Lex Fridman Podcast - #141 - Erik Brynjolfsson: Economics of AI, Social Networks, and Technology

The following is a conversation with Erik Brynjolfsson.

He’s an economics professor at Stanford

and the director of Stanford’s Digital Economy Lab.

Previously, he was a long, long time professor at MIT

where he did groundbreaking work

on the economics of information.

He’s the author of many books,

including The Second Machine Age

and Machine Platform Crowd,

coauthored with Andrew McAfee.

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As a side note, let me say that the impact

of artificial intelligence and automation

on our economy and our world

is something worth thinking deeply about.

Like with many topics that are linked

to predicting the future evolution of technology,

it is often too easy to fall into one of two camps.

The fear mongering camp

or the technological utopianism camp.

As always, the future will land us somewhere in between.

I prefer to wear two hats in these discussions

and alternate between them often.

The hat of a pragmatic engineer

and the hat of a futurist.

This is probably a good time to mention Andrew Yang,

the presidential candidate who has been

one of the high profile thinkers on this topic.

And I’m sure I will speak with him

on this podcast eventually.

A conversation with Andrew has been on the table many times.

Our schedules just haven’t aligned,

especially because I have a strongly held to preference

for long form, two, three, four hours or more,

and in person.

I work hard to not compromise on this.

Trust me, it’s not easy.

Even more so in the times of COVID,

which requires getting tested nonstop,

staying isolated and doing a lot of costly

and uncomfortable things that minimize risk for the guest.

The reason I do this is because to me,

something is lost in remote conversation.

That something, that magic,

I think is worth the effort,

even if it ultimately leads to a failed conversation.

This is how I approach life,

treasuring the possibility of a rare moment of magic.

I’m willing to go to the ends of the world

for just such a moment.

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And now here’s my conversation with Erik Brynjolfsson.

You posted a quote on Twitter by Albert Bartlett

saying that the greatest shortcoming of the human race

is our inability to understand the exponential function.

Why would you say the exponential growth

is important to understand?

Yeah, that quote, I remember posting that.

It’s actually a reprise of something Andy McAfee and I said

in the second machine age,

but I posted it in early March

when COVID was really just beginning to take off

and I was really scared.

There were actually only a couple dozen cases,

maybe less at that time,

but they were doubling every like two or three days

and I could see, oh my God, this is gonna be a catastrophe

and it’s gonna happen soon,

but nobody was taking it very seriously

or not a lot of people were taking it very seriously.

In fact, I remember I did my last in person conference

that week, I was flying back from Las Vegas

and I was the only person on the plane wearing a mask

and the flight attendant came over to me.

She looked very concerned.

She kind of put her hands on my shoulder.

She was touching me all over, which I wasn’t thrilled about

and she goes, do you have some kind of anxiety disorder?

Are you okay?

And I was like, no, it’s because of COVID.

This is early March.

Early March, but I was worried

because I knew I could see or I suspected, I guess,

that that doubling would continue and it did

and pretty soon we had thousands of times more cases.

Most of the time when I use that quote,

I try to, it’s motivated by more optimistic things

like Moore’s law and the wonders

of having more computer power,

but in either case, it can be very counterintuitive.

I mean, if you walk for 10 minutes,

you get about 10 times as far away

as if you walk for one minute.

That’s the way our physical world works.

That’s the way our brains are wired,

but if something doubles for 10 times as long,

you don’t get 10 times as much.

You get a thousand times as much

and after 20, it’s a billion.

After 30, it’s a, no, sorry, after 20, it’s a million.

After 30, it’s a billion.

And pretty soon after that,

it just gets to these numbers that you can barely grasp.

Our world is becoming more and more exponential,

mainly because of digital technologies.

So more and more often our intuitions are out of whack

and that can be good in the case of things creating wonders,

but it can be dangerous in the case of viruses

and other things.

Do you think it generally applies,

like is there spaces where it does apply

and where it doesn’t?

How are we supposed to build an intuition

about in which aspects of our society

does exponential growth apply?

Well, you can learn the math,

but the truth is our brains, I think,

tend to learn more from experiences.

So we just start seeing it more and more often.

So hanging around Silicon Valley,

hanging around AI and computer researchers,

I see this kind of exponential growth a lot more frequently

and I’m getting used to it, but I still make mistakes.

I still underestimate some of the progress

in just talking to someone about GPT3

and how rapidly natural language has improved.

But I think that as the world becomes more exponential,

we’ll all start experiencing it more frequently.

The danger is that we may make some mistakes in the meantime

using our old kind of caveman intuitions

about how the world works.

Well, the weird thing is it always kind of looks linear

in the moment.

Like it’s hard to feel,

it’s hard to like introspect

and really acknowledge how much has changed

in just a couple of years or five years or 10 years

with the internet.

If we just look at advancements of AI

or even just social media,

all the various technologies

that go into the digital umbrella,

it feels pretty calm and normal and gradual.

Well, a lot of stuff,

I think there are parts of the world,

most of the world that is not exponential.

The way humans learn,

the way organizations change,

the way our whole institutions adapt and evolve,

those don’t improve at exponential paces.

And that leads to a mismatch oftentimes

between these exponentially improving technologies

or let’s say changing technologies

because some of them are exponentially more dangerous

and our intuitions and our human skills

and our institutions that just don’t change very fast at all.

And that mismatch I think is at the root

of a lot of the problems in our society,

the growing inequality

and other dysfunctions in our political

and economic systems.

So one guy that talks about exponential functions

a lot is Elon Musk.

He seems to internalize this kind of way

of exponential thinking.

He calls it first principles thinking,

sort of the kind of going to the basics,

asking the question,

like what were the assumptions of the past?

How can we throw them out the window?

How can we do this 10X much more efficiently

and constantly practicing that process?

And also using that kind of thinking

to estimate sort of when, you know, create deadlines

and estimate when you’ll be able to deliver

on some of these technologies.

Now, it often gets him in trouble

because he overestimates,

like he doesn’t meet the initial estimates of the deadlines,

but he seems to deliver late but deliver.

And which is kind of interesting.

Like, what are your thoughts about this whole thing?

I think we can all learn from Elon.

I think going to first principles,

I talked about two ways of getting more of a grip

on the exponential function.

And one of them just comes from first principles.

You know, if you understand the math of it,

you can see what’s gonna happen.

And even if it seems counterintuitive

that a couple of dozen of COVID cases

can become thousands or tens or hundreds of thousands

of them in a month,

it makes sense once you just do the math.

And I think Elon tries to do that a lot.

You know, in fairness, I think he also benefits

from hanging out in Silicon Valley

and he’s experienced it in a lot of different applications.

So, you know, it’s not as much of a shock to him anymore,

but that’s something we can all learn from.

In my own life, I remember one of my first experiences

really seeing it was when I was a grad student

and my advisor asked me to plot the growth of computer power

in the US economy in different industries.

And there are all these, you know,

exponentially growing curves.

And I was like, holy shit, look at this.

In each industry, it was just taking off.

And, you know, you didn’t have to be a rocket scientist

to extend that and say, wow,

this means that this was in the late 80s and early 90s

that, you know, if it goes anything like that,

we’re gonna have orders of magnitude more computer power

than we did at that time.

And of course we do.

So, you know, when people look at Moore’s law,

they often talk about it as just,

so the exponential function is actually

a stack of S curves.

So basically it’s you milk or whatever,

take the most advantage of a particular little revolution

and then you search for another revolution.

And it’s basically revolutions stack on top of revolutions.

Do you have any intuition about how the head humans

keep finding ways to revolutionize things?

Well, first, let me just unpack that first point

that I talked about exponential curves,

but no exponential curve continues forever.

It’s been said that if anything can’t go on forever,

eventually it will stop.

And, and it’s very profound, but it’s,

it seems that a lot of people don’t appreciate

that half of it as well either.

And that’s why all exponential functions eventually turn

into some kind of S curve or stop in some other way,

maybe catastrophically.

And that’s a cap with COVID as well.

I mean, it was, it went up and then it sort of, you know,

at some point it starts saturating the pool of people

to be infected.

There’s a standard epidemiological model

that’s based on that.

And it’s beginning to happen with Moore’s law

or different generations of computer power.

It happens with all exponential curves.

The remarkable thing is you elude,

the second part of your question is that we’ve been able

to come up with a new S curve on top of the previous one

and do that generation after generation with new materials,

new processes, and just extend it further and further.

I don’t think anyone has a really good theory

about why we’ve been so successful in doing that.

It’s great that we have been,

and I hope it continues for some time,

but it’s, you know, one beginning of a theory

is that there’s huge incentives when other parts

of the system are going on that clock speed

of doubling every two to three years.

If there’s one component of it that’s not keeping up,

then the economic incentives become really large

to improve that one part.

It becomes a bottleneck and anyone who can do improvements

in that part can reap huge returns

so that the resources automatically get focused

on whatever part of the system isn’t keeping up.

Do you think some version of the Moore’s law will continue?

Some version, yes, it is.

I mean, one version that has become more important

is something I call Coomey’s law,

which is named after John Coomey,

who I should mention was also my college roommate,

but he identified the fact that energy consumption

has been declining by a factor of two.

And for most of us, that’s more important.

The new iPhones came out today as we’re recording this.

I’m not sure when you’re gonna make it available.

Very soon after this, yeah.

And for most of us, having the iPhone be twice as fast,

it’s nice, but having the battery lifelonger,

that would be much more valuable.

And the fact that a lot of the progress in chips now

is reducing energy consumption is probably more important

for many applications than just the raw speed.

Other dimensions of Moore’s law

are in AI and machine learning.

Those tend to be very parallelizable functions,

especially deep neural nets.

And so instead of having one chip,

you can have multiple chips or you can have a GPU,

graphic processing unit that goes faster.

Now, special chips designed for machine learning

like tensor processing units,

each time you switch, there’s another 10X

or 100X improvement above and beyond Moore’s law.

So I think that the raw silicon

isn’t improving as much as it used to,

but these other dimensions are becoming important,

more important, and we’re seeing progress in them.

I don’t know if you’ve seen the work by OpenAI

where they show the exponential improvement

of the training of neural networks

just literally in the techniques used.

So that’s almost like the algorithm.

It’s fascinating to think like, can I actually continue?

I was figuring out more and more tricks

on how to train networks faster and faster.

The progress has been staggering.

If you look at image recognition, as you mentioned,

I think it’s a function of at least three things

that are coming together.

One, we just talked about faster chips,

not just Moore’s law, but GPUs, TPUs and other technologies.

The second is just a lot more data.

I mean, we are awash in digital data today

in a way we weren’t 20 years ago.

Photography, I’m old enough to remember,

it used to be chemical, and now everything is digital.

I took probably 50 digital photos yesterday.

I wouldn’t have done that if it was chemical.

And we have the internet of things

and all sorts of other types of data.

When we walk around with our phone,

it’s just broadcasting a huge amounts of digital data

that can be used as training sets.

And then last but not least, as they mentioned at OpenAI,

there’ve been significant improvements in the techniques.

The core idea of deep neural nets

has been around for a few decades,

but the advances in making it work more efficiently

have also improved a couple of orders of magnitude or more.

So you multiply together,

a hundred fold improvement in computer power,

a hundred fold or more improvement in data,

a hundred fold improvement in techniques

of software and algorithms,

and soon you’re getting into a million fold improvements.

So somebody brought this up, this idea with GPT3 that,

so it’s trained in a self supervised way

on basically internet data.

And that’s one of the, I’ve seen arguments made

and they seem to be pretty convincing

that the bottleneck there is going to be

how much data there is on the internet,

which is a fascinating idea that it literally

will just run out of human generated data to train on.

Right, I know we make it to the point where it’s consumed

basically all of human knowledge

or all digitized human knowledge, yeah.

And that will be the bottleneck.

But the interesting thing with bottlenecks

is people often use bottlenecks

as a way to argue against exponential growth.

They say, well, there’s no way

you can overcome this bottleneck,

but we seem to somehow keep coming up in new ways

to like overcome whatever bottlenecks

the critics come up with, which is fascinating.

I don’t know how you overcome the data bottleneck,

but probably more efficient training algorithms.

Yeah, well, you already mentioned that,

that these training algorithms are getting much better

at using smaller amounts of data.

We also are just capturing a lot more data than we used to,

especially in China, but all around us.

So those are both important.

In some applications, you can simulate the data,

video games, some of the self driving car systems

are simulating driving, and of course,

that has some risks and weaknesses,

but you can also, if you want to exhaust

all the different ways you could beat a video game,

you could just simulate all the options.

Can we take a step in that direction of autonomous vehicles?

Next, you’re talking to the CTO of Waymo tomorrow.

And obviously, I’m talking to Elon again in a couple of weeks.

What’s your thoughts on autonomous vehicles?

Like where do we stand as a problem

that has the potential of revolutionizing the world?

Well, I’m really excited about that,

but it’s become much clearer

that the original way that I thought about it,

most people thought about like,

you know, will we have a self driving car or not

is way too simple.

The better way to think about it

is that there’s a whole continuum

of how much driving and assisting the car can do.

I noticed that you’re right next door

to the Toyota Research Institute.

That is a total accident.

I love the TRI folks, but yeah.

Have you talked to Gil Pratt?

Yeah, we’re supposed to talk.

It’s kind of hilarious.

So there’s kind of the,

I think it’s a good counterpart to say what Elon is doing.

And hopefully they can be frank

in what they think about each other,

because I’ve heard both of them talk about it.

But they’re much more, you know,

this is an assistive, a guardian angel

that watches over you as opposed to try to do everything.

I think there’s some things like driving on a highway,

you know, from LA to Phoenix,

where it’s mostly good weather, straight roads.

That’s close to a solved problem, let’s face it.

In other situations, you know,

driving through the snow in Boston

where the roads are kind of crazy.

And most importantly, you have to make a lot of judgments

about what the other driver is gonna do

at these intersections that aren’t really right angles

and aren’t very well described.

It’s more like game theory.

That’s a much harder problem

and requires understanding human motivations.

So there’s a continuum there of some places

where the cars will work very well

and others where it could probably take decades.

What do you think about the Waymo?

So you mentioned two companies

that actually have cars on the road.

There’s the Waymo approach that it’s more like

we’re not going to release anything until it’s perfect

and we’re gonna be very strict

about the streets that we travel on,

but it better be perfect.


Well, I’m smart enough to be humble

and not try to get between.

I know there’s very bright people

on both sides of the argument.

I’ve talked to them and they make convincing arguments to me

about how careful they need to be and the social acceptance.

Some people thought that when the first few people died

from self driving cars, that would shut down the industry,

but it was more of a blip actually.

And, you know, so that was interesting.

Of course, there’s still a concern

that if there could be setbacks, if we do this wrong,

you know, your listeners may be familiar

with the different levels of self driving,

you know, level one, two, three, four, five.

I think Andrew Ng has convinced me that this idea

of really focusing on level four,

where you only go in areas that are well mapped

rather than just going out in the wild

is the way things are gonna evolve.

But you can just keep expanding those areas

where you’ve mapped things really well,

where you really understand them

and eventually all become kind of interconnected.

And that could be a kind of another way of progressing

to make it more feasible over time.

I mean, that’s kind of like the Waymo approach,

which is they just now released,

I think just like a day or two ago,

a public, like anyone from the public

in the Phoenix, Arizona to, you know,

you can get a ride in a Waymo car

with no person, no driver.

Oh, they’ve taken away the safety driver?

Oh yeah, for a while now there’s been no safety driver.

Okay, because I mean, I’ve been following that one

in particular, but I thought it was kind of funny

about a year ago when they had the safety driver

and then they added a second safety driver

because the first safety driver would fall asleep.

It’s like, I’m not sure they’re going

in the right direction with that.

No, they’ve Waymo in particular

done a really good job of that.

They actually have a very interesting infrastructure

of remote like observation.

So they’re not controlling the vehicles remotely,

but they’re able to, it’s like a customer service.

They can anytime tune into the car.

I bet they can probably remotely control it as well,

but that’s officially not the function that they use.

Yeah, I can see that being really,

because I think the thing that’s proven harder

than maybe some of the early people expected

was there’s a long tail of weird exceptions.

So you can deal with 90, 99, 99.99% of the cases,

but then there’s something that just never been seen before

in the training data.

And humans more or less can work around that.

Although let me be clear and note,

there are about 30,000 human fatalities

just in the United States and maybe a million worldwide.

So they’re far from perfect.

But I think people have higher expectations of machines.

They wouldn’t tolerate that level of death

and damage from a machine.

And so we have to do a lot better

at dealing with those edge cases.

And also the tricky thing that if I have a criticism

for the Waymo folks, there’s such a huge focus on safety

where people don’t talk enough about creating products

that people, that customers love,

that human beings love using.

It’s very easy to create a thing that’s safe

at the extremes, but then nobody wants to get into it.

Yeah, well, back to Elon, I think one of,

part of his genius was with the electric cars.

Before he came along, electric cars were all kind of

underpowered, really light,

and there were sort of wimpy cars that weren’t fun.

And the first thing he did was he made a roadster

that went zero to 60 faster than just about any other car

and went the other end.

And I think that was a really wise marketing move

as well as a wise technology move.

Yeah, it’s difficult to figure out

what the right marketing move is for AI systems.

That’s always been, I think it requires guts and risk taking

which is what Elon practices.

I mean, to the chagrin of perhaps investors or whatever,

but it also requires rethinking what you’re doing.

I think way too many people are unimaginative,

intellectually lazy, and when they take AI,

they basically say, what are we doing now?

How can we make a machine do the same thing?

Maybe we’ll save some costs, we’ll have less labor.

And yeah, it’s not necessarily the worst thing

in the world to do, but it’s really not leading

to a quantum change in the way you do things.

When Jeff Bezos said, hey, we’re gonna use the internet

to change how bookstores work and we’re gonna use technology,

he didn’t go and say, okay, let’s put a robot cashier

where the human cashier is and leave everything else alone.

That would have been a very lame way to automate a bookstore.

He’s like went from soup to nuts and let’s just rethink it.

We get rid of the physical bookstore.

We have a warehouse, we have delivery,

we have people order on a screen

and everything was reinvented.

And that’s been the story

of these general purpose technologies all through history.

And in my books, I write about like electricity

and how for 30 years, there was almost no productivity gain

from the electrification of factories a century ago.

Now it’s not because electricity

is a wimpy useless technology.

We all know how awesome electricity is.

It’s cause at first,

they really didn’t rethink the factories.

It was only after they reinvented them

and we describe how in the book,

then you suddenly got a doubling and tripling

of productivity growth.

But it’s the combination of the technology

with the new business models, new business organization.

That just takes a long time

and it takes more creativity than most people have.

Can you maybe linger on electricity?

Cause that’s a fun one.

Yeah, well, sure, I’ll tell you what happened.

Before electricity, there were basically steam engines

or sometimes water wheels and to power the machinery,

you had to have pulleys and crankshafts

and you really can’t make them too long

cause they’ll break the torsion.

So all the equipment was kind of clustered

around this one giant steam engine.

You can’t make small steam engines either

cause of thermodynamics.

So you have one giant steam engine,

all the equipment clustered around it, multi story.

They have it vertical to minimize the distance

as well as horizontal.

And then when they did electricity,

they took out the steam engine.

They got the biggest electric motor

they could buy from General Electric or someone like that.

And nothing much else changed.

It took until a generation of managers retired

or died three years later,

that people started thinking,

wait, we don’t have to do it that way.

You can make electric motors, big, small, medium.

You can put one with each piece of equipment.

There’s this big debate

if you read the management literature

between what they call a group drive versus unit drive

where every machine would have its own motor.

Well, once they did that, once they went to unit drive,

those guys won the debate.

Then you started having a new kind of factory

which is sometimes spread out over acres, single story

and each piece of equipment has its own motor.

And most importantly, they weren’t laid out based on

who needed the most power.

They were laid out based on

what is the workflow of materials?

Assembly line, let’s have it go from this machine

to that machine, to that machine.

Once they rethought the factory that way,

huge increases in productivity.

It was just staggering.

People like Paul David have documented this

in their research papers.

And I think that that is a lesson you see over and over.

It happened when the steam engine changed manual production.

It’s happened with the computerization.

People like Michael Hammer said, don’t automate, obliterate.

In each case, the big gains only came once

smart entrepreneurs and managers

basically reinvented their industries.

I mean, one other interesting point about all that

is that during that reinvention period,

you often actually not only don’t see productivity growth,

you can actually see a slipping back.

Measured productivity actually falls.

I just wrote a paper with Chad Severson and Daniel Rock

called the productivity J curve,

which basically shows that in a lot of these cases,

you have a downward dip before it goes up.

And that downward dip is when everyone’s trying

to like reinvent things.

And you could say that they’re creating knowledge

and intangible assets,

but that doesn’t show up on anyone’s balance sheet.

It doesn’t show up in GDP.

So it’s as if they’re doing nothing.

Like take self driving cars, we were just talking about it.

There have been hundreds of billions of dollars

spent developing self driving cars.

And basically no chauffeur has lost his job, no taxi driver.

I guess I got to check out the ones that.

It’s a big J curve.

Yeah, so there’s a bunch of spending

and no real consumer benefit.

Now they’re doing that in the belief,

I think the justified belief

that they will get the upward part of the J curve

and there will be some big returns,

but in the short run, you’re not seeing it.

That’s happening with a lot of other AI technologies,

just as it happened

with earlier general purpose technologies.

And it’s one of the reasons

we’re having relatively low productivity growth lately.

As an economist, one of the things that disappoints me

is that as eye popping as these technologies are,

you and I are both excited

about some of the things they can do.

The economic productivity statistics are kind of dismal.

We actually, believe it or not,

have had lower productivity growth

in the past about 15 years

than we did in the previous 15 years,

in the 90s and early 2000s.

And so that’s not what you would have expected

if these technologies were that much better.

But I think we’re in kind of a long J curve there.

Personally, I’m optimistic.

We’ll start seeing the upward tick,

maybe as soon as next year.

But the past decade has been a bit disappointing

if you thought there’s a one to one relationship

between cool technology and higher productivity.

Well, what would you place your biggest hope

for productivity increases on?

Because you kind of said at a high level AI,

but if I were to think about

what has been so revolutionary in the last 10 years,

I would 15 years and thinking about the internet,

I would say things like,

hopefully I’m not saying anything ridiculous,

but everything from Wikipedia to Twitter.

So like these kind of websites,

not so much AI,

but like I would expect to see some kind

of big productivity increases

from just the connectivity between people

and the access to more information.

Yeah, well, so that’s another area

I’ve done quite a bit of research on actually,

is these free goods like Wikipedia, Facebook, Twitter, Zoom.

We’re actually doing this in person,

but almost everything else I do these days is online.

The interesting thing about all those

is most of them have a price of zero.

What do you pay for Wikipedia?

Maybe like a little bit for the electrons

to come to your house.

Basically zero, right?

Take a small pause and say, I donate to Wikipedia.

Often you should too.

It’s good for you, yeah.

So, but what does that do mean for GDP?

GDP is based on the price and quantity

of all the goods, things bought and sold.

If something has zero price,

you know how much it contributes to GDP?

To a first approximation, zero.

So these digital goods that we’re getting more and more of,

we’re spending more and more hours a day

consuming stuff off of screens,

little screens, big screens,

that doesn’t get priced into GDP.

It’s like they don’t exist.

That doesn’t mean they don’t create value.

I get a lot of value from watching cat videos

and reading Wikipedia articles and listening to podcasts,

even if I don’t pay for them.

So we’ve got a mismatch there.

Now, in fairness, economists,

since Simon Kuznets invented GDP and productivity,

all those statistics back in the 1930s,

he recognized, he in fact said,

this is not a measure of wellbeing.

This is not a measure of welfare.

It’s a measure of production.

But almost everybody has kind of forgotten

that he said that and they just use it.

It’s like, how well off are we?

What was GDP last year?

It was 2.3% growth or whatever.

That is how much physical production,

but it’s not the value we’re getting.

We need a new set of statistics

and I’m working with some colleagues.

Avi Collis and others to develop something

we call GDP dash B.

GDP B measures the benefits you get, not the cost.

If you get benefit from Zoom or Wikipedia or Facebook,

then that gets counted in GDP B,

even if you pay zero for it.

So, you know, back to your original point,

I think there is a lot of gain over the past decade

in these digital goods that doesn’t show up in GDP,

doesn’t show up in productivity.

By the way, productivity is just defined

as GDP divided by hours worked.

So if you mismeasure GDP,

you mismeasure productivity by the exact same amount.

That’s something we need to fix.

I’m working with the statistical agencies

to come up with a new set of metrics.

And, you know, over the coming years,

I think we’ll see, we’re not gonna do away with GDP.

It’s very useful, but we’ll see a parallel set of accounts

that measure the benefits.

How difficult is it to get that B in the GDP B?

It’s pretty hard.

I mean, one of the reasons it hasn’t been done before

is that, you know, you can measure it,

the cash register, what people pay for stuff,

but how do you measure what they would have paid,

like what the value is?

That’s a lot harder, you know?

How much is Wikipedia worth to you?

That’s what we have to answer.

And to do that, what we do is we can use online experiments.

We do massive online choice experiments.

We ask hundreds of thousands, now millions of people

to do lots of sort of A, B tests.

How much would I have to pay you

to give up Wikipedia for a month?

How much would I have to pay you to stop using your phone?

And in some cases, it’s hypothetical.

In other cases, we actually enforce it,

which is kind of expensive.

Like we pay somebody $30 to stop using Facebook

and we see if they’ll do it.

And some people will give it up for $10.

Some people won’t give it up even if you give them $100.

And then you get a whole demand curve.

You get to see what all the different prices are

and how much value different people get.

And not surprisingly,

different people have different values.

We find that women tend to value Facebook more than men.

Old people tend to value it a little bit more

than young people.

That was interesting.

I think young people maybe know about other networks

that I don’t know the name of that are better than Facebook.

And so you get to see these patterns,

but every person’s individual.

And then if you add up all those numbers,

you start getting an estimate of the value.

Okay, first of all, that’s brilliant.

Is this a work that will soon eventually be published?

Yeah, well, there’s a version of it

in the Proceedings of the National Academy of Sciences

about I think we call it massive online choice experiments.

I should remember the title, but it’s on my website.

So yeah, we have some more papers coming out on it,

but the first one is already out.

You know, it’s kind of a fascinating mystery

that Twitter, Facebook,

like all these social networks are free.

And it seems like almost none of them except for YouTube

have experimented with removing ads for money.

Can you like, do you understand that

from both economics and the product perspective?

Yeah, it’s something that, you know,

so I teach a course on digital business models.

So I used to at MIT, at Stanford, I’m not quite sure.

I’m not teaching until next spring.

I’m still thinking what my course is gonna be.

But there are a lot of different business models.

And when you have something that has zero marginal cost,

there’s a lot of forces,

especially if there’s any kind of competition

that push prices down to zero.

But you can have ad supported systems,

you can bundle things together.

You can have volunteer, you mentioned Wikipedia,

there’s donations.

And I think economists underestimate

the power of volunteerism and donations.

Your national public radio.

Actually, how do you, this podcast, how is this,

what’s the revenue model?

There’s sponsors at the beginning.

And then, and people, the funny thing is,

I tell people they can, it’s very,

I tell them the timestamp.

So if you wanna skip the sponsors, you’re free.

But it’s funny that a bunch of people,

so I read the advertisement

and then a bunch of people enjoy reading it.

And it’s.

Well, they may learn something from it.

And also from the advertiser’s perspective,

those are people who are actually interested.

I mean, the example I sometimes get is like,

I bought a car recently and all of a sudden,

all the car ads were like interesting to me.


And then like, now that I have the car,

like I sort of zone out on, but that’s fine.

The car companies, they don’t really wanna be advertising

to me if I’m not gonna buy their product.

So there are a lot of these different revenue models

and it’s a little complicated,

but the economic theory has to do

with what the shape of the demand curve is,

when it’s better to monetize it with charging people

versus when you’re better off doing advertising.

I mean, in short, when the demand curve

is relatively flat and wide,

like generic news and things like that,

then you tend to do better with advertising.

If it’s a good that’s only useful to a small number

of people, but they’re willing to pay a lot,

they have a very high value for it,

then advertising isn’t gonna work as well

and you’re better off charging for it.

Both of them have some inefficiencies.

And then when you get into targeting

and you get into these other revenue models,

it gets more complicated,

but there’s some economic theory on it.

I also think to be frank,

there’s just a lot of experimentation that’s needed

because sometimes things are a little counterintuitive,

especially when you get into what are called

two sided networks or platform effects,

where you may grow the market on one side

and harvest the revenue on the other side.

Facebook tries to get more and more users

and then they harvest the revenue from advertising.

So that’s another way of kind of thinking about it.

Is it strange to you that they haven’t experimented?

Well, they are experimenting.

So they are doing some experiments

about what the willingness is for people to pay.

I think that when they do the math,

it’s gonna work out that they still are better off

with an advertising driven model, but…

What about a mix?

Like this is what YouTube is, right?

It’s you allow the person to decide,

the customer to decide exactly which model they prefer.

No, that can work really well.

And newspapers, of course,

have known this for a long time.

The Wall Street Journal, the New York Times,

they have subscription revenue.

They also have advertising revenue.

And that can definitely work.

Online, it’s a lot easier to have a dial

that’s much more personalized

and everybody can kind of roll their own mix.

And I could imagine having a little slider

about how much advertising you want or are willing to take.

And if it’s done right and it’s incentive compatible,

it could be a win win where both the content provider

and the consumer are better off

than they would have been before.

Yeah, the done right part is a really good point.

Like with the Jeff Bezos

and the single click purchase on Amazon,

the frictionless effort there,

if I could just rant for a second

about the Wall Street Journal,

all the newspapers you mentioned,

is I have to click so many times to subscribe to them

that I literally don’t subscribe

just because of the number of times I have to click.

I’m totally with you.

I don’t understand why so many companies make it so hard.

I mean, another example is when you buy a new iPhone

or a new computer, whatever,

I feel like, okay, I’m gonna lose an afternoon

just like loading up and getting all my stuff back.

And for a lot of us,

that’s more of a deterrent than the price.

And if they could make it painless,

we’d give them a lot more money.

So I’m hoping somebody listening is working

on making it more painless for us to buy your products.

If we could just like linger a little bit

on the social network thing,

because there’s this Netflix social dilemma.

Yeah, no, I saw that.

And Tristan Harris and company, yeah.

And people’s data,

it’s really sensitive and social networks

are at the core arguably of many of societal like tension

and some of the most important things happening in society.

So it feels like it’s important to get this right,

both from a business model perspective

and just like a trust perspective.

I still gotta, I mean, it just still feels like,

I know there’s experimentation going on.

It still feels like everyone is afraid

to try different business models, like really try.

Well, I’m worried that people are afraid

to try different business models.

I’m also worried that some of the business models

may lead them to bad choices.

And Danny Kahneman talks about system one and system two,

sort of like a reptilian brain

that reacts quickly to what we see,

see something interesting, we click on it,

we retweet it versus our system two,

our frontal cortex that’s supposed to be more careful

and rational that really doesn’t make

as many decisions as it should.

I think there’s a tendency for a lot of these social networks

to really exploit system one, our quick instant reaction,

make it so we just click on stuff and pass it on

and not really think carefully about it.

And that system, it tends to be driven

by sex, violence, disgust, anger, fear,

these relatively primitive kinds of emotions.

Maybe they’re important for a lot of purposes,

but they’re not a great way to organize a society.

And most importantly, when you think about this huge,

amazing information infrastructure we’ve had

that’s connected billions of brains across the globe,

not just so we can all access information,

but we can all contribute to it and share it.

Arguably the most important thing

that that network should do is favor truth over falsehoods.

And the way it’s been designed,

not necessarily intentionally, is exactly the opposite.

My MIT colleagues are all, and Deb Roy and others at MIT,

did a terrific paper in the cover of Science.

And they documented what we all feared,

which is that lies spread faster than truth

on social networks.

They looked at a bunch of tweets and retweets,

and they found that false information

was more likely to spread further, faster, to more people.

And why was that?

It’s not because people like lies.

It’s because people like things that are shocking,

amazing, can you believe this?

Something that is not mundane,

not something that everybody else already knew.

And what are the most unbelievable things?

Well, lies.

And so if you wanna find something unbelievable,

it’s a lot easier to do that

if you’re not constrained by the truth.

So they found that the emotional valence

of false information was just much higher.

It was more likely to be shocking,

and therefore more likely to be spread.

Another interesting thing was that

that wasn’t necessarily driven by the algorithms.

I know that there is some evidence,

Zeynep Tufekci and others have pointed out on YouTube,

some of the algorithms unintentionally were tuned

to amplify more extremist content.

But in the study of Twitter that Sinan and Deb and others did,

they found that even if you took out all the bots

and all the automated tweets,

you still had lies spreading significantly faster.

It’s just the problems with ourselves

that we just can’t resist passing on the salacious content.

But I also blame the platforms

because there’s different ways you can design a platform.

You can design a platform in a way

that makes it easy to spread lies

and to retweet and spread things on,

or you can kind of put some friction on that

and try to favor truth.

I had dinner with Jimmy Wales once,

the guy who helped found Wikipedia.

And he convinced me that, look,

you can make some design choices,

whether it’s at Facebook, at Twitter,

at Wikipedia, or Reddit, whatever,

and depending on how you make those choices,

you’re more likely or less likely to have false news.

Create a little bit of friction, like you said.


You know, that’s the, and so if I’m…

It could be friction, it could be speeding the truth,

either way, but, and I don’t totally understand…

Speeding the truth, I love it.

Yeah, yeah.

Amplifying it and giving it more credit.

And in academia, which is far, far from perfect,

but when someone has an important discovery,

it tends to get more cited

and people kind of look to it more

and sort of, it tends to get amplified a little bit.

So you could try to do that too.

I don’t know what the silver bullet is,

but the meta point is that if we spend time

thinking about it, we can amplify truth over falsehoods.

And I’m disappointed in the heads of these social networks

that they haven’t been as successful

or maybe haven’t tried as hard to amplify truth.

And part of it, going back to what we said earlier,

is these revenue models may push them

more towards growing fast, spreading information rapidly,

getting lots of users,

which isn’t the same thing as finding truth.

Yeah, I mean, implicit in what you’re saying now

is a hopeful message that with platforms,

we can take a step towards a greater

and greater popularity of truth.

But the more cynical view is that

what the last few years have revealed

is that there’s a lot of money to be made

in dismantling even the idea of truth,

that nothing is true.

And as a thought experiment,

I’ve been thinking about if it’s possible

that our future will have,

like the idea of truth is something we won’t even have.

Do you think it’s possible in the future

that everything is on the table in terms of truth,

and we’re just swimming in this kind of digital economy

where ideas are just little toys

that are not at all connected to reality?

Yeah, I think that’s definitely possible.

I’m not a technological determinist,

so I don’t think that’s inevitable.

I don’t think it’s inevitable that it doesn’t happen.

I mean, the thing that I’ve come away with

every time I do these studies,

and I emphasize it in my books and elsewhere,

is that technology doesn’t shape our destiny,

we shape our destiny.

So just by us having this conversation,

I hope that your audience is gonna take it upon themselves

as they design their products,

and they think about, they use products,

as they manage companies,

how can they make conscious decisions

to favor truth over falsehoods,

favor the better kinds of societies,

and not abdicate and say, well, we just build the tools.

I think there was a saying that,

was it the German scientist

when they were working on the missiles in late World War II?

They said, well, our job is to make the missiles go up.

Where they come down, that’s someone else’s department.

And that’s obviously not the, I think it’s obvious,

that’s not the right attitude

that technologists should have,

that engineers should have.

They should be very conscious

about what the implications are.

And if we think carefully about it,

we can avoid the kind of world that you just described,

where truth is all relative.

There are going to be people who benefit from a world

of where people don’t check facts,

and where truth is relative,

and popularity or fame or money is orthogonal to truth.

But one of the reasons I suspect

that we’ve had so much progress over the past few hundred

years is the invention of the scientific method,

which is a really powerful tool or meta tool

for finding truth and favoring things that are true

versus things that are false.

If they don’t pass the scientific method,

they’re less likely to be true.

And that has, the societies and the people

and the organizations that embrace that

have done a lot better than the ones who haven’t.

And so I’m hoping that people keep that in mind

and continue to try to embrace not just the truth,

but methods that lead to the truth.

So maybe on a more personal question,

if one were to try to build a competitor to Twitter,

what would you advise?

Is there, I mean, the bigger, the meta question,

is that the right way to improve systems?

Yeah, no, I think that the underlying premise

behind Twitter and all these networks is amazing,

that we can communicate with each other.

And I use it a lot.

There’s a subpart of Twitter called Econ Twitter,

where we economists tweet to each other

and talk about new papers.

Something came out in the NBER,

the National Bureau of Economic Research,

and we share about it.

People critique it.

I think it’s been a godsend

because it’s really sped up the scientific process,

if you can call economic scientific.

Does it get divisive in that little?

Sometimes, yeah, sure.

Sometimes it does.

It can also be done in nasty ways and there’s the bad parts.

But the good parts are great

because you just speed up that clock speed

of learning about things.

Instead of like in the old, old days,

waiting to read it in a journal,

or the not so old days when you’d see it posted

on a website and you’d read it.

Now on Twitter, people will distill it down

and it’s a real art to getting to the essence of things.

So that’s been great.

But it certainly, we all know that Twitter

can be a cesspool of misinformation.

And like I just said,

unfortunately misinformation tends to spread faster

on Twitter than truth.

And there are a lot of people

who are very vulnerable to it.

I’m sure I’ve been fooled at times.

There are agents, whether from Russia

or from political groups or others

that explicitly create efforts at misinformation

and efforts at getting people to hate each other.

Or even more important lately I’ve discovered

is nut picking.

You know the idea of nut picking?

No, what’s that?

It’s a good term.

Nut picking is when you find like an extreme nut case

on the other side and then you amplify them

and make it seem like that’s typical of the other side.

So you’re not literally lying.

You’re taking some idiot, you know,

renting on the subway or just, you know,

whether they’re in the KKK or Antifa or whatever,

they’re just, and you,

normally nobody would pay attention to this guy.

Like 12 people would see him and it’d be the end.

Instead with video or whatever,

you get tens of millions of people say it.

And I’ve seen this, you know, I look at it,

I’m like, I get angry.

I’m like, I can’t believe that person

did something so terrible.

Let me tell all my friends about this terrible person.

And it’s a great way to generate division.

I talked to a friend who studied Russian misinformation

campaigns, and they’re very clever about literally

being on both sides of some of these debates.

They would have some people pretend to be part of BLM.

Some people pretend to be white nationalists

and they would be throwing epithets at each other,

saying crazy things at each other.

And they’re literally playing both sides of it,

but their goal wasn’t for one or the other to win.

It was for everybody to get behaving

and distrusting everyone else.

So these tools can definitely be used for that.

And they are being used for that.

It’s been super destructive for our democracy

and our society.

And the people who run these platforms,

I think have a social responsibility,

a moral and ethical, personal responsibility

to do a better job and to shut that stuff down.

Well, I don’t know if you can shut it down,

but to design them in a way that, you know,

as I said earlier, favors truth over falsehoods

and favors positive types of

communication versus destructive ones.

And just like you said, it’s also on us.

I try to be all about love and compassion,

empathy on Twitter.

I mean, one of the things,

nut picking is a fascinating term.

One of the things that people do,

that’s I think even more dangerous

is nut picking applied to individual statements

of good people.

So basically worst case analysis in computer science

is taking sometimes out of context,

but sometimes in context,

a statement, one statement by a person,

like I’ve been, because I’ve been reading

The Rise and Fall of the Third Reich,

I often talk about Hitler on this podcast with folks

and it is so easy.

That’s really dangerous.

But I’m all leaning in, I’m 100%.

Because, well, it’s actually a safer place

than people realize because it’s history

and history in long form is actually very fascinating

to think about and it’s,

but I could see how that could be taken

totally out of context and it’s very worrying.

You know, these digital infrastructures,

not just they disseminate things,

but they’re sort of permanent.

So anything you say at some point,

someone can go back and find something you said

three years ago, perhaps jokingly, perhaps not,

maybe you’re just wrong and you made them, you know,

and like that becomes, they can use that to define you

if they have ill intent.

And we all need to be a little more forgiving.

I mean, somewhere in my 20s, I told myself,

I was going through all my different friends

and I was like, you know, every one of them

has at least like one nutty opinion.

And I was like, there’s like nobody

who’s like completely, except me, of course,

but I’m sure they thought that about me too.

And so you just kind of like learned

to be a little bit tolerant that like, okay,

there’s just, you know.

Yeah, I wonder who the responsibility lays on there.

Like, I think ultimately it’s about leadership.

Like the previous president, Barack Obama,

has been, I think, quite eloquent

at walking this very difficult line

of talking about cancel culture, but it’s a difficult,

it takes skill.

Because you say the wrong thing

and you piss off a lot of people.

And so you have to do it well.

But then also the platform of the technology is,

should slow down, create friction,

and spreading this kind of nut picking in all its forms.


No, and your point that we have to like learn over time,

how to manage it.

I mean, we can’t put it all on the platform

and say, you guys design it.

Because if we’re idiots about using it,

nobody can design a platform that withstands that.

And every new technology people learn its dangers.

You know, when someone invented fire,

it’s great cooking and everything,

but then somebody burned themself.

And then you had to like learn how to like avoid,

maybe somebody invented a fire extinguisher later.

So you kind of like figure out ways

of working around these technologies.

Someone invented seat belts, et cetera.

And that’s certainly true

with all the new digital technologies

that we have to figure out,

not just technologies that protect us,

but ways of using them that emphasize

that are more likely to be successful than dangerous.

So you’ve written quite a bit

about how artificial intelligence might change our world.

How do you think if we look forward,

again, it’s impossible to predict the future,

but if we look at trends from the past

and we tried to predict what’s gonna happen

in the rest of the 21st century,

how do you think AI will change our world?

That’s a big question.

You know, I’m mostly a techno optimist.

I’m not at the extreme, you know,

the singularity is near end of the spectrum,

but I do think that we’re likely in

for some significantly improved living standards,

some really important progress,

even just the technologies that are already kind of like

in the can that haven’t diffused.

You know, when I talked earlier about the J curve,

it could take 10, 20, 30 years for an existing technology

to have the kind of profound effects.

And when I look at whether it’s, you know,

vision systems, voice recognition, problem solving systems,

even if nothing new got invented,

we would have a few decades of progress.

So I’m excited about that.

And I think that’s gonna lead to us being wealthier,

healthier, I mean,

the healthcare is probably one of the applications

that I’m most excited about.

So that’s good news.

I don’t think we’re gonna have the end of work anytime soon.

There’s just too many things that machines still can’t do.

When I look around the world

and think of whether it’s childcare or healthcare,

cleaning the environment, interacting with people,

scientific work, artistic creativity,

these are things that for now,

machines aren’t able to do nearly as well as humans,

even just something as mundane as, you know,

folding laundry or whatever.

And many of these, I think are gonna be years or decades

before machines catch up.

You know, I may be surprised on some of them,

but overall, I think there’s plenty of work

for humans to do.

There’s plenty of problems in society

that need the human touch.

So we’ll have to repurpose.

We’ll have to, as machines are able to do some tasks,

people are gonna have to reskill and move into other areas.

And that’s probably what’s gonna be going on

for the next, you know, 10, 20, 30 years or more,

kind of big restructuring of society.

We’ll get wealthier and people will have to do new skills.

Now, if you turn the dial further, I don’t know,

50 or a hundred years into the future,

then, you know, maybe all bets are off.

Then it’s possible that machines will be able to do

most of what people do.

You know, say one or 200 years, I think it’s even likely.

And at that point,

then we’re more in the sort of abundance economy.

Then we’re in a world where there’s really little

for the humans can do economically better than machines,

other than be human.

And, you know, that will take a transition as well,

kind of more of a transition of how we get meaning in life

and what our values are.

But shame on us if we screw that up.

I mean, that should be like great, great news.

And it kind of saddens me that some people see that

as like a big problem.

I think that would be, should be wonderful

if people have all the health and material things

that they need and can focus on loving each other

and discussing philosophy and playing

and doing all the other things that don’t require work.

Do you think you’d be surprised to see what the 20,

if we were to travel in time, 100 years into the future,

do you think you’ll be able to,

like if I gave you a month to like talk to people,

no, like let’s say a week,

would you be able to understand what the hell’s going on?

You mean if I was there for a week?

Yeah, if you were there for a week.

A hundred years in the future?


So like, so I’ll give you one thought experiment is like,

isn’t it possible that we’re all living in virtual reality

by then?

Yeah, no, I think that’s very possible.

I’ve played around with some of those VR headsets

and they’re not great,

but I mean the average person spends many waking hours

staring at screens right now.

They’re kind of low res compared to what they could be

in 30 or 50 years, but certainly games

and why not any other interactions could be done with VR?

And that would be a pretty different world

and we’d all, in some ways be as rich as we wanted.

We could have castles and we could be traveling

anywhere we want and it could obviously be multisensory.

So that would be possible and of course there’s people,

you’ve had Elon Musk on and others, there are people,

Nick Bostrom makes the simulation argument

that maybe we’re already there.

We’re already there.

So, but in general, or do you not even think about

in this kind of way, you’re self critically thinking,

how good are you as an economist at predicting

what the future looks like?

Do you have a?

Well, it starts getting, I mean,

I feel reasonably comfortable the next five, 10, 20 years

in terms of that path.

When you start getting truly superhuman

artificial intelligence, kind of by definition,

be able to think of a lot of things

that I couldn’t have thought of and create a world

that I couldn’t even imagine.

And so I’m not sure I can predict what that world

is going to be like.

One thing that AI researchers, AI safety researchers

worry about is what’s called the alignment problem.

When an AI is that powerful,

then they can do all sorts of things.

And you really hope that their values

are aligned with our values.

And it’s even tricky to finding what our values are.

I mean, first off, we all have different values.

And secondly, maybe if we were smarter,

we would have better values.

Like, I like to think that we have better values

than we did in 1860 and, or in the year 200 BC

on a lot of dimensions,

things that we consider barbaric today.

And it may be that if I thought about it more deeply,

I would also be morally evolved.

Maybe I’d be a vegetarian or do other things

that right now, whether my future self

would consider kind of immoral.

So that’s a tricky problem,

getting the AI to do what we want,

assuming it’s even a friendly AI.

I mean, I should probably mention

there’s a nontrivial other branch

where we destroy ourselves, right?

I mean, there’s a lot of exponentially improving

technologies that could be ferociously destructive,

whether it’s in nanotechnology or biotech

and weaponized viruses, AI and other things that.

nuclear weapons.

Nuclear weapons, of course.

The old school technology.

Yeah, good old nuclear weapons that could be devastating

or even existential and new things yet to be invented.

So that’s a branch that I think is pretty significant.

And there are those who think that one of the reasons

we haven’t been contacted by other civilizations, right?

Is that once you get to a certain level of complexity

in technology, there’s just too many ways to go wrong.

There’s a lot of ways to blow yourself up.

And people, or I should say species,

end up falling into one of those traps.

The great filter.

I mean, there’s an optimistic view of that.

If there is literally no intelligent life out there

in the universe, or at least in our galaxy,

that means that we’ve passed at least one

of the great filters or some of the great filters

that we survived.

Yeah, no, I think Robin Hansen has a good way of,

maybe others have a good way of thinking about this,

that if there are no other intelligence creatures out there

that we’ve been able to detect,

one possibility is that there’s a filter ahead of us.

And when you get a little more advanced,

maybe in a hundred or a thousand or 10,000 years,

things just get destroyed for some reason.

The other one is the great filters behind us.

That’ll be good, is that most planets don’t even evolve life

or if they don’t evolve life,

they don’t evolve intelligent life.

Maybe we’ve gotten past that.

And so now maybe we’re on the good side

of the great filter.

So if we sort of rewind back and look at the thing

where we could say something a little bit more comfortably

at five years and 10 years out,

you’ve written about jobs

and the impact on sort of our economy and the jobs

in terms of artificial intelligence that it might have.

It’s a fascinating question of what kind of jobs are safe,

what kind of jobs are not.

Can you maybe speak to your intuition

about how we should think about AI changing

the landscape of work?

Sure, absolutely.

Well, this is a really important question

because I think we’re very far

from artificial general intelligence,

which is AI that can just do the full breadth

of what humans can do.

But we do have human level or superhuman level

narrow intelligence, narrow artificial intelligence.

And obviously my calculator can do math a lot better

than I can.

And there’s a lot of other things

that machines can do better than I can.

So which is which?

We actually set out to address that question

with Tom Mitchell.

I wrote a paper called what can machine learning do

that was in science.

And we went and interviewed a whole bunch of AI experts

and kind of synthesized what they thought machine learning

was good at and wasn’t good at.

And we came up with what we called a rubric,

basically a set of questions you can ask about any task

that will tell you whether it’s likely to score high or low

on suitability for machine learning.

And then we’ve applied that

to a bunch of tasks in the economy.

In fact, there’s a data set of all the tasks

in the US economy, believe it or not, it’s called ONET.

The US government put it together,

part of the Bureau of Labor Statistics.

They divide the economy into about 970 occupations

like bus driver, economist, primary school teacher,

radiologist, and then for each one of them,

they describe which tasks need to be done.

Like for radiologists, there are 27 distinct tasks.

So we went through all those tasks

to see whether or not a machine could do them.

And what we found interestingly was…

Brilliant study by the way, that’s so awesome.

Yeah, thank you.

So what we found was that there was no occupation

in our data set where machine learning just ran the table

and did everything.

And there was almost no occupation

where machine learning didn’t have

like a significant ability to do things.

Like take radiology, a lot of people I hear saying,

you know, it’s the end of radiology.

And one of the 27 tasks is read medical images.

Really important one, like it’s kind of a core job.

And machines have basically gotten as good

or better than radiologists.

There was just an article in Nature last week,

but they’ve been publishing them for the past few years

showing that machine learning can do as well as humans

on many kinds of diagnostic imaging tasks.

But other things that radiologists do,

they sometimes administer conscious sedation.

They sometimes do physical exams.

They have to synthesize the results

and explain it to the other doctors or to the patients.

In all those categories,

machine learning isn’t really up to snuff yet.

So that job, we’re gonna see a lot of restructuring.

Parts of the job, they’ll hand over to machines.

Others, humans will do more of.

That’s been more or less the pattern all of them.

So, you know, to oversimplify a bit,

we’re gonna see a lot of restructuring,

reorganization of work.

And it’s real gonna be a great time.

It is a great time for smart entrepreneurs and managers

to do that reinvention of work.

I’m not gonna see mass unemployment.

To get more specifically to your question,

the kinds of tasks that machines tend to be good at

are a lot of routine problem solving,

mapping inputs X into outputs Y.

If you have a lot of data on the Xs and the Ys,

the inputs and the outputs,

you can do that kind of mapping and find the relationships.

They tend to not be very good at,

even now, fine motor control and dexterity.

Emotional intelligence and human interactions

and thinking outside the box, creative work.

If you give it a well structured task,

machines can be very good at it.

But even asking the right questions, that’s hard.

There’s a quote that Andrew McAfee and I use

in our book, Second Machine Age.

Apparently Pablo Picasso was shown an early computer

and he came away kind of unimpressed.

He goes, well, I don’t see all the fusses.

All that does is answer questions.

And to him, the interesting thing was asking the questions.

Yeah, try to replace me, GPT3, I dare you.

Although some people think I’m a robot.

You have this cool plot that shows,

I just remember where economists land,

where I think the X axis is the income.

And then the Y axis is, I guess,

aggregating the information of how replaceable the job is.

Or I think there’s an index.

There’s a suitability for machine learning index.


So we have all 970 occupations on that chart.

It’s a cool plot.

And there’s scatters in all four corners

have some occupations.

But there is a definite pattern,

which is the lower wage occupations tend to have more tasks

that are suitable for machine learning, like cashiers.

I mean, anyone who’s gone to a supermarket or CVS

knows that they not only read barcodes,

but they can recognize an apple and an orange

and a lot of things cashiers, humans used to be needed for.

At the other end of the spectrum,

there are some jobs like airline pilot

that are among the highest paid in our economy,

but also a lot of them are suitable for machine learning.

A lot of those tasks are.

And then, yeah, you mentioned economists.

I couldn’t help peeking at those

and they’re paid a fair amount,

maybe not as much as some of us think they should be.

But they have some tasks that are suitable

for machine learning, but for now at least,

most of the tasks of economists

didn’t end up being in that category.

And I should say, I didn’t like create that data.

We just took the analysis and that’s what came out of it.

And over time, that scatter plot will be updated

as the technology improves.

But it was just interesting to see the pattern there.

And it is a little troubling in so far

as if you just take the technology as it is today,

it’s likely to worsen income inequality

on a lot of dimensions.

So on this topic of the effect of AI

on our landscape of work,

one of the people that have been speaking about it

in the public domain, public discourse

is the presidential candidate, Andrew Yang.


What are your thoughts about Andrew?

What are your thoughts about UBI,

that universal basic income

that he made one of the core ideas,

by the way, he has like hundreds of ideas

about like everything, it’s kind of interesting.

But what are your thoughts about him

and what are your thoughts about UBI?

Let me answer the question about his broader approach first.

I mean, I just love that.

He’s really thoughtful, analytical.

I agree with his values.

So that’s awesome.

And he read my book and mentions it sometimes,

so it makes me even more excited.

And the thing that he really made the centerpiece

of his campaign was UBI.

And I was originally kind of a fan of it.

And then as I studied it more, I became less of a fan,

although I’m beginning to come back a little bit.

So let me tell you a little bit of my evolution.

As an economist, we have, by looking at the problem

of people not having enough income and the simplest thing

is, well, why don’t we write them a check?

Problem solved.

But then I talked to my sociologist friends

and they really convinced me that just writing a check

doesn’t really get at the core values.

Voltaire once said that work solves three great ills,

boredom, vice, and need.

And you can deal with the need thing by writing a check,

but people need a sense of meaning,

they need something to do.

And when, say, steel workers or coal miners lost their jobs

and were just given checks, alcoholism, depression, divorce,

all those social indicators, drug use, all went way up.

People just weren’t happy

just sitting around collecting a check.

Maybe it’s part of the way they were raised.

Maybe it’s something innate in people

that they need to feel wanted and needed.

So it’s not as simple as just writing people a check.

You need to also give them a way to have a sense of purpose.

And that was important to me.

And the second thing is that, as I mentioned earlier,

we are far from the end of work.

I don’t buy the idea that there’s just like

not enough work to be done.

I see like our cities need to be cleaned up.

And robots can’t do most of that.

We need to have better childcare.

We need better healthcare.

We need to take care of people who are mentally ill or older.

We need to repair our roads.

There’s so much work that require at least partly,

maybe entirely a human component.

So rather than like write all these people off,

let’s find a way to repurpose them and keep them engaged.

Now that said, I would like to see more buying power

from people who are sort of at the bottom end

of the spectrum.

The economy has been designed and evolved in a way

that’s I think very unfair to a lot of hardworking people.

I see super hardworking people who aren’t really seeing

their wages grow over the past 20, 30 years,

while some other people who have been super smart

and or super lucky have made billions

or hundreds of billions.

And I don’t think they need those hundreds of billions

to have the right incentives to invent things.

I think if you talk to almost any of them as I have,

they don’t think that they need an extra $10 billion

to do what they’re doing.

Most of them probably would love to do it for only a billion

or maybe for nothing.

For nothing, many of them, yeah.

I mean, an interesting point to make is,

do we think that Bill Gates would have founded Microsoft

if tax rates were 70%?

Well, we know he would have because they were tax rates

of 70% when he founded it.

So I don’t think that’s as big a deterrent

and we could provide more buying power to people.

My own favorite tool is the Earned Income Tax Credit,

which is basically a way of supplementing income

of people who have jobs and giving employers

an incentive to hire even more people.

The minimum wage can discourage employment,

but the Earned Income Tax Credit encourages employment

by supplementing people’s wages.

If the employer can only afford to pay them $10 for a task,

the rest of us kick in another five or $10

and bring their wages up to 15 or 20 total.

And then they have more buying power.

Then entrepreneurs are thinking, how can we cater to them?

How can we make products for them?

And it becomes a self reinforcing system

where people are better off.

Ian Drang and I had a good discussion

where he suggested instead of a universal basic income,

he suggested, or instead of an unconditional basic income,

how about a conditional basic income

where the condition is you learn some new skills,

we need to reskill our workforce.

So let’s make it easier for people to find ways

to get those skills and get rewarded for doing them.

And that’s kind of a neat idea as well.

That’s really interesting.

So, I mean, one of the questions,

one of the dreams of UBI is that you provide

some little safety net while you retrain,

while you learn a new skill.

But like, I think, I guess you’re speaking

to the intuition that that doesn’t always,

like there needs to be some incentive to reskill,

to train, to learn a new thing.

I think it helps.

I mean, there are lots of self motivated people,

but there are also people that maybe need a little guidance

or help and I think it’s a really hard question

for someone who is losing a job in one area to know

what is the new area I should be learning skills in.

And we could provide a much better set of tools

and platforms that maps it.

Okay, here’s a set of skills you already have.

Here’s something that’s in demand.

Let’s create a path for you to go from where you are

to where you need to be.

So I’m a total, how do I put it nicely about myself?

I’m totally clueless about the economy.

It’s not totally true, but pretty good approximation.

If you were to try to fix our tax system

and, or maybe from another side,

if there’s fundamental problems in taxation

or some fundamental problems about our economy,

what would you try to fix?

What would you try to speak to?

You know, I definitely think our whole tax system,

our political and economic system has gotten more

and more screwed up over the past 20, 30 years.

I don’t think it’s that hard to make headway

in improving it.

I don’t think we need to totally reinvent stuff.

A lot of it is what I’ve been elsewhere with Andy

and others called economics 101.

You know, there’s just some basic principles

that have worked really well in the 20th century

that we sort of forgot, you know,

in terms of investing in education,

investing in infrastructure, welcoming immigrants,

having a tax system that was more progressive and fair.

At one point, tax rates were on top incomes

were significantly higher.

And they’ve come down a lot to the point where

in many cases they’re lower now

than they are for poorer people.

So, and we could do things like earned income tax credit

to get a little more wonky.

I’d like to see more Pigouvian taxes.

What that means is you tax things that are bad

instead of things that are good.

So right now we tax labor, we tax capital

and which is unfortunate

because one of the basic principles of economics

if you tax something, you tend to get less of it.

So, you know, right now there’s still work to be done

and still capital to be invested in.

But instead we should be taxing things like pollution

and congestion.

And if we did that, we would have less pollution.

So a carbon tax is, you know,

almost every economist would say it’s a no brainer

whether they’re Republican or Democrat,

Greg Mankiw who is head of George Bush’s

Council of Economic Advisers or Dick Schmollensie

who is another Republican economist agree.

And of course a lot of Democratic economists agree as well.

If we taxed carbon,

we could raise hundreds of billions of dollars.

We could take that money and redistribute it

through an earned income tax credit or other things

so that overall our tax system would become more progressive.

We could tax congestion.

One of the things that kills me as an economist

is every time I sit in a traffic jam,

I know that it’s completely unnecessary.

This is complete wasted time.

You just visualize the cost and productivity.

Exactly, because they are taking costs for me

and all the people around me.

And if they charged a congestion tax,

they would take that same amount of money

and people would, it would streamline the roads.

Like when you’re in Singapore, the traffic just flows

because they have a congestion tax.

They listened to economists.

They invited me and others to go talk to them.

And then I’d still be paying,

I’d be paying a congestion tax instead of paying in my time,

but that money would now be available for healthcare,

be available for infrastructure,

or be available just to give to people

so they could buy food or whatever.

So it’s just, it saddens me when you sit,

when you’re sitting in a traffic jam,

it’s like taxing me and then taking that money

and dumping it in the ocean, just like destroying it.

So there are a lot of things like that

that economists, and I’m not,

I’m not like doing anything radical here.

Most, you know, good economists would,

I probably agree with me point by point on these things.

And we could do those things

and our whole economy would become much more efficient.

It’d become fairer, invest in R&D and research,

which is close to a free lunch is what we have.

My erstwhile MIT colleague, Bob Solla,

got the Nobel Prize, not yesterday, but 30 years ago,

for describing that most improvements

in living standards come from tech progress.

And Paul Romer later got a Nobel Prize

for noting that investments in R&D and human capital

can speed the rate of tech progress.

So if we do that, then we’ll be healthier and wealthier.

Yeah, from an economics perspective,

I remember taking an undergrad econ,

you mentioned econ 101.

It seemed from all the plots I saw

that R&D is an obvious, as close to free lunch as we have,

it seemed like obvious that we should do more research.

It is.

Like what, what, like, there’s no.

Well, we should do basic research.

I mean, so let me just be clear.

It’d be great if everybody did more research

and I would make this issue

between applied development versus basic research.

So applied development, like, you know,

how do we get this self driving car, you know,

feature to work better in the Tesla?

That’s great for private companies

because they can capture the value from that.

If they make a better self driving car system,

they can sell cars that are more valuable

and then make money.

So there’s an incentive that there’s not a big problem there

and smart companies, Amazon, Tesla,

and others are investing in it.

The problem is with basic research,

like coming up with core basic ideas,

whether it’s in nuclear fusion

or artificial intelligence or biotech.

There, if someone invents something,

it’s very hard for them to capture the benefits from it.

It’s shared by everybody, which is great in a way,

but it means that they’re not gonna have the incentives

to put as much effort into it.

There you need, it’s a classic public good.

There you need the government to be involved in it.

And the US government used to be investing much more in R&D,

but we have slashed that part of the government

really foolishly and we’re all poorer,

significantly poorer as a result.

Growth rates are down.

We’re not having the kind of scientific progress

we used to have.

It’s been sort of a short term eating the seed corn,

whatever metaphor you wanna use

where people grab some money, put it in their pockets today,

but five, 10, 20 years later,

they’re a lot poorer than they otherwise would have been.

So we’re living through a pandemic right now,

globally in the United States.

From an economics perspective,

how do you think this pandemic will change the world?

It’s been remarkable.

And it’s horrible how many people have suffered,

the amount of death, the economic destruction.

It’s also striking just the amount of change in work

that I’ve seen.

In the last 20 weeks, I’ve seen more change

than there were in the previous 20 years.

There’s been nothing like it

since probably the World War II mobilization

in terms of reorganizing our economy.

The most obvious one is the shift to remote work.

And I and many other people stopped going into the office

and teaching my students in person.

I did a study on this with a bunch of colleagues

at MIT and elsewhere.

And what we found was that before the pandemic,

in the beginning of 2020, about one in six,

a little over 15% of Americans were working remotely.

When the pandemic hit, that grew steadily and hit 50%,

roughly half of Americans working at home.

So a complete transformation.

And of course, it wasn’t even,

it wasn’t like everybody did it.

If you’re an information worker, professional,

if you work mainly with data,

then you’re much more likely to work at home.

If you’re a manufacturing worker,

working with other people or physical things,

then it wasn’t so easy to work at home.

And instead, those people were much more likely

to become laid off or unemployed.

So it’s been something that’s had very disparate effects

on different parts of the workforce.

Do you think it’s gonna be sticky in a sense

that after vaccine comes out and the economy reopens,

do you think remote work will continue?

That’s a great question.

My hypothesis is yes, a lot of it will.

Of course, some of it will go back,

but a surprising amount of it will stay.

I personally, for instance, I moved my seminars,

my academic seminars to Zoom,

and I was surprised how well it worked.

So it works?

Yeah, I mean, obviously we were able to reach

a much broader audience.

So we have people tuning in from Europe

and other countries,

just all over the United States for that matter.

I also actually found that it would,

in many ways, is more egalitarian.

We use the chat feature and other tools,

and grad students and others who might’ve been

a little shy about speaking up,

we now kind of have more of ability for lots of voices.

And they’re answering each other’s questions,

so you kind of get parallel.

Like if someone had some question about some of the data

or a reference or whatever,

then someone else in the chat would answer it.

And the whole thing just became like a higher bandwidth,

higher quality thing.

So I thought that was kind of interesting.

I think a lot of people are discovering that these tools

that thanks to technologists have been developed

over the past decade,

they’re a lot more powerful than we thought.

I mean, all the terrible things we’ve seen with COVID

and the real failure of many of our institutions

that I thought would work better.

One area that’s been a bright spot is our technologies.

Bandwidth has held up pretty well,

and all of our email and other tools

have just scaled up kind of gracefully.

So that’s been a plus.

Economists call this question

of whether it’ll go back a hysteresis.

The question is like when you boil an egg

after it gets cold again, it stays hard.

And I think that we’re gonna have a fair amount

of hysteresis in the economy.

We’re gonna move to this new,

we have moved to a new remote work system,

and it’s not gonna snap all the way back

to where it was before.

One of the things that worries me is that the people

with lots of followers on Twitter and people with voices,

people that can, voices that can be magnified by reporters

and all that kind of stuff are the people

that fall into this category

that we were referring to just now

where they can still function

and be successful with remote work.

And then there is a kind of quiet suffering

of what feels like millions of people

whose jobs are disturbed profoundly by this pandemic,

but they don’t have many followers on Twitter.

What do we, and again, I apologize,

but I’ve been reading the rise and fall of the Third Reich

and there’s a connection to the depression

on the American side.

There’s a deep, complicated connection

to how suffering can turn into forces

that potentially change the world in destructive ways.

So like it’s something I worry about is like,

what is this suffering going to materialize itself

in five, 10 years?

Is that something you worry about, think about?

It’s like the center of what I worry about.

And let me break it down to two parts.

There’s a moral and ethical aspect to it.

We need to relieve this suffering.

I mean, I’m sure the values of, I think most Americans,

we like to see shared prosperity

or most people on the planet.

And we would like to see people not falling behind

and they have fallen behind, not just due to COVID,

but in the previous couple of decades,

median income has barely moved,

depending on how you measure it.

And the incomes of the top 1% have skyrocketed.

And part of that is due to the ways technology has been used.

Part of this been due to, frankly, our political system

has continually shifted more wealth into those people

who have the powerful interest.

So there’s just, I think, a moral imperative

to do a better job.

And ultimately, we’re all gonna be wealthier

if more people can contribute,

more people have the wherewithal.

But the second thing is that there’s a real political risk.

I’m not a political scientist,

but you don’t have to be one, I think,

to see how a lot of people are really upset

with they’re getting a raw deal

and they want to smash the system in different ways,

in 2016 and 2018.

And now I think there are a lot of people

who are looking at the political system

and they feel like it’s not working for them

and they just wanna do something radical.

Unfortunately, demagogues have harnessed that

in a way that is pretty destructive to the country.

And an analogy I see is what happened with trade.

Almost every economist thinks that free trade

is a good thing, that when two people voluntarily exchange

almost by definition, they’re both better off

if it’s voluntary.

And so generally, trade is a good thing.

But they also recognize that trade can lead

to uneven effects, that there can be winners and losers

in some of the people who didn’t have the skills

to compete with somebody else or didn’t have other assets.

And so trade can shift prices

in ways that are averse to some people.

So there’s a formula that economists have,

which is that you have free trade,

but then you compensate the people who are hurt

and free trade makes the pie bigger.

And since the pie is bigger,

it’s possible for everyone to be better off.

You can make the winners better off,

but you can also compensate those who don’t win.

And so they end up being better off as well.

What happened was that we didn’t fulfill that promise.

We did have some more increased free trade

in the 80s and 90s, but we didn’t compensate the people

who were hurt.

And so they felt like the people in power

reneged on the bargain, and I think they did.

And so then there’s a backlash against trade.

And now both political parties,

but especially Trump and company,

have really pushed back against free trade.

Ultimately, that’s bad for the country.

Ultimately, that’s bad for living standards.

But in a way I can understand

that people felt they were betrayed.

Technology has a lot of similar characteristics.

Technology can make us all better off.

It makes the pie bigger.

It creates wealth and health, but it can also be uneven.

Not everyone automatically benefits.

It’s possible for some people,

even a majority of people to get left behind

while a small group benefits.

What most economists would say,

well, let’s make the pie bigger,

but let’s make sure we adjust the system

so we compensate the people who are hurt.

And since the pie is bigger,

we can make the rich richer,

we can make the middle class richer,

we can make the poor richer.

Mathematically, everyone could be better off.

But again, we’re not doing that.

And again, people are saying this isn’t working for us.

And again, instead of fixing the distribution,

a lot of people are beginning to say,

hey, technology sucks, we’ve got to stop it.

Let’s throw rocks at the Google bus.

Let’s blow it up.

And there were the Luddites almost exactly 200 years ago

who smashed the looms and the spinning machines

because they felt like those machines weren’t helping them.

We have a real imperative,

not just to do the morally right thing,

but to do the thing that is gonna save the country,

which is make sure that we create

not just prosperity, but shared prosperity.

So you’ve been at MIT for over 30 years, I think.

Don’t tell anyone how old I am.

Yeah, no, that’s true, that’s true.

And you’re now moved to Stanford.

I’m gonna try not to say anything

about how great MIT is.

What’s that move been like?

What, it’s East Coast to West Coast?

Well, MIT is great.

MIT has been very good to me.

It continues to be very good to me.

It’s an amazing place.

I continue to have so many amazing friends

and colleagues there.

I’m very fortunate to have been able

to spend a lot of time at MIT.

Stanford’s also amazing.

And part of what attracted me out here

was not just the weather, but also Silicon Valley,

let’s face it, is really more of the epicenter

of the technological revolution.

And I wanna be close to the people

who are inventing AI and elsewhere.

A lot of it is being invested at MIT for that matter

in Europe and China and elsewhere, in Nia.

But being a little closer to some of the key technologists

was something that was important to me.

And it may be shallow,

but I also do enjoy the good weather.

And I felt a little ripped off

when I came here a couple of months ago.

And immediately there are the fires

and my eyes were burning, the sky was orange

and there’s the heat waves.

And so it wasn’t exactly what I’ve been promised,

but fingers crossed it’ll get back to better.

But maybe on a brief aside,

there’s been some criticism of academia

and universities and different avenues.

And I, as a person who’s gotten to enjoy universities

from the pure playground of ideas that it can be,

always kind of try to find the words

to tell people that these are magical places.

Is there something that you can speak to

that is beautiful or powerful about universities?

Well, sure.

I mean, first off, I mean,

economists have this concept called revealed preference.

You can ask people what they say

or you can watch what they do.

And so obviously by reveal preferences, I love academia.

I could be doing lots of other things,

but it’s something I enjoy a lot.

And I think the word magical is exactly right.

At least it is for me.

I do what I love, you know,

hopefully my Dean won’t be listening,

but I would do this for free.

You know, it’s just what I like to do.

I like to do research.

I love to have conversations like this with you

and with my students, with my fellow colleagues.

I love being around the smartest people I can find

and learning something from them

and having them challenge me.

And that just gives me joy.

And every day I find something new and exciting to work on.

And a university environment is really filled

with other people who feel that way.

And so I feel very fortunate to be part of it.

And I’m lucky that I’m in a society

where I can actually get paid for it

and put food on the table

while doing the stuff that I really love.

And I hope someday everybody can have jobs

that are like that.

And I appreciate that it’s not necessarily easy

for everybody to have a job that they both love

and also they get paid for.

So there are things that don’t go well in academia,

but by and large, I think it’s a kind of, you know,

kinder, gentler version of a lot of the world.

You know, we sort of cut each other a little slack

on things like, you know, on just a lot of things.

You know, of course there’s harsh debates

and discussions about things

and some petty politics here and there.

I personally, I try to stay away

from most of that sort of politics.

It’s not my thing.

And so it doesn’t affect me most of the time,

sometimes a little bit, maybe.

But, you know, being able to pull together something,

we have the digital economy lab.

We’ve got all these brilliant grad students

and undergraduates and postdocs

that are just doing stuff that I learned from.

And every one of them has some aspect

of what they’re doing that’s just,

I couldn’t even understand.

It’s like way, way more brilliant.

And that’s really, to me, actually I really enjoy that,

being in a room with lots of other smart people.

And Stanford has made it very easy to attract,

you know, those people.

I just, you know, say I’m gonna do a seminar, whatever,

and the people come, they come and wanna work with me.

We get funding, we get data sets,

and it’s come together real nicely.

And the rest is just fun.

It’s fun, yeah.

And we feel like we’re working on important problems,

you know, and we’re doing things that, you know,

I think are first order in terms of what’s important

in the world, and that’s very satisfying to me.

Maybe a bit of a fun question.

What three books, technical, fiction, philosophical,

you’ve enjoyed, had a big, big impact in your life?

Well, I guess I go back to like my teen years,

and, you know, I read Sid Arthur,

which is a philosophical book,

and kind of helps keep me centered.

By Herman Hesse.

Yeah, by Herman Hesse, exactly.

Don’t get too wrapped up in material things

or other things, and just sort of, you know,

try to find peace on things.

A book that actually influenced me a lot

in terms of my career was called

The Worldly Philosophers by Robert Halbrenner.

It’s actually about economists.

It goes through a series of different,

it’s written in a very lively form,

and it probably sounds boring,

but it did describe whether it’s Adam Smith

or Karl Marx or John Maynard Keynes,

and each of them sort of what their key insights were,

but also kind of their personalities,

and I think that’s one of the reasons

I became an economist was just understanding

how they grapple with the big questions of the world.

So would you recommend it as a good whirlwind overview

of the history of economics?

Yeah, yeah, I think that’s exactly right.

It kind of takes you through the different things,

and so you can understand how they reach,

thinking some of the strengths and weaknesses.

I mean, it probably is a little out of date now.

It needs to be updated a bit,

but you could at least look through

the first couple hundred years of economics,

which is not a bad place to start.

More recently, I mean, a book I really enjoyed

is by my friend and colleague, Max Tegmark,

called Life 3.0.

You should have him on your podcast if you haven’t already.

He was episode number one.

Oh my God.

And he’s back, he’ll be back, he’ll be back soon.

Yeah, no, he’s terrific.

I love the way his brain works,

and he makes you think about profound things.

He’s got such a joyful approach to life,

and so that’s been a great book,

and I learn a lot from it, I think everybody,

but he explains it in a way, even though he’s so brilliant,

that everyone can understand, that I can understand.

That’s three, but let me mention maybe one or two others.

I mean, I recently read More From Less

by my sometimes coauthor, Andrew McAfee.

It made me optimistic about how we can continue

to have rising living standards

while living more lightly on the planet.

In fact, because of higher living standards,

because of technology,

because of digitization that I mentioned,

we don’t have to have as big an impact on the planet,

and that’s a great story to tell,

and he documents it very carefully.

You know, a personal kind of self help book

that I found kind of useful, People, is Atomic Habits.

I think it’s, what’s his name, James Clear.

Yeah, James Clear.

He’s just, yeah, it’s a good name,

because he writes very clearly,

and you know, most of the sentences I read in that book,

I was like, yeah, I know that,

but it just really helps to have somebody like remind you

and tell you and kind of just reinforce it, and it’s helpful.

So build habits in your life that you hope to have,

that have a positive impact,

and don’t have to make it big things.

It could be just tiny little.

Exactly, I mean, the word atomic,

it’s a little bit of a pun, I think he says.

You know, one, atomic means they’re really small.

You take these little things, but also like atomic power,

can have like, you know, big impact.

That’s funny, yeah.

The biggest ridiculous question,

especially to ask an economist, but also a human being,

what’s the meaning of life?

I hope you’ve gotten the answer to that from somebody else.

I think we’re all still working on that one, but what is it?

You know, I actually learned a lot from my son, Luke,

and he’s 19 now, but he’s always loved philosophy,

and he reads way more sophisticated philosophy than I do.

I went and took him to Oxford,

and he spent the whole time like pulling

all these obscure books down and reading them.

And a couple of years ago, we had this argument,

and he was trying to convince me that hedonism

was the ultimate, you know, meaning of life,

just pleasure seeking, and…

Well, how old was he at the time?

17, so…


But he made a really good like intellectual argument

for it too, and you know,

but you know, it just didn’t strike me as right.

And I think that, you know, while I am kind of a utilitarian,

like, you know, I do think we should do the grace,

good for the grace number, that’s just too shallow.

And I think I’ve convinced myself that real happiness

doesn’t come from seeking pleasure.

It’s kind of a little, it’s ironic.

Like if you really focus on being happy,

I think it doesn’t work.

You gotta like be doing something bigger.

I think the analogy I sometimes use is, you know,

when you look at a dim star in the sky,

if you look right at it, it kind of disappears,

but you have to look a little to the side,

and then the parts of your retina

that are better at absorbing light,

you know, can pick it up better.

It’s the same thing with happiness.

I think you need to sort of find something, other goal,

something, some meaning in life,

and that ultimately makes you happier

than if you go squarely at just pleasure.

And so for me, you know, the kind of research I do

that I think is trying to change the world,

make the world a better place,

and I’m not like an evolutionary psychologist,

but my guess is that our brains are wired,

not just for pleasure, but we’re social animals,

and we’re wired to like help others.

And ultimately, you know,

that’s something that’s really deeply rooted in our psyche.

And if we do help others, if we do,

or at least feel like we’re helping others,

you know, our reward systems kick in,

and we end up being more deeply satisfied

than if we just do something selfish and shallow.

Beautifully put.

I don’t think there’s a better way to end it, Eric.

You were one of the people when I first showed up at MIT,

that made me proud to be at MIT.

So it’s so sad that you’re now at Stanford,

but I’m sure you’ll do wonderful things at Stanford as well.

I can’t wait till future books,

and people should definitely read your other books.

Well, thank you so much.

And I think we’re all part of the invisible college,

as we call it.

You know, we’re all part of this intellectual

and human community where we all can learn from each other.

It doesn’t really matter physically

where we are so much anymore.


Thanks for talking today.

My pleasure.

Thanks for listening to this conversation

with Eric Brynjolfsson.

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It has become appallingly obvious

that our technology has exceeded our humanity.

Thank you for listening and hope to see you next time.

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