November 1, 2018 • 26 mins
Few narratives in economics and social policy are as alarmist as the one about the penetration of automation and artificial intelligence into the workplace, especially in manufacturing. Craig Torres digs into the story and finds the automation paradox: The infusion of artificial intelligence, robotics and big data into the workplace is elevating human expertise. More than ever, we need human ingenuity to reinvent a process or rapidly solve problems in an emergency.
Also, Stephanie and Bloomberg opinion’s Noah Smith discuss whether there are lessons to be found in the industrial revolution, and what the future of technology might look like.
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Episode Transcript
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Speaker 1 (00:02):
Hello, and welcome to the New Economy. I'm Stephanie Flanders,
head of Bloomberg Economics. The factory of the future will
employ only one man and one dog. The man's job
will be to feed the dog. The dogs will be
to keep the man away from the machines. You can't
(00:22):
talk about the New economy without talking about technology, and
that joke captures one vision of what it has in
store for us. Robots are going to take all of
our jobs. But there's another view that says robots aren't
a threat but a liberation, an opportunity to get away
from the daily grind and focus on what's really special
about being human. It's a nice idea. I think it's
(00:46):
particularly appealing to Silicon Valley types who are programmed to
always believe that technology will make the world a better place.
Later on, I'm going to talk to Bloomberg columnist Noah
Smith about whether that's a realistic vision for the whole economy.
But first, blob Like Economy report to Craig Torres went
to a corner of Virginia where robots and humans do
seem to be each playing to their strengths with not
(01:08):
a dog insight. Every day we read stories about how
automation and artificial intelligence will make humans less relevant. Many
of these stories are alarmists suggesting robots will be doing
what shop floor machinists or even doctors do every day.
(01:30):
The real story is more subtle. I visited a state
of the art Rolls Royce facility outside of Richmond, Virginia
to find out how automation is changing human work. I
asked the plant manager, Laurence O'Dell, to take us on
a tour and explain how humans and machines shake hands
here to produce something very valuable and very precise. This
(01:54):
plant makes jet engine parts. I have pulled into the
parking lot of Rolls Royce. The plant stands kind of
in a forest, and as you drive down the road
you see this modern looking facility with the very familiar insignia.
(02:19):
Now I have to watch a safety video as I
go into the plant. Once I am inside, I asked
Lauren about what work was like when he was a
young engineer in the night and an automotive engine plant.
How close were men and women too, metal very close,
(02:42):
very very close. In many ways. There were a lot
of hand assembly operations. If you were assembling an enginengine.
It wasn't what we would recognize in any way today
as smart automation. It was electrically speaking, following some some
pain to dance steps on the floor one to three four,
(03:03):
one to three four. When I look at the parts
Rolls Royce produces here, they are finely cut like pieces
of ornate industrial jewelry. One part, called a fan disc,
starts out as a big cylinder of titanium, about as
round as a tire you might see on a tractor
trailer on the highway. This disc is integrated with the
(03:23):
fan blades you see on the front of a jet
engine as you board a plane. The raw disc costs
as much as fifty thousand dollars, so it has kept
in a big suitcase until it has loaded onto the
machining process to protect it from scratching and damaged. After
it's machined, it's about the size of an all weather
tire on my pickup truck, and it's just as articulated
(03:46):
and shiny and deeply grooved. There their ultra precise parts.
They're made out of materials that are right at the
ende of right at the at the cutting edge of
of what engineers can create so these are hearts that
are spinning at between six thousand and twelve thousand revolutions
per minute, and they're they're extremely highly stressed, uh physically
(04:09):
as well. You don't just walk on the shop floor here,
you have to dress for it works pretty good. So
we're all dressed in steel toe slippers. I have my
belt covered with a velcrow pad, my wedding ring is off,
and I have something over my eyes, so I'm pretty
(04:30):
scratch proof, i'd like to think. So if you were
wearing earrings, we let me go with those work wears.
Who I haven't. I haven't been out here since about
eight o'clock this morning, so I have no idea what's
going on. The rolls Royce plant is cavernous, albeit not gigantic.
(04:54):
There is lots of natural light. I feel like I'm
in a workshop, but already you can tell there's something
different about what we mean by work here. There aren't
rows of men and women standing at stations doing an
activity as in an auto plant. Instead, there are a
series of large pods about half the size of a
subway car with a window. Parts are placed within them,
(05:17):
and this is where the magic happens. Inside a robotic
arm picks out its own cutting tool, It measures itself
and stores this data along the way. Lauren explains, this
whole process is about turning a mathematical description of a
physical thing, a jet engine part, into reality. The car
(05:40):
by drill bit trims cuts amid a splashing milk colored lubricant.
The plant is relatively quiet and very clean. Where are
the humans. They're not running this, They're making sure this
automated process is repeating and repeating and repeating again without
much variant. There are essentially no manual operations here anymore. Though.
(06:08):
UM we're using in our machines what you would recognize
as big data, okay, to understand not only the parts
and how the parts are being manufactured, but also the
basic condition of the machine itself. Seems to be accelerating
a little bit every week, where every week we're either
(06:29):
implementing or we're taking a step toward implementing, UM a
new kind of predictive technology. I have never seen anything
like the shop floor at Rolls Royce. This plant is
less than eight years old. Something entirely new and different
is happening here. Humans are not on the clock like
pace of a typical manufacturing plant. The robots operating behind
(06:54):
the windows of the big boxes are constantly self optimizing,
that is, re measure ring themselves. I asked Lauren to
explain this to me. What he describes is straight out
of what twenty years ago would have been a comic
book kind of futurism. Humans are maintaining the process but
(07:14):
also standing back from it in a way that seems unusual.
This is how Lauren explains it. What it frees the
humans up to do is to think about how can
we make something better? So people to a significant extent
aren't as tied to the equipment as they have been
(07:35):
in the past, and they're really being freed up to
work on higher order activities. Is that more human work?
Would you say? Or not? Right now? I think it's
more human work. Okay, I'm starting to get it. In
an ultra automated plant like this, humans are a step
away from the whole process, less close to the metal,
(07:58):
but still very familiar with exactly what is going on.
So familiar, Lauren says that even the flex of titanium
spinning off the cutting tool can tell them something is off.
Lauren says he needs both skills people who know the
guts of the process, but can think beyond it, troubleshoot it,
(08:19):
and make it better. How do you train for something
like this? It sounds challenging to answer that question. I
drove across the bottom of the state to Danville, Virginia,
where Troy Simpson trains young men and women at a
community college with a special third year program in advanced manufacturing.
(08:41):
The location of Danville is highly significant. This city once
hosted one of the South's largest textile companies, Dan River Mills.
It's a bit like visiting an Athens of the South.
There are old brick temples from the tobacco and textile
industry everywhere. It's some are falling into ruin, while others
(09:02):
have been repurposed and restored. This area was hit hard
by NAFTA and the China trade. The state and county
are focused on restoring its prosperity by creating a new
kind of workforce that modern manufacturers will find attractive. They've
had some success. Here's Troy explaining how they are trying
(09:22):
to create this new workforce as factories are looking towards
a digital factory and pushing so much data, making intelligent
decisions through AI. Then we still got to have somebody
to set those factories up and the troubleshoot those same
problems that we have whether factor is digital or not. Right,
But those technicians and now need to understand what is
(09:48):
happening upstream and downstream of a process. So now we've
got a technician that it's a whole plethora of things
that could be causing those problems to maintain the process
this so we have built on the facility to do
to train that technician in that very environment. So let's
take a step back and figure out what we heard
and saw. Rolls Royce has a perfect mathematical description of
(10:12):
an engine part humans and machines joined forces to try
and hit that perfection. Every day humans are looking at
data as much as the things they work with. The
machines and the metal work is becoming more conceptual, less
heavy and dirty, plants are less hierarchical, and there's more
(10:34):
gender equality as work migrates in this direction. I like
the way Lauren puts it. If we blindly believe data
and we don't challenge it to say, does that make sense?
Does what the machine told me makes sense? Can I
challenge that? And I can can I verify it? Or
(10:54):
am I just going to blindly believe it and make
an offset to thinking Whatever it is, whether it's a
physical machine or computer model or whatever, Am I just
going to make that make that change on the basis
of of what it's told me. If it doesn't make sense,
then why would you allow it to make the change?
(11:16):
Or maybe we need to change our paradigm. Maybe we
need to update our paradigm of how that little piece
of the world works. And I think that for now,
and I think that until artificial intelligence really becomes prevalent,
I think that's where humans are always going to be
taking a step up, not a step away. Don't think
I don't think of it as a step away. I
(11:36):
think of as a step up the humans role. And
there is no organism or machine better equipped to do
this on the planet than a human being is to recognize,
in the purest sense, a non standard situation. Yes, there
is a quiet revolution happening in all trades, from medicine
(11:59):
to making term both fan disks as artificial intelligence and
automation creep into more of what we do. What does
it mean for humans? The stories I'm hearing are enormously
positive yes, there will be fewer people working in a
particular plant, but that doesn't mean there are going to
be fewer people working in manufacturing. That depends on how
(12:23):
quickly we can train people to work in this space
where they understand a job as not producing one thing,
but it's part of a larger creative process, full of
data and feedback that aims at a more repeatable, more
perfect thing. I'm Craig Trusts, an economics writer for Bloomberg News. Well,
(12:50):
I'm joined now by Noah Smith, Bloomberg opinion columnist on
all Things interesting in economics. He's in our San Francisco studio.
I mean, no, I think I guess the big question
first coming out of that report from from Craig Torres,
I mean, do you buy the idea that the robots
and artificial intelligence could be the best thing that ever
happened to humanity? I think the best thing that ever
(13:11):
happened to humanity is a little exaggerated. Um, But what's
interesting is that most jobs can probably not be completely automated. Usually,
what happens is that some tasks in the middle of
the job get automated. In other words, a robot won't
take your job it'll take half your job. And of
course that really reminds me of what happened to menial
(13:34):
labor and factory work during the Industrial Revolution. Before the
Industrial Revolution, you had people putting together every bit of
every product themselves. Now you have these machines that can
do some of the things, but they don't do all
of the things. So you have assembly line workers doing
some of the things, and you have um basically manufacturing
workers doing some of the tasks. And that made the
(13:56):
manufactured goods a lot cheaper. Demand for the manufactured goods
released biked, and therefore, even though half of craftsmen's jobs
are being taken by these machines, you really had a
lot more employment and a lot higher wages in the
manufacturing sector during the Industrial Revolution. And I think this
could be the same thing for service jobs. You know,
service sectors really have had low productivity growth. We've seen
(14:19):
huge productivity growth and manufacturing very slow productivity and services.
It's possible that this could change that and could make
services behave more like manufacturing used to. I mean, it's
interesting you talk about productivity. I do always find it
quite funny when you're in these conversations where people are talking.
You know, what should we most worry about, you know,
the end of the terrible things happening in the global economy.
(14:40):
And of course, you know, one of the big things
we talk about is the very slow rate of productivity growth,
the fact that we're not getting more out of the
same number of people, are not at the same rate
we have in the past. And that's obviously hugely important
because it drives wage growth and standard of living in growth.
But the people who are really gloomy about productivity growth
are often also the ones who are really gloomy about
(15:01):
everyone losing all their jobs because of technology, and you
sort of you can't really have you know, if technology
does get rid of all those jobs, it is inevitably
going to raise productivity. I guess the question is whether
the benefits of that get um distributed equally. But I
guess when people think about this what they tend to
come back at and say, well, we know that's true,
we know that it can't be completely automated. But if
(15:23):
it can be automated, I mean, you know, if you
had all of the picking for you know, in a warehouse,
for grocery shopping or whatever else, if it all gets
done by robots, but it's not accurate, and you need
to have one or two people wandering around, you know,
putting the robots back up when they fought, when they
(15:44):
bump into something, or you know, correcting it when they've
when they've gone wrong. You know, that is still a
loss of jobs that have been lost in that process.
And this just feels like it's going to be on
a greater scale than in the past. You buy that
it could happen. The entire question is where or not
demand expands. So what was interesting about manufacturing is that
(16:04):
you suddenly had um people having so much more stuff,
physical stuff, manufactured things. People now had cars, and they
had TVs, and they had much larger houses, and they
had many more bulls and many more dresses and blah
blah blah blah blah. And because this demand expanded, even
though each each dress, for example, that was made didn't
(16:25):
require as much human labor as before, overall people were
getting paid more for doing human labor involved in dressmaking
than they were before, much more, much much more so.
Because demand expanded in this way, UM, we had increase
in employment, increase in wages, increase in productivity. All at
the same time the question is could a similar thing
(16:46):
happened with services the The dissatisfying answer is that a
lot of different things could happen. But I believe people
aren't paying enough attention to the good possibility. People are
fixating on this extremely simple story of like you get
replaced by a robot and now you have nothing to do,
and people are completely ignoring that upside of the story,
you know, focusing less on trying to bring about the
(17:09):
good future because they're not even trying to visualize it.
Your argument is it goes back to what people point out,
even with the adjustments that you've had so far, you know,
since the introduction of the a t M. I know,
lots of bankers in the US like to point out
that the number of factual cashiers in the U. S
(17:31):
economy has increased dramatically since the introduction of a t
M S. So you don't end up necessarily with the replacement.
But I think even your own examples highlight that there's
going to be adjustment costs and a transition where the
jobs that are created. You know, even on the certainly
on the old version of the way it happened in
(17:52):
the past, you had a lot of jobs created that
were very different from the jobs that have been lost.
And if you're thinking about what happened to those individuals
in those particularly in sectors that were really affected by technology,
and in the sort of manufacturing technology, you know, the
story of those people and their ability to get into
(18:12):
the newer sectors is not not really a very happy one.
I'm certainly thinking of large parts of the manufacturing sector
in the UK where people lost their jobs and the
industries moved on, and those people did not necessarily ever
work again, or they certainly spend a long time unemployed.
So you know that has to be a question mark. Sure,
(18:33):
Nor can I say that you know we're going to
have a happy future where absolutely everybody has higher wages
and better jobs, etcetera. We're not, um, We're going to
have a complicated future where some people lose out and
some people win. And the question is on balance what happens,
is it good or bad? What can we do to
make sure that there's more people out there who are
gaining than losing a lot more, What can we do
(18:55):
to sort of steer this CATEC universe generally in the
right direction. With our limited powers of government and are
limited understanding of what's going on and the you know,
the results of our actions and the current situation. We
have all this limited understanding. We're operating in the dark,
and we have limited control, and we don't really know
what our control does so much. How do we steer
things in a better direction? And that is the question.
(19:18):
I mean, that was interested. It seems to me we
don't need to just it's not satisfying to say we're
just going to wait and see why where the cards fall,
and wait and see if a meteor lands on your
head or not. I mean, you kind of want to
know whether you're government actually is willing to give you
an umbrella or something stronger to prevent the meteor hitting
knocking you out. What I sometimes think is when I
(19:39):
look at the US, but it doesn't seem like there's
a much appetite or capacity for government or for other
institutions to help people manage the human consequences of this
kind of dislocation. I mean, we've got I was looking
at some of the numbers the spending on public spending
on sort of training and labor market stuff as one
(19:59):
of the lower in the O E c D. It's
gone down by as a share of GDP since the
eighties in the US trade unions, who might have been
the kind of natural institution that could have sort of
stood between kind of workers and companies and helped people
up skill. You know, they're clearly not and on the
horizon at all in the US now. So what do
(20:23):
you when you think about what tools government has, even
with all this uncertainty about the impact, You know, are
there things if you hadn't if you had a different
kind of appetite political appetite, are the things that you
would say would make a real difference. Absolutely, Now we
don't really know what will work. By the way, we
tried a lot of government retraining programs in previous decades
(20:46):
and all the evaluations say that they were completely ineffectual.
They didn't help anybody get jobs in long term. They
were a giant waste, and that's why we cut back.
The government was terrible retraining people. UM companies are really
good at training people because they know what to train
people for, they know what they need. The thing is,
you know, American companies have just been in this orgy
(21:06):
of offshoring and outsourcing, especially outsourcing, so offshoring isn't as
big of an issue as people think it's it's more
outsourcing to contractors, you know, within the United States. Because
of that, companies don't spend a lot of money and
time training people anymore, and you get this equilibrium where
it's very easy for people to jump to other companies,
unlike in a lot of other countries. But then because
(21:28):
it's so easy, companies don't want to spend the time
and effort training workers. And so we need to shift
back to this equilibrium where companies give people more job
security and and more training and more investment in the workers,
and um, we need to have a more harmonious labor
management relationship. And I think the most interesting proposal for
(21:51):
how to do that has come from Elizabeth Warren, who
proposed codetermination. I don't know if they're familiar with this,
but it's yeah, so it's I mean, Germany does it.
It's it's corporatist. It's not the sort of socialist model
of the benevolent, all knowing government will do everything for you,
but it is. It definitely makes companies more democratic and
give work, gives workers more of a saying company policy,
(22:13):
and of course workers are going to want more investment
in workers and her proposal didn't have the sort of
workers councils that Germany has, but I think that would
be a good addition as well. So I think that
policy has the largest chance of anything that we've thought
of are proposed of shifting back to this good equilibrium
where companies invest a ton in training workers. But that
(22:35):
policy is a million miles away. I mean, although it's
been proposed by a US politician, it is a long
way from the the culture that we've seen of American
companies and American capitalism really for for many, many years.
I mean, there's not that the few bits of worker
code inmidentation that I think you did have, or we
(22:55):
started to have in the US, I think in the
thirties got um overwhelmed by efforts against it and never
really got established in the US. I mean your it
is certainly true. There's a lot of this kind of
thing in Europe. Jeremy Corbyn and the Labor Party has
has talked about it. I was out of something where
there was a lot of people from different parts of
(23:16):
Europe talking about the challenge of technology and digitalization of
the workforce, and the Swedish trade unionists said, you know,
we've with our members. We don't think that the new
machine opposes a threat to our members. It's the old
machine that poses a threat. And it's it's a completely
different psychology, which comes from the social partnership they have
and the kind of funds that they've come together. Employeed
(23:38):
and employers contribute to retraining programs that, as you say,
are closer to the work. But it feels just culturally,
it's not just the current administration. It feels a million
miles away from the from the traditions of the US.
Is it really something that could easily start to be
effective in the US? Uh So, I don't know, um,
(23:59):
but I do know that it's closer to our culture
and traditions than a government managed thing, and it would
be more effective as well. We have a history of
doing corporatest things. We have a very corporatist health insurance system.
We say, you know, in America, if you're not old
or you know, your your health insurance goes through your employer.
So we have a history of having things go through
(24:22):
people's employers. I think there's much more hope for that
kind of of corporatist you know, retraining and worker investment system.
Then there is for a government system, even before you
take into account the fact that the government system would
probably be ineffectual and sale. So, Um, the answer is
is it a long shot? Maybe so, I don't really know. Uh.
(24:45):
Is it a longer shot than other things? Probably not?
And if a if a third option, if a third
option is to somehow have you know, because we can't
do any of these other things, we should instead have
the government try to disincentivized adoption of automation technology. That
would be dumb and a disaster. Um. We should not.
(25:07):
That is what we should absolutely not ever do, and
we should firmly resist anyone who brings that idea up.
And the reason is because our companies are competing with
companies from Asia and Europe that are not going to
do that, that are going to do something a lot
smarter than that. If we simply limit technology, this one
technology because we're sort of afraid of it, and we
spin these sci fi scenarios that haven't come true at
(25:30):
all yet, Um, then we'll just be, you know, turning
ourselves into a backwater while other countries take over. We
are going to have some interesting conversations about this with
all the movers and Shakers and the New Economy Forum
in Singapore. No Smith, thanks very much, Thank you, thanks
(25:50):
for listening to The New Economy. Today's episode was reported
by Quaig Toras with editor Scott Lamon and produced by
Magnus Hendrison. Special thanks to Noah Smith. Francesco Levy is
the head of Bloomberg Podcast h
Hello, and welcome to the New Economy. I'm Stephanie Flanders,
head of Bloomberg Economics. The factory of the future will
employ only one man and one dog. The man's job
will be to feed the dog. The dogs will be
to keep the man away from the machines. You can't
(00:22):
talk about the New economy without talking about technology, and
that joke captures one vision of what it has in
store for us. Robots are going to take all of
our jobs. But there's another view that says robots aren't
a threat but a liberation, an opportunity to get away
from the daily grind and focus on what's really special
about being human. It's a nice idea. I think it's
(00:46):
particularly appealing to Silicon Valley types who are programmed to
always believe that technology will make the world a better place.
Later on, I'm going to talk to Bloomberg columnist Noah
Smith about whether that's a realistic vision for the whole economy.
But first, blob Like Economy report to Craig Torres went
to a corner of Virginia where robots and humans do
seem to be each playing to their strengths with not
(01:08):
a dog insight. Every day we read stories about how
automation and artificial intelligence will make humans less relevant. Many
of these stories are alarmists suggesting robots will be doing
what shop floor machinists or even doctors do every day.
(01:30):
The real story is more subtle. I visited a state
of the art Rolls Royce facility outside of Richmond, Virginia
to find out how automation is changing human work. I
asked the plant manager, Laurence O'Dell, to take us on
a tour and explain how humans and machines shake hands
here to produce something very valuable and very precise. This
(01:54):
plant makes jet engine parts. I have pulled into the
parking lot of Rolls Royce. The plant stands kind of
in a forest, and as you drive down the road
you see this modern looking facility with the very familiar insignia.
(02:19):
Now I have to watch a safety video as I
go into the plant. Once I am inside, I asked
Lauren about what work was like when he was a
young engineer in the night and an automotive engine plant.
How close were men and women too, metal very close,
(02:42):
very very close. In many ways. There were a lot
of hand assembly operations. If you were assembling an enginengine.
It wasn't what we would recognize in any way today
as smart automation. It was electrically speaking, following some some
pain to dance steps on the floor one to three four,
(03:03):
one to three four. When I look at the parts
Rolls Royce produces here, they are finely cut like pieces
of ornate industrial jewelry. One part, called a fan disc,
starts out as a big cylinder of titanium, about as
round as a tire you might see on a tractor
trailer on the highway. This disc is integrated with the
(03:23):
fan blades you see on the front of a jet
engine as you board a plane. The raw disc costs
as much as fifty thousand dollars, so it has kept
in a big suitcase until it has loaded onto the
machining process to protect it from scratching and damaged. After
it's machined, it's about the size of an all weather
tire on my pickup truck, and it's just as articulated
(03:46):
and shiny and deeply grooved. There their ultra precise parts.
They're made out of materials that are right at the
ende of right at the at the cutting edge of
of what engineers can create so these are hearts that
are spinning at between six thousand and twelve thousand revolutions
per minute, and they're they're extremely highly stressed, uh physically
(04:09):
as well. You don't just walk on the shop floor here,
you have to dress for it works pretty good. So
we're all dressed in steel toe slippers. I have my
belt covered with a velcrow pad, my wedding ring is off,
and I have something over my eyes, so I'm pretty
(04:30):
scratch proof, i'd like to think. So if you were
wearing earrings, we let me go with those work wears.
Who I haven't. I haven't been out here since about
eight o'clock this morning, so I have no idea what's
going on. The rolls Royce plant is cavernous, albeit not gigantic.
(04:54):
There is lots of natural light. I feel like I'm
in a workshop, but already you can tell there's something
different about what we mean by work here. There aren't
rows of men and women standing at stations doing an
activity as in an auto plant. Instead, there are a
series of large pods about half the size of a
subway car with a window. Parts are placed within them,
(05:17):
and this is where the magic happens. Inside a robotic
arm picks out its own cutting tool, It measures itself
and stores this data along the way. Lauren explains, this
whole process is about turning a mathematical description of a
physical thing, a jet engine part, into reality. The car
(05:40):
by drill bit trims cuts amid a splashing milk colored lubricant.
The plant is relatively quiet and very clean. Where are
the humans. They're not running this, They're making sure this
automated process is repeating and repeating and repeating again without
much variant. There are essentially no manual operations here anymore. Though.
(06:08):
UM we're using in our machines what you would recognize
as big data, okay, to understand not only the parts
and how the parts are being manufactured, but also the
basic condition of the machine itself. Seems to be accelerating
a little bit every week, where every week we're either
(06:29):
implementing or we're taking a step toward implementing, UM a
new kind of predictive technology. I have never seen anything
like the shop floor at Rolls Royce. This plant is
less than eight years old. Something entirely new and different
is happening here. Humans are not on the clock like
pace of a typical manufacturing plant. The robots operating behind
(06:54):
the windows of the big boxes are constantly self optimizing,
that is, re measure ring themselves. I asked Lauren to
explain this to me. What he describes is straight out
of what twenty years ago would have been a comic
book kind of futurism. Humans are maintaining the process but
(07:14):
also standing back from it in a way that seems unusual.
This is how Lauren explains it. What it frees the
humans up to do is to think about how can
we make something better? So people to a significant extent
aren't as tied to the equipment as they have been
(07:35):
in the past, and they're really being freed up to
work on higher order activities. Is that more human work?
Would you say? Or not? Right now? I think it's
more human work. Okay, I'm starting to get it. In
an ultra automated plant like this, humans are a step
away from the whole process, less close to the metal,
(07:58):
but still very familiar with exactly what is going on.
So familiar, Lauren says that even the flex of titanium
spinning off the cutting tool can tell them something is off.
Lauren says he needs both skills people who know the
guts of the process, but can think beyond it, troubleshoot it,
(08:19):
and make it better. How do you train for something
like this? It sounds challenging to answer that question. I
drove across the bottom of the state to Danville, Virginia,
where Troy Simpson trains young men and women at a
community college with a special third year program in advanced manufacturing.
(08:41):
The location of Danville is highly significant. This city once
hosted one of the South's largest textile companies, Dan River Mills.
It's a bit like visiting an Athens of the South.
There are old brick temples from the tobacco and textile
industry everywhere. It's some are falling into ruin, while others
(09:02):
have been repurposed and restored. This area was hit hard
by NAFTA and the China trade. The state and county
are focused on restoring its prosperity by creating a new
kind of workforce that modern manufacturers will find attractive. They've
had some success. Here's Troy explaining how they are trying
(09:22):
to create this new workforce as factories are looking towards
a digital factory and pushing so much data, making intelligent
decisions through AI. Then we still got to have somebody
to set those factories up and the troubleshoot those same
problems that we have whether factor is digital or not. Right,
But those technicians and now need to understand what is
(09:48):
happening upstream and downstream of a process. So now we've
got a technician that it's a whole plethora of things
that could be causing those problems to maintain the process
this so we have built on the facility to do
to train that technician in that very environment. So let's
take a step back and figure out what we heard
and saw. Rolls Royce has a perfect mathematical description of
(10:12):
an engine part humans and machines joined forces to try
and hit that perfection. Every day humans are looking at
data as much as the things they work with. The
machines and the metal work is becoming more conceptual, less
heavy and dirty, plants are less hierarchical, and there's more
(10:34):
gender equality as work migrates in this direction. I like
the way Lauren puts it. If we blindly believe data
and we don't challenge it to say, does that make sense?
Does what the machine told me makes sense? Can I
challenge that? And I can can I verify it? Or
(10:54):
am I just going to blindly believe it and make
an offset to thinking Whatever it is, whether it's a
physical machine or computer model or whatever, Am I just
going to make that make that change on the basis
of of what it's told me. If it doesn't make sense,
then why would you allow it to make the change?
(11:16):
Or maybe we need to change our paradigm. Maybe we
need to update our paradigm of how that little piece
of the world works. And I think that for now,
and I think that until artificial intelligence really becomes prevalent,
I think that's where humans are always going to be
taking a step up, not a step away. Don't think
I don't think of it as a step away. I
(11:36):
think of as a step up the humans role. And
there is no organism or machine better equipped to do
this on the planet than a human being is to recognize,
in the purest sense, a non standard situation. Yes, there
is a quiet revolution happening in all trades, from medicine
(11:59):
to making term both fan disks as artificial intelligence and
automation creep into more of what we do. What does
it mean for humans? The stories I'm hearing are enormously
positive yes, there will be fewer people working in a
particular plant, but that doesn't mean there are going to
be fewer people working in manufacturing. That depends on how
(12:23):
quickly we can train people to work in this space
where they understand a job as not producing one thing,
but it's part of a larger creative process, full of
data and feedback that aims at a more repeatable, more
perfect thing. I'm Craig Trusts, an economics writer for Bloomberg News. Well,
(12:50):
I'm joined now by Noah Smith, Bloomberg opinion columnist on
all Things interesting in economics. He's in our San Francisco studio.
I mean, no, I think I guess the big question
first coming out of that report from from Craig Torres,
I mean, do you buy the idea that the robots
and artificial intelligence could be the best thing that ever
happened to humanity? I think the best thing that ever
(13:11):
happened to humanity is a little exaggerated. Um, But what's
interesting is that most jobs can probably not be completely automated. Usually,
what happens is that some tasks in the middle of
the job get automated. In other words, a robot won't
take your job it'll take half your job. And of
course that really reminds me of what happened to menial
(13:34):
labor and factory work during the Industrial Revolution. Before the
Industrial Revolution, you had people putting together every bit of
every product themselves. Now you have these machines that can
do some of the things, but they don't do all
of the things. So you have assembly line workers doing
some of the things, and you have um basically manufacturing
workers doing some of the tasks. And that made the
(13:56):
manufactured goods a lot cheaper. Demand for the manufactured goods
released biked, and therefore, even though half of craftsmen's jobs
are being taken by these machines, you really had a
lot more employment and a lot higher wages in the
manufacturing sector during the Industrial Revolution. And I think this
could be the same thing for service jobs. You know,
service sectors really have had low productivity growth. We've seen
(14:19):
huge productivity growth and manufacturing very slow productivity and services.
It's possible that this could change that and could make
services behave more like manufacturing used to. I mean, it's
interesting you talk about productivity. I do always find it
quite funny when you're in these conversations where people are talking.
You know, what should we most worry about, you know,
the end of the terrible things happening in the global economy.
(14:40):
And of course, you know, one of the big things
we talk about is the very slow rate of productivity growth,
the fact that we're not getting more out of the
same number of people, are not at the same rate
we have in the past. And that's obviously hugely important
because it drives wage growth and standard of living in growth.
But the people who are really gloomy about productivity growth
are often also the ones who are really gloomy about
(15:01):
everyone losing all their jobs because of technology, and you
sort of you can't really have you know, if technology
does get rid of all those jobs, it is inevitably
going to raise productivity. I guess the question is whether
the benefits of that get um distributed equally. But I
guess when people think about this what they tend to
come back at and say, well, we know that's true,
we know that it can't be completely automated. But if
(15:23):
it can be automated, I mean, you know, if you
had all of the picking for you know, in a warehouse,
for grocery shopping or whatever else, if it all gets
done by robots, but it's not accurate, and you need
to have one or two people wandering around, you know,
putting the robots back up when they fought, when they
(15:44):
bump into something, or you know, correcting it when they've
when they've gone wrong. You know, that is still a
loss of jobs that have been lost in that process.
And this just feels like it's going to be on
a greater scale than in the past. You buy that
it could happen. The entire question is where or not
demand expands. So what was interesting about manufacturing is that
(16:04):
you suddenly had um people having so much more stuff,
physical stuff, manufactured things. People now had cars, and they
had TVs, and they had much larger houses, and they
had many more bulls and many more dresses and blah
blah blah blah blah. And because this demand expanded, even
though each each dress, for example, that was made didn't
(16:25):
require as much human labor as before, overall people were
getting paid more for doing human labor involved in dressmaking
than they were before, much more, much much more so.
Because demand expanded in this way, UM, we had increase
in employment, increase in wages, increase in productivity. All at
the same time the question is could a similar thing
(16:46):
happened with services the The dissatisfying answer is that a
lot of different things could happen. But I believe people
aren't paying enough attention to the good possibility. People are
fixating on this extremely simple story of like you get
replaced by a robot and now you have nothing to do,
and people are completely ignoring that upside of the story,
you know, focusing less on trying to bring about the
(17:09):
good future because they're not even trying to visualize it.
Your argument is it goes back to what people point out,
even with the adjustments that you've had so far, you know,
since the introduction of the a t M. I know,
lots of bankers in the US like to point out
that the number of factual cashiers in the U. S
(17:31):
economy has increased dramatically since the introduction of a t
M S. So you don't end up necessarily with the replacement.
But I think even your own examples highlight that there's
going to be adjustment costs and a transition where the
jobs that are created. You know, even on the certainly
on the old version of the way it happened in
(17:52):
the past, you had a lot of jobs created that
were very different from the jobs that have been lost.
And if you're thinking about what happened to those individuals
in those particularly in sectors that were really affected by technology,
and in the sort of manufacturing technology, you know, the
story of those people and their ability to get into
(18:12):
the newer sectors is not not really a very happy one.
I'm certainly thinking of large parts of the manufacturing sector
in the UK where people lost their jobs and the
industries moved on, and those people did not necessarily ever
work again, or they certainly spend a long time unemployed.
So you know that has to be a question mark. Sure,
(18:33):
Nor can I say that you know we're going to
have a happy future where absolutely everybody has higher wages
and better jobs, etcetera. We're not, um, We're going to
have a complicated future where some people lose out and
some people win. And the question is on balance what happens,
is it good or bad? What can we do to
make sure that there's more people out there who are
gaining than losing a lot more, What can we do
(18:55):
to sort of steer this CATEC universe generally in the
right direction. With our limited powers of government and are
limited understanding of what's going on and the you know,
the results of our actions and the current situation. We
have all this limited understanding. We're operating in the dark,
and we have limited control, and we don't really know
what our control does so much. How do we steer
things in a better direction? And that is the question.
(19:18):
I mean, that was interested. It seems to me we
don't need to just it's not satisfying to say we're
just going to wait and see why where the cards fall,
and wait and see if a meteor lands on your
head or not. I mean, you kind of want to
know whether you're government actually is willing to give you
an umbrella or something stronger to prevent the meteor hitting
knocking you out. What I sometimes think is when I
(19:39):
look at the US, but it doesn't seem like there's
a much appetite or capacity for government or for other
institutions to help people manage the human consequences of this
kind of dislocation. I mean, we've got I was looking
at some of the numbers the spending on public spending
on sort of training and labor market stuff as one
(19:59):
of the lower in the O E c D. It's
gone down by as a share of GDP since the
eighties in the US trade unions, who might have been
the kind of natural institution that could have sort of
stood between kind of workers and companies and helped people
up skill. You know, they're clearly not and on the
horizon at all in the US now. So what do
(20:23):
you when you think about what tools government has, even
with all this uncertainty about the impact, You know, are
there things if you hadn't if you had a different
kind of appetite political appetite, are the things that you
would say would make a real difference. Absolutely, Now we
don't really know what will work. By the way, we
tried a lot of government retraining programs in previous decades
(20:46):
and all the evaluations say that they were completely ineffectual.
They didn't help anybody get jobs in long term. They
were a giant waste, and that's why we cut back.
The government was terrible retraining people. UM companies are really
good at training people because they know what to train
people for, they know what they need. The thing is,
you know, American companies have just been in this orgy
(21:06):
of offshoring and outsourcing, especially outsourcing, so offshoring isn't as
big of an issue as people think it's it's more
outsourcing to contractors, you know, within the United States. Because
of that, companies don't spend a lot of money and
time training people anymore, and you get this equilibrium where
it's very easy for people to jump to other companies,
unlike in a lot of other countries. But then because
(21:28):
it's so easy, companies don't want to spend the time
and effort training workers. And so we need to shift
back to this equilibrium where companies give people more job
security and and more training and more investment in the workers,
and um, we need to have a more harmonious labor
management relationship. And I think the most interesting proposal for
(21:51):
how to do that has come from Elizabeth Warren, who
proposed codetermination. I don't know if they're familiar with this,
but it's yeah, so it's I mean, Germany does it.
It's it's corporatist. It's not the sort of socialist model
of the benevolent, all knowing government will do everything for you,
but it is. It definitely makes companies more democratic and
give work, gives workers more of a saying company policy,
(22:13):
and of course workers are going to want more investment
in workers and her proposal didn't have the sort of
workers councils that Germany has, but I think that would
be a good addition as well. So I think that
policy has the largest chance of anything that we've thought
of are proposed of shifting back to this good equilibrium
where companies invest a ton in training workers. But that
(22:35):
policy is a million miles away. I mean, although it's
been proposed by a US politician, it is a long
way from the the culture that we've seen of American
companies and American capitalism really for for many, many years.
I mean, there's not that the few bits of worker
code inmidentation that I think you did have, or we
(22:55):
started to have in the US, I think in the
thirties got um overwhelmed by efforts against it and never
really got established in the US. I mean your it
is certainly true. There's a lot of this kind of
thing in Europe. Jeremy Corbyn and the Labor Party has
has talked about it. I was out of something where
there was a lot of people from different parts of
(23:16):
Europe talking about the challenge of technology and digitalization of
the workforce, and the Swedish trade unionists said, you know,
we've with our members. We don't think that the new
machine opposes a threat to our members. It's the old
machine that poses a threat. And it's it's a completely
different psychology, which comes from the social partnership they have
and the kind of funds that they've come together. Employeed
(23:38):
and employers contribute to retraining programs that, as you say,
are closer to the work. But it feels just culturally,
it's not just the current administration. It feels a million
miles away from the from the traditions of the US.
Is it really something that could easily start to be
effective in the US? Uh So, I don't know, um,
(23:59):
but I do know that it's closer to our culture
and traditions than a government managed thing, and it would
be more effective as well. We have a history of
doing corporatest things. We have a very corporatist health insurance system.
We say, you know, in America, if you're not old
or you know, your your health insurance goes through your employer.
So we have a history of having things go through
(24:22):
people's employers. I think there's much more hope for that
kind of of corporatist you know, retraining and worker investment system.
Then there is for a government system, even before you
take into account the fact that the government system would
probably be ineffectual and sale. So, Um, the answer is
is it a long shot? Maybe so, I don't really know. Uh.
(24:45):
Is it a longer shot than other things? Probably not?
And if a if a third option, if a third
option is to somehow have you know, because we can't
do any of these other things, we should instead have
the government try to disincentivized adoption of automation technology. That
would be dumb and a disaster. Um. We should not.
(25:07):
That is what we should absolutely not ever do, and
we should firmly resist anyone who brings that idea up.
And the reason is because our companies are competing with
companies from Asia and Europe that are not going to
do that, that are going to do something a lot
smarter than that. If we simply limit technology, this one
technology because we're sort of afraid of it, and we
spin these sci fi scenarios that haven't come true at
(25:30):
all yet, Um, then we'll just be, you know, turning
ourselves into a backwater while other countries take over. We
are going to have some interesting conversations about this with
all the movers and Shakers and the New Economy Forum
in Singapore. No Smith, thanks very much, Thank you, thanks
(25:50):
for listening to The New Economy. Today's episode was reported
by Quaig Toras with editor Scott Lamon and produced by
Magnus Hendrison. Special thanks to Noah Smith. Francesco Levy is
the head of Bloomberg Podcast h
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