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