Marc Andreessen on Why This Is the Most Important Moment in Tech History

If we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. Because what we'd be staring at is a future of depopulation, and like depopulation without new technology would just mean that the economy shrinks. My friend Larry Summers used to tell people he said, "The key for replanting is he said, don't be fungible." He's an economist and said, "That was economic speaking. That means essentially, don't be replaceable." We're going to have AI coders that are actually better coders than the best human coders. I think we're going to have AI dockers that are better than the best human dockers. I think we're going to have AI lawyers that are better than the best human lawyers. I think we're used to living in a world where we just don't understand how good, good can get, because we've been tapped by our own biology. And we're going to get to experience what it's like when you have the capability at your fingertips that's actually better than human in these domains. What if the most consequential technology shift in human history is happening right now, and most people are still debating whether it's real? In November 1989, when the Berlin Wall fell, few understood they were watching the end of one world order in the beginning of another. The Cold War had lasted 44 years, its collapse took weeks. Within a decade, a young programmer would find himself at the center of the next transformation, building a browser that brought the internet to everyone. Three decades later, that same person believes 2025 rivals those women's immagnitude. AI models have crossed from creative parlor tricks into genuine reasoning, solving problems in medicine, law, and science that seemed impossible just 18 months ago. But here's what's unsettling. We don't yet know what this means for the people who build software, product managers, engineers, designers. The roles that define the last 30 years of tech face fundamental questions about their future. The optimistic view and the pessimistic view can't both be right, yet both have evidence. This conversation examines what's actually changing, what skills matter now, and how the most AI-native founders are building different. Today we're sharing a conversation between Lenny Richitsky and Mark Andreessen from a recent episode of Lenny's podcast. Mark Andreessen, thank you so much for being here and welcome to the podcast. Awesome, Lenny. Thank you. It's great to be here. I want to start with just a big picture question. I have a billion directions I want to go, but I think this is going to give us a little bit of a frame of reference. How big of a deal is the moment in time that we are living through right now? This is a very, very historic time. I think 2025 was maybe the most interesting year in my entire career and probably life. I would expect 20, 26 to see that. Wow. That says a lot. Yeah, I've seen some stuff. So it feels like two things are happening. One is the trust that a lot of people have had in kind of what you describe as kind of legacy institutions around the world is I think in kind of full scale collapse right now. By the way, there's a lot of data to support that. And so I think there's just there's like a lot of structures and orders and institutions that people have just relied on for a long time that have just proven to not be up for the challenge. And then kind of corresponding with that is the national and global conversation to become like let's say liberated. And so, you know, this sort of incredible revolution that we have in kind of, you know, I would describe as freedom of speech, freedom of thought ability for people to openly discuss things that maybe they couldn't discuss even a few years ago. You know, it's just dramatically expanded. And I think that's that's now on one way train for just a much broader range of discourse. And then, you know, there's also just these like incredibly massive geopolitical shifts that are happening. And obviously the U.S. is changing a lot. Europe is changing a lot. China's changing a lot. Latin America, by the way, is changing a lot. You're very dramatic. You know, events playing out down there right now. You know, kind of all over the world. Like I think a lot of assumptions are being pulled out into the daylight and and resam. And then it's kind of the fact that all these things are happening at the same time. Right. And so you've got all of these countries and industries. You know, where things are kind of increasingly in people, but you have AI is this kind of new technology that's going to really affect things. And then you've got, you know, people, you know, citizens being able to fully participate. Being able to argue things out. So it's kind of like those three kind of big mega things are kind of all colliding at the same time. And I think we're probably just the very beginning of all three of those. And those I'll feel like kind of, you know, historical, you know, moment shifts. It, you know, comparable in magnitude to maybe the fall of the Berlin Wall in 1989, you know, maybe maybe the end of World War II. You know, kind of moments like that. It certainly feels like that. Good God. What a time to be alive. Yeah. In terms of the AI piece, which is where a lot of people are trying to figure out what to do. What do you think isn't being priced in yet in terms of the impact? Yeah, he's going to have on say the world or just people listening. I think at this point, I think it's pretty clear with that with, you know, our technology hats on that like this stuff is really working not right. And so there was this, you know, kind of queen. And when there was a chat GPD moment, you know, three years ago, it was only by the way, only three years ago, right, was the chat GPD moment. And the big question was, all right, this is like incredibly fun and creative and like we have machines now they can compose. Shakespeare and science and rap lyrics and like, you know, this is amazing. But then there was there, you know, there's this great question like, can you harness this technology for reasoning and for, you know, problem solving and domains that like really matter, you know, medicine and science and law and so forth. And, you know, it turns out the answer to that is yes, right. And, you know, the last 12 months and especially the last, even just the last three months have really proven that like AI can really do like, you know, you're seeing it all now, you know, it can actually, you know, AI is not developing new math theorems. You know, they're, you know, over the holiday break, you know, there's sort of the, but it feels like the AI coding thing, you know, really hit critical mass. And the world's best, you know, the ring best programmers, right, including like Lina's turboval, you know, for the first time over the holiday break basically said, yeah, AI is now coding better than we can. And so that, you know, that's, that's incredibly incredible powerful. And I think we all, you know, kind of I think assume that AI now is going to get really good at reasoning in any domain in which there are verifiable answers. And so that, you know, that's going to include like many very important domains. So, so like the technology feels like it's moving fast and it's going to be working really well. I think this thing that is not well understood, I think a lot of people have a, I think a lot of people in the industry have kind of what I described as this one dimensional thing, which is okay as a result of the technology now working AI just kind of sweeps the world changes everything. And I think that's that's kind of the wrong, that's kind of the wrong frame or I think it's based on incomplete understanding of the world that we live in or the world that we've been living in for the last, you know, 80 years. And I recall two things in particular. So one is it has, I think it's felt to us like in the US and the West for the last, you know, whatever 30 years or 50 years it felt like we've been in a time of great technological change, but actually, if you look for actually evidence of that, like in statistic in statistical evidence of that and a little evidence of that, like you basically can't find it. In a particular economist have a way of measuring the rate of technological change in the economy that is productivity growth, which we could talk about that means, but basically it's sort of the mathematical expression of the impact of technology on the economy and productivity growth for the last 50 years has actually been very low, not very high. So we all feel like it's been very high, there's been lots of technological change was actually happening is it's been very low. And in fact, the pace of productivity growth like in the US is is running at like a half of what it worked in my lifetime in our lifetimes. It's been running it about a half the pace that it ran in between 1940 and 1970, and it's been running at about a third the pace that it ran between about 1870 to about 1940. And so statistically in the US in the West technology progress and economy technology impact economy has actually slowed way down. And so we, you know, the thing is going to hit, but it's hitting an environment in which we have actually had almost no technological progress in the actual economy for a very long time. So we could talk about that, and then there's this other like just incredible thing that's happening, which is the, you know, the demographic collapse, right, it's sort of a Western phenomenon, an increasingly global phenomenon, which is, you know, the rate of reproduction of the human species is is in rapid climb. And you know, there are many countries, you know, including the US where, you know, the rate of reproduction is, you know, under two, you know, meaning, meaning that, you know, many, many countries around the world, by the way, including China, which is a really big deal. I'm actually going to depopulate over the next century. And so you have this kind of precondition that says there's actually been very little technical technological progress happening in the world. And the world is going to depopulate. And so AI is going to enter the world in which those two things are true. And I think it's, this is incredibly important because we actually need AI to work in order to get productivity growth up, which is what we need to get economic growth up. And we actually need AI to work because we're going to need, you know, we're going to need machines to do all the jobs that we're not going to have people to do because we're literally going to depopulate. We're going to depopulate the planet over the next 100 years. And so I, I think the interplay of these factors is going to be much more interesting and frankly more complex that a lot of people have been thinking. I'm going to follow this thread about kids. I know you have a kid. And one of my most, my favorite lenses into how people think and what they value is what they're teaching their kids, what they're steering their kids towards. Are there specific skills or, I don't even careers that you're steering your kid towards? The way I think about this, anyway, yeah, we have a 10 year old. And so, you know, we, I'm actually at home school. And so we think a lot about this. So I think the way to think about the impact of AI on people on specifically people as individuals, I think it's, it's actually, you know, a lot of people just focus on kind of this, you know, that's kind of very, I would say straight forward and overly simplistic view of just literally job gains, you know, job losses. Which we can talk about, but there's two specific things at the level of like an individual person or an individual kid. So I think it's pretty clear that AI is going to take people who are good at doing things and it's going to make them very good at doing things. Right. And so it's going to be a tool that's going to sort of raise the average kind of across the board. And you know, look, you see that playing out already. You know, anybody who's in a position where they need to, you know, write something or design something or write code or whatever, if they're pretty good at it today, they use, they use AI and all of a sudden they're very good at it. So they're, they're sort of that aspect to it. And I think the way the education system rate large is going to teach is going to kind of teach AI is is going to be based, you know, hopefully a lot on that. But then there's this other thing that's happening, which we're also starting to see and we're really seeing it, particularly in coding right now, where the really great people are becoming likes spectacular great, right. And so you kind of use it use the term think about like the super empowered individual, right. So the individual who is like really good at coding or really good at making movies or really good at making songs or really good at designing, you know, making art or whatever. Whatever, whatever those things are or, you know, or podcasting or, you know, hopefully venture capital, you know, if you're very good at it and you can really harness AI, you can become spectacular great. And like super productive, right. And, you know, I'm sure you have a lot of friends in this in this category as well, but like, you know, the really really good quarters experience this right now, my friends are really good quarters are like, oh my God, all of a sudden, I'm not twice as good as I used to be. I'm like 10 times as good as I used to be. And so I think at the at the unit of like n equals one of like an individual kid, I think the question is kind of, how do you get them into position where they're kind of this kind of super empowered individual such that they're going to be really kind of deep in whatever it is they're going to do, but they're going to they're going to be deep in a way that's going to let them fully use the part of AI to be not just great, but to be like spectacular great. And I think that that that's that's going to be the real you know that that that that's the real opportunity and that you know, at least that's what we're shooting for and that's what I would encourage parachute for. So when I heard there is essentially agency this where that we see on Twitter all the time is building agency them not waiting for someone to tell them what to do figuring out what to do. Yeah, yeah, so this this this thing with this this term agency has become very, very, you know, very popular. It's really interesting because it's I had a lot of trouble with this early on because I'm like agency, okay, what are they talking about and what they're kind of talking about is like, you know, initiative, you know, will is, you know, you can just do things. Um, you know, what is it, uh, the the semi birthday has the great term live player, you know, you you can be a like a primary participant in events. And at first I was like, well, yeah, like that's kind of obvious, right? Like, of course, and then I'm like, oh, actually it's not so obvious anymore because kind of your point, I think so much of our society is based on like there are all these rules. And everybody gets taught kind of a default. You're supposed to follow all these rules, right? And then everybody gives you like break the rules like everybody gets freaked out. It's like, oh my god, he broke the rules. And so like we we have somehow worked our way, our way, kind of, you know, I don't know, psychologically sociologically, you know, kind of into a state in which I guess the natural assumption for a lot of people is, you know, the thing that you, for example, then you want to train kids to do is like follow all the rules. Um, and you know, you could argue the kind of, you know, for example, the, you know, the school system, you came through 12 school system, whatever has gotten kind of more and more focused at over time. And it's like, yeah, it's like, no, you should actually, and again, especially unit unit, unit, and equals one, like of your kid. It's like, look, there's, there's something to be had. We, I just had this conversation, my 10 year old last night, actually, I rolled out the concept of, you know, in order to lead, you must first learn to obey, right? In order to, you know, issue orders, you must learn how to follow orders and, you know, you know, it's kind of trying to keep them with some level of structure in his life. And not just, and not just your agency. But yeah, I mean, so, look, you know, some rules are important and so forth. But yeah, no, look, there, there is like a huge, there's just a huge frame of life on being somebody who is able to like fully take responsibility for things, fully take charge, run an organization, lead a project, create something new. Um, and, you know, maybe, yeah, that, that has been maybe a little bit diminishing our culture over the last 30 years. And, you know, it's, it's healthy, you know, that, you know, that, that there's now a term for that, that, that has come back, back into Vogue. And then, and again, that's how I view AI for kids is like, okay, AI should be the ultimate letter on the world for a kid with agency to be able to say, okay, I can actually be a primary contributor, right? Whether that's going to be a primary contributor in everything from, you know, developing new areas of physics to writing code to being an artist, you know, to writing, you know, to writing novels, like, you know, whatever that thing is, I can fully participate in the world. I can really change things and I, and I, that, that feel that the combination of that idea combined with this technology feels very healthy to me. What does that quote about and give me a lever and I'll move the world. And I'll move the world. Yeah, that's exactly right. Well, so it's actually funny. You mentioned that. So the, the, the early kind of scientists, including like Isaac Newton, were super obsessed with, with, you know, his concerts with Alchemy, right? It's like, you know, they, you know, they, you know, they develop like, you know, Newton is like a developer, Newtonian physics, and he developed like calculus, all these things. He's really obsessed with was Alchemy, which was the thing he could never get to work, right? And, and Alchemy was the transmutation of lead into gold, which meant the transmutation of something that was very common, which was led into something that was very rare and valuable, which was gold. And, you know, they, there was this, the, he's bad, you know, decades trying to figure out this thing called the philosopher stone, which would be basically the machine or the process that would be able to transmit the rare, you know, the common thing into the rare thing. In the gold, and he never figured out it, you know, incredibly frustrating, nobody ever figured that out. And now we literally say, I have a technology that transfers sand into thought. Just blew my mind, right? The most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought, right? And so, AI is, it is, it is the, it is the philosopher stone, like it is that it actually is that, and it's just this incredibly powerful tool. And, and that's where I, that's where I get so excited. I mean, and again, this while we're doing other 10 year old, which is like, all right, it's primary thing that we want to make sure to do is to make sure that he knows fully how to leverage and get, and get benefit out of the philosopher stone, right, which is, you know, which is to say AI. And that, and then, you know, that's certainly central everything we're teaching him. You know, there's, there's this meme going around that, you know, Silicon Valley people don't let their kids use computers. And I just, there may be a handful of people who are like that. I don't, you know, I don't know. I think it's more, honestly, the other way around, which is the, you know, the more you're kind of plugged into stuff in Silicon Valley, the more important is to make sure that your kids actually fully understand this and know how to use it. And that's certainly the mode that we're in. And that's, that's certainly the mode that I would encourage Paris to think about. And I know your kid was homeschool that is super interesting. There's almost a statement on, you know, education in today's day. Maybe is there any thoughts there? I'm just for folks that maybe aren't in your tax bracket that want to help their kids be successful. Maybe homeschool, maybe not would, what advice would you have? And this is the challenge. And again, this kind of goes to how your, you know, kind of your original question, which is education, there's two completely different ways to talk about and think about education. The way that's usually thought about and talked about is kind of at the level of white a nation, right? So, so, you know, it's like a national level issue or maybe a state level issue in the US, which is basically like how do you educate all the kids? And of course, that's incredibly important. And of course, you're going to need like some level of large scale system, like, you know, the national K through 12 school system or something like that. In order to do that. But then there's this other question, which is like at n equals one for an individual kid, like what can you do with with an individual kid? And so I'll just give you kind of the ultimate, you know, kind of the ultimate answer to that question, which is it's been known for centuries that the ideal way to teach a kid at the unit of n equals one by far, the ideal way to do it is with one and one tutoring. Like if you just have an individual kid and the goal is to maximize an individual kid by far, you get the best results with one or one tutoring. And this is something that like every world family knew in history. It's something that every aristocratic class knew in history. There's all these amazing examples. Alexander the Great was treated by Aristotle. He took over the world, right? Like, you know, many of the great kings and queens, you know, world families and aristocrats and so forth, you know, over the course of centuries. You know, kind of always had always had this approach. There's actually also statistical evidence analytical evidence that this is correct. There's this, you know, massive question in the field of education, which is how do you improve educational outcomes? And basically it turns out it's just it's very hard to improve educational outcomes except there's one method that always does it, which is called the it's called the bloom to sigma effect, which is there's one method of education that routinely raises student outcomes by two status of deviation. And we'll take a kid from the 50th percentile of the 99th percentile and that's one and one tutoring, right? So again, if you go back to like it equals one, you have a kid and a tutor and they're in this like, you know, very tight loop with each other. You know, where the kid is able to constantly kind of be on the leading edge of what's our capable of doing and they can they, you know, they can move incredibly fast and they get kind of correction real time. You get these better outcomes, but you know, to your question like it's never been economically feasible for anybody other than the richest people in society to be able to provide one or one tutoring kids. AI provides the very real prospect of being able to do that, right? Because obviously now, right? If you have a kid that's like super interested in something and they can talk to, you know, an LLM about it and they can ask an infinite number of questions that they can get instantaneous feedback. And in fact, you can even tell an LLM it's like, you know, teach me how to do the following and you can say, you know, wow, that's like, I don't quite understand what you're saying. Like, dumb it down for me a little bit. Okay, now, quiz me, you know, do I actually understand this? Like people can just do this today, right? And so I think there's this like massive opportunity for parents, you know, in many walks of life to be, you know, with with a little bit of time at focus to be able to say, okay, you know, my kids probably still going to go through traditional education system, but I'm going to augment this with AI tutoring. And of course, there's going to be tons of startups, right? And there already are that are going to try to build on all the products and services for this kind of academy, you know, the nonprofit side has a big push to do this. And so, you know, I think the broad answer might be a hybrid approach with schools plus one to one tutoring through AI. There's also this great, you may have heard there's this great school, new private school system called Alpha, in which everything I just described is kind of the basis of their philosophy, which is, you know, it's a combination of in-person schools and teachers, but it's also, you know, heavily based on AI and AI tutoring. And so I think there's like a, there is a magic formula in here that I think is going to apply much more broadly. And I really, for parents interested in this, I now would be a great time to really start to think hard about that and to look at the options. It's interesting because there's all this concern that young people jobs are not going to be there for them. AI is replacing them on the flip side. There's what you're describing here. It feels like people coming into learning today are going to be move so fast and learn so much more. And where do you sit on this divide of like young people are in big trouble or they're actually going to be the ones winning in the end. Yeah, so the job, the job substitution job loss thing is just it's very reductive. I think it's an overly simplistic model. And again, it goes back to what I said at the very beginning, which is, we've actually been in a regime for 50 years of very slow technological change in the economy. And so, you know, again, like I said, it's like a half the rate of the previous era and then a third the rate of like 100 years ago. And so we're coming out of this kind of phase where we've had like almost a technological progress in the economy. We've had a remarkably little job churn as a result of that relative to any historical period. And so even if AI like kicks up even if AI triples productivity growth in the economy, which would like be a massively big deal, you would take us back to the same level of job churn that was happening between 1870 and 1930. And if you go back and you read accounts of 1870 to 1930, people just thought the world was a watch with opportunity right at that rate of technological transformation. Kids were able to like develop new careers into new areas of the economy building new kinds of products and services. I mean, you know, a huge part of our everything in our modern world today was kind of invented and proliferated kind of during that period. And so even if AI like triples the pace of economic change in the economy, it's going to just translate to like a much higher rate of economic growth has been transferred translate too much higher rate higher rate of job growth. And you know, there will be some level of like task level and job level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation that will happen. And then corresponding to that, there will be, you know, there will be hiring blooms, you know, I quite honestly, I think all over the place. And then again, go back to the other thing, which is like this is all happening in the face of declining population growth and and and increasingly population shrinkage. And so human workers in many, many, many countries over the next 10, 20, 30 years are going to be at more and more of a premium literally because you're going to have shrinking population levels. You know, we don't really want to get into politics, particularly, but it does feel like the world broadly is going to reverse course on on the rates of immigration, we've had for less than 50 years. It seems to be kind of a broad based, you know, kind of thing happening, you know, kind of was right rise in nationalism, you know, concerns about the rate of immigration and immigration historically in countries like the U.S. and so it's kind of have been floated over time based on kind of how, you know, kind of how the national mood shifts. And so if you sort of combine in a country like the U.S. or any country in Europe, if you combine declining population with less immigration, the remaining human workers are going to be at a premium, not at a discount. And so I think I think that combination of kind of faster productivity growth, faster economic growth and then slower population growth and less immigration actually means there's going to be much less of this kind of dystopian, you know, no jobs thing. I just think it's probably totally off base. That is extremely interesting. So what I'm hearing is you're not super worried about job loss is the key here that the timing kind of just works out, this population decrease, you know, like all these kind of have to line up for there not to be this massive job loss with AI. Yeah, well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. Right. Because what we will be staring at as a future of depopulation and like depopulation without new technology would just mean that the economy shrinks. Right. So it would mean that the economy kind of itself kind of shrinks over time, you know, opportunity diminishes, there are no new, there are no new jobs, there are no new fields, there's no new, there's no source of consumer demand for spending on things. And so you would be very worried about going into period of like severe decline of stagnation. And you know, well, you know, essentially you'd be looking at these very dystopian scenarios of like an economy kind of self euthanizing itself over time. And it's going to be very worried about like the opposite of what everybody thinks that they're worried about. The only reason we're not worried about that is because we now know that we have the technology, the consumption for the lack of population growth and then, you know, also for the lack of immigration. And so, you know, I would say the timing has worked out miraculously well in a sense of we're going to have AI and robots precisely when we actually need them to keep the economy from actually shrinking. And I just think like that, that's just like a fundamentally, a fundamentally good news story. To get to the mass job loss thing that people are worried about on the other side of things, you know, you have to, you'd have to look at like far, far, far, far higher rates of productivity grows. You'd have to look at rates of productivity growth that are 20, 20, 30, 50 percent a year, you know, something like that, which are orders of magnitude higher than we've ever had in any, in any economy and history of the planet. You know, it's possible that we get that. I mean, look, I'm, you know, I have my utopian kind of, you know, kind of, you know, temptation all along with everybody else. If AI like radically transforms everything overnight, then maybe you, you know, let's, let's play out the kind of utopian scenario. You get to a much higher level of productivity growth, you get to much higher level technological change. Corresponding to that, you'll have a massive economic boom. You'll have a massive growth in the economy. And then corresponding with that, you'll have a collapse in prices. And so the price of goods and services that are that are that are sort of, you know, whatever you're going to call it, affected by a commoditized by AI, the prices of those goods and services will collapse. It'll be price deflation. And then as a consequence of price deflation, everything that people are buying today gets a lot cheaper. And that's the equivalent of a gigantic increase in wealth, right across the society. Right. Take it this way. This is actually what we're talking about because people, I think, get kind of sideways on this issue. So if AI is going to transform the economy as much as the, you know, whatever utopians are distorted because they're whatever kind of think that it will. The necessary economic calculation of what happens is massive, massive productivity growth. The consequence of massive productivity growth, what that literally means mechanically is more output required less input, right. So you get more economic output for less input, right. So you're substituting in AI for human workers or whatever. And as a consequence, you get like this massive boom and output with much lower input costs. The result of that is you get the lots of goods and services in all those effective sectors. The result of those blots is you get collapsing prices, right. The collapsing prices mean that the thing today that costs you $100 now costs you $10 and now costs you $1. That's the equivalent of giving everybody a giant raise, right, because now they have all this additional spending power. That additional spending power then translates to economic growth, right, the development of new fields. Everybody's like materially like much better off for it quickly. And then by the way, if you do the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the kind of social safety net to prevent people from being immiserated, right. Because the prices of all the goods and services that like a welfare program has to pay from, they're all collapsing, right. And so the price of health care collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses. Because this, this, this, this, this incredible impact that AI is having. And so in this kind of utopian dystopian scenario that people have, it's not, there's no scenario in which like everybody's just poor. In fact, it's quite the opposite, which is everybody gets a lot richer because prices collapse. And then it's actually much easier to pay for the social safety net for the people who, you know, for some reason can't find a job. And so like, like maybe we end up in that scenario. I mean, the kind of optimistic part of me says, yeah, maybe AI is that powerful and maybe the rest of the economy can actually change to accommodate that and maybe that will happen. But the result of that is going to be a much better new story than people think it's going to be. And again, everything I've just described by the way is like just a very straightforward extrapolation of very basic economics. I'm not making any like bold predictions of what I just said. This is just like a straightforward mechanical process that plays itself out if you have higher rates of productivity growth, which are necessarily the results of higher rates of technological growth. And so I think we're, I think we're looking yet, and to be clear, I think we're looking at a world that's not like radically transformed the way that maybe the utopian think that it will be or the dystopia think it will be. I think it will be more incremental for races we can discuss, but I think that incremental is a is a is a lonely I think that process is going to be a good news process. And then even if it's much faster, it's also going to be a good news process. It'll just be a good news process and the other way that I just describe. I love hearing optimism and good news. I will also add that you've been I was researching you ahead of this chat and you've been right so many times about where the world is heading. That's why I'm especially excited to talk to you. Maybe a short list. I imagine there are many more things. Okay, so one you were right about the web and web browsers becoming important. You were right about software eating the world. Check. You in 2011, you said that in 10 years, we're going to have 5 billion people using smartphones. And I believe that full number into being 6 billion. You also you have this debate with Peter Teal that I came across where you are debating whether technology is stopped progressing or if new technology will continue to emerge and you are arguing there's progress progress will continue when he is like, no, I think we're done with cool technology you were right. I imagine there are many more things you were right about. So so again, I'm just I love hearing your predictions because I feel like they're actually going to turn out to be correct. So I was just start by saying, I've been wrong about tons of things, but you know, I buried those out back behind the shed. Delete them from the internet. No browser can discuss. I have them nuked out of the internet archives so they never see it again. So, you know, I'm wrong plenty of times also. But yeah, I mean, look, I think in yeah, some of those are right. By the way, I will say on the Peter one, I have come I've come much for around the Peter's point of view. We argue that one like quite a bit differently today than I did and I would give his view. I think I think a lot more credit. And it actually goes to kind of the discussion that we be the kind of conversation we just had, which is the. The real form of what Peter was arguing was we have lots of process in bit. We have lots of progress in bits, right, but we have very little progress in atoms. Right. And that's the real core of what he was arguing. And I think I think I was a little bit. I don't know missing that or kind of, you know, kind of glossing it over a little bit because I was so focused on making sure people understood. There actually is still progress happening in in bits. But I think, you know, a lot of his critiques around the lack of progress in atoms is real. And again, this goes back to this thing of like in the lab and he, you know, he's talking about this for a long time. In the last 50 years, there has just been very little technological innovation in most of the economy. There's been very little technological innovation in particular, anything involving atoms. You know, there's been very little real world technological change. There just hasn't been like the built world is just not that different today than it was 50 years ago. And again, if you contrast that, you know, if you compare and contrast 1870 and 1930, it was dramatically different world. If you contrast 1930 and 1970, it was dramatically different world. If you contrast 1970, it's not that different. Right. And look, you just see that you could just like walk around. And it's just like, oh, yeah, there's a bunch of buildings that were built in like 1960. Right. And there's a bridge that was built in like 1930. And there's a dam that was built in like 1910. And there's a city that was founded in, you know, 1880. And like, what have we done? Where are new cities? Where are new dams? Where, you know, where is the California High Street Rail? Like, you know, like, what's going on here? And so like, I think he is, I think he is right about a lot of that. Again, this is also why I think that AI is not going to have as rapid an impact. It's not going to be, again, this kind of utopian or dystopian view of like, everything changes overnight. I think it just kind of can't happen because of the reasons the Peter articulates, which is there's just, there's so much about how the world works. It's basically just like wrapped up in red tape, like bureaucratic process, rules, restrictions, you know, the politics. By the way, you know, unions, cartels, oligopolys, there's all these structures in the world that are kind of economic or political or regulatory structures that basically prevent things from changing. And so, I mean, let's take a great example. Like, AI is impacted on the healthcare system. Like, by rights, AI is going to have a dramatic impact on the healthcare system in very positive ways. But, you know, large parts of the medical system today are, they are cartels, right? And so there's like, there's the doctors are cartel and nurses are cartel, like hospitals are cartel. And then there's this push to like nationalize all the healthcare systems. And then you've got, you know, then you've got a government monopoly, right? And it's like, and guess what cartels and monopolies don't like is they don't like like rapid change, right? And so, you know, you show up as a kid and you're like, wow, I've got like this new technology to do like AI medicine. And they're like, oh, well, does it threaten doctors, actually, in that case, we're going to block it. So, and I think a lot of consumers, by the way, you know, I don't, I see this in my life, and you'll probably see this in your life also, which is, you know, like Cheshire PT is like almost certainly a better doctor than your doctor today. But like, Cheshire PT can't get a license to practice medicine, right? So it can't substitute for a doctor, it can't prescribe medications, right? It can't, you know, perform procedures. Right. And so there, there are these, anyway, anyway, so Peter, Peter, I think, was very articulate and has been for a long time. I'm like, no, there are actually real structural impediments in the economy and in the political system that we have that actually prevent any, the race of change that are anywhere near the race of change that people have in the past. And you can maybe say optimistically, you know, maybe the presence of it of the new of the new magic technology of AI, maybe it causes us to revisit a lot of these assumptions, assumptions for the first time in decades to really say, okay, is this really the world we want to live in? Don't we actually want to get to the future faster? So maybe that would be the optimistic. It's time to build somebody famously said, I in my calendar, I actually have that as my, when I start to work, it's time to build my block in the morning today. Thank you for that. Okay, I love, I love the way you go from just like macro to just like end of one. And I want to go to end of one. A lot of the listeners of this podcast are product managers, their engineers, their designers, they're not a lot of, there's a lot of founders, but there's also a lot of non founders. There's a lot of people building product that aren't founders. And obviously a lot of people are worried about where their career is going is one of these roles going to disappear is one of these roles going to do really well, how do I? Stay up to date, you're close with a lot of teams, a lot of product teams. What's your sense of just the future of these three very specific roles, product manager, engineer designer? This, I think, is really funny question. So these three roles in particular, obviously are kind of the central roles for building, you know, for tech companies. So the way I've been describing it is, you know, the concept of the Mexican standoff, right, which is the movie scene where the, you know, the two guys have guns pointing each other's heads. And then there's if you watch like John Wu movies, he loves to have he does the three way Mexican standoff where you've got like a triangle, you know, people and like that, you know, and of course, it's John Wu movies, they've got guns and both hands. So they're all each is aiming at the other two. Yeah. And you got this kind of standoff situation. So the way I've been describing this is there's like a Mexican standoff happening between those three roles between product manager designer and coder, specifically the following, which is every coder now believes they can also be a product manager and a designer. Right, because they have AI every product manager thinks they can be a coder and a designer and then every designer knows they can be a product manager, right, and a coder. Right. And so people in each of those roles now, you know, know or believe that with AI, they don't need the other two roles anymore, right, they can do that because they can have AI do that. And then of course, and then of course, there's the real irony, which is, you know, all three of them are going to realize that AI can also be a better manager, right. So they're going to be aiming the guns off the road chart, but that's probably the next phase. And what I think is so fascinating about this Mexican, Mexican staff is they're actually all kind of correct, I think, right, which is AI is actually a pretty good, you know, now it's actually now a really good coder. It's actually now a really good designer and it's also a really good product manager, right, it's actually good at doing all three of those things or at least doing a lot of the tasks involved in those three jobs. And so again, this goes back to the the super part is kind of idea of the super empowered individual where if I'm a coder like, you know, I mean, step one is like I need to make sure that I really understand AI coding and like what that means and what how coding is going to change in the future. I need to understand, you know, specifically how to go from being a coder who writes code entirely by hand to being a coder who orchestrates, you know, a dozen instances of coding bots, you know, there's a change in the actual job coding itself, which is which is happening right now. But the other part of it is, okay, how do I become that super part individual? How do I become a coder that also then harnesses AI so that I can also be a great product manager and I can also be a great designer, right. And the same thing for the product manager, which is how do I make sure that I can now use coding tools? How do I make sure I can also, you know, do AI based design. And the same thing for the designer, which is how do I use AI to be also become a coder and also become a product manager. And then what you did is maybe the maybe the those individual roles change like maybe those are not any more sort of safe pipe roles with the way that they have been for the last 30 years or whatever. But what happens is that the talented people in any of those roles become super empowered and they become good at doing all three of those things. And then and then those people become incredibly valuable because then those are people who can actually like, you know, build and design right new products right from scratch, which is like the which is which is the most valuable thing. And so I think I think that's I think that's the opportunity. So I love this answer, so what I'm hearing is essentially if you're amazing at any of these three roles, you will do well number one, if you're amazing, these roles, that's great. But also you part of being amazing, these roles is also being able to fully harness the technology, right. So if you're if you're a master coder today and you don't ever get to the point where you figure out how to use AI to leverage your coding skills and do more, right. At some point, you are going to hit an issue, right. Here's another way economists talk about this, which is there's a concept of the job, but the job is not actually the atomic unit of what happens in the workplace, the atomic unit of what happens replaces the task. And so and then what the way the economists think about it is that job is a bundle of tasks and everybody wants to talk about job loss, but really what you want to look at is task task loss, right. The tasks changing, I mean, the classic, the classic example of task changing, class example of task changing was once upon a time executives never used typewriter or personal computers themselves, right. You know, if you were a vice president, a company in 1970 or whatever, you did not have like a typewriter or computer and your desk typing things, you had a secretary who you dictated my most to, right. And then there was this change or like email started to show up and what would happen was the job of the secretary then went from, you know, it went from, you know, the job of the secretary changed for sending out letters with stamps on them to like sending receive emails with the other admins. And then the secretary would print out the email and bring it into the executive's office. And the executive office would read the email and paper, scroll, scroll the reply and give and give that message back to the secretary who would go back and type into the computer on his or her desk and send it as an email. Fast forward to today, none of that happens. Now executives just do all their own email. They still have secretaries or admins, but they're now doing different tasks. You know, they're travel planning and orchestrating events and like doing all these other things, you know, that the great admins do. And then the test, the test sat ironically of the executive has expanded to do actually more of the character work themselves actually like sit there and like type their memos, which again, 50 years ago, they never would have done that. So the executive job still exists, the secretary job still exists, but the tasks have changed. And I think that's like a great example what's going to happen in coding, the tasks are going to change is what's got product management, the tasks are going to change design or task are going to change. And so the job, the job persists longer than the individual tasks. And then as the tasks change enough, then that's when the jobs change. And so at the at the level of an individual, you kind of want to think of like, okay, I have this job, the job is a bundle of tasks. I need to be really good at making sure that I can like swap the tasks out, right? I can really adapt using your technology, you know, get really good at a coding, for example. I can, you know, and then and then you want to kind of add skills. I can also get really good at design. I can also get really good at product management, because I've got this new tool. So you want to kind of pick up more and more scope as you do that. And then, you know, 10 years from now is your job title coder or coder designer product manager, or is it just I build products, or is it just I tell the AI how to build products. It's like, whatever that, whatever that job is called, who even knows what it's going to be. But it's going to be incredibly important, because the people doing that job are going to be orchestrating the AI. And so that that's the track that the best people are going to be on. And I think that that's the thing that we heard lean hard into. I think people aren't fully grasping just specifically software engineering and how much that is changing. Like it's pretty clear we're going to be in a world soon where engineers are not actually writing code, which I think a year ago, we would not have thought. And now it's just clearly this is where it's heading. It's like this is going to be this artisanal experience of sitting there writing code, which is so crazy. How much that job is going to change. Yeah, so again, here I go back and again, pardon, maybe the history lesson, but like I go back like going. So the first you know that you know that the original definition of the term calculator, do you know what that referred to? No, referred to people. Right. So back before there were like electronic calculators or computers or any of these things, the way that you would actually do computing, the way that you would do calculating like the way the insurance company would calculate actual real tables or the military would like calculate. You know, I don't know whatever true logistics, you know, formulas or whatever it was. The way that you would do it is you would actually have a room full of people. And by the way, these are like big rooms, you could have hundreds or thousands or tens of thousands of people doing this. And you would actually you would actually figure out you have somebody to the end of the room, who is like responsible for like whatever the mathematical equation was. And then they would personal out the individual mathematical calculations to people sitting at desks who were doing them all by hand. Right. And those that that job title was those people were calculators, right. And so we've gone from a world in which you literally have people doing mathematical equations by hands, by hands, then we got the first computers. The first computers of course didn't have programming languages. Right. They only had machine code. Right. So the first computers are programmed with ones and zeros. And so the task of the programmer became do the ones and zeros. And then that became punch cards. And you can still, you know, there's still people, you know, kicking today who you know whose job as a programmer was to like yield the punch cards. And then you got actually this big breakthrough, which was called assembly language, which was basically the way to do machine code, but like with some level of like English kind of attitude. And then the best programmers did assembly language. And then you know, when I was coming up, it was higher level languages like C, the compiled into machine code. And that's what programmers did. And then I still remember when when scripting, you know, with scripting languages, you know, we developed JavaScript and escape and then you know Python took off a pearl and these other scripting languages. But scripting languages, you know, took off in the in the in the in the 2000s, there was this big fight in the technical community, which is scripting real programming or not. Right. Because it's like it's kind of cheating. Right. Because real programmers write code, the compiles to machine code. And like real programmers like do like memory management themselves and they do all, you know, this this whole craft of writing writing, you know, writing, writing C code. And, you know, these JavaScript for Python programmers are just doing this kind of lightweight thing. It does even really count as coding. And of course, the answer is yes, it very much counted. And now most coding is done with the scripting languages, right. Which that is, you see my point, the scripting languages have abstracted away like five layers of detail underneath that that people used to do by hand that they don't anymore. And then, and then there's, and then your point like AI coding is the next layer on that AI coding actually abstracts the way that brought us actually writing the scripting code. Right. And so in one sense, this is a really big deal for all the obvious reasons. But on the other hand, it's like, OK, this is the next layer of the task redefinition under the job of programmer. Right. Now, what's the job of that programmer? It's your point. It's not necessarily to write the code by hand. But what it is now is all right. Now, you know, if you talk to the world's best program yesterday, what they'll tell you is, oh, my job is I'm sitting there and I'm orchestrating 10 code bots, right, coding bots that are running in parallel. And literally, it is that they're in the shift for browser, you know, browser browser terminal terminal and they're and they're what they're they're they're they're they're day job now is kind of arguing with AI boss trying to get them to like write the right code. Right. And then in the debug it and fix the problems and change change change this back and do all these things. So now the job of the programmer is to argue with the coding bots. But like, if you don't know how to write the code yourself, you don't know how to evaluate what the coding bots are giving you. Right. And so you, you know, you asked about pretending, you know, there are 10 year old is, you know, super in a computer is a super in a programming. And what I'm, what I'm telling you. And what I'm telling you, you know, he's easy as in cloud and chat GPT at copilot and all these things. I'm telling him is like, look, and by the way, he loves vibe coding. He's on Repplet all the time doing vibe coding, you know, doing games, you know, he's sitting there, you know, it's hysterical, right, because he's sitting there. It's a 10 year old basically who's, you know, spend two hours at dinner arguing with an AI for fun. Right. Right. But what I'm telling him is, no, look, you need to still fully understand and learn how to write and understand code because the coding bots are giving you code. If it doesn't work or if it's not doing what you expect or it's not fast enough for whatever, like you need to be able to understand the results of what the AI is giving you. Right. And in the same way that somebody who is writing scripting language code, nothing to understand ultimately how the micropress this works. And so again, it's kind of this up leveling of capability where you actually want the debts to be able to go down and be able to understand what the thing is actually doing. Even if you're not spending your day actually doing that by hand. And again, I look at that and I'm like, okay, now programmers are going to be 10 times or a hundred times or a thousand times more productive than they used to be. Right. And that is overwhelmingly a good thing that the tasks are definitely changing the nature of the job is changing. But our human being is going to be involved in like in the coding process and overseeing the AI coding and all that. And the answer is, of course, absolutely 100%. No question. So you're in the camp of still learning to get still a valuable skill. Oh, yeah, totally. Well, again, if you want to be one of these super, look, look, if you just want to put your like self out of pilot and like, I can't be bothered. And I'm just going to have AI right the code and it's going to generate whatever it does. And that's fine. And I'm going to be, you know, I'm going to be. It's a goal is to be a mediocre coder. Then just let the AI do it. It's fine. The AI is going to be perfectly ready to generate an instant amounts of media or code. No problem. It's all good. If the goal is, I want to be one of the best software people in the world. And I want to build new software products and technologies that like really matter. Then yeah, you 100% watch you still, but you want to go all the way down. You want your skill set to go all the way down to the assembly to assembly a machine code. You want to understand every layer of the stack. You want to deeply understand what's happening at the level of the chips, right? And the network and so forth. By the way, you also really deeply want to understand how the AI itself works, right? Because if people understand how the AI works are able to clearly able to get more value out of it than somebody doesn't understand how it works. I mean, you're always more productive if you know how the machine works, right? When you use the machine. And so, yeah, the super empowered individual on the other end of this that wants to do great things with the new technology. Yes, you 100% want to understand this thing all the way down the stack. Because you want to be able to understand what it's giving you, right? And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is. By the way, again, this goes back to education. AI is your best friend at helping you learn all that, right? Because it's like, oh, I need to understand. I don't know like this isn't fast enough. I need to figure out as a coder. I need to figure out how to do a different approach to memory management or something. And you'd be like, well, you know, shit, like I, you know, I don't quite know how to do that. Okay, AI, let's spend 10 minutes. Teach me how to do this, right? Teach me what this all means, right? So all of a sudden you have this like incredibly synergistic relationship with the AI where it's also helping you get better at the same time that's doing a lot of work for you. By the way, I was going to say I was a big pearl programmer. I was an engineer for 10 years and that was my language of choice. Do you remember? I don't know when you were doing it, but do you remember that at least early on? Did you ever did you ever hit this where like sea coders were like looking down their nose at you being like for sure. For sure is like this is so slow. It's not going to scale. What are you? What are you spending out of time on this thing? Yeah, exactly. And of course, you know, and again, it started this thing where, you know, they were sort of correct, which is at the beginning, it wasn't, you know, fast never, whatever. By the end, they were definitely wrong, right? Which is it got much better, much faster. And you know, it's swept the world. And today happens the scripting languages. And then by the way, the people along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they were the ones who were able to actually make the scripting languages I should work really well. Right. And so that was, that was a great example. This kind of adaptation. And then again, the result of that was, you know, a far higher number of people writing code of scripting languages than were ever writing code of the lower level languages. And I think this will just kind of be a more dramatic burden of that. I love that pearl is designed by a linguist. I don't know if you remember that part. And that's what made it so nice to to code with. Well, that's funny because, of course, it was so notorious for being impossible to understand. So how ironic. Coming back to these, this kind of triad, the other element that I hear more and more of is just as is the skill of taste and design and user experience. It feels like that's a very hard skill to learn. And to me, it tells me design is going to be much more valuable in the future. Yeah, that's right. And again, here, this is a great example. So the, again, the task level, the task level of like design, the perfect icon, right, is going to be like, all right, the ass is going to do that all day long. It's going to give you a thousand icon designs. It's going to be great. Like it's going to be fantastic, like whatever, you know, and there will still, by the way, there will still be some level of human icon design or whatever. But like, it's going to get really good at that. But like, what are we trying to do? Like the, you know, kind of capital D design of like, all right, what is this thing for? And how does this, how is this going to function in the world of human beings? And like, you know, what, what's going to, you know, is this going to make people happy when they use it as it's going to make people go good about themselves. Is it going to fit into the rest of their life? Is it going to, you know, I don't know, challenge them in the right way. You know, all these kinds of higher level questions with the great designers have always thought about like that, that the job of designer, right, will involve much more of those higher level, more important components. And then again, with it with AI doing a lot more of interlite tasks. And so, you know, one way to think about it is, you know, I don't know, you think of like that with the world's best designers, you know, Johnny Iber, whenever it could be like, wow, like, if I'm a designer today, if I'm a 25 year old designer, and I, and I aspire to be, you know, Johnny I've been a decade. It's all of a sudden I have a new path that I can use to kind of get to get there, which is I can, you know, because Johnny I've did everything you did without AI. Now, you know, a young designer can be like, wow, if I really harness AI in a decade, I'm going to be like the best designer the world's ever seen, because it's not just going to be me, it's going to be me plus being so super empowered by this technology to be able to do so much more. And then so much more of my time and attention is going to be is going to be able to be focused on these higher level things that most most designers ever get to. And I think that that's going to be another great example of that. So maybe what I'm hearing here is kind of this t-shaped strategy of the if you want to be successful in any three of these roles. Be very, very, very good at that specific role product management engineering design and then get good enough at these other two roles. Well, so I think that's great. I think that's really really relevant. And then, you know, Scott, you know, Scott Adams and firstly just passed away, you know, which is a real tragedy, but I was always ever referred for years to actually Scott, Scott Adams. He had this famous, he had his famous kind of career advice. He would get people, which I think makes a lot of sense, which, which dovetails about your saying, which is he used to say used to say it's like, look, he said, you know, I could he said, you know, I could have been a pretty good cartoonist. Or I could have been like pretty good at business. But the fact that I was a cartoonist who understood business made me like spectacularly great at making dobert. Right. Because even the world's best cartoonists who didn't understand business could have never written dobert. And then the world's best business people who didn't know how to do cartoons couldn't have done dobert. It took somebody who actually had both of those skills to be able to make dobert, right, which is one of the most successful cartoons in history. Right. And so, so the way Scott always described it was that that from a career development standpoint, the additive effect of being good at two things is like more than double. Right. The additive effect of being good at three things is more than triple. Right. Because you become a super relevant specialist in the combination of the domains. And you, like you see this all, I mean, you see this all over, you know, you see this all over the economy. You see this all over the economy. But I'll give you an example. Hollywood, you know, just Hollywood as an example. You know, there are a lot of writers who can't direct a movie and they can be very successful writers. There are a lot of directors who can't write a movie. They can be very successful directors. But the superstars entertainment industry are the people who can write and direct. Right. And, you know, they don't have a term for those. They call it a tourist. Right. And that's, you know, those are the people who are like the real creative forces that move the field. And so, and so again, and by the way, Hollywood, it's just really fun. It's been spent a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican standoff going right now that we that we describe an attack except in Hollywood, for example, for filmmaking is the director is the writer in the actor. Right. Because the director is now thinking, wow, I don't need the writer anymore because the AI can write the script that I don't need the actor anymore because I can have AI actors. The writer is saying, wow, I don't need the director because I can direct the movie and I can do the actors. And the actor is saying, I don't need either one of these guys, I can have AI director saying I can have the AI writer saying I'm just going to show I could do my performance. Right. And so it's the same kind of trying, triangular configuration. And again, what's great about it is they're all correct. Right. Each person, each of those three fields is going to be able to expand laterally and pick up those other those additional skills. And then as a consequence, you're going to have more people who can write and direct or write and act or direct and act. Or direct and act or do all three. And I think, you know, to your point, like your T-Shift thing, like I think that's going to be true, basically across the entire economy. And if you think about the T, if you think about the T configuration, it's like, yeah, the breath, the breath, the breath, the top of the T is like, how many individual domains are you familiar enough with to be able to use the AI tools to be able to do really good work. And then this part of the T is how deep can you go in at least one of those domains so that you really, really deeply know what you're doing. But like if you're like super deep on coding and you can use AI to do design and you can use AI to do product management. Right. That's your T right there. And you're a triple threat at the top of the T, but with this level of technical grounding underneath that. And I mean, at that point, you're, again, you're the super part individual, you're going to be able to just perform like seats of magic. For example, the terms of designing and building your products, you know, the people in my generation couldn't even dreamed of. And so I think I think that this is a universal kind of theory that I think can apply across the entire economy. I'm going to invent a new framework right now. Okay, forget the T framework. I'm picturing an F sideways or an E or there's three two or three, I don't know, downward parts. And so what I'm hearing is get good at least two. Yeah, I think that's right. I think that's right. Yeah, the combination. Yeah. My friend Larry Summers had a different version of the Scott Adams thing, which is he used to tell people, he said, the key for a career planning. He said, don't be fungible. Right. And you know, that's it. He's an economist. And so that was economics. You can why and what that means is what that means essentially is don't be replaceable. And so don't be a cog. Right. So and what that meant was don't just be one thing. Right. So if you're, if you're, if you're quote unquote, you know, again, just a designer. It's just a product manager as a coder like then in theory, you can be swapped in around. But if you have this, if you have this, yeah, if you have this e or F, you know, laying on a side kind of thing. And if you have, if you have this combination of things, it's actually quite rare. Then all of a sudden, you're not fungible, not only not funcible, like you're actually massively important because you're one of the only people in the world who can actually do that combination of things. And yeah, that your ability not become one of those people is like titanically enhanced with AI is compared to anything we've ever seen before. This is so interesting because I've worked with people that are good at these two skills and they were always called unicorns at the company. She can coat and design. And what I'm hearing here is this is what you need to become. You need to be com really good at least two things. I think you use the term smoke stack or something where it's like PM over here, engineer design. And what I'm hearing here is you need to get good at least two of these skills. The silos of these two roles are disappearing. That's right. That's right. And again, I can't overstress the following for anybody listening to this. The thing about AI that I think people are just like not getting enough benefit out of yet is just it will teach you. Like this is amazing. Like there's never been a technology before where you can ask it like teach me how to do this thing. So it's I always feel like it's like it's like people spend too much is one of these things where it's so much focus on figuring out how to use like a large language model is like, OK, what am I going to try to get it to do for me, right, which is first very important. The other side of it is what can I get you to teach me how to do, right. And it's just as good at that. Right. And so again, this is this level is level of late superpower. Like, you know, people who really want to like improve themselves and like develop their career should be spending every every spare hour in my view. At this point, talking to an AI being like, all right, train, train me up like tell me, tell me, tell me how to, you know, train, train me, train me out of the, you know, I'm a coach, train me out of the product manager. It will happily do that. It knows exactly how to do that, you know, run me, you know, make me problems, you know, make me assignments that evaluate my results. Right. And it will do it will do that just as happily as it will do work, quote unquote for you. Two tricks I've heard along those lines. One is to watch the output what the agent is doing and thinking as it's doing the work. So if you're not an engineer is just sit there and watch it think and make decisions. And it's almost become this like layer on top of learning to code is learning to see what the agent is doing and thinking is that teaches you about architecture. And the other is a couple podcast guests have mentioned this when you get stuck and then you figure out how to unstuck yourself. You ask it, what could I have done differently? What could I have said that would have avoided this error in the first place. Yeah, that's right. That's right. Yeah, look on that first one and then say, again, that's what I'm doing on my 10 year old. Yeah, look, if if you ask an idea, this is a really good point. So if you ask an AI, I don't write me this code and then it doesn't it comes back and it doesn't work right. Like if all you know is like single function, I asked it and it gave me back something that's not good. Like what do you like what do you even do with that? Right. Like you don't understand why a debut that result. Do you really understand it even what you even understand what to tell it's trying to get it to do something different. But to your point, like if you actually walk if you actually watch what it's doing. And then you have the grounding, you know, kind of that like that you have your year year F. If you have that grounding, then you can be like, Oh, I see what it's doing. I see where it made the mistake. I see where it went sideways. And then you're all of a sudden able to intervene and able to say, No, that's not what I'm mad at is everything. Right. And again, this is this this is a big part of having having the actual kind of, you know, synergistic relationship. Is that you understand. And by the way, look, I mean, this is like everything I'm saying is, you know, everything, everything that we're saying right now also is the same as if you're working with him and things, right. Like, you know, you and I are colleagues and I, you know, ask you to do something you come back with something completely different. Like I do need to understand what was happening in your head, right, in order to be able to get you to give you feedback. Right. If I just tell you, Oh, that's wrong. It doesn't like nothing happens. I need to actually understand. I do have theory of mind, right. I need to understand what you were thinking in order to really give you the right to be back. And so, and, you know, and again, the great thing with AI is AI will happily sit there and explain all day long, why it's doing what it's doing. It'll, you know, it'll happily critique itself. You know, you can do this. By the way, it's also a very fun thing where you can have one AI critique the other AI, right. Which is another thing, which is like, you have one AI right the code. You have another AI debunk the code. And so you can actually use you can play the eyes off against each other and get some argue with each other. And yeah, these are all these are all the kinds of skills that are going to become I think incredibly valuable. Having people call those LLM councils. Yes, they're talking each other. Yeah, that's right. That's right. I do feel like if I were like I'm I have no design background. I've always wanted to design. I would I've always wanted to be a great designer. It feels like that's the hardest one to learn of all these three by just watching and talking, right. Because there's a lot of exposure hours as folks have used this term. Just like, how do you learn to be a great designer that feels like that's going to be really hard and valuable. So my true confession is I've always kind of wanted to be a cartoonist. But I have no like art skills. But as we're talking, I'm like, it might be time. Your time has come, Mark. Yes. I want to pivot to founders. You're maybe your bread and butter. I spend a lot of time with the most cutting edge AI forward founders. I'm curious what you see them do. How you see them. Some way they operate. That's maybe blowing your mind about how the future of starting company looks. How the future of AI forward companies look. Yeah. So this is a great, very topical topic. This is all playing out in real time right now on the leading edge. So I think there's like three layers of it. See if this makes sense. I think there's like three layers of it. I think layer one is they're thinking, all right, how does AI redefine the products themselves? Right. And this is kind of the this is kind of the time honored, you know, kind of thing that happens to technology transitions. And this is kind of what a lot of entry capital is based on, which is, you know, okay, there's a new technology that comes out. And you know, maybe it's the personal computer or the iPhone or the internet or now say, I and it's like, all right. Is this a new capability that gets added to existing products? Right. So all of a sudden you've got, I don't know, an existing, you know, software business. And now you've got your, you know, PC version of it. And now you got your iPhone version of it. And you just kind of keep on going. And you know, you kind of add the new technology kind of gets kind of added into the mix. You know, it's kind of another reading into an existing formula. And of course, you know, a lot of new technologies are like that, right. You know, I don't know when I don't know when flash, when flash storage came out or something, you know, you didn't really, you didn't really redefine the software industry because people just want to be using, you know, hard disk using flash storage or something. But when the internet came out like basically old school on print software for the most part, you know, not entirely, but like a lot of it died and just got replaced by like what software. Right. And so, so sometimes you get the kind of, it's additive to an existing thing. Sometimes you get the actually it redefines a product category or redefines an industry. The actual, you know, the medications of the companies themselves turn over. And so, so, so, you know, so there's sort of this question of like, you know, an example, you just mentioned nano banana. So like a great example is there are, you know, there are these businesses like, you know, just take Adobe like, you know, Photoshop is built of whatever 40 or franchise in image editing. Okay, is AI as sort of a feature now that gets added to Photoshop to be able to do AI based image editing. Or, you know, do you just like stop editing images entirely because you're using nano banana and you're all images are just being generated and it's just easier to just have AI generate a new image that it is to try to add to the end of the mold woman. So, I think, you know, there's many areas of of tack in which that question is being asked and, you know, the answers, I think will vary by domain. But, you know, obviously as as a venture firm, we're betting hard on many of these categories being being totally reinvented and a lot of the, a lot of the best founders are trying to figure out how to do that. So that, so that's kind of AI, you know, changing the definition of the product. I think the next layer is actually a lot of what we've already talked about, which is AI changing the jobs. And so it's, you know, a lot of what we already talked about, but like, okay, if I'm a founder of a company and I've got, you know, if I have, you know, room in my budget for 100 coders, you know, how do I get those coders to be super power day, I coders not, you know, not the kind of coders I used to have. Super power day, I coders that, does that mean, you know, do I still need 100, maybe not only 10 or does that mean I still want 100, but now they're doing 10 times more. Right. And so that, you know, as, you know, like a lot of the best founders are working on that right now. And then I think the third shoot of drop hasn't quite dropped yet, but it's, you know, it's kind of the big one, which is like, all right, like the basic idea of having a company. Right. You know, does that change? And again, here you've got this concept of the super power individual, which is like, okay. You know, can you have entire companies where you have basically the founder does everything, right, because what the founder is doing is like overseeing an army of AI bots. And there's sort of this, you know, this kind of this holy grail in our industry that's been running for a long time, which is like, can you have the, can you have like the one person billion dollar outcome. And we've had a few of those over the years, Bitcoin is probably the most spectacular example, you know, with the Syrian right behind it, you know, which wasn't quite one person, but you know, a very small team, you know, you had, you know, kind of Instagram and WhatsApp that had very big outcomes with very small teams. You know, every once in a while, you get one of these things where you just, you know, something hits and you just have a, you know, very small number of people associated with it. And so that said, you know, most of our companies obviously end up with, you know, huge numbers of employees. And so I think, you know, so the, the most leading a shoulders of thinking of like, okay, how do I reconstitute the actual very definition or idea of a, of having a company. And, you know, can you have a company that's literally basically just all AI. And so, and if you're doing something, you know, if you're doing anything in the real world, that's hard. But if you're doing software like that, that seems like it might be feasible in some cases. You know, there's like the ultimate example that which is like, you know, can you have like AI, can you have like autonomous like AI economy stuff happening where you have like AI bots and the blockchain or something. You know, that are basically out there like functioning as a, as a, as a business like making money and just, you know, literally where the AI does all the work itself. And just get, you know, issues meet up. It is. And so maybe, you know, maybe that, you know, maybe that's the final outlier result. We have, we have a few founders are chasing that kind of thing. So I would describe that as, I would describe that as kind of the, the, the ladder that the best founders around. Super interesting. This whole idea of a one person billion dollar company. I think it depends on your definition of what this is like an outcome I could see. Having run, running my newsletter as one person with some contractors. There's so many little annoying things that I have to deal with with just support tickets and issues and bugs. And like it's hard for me to imagine actually a one person billion dollar company, even if AI is handling so much of your support. Because there's just so many random edge cases that I'm just constantly filling out forms. And so, I guess depends on do you have contractors of that count as, you know, like what does it can, what does it mean to be a one person. But I'm just like, I can't see that happening. Yeah, I mean, look, Bitcoin's, it's actually pulled it off. But like, you know, the open source community, you know, like it does that count. I don't know. Yes, I guess it counts. Okay. Yeah, exactly. Right. So, yeah. Yeah. And I was I was I don't propose to have answers here, but more just like the smartest people I know or many of the many of the smartest people I know are thinking hard about this. Yeah. What do you think about moats a big question constantly in AI, you know, the fact that everything's changing. Just what's your guys is thesis on moats in AI? Is that even a thing? Do you care? My experience with like really big technological transformations. And of course, I kind of lived this directly with the internet. And I saw this happen is the really big technological transformations. They take a long time to play out. And there's there's all of these structural implications that just kind of cascade out over time. And then there's kind of this, this, there's this like rush to judgment up front where people kind of say, oh, it's therefore obvious that, you know, XYZ. It's therefore obvious that this kind of company is going to be the company of the future and not that kind. It's obvious that this incumbent's going to be able to adapt and so everyone isn't. It's obvious that there's economic opportunity and this kind of start up and not any others. It's obvious that the moats are going to be in this area of the technology, but not in this other area. And they're and you know, what everybody does is they kind of state those things with like just an enormous amount of self assurance where they, you know, where they really sound like they have all the answers. And then, you know, what happens is this these ideas kind of saturate the media, right? Because the media naturally prizes like definitive answers over open questions. Because you know, you want, you know, when CNBC is like booking guests, they want a guest who's going to come on and say, yes, this is the way it's going to be acts. Not like, you know, I think that's a really good question. And let's like debate it from like eight different angles. And what I found is if you look back on those predictions a few years later and you can do this, by the way, if you pull up like coverage of the internet, you're from like 1993 through like 1997 or even through like that matter, even through like 2005 or 2010. And you look at like the kinds of confidence statements people were making in the first 10 or 15 years. Like I would say like almost all of them are wrong. Again, generally like quite badly wrong. And so I just, I think the process, I think with massive, there's going to be a massive technological change. It's going to be like, I don't know, five or six layers of like structural change that will play out over time. And again, a lot, we've talked about a lot of this, but like it implications on like, what are the definition of products? What are the definitions of companies? What are the definitions of jobs? What are the definitions of industries? How does this play out of the national level? How does this play out of the global level? You know, how does this interest, by the way, how does this intersect with politics? How does this intersect with, you know, unions? How does this intersect with, you know, war? You know, what's China going to do? You know, and so it's just like, there's just, there's, there's just a tremendous number of unknowns, like a very, very large number of unknowns. And I think it's just like really, really dangerous to pre-judge these things. And so I just give it, I'll just give it, and it's just, I'll just run this as a thought experiment. You know, see what you think on this, but it's like, you know, like do you do AI models, our AM models themselves, like, defensible, like, is there a mo on AM models? And on the one hand, you'd be like, wow, it certainly seems like there is or should be because, like, if something takes, you know, billions of dollars to build. And you, you know, you need this, like, incredible, critical, massive, like, compute data. And there's only a certain number of engineers in the world that know how to do this. And, you know, they are getting paid, like, MBA stars. And, you know, and then these companies have to deal with all these, like, crazy, you know, political issues and press issues and reputational stuff and regulatory and legal. Like, all of that translates to, like, you know, okay, probably, at the end of this, there's going to be two or three companies that are going to end up with, like, you know, 100%. You know, I don't know whatever, 50, 50, or 30, 30, or 90, 10, 1, or whatever it is, market share, and then they're going to have whatever probability they have. And it's going to be a kind of a classic oligopoly and, or maybe, you know, maybe one company's indefinitely, it will be, it will be a monopoly on that. And by the way, those outcomes have happened and suffered many times before. And so maybe that, that will be the outcome. You know, the other side of it is, you know, if you had told me three years ago, you know, that in the, you know, kind of Christmas such as a PT that, like, within basically a year to year and a half, there would be, you know, five other American companies that would have basically, you know, exactly capable products. And then there would be another five companies out of China that would have exactly capable products. And then there would additionally be open source that was basically the same. I would have been like, wow, like, you know, the thing that seemed like it was black magic all of a sudden, you know, has become like commoditized really fast. You know, which, by the way, is exactly what happened, right? Like, you know, within, within a year of, of GPT 3 coming out, where there were their open source, GPT 3 is running on a fraction of the hardware, right, they were available for free. And then they were, and then, you know, there were five, you know, now you've got, you know, in the game, you know, fully in the game, you've got Google and you've got Anthropic and you've got XAI and you've got meta and you've got, you know, all these sort of companies that are in the deep seek and, you know, can be in all these other Chinese companies. And so, like, even at the level of like LLMs or, you know, AI models, like, you can squint and make that argument either way, by the way, same thing at the level of apps, right. It's like, you know, one school of thought is, you know, apps, apps are not a thing because like the model is just going to do everything. But another way of looking at it is, no, actually like actually adapting the model is kind of the engine into it, into a domain involving human beings, where you need to like actually have it fit for purpose to be able to function in the medical industry or the legal industry or whatever. Or coding, you know, no, you actually need like the application levels actually going to matter enormously and maybe the LLMs quantitize and maybe the value goes to the apps. And again, you can kind of squint either way on that one. And I know various smart people who are on both sides of that argument. And so I, my honest answer on this is I think we're in a process of discovery over time, which is, you know, the way I think about this kind of structurally is it's a complex adaptive system. The technology itself, you know, provides one of the inputs, the legal and regulatory process, you know, as another input. And, you know, actual individual choices made by entrepreneurs, you know, matter a lot, you know, the economics matter a lot availability of investor capital varies over time that matters a lot. And this is a, this is a complex system. And so we actually don't know the outcomes on this yet. And we need to basically be, we need to be open to surprises at the structural level of what happens. And of course, as a, as if you see, this is very exciting because it means we, you know, we're doing this now. It kind of makes that's along everyone any strategies and kind of see and see how this plays out. And I just say, like, there may be like one, I don't know, there may be like one particularly brilliant. I don't know. Edgeman manager or something else is all figured out. But I guess I would say if, if, if, if things just I haven't met them yet. So what I'm hearing here is don't over obsess with modes at this point because we have no idea what'll end up being in as much as it may feel like, okay, there's no way open and I will lose this lead. We're seeing a lot of competition, GPT wrapper point is really great. A lot of such a derogatory term. I don't know, a year ago, just like, you're just GPT wrapper. Now it's like the companies that are the biggest companies as fast as growing companies in the world. Yeah, well, it's like a little bit like, I don't know. I mean, even just like with, you know, the, you know, this has been the, you know, the, the holiday, if, you know, three years ago was the holiday of. I mean, we have, if you had to be the last, you know, month or whatever has been the holiday of a cloud, particularly cloud code, right for coding. But it's like, you know, it's pretty amazing because it's like, okay, there was cloud, which is obviously great accomplishment. But then there's cloud code, which is, which is, which is an app, which is an app, right? It's a, it's a, it's a cloud wrapper. Right. It's, you know, agent harness. And then, and then they did this amazing thing where they came out with. It's a, uh, co work, co work, co work, um, and, uh, and remember what they said of co work, which is a cloud code work, co work in a week. Yeah, we can have you up 100%. Well, and that's, and there's two ways looking at that, which is like, wow, that's really impressive. I mean, obviously, that's really impressive. The cloud code was able to build co work in a week and a week and a half. That's great. That's amazing. The other way to look at it is co work was developed in a week and a half, like, like, how, how much complexity could there be? How much of a varied entry can there be and something that was developed in a week and a half. And so, and then, and then, you know, and then again, it's, it's this, it's this push this pull thing where it's like, it's like, wow, it's incredibly, it's incredibly functional and incredibly valuable. And people are like, all over the world every day now, we're like, wow, I can't believe what I can do with this. It's like the most magical product ever, but at the same time, it's a cool week and a half. Right. And so, right. And so every other, every other model company, you know, I'm sure you'd have to expect to sit in there being like, okay, obviously, we need to build, you know, an Asian artist. And then obviously, we need to build a co work, you know, thing for, for regular people. And obviously, you know, I don't, I don't, I don't even say I know anything, but just like, obviously, they're all going to do that. Right. And so, you know, how defensible is that? And, you know, in six months, you know, and we've seen this happen before. Like, it's, it's, it's quad code going to get laps the same way that, you know, get up co-pilot got laps. You know, the, the history in the last three years is bad. Everything that looks like it's like the fundamental breakthrough gets gets basically replicated in lap brick way. Like, many of the smartest people I know in the field, when I, when I really kind of talked to them, kind of, you know, get a couple drinks in them, they're like, yeah, they're basically, you know, one theory is, like, they really aren't any secrets among the big laps. Like, the big laps kind of all have the same information. And they kind of have all the same knowledge. And they're, you know, they're kind of, they lap each other on a regular basis, but, you know, there's not a lot of proprietary anything at this point. And then, and then, you know, again, evidence of that is, you know, deep seek, you know, came out of left field and basically was like, you know, you're reaffirmation of a lot of the ideas on American big labs and, you know, and had some original ideas of its own. But like, you know, wow, it wasn't that hard for, you know, some, you know, basically hedge fund in China to do it. And so like how much responsibility is there. But on the other side of it, you've got, wow, these big labs are not paying, you know, individual engineers like their rock stars. And they're, you know, incredibly bright to create a people. And, you know, maybe there's, you know, it doesn't nascent ideas any one of these labs that it's actually going to be a huge breakthrough that's going to be hard to replicate. And so, again, it's just like, I think we just need to, I don't know, in my view, I, my view of myself, I need to put like a big discount on my forecasting ability on this one. Like it, for me, it's much less interesting to try to say, okay, as a consequence, industry structure in five years is going to be X. The big winner in the category is going to be company Y. The big, you know, product killer app is going to be Z. It's like, I, this is a, I don't think I can predict that. I think, I think I'm much, much better use of my time as it being being very flexible and adaptable at a time like this. So with all of us in mind, do you feel like there's something you're paying attention to more to help you decide? Okay, this is where we want to place our bet. Or is the answer, essentially the strategy you guys have, which is place a lot of bets. You guys raised the largest fund in history. Is that, is that the way you win in this world? Yeah, so for, I mean, for us, yeah, for us, we have, we obviously have a very, very deliberate strategy. When, when we think about this, use the Peter Tiel for you remember the Peter Tiel formulation of, you said there's a two by two, there's optimism and pessimism. And then there's determinant and as it indeterminate and indeterminate, right? And so, and he always argued like there's, he always argued in the like Silicon Valley is characterized by too much, what he calls indeterminate optimism. Right? And what he, what he always described, what he meant by that is it basically, I think the way he would describe it is an indeterminate optimist who thinks the world is going to be better, but can't explain why. Right? Like some combination of things is going to happen to make the world be better, even if we don't know what those things are. And, and you know, I think he, he at least historically would say like that's, that's basically, you know, that, that, that, that risks at least being just like wishful thinking or delusional thinking. And with the world needs more is determinant optimists, which are people who are like, no, the world is going to be better because I'm going to do this specific thing, right? And he would classify, for example, Elon, you know, he was sort of maybe say, you know, VCs are indeterminate optimists. And then he would say, you know, Elon is the determinant, determinant, determinant optimist where it's like, no, I'm going to build the electric car. I'm going to, you know, solar and then I'm going to spit, you know, Mars, you know, I'm going to make sure I concrete things. And I think there's a lot, I think there's a lot to Peter's framework, but the way I would describe it is I think maybe, you know, if you know, just a real part of that, it would be, I think the indeterminate optimism is a stronger phenomenon than at least I think he's historically represented it as, and I would put myself firmly in the indeterminate optimist category. And that's the strategy that we that we have at a 16 Z, which is, and the reason for that is it's not, it's not, hopefully it's not so much wishful thinking. It's more, no, what the indeterminate optimism of venture capital or the indeterminate optimism of a 16 Z or Silicon Valley is very, it's actually very specific, which is there are these extremely bright and capable people like Elon and many others who are founders, right, and product, and you know, kind of product, product creators. Right. And and each of those individual people is a determinant optimist, like each of them, each of them individually has like a very strong view what they're going to do. But the great virtue of the capitalist system, the great virtue of the American economy, the great virtue of Silicon Valley is we don't just have one of those, we don't just have 10 of those, we have 100 and 1000 and 10,000 of those. And the way to optimize the outcome is to have as many of those as possible be as good as possible, run as hard as possible. And then just the nature of, you know, the nature of the future is like we just don't know all the answers, and that's okay. And then the right way to deal with that is to run as many experiments as possible and have as many people try to do as many interesting things as possible. And so yeah, I would, I would put myself firmly on the side of the indeterminate optimist. I mean, I'm wondering if the answer to the question of what you look for now more and more is this determinant optimistic founder that has this massive ambition and is actually working on achieving it. Yeah, no, that's right, that's right. I mean, the founders need to be determined and optimists like they need to have a very specific plan. Now, and you look the critique, the critique always, you know, the critique from the founders is, oh, UVCs have it easy because like you don't have to like, you don't actually have to commit, right? You don't actually have to like make, you don't actually have to like, you know, you don't have to make the bad you lay in, you can like place multiple bads, you can offer as a portfolio, you know, you should have a lot more sympathy for us as founders. You know, because we, you know, we only get to make one bet, you know, and there's, there's truth to that, you know, the kind of argument on that is the founders get to run their companies, we don't. So, you know, we don't, we don't get to put our hand on the steering wheel. And so, you know, the great virtue of being a determinant optimist is you actually get to get to single mightily execute against that goal. And, and, and you look in the long run, who, who does history remember history remembers Henry Ford, right, not, you know, whoever was that, you know, whatever the seed investor is for a Ford motor company and, you know, 10 other car companies have failed. Right. And so, you know, the determinant optimist is the per, you know, the founder is the founder and the company builder and the engineer and these are the people who actually use the same and, you know, deserve 99.99% of the credit. But, you know, having said that, I do think there is a role for absolutely having something to German optimist in the, in the background, no, helping one way and helping keep the whole cycle going. Do you think about AGI in shifting your investment thesis like as we approach AGI and hit AGI as an investor. How do you think about your investment thesis changing? Yeah. So, I've always kind of had a little bit of an issue. I've always kind of struggled with the concept of EGI because it at least was fine terms, which is where I kind of struggle with it, which is there's like the prosaic, there's the, there's the prosaic definition of AGI and then there's like the, I don't know cosmic definition. And then we have described it as, well, start with the cosmic one. So the cosmic one is basically this is the singularity, right? And so AGI is the, is the moment where you enter the singularity, which is to say where the world fundamentally changes and like the rules, the old world are gone. We're not operating in a new domain. And then, you know, the kind of the full definition of singularity is like it's a world in which, you know, human judgment is no longer really relevant because the, you know, you get this self improvement loop. The AGI is improving itself and it's sort of racing, you know, so called takeoff scenarios, you see if this takeoff thing or the AGI is improving itself and the machines are making decisions so much faster to the people and people are just sitting there watching the machine do its thing. You know, and I kind of described it. I don't really, I don't really think that's, I don't, I don't think we live in that world like where they could call that utopian or dystopian, like I don't think we're lucky or unlucky enough to live in that world, we could debate that. We're going to talk about that more, but the, the, the prosaic definition of the AGI that at least I think the industry purchases, but it's kind of conversant and tell me to agree with this is, it's when the AGI could do every economically relevant task as good as a person. The way the co-founder of Anthropic put it is like a basket of the most valuable economic tasks. So it's like 10, 15, not every single economically valuable task. Okay. Got it. Yeah. So it's maybe even a slightly reduced, slightly reduced definition. And by the way, we're going to, you're clearly getting close to that if we're not already there. And so on that one, I kind of feel like, so I kind of feel like the cosmic one over states, what's going to happen. And then I kind of feel like the kind of AGI definition that you just gave. I think it kind of understates what's going to happen. It's almost too reductionist. And the reason for that is, I don't think there's any reason to assume that human skill level is the cap on anything. Right. And so the way we say that as AGI always is, you know, the definition you gave the definite idea of it's kind of, it's always kind of relative in comparison to a human worker. Right. And it's like, I don't know like human skill level caps out at a certain point, but that's because of the inherent like biological limitations of the human organism, right. Like we're all, you know, human idea, example, human IQ, human IQ, you know, kind of like I'll fluid intelligence or the sort of G factor or kind of, you know, fluid intelligence. IQ, I think tops out in, in humans as a species, it tops out around 160, right. We're at like 160, it's like Einstein level, Einstein, five minutes by Q. Mentures by Q, like you just tops out of 160. The 160 IQ people are the ones who come up with new physics. There's only a small handful of those. The generally speaking, when we run into somebody in the world who's like incredibly smart, who's like a best selling author or like a, you know, one of the world's best, I don't know, research scientists are one of the world's best doctors. You know, whatever it would be probably 140. It's kind of the IQ that you're looking for there. If you're looking for like a really good lawyer, it's probably 130. If you're looking for like a really good like online manager and a business is probably 110. You know, if you're looking for like an accountant, like a small business accountant who's good at doing the books or small businesses is probably 105. Right. And so the kind of scope of like impressive human, you know, the ability of the human organism to do it, intellectually impressive things, you know, it's sort of that 110 to 160 is kind of the spectrum. And you know, good news is there's a lot of those people running around, but like there's not that many at 140, 150, 160. But it's like, that's just that's like limitations of what can fit in here, right. And it's like, there's no theoretical limit on where this goes if you release the limitations of human biology, right. And so can you have a, and you already have people running these extremists to kind of do human equivalent, you know, kind of IQ. I'm, you know, for for existing and by the way, existing and models right now are kind of testing around the 130 140 level, which means they're going to get to the 160 level and they're, you know, they're arguably on the mass size starting to get to the 160 level now. But like I, I think we're going to have AM models relatively quickly that are going to be like 160, 180, 200, you know, 250, 300. By the way, and I think that's great, right. Like I feel, I feel, I feel is great about that as I do about the fact that we occasionally get a nice time. Right. It's like with the world be better off or worse off, take more of your Einstein's and the answer is of course there will be better off with more Einstein's and of course they will be better off with machines that have IQ, you know, more IQ like Einstein agree to the Einstein. But like I think IQ, IQ of the machines is going to see that in the humans. I think that's really good. And then the performance, you know, again, it goes back to like the coding thing is happening. The performance against task is going to get better also like I think, you know, this is where line of struggles in particular is like, yeah, okay. Like this thing is starting to generate better code than I can. Okay, so now we're going to have AI coders that are actually better coders than the best human coders. I think that's great. I think we're going to have AI doctors that are better than the best human doctors. I think we're going to have AI lawyers that are better than the best human lawyers, which actually is going to be very interesting to see. I think it's also great. And so like I don't think there's a, I think we're used to living in a world where we just don't understand how good, good can get because we've been capped by our own biology. And so you see what I'm saying, which is like, I think this idea of like human equivalent is just going to be like a footnote. It's like, oh, yeah, that was just on Tuesday, you know, in 2026 is when they hit that and it kind of didn't matter because the next question is like, okay, what are we going to, what are we going to, what are we get to do in a world in which we actually have machines that are better than that. Right. And so, so I think this is going to be much more of an exploratory process for actually exceeding human capability than it's going to be any sort of particular singular singularity moment or whatever that happens just to just happen to coincide with the human threshold. 200 IQ. I just like that frame of reference is such a mind expanding way to think about just how fast and how smart these things are going to get and quickly. Well, I don't know if you have this experience, I have this experience all the time. Well, two two experiences I have all the time. One is just like, I'm just like, like, I know I ought to be able to do this, but like, I just can't. Like, it's going to take too long, you know, I want to write this thing or I want to like, whatever I want to have this theory on this thing or I have a plan or whatever it's just like fuck like. I don't have the eight hours or, or by the way, the eight weeks or the eight years, right. And like, I just don't know enough yet. And I'm just like, I can't do the math in my head and my memory isn't perfect. And like, I can't remember. And I read, you know, if you have this, you get interested in something you read 10 bucks. And then you're like, shit, I forgot almost everything that I just read. I wish I could retain it all, but I can't. It's just like, you just have this, I sort of live in this kind of state of like endless frustration. I was just like, like, if I could just be smarter than I was, like, I'd be so much better at what I do, but I'm not. So, so there's that. And then I don't know how often you have this, but I have this on a regular basis. It's just like, you know, I, you know, because of what we do. Like, I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point. And it's like, for the first half of the conversation, I've just taken notes the entire time. And for the second half of the conversation, I just like, fuck, like, fuck me. Like, this person is just smarter than I am. And they're just out thinking me, and they're going to keep out thinking me, and I just can't. And I'm just like, all right, damn it. Like, I got to go home. And I got to like have a drink because I'm just not, you know, I'm just not, whatever that is, I'm not that. And so we're just so used to having this limitations. Um, that the idea of having machines that work for us that don't have those limitations, I just, I think that's much more exciting than people are giving your credit for. Oh, man, I could talk to you for hours, Mark. I'm thinking to close out the conversation. I want to ask about your media diet and your product diet. You just talked about books reading 10 books. I think you famously read constantly. I saw an interview with you where you're just like AirPods, changing my life. I'm just listening to audiobooks now. So in terms of media diet, what do you, what are you reading, what are you paying attention to these days in terms of a podcast, newsletter, blogs, things like that, and then any books in particular? Yeah, yeah. So what I read is basically, I mean, I would say read basically three categories of things. So like in terms of like general media, it's basically I sort of, I was describing as I have almost a perfect barbell strategy, which is I read acts and I read old books. Right. So it's basically either like up to the minute, what's happening right now, or it's like a book that was written 50 years ago, that has stood the test of time, and then we're presumably there's something timeless in it. And then it's sort of everything in the middle. I'm always like much more skeptical about. And in particular, it's kind of what I already said, which is, I think if you go back and you read old, nobody ever does this. It's actually really funny. Nobody ever does this. There's no market for it. But so you go back and you read old newspapers. And by the way, you can say you can do this. Just read last week's newspaper, right? I'd say sort of retaining on Friday. So read last Friday's newspaper. Right. And just go back and read it and be like, oh my God. Like none of this happened. Like not that none of what they predicted played out the way that they said that it would none of this turned out to actually be that like relevant or correct. Like they didn't understand like, you know, the way they had no view of what was going to happen this week, they couldn't know. And so they were making predictions and forecasts and so forth based on like not have any information. But this was like, wow, like, you know, like none of this happened. Like I wish I had never read this. Like, oh my God. And then, you know, it's kind of the same thing with magazines that go back and read old magazines. And just like the level of the, you know, the endless numbers of predictions that they make. And kind of, you know, the problem with newspapers at least they're going day to day, the thing with magazines is like every it's like a week or months, you know, kind of a long cycle. And so it's even, you know, by the time an article even hits publication, it's, you know, it's often out of date. So I just, I just have like a big problem with kind of everything in the middle. And so it's either it's either it's either it's either of the moment or timeless. But then yeah, you mentioned like newsletters. I mean, so the other thing. And you know, this is maybe obvious, but I think it's probably still underrated, which is actual practitioners in the field who are actually creating content. I think probably is still like dramatically underrated. And I think this is a huge part of like the sub stack phenomenon and the newsletter phenomenon and the podcast phenomenon is like direct exposure to the people who are actually principles in the field who actually know what they're talking about is probably still dramatically underrated. And I think again, the reason for that is like we're used to being in this mass media kind of culture in which basically everything is mediated. Right, everything got filtered through like TV interviews or a newspaper interviews for magazine interviews. And, and you know, obviously now more and more is just no, you actually want like smart people who are actually working on something you explain themselves. And then you have, you know, you have news, kinds of intermediation like podcasts that the kind of open that up for people, it makes that possible. And so yeah, like domain practitioners are, you know, really great. I mean, they just just take the obvious and AI, you know, obviously your stuff, but also like, you know, let's, you know, the fact that like life's treatment can have, you know, the world's leading or, you know, whoever the, you know, any of you guys, you know, there's small handful of you guys who have access to these people, you have the world's, you know, kind of leading experts in the domain actually show up. And by the way, as you know, it looks, the critique always is, you know, people talk their book like if I'm running a startup or whatever I'm just selling, it's like, and there's always a little bit of that. But it's also, you know, my experience is people love to talk about what they do. And, you know, and they fundamentally like want to express what they do and, and they want to explain it and they want people to understand it. And everybody kind of enjoys that and they get to contribute to can human knowledge by doing that and they get eager gratification by doing that. And so I think there's just actually just tremendous amounts of alpha in listening to the world's leading experts in the space who actually just like show up and talk about what they're doing. And of course, like the world is a wash in that today in a way that it wasn't as recently as 10 years ago. So, yeah, I do as much of that as I can too. And there's also just this culture and in tech Silicon Valley in particular sharing, not trying to keep these secrets. Everyone on LinkedIn is always like, how is this for eight? Like, it's just the way it works. Yeah, it's somebody said Silicon Valley is a company town, but the company is Silicon Valley, right? And again, the level is going to be one of these great end equals one. The level of end equals one is somebody, you know, I've run starters before, I've run companies before, at the level of end equals one of like running a company, that's just a giant pain in the fucking butt. Like because, you know, your secrets are walking out the door and your employees are walking out the door and the whole thing sucks. But, you know, the other side of it is you also benefit from that, right? Because you get to hire people with all these skills and experiences, right? And you're in this year and this ecosystem that adapts, right? And channels, talents and skill and knowledge and people into the new fields. And so, you know, so there's kind of the push and pull of that at the level of just being an individual, individual CEO. At the level of just being in the ecosystem to your point, like, yeah, it's an absolutely magical phenomenon. And by the way, like, you know, one of the, you know, for all the, for all the issues in Silicon Valley, you know, I think AI, I did the comment once I think AI is the ninth major technology platform in history of Silicon Valley, right? The, you know, Silicon Valley is Silicon Valley still called Silicon Valley. We haven't made Silicon here in decades, right? We used to actually, you know, this called Silicon Valley because they used to make chips, right? They just have the, like, the actual fabs were in Silicon Valley and then they, you know, they designed them and they made the chips. And so, and that was, you know, wave one starting in the 19th, you know, actually, that was like, actually, no, actually more like three or whatever. But like, it was, you know, that was when the, the, the area was named like in the 1950s, but now we're on like wave nine, right? And, and the company town phenomenon where the company is the industry, like the, the, the, you get the indeterminate optimism. The, nobody had, nobody had to sit and plan and say, okay, in the 1990s, Silicon Valley is going to do the internet in the 2000s. They're going to this smartphone and the 2010s are going to do the cloud and the 2020s are going to AI. It's just the, the, the, right, the indeterminate optimism, optimism of the ecosystem, the flexibility of the ecosystem met that the, the Silicon Valley could, could morph into all these categories. And again, maybe a testimony to indeterminate optimism. This reminds me of the meme of how we're all just rappers over sand. Everything we're building is just rapper, rapper, rapper, rapper. The rapper thing is the circle. Yeah, I'm a, I'm a software company. I'm a chip rapper, right? Yeah, I'm a, I'm a business application. I'm a database rapper. Yeah, exactly. I'm a Sandra. I mean, yeah, you and I are, we're all now Sandra rappers. Sandra. Perfect. Okay. One more question along the media diet. I asked your partner, Ben Harwitz, what to talk about the Z and A16, Z, if people don't know him. And he said that you're really into movies these days. Yeah. And so I don't know any movies, any movies you really into these days, any movies you've absolutely loved recently. Yeah. So the movie that blew my socks off last year, which I think is the best movie of the decade for sure. Maybe of the last like 15 years. Is this movie? Unfortunately, one of these things, not a lot of people have seen it, but I would highly encourage it. It's called Eddington. I heard of it. Have you not heard of it? Okay. So Ed, you're going to really enjoy it. So I will, I will spoil too much of it. So at, at, at, at the service level, the following spoil is nothing at the service level. It's set in a small town in New Mexico called Eddington, which is a small town of about 600 people. And there's a sheriff who's played by Joaquin Phoenix, who's like an old crusty, basically right-winger. And then there's a, there's a mayor played by Pedro Pascal, who's basically a young hip progressive. And, and then the movie starts, I think in March of 2020. And so it starts when COVID first hits. And then it's sort of as it plays out over the next few months, if then it intersects. And it sort of extends into the summer of 2020. So, you know, kind of the George Floyd moment and then the, you know, the protests and riots and kind of everything. So it's sort of the convergence of COVID and then the, and then the, and then the, and then the, all the, all the BLM stuff. And, and, and, and then, and then, and then there's a third kind of element to it, which is, there's a company, which is basically a loosely disguised version of Meta, if you read the backstory of it, which is building an AI data center on the auscripts of town. So they kind of pull that in as sort of a thing that looms larger and larger over time. And then the thing that really is great at is it really shows, you know, this is a small town in New Mexico. And so everybody in the town gets kind of fully wrapped up in all the COVID stuff and they get fully wrapped up in all the BLM stuff. And they get fully wrapped up in all the like, you know, tech anxiety stuff. But they're all experiencing it basically through the internet, right, which, which is, which is, you know, what, what actually happened, right. And so, so it is, so, so the reason I love the movie so much is one is it's the first movie that directly grapples with 2020 of what happened in 2020. And it's just like fully, fully engages and grapples with like all the dynamics that we're playing out the country. But the other reason is it's the first movie that has a really good job of showing what it, what it was like, especially of that era to live in a world in which there were things happen in the real world. And people were kind of experiencing events online, you know, like in a way that was like very central in their lives, right. And so it does like a really good job of pulling in like smartphones and social media in a way that in a way that movies really, really is shared with them. And the whole thing comes together in an incredibly entertaining way. And so I wouldn't even say I, I wouldn't even say I completely agree with the movie or whatever. And I think the director movie and I would probably disagree about a lot, but you really tries hard to like really grapple with like what is actually like to live like a human being in the 2020s in America. In a way that I think many other filmmakers who are very talented have just been very scared of touching. And this guy for some reason, he's just like, yeah, I'm just going to find all the third rails and I'm just going to like fucking gravel. That's great. That's great. That's great. Everybody should see it. Oh, man. Okay. Final question. I want to ask you that your product diet or any products you use that maybe are less known that you love that you want to recommend. You can, you know, mention products, your investors, and if you use them constantly. I mean, we have so many that it's really hard to, you know, I always feel it's like, you know, who's your favorite children. So it's really hard to, to, to, you know, to, to pull out specific ones, but I'll, you know, I'll talk about a few. I mean, I just, I'll just observation. So one is my 10 year old, my 10 year old, my 10 year old right now is 100% obsessed with replete. And by the way, it was not from me. Do you have kids? I do have one, two and a half year old. Two and a half. Okay. So you haven't run into what I'm running into now, which is whatever it is you do is not cool. Right? Like two and a half, whatever daddy does is like the coolest thing in the fucking world. I can tell you by the time he's 10, whatever you do is like deeply uncool, right? And I'm highly aware of that. And so like if I mentioned, oh, yeah, we work on XYZ, you know, he's like, okay. But when he discovers something, then it's cool. Or when his friends tell him about it, it's cool. And so he, he threw no interference on my part. I discovered a template about about three months ago and discovered by coding and it's like completely obsessed with my coding games and all kinds of all kinds of things. I'm like, what are we going to do it for hours? And so I'm seeing that phenomenon play out, which is super fun. That's one, two is I am just completely in love with all the AI voice stuff. I think it's just absolutely amazing hysterical. My favorite party trick at dinner parties now is to pull out rock with bad Rudy. Which is if you're seeing us, it's the, it's a foul mouse raccoon. Avatar on the in the Elon's rock app. So I think that's super fun. We had this company sesame that had, you know, they, they went viral last year for this. You know, these, these, these, just incredibly like, you know, intimate emotional, you know, kind of voice experiences. So I think the voice stuff is fantastic. I'm also super fascinated by all the voice input stuff. And so, you know, I'm, you know, one of the most sweet, you know, one of the most recently company recently sold. But, you know, that all the, I think like dependence, the wearables, like all that stuff is going to be big, the meta glasses. I think there's going to be a whole wearables revolution here. I love the voice input stuff. I have this app on my, there's this app on my phone now called whisper flow, which is voice transcription, which works like staggeringly well. It's like a voice transcription function, but you can actually talk to the AM model while you're doing voice transcription. So you can kind of, it kind of understands when you're telling it, no, no, you know, I want bullet points over there. And I want this and that. And it understands that you're not telling it to type in the words, I want bullet points. It just actually understands that you want bullet points. And so like that's a great example of a super useful thing. And so I think the voice mode stuff is going to be, is going to be, is going to be really great. Subscribers of my newsletter get a year free of Repplet and whisper flow. So there we go. What's the, what's the most memorable thing your son built with Repplet? Oh, so he's gotten super into Star Trek. And so so far it's been, he's writing like writing like Star Trek simulators. So like all the, you know, all the, my next generation, they actually have a generation. Okay, I was going to ask which, well, you like, we actually like them all. We watched the new Starfleet Academy last night, which actually is quite good, but we watched the original, you know, we watched, we watched them all. But it was in next generation where they actually developed an actual design language for the computers. If you watch the original series, they just had like basically, you know, now it's with lights. And they didn't really, you know, they just like were like, you know, fuck around on set and trying to pretend they were doing it. But by next generation, they actually had designed, they actually had a UI design language. And so one of the, one of the fun things you can divide coding is you can say, give me a Star Trek next generation. You know, user interface, but you know, whatever this that, whatever it actually uses the, they called it, the seven of nerd out, they call it L cars. Design language, and it'll, you know, it'll actually build you like Star Trek next generation, bridge, castles using that design language. But you know, with your choice of like Star Trek game, for example. And so he's, he's gone crazy for that kind of thing. And that sounds extremely delightful. You guys should open source to release that. Mark, like I said, I could talk to you for hours. Well, you got things to do. Anything you want to leave listeners with before we wrap up anything you want to double down on or just leave listeners with. Yeah, so a couple things. So one is we got super lucky last week. Packy, McCormick, right, the best piece I've ever written about us. Actually, which he released. And so it's the best explanation of what we do. And how we think. And so I definitely recommend that. And then you know, we're putting a lot. We have a great team of folks now. We're putting a lot of effort ourselves in a video. And you know, in content. And so I definitely recommend our YouTube channel, which I think has a lot of great stuff. And it's going to be very exciting in the next year. Awesome. I'll link to that. I think it's just YouTube.com/A16Z something like that. And you guys have great stuff. Mark, thank you so much for being here. Awesome. Thank you for having me. I really, I really appreciate it. Bye, everyone. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or a review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our substack at A16Z.Substack.com. Thanks again for listening, and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies, and individuals are not endorsed by AH Capital Management LLC, A16Z, or any of its affiliates. Information is from sources deemed reliable on the data publication, but A16Z does not guarantee its accuracy. (upbeat music)