Erik Brynjolfsson: Economics of AI, Social Networks, and Technology
嘉宾：Erik Brynjolfsson
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本篇目录13 章节
§01Transcript · 00:00–08:34§02Transcript · 08:36–17:09§03Transcript · 17:11–24:39§04Transcript · 24:40–32:17§05Transcript · 32:18–40:24§06Transcript · 40:29–48:54§07Transcript · 48:56–56:50§08Transcript · 56:52–1:05:03§09Transcript · 1:05:05–1:12:41§10Transcript · 1:12:44–1:21:03§11Transcript · 1:21:06–1:29:08§12Transcript · 1:29:10–1:37:04§13Transcript · 1:37:05–1:39:42§01Transcript · 00:00–08:34
The following is a conversation with Eric Brinjalsen.
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,"
co-authored 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's 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.
If you enjoy this thing, subscribe on YouTube,
review it with 5 Stars on Apple Podcasts,
follow on Spotify, support on Patreon,
connect with me on Twitter @LexFriedman.
And now, here's my conversation with Eric Greenjohnson.
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 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 kinda 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 'cause 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 1,000 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 GPT-3
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 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 under the digital umbrella.
It feels pretty calm and normal and gradual.
- A lot of stuff, I think there are parts of the world,
most of the world 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
'cause 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
create deadlines and estimate when you'll be able to deliver
on some of these technologies.
Now, it often gets them in trouble
because he overestimates,
like he doesn't meet the initial estimates of the deadlines
but he seems to deliver late but deliver.
- Right.
- 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.
§02Transcript · 08:36–17:09
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
could 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 exponentially growing curves.
And I was like, holy shit, look at this.
In each industry, it was just taking off.
And 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 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-- - Yes.
- Revolutions stack on top of revolutions.
Do you have any intuition about how the heck 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. (laughs)
And-- - That's very profound.
- It's very profound, but 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 happened with COVID as well.
I mean, it was, it went up and then it sort of,
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, as you allude,
in 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 Cume's Law,
which is named after John Cume,
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,
that's nice, but having it the battery life longer,
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,
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, a graphic processing unit,
that goes faster, and 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 us figuring out
more and more tricks on how to train networks faster?
- The progress has been staggering.
As 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 amount of digital data
that can be used as training sets.
And then last but not least,
as they mentioned at OpenAI,
there have 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 hundredfold improvement
in computer power, a hundredfold or more improvement
in data, a hundredfold improvement
in techniques of software and algorithms,
and soon you're getting into millionfold improvements.
- You know, somebody brought this up,
this idea with GPT-3 that it's,
so it's training 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.
- I know, we're making 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 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?
Make sure talking to the CTO of Waymo tomorrow,
and obviously, I'm talking to Elon again in a couple weeks.
What's your thoughts on autonomous vehicles?
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 it,
§03Transcript · 17:11–24:39
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 Toyota Research Institute.
- That's 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,
'cause 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's 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'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'd be perfect.
- Yeah.
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.
So that was interesting.
Of course, there's still a concern
that there could be setbacks if we do this wrong.
Your listeners may be familiar
with the different levels of self-driving,
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 they 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,
anyone from the public in the Phoenix, Arizona,
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, 'cause 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
'cause the first safety driver would fall asleep.
That's like, I'm not sure they're going
in the right direction with that.
- No, they've Waymo in particular
have done a really good job of that.
They actually have a very interesting infrastructure
of remote 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-
- Yeah, I can see that being really,
because I think the thing that's proven harder
than maybe some of the early people expected
is 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 they 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 requires guts and risk-taking.
It also requires, you know, 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, you know,
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.
You know, 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,
said, "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 the 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.
§04Transcript · 24:40–32:17
So you have one giant steam engine,
all the equipment clustered around it, multi-story.
They'd 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,
30 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 was this big debate,
if you read the management literature
between what they called 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 there's a,
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 you 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 on the ones that-
- 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 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.
- 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,
hope I'm not saying anything ridiculous,
but everything from Wikipedia to Twitter.
So like these kind of websites,
not so much AI,
but 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, 'cause that makes it--
- 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,
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 well-being,
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 Kallis and others,
to develop something we call GDP-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, 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 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 can measure at 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.
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.
§05Transcript · 32:18–40:24
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 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.
You know, 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 a bunch of people enjoy reading it.
- Well, they may learn something from it.
And also, from the advertiser's perspective,
those are people who are actually interested, you know?
Like, I mean, the example I sometimes give,
like, I bought a car recently,
and all of a sudden, all the car ads were like,
interesting to me, I was paying attention.
And then, like, now that I have the car,
like, I sort of zone out on it.
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, you know, 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 you, 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.
- What about a mix?
Like this is what YouTube is, right?
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 is 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, you know, the done right part is a really good point.
Like with 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 to subscribe.
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 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 linger a little bit
on the social network thing,
because there's this Netflix social dilemma.
- Yeah, no, I saw that.
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 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 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 our 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 we can all access information,
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, Aral and Deb Roy and others at MIT
§06Transcript · 40:29–48:54
did a terrific paper on the cover of Science.
And when they document 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 that something 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,
you know, Zainab Tufekci and others have pointed out
in 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 this salacious content.
But I also blame the platforms because, you know,
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,
you know, the guy who helped found Wikipedia.
And he convinced me that, look, you know,
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.
- Yeah.
- You know, that's the--
- It could be friction or it could be speeding the truth,
you know, either way, but, and I don't totally understand--
- Speeding the truth, I love it.
- Yeah, yeah, you know, amplifying it
and giving it more credit and, you know,
like in academia, which is far, far from perfect,
but, you know, 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, you know, 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 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, like 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 it,
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 scientists,
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 the 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 sub part of Twitter called econ Twitter,
where we economists tweet to each other
and talk about new papers.
Something came out in NBER,
the National Bureau of Economic Research,
and we share about it.
People critique it.
I think it's been a godsend
'cause it's really sped up the scientific process,
if you can call economics 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 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,
ranting 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,
§07Transcript · 48:56–56:50
I'm like, I get angry.
I'm like, I can't believe that person
did such things, it's so terrible.
Let me tell all my friends about this terrible person.
(laughing)
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, 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,
not picking is a fascinating term.
One of the things that people do
that's, I think, even more dangerous
is not 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%.
'Cause, well, it's actually a safer place
than people realize 'cause it's history,
and history in long form is actually very fascinating
to think about, but I could see how that could be taken
totally out of context, and it's very worrying.
- Right, 'cause you think about these digital infrastructures,
not just the semi-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 a, you know?
And 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 one nutty opinion.
(both laughing)
- Yeah, exactly.
- And I was like, there's nobody who's completely,
except me, of course.
- Yeah, exactly. - But I'm sure they thought
that about me, too.
And so you just kinda learn to be a little bit tolerant
that, okay, there's just, you know.
- Yeah, I wonder who the responsibility lays on there.
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 difficult.
It takes skill.
'Cause 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, the technology,
should slow down, create friction,
and spreading this kind of nut picking in all its forms.
- Absolutely.
No, and your point that we have to learn over time
how to manage it.
I mean, we can't put it all on the platform
and say, you guys design it.
'Cause if we're idiots about using it,
nobody can design a platform that withstands that.
And every new technology people learn, it's dangerous.
You know, when someone invented fire,
it's great cooking and everything,
but then somebody burned himself.
And then you had to learn how to avoid,
maybe somebody invented a fire extinguisher later.
So you kinda 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 try 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
kinda 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 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 100 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 it 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'll be surprised to see what the 20,
like if we were to travel in time,
a hundred 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 is 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?
- Yeah.
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.
§08Transcript · 56:52–1:05:03
You know, 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.
You know, 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, you know, in some ways be as rich as we wanted
you know, we could have castles
we could be traveling anywhere we want
and it could obviously be multisensory.
So that would be possible.
And you know, there's people, you know,
you've had Elon Musk on and others, you know,
there are people, Nick Bostrom, you know,
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?
- Well, it starts getting, I mean,
I feel reasonably comfortable next, you know,
five, 10, 20 years in terms of that path.
When you start getting truly superhuman
artificial intelligence, kind of by definition,
I'll 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.
We 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, you know, I like to think that we have better values
than he did in 1860 and, or in, you know,
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 non-trivial 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.
- 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 it's Robin Hanson has a good way of,
maybe others, they 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 100 or 1,000 or 10,000 years,
things just get destroyed for some reason.
The other one is the great filter's 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 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, 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 O*NET.
The US government put it together,
part of 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 radiologists do, you know,
they sometimes administer conscious sedation,
they sometimes do physical exams,
they have to synthesize the results
and explain 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 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.
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.
§09Transcript · 1:05:05–1:12:41
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, GPT-3, 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.
- That's the suitability for machine learning index, exactly.
So we have all 970 occupations on that chart.
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 that 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 that economists do
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 the 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.
- Yeah.
- 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.
- He does.
- It's kind of interesting.
- Yeah.
- 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.
You know, 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.
You know, Voltaire once said
that work solves three great ills,
boredom, vice, and need.
And you know, 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, you know, 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,
you know, we are far from the end of work.
You know, 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.
You know, 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, you know, an interesting point to make is,
you know, like, 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.
You know, 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.
You know, if the employer can only afford
to pay him $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.
Andrew Ng 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 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.
- Well, 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
§10Transcript · 1:12:44–1:21:03
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 map to,
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,
but 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's head of George Bush's
Council of Economic Advisers,
or Dick Shmolensey,
who is another Republican economist degree,
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 could just visualize the cost and productivity.
- Exactly, because they are taking costs from me
and all the people around me,
and if they charged a congestion tax,
they would take that same amount of money
and it would streamline the roads.
Like when you're in Singapore,
the traffic just flows 'cause they have a congestion tax.
They listen 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 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 saddens me 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 doing anything radical here.
Most 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 fair, invest in R&D and research,
which is close to a free lunch is what we have.
My erstwhile MIT colleague, Bob Sola,
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,
that's close to a free lunch as we have.
It seemed like obvious that we should do more research.
- It is.
- Like 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 thing to be to apply development
versus basic research.
So apply development, like,
how do we get this self-driving car feature
to work better in the Tesla?
That's great for private companies
'cause 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 they'll 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, a 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.
I, 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,
§11Transcript · 1:21:06–1:29:08
my academic seminars to Zoom
and I was surprised how well it worked.
- That's how 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, it's 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, you know,
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, you know,
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.
You know, bandwidth has held up pretty well
and all of our email and other tools have just,
you know, 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 going to have a fair amount
of hysteresis in the economy.
We're going to move to this new,
we have moved to a new remote work system
and it's not going to 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,
you know, 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.
You know, there's a moral and ethical aspect to it
that we need to relieve this suffering.
I mean, I'm share 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,
a 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 has 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 their getting a raw deal.
And they are going to, you know,
they wanna 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.
You know, 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 were 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.
- 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, 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 movement like?
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
§12Transcript · 1:29:10–1:37:04
who are inventing AI and elsewhere.
A lot of it is being invested at MIT, for that matter,
in Europe and China and elsewhere, India.
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.
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'd been promised,
but fingers crossed it'll get back to better.
- Then 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 revealed preferences,
I love academia.
I'm out here, 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.
Hopefully my dean won't be listening,
but I would do this for free.
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,
kinder, gentler version of a lot of the world.
- Yes, that's true, exactly.
- We sort of cut each other a little slack
on things like, on just a lot of things.
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 being able to pull together something,
we have the Digital Economy Lab.
We get 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 those people.
I just say I'm gonna do a seminar or 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
and we're doing things that 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 impact in your life?
- Well, I guess I go back to my teen years
and I read "Sidartha" which is a philosophical book
and kinda helps keep me centered.
- Herman Hest.
- Yeah, my Herman Hest, exactly.
Don't get too wrapped up in material things or other things
and just sort of 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 Heilbroner.
It's actually about economists.
It goes through a series of different companies
written in a very lively form.
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 grappled 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 kinda takes you through the different things
so you can understand how they reach,
thinking some of the strengths and weaknesses.
I mean, probably it's 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, 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.
- It was episode number one.
- Oh my God.
- And he's 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 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 co-author 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
'cause he writes very clearly
and most of the sentences I read in that book,
I was like, yeah, I know that,
but it just really helps to have somebody remind you
and tell you and kind of just reinforce it.
- So build habits in your life
that you hope to 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
and you take these little things,
but also like atomic power, it can have 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?
(laughing)
- I hope you've gotten the answer to that from somebody.
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 once took him to Oxford and he spent the whole time
pulling all these obscure books down and reading them.
And a couple years ago, we had this argument,
and he was trying to convince me that hedonism
was the ultimate meaning of life, just pleasure seeking.
(laughing)
- Well, how old was he at the time?
- 17.
(laughing)
But he made a really good intellectual argument for it too.
- Of course.
- But it just didn't strike me as right.
And I think that while I am kind of a utilitarian,
like I do think we should do the greatest good
for the greatest 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.
§13Transcript · 1:37:05–1:39:42
Like if you really focus on being happy,
I think it doesn't work.
You gotta be doing something bigger.
I think the analogy I sometimes use is
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 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, 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 help others.
And ultimately, 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,
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.
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.
- Beautiful.
Thanks for talking today.
- My pleasure.
- Thanks for listening to this conversation
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And now, let me leave you with some words
from Albert Einstein.
"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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