Speaker 1: This is Wall Street Week. I'm David Weston bringing you
Speaker 1: stories of capitalism. This week an industrial boom south of
Speaker 1: the border. We went to Mexico to see how tariffs
Speaker 1: and transportation costs are reshaping the American supply chain and
Speaker 1: what that could mean for China. But we begin with
Speaker 1: a biography, not one about a person or even really
Speaker 1: a thing.
Speaker 2: How do we.
Speaker 1: Classify something like artificial intelligence, something that has never existed
Speaker 1: before and something that might be beyond our understanding. This
Speaker 1: is a story about identity, who we are as humans,
Speaker 1: whether our identity is being merged with computers, and if so,
Speaker 1: what we need to do about it.
Speaker 2: There's nothing special that people.
Speaker 3: They are very wonderful things, and incredibly we're incredibly complicated,
Speaker 3: and we're very wonderful to other people.
Speaker 2: That's people and what we care about. We've evolved that way.
Speaker 2: But there's nothing that you can't replicate in other material.
Speaker 1: If anyone should know about whether computers can do everything
Speaker 1: that humans can, it would be Jeffrey Hinton, the Computer
Speaker 1: scientists and University of Toronto professor emeritus, is known as
Speaker 1: the godfather of AI. After working for over a decade
Speaker 1: at Google, he quit his job in twenty twenty three
Speaker 1: and became outspoken about the risks of the technology. He
Speaker 1: helped pioneer this, even as he continues to receive accolades
Speaker 1: for his work. We sat down with him after he
Speaker 1: had just received the Nobel Prize in physics.
Speaker 3: I was in a hotel in California and my phone
Speaker 3: went off at one in the morning, and I thought
Speaker 3: about not answering it, and then they said I won
Speaker 3: the Nobel Prize in physics, and I thought, wait a minute,
Speaker 3: I'm not a physicist.
Speaker 2: This can't be right.
Speaker 1: But it was right. And in December it will be
Speaker 1: Professor Hinton, along with John Hopfield, who will be receiving
Speaker 1: the award at a ceremony in Stockholm for their breakthrough
Speaker 1: work on machine learning. That generative AI is reshaping the
Speaker 1: business landscape is beyond doubt. Bloomberg Intelligence expects it to
Speaker 1: drive big tech firms to nearly double their CAPEX spending
Speaker 1: from twenty twenty three to twenty twenty five, with revenue
Speaker 1: from AI set to approach a trillion dollars by the
Speaker 1: end of the decade. But before we get to the
Speaker 1: future of AI, we turned to its past. We asked
Speaker 1: Jeffrey Hinton to go back to the beginning, when this
Speaker 1: new creature that can do what humans can do first arrived.
Speaker 3: I would say it was born in about nineteen eighty
Speaker 3: three with something that Terry Sinowski and I did, which
Speaker 3: was called the Boltsham Machine. It never really panned out,
Speaker 3: but it was a generaty of AI. It was a
Speaker 3: model that would generate images, not very good images, but
Speaker 3: it would generate them.
Speaker 4: Well.
Speaker 5: There could be a debate about the birth of it,
Speaker 5: but I would go all the way back to Alan
Speaker 5: Turing and then John von Neumann, so that would be
Speaker 5: in the thirties, forties and fifties.
Speaker 1: Marty Chavez of Sixth Street has spent his career developing
Speaker 1: AI and he traces its birth back to the very
Speaker 1: foundations of computer science.
Speaker 5: So it's been around a while, right, people thinking about
Speaker 5: what is computation, What is the nature of computation? And
Speaker 5: do human beings do computations? Is everything that's happening in
Speaker 5: our brains or our minds just a form of computation.
Speaker 5: People were asking these questions really almost almost one hundred
Speaker 5: years ago and starting to work on it.
Speaker 1: Whatever the beginnings of AI, most agree that it took
Speaker 1: many people in many decades to get here, But they
Speaker 1: also agree that in just the last few years the
Speaker 1: game has changed. A series of rapid breakthroughs has brought
Speaker 1: what were far out predictions much closer to reality.
Speaker 3: The big milestone is the large language models, which work
Speaker 3: by looking at a context and then generating guesses about
Speaker 3: what they think the next word should be.
Speaker 2: That's why they generative.
Speaker 3: Then that was followed by things like Dali from open
Speaker 3: Ai that could generate images. Before that, there were things
Speaker 3: that could generate captions for images, so they would look
Speaker 3: at an image and then they would generate a string
Speaker 3: of words that describe the image.
Speaker 2: Those were the early versions of generative area.
Speaker 3: Now we're getting multimodal chatbots which can generate everything. They
Speaker 3: can generate images and language.
Speaker 5: The huge milestone that I think of is one that
Speaker 5: we hit twenty sixteen twenty seventeen. This was a milestone
Speaker 5: in the evolution of the of the neural networks. It
Speaker 5: maybe didn't get a lot of public attention, but it
Speaker 5: certainly got the attention of everybody in the field. And
Speaker 5: this milestone came about because of the work of many
Speaker 5: of the researchers. I mentioned a few of them for
Speaker 5: over decades, but another contributor to the milestone was the
Speaker 5: Internet and the a sudden availability of vast amounts of
Speaker 5: labeled data.
Speaker 1: The breakthroughs that Hinton and others worked to achieve have
Speaker 1: developed artificial intelligence to the point that now, in a
Speaker 1: text conversation, it might be indistinguishable from a human being.
Speaker 1: A study this year by cognitive scientists that you see
Speaker 1: San Diego showed that people mistook chat GPT for a
Speaker 1: human more than fifty percent of the time, clearing the
Speaker 1: bar for the so called touring test. Humans themselves only
Speaker 1: passed the test about two thirds of the time. But
Speaker 1: as surprising as that is, it could be just the beginning.
Speaker 1: How far along is AI in its life? Again, if
Speaker 1: we're anthropomorphizing, here, is it a child? Is an adolescent?
Speaker 1: Where is it?
Speaker 3: It's more like a toddler, maybe like a three year old.
Speaker 5: Let's imagine that we're in that gangly, awkward teenager phase
Speaker 5: of AI. What happens next? There's a huge debate.
Speaker 1: Whether AI is a toddler or an adolescent. Today. The
Speaker 1: question is how far can it go? How much can
Speaker 1: it learn? And perhaps most important, will it catch up
Speaker 1: to us humans? For Jeffrey Hinton. It's a matter of when,
Speaker 1: not if. Does that mean that general of artificial intelligence
Speaker 1: will be able to do whatever humans can do? Is
Speaker 1: there anything that it cannot do that we humans can do?
Speaker 3: Yes, most people have the belief there's something very special
Speaker 3: at people, which is consciousness or sentience or subjective experience,
Speaker 3: and we have this in a theater.
Speaker 2: Probably all nonsense.
Speaker 3: The history of it is, these large language models predicting
Speaker 3: the next words using backpropagation were initially designed as a
Speaker 3: model of how people work. Now there's a whole bunch
Speaker 3: of people who believe in symbolic logic and that's how
Speaker 3: people must think. They think it must be true, who
Speaker 3: think people work a completely different way, and they're wrong.
Speaker 3: They never managed to produce stuff that works nearly as
Speaker 3: well as these big language models. So they'll tell you
Speaker 3: these big language models are quite unlike people. They're just
Speaker 3: using correlations and so on, and it's all just not
Speaker 3: really understanding. No, our best model of how people understand
Speaker 3: is exactly the same model as how these big language
Speaker 3: models understand. They're just like us, and they're much more
Speaker 3: like us than they are like standard computer software. So
Speaker 3: understanding is all about converting words into features, having the
Speaker 3: features interact, and having those predict the next word. We
Speaker 3: do it pretty much the same way they do it.
Speaker 1: What about creativity? Can general AI be as creative as
Speaker 1: humans are?
Speaker 3: As someone else might have said, There you go again,
Speaker 3: you want people to be special. If you take standard
Speaker 3: tests of creativity, AIS already do better than most people.
Speaker 1: What about in a large corporation, you have worker bees
Speaker 1: we often have heard of them. Then you have middle management,
Speaker 1: upper management and CEOs? Could they replace CEOs?
Speaker 3: Okay, so there's a good scenario for the future which
Speaker 3: goes like this.
Speaker 2: Suppose you have a big.
Speaker 3: Company with a rather dumb CEO. Maybe he's the son
Speaker 3: of the previous CEO, but he has an executive assistant.
Speaker 2: Who's very good, almost certainly a woman.
Speaker 3: This executive assistant is asked to achieve things by the
Speaker 3: dumb CEO, and she achieves them in ways he doesn't
Speaker 3: understand and would never have thought of, so he becomes
Speaker 3: much more effective. He gets what he wants, he thinks
Speaker 3: he's doing it. It all happens behind the scenes and
Speaker 3: very efficiently. That's how it could be if we all
Speaker 3: had our own AI assistants who were smarter than us,
Speaker 3: and they were thoroughly benevolent, and they never wanted to
Speaker 3: take over because somehow achieve that that's the good scenario.
Speaker 1: I had a boss once in business who said, a
Speaker 1: danger is wishing yourselves to success. Do we wish ourselves
Speaker 1: to a safer world by hoping that General AI cannot
Speaker 1: do everything we can do, because it might be very
Speaker 1: threatening to many people if we believed it can do
Speaker 1: everything we can do.
Speaker 3: Yes, it is very threatening and I'm very worried about it,
Speaker 3: and we should definitely put huge effort right now into
Speaker 3: figuring out whether we can keep control of it.
Speaker 1: What effort should we put forward, what should we do?
Speaker 3: Okay, so for something like climate change, everybody knows what
Speaker 3: we should do. There's a very simple solution, just don't
Speaker 3: burn carbon. It's just we don't have the political will.
Speaker 2: We still give big subsidies to.
Speaker 3: Oil companies, which is lunatic. So we know what to do,
Speaker 3: we just won't do it with AI. To keep AI
Speaker 3: safe from the long term danger of more INtime and
Speaker 3: things than us just taking over from us, we don't
Speaker 3: even know what to do. What we need to do
Speaker 3: now is understand that there really is this danger. These
Speaker 3: things really do understand and they really are going to
Speaker 3: get smarter than us. But also how are we going
Speaker 3: to keep control of them? We have some chance because
Speaker 3: we're creating.
Speaker 1: Them, and that's where we turn next in the biography
Speaker 1: of AI. How much good can it do? And what
Speaker 1: do we need to do to make sure it becomes
Speaker 1: a responsible citizen instead of a threat to us.
Speaker 3: All many people say, you know, I'll create more jobs
Speaker 3: for this particular thing. I'm not convinced of that. What
Speaker 3: we're doing in the Industrial Revolution we make human strength irrelevant.
Speaker 3: Now we're making human intelligence irrelevant, and that's very scary.
Speaker 6: You're listening to Bloomberg Wall Street Week with David Weston
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Speaker 1: The life of artificial intelligence is connected to the lives
Speaker 1: of people and also to their livelihoods. AI is already
Speaker 1: being used to make processes more efficient. What could this
Speaker 1: mean for productivity and therefore economic growth and who will
Speaker 1: benefit from it? We pose those questions to Jeffrey Hinton.
Speaker 3: It will be a wonderful thing for productivity, that's true.
Speaker 3: Whether it be a wonderful thing for society is something else. Altogether.
Speaker 3: In a decent society, if you increase productivity a lot,
Speaker 3: everybody's better off. But here, what's going to happen if
Speaker 3: you increase productivity a lot. The rich and the big
Speaker 3: companies are going to get much richer, and ordinary people
Speaker 3: are probably going to be worse off because they lose
Speaker 3: their jobs.
Speaker 1: AI's ability to reshape productivity could also give new life
Speaker 1: to developed economies whose productivity has stalled. US productivity growth
Speaker 1: has been significantly lower in the twenty first century than
Speaker 1: it was between nineteen thirty and two thousand. But for
Speaker 1: all the talk of the sweeping effects of AI, economic
Speaker 1: change has been slow to materialize. So far. Goldwyn Sachs
Speaker 1: reports that only five percent of companies claim to use
Speaker 1: generative AI in regular production, with tech and information businesses
Speaker 1: leading the way. When wider adoption does come, Hinton anticipates
Speaker 1: it will have very different impacts on different parts of
Speaker 1: the workforce.
Speaker 3: Many people say, you know, illegrate more jobs for this
Speaker 3: particular thing.
Speaker 2: I'm not convinced of that.
Speaker 3: What we're doing in the Industrial Revolution we make human
Speaker 3: strength irrelevant. Now we're making human intelligence irrelevant, and that's
Speaker 3: very scary. So there's some areas where demand is very elastic.
Speaker 3: An example would be healthcare. If I could get ten
Speaker 3: hours a week talking to my doctor, I'm over seventy
Speaker 3: I'd be very happy. So if you take someone and
Speaker 3: make them much more efficient by having them work with
Speaker 3: a very intelligent AI, they're not gonna become unemployed. It's
Speaker 3: not that you're now only gonna need a few of them.
Speaker 3: You're just gonna get much more healthcare.
Speaker 2: Great.
Speaker 3: So in elastic areas, it's great. The some areas that
Speaker 3: are less elastic, like I have a niece who answers
Speaker 3: letters a complaint to a health service. She used to
Speaker 3: take twenty five minutes to answer a letter. Now she
Speaker 3: can just scan the letter into chat GBT, it'll give
Speaker 3: an answer. She'll look at it, check it is okay,
Speaker 3: that's five minutes now. I suspect they'll need less people
Speaker 3: like that. It may be they can just everybody can
Speaker 3: complain a lot more, but I suspect they'll need less.
Speaker 2: People like that.
Speaker 3: So some jobs are Some jobs are elastic, others aren't.
Speaker 3: The non elastic ones, I think people will lose their jobs.
Speaker 3: And what's going to happen is the extra wealth created
Speaker 3: by the increasing productivity is not going to go to them.
Speaker 1: Hinton's prize in physics wasn't the only Nobel awarded this
Speaker 1: year for artificial intelligence. Two Google DeepMind scientists, a professor
Speaker 1: at the University of Washington receive Nobels in chemistry for
Speaker 1: using AI to predict the structure of millions of proteins
Speaker 1: and even to invent a whole new protein. Marty Chavez
Speaker 1: A sixth Street says that tool called alpha fold shows
Speaker 1: the power of machine learning.
Speaker 5: As important a breakthrough as this Alpha fold work is
Speaker 5: if you think about the problems in biology, the problems
Speaker 5: in biology are so much more complicated.
Speaker 10: Right.
Speaker 5: Proteins are the basic building block of life. But then
Speaker 5: those proteins organize themselves into organ eels of a cell,
Speaker 5: and then cells, which organize themselves into tissues, which organize
Speaker 5: themselves into organs which go to organ systems human beings,
Speaker 5: populations of human beings.
Speaker 1: Beyond biology and health sciences, generative AI could also have
Speaker 1: profound effects on the climate. For one thing, it will
Speaker 1: require a lot more electricity to drive it. Wells Fargo
Speaker 1: projects a five hundred and fifty percent surge in AI
Speaker 1: power demand by twenty twenty six. Ireland is a poster
Speaker 1: child for the extent of that demand. Today, it's growing
Speaker 1: number of data centers used up twenty one percent of
Speaker 1: the country's electricity in twenty twenty three.
Speaker 11: The good news is it's not new in our history.
Speaker 11: We've been through many periods as a country where we
Speaker 11: have had significant energy demand. But what this is going
Speaker 11: to mean is AI is now adding to this as
Speaker 11: we electrify transportation, as we electrify our buildings, we now
Speaker 11: have this big near term demand for energy to power
Speaker 11: these data centers.
Speaker 1: Brian Deese served as the Director of the National Economic
Speaker 1: Council under President Biden and is now focused on studying
Speaker 1: the effects of AI on energy at MIT.
Speaker 12: SO.
Speaker 11: In the near term, it creates this tension of how
Speaker 11: do we bring that additional electricity online? And are we
Speaker 11: bringing cleaner sources online or dirtier sources online, which obviously
Speaker 11: affects emissions. But the longer term, the bigger picture way
Speaker 11: these interact is AI is a technology which will then
Speaker 11: get deployed in lots of aspects of our economy, including
Speaker 11: our energy system. And so there are also opportunities where
Speaker 11: these two technological developments coming at the same time could
Speaker 11: actually be one plus one equals three help us find
Speaker 11: more efficient ways to innovate in the energy space.
Speaker 1: For all the good that generative AI could potentially do
Speaker 1: for the human race, it also poses some real dangers,
Speaker 1: perhaps none so apparent as its application to armed warfare.
Speaker 1: Anya Manual's Aspens Strategy Group publisher report on this very subject.
Speaker 7: AI by its nature is dual use because it is
Speaker 7: a multipurpose technology. It's like electricity or the steam engine.
Speaker 7: It's going to power everything, from the most amazing positive
Speaker 7: use cases to the really dangerous ones.
Speaker 1: All of my lifetime, the biggest geopolitical risk terror even
Speaker 1: has been the threat of thermonuclear destruction. Recently was a
Speaker 1: meeting over in South Korea with an attempt to have
Speaker 1: everybody agreed that if there were to be nuclear weapons used,
Speaker 1: a human had to be in that chain of command,
Speaker 1: and the countries there agreed to it except for one China.
Speaker 1: What do we make of that?
Speaker 7: We are really on day one of this technology and
Speaker 7: how it can be used visa v National security. There
Speaker 7: have been a number of attempts in the last couple
Speaker 7: of years, a lot of them thankfully led by the
Speaker 7: United States to think carefully about AI and nuclear weapons
Speaker 7: use one two. How much you would allow lethal autonomous weapons?
Speaker 7: And this means really, you know, if you have a
Speaker 7: predator drone, there's still a human directing it and deciding
Speaker 7: who to target and who to shoot at. In the
Speaker 7: Ukraine War, You're getting very close now to drone on
Speaker 7: drone dog fights, drones doing their own targeting and shooting,
Speaker 7: and very soon you have a situation that's very dangerous
Speaker 7: where if the AI weapon is always going to be faster,
Speaker 7: the incentive for everyone is to use lethal autonomous weapons
Speaker 7: because they'll always win, and then you'll have huge escalation,
Speaker 7: super dangerous. Lots of different people are trying to get
Speaker 7: at this in different ways. The Korea Conference was one
Speaker 7: of them. I'm not surprised that China hasn't signed on.
Speaker 7: I would just give you one example. You use nuclear weapons.
Speaker 7: You know, when the US came first in the nuclear race,
Speaker 7: we proposed some limited limits on nuclear weapons, I think
Speaker 7: as early as nineteen forty six. Then no real arms
Speaker 7: controlled treaties were negotiated until after you had the Cuban
Speaker 7: missile crisis and we got to the brink, and then
Speaker 7: we had some pretty good ones. But what was going
Speaker 7: on all the time in the background, and what is
Speaker 7: going on now, and it's super important and bears emphasizing,
Speaker 7: is quietly behind the scenes. There is some Chinese sign
Speaker 7: scientists and American and Western scientists talking about these issues.
Speaker 7: There are other track twos that are more on the
Speaker 7: policy area. I'm part of one of them, quiet conversations
Speaker 7: with the Chinese about the dangers of AI and the
Speaker 7: Chinese interlock unders I've talked to are equally worried about
Speaker 7: very similar things than the ones you and I are
Speaker 7: talking about here.
Speaker 3: And for a long time people have realized there's going
Speaker 3: to be an arms race who can get the best
Speaker 3: lethal autonomous weapons fastest. All of the defense departments of
Speaker 3: the people who sell arms, like the US, China, Britain, Israel, Russia,
Speaker 3: all those defense departments are busy working on autonomous lethal weapons.
Speaker 3: They're not going to stop. If one of them stopped,
Speaker 3: the others wouldn't. What we need for that is not
Speaker 3: to stop working on it, but to have a Geneva convention. Now,
Speaker 3: you don't get Geneva conventions things like the Geneva Conventions
Speaker 3: for chemical weapons until after something very nasty has happened.
Speaker 2: So I think realistically, very.
Speaker 3: Nasty things are going to happen with ethan autonomous weapons,
Speaker 3: and then maybe we'll be able to get conventions. So
Speaker 3: for chemical weapons, the conventions have basically worked. Putin isn't
Speaker 3: using them in the Ukraine and they haven't been used much,
Speaker 3: so basically the conventions worked. I'm hoping they would work
Speaker 3: for lethal autonomous weapons, although I'm less confident but nothing's
Speaker 3: going to happen till after some very nasty things have happened.
Speaker 1: Whether AI works for good or for ill, a fundamental
Speaker 1: question is whether humans can maintain control or at least
Speaker 1: influence over it. Professor Hinton says we shouldn't assume that
Speaker 1: we can, at least for long.
Speaker 3: As soon as you make agents which people are busy
Speaker 3: doing now, things that can act in the world. To
Speaker 3: make an effective agent, you have to give it the
Speaker 3: ability to create subgoals. So if you want to get
Speaker 3: to Europe, you have a sub goal of getting to
Speaker 3: an airport, and you don't have to sort of think
Speaker 3: about Europe while you're solving that subgoal. That's why subgoals
Speaker 3: are helpful, and these big eye systems create sub goals. Now,
Speaker 3: the problem with that is if you give something the
Speaker 3: ability to create subgoals, it will quickly realize there's one
Speaker 3: particular subcoal that's almost always useful. So if I have
Speaker 3: the goal of just getting more control over the world,
Speaker 3: that will help me achieve everything I want to achieve.
Speaker 3: So these things will realize that very quickly, even if
Speaker 3: they've got no self interest. Then understand that if I
Speaker 3: get more control, I'm it'll be better at doing what
Speaker 3: they want me to do, and so they will try
Speaker 3: and get more control. That's the beginning of a very
Speaker 3: slippery slope.
Speaker 1: But that suggests times are wasting. Then, in fact, we
Speaker 1: don't have that much time. We don't to be able
Speaker 1: to get some control over general they are.
Speaker 3: We control it right now, but we don't have that
Speaker 3: much time to figure out how we're going to stay
Speaker 3: in control when it's smarter than us.
Speaker 1: How do we go about that. Let's take the United
Speaker 1: States for example, before we go globally. In the United States,
Speaker 1: we have a government. There are good people in the government,
Speaker 1: smart people, some probably less smart, But are they up
Speaker 1: to the job of really understanding what you're talking about
Speaker 1: and getting their arms around.
Speaker 3: I think with the current government they are taking it seriously.
Speaker 3: It's just very difficult to know what to do. One
Speaker 3: clear thing they should be doing that I believe they
Speaker 3: should be doing. We need many of the smartest young
Speaker 3: researchers to be working on this problem, and we need
Speaker 3: them to have resources. Now, the government doesn't have the resources.
Speaker 3: The big companies have the resources. The government I think
Speaker 3: should be insisting that the big companies spend much more
Speaker 3: of their resources on safety, on this safety research or
Speaker 3: how will we stay in control? Compared with what they
Speaker 3: do now right now, they spend like a few percent
Speaker 3: on that, and nearly all their resources go into building
Speaker 3: even better, bigger models.
Speaker 1: As you say, big companies don't like to be told
Speaker 1: by the government how to spend their money, but they
Speaker 1: are often. I mean, you have big accounting departments, for example,
Speaker 1: to comply with various regulatory requirements and accounting from everything
Speaker 1: you know. Everything you understand is general AI potentially an
Speaker 1: existential threat to the human species.
Speaker 3: I think that's what I've been saying. Yes, it really
Speaker 3: is an exercenti threat. Some people say this is just
Speaker 3: science fiction, and until fairly recently I believed it was
Speaker 3: a long way off. I always thought it would be
Speaker 3: a very long term threat, but I thought it would
Speaker 3: be one hundred years before we had.
Speaker 2: Really smart things. Fifty years.
Speaker 3: Maybe we have plenty of time to think about it now.
Speaker 3: I think it's quite likely that sometime in the next
Speaker 3: twenty years these things will get smarter than us, and
Speaker 3: we really need to worry about what happens.
Speaker 1: Then, coming up a race for resources south of the border,
Speaker 1: we go to Mexico, to see how global trade is
Speaker 1: evolving in real time. That's next on Wall Street Week.
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Speaker 1: This is a story about potential, the potential that further
Speaker 1: economic integration between the United States and Mexico holds for
Speaker 1: both countries, a potential for good, but also a potential
Speaker 1: for unintended consequences.
Speaker 13: The plastic comes from hyping.
Speaker 1: Baldwin Britain is a manufacturer based in Monterey, trying to
Speaker 1: seize what some are calling the Mexican moment.
Speaker 13: And everything comes us as pellets and it gets melted
Speaker 13: in this machine.
Speaker 1: As CEO of Plastic Exports, he runs several factories in
Speaker 1: northern Mexico making plastic components and finished consumer products in
Speaker 1: his newest plant kitchen containers.
Speaker 13: So this is for consumer products that we're selling to
Speaker 13: the United States that basically came from near sharing projects.
Speaker 13: So these products were previously made in China and they're
Speaker 13: relocating into Mexico.
Speaker 1: What was once offshoring to China and other exporters in
Speaker 1: Ocean a Way is beginning to give way to nearshoring
Speaker 1: from Mexico. The share of US imports coming from China,
Speaker 1: which hit twenty one percent in twenty eighteen, is on
Speaker 1: the decline, down to under fourteen percent last year. The
Speaker 1: share coming from Mexico, over thirteen percent in twenty eighteen,
Speaker 1: is on the rise, topping fifteen percent last year. This
Speaker 1: closer integration between Mexico and the United States was jump
Speaker 1: started by the North American Free Trade Agreement or NAFTA,
Speaker 1: back in nineteen ninety four, covering Canada as well as
Speaker 1: the US and Mexico. At that time, a young American
Speaker 1: named David Eaton was an advisor on the treaty. The
Speaker 1: promise of the agreement and the opportunities it created took
Speaker 1: him to Monterey and he has never left. Now he's
Speaker 1: the Mexico Director for Business development of the recently merged
Speaker 1: Canadian Pacific Kansas City Rail forming what he calls the
Speaker 1: near shoring backbone. His goal is to fuse together a
Speaker 1: North American rail system reaching from Upper Canada through the
Speaker 1: United States all the way to southern Mexico. How fast
Speaker 1: is that need growing?
Speaker 10: Ercardo.
Speaker 12: We're investing and getting ready in the planning process right now.
Speaker 12: Because you look at lots of the announcements out there, BMW, Volvo,
Speaker 12: mattel Lego, Arsolo, Midal Turnium, the pipeline is really long.
Speaker 1: Britain is willing to bet you'll find something he makes
Speaker 1: in your home. As a longtime supplier of parts to
Speaker 1: US and European home appliance companies, his new customers are
Speaker 1: just as likely to be Chinese multinationals you've never heard of.
Speaker 13: Thirty years ago we saw a lot of shifts of
Speaker 13: business from Mexico to China. Four years we have seen
Speaker 13: the shift come back to Mexico.
Speaker 1: Are Chinese companies coming in to compete with you?
Speaker 13: Yes, they are coming. They're setting up plants in Mexico
Speaker 13: as a matter of fact, and Leveloon, the state of Levelone,
Speaker 13: which is an industrial helb of Mexico. They're setting up
Speaker 13: so that basically forces companies like us to be in
Speaker 13: our a game to compete with them.
Speaker 1: Today, industrial parks full of Chinese companies are sprouting up
Speaker 1: around the Monterey Hub.
Speaker 13: There's a city in China called sheen Sen. Thirty five
Speaker 13: years ago there was a village to thirty thousand people.
Speaker 13: Today they have over twenty million. There's a manyfacturing hub.
Speaker 13: We see that happening to Mexico.
Speaker 10: There is a philosophy among these companies coming from China
Speaker 10: to go abroad, go to these other countries, learn how
Speaker 10: to do this, integrate to move.
Speaker 1: For decades, Alan Russell, CEO of TECHMA, has been in
Speaker 1: the business of helping companies to open and operate factories
Speaker 1: in Mexico, overcoming some of the friction and red tape.
Speaker 10: Our technology is Mexico and we help companies skip the
Speaker 10: pain and the time that it takes to learn that
Speaker 10: on their own.
Speaker 1: Over your thirty eight years, has there been some harmonization
Speaker 1: of some of those differences in regulations and rules, tax flaws,
Speaker 1: things like that.
Speaker 10: They've gotten more strict, more rules and regulations. It's more
Speaker 10: complex today than it ever has been. So we put
Speaker 10: an umbrella over our client bring them into the country.
Speaker 1: In mexicoolt Annie Ching is the plant manager at Leos,
Speaker 1: one of the companies using Techma for its new operations
Speaker 1: in Saltio, an hour and a half outside of Monterey.
Speaker 1: Leos makes batteries for data centers, cars and golf carts.
Speaker 1: It was established in China twenty five years ago and
Speaker 1: still manuf factors there, but in twenty eleven Leoch relocated
Speaker 1: its headquarters to Singapore, and just last year it opened
Speaker 1: a new factory in Mexico with plans to open two more.
Speaker 4: For the export process, we started the export of the
Speaker 4: first containers to the United States last week.
Speaker 1: Russell has seen an uptick in his business as companies
Speaker 1: begin to shift their supply chains from China to Mexico,
Speaker 1: at first because of the cost of fuel and the
Speaker 1: delays and transportation, and then when the near shoring process
Speaker 1: was turbocharged by the pandemic and tariffs, as companies sought
Speaker 1: alternatives to China.
Speaker 10: So there started to be a movement where the boardrooms
Speaker 10: across the United States were saying, we should consider diversifying
Speaker 10: from China. We have all of our eggs there. Coster
Speaker 10: going up where a lot of these public companies actually
Speaker 10: carry on their books a China risk factor until they
Speaker 10: can adjust and minimize that risk by diverse ye into
Speaker 10: Vietnam and the laos into India into Mexico.
Speaker 1: We visited a Mattel plant in Monterey, a facility that
Speaker 1: was close to closing at one point when production moved
Speaker 1: to China, but since the pandemic, Mattel has taken a
Speaker 1: u turn moving production closer to home. The Monterey plant
Speaker 1: is now its largest in the world. The place where
Speaker 1: they make, among other things, the Barbie Dreamhouse. They're all
Speaker 1: destined for Christmas trees. The raw material for the Barbie
Speaker 1: Dreamhouse comes from the United States by rail straight to
Speaker 1: the factory doorstep.
Speaker 12: Of course, toys are made of resin, and we've got
Speaker 12: resin cars that are coming down with those cars are
Speaker 12: pull Yeah Nindra sons of resin produced from the US
Speaker 12: golf coast, primarily.
Speaker 1: Although some have been quick to declare Mexico the victor
Speaker 1: of the US trade war with China, Eaton says the
Speaker 1: United States also benefits from deeper integration with its neighbor.
Speaker 1: He points to the auto sector under the United States
Speaker 1: Mexico Canada Agreement the USMCA as a future blueprint for
Speaker 1: trade that will help US, Canada, and Mexico thrive together.
Speaker 12: You really can't talk about a US made car or
Speaker 12: a Canadian made car a Mexican car. These autos are
Speaker 12: really North American. You know, the cars and the parts
Speaker 12: and components across the three borders thousands and times. Literally
Speaker 12: before you produce a before you produce a car.
Speaker 1: Auto sector is really big here in Northern Mexico. Give
Speaker 1: us a sense of where that is and where that's going.
Speaker 12: The USMCA says that seventy or seventy five percent of
Speaker 12: the value or content of a car must meet with
Speaker 12: North American content requirements to get duty free treatment under
Speaker 12: the USMCA, and that's creating incentives for a lot of
Speaker 12: companies to change their sourcing. For example, the steel company
Speaker 12: Urnium is now investing billions of dollars here in Northern
Speaker 12: Mexico to process their own slab on site. They're going
Speaker 12: towards the production of an auto coil that meets the
Speaker 12: USMCA standards, and so instead of purchasing raw slab from Brazil,
Speaker 12: they're going to start processing that slab here on site,
Speaker 12: consuming iron ore and other products from the United States.
Speaker 12: We're stronger together, frankly, when we manufacture in Mexico. It
Speaker 12: creates more synergies and more jobs in the US.
Speaker 1: The near shoring money coming into Mexico isn't just about
Speaker 1: ways of transporting raw materials and finished goods. It also
Speaker 1: requires real estate investment, which we saw in dozens of
Speaker 1: large industrial parks through the area. But if you look
Speaker 1: at this area, this industrial part right now, if you
Speaker 1: came here twenty years ago, what would you see. Well?
Speaker 2: Nothing.
Speaker 1: Alberta Cretine spent a decade accumulating industrial real estate, some
Speaker 1: forty two million square feet of it. Six bidders competed
Speaker 1: to buy the company he led, Febra Tarafina, a competition
Speaker 1: ultimately won by Prologis when it paid two point eight
Speaker 1: billion dollar to top Blackstone's bid, doubling the size of
Speaker 1: its portfolio in Mexico. Tell us about that bidding war.
Speaker 14: Tearina was a public company, so there were some unsolicit
Speaker 14: offers or a takeover office, you know, from one company,
Speaker 14: and then an OI wanted, I wanted, and I allow
Speaker 14: my investors to make the issue which way they wanted
Speaker 14: to go?
Speaker 1: Is a commercial estate for manufacturing in northern Mexico is
Speaker 1: still a good investment if you were starting out today.
Speaker 1: Would you invest in it?
Speaker 14: Absolutely?
Speaker 1: Despite his continued enthusiasm for the business, Cretine acknowledges some limits,
Speaker 1: particularly when it comes to a reliable source of energy
Speaker 1: to power the plants. Addressing the infrastructure needs with restrict
Speaker 1: to electricity you talk about requires a fair amount of
Speaker 1: capital investment. Yes, is there enough public investment to be
Speaker 1: able to take care of that or do you need
Speaker 1: private capital to come in?
Speaker 14: Absolutely, we need a private capital to come in. The
Speaker 14: administration acknowledges that the last administration almost have no investment
Speaker 14: whatsoever in generation or in high tension lines, that there
Speaker 14: had been interdeability of to continue to provide environment for
Speaker 14: new companies to come to Mexico.
Speaker 4: Part of Mexico's challenges are self made. Is its own
Speaker 4: barriers that's putting in front. So it's things like not
Speaker 4: having access to affordable, consistent, clean energy.
Speaker 1: Shannon O'Neil is Senior Vice President of the Council on
Speaker 1: Foreign Relations. She sees the window of opportunity others do,
Speaker 1: but also some troublesome risks for investors. As you've talked
Speaker 1: to investors in Mexico, how concerned are they about some
Speaker 1: of the issues you identified like judicial reform, for example.
Speaker 4: These really are front and center and investors' minds. So
Speaker 4: the justice reform will mean that judges are now elected,
Speaker 4: and so businesses worry that those judges could be bought
Speaker 4: or influenced by the government in particular cases.
Speaker 1: What about security in particular, because it wasn't that long
Speaker 1: ago that it felt like some of the individual Mexican
Speaker 1: states were almost in a state of civil war with
Speaker 1: the cartels.
Speaker 4: Security has worsened under this last government. Homicide rates have
Speaker 4: remained at near record highs, but things like extortion, embezzlement,
Speaker 4: and other kidnappings have grown.
Speaker 1: One of the things an investor tends not to like
Speaker 1: is uncertainty.
Speaker 4: And it's building an uncertainty that happened under the last administration.
Speaker 4: It's continuing and even deepening, and we've seen it in
Speaker 4: the investment numbers. You know what, Mexicans were hoping twenty
Speaker 4: twenty four would be a record highs in terms of investment,
Speaker 4: and most of it has been frozen, both international investment,
Speaker 4: foreign investment, but also domestic investments. So this is a
Speaker 4: time of uncertainty in Mexico, even as companies are looking
Speaker 4: for alternatives to Asia.
Speaker 1: And if that weren't enough the USMCA itself, that successor
Speaker 1: agreement to NAFTA underpinning the near showing system, will be
Speaker 1: up for review in twenty twenty six, with new governments
Speaker 1: in both the United States and Mexico.
Speaker 4: I do think these are going to be pretty difficult negotiations.
Speaker 4: The really looming issue for the review of the USMCA
Speaker 4: is China and where does China fit into supply chains
Speaker 4: in North America And where does China fit in to
Speaker 4: production more broadly in North America.
Speaker 1: That does it for this edition of Bloomberg Wall Street Week.
Speaker 1: If you missed any part of today's program, you can
Speaker 1: listen on demand with our Wall Street Week podcast. Find
Speaker 1: that on Apple, Spotify, or anywhere else you get your podcasts.
Speaker 1: I'm David Weston. Stay with us. Today's top stories and
Speaker 1: global business headlines are coming up right now.