TED2017
Martin Ford: How we'll earn money in a future without jobs
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Machines that can think, learn and adapt are coming -- and that could mean that we humans will end up with significant unemployment. What should we do about it? In a straightforward talk about a controversial idea, futurist Martin Ford makes the case for separating income from traditional work and instituting a universal basic income.
Martin Ford - Futurist
Martin Ford imagines what the accelerating progress in robotics and artificial intelligence may mean for the economy, job market and society of the future. Full bio
Martin Ford imagines what the accelerating progress in robotics and artificial intelligence may mean for the economy, job market and society of the future. Full bio
Double-click the English transcript below to play the video.
00:12
I'm going to begin with a scary question:
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Are we headed toward
a future without jobs?
a future without jobs?
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The remarkable progress that we're seeing
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in technologies like self-driving cars
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has led to an explosion
of interest in this question,
of interest in this question,
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but because it's something
that's been asked
that's been asked
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so many times in the past,
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maybe what we should really be asking
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is whether this time is really different.
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The fear that automation
might displace workers
might displace workers
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and potentially lead
to lots of unemployment
to lots of unemployment
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goes back at a minimum 200 years
to the Luddite revolts in England.
to the Luddite revolts in England.
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And since then, this concern
has come up again and again.
has come up again and again.
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I'm going to guess
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that most of you have probably never
heard of the Triple Revolution report,
heard of the Triple Revolution report,
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but this was a very prominent report.
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It was put together
by a brilliant group of people --
by a brilliant group of people --
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it actually included
two Nobel laureates --
two Nobel laureates --
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and this report was presented
to the President of the United States,
to the President of the United States,
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and it argued that the US was on the brink
of economic and social upheaval
of economic and social upheaval
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because industrial automation
was going to put millions of people
was going to put millions of people
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out of work.
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Now, that report was delivered
to President Lyndon Johnson
to President Lyndon Johnson
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in March of 1964.
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So that's now over 50 years,
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and, of course, that
hasn't really happened.
hasn't really happened.
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And that's been the story again and again.
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This alarm has been raised repeatedly,
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but it's always been a false alarm.
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And because it's been a false alarm,
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it's led to a very conventional way
of thinking about this.
of thinking about this.
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And that says essentially that yes,
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technology may devastate
entire industries.
entire industries.
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It may wipe out whole occupations
and types of work.
and types of work.
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But at the same time, of course,
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progress is going to lead
to entirely new things.
to entirely new things.
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So there will be new industries
that will arise in the future,
that will arise in the future,
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and those industries, of course,
will have to hire people.
will have to hire people.
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There'll be new kinds of work
that will appear,
that will appear,
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and those might be things that today
we can't really even imagine.
we can't really even imagine.
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And that has been the story so far,
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and it's been a positive story.
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It turns out that the new jobs
that have been created
that have been created
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have generally been
a lot better than the old ones.
a lot better than the old ones.
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They have, for example,
been more engaging.
been more engaging.
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They've been in safer,
more comfortable work environments,
more comfortable work environments,
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and, of course, they've paid more.
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So it has been a positive story.
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That's the way things
have played out so far.
have played out so far.
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But there is one particular
class of worker
class of worker
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for whom the story
has been quite different.
has been quite different.
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For these workers,
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technology has completely
decimated their work,
decimated their work,
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and it really hasn't created
any new opportunities at all.
any new opportunities at all.
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And these workers, of course,
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are horses.
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(Laughter)
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So I can ask a very provocative question:
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Is it possible that at some
point in the future,
point in the future,
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a significant fraction of the human
workforce is going to be made redundant
workforce is going to be made redundant
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in the way that horses were?
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Now, you might have a very visceral,
reflexive reaction to that.
reflexive reaction to that.
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You might say, "That's absurd.
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How can you possibly compare
human beings to horses?"
human beings to horses?"
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Horses, of course, are very limited,
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and when cars and trucks
and tractors came along,
and tractors came along,
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horses really had nowhere else to turn.
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People, on the other hand,
are intelligent;
are intelligent;
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we can learn, we can adapt.
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And in theory,
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that ought to mean that we can
always find something new to do,
always find something new to do,
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and that we can always remain
relevant to the future economy.
relevant to the future economy.
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But here's the really
critical thing to understand.
critical thing to understand.
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The machines that will threaten
workers in the future
workers in the future
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are really nothing like those cars
and trucks and tractors
and trucks and tractors
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that displaced horses.
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The future is going to be full
of thinking, learning, adapting machines.
of thinking, learning, adapting machines.
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And what that really means
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is that technology is finally
beginning to encroach
beginning to encroach
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on that fundamental human capability --
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the thing that makes us
so different from horses,
so different from horses,
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and the very thing that, so far,
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has allowed us to stay ahead
of the march of progress
of the march of progress
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and remain relevant,
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and, in fact, indispensable
to the economy.
to the economy.
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So what is it that is really so different
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about today's information technology
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relative to what we've seen in the past?
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I would point to three fundamental things.
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The first thing is that we have seen
this ongoing process
this ongoing process
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of exponential acceleration.
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I know you all know about Moore's law,
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but in fact, it's more
broad-based than that;
broad-based than that;
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it extends in many cases,
for example, to software,
for example, to software,
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it extends to communications,
bandwidth and so forth.
bandwidth and so forth.
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But the really key thing to understand
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is that this acceleration has now
been going on for a really long time.
been going on for a really long time.
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In fact, it's been going on for decades.
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If you measure from the late 1950s,
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when the first integrated
circuits were fabricated,
circuits were fabricated,
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we've seen something on the order
of 30 doublings in computational power
of 30 doublings in computational power
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since then.
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That's just an extraordinary number
of times to double any quantity,
of times to double any quantity,
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and what it really means
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is that we're now at a point
where we're going to see
where we're going to see
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just an extraordinary amount
of absolute progress,
of absolute progress,
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and, of course, things are going
to continue to also accelerate
to continue to also accelerate
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from this point.
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So as we look forward
to the coming years and decades,
to the coming years and decades,
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I think that means
that we're going to see things
that we're going to see things
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that we're really not prepared for.
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We're going to see things
that astonish us.
that astonish us.
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The second key thing
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is that the machines are,
in a limited sense, beginning to think.
in a limited sense, beginning to think.
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And by this, I don't mean human-level AI,
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or science fiction
artificial intelligence;
artificial intelligence;
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I simply mean that machines and algorithms
are making decisions.
are making decisions.
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They're solving problems,
and most importantly, they're learning.
and most importantly, they're learning.
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In fact, if there's one technology
that is truly central to this
that is truly central to this
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and has really become
the driving force behind this,
the driving force behind this,
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it's machine learning,
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which is just becoming
this incredibly powerful,
this incredibly powerful,
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disruptive, scalable technology.
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One of the best examples
I've seen of that recently
I've seen of that recently
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was what Google's DeepMind
division was able to do
division was able to do
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with its AlphaGo system.
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Now, this is the system that was able
to beat the best player in the world
to beat the best player in the world
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at the ancient game of Go.
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Now, at least to me,
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there are two things that really
stand out about the game of Go.
stand out about the game of Go.
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One is that as you're playing the game,
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the number of configurations
that the board can be in
that the board can be in
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is essentially infinite.
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There are actually more possibilities
than there are atoms in the universe.
than there are atoms in the universe.
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So what that means is,
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you're never going to be able to build
a computer to win at the game of Go
a computer to win at the game of Go
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the way chess was approached, for example,
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which is basically to throw
brute-force computational power at it.
brute-force computational power at it.
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So clearly, a much more sophisticated,
thinking-like approach is needed.
thinking-like approach is needed.
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The second thing
that really stands out is that,
that really stands out is that,
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if you talk to one
of the championship Go players,
of the championship Go players,
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this person cannot necessarily
even really articulate what exactly it is
even really articulate what exactly it is
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they're thinking about
as they play the game.
as they play the game.
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It's often something
that's very intuitive,
that's very intuitive,
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it's almost just like a feeling
about which move they should make.
about which move they should make.
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So given those two qualities,
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I would say that playing Go
at a world champion level
at a world champion level
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really ought to be something
that's safe from automation,
that's safe from automation,
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and the fact that it isn't should really
raise a cautionary flag for us.
raise a cautionary flag for us.
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And the reason is that we tend
to draw a very distinct line,
to draw a very distinct line,
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and on one side of that line
are all the jobs and tasks
are all the jobs and tasks
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that we perceive as being on some level
fundamentally routine and repetitive
fundamentally routine and repetitive
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and predictable.
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And we know that these jobs
might be in different industries,
might be in different industries,
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they might be in different occupations
and at different skill levels,
and at different skill levels,
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but because they are innately predictable,
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we know they're probably at some point
going to be susceptible
going to be susceptible
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to machine learning,
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and therefore, to automation.
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And make no mistake --
that's a lot of jobs.
that's a lot of jobs.
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That's probably something
on the order of roughly half
on the order of roughly half
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the jobs in the economy.
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But then on the other side of that line,
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we have all the jobs
that require some capability
that require some capability
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that we perceive as being uniquely human,
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and these are the jobs
that we think are safe.
that we think are safe.
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Now, based on what I know
about the game of Go,
about the game of Go,
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I would've guessed that it really ought
to be on the safe side of that line.
to be on the safe side of that line.
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But the fact that it isn't,
and that Google solved this problem,
and that Google solved this problem,
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suggests that that line is going
to be very dynamic.
to be very dynamic.
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It's going to shift,
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and it's going to shift in a way
that consumes more and more jobs and tasks
that consumes more and more jobs and tasks
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that we currently perceive
as being safe from automation.
as being safe from automation.
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The other key thing to understand
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is that this is by no means just about
low-wage jobs or blue-collar jobs,
low-wage jobs or blue-collar jobs,
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or jobs and tasks done by people
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that have relatively
low levels of education.
low levels of education.
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There's lots of evidence to show
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that these technologies are rapidly
climbing the skills ladder.
climbing the skills ladder.
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So we already see an impact
on professional jobs --
on professional jobs --
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tasks done by people like accountants,
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financial analysts,
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journalists,
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lawyers, radiologists and so forth.
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So a lot of the assumptions that we make
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about the kind of occupations
and tasks and jobs
and tasks and jobs
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that are going to be threatened
by automation in the future
by automation in the future
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are very likely to be
challenged going forward.
challenged going forward.
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So as we put these trends together,
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I think what it shows is that we could
very well end up in a future
very well end up in a future
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with significant unemployment.
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Or at a minimum,
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we could face lots of underemployment
or stagnant wages,
or stagnant wages,
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maybe even declining wages.
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And, of course, soaring levels
of inequality.
of inequality.
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All of that, of course, is going to put
a terrific amount of stress
a terrific amount of stress
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on the fabric of society.
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But beyond that, there's also
a fundamental economic problem,
a fundamental economic problem,
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and that arises because jobs
are currently the primary mechanism
are currently the primary mechanism
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that distributes income,
and therefore purchasing power,
and therefore purchasing power,
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to all the consumers that buy the products
and services we're producing.
and services we're producing.
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In order to have a vibrant market economy,
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you've got to have
lots and lots of consumers
lots and lots of consumers
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that are really capable of buying
the products and services
the products and services
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that are being produced.
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If you don't have that,
then you run the risk
then you run the risk
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of economic stagnation,
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or maybe even a declining economic spiral,
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as there simply aren't enough
customers out there
customers out there
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to buy the products
and services being produced.
and services being produced.
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It's really important to realize
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that all of us as individuals rely
on access to that market economy
on access to that market economy
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in order to be successful.
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You can visualize that by thinking
in terms of one really exceptional person.
in terms of one really exceptional person.
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Imagine for a moment you take,
say, Steve Jobs,
say, Steve Jobs,
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and you drop him
on an island all by himself.
on an island all by himself.
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On that island, he's going
to be running around,
to be running around,
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gathering coconuts just like anyone else.
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He's really not going to be
anything special,
anything special,
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and the reason, of course,
is that there is no market
is that there is no market
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for him to scale
his incredible talents across.
his incredible talents across.
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So access to this market
is really critical to us as individuals,
is really critical to us as individuals,
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and also to the entire system
in terms of it being sustainable.
in terms of it being sustainable.
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So the question then becomes:
What exactly could we do about this?
What exactly could we do about this?
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And I think you can view this
through a very utopian framework.
through a very utopian framework.
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You can imagine a future
where we all have to work less,
where we all have to work less,
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we have more time for leisure,
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more time to spend with our families,
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more time to do things that we find
genuinely rewarding
genuinely rewarding
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and so forth.
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And I think that's a terrific vision.
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That's something that we should
absolutely strive to move toward.
absolutely strive to move toward.
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But at the same time, I think
we have to be realistic,
we have to be realistic,
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and we have to realize
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that we're very likely to face
a significant income distribution problem.
a significant income distribution problem.
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A lot of people are likely
to be left behind.
to be left behind.
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And I think that in order
to solve that problem,
to solve that problem,
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we're ultimately going
to have to find a way
to have to find a way
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to decouple incomes from traditional work.
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And the best, more straightforward
way I know to do that
way I know to do that
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is some kind of a guaranteed income
or universal basic income.
or universal basic income.
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Now, basic income is becoming
a very important idea.
a very important idea.
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It's getting a lot
of traction and attention,
of traction and attention,
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there are a lot of important
pilot projects
pilot projects
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and experiments going on
throughout the world.
throughout the world.
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My own view is that a basic income
is not a panacea;
is not a panacea;
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it's not necessarily
a plug-and-play solution,
a plug-and-play solution,
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but rather, it's a place to start.
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It's an idea that we can
build on and refine.
build on and refine.
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For example, one thing that I have
written quite a lot about
written quite a lot about
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is the possibility of incorporating
explicit incentives into a basic income.
explicit incentives into a basic income.
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To illustrate that,
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imagine that you are a struggling
high school student.
high school student.
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Imagine that you are at risk
of dropping out of school.
of dropping out of school.
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And yet, suppose you know
that at some point in the future,
that at some point in the future,
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no matter what,
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you're going to get the same
basic income as everyone else.
basic income as everyone else.
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Now, to my mind, that creates
a very perverse incentive
a very perverse incentive
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for you to simply give up
and drop out of school.
and drop out of school.
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So I would say, let's not
structure things that way.
structure things that way.
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Instead, let's pay people who graduate
from high school somewhat more
from high school somewhat more
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than those who simply drop out.
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And we can take that idea of building
incentives into a basic income,
incentives into a basic income,
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and maybe extend it to other areas.
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For example, we might create
an incentive to work in the community
an incentive to work in the community
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to help others,
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or perhaps to do positive
things for the environment,
things for the environment,
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and so forth.
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So by incorporating incentives
into a basic income,
into a basic income,
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we might actually improve it,
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and also, perhaps, take at least
a couple of steps
a couple of steps
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towards solving another problem
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that I think we're quite possibly
going to face in the future,
going to face in the future,
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and that is, how do we all find
meaning and fulfillment,
meaning and fulfillment,
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and how do we occupy our time
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in a world where perhaps
there's less demand for traditional work?
there's less demand for traditional work?
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So by extending and refining
a basic income,
a basic income,
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I think we can make it look better,
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and we can also, perhaps, make it
more politically and socially acceptable
more politically and socially acceptable
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and feasible --
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and, of course, by doing that,
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we increase the odds
that it will actually come to be.
that it will actually come to be.
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I think one of the most fundamental,
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13:14
almost instinctive objections
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that many of us have
to the idea of a basic income,
to the idea of a basic income,
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13:19
or really to any significant
expansion of the safety net,
expansion of the safety net,
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13:23
is this fear that we're going to end up
with too many people
with too many people
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riding in the economic cart,
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13:28
and not enough people pulling that cart.
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And yet, really, the whole point
I'm making here, of course,
I'm making here, of course,
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is that in the future,
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machines are increasingly going
to be capable of pulling that cart for us.
to be capable of pulling that cart for us.
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That should give us more options
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13:41
for the way we structure
our society and our economy,
our society and our economy,
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And I think eventually, it's going to go
beyond simply being an option,
beyond simply being an option,
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and it's going to become an imperative.
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The reason, of course,
is that all of this is going to put
is that all of this is going to put
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such a degree of stress on our society,
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13:55
and also because jobs are that mechanism
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that gets purchasing power to consumers
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13:59
so they can then drive the economy.
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If, in fact, that mechanism
begins to erode in the future,
begins to erode in the future,
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14:05
then we're going to need to replace
it with something else
it with something else
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14:08
or we're going to face the risk
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14:10
that our whole system simply
may not be sustainable.
may not be sustainable.
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14:12
But the bottom line here
is that I really think
is that I really think
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that solving these problems,
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and especially finding a way
to build a future economy
to build a future economy
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that works for everyone,
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14:23
at every level of our society,
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is going to be one of the most important
challenges that we all face
challenges that we all face
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in the coming years and decades.
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Thank you very much.
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(Applause)
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ABOUT THE SPEAKER
Martin Ford - FuturistMartin Ford imagines what the accelerating progress in robotics and artificial intelligence may mean for the economy, job market and society of the future.
Why you should listen
Martin Ford was one of the first analysts to write compellingly about the future of work and economies in the face of the growing automation of everything. He sketches a future that's radically reshaped not just by robots but by the loss of the income-distributing power of human jobs. How will our economic systems need to adapt?
He's the author of two books: Rise of the Robots: Technology and the Threat of a Jobless Future (winner of the 2015 Financial Times/McKinsey Business Book of the Year Award ) and The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, and he's the founder of a Silicon Valley-based software development firm. He has written about future technology and its implications for the New York Times, Fortune, Forbes, The Atlantic, The Washington Post, Harvard Business Review and The Financial Times.
Martin Ford | Speaker | TED.com