Wait, The Robots Didn't Take Our Jobs? — With Erik Brynjolfsson

Channel: Alex Kantrowitz

Published at: 2022-06-04

YouTube video id: uWPes4aj2Es

Source: https://www.youtube.com/watch?v=uWPes4aj2Es

hello and welcome to the big technology
podcast a show for cool headed nuance
conversation of the tech world and
beyond and we are coming to you for one
more episode uh from davos we've had a
series of them and uh we're here in
collaboration with the web3 foundation
in unfinished it's been a heck of a week
five shows in two weeks
love to hear what you think about it so
please send feedback to
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gmail.com
our guest today is professor eric
brynjolfsson he is a professor and
senior fellow at stanford university
director of the digital economy lab
there and author of co-author of a great
book the second machine age which i
recommend you pick up eric welcome to
the show pleasure to be here alex so
the way i came across your work was i
was in amazon headquarters reporting for
my book always day one talking about how
ai and corporations will will mesh
and how that changes work and i'm having
a conversation about it with jeff wilke
who is the ceo of
consumer worldwide there before he
stepped down
last year
and
he said you've got to read eric
berniel's book so
i read it and i think that your work on
the way that ai and
and work combine
is is really fascinating and i'm excited
to have you here
well uh that's great to hear i'm a huge
fan of jeff wilkey's uh we first met
back when he was at mit and uh he's
responsible for a lot of amazon success
so i'm glad he liked the book yeah and
we'll talk and we'll keep by the way for
listeners and viewers he um
he was responsible for running the
entire amazon retail operation among
other things so basically was doing the
amazon style of amazon business while
jeff bezos was ceo and then eventually
left
so the study of ai and work it's really
interesting it's nascent
and it's not quite a technology study
not quite a sociology study not quite a
labor study but kind of
falls squarely in the middle of all
three right what got you into it
professor well you know uh since i was a
kid i was a fan of science fiction and i
always saw and imagined the way that
technology could change the world and
you know i read isaac asimov's
foundation series where he made up this
profession of psycho historians
that can
understand the great sweep of history
and these mega trends and i thought that
was kind of cool so i think that kind of
got me into economics and and i was
debating
you know in college and afterwards about
whether to be go more into ai and i did
actually take a number of ai courses and
teach a course on ai right after i
graduated or more into economics i was
hoping i could do both of them
simultaneously when i went to mit
it turned out the two groups didn't
really talk to each other that much
so i ended up doing the economics track
but focusing on how technology was
changing the world and ever since i've
been thinking about okay
when i look at
a change in the economy
to what extent can technology explain
that or conversely
to what extent can a change in
technology lead to changes in the
economy and what can we do to shape
those changes in a way that lead to
better outcomes
was there a moment where artificial
intelligence and machine learning
started to come onto the radar for you
and made you think that it was worth
studying in deeper depth
well pretty early honestly i mean i
definitely by high school i was
reading uh you know the mind's eye and
douglas hofstetter and patrick winston
had a book on artificial intelligence
and of course if you read isaac asimov
it's you know irobot goes back to the
50s and 60s when he was writing some of
those stories so that's been something
that
like generations of people i thought was
important um
for me i also just more broadly was
looking at digital technologies a lot of
my work in the 90s was about the
internet and search costs and
information goods you know things made
out of bits versus out of atoms have
very different properties so trying to
understand the economics of information
um but always keeping a an eye and doing
occasional work on ai in particular
and then uh i think in that you know the
early around 2010 or thereabouts
um
when a lot of people were worried about
uh
what was happening to wages and work and
productivity i started diving more
closely and saw the role of of ai i took
a ride in the google self-driving car i
think it was 2011 actually we drove up
route 101
up to san francisco and turned around
and i was like wow this is seems like
it's almost ready for prime time the
highway was driving fine
they had the human take it when we
turned around the clover leaf to turn
around come back down but i said okay
well they'll figure that out soon and so
that was something that i i think was a
little prematurely optimistic about how
rapidly the cars would be rolling out
but really since the early 2010s have
been focusing more and more on machine
learning and ai
because of well we've seen this this
revolution driven by deep learning
and supervised learning systems that
have been able to solve a lot of
problems that previously they couldn't
do
right now i don't think it's
controversial to say that we're living
in the most advanced moment for machine
learning and artificial intelligence
especially as it's applied in the
workplace for sure we've never had
anything like this and it's accelerated
extremely fast
however
the
rumors that the ai was going to take
people's jobs
and lead to mass unemployment
that hasn't come true right andrew yang
ran a political campaign saying we need
universal basic income right turns out
we didn't we're as close to as full
employment as you can get in the u.s
that's right and artificial intelligence
doesn't seem to be threatening jobs now
maybe i'm making some assumptions here
but can you square those two ideas yeah
no absolutely and
i i i love andrew yang he's a super
smart guy i've had some good
conversations with him i'm very
flattered that he he cites our work in
some of his uh campaign literature etc
and i appreciate that um
but you know we actually in the book we
were not
pushing for universal basic income and
we did not predict mass unemployment
some people uh didn't read the book that
carefully and maybe you know saw that we
were talking about some big changes
coming but we always talked about what
we call the great restructuring the
reality is not the great mass
unemployment or great resignation even
the reality is is that while ai is very
powerful human level or even superhuman
in certain specific narrow tasks we're
still very far from artificial general
intelligence you know ai that can do the
broad set of things that humans can do
in fact i did a study that was published
in science with tom mitchell of carnegie
mellon university where we looked at
about eighteen thousand specific tasks
that humans do in the in the economy uh
onet actually publishes a list of 950
occupations where each of them is about
20 or 30 tasks so that's up to about 18
000 tasks
and
we evaluated them on a set of criteria
as to whether machine learning was
likely to be able to do and we call this
the suitability for machine learning
rubric
what we found was that in most jobs
there were some tasks that machine
learning could do better than humans or
would be able to um if you apply the
technology and there's kind of a gold
rush now to do that but in not a single
occupation we looked at all of them
did we find that machine learning could
run the table and do all of the tasks
you know for instance people often talk
about
reading medical images and
radiologists being put out of work i've
heard a number of machine learning
people talk about that and it's true
that
machine learning can read medical images
very well in some cases better than
humans to detect cancer or other
anomalies
but
there are
26 tasks the radiologists do according
to to the one taxonomy and for most of
the machine learning is not very helpful
it's not helpful in counseling patients
and comforting them after they get a
diagnosis or coordinating care with
other physicians or setting up the
machinery or or one of the tasks is
administers conscious sedation i'm like
i'm not i don't think i want a machine
to be administering sedation to people
so um so those are you know and that's
even one of the jobs that is in some
ways more vulnerable and many other jobs
like
you know carpenter it we're even further
from having machine learning doing so
that said um if you look at the subset
of tasks where machine learning can
help or or take over for humans or
augment humans it adds up to about a
trillion dollars worth of work if you
were most conservative and so there's a
lot of restructuring and reorganization
that will happen in the economy um but
for quite a while there'll still be
demand for human labor and so i've
always focused more on how can we
redeploy retrain people as some tasks
become less important other tasks become
more important
in short there's no shortage of
work that needs to be done if you look
around the economy if you look around
you know people taking care of
elderly or children or cleaning the
environment or just even art and science
that only humans can do at least with
existing technology so we need people to
be working on all of those areas
and we're pretty far from saying that
there's there's nothing left for people
to be working on do you think that the
fear of artificial intelligence taking
our jobs was overblown
is overblown well
so let me nuance it
it's taking certain tasks
it's not leading to mass unemployment
but it is shifting the demand for
different types of skills and so there
are places where there's less demand and
there's more demand and wages in some
areas are depressed when a machine can
do some of the jobs especially some of
the repetitive rope kind of work so
middle skill routine information
processing work a lot of that has been
depressed and the median wages have been
stagnating in those areas
so there's a
effects on demand but it's not like mass
unemployment it's more uh what's
happening to wages and income and income
inequality that have been the drivers
and i do think it's worth thinking about
those and taking steps in terms of
education retraining maybe income
support progressive taxation to help
cushion that and help smooth the
transition to new kinds of jobs but it
you know it's not the kind of thing
where we like throw up our hand say well
there's nothing these people can do
let's just give them uh ubi
when i read you have a paper out called
touring trap she talks a little bit
about this and one of the things that
surprised me in reading it was
that you very concretely drew the line
between automation and machine learning
into
bigger societal problems like
concentration of wealth among right
fewer people
and income inequality for instance
societal unrest do we have any evidence
that um that that's happening now
because you know i was trying to see i
always thought that the link between
what machine learning was doing in the
workplace and these bigger issues um
wasn't as firm um and and it seems like
from your research there's actually a
bigger link than i was well imagining
there's a very strong link between
technological advances especially
information technology and changes in
the weight structure so depending on how
broadly you define it now
ai and machine learning is this sort of
the leading edge of that and there's a
new set of things that are being
affected by that but going back um for
20 or 30 years there's a mountain of
research some of which i did people like
larry katz david otter duronas omoglu
that have documented
the link between
chain in uses of technology and changes
in the wage structure they're really
three big forces that are affecting it
one is technology another is
globalization and trade and the third
one is uh you know government tax
structure and and those things of the
three most economists would say that
technology's the single biggest one
larry katz who runs the quarterly
journal of economics
and who's you know leading labor
economist looked at many of these issues
uh says that it's not even close
so
in that sense there is is strong
evidence now you go into specific
technologies and then you need to look
at particular case studies and then
there are a number of studies that where
technology has had beneficial effects
negative effects in different ways but
the broad story is that technology can
certainly move the wage structure and
going back to what i described earlier
that work on suitability for machine
learning that's a little bit more
forward-looking that looks at the tasks
that potentially could be done by
machine learning and if you if you you
know run the math through if some
percentage of those tasks are done by
machines then there will be less labor
demand for those kinds of tasks more
labor demand for complements to them and
that will shift the demand for for
skills significantly
our
analysis that we did in a follow-up
paper with daniel rock of wharton
suggests that as with the previous wave
the next wave of technology also will
disproportionately affect some of the
lower and middle skill jobs compared to
the higher skill jobs
you know cashiers and
bookkeepers are more likely to be in the
affected part but also airline pilots
who are among the higher paid ones will
also be somewhat affected radiologists i
met mentioned but on average um
the
the effect
could be to increase inequality
depending on how the technology is used
one of the examples that i've heard that
i think is pretty illustrative or
illustrative i don't know how you say
that word is um the accountants
you know lots of families would have
their own family accountant you know
back in the day and an accountant was a
pretty good middle-class job
and you could do that work and now
they've moved to turbo tax and the
benefits are accruing to intuit
yeah so so that's that there's an
example of some of that but but i want
to come to the
one of the main points from the the
turing trap paper which is that there's
different ways of using technology
technology can be used
to imitate
and replicate and automate what humans
are doing it could also be used to
complement and extend or augment what
humans are doing um and both of those
can be very profitable both of those are
you know successful strategies and there
are definitely some things we would love
to have automated away if a job is dirty
or dangerous or dull
you know let's let's go ahead and have
machines do it but most human progress
has actually not come from that kind of
automation from replacing what we're
doing and i give a little example or
hypothetical thought experiment in the
paper if you go back to ancient athens
and i suppose that somebody had invented
a miraculous robot
that
a set of robots that automated every job
in ancient athens but only the jobs in
ancient athens so you just automated
what they were already doing whether
it's making clay pots or you know tunics
or burning incense for sick people all
of that could be automated
i think it's pretty clear that
you know even having lots of free clay
pots and burning incense their living
standards wouldn't be all that high and
it's true they wouldn't have to work at
all so there'd be zero labor they have a
lot of leisure but ultimately their
living streams would be nowhere close to
ours they wouldn't have iphones or jet
planes or or penicillin or covert
vaccines
so um
most progress since that time has not
come from taking existing tasks and
simply automating them it's come from
using the technology to allow us to do
new things
and
a second important point we make in that
paper i make in that paper is that when
you do use the technology to automate
tasks not only aren't you
making the pie as big as it could be
you're not really raising the level as
much but you're also shifting things
around as you mentioned earlier it leads
to more of a concentration of wealth
among capital owners
when you use technology to automate what
a worker is doing there's less demand
for the worker there's less
labor income lower wages maybe even zero
wages
and more income for the capital owner
that leads to more concentration of
wealth capital is more concentrated
than labor
conversely if you use the technology to
augment people that is use the
technology to allow them to do new
things they couldn't do
before and complement what they're doing
then that tends to raise wages and
create more widely shared prosperity
in the touring trap article i argue that
as i said earlier while both of those
strategies can in principle be
profitable
right now there are excess incentives to
focus on substitution and automation and
not enough to focus on complementing
humans whether you're a technologist
trying to you know
solve the turing test which is you know
the title of the paper's homage to the
turing test you know that which
was originally called the imitation game
where you're trying to make a machine
that imitates humans that is focusing
too much on substitution or whether
you're a manager looking to take
the labor out of your factory or out of
your process by replacing each worker
with a machine that's focused on
substitution or whether you're doing tax
policy and
you give significantly lower tax rates
to
capital owners as opposed to labor which
is what the us currently does it wasn't
always like that in 1986 they were even
so in each of those cases
there's sort of the thumb is on the
scale towards
automation and substitution
and i think that's a mistake at a
minimum we should have a level playing
field and just you know do whichever one
works better and i think you can even go
further and say if we're going to put
the thumb on either side of the scale i
think we should push more towards
augmenting rather than substitution and
that's the that's the message of that
paper is we need to rethink
how we're using these technologies and
ultimately i hope technologists and
managers and policymakers will think
harder about how we can use the
technology not only make the pie bigger
but to create shared prosperity through
this complementing strategy professor
eric bernielson is with us he is
professor and senior fellow at stanford
university where he directs the digital
economy lab we'll be back right after
this and we're back here on big
technology podcast with professor eric
brennelson
from stanford he wrote the second
machine age a great book that you should
check out professor very interesting
hearing you talk about the way that
machine learning can change work i
when i watched what amazon had been
doing i always felt that they were
working towards the automate versus
augment and i want to give you a chance
to respond to this but
it seems to me that they automated a lot
of work in the retail organization to
make room for those retail workers to
end up building new products amazon go
for instance came out of a group that
had been in the retail organization
dilip kumar who led pricing and
promotions all that stuff got automated
when he finishes spending a year and a
half working as jeff bezos's technical
advisor he then goes ahead and creates
the amazon go store which is now
core to the company's strategy right and
so i always thought okay you automate
the the execution work work that's
keeping the business running you make
room for idea work anything involved in
building something new
and that's sort of how you make progress
but it's the augmentation side of it is
is a different wrinkle that i hadn't
considered yeah and let me be clear
there are places where automation is
great and and there's i've visited those
uh amazon distribution centers and some
of that work is pretty boring and
routine and it would be great if a robot
could take over some of that and this is
even in the white collar yeah even the
white collar side people ordering the
products and stuff yeah stocking routine
work like that i mean i think it's
actually a fortunate
coincidence that the kinds of things
that most people don't really like doing
repetitive boring
kinds of work that don't involve much
creativity machines are pretty good at
that the kinds of things most people
prefer
creative work or interacting with other
humans you know the human
touch and relationships that's stuff
that the machines are not very good at
so we have a kind of a nice division of
labor forming where you know at least
with current technologies um
we can have people focus on things that
they actually like a little bit more but
you know amazon's an interesting example
of the amazon go store in particular i i
wish them success with that and it's
great that they're pushing the envelope
on that but i want to do another thought
experiment which is
you know
amazon's about a 2 trillion dollar
company now a super successful company
and back in the 1990s uh jeff bezos was
looking at bookstores and thinking hey
we can use technology to change the way
bookstores run
if he had been uncreative he would have
walked into a bookstore and said you
know that cashier we could automate the
cashier we could put a robot where the
cashier is and check people out with a
robot cashier
and you know maybe that would have led
to a little labor savings i don't know
it would not really have moved the dial
much in terms of uh i'm sure there
wouldn't have been a two trillion dollar
company if that's simply what they had
done and in many ways it was a lot
harder i mean even today amazon go
trying to do something like that is
you know beta technology
instead
what he did was he looked at existing
technology and said you know we can do
things entirely different now with the
internet we don't need a
physical cache we need physical stores
you know we can have people order
through a browser etc
and that much more creative way of using
the technology created a lot more value
for all of us including for amazon
shareholders eventually
and i think that's a a good example of
how simply trying to take the existing
process
and automate what people are doing with
machinery is
is uncreative and usually
not something that that makes a big
impact managers who are able to think
more broadly to do things differently
and combine the components in new ways
are able to usually create a lot more
value
and so amazon's a great example of that
and that's one of my messages when i
teach you know the business school to to
my mba students is try to think a little
bit more creatively about how to use the
technology isn't there a darker side to
all this i was uh listening to amazon's
shareholder meeting last week and it was
striking to me that uh there were
shareholder proposals coming through
from workers who felt that they were
being managed by robot managers who were
tracking their time off task and you
know signing them goals and firing them
in some cases
is that the type of automation we want
but it doesn't sound like it you know
i'm not familiar with that particular
set of uh of what they're doing with
amazon in that
category
but the broader point is spot on which
is
that
these technologies can be used to
make the pie bigger to create more
shared prosperity create more freedom
and well-being or the technologies can
be used to concentrate wealth to reduce
freedom uh
to uh make people a lot most people
worse off there's no
uh law of economics that says that
everyone's gonna benefit from these
technologies automatically and i think
it's really important for us to think
about what our values are what are we
trying to achieve with these
technologies
our technologies today as you said the
opening of the podcast are more powerful
now than they've ever been
and almost by definition that means that
our tools are able to change the world
more than they ever could before so we
have more agency our decisions make a
big impact
there's only so much somebody could do
you know with a spear or rock you know a
thousand years ago but today you can
literally like change the world in quite
dramatic ways if if you use the
technology and that can be done in a way
that that makes the next 10 years the
best decade we've ever had or in a way
that makes it one of the worst or the
worst so i i really put a lot more
agency on us and i think it's really
important that we think carefully about
how we use these technologies and think
about what our values are what kind of
world we want to build
i know don't buy the idea that you know
technologist job is just to to make you
know technologies that that work and
leave it to other people everybody has
to be thinking about
what their values are when they when
they build technologies
i have a couple more questions for you i
want to know what you think about
robotic process automation because a
couple years ago this is especially when
i was writing my book it seemed like it
was the hottest thing in the world
companies like uipath are raising
massive valuations talking about giving
putting a robot at every desk giving
every every employee their own robot
right um they're struggling pretty bad
right now was the technology not there
was the fact that organizations can't
implement it well enough not there what
happened in that situation just to make
sure your listeners know robotic process
automation generally doesn't refer to
that kind of physical robots that we
think of right it's referring to um you
know uh information robots like that
will automate filling out a form and
there's a huge amount of white collar
work that's like incredibly boring and
routine we talked about that where you
know insurance forms or medical forms
whatever all need to be filled out and
processed and the idea of robotic
process automation was to automate a lot
of that and um
you know like i said these dirty these
dull boring jobs can be um
can be automated and more power to them
i think that uh rpa was a bit uh
overhyped and so there's a bit of a
bubble there's definitely some value
there but in the end it's it's what i
said earlier it's it's kind of focusing
on taking what people are already doing
and automating that um often they'll
literally like fill in forms that were
made for humans and so you're just
it's like my cashier example you're not
really thinking broadly enough what you
probably want to do is have the
information systems connect to each
other at a much more fundamental level
not going through this
form that was designed for humans
and uh and and i think it's just not
being creative enough about how
data systems can communicate with each
other so yeah it automates a bit of of
human labor but it isn't really
reimagining work the way i was calling
for
you know i'm struck by your example of
it was athens ancient athens right
digital oh yeah yeah yeah what well it's
interesting cars our economy right now a
lot of the production of goods you know
the way that we used to think of
production
is you know being done with a lot of
capital little labor a lot of automation
very efficient factories
what what then what is our and our
economy is like service or maybe it's
entertainment
you ever think about like what the heck
we're doing i mean why where what is our
economy actually if we can build
everything we need
you know with with very little labor
what exactly are we all working for i
think that's exactly the question we
should all be asking ourselves is um and
you know policy makers managers
economists like me um we can make gdp
higher and higher um we can reduce the
labor content more and more you know
therefore increasing productivity um and
if we're just making more you know flat
screen tvs you know to what end
i'm i'm not a philosopher but one thing
i would look to is maslow's hierarchy of
needs you know those of you know there's
some basic needs like food safety
clothing shelter and then there's some
intermediate leads needs like
relationships and status and then
there's
self-actualization and i i think that's
not a bad uh
template for how we should think about
progress
there's still a lot of people who don't
have enough food clothing shelter so
let's take care of those needs i'm glad
to see that that you know absolute
poverty has decreased tremendously over
the past 30 years actually even faster
than the un development goals called for
so so uh hooray for technology and i
think
the way that we've made progress on that
um there's still a lot of work to be
done but in wealthier societies like
ours you know we need to think about
okay how are we going to get meaning and
what do we want to spend our time on do
we want to spend time you know on on
facebook debating politics um do we want
to you know be gossiping with each other
do we want to be in virtual reality
playing video games you know what are
the things we want to do with this time
and leisure that have been freed up what
are the things that matter to us
i would uh hypothesize that
you know philosophy will start becoming
more important as people step back and
think okay you know as i said earlier we
have these powerful tools our values
matter more now than they ever did
before
we're not just having to spend 12 hours
a day scraping out a living you know by
getting some uh grain to grow in the
ground
we can now have the luxury of of
thinking beyond that
last question for you um yeah i think
you know we're here at davos um and
again we've been doing two weeks of
these shows so
folks bear with us the davos references
will will end next week but i haven't
seen anyone uh here more busy than you
i've seen you've been walking back and
forth
on the promenade um
the whole week you must have been
speaking with corporate leaders do you
think they are interested in
uh augmentation of labor or do you think
they're mostly trying to figure out hey
how do i automate as many jobs as i can
especially as we head to an economic
downturn there's some of each i think a
big misconception about davos is uh is
you know there's all the plutocrats and
all the wealth and power that's here but
the people who come here uh they really
do seem sincerely interested in changing
the world to make it better so the ones
i end up talking to are asking me how
can we create more widely shared
prosperity you know how can we do it in
a way that helps that's sustainable and
helps the environment how can we address
some of the challenges of diversity
equity and inclusion and
i think they're sincere when they say
they want to work on those things and
they they put them forward addressing
poverty i could go through the whole
list
and um they want to use the technology
to make the world better by and large um
there's a lot of uh
inertia and forces that go in the other
direction so it's not easy but i'm
heartened by how many
smart people i just had a breakfast
meeting this morning with with uh
i can say his name frank mccourt because
we're here in the uh the web 3 area and
uh he made a lot of money in in real
estate in other areas and he's taking
his energy to try to create a
decentralized
social network protocol that allows
people to own their own connections
their own data and uh and have more
freedom to interact with each other
and
deal with some of the misinformation i
think he's doing it mainly because he
wants to try to make the world a better
place as he sees it not because he's uh
trying to concentrate more wealth and
power in his in his own hands and
there's a lot of people technologists
and others
that have as their agenda to do that
one of the things i love about being out
in silicon valley is i run into a lot of
mission-driven companies and individuals
who um
they are pretty far up maslow's
hierarchy of needs i guess most of them
probably don't need to work anymore for
for food if they didn't want to but they
work pretty hard i work pretty hard
because we're energized by that we think
we can make a difference in the world
and uh
we have these powerful tools now and
this is a an inflection point in society
where the choices we make the next five
ten years
could put us on a very different path
depending on whether we make the right
choices so it's worth you know spending
the time and energy to to do what we can
to to move the down the right direction
professor eric for nielsen thank you so
much for joining us it's been a pleasure
thanks everybody for listening it's been
a heck of a run here at davos we are now
officially coming to a close i really
appreciate you listening to all the
shows so please give us feedback
and stay tuned for next week's show
we're going to go back on our normal
schedule we'll be talking about tech
news
thank you to simon hipkins from key
pictures for doing the audio and the
video you can check out the video on my
youtube page thank you to linkedin for
having me as part of your podcast
network thank you to unfinished
and the web3 foundation again for this
great collaboration here and thanks to
all of you the listeners we will see you
next week