Marc Andreessen on AI Winters and Agent Breakthroughs

This episode originally aired on the Layton Space Podcast. Mark Andreessen has watched AI cycle through summers and winters for more than 35 years, from coding and list in 1989 to back in the foundation model companies today. He argues that the current moment is not another false start, but the payoff from eight decades of foundational research, catalyzed by four distinct breakthroughs, large language models, reasoning, agents, and self-improvement. He also makes the case that the combination of a language model, a unique shell, and a file system represent one of the most important software architectures in a generation. Swix and Alessio Finnelly speak with Mark Andreessen, co-founder and general partner at A16Z. 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. For example, we now know the neural network is the correct architecture. I will tell you, there was a 60-year run where that was like 70 years or that was controversial. The way I think about what's happening is basically, I think about basically the period we're in right now is it's called 80-year overnight success, which is an overnight success, because it's like bam, chat GPT hits and then O1 hits and then open claw hits. These are overnight radical overnight transformative successes, but they're drawing on an 80-year wellspring backlog 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 very serious hard-core research. If I were 18, this is what I would be spending all of my time on. This is 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 entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content. We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribe to us to keep all this sustainable without ads and we want to keep it that way. But I just have one favor to ask all of you. The single most powerful, completely free thing you can do is to click that subscribe button. It's the only thing I'll ever ask of you and it means absolutely everything to me and my team that works so hard to bring the inspace to you each and every week. If you do it, I promise you we'll never stop working to make the show even better. Now let's get into it. [Music] Hey everyone, welcome to the Lidenspace Podcast. This is Alasio from the Colonel Labs and I'm joined by Spooks at the Lidenspace. Hello and we're in A16Z with A Mark and Jason Welcome. Yes. Yes A and what half of 16? A1. Exactly. Apparently this is the final few days in your current office you're moving across the road. We have a little bit of some of your projects underway but yeah. Actually, this is the original. We're in the original office. We're in the, we're in the, we're in the, we're in the whole thing. It's beautiful. Yeah, great. Thank you. So I have to come out. I wanted to pick a spicy start in October, 2022. I just made friends with Roon and I wanted to give him something to be spicy about and I said, you'll never not be funny that A16Z was constantly going. The future is where the smart people should spend their time and then going deep into crypto and not in AI. And that was in October, 2022. And Roon says there was an internal meeting in A16Z to reorient around Genie I. Obviously you have, but was there a meeting? What was that? I mean, I don't look, I've been doing AI since the late 80s. So I don't know, as far as I'm concerned, the stuff is all Johnny come like, like, yeah, I mean, look, we've been doing AI our entire existence. I mean, we've been doing AI machine learning deep, you know, deep, we've been doing this stuff way from the beginning. Obviously, AI is just a quarter computer science. I actually view them as like quite, quite continuous, you know, Ben and I both have computer science degrees. You know, we both, Ben and I actually both are well enough to remember the actual AI boom in the 1980s. There was a big AI boom at the time. And there was a, just limited names like expert systems and live in the air of like list and list machines. I coded it list. I was coding a list in 1989, where that was the language of the AI future. Yeah. So this is something that we're like completely, completely comfortable with and been doing the whole time in a very enthusiastic about. Is there a strong like this time is different because my closest analog was 2016, 2017. There was a new AI boom and it petered out very, very quickly. Well, just just just in terms of investing sort of sort of investment, investment, investment excitement. Well, though that's really when they did the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at the time, it did vocabulary was more machine learning, but it was very clear at that time that machine learning was hitting some sort of takeoff point. Yeah. You guys have talked about this length on your thing, but if you really track what happened, I think the real story as it was the Alex net basically breakthrough in like 2013, that was the real knee in the curve. And then it was obviously the transformer breakthrough in 17 and then everything that followed. But you know, look machine learning, you know, they were, you know, look, I mean, look, I've been working, you know, I've been working with one of my kind of projects working with Facebook since 2004 and on the board since 2007. And of course, they based started using machine learning very early. And you know, I've used it basically for like 20 years for, you know, content, you know, feed optimization and advertising optimization. And obviously many, you know, financial services, you know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things. It's like, it's not a single thing like it's like, it's like layers, right? And then the layers arrive at different paces, but they kind of build up. Yeah. They kind of look over time. And then, and then look in retrospect, it was 2017 was kind of the, you know, the key point with the rest of the transformer. And then as you guys know, there was this really weird like four year period where it's like the transformer existed. And then it was just like, let's go. Yeah. Well, but between 20, 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 open AI developed chat GPT or GPT 2. And then they told everybody, this is way too dangerous to do that. Right. You know, we can't possibly let normal people, normal people use this thing. And then you guys, I'm sure remember AI Dungeon. Yeah. So the only, for there was like a year where like the only way for a normal person to use GPT 3 was an AI dungeon. Yeah. And so you, we would do this. You'd go in there and you pretend to play Dungeons and Dragons. And 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, the big companies, you know, big companies are cautious and you know, the big companies were cautious. It, by the way, it took open AI, you know, they, they, they talked about this. It took open AI time to actually adjust, you know, kind of redirect their research path. I think it was at Rosewood, right? The dinner that founded Open AI was right there. Right. But that dinner would have taken place in 2018. The formation of Open AI as late as 2018. Sorry. No, I'm wrong. Probably. It should be 20. Yeah, they just said we did a 10 year anniversary. So it is 2025. Yeah. So 2015. Yeah, 2015. Now 2015. But then Alec Radford did GPT 1 in what probably 17 18, 18 17 18. So it is yeah, for the, and then they didn't really, and then GPT 3 was what 2020, 2020, 2020, because that became called by the 21. Even Open AI, which has been, you know, the leader of this thing in the last decade, you know, even they had to adapt and lean into the new thing. And so, yeah, I think it's just this process of basically sort of wave after wave, layer after layer, you know, building on itself. And then you kind of get these catalytic moments where the whole thing pops. And obviously that's what's happening now. Is it useful to think about will there be any, I went to because there's always these patterns like is this endless summer? Is something I constantly think about because do I get, do I just like, just get endlessly hyped and just trust it, I will only be early and never wrong. Or will there be a winter? So there's something about, let's say the following, there's something about AI that has led to this repeated pattern. And you guys know this, but it's a 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. It was, it was far back that long. And then there was, I've, if 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. And they got an NSF grant to, for all the AI experts at the time, spend the summer together. And they figured if they had 10 weeks together, they could get AGI of the other end. And they got there, by the way, they got the grant, they got the 10 weeks and then, you know, making 50, you know, no, no AGI. And like I said, I lived through the 80s version of this where there was a big, a big woman of crash. So there is this thing. And there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. And it's probably on both sides of like the, the boom bus cycle, you kind of see that play out. Having said that, I think what's actually happened is like just, you know, and we now know on retrospect like an enormous amount of technical progress that built up over time. And like, for example, we now know the neural network is the correct architecture. And I will tell you, like there was a 60 year run where that was like, you know, or even 70 years or that was controversial. And we now know that that's the case. And so we now, you know, everything we're building on today, sort of derives from the original idea in 1943. So in retrospect, we now know that like these guys were right, they would get the timing wrong. And they thought capabilities would arrive faster. Or they 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. And the payoff from from all their work is happening now. And so the way I think about what's happening is basically, I think about basically the period we're in right now is it's like all 80 year overnight success, right? Which is like, it's an overnight success because it's like, bam, you know, chat GPT hits and then in the no one hits and then you know, open claw hits and like, you know, these are open, 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 and thinking. Look, there were AI researchers who spent their entire lives. They got their PhD. They worked for, they've researched for 40 years. They retired. And a lot of cases they passed away and they never actually saw it. Yeah, so sad it is it is sad it was like the last guy Yeah, well, there were guys that are doing Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventories of the field. He's one of the guys that organized the Dartmouth conference And you know, he taught at Stanford for 40 years and passed you know, passed away. I don't know whatever 10 years ago or something Never never actually 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, say say history doesn't repeat but it rhymes It's like okay. Does that mean that there's gonna be another like, you know, basically boom bus cycle and I will tell you like let's like In a sense like yes, everything goes through cycles and you know people get overly enthusiastic and overly depressed and there's there's a time There's a timelessness to that having said that there's just no question So the foremost the foremost dangerous words and the time is different. You know the 12 most dangerous words Investing no the former state foremost dangerous words and the best time is different The 12 most dangerous words and so like I tell you what's different like Now it's working like like there's just no I mean look. There's just no question And by the I'll just give you guys my take like LLM's like from from basically the chat GPT moment through to Spring of 25. I think you could still I think well intentioned well in a form 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 gonna be great for creative writing and creating you know Shakespeare and son son and son, you know as as rap lyrics or whatever like it's gonna be great and all that stuff But we're not gonna be able to harness this to make this relevant in you know coding or in medicine or in law or and you know You know kind of feels you know kind of really really matter And I think basically it was the reasoning breakthrough It was oh one and then r1 that basically answered that question basically said oh no We're gonna be able to actually turn this into something that's gonna work in the real world And then it obviously the coding breakthrough over the over basically of the coding breakthrough the kind of catalyzed over the holiday break Was kind of the third step in that We're just like all right if you know if Linus Torvalds is saying that AI coding is not better than he is like That's that's never happened before it's the benchmark yeah, that's never happened before and so now we know that it's gonna sweep through coding and then and then we we know You know, we know that if it's gonna work in coding it's gonna work and everything else right is just that because that's like that's like that's like the hardest In many ways that's the hardest example and now everything else is gonna be a 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 it incredibly powerful and then we just got the the daughter research You know the self-improvement, you know, we're now into the self-improvement breakthrough and so the so the way I think about it is We've had four fundamental breakthroughs and functionality LAMs reasoning agents And then and then now RSI And they're all actually working and so I'm just as you I'm jumping out of my shoes 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, I am completely convinced I think the exciting that people feel is like during the transistor Are you more so 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 in like the jumps where like you said is like in three months you have like this huge jump like And people are like well, this can keep happening right but then it keeps happening and we'll keep happening And so like how do you think about also timelines of like what's we're building? I think we always have this question with guests, which is like you know Should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead and 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 six months it like drastically changes and 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 more's law was what we now call a scaling wall like where's law was a scaling wall and for your younger viewers More or as law was every chip chips either get twice as powerful or twice the shape every every 18 months And that and that and then you know that it's got 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 million dollar current dollar thing into You know the phone in your pocket being you know a million times more powerful than that like that you know for 500 bucks And so that that was a scaling law and then and then key to any scaling law including more's law And AI scaling laws is you know, they're not really lost right there. They're their predictions But when they work they become self-fulfilling predictions because they they said a benchmark and then the entire industry Right all the smart people of the industry kind of work to make sure that that actually happens And so they kind of motivate the breakthroughs that are required to keep that going and and and and chips That was a 50 year that was a 50 year run right and it was amazing and it's still happening in some areas of Of chips I think the same thing is happening with the the course scaling was the course scaling was in in in AI You know, they're not really laws, but like they they are basically Their predictions and then they're motivating catalysts for the research work that is required to be and by the way Also the investment dollars You know required to basically keep you know keep the curves going And look it is it's going to be complicated and it's going to be variable and they're you know They're 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 forget a way to punch through the walls Then obviously that's you know, that's been happening a lot You know, then look there's going to be times when it looks like the walls have you know the the loss of Peter to out And then they're going to pick up again and search and then and then and then it appears what's happening to the eyes There's not multiple you know multiple scaling laws There's multiple areas of improvement and and I think you know, I don't know how many more there are yet to be discovered But there are probably some more that we don't know about yet You know, they're like for example, there's probably some scaling law around world models and robotics that we don't fully understand You know kind of acquisition of data at scale in the real world that we don't fully understand yet So that that one will probably kick in at some point here. There's a bunch of really smart people working on that And so yeah, I think the expectation is that you know the scaling laws generally are going to continue Yeah, the pace of improvement will continue to move really fast To your question. I'm like what to build so I'm a complete believer the scaling laws are going to continue I'm a complete believer the capabilities are going to keep getting amazing You know leaps and bounds The part where I kind of part ways a little bit with what I would describe as the AI pierists You know, which is which I would characterize as like the people who are In many ways the smartest people in the field, but also the people who spend their entire life like in a lab And have to have those they have very little experience in the outside world The newest 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 and and doesn't you know 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 much of the ASEOs have this thing which is just like there 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 these these obvious set of things that society used to do And then like society's not doing any of those things Right, and it's like how can society not you know, whatever their theory is how can society not see 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 And so the specific answer your question is like as usual it depends You know, it depends look there's no question people are kind of like there's no question. They're going to be companies It's already happening their companies that think that they're building value on top of models Then they're just going to get bliss 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 and into the real messy world The humanity is just going to be messy and complicated. It's it's not going to be simple. It's going to be messy and complicated And there are going to be a lot of companies and a lot of products And in fact, entire industries that are going to get built to basically actually help all of this technology actually reach real people The amount of capital going into these companies. I mean Dario talked about it on the Dork Cash Podcast and Dork 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 They know but now I think 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 sitting? I go like and say like things Peter out You think you can kind of like a roof structure these buildouts and you know capital investments Yeah, so just start by saying so I live through the dot com crash And I can tell you stories for hours about the dot com crash and it was horrible. No, it was awful. It was it was it was apocalyptic Well, by the way, a lot of the dot com crash was actually at the time it was actually a telecom crash It was a bandwidth crash like the thing that actually crashed the wiped out of the money with the telecom companies global crossing global Yes, I'm from Singapore and the lead so much cable over over our oceans Actually, there was a scaling law on the dot com era and it was literally the the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter And actually in 1995 and 1996 internet traffic actually did double every quarter And so that became the scaling a lot So what all these telecom entrepreneurs did was they went out and they raised money to build fiber anticipating that the demand for Ben With this gonna keep doubling every quarter doubling every quarter though is like you know grains of chess and 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 sense andception is you know, it's continuously grown It's never shrunk and it's grown really fast compared to anything else In human history, but it wasn't doubling every quarter as of 1998 1999 And so there was this gap in the expectation of what they thought was a scaling law versus reality And that's actually what caused the dot com crash which was the way over companies like global crossing way overbuilt fiber Which is sort of the by the way fiber telecom equipment, you know So all the all the networking gear you know and then and then by the way the actual physical data center So like that was the beginning of the of the data center build and then data center overbuild And so you had that but it was it was literally I think it was like two trillion dollars got wiped out Right it was like It was like a big it was and by the way the other the other subtlety in it was the internet companies themselves Never really had any debt because tech companies generally don't run on debt But the telecom companies run on debt physical infrastructure companies run on debt And so the companies like global crossing not just raised a lot of equity they else raised a lot of debt So they're highly levered and so then you just do the thing It's just like okay, you have a highly levered thing where you're just oh, you're over building capacity Demand is growing but not as fast as you hoped and then boom bankrupt right and then and then it's like they say about the hotel industry which is it's always that third owner of a hotel that makes money. Right? It has to go bank. up twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state and then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today, but 25 years later. But it's actually the elapsed time was it took 15 years, it took 15 years from 2000 to 2015 to actually fill it fillable the capacity. The question or warning is the overbilled can happen. And you know, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital is like, wow, it's just I don't know how to invest in these crazy software things. But for sure, I can put build data centers and for sure I can buy GPUs and I can deploy, you know, compute grids and all these things. And so, you know, if you're a pessimist, you can look at this and you can say, wow, this is like really set up to be able to basically replicate, you know, what we went through, we're going through in 2000. Obviously, that would be bad. The counter argument, which is the one I agree with, which is the counter on the other side is a couple things. One is the companies that are investing all the companies that are investing the money are like the blue is chip of companies. And so back back in the, like global crossing was like, it was like an entrepreneur, it's like a new venture. But like the money that's being deployed now at scale is Microsoft and at, you know, an Amazon and Google Facebook and Facebook and Nvidia and, you know, these these these and now, you know, by the way, open and then through up, I quit from now, it like, you know, really serious size, you know, as companies with, you know, very serious revenue. These are very large scale companies with like lots, lots of cash, lots of debt capacity that they've never used. 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 you guys know this, like everybody's starve for capacity, everybody's starve for compute capacity. And then you know, all the associated things memory and, and interconnect and everything else, data center space. And so every dollar right now that's being put in the ground is turning into revenue. And in fact, I actually think there's an interesting thing happening, which is because everybody's starve 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. It's right to pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful. The models would be much better because you would just allocate a lot more money to training and you'd just build better models and they would be better. And so we're actually getting the sandbag version of the technology. No, everything we use is quantized because the labs have to keep the full versions. Right. We're not even getting the good stuff. But getting the good stuff is just even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all the things that have to happen in the course of the next five or 10 years, once it happens, even the current technology is going to get, going to get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Like it, you know, this isn't just sending packets across a thing, whatever and hoping people find something to do with it. This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Here's what I know. Here's what I know. In the next three or four years, it's like some of the three or four years out, basically everything is selling out. So like the entire supply chain is sold out or selling out. And so there's no like it, we're just going to have like chronic supply shortage for, you know, for years to come. There's going to be a response from the market that's going to result in an enormous, you know, it's happening now, an enormous flood of investment in a new fat capacity and, you know, everything else to be able to do that at some point of supply chain constraints will unlock, you know, at least to some degree, that will be another accelerant to industry growth when that happens because the products will get better and everything will get cheaper. And so I know that's going to happen. I know that, you know, the deployments, you know, the actual use cases are like really compelling. And then like I said, you know, with reasoning and agents and so forth, like I know they're just going to get like much, much better from here. And so I know the capabilities are like really, really serious. I also know that the technical progress is not going to stop. It is accelerated is accelerating. Like the breakthroughs are tremendous. I mean, even just month over a month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic in their, their are cynics, you can look at 2000, you can find echoes, but I can't even imagine betting on this. It's just 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. It was up Michael Burry. Uh, oh, it's an interesting thing. Well, pick on a guy. Well, pick on one guy. Well, because he did it. He came out with it was it was the, it was the envy. It was the Nvidia short, right? Yeah. The Nvidia short. And then you guys probably talked about this, which is the now. So 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 three years old. You're making more money on it today than you did three years ago, because the pace of improvement of the software is faster than the depreciation cycle to chip. And then my understanding is Google is running. I don't think I don't know exactly what I do. These rumors that I've heard or maybe it's public, but I think Google is running very old TPUs. Very profit. Yeah. Very profit. Very profit. Very profitably. And so, so it actually turns out as far as I can tell, it's actually the opposite of the brewery thesis is actually he was actually 180 degrees wrong. It's actually the, the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like, it's never been the case that you have an older model chip that becomes more valuable. Yeah. It's valuable. And again, that's an expression of that just in ferocious pace of software progress, ferocious pace of capability payoff that you're getting on the other side of this. And so I just, the idea of betting against that, like, yeah, what it means, like an invitation to get your face ripped off. Well, my early hits was like modeling the lifespan of the H 100 and H 200s and going, like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically care cut it down to two to three, but actually it's going up and not down. And, and that's, I mean, that's, I think that's the dream. We are finding utilization. And I think utilization solves all problems. Like, you can, you can find use, use cases for even like the port, like even memory we're having a shortage, right? And even like the, the shitty air versions of memory that we do have, we are finding use cases for it. So like, it's great. How important is open source AI and kind of like edge inference in a world in which you have three years of supply crunch? Like, 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 think just practically speaking, if we're just going to have fundamental construct, so supply crunches for the next, I mean, you guys, if you just project forward demand over the next three years, relative to supply, one of the, it's main predictions you can do is what's going to, what's going to happen to the cost of inference in the core over the next three years? And like it may rise dramatically, right? So, so what is it? And then he's, is you know, like the big model competition subsidizing heavily right now? Right. And so, so what's the, what will be the average person's, you know, per day per month token cost, you know, three years from now to do all the things that they want to do? And I don't know, it's going to be, I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw for claw tokens to run open claw. Right? And so, okay, 30,000 dollars a month, right? And by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do. Right? And so you can imagine, there's like a latent demand of up to, I don't know, five or 10,000 dollars a day of tokens for a fully deployed, you know, personal agent. And obviously consumers can't pay that. Right. And so, so, but it gives you a sense of the few of the future scope of demand. Right? And so, so even, even if there's a 10x improvement in price performance, that's still, you know, it goes to a hundred dollars a day, which is still way beyond what people can pay. So there's just going to be like for row system. And by the way, the agent thing, the other interesting thing is I think the agent thing. So up until now, a lot of the constraints of GPU constraints, I think the agent thing now also translates into CPU constraints. Right? CPU memory, CPU memory, right? And so like the entire chip ecosystem is just going to get within network constraints that will be the killer. Let's all bottle like to potentially for years. And so, so I think the brat, 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 complex supply constraints. And then at some point, maybe the lab stops subsidizing so much and that, that again will be, be an issue. And so there's just going to be so much more demand for inference than then can be satisfied, you know, kind of with the centralized model. And then, and then you know, you guys know this, but like all the just the dramatic, I mean, just the dramatic innovations that have happened in the Apple Silicon to be able to do inferences is quite amazing. A level of effort being put like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a PC and then six months later, up and run a PC, right? It's like amazing. And there's various smart people working on that. So there's all that. And then look, there's also, you know, there's also like other, there's other motivators, there's other motivators, which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for in certain cases with some people for certain use cases, people being like, well, I'm not willing to just like turn everything over. So 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 want your turn up to have an AI model and, 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 and a lot of those are going to be local. And so yeah, no, like I think I think you're going to have time. And then you're going to, by the way, also wearable devices, you know, you don't want to do a complete round trip. You want, you know, you're 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 biggest news this week was the collapse of AI to the Allen Institute, one of the actual American open source model labs. And I'm not that optimistic on American open source. Like 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. I look, number one, I do think we care. I do think we, I do think we care who makes it. I would say this the previous presidential administration wanted to kill it in the US. Oh, yeah. They wanted to drown in the bathtub. And so they wanted to kill it. So at least we have a government now that actually like actually wants it to wants it to come to council. And then the new and the peak cast. Yeah. So that, you know, this is minute for whatever other political issues people have, which are many, you know, this administration has, I think, a very enlightened view and a particular and enlightened view on AI and a particular open source AI. And so they're very supportive. My read is the Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is they, they don't fundamentally, they don't think they can sell commercial AI outside of China right now and at least specifically not, not in the US for a combination of reasons. And so they kind of view, I think, open source AI. a bit of a loss leader against basically domestic paid services and then kind of ancillary products. They're very excited about it. By the way, I think it's great. I think it's great that they're doing it. I think DeepSeaf was like a gift to the world. The great thing about open source, the impact of open source is felt two ways. One is you get the software for free. But the other is you get to learn how it works. And so the paper, the paper and the code, right and the code. And so like, for example, I thought this was amazing. So OpenAX comes out with a one. And it's an amazing technical breakthrough. And it's just like absolutely fantastic. But of course, they don't explain how it works in detail. And then of course, they hide the reasoning traces. Right. And then and then everybody's like, okay, this is great. But like, who's going to be able to replicate this? Are other people going to be able to do this? You know, is there secrets often there? And then our one comes out and it's just like, there's the code and there's the paper. And now the whole world knows how to do it. And then, you know, three months later, every other AI model is adding reasoning. And so so you get this kind of double, like even if the Chinese models themselves are not the models to get used. The education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful. So that happens. And then I don't know. We'll see. You know, there are a bunch of American, you know, open source, you know, AI model companies. I mean, look, there's going to be tremendous, you know, there already is. There's, you know, there's going to be tremendous, tremendous competition among the primary model companies. You know, there's depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck in different ways, you know, and, and, you know, and then obviously both X and then matter where I'm involved are, you know, both have huge, you know, huge attempts to, you know, kind of to kind of leapfrog underway. And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're back in that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know. How many, how many like main line foundation model companies are there in China at this point? It's probably six. It's five tigers. It's like they call it. Quinn is in questionable because there's changing leadership. But that does that include that includes like moonshot. Yes. The CME and deep seek, Z AI, Quinn, O1 is in there. Right. And then by dance, and then you see, by dance would be like the next year. By dancing, they weren't as prominent. They weren't have a lead in the audience. Yeah, but they're, you know, you know, see, dance is very inspiring. And presumably they have more stuff coming in 10 cent probably has more stuff coming and so forth. And so, so it's like, look, here would be a thing you can anticipate, which is there are not, these markets, they're not going to be between the US and China right now. There's like a dozen primary foundation model companies that are like at scale at that some level of like vertical mass. It's not going to be a dozen and three years. Right. Like it just because these industries don't bear it doesn't it's it's going to be three, you know, there's going to be three or four big winners or maybe one or two big winners. And so there's going to be like a whole bunch of those guys that are going to have to figure out alternate strategies. And I think like open source is one of those strategies. And so I think you could see like a whole, I think the questions like who's going to do open source. I think that could change really fast. I think that that's a very dynamic thing. I think it's very hard to predict what happens. And I think it's very important. And Vity is doing a lot in you. Well, I was going to say, well, exactly. And then you got a video. And then, and then, you know, just to get an industrial, there's this old thing in business strategy, which is called commodity, commodity, commodity, the complement. And so if your Jensen is just kind of obvious, of course, you want to come out of it has the software. And he's and to his enormous credit, he's putting enormous resources behind that. And so maybe it's literally a video. And I think that would be great. Yeah. Narrative violation to European projects in the living. Damn. I was in my Europe conference soon and I got both of them. They got us. They got us. Okay, wait a minute. Where was Peter? So where was Stuyberger when he did it? He was in Vienna. He was in Vienna. And then where is he now? He's moving to SF. Okay. All right. Okay. There we go. And then yeah, the pie guy right, the pie guys are European. Yeah, there was everybody's in the smart Mario's also there. Right. And are they? Yeah, they haven't announced yet any sort of change. Change or have they no, they're the devil company there. Okay. Okay. Okay. Yeah. Yeah. Yeah. Yeah. Good. Anyway, I think pie and open claw very important software things and I just wanted you to just go off on what do you think? Yeah. So I think in the combination of the two of them, I think is one of the 10 most important software. Open claw got all the attention, but right. Talk about pie. Pie's kind of the idea. Pie'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 like 1972, I don't know, it still is very important, but like 1973, from 1973 to like basically the creation of Linux, which is basically this thing used to call it the Unix mindset. Like so, so because there were all these different theories, there are all these different operating systems and mainframes and then you know, all these windows and Mac and all these things. And then there was this 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 the operating system that like made the computer industry really work like in the 1960s, which was the thing called OS 360, which was this big operating system IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a giant castle of software. And by the way, it worked really well and they were very successful with it, but like it was this huge castle in the sky. But it was this thing, it was almost unapportable, 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 system work. And then the Unix guys originally out of AT&T and then out of Berkeley came out and they said, no, let's have a completely different architecture and the way architecture is going to work is we're going to have we're going to have a prompt and a shell. And then 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 it's almost like the operating system itself is going to be a programming language. And then that led to the sort of centrality of the shell. And then that led to a sort of you know, basically changing the other Unix tools. And then that led to the emergence of these scripting languages like Pearl, where you could basically kind of very easily do this. And then the shells got more sophisticated. And then and then looked like you know that that number one that worked. And that was the world I grew up in like I was I was a Unix guy, you know, sort of from call it 1988 to you know kind of all the way through my work. And it worked really well. It's in the background you know normal people don't need to need to 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 Windows shell is kind of a sort of a weird derivative of that. But you know, but look the internet runs on Unix. And that's smartphones actually both iOS and Android are Unix derivatives. And so you know kind of Unix did end up winning. But anyway, and then we just started taking that for granted. And then so basically the way I think about what happened with Pi and then with OpenClaw is basically with those guys figured out is always say the great breakthroughs are obvious in retrospect, right? Which is the best kind to the best kind. They were not 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 backwards looking and you're just like, oh, of course, like to me, those are always the best breakthroughs. So actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course. Yeah, what other objective mattered. Yeah, exactly. But like it, but she's even saying it wasn't obvious until somebody actually did it, right? And so the conceptual breakthrough is real and deep and powerful and very important. And so the way I think about Pi and OpenClaw is it's basically marrying the language model mindset to the unit to the Unix basically shell prompt mindset. And so it's basically this idea that what so what is an agent, right? And as you know, like many smart people have been trying to figure out what an agent is for decades. And they've had many architectures to build agents and the whole thing in a terms of what is an agent. So it turns out what we now know is an agent is the following. So it's language model. And then above that, it's a bash. It's a bash shell. So it's a unique shell. And then as in, then the agent has access has access to to the shell and hopefully in a sandbox, maybe in a sandbox. So it's the model. It's the shell. And then it's a file system. And then the state is stored in files. And then there's the markdown format for the files themselves. And then there's basically what in Unix is called a cron job. There's a loop and then there's a heartbeat for this heartbeat. And the thing basically wakes up wakes up. So it's basically LOM 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 a enormous number of command line interfaces into all kinds of things already in the, you know, you're entire, I mean, you're entire just to start with your computer runs on a shell. If you're running a Mac or a phone, your computer's 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 whole idea where we need like MCP and these like grow to fancy protocols, whatever it's like, no, we don't. We just need like a command line thing. So that's the architecture. And then it turns out what is your agent? Your agent is a bunch of files stored in a file system. And then there's the thing that just like completely blew my mind when I ran my head around it as a result of this, which is like, okay, this means your agent is now actually independent of the model that is running on because you can actually swap out a different LLM underneath your agent. And your agent will change personality somewhat because the model is different. But all of the state stored in the files will be retained different instructions set, but you just compiled it. Exactly. And it's all right. It's like swapping out a ship and recompiling. But it's still your agent with all of its memories and with all of its capabilities. And then by the way, you can also swap out the shell. So you can move it to a different execution environment that is also a bad shell. By the way, you can also switch out the file system. And you can swap out the heartbeat for the ground framework, the loop, the agent framework itself. And so your agent basically is basically the end of the day. It's just it's files. And then there's of course, yeah, it's basically it's just the files. And then by the way, as a consequence of that, the agent itself, it turns out a couple important things. So one is it can migrate itself. Right. And so you can instruct your agent migrate yourself to a different runtime environment. Migrate yourself to a different file system. Migrate yourself to a different, you know, like you swap out the language model, your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. And actually it actually knows about its own files. And it can rewrite its own files. Right. Which by the way is basically no widely deployed software system in history where the the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself like that. That I mean, there have been toy systems that have had that, but there's never been a widely deployed system that has a capability. And then that leads you to the capability that just like completely blew my mind to when I ran my head around it, which is you can tell the agent to add new files. functions and features to itself and it can do that. >> Thank you. >> Right. >> Extend yourself, like you stand yourself. Give yourself a new capability. Right? And so literally it's just like you run into somebody at a party and they're like, oh, I have my open cloud do whatever, connect to my eat sleep bed and it gives me better advice to sleep and you go home at night and you tell your claw or if they're at the party by the way, you tell your claw, oh, add this capability to yourself and your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to cloud code or whatever, it'll write whatever it needs. And then the next thing you know, it has this new capability. 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 to do that. And so anyway, so the combination of all this is just, I mean, this is just like a massive incredible, I mean, it's just incredible. Like if I were, if I were 18, like this is 100, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. And again, people are going to look at it and they already get this. There's fun people are going to look at it and they're going to say, oh, where's the breakthrough? Because these, all of these components were already known before. 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 is buried in there. And so, and so for example, computer use all of a sudden just kind of falls trivially, trivially, of course, it's going to be able to use your computer. It has full access to the shell, right? And then you just, you give it access to a browser and then you've got the computer in the browser and often away it goes. And then you've got all the abilities of the browser also. And so, and so the capability, unlike 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 your challenges at the thing. And by the way, it's early and you know, these are, you know, these are prototypes and there are, you know, I see you guys know, there's security issues. And so, you know, there's a bunch of stuff to be ironed out. But the unlock of capability is just incredible. And I have absolutely no doubt that everybody in the world is going to have at least, you know, an agent like this, if not an entire family of agents and we're going to be living in a world where I think it's almost inevitable now that this is the way people are going to use computers. I was going to say for someone who is deeply familiar with social networks, the next step is your claw talking to my claw, posting on claw Facebook, posting their jobs on claw LinkedIn and posting their tweets on claw XAI or whatever, you know, I do think that that is how, you know, we get into some danger there in terms of like alignment and whether or not we want these things to run. You guys know, rentihuman.com? Yeah, rentihuman.com. Yeah, yeah, yeah, yeah. I mean, it's fiber. It's task time. Sure. Of course. Mechanical Turk. Yeah. Which of course is going to happen. It's obviously going to happen. I'm curious if you have any thoughts on the engineering side. So when you build the browser, the internet, you know, just a bunch of mostly plain text file plus some images. And today, the every website and app is like so complex and like somehow, you know, the browser kept evolving to fit that in. Are there any design choices that were made like early in the browser and kind of like the internet and the protocols they, you're seeing agents similar today. Like, hey, this thing is just not going to work for like this type of new compute. And we should just rip it out right now. There were a whole bunch, but I'll give you a couple. So one is, and we didn't need 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 of that human readability actually mattered a great deal. And so it's particularly in those days, it was not so much English language, but it was there was a design decision to be made between binary protocols and text protocols. Basically every, every, every basically old school systems architect that had grown up between like the 1960s and the 1990s, basically sad, you know, the internet, it's, what do you know about the internet? It's star for bandwidth. You just, you have these very narrow straws. You know, look, people, when we did the work on a mosaic, like people who had the internet at home had a 14-kilometre modem, right? So you're trying to like hyper optimize every bit of data that the travels over the network. And so obviously, if you're going to design a protocol like HTTP, you're going to want to be binary, 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 your, the last thing you're going to want to do is like bring up and tear down new connections. And you definitely, you're not going to want a text protocol. And so of course, we said, no, we actually want to go completely the other direction. It's obviously we only want text protocols. By the way, same thing in HTML itself, we want HTML to be relatively verbose, you know, we want the tags to actually be like human readable. We want to use the most inefficient things possible. Yeah, we want to do the, we want to do the inefficient things. We're the original token max here. Yeah, exactly. Yeah, yeah, basically it's just like, well, yeah, well, actually this was, this was actually the conscious thing, which basically says just like, assume, assume a future of infinite bandwidth built for that. And then basically what it was, it was a bet that it was a bet that if the system, if the latent capabilities of the system were powerful enough, and that was obvious enough to people that would create the demand for the bandwidth that would cause the supply of bandwidth to get built that would actually make the whole thing work. And then specifically what we wanted was we wanted everything to be human readable because at the engineering level, we want 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, to have it converted out of binary, right. And so the, all the, you know, HTTP and everything else where it was always text protocols, and the same thing with HTML. And in many ways, some people say that the key breakthrough in the browser was the ViewSource option, which is every web page you go to, you could view source, which means you could see how it worked, which means you could teach yourself how to build, right, new, to build do web pages. There was that. So human readability, and again, human readability in those days still Matt 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 I see how it's working. And that worked really well for the web. And I think it's working really well for AI. That was one. What was the other big part of the idea of web servers was to actually surface the underlying latent capability of the operating system and to be able to surface the, also the underlying latent capability of the database. Because basically what was a web server? What is a web server fundamentally? Or, actually, it's, it's the operating system. So it's the operating system's ability to, you know, it's running on top of the OS. 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 of websites are, our friend instant databases. And so you wanted to, you wanted to unleash the underlying latent power of whether it was an Oracle database or some other, you know, some other postgres or whatever 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 in 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? Data basis 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, fair enough. But on the other side of that is just like this is now a much better interface to databases and one that eight billion people are going to use and is going to be like far easier to use and far more flexible. And, and, and, and you're not just 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 and layers. Some of the smartest people in the industry look at any new challenge and they're like, okay, I need to build a new kind of application. So the first thing I need to do is build a new program in language. Right. And then the next thing I need to do is build a new operating system. Right. And the next thing I need to do is I need to build a new chip. Right. And they kind of want to reinvent everything. And I've, I've always had, maybe it's just, I don't know, pragmatic mentality or something or maybe an engineering over science mentality. But it's more like, no, you have just like all of this latent power in the existing systems. And you don't want to be held back by their constraints. But what you want to do is you want to kind of liberate that power and open it up. Yeah. And I think the web did that for those reasons. And I think it's the same thing that was happening. It's good perspective in the web. The programming language is another good thing. We have Brett Taylor on the podcast and we were talking about Rust. And you know, Rust is memory safe, but the following. So why are we teaching the model to not write memory unsivecologist use Rust and then you get a portrait? How much do you think there's like time to be spent like recreating some of these things instead of taking them from granted? I'll be like, okay, Python is kind of slow. Python typescript. You know, as a, yeah. As imperfect as they are, they are the lingua franca. I mean, I think this is going to change a lot because I don't think the models care what language they program in. And I think they're going to be good at programming every language. And I think they're going to be good at translating many language to any other language. Like, okay, so this gets into the coding side of things. I think we're going through a really fundamental change. And I look, I grew up, you know, I grew up handcoding. I grew up handcoding. Everything I did was actually, everything I did actually was written in C. I was back in the day. I was 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'm still from the generation that, you know, I knew assembly language and, you know, I, you know, so I could drop down and do things right on the ship. And so we've just, we've all of us, we've always lived in a world in which software is like this precious thing 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 they much software can human beings, you know, kind of maintain? And I think like all those assumptions are being shot right off the window right now. Like I think they're, I think those days are just over. And I think the new world is like actually high quality software is just like infinitely available. And if you need new software to do X, Y, Z, but 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 languages written and you just tell the thing, all right, I want the right now. I want the rest version. Or, you know, secure, you know, secure, we're about to, by the way, we're about to go through computer securities about to go through the most dramatic change ever, which is number one, like every single latent security bug is about to be exposed. Right. So we're going to have like the, we're set up here for like the computer security apocalypse for a while. But on the other side of it, now we have coding agents that can go in and actually fix all the security bugs. And so how are you going to secure a software in the future? You're going to tell the bot to secure it and it's going to go through and fix it all. And so, 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? And that has like, you know, that has like tons and tons of consequences. In some sense, the answer to the question that you posed, I think it's just somewhat, I don't know, simple or something or straightforward, which is if you want all your software and rest, you just all about you want all your software and rest like things that used to be like the hard or even like seem like an insurmountable mountain to get through all of a sudden, I think become very easy. I think Brad had a theory that there would be a more optimal language for LMS. And so the contention is there isn't like just don't bother. Just whatever humans already use, LMS are perfectly capable of pointing. I think we're pretty close to being, I don't know if this works. I think we're pretty close to being able to ask the AI, what would it's optional optimal language be and let it design it? That's true. Okay, here's a question. Are you even going to have programming languages in the future? Or are there, are there, are there, are there, are there, are there, are there, are there just coding binary directly? Did you see there's actually an experiment, somebody just did this thing where they have a language model now that actually emits model weights for a new language model? Right, and so will the bots predict the weights? Yeah, well the bots literally be emitting not just coding binaries, but will they actually be emitting weights for new models directly directly? And conceptually, there's no reason why they can't do both of those things. They're like, just architecturally both of those things seem completely possible. Very inefficient. You basically, very inefficient simulation of a simulation in a simulation inside of weights. Very inefficient, but like, look, LMs are already incredibly inefficient. I'm a favorite thing, ask Clawed to add two plus two equals four, right? It's just like, you know, it's like, it's like whatever, billions and billions of times more inefficient than using your pocket calculator. But the payoff is so great of the general capability. So anyway, like I kind of think in ten 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 trying to understand why the bots have decided to structure a code in the way that they have. And me, if you play it through, you don't need browsers then. That's the death of the browser. Well, so I would take it a step further, which is you may not need to use your interfaces. So who is going to use software in the future? Other bots? Other bots. Yeah. And so you still need to, I don't know, pipe information in. Do we? Inout. Really? Well, what are you going to do then? Are you sure? You're just going to log off and touch grass? Whatever you want, exactly. Is that better? I want some way to do stuff for me. Is that, but is that better? I mean, look, I don't look like I got, you know, the arguments here, it was not that long ago, that 99% of humanity was behind a plow. Right? And what are people going to do if they're not plowing fields all day to grow food? And it just turns out there's like much better waste for people to spend time than plowing fields. Yeah. Exactly. Talking to their friends. And look, and I'm not an absolutist and I'm not a utopian. And to be clear, like I have an 11 year old and he's learning how to code and like I'm, you know, I think it's still a really good idea to learn how to code and so forth. But I just, if you project forward, you just have to think forward in a world in a way, 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 it's going to do it in whatever way is most optimal for it to do it. Yeah. Unless I tell it to do it non optimally, like if I tell it to do it in Java or in restaurant, 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 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 interpretive. It's going to be the engine of interpretability to explain itself. And I just am not convinced that that I'm not convinced that in that world, you have these historical, the goals of the abstractions will be whatever the boss need at what the human's right. Yeah. Yeah. Well, I'm curious like if that's true, then shouldn't the models providers be building some internal language representation that they can do extreme kind of like RL and reward modeling around because it's like today, they're kind of like tied to like tap script and Python because the users need to write in that language versus they can have their own thing internally and like they don't need to teach it to anybody. They just need to teach their model. And I think that's how you get maybe the version between the models, like going back to like the pie, open clotting. It's like, oh, I built all the software using the open AI model. And I switched to the entropic model, but the entropic model doesn't understand the thing. So it feels like there still needs to be some obstruction, but maybe not. Maybe that's the lock-in that the model providers want to have. I'm not even sure that's locking though because why can't the second model just learn what the first model has done? Like, the exact, okay, so okay, give me some. Yes, you know, models can now reverse engine or software, right? So it's the whole thing now with people are reverse engineering like Nintendo game binaries. Yeah. You have like, you see 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 bird into a chip or something, another reverse engineer and a version of the Mac, right? And so if you reverse it, if it's quite kind of say, if you're reversing like X86 binaries, then why can't you reverse engineer whatever they create? Yeah. And because we're all on a unix-based system, it has to be reversible because it needs to run on the target. Yeah. Yeah. Yeah. Basically. And so I just think it's this thing where it's just like, and by the way, and everything we're describing is something that human beings in theory could have done before, but just with, but with enormous, but it was just always like cost and labor prohibitive. And I learned how to reverse engineer. It's like human beings in reverse engineer binaries. It's just for any complex binary, and you 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 a human built system sort of compensate for the human limitations. Yep. Right? And if you don't have the human limitations anymore, then all of a sudden you have, and it's not that you won't have it distractions, but you'll have a different kind of abstraction. Yep. I have two topics to bring us to close and you can pick whichever ones. I'm just talking about protocols. I said you or someone else, I forget my internet issue, who said that like the biggest mistake that we didn't figure out in the early days was payments. Yes. Was that you? Yes. It was a 402 payment required. We have a chance now. I don't think we're going to figure it out. I don't know. Like what's your take? Oh, I think we will. Yeah. No, no, I think it's going to happen for sure. Yeah. And there's two reasons to get for sure. One is we actually have internet native money now in the former crypto, and this is the tablecoins and crypto. And I think this is the grand unification basically of AI crypto. Is what's about to happen now. And the AI is the crypto killer app. I think it's where this is really going to come out. And then the other is just, I think it's now obvious. It's like obviously, AI agents are going to need money. It's already happening. 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 about 0.1% if that. But yeah. Today, yeah, yeah, yeah. Think forward. Think forward. Think forward. Think forward thinking. The ultimate principle of everything and everything that I think we do is the William Gibson quote, which is the future is already here. It just isn't distributed yet. My friends who are the most aggressive users of open claw just like have given their claws banker accounts. They've got credit cards. And and and and and and only they've done it. It's obvious that they needed to do it because it's obvious that they needed to be able to spend money on their. Yeah. It's just completely obvious. And so and again, like so the number of people who have done that today to your point is like, I don't know, probably 5,000 or something. But it will go. That's how these things start. Actually, I mean, since you keep mentioning it. And by the way, open claw, by the way, if you don't give it a back account, it's just going to break into your court. You remember? It's going to be breaking into your back account anyway. And take your money. So you might as you might as well do it. You might as well do it. By the way, I really love I got to tell you I really love the phenomenon. I love the yellow. I'm not doing it myself to be clear, but I love the people that are just like, what is it? What is it? Dangerously dangerous. But yeah, anyway, it's a Facebook thing. Okay. Because yeah, in Facebook, they have this culture to name the thing dangerous so that you are aware when you enable the flag that you are opting into dangerous thing. Okay. They brought it into open AI. And of course, that makes it enticing. Yes, Sam runs codex with skip permissions on his laptop. Yes, 100%. And so I think the way to actually see the future is to find the people who are doing that. There's a man to steal you know, and they're, they're, they're, they're not giving everything, you know, just watch it and watch the logs. But like, let's actually find out what the thing can do. Yeah. The way to find out what the thing can do is just try everything. Yeah, let it try everything. Let it unlock everything. By the way, that's how you're going to find all the good stuff it can do. By the way, that's also how you're going to find all the flaws. I think the people who turn that on for bots are like, they're like murders to the progress of human civilization. Like I feel very bad for their descendants that they're like, the cons are going to get looted by their bots for like 20 minutes. But I think the contribution that they're making to the future of our species is amazing. Is that a gentleman's science? Yes, it's, yes, very many. It's Ben Franklin out with the trying to, trying to get lightning strike as his balloon and seeing if he gets electrocuted. Yeah. It's a, Jonah Salk with the polio vaccine. Yeah. Injecting in. Yes. So yes, I, I, I think we should have like a glory should have like flags and like, we should have like monuments to the people that just let open clover on their lives. More anecdotes are like, what are the craziest or interesting things that people listening to this should go and do? I mean, this is, this is the, this is the extreme thing is just like the straight yellow, like just, yeah, turn your life. That's a general capability. Yeah, yeah, yeah. It's like a specific story that was like, wow, and everyone in a 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 a, the health dashboard stuff is just, it's just absolutely, absolutely amazing. The number of stories on, I'm trying to just want to violate people's, you know, obviously personal. But, you know, one of the things OpenClaw is really good at us, hacking into all this stuff in your land. It's really good. And so, you know, internet of things, AKA internet of shit, like super insecure, but great, it's all discoverable. It's like, discoverable. OpenClaw is happy to scan your network. I didn't find all the things. And then my, my friends are most aggressive at this are having OpenClaw takeover everything in their house. Yeah. Yeah. It takes over their security cameras. It takes over their, you know, they're, whatever, their access control systems. It takes over their webcams. I have a friend who's claw watches him sleep, put a webcam in your bedroom, put the, put the claw in the loop. I have a wake up frequently and have it watch it and just tell it, watch me sleep. And I've seen the transcripts and it's literally like, Joe's asleep. This is good. This is good. The Joe's asleep. You know, I have, I have his health date and I know that he hasn't been getting enough sleep. And so it's really good that he's getting asleep. I really hope he gets his full, whatever, you know, five hours of sleep. That's it. Joe's moving. Joe's moving. Joe might be waking up. This is a real price Joe wakes up now. He's going to ruin his sleep cycle. Oh, okay. It's okay. Joe just rolled over. He's gone back a bit. Okay. Good. All right. Okay. I can relax. This is fine. He's monitoring the situation. I'm monitoring the situation. And being a bot, like, you know, is just like very focused. It's just like, this is like his reason for existence is to watch Joe sleep. And then I was talking to my friend and did this. He's like, you know, on the one hand, it's like, all right, this is weird and creepy. And I need to, I need to, maybe this is taking my life. And then the other thing is like, you know, what? If I had a heart attack in the middle of the night, this thing literally would freak out and call 911. Like, there's no question. This thing would figure out how to like alert medical authorities and like probably someone's watch teams and like, do whatever would be required to save my life, right? And so it's like, like, yeah, that's happening. What else? I've got to give you some company unitary that makes the robot docs. And I actually have one in home, which is actually really fun with the Chinese companies. The Chinese companies are so aggressive at adopting new technology, but they don't always just take the time to really package it, package it, maybe think it all the way through. And so at least the unitary dog I have. So it has a old non-LM just control system, which by the way is not very good in markets well, but in practice, it's not that good. It has trouble with stairs and so forth. And so it's not quite what it should be. But then the language model thing comes out of the voice. So they add to the add LLM capability, and then they add a voice mode to it. But that LLM capability is not at all connected to the control system. So you've got this schizophrenic dog that like is a complete idiot when it comes to climbing the stairs, but it will happily teach you quantum mechanics, right? In like a plumb English accent, right? Like it's just like absolutely amazing. Yeah, you can't get into intelligence. Yeah, you can't really talk about jagging. And then now obviously what's going to happen in the future is they're going to connect together, but right now, it's not that useful. And so I have a friend who has one of these who had his claw basically hack in and rewrite the code, write new firmware for the unitary robot. And now it's an actual pet dog for his kids. She did it before after, like the motion. Yeah, you said it's completely different. He said it's a complete transformation. And whenever there's an issue in the thing now, the claw just like rewrites the code. You know, you know, you go say it does the code as well. It kind of goes to your thing here. So like all of a sudden, this is why we're going to think about a code. Yeah, coding is not just like writing new apps. It's also going in and rewriting all the old stuff that shouldn't work that never worked. And so like I think I think basically, I think the internet, the internet of shit is basically over. Like I think everything, there's a potentially here where like all of 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, you have to decide if, yes, there are horror movies in which this is the premise. And so you have to decide if you want this. But, but this is the first time I can say with confidence, I now know how you could actually have a smart home with 30 different kinds of things with chips and internet access. Where it actually all makes sense and all works together. And it's all coherent and the whole thing. And to have that unlock without a human being having to go to any of that work like, yeah, yes. Yeah, I'm waiting for a sorry, Mark. I can't let you open that fridge door. You know, like exactly, exactly. Yes, yes, because I'm not supposed to eat. I have all of, yes, I have every threat of health information. You know, and I know you think you're doing, you know, I don't know, I don't think you do. But, you know, this is a real, are you really, you know, are you really sure? And, you know, you told, you know, you told me last night, you really don't want me to do this. So, you know, I'm sorry, but the fridge door is locked. Open the fridge door is exactly. And by the way, I know you're supposed to be studying for a test. Why don't you go, when you can pass the test, I will open the fridge door for you. Yeah. Final photo call. And then we can wrap up a proof of human. Yeah, right? That's the last piece that we got to figure out. Yeah. So I would say there's two massive, I would say, sort of, these symmetries in the world right now, where we've known these asymmetries exist and we, we societally have an unwilling to grapple with them and I think they're both tipping right now. And they're the same thing as virtual world version, physical world version. So the virtual world version is the bot problem. We're just like, you know, the internet is just like a wash and bots. The internet's a wash and fake people. It has been forever. By the way, a lot of that has to do with lack of money, you know, and so this is, you know, this is this. My spacey take was these two at the same thing and corporations of people too, you know. Interesting. Yeah, yeah. Okay. So a mega count is for the few of it. Yeah. Okay. Yeah, until you give the bots mega counts. Yeah, exactly. So, okay. Yeah. So there's that. But yeah, look, look, the bot, I mean, every social media user knows this. The bot problem is a big problem. You know, the bot problem has been a big problem forever. It's a huge problem and it's never really been confronted directly like at an point. By the way, the physical world version of this is the drone problem. Right. And so we've known for, you know, we've known for 20 years now that the asymmetric threat, both in military and actual military conflict, but also in just like security, like, like, you know, security on the home front. The big threat is the cheap attack run. Right. 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 cut, you know, in the world is like on protector from drone attacks, every, every stadium, every school, every prison, like, is like sure. Okay. We've known that. We've never done anything to do about it. Yeah. One possibility is just leave them a protector forever and live in a world of like asymmetric chairs and forever. The other is take the problem seriously and figure out the set of techniques and technologies required to be able to deal with that, whether those are lasers or jammers or really warning systems or, you know, personal force fields, I think personal for doing personal force fields. Exactly. In both cases, these are these are economic asymmetry. These are economic asymmetry, right? Because it's really cheap to feel the bot, but it's very hard to tell something about it's very cheap to feel to drone. It's very hard. It's very expensive to defend against a drone. But you see what I'm saying is it's it's the virtual version of the problem and it's the physical version of the problem. The virtual version of the problem, what we need quite literally is proof of human. The reason is because you're not going to proof a bot. The especially now that the bots are too good. The bots can pass the touring test and if the bots can pass the touring test, then you can't you can't screen for bot. You can't have proof of not a bot, but what you can have is you can have proof of human. You can have, you know, cryptographic validated. This is definitely a person and this is and then you can have cryptographic validated. This is definitely like something that a person said. This video is real, right? Just a double click on. Do you think Alex Blania with worlds? Do you think he's got it? Oh, yeah. Alternative. So I mean, there's going to be I think there'll be many people will try. We're one of the key, you know, participants in the world in the world project. I don't know. Yeah. So we're part of this. But yeah, I think so we think world is exactly correct. Okay. And the reason is it has it has to be it has to be perfect human. It has because you can't do proof of not bot. You have to do perfume and to do perfume and you need you need biological validation. You need to start with this was actually a person, right? Because otherwise you have bots signing up as fake people. That right. So you have to have like something. You have to have a biometric. And then you have to have cryptographic validation and then the ability to do to do the look up. And then by the way, the other thing you need, which you also need to select a disclosure. So you need to be able to do perfect human without reviewing all the underlying information. By the way, another thing you're going to need, you're going to prove of age, right? Because there's all these laws in all these different countries now around. You need to be 13 or 16 or 18 or whatever to do different things. So you're going to need to, you know, sort of validate a proof of age, you know, to be able to legally operate. Right. And so that's coming. And then you're going to want like proof of credit score and you know, proof of like, you know, 100 other. That's a tricky one. But it is a tricky one, but you're going to, there's no reason, like if somebody's checking on your credit, somebody should, put it giving example, somebody shouldn't need to know your name in order to be able to find out whether you're credit worthy. Right. Independently verifiable and pieces of information. It's like just like just close. And this is the answer to the privacy problem writ large, which is I only need to prove and I need to prove at that moment. So like you're going to need that. And I think their architecture makes sense. So that needs to get solved. I think language models have tipped. The bots are not too good. And so they're undetectable. And so as a consequence, we now need to confront that problem directly. And then like I said, and then the other problem is we need to go actually confront the problems. The Ukraine conflict has really unlocked a lot of thinking on that now the, and now the, the, the, the Iran situation is also unlocked in that. And so I think there's going to be just like this incredible explosion of both drone and counter drone. Are Jones a beard and their guns? Is that the key that way? Yeah. Yeah. Encounter drones. I think we can sneak in one more question. I'm trying to get our a lot of things that you said over the year. So at the milk and institute debate with Teal, which is amazing. You talked about the lag between a new technology and kind of like the GDP impact of it. The other idea you talked about is bourgeois capitalism and how, you know, it's kind of mandatory. Your class was needed because of this complexity. And I think if you bring AI into the fold, you have like much higher leverage for people. So like if you have, you know, the musk industries, and you give Elon a GI, you can run a lot more things at once. That's right. And then you have the social contract. And I know you'd receive a clip of some all-ment saying, where we think in the whole thing and you're like, absolutely not. Yes. And I was at an event with Sam last night. And he actually said in the last couple of weeks, if I'll 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 such companies. And yeah, just like how the impact of work structure and all of that is playing out. Yeah. So there's a whole bunch of time. I know. I don't know. I'd be happy to spend more time, but we could spend more time on all that. So just for people who haven't followed this. So this term manager, who comes from this thinker in the 20th century James Burnham, who is one of the great kind of 20th century political thinkers, societal thinkers. And he sort of said, and he was writing in like the 1940s, 1950s. And he said kind of the whole history capitalism until that point had been in two phases. Number one had been what he called bourgeois capitalism, which was think about as like name on the door. Like Ford Motor Company, because Henry Ford runs the company. And Henry, it's like a dictatorial model. And Henry Ford just like tells everybody what to do. And he said the problem with bourgeois capitalism is it doesn't scale, because Henry Ford can only tell so many people to do so many things. And then he runs out of time in the day. And so he said the second phase of capitalism, what he called managerial capitalism, which was the creation of a professional class of managers that are trained not to be like car experts or to be whatever experts in any particular field, but are trained to be experts in management. And then that led to the importance of like Harvard Business Schools and Management Consulting firms and all these things. And then you look at every big company today. And like most of the executives and most of the fortune companies are not domain experts in whatever the company does. And they're certainly not the founders of those companies, but they're professional managers. And in fact, in the course of their careers, they'll probably manage many different kinds of businesses. They'll rotate around and they might work in healthcare for a while and then work in financial services and then go work in something else. You know, come work in tech. And what Burnham said is he said that transition is absolutely required because the problem with bourgeois capitalism is it doesn't scale. Henry Ford doesn't scale. And so if you're going to run capitalist enterprises that are going to have millions to billions of customers, they're going to be operating a level of scale and complexity that's going to require this professional management class. And he said, look, the professional management class has its downsides. Like they're not necessarily experts at doing the thing. They're not as inventive. You know, they're not going to create the next breakthrough thing. But he's like, whether you think that's good or bad or whatever is what's going to be required. And basically that's what happened. Right. And so he wrote that book originally in like 1940. You know, over the course of the next 50 years, basically, managerialism, I mean, today, up till today, managerialism basically took over everything. And what I'm describing is basically how all big companies run and how all governments run and how our large scale nonprofits run and kind of everything runs. Basically, what venture capital does is we basically are a sort of protest movement to that to try to find the next Henry Ford, or just to say Elon Musk or the next Elon Musk or the next Steve Jobs, the next Bill Gates, the next Mark Zuckerberg. And so we start these companies in the old model, right? We start them out as Henry Ford model. And so we start them out with a founder or a founder with colleagues, but you know, there's a founder CEO. And then we basically bet that the startup is going to be able to do things specifically innovate in ways that the big incumbents in the industry are not going to be able to do. And so it's a bet that by basically by relighting this sort of name on the door kind of thing, this new innovative thing with like a king, monarchical political structure, that they're going to be able to innovate in a way that the incumbent is not going to be able to because the incumbent is being run by managers, right? And by the way, of course, venture being what it is sometimes that works, sometimes it doesn't, but we're constantly doing that. But I've always viewed it my entire life as like we're like raging against the dying of the life. Like we're sort of constantly trying to fight off managerialism, just basically swapping everything and everything getting basically boring and gray and dumb and old, right? And we're trying to keep some level of energy vitality in the system. AI is the thing that would lead you to think, wow, maybe there's a third model, right? And maybe, and wait, think about it would be maybe it's a combination of the two. Maybe the new Henry Ford or the new E-Line or the new Steve Jobs plus AI is the best of both, right? Because it's sort of the spark of genius of the name of the door model, the Henry Ford model. But then it's give that person AI superpowers to do all the managerial stuff and let the boss drill the managerial stuff. That may be the actual secret formula. And we've never even known that we wanted this because we never even thought it was a possibility. But yeah, I mean, you know this, what is the thing that these bots are really good at doing paperwork? Like they're really good at filling out forms. Like they're really good at writing reports. They're really good at reading, they're really good at doing all the managerial work. Like they're amazing at it. And so yeah, so I think I think the I 100%, I think the answer, the answer, very well, might be to get the best best of both worlds we're doing this. And then the challenge is going to be twofold. The challenge is going to be for the innovators to really figure out how to leverage AI to actually do this, right? And then the other challenge is going to be for the incumbents that are managerial to figure out like, okay, what does that mean? Because now they're going to be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to. And so it's really, I think, is going to force a lot of big companies to kind of figure at innovation. Either say figure out innovation or die trying. Do you feel like that structure accelerates the impact on the actual GDP and economy? If you guys pay sex is like the growth is like so fast. And like instead of having these companies kind of like peed around and grow to an impact, they can kind of like keep going. Have not accelerating. That's for sure the hope. The challenge and you know, I look to AI utopian view is of course, of course. And then that's going to be the future of the economy. And it's going to grow 10,000 X and a hundred X and a thousand X and we're going to train this regime of like much higher economic growth forever and consumer cornucopia of everything. And it's going to be great. And I hope that's true. I hope that's like the you know, you that's the current kind of utopian vision. I hope that's true. The problem is it goes back again. The real world is really messy. And I'll give you an example of how the real world is really messy. It requires 900 hours of professional certification training to become a hairdresser in the state of California. So it's like 35% of the economy, something like that. You have to get some sort of professional certification to do the job. Which is to say that the professions are all cartels. Right. And so you have to get licensed as a doctor. You have to get licenses of lawyer. You have to get licensed as a you have to get into a union. By the way, to work for the government, you need to be you have both civil service protections and you have public sector unions. You have two layers of insulation against ever getting fired for anything or anything. And you've been there for changing. I'll give you another example that the dock workers went on strike a couple of years ago, because they're robotics. You know, if you go look at a modern dock like in Asia, it's all robots. If you go to American docks, it's like all still guys, dragon, strike and stuff by hand. The dock workers on a strike, it turns out there 25,000 dock workers working on on the dock in America turns out they have incredible political power because it's one of these unified blocks of things. They won their strike. And so they got commitments from the dock owners to not implement more automation. We learned a couple of things in that. So number one, we learned that even a union in the smallest 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 because there's 25,000 people working at the dock. So if 25,000 people are doing 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 government agencies, the strike new collective bargaining agreements during COVID. We're 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 say and then what they do it turns out what the employees do is they're very smart in this way. And so they figure out they come in on the last day of a month and first day the next month. And so they're in there they're in the office two days per 60 days, which means these buildings are empty for 58 days at a time. And you see them you see where I'm heading with this like this is like locked in right? This is like locked in in a way that has nothing to do with like people say capitalist it's like anti-capitalistic. It's like it's basically it's restrictions on trade. It's restrictions on the ability to like change the workforce. And so so much of our economy is is you know the I'm describing the entire healthcare system. I'm describing the entirely go profession. I'm describing the entire housing industry. I'm describing the entire education system right. Gave through 12 schools in the United States. They're a literal government monopoly. How are we going to apply an education? The answer is we're not because it's a literal government monopoly. It is never going to change the end and there is nothing to do. By the way, you can create an entirely new school system like that's the one thing you can do is you can do what alpha school is doing. You can create an entirely new school system. Other than that, you're not going to go in and change what's happening in the American classroom. Like K through 12, there's no chance. The teachers are 100% opposed to it. It's 100% not going to happen. So you see what I'm saying is like there's this like massive slippage that's going to take place. Both the AI utopians and the AI tumors are far too optimistic. Right. You see what I'm saying? Because they believe that because the technology makes something possible that 8 billion people all of a sudden are going to change how they behave and it's just like no so much of how the existing economy works. It's just it's just like why you're in. And so we're going to be lucky as a society. We're going to be lucky if AI adoption happens quickly. Right. Because if it doesn't, what we're just going to have is stagnation. Awesome. I know you got to run. Yeah. I don't know if you're still welcome, but it was such a pleasure talking to you. We're truly living in an age of science fiction coming to your life. Yes. Yes. Could not be more exciting. Yeah. Really. Thank you, Martha. Awesome. 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