Marc Andreessen: The real AI boom hasn’t even started yet

If we didn't have AI, we'd be in a panic right now about what's going to happen to the economy.

We've actually been in a regime for 50 years of very slow technological change in the face of declining population growth.

The timing has worked out miraculously well.

We're going to have AI and robots precisely when we actually need them.

The remaining human workers are going to be at a premium, not at a discount.

How big of a deal is the moment in time that we are living through right now?

This is a very, very historic time.

AI is the philosopher's stone.

Now we have a technology that transfers the most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought.

I spent a lot of time with the cutting-edge AI-forward founders.

The most leading-edge founders are thinking of, can you have entire companies where the founder does everything?

There's all this concern that young people, jobs are not going to be there for them.

AI is replacing them.

Everybody wants to talk about job loss, but really what you want to look at is task loss.

The job persists longer than the individual tasks.

What's your sense of just the future of 3 very specific roles?

Product manager, engineer, designer.

There's like a Mexican standoff happening between those 3 roles.

Every coder now believes they can also be a product manager and a designer 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 and a coder.

They're actually all kind of correct.

What happens is the additive effect of being good at two things is more than double.

The additive effect of being good at three things is more than triple.

You become a super relevant specialist in the combination of the domains.

People aren't fully grasping how much this is changing.

People who really want to improve themselves and develop their careers should be spending every spare hour, in my view, at this point, talking to an AI, being like, all right, train

me up.

Today, my guest is Marc Andreessen, one of the most seminal figures in tech and in business.

He invented the web browser, built the world's largest venture firm.

He's also a multi-time founder and an investor in essentially every generational tech company, and is also one of the most clear-minded, lateral, and insightful thinkers about both

the past and the future of technology.

In this very special conversation, we chat about how unique and significant the moment that we are all living through right now is, what skills he's teaching his kids to thrive in the

AI future, what happens to product managers, designers, and engineers in the coming years, where moats exist in AI, what the most AI-native founders are doing differently, and so much

more that is just scratching the surface of this very deep and important conversation.

You are going to walk away from this chat being smarter about what is going on in the world right now and where things are heading.

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For suggesting topics and questions for this conversation.

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

Awesome, Lenny.

Thank you.

It's great to be here.

I want to start with just a big picture question.

I have a billion directions I want to go, but I think this is going to give us a little bit of a frame of reference.

How big of a deal is the moment in time that we are living through right now?

This is a very, very historic time.

I think 2025 was maybe the most interesting year in my entire career and probably life.

And I think I would expect 2026 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 think, in kind of full-scale collapse

right now.

By the way, there's a lot of data to support that.

And so I think there's just, there's, 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 kind of corresponding with that is the national and global conversation have 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 would describe 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, it's just dramatically expanded.

And I think that's, that's now on a one-way train for just a much broader range of discourse.

And then, you know, there's also just these like incredibly massive geopolitical shifts that are happening.

And obviously the U.S.

is changing a lot.

Europe is changing a lot.

China's changing a lot.

Latin America, by the way, is changing a lot.

Very dramatic, you know, events playing out down there right now.

You know, kind of all over the world, like I think a lot of assumptions are being pulled out into the daylight and reexamined.

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 going to 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 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 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 1989, you know, maybe, maybe the end of World War II.

Um, you know, kind of moments like that.

It certainly feels like that.

Good God.

What a time to be alive.

Yeah.

In terms of the AI piece, which is where a lot of people are trying to figure out what to do, what do you think isn't being priced in yet in terms of the impact AI is going to have

on, say, the world or just people listening?

The AI thing, I think at this point, I think it's pretty clear with the, with, you know, our technology hats on that like this stuff is really working now.

Right.

And so there, there was this, you know, kind of, you know, when, when there was a ChatGPT moment, you know, 3 years ago, it was only, by the way, only 3 years ago.

Right.

Um, was the ChatGPT moment.

And 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, you know, there's this big question like, can you harness this technology for reasoning and for, you know, problem solving and in domains that like really matter,

you know, medicine and science and law and so forth.

And, you know, it turns out the answer to that is yes.

Right.

And, you know, the last 12 months and especially the last even just the last 3 months have really proven that like AI can really do like, you know, I mean, you're seeing it all now,

you know, You can actually, you know, AI is now developing new math theorems.

Um, you know, they're, you know, over the holiday break, you know, there's sort of the, but it feels like the AI coding thing, you know, really hit critical mass.

Uh, and the world's best, the world's best programmers, right?

Including like Linus Torvalds, you know, for the first time over the holiday break basically said, yeah, AI is now coding better than we can.

And so that, you know, that's, that's incredibly, incredibly powerful.

And I think we, 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, you that's going to include like many very important domains.

So, so like the technology feels like it's moving fast and it's going to be working really well.

I think the thing that is not well understood, I think a lot of people have a—

I think, you know, a lot of people in the industry have kind of what I would describe as this one-dimensional thing, which is, okay, as a result of the technology now working, AI just

kind of sweeps the world and changes everything.

And I think that's kind of the wrong— that's kind of the wrong frame.

Or I think it's based on an incomplete understanding of the world that we live in or the world that we've been living in for the last 80 years.

And I would call 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, 30 years or 50 years, it's felt like we've been in a time of great technological change.

But actually, if you look for actually evidence of that, like in statistical evidence of that, analytical evidence of that, like you basically can't find it.

And in particular, economists have a way of measuring the rate of technological change in the economy that is productivity growth, which which we could talk about what that means, but

basically it's sort of the mathematical expression of the impact of technology on the economy.

And productivity growth for the last 50 years has actually been very low, not very high.

So we all feel like it's been very high.

There's been lots of technological change.

What's actually happening is it's been very low.

And in fact, the pace of productivity growth, like in the US, is running at like a half of what it— in my lifetime, in our lifetimes, it's been running at about half the pace that it

ran in, um, between 1940 and 1970.

And it's been running at about a third the pace that it ran between about 1870 to about 1940.

And so statistically, in the U.

S., in the West, technology progress in the economy, technology impact on the economy has actually slowed way down.

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

time.

So we could talk about that.

And then there's this other, like, just incredible thing that's happening, which is the, you know, sort of the demographic collapse, right?

It's sort of a Western phenomenon and increasingly global phenomenon, which is, you know, the rate of reproduction of the human species is, is in rapid decline.

And, you know, there are many countries, you know, including the U.S., where, you know, the rate of reproduction is, you know, under 2, you know, meaning, meaning that, you know, many,

many countries around the world, by the way, including China, which is a really big deal.

Are actually going to depopulate over the next century.

And so you have this kind of precondition that says there's actually been very little technological progress happening in the world and the world is going to depopulate.

And so AI is going to enter a world in which those two things are true.

And I think it's— this is incredibly important because we actually need AI to work in order to get productivity growth up, which is what we need to get economic growth up.

And we actually need AI to work because we're going to need, you know, we're going to need machines to do all the jobs that we're not going to have people to do.

Because we're literally going to depopulate, we're going to depopulate the planet over the next 100 years.

And so I, I think the interplay of these factors is going to be much more interesting and frankly more complex than a lot of people have been thinking.

I'm going to follow this thread about kids.

I know you have a kid and one of my most, my favorite lenses into how people think and what they value is what they're teaching their kids, what they're steering their kids towards.

Are there specific skills or I don't even careers that you're steering your kid towards?

The way I think about this, and you know what, yeah, we have a 10-year-old and so, you know, and we actually homeschool and so we think a lot about this.

So I think the way to think about the impact of AI on people, on specifically people as individuals, I think it's actually, you know, 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 can talk about.

But there's, 2 specific things at the level of like an individual person or an individual kid.

So I think it's pretty clear that AI is going to take people who are good at doing things and it's going to make them very good at doing things, right?

And so it's going to be a tool that's going to sort of raise the average kind of across the board.

And, you know, look, you see that playing out already.

You know, anybody who's in a position where they need to, you know, write something or design something or write code or whatever, if they're, 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, So there's sort of that aspect to it.

And I think the way the education system writ large is going to teach AI is going to be based, you know, hopefully a lot on that.

But then there's this other thing that's happening, which we're also starting to see, and we're really seeing it particularly in coding right now,

where the really great people are becoming like spectacularly great.

Right.

And so you kind of use it, use the term, you think about like the super empowered individual, right?

The individual who is like really good at coding or really good at making movies or really good at making songs or really good at designing, you know, making art or whatever, whatever

those things are, or, you know, or podcasting or, you know, hopefully venture capital, you know, if you're very good at it and you can really harness AI, you can become spectacularly

great and like super productive.

Right.

And, you know, I'm sure you have a lot of friends in this, in this category as well.

But like, you know, the really, really good coders are experiencing this right now.

My friends who are really good coders are like, oh my God.

All of a sudden I'm not twice as good as I used to be.

I'm like 10 times as good as I used to be.

And so I think at the unit of like n equals 1 of like an individual kid, I think the question is kind of how do you get them in a position where they're kind of this kind of super empowered

individual such that they're going to be really kind of deep in whatever it is they're going to do, but they're going to be deep in a way that's going to let them fully use the power

of AI to be not just great, but to be like spectacularly great.

And I think that's going to be the real, you know, 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 shoot for.

So what I heard there is essentially agency.

That's where that we see on Twitter all the time is building an agency, them not waiting for someone to tell them 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 California for the last couple of years.

It's really interesting because it's, it's, I had a lot of trouble with this early on because I'm like agency, okay, what are they talking about?

And what they're kind of talking about is like, you know, initiative, you know, initiative, you know, um, you know, willingness to, you know, You could just do things.

Um, you know, uh, what is it?

Uh, the, the Semmelberf has the great term live player.

Um, you know, you, you, you can be like a primary participant in events.

And at first I was like, well, yeah, like that's kind of obvious, right?

Like, of course.

And then I'm like, oh, actually it's not so obvious anymore because kind of your point, I think so much of our society is based on like, there are all these rules and everybody gets

taught kind of by default.

You're supposed to follow all these rules.

Right.

And then everybody, if you like break the rules, like everybody gets freaked out.

It's like, oh my God, he broke the rules.

And so like we, we, we have somehow worked our way, our way kind of, you know, I don't know, psychologically, sociologically, you know, kind of into a state in which I guess the natural

assumption for a lot of people is, you know, the thing that you, for example, 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 12 school system or whatever has gotten kind of more and more

focused on it over time.

And it's like, yeah, it's like, no, you should actually.

And again, especially unit n equals 1, like of your kid, it's like, okay, look, there's something to be had.

We, I just had this conversation with my 10-year-old last night, actually.

I rolled out

the concept of, you know, in order to lead, you must first learn to obey, right?

In order to, you know, issue orders, you must learn how to follow orders.

And, you know, you know, kind of trying to keep him with some level of structure in his life and not just pure agency.

But yeah, I mean, and so look, you know, some rules are important and so forth.

But yeah, no, look, there is like a huge

there's just a huge premium in life on being somebody who is able to like fully take responsibility for things, fully take charge, run an organization, lead a project, create something

new.

And, you know, maybe, yeah, that has been maybe a little bit diminished in our culture over the last 30 years.

It's healthy, you know, that there's now a term for that that is coming back into vogue.

And then, and again, that's how I view AI for kids is like, okay, AI should be the ultimate lever 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 can fully participate in the world.

I can really change things.

And I, and I, that, that feel that the combination of that idea combined with this technology feels very healthy to me.

What is that quote about?

Give me a lever and I'll move the world.

And I'll move the world.

Yeah, that's exactly right.

Well, so it's actually funny you mentioned that.

So the early kind of scientists, including like Isaac Newton, were super obsessed with this concept of alchemy, right?

It's like, 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 alchemy was the transmutation of lead into gold, which meant the transmutation of something that was very common, which was lead, into something that was very rare and valuable,

which was gold.

And, you know, they, there was this, the, he spent, you know, decades trying to figure out this thing called the Philosopher's Stone, which would be basically the, the machine or the

process that would, it would be able to transmute the rare, you know, the common thing into the rare thing, lead into gold.

And he never figured it out.

And, 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.

Just blew my mind.

Right.

The most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought.

Right.

And so AI is— it is the Philosopher's Stone.

Like, it is that.

It actually is that.

And it's just this incredibly powerful tool.

And that's why I get so excited.

I mean, and again, this is what we're doing with our 10-year-old, which is like, all right, a primary thing that we want to make sure to do is to make sure that he knows fully how to

leverage 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 essential 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, 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 kind of plugged into stuff in Silicon Valley, the more important it is to make sure

that your kids actually fully understand this and know how to use it.

And that's certainly the mode that we're in.

And that's, 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.

It's almost a statement on, you know, education in today's day.

Maybe is there any thoughts there?

I'm just— for folks that maybe aren't in your tax bracket that want to help their kids be successful, maybe homeschool, maybe not.

What advice would you have?

This is the challenge.

And again, this kind of goes to how you're, you know, kind of your original question, which is

education.

There's two completely different ways to talk about and think about education.

The way that's usually thought about and talked about is kind of at the level of like a nation, right?

So, so, you know, it's like a national level issue or maybe a state level issue in the US, which is basically like, how do you educate all the kids?

And of course, that's incredibly important.

And of course, you're going to need like some level of large scale system, like, you know, the national K through 12 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 at n equals 1 for an individual kid, like what can you do with, with an individual kid?

And so I'll just give you kind of the ultimate, you know, kind of the ultimate answer to that question, which is it's been known for centuries that the ideal way to teach a kid at the

unit of n 1, by far the ideal way to do it is with one-on-one tutoring.

Like if you just have an individual kid and the goal is to maximize an individual kid, by far you get the best results with one-on-one tutoring.

And this is something that like every royal family knew in history.

It's something that every aristocratic class knew in history.

There's all these amazing examples.

Alexander the Great was tutored by Aristotle.

He took over the world, right?

Like, you know, many of the great kings and queens and, you know, royal families and aristocrats and so forth, you know, over the course of centuries, you know, kind of always had,

always had this approach.

There's actually also statistical evidence, analytical evidence that this is correct.

There's this, you know, massive question in the field of education, which is how do you improve educational outcomes?

And basically it turns out it's just, it's very hard to improve educational outcomes, except there's one method that always does it.

Which is called the, it's called the Bloom 2 Sigma effect, which is there's one method of education that routinely raises student outcomes by 2 standards of deviation and will take

a kid from the 50th percentile to the 99th percentile.

And that's one-on-one tutoring, right?

So again, if you go back to like n equals 1, you have like a kid and a tutor and they're in this like, you know, very tight loop with each other, you know, where the kid is able to

constantly kind of be on the leading edge of what they're capable of doing.

And they can, they, you know, they, they can move incredibly fast and they get kind of correction in real time.

You get these better outcomes, but you know, to your question, like it's never been economically feasible for anybody other than the richest people in society to be able to provide

one-on-one tutoring for kids.

AI provides the very real prospect of being able to do that, right?

Because obviously now, right?

If you have a kid that's like super interested in something and they can talk to, you know, an LLM about it and they can ask an infinite number of questions and they can get instantaneous

feedback.

Um, and in fact, you can even tell an LLM, it's like, you know, teach me how to do the following.

And you can say, you know, wow, that's like, I don't quite understand what you're saying.

Like dumb it down for me a little bit.

Okay, now quiz me, you know, do I actually understand this?

Like, people can just do this today, right?

And so I think there's this like massive opportunity for parents, you know, in many walks of life to be, you know, with a little bit of time and focus to be able to say, okay, you know,

my kid's probably still going to go through a traditional education system, but I'm going to augment this with AI tutoring.

And of course, there's going to be tons of startups, right?

And there already are that are going to try to build on all the products and services for this.

Khan Academy, you know, on the nonprofit side has a big push to do this.

And so, you know, I think the broad answer might be a hybrid approach with schools plus one-to-one tutoring through AI.

There's also this great— you may have heard there's this great school, new private school system called Alpha, in which everything I just described is kind of the basis of their philosophy,

which is, you know, it's a combination of in-person schools and teachers, but it's also, you know, heavily based on AI and AI tutoring.

And so I think there's like a— there is a magic formula in here.

That I think is going to apply much more broadly.

And I really, for parents interested in this, now would be a great time to really start to think hard about that and to look at the options.

It's interesting because there's all this concern that young people, jobs are not going to be there for them, AI is replacing them.

On the flip side, there's what you're describing here.

It feels like people coming in learning today are going to be moved so fast and learn so much more.

And where do you sit on this divide of like young people are in big trouble or they're actually going to be the ones winning in the end?

Yeah, so the job substitution, job loss thing is just, it's very reductive.

It's, it's, I think it's an overly simplistic model.

And again, it goes back to what I said at the very beginning, which is we've actually been in a regime for 50 years of very slow technological change in the economy.

And so, you know, and again, like I said, it's like at half the rate of the previous era and then a third the rate of like 100 years ago.

And so we're coming out of this kind of phase where we've had like almost no technological progress in the economy.

We've had remarkably little job churn as a result of that relative to any historical period.

And so even if AI like ticks up, even if AI triples productivity growth in the economy, which would like be a massively big deal, it would take us back to the same level of job churn

that was happening between 1870 and 1930.

And if you go back and you read accounts of 1870 to 1930, people just thought the world was awash with opportunity, right?

At that rate of technological transformation, kids were able to like develop new careers into new areas of the economy, building new kinds of products and services.

I mean, you know, a huge part of our, of everything in our modern world today was kind of invented and proliferated kind of during that period.

Um, and so even if AI like triples the pace of economic change in the economy, it's going to just translate to like a much higher rate of economic growth is going to translate to a

much higher rate of job growth.

And, you know, there'll be some level of like task level and job level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation,

uh, that will happen.

And that then corresponding to that, there will be, you know, there will be hiring booms.

You know, I, Quite honestly, I think all over the place.

And then again, go back to the other thing, which is like, this is all happening in the face of declining population growth and increasingly population shrinkage.

Um, and so human workers in many, many, many countries over the next, you know, 10, 20, 30 years are going to be at more and more of a premium, uh, literally because you're going to

have shrinking population levels.

You know, we don't really want to get into, you know, politics particularly, but it does feel like the world broadly is going to reverse course on the rates of immigration that we've

had for the last 50 years.

It seems to be kind of a broad-based, you know, kind of thing happening, you know, kind of with, you know, rise of nationalism, you know, concerns about the rate of immigration and

immigration historically in countries like the U.S., you know, it's kind of ebbed and flowed over time based on kind of how, you know, kind of how the national mood shifts.

And so if you sort of combine in a country like the U.S.

or any country in Europe, if you combine declining population with less immigration,

the remaining human workers are going to be at a premium, not at a discount.

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 it's probably totally off base.

That is extremely interesting.

So what I'm hearing is you're not super worried about job loss.

Is the key here that the timing kind of just works out?

Does population decrease?

You know, like all these kind of have to line up for there not to be this massive job loss with AI?

Yeah, well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy, right?

Because what we would be staring at is a future of depopulation, and like depopulation without new technology would just mean that the economy shrinks, right?

So it would mean that the economy kind of itself kind of shrinks over time.

You know, the opportunity diminishes.

There are no new, there are no new jobs.

There are no new fields.

There's no new, there's no new source of consumer demand for spending on things.

And so you would be very worried about going into a period of like severe decline and stagnation.

And, you know, essentially you'd be looking at these like very dystopian scenarios of like an economy kind of self-euthanizing itself over time.

And so you'd be very worried about like the opposite of what everybody thinks that they're worried about.

The only reason we're not worried about that is because we now know that we have the technology that can substitute for the lack of population growth and then, you know, also for the

lack of immigration that's likely And so, you know, I would say the timing has worked out miraculously well in the sense that we're going to have AI and robots precisely when we actually

need them to keep the economy from actually shrinking.

And I just think like that, that's just like a fundamentally good news story.

To get to the mass job loss thing that people are worried about on the other side of things, you know, you'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 10%, 20%, 30%, 50% a year.

you know, something like that, which are, you know, orders of magnitude higher than we've ever had in any, in any economy in the history of the planet.

Um, you know, it's possible that we get that.

I mean, look, I'm, you know, I have my utopian kind of, you know, kind of, you know, temptation along with everybody else.

If AI like radically transforms everything overnight, then maybe you, you know, let's, let's play out the kind of utopian scenario.

Uh, you get to a much higher level of productivity growth, you get to a much higher level of technological change.

Corresponding to that, you'll have a massive economic boom.

You'll have a massive growth in the economy.

And then corresponding with that, you'll have a collapse in prices.

And so the price of goods and services that are sort of, you know, whatever you want to call it, affected by or commoditized by AI, the prices of those goods and services will collapse,

right?

There'll be price deflation.

And then as a consequence of price deflation, everything that people are buying today gets a lot cheaper.

And that's the equivalent of a gigantic increase in wealth.

Right across the society, right?

Well, you think of this way.

This is actually worth talking about because people, I think, get kind of sideways on this issue.

So if AI is going to transform the economy as much as the, you know, whatever utopians or dystopians or whatever kind of think that it will, the necessary economic calculation of what

happens is massive, massive productivity growth.

The consequence of massive productivity growth, what that literally means mechanically, is more output requiring less input, right?

So you get more economic output for less input, right?

So you're substituting in AI for human workers or whatever.

And as a consequence, you get like this massive boom in output, which with much lower input costs.

The result of that is you get lots of goods and services and all those affected sectors.

The result of those gluts is you get collapsing prices, right?

The collapsing prices mean that the thing today that costs you $100 now costs you $10 and now costs you $1.

That's the equivalent of giving everybody a giant raise, right?

Because now they have all this additional spending power.

That additional spending power then translates to economic growth, right?

The development of new fields.

Everybody's like materially, 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 from, they're all collapsing.

Right?

And so the price of healthcare collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses because this, this, this, this, this

incredible impact that AI is having.

And so in this kind of utopian dystopian scenario that people have, it's not, there's no scenario in which like everybody's just poor.

In fact, it's quite the opposite, which is everybody gets a lot richer because prices collapse.

And then it's actually much easier to pay for the social safety net for the people who, you know, for some reason can't find a job.

And so like, like maybe we end up in that scenario.

I mean, the kind of optimistic part of me says, yeah, maybe AI is that powerful and maybe the rest of the economy can actually change to, to accommodate that.

And maybe that'll happen.

But the result of that is going to be a much better news story than people think it's going to be.

And again, everything I've just described, by the way, is like just a very straightforward extrapolation of very basic economics.

I'm not making any like bold predictions of what I just said.

This is just like a straightforward mechanical process that plays itself out if you have higher rates of productivity growth.

Which are necessarily the results of higher rates of technological growth.

And so I think we're, I think we're looking at, and to be clear, I think we're looking at a world that's not like radically transformed the way that maybe the utopians think that it

will be, or the dystopians think it will be.

I think it'll be more incremental for reasons we can discuss.

But I think that incremental,

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 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 2011, you said that in 10 years we're going to have 5 billion people using smartphones.

And I believe the actual number ended up being 6 billion.

You also, uh, you had this debate with Peter Thiel that I came across where you were debating whether technology has stopped progressing or if new technology will continue to emerge.

And you were arguing there is progress, progress will continue.

And he, 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 love hearing your predictions because I feel like they're actually going to turn out to be correct.

So I should start by saying I've been wrong about tons of things, but you know, I buried those out back behind the shed.

Delete them from the internet.

No web browser can discover them.

I have them nuked out of the Internet Archive so that they're never seen again.

Um, so, uh, you know, I'm wrong plenty of times also.

Um, but yeah, I mean, look, I think, yeah, some of those I got right.

By the way, I will say on the Peter one, I have come I've come much more around to Peter's point of view.

I would probably argue that one like quite a bit differently today than I did.

And I would give his view, I think, a lot more credit.

And it actually goes to kind of the discussion that we did, kind of conversation we just had, which is the real form of what Peter was arguing was we have lots of progress in bits,

right?

But we have very little progress in atoms, right?

And that's the real core of what he was arguing.

And I think I was a little bit, I don't know, missing that or kind of, you know, kind of glossing that over a little bit.

Because I was so focused on making sure people understood, no, there actually is still progress happening in bits.

But I think, you know, a lot of his critiques around the lack of progress in atoms is real.

And again, this goes back to this thing of like in the last, and he, you know, he's talked about this for a long time.

In the last 50 years, there has just been very little technological innovation in most of the economy.

There's been very little technological innovation in particular, anything involving atoms.

You know, there's been very little real-world technological change.

There just hasn't been.

Like the built world is just not that different today than it was 50 years ago.

And if you, and again, if you contrast that, you know, if you, if you compare and contrast 1870 to 1930, it was a dramatically different world.

If you contrast 1930 to 1970, it was a dramatically different world.

If you contrast 1970 to today, it's not that different.

Right.

And look, you just see that you could just like walk around and it's just like, oh yeah, there's a bunch of buildings that were built in like 1960.

Right.

And there's a bridge that was built in like 1930.

And there's a dam that was built in like 1910.

And there's a city that was founded in, you know, 1880.

And like,

what have we done?

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 think he is— I think he is right about a lot of that.

Again, this is also why I think that AI is not going to have as rapid an impact.

It's not going to be, again, this kind of utopian or dystopian view of like everything changes overnight.

I think it just kind of can't happen because of the reasons that Peter articulates, which is there's just— there's so much about how the world works that's basically just like wrapped

up in red tape, like bureaucratic process, rules, restrictions, you know, the politics, by the way, you know, unions, cartels, oligopolies.

There's all these structures in the world that are kind of economic or political or regulatory structures to basically prevent things from changing.

And so, I mean, let's take a great example, like AI's impact on the healthcare system.

Like, by rights, AI is going to have a dramatic impact on the healthcare system and in very positive ways.

But, you know, large parts of the medical system today are, they are cartels, right?

And so there's like a, there's the doctors are cartel and like nurses are cartel, like hospitals are cartel.

And then there's this push to like nationalize all the healthcare systems.

And then you've got, you know, then you've got a government monopoly, right?

And it's like, and guess what cartels and monopolies don't like is they don't like like rapid change.

Right.

And so, you know, you show up as a kid and you're like, wow, I've got like this new technology to do like AI medicine.

And they're like, oh, well, does it threaten doctor jobs?

Well, in that case, we're going to, we're going to block it.

So, and I think a lot of consumers, by the way, you know, I don't know if I see this in my life and you'll probably see this in your life also, which is, you know, like ChatGPT is like

almost certainly a better doctor than your doctor today.

But like ChatGPT can't get a license to practice medicine.

Right.

So it can't substitute for a doctor.

It can't prescribe medications.

Right.

It can't perform procedures.

Procedures, right?

And so there, there are these— anyway, so Peter, Peter, I think, was very articulate and has been for a long time on like, no, there are actually real structural impediments in the

economy and in the political system that we have that actually prevent any— the rates of change that are anywhere near the rates of change that people had in the past.

And, and you can maybe say optimistically, you know, maybe the presence of it, of the new, of the new magic technology of AI, maybe it causes us to revisit a lot of these 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 of the day.

Thank you for that.

Okay.

I love, I love the way you go from just like macro to just like N of 1.

And I want to go to N of 1.

A lot of the listeners of this podcast are product managers.

They're engineers, they're designers.

They're not a lot of, there's a lot of founders, but there's also a lot of non-founders.

There's a lot of people building product.

That aren't founders.

And, 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.

This, I think, is a really funny question.

So these three roles in particular, obviously, are kind of the central roles for building, you know, for tech companies.

So the way I've been describing it is, you know, you know, the concept of the Mexican standoff.

Right?

Which is the movie scene where the, you know, the two guys have guns pointed at each other's heads.

And then there's, if you watch like John Woo movies, he loves to have, he does the three-way Mexican standoff where you've got like a triangle, you know, people and like the, you know,

and of course it's John Woo movies, they've got, you know, guns in both hands.

So they're all, each is aiming at the other two.

Yeah.

And you've got this kind of standoff situation.

And so the way I've been describing this is there's like a Mexican standoff happening between those three roles, between product manager, designer, and coder.

Specifically of the following, which is every coder now believes they can also be a product manager and a designer, right?

Because they have AI.

Every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager, right?

And a coder, right?

And so people in each of those roles now, you know, know or believe that with AI, they don't need the other two roles anymore, right?

They can do that because they can have AI do that.

And then of course, and then of course there's the real irony, which is, you know, all the three, all three of them are going to realize that AI can also be a better manager.

Right?

So they're going to end up aiming the guns up through our chart, but that's probably the next phase.

And what I think is so fascinating about this Mexican stat is they're actually all kind of correct, I think, right?

Which is AI is actually a pretty good, you know, it's now, it's actually now a really good coder.

It's actually now a really good designer and it's also a really good product manager, right?

It's actually good at doing all three of those things, or at least doing a lot of the tasks involved in those three jobs.

And so again, this goes back to the super empowered, this kind of idea of the super empowered individual.

where if, if I'm a coder, like, you know, I mean, step one is like, I need to make sure that I really understand AI coding and like what that means and what, how coding is going to

change in the future.

You know, that, that I need to understand, you know, specifically how to go from being a coder who writes code entirely by hand to being a coder who, you know, orchestrates, you know,

a dozen instances of, of, of, you know, coding bots.

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 superpowered individual?

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, do AI, AI-based design?

And the same thing for the designer, which is how do I use AI to be a, 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 30 years or whatever.

Uh, but what happens is that the talented people in any of those roles become superpowered and they become good at doing all 3 of those things.

and then, and then those people become incredibly valuable because then those are people who can actually like, you know, build and design, right?

New products, right?

From scratch, which is like the, you know, 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.

Uh, so I love this answer.

So what I'm hearing is essentially, uh, if you're amazing at any of these 3 roles, you will do well.

Number 1, 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 if you're, if you're a master coder today and you don't ever get to the point where you figure out how to use AI to leverage your coding skills and do more, right?

Like at some point you are going to hit an issue, right?

Here's another way economists talk about this, which is there's the concept of the job, but the job is not actually the atomic unit of what happens in the workplace.

The atomic unit of what happens in the workplace is the task.

And so, and then what the way the economists think about it is a job is a bundle of tasks.

And everybody wants to talk about job loss, but really what you want to look at is task loss, right?

Tasks changing.

I mean, the classic example of task changing, classic example of task changing was once upon a time, executives never used typewriters or personal computers themselves, right?

You know, if you were a vice president of a company in 1970 or whatever, you did not have like a typewriter or computer on your desk typing things.

You had a secretary who you dictated memos to.

Right.

And then there, and then there was this change where like email started to show up.

And what would happen was the job of the secretary then went from, you know, it went from, you know, the, the, the job of the secretary changed from sending out letters with stamps

on them to like sending or receiving emails with the other admins.

And then, and then the secretary would print out the email and bring it into the executive's office.

And the executive office would read the email on paper, scroll 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 his or her desk and send it as an email.

Fast forward to today, none of that happens.

Now executives just do all their own email.

They still have secretaries or admins, but they're now doing different tasks.

You know, they're travel planning and orchestrating events and like doing all these other things, you know, that, you know, the great admins do.

And then 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,

50 years ago, they never would have done that.

And so The executive job still exists.

The secretary job still exists.

Uh, but the tasks have changed.

And I think that's like a great example of what's going to happen in coding.

The tasks are going to change.

Product management, the tasks are going to change.

Designer tasks are going to change.

And so the job persists longer than the individual tasks.

And then as the tasks change enough, then that's when the jobs change.

And so at the level of an individual, you kind of want to think of like, okay, I have this job.

The job is a bundle of tasks.

I need to be really good at making sure that I can like swap the tasks out, right?

I can really adapt, use the new technology, you know, get really good at AI coding, for example.

I can, you know, and then you want to kind of add skills.

I can also get really good at design.

I can also get really good at product management because I've got this new tool.

So you want to kind of pick up more and more scope as you do that.

And then, you know, 10 years from now, is your job title coder or coder, designer, product manager, or is it just I build products?

Or is it just, I tell the AI how to build products?

It's like, whatever that, whatever that job is called, who even knows what it's going to be, but it's going to be incredibly important because the people doing that job are going to

be orchestrating the AI.

And so that, that's the track that the best people are going to be on.

Um, and, and I think that that's the thing to lean hard into.

I think people aren't fully grasping just specifically software engineering and how much that is changing.

Like, it's pretty clear we're going to be in a world soon where engineers are not actually writing code, which I think a year ago we would not have thought.

And now it's just clearly this is where it's heading.

It's like, there's going to be this artisanal experience of sitting there writing code, which is so crazy how much that job is going to change.

Yeah.

So again, here I go back and again, pardon maybe the history lesson, but like I go back like coding.

So the first, you may know that, do you know that the original definition of the, of the term calculator?

Do you know what that referred to?

Um, no.

It referred to people.

Right.

So back before there were like electronic calculators or computers or any of these things, the way that you would actually do computing, the way that you would do calculating, like

the way that an insurance company would calculate actuarial tables or the military would like calculate, you know, I don't know, whatever troop logistics formulas or whatever it was,

the way that you would do it is you would actually have a room full of people.

And by the way, these are like big rooms.

You could have hundreds or thousands or tens of thousands of people doing this.

And you would actually figure out, you have 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 that job title was those people were calculators.

Right.

And so we've gone from a world in which you literally have people doing mathematical equations by hand.

Then we got the first computers.

The first computers, of course, didn't have programming languages.

Right?

They only had machine code, right?

So the first computers were programmed with 1s and 0s.

And so the task of the programmer became do the 1s and 0s.

And then that became punch cards.

And you can still, you know, there's still people, you know, kicking, you know, today who, you know, whose job as a programmer was to like 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 scripting languages, you know, we developed JavaScript at Netscape, and then Python took off, and Perl, and these other scripting languages.

When scripting languages took off in the 2000s, there was this big fight in the technical community, which is, is scripting real programming or not?

Right?

Because it's like, it's kind of cheating, right?

Because real programmers write code that compiles to machine code, and like real programmers do memory management themselves, and they do all, you know, this whole craft of writing

C code.

And, you know, these, these JavaScript or Python programmers are just doing this kind of lightweight thing.

It doesn't even really count as coding.

And of course the answer is yes, it very much counted.

And now most coding is done with the scripting languages, right?

Um, which have, you see my point, the scripting languages have abstracted away like 5 layers of detail underneath that, that people used to do by hand and they don't anymore.

And then, and then there's, and then to your point, like AI coding is the next layer on that.

AI coding actually abstracts away the process of actually writing the scripting code.

Right.

And so in one sense, this is a really big deal for all the obvious reasons.

But on the other hand, it's like, okay, this is the next layer of the task redefinition under the job of programmer right now.

What's the job of the programmer?

It's to your point, it's not necessarily to write the code by hand, but what it is now is, all right, now, you know, if you talk to the world's best programmers today, what they'll

tell you is, oh, my job is I'm sitting there and I'm orchestrating 10 code bots, right?

Coding bots that are running in parallel.

And literally they sit there and they shift from browser to browser or terminal to terminal.

And their day job now is kind of arguing with AI bots to try to get them to like write the right code.

And then debug it and fix the problems and change the spec and do all these things.

And so now the job of the programmer is to argue with the coding bots.

But like, if you don't know how to write the code yourself, you don't know how to evaluate what the coding bots are giving you.

And so, you asked about the tenant, our 10-year-old is, you know, super into computers and super into programming.

And what I'm, what I'm telling, you know, he's using Claude and ChatGPT and Copilot and all these things.

What I'm telling him is like, look, and by the way, he loves vibe coding.

He's on Replit all the time doing vibe coding, you know, doing games, you know, he's sitting there, you know, it's hysterical, right?

Because he's sitting there, it's a 10-year-old basically who spends 2 hours at dinner arguing with an AI for fun.

Right.

Right.

But, but what I'm telling him is, no, look, you need to still fully understand and learn how to write and understand code because the coding bots are giving you code.

If it doesn't work or if it's not doing what you expect or it's not fast enough or whatever, like you need to be able to understand the results of what the AI is giving you, right?

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

And so again, it's kind of this upleveling 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 1,000 times more productive than they used to be.

Right.

And that is overwhelmingly a good thing.

The tasks are definitely changing.

The nature of the job is changing.

But are human beings going to be involved in like in the coding process and overseeing the AI coding and all that?

And the answer is, of course, absolutely, 100%.

Like, no question.

So you're in the camp of still learn to code, still a valuable skill.

Oh yeah, totally.

Well, again, if you want to be one of these super— look, if you just want to put yourself on autopilot, And like, I can't be bothered and I'm just going to have AI write the code and

it's going to generate whatever it does and that's fine.

And I'm going to be, you know, I'm going to be— if the goal is to be a mediocre coder, then just let the AI do it.

It's fine.

The AI is going to be perfectly good at generating infinite amounts of mediocre code.

No problem.

It's all good.

If the goal is I want to be one of the best software people in the world and I want to build new software products and technologies that like really matter, then yeah, you 100% want

to still— you want to go all the way down.

You want your skill set to go all the way down to the assembly, to assembly and machine code.

You want to understand every layer of the stack.

You want to deeply understand what's happening at the level of the chip, right?

And the network and so forth.

By the way, you also really deeply want to understand how the AI itself works, right?

Because you want to, right?

Because if people understand how the AI works, are able to, they're clearly able to get more value out of it than somebody who doesn't understand how it works.

Right?

I mean, you're always more productive if you know how the machine works, right?

When you use the machine.

And so, yeah, the super empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100% want to understand this thing all the way

down the stack.

Because you want to be able to understand what it's giving you, right?

And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is.

By the way, again, this goes back to education.

AI is your best friend at helping you learn all that, right?

Because it's like, oh, I need to understand.

I don't know, like this isn't fast enough.

I need to go, I need to figure out as a coder, I need to figure out how to do a different approach to memory management or something.

And you can be like, well, you know, shit, like I, you know, I don't quite know how to do that.

Okay, AI, let's spend 10 minutes teach me how to do this, right?

Teach me what this all means, right?

So all of a sudden you have this like incredibly synergistic relationship with the AI where it's also helping you get better at the same time that's doing a lot of work for you.

By the way, I was going to say, I was a big Perl programmer.

I was an engineer for 10 years and that was my language of choice.

Do you remember?

I don't know when you were doing it, but do you remember that at least early on?

Do you remember?

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 going to 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 swept the world.

Uh, you know, most coding today happens in scripting languages.

And then by the way, the people along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they were the ones

who were able to actually make the scripting languages actually work really well.

Right.

And so that was, that was a great example of this kind of adaptation.

And then again, the result of that was, you know, a far higher number of people writing code with scripting languages than were ever writing code with lower-level languages.

And I think this will just kind of be a more dramatic version of that.

I love that Perl was designed by a linguist.

I don't know if you remember that part.

And that's what made it so nice to code with.

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

So how ironic.

Yes.

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Coming back to these, this kind of triad, the other element that I hear more and more of is just as, is the skill of taste and design and user experience.

It feels like that's a very hard skill to learn.

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

Yeah, that's right.

And again, here, this, this is a great example.

So

again, the task level, the, the, the, the, the task level of like Design the perfect icon, right?

Is going to be like, all right, the AI is going to do that all day long.

It's going to give you a thousand icon designs.

It's going to be great.

Like, it's going to be fantastic.

Like, whatever, you know, and there will still, by the way, there will still be some level of human icon design or whatever, but like, AI is going to get really good at that.

But like, what are we trying to do?

Like the, you know, kind of capital D design of like, all right, what is this thing for?

And how does this, how is this going to function in a world of human beings?

And like, you know, what, what's going to, is this going to make people happy when they use it?

It's going to make people feel good about themselves.

is it going to fit into the rest of their life?

Is it going to, you know, I don't know, challenge them in the right way?

You know, all these kinds of higher level questions that the great designers have always thought about, like the job of designer, right, will involve much more of those higher level,

more important components.

And then again, with AI doing a lot more of the underlying tasks.

And so, you know, one way to think about it is, you know, I don't know, you think of like, I don't know, the world's best designers, you know, Jony Ive or whatever.

It could be like, wow, like if I'm a designer today, if I'm a 25-year-old designer and I aspire to be, you know, Jony Ive in a decade.

It's all of a sudden I have a new path that I can use to kind of get there, which is, you know, because Johnny I did everything he did without AI.

Now, you know, a young designer can be like, wow, if I really harness AI in a decade, I'm going to be like the best designer the world's ever seen because it's not just going to be

me.

It's going to be me plus being so super empowered by this technology to be able to do so much more.

And then so much more of my time and attention is going to be able to be focused on these higher level things that most designers never get to.

And I think that's going to be another great example of that.

So maybe what I'm hearing here is kind of this T-shaped strategy of if you want to be successful in any 3 of these roles, be very, very, very good at that specific role— product management,

engineering, design— and then get good enough at these other 2 roles.

Well, so I think that's great.

I think that's really, really relevant.

And then, you know, Scott Adams unfortunately just passed away, you know, which is a real tragedy.

But I was always— I referred for years to actually Scott Adams.

He had this famous He had this famous kind of career advice he would give people, which I think makes a lot of sense, which dovetails with what you're saying, which is he used to say,

he used to say, it's like, look, he said, you know, I could, he said, you know, I could have been a pretty good cartoonist, or I could have been like pretty good at business.

But the fact that I was a cartoonist who understood business made me like spectacularly great at making Dilbert,

right?

Because even the world's best cartoonist who didn't understand business could have never written Dilbert.

And then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert.

It took somebody who actually had both of those skills to be able to make Dilbert, right?

Which is one of the most successful cartoons in history.

Right.

And so, so the way Scott always described it was that from a career development standpoint, that additive effect of being good at two things is like more than double, right?

The additive effect of being good at three things is more than triple, right?

Because you become a super relevant specialist in the combination of the domains.

Um, and you, you look, you see this all, I mean, you see this all over, you know, you see this all over the economy.

I mean, you see this all over the economy, but I'll just, you know, give you an example.

Hollywood, you know, just Hollywood as an example, you know, there are a lot of writers who can't direct a movie and they can be very successful writers.

There are a lot of directors who can't write a movie.

They can be very successful directors, but the superstars in the entertainment industry are the people who can write and direct.

Right.

And, you know, they don't have a term for those.

They call those auteurs, right?

And that's, you know, those are the people who are like the real creative forces that move the field.

And so again, and by the way, Hollywood, actually it's really funny, I've been spending a lot of time talking to Hollywood people about AI.

Hollywood has the same Mexican standoff going right now that we described in tech, except in Hollywood, for example, for filmmaking, it's the director, it's the writer, and the actor,

right?

Because the director is now thinking, wow, I don't need the writer anymore 'cause the AI can write the script, and I don't need the actor anymore 'cause I can have AI actors.

The writer is saying, well, I don't need the director 'cause the AI can direct the movie and the AI can do the actors.

And the actor is saying, I don't need either one of these guys.

I can have 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.

And so it's the same kind of triangular configuration.

And again, what's great about it is they're all correct.

Each person in each of those three fields is going to be able to expand laterally and pick up those additional skills.

And then as a consequence, you're going to have more people who can write and direct, or write and act, or direct and act, or do all three.

And I think, you know, to your point, like your T-shape thing, like I think that's going to be true basically across the entire economy.

And if you think about the T, if you think about the T configuration, it's like, yeah, the breadth, 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.