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