a modern dock, like in Asia, it's all robots. If you go to u- u- American dock, it's like all still guys dragging stri- dragging stuff by, by hand. The dockworkers went on a strike. It turns out there are 25,000 dockworkers working on, on, on docks in America. It turns out they have incredible political power- **Swyx** [1:13:23] Mm **Marc Andreessen** [1:13:23] ... 'cause it's a, it's, it's one of these u- 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 things in that. So number one, we learned that even a union as small as 25,000 people still has, like, tremendous political stroke. We also learned that they... It actually turns out the dockworkers' union has 50,000 people in it, 'cause there's 20... They have 25,000 people working at the docks, they have 25,000 people drawing full paychecks sitting at home from prior union agreements. **Swyx** [1:13:47] Oh my God. **Marc Andreessen** [1:13:47] From prior union agreements. I'll give you another great example. There are government agencies, there are federal government agencies where the employees, right, have, have civil service protections and they're in public sector unions. There are entire federal government agencies that struck new collective bargaining agreements during COVID, where not only are they... have their jobs guaranteed in perpetuity, but they only have to report to work in an office one day per month. And so there are entire office buildings in Washington, DC that are empty 29 out of 30 days of the year that are still operating and are still... we're all still paying for it, 20... And so, and then what they do, it turns out what the employees do is they're very, they're very smart in the, in, in this way. And so they figure out, they come in on the last day of a month and the first day of the next month. And s- and so they're, so they're in their, they're in the office two days per 60 days. **Swyx** [1:14:28] Gotcha. **Marc Andreessen** [1:14:28] Which means these buildings are empty for 58 days at a time. And you see what I'm, you see where I'm heading with this. Like, th- th- this is, like, locked in. **Swyx** [1:14:35] Mm-hmm. **Marc Andreessen** [1:14:36] Right? This is, like, locked in in a way that has nothing to do with like... And people say capital- it's, it's like anti-capitalistic. It's like, it's, it's basically, it's restrictions on trade. It's restrictions on the ability to, like, change the workforce. And so, so much of our economy is, is, you know, the, the... I, 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. How are we gonna 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. **Swyx** [1:15:17] Mm-hmm. **Marc Andreessen** [1:15:17] Other than that, you're not gonna 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 gonna happen. So, so you see what I'm saying, is like there's this, like, massive slippage that's gonna take place. **Swyx** [1:15:29] Yeah. **Marc Andreessen** [1:15:29] Both the AI utopians and the AI doomers are far too optimistic. **Swyx** [1:15:32] Right. **Marc Andreessen** [1:15:34] You see what I'm saying? Because they believe that because the technology makes something possible, that eight billion people all of a sudden are gonna change how they behave, and it's just like, nope. So much of how the existing economy works- **Swyx** [1:15:44] Mm-hmm **Marc Andreessen** [1:15:44] ... is just, it, is just like wired in. **Swyx** [1:15:46] Yep. **Marc Andreessen** [1:15:46] And so we're gonna be lucky... As a society, we're gonna be lucky if AI adoption happens quickly, right? 'Cause if it doesn't, what we're just gonna have is stagnation. **Swyx** [1:15:53] Awesome. Marc, I know you gotta run. **Alessio** [1:15:55] Yeah. We all know... Or stay welcome. But, uh, it was such a pleasure talking to you. Uh, we're truly living in an age of science fiction coming to real life. **Marc Andreessen** [1:16:02] Yes. Yes. Could not be more exciting. **Alessio** [1:16:03] Yeah. **Marc Andreessen** [1:16:04] Thank you, Marc. **Swyx** [1:16:04] Really thank you, Marc. You guys, awesome. **Alessio** [1:16:05] Thank you. **Marc Andreessen** [1:16:06] Good. **Alessio** [1:16:06] That's it. **Marc Andreessen** [1:16:06] Good. Thank you. **Swyx** [1:16:07] That's it. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.