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

If we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. Because what we'd be staring at is a future of depopulation and like depopulation without new technology would just mean that the economy shrinks. My friend Larry Summers used to talk Key for planning, as you said. He is an economist, and so that was economic speaking. I think we're gonna have AI doctors that are better than the best human doctors. I think we're gonna have AI lawyers.

I think we're useless. And we're gonna The capability at your fingertips that's actually better than human. What if the most consequential technology shifted humanity? In november nineteen eighty nine when the Berlin Wall Street The Cold War had lasted forty five years. A young programmer would find himself at the center of the next transformation, building a browser that brought the internet

Three decades later, that same person believes 2025 rivals those movements in magnitude. AI models From creative parlor tricks into genuine reasoning, solving problems in medicine, law, and science that seemed impossible just eighteen months ago. But here's what's unsettling. We don't yet know what this means for the people who build Product managers education. Designers. The roles that defined the last 30 years of You can't both be right. This conversation examples.

What skills matter now and how the most AI needed for the world? Today we're sharing a conversation between Lenny Richardson. from a recent episode of Lenny's podcast.

Mark Andreessen, thank you so much for being here and welcome to the podcast. Awesome, Lenny. Thank thank you. It's great to be here. I want to start with just a big picture question. I have a billion directions I want to go, but I think this is gonna give us a little bit of a frame of reference.

How big of a deal is the moment in time that we are living through right now? This is a very, very historic time. I think twenty twenty five was maybe the most interesting year in my entire career and and probably life. And I think I I would expect twenty twenty six to exceed that. Wow, that says a lot. Yeah, I see I've seen some stuff. So um 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 could describe 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 uh 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 kinda corresponding with that is the national and global conversation to become like, let's say liberated. Um, and so, you know, this sort of incredible revolution that we have in in kind of uh, you know, I was what I've described as freedom of speech, freedom of thought.

um, 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 that's now on a 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 U the US is changing a lot. Europe is changing a lot. China's changing a lot. Latin America, by the way, is changing a lot. You're very dramatic, you know, events playing out down there right now.

you know, kind of all over the world. Like I'll I think a lot of assumptions are being pulled out in the in into the daylight and and and re examined and and then it's kind of the fact that all these things are happening at the same time, right? And so you've got all of these countries and industries

you know, where things are kind of increasingly upheaval, but you have AI as this kind of new technology that's gonna really affect things. And then you've got, you know, people, you know, citizens being able to fully participate. uh being able to argue things out. So it's it's kind of like those three kind of big mega things are kind of all colliding um at the same time. And I I think we're probably just the very beginning of all three of those.

And and those all feel like kind of, you know, historical, you know, moment shifts. It you know, comparable in magnitude to maybe the fall of the Berlin Wall in nineteen eighty nine, you know, maybe maybe the end of World War Two, um, you know, kind of moments like that. It certainly feels like that. Good God.

What a time to be alive. In terms of the AI piece, which is where a lot of people are trying to figure out what to do, what do you think isn't being priced in yet in terms of the impact? Yeah, he's gonna have on say the world or just People listening.

The I think it's I think at this point I think it's pretty clear with the with you know our technology hats on that like i th this stuff is really working now, right? And so there there was this, you know, kinda qu you know, when when the there was a chat GPT moment, you know, three years ago. It is only by the way, only three years ago, right?

Right, um was the Chat G P D moment. And and the the big question was all right, this 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 really big question of like can you can you harness this technology for reasoning um and for, you know, problem solving in in domains that like really matter, you know, medicine and science and and and law and And so forth.

Um and and you know it it turns out the answer to that is yes, right? Um and you know the the the last twelve months and especially the last even just the last three months have really proven that like AI can really do like you know you're seeing it all now, you know it can actually you know AI is now developing new math theorems. Um, you know, th they're you know, uh 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.

Uh, and the world's best you know the the world's best programmers, right, including like Linus Torvalds, you know, for 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 in incredibly f powerful. And I think we we all, you know, kind of I think assume that AI now is going to get really good at reasoning um in in any domain in which there are verifiable answers. And so that that that you know, that's gonna include like many very important domains.

So um so like for the the technology feels like it's it's it's moving fast and and and it's gonna be working really well. Um I think this thing that is not well understood i I 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 kinda sweeps the sweeps the world and changes everything.

And I think that's g that's kind of the wrong that's kind of the wrong framework. I think it's based on an incomplete understanding of of the world that we live in or the world that we've been living in for the last, you know, eighty years.

And I I recall out 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 thirty years or fifty years, it's felt like we've been in a time of great t technological change, but actually If you look for actually evidence of that, like in static in statistical evidence of that, analytical evidence of that, like you basically can't find

Um and in particular, um, economists have a way of measuring the rate of technological change in the economy that is productivity growth, which which which we could talk about what that means, but basically it's it's uh it's it's sort of the mathematical expression of the impact of technology.

uh on the economy and productivity growth for the last fifty 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 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 were in my lifetime, in our lifetime.

It's been running at about a half the pace um that it ran in um between nineteen forty and nineteen seventy, and it's been running at about a third the pace that it ran between about eighteen seventy to about nineteen forty. And so statistically, in the US, in the West, technology progress in the economy, technology impact on the economy has actually slowed way down.

And so we you know, the the the AI thing is is gonna hit, but it's hitting an environment in which we we have actually had almost no technological progress in the actual economy for a very long time.

So so we could talk about that. And then there's this other like just incredible thing that's happening, which is the the you know, sort the the d demographic collapse, right? Uh it's sort of a a Western phenomenon, an increasingly global phenomenon, which is, you know, the rate of reproduction of the human species is is in rapid decline.

And, you know, there are many countries, you know, including the US where, you know, the rate of reproduction is, you know, under two, you know, meaning meaning that, you know, man many, many countries around the world, by the way, including China, which is a really big deal, um are actually going to depopulate over the next century.

Um and so you have this kind of precondition that says there's actually been very little technical pro technological progress happening in the world um and the world is going to depopulate. Um, and so AI is gonna enter the wor a world in which those two things are true. And I think it's incr 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.

'Cause we're gonna need s you know we're we're gonna need machines to do all the jobs that we're not gonna have people to do. 'Cause we're we're literally gonna depopulate the we're gonna depopulate the planet over the next hundred years. And so I I think the interplay of these factors i i i is going to be much more interesting and and frankly more more more complex than a lot of people have been thinking. I'm gonna 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 know, even careers that you're Steering your kid towards the Peter Peter. The way I think about this, uh anyway, yeah, we we have a 10-year-old and so I, you know, we and we actually homeschool, and so we we we think a lot about this. Um

I think the way to think about the impact of AI on on people, on specifically people as individuals, I think it's it's it's actually, you know, th a 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 or an individual kid. So I think it's pretty clear that AI is going to take people who are good at doing things and it's gonna make them very good at doing things. Right. And so it's gonna be a tool that's gonna sort of raise the average kind of across the board. And, you know, look, you 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 they're There's sort of that aspect to it. And I think the the the the way the education system right large is gonna teach is gonna kinda teach AI is is is gonna be based, you know, uh 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 encoding right now. Um, where the really great people are becoming like spectacularly great.

Right. Um and so you you you you you just you kinda use it use the term you think about like the super empowered individual, right? So the individual who is like really good um 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 or you know, or podcasting, or, you know, hopefully venture capital.

You know, if if you're very good at it and you can really harness AI, you can become spectacularly great. Uh and like super productive, right? And you know, you I'm sure you you have a lot of friends in this in this category uh as well. But like, you know, co c the the the really, really good coders are experiencing this right now. My friends are really good coders, they're like, oh my god. All of a sudden I'm not twice as good as I used to be. I'm like ten times as used as good as I used to be.

And so I think at the uh 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 gonna be really kind of deep in whatever it is they're gonna do, but they're gonna they're gonna be deep in a way that's gonna let them fully use the power of AI to be not just great, but to be like spectacularly great.

Um and and I think that that that's that's gonna be the real you know, that that that that that's the real opportunity and that, you know, at least that's what we're shooting for and that's what I would encourage parents to shoot for. So what I heard there is essentially agency, this word that we see on Twitter all the time is

building uh agency them not waiting for someone to tell'em what to do, figuring out what to do. Yeah, yeah. So this this this this thing with this this term agency that's become very, very um, you know, very popular um certainly in California for the last couple of years.

It's really interesting'cause it's it's I had a lot of trouble with this early on because I'm like agency actor, what are they talking about? And what what they're kinda talking about is like, you know, initi you know, initiative you know, um, you know, willingness to, you know, y you can just do things. Um, you know, uh what is it? Uh the the SEMO Burger has the great term live player. Um, you know, you you you can be a like a primary participant in advance.

And at at first I was like, Well, yeah, like that's kind of obvious, right? Like, of course. And and then and then I'm like, oh Actually it's not so obvious anymore because kind of to your 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 we we have somehow worked our s our our way our way kind of, you know, I don't know, psychologically, sociologically. you know, kind of into a state in which I guess the natural assumption for a lot of people is, you know, the thing that you you want for example, the thing you want to train kids to do is like follow all the rules.

Um and you know, you could argue that kind of, you know, for example, the you know, the school system the K through twelve school system or whatever has gotten kind of more and more focused on over time. And it's like, yeah, it's like, no, you you should actually and and again, especially unit unit N equals one, 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 ten year old last night, actually. I I I f I rolled out uh uh the concept of uh you know, i uh 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 uh You know, kinda trying to keep keep him with some level of structure in his life.

And that's just and that's just pure agency. But yeah, I mean so and so look, you know, some rules are important and so forth. But yeah, no, look, there there is like a huge br there 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.

Um, and you know, maybe yeah, that that has been maybe a little bit diminished in our culture over the last thirty years. It it it the admitt, you know, it's it's healthy.

you know, that that you know, that that there's now a term for that that that that is coming back back into vogue and then and then and again like 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.

uh, you know, to writing, you know, to writing novels, like, you know, whatever that thing is, I 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. What is that quote about give me a a lever and I'll move the world? And I'll move the world. Yeah, that's exactly right. Well, so it's actually funny you mentioned that. So the the um

The uh the the early kind of scientists, including like Isaac Newton, were super obsessed with with the you know, this concept of alchemy, right? It's like, you know, they you know, they div you know, they developed like you know, Newton. He's like developed Newtonian physics and he developed like calculus and all these things, but the thing he was 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 y 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, le lead into gold. And he never figured it out. And 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. Right.

Right. And and so AI is it it it is it is the it is the philosopher's stone. Like it it it is that. It it actually is that and it's just this incredibly powerful tool.

Um and and that's where I get that's where I get so excited. I mean and and again this is what we're doing with our ten year old which was like, All right, it primary thing that we want to make sure to to do is th 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

uh you know, which is to say AI and that that and then you know that's certainly central to everything we're teaching them. You know, there's there's this meme going around that um, you know, Silicon Valley people don't let their kids use computers. And 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. Um I I think it's more honestly the other way around, which is

Uh the you know, the more you're kinda plugged into stuff in Silicon Valley, the more 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 and that's that's certainly the mode that I would encourage parents to think about. I did not know your kid was homeschooled. That is super interesting. There it's almost a a statement on, you know, education in today's day.

Maybe is there any thoughts there? I'm just for folks that maybe aren't in your tax bracket that want to help their kids be successful, maybe homeschool, maybe not, what what advice would you have? This is the challenge. And and again, this this kind of goes to how your you know kind of your original question, which is Education th there's two completely different ways to talk about think about education.

The way it's usually thought about and talked about is kind of at at the level of like a a nation, right? So so you know, it's 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 gonna need like some level of large scale system like the, you know, the national K through twelve school system or something like that.

you know, in order in order to do that. Um, but then there's this other question which is like and n equals one, uh for an individual kid, like what can you do with with an individual kid? Um 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 uh a kid at the unit of n equals one, th by far the ideal way to do it is 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 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 and so you know, royal families and aristocrats and so forth, you know, uh over the course of centuries. Um all you know, kind of always had the always had this approach. There's actually also statistical evidence, um, uh analytical evidence that this is correct. Um

there there's this, you know, massive question in 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 fiftieth percentile to the ninety ninth percentile, and that's one on one tutoring.

Right. So again, right, if you go back to like it at N equals one, 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 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 an an LM about it and they can ask an infinite number of questions and they can get instantaneous feedback.

Um, and in fact, you can even tell an LM, 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. Um, okay, now quiz me, you know, do I actually understand this? Like people can just do this today.

Right. Um 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, uh, to be able to say, Okay, you know, my my kids probably still gonna go through a traditional education system, but I'm gonna augment this with AI tutoring.

Um and of course there you know and of course there's gonna be tons of startups, right? And there already are that are that are gonna try to build on all the all the products and services for this. Khan Academy, you know, on the nonprofit side has a big push to do this. Um and so you know, I I think the the broad answer might be a hybrid approach with schools plus one to one tutoring through AI.

Um, there's also this great, you may have heard there's this great school new private school system called Alpha, um 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 teaching. But it's also, you know, heavily based on AI and AI tutoring. And so I I think there's like a uh there there is a magic formula in here.

Um that I think is gonna apply much more broadly. Um and I and and it really for parents interested in this, I now would be a great time to really start to think hard about that. Um and and and to look at the options. It's interesting because there's all this concern that young people

uh jobs are not gonna 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 gonna be move so fast and learn so much more. And in where where do you

sit on this divide of like young people are in big trouble or they're actually gonna be the ones winning in the end. Yeah. So the job the job substitution, job loss thing is just it's very reductive. It's it's a 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 fifty years of very slow technological change in the economy.

And so, you know, and again, like I said, it's like at a at a half the rate of the of the previous era and then a third the rate of like a hundred years ago. And so we we're we we're we're we're coming out of this kind of phase where we've had like almost no technological progress in the economy.

We've had remarkably little job churn as a result of that, relative to a to any historical period. And so even if AI like kicks up, even if AI triples productivity growth in the economy, which would like be a massively big deal. It would take us back to the same level of job churn that was happening between eighteen seventy and nineteen thirty. And if you go back and you read accounts of eighteen seventy and nineteen thirty, people just thought the world was a wash with opportunity.

Right. At at that rate of technological transformation, kids were able to like develop new careers into new areas of of of of the economy, building new kinds of products and services. I mean, you know, a huge part of our of everything in our modern world today was kind of invented and and uh and proliferated kind of during that period.

triples the pace of economic change in the economy, it's gonna just translate to a like a much higher rate of economic growth is gonna transfer translate to a much higher r higher rate of job growth. And, you know, there 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 uh that will happen and that then corresponding to that there will be, you know, there will there will be hiring blooms I you know, I quite honestly think all over the place. And then again, go back to the the the other thing, which is like this is all happening in the face of declining population growth and and and increasingly population shrinking.

Um, and so human workers in many, many, many countries over the next, you know, ten, twenty, thirty years are going to be at more and more of a premium.

Um literally because you're gonna 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 go is is gonna reverse course on on on the rates of immigration that we've had for the last fifty years. It seems to be kind of a a broad based, you know, kind of thing happening.

Um, you know, kind of with ri you know, ri rise of nationalism, you know, concerns about the rate of immigration and immigration historically in countries like the US, you know, it's it's kind of ebbed and flowed over time.

based on kind of how you know kind of how the 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 the remaining human workers are going to be at a premium, not at a discount. Um and so I think I think that combination of kind of faster productivity growth, faster economic growth, and then slower population growth and less immigration.

Um, actually means there's going to be much less of this kind of dystopian, you know, no jobs thing. I just think is probably totally off base. That is extremely interesting. So what I'm hearing is you're not.

Super worried about job loss. Is the key here that the timing kind of just works out? Does population decrease? You know, like all these kind of have to line up. for there not to be this massive job. Yeah, well look, if we didn't have AI, we'd be in a panic right now about what's gonna happen to the economy. Right? Uh because what we what we'd be staring at is a 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, d opportunity diminishes. There are no new there there are no new jobs, there are no new fields, there's no new d there's no new source of consumer demand for spending on things.

Um and so you you would you would you would be very worried about going into a period of like severe decline and stagnation. Um and you know the b you know, essentially you'd you'd be looking at these like very dystopian scenarios of like an economy kind of self euthanizing itself. uh over time. Um and and you it's 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 for the lack of immigration that's like. And so we you know, it i I would say the timing has worked out mirac miraculously well in the sense that we're gonna have AI and robots precisely when we actually need them.

uh to keep the economy from actually shrinking. Um and and I just think like that that's just like a fun uh a fundamentally a fundamentally good news story. Um, to get to the mass job loss thing that people are worried about, um, on the other side of things, you know, you have to you'd have to look at like far, far, far higher rates of productivity growth. You'd have to look at rates of productivity growth that are trend, twenty, thirty, fifty percent 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. Um, you know, it's possible that we get that. I mean, look, I'm you know, I I have my utopian kind of, you know, kind of uh you know temptation along with everybody else. If if AI like radically transforms everything overnight, then maybe you you know, let's let's play out the kind of utopian scenario.

uh you get to a much higher l level of of 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 you know massive growth in the economy. Uh and then corresponding with that, you'll have a collapse in price.

Um and so the price of goods and services that are that are that are sort of, you know, whatever you're gonna call it, affected by or commoditized by AI, the prices of those goods and services will collapse. Right. It'll be price deflation. And then as a consequence of price deflation, everything that people are buying today gets a lot cheaper. And that's the equivalent of a gigantic increase in wealth, right, across the society.

Right. This is actually worth talking about because peop people I think get get kinda sideways on uh uh 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 e massive productivity growth.

The consequence of massive productivity growth, what that literally means mechanically, is more output 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 cost. 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 a hundred dollars now costs you ten dollars and now costs you one dollar. That's the equivalent of giving everybody a giant raise, right?'Cause now they have all this additional spending power. That additional spending power then translates to economic growth, right? The development of new fields.

Everybody's like maturely like much better off very quickly. And then by the way, if you to the extent that you do have unemployment coming out the other side of that, it's it's now much cheaper to provide the kind of social safety net. to prevent people from being immiserated, right? Because the prices of all the goods and services that like a welfare program has to pay for them, 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. 'Cause this this this this this incredible impact that AI is having. And so in this kind of utopian, dystopian scenario that people have, it's not there there's no scenario in which like everybody's just poor. In fact, it's it's quite the opposite, which is everybody gets a lot richer because prices collapse.

And then it's actually much easier to pay for the social safety net for the people who, you know, for some reason can't find a job. And so like Like maybe we end up in that scenario. I mean the 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 gonna be a much better news story than people think it's going to be.

Um 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 that that that plays itself out if you have higher rates of productivity growth.

which are necessarily the results of higher gra race of technological growth. And so I think we're I think we're looking at and 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 d the dystopians think it will be.

I think it'll be more incremental for races we can discuss. But I think that incremental i is a is a is 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.

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. Uh okay, so one you were right about the web and web browsers becoming important. You were right about software eating the world. Check.

You uh in twenty eleven you said that in ten years we're gonna have five billion people using smartphones. And I believe the actual number ended up being six billion. You also uh you have 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's progress. Progress will continue. And he he was like, No, I think we're done with cool technology. You were right. Uh I imagine there are many more things you were right about. So So again, I'm just I I I love hearing your predictions because I feel like they're actually gonna turn out to be correct.

So I should start by saying I've been wrong about tons of things, but you know, I bury those up back behind the shed. Delete them from the internet. No web browser can disguise them. I have them nuked out of the internet archives so that they know they they're never seen again. Um So uh you know, we're I I'm wrong plenty of times also. Um

But yeah, I mean look, I I think in yeah, and some some of those are right. By the by the way, I will say on the on the Peter one, I I have come I have come much more around to Peter's point of view. Um 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. Um

And and it actually goes to the kind of the discussion that we the kind of conversation we just had, which is the the the the the real form of what Peter was arguing was we have lots of process in bits, we have lots of progress in bits, right? But we have we have very little progress in atoms.

Right. Um and th and that's the real core of what he was arguing. And I think I I I think I I was a little bit, I don't know, missing that or kind of, you know, kind of glossing that over a little bit, um, because I was so focused on making sure people understood no, there actually is still progress happening in

um in bits. But I think, you know, a lot of his critiques around the lack of progress in Adams is real. And and again, this goes back to this thing of like in the la and he, you know, he's talked about this for a long time. In the last fifty 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.

Th it just there just hasn't been. Like the the the the built world is just not that different today than it was fifty years ago. And if you and again, if you contrast that, you know, if you if if you compare and contrast eighteen seventy and nineteen thirty, it was a dramatically different world. If you contrast nineteen thirty to nineteen seventy, it was a dramatically different world. If you contrast nineteen seventy to date, it's not that different.

Right. And you 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 nineteen sixty. Right. And there's a bridge that was built in like nineteen thirty, and there's a dam that was built in like nineteen ten. And there's a city that was founded in, you know, eighteen eighty. And like Yeah.

What have we done? Right? Like, where are our new cities? Where are our 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 I think he is I I think he is right about a lot of that. Um again. This is also why I think that AI is not going to have as rapid an imp it's not going to be th 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 y bureaucratic process, rules, restrictions, um, you know, the the the the politics. Um by the way, you know, unions, cartels.

oligopolies. There there's all these structures in the world that are kind of economic or political or regulatory structures that basically prevent things from changing. And so I mean, uh let's take let's take a great example. Like A AI's impact on the healthcare system. Like uh by rights, AI is gonna have a dramatic impact on the healthcare system and in in in very positive ways.

But, you know, the a large parts of the medical system today are they are cartels, right? And so there's like a there's the the doctors are a cartel and like nurses are a cartel and like hospitals are a 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 and and guess what cartels of monopolies don't like is they don't like like rapid change.

Right. Um and so, you know, you show up as a kid and you're like, Wow, I've got like this new technology to do like AI medicine and they're like, Oh, well, does it threaten doctors valves? 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 know if 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 it can't substitute for a doctor, it can't prescribe medications, right? It can't, you know, perform procedures.

Right. And so there there there are these anyway anyway, so Peter Peter I think was very articulate and 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 s uh the race of change that are anywhere near the race 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 t of the new magic technology of AI, maybe it causes us to revisit a lot of these assumptions assumptions for the first time in decades to really say, okay, is this really the world we want to live in? Don't we actually want to get to the future faster?

So maybe that would be the optimistic view. It's time to build, somebody famously said. I uh 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. Okay. I love I love the way you go from just like macro to just like N of one. And I'm gonna go to N of one.

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 products.

that aren't founders. And uh 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.

So the way I've been describing it is, you know, you know the concept of the Mexican standoff, right? Which is the the movie scene where the, you know, the two guys have guns pointed at each other's heads. Mm-hmm. Um 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, pe people in like the you know, and of course it's Sun Wu movie is they've got, you know, guns in both hands.

So they're all each each is aiming at the other two. Yeah. Um, and you got this kind of standoff situation. And so the the the way I've been describing this is there's like a Mexican standoff happening between those three roles between product manager, designer, and 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 and a coder. Right. And so people in each of those roles now, you know, know or believe that with AI they they don't need the other two roles anymore, right? They they they can do that'cause they can have AI do that.

And then of course and then of course there's the real irony, which is, you know, the all the the three all three of them are going to realize that AI can also be a better manager, right? So they're gonna they're gonna ba aim aiming the guns up the org chart, but that th that's probably the th that's the next phase. And what I think is so fascinating about this mechanist messaging staff is they're actually all kind of correct, I think, right?

Which is AI is actually a pretty good, you know, it's now I it's actually now a really good coder, it's actually now a really good designer, and it's also a really good product manager, right? It's actually good at doing all three of those things, or at least doing a lot of the tasks involved in in in those three jobs. Um and so again this this goes back to the the the super empowered this kind of idea of the super empowered individual.

Uh, 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 gonna change in the future, you know, the the I need to under you know, specifically uh 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 of of you know coding bots.

uh you know, you know, there's a 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 superpower individual? How do how do I become a coder that also then harnesses AI so that I can also be a great product manager and I 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, you do AI, AI based design. And the same thing for the designer, which is how do I use AI to be a be also become a coder and also become a product manager.

And then what you get is maybe the maybe the 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 thirty years or whatever. Uh but what happens is the the talented people in any of those roles become super empowered and they become good at doing all three of those things.

Um 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, the which is which is the most valuable thing.

Uh and so I I think I think that's I think I think that's the opportunity. So I love this answer. So what I'm hearing is essentially uh if you're amazing at any of these three roles, you will do well. Number one, if you're amazing at these roles, that's great. But also you part part of being amazing in these roles is also being being able to fully harness the new technology.

Right. So y 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 gonna hit an issue. Right.

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 is task task loss, right? The tasks changing. I mean the the the the the c the classic examp 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 of a company in nineteen seventy 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 the Secretary would print out the email and bring it into the executive's office.

The executive officer would read the email on paper, sprawl scr sprawl the reply, um and and and and give and give that message back to the secretary who would go back and type it into the computer on 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 that, you know, the that the the great admins do. And then that and then the test, the task 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, fifty years ago, they never never never would have done that. And so the executive job still exists, the secretary job still exists, um, but the tasks have changed. And I think that's like a great example of what's gonna happen in coding, the tasks are gonna change, this is what's gonna have product management, the tasks are gonna change, designer tasks are gonna change. And so the the the job can per the job persists longer than the individual tasks.

And then as the tasks change enough, then that's when the jobs change. And so at the at the level of an individual, you kind of want to think of like, okay, I have this job. The job is a bundle of tasks. I need to be really good at making sure that I can like swap the tasks out, right? I can I can really adapt.

use the new technology, you know, get really good at AI coding, for example. Um I can, you know, and then and then you want to kinda add skills. I can also get really good at design. I can also get really good at product management because I 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 gonna be? But it's gonna be incredibly important because the people doing that job are gonna be orchestrating the AI. And so that that that's the track that the best people are going to be on.

Um and and I think that that's the thing to lead her lean heart into. I think people aren't fully grasping just specifically software engineering and how much that is changing. Like it's pretty clear we're gonna 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 gonna be this artisanal experience of sitting there writing code, which is so crazy how much that job is gonna change.

Yeah, so again here I go back and again I d pardon maybe the history lesson, but like I go back like Cody, so the first I do you know the I do you know the the original definition of the of the term calculator? Do you know what that referred to? No. I referred to people. Uh right. So uh back before there were like electronic calculators or computers or any of these things.

Um, 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 you know, formulas or whatever it was. The way that you would do it is you would actually have a room full of people. Um, and by the way, these are like big rooms. You could have hundreds or thousands or tens of thousands of people doing this.

And you would actually br 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 th that that job title was those people were calculating. 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 you know whose job as a programmer was to like deal with 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 with scripting languages, you know, we developed JavaScript at Netscape and then Python took off and Perl and these other scripting languages. When scripting languages you know took off in the in the in the in the in the two thousands, there was this big fight in the technical community, which is is scripting real programming or not?

Right,'cause 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, they get this this this whole craft of writing writing uh 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 as coding? And of course the answer is yes, it very much counted. And now most coding is done with the scripting languages, right? Um which DevIps 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 your 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 I'm 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 what they're what their their their their day just 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 know, you asked about the ten ye you know, uh our ten 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 Chat GPT to copilot and all these things, and what I'm telling him is like look.

And he by the way, he loves vibe coding. He's on replic all the time doing vibe coding, you know, d doing game doing games. You know, he's sitting there, you know, it's hysterical, right?'Cause he's sitting there it's a ten year old basically who's, you know, spends two hours at dinner arguing with an AI for fun. Right. Um

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 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 in the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works.

Um, and so again, it's 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 the thing is actually doing. even if you're not spending your day actually doing that by hand. And again, I look at that and I'm like, okay, now programmers are going to be 10 times or 100 times or a thousand times more productive than they used to be.

Right. And and that and that is overwhelmingly a good thing. The the the the the the tasks are definitely changing, the nature of the job is changing. Um but are human beings going to be involved in like in in the coding process and overseeing the the AI b coding and all that? And and and the answer is of course absolutely a hundred percent. Like no question.

So you're in the camp of still learn to cope, still a valuable skill. Oh yeah, totally. Well again, if you wanna be one of these super imp look, look, if you just wanna put your like self an autopilot and like I can't be bothered and I'm just gonna have an AI write the code and it's gonna generate whatever it does and that's fine and I I'm gonna be you know, I'm gonna be If if the goal is to be a mediocre coder

Then just let the AI do it. It's fine. The AI is gonna be perfectly good at generating infinite amounts of immediate overcode. No problem. It's all good. If if if the goal is I want to be one of the best software people in the world and I wanna build new software products and technologies that like really matter.

Then yeah, you 100% watch you still but you want to go all the way down. You want your skill set to go all the way down to the assembly to assembly and machine code. You wanna understand every layer of the stack. You wanna deeply understand what's happening at the level of the of the chip, right, and and and the network and so forth.

By the way, you also really deeply want to understand how the AI itself works, right? Because you wanna right, because if you people who understand how the AI works are able to they're clearly able to get more value out of it than somebody who does doesn't understand how it works. But I mean you're you're always more productive if you know how the machine works, right, when you use the machine.

And so yeah, you the the super empowered individual on the other end of this that wants to do great things with the new technology, yes, you a hundred percent 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 and and when something doesn't work or when something isn't right, you wanna be able to really quickly understand why that is. Um 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. Um, 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, I was gonna say, uh, I was a big Pearl uh programmer. I was an engineer for ten years and that was my my language of choice.

You do you remember I don't know when you were doing it, but do do you remember that at the at least early on, do you remember did you ever did you ever hit this where like

C coders were like looking down their nose at you, being like For sure. For sure. It's like this is so slow. It's not gonna scale. What are you what are you spending all your time on this thing? 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's it swept the world. Uh you know, most coding today happens in scripting languages. And and then by the way, the people alo along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they 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 and then again, the result of that was, you know, a far higher number of people writing code with scripting languages than were ever writing code with lower level languages. And I I think this will just kind of be a more dramatic version of that.

I love that Pearl 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. Well that's funny because of course it was so notorious for being i impossible to understand. So how ironic. Coming back to these this kind of triad, the other element that I hear more and more of is just as is the skill of taste and design and user experience. It feels like

That's a very hard skill to learn. And to me, it tells me design is gonna be much more valuable in the future. Yeah, that's right. And again, here this this is a great example. So

Right, is gonna be like, all right, the AI's gonna do that all day long. It's gonna escape give you a thousand icon designs, it's gonna be great. Like it's gonna 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 they is gonna get really good at that. But like What are we trying to do? Like th th the you know, kind of capital D design of like, all right, what is this thing for?

And how does this yeah, how is this going to function in a world of human beings? And like, you know, what what's gonna is this gonna make people happy when they use it? Is it's gonna make people feel good about themselves?

Um, is it gonna fit into the rest of their life? Is it gonna, 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 the the job of designer, right, will involve much more of those higher level, more important components. And and then again with a with AI doing a lot more of uh of the underlying

tasks. And so, you know, one one way to think about it is, you know, I don't know, you could you think of like I don't know, the world's best designers, you know, Johnny Ive or whatever, and you could be like, wow, like if I'm a designer today, if I'm a twenty five year old designer and I and I aspire to be, you know, Johnny Ive in a decade

Um it it's it's all of a sudden I have a new path that I can use to kind of get to t to to get there, which is I c you know, cause Jeff 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 gonna be like the best designer the world's ever seen because it's not just gonna be me. It's gonna be me plus being so super empowered by this technology to be able to do so much more.

Um and then so much more of my time and attention is gonna be is gonna 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. So maybe what I'm hearing here is kind of this T-shaped strategy of be if you want to be successful in any three of these roles.

Be very, very, very good at that specific role of product management, engineering, design, and then get good enough at these other two roles. Well, so y I think that's great. I think that's really really relevant. And then, you know, the Scott, you know, Scott Adams unfortunately just passed away. Um, you know, which is which is a a real tragedy, but um

I I was always I've always I referred for years to actually Scott's uh Scott Adams he had this uh famous um he had this famous uh kind of career advice he would give people which I I think makes a lot of sense, which which which dovetails of what you're saying, which is he he used to say used to say it's like look

He said, um, you know, I c I could he he's he said, you know, I could have been a pretty good cartoonist, um, or I could have been like pretty good at business. But the fact that I was a cartoonist who understood business made me like spectacularly great at Megan Dilbert.

Right. Because even the world's best cartoonists 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 so the the way Scott always described it was that that that from a career development standpoint, the the additive effect of being good at two things is like more than double. Right. Um the additive effect of being good at three things is more than triple. Right. Um be because you you you be you become a super relevant specialist in the combination of the domain.

Um and and you you know look you see this all you I mean you see this all over you know you see this all over the economy. Yeah I mean you see this all over the economy, but I'll just you know give you an example, Hollywood

Yeah, just Hollywood as an example. You know, there are a lot of writers who can't direct a movie and they can be very successful writers. There are a lot of directors who can't write a movie and they can be very successful directors. But the superstars in the entertainment industry are the people who can write and direct.

Right. And you know, they they they don't have a term for those. They call those autures. Right. And that's you know, th those are the people who are like the real creative forces that move the field.

And so and so again, and by the way, Hollywood actually is really funny I've spent spending a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican standoff going um right now that we that we described in a tech, except in Hollywood, for example, for filmmaking, is 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 actor. The writer is saying, Well, I don't need the director,'cause the AI can direct the movie and the AI can do the actress. And the actor is saying, I don't need either one of these guys. I can have the 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 it's it's the say it's the same kind of tri t triangular configuration. And again, what what's great about it is they're all correct, right? e each person in each of those three fields is going to be able to expand laterally and pick up those other those 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 and I think t you know, to your point, like your your your T shift thing, like I I think that's gonna be true basically across the entire economy. And and and if you think about the T is you know if you think about the T configuration, it's like yeah, the the bread the breadth, 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 the the 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 that's your T right there.

And and you're a triple threat at the top of the T, but with this level of technical grounding underneath that. And I mean, at that point you're again, you're the super empowered individual. You're gonna be able to just perform like seats of magic.

uh for example in terms of designing and building new products, you know, the the people in my generation couldn't have even dreamed of. And so I I I think I think that this is a universal kind of theory that I think c can can apply across the entire economy. I'm gonna invent a new framework right now.

Okay, forget the T framework. Uh 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. I think that's right. I think that's right. I know the yeah, the combination, yeah. Uh um uh uh my my friend Larry Summers uh had a had a different version of the Scott Adams thing, which is he used to tell people he said the key for uh 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. Well, and what that means is what that means essentially is don't be replaceable. And so don't be a cog, right? So and 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 a decoder, like then in theory you can be swapped in or out.

But if if you have this, if you have this, yeah, to your if you have this E or F, you know, laying on the side kind of thing, and if you have if you have this combination of things, it's actually quite rare, then all of a sudden you're not fungible. Not only 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. Um and yeah, that that your ability not to become one of those people is like

Uh with AI as compared to anything we've ever seen before. This is so interesting because I've worked with people that are good at these two skills and they were always called unicorns at the company. She can 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. That's right. That's right. And again, I can't I can't overstress the following for 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. And so

It's I always feel like it's like it's like people spend too much i it's it's one of these things where it's like so much focus on figuring out how to use like a large language model is like, okay, what am I gonna 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 it's just as good at that.

Right. Um and so th th again, this is this level this level of latent superpower. Like you know, people who really want to like improve themselves and like dri develop their career should be spending every every spare hour, in my view, at this point talking to an AI, being like, All right, train train me up, like tell me tell superempower me.

Tell me how to be, you know, train 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's it it knows exactly how to do that. You know, r run me drill, you know, make me problems, you know, yeah, make me assignments that evaluate my results.

Right. And it it will do it will do that just as happily as it will do work quote unquote for you. Two tricks I've heard along those lines. One is uh 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 uh a couple of podcast guests have mentioned this. When you get stuck. And then you figure out how to unstuck yourself, you ask it, what could I have done differently? What could I have said that would have avoided this error in the first place? Yeah, that's right. That's right. Yeah. Oh, look on that first one. And this again, this is what I'm doing with my 10-year-old.

Yeah, look if if if you ask an A yeah, this is this is a really good point. So if you ask an AI write me this code and then and then it does it and 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 you 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, um and and and and then you and then you you have the grounding, you know, kind of that leg of the e of your ear, your F. Um, 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'm at do, this is everything.

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 um is that you understand and by the way, look, I mean th this is like everything I'm saying is you know, everything I everything that we're saying right now also is the same as if you're working with human beings, right?

Like it, you know, if you and I are colleagues and I, you know, 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 do need to give you feet give you feedback. Right. If I just tell you, Oh, that's wrong, it it doesn't like not 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.

Um and so and and you know and again the great thing with AI is AI will happily sit there and explain all day alarm why it's doing what it's doing. It'll you know, it'll happily critique itself. But you know, and you can do s by the way, you can solve a very fun thing where you can have w have one AI critique the other AI. Right. Um, which is another thing, which is like you have one AI write the code, you have another AI debunk the code.

Um and so you can actually use you can play the ads off against each other and get them to argue with each other. Um and yeah, the these are all these are all the kinds of skills that are gonna become, I think, incredibly valuable. I think people call those LLM councils. Yes. They're talking to each other. Yeah, that's right. That's right. I do feel like if I were like I'm I have no design background, I've always wanted to design. I would I I've always wanted to be a great designer.

Uh, it feels like that's the hardest one to learn of all these three by just watching and talking, right?'Cause there's a lot of exposure hours as as folks have used this term, just like how do you learn to be a great designer? That feels like that's gonna be really hard and valuable. So my 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, Hmm, it might be time. Your time has come, Ark. Yes.

I want to pivot to founders, your maybe your bread and butter. You spent 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.

Yeah, so this is a great it's a very, you know, topical type topic, but it's all playing out in real time right now, um, on the on the leading edge. So I I think there's like three layers of it. See if 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 how does AI redefine the products themselves?

Right. Um, and and this is kind of the this is kind of the time honored, you know, kind of thing that happens with technology transitions. And this is kind of what, you know, a lot of venture capital is based on, which is Um, you know, okay, there's a new technology that comes out and you know, maybe it's the personal computer or the iPhone or the internet or now it's AI and it's like, all right. Um, 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 kinda keep on going and you know, you kinda add the the new technology kind of gets kind of added into the mix. Um, you know, it's kind of another ingredient into into an existing formula.

And and of course, you know, a lot of new technologies are like that, right? Um, you know, I don't know when I don't know when flash when flash storage came out or something, you know, P P it didn't really you didn't really redefine the the software industry because people just wanted to be using, you know, hard disk using flash storage or something. Um

But when the internet came out, like basically old school on prem software for the most part, you know, not not entirely, but like a lot of it died and just got replaced by like web software. Um right. And so so sometimes you get the kind of it's it's additive to an existing thing. Sometimes you get the actually it redefines a entire product category, redefines an industry, the actual comp 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 a great example is there are you know, the there are these businesses like you know, just take Adobe, like, you know, Photoshop has built a whatever, forty year franchise in image editing. Um, 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 your all images are just being generated and it's just easier to just j have AI generate a new image than it is to try to edit an 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 answers I think will vary by domain, but um, 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 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 job. Um, 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 hundred? Maybe now I only need ten. Or does that mean I still want a hundred, but now they're doing ten times more? Right. And so the you know, as you know, like a lot of the best founders are 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 the the the the basic idea of having a company, right? You know, does that change? And and again, here you've got this concept of the superpowered individual. Which is like, okay, um, you know, can you have entire companies where you have basically the founder does everything?

Right, because what the founder's doing is like overseeing an army of A AI bots and 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 we you know, we've had a few of those over the years. Bitcoin is probably the most spectacular example.

you know, was Ethereum right behind it, um, 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, you 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 most software companies obviously end up with, you know, huge numbers of employees.

Um, and so I I think, you know, so so the the most leading edge founders are thinking of like, okay, how how do I reconstitute the actual very definition or idea um of a um uh uh uh of having a company? And and you know, can you have a company that's that's literally basically just all AI? Um and so and 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 out basically out there like functioning as a as a as a business of like making money and just, you know, l literally where the the AI does all the work itself and just di you know, issues me dividends.

And so maybe you know, maybe that the that you know, maybe that maybe that's the the final outlier result. We have we have a few uh founders who are chasing that kind of thing. Um so I I would describe that as i I would describe that as kind of the the the ladder that the best founders are on.

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. Uh having run running my newsletter uh 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 constant like filling out forms. Uh and so I guess depends on do you have contractors, does that count? Yeah. I mean look Bitcoin's

Satoshi pulled it off. But like, you know, the open source commun you know, like does that count? I don't know. I guess I guess it counts, okay. Yeah, exactly. Right. So yeah, that that that yeah. Um and I was I I would say I don't propose to have answers here, but more just like The smartest people I know are are many of the s many of the smartest people I know are are thinking hard about this. Yeah.

What do you think about motes? A big question constantly in AI, you know, the fact that everything's changing, just what's your guys' thesis on motes in AI. Does is that even a thing? Do you care? My experience with like really big technological transformations, and of course I I kind of lived this directly with the internet and I saw this happen.

is the really big technological transformations, they they take a long time to play out and there's there's all of these structural implications that just kind of cascade out over time. And then there's kind of this this there's this like rush to judgment up front.

where people kind of say, oh, it's therefore obvious that, you know, XYZ, it's therefore obvious that this kind of company is gonna be the company of the future and not that kind. It's obvious that this incumbent's gonna be able to adapt and this other one isn't. It's it's obvious that there's economic opportunity in this kind of startup and not in these others.

Um it's obvious that the motes are gonna be in this area of the technology, but not in this other area. And and there and you know what everybody does is they 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 the media naturally prizes like definitive answers over open questions.

Is it you know, you you want you know like when C N B C is like booking guests, they want a guest who's gonna come on and say, Yes, this is the way it's gonna 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 from like nineteen ninety three through like nineteen ninety seven.

Or even through like for that matter, even through like two thousand five or two thousand ten and you look at like the kinds of confidence statements people were making in the first ten or fifteen years, like I would say like almost all of them were wrong. Again, 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 and again, a lot of we've talked about a lot of this, but like it it the implications on like what are the definitions of products? What are the definitions of company? What are the definitions of 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 gonna do? Um, you know, uh and so

It's just like there's just there's there are just a tremendous number of unknowns. Like a a a a a a very, very large number of unknowns. And I think it's just like really, really dangerous to prejudge these things. And so I'll I'll just give I'll just give and it's just I'll just run this as a thought experiment, you know, so you can see what you think on this. But it's like, you know, i like do do do AI models them s are AI models themselves like defensible? Like uh is there a moat?

uh 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, Um, 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 and you know, they are getting paid like MBA stars.

Um 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 gonna be two or three companies that are gonna end up with like, you know, a hundred percent, you know, I don't know, whatever, fifty fifty or

thirty thirty thirty or ninety ten one or whatever it is, market share and then they're gonna have whatever profability they have and it's gonna be a kind of a classic oligopoly and or or maybe, you know, or maybe one company's gonna win definitively 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 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, um, you know, that in the uh, you know, kind of Christmas of Chat GPT that like within basically a year to a year and a half there would be, you know

five other American companies that would have basically, you know, exactly capable products. Um, 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. Um, I would have been like, wow, like it, you know, the the thing that seemed like it was black magic 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 ch of GPT three coming out, there were their open source GPT threes running on a fraction of the hardware, right, that were available for free.

Um 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 f and then Deep Seek and, you know, Kimmy and all these other advanced companies.

Um and so like even at the level of like LLMs or you know AI models, like you can squint and make that argument either way. By the way, same thing at the level of apps, right? It's like, you know, one school of thought is, you know, the apps the apps are not a thing'cause like the model's just gonna do everything. Um but an another way of looking at it is no, actually like actually adapting the model is kind of the engine into a it into a domain involving human beings.

um where you need to like actually have it fit for purpose to be able to function in the medical industry or the legal industry or you know or whatever uh or coding, you know, no, you actually need like the application level is actually gonna matter enormously. And maybe the L LM's commoditize and maybe the value goes to the Um and and and again, you can kind of squint either way on that one. And I and I know very smart people who are on both sides of that argument.

Um and so I I my honest answer on this is I think we're in a process of discovery over time. Um, which is, you know, it's in the way I think about this kind of structurally is it's 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. Um in you know, actual individual choices made by entrepreneurs um, you know, matter a lot.

Um, you know, the economics matter a lot. Availability of investor capital varies over time. That matters a lot. Um and this is a this is a complex system. And so we we actually don't know the the outcomes on this yet. And and we need to basically be we need to be open to surprises at the structural level.

uh uh uh of what happens. And of course as a as a VC this is very exciting because it means we sh you know we we're doing this now. We should kind of make bets uh along every one of these strategies um and kind of see and and and see how this plays out.

Uh 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 I guess I would say if if if if they exist, I haven't met them yet.

So what I'm hearing here is don't over obsess with Motes at this point because we have no idea what it'll end up being and as much as it may feel like, okay, there's no way open AI will lose this lead. Clearly we're seeing a lot of competition. GPT wrapper point is really great. A lot as such a derogatory term. I don't know, 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.

Yeah, well it's it's like a little bit like I don't know, I mean even just like with you know the you know the bit the you know this has been the you know the the the holiday if you know three years ago was the holiday of of Chat GPT this last, you know, month or whatever has been the holiday of of uh Claude particularly Claude Code, right, for for coding.

But it's like, you know, so it's it's pretty amazing because it's like, okay, there was Claude, which is obviously a great accomplishment, but then there's Claude Code, which is which is an app which is an app, right? So it's it's a clod wrapper. Mm-hmm. Right? It's a you know agent harness. Um and then um and then they did this amazing thing where they came out with uh was it uh cowork. Cowork. Um and uh and remember what they said cowork, which is uh Clod Code wrote co-work in a week.

Yeah. A week and a half. Yep. A hundred percent. Well, and that's r th and there's two ways of looking at that, which is like, wow, that's really impr I mean obviously th 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, yeah.

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 and 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, y I 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 for for regular people.

And obviously I you know, I I don't I'm not even saying I know anything, but just like obviously they're all gonna do that. Right. Um and so you know, how defensible is that? And in you know, in six months, y you know, and we've seen this happen before. Like in six is is quad code gonna get lapped the same way that you know GitHub copilot got lapped.

You know, the the history in the last three years has been ev everything that looks like it's like the fundamental breakthrough gets gets basically replicated in lab break 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 of drinks into 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.

Um and then and then you know, again, evidence of that is, you know, Deep Seek, you know, came out of left field and basically was like a, you know, y re implementation of a lot of the ideas under American Big Labs and you know, had and had some original ideas of of its own. Um, but like, you know, wow, it wasn't that hard for, you know, some, you know, basically a 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 their rock stars, um, and they're, you know, incredibly bright and creative people.

Um and you know, maybe there's, you know, a dozen nascent ideas in any one of these labs that it's actually gonna be a huge breakthrough that's gonna be hard to replicate. And so again, it's just like I I think we just need to I don't know, my view is or I my view myself I need to put like a big discount on my forecasting ability on this one. Like it for me it's much less interesting to try to say okay, as a consequence industry structure in five years is gonna be active.

the big winner in the category is gonna be company Y, the big, you know, product killer app is gonna be Z. It's like I This is to say I don't think I can predict that. Um I I think we're I I think a much much better use of my time is is being being very flexible and adaptable at at a time like this.

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 raise the the largest fund in history.

Is that is that the way you win in this world? Yeah. So for I mean for us, yeah, for for us, I mean we have we obviously have a very very deliberate strategy. One one way to think about this use the Peter Thiel for you remember the Peter Thiel formulation of uh you said there's a two by two, there's optimism and pessimism.

And then there's determinate and uh is it indeterminate and uh indeterminate. Uh right. Um and so um and he always argued that 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, um, 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 dil delusional thinking.

And what the world needs more is tr 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 he would sort sort of maybe say, you know, VCs are indeterminate optimists

Um and then he would say, you know, El Elon is the d determinate determinate determinate optimist where it's like, No, I'm going to build the electric car and I'm gonna do you know, solar and then I'm gonna do sp you know, Mars, you know. And I'm gonna these very concrete things. And I 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 I think maybe you know if you and I disagree with 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 sixteen Z, which is ah and the and the reason for that is it's 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 sixteen z or Silicon Valley is 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 pro product creators. Right. And and and each of those individual people is a determinate optimist, like each of them e each of them individually has like a very strong view of what they're gonna 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 ten of those. We have a hundred and a thousand and then ten thousand of those. And the 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 then just the the nature of you know, the nature of the future is like we just don't know all the answers and that's okay. But and then 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.

Um and so yeah, I would I would put myself firmly on the side of the indeterminate optimist. I'm in uh I'm wondering if the answer to the question of what you look for now more and more is this determinate optimistic founder that has this massive ambition and is Actually working on achieving it. Yeah. Yeah. No, that's right. That's right. I mean, look, the founders need to be deterrent determined and optimists. Like they need to have a very specific plan. Now,

And yeah, yeah, look, the cr the critique the critique always you know, the critique from the founders is, Oh, UVCs have it easy'cause 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 have to make the bed you lay in, you can like place multiple bets, you can operate 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. Um, you know, and th and there's there's truth to that. You know, the counter argument on that is the founders get to run their companies, we don't. So So you know, we don't 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 determinate optimist is you actually get to get to single mindedly execute against that goal. And and and you know, look, in the long run, who who does history remember? History remembers Henry Ford. Right, not you know, whoever was the you know, whatever the seat investor who seated for a motor company and and, you know, ten other car companies have failed.

Right. Um and so, you know, the determinant optimist is the per you know, the founder is the the founder and the company builder and the engineer. I mean, these are the people who actually do the same and you know deserve ninety nine point nine nine nine nine percent of the credit.

But uh you know, having said that I I do think there is a role for having s having something determined at Optimus in the in the uh in in the background, no, helping along the way and and helping keep the whole the whole cycle going. Do you think about

AGI in shifting your investment thesis. Like as we approach AGI and hit AGI as an investor. How do you think about your investment thesis changing? Yeah. So I've always kind of had a little bit of an issue. I've always kind of struggled with the concept of AGI, um, because it at least Well it's why there there's those defined terms which is w where I kinda struggle with it, which is

There's like the prosaic there's the there's the prosaic uh definition of AGI. And then there's like the, I don't know, cosmic definition. And the way I would describe it as, well, I'll start with the cosmic one. So the c the cosmic one is basically the s it's the singularity, right? Um, and so AGI is the is the moment where you enter the singularity, which is to say the where the world fundamentally changes. So like the r the rules of the old world are gone. We're not operating in a new domain.

And then, you know, the kind of the full definition of singularity is like it's a world in which, you know, human judgment is no longer really relevant because the, you know, you get this self improvement loop, the AI is the AI is improving itself and it's sort of racing, you know, so called takeoff scenarios. You can see you get this takeoff thing.

Well, the AI is improving itself and the machines are making decisions so much faster than people and people are just sitting there watching the the the machine do its thing. You know, I and I kinda 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 I 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 can talk about that more. But um the the 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 Uh it's when the AI could do every economically relevant task as good as a person. The way um the co founder of Anthropic put it is like a basket of the most valuable economic tasks. So it's like

ten fifteen, not every single economically valuable task. Okay, got got it. Yeah. So that's maybe even a slightly reduced slightly reduced definition. Um and by the way, we're gonna you're clearly getting close to that if we're not already there. And so I on that one, I kind of feel like

So I kind of feel like the cosmic one overstates what's gonna 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 it's almost too reductionist. And 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 so the way we say that is HI always is the y you know, the definition you gave the definite idea of it's kind of incom it's always kind of relative in comparison to a human worker. 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 an example, human IQ.

Human IQ, you know, kind of what they call fluid intelligence, or the sort of G factor of kind of uh, you know, uh uh fluid intelligence. Uh IQ, I think, tops out in humans as a species. It tops out around 160. Right. We're at at at like one sixty it's like Einstein level. Einstein finds it. Mintures by Q. In terms of IQ. Like you just tops out of one sixty. The the one sixty 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, um, it would be probably 140 um is kind of the IQ that you're looking for there. Um if you're looking for like a really good lawyer, it's probably one thirty. Um if you're looking for like a really good like online manager in a business, it's probably one ten.

Um, 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, is probably one oh five. Right. And so the the kind of scope of like impressive human, you know, the the the the the ability of the human organism to do intellectually impressive things

You know, it it it's sort of that one ten to one sixty 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 one forty, one fifty, one sixty. 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 and you know, you already have people running these extributes to kinda do human equivalent, you know, kind of IQ uh uh you know, for for existing A model. And by the way, existing A models right now are kinda testing around the one thirty, one forty level. Which means they're gonna get to the one sixty level and they're you know, they're arguably on the mass side starting to get to the one sixty level now.

But like I I think we're gonna have AI models relatively quickly that are gonna be like one hundred sixty, one eighty, two hundred, you know, two two two fifty, three hundred. 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 Einstein's and of course the world would be better off with machines that have IQ, you know, more IQ like Einstein or greater than Einstein.

But like I think IQ's IQ of the machines is gonna exceed that of the humans. I think that's that's really good. Um and then the performance, you know, again it goes back to like the AI coding thing is happening. The performance against task is going to get better also. Like I th I think, you know, this is where line of Star Waltz in particular, it was like, yeah, okay. Like this thing is starting to generate better code than I can.

Okay, so now we're gonna have AI coders that are actually better coders than the best human coders. I think that's great. I think we're gonna have AI doctors that are better than the best human doctors. I think we're gonna have AI lawyers that are better than the best human lawyers.

Which actually is gonna be very very interesting to see. Uh which we can talk about. Which I think is also great. Um 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'cause we've been capped by our own biology. And we're gonna get to experience what it's like when you have the capability at your fingertips that's actually better than human in these domains.

Um and so I I kinda you see what I'm saying, which is like i I think this idea of like human equivalent is just gonna be like a footnote. It's like, oh yeah, that was just on Tuesday. you know, in in twenty twenty six is when they hit that and it kind of didn't matter because the the next question was like, okay, what are we gonna what are we gonna what do we gift to do in a world in which ra we actually have machines that are better than that?

Right. And so I so so I think this is gonna be much more of an exploratory process for actually exceeding human capability than it's gonna be any sort of particular singular singularity moment or whatever that happens just that just happens to coincide with the human threshold.

Two hundred IQ. I uh just like that frame of reference is such a uh mind expanding way to think about just how fast and how smart these things are gonna get and and quickly.

Well, I don't know if you have this experience. I I have this experience all the time. Well, two two experiences I have all the time. One is just like I've just like sh like I know I ought to be able to do this, but like I just can't like it's gonna take too long you know, I I I wanna write this thing or I wanna like whatever, I wanna have this theory on this thing or I have a plan or whatever and it's just like fuck like I 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 I've you had this, you get interested in something, you read ten books and then you're like, Shit, I forgot almost everything that I just read. Like I I can't

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 end endless frustration. Yeah, so it's 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 so there's that and then I I don't know how often you have this, but I have this on a regular basis. It's just like uh you know uh I I you know because of what we do, like I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point, 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 outthinking me and they're gonna keep outthinking me and I just can't and I'm just like all right goddammit like I gotta go home and I gotta like have a drink because I'm just not you know I'm I'm just not whatever that is, I'm not that.

And so we're just so used to having those limitations. Um, 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 your credit for. 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 ten 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 audio books now all the time. So in terms of media data, what do you what are you reading, what are you paying attention to these days in terms, I don't know, podcasts, newsletters, blogs, things like that, and then any books in particular?

Yeah, yeah. So what I read is basically I mean I it's I was read basically three categories of things. So like in terms of like general media, um it's basically I I sort of um I always describe it as I have like a almost a perfect barbell strategy, um, 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, um, or it's like a book that was written fifty years ago that has stood the test of time.

And then you know, we're presumably there's something timeless in it. Um and and then it it's sort of everything in the middle I'm always like much more skeptical about and and it's in particular it's 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 and by the way, you can t you can do this. Just read last week's newspaper, right? So we're 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. N 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 it's just like wow, like you know, n like none of this happened. Like I wish I had never read this, like, Oh my God. Um and then you know it's kind of the same thing with magazines. Like go back and read old magazines um and just like the the the the the level of the you know the just the the endless numbers of predictions that they make.

Yeah, and and kind of you know, the problem with you know, newspapers at least are going day to day. The thing with magazines is like every it's like a a week or month, you know, kind of a long cycle. And so it's even, you know, by the time an article even hits publication, it's you know it's it's often out of out of date. So I just I just have like a big problem with kind of everything in the middle. Um and so it it's either it's either it's either of the moment or timeless.

But then yeah, you mentioned like newsletters. I mean, so the the the other thing and then, you know, this is maybe obvious, but I think it's probably still underrated, which is uh the actual practitioners in the field who are actually creating content, I think probably is still like dramatically under underrated.

Um, 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 we we're we're used to being in this mass media kind of culture in which basically everything is mediated.

Right. Everything got filtered through like T V 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 that that that kind of open that up for people and make that possible.

Um and so yeah, like domain practitioners are um, you know, really great. I mean, i i just to state the obvious in AI, you know, it's obviously your your stuff, but also like, you know, let Lex you know, like 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, is you know, is any looks i the the critique always is, you know, people talk their book, like if I'm running a startup or whatever, I'm just selling. But it's like and there's and there's always a little bit of that. Um, but it's also uh you know, my experience is people love to talk about what they do.

And and you know, and they they fundamentally like want to express what they do and and and they want to explain it and they want people to understand it. And everybody kind of enjoys that and they get to contribute to kind of human knowledge by doing that and they get ego gratification by doing that. Um 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 ten years ago. So I yeah, I do as much of that as I can too. And there's also just this culture in 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. Yeah. It's uh somebody said uh Silicon Valley is a company town, but the the the the the company is Silicon Valley. Right.

But and but and again at the at the level this goes to again there's one of these great n equals one. At the level of n equals one is somebody, you know, and I've I've run startups before, I've run companies before. Um at the level of n equals one of like running a company, that's just a giant pain in the fucking butt. Like

'Cause 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?'Cause you get to hire people with all these skills and experiences.

Right. And you you're in this you're in this ecosystem that it that adapts, right? And channels talents and and and and skill and knowledge and people into in the into the new fields. And so you know, so there you know, there's kind of the push and pull of that at the level of just being an individual individual CEO.

Um at the level of of of just being in the ecosystem to your point, like yeah, it's a 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, um, you know, I think AI, I I did the comment 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's still called Silicon Valley. We haven't made silicon here in decades. Right? Uh we used to actually you know, they used to call it Silicon Valley because they used to make chip.

Right. They just said that like the actual fabs were in Silicon Valley and then they and they designed them and they made the chips. Um and and so and that was, you know, wave one starting in the nineteen f you know, actually that was like actually no, actually more or less like wave three or whatever, but like it was you know, that was when the the in the sh the the area was named like in the nineteen fifties.

But now we're on like wave nine, right? Um and and the the company town phenomenon where the company is, the industry. Like the the the again, the indeterminate optimism. Nobody had nobody had to sit and plan and say, okay, in the nineteen nineties Silicon Valley's going to do the internet, in the two thousands they're going to do the smartphone, in the twenty tens they're going to do the cloud, in the twenty twenties they're going to do AI.

It it just the the the the right, the indeterminate optimist optimism of ecosystem, flexibility of the ecosystem meant that the the the Silicon Valley could could morph um i into all these categories and and again a maybe a a testimony to in determinate optimism. This reminds me of the meme of how we're all just wrappers over sand. Everything we're building is just

Rapper, rapper, rapper, rapper. The rapper thing is hysterical. Yeah, yeah. I'm a I'm a software company, I'm I'm a I'm a chip wrapper, right? Um yeah, I'm a I'm a I'm a I'm a business application, I'm a database wrapper. Um yeah, exactly. I'm a sand rapper and yeah, you and I are all you we're all now sand wrappers.

Sandra. Perfect. Okay. One more question along the media diet I asked your partner Ben Horowitz, uh, what to talk to you about, uh, the Z in A sixteen Z 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're really into these days? Any movies you've absolutely loved recently? Yeah. So the movie that blew my socks off uh last year, which I think is the best movie of the decade for sure, maybe of the last like fifteen years. Is this movie uh unfortunately it's one of these things, but 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 gonna really enjoy it. So I won't s I won't spoil too much of it. So it it at at at at the surface level th th this well, the following spoils nothing. At the surface level, it's sat in a small town in New Mexico called Eddington, which is a small town of about six hundred people. Um

And um there's a uh sheriff uh who's played by Joaquin Phoenix, who's like an old, crusty, basically right winger. And then there's a um uh there's a uh mayor uh played by Pedro Pascal, who's basically a young hip progressive. And uh 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, it it then in it intersects and it it sort of extends into the summer of twenty twenty. So, you know, kind of the the George Floyd moment and then the you know, the the protests and riots and kind of everything that's so sort of the convergence of COVID and then the um and then all the BLN stuff.

And and and and then um it it and then and and then there's a third kind of element to it, which is um 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 kinda pull that in uh as sort of a thing that looms larger and larger over time. And then um the thing it really is great at is it really shows, um, 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 which is which is, you know, what what actu what actually happened, right? And so

So it it's it's so so the reason I love the movie so much is one one is it's the first movie that directly grapples with twenty twenty, of what happened in twenty twenty, and that just like fully, fully engages and grapples with like all the dynamics that were 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 happened in the real world and people were kind of experiencing events online i you know, with like in a way that was like very central in their lives. Right. Um and so it does like a really good job of pulling in like smartphones and social media.

Um in a way that um uh in a way that movies really, really, really struggle with and then the whole thing comes together in an incredibly entertaining way.

Um and so and I won't even say I I I won't even say I completely agree with the movie or whatever, and I I think the director of the 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 twenty twenties in America in a way that I think many other filmmakers who are very talented have just been very scared of touching.

Uh and and this guy for some reason he's just like, Yeah, I'm just gonna find all the third rails and I'm just gonna like fucking grab'em. I can see why that's your favorite movie of the year. It's great. It's great. It's great. Everybody should see it. Oh man.

Okay, f final question. I want to ask about your prior uh 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. I mean, we have s 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 it's really hard to to to uh to uh

you know, to to to pull out specific ones. Um but I'll uh you know I'll I'll I'll talk about a few. Um yeah, I mean or just I I'll just observation. So one is my my ten year old um I've my ten year old my ten year old right now is a hundred percent obsessed with replit. Um and and by the way, it was not from me. Do you have kids? I do, I have one, two and a half year old. Two and a half. Okay. So you haven't run into what I'm running into now, which is whatever it is you do is not cool.

Right? Like it's 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 ten, whatever you do is like deeply uncool. Right. And I'm and I'm highly aware of that. Um and so like if I mentioned, oh yeah, we work on XYZ, you know, he's like Okay. Um but when he discovers something, then then it's cool.

Or when his friends tell him about it, it's cool. And so he d he he through no inter interference on my part, uh discovered Replit about uh about uh three months ago and discovered vibe coding and is like completely obsessed with vibe coding games and all kinds of all kinds of things and like literally do it for hours. And so I'm I'm seeing that phenomenon play out, uh, which is super fun. Um uh that's one. Two is I am just completely in love with all the AI voice stuff.

Um, I think it's just absolutely amazing, hysterical. Um, my favorite uh party trick at dinner parties now is to fall out uh Groc uh with uh bad Rudy. Which is if you've seen it's uh it's the uh it's a follow mouse raccoon. uh a uh avatar uh on the uh in the in the

So um uh I think that's super fun. We have this company, Sesame, that had, you know, they they went viral last year for this, uh, you know, for these this that these just incredibly like uh, you know, i intimate emotional, you know, kind of voice experiences. Um so I think the voice stuff is fantastic.

I'm also super fascinated by all the voice input stuff. Um and so um you know, lemma you know, one of the most sweet uh one of the most recently um c company recently um uh sold. But um, you know, the the all the the I I think like the pendants, the wearables, like all that stuff is gonna be big, the meta glasses.

Um I you know, I think there's gonna be a whole wearables revolution here. Um I I love the voice input stuff. Um I have this app on my there's this app on my phone now called WhisperFlow, um, which is uh voice transcription, um, which works like staggeringly well. Um uh it's like incredible. 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 kinda it kind of understand 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 I think the voice mode stuff is gonna be is gonna be uh is gonna be really great.

Uh subscribers of my newsletter get a year free of Replit and Whisper Flow. So there we go. Uh uh what's the what's the most memorable thing your son built with Replit? Oh well so he's gotten super into Star Trek. Um and so uh so far it's been he's really like writing like Star Trek simulators. Um so uh like all the uh you know, all the uh uh by next generation they actually have next generation. Okay, I was gonna ask what.

Well he like actually we like them all. We watched the new Starfleet Academy last night, which actually is quite is actually quite good. Um but uh we we watched the original, you know, we watch we watched them all. But it was in Next Generation where they actually developed an actual design language for the computers.

'Cause if if you watch the original series, they just had like basically, you know, knobs with lights and th y 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

It actually had a UI design language. And so uh 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 or you know, whatever this, that or whatever. And it actually uses the they called it the seven inner now, they call it L cars.

um uh d design language. And um it'll you know, it'll actually build you like Star Trek next generation, British consoles um using that design language, but you know, with your choice of like a Star Trek game, for example. Um and so he's he's going crazy for that kind of thing. That sounds extremely delightful. You guys should uh open source or release that.

Mark, I like I said, I could talk to you for hours. Uh well you got things to do. Uh anything you wanna leave listeners with before we wrap up, anything you wanna double down on or just leave listeners with? Yeah, so a couple of things. So one is we got super lucky last week, uh Packy McCormick uh wrote the best piece ever written about us, actually, um, which he released. Um and so it's the best explanation of what we do.

Uh and how we think. And so I I would definitely recommend that. Um 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, um, in a you know, and content. Um and so I'd definitely recommend our YouTube channel.

Which I I think has a lot of great stuff and is gonna be very exciting in the next year. Awesome. We'll link to that. I think it's just youtube dot com slash eight sixteen Z, something like that. And you guys have great stuff. Mark, thank you so much for being here. Awesome. Thank you for having me. I really I really appreciate it. This episode of the A sixty Z If you like this episode, Leave us a rating or review and share it with your friends and families.

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