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Daniel Susskind: 3 myths about the future of work (and why they're not true)
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"Will machines replace humans?" This question is on the mind of anyone with a job to lose. Daniel Susskind confronts this question and three misconceptions we have about our automated future, suggesting we ask something else: How will we distribute wealth in a world when there will be less -- or even no -- work?
Daniel Susskind - Economist
Daniel Susskind explores the impact of technology, particularly artificial intelligence, on work and society. Full bio
Daniel Susskind explores the impact of technology, particularly artificial intelligence, on work and society. Full bio
Double-click the English transcript below to play the video.
00:12
Automation anxiety
has been spreading lately,
has been spreading lately,
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a fear that in the future,
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many jobs will be performed by machines
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rather than human beings,
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given the remarkable advances
that are unfolding
that are unfolding
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in artificial intelligence and robotics.
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What's clear is that
there will be significant change.
there will be significant change.
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What's less clear
is what that change will look like.
is what that change will look like.
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My research suggests that the future
is both troubling and exciting.
is both troubling and exciting.
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The threat of technological
unemployment is real,
unemployment is real,
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and yet it's a good problem to have.
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And to explain
how I came to that conclusion,
how I came to that conclusion,
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I want to confront three myths
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that I think are currently obscuring
our vision of this automated future.
our vision of this automated future.
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A picture that we see
on our television screens,
on our television screens,
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in books, in films, in everyday commentary
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is one where an army of robots
descends on the workplace
descends on the workplace
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with one goal in mind:
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to displace human beings from their work.
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And I call this the Terminator myth.
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01:11
Yes, machines displace
human beings from particular tasks,
human beings from particular tasks,
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01:15
but they don't just
substitute for human beings.
substitute for human beings.
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They also complement them in other tasks,
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making that work more valuable
and more important.
and more important.
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Sometimes they complement
human beings directly,
human beings directly,
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making them more productive
or more efficient at a particular task.
or more efficient at a particular task.
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So a taxi driver can use a satnav system
to navigate on unfamiliar roads.
to navigate on unfamiliar roads.
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An architect can use
computer-assisted design software
computer-assisted design software
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to design bigger,
more complicated buildings.
more complicated buildings.
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But technological progress doesn't
just complement human beings directly.
just complement human beings directly.
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It also complements them indirectly,
and it does this in two ways.
and it does this in two ways.
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The first is if we think
of the economy as a pie,
of the economy as a pie,
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technological progress
makes the pie bigger.
makes the pie bigger.
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As productivity increases,
incomes rise and demand grows.
incomes rise and demand grows.
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The British pie, for instance,
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is more than a hundred times
the size it was 300 years ago.
the size it was 300 years ago.
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And so people displaced
from tasks in the old pie
from tasks in the old pie
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could find tasks to do
in the new pie instead.
in the new pie instead.
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02:12
But technological progress
doesn't just make the pie bigger.
doesn't just make the pie bigger.
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It also changes
the ingredients in the pie.
the ingredients in the pie.
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As time passes, people spend
their income in different ways,
their income in different ways,
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changing how they spread it
across existing goods,
across existing goods,
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and developing tastes
for entirely new goods, too.
for entirely new goods, too.
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New industries are created,
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new tasks have to be done
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and that means often
new roles have to be filled.
new roles have to be filled.
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So again, the British pie:
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300 years ago,
most people worked on farms,
most people worked on farms,
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150 years ago, in factories,
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and today, most people work in offices.
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And once again, people displaced
from tasks in the old bit of pie
from tasks in the old bit of pie
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could tumble into tasks
in the new bit of pie instead.
in the new bit of pie instead.
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Economists call these effects
complementarities,
complementarities,
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but really that's just a fancy word
to capture the different way
to capture the different way
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that technological progress
helps human beings.
helps human beings.
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Resolving this Terminator myth
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shows us that there are
two forces at play:
two forces at play:
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one, machine substitution
that harms workers,
that harms workers,
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but also these complementarities
that do the opposite.
that do the opposite.
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Now the second myth,
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what I call the intelligence myth.
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What do the tasks of driving a car,
making a medical diagnosis
making a medical diagnosis
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and identifying a bird
at a fleeting glimpse have in common?
at a fleeting glimpse have in common?
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Well, these are all tasks
that until very recently,
that until very recently,
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leading economists thought
couldn't readily be automated.
couldn't readily be automated.
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And yet today, all of these tasks
can be automated.
can be automated.
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You know, all major car manufacturers
have driverless car programs.
have driverless car programs.
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There's countless systems out there
that can diagnose medical problems.
that can diagnose medical problems.
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And there's even an app
that can identify a bird
that can identify a bird
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at a fleeting glimpse.
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Now, this wasn't simply a case of bad luck
on the part of economists.
on the part of economists.
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They were wrong,
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and the reason why
they were wrong is very important.
they were wrong is very important.
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They've fallen for the intelligence myth,
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the belief that machines
have to copy the way
have to copy the way
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that human beings think and reason
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in order to outperform them.
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When these economists
were trying to figure out
were trying to figure out
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what tasks machines could not do,
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they imagined the only way
to automate a task
to automate a task
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was to sit down with a human being,
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get them to explain to you
how it was they performed a task,
how it was they performed a task,
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and then try and capture that explanation
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in a set of instructions
for a machine to follow.
for a machine to follow.
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This view was popular in artificial
intelligence at one point, too.
intelligence at one point, too.
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I know this because Richard Susskind,
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who is my dad and my coauthor,
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wrote his doctorate in the 1980s
on artificial intelligence and the law
on artificial intelligence and the law
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at Oxford University,
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and he was part of the vanguard.
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And with a professor called Phillip Capper
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and a legal publisher called Butterworths,
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they produced the world's first
commercially available
commercially available
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artificial intelligence system in the law.
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This was the home screen design.
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He assures me this was
a cool screen design at the time.
a cool screen design at the time.
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(Laughter)
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I've never been entirely convinced.
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He published it
in the form of two floppy disks,
in the form of two floppy disks,
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at a time where floppy disks
genuinely were floppy,
genuinely were floppy,
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and his approach was the same
as the economists':
as the economists':
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sit down with a lawyer,
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get her to explain to you
how it was she solved a legal problem,
how it was she solved a legal problem,
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and then try and capture that explanation
in a set of rules for a machine to follow.
in a set of rules for a machine to follow.
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In economics, if human beings
could explain themselves in this way,
could explain themselves in this way,
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the tasks are called routine,
and they could be automated.
and they could be automated.
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But if human beings
can't explain themselves,
can't explain themselves,
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the tasks are called non-routine,
and they're thought to be out reach.
and they're thought to be out reach.
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Today, that routine-nonroutine
distinction is widespread.
distinction is widespread.
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Think how often you hear people say to you
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machines can only perform tasks
that are predictable or repetitive,
that are predictable or repetitive,
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rules-based or well-defined.
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Those are all just
different words for routine.
different words for routine.
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And go back to those three cases
that I mentioned at the start.
that I mentioned at the start.
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Those are all classic cases
of nonroutine tasks.
of nonroutine tasks.
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Ask a doctor, for instance,
how she makes a medical diagnosis,
how she makes a medical diagnosis,
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and she might be able
to give you a few rules of thumb,
to give you a few rules of thumb,
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but ultimately she'd struggle.
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She'd say it requires things like
creativity and judgment and intuition.
creativity and judgment and intuition.
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And these things are
very difficult to articulate,
very difficult to articulate,
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and so it was thought these tasks
would be very hard to automate.
would be very hard to automate.
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If a human being can't explain themselves,
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where on earth do we begin
in writing a set of instructions
in writing a set of instructions
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for a machine to follow?
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Thirty years ago, this view was right,
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but today it's looking shaky,
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and in the future
it's simply going to be wrong.
it's simply going to be wrong.
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Advances in processing power,
in data storage capability
in data storage capability
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and in algorithm design
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mean that this
routine-nonroutine distinction
routine-nonroutine distinction
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is diminishingly useful.
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To see this, go back to the case
of making a medical diagnosis.
of making a medical diagnosis.
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Earlier in the year,
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a team of researchers at Stanford
announced they'd developed a system
announced they'd developed a system
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which can tell you
whether or not a freckle is cancerous
whether or not a freckle is cancerous
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as accurately as leading dermatologists.
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How does it work?
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It's not trying to copy the judgment
or the intuition of a doctor.
or the intuition of a doctor.
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It knows or understands
nothing about medicine at all.
nothing about medicine at all.
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Instead, it's running
a pattern recognition algorithm
a pattern recognition algorithm
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through 129,450 past cases,
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hunting for similarities
between those cases
between those cases
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and the particular lesion in question.
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It's performing these tasks
in an unhuman way,
in an unhuman way,
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based on the analysis
of more possible cases
of more possible cases
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than any doctor could hope
to review in their lifetime.
to review in their lifetime.
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It didn't matter that that human being,
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that doctor, couldn't explain
how she'd performed the task.
how she'd performed the task.
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Now, there are those
who dwell upon that the fact
who dwell upon that the fact
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that these machines
aren't built in our image.
aren't built in our image.
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As an example, take IBM's Watson,
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the supercomputer that went
on the US quiz show "Jeopardy!" in 2011,
on the US quiz show "Jeopardy!" in 2011,
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and it beat the two
human champions at "Jeopardy!"
human champions at "Jeopardy!"
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The day after it won,
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The Wall Street Journal ran a piece
by the philosopher John Searle
by the philosopher John Searle
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with the title "Watson
Doesn't Know It Won on 'Jeopardy!'"
Doesn't Know It Won on 'Jeopardy!'"
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Right, and it's brilliant, and it's true.
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You know, Watson didn't
let out a cry of excitement.
let out a cry of excitement.
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It didn't call up its parents
to say what a good job it had done.
to say what a good job it had done.
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It didn't go down to the pub for a drink.
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This system wasn't trying to copy the way
that those human contestants played,
that those human contestants played,
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but it didn't matter.
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It still outperformed them.
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Resolving the intelligence myth
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shows us that our limited understanding
about human intelligence,
about human intelligence,
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about how we think and reason,
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is far less of a constraint
on automation than it was in the past.
on automation than it was in the past.
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What's more, as we've seen,
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when these machines
perform tasks differently to human beings,
perform tasks differently to human beings,
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there's no reason to think
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that what human beings
are currently capable of doing
are currently capable of doing
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represents any sort of summit
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in what these machines
might be capable of doing in the future.
might be capable of doing in the future.
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Now the third myth,
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what I call the superiority myth.
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It's often said that those who forget
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about the helpful side
of technological progress,
of technological progress,
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those complementarities from before,
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are committing something
known as the lump of labor fallacy.
known as the lump of labor fallacy.
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Now, the problem is
the lump of labor fallacy
the lump of labor fallacy
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is itself a fallacy,
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and I call this the lump
of labor fallacy fallacy,
of labor fallacy fallacy,
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or LOLFF, for short.
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Let me explain.
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The lump of labor fallacy
is a very old idea.
is a very old idea.
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It was a British economist, David Schloss,
who gave it this name in 1892.
who gave it this name in 1892.
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He was puzzled
to come across a dock worker
to come across a dock worker
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who had begun to use
a machine to make washers,
a machine to make washers,
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the small metal discs
that fasten on the end of screws.
that fasten on the end of screws.
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And this dock worker
felt guilty for being more productive.
felt guilty for being more productive.
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Now, most of the time,
we expect the opposite,
we expect the opposite,
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that people feel guilty
for being unproductive,
for being unproductive,
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you know, a little too much time
on Facebook or Twitter at work.
on Facebook or Twitter at work.
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But this worker felt guilty
for being more productive,
for being more productive,
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and asked why, he said,
"I know I'm doing wrong.
"I know I'm doing wrong.
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I'm taking away the work of another man."
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In his mind, there was
some fixed lump of work
some fixed lump of work
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to be divided up between him and his pals,
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so that if he used
this machine to do more,
this machine to do more,
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there'd be less left for his pals to do.
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Schloss saw the mistake.
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The lump of work wasn't fixed.
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As this worker used the machine
and became more productive,
and became more productive,
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the price of washers would fall,
demand for washers would rise,
demand for washers would rise,
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more washers would have to be made,
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and there'd be more work
for his pals to do.
for his pals to do.
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The lump of work would get bigger.
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Schloss called this
"the lump of labor fallacy."
"the lump of labor fallacy."
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And today you hear people talk
about the lump of labor fallacy
about the lump of labor fallacy
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to think about the future
of all types of work.
of all types of work.
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There's no fixed lump of work
out there to be divided up
out there to be divided up
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between people and machines.
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Yes, machines substitute for human beings,
making the original lump of work smaller,
making the original lump of work smaller,
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but they also complement human beings,
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and the lump of work
gets bigger and changes.
gets bigger and changes.
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But LOLFF.
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Here's the mistake:
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it's right to think
that technological progress
that technological progress
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makes the lump of work to be done bigger.
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Some tasks become more valuable.
New tasks have to be done.
New tasks have to be done.
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But it's wrong to think that necessarily,
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human beings will be best placed
to perform those tasks.
to perform those tasks.
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And this is the superiority myth.
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Yes, the lump of work
might get bigger and change,
might get bigger and change,
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but as machines become more capable,
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it's likely that they'll take on
the extra lump of work themselves.
the extra lump of work themselves.
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Technological progress,
rather than complement human beings,
rather than complement human beings,
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complements machines instead.
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To see this, go back
to the task of driving a car.
to the task of driving a car.
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Today, satnav systems
directly complement human beings.
directly complement human beings.
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They make some
human beings better drivers.
human beings better drivers.
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But in the future,
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software is going to displace
human beings from the driving seat,
human beings from the driving seat,
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and these satnav systems,
rather than complement human beings,
rather than complement human beings,
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will simply make these
driverless cars more efficient,
driverless cars more efficient,
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helping the machines instead.
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Or go to those indirect complementarities
that I mentioned as well.
that I mentioned as well.
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The economic pie may get larger,
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but as machines become more capable,
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it's possible that any new demand
will fall on goods that machines,
will fall on goods that machines,
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rather than human beings,
are best placed to produce.
are best placed to produce.
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The economic pie may change,
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but as machines become more capable,
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it's possible that they'll be best placed
to do the new tasks that have to be done.
to do the new tasks that have to be done.
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In short, demand for tasks
isn't demand for human labor.
isn't demand for human labor.
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Human beings only stand to benefit
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if they retain the upper hand
in all these complemented tasks,
in all these complemented tasks,
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but as machines become more capable,
that becomes less likely.
that becomes less likely.
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So what do these three myths tell us then?
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Well, resolving the Terminator myth
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shows us that the future of work depends
upon this balance between two forces:
upon this balance between two forces:
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one, machine substitution
that harms workers
that harms workers
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but also those complementarities
that do the opposite.
that do the opposite.
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And until now, this balance
has fallen in favor of human beings.
has fallen in favor of human beings.
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But resolving the intelligence myth
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shows us that that first force,
machine substitution,
machine substitution,
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is gathering strength.
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Machines, of course, can't do everything,
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12:16
but they can do far more,
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encroaching ever deeper into the realm
of tasks performed by human beings.
of tasks performed by human beings.
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What's more, there's no reason to think
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that what human beings
are currently capable of
are currently capable of
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represents any sort of finishing line,
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that machines are going
to draw to a polite stop
to draw to a polite stop
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once they're as capable as us.
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Now, none of this matters
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so long as those helpful
winds of complementarity
winds of complementarity
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blow firmly enough,
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but resolving the superiority myth
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12:40
shows us that that process
of task encroachment
of task encroachment
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not only strengthens
the force of machine substitution,
the force of machine substitution,
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but it wears down
those helpful complementarities too.
those helpful complementarities too.
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Bring these three myths together
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12:53
and I think we can capture a glimpse
of that troubling future.
of that troubling future.
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Machines continue to become more capable,
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encroaching ever deeper
on tasks performed by human beings,
on tasks performed by human beings,
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strengthening the force
of machine substitution,
of machine substitution,
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weakening the force
of machine complementarity.
of machine complementarity.
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13:08
And at some point, that balance
falls in favor of machines
falls in favor of machines
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rather than human beings.
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This is the path we're currently on.
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I say "path" deliberately,
because I don't think we're there yet,
because I don't think we're there yet,
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but it is hard to avoid the conclusion
that this is our direction of travel.
that this is our direction of travel.
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That's the troubling part.
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Let me say now why I think actually
this is a good problem to have.
this is a good problem to have.
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For most of human history,
one economic problem has dominated:
one economic problem has dominated:
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13:34
how to make the economic pie
large enough for everyone to live on.
large enough for everyone to live on.
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13:38
Go back to the turn
of the first century AD,
of the first century AD,
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and if you took the global economic pie
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and divided it up into equal slices
for everyone in the world,
for everyone in the world,
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everyone would get a few hundred dollars.
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Almost everyone lived
on or around the poverty line.
on or around the poverty line.
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And if you roll forward a thousand years,
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roughly the same is true.
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But in the last few hundred years,
economic growth has taken off.
economic growth has taken off.
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Those economic pies have exploded in size.
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Global GDP per head,
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the value of those individual
slices of the pie today,
slices of the pie today,
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they're about 10,150 dollars.
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If economic growth continues
at two percent,
at two percent,
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our children will be twice as rich as us.
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If it continues
at a more measly one percent,
at a more measly one percent,
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our grandchildren
will be twice as rich as us.
will be twice as rich as us.
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By and large, we've solved
that traditional economic problem.
that traditional economic problem.
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Now, technological unemployment,
if it does happen,
if it does happen,
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in a strange way will be
a symptom of that success,
a symptom of that success,
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will have solved one problem --
how to make the pie bigger --
how to make the pie bigger --
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but replaced it with another --
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how to make sure
that everyone gets a slice.
that everyone gets a slice.
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As other economists have noted,
solving this problem won't be easy.
solving this problem won't be easy.
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Today, for most people,
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their job is their seat
at the economic dinner table,
at the economic dinner table,
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and in a world with less work
or even without work,
or even without work,
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it won't be clear
how they get their slice.
how they get their slice.
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There's a great deal
of discussion, for instance,
of discussion, for instance,
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about various forms
of universal basic income
of universal basic income
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as one possible approach,
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and there's trials underway
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in the United States
and in Finland and in Kenya.
and in Finland and in Kenya.
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And this is the collective challenge
that's right in front of us,
that's right in front of us,
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to figure out how this material prosperity
generated by our economic system
generated by our economic system
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15:11
can be enjoyed by everyone
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in a world in which
our traditional mechanism
our traditional mechanism
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15:15
for slicing up the pie,
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15:17
the work that people do,
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15:19
withers away and perhaps disappears.
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Solving this problem is going to require
us to think in very different ways.
us to think in very different ways.
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There's going to be a lot of disagreement
about what ought to be done,
about what ought to be done,
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but it's important to remember
that this is a far better problem to have
that this is a far better problem to have
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than the one that haunted
our ancestors for centuries:
our ancestors for centuries:
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how to make that pie
big enough in the first place.
big enough in the first place.
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Thank you very much.
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(Applause)
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ABOUT THE SPEAKER
Daniel Susskind - EconomistDaniel Susskind explores the impact of technology, particularly artificial intelligence, on work and society.
Why you should listen
Daniel Susskind is the co-author, with Richard Susskind, of the best-selling book, The Future of the Professions, and a Fellow in Economics at Balliol College, Oxford University. He is currently finishing his latest book, on the future of work. Previously, he worked in the British Government -- as a policy adviser in the Prime Minister's Strategy Unit, as a policy analyst in the Policy Unit in 10 Downing Street, and as a senior policy adviser in the Cabinet Office. Susskind received a doctorate in economics from Oxford University and was a Kennedy Scholar at Harvard University.
Daniel Susskind | Speaker | TED.com