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 the 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 career planning is, he said, don't be fungible. He's an economist. And so that was economics speaking. What that means essentially is 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 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. I think we're used to living in a world where we just don't understand how good good can get because 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.

0:00

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 and 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 moments of magnitude. 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 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 differently today. We're sharing a conversation between Lenny Ryczynski and Marc Andreessen from a recent episode of Lenny's podcast.

0:41

Marc Andreessen, thank you so much for being here and welcome to the podcast.

1:50

Awesome, Lenny. Thank you. It's great to be here.

1:54

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?

1:57

This is a very, very historic time. I think 2025 was maybe the most interesting year in my entire career and probably life. And I think I would expect 2026 to exceed that.

2:08

Wow, that says a lot.

2:18

Yeah, I've seen some stuff. So it feels like two things are happening. One is the, the, the trust that a lot of people have had in kind of what you described as kind of legacy institutions around the world is I, I think in kind of full scale collapse right now. By the way, there's a lot of data, data to support that. And so I think there's just, there's, 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, 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, what I've described 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, is just dramatically expanded. And I think that's now on a 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 US is changing a lot, Europe is changing a lot, China's changing a lot of Latin America, by the way, is changing a lot. 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 in the, into the daylight and re examined. 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 increasing upheaval, but you have AI as 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 all feel like kind of historical moment shifts comparable in Magnitude to maybe the fall of the Berlin Wall in 1989, maybe the end of World War II kind of moments like that. It certainly feels like that.

2:19

Good God, what a time to be alive.

4:07

Yeah.

4:10

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 AI is going to have on, say, the world or just people listening?

4:11

The. I think, I think at this point, I think it's pretty clear with our technology hats on, that, like, this stuff is really working now, right? And so there was this kind of, you know, when There was a ChatGPT moment, you know, three years ago. It was only, by the way, only three years ago, right, was the ChatGPT moment. And the big question was, all right, this is like incredibly fun and creative and like we have machines now that can compose Shakespearean sonnets and rap lyrics and like, you know, this is amazing. But then there was there, you know, there's this big question like, can you, can you harness this technology for reasoning and for, you know, problem solving in 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, I think you can actually, you know, AI is now 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 world's best programmers, right, including like Linus Torvalds, 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, incredibly 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 like the technology feels like it's moving fast and it's going to be working really well. I think the thing that is not well understood, I think a lot of people have a, I think, you know, a lot of people in the industry have kind of what I would describe as this one dimensional thing, which is okay, As a result of the technology now working, AI just kind of sweeps the world and changes everything. And I think that's kind of the wrong framer. I think it's based on an incomplete understanding of the world that we live in or the world that we've been living in for the last 80 years. And I recall it 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's felt like we've been in a time of great technological change. But actually, if you look for actually evidence of that, like in statistical evidence of that, analytical evidence of that, like, you basically can't find it. And in particular, economists have a way of measuring the rate of technological change in the economy, that is productivity growth, which we could talk about what 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. What's 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. In my lifetime, in our lifetimes, it's been running at 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 in the economy, technology impact in the economy has actually slowed way down. And so we, you know, the AI 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 and increasingly global phenomenon, which is, you know, the rate of reproduction of the human species is in rapid decline. And, you know, there are many countries, you know, including the U.S. 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, are 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, a world in which those two things are true. And I think 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 think the interplay of these factors is going to be much more interesting and frankly more complex than a lot of people have been thinking.

4:21

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?

8:33

The way I think about this, anyway, yeah, we have a 10 year old and so, you know, and we actually homeschool 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 actually, you know, lot of people just focus on kind of this, you know, this kind of very, I would say straightforward and overly simplistic view of just literally job gains, you know, job losses, which we could talk about. But there's two specific things at the level of like an individual person and 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, if they're pretty good at it today, they use, they use AI and all of a sudden they're very good at it. And so there's sort of that aspect to it. And I think the way the education system writ large is going to teach, is going to kind of teach. AI 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, like, spectacularly great, right? And so you kind of use the term, you 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 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 spectacularly 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 coders are experiencing this right now. My friends who are really good coders 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, 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 in a 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 be deep in a way that's going to let them fully use the power of AI to be not just great, but to be, like, spectacularly great. And I think that's going to be the real. That's the real opportunity. And at least that's what we're shooting for. And that's what I would encourage parents to shoot for.

8:50

So what I heard there is essentially agency. This word 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.

11:11

Yeah, yeah. So this thing with this term agency that's become very, very, you know, very popular, certainly California for the last couple years. It's really interesting because I had a lot of trouble with this early on because I'm like, agency. What are they talking about? And what they're kind of talking about is like, you know, initiative, you know, you know, willingness to, you know, you could just do things. You know, what is it? The SEMO version has the great term live player. You know, you can be 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 by default you're supposed to follow all these rules, right? And then everybody, if you like, break the rules, like, everybody gets freaked out. It's like, oh my God, he broke the rules. And so, like, we have somehow worked 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, the thing you want to train kids to do is like, follow all the rules. And you know, you could argue that kind of, you know, for example, that, you know, the school system, you K through 12 school system or whatever has gotten kind of more and more focused on it over time. And it's like, yeah, it's like, no, you, you should actually. And again, especially unit, Unit N equals 1 like of your kid. It's like, okay, and look, there's, there's something to be had. We. I just had this conversation with 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, kind of trying to keep, keep him with some level of structure in his life and not just, and not just pure agency, but yeah, I mean, and 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 premium in 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. And you know, maybe, yeah, that, that has been maybe a little bit diminished in our culture over the last 30 years. It, you know, it's, it's healthy, you know, that, you know, that there's now a term for that that is coming back, back into vogue. And then, 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. I can 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, I can fully participate in the world. I can really change things. And I, and I, that, that feel, the, the combination of that idea combined with this technology feels very healthy to me.

11:20

What is that quote about? Give me a lever and I'll move the world.

13:53

And I'll move the world. Yeah, that's exactly right. Well, so it's actually funny you mentioned that. So the, the, the, the, the early kind of scientists, including like Isaac Newton, were super obsessed with, with, you know, this concept of alchemy, right? It's like, you know, they, you know, they, you know, they developed like, you know, Newton, he's like, developed Newtonian physics and he developed like calculus and all these really obsessed with, was alchemy, which was the thing he could never get to work, right? And, and, and alchemy was the transmutation of lead into gold, which meant the transmutation of something that was very common, which was lead, into something that was very rare and valuable, which was gold. And you know, they, there was this, the, he spent you know, decades trying to figure out this thing called the philosopher's stone, which would be basically the, the machine or the process that would, that would be able to transmute the rare, you know, the common thing into the rare thing let, let into gold. And he never figured it out. It, you know, it's incredibly frustrating. Nobody ever figured that out. And now we literally with AI, have a technology that transfers sand into thought.

13:56

Just blew my mind, right?

14:49

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, it is the philosopher's stone. It is that. It actually is that. And it's just this incredibly powerful tool. And that's why I get so excited. I mean, and again, this is what we're doing with our 10 year old, which is like, all right, a primary thing that we want to make sure to, to do is to make sure that he knows fully how to leverage and, and get, and get benefit out of the Philosopher's stone, right? Which is, you know, which is to say AI and that, that, and then, you know, that's certainly central to 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, I just, I, I, there may be a handful of people who are like that. I, I don't, you know, I don't know. I, 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 it 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 certainly the mode that I would encourage parents to think about.

14:51

I did not know your kid was homeschooled. That is super interesting. It's almost a statement on education in today's day. Maybe. Is there any thoughts there for folks that maybe aren't in your tax bracket that want to help their kids be successful? Maybe homeschool, maybe not. What advice would you have?

15:44

This is the challenge. And again, this kind of goes to how 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 like 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 the, you know, the national K through 12 school system or something like that, you know, in order, in order to do that. But then there's this other question which is like at n equals 1 for an individual kid, like what can you do 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 1. By far the ideal way to do it is with one on 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 on one tutoring. And this is something that like every royal family knew in history. It's something that every aristocratic class knew in history. There's all these amazing examples. Alexander the Great was tutored by Aristotle. He took over the world, right? Like, you know, many of the great kings and queens, you know, royal 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 two sigma effect, which is there's one method of education that routinely raises student outcomes by two standards of deviation and will take a kid from the 50th percentile to the 99th percentile. And that's one on one tutoring, right? So again, if you go back to like at n equals 1, you have like 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 they're capable of doing and they can, they, you know, they, they can move incredibly fast and they get kind of correction in 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 on one tutoring for 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 and 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, I think there's this like massive opportunity for, for parents, you know, in, in many walks of life to be, you know, with, with, with a little bit of time and focus to be able to say, okay, you know, my, my kid's probably still going to go through a traditional education system, but I'm going to augment this with AI tutoring. And of course there, you know, and of course there's going to be tons of startups, right, and there already are that, that are going to try to build on all the products and services for this. Khan Academy, you know, on 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 really for parents interested in this, now would be a great time to really start to think hard about that and to look at the options.

16:00

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?

19:36

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, and again, like I said, it's like at 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 no 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 ticks up, even if AI triples productivity growth in the economy, which would like be a massively big deal, it 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 and 1930, people just thought the world was awash 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, and proliferated kind of during that period. And so even if I 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 is going to transfer translate to a much higher, higher rate of job growth. And you know, there'll 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 that then corresponding to that there will be, you know, there will be hiring blooms. You know, I think, 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 increasingly population shrinkage. And so human workers in many, many, many countries over the next, you know, 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, you know, politics particularly, but it does feel like the world broadly is going to reverse course on the rates of immigration that we've had for the last 50 years. It seems to be kind of a broad based, you know, kind of thing happening, you know, kind of with, you know, rise in nationalism, you know, concerns about the rate of immigration and immigration historically in countries like the U.S. you know, it's, it's kind of ebbed and flowed 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 US or any country in Europe, if you combine declining population with less immigration, you, the remaining human workers are going to be at a premium, not at a discount. And so 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 no jobs thing. I just think it's probably totally off base.

19:55

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? Does population decrease? All these kind of have to line up for there not to be this massive job loss with AI.

22:28

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, what we'd be staring at is the future of depopulation. And like, depopulation without new technology would just mean that the economy shrinks, right? So, 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 new source of consumer demand for spending on things. And so you would be very worried about going into a period of like, severe decline of stagnation. And, you know, essentially you'd be looking at these, like, very dystopian scenarios of like an economy kind of self euthanizing itself over time. And so you'd be very worried about, like, the opposite of what everybody, you know, 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 that can substitute for the lack of population growth and then, you know, also for the lack of immigration. That's likely. And so I would say the timing has worked out miraculously well in the sense that 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 that's just 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'd have to look at far higher rates of productivity growth. You'd have to look at rates of productivity growth that are 10, 20, 30, 50% a year. You know, something like that, which are, you know, orders of magnitude higher than we've ever had in any, in any economy in the 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 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 a much higher level of 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 sort of, whatever you want to call it, affected by or commoditized by AI, the prices of those goods and services will collapse. There'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 worth talking about because people, I think, get, get kind of sideways on, on this issue. So if AI is going to transform the economy as much as the, you know, whatever utopians or dystopians or 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, more, requiring 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 in output which, with much lower input costs. The result of that is you get gluts of goods and services in all those affected sectors. The result of those gluts 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, the development of new fields. Everybody's materially much better off very quickly. And then, by the way, to 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, because the prices of all the goods and services that a welfare program has to pay from, they're all collapsing, right? And so the price of healthcare collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses because this incredible impact that AI is having. And so in this kind of utopian dystopian scenario that people have, there's no scenario in which 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 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, to accommodate that. And maybe that'll happen. But the result of that is going to be a much better news 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 on 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 at, and to be clear, I think we're looking at a world that's not like radically Transformed the way that maybe the utopians think that it will be, or the dystopians think it will be. I think it'll be more incremental for reasons we can discuss. But I think that incremental over, over overwhelmingly, 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 in the other way that I was described.

22:43

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. I'll give you 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 the actual number ended up being 6 billion. You also, you had this debate with Peter Thiel that I came across where you were debating whether technology has stopped progressing or if new technology will continue to emerge. And you were arguing there is progress, progress will continue. And he was like, no, I think we're done with cool technology. You were right. 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.

27:33

So I should start by saying I've been wrong about tons of things. But, you know, I bury those out back behind the shed, delete them from the Internet.

28:33

No web browser can discover them.

28:40

Yes. I have them nuked out of the Internet archives so that they're never seen again. So, you know, I'm wrong plenty of times also. But yeah, I mean, look, I think, yeah, some of those are right. By the way, I will say on the Peter one, I have come, I've come much more around to Peter's point of view. I would probably 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 the kind of conversation we just had, which is 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 that over a little bit because I was so focused on making sure people understood. No, 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 last. And he, you know, he's talked 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 if you, and again, if you contrast that, you know, if you, if you Compare and contrast 1870 and 1930, it was a dramatically different world. If you contrast 1930 to 1970, it was a dramatically different world. If you contrast 1978, 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 our new cities? Where are new dams? Where, you know, where's, where's the California high speed rail? Like, you know, 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 that Peter articulates, which is there's just, there's so much about how the world works that'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, oligopolies. There's all these structures in the world that are kind of economic or political or regulatory structures to basically prevent things from changing. And so, I mean, let's take a great example, like AI's impact on the healthcare system. Like, by rights, AI is going to have a dramatic impact on the healthcare system and in very positive ways. But, you know, large parts of the medical system today are, they are cartels, right? And so there's like a, there's the doctors are a cartel, and like, nurses are a 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 of 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 doctor jobs? Well, in that case, we're gonna, we're gonna block it. So. And I think a lot of consumers, by the way, you know, I don't, I, I, I see this in my life. And you, you'll probably see this in your life also, which is, you know, like, ChatGPT is like, almost certainly a better doctor than your doctor today. But, like, ChatGPT 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'. Procedures. Right? And so there, there, there are these. Anyway, anyway, so Peter, Peter, I think, was very articulate and has been for a long time on, like. No, there are actually real structural impediments in the economy and in the political system that we have that actually prevent any, the rates of change that are anywhere near the rates of change that people had in the past. And, 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 assumption 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 after Mystic View.

28:42

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. That's my block in the morning today. Thank you for that.

32:44

Okay.

32:54

I love, I love the way you go from just like macro to just like N of 1. And I want to go to N of 1. A lot of the listeners of this podcast are product managers. They're engineers, they're 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.

32:55

This, I think is a really funny question. So these three roles in particular obviously are kind of the central roles for building for tech companies. So the way I've been describing it is the concept of the Mexican standoff, right? Which is the, the movie scene where the, you know, the two guys have guns pointing at each other's heads. And then there's, if you watch like John Woo movies, he loves to have, he does the three way Mexican standoff where you've got like a triangle. You know, people like, you know, John Woo movie is they've got, you know, guns in both hands. So they're all, each, each is aiming at the other two. Yeah. And you got this kind of standoff situation. And 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 of 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, 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 the, the three, all three of them are going to realize that AI can also be a better manager, right? So they're going to end up aiming the guns up the org chart. But that's probably, that's the next phase. And what I think is so fascinating about this Mexican stat is they're actually all kind of correct, I think. Right. Which is AI is actually a pretty good, you know, 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.

33:28

Right.

34:54

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 super empowered, this kind of idea of the super empowered individual where if, 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, you know, that, that 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, you know, orchestrates, you know, a dozen instances of, you know, coding bots. You know, you know, there's, there's a change in the actual job of coding itself, which is, which is happening right now. But the other part of it is, okay, how do I become that super powered individual? How, how do I become a coder that also then harnesses AI so that I can also be a great product manage. I can also be a great designer, right? And then 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, 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 get is maybe the, maybe the, those individual roles change. Like, maybe those are not any more sort of stovepipe roles of the way that, you know, they have been for the last 30 years or whatever. But what happens is 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, you know, which is, which is the most valuable thing. And so I think, I think that's, I think, I think that's the opportunity.

34:54

So I love this answer. So what I'm hearing is essentially if you are amazing at any of these three roles, you will do well.

36:19

Number one, if you're amazing at these roles, that's great. But also you, part of being amazing these roles is also being, being able to fully harness the new technology, right? So if you're, if you're a master coder today and you, you don't ever get to the point where you, you figure out how to use AI to leverage your coding skills, you know, and, and, and do more Right. Like at some point you are going to hit an issue, right? Here's another way economists talk about this, which is there's the 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 in the workplace is the task. And so, and, and then what, what the way the economists think about it is a job is a bundle of tasks. And everybody wants to talk about job loss, but really what you want to look at is task loss, right? Tasks changing. I mean the classic example of task changing, classic example of task changing was once upon a time, executives never used typewriters 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 on your desk typing things. You had a secretary who you dictated memos to. Right? And then there, and then there was this change where like emails 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, the, the, the job of the secretary changed from sending out letters with stamps on them to like sending or receiving emails with the other admins. And then, and then the secretary would print out the email and bring it into the executive's office. The executive office would read the email and paper, scroll, scroll the reply and, and, and, and dip and give that message back to the secretary who would go back and type it into the computer on, on, on, 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, you know, the great admins do. And then, and then the test, the test set, ironically of the executive has expanded to do actually more of the clerical work themselves actually like sit there and like type their own memos, which again, 50 years ago they never would have done that. And 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 of what's going to happen in coding. The tasks are going to change. It's what's going to have product management tasks are going to change, designer tasks are going to change. And so 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 level 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, I can really adapt, use the new technology, you know, get really good at AI 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's the track that the best people are going to be on. And I think that's the thing to lean hard into.

36:26

I think people aren't fully grasping just specifically software engineering and how much that is changing. 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 there's going to be this artisanal experience of sitting there writing code, which is so crazy how much that job is going to change.

39:35

Yeah. So again, here I go back and again, pardon, maybe the history lesson, but like, I go back, like, coding. So the first, you know. Do you know the original definition of the, of the term calculator? Do you know what that referred to?

39:57

No, I referred to people.

40:10

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 that an insurance company would calculate actuarial tables or the military would, like, calculate, you know, I don't know, whatever troop logistics 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, 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 know, somebody at the head of the room who was, like, responsible for like, whatever the mathematical equation was, and then they would parcel out the individual mathematical calculations to people sitting at desks who were doing them all by hand. Right? And, and those, that, that, that job title was those people were calculators. Right? Um, and so we've gone from a world in which you literally have people doing mathematical equations by hands, by hand. Then we got the first computers. The first computers, of course, didn't have programming languages, right? They, they only had machine code, right? So the first computers were 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, you know, today who, you know, whose job as a programmer was to like build 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 added to it. And then the best programmers did assembly language. And then, you know, when I was coming up, it was higher level languages like C that compiled into machine code. And that's what programmers did. And then I still remember when, when scripting, you know, when scripting languages, you know, we developed JavaScript at Netscape and then, you know, Python took off and Perl and these other scripting languages, those scripting languages, you know, took off in the, in the, in the, in the, in the 2000s, there was this big fight, you know, in the technical community, which is, is scripting real programming or not? Right? Because it's, it's like, it's kind of cheating, right? Because real programmers write code that compiles to machine code. And like, real programmers like, do like memory management themselves and they do all, you know, this, this, this whole craft of writing, writing, you know, writing, writing C code and you know, these, these JavaScript or Python programmers are just doing this kind of lightweight thing and does it 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 have. You see my point, the scripting languages have abstracted away like five layers of detail underneath that, that people used to do by hand, and they don't anymore. And then, and then there's, and then to your point, like AI coding is the next layer on that AI coding actually abstracts away the process of 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, okay, this is the next layer of the task Redefinition under the job of programmer, right? Now, what's the job of the programmer? It's, to 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 programmers today, 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, right? And literally they sit there and they shift from browser, you know, browser to browser or terminal to terminal. And they're, and they're, they're what? Their, their, their, their day job, their day job now is kind of arguing with the AI bots to try to get them to like write the right code, right? And then, and then debug it and, and fix the problems and change, change, change this back and, and do all these things. And so now, 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 asked about the 10, you know, our 10 year old is, you know, super into computers and super into programming and what I'm, what I'm telling, you know, and he's, he's using Claude and ChatGPT, Copilot and all these things. What I'm telling him is like, look, and by the way, he loves vibe coding. He's on replit all the time doing vibe coding, you know, doing ga, 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, you know, spends two hours at dinner arguing with an AI for fun. Right, right. But, 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 or whatever. Like you need to be able to understand the results of what the AI is giving you, right? In the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works. And so again, it's kind of this up leveling of capability where you actually want the depth to be able to go down and be able to understand what this 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 100 times or a thousand times more productive than they used to be, right? And that is overwhelmingly a good thing. The tasks are definitely changing, the nature of the job is changing. But are human beings 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%, like, no question.

40:13

So you're in the camp of still learning to code, still a valuable skill?

44:40

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 on autopilot and like, I can't be bothered and I'm just going to have AI write 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. If the goal is to be a mediocre coder, then just let the AI do it. It's fine. The AI is going to be perfectly good at generating infinite amounts of mediocre 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 really matter, then yeah, you 100% want, you still, you want to go all the way down, you want your skill set to go all the way down to the assembly, to assembly and machine code. You want to understand every layer of the stack. You want to deeply understand what's happening at the level of the chip, right? And the network and so forth. By the way, you also really deeply want to understand how the AI itself works, right? Because you want to, right? Because if people understand how the AI works, are able to, they're clearly able to get more value out of it than somebody who doesn't understand how it works, right? 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 go. 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 can 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.

44:43

I was going to say I was a big Perl programmer. I was an engineer for 10 years, and that was my language of choice.

46:26

Do you remember, I don't know when you were doing it, but do you remember that, at least early on, did you ever hit this where like C coders were like, looking down their nose at you, being like, for sure, for sure.

46:32

It's like, this is so slow. It's not going to scale. What are you spending all your time on this?

46:41

Yeah, exactly. And of course, you know, and again, it was sort of this thing where, you know, they were. They were sort of correct, which is at the beginning, it wasn't, you know, fast enough or whatever. By the end, they were definitely wrong. Right. Which is. It got much better, much faster and it, you know, it swept the world. You know, most coding today happens in 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 actually work really well. Right. And so that, that was, that was a great example of this kind of adaptation. And then again, the result of that was, you know, a far higher number of people writing code or scripting languages than were ever writing code with lower level languages. And I think this will just kind of be a more dramatic version of that.

46:45

I love that. Perl was 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.

47:22

Well, that's funny, because of course, it was so notorious for being impossible to understand. So how ironic. Yes.

47:28

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 tells me design is going to be much more valuable in the future.

47:35

Yeah, that's right. And again here, this, this is a great example. So the, again the task level, the, the, the, the, the task level of like design the perfect icon, right, is going to be like, all right, the AI is going to do that all day long. It's the end escape, 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 a, is going to get really good at that. But like what are we trying to do? Like the, the, 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 a world of human beings? And like, you know, what, what's going to, is this going to make people happy when they use it? It's going to make people feel 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 that the great designers have always thought about. Like the job of designer, right, will involve much more of those higher level, more important components and then again with AI doing a lot more of the underlying tasks. And so, you know, one way to think about it is, you know, I don't know, you think it's like the world's best designers, you know, Johnny Ive or whatever, you could be like, wow, like if I'm a designer today, if I'm a 25 year old designer and I aspire to be, you know, Jony, I've, in a decade, it's, 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, you know, because Johnny, I 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 never get to. And I think that that's gonna be another great example of that.

47:50

So maybe what I'm hearing here is kind of this T shape strategy of be, 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.

49:27

Well, so I think that's great. I think that's really, really relevant. And then, you know, Scott, you know, Scott Adams unfortunately just passed away, you know, which. Which is a real tragedy. But I was always. I've always. I referred for years to actually Scott's Scott Adams. He had this famous. He had his famous kind of career advice he would give people, which I think makes a lot of sense, which dovetails with what you're saying, which is. He used to say. He used to say. It's like, look, he said. He said, I could have been a pretty good cartoonist or I could have been pretty good at business. But the fact that I was a cartoonist who understood business made me spectacularly great at making Dilbert. Because even the world's best cartoonist who didn't understand business could have never written Dilbert. And then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert. It took somebody who actually had both of those skills to be able to make Dilbert. Right? Which is one of the most successful cartoons in history. Right? And so the way Scott always described it was 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 look, you see this all. I mean, you see this all over. You see this all over the economy. I mean, you see this all over the economy. But I was giving you an example. Hollywood, just Hollywood as an example. 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 in the entertainment industry are the people who can write and direct. They don't have a term for those. They call those auteurs. And those are the people who are the real creative forces that move the field. And so. And so again. And by the way, Hollywood actually, it's really funny. It's been spending a lot of time talking to Hollywood people about AI Hollywood has the same Mexican standoff going right now that we. That we described in Attack. Except in Hollywood, for example, for filmmaking, it's the director, it's the writer and the actor, right? Because the director is now thinking, wow, I don't need the writer anymore because the AI can write the script and 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 the AI can do the actors. And the actor is saying, I don't need either one of these guys. I can have AI direct the thing, I can have the AI write the thing, and I'm just going to show up and do my performance, right? And so, so it's the same, it's the same kind of triangular configuration. And again, what's great about it is they're all correct, right? Each person in 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 do all three. And I think to your point, like your T shape thing, 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 breadth, 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, again, you're the super empowered individual. You're going to be able to just perform like feats of magic, for example, in terms of designing and building your products, you know, the people in my generation couldn't even dreamed of. And so I think that this is a universal kind of theory that I think can apply across the entire economy.

49:41

I'm going to invent a new framework right now. Okay, forget the T framework. I'm picturing an F sideways or an E where there's three, two or three, I don't know, downward parts. And so what I'm hearing is get good at least two.

53:03

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 career planning is he said, don't be fungible. Right. And you know, that's. He's an economist. And so that was economics speaking. 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, just a product manager, just decoder, like, then in theory, you can be swapped in or out. But if you have this. If you have this. Yeah. If you have this E or F, you know, laying on its side kind of thing, and if you have. If you have this combination of things that's actually quite rare, then all of a sudden you're not fungible. Not only you're not fungible, 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. That your ability now to become one of those people is, like, just titanically enhanced with AI as compared to anything we've ever seen before.

53:17

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 code and design. Oh, my God. And what I'm hearing here is this is what you need to become. You need to become really good at at least two things there. I think you used the term smokestack or something where it's like PM over here, engineer, design. And what I'm hearing here is you need to get good at at least two of these skills. The silos of these two roles are disappearing.

54:11

That's right. That's right. And again, I can't. 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. It's one of these things where it's like so much focus on figuring out how to use Like a large language model. It's like, okay, what am I going to try to get it to do for me? Right? Which is of course very important. But the other side of it is, what can I get it to teach me how to do, right? And it's just as good at that, right? And so again, this is this level, this level of latent 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 me up, like, super empower me. Tell me how to, you know, train me, train me how to be, you know, I'm a coder. Train me how to be a product manager. It will happily do that. It knows exactly how to do that. You know, run me driven, you know, make me problems, you know, make me assignments, then evaluate my results, right? And it will do, it will do that just as happily as it will do work, quote, unquote.

54:34

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, because 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?

55:40

Yeah, that's right, that's right. Yeah, look on that first one. And this again is what I'm doing with my 10 year old. Yeah, look, if, if, if you ask an AI, this is a really good point. So if you ask an AI, I don't write me this code, and then, and then it doesn't, it comes back and it doesn't work, right? Like if, if all, you know is like, single function, I asked it and it gave me back something that's not good. Like, well, what do you, like, what do you even do with that, right? Like, you, you don't understand why it gave you that result. Do you really understand it? Even what to do? You even understand what to tell it to try 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 leg of the. Of your E or your 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 be able to say, no, no, that's not what I meant to do this other thing, right? And so, and again, this is. This, 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 human beings, right? Like, you know, if you and I are colleagues and I, you know, would ask you to do something, you come back with something completely different. Like, I. I do need to understand what was happening in your head, right? In order to, in order to be able to get. You need to give you feet, 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 need to have theory of mind, right? I need to understand what you were thinking in order to really give you the right feedback. 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, and you can do this. By the way, this is a very fun thing where you can have, have one AI critique the other AI, right? Which is another thing which is like, you have one AI write the code, you have another AI debunk the code. And so you can actually use. You can play the AIs off against each other and get them to 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.

56:10

I think people call those LLM counsels.

57:57

Yes.

57:59

They're talking to each other.

58:00

Yeah, that's right. That's right.

58:01

I do feel like if I were, like, I have no design background. I've always wanted to design. 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.

58:03

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.

58:21

Your time has come, Arc.

58:32

Yes.

58:33

I want to pivot to founders, maybe your bread and butter. You 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 a company looks, how the future of AI forward companies look.

58:34

Yeah. So this is a great, very topical topic that's all playing out in real time right now on the leading edge. So I think there's like three layers of it and 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 time honored kind of thing that happens with technology transitions. And this is kind of what a lot of venture capital is based on, which is, okay, there's a new technology that comes out, and maybe it's the personal computer or the iPhone or the Internet, or now it's AI 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. The new technology kind of gets kind of added into the mix. You know, it's kind of another ingredient into. 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 redefine the software industry because people just went from using, you know, hard disk, using Flash storage or something. But when the Internet came out, like basically old school on prem software, for the most part, you know, not entirely, but like, a lot of it died and just got replaced by, like, web software, right? And so sometimes you get the kind of. It's additive to an existing thing. Sometimes you get the. Actually it redefines an entire product category, redefines an industry. The actual, you know, in many cases, the companies themselves turn over. And so, so, so, you know, so there's sort of this question and 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 a whatever 40 year franchise in image editing. Okay, is AI a 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 gener a new image than it is to try to edit, edit an old one. And so I think, you know, there's many areas of, of tech in which that question is being asked and you know, the answer is, I think will vary by domain. But you know, obviously as, as a venture firm, we're, 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, 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 a hundred coders, you know, how do I get those coders to be super empowered AI coders? Not, you know, not the kind of coders I used to have. And if they're super empowered AI coders, then does that mean, you know, do I still need the 100, maybe now I only need 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 shoe to drop hasn't quite dropped yet. But it's, 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 superpowered 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, there's 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 you know, we've had a few of those over the years. Bitcoin is probably the most spectacular example, you know, with Ethereum 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, some something hits and you just have a, you know, very small number of people associated with it, you know. But that said, you know, most, most software companies obviously end up with, you know, huge numbers of employees. And so I, I think, you know, the most leading edge founders are thinking of like, okay, how, how do I reconstitute the actual very definition or idea of a, of having a company? And, and you know, can you have a company that's, that's literally basically just all AI and so, and if you're doing so, you know, if you're doing anything in the real world, that's hard but if you're doing software like that, that, that seems like it might be feasible in some cases. And then you know, there's like the ultimate example of 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 in the blockchain or something, you know, that are basically out there like functioning as a, as a business and like making money and just you know, literally where the AI does all the work itself and just get, you know, issues me dividends and so maybe, you know, maybe that, you know, maybe that maybe that's the final outlier result. We have, we have a few founders who are chasing that kind of thing. So I would describe that as, I would describe that as kind of the ladder that the best founders are on.

58:53

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, and so I guess depends on, do you have contractors? Does that count? You know, like, what does it, what does it mean to be a one person? But I'm just like, I can't see that happening.

1:03:33

Yeah, I mean look, Bitcoin Satoshi pulled it off.

1:04:09

But like, you know, the open source community, you know, like, does that count? I don't know. I guess, I guess it counts.

1:04:12

Okay. Yeah, exactly. Right. So. Yeah, yeah. And I would say, I would say I don't propose to have answers here, but more just like the smartest people I know are. Many of the, many of the smartest people I know are thinking hard about this.

1:04:17

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's thesis on Moats? In AI? Does, is that even a thing? Do you care?

1:04:31

My experience with like really big technological transformations and of course I kind of live 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 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. Not that kind. It's obvious that this incumbent's going to be able to adapt and this other one isn't. It's obvious that there's economic opportunity in this kind of startup and not in these others. It's obvious that the Moats are going to be in this area of the technology, but not in this other area. And, and there, and you know what everybody does is they kind of state those things with like just an enormous amount of self assurance where they, they, you know, where they really sound like they have all the answers. And then you know what happens is this, these, these ideas kind of saturate the media, right? Because the, the, the media naturally prices like definitive answers over open questions. Because you know, you, you want, you know, like 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. X. 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, you can do this, by the way, if you pull up, like, coverage of the Internet, you know, from like, 1993 through, like, 1997, or even through, like, for 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 were wrong. Generally, like, quite badly wrong. And so I just. I think the process, I think with massive. With if there's going to be a massive amount of 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. We've talked about a lot of this, but, like, the 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 at the national level? How does this play out at the global level? You know, how does this inter, 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 are just a tremendous number of unknowns. Like, a very, very large number of unknown. And I think it's just, like, really, really dangerous to prejudge these things. And so I'll just give. I'll just give you. And it's just. I'll just run this as a thought experiment. You know, you can see what you think on this. But it's like, you know, like, do. Do AI models. Are AI models themselves, like, defensible? Like, is there a moat on AI models? And on the. 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 need, you know, you need this, like, incredible critical mass of, like, compute and data, and there's only a certain number of engineers in the world that know how to do this, are getting paid, like, NBA 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, 30 or 90, 10, 1 or whatever. It is market share and then they're going to have whatever profitability they have and it's going to be a kind of a classic oligopoly and, or maybe, you know, maybe one company's going to win definitively and it'll be, it'll be a monopoly and that. And by the way, those outcomes have happened in software 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 of chatgpt 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 blackmagic all of a sudden, you know, has, has become like commoditized really fast, you know, which, which by the way is exactly what happened, right? Like, you know, within, within a year of GPT3 coming out, there were their open source GPT3 is running on a fraction of the hardware, right, that were available for free. And then there were, and then, you know, there were five. You know, now, 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 other companies that are, and then Deepseek and, you know, Kimmy and all these other Chinese companies. And so even at the level of LLMs or AI models, you can squint and make that argument either way. By the way, same thing. At the level of apps, it's like one school of thought is apps are not a thing because the model's just going to do everything. But another way of looking at it is no, actually adapting the model is the engine into a domain involving human beings where you need to 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 level is actually going to matter enormously and maybe the LLMs commoditize and maybe the value goes to the apps. And again, you can kind of squint either way on that one. And I know very smart people who are on both sides of that argument and so 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, is another input. Actual individual choices made by entrepreneurs matter a lot. The economics matter a lot. Availability of investor capital varies over time. That matters a lot. And 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 if. You see, this is very exciting because it means we're doing this now. We should kind of make bets along every one of these strategies and kind of see and see how this plays out. And I would just say, like, there may be like, one. I don't know, there may be like one particularly brilliant, I don't know, Edge fund manager or something who has this all figured out. But I guess I would say if they exist, I haven't met them yet.

1:04:41

So what I'm hearing here is don't over obsess with moats at this point, because we have no idea what it'll end up being. As much as it may feel like, okay, there's no way OpenAI will lose this lead. Clearly we're seeing a lot of competition. GPT wrapper point is really great. A lot. It was 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, fastest growing companies in the world.

1:10:36

Yeah, well, it's, it's like a little bit like, I don't know, I mean, even just like with, you know, you know, the, you know, this has been the, you know, the, the holiday, if, you know, three years ago was the holiday of chatgpt. This last, you know, month or whatever has been the holiday of Claude, particularly Claude code. Right. For. For coding. But it's like, you know, it's pretty amazing because it's like, okay, there was Claude, which was, you know, obviously a great accomplishment, but then there's Claude code, which. Which is an app. Which is an app. Right. It's a cloud wrapper. Right. It's a, you know, agent harness. And then, and then they did this amazing thing where they came out with coworker, Cowork, cowork. And, and remember what they said? A cowork, which is a cloud code, wrote Cowork in a week?

1:10:57

Yeah. A week and a half.

1:11:32

Yep.

1:11:33

A hundred percent.

1:11:34

Well, and that's. And there's two ways of looking at that, which is like, wow, that's really impressive. I mean, obviously, that's really impressive. That cloud code was able to build Cowork in a week, In a week and a half. That's great. That's amazing. The other way to look at it is Cowork was developed in a week and a half. Like, like, how. How much complexity could there be? How much of a barrier to entry can there be in something that was developed in a week and a half? And so, and, and. And then, you know, and then again, it's this. It's this. It's this push, this pull thing where it's like, it's like, wow, it's incredibly val. It's incredibly functional, incredibly valuable. And people are like, all over the world every day now are 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 took a 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 is sitting there being like, okay, obviously we need to build, you know, an Asian artist, and then obviously we need to build a cowork, you know, thing for regular people. And obviously, you know, I don't. I'm not even saying 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, is squad code going to get lapped the same way that, you know, GitHub Copilot got lapped? You know, the, the history in the last three years has been everything that looks like it's like the fundamental breakthrough gets. Gets basically replicated in lab very quickly. Like many of the smartest people I know in the field, when I, When I really kind of talk to them, kind of, you know, get a couple drinks from them, they're like, yeah, they're basically, you know, one theory is, like, there really aren't any secrets among the big labs. Like, the, the big labs 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. 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 seq, you know, came out of left field and basically was like a, you know, reimplementation 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 defensibility is there? But on the other side of it, you've got, wow. These big labs are now paying, you know, individual engineers like they're rock stars and they're, you know, incredibly bright and creative people. And, you know, maybe there's, you know, a dozen nascent ideas in 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, my view is, my view myself, I need to put like a big discount on my forecasting ability on this one. Like, 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 say, I don't think I can predict that. I think a much, much better use of my time is being very flexible and adaptable at a time like this.

1:11:35

So with all this 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, the largest fund in history. Is that, is that the way you win in this world?

1:14:03

Yeah. So for, I mean, for us, yeah, for, for us, we have, we obviously have a very, very deliberate strategy. One way to think about this. Use the Peter Thiel, you remember the Peter Thiel formulation of, you said there's a two by two. There's optimism and pessimism and then there's determinate. And is it indeterminate? And indeterminate. Right. And so, and he always argued like there's, he always argued that, like, Silicon Valley is characterized by, in too much, what he calls indeterminate optimism. Right. And what he, what he, what he always described, what he meant by that is 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 what 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 would sort of, sort of maybe say, you know, VCs are indeterminate optimists. And then he would say, you know, Elon is the determinate, determinate, 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 do, you know, Mars, right? I'm going to these very 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 if you and I disagree on 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 16Z, which is, and the reason for that is 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 6GZ 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 creators, right? And each of those individual people is a determinant optimist. Like, each of them, each of them individually has like a very strong view of 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 and we don't just have 10 of those. We have 101,000 and then 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 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 smart 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.

1:14:19

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.

1:16:51

Yeah, yeah, no, that's right, that's right. I mean look, the founders need to be determinant optimists. Like they need to have a very specific plan now. And yeah, 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, you don't actually have to like, you know, you don't have to make the bad. You lay. You can like place multiple bats, you can operate 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 the one bet, you know, and there's, there's truth to that. You know, the counterargument on that is the founders get to run their companies. We don't. So you know, we, 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 mindedly execute against that goal. And you know, look, in the long run, who does history remember? History remembers Henry Ford, right? Not you know, whoever was the, you know, whatever the SEAT investor who's heated 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. I mean these are the people who actually use this thing and you know, deserve 99.9999% of the credit. But you know, having said that, I do think there is a role for having some indeterminate optimists in the, in the background helping along the way and helping keep the whole, the whole cycle going.

1:17:02

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?

1:18:14

Yeah, so I've always kind of had a little bit of an issue. I've always kind of struggled with the concept of AGI because it at least, well, let's put it this way. There's, let's define terms, which is where I kind of struggle with it, which is there's like the prosaic, there's the prosaic definition of AGI and then there's like the, I don't know, cosmic definition. And the way I would describe it as. Well, let's start with the cosmic one. So the cosmic one is basically the singularity, right? And so AGI is the, is the moment where you enter the singularity, which is to say that where the world fundamentally changes and like the rules of the old world are gone. We're now 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 AI is improving itself and it's sort of racing, you know, so called takeoff scenarios. You can see at this takeoff thing where the AI is improving itself and the machines are making decisions so much faster than people and people are just sitting there watching the machine do its thing, you know, and I kind of described why I don't really, I don't really think that's, I don't, I don't think we live in that world. Like whether you 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 could talk about that more. But the prosaic definition of AGI, that at least I think the industry participants have kind of converged on and tell me if you agree with this, is it's when the AI could do every economically relevant task as good as a person.

1:18:25

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.

1:19:43

Okay, got it. Yeah. So that's maybe even a slightly reduced definition. And by the way, 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 overstates that 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. Like 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 is 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. I give you example, human iq, human iq, you know, kind of what they call fluid intelligence or the, the sort of G factor of kind of, you know, fluid intelligence IQ I think tops out in humans as a species. It tops out around 160, right? Where at at like 160, it's like Einstein level, Einstein, Feynman in terms of IQ. In terms of IQ, like you just tops out at 160. The, 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 or one of the world's best doctors, you know, or whatever it would be, probably 140 is 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 line manager in a business, it's probably 110. You know, if you're looking for like an accountant, like a small business accountant who's good at doing the books for small businesses, it's probably 105, right? And so the, the kind of scope of like impressive human, you know, that the, the, the ability of the human organism to do 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 the 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 experiments to kind of do human equivalent, you know, kind of IQ, you know, for, for existing AI model. And by the way, existing AI 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 side starting to get to the 160 level now. But like I said, I think we're going to have AI 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 as great about that as I do about the fact that we occasionally get an Einstein, right? It's like, would the world be better off or worse off with more or fewer Einsteins? And the answer is, of course the world would be better off with more Einsteins. And of course the world would be better off with machines that have iq, you know, more iq like Einstein are greater than Einstein. But, like, I think IQ's IQ of the machines is going to exceed that in humans. I think that's really good. And then the performance, you know, again, it goes back to like the AI code coding thing that's happening. The performance against task is going to get better also. Like, I, I think, you know, this is where Linus TR 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, very interesting to see which we can talk about, which I think is 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 biolo. 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. And so you see what I'm saying, which is like, I think this idea of human equivalent is just going to be like a footnote. It's like, oh yeah, that was just on Tuesday in 2026 is when they hit that. And it kind of didn't matter because the next question was like, okay, what do we get to do in a world in which we actually have machines that are better than that? Right? And 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, that just happens to coincide with the human threshold 200 IQ.

1:19:53

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.

1:24:08

Well, I don't know if you have this experience. I have this experience all the time. Well, 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, I want to write this thing or I want to like, whatever, I want to have this theory on this thing or have a plan or whatever. And 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, after you have this, you get interested in something you read and then you're like, shit, I forgot almost everything that I just read. Like I, I, I wish I could retain it all, but I can't. It's just like I, you just have this. I, I sort of live in this kind of state of like an endless frustration. I was like, I, 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, 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 sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point. You know, 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'm 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, God 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 those limitations that the idea of having machines that work for us that don't have those limitations, I. I just. I think that's much more exciting than people are giving you credit for.

1:24:17

Oh, man. I could talk to you for. 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. I think you famously read constantly. I saw a interview with you where you're just like, AirPods changed my life. I'm just listening to audiobooks now all the time. So in terms of media debt, what do you. What are you reading? What are you paying attention to these days in terms, I don't know, podcast, newsletters, blogs, things like that. And then. Any books in particular?

1:25:56

Yeah, yeah. So what I read is basically. I mean, I read basically three categories of things. So, like, in terms of, like, general media, it's basically. I sort of. I always describe it as. I have, like, a 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, you know, where 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 if you go back and you read old newspapers, and by the way, you can do this. Just read last week's newspaper, right? I'm taping 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, they, by the way, they had no view of what was gonna happen this week. Then they couldn't know. And so they were making predictions and forecasts and so forth based on, like, not having 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. Like, go back and read old magazines and just like the level of the, you know, just the endless numbers of predictions that they make and kind of, you know, the problem with, you know, newspapers, at least they're going day to day. The thing with magazines is like, every. It's like a week or month, you know, kind of 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 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 the 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 substack phenomenon and the newsletter phenomenon and the podcast phenomenon is like, direct exposure to the people who are actually principals 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 like newspaper interviews or magazine interviews. And. And, you know, obviously now more and more, it's just. No, you actually want, like, smart people who are actually working on something, explaining themselves. And then you have, you know, you have new kinds of intermediation like podcasts that kind of open that up for people and make that possible. And so, yeah, like, domain practitioners are, you know, really great. I mean, just to state the obvious in AI, you know, it's obviously your. Your stuff, but also, like, you know, let, let's, you know, you know, the fact that like, Lex Friedman can have, you know, the world's leading or, you know, whoever the, you know, and any of you guys, you know, there's a small handful of you guys who have access to these people. You could have the world's, you know, kind of leading experts in the domain actually show up. And. And by the way, as, you know, 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 they want to explain it and they want people to understand it, and everybody kind of enjoys that, and they get to contribute to kind of human knowledge by doing that, and they get ego 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 awash in that today in a way that it wasn't as recently as 10 years ago. So I, Yeah, I do as much of that as I can, too.

1:26:21

And there's also just this culture in tech, Silicon Valley in particular, of sharing, of not trying to keep these secrets. Everyone on LinkedIn is always like, how is this free? Like, it's just the way it works.

1:29:50

Yeah, it's. Somebody said Silicon Valley is a company town, but the company is Silicon Valley, right? And again, at the level, again, there's one of these great n equals 1. At the level n equals 1 is somebody, you know. And I've. I've run Stratus before. I've run companies before at the level of N 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 ecosystem that adapts, right, and channels talents and skill and knowledge and people into the new fields. And so, you know, so, you know, there's kind of the push and pull of that at the level of just being an 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, One of the, you know, for all the. For all the issues in Silicon Valley, you know, I think AI, I did the count once, I think AI is the ninth major technology platform in the history of Silicon Valley. Right. That, you know, Silicon Valley is. Silicon Valley is still called Silicon Valley. We haven't made silicon here in decades. Right. We used to actually, you know, it's called Silicon Valley because they used to make chips. Right. They used to have the, like, the actual fabs were in Silicon Valley. And then they. And they designed them and they made the chips and. And so. And that was, you know, wave one starting in the 19. Actually, that was like. Actually, no, actually more wave three or whatever. But like, it was, you know, that was when the area was named, like in the 1950s. But now we're on, like wave nine. Right. And the company town phenomenon, where the company is the industry. Like, you get the indeterminate optimism. 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 do the smartphone. In the 2010s, they're going to do THE CLOUD. And the 2020s are going to do AI. It just the indeterminate optimism of ecosystem, the possibility of the ecosystem meant that the Silicon Valley could. Could morph into all these categories and again, maybe a testimony to indeterminate optimism.

1:30:00

This reminds me of the meme of how we're all just wrappers over sand. Everything we're building is just wrapper over wrapper. Wrapper wrapper.

1:31:54

The wrapper thing is hysterical. Yeah, yeah. I'm a software company and I'm a chip wrapper. Right. Yeah. I'm a business application. I'm a database wrapper. Yeah, exactly. I'm a sand. Yeah. You and I are. We're all now sand wrappers. Sand wrapper, Perfect.

1:32:00

Okay, one more question. Along the media diet, I asked your partner, Ben Horowitz, what to talk to you about The Z in a 16Z, if people don't know him. And he said that you're really into movies these days. And so I don't know any movies. Any movies you really into these days? Any movies you've absolutely loved recently?

1:32:14

Yeah. So the movie that blew my socks off last year, which I think is the best movie of the decade for sure, and maybe of the last, like 15 years is this movie. Unfortunately, it's one of these things. Not a lot of people have seen it, but I would highly encourage it. It's called Eddington. Not heard of it. Have you not heard of it? Okay, so, Eddie, you're going to really enjoy it, so I won't. I Won't spoil too much of it. So at the surface level, the following spoils nothing at the surface 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 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 sort of, as it plays out over the next few months, event, it intersects and it sort of extends into the summer of 2020. So, you know, kind of the. The George Floyd moment, and then the, you know, the. The protests and riots and kind of everything. So sort of the convergence of COVID and then the. And then the. And then. And then the. The. All the. All the BLM stuff. 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 outskirts 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 it 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. What actually happened. Right? And so. So it's. It's so. So the reason I love the movie so much is one. One is it's the first move that directly grapples with 2020 of what happened in 2020, and just like, fully, fully engages and grapples with, like, all the dynamics that we're playing out in the country. But the other reason is it's the first movie that does a really good job of showing what it. What it. What it was like, especially in 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, really struggle with. And then the whole thing comes together in an incredibly entertaining way. And so. And I won't even say I. I won'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 he really tries hard to like, really grapple with like what it is actually like to live like a human being in the 2000s 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 grab them.

1:32:30

I can see why that's your favorite movie.

1:35:16

It's great, it's great. It's great. Everybody should see it.

1:35:18

Oh man. Okay, final question I want to ask about your product diet. Are there 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 in if, if you use them constantly?

1:35:20

I mean, we have, you know, we have so many that it's really hard to, you know, I always feel it's like, you know, who's. Here's your favorite children. So it's really hard to, you know, to pull out specific ones. But I'll, you know, I'll talk about a few, I mean, or just, I'll just. Observation. So one is my 10 year old. My 10 year old right now is 100% obsessed with Replit. And by the way, it was not from me. Do you have kids?

1:35:33

I do, I have one. Two and a half year old.

1:35:56

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. And I'm highly aware of that. And so like, if I mention, oh yeah, we work on xyz, 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, through no interference on my part, discovered replit about three months ago and discovered vibe coding and is like completely obsessed with vibe coding games and, and all kinds of things. And like litter will sit and 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 Grok with bad Rudy, which is, if you've seen, it's a foul mouse raccoon avatar in the Grok app. So I think that's super fun. We have this company, Sesame, that had, you know, they went viral last year for this, you know, 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, you know, one of the most sweet one of us recently, that company recently sold. But you know that all the, I think like the pendants, the wearables, like, all that stuff is going to be big. The metaglasses, 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 incredibly, it's like a voice transcription function, but you can actually talk to the AI 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 really great.

1:35:58

Subscribers of my newsletter get a year free of Replit and Whisper Flow. So there we go. What's the most memorable thing your son built with Replit?

1:38:07

Oh, so he's gotten super into Star Trek and so, so far it's been. He's writing like Star Trek simulators. So like all the, you know, all the. My Next Generation. They actually have Next Generation.

1:38:15

Okay. I was going to ask which.

1:38:26

Well, he, like, we actually, we like them all. We watched the new Starfleet Academy last night, which actually is quite, it was actually quite good. But we, 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. Because if, if you watch the original series, they just had like, basically, you know, knobs with lights and they didn't really, you know, they just like, were like, you know, fucking 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 do vibe coding is you can say, give me a Star Trek Next Generation, you know, user interface for, you know, whatever this, that or whatever. And it actually uses the. They called it this. I'm an. They call it LCARS design language. And it'll, you know, it'll actually build you like Star Trek Next Generation bridge consoles using that design language, but, you know, with your choice of like a Star Trek game, for example. And so he's, he's gone crazy for that kind of thing.

1:38:27

That sounds extremely delightful. You guys should open source or release that. Mark, I. Like I said I could talk to you for hours. Well, you got things to do. Anything you want to leave listeners with.

1:39:15

Before we wrap up?

1:39:26

Anything you want to double down on or just leave listeners with?

1:39:26

Yeah, so a couple things. So one is we got super lucky last week. Paki McCormick wrote the best piece 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 would definitely recommend that. And then, you know, we're putting a lot. We have a, you know, great team of folks now. We're putting a lot of effort ourselves into video and 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.

1:39:29

Awesome. We'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.

1:39:55

Awesome. Thank you for having me. I really, I really appreciate it.

1:40:02

Bye, everyone.

1:40:04

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to, like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X1 6Z and subscribe to our substack@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 date of publication, but A16Z does not guarantee its accuracy.

1:40:08

I can't like.

1:41:05