# Marc Andreessen introspects on Death of the Browser, Pi + OpenClaw, and Why "This Time Is Different"

*Latent Space* · https://www.youtube.com/watch?v=knx2wrILP1M

## Summary

Marc Andreessen joins Alessio and Swyx at the a16z office for a wide-ranging conversation on why this AI moment is an "80-year overnight success," why the scaling laws will continue, and why betting against AI would be "suicidal." He walks through the dot-com telecom crash as a cautionary analogy for today's GPU buildout, explains why Pi plus OpenClaw is one of the ten most important software breakthroughs ever, and predicts the death of browsers, user interfaces, and possibly programming languages. He closes with arguments for proof-of-human (World), crypto as the agent payment layer, and why both AI utopians and doomers are too optimistic given how locked-in and cartelized the real economy is.

## Key points

- Andreessen frames current AI progress as an "80-year overnight success" unlocking decades of research dating back to the 1943 neural network paper.
- He identifies four fundamental breakthroughs now working: LLMs, reasoning (o1/R1), agents (OpenClaw), and self-improvement (RSI).
- He argues old Nvidia chips are gaining value because software improvement outpaces chip depreciation, calling Michael Burry's Nvidia short thesis "180 degrees wrong."
- He describes an agent as LLM plus shell plus file system plus markdown plus cron, with the agent's state living entirely in its files, making it model-independent and self-modifying.
- He predicts high-quality software becomes infinitely available, computer security goes through an apocalypse-then-fix cycle, and programming languages may cease to be a salient concept.
- He says AI is the crypto killer app: agents need money, and stablecoins finally solve the HTTP 402 "payment required" gap.
- He argues proof-of-human with biometrics, cryptography, and selective disclosure (World) is required because bots can now pass the Turing test, paralleling the physical-world drone defense problem.
- Drawing on James Burnham, he suggests founder-plus-AI may combine bourgeois and managerial capitalism, but warns that cartels, unions, and government monopolies (e.g., K-12 education) will block rapid AI adoption.
- He views the dot-com crash as a telecom bandwidth overbuild that wiped out ~$2 trillion, but says today's AI capex is safer because blue-chip companies fund it and every GPU dollar converts to revenue immediately.
- He expects a chronic compute supply shortage for years, with inference demand (friends paying $1,000/day in tokens) far exceeding what consumers can pay.

## Notes

### Why This Time Is Different
- Andreessen calls the current era an "80-year overnight success": ChatGPT, o1, and Claude look like sudden breakthroughs but unlock research going back to the 1943 neural network paper and the 1955 Dartmouth AGI conference.
- He identifies four fundamental breakthroughs that are all working: LLMs, reasoning (o1 and R1), agents (OpenClaw), and now self-improvement (RSI).
- The Linus Torvalds comment that AI coding is better than he is serves as a benchmark that coding will sweep through and everything else will follow.

### Scaling Laws and Investment Risk
- Moore's law was itself a scaling law: a prediction that became self-fulfilling because the whole industry worked to keep it true. He expects the same for AI scaling laws, including undiscovered ones like world-model data acquisition.
- The dot-com crash was really a telecom bandwidth crash: fiber was overbuilt against a "traffic doubles every quarter" assumption, wiping out ~$2 trillion, with capacity taking until 2015 to fill.
- Today's buildout differs: blue-chip companies (Microsoft, Amazon, Google, Nvidia, OpenAI, Anthropic) fund it, and every dollar into running GPUs converts to revenue immediately amid a chronic supply shortage.
- Old Nvidia chips are getting more valuable, not less, because software progress outpaces chip depreciation — Michael Burry's Nvidia short was "180 degrees wrong."

### Pi + OpenClaw
- An agent is: LLM + Unix shell + file system + markdown + cron/heartbeat. The agent's state is just its files, so you can swap the model, shell, or file system and keep the same agent.
- Agents have full introspection, can rewrite their own files, migrate themselves, and add new capabilities on request.
- This liberates latent power in existing systems rather than reinventing them — the same philosophy behind the browser's text protocols, human-readable HTML, and view source.

### Economy and Society
- AI is the crypto killer app: agents need money, and stablecoins finally solve the HTTP 402 payments gap.
- Proof of human (World) with biometrics, cryptography, and selective disclosure is needed because bots pass the Turing test; the physical analog is the drone defense problem.
- Via James Burnham: founder-plus-AI may combine bourgeois and managerial capitalism, but cartels, unions, licensing, and government monopolies mean both utopians and doomers are too optimistic about how fast 8 billion people will change.

## Transcript

[00:00] 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 you know, or even 70 years where that was controversial. And so so the way I think about what's happening is basically I think I think about basically the the period we're in right now is it's I call it 80-year overnight success. Right, which is like it's an overnight success cuz it's like bam, you know, chat GPT hits and then

[00:30] and then 01 hits and then you know, open claw hits and like you know, these are open these are these are like overnight like radical overnight transformative successes, 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 [music] research. If I were 18, like this is a hundred this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. 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

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[01:44] Everyone, welcome to the Latent Space podcast. This [music] is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Hello, and we're in a16z with A uh Martin Casado, welcome. Yes. Yes, A and what, half of 16? >> [laughter] >> A1. Exactly. Uh apparently this is the the final few days in your your current office. You're moving across the road. Uh we're yeah, we have a we have we have some we have some projects underway, but yeah, this is actually this is the original. We're in actually the original office. We're in the we're in the we're in the we're in the whole thing. Yeah. >> Great. Thank you. So I have to come out. This is a you know, I just wanted to pick a spicy start. In October 2022, I

[02:18] just made friends with Rune and I wanted to give him something to sort of be spicy about and I said uh you'll never not be funny that A16Z was constantly going the future is where the smart people choose to spend their time and then going deep into crypto and not in AI and that was in October 2022 and Rune says there was an internal meeting in A16Z to reorient around Gen AI. Obviously you have, but was there a meeting? What what was that? I mean, I don't look, I've been doing AI since the late '80s. So I don't know like all that. As far as I'm concerned, this stuff is all Johnny-come-lately. Yeah, yeah, I mean look, we've been doing AI our entire existence. I mean, we've been doing AI machine learning

[02:53] deep you know, deep learning. We've been doing this stuff way from the beginning. But obviously, AI is just core to computer science. I I I actually view them as like quite >> [snorts] >> 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. There was like a there was a big AI boom at the time. Um and there was a there was one under names like expert systems um and with the era of like Lisp and Lisp machines. Um I I coded in Lisp. I was coding in Lisp in 1989. Where that was the the language of the AI future. Um yeah, so this is something that we're like completely you're completely

[03:25] comfortable with. We've been doing the whole time and are very enthusiastic about. Is there a strong like this time is different because uh my closest analog was 2016-17. There was there was an AI boom and it petered out very very quickly. Uh >> Well, it just it it just didn't sort of invest. >> Sort of. Sort of. Investment investment excitement. Although that's really when the the Nvidia phenomenon really it was it I would say it was in that period when it was very clear that it at the time 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. Yeah. Well, and as you guys you guys have talked about this at length on your

[03:56] 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 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. Yeah. 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 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, you know, they they started using machine learning very early. Um and you know, I've used it basically, you know, for like 20 years for, you

[04:25] know, content, you know, feed optimization and advertising optimization and obviously many, you know, [snorts] 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? Um and and the layers arrive at different paces. And but they kind of build up. Yeah. 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 transformer. And then and then as you guys know, there was this really weird like four-year period where it's like

[04:52] the the transformer existed and then it was just like >> [snorts] >> It's cool. Yeah. Well, [laughter] but but it was just but but between 2020 but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chatbots, but they weren't letting anybody use them. Yeah. Right. And then, you know, and then OpenAI developed ChatGPT or GPT-2, and then they told everybody this is way too dangerous to deploy, right? We're 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 It's a So, the old for there was like a year where like the only way for a normal person to use GPT-3 was in in AI Dungeon. Yeah. And so, you you we

[05:24] 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 big companies you know, big companies are cautious. And you know, the big companies were cautious. It It By the way, it took OpenAI you know, they they they talk about this. It took OpenAI time to actually adjust, you know, kind of re- redirect their research path. >> I think it was at Rosewood, right? Uh that the dinner they founded OpenAI was right there. Right. But that that dinner would have taken place in 20 >> 18 18 The formation of OpenAI is late as 2018? I I sorry no I'm I'm I'm I'm

[05:55] wrong. Probably should be 20 They just celebrated their 10-year anniversary so it it is 2025. Yeah. That's so 2015. Yeah, 2015 yeah 2015. But then Alec Radford did GPT-1 in >> What? Probably 17 18 yeah. >> 17 18 so it yeah for the and then and then they didn't really and then GPT-3 was what? 20 20 20 2020 Because that became co-pilot immediately yeah. Even OpenAI which has been you know the leader of the of this thing in the last decade you know even they had to adapt and and and lean into the new thing and so yeah I I think it's just this process of basically sort of wave after wave layer after layer you know building on itself and then you kind of get these catalytic moments where where the whole

[06:30] thing pops. And obviously that's what's happening now. Is it useful to think about will there be an AI winter because there's always these patterns? Like is it 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 >> [laughter] >> Or are we will there be a winter? So there's something about say the following there's something about AI that has led to this repeated pattern. And and and you you guys know this It's summer winter summer winter summer winter summer winter and it goes back 80 years. 80 years. So the original neural network paper was 1943 right which is which is amazing that it was it was far back that

[07:09] long. And then there was you have you guys ever talked about this on your show but there was this there was a big there was an AGI conference at Dartmouth University in 1955. 55 Yeah. And they got an NSF grant to for the all the AI experts at the time to spend the summer together and they figured if they had 10 weeks together they could get AGI >> [laughter] >> out the other end. And they got there by the way they got the grant they got the 10 weeks and then you know 1955 you know no no AGI. And like I said 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 there there is something about AI that causes the people in the field I would say to become both

[07:39] excessively utopian and excessively apocalyptic Um and it's we're probably on both sides of like the the the boom bust cycle you you kind of see that play out. Having said that, I think what's actually happened is like just at in you know in 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 that neural network is the correct architecture. And I I will tell you like there was a 60-year run where that was like a you know or even 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 is sort of derived from the original idea 1943. And so so in retrospect we

[08:10] now know that like these these guys were right they you know they would get the timing wrong and they thought you know capabilities would arrive faster or they were it could be turned into businesses sooner or whatever. But like they were fundamentally the scientists who worked on this over the course of decades were fundamentally correct about what they were doing. >> [snorts] >> And and and the payoff from from from all their work is happening now. And so so the way I think about what's happening is basically I think 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 cuz it's like bam you know chat GPT hits and then and

[08:38] then 01 hits and then you know Claude 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. Look, there were AI researchers who spent their entire lives. They got their PhD. >> [laughter] >> They worked for they researched for 40 years. They retired. In a lot of cases they passed away and they never actually saw it work. Yeah. So sad. It is it is sad. It is sad. Jeff Hinton was like the

[09:11] last guy. Yeah [laughter] yeah well there were guys out there Alan Newell I mean there was tons of John McCarthy John McCarthy was like one of the inventors of the field. He's one of the guys who organized the Dartmouth conference and you know he taught at Stanford for 40 years and passed you know passed away I don't know whatever 10 10 years ago or something. Never never actually got got to see it happen. But like 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 as they say history doesn't repeat but it rhymes. It's like okay does that mean that there's going to be another like you know basically boom bust cycle and I I

[09:39] will tell you like look like in a sense I guess everything goes through cycles and you know people get overly enthusiastic and overly depressed and there's there's a time there's a timelessness to that. Having said that there's just no question. So The four most the four most dangerous words in the >> is different. This time is different. Do you know the 12 most dangerous words in investing? >> No. The four most four most dangerous words in investing [laughter] are this time is different. So the 12 most dangerous words. And so like I'll tell you what's different like Now it's working. Like like there's just no I mean look there's just no question. And by the way I'll just give you guys my take like LLMs like from from

[10:13] basically the chat GPT moment through to spring of 25 I think you could still I think well intentioned well informed skeptics could still say oh this is just pattern completion and oh these things don't really understand what they're doing and you know the hallucination rates are way too high and you know this is going to be great for creative writing and creating you know Shakespearean sonnets and you know as as rap lyrics or whatever like it's going to be great at all that stuff but we're not going to be able to harness this to make this relevant in you know coding or in medicine or in law or in you know kind of fields that you know kind of really really matter. And I think basically it was the reasoning

[10:45] breakthrough. It was 01 and then R1 that basically answered that question basically said oh no we're going to be able to actually turn this into something that's going to work in the real world and then and then it obviously the coding breakthrough over the basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that. Where it's like [snorts] all right if if you know if Linus Torvalds is saying that AI coding is now better than he is like >> [laughter] >> Like that that's that's never happened before. >> That's the benchmark yeah. That's never happened before and so now we know that it's it's going to sweep through coding and and then and then we we know you

[11:12] know we know that if it's going to work in coding it's going to work in everything else right? It's just that that cuz that's that's like that's like that's like the hardest in many ways that's the hardest example and now everything else is going to be a derivative of that. And then on top of that we just got the agent breakthrough but you know with open claw which is fantastic which is amazing and incredibly powerful. And then we just got the the the auto research you know know, the self improvement you know we're now into the self improvement breakthrough. And so the so the way I think about it is we've had four fundamental breakthroughs in functionality. LLMs reasoning, agents um and then and and then now RSI. Um and

[11:42] they're all actually working. Um and so I'm I'm just as you can I'm jumping out of my shoes. [laughter] Like like this is like this is it. Like this is the culmination of 80 years worth of worth of work and this is the time it's becoming real. Yeah. Yeah, I I'm completely convinced. 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. With AI it's it's so jagged and like the jumps where like like you said it's like in 3 months you have like this huge jump like and people are like, well, this can't keep happening, right? But then it keeps happening.

[12:13] >> It'll keep happening. 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 for a model versus like the next model just going to do it one shot in the latest space. And how does that inform like how you think about the shape of the technology? You know, you talk about how it's a new computing platform. If you have a computing platform then like every 6 months it like drastically changes in what it looks like. It's hard to build companies on top of it. Yeah, so so a couple things. So one is like, look, the the Moore's law was what we now call a scaling law. Like Moore's law was a

[12:45] 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, that's what took the mainframe computer from a $25 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,

[13:14] 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 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 that was a 50-year run, right? And it it was amazing and it's still happening in in some areas of 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 they're not really laws, but like they they are

[13:43] 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 dollars you know, required to basically keep you know, keep the curves going. And and look it's going to be complicated and it's going to be variable and there you know, there going to be walls that are going to look like they're fast approaching and then they're going to be you know, engineers are going to get to work and they're going to figure out a way to punch through the walls. And obviously that's you know, that's been happening a lot. You know, and then look there's going to be times when it looks like the walls have you know, the the the laws have

[14:08] petered out and then they're going to they're going to pick up again and surge and then and then and then it it appears what's happening to the eyes 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 already have to be discovered, but there are probably some more that we don't know about yet. You know, like for example, there's probably some scaling law around um world models and that we don't fully 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

[14:35] And so yeah, I I think the expectation is that you know, the the scaling laws generally are going to continue. Yeah, the the pace of improvement will continue to move really fast. Um To your question on like what to build. So I'm a complete believer the scaling laws are going to continue. I'm a complete believer the capabilities are going to keep getting amazing you know, leaps and bounds. The part where I kind of part ways a little bit with I would what I would describe as the AI purist you know, which is which I would characterize as like the people who are many ways the smartest people in the field, but also the people who spend their entire life like in a lab. Um [snorts] and have to have I would say

[15:05] have very little experience in the outside world. Um the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated. Um and and doesn't you know, it it 8 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 they're 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 this obvious set of things that society needs to do. Mhm. [snorts] And then they're like society's not doing any of those

[15:38] things. Right? And it's like how can society not you know what whatever their theory is how can society not see XYZ? And the answer is well society is number one there's no single society it's like 8 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 there's no question people are going to like there's no question there're going to be companies it's already happening. There are companies that think that they're

[16:07] building value on top of the models and then they're just going to get blitzed by the by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real 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 going to be messy and complicated. And there are going to be a lot of companies and a lot of products um and in in in fact entire industries that are going to get built that to basically actually help all of this technology actually reach real people. >> [snorts] >> The amount of capital going into these companies I mean Dario talked about it

[16:36] 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 going to go bankrupt if the model doesn't exactly hit the the performance level. How do you think about that also as a risk on you know you guys are investors at Inflection and Inflection AI and Thinking Machines and World Apps it seems like we're leveraging this gaming loss at a pretty high rate. Like how comfortable I guess do you feel with the downside scenario? Like and say like things peter out you think you can kind of like restructure these build outs and you know capital investments? Yeah so I should start by saying so I lived through the dot com crash and I can tell you stories for hours

[17:12] about the dot com crash and it was horrible. No it was awful. It was it was it was apocalyptic. By the way the 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 with the tele- the telecom companies. Global Crossing. Global Global Yes. I'm from Singapore and they laid so much cable over over our oceans. Actually, there was a scaling law in the dot-com era and it was literally the the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter. Uh and and actually in 1995 and 1996 internet traffic actually did double every quarter. And so that became the

[17:45] scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber anticipating that the demand for bandwidth was going to keep doubling every quarter. Doubling every quarter though is like, you know, grains of chess on the chessboard. Like at some point the numbers become extremely large, right? And and and it really and really what happened was the internet the internet by the way continuously kept growing basically since inception and it's, you know, it's it's continuously grown. It's never shrunk. And it's grown really fast compared to anything else, you know, in in in human history. But it wasn't doubling every quarter as of 1998-1999. And so there was this gap in the

[18:13] expectation of what they thought was the scaling law versus reality. And that's actually what caused the dot-com crash, which was they they wait over companies like Global Crossing wait overbuilt fiber. Which is sort of the By the way, fiber, telecom equipment, you know, so all that 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 overbuilt. And so you had the but it was it was literally I think it was like $2 trillion got wiped out, right? It was like >> Jesus It was like a big It was And by the way, the other the other subtlety in it was the internet companies themselves never

[18:46] really had any debt cuz 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 over over you're overbuilding capacity. Demand is growing but not as fast as you hoped. And then boom, bankrupt, right? And then and then it's 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 has to go bankrupt twice.

[19:12] 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. >> [gasps] >> So, by the way, all of those data centers and all of those all the fiber that they're in use it's all in use today. But 25 years later. >> [snorts] >> But it took it actually the elapsed time was it took 15 years. It took 15 years from 2000 to 2015 to actually fill the fill up all the 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.

[19:43] But for sure I can put build data centers and for sure I can buy GPUs and I can deploy you know the compute grids and and all these things. Um and so you know you 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 we went through in 2000. Obviously that would be bad. The counterargument which is the one I I agree with which is the counterargument on the other side is a couple things. One is the companies that are investing all the the companies that are investing the money are like the bluest chip of companies. So that back back in the in the like Global Crossing was like an it was like an entrepreneur

[20:13] it's 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 right and Facebook and Nvidia and you know these these these and and now you know by the way Open AI and Anthropic which are now at like you know really serious size um you know as companies with you know very serious revenue. These are very large scale companies with like lots lots of cash lots of debt capacity that they've they've never used. And so this is institutional in a way that that really wasn't at the time. And then the other is at least for now every dollar that's being put into anything that results in a running GPU is being turned into revenue right away. Like so and you

[20:47] 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 every dollar right now that's being put in the ground is turning into revenue. And and it 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. That's true. If if right, suppose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful, the models would be much

[21:16] better cuz you would just allocate a lot more money to training and you'd just build better models and they would be better. And so we're we're actually getting the sandbag version of the technology. >> Yeah, no everything we use is quantized because the the labs have to keep the the full versions. Right. >> [laughter] >> We're not even getting the good stuff. Yeah. But but getting the good stuff is is just even if technical progress stops, once there's like a much bigger build of like GPU manufacturing capacity and memory, you [clears throat] 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 is going to get going to get

[21:46] much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Like 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 in every domain of human activity and then it works like incredibly well. Yeah. Um here's what I know. Here's what I know. Um in the next three or four years, it's like somewhere between three or four years out, basically everything is selling out. So like the the entire supply chain is is is is sold out or or selling out. And so there there's no like it I we're just going to have like chronic

[22:15] supply shortage for you know, for years to come. Um there's going to be a response from the market that's going to result in an enormous, you know, it's happening now, an enormous flood of investment in a new fab capacity and you know, 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 cuz the products will get better and everything will get cheaper. Um and so so I know that's going to 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

[22:44] I know they're just going to get like much much better from here. And so I 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 it it is accelerating. Like the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you You I think if you were a cynic and there there are cynics, you can look at 2000, you can find echoes, but I can't even imagine betting on that this is going to like somehow disappoint and you know, at least for years to come. I think it would be essentially suicidal to make that bet. Um is that Michael Burry? Uh oh That's an interesting guy, huh?

[23:15] >> [laughter] >> We'll pick on a guy we'll pick let's pick on one guy. We'll pick cuz he did he came out with it was it was the He doesn't mind. It was the Nvidia short, right? He came out with the Nvidia short. And then did if you guys probably talked about this which is the the analysis now that the current models are getting better faster at such a rate that if you are running an Nvidia If you're running an Nvidia inference chip today that's 3 years old, you're making more money on it today than you did 3 years ago. Because the pace of improvement of the software is is faster than the the depreciation cycle of the chip. And then my understanding is Google is running I don't know if they I don't know exactly what I this is rumors

[23:44] that I've heard or maybe it's public, but I think Google's running very old TPUs Very profitably. And very profitably and very profitably. Um and so so it actually turns out as far as I can tell that it's actually the opposite of the Burry thesis is actually he was actually 180° wrong. It's actually the the old Nvidia chips are getting more valuable. Which is something that's like literally never happened [laughter] before. Like it's never been the case that you have an older model chip that becomes more valuable not less valuable. And then and again that's an expression of that just ferocious pace of software progress, ferocious pace of capability payoff that you're getting on the other side of

[24:15] this. And so I just the idea of betting against that like Yeah, yeah. It's like an invitation to get your face ripped off. >> [laughter] >> What my really hits was like modeling the lifespan of the H100 and H200s and and going like you know, usually they advise like 4 to 7 years and it was you know, maybe you sort of realistically cut it down to 2 to 3, but actually it's going up and not down. And and that's I mean that's I think that's the dream. We are finding utilization and I think utilization solves all problems. Like you can you can find use use cases for even like the poor like even memory we're having a shortage, right? And and even like the the shittier versions of of memory that we do have we are finding

[24:51] use cases for it. So like that's great. Yeah. How how important is open source AI and kind of like edge inference in a world in which you have three years of supply crunch. Like you think in the like you know, if you fast forward like five years, like how do you think about inference in the data center versus at the edge? 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 going to have fundamental construct supply crunches for the next I mean, you guys know that if you just project forward demand over the next three years

[25:21] relative to supply, one of the dismaying predictions you can do is what's going to what's going to happen to the cost of of inference in the core over the next three years. And like it may rise dramatically, right? Like So, so what and then is is you know, like the the big model competition is subsidizing heavily right now, right? And so, so what's the what will be the average person's you know, per day per month token cost, you know, three years from now to do all the things that they want to do? And I I don't know what's going to be like I mean, I have you guys probably have friends. I have friends today who are paying a thousand dollars a day for open cloud for cloud tokens to run open cloud, right? And so, okay,

[25:50] $30,000 a month, right? And and by the way, those friends have like a thousand more ideas of the things that they want to claw to do, right? And so, you could imagine there's like latent demand of up to I don't know, five or ten thousand dollars a day of of of tokens for a fully deployed, you know, personal agent. And obviously, consumers can't pay that, right? And so, so but it gives you a sense of the of the few of the future scope of demand, right? And so, so even even if there's a ten x improvement in price performance, that's still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay. So, there's just going to be like ferocious demand. By the way, the agent

[26:19] thing the other interesting thing is I think the agent thing so, up until now a lot of the constraints have been GPU constraints. I think the agent thing now also translates into CPU constraints, CPU and memory, yes. CPU and memory, right? And so, like the entire chip ecosystem is just going to get >> With with our network constraints, that will be the killer. It's all bottlenecked potentially for years. And so, so I I think that Brad and I think it's actually possible, I mean, generally inference costs are going to keep coming down, but I think the let's put it this way, the rate of decline I think may level out here for a bit because of these supply constraints. And then at some point maybe the labs stop

[26:46] subsidizing so much and that that that again will be a be an issue. And so, there's just going to be so much more demand for inference than than can be satisfied you know kind of with a centralized model and then and then you know you guys know this but like all the just the dramatic I mean just the dramatic innovations that happened in the Apple Silicon to be able to do inferences it's quite amazing. The level of effort being put like the open source guys are putting incredible effort into getting you know this recurring pattern where the big model will never run on a PC and then six months later up 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

[27:16] also you know there's also like other there's other motivators there's other motivators which is just like okay how much trust are the big centralized model providers you know how much trust are they building in the market versus you know how much are you know at least for in certain cases with some people for certain use cases people being like well I'm not willing to just like turn everything over. So there there there's all the trust issues. By the way there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud you just need like a smart local model. There's also performance issues where you want to you know you want you know you're going to

[27:47] want your doorknob to have an AI model in it. >> Right. You know to be able to you know do you know to be able to do access control. Obviously like everything with a chip is going to have an AI model in it and a lot of those are going to be local. And so yeah no like I think I think you're going to have and then you're going to by the way also wearable devices you know you you don't want to do a complete round trip you want you know whatever your smart devices are you want it to be like super low latency. Yeah. The question do we care who makes it? One of the biggest news this week was the collapse of AI2 the Allen Institute one of the actual American open source model labs. Yeah. And I'm not that optimistic in the on

[28:20] American open source. Like you you guys invested in Mistral and Mistral's doing extremely well outside of China. That's about it. Yeah we'll see. We'll see. Look I number one I do think we care I do think we I do think we care who makes it. I would say this the the previous presidential administration wanted to kill it in the US. They wanted to drown it in the bathtub. And [snorts] so they wanted to kill it. So at least we have a government now that actually like actually wants it wants it to happen. >> And you're on the council? And and I yeah and the new and the PCAST yeah. So the you know this admin a for whatever other political issues people have, which are many, you know, this administration has I think a very

[28:50] enlightened view and in particular an enlightened view on AI and in particular on open source AI. Uh and so, they're very supportive. Um my read is the Chi- the Chinese have a very the various Chinese companies have a very specific reason to do open source, which is 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 is a bit of a loss leader against basically domestic um you know, paid paid services and then kind of you know, kind of ancillary products. You know, they're they're very excited about it. By the way, I think it's great. I think

[29:21] it's great that they're doing it. Um you know, I think Deep Seek 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 the paper 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. [snorts] And and then and then

[29:50] everybody's like, okay, this is great, but like who's going to be able to replicate this? How are other people going to 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, 3 months later every other AI model is is adding reasoning. And so, so you get this kind of double like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world the information diffusion, you know, is incredibly powerful. So, that happens. And then I don't know. We'll we'll see. You know, there are a bunch of American, you know,

[30:17] open source you know, AI model companies. I mean, look, there's going to be tremendous, you know, there already is. There's you know, there's going to be treme- there's tremendous competition among the primary model companies. You know, there's depending on how you count, there's like four or five, you know, big co- model companies now that are, you know, kind of neck and neck in different ways. Um you know, and and and um you know, and then obviously both both both X and then Meta where I'm involved you know, both have huge, you know, huge attempts to, you know, kind of to kind of kind of leapfrog on the way. And then you've got, you know, a whole fleet of startups, new companies, including a

[30:46] whole bunch that were backed in that are, you know, trying to kind of come up with different approaches. And then you've got whatever it is, I don't know. How How many How many like main line foundation model companies are there in China at this point? It's probably six >> tigers, is what they call it. Uh Quen is in in questionable because of there's changing leadership. >> Right. Yeah. But that doesn't include that that includes like Moonshot. >> Yes. Kimi, DeepSeek, uh ZAI, um Quen, 01 is in there. Right. And then um and ByteDance and and then you you see that ByteDance would be like the next tier. They weren't as prominent. >> They weren't but now they're Yeah, but they're you know, at least you know,

[31:19] SeaDance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and and so forth. And so So So like look, here here would be a thing you can 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 some level of like critical mass. It's not going to be a dozen in 3 years, right? Like it just because these industries don't bear a dozen. It's It's going to be three or you know, there's going to be three or four big winners or maybe one or two big winners. And so there's going to be like a whole bunch of those guys that are going to have to

[31:45] 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 question's like who's going to do open source? I think that could change really fast. I I think that 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. Nvidia's doing a lot. You mentioned Well, I was going to say Well, exactly. And then you got Nvidia and then and then, you know, just again industry for There's an old thing in business strategy which is called a commoditize the commoditize the complement. And so right, it's like if your Jensen is just kind of obvious. Of course you want to commoditize the

[32:12] software. And he's And to his enormous credit, he's putting enormous resources behind that. And so maybe maybe it's literally Nvidia. And I think that would be great. Yeah. Uh narrative violation two European projects. Uh innovating. >> [laughter] >> Bam. I I'm mostly in my uh Europe uh conference soon and I got both of them. They got us. They got us, Mark. They got us all. Wait a minute. Where was Peter So where was Steinberg when he did Open Indiana? >> Yeah, yeah, yeah. He was in what? He was in Vienna. >> Oh, he was in Vienna. And then where is he now? Uh he's moving to SF. Okay. Okay. [laughter] All right. Okay. There we go. And then yeah, the Pi guy right? The Pi guys are European. Yeah, they're they're

[32:44] buddies in Austria. Mario's also there. Yeah. Right. And are they Yeah, they haven't announced yet any sort of change change or have they? No, they're they have a company there. Okay, good. Okay, good. Good, good. Um Yeah, good. Anyways, yeah, I think Pi and Open Claw are very important software things and and I just wanted you to just go off on what you think. Yeah. So, I think in the combination of the two of them, I think it's one of the 10 most important software Open Claw got all the attention, but talk about Pi. Pi Pi's kind of the end Yeah, Pi's Pi's 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

[33:17] like 1970 to I don't know. It still is very important, but like 19 from 1970 through to like basically the creation of Linux, which is basically this this thing we used to call like the Unix mindset. Like so so cuz there were all these different, you know, theories there all these different operating systems and mainframes and and then, you know, all these Windows and Mac and all these things. And then there was this but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these like like in the old days like like the operating system that like made the computer actually really work like in the 1960s was this thing called OS 360, which was

[33:47] this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a you know, it was like a giant castle um of software. And and by the way, it worked really well and they were very successful with it, but like it was this huge castle in the sky, but it was this thing it was almost unapproachable, which is like you had to be kind of inside IBM or very close to IBM and you had to really understand every aspect 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

[34:13] architecture is going to work is we're going to have we're going to have a a prompt and and a shell. And then and then we're going to all the functionality is going to be in the form of these discrete modules. And then you're going to be able to chain the modules together. And so like the the it's almost like the operating operating system itself is going to 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 the sort of you know, basically chaining together Unix tools and then that led to the emergence of these these scripting languages like Pearl where you could basically kind of very easily do this. And then the shells got more

[34:40] sophisticated. And then and then and then look like you know that that number one that worked. And that that was the world I grew up in like I was I was a Unix guy you know sort of from call it 1988 to you know kind of all the way through my work. And it worked really well. It it's in the background you know normal people don't need it didn't need it necessarily know about it. But like if you were doing like system architecture application development you knew all about it. And then you know it's been in the background ever since and you know look your Mac still has a Unix shell you know kind of in there and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there and then you know the

[35:10] Windows shell is kind of a you know sort of a weird derivative of that but you know but look the the internet runs on Unix and then smartphones actually both iOS and Android are Unix derivatives and so you know kind of Unix did end up winning but but anyway and then we just started taking that for granted and then and then so so basically the way I think about what happened with Pi and then with Open Claw is basically what those guys figured out is I would say the the great breakthroughs are obvious in retrospect right which is the best kind. The best kind. They weren't obvious at the time or somebody else would have done them already. And so there is a like a real conceptual leap but then you look at it sort of

[35:40] backwards looking and you're just like oh of course. Like to me those are always the best breakthroughs. Well actually language models themselves are like that it's just like oh next token completion oh of course. Yeah what other objective mattered? Yeah exactly [laughter] but but like right but she's even saying it wasn't obvious until somebody actually did it right. And so the conceptual breakthrough is real and deep and powerful and and very important. And so the way I think about Pi and Open Claw is it's basically marrying the the language model mindset to the to the Unix basically shell prompt mindset. And so it's it's basically this idea that what what so what is an agent? Right and as you know like many smart people

[36:11] 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 it's it's language model and then above that it's a bash it's a bash shell. So it's it's a Unix shell. And then it's in then the agent has access has access to to the shell in you know hopeful hopefully in a sandbox maybe in maybe in a sandbox. So it's it's the model it's the shell and then it's a it's a file system and then the state is stored in files. And then you know there's the markdown format for the you know for for the files themselves and then and then

[36:40] there's basically what in Unix is called a cron job. There's a loop and then there's a heartbeat there's a heartbeat and 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 every part of that other than the model is something that we already completely know and understand and in fact it turns out that like the latent power of the Unix shell is like extraordinary. Because basically like all like there's just like there's just enormous latent power in the shell there's enormous numbers of Unix commands there's enormous number of command line interfaces into all kinds of things already in the you know your entire I

[37:11] mean your entire just to start with your computer runs on a shell. If you're running a Mac or a phone your computer your computer is running on a shell. Already and so like the full power of your computer is available at the command line level and then it turns out it's really easy to expose other functions as a command line interface. And so like this this whole idea where we need like MCP and these like product 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

[37:39] 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 cuz the model is different but all of the state stored in the files will be retained. Different instruction set but you just compile it. >> Right exactly and then 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 and with all of its capabilities and then by the way you can also swap out the shell so you can move it to a different execution environment

[38:06] that is also is also a bash shell. By the way you can also switch out the file system. Right and you can and you can 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 basically at the end of the day it's just it's just its files Um, then and then there is of course the purple cloth. Yeah, it's it's basically it's just the files. >> [laughter] >> 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, you you you you can instruct your agent, "Migrate yourself to a different uh runtime environment.

[38:36] Migrate yourself to a different file system. Migrate yourself to a different you know, like we swap out the language model." Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually it actually knows about its own files, and it could 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 like that. That I mean, there've been toy systems that have had that, but there there's never been a widely

[39:03] deployed system that has that capability. And then that leads you to the capability that just like completely blew my mind when I wrapped my head around it, which is you can tell the agent to add new functions and features to itself, and it can do that. Right? 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 open claw do whatever, connect to my eight sleep bed, and it gives me better advice in 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

[39:30] 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 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 a hundred this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. And again, pe-

[39:58] people are going to look at it, and they already get this response. People are going to look at it, and they're going to say, "Oh, well, where's the breakthrough?" Cuz these all of these components were already known before. But but this is the key. The key to the breakthrough was by using all these components that were known before, you get all of the underlying capability that's buried in there. And so, all and so, for example, computer use all of a sudden just kind of falls trivially trivial. Of course, it's going to be able to use a computer. It [snorts] 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

[40:23] and away it goes. And then then you've got all the abilities of the browser, also. Um and so and so the capability unlock here is profound. My friends who were, you know, deepest into this are having their claw do like like literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and you know, these are you know, these are prototypes and there is you know, as you guys know, there's security issues. And and so you know, there's a bunch of stuff to be ironed out, but the the unlock of capability is just incredible. And I I have absolutely no doubt that everybody in the world is going to is going to have at least, you

[40:54] know, an agent like this, if not an entire family of agents, and we're going to be living in a world where I think it's almost inevitable now that this is the way people are going to use computers. I was going to say for someone who's deeply familiar with social networks, the next step is your claw talking to my claw. Posting on claw Facebook, posting their jobs on claw LinkedIn, and close posting their tweets on claw XAI or whatever, you know. >> [laughter] >> Um I do think that that is how uh you know, we we get into some danger there in terms of like alignment and whether or not we want these things to to to run. You guys know about Rent-A- Rent-A-Human.com? >> Yeah, Rent-A- Yeah. I mean, it's Fiverr, it's Task Rabbit,

[41:28] it's >> course. Mechanical Mechanical Turk. >> Yeah, but flipped. Right? The agent hiring the people. Yeah. Which of course is going to happen. >> [laughter] >> Right? It's obviously going to happen. I'm curious if you have any thoughts on the engineering side. So, when you built the browser, the internet, you know, just a bunch of mostly plain text file plus some images. And today the every website and app is like so complex and like somehow, you know, the browser kept evolving to fit that in. Are there any design choices that were made like early in the browser and kind of like the internet and the protocols that you're seeing agents similar today say, "Hey, this thing is just not going to work for like this type of new

[42:05] compute and we should just rip it out right now." There were a whole bunch, but I'll give you a couple. So, one is um and we did you know, to be clear, like this this was not you know this was totally different we didn't have the capabilities we have today but we didn't have the language models underneath this but we did have this idea that human readability actually mattered a great deal and so specifically in those days it was it was not so much English language but it was there was a design decision to be made between binary protocols and text protocols. And basically every every every basically old school systems architect that had grown up between like the 1960s and 1990s basically said you know the

[42:37] internet is what do you know about the internet it's starved for bandwidth. You just you have these very narrow straws you know like people when we did the work on Mosaic like people who had the internet at home had a 14 kilobit modem right so you're you're trying to like hyper optimize every bit of data that that travels over the network. And so obviously if you're going to design a protocol like HTTP you're going to want it to be a you know highly compressed binary protocol for maximum efficiency and you're going to want to have it be like a single connection that persists and you're you're the last thing you're going to want to do is like bring up and tear down new connections. And you definitely you're not going to

[43:04] not going to want a text protocol and so of course we said no we actually want to go completely the other direction and it's obviously we only want text protocols. By the way same thing in HTML itself we want HTML to be relatively verbose you know we want the tags to actually be like human readable. We want to use the >> The most inefficient things possible. >> Yeah we want to do the do the do the inefficient things. You're the original token maxer. Yeah exactly yeah yeah yeah basically it's just like Well yeah well actually this was this was actually the the conscious thing which basically says just like assume assume a future of infinite bandwidth build for that and then basically what it was is it was a bet that it was a bet

[43:35] that if the system 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 you have to convert it out of binary right and so the the all the you know HTTP and everything else were were it was always text protocols

[44:02] and the same thing with HTML and in many ways some people say that the key break through in the browser was the view source option, which is every webpage you go to you could view source, which means you could see how it worked, which means you could teach yourself how to build right new to build new webpages. There was that. So, human readability and again human readability in those days still meant technical, you know, specs. You know, now it means English language, but there's an incredible latent power in giving everybody who uses the system the option to be able to drop down and actually understand and see how it's working. And that worked really well for the web and I think it's working really well for AI.

[44:32] That was one. What was the other um A big part of the idea of web servers was actually surface the underlying latent capability of the operating system and to be able to surface the also the underlying latent capability of the database. Because basically what was a web server? What 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 that you want to do, process everything. And then of course a lot of really, you know, a lot a lot of websites are front ends to databases. And so, you wanted to

[45:00] you wanted to unleash the underlying latent power of whether it was an Oracle database or some other, you know, some other postgress or whatever whatever it was. And so, a lot of the function of the web server was to just bridge from that internet connection coming in to be able to unlock the underlying power of the OS and the database. And again, people looked at it at the time and they were like, well, is this really does this really matter? Like is this important because we've had databases forever and we've always had, you know, user interfaces for databases and this is just another user interface for a database. It's like, okay, yeah, fair enough. But on the other side of that is just like

[45:28] this is now a much better interface to databases and one that 8 billion people are going to use and is going to be like far easier to use and far more flexible and and and and you're not just going to have old databases. Now you have a system where people can actually understand why they want to build, you know, a million times more database apps than they have in the past. And then the number of databases in the world exploded. And so, again this goes to this thing of like building building in layers. Some of the smartest people in industry look at any new challenge and they're like, okay, I I need to build a new kind of application. So, the first thing I need to do is build a new programming language.

[45:55] Right? And then the next thing I need to do is build a new operating system. [laughter] Right? And then the next thing I need to do is I need to build a new chip. Right? And they they kind of want to reinvent everything. And I've I've always had maybe it's just I don't know pragmatic mentality or something or maybe an engineering over science mentality, but it's more like no, you have just like all of this latent power in the existing systems and you don't want to be held back by their constraints, but what you want to do is you want to kind of liberate that power and open it up. So I I think I think that and I think the web did that for those reasons and I think it's the same thing that's happening.

[46:23] Programming languages is another good thing. We have Brett Taylor on the podcast and we were talking about rust and you know, rust is memory safe by default. 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? I'll be like, oh okay, python is kind of slow. You know, it's like yeah. As as imperfect as they are, they are the lingua franca. I mean, I think this is going to change a lot cuz I don't think the models care what language they program in and I think they're going to be good at programming in every language and I

[46:53] think they're going to be good at translating from any language to any other language. Like okay, so this gets into the coding side of things. I think we're going through a really fundamental change. And then look, I I grew up you know, I grew up hand code. You know, I I grew up hand coding. Everything I did was actually everything I did actually was written in C. I wasn't even I wasn't even using C++. So I or like Java or any of this stuff, right? And so everything everything I ever did I was like managing my own memory at the level of C. And then I you know, I still from the generation that you know, I knew assembly language and you know, I I you know, so I could drop down and do things right

[47:25] on the ship. And so we've just we've all all of us we've always lived in a world in which software is like this precious thing that like you have to think about very carefully and it's like really hard to generate good software and there's only a small number of people who can do it and like you have to be very like jealous in terms of thinking about like how do you allocate? Like what are your engineers working on and how many good engineers do you actually have and how much software can they write and how can how much software can human beings you know, kind of maintain? And I think like all those assumptions are being shot right out the window right now. Like I think they're I think those days are just over and I

[47:52] think the new world is like actually high quality software is just like infinitely available. And [snorts] if you need new software to do XYZ, like you're just going to wave your hand and you're going to 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 right now I want the rest version. Um or you know, secure you know, secure we're about to by the way, we're about to go through computer security about to go through the most dramatic change ever, which is number one, like every single latent security bug is about to be exposed. >> Right. So it's we're going to have like the we're we're set up here for like the computer security apocalypse for a

[48:20] while. But but but on the other side of it, now we have coding agents that can go in and actually fix all the security bugs. And so how how are you going to secure software in the future? You're going to tell the tell the bot to secure it and it's going to 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 going to have as much as you want. >> Right. Uh and and that has like, you know, that has like tons and tons of consequences. In some sense, the answer to the question you posed I I think is just somewhat, I don't know, simple or something or straightforward, which is

[48:47] just if you want all your software in rest, you just tell the bot you want all your software in rest. Like the things that used to be like hard or even like seem like an insurmountable mountain to get to get through all of a sudden I think become very easy. Uh I think Brett had a theory that there would be a more optimal language for LLMs. And so the contention is there isn't. Like just don't bother. Just whatever humans already use, LLMs are perfectly capable porting. 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 what its optimal language be and let it right and let it design it. >> [laughter] >> True. Okay, here's the question.

[49:21] Are you going to even going to have programming languages in the future? Or the AI or the AI is just going to be emitting binaries. Let's assume for a moment that humans aren't coding anymore. Let's assume it's all bots. The what levels of intermediate abstraction do the bots even need? Yeah. Or are they just coding binary directly? Did you see there's actually an exper- there's somebody just did this thing where they have a they have a language model now that actually emits model weights for a new language model. >> [laughter] >> Right. And so will the bots be >> Just predict the weights. Well, yeah, will the bots literally be emitting not just coding binaries, but will they will will they actually be emitting weights

[49:50] for new for new models. Direct, directly. And conceptually there's no reason why they can't do both of those things. Just architecturally both of those things seem completely possible. >> Very inefficient. You're basically a simulation of a simulation in a simulation inside of the weights. Yeah. Yeah, very inefficient. But like look, LLMs are already like incredibly inefficient. Ask I'm saying the same thing. Ask Claude 2 + 2 = 4, right? It's just like you know, it's like you know, it's it's it's like whatever billions and billions of times more inefficient than using your pocket calculator. But but but yeah, the the payoff is so great of the general capability. So anyway, like I I kind of think in 10

[50:25] years like I'm not sure yeah, like I'm not sure there will even be a salient concept of a programming language in the way that we understand it today. And in fact, what we may be doing more and more is a form of interpretability, which is we're trying to understand why the bots have decided to structure code in the way that they have. I mean, if you play it through, you don't need browsers then. Like that's the death of the browser. Well, so I I would take it a step further, which is you may not need user interfaces. So who is going to use software in the future? Other bots. Other bots. Yeah. And so >> need to I don't know pipe information in Do we? and out. Really? >> [laughter] >> Well, what are you going to do then?

[51:01] >> Are you sure? You're just going to log off and touch grass? >> Whatever you want, exactly. >> [laughter] >> Isn't that better? I want software to do stuff for me. Isn't that but isn't that better? You know, I don't know like like you know, you know, you know, there are arguments here, you know. >> [laughter] >> It was not that long ago that 99% of humanity was behind a plow. Right. Right. And what are people going to do if they're not plowing fields all day to to grow food, right? And it just turns out there's like much better ways for people to spend time than plowing fields. >> Yeah, do a scroll in. Uh exactly. >> [laughter] >> Exactly, or you know, talking to their friends. And look, and I'm not an

[51:29] absolutist and I'm not a utopian and I and to be clear like I have 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 like a really good idea to learn how to code and so forth. But I just if you project forward, you just have to think forward to a world in which it's just like okay, I'm just going to tell the thing what I need and it's going to do it. And then and then it's going to do it in whatever way is most optimal for it to do it. Yeah. I tell it to do it non-optimally. Like if I tell it to do it in Java or in Rust or whatever, it'll do it, I'm sure. But like if I'm just going to tell it to do it, it's going to do it in whatever way is like the optimal way to do it. And

[51:56] then I And then if I need to understand how it works, I'm going to ask it to explain to me how it works. Right? And so it's going to be doing its own interpret- it's going to be the engine of interpretability to explain itself. And I I just am not convinced that that I'm not I'm not convinced that in that world you have these historical The goals of the abstractions will be whatever the boss needed with the user. Yeah. Yeah, that Well, I I'm curious though, 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

[52:29] because the users need to write in that language. Versus they can have their own thing internally and like they don't need to teach it to anybody. They just need to teach their model. And I think that's how you get maybe the version between the models. Like going back to like the Pi open cloud thing, it's like, "Oh, I built all the software using the open AI model, and now switch to the Anthropic model, but the Anthropic model doesn't understand the thing." So I would It feels like there still needs to be some abstraction. But maybe not. Maybe that's the lock-in that the model providers want to have. I don't I'm not even sure that's lock-in though, because why can't the second model just learn what the first model

[53:02] has done? Like Exactly. >> Okay, so Okay, give me an example. So as you know, models can now reverse engineer software binaries. This whole thing now where people are reverse engineering like Nintendo Nintendo game binaries. Yeah. So you have like You've You've seen a bunch of reports like this where somebody has like a favorite game from the 1980s. And the source code is like long dead, but they have like a binary burned into a chip or something, and now they're reverse engineering to get a version that runs on their Mac, right? And so if you reverse it if This is what I kind of say. If you're reversing like x86 binaries, then why can't you reverse engineer whatever they create. Yeah. And because

[53:29] we're on a Unix-based system, it has to be reversible because it needs to run on the target. Yeah. Yeah, yeah. Yeah, yeah, basically. And so I just I just think it's this thing where it's just like And by the way, everything we're describing is something that human beings in theory could have done before, but just with >> Right, yeah. but with enormous but it was just always like cost and labor prohibitive. Reverse engineer I learned how to reverse engineer was like human beings can reverse engineer binaries. It's just for any complex binary you need like a thousand years to do it. But now with the model you don't. And so all of a sudden you get you get these things or another way to think about it is so much of human built

[54:02] systems are to compensate for the human limitations. Right? And if you don't have the human limitations anymore then all of a sudden you have won't have abstractions but you'll have a different kind of abstraction. I have two topics to bring us to a close and you can pick whichever ones. I just talking about protocols. Was it you or someone else I forget my internet history who said that like the biggest mistake that we didn't figure out in the early days was payments. Was that you? Yes, it was a 402 402 payment required. We have a chance now. I don't think we're going to figure it out. I don't know. Like what's your take? >> we will. Yeah. No, now I think it's going to happen for sure. Yeah. Yeah,

[54:34] and there's two reasons it's going to happen for sure. One is we actually have internet native money now in the form of crypto stable coins and crypto. And this is I think this is the grand unification basically of AI and crypto is what's about to happen now. I think AI is the crypto killer app. I think it's where where this is really going to come out. 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 going to need money. And it's already happening, right? If you've got a if you've got a claw and you want it to buy things for you, you have to give it money in some form. I would say the adoption is probably like 0.1% if if that, but yeah.

[55:01] >> Oh, today? Yeah, yeah, yeah, but think forward. Like where is it going? Forward thinking. The ultimate principle of everything and and everything that I think I we do is it's the William Gibson quote which is the future is already here it just isn't distributed. It isn't distributed yet. My friends who are the most aggressive users of open claw just like have given their claws bank accounts and credit cards. And and and and not only have they done it, it's obvious that they needed to do it because it's obvious that they needed to be able to spend money on their behalf. Yeah, yeah. It's just completely obvious. And so and again like so the number of people who have done that today to your point is

[55:30] like I don't know probably 5,000 or something. But it'll grow. That's how these things start. Actually, I mean since you keep mentioning And by the way open claw by the way if you don't give it a bank account, it's just going to break into your your bank account. >> [laughter] >> It's going to break It's going to break It's going to break into your bank account anyway and take your money. So, you you might as you might as well do it. You might as well do it. Uh yeah. By the way, I really love I got to tell you I really love the phenomenon. I love the YOLO. Um I'm not doing it myself, to be clear, but but I love the people that are just like you What is it? Skip Skip permissions? By the way, it's a Facebook thing.

[55:59] >> Okay. >> [laughter] >> Cuz cuz we In Facebook, they they have this culture to name the thing dangerous so that you are aware when you enable the flag that you're opting into a dangerous thing. Okay, good. They brought it into OpenClaw AI. And of course that makes it enticing. Sam runs Codex with skip permissions on on his laptop. Yes, 100%. And so, I I I think the way to actually see the future is to find the people who are doing that. >> [laughter] >> There's a man who you know, they're you know Log everything, you know, just watch it watch the logs. >> But like, let's actually find out what the thing can do. And the way to find out what the thing can do is just like Yeah, let it try everything. Let it

[56:32] unlock everything. By the way, that's how you're going to find all the good stuff it can do. By the way, that's also how you're going to find all the flaws. Yeah. I think the people who turn that on for bots are like they're like murders to the progress of human civilization. Like I feel very bad for their descendants that their bank accounts are going to get looted by their bots in the first like 20 minutes, but I think the contribution that they're making to the future of our species is amazing. >> It's like gentleman science, you know. Yes, it's Yes, yes. It's a Ben Franklin out with a trying to trying trying to get lightning strike his [laughter] his his balloon and see seeing if he gets electrocuted. Yeah. It It's a Jonas Salk

[57:00] with the polio vaccine. >> [laughter] >> Injecting it Yes. So, yes, I I I I think we should have like a glory we should have like flags and like we should have like monuments to [laughter] the people who just let OpenClaw run their lives. More anecdotes. What what are the craziest or interesting things that people listening to this should go go home and do? I mean, this is this is the this is the the extreme thing is just like the straight YOLO. Like just yeah, turn turn your life >> But that's a general capability. Is there like a specific story that was like wow and and everyone in the group chat just lit up? I mean, like you know, so there's tons of there's already tons of health. You know, there's you know,

[57:31] that the health dashboard stuff is just is just absolutely absolutely amazing. >> Yeah. The number of stories on I'm trying I just don't want to violate people's, you know, obviously personal anonymities, but you know, one of the things open claws are really good at is hacking into all the stuff in your LAN. It's really good at it. So, you know, internet of things aka internet of Like super insecure, but great. Discoverable. Yeah, it's a discoverable. Open claw is happy to scan your network, identify all the things and then my my my friends who are most aggressive at this are having open claw take over everything in their house. Yeah. It takes over their security cameras, it takes over their their, you know, their

[58:02] whatever their their access control systems, it takes over their webcams. I have a friend whose claw watches him sleep. Put a webcam in your bedroom, put the put the claw put the claw on a loop. I have it wake up frequently and have it watch it 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 cuz, 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, 5 hours of REM sleep. Uh Joe's moving. Joe's moving. Uh Joe might be waking waking up. This is a real pri If Joe wakes up now, he's

[58:33] going to ruin his sleep cycle. Oh, okay, it's okay. Joe just rolled over. Okay, he's gone back to bed. Okay, good. All right. Okay, I can relax. This is fine. >> He's monitoring the situation. >> [laughter] >> Monitoring the situation. And and being a bot like, you know, it's just like very focused. [laughter] It's just like this is like his reason for existence is to watch Joe sleep. And then and then I was telling my friend who did this is like, you know, on the one hand it's like, all right, this is weird and creepy. And I need to I need to I 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, literally would like freak

[59:02] out and call 911. Like there's no question this thing would figure out how to like alert medical authorities and like probably summon SWAT teams and like do whatever would be required [laughter] to save my life, right? And so it's like, you know, like yeah, like that's happening. What else? Um They give us this company Unitree that makes the robot dogs. And I actually have one at home which is is it's actually really the Chinese companies. The Chinese companies are so aggressive at adopting uh new technology, but they don't always like take the time to really package it. >> Package it and maybe think it all the way through. And so so the at least the Unitree dog I have so it has a old non-LLM just control system. Which by

[59:40] the way is not very good. Markets well, but it in practice is 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 and 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. >> [laughter] >> 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. >> [laughter] >> Right. And like a plum English accent, right? It's just like absolutely amazing. >> Jagged intelligence. Yeah. Talk about jagged. And then now

[1:00:10] obviously what's going to happen in the future is is they're going to connect together, but >> They'll do it. 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 rewrite the write new firmware. >> Yeah. Rewrite new firmware for the for the Unitree robot. And now it's now it's an actual dog for his kids. You can do that before and after like the motion? Yeah. It's He said it's completely different. He said it's a complete transformation. And whenever there's an issue in the thing now the claw just like rewrites the code. You know you know you go you does does the code and so it's a good kind of

[1:00:36] goes to your thing here. So so like all of a sudden this is why like we're going to 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 is basically over. Like I I think everything there's a potentially here where like all these devices in your house that have been like basically marginal or you know basically dumb, you know, like all of a sudden they might all get really smart. Now Smart home. you have to decide if yes, there are horror movies in which this is of which this is the premise. And so you have to decide

[1:01:05] if you want this. >> Yeah. But but but this is the first time I can say with confidence I now know how you could actually have a smart home Yeah. with 30 different kinds of things with chips and internet access where it actually all makes sense and all works together and it's all coherent and the and the whole thing. And to have that unlock without a human being having to go do do of that work like Yeah. So, it's fine. I'm waiting for a sorry, Mark. Uh I can't let you open that fridge door. You know, like Exactly. [laughter] Exactly. Yes. YES. >> BECAUSE YOU'RE NOT SUPPOSED TO EAT RIGHT NOW. >> all of you. Yes. I have every shred of health information. You know, and I know you think you're doing, you know, da da

[1:01:35] da. And I know you think you can do this, but you know, this is a real Are you really, [laughter] 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. And so, you know, I'm sorry, but the fridge door is locked. >> [laughter] >> Um yes. >> Open the fridge doors. >> Exactly. And by the way, I know you're supposed to be studying for a test. So, why don't we why don't you go when you can pass the test, um I will open the fridge door for you. Final photo call and then and then we can wrap up. Uh proof of human. Yes. >> right? That's the last piece that we got to figure out. >> Yeah. So, I would say there's there's two massive, I would say, um uh sort of

[1:02:04] 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. It's virtual world version. It's physical world version. So, the virtual world version is is the bot problem. Where just like, you know, the internet internet is just like a washing bots. The internet's a washing fake people. It has been forever. Um by the way, a lot of that has to do with lack of money, you know, and so this you know, this is the this is the this is the My spicy take was these two are the same thing and corporations are people, too. You know,

[1:02:31] >> [laughter] >> Interesting. Yeah. Yeah. Yeah. Okay. >> So, a bank account is proof of human. Yeah. Okay. >> [laughter] >> Yeah, until you until you give the bots bank accounts. Yeah, exactly. So, okay. Yeah, so there's that. But yeah, look Look, the bot I mean, every social media user knows this. The bot The bot problem is a big problem. You know, the bot The bot problem has been a big problem forever. It's It's a huge problem. And it's never really been confronted directly. Like, at any point. By the way, the physical world version of this is the drone the drone problem. Um right? And so, we we've known for, you know, we've known for 20 years now that the asymmetric threat both in military in

[1:03:00] actual military conflict, but also in just like security, like like, you know, security on the home front, the big threat is is the cheap attack drone. Right? The the the cheap the cheap suicide, you know, drone with a bomb. And we've known that forever. And by the way, like, you know, it's very disconcerting how like every, you know, every office complex in the in the cut, know, in the world is like unprotected from drone attacks. Every every stadium, every school, every prison like is like >> Sure. Okay, we've known that. We've never done anything about it. What are we going to do about it? Well, one possibility is just leave leave them unprotected forever and live in a world of like asymmetric terrorism forever. The other

[1:03:30] 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, don't Personal force fields. >> Kinetic personal For Dune, >> [laughter] >> uh, for For personal force fields. Exactly. And in both cases, the these are these are economic asymmetries. These are economic asymmetries, right? Cuz it's really cheap to field a bot, but it's very hard to tell if 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

[1:03:57] physical version of the problem. Uh, the virtual version of the problem what we what we need quite literally is proof of human. The reason is because you're you're not you're not going to have proof of bot. The the the especially now that the bots are too good. The 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. This video is real. Right? Just to double click on

[1:04:25] on, uh, do you think Alex Blania with World, do you think he's got it or is there alternative? Oh, so I mean there's going to be I think there'll be I mean many people will try. We're one of the key, you know, participants in in the World in the World project and I didn't know that. I didn't know that. Yeah, so we're we're partisans. But yeah, I I think So we think World is exactly correct. >> Okay. And the reason is it has it has to be it it has to be proof of human. It it has because you can't do proof of not bot. You have to do proof of human. To do proof of human, you you need you need biological validation. You you need it to start with this was actually a person. Right? Cuz otherwise you have

[1:04:53] bots signing up as fake people. All right? So you you have to have like something You have to have a bio biometric. And then you have to have cryptographic validation and then the ability to do to do to do the lookup. And then by the way, the other thing you need which is that you you also need selective disclosure. Um, so you need to be able to do proof of human without revealing all the underlying information. By the way, another thing you're going to need you're going to need proof of age, right? Cuz there's all these laws in all these different countries now around you need to be 13 or 16 or 18 or whatever to do different things. And so you're going to you're going to need a you know, sort of

[1:05:18] validate a proof of age. Um you know, to be able to legally operate, right? And so that that's coming and then you're going to want like proof of credit score and you know, proof of like, you know, 100 other >> That's a tricky one. It is a tricky one, but you're going to you're going to need there's no reason like if somebody's checking on your credit somebody shouldn't I'll put this I'll give you an example. Somebody shouldn't need to know your name in order to be able to find out whether you're credit worthy, right? I see. Independently verifiable pieces of information. >> Pieces of information. So like so like disclosed and this is the answer to the privacy problem writ large, which is I I

[1:05:43] only need to prove I need to prove at that moment. >> So like you're going to need that and I think that their architecture makes sense. So that needs to get solved. I think language models have tipped the bots are now too good. Uh and and so they're undetectable and so as a consequence you we now need to go and confront that problem directly. And then and like I said and then the other problem is we we need to go actually confront the drone problem. >> Business. The Ukraine conflict has really unlocked a lot of thinking on that. And now the um and now the the the the the Iran situation is also unlocking that. And so I think there's going to be >> just like this incredible explosion of of both drone and counter drone. Our

[1:06:12] drones are better than their guns. >> [laughter] >> It's also keep it that way. And counter and counter drones. I think we can sneak in one more question. Um I'm trying to tie together a lot of things that you said over the years. So at the Milken Institute debate with Thiel, which is amazing, um you talked about the lag between a new technology and kind of like the GDP um impact of it. The other idea you talked about is bourgeois capitalism and how you know, this kind of managerial class was needed because of this complexity and I think if you bring AI into the fold you have like much higher leverage of people. So like if you have, you know, the Musk industries um and you give Elon a GI you can run a lot more things uh at once.

[1:06:51] That's right. 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. >> Yes. Under Uh and I was I was at that event with Sam last night uh and he actually said in the last couple weeks he felt like now people are taking that seriously. So I'm just curious like how you're seeing the structure of organization changing, especially when you invest in early stage companies, and um yeah, just like how the impact of work structure and all of that is playing out. >> Yeah. So, there's a whole bunch of there's a whole bunch of time we [laughter] could spend I don't know by the way we'd be happy to spend more

[1:07:21] time, but we could we could spend more time on all that. So, just it for people who haven't followed this. So, that this this this term managerial comes from this thinker in the 20th century James Burnham who um is one of the great kind of 20th century political thinkers uh societal thinkers. And he sort of said as and he was writing in like 1940s, 1950s. Um and he said kind of that the whole history of capitalism until that point had been in two phases. Number one had been what he called bourgeois capitalism which was think about it as like name on the door. Like Ford Motor Company cuz Henry Ford runs the company. Um and Henry it's like a dictate dictatorial model and Henry Ford just like tells

[1:07:49] everybody what to do. And he said the problem with bourgeois capitalism is it doesn't scale cuz Henry Ford can only tell so many people to do so many things and then he runs out of time in the day. And so, um he said the second phase of capitalism was what he called managerial capitalism which was the creation of a professional class of managers. Um that are trained not to be like car experts or to be whatever experts in any particular field, but are trained to be experts in management. And then that led to, you know, the importance of like Harvard Business, you know, business schools and management consulting firms and all these things. And then you look at every big company today and like most

[1:08:19] 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 health care for a while and then work in financial services and then go work in something else, you know, come work in tech. And what Burnham said is he said that transition is absolutely required because the the the problem with bourgeois capitalism is is it doesn't scale. Henry Ford doesn't scale. And so, if you're going to run capitalist enterprises that are going to have

[1:08:47] millions to billions of customers, um you're going to need to you they're going to be operating a level of scale and complexity that's going to require this professional management class. And he said, "Look, that the professional management class has its downsides. Like they're not necessarily experts at doing the thing. They're not as inventive, you know, they're not going to create the next breakthrough thing." But he's like whether you think that's good or bad or whatever it's what's going to be required. And basically that's what happened, right? And so he wrote that book originally like 1940. You know, over the course of the next 50 years basically managerialism Well, I mean, today up till today managerial

[1:09:14] managerialism basically took over everything and you know, what I'm describing is basically how all big companies run and how all governments run and how all large-scale nonprofits run and kind of everything you know, everything runs. Basically what what what venture capital does is we basically are a rump sort of protest movement to that to try to find the next Henry Ford or or just to say Elon Musk or 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 the old model, right? We we we start them out as as as as like in the Henry Ford model. And so we start them out with a founder or a or a or a

[1:09:45] founder with with colleagues, but you know, there's there's a founder CEO. Um and then we basically bet that we basically bet that the startup is going to be able to do things specifically innovate in ways that the big incumbents in that industry are not going to be able to do. And so it's a bet that by basically by relighting the sort of name you know, kind of thing this new innovative thing with like a king monarchical political structure. Um that they're going to be able to innovate in a way that the incumbent is not going to be able to because the incumbent is 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

[1:10:15] doesn't, but but we're constantly doing that. But I've always viewed it my entire life as like we're like raging against the dying of the light. Like we're we're we're [clears throat] 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 we're trying to keep some level of energy and vitality in the system. AI is the thing that would lead you to think wow, maybe there's a third model. Right. And and maybe and way to think about it would be maybe it's a combination of the two. Maybe the new Henry Ford or the new Elon or the new Steve Jobs plus AI is the best of both. Right? Cuz it's it's it's sort of the

[1:10:48] 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 it was a possibility. But yeah, I mean, you know this. The What is the thing that these bots are really good at? They're really good at doing paperwork. Like, they're really good at filling out forms. Like, they're really good at writing reports. They're really good at reading them. 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

[1:11:20] think the answer the answer very well might be to get the best best best of both worlds by doing this. And then the challenge is going to be twofold. It's going to be for the innovators to really figure out how to leverage AI to actually do this. Right? And then And then the the other challenge is going to be for the for the incumbents that are managerial to figure out like, okay, what does that mean? Cuz now they're going to they're they're going to be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to. And so, this really, I think, is going to force a lot of big companies to kind of figure out innovation. Either I say figure out innovation or die trying.

[1:11:50] Do you feel like that structure accelerates the impact on the actual GDP and economy? If you could space out, it's like the growth is like so fast. And like, instead of having these companies kind of like peter out in growth and impact, they can kind of like keep going, if not accelerating. >> Yeah. That's for sure the hope. The The challenge and and you know, and and look, the AI utopian view is a Of course. And and and that's going to be the future of the economy, and it's going to grow 10x and 100x and 1,000x, and we're entering this regime of like much higher economic growth forever, and consumer cornucopia of everything, and it's going to be great. And I And I hope that's true. I hope that's That's like

[1:12:20] the you know, that's the current kind of utopian vision. I hope that's true. The problem is goes back again. The real world is really messy. Um, and I'll give you an example of how the real world is really messy. It requires 900 hours of professional certification training to become a hairdresser in the state of California. Um, so, it's like 35% of the economy, something like that, you have to get some sort of professional certification to do the job. Which [snorts] is to say that the the professions are all cartels, right? And so, you have to get licensed as a doctor. You have to get licensed as a lawyer. You have to get licensed as a You have to get into a union. By the way, to to work for the

[1:12:54] government, you need to be you have both civil service protections and you have public sector unions. You have two layers of insulation against ever getting fired for anything or anything anything ever changing. [laughter] I'll give you another example. The the dock work the dock workers went on strike a couple years ago cuz they're robotics if you go look at a modern dock like in Asia it's all robots. If you go to American dock it's like all still guys dragging dragging stuff by hand. The dock workers went on a strike. It turns out there are 25,000 dock workers working on docks in America. Turns out they have incredible political power cuz it's a it's one of these unified blocks of things. They won their strike and so

[1:13:27] they got commitments from the dock owners to not implement more automation. We learned a couple of things in that. So number one we learned that even a union as small as 25,000 people still has like tremendous political stroke. We also learned that they it actually turns out the dock workers union has 50,000 people in it cuz there's 20 they have 25,000 people working at docks they have 25,000 people drawing full paycheck sitting at home from prior union agreements from prior union agreements. I'll give you another great example. There are government agencies there are federal government agencies where the employees right of have civil service protections and they're in public sector unions. There are entire federal

[1:13:57] government agencies that struck new collective bargaining agreements during COVID. Where not only are they have their jobs guaranteed in perpetuity but they only have to report to work in an office one day per month. And so there are entire office buildings in Washington DC that are empty 29 out of 30 days of the year that are still operating and are still we're all still paying for it. And so and then what they do it turns out what the employees do is they're very they're very smart in the in in this way and so they figure it out. They come in on the last day of a month and the first day of the next month. And so they're in the they're in the office two days per 60 days. Which means these buildings are empty

[1:14:28] for 58 days at a time. >> [laughter] >> And you see you see where I'm heading with this? Like this is like locked in. Right? This is like locked in in a way that has nothing to do with like people say capitalist it's like anti-capitalistic. It's like it's 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 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-12 schools in the United States they're a literal government monopoly. How are we going to apply AI in education? The answer is we're not

[1:15:06] because it's a literal government monopoly. It is never going to change. The end and there is nothing to do. By the way, you can create an entirely new school system. Like that's the one thing you can do is you can do what Alpha School is doing. You can create an entirely new school system. Other than that, you're not going to go and change what's happening in the American classroom like K-12. There's no chance. The teachers are 100% opposed to it. It's 100% not going to happen. So So you see what I'm saying is like there's this like massive slippage that's going to take place. Both the AI utopians and the AI doomers are far too optimistic. Right. >> [laughter] >> You see what I'm saying because because

[1:15:35] they believe that because the technology makes something possible that 8 billion people all of a sudden are going to change how they behave and it's just like, nope. So much of how the existing economy works is just it's just like wire done. And so, we're going to be lucky as a society we're going to be lucky if AI adoption happens quickly. Right, because if it doesn't what we're just going to have is stagnation. As a matter of fact, I know you got to run. Yeah, we all know if or still 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. Yes. Yes. Could not be more excited. Really thank you Mark for you guys. Awesome. Thank you.

[1:16:05] Good. Thank you.

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