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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: This is Wall Street Week. I'm David Weston bringing you

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Speaker 2: stories of capitalism. Unicorns used to be rare, but they're

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Speaker 2: popping up more often and getting bigger by the day.

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Speaker 2: We look into why larger companies are staying private for

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Speaker 2: longer and whether we should be concerned. Plus, we're paying

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Speaker 2: a lot of attention to the tariffs President Trump is

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Speaker 2: imposing on China and Europe and Brazil. But we go

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Speaker 2: to Lusutu to see what the Trump tariffs mean for

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Speaker 2: a country whose exports may seem small by US standards,

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Speaker 2: but are big enough to threaten its local economy. And

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Speaker 2: the pandemic made the world far more aware of vulnerabilities

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Speaker 2: in supply chains. What is technology doing to overcome the

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Speaker 2: problems we found? But we start with a major public

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Speaker 2: university transforming itself and its students for a very different world.

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Speaker 2: Michael Crowe is the longtime president of Arizona State University.

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Speaker 2: He's won just about every award there is for innovation

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Speaker 2: in higher education, and his institution is working to make

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Speaker 2: sure it keeps up with the times.

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Speaker 1: We decided to change everything, change culture, change design, change,

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Speaker 1: intellectual structure, use technology move in new directions, and then

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Speaker 1: we began measuring learning outcomes.

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Speaker 2: For all that talk about changes in higher ed, including

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Speaker 2: just this week when the University of Virginia became the

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Speaker 2: first public college to agree to Trump administration oversight, the

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Speaker 2: biggest cause for change. AI doesn't come up all that

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Speaker 2: often in the political debate. For nearly a quarter century,

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Speaker 2: Michael Crowe has led the university that is by some measures,

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Speaker 2: the largest in the United States. Few people have thought

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Speaker 2: as much about American higher education while also having the

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Speaker 2: ability to influence it on such a large scale. But

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Speaker 2: when we first talked with him a little over a

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Speaker 2: year ago, we didn't cover artificial intelligence. A lot has

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Speaker 2: changed since then.

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Speaker 3: AI will leave a lot of white colar people behind.

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Speaker 2: Another uncomfortable truths linked to.

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Speaker 4: AI AI AI AI AI.

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Speaker 5: It's not a bubble.

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Speaker 2: Obviously, the most disruptive technology in the history of man.

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Speaker 1: Can Egalitarian access to knowledge is at the highest level

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Speaker 1: in the history.

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Speaker 5: Of our species.

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Speaker 1: What we have as a walking, talking reference library on

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Speaker 1: any subject, and we never had anything like that in

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Speaker 1: our society before.

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Speaker 2: We wanted to know how artificial intelligence is changing the

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Speaker 2: American college experience, what it means for the teachers and

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Speaker 2: students who have now made it a regular part of

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Speaker 2: their lives. But we also asked Crow about the outcomes

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Speaker 2: for recent graduates and how his school is preparing students

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Speaker 2: for an economy that is moving very fast. What does

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Speaker 2: that do to teaching? I mean, back in the olden days,

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Speaker 2: we wrote essays.

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Speaker 5: We wrote blue books back when I was there.

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Speaker 2: How do you do things like essays and evaluations?

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Speaker 1: Well, I think what has to happen And we've experienced

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Speaker 1: this at ASU with our six thousand factor members, several

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Speaker 1: thousand of which are already AI trained, is you have

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Speaker 1: to up the game. Perhaps we were learning too slowly,

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Speaker 1: too incrementally, too much in a regimented or industrial way.

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Speaker 5: With the AI tools.

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Speaker 1: That are available now, you can up the game, enhance

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Speaker 1: the question complexity, enhance the answer complexity, expect more of

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Speaker 1: the students. We had somebody give their test the Business

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Speaker 1: School to an AI system to get everything right instantly,

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Speaker 1: and the test is too easy.

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Speaker 5: The test is too.

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Speaker 1: Simple, so you need It's basically a way in our view,

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Speaker 1: to accelerate learning, to broaden learning into speed learning. So

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Speaker 1: you have to look at it as a new way

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Speaker 1: to basically make the game more intensive. The model is

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Speaker 1: always changing. So Plato was against the written word. He

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Speaker 1: thought everything should be thought through verbally and communicated verbally.

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Speaker 1: There were unbelievable forces against the development of the printed book,

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Speaker 1: and so the internet in its development, the web and

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Speaker 1: its development all had.

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Speaker 5: People that were against it.

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Speaker 1: And so AI changes the model in the sense that

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Speaker 1: it speeds it up and intensifies it. It personalized the

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Speaker 1: learner's experience, but it doesn't teach those core things. There's

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Speaker 1: no values being taught, there's no values being experienced, there's

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Speaker 1: no lived experiences being built. So what we really have

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Speaker 1: here now is we just have this massive hyper speed

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Speaker 1: calculator capable of going to all of the digitized information

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Speaker 1: you asking a question about that information and getting the

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Speaker 1: most probable answer. It's all about the questions that you're asking.

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Speaker 1: It's not about the answers. It's about the questions, and

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Speaker 1: that's what people need to really figure out.

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Speaker 2: Does it change the notion of cheating.

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Speaker 1: I'll bet humans have cheated for quite a while. It

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Speaker 1: does change the nature of what is your work now.

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Speaker 1: If you're answering a complex question and you're using a

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Speaker 1: reference library and an AI system, to answer that question

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Speaker 1: that seems legitimate. If you're using it to produce your

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Speaker 1: analytical response that's supposed to be demonstrative.

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Speaker 5: Of your ability, well then your cheating.

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Speaker 1: Now you have to then build a system which recognizes

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Speaker 1: the ability to gain access to these tools. Now, sometimes

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Speaker 1: it's just going to be you in there by yourself

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Speaker 1: taking the test, because they've got to know that you

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Speaker 1: know how to ask the question, you know how to

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Speaker 1: derive the answer, that your brain works in a certain way.

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Speaker 1: Beyond that, the AI systems are going to enhance learning

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Speaker 1: in every possible way, and that the idea of cheating will.

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Speaker 5: Change at this point.

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Speaker 2: Are there some things that you can learn that AI

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Speaker 2: cannot teach you?

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Speaker 5: Absolutely?

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Speaker 1: I mean, you know, an AI system can't teach you

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Speaker 1: to be innovative.

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Speaker 5: It can't teach you to be creative.

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Speaker 1: It cannot teach you grit, It cannot teach you the

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Speaker 1: discovery process. It cannot teach you. I mean, it's a machine.

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Speaker 5: It's a it's.

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Speaker 1: An advanced hyper speed calculator. It can do things that

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Speaker 1: you can't do. It can think around corners that you

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Speaker 1: can't see. But of course, soakn a dog and so

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Speaker 1: and so's it's a powerful analytical tool to enhance our

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Speaker 1: mental capabilities, not to replace them.

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Speaker 2: In order to ensure that AI is a springboard rather

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Speaker 2: than a crutch, Crow says, students and teachers will have

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Speaker 2: to raise the bar. And one place he's already seen

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Speaker 2: signs of AI's ability to supercharge progress is in the

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Speaker 2: school's research programs.

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Speaker 1: It's almost unbelievable. We have probably fifty research groups that

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Speaker 1: are using advanced AI to solve unsolvable problems, to figure

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Speaker 1: out how to process materials or manage the Mississippi River

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Speaker 1: in a different way in terms of the flow of

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Speaker 1: the water and the flow of the dirt and other

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Speaker 1: things that go down the river. We've got people doing

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Speaker 1: advanced chemistry. Now, we're using AI systems to think beyond

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Speaker 1: the way that we normally think to create more revolutionary opportunity.

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Speaker 1: There was a study recently done by some of our

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Speaker 1: faculty at the speed with which you could complete the

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Speaker 1: work equivalent to a dissertation in genetics fourteen days. What

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Speaker 1: that means then, is that the PhD that normally takes

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Speaker 1: four or five years to set up the experiments, do

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Speaker 1: the experiments, do the work be evaluated? Maybe the PhD

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Speaker 1: student of the future will do the equivalent of twenty PhDs.

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Speaker 1: That will speed up the cures for cancer, that will

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Speaker 1: speed up the analytical tools that will help restore site

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Speaker 1: in human beings, that will speed up the techniques that

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Speaker 1: use electromagnetic current to affect people with Alzheimer's disease and

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Speaker 1: other neurodegenerative diseases, all of which are computationally limited.

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Speaker 2: Certainly, the world has changed in enormous sin since I

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Speaker 2: came out of college a long time ago now, But

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Speaker 2: my sense is the rate of change has really increased,

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Speaker 2: maybe even geometrically how fast it's changing. How do you

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Speaker 2: prepare graduates today for a world twenty thirty forty years

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Speaker 2: down the road that I can't even imagine.

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Speaker 1: So there's no way to prepare someone for something you

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Speaker 1: don't know what it will be, except one thing what

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Speaker 1: we call we're attempting to take all the people that

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Speaker 1: are coming to our university one hundred and twenty thousand

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Speaker 1: degree seeking students, seven hundred thousand other learners who are

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Speaker 1: just taking courses with us digitally and otherwise. Can we

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Speaker 1: help create you to be a master learner? Can we

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Speaker 1: help you to be a person capable of learning anything,

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Speaker 1: adjusting to anything, adapting to anything. It's really that because

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Speaker 1: we don't know what all of the adaptations that will

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Speaker 1: be required are. We do know that you should be

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Speaker 1: grounded in American history and economics and the role of democracy,

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Speaker 1: and certain subjects in math and science and so forth

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Speaker 1: and so on, and then after that we find a

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Speaker 1: learning path.

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Speaker 5: For you to take where you learn to learn. We

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Speaker 5: don't care what your major is.

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Speaker 4: You can.

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Speaker 1: I met a kid the other day. It was majoring

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Speaker 1: in opera and physics. Great, fine, fantastic. That's how that

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Speaker 1: kid learns and so and so. That's what we're after.

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Speaker 1: How do you how do you create universal learners capable

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Speaker 1: of learning anything.

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Speaker 2: That's the pathway We here at forty thousand feet about

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Speaker 2: a shifting employment situation for recent college graduates because of AI.

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Speaker 2: Are you seeing any of that in the real world.

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Speaker 5: We're not seeing that in our graduates.

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Speaker 4: Now.

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Speaker 1: The problem with people talking about all college graduates. There's

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Speaker 1: more than twenty million people in college. A couple million

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Speaker 1: go to what you think of as sleep away colleges.

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Speaker 1: You know, they go to places where you're you know,

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Speaker 1: you're living on campus. The other eighteen million go to

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Speaker 1: college in some other way, community college online, some kind

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Speaker 1: of other course and so forth. So we're not seeing

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Speaker 1: any change, you know, we're seeing the same level of anxiety.

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Speaker 1: We're seeing the same level of the process where you know,

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Speaker 1: more than ninety five percent of our students that graduate

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Speaker 1: as undergraduates are employed or in graduate school within the

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Speaker 1: first year, almost all within the second year. So we're

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Speaker 1: still seeing good rois. But what we are seeing is students,

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Speaker 1: you know, are quite savvy, you know, adjusting their trends.

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Speaker 1: So we're seeing a slightly downward trend in computer science,

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Speaker 1: and a slight way slightly upward trend in double majoring

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Speaker 1: and triple majoring, more people moving into analytics and supply

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Speaker 1: chain and all kinds of other things, and so the

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Speaker 1: market for learning is also adjusting. We measure our success

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Speaker 1: based on who we include.

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Speaker 2: Crow hopes the size and scope of ASU will help

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Speaker 2: with that adjustment, allowing students to react quickly and build

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Speaker 2: new skills for the changing world. And in a school

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Speaker 2: made famous for opening its doors rather than being exclusive,

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Speaker 2: he thinks the most important skills of all can come

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Speaker 2: from unlikely places.

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Speaker 1: We've even got ways now that we're using advanced AI,

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Speaker 1: enhanced robots to help people that aren't qualified to get

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Speaker 1: into a particular college to do what they want to

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Speaker 1: do to get them qualified. Guess what, when we get

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Speaker 1: them qualified, they have more grit and determination than anyone

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Speaker 1: else who sort of walked into it from high school,

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Speaker 1: and they outperform everyone.

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Speaker 2: There's a theme going on right now. Maybe college has

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Speaker 2: been overrated, over sold. What do you say to parents?

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Speaker 1: What we have is a way for your child, your

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Speaker 1: student to learn on the path that's going to enhance

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Speaker 1: their ability to be most adaptive throughout their life.

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Speaker 5: So don't worry about their major.

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Speaker 1: So we get these parents that say, well, my kid

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Speaker 1: needs to major in accounting so they can get a job,

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Speaker 1: or they need to major in anything other than political

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Speaker 1: science or history or English where they'll never get a job.

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Speaker 5: That's actually not true.

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Speaker 1: Some of the hottest things that we have that we're

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Speaker 1: producing right now are English majors that can code, and

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Speaker 1: so they have a broader perspective and they can code,

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Speaker 1: and so we provide free coding classes to everyone in

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Speaker 1: the institution. We provide other ways in which you can

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Speaker 1: double major, triple major, take other kinds of things, and

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Speaker 1: so what we say to parents is let's find the

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Speaker 1: way where your kid is going to smile while learning,

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Speaker 1: while pairing themselves to be a master learner, and you'll

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Speaker 1: have to worry about them less. If they take a

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Speaker 1: fixed thing in a fixed way and a fixed pathway,

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Speaker 1: they could find themselves in an alley in no way out.

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Speaker 1: We're trying to make sure that that doesn't happen.

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Speaker 2: Coming up, transforming the US economy by taking the bugs

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Speaker 2: and supply chains and turning them into features. That's next

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Speaker 2: on Wall Street Week. This is a story about featuring

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Speaker 2: the things we thought we needed to fix. The pandemic

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Speaker 2: focused all of us on the need to fix our

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Speaker 2: supply chains. But what if we could use technology not

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Speaker 2: just to fix our supply chains, but to rethink them

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Speaker 2: all together. Could we use them to transform our markets,

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Speaker 2: our workforce, and our economy itself.

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Speaker 4: Stopping the spread of the deadly coronavirus has men stopping

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Speaker 4: the spread of.

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Speaker 6: More than just germs.

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Speaker 7: Good sparts, and people have been halted in place.

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Speaker 8: I think that pandemic sort of both logistics to the

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Speaker 8: limelight from this like tucked in the corner industry to

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Speaker 8: be really be in the forefront of all of us,

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Speaker 8: of consumers, and society. And when supply chain stop, economy stop.

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Speaker 9: Going back to the COVID days of twenty twenty, we

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Speaker 9: started buying online like we had never seen before. And

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Speaker 9: so what happened then with all of that cargo rushing in?

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Speaker 9: It was like taking ten lanes of LA freeway traffic

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Speaker 9: and squeezing them into five.

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Speaker 2: But what if we could use technology not just to

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Speaker 2: fix supply chains, but to rethink them all together. Could

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Speaker 2: we use them to transform our markets, our workforce, and

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Speaker 2: our economy itself.

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Speaker 8: Logistics is the largest cost for the manufacturing and retail economy,

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Speaker 8: and if you drive that cost down, like anything else

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Speaker 8: we have seen in the history of economy, you unleash

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Speaker 8: the next epoch of economic activity.

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Speaker 2: Lee or Run recently became COO of autonomous trucking company

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Speaker 2: WABI after co founding Uber Freight back in twenty seventeen.

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Speaker 8: Their cost of good moved is sometimes thirty forty percent

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Speaker 8: of the cost of operation. It's a prohibitor for them

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Speaker 8: to scale and to be bigger. If you take that

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Speaker 8: cost down, you essentially democratize access to logistics. You encourage

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Speaker 8: more economical activity. If you would use the friction in them.

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Speaker 8: It's friction in the system. If you would use the

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Speaker 8: friction in the system on manufacturing, on distribution, on moving

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Speaker 8: those goods around, you just unlock a bunch of new

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Speaker 8: economical opportunity. You unlock manufacturing strength, you unlock new productability.

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Speaker 8: Back to COVID, you unlock resiliency, and that set the

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Speaker 8: stage for a rapid economical activity.

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Speaker 2: The stakes are enormous, but how to get from here

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Speaker 2: to there? Says The answer lies in part in that

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Speaker 2: artificial intelligence everyone's talking about.

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Speaker 8: I think I allows you. The way I think about

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Speaker 8: it really like free levels of optimization and supporting function.

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Speaker 8: One is logistics is a highly fragmented, highly manual ecosystem.

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Speaker 8: You have hundreds of thousands of opera ittles acting as glue,

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Speaker 8: overcoming all those information challenges, doing a lot of repeated

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Speaker 8: manual task which is really sort of like taxing the

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Speaker 8: system from a cost perspective and from an efficiency perspective,

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Speaker 8: and more importantly just from a time perspective. The second

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Speaker 8: level is optimization. Can we now actually start looking at

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Speaker 8: the supply chain holistically and start driving smoulted decisions? Can

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Speaker 8: we optimize those empty miles? Why does it need to

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Speaker 8: be forty percent empty miles. If we know everything, we

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Speaker 8: could connect everything together and we can actually start smartly

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Speaker 8: designing the network so you can actually minimize those empty miles.

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Speaker 8: And the last level is using AI to tully drive

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Speaker 8: better decision making. Let's have a chat shipt for my

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Speaker 8: supply chain. And the most important one is unleashing AI

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Speaker 8: to the physical world with physical AI and self driving,

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Speaker 8: which I think is really sort of the deepest disruption

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Speaker 8: and the most profound change that would see with AI

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Speaker 8: in supply chain in the next decade.

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Speaker 2: Part of what Ron and visions for the future is

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Speaker 2: happening right now in the Port of Los Angeles.

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Speaker 10: There's so much here and I think we're just scratching

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Speaker 10: the surface.

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Speaker 2: Gene Soroca is the executive director of the Port of

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Speaker 2: Los Angeles, the busiest container port in the United States.

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Speaker 10: One of the areas that we stepped into right away.

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Speaker 9: Was digitalization of all the port's information. How many ships

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Speaker 9: are coming in containers, what trucks and trains need to

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Speaker 9: be planned, how do we get our great skilled labor

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Speaker 9: at the right place anticipating the cargo that was coming in.

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Speaker 9: So we worked with the wab Tech company to develop

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Speaker 9: the first information sharing system for a port here in

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Speaker 9: the United States.

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Speaker 10: We call it the Port Optimizer.

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Speaker 9: We can now see cargo forty days before the ship

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Speaker 9: arrives into Los Angeles, giving us ample time to plan

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Speaker 9: all those things, the skilled labor, the land, the machinery,

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Speaker 9: and just that intuitiveness about how we're going to handle things.

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Speaker 10: If something doesn't go to schedule.

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Speaker 9: And normally that's the case, there's always an adjustment that

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Speaker 9: has to be made in the supply chain. So now

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Speaker 9: we can see things every morning, a dashboard of information

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Speaker 9: about the velocity, the vital statistics of the port, and

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Speaker 9: I could tell you after about ninety seconds of a

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Speaker 9: review how we're doing and what we need to do next.

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Speaker 9: That digitalization started to introduce prescriptive and predictive analysts. Then

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Speaker 9: we start to get into really really interesting engineering work.

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Speaker 9: We're going to have a project here on the Vincent

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Speaker 9: Thomas Bridge to resurface it. So we're using geospatial mapping

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Speaker 9: and great companies like EZRI and the Jet Propulsion Lab

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Speaker 9: to help us now simulate traffic patterns ahead of time

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Speaker 9: and in real time allow drivers to move around with

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Speaker 9: much more knowledge than they had before. It's going to

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Speaker 9: help this trucking community out in a tremendous way.

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Speaker 2: What so Roca is pursuing within the Port of Los Angeles,

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Speaker 2: Roger Penske is doing on roads coast to coast and

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Speaker 2: even around the world.

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Speaker 4: We'll do five hundred thousand vehicles. We're in poor contents

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Speaker 4: in nine countries, and we have our truck leasing, rental

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Speaker 4: and logistics and we have forty four thousand people.

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Speaker 10: So it's a.

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Speaker 4: Real enterprise from artificial intelligence. What we're using in the

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Speaker 4: truck leasing business. We're taking downloading two hundred thousand vehicles

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Speaker 4: a night with operating data, and I look at the

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Speaker 4: amount that we capture on an annual basis, that's a

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Speaker 4: billion units of data. We run five million, six hundred

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Speaker 4: million miles with our trucks, and when you take that,

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Speaker 4: we need somewhere to aggregate it. So we're starting to

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Speaker 4: use what we call Catalyst AI. It's a AI product

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Speaker 4: where we look at the data, we diagnose the data,

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Speaker 4: we go to our customers, and we use this as

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Speaker 4: a connection.

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Speaker 2: The Penske Company's use of AI is not limited to

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Speaker 2: monitoring its massive fleet. It also uses it in the

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Speaker 2: maintenance required to keep that fleet on the road.

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Speaker 4: We then become to build a process where we have

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Speaker 4: predictive maintenance. So AI is telling us this truck should

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Speaker 4: come into our shop. You might have the same truck

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Speaker 4: but a different duty cycle. So when that truck comes

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Speaker 4: in our shop, what happens is mechanic goes to the computer,

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Speaker 4: scans the barcode or the VEN number on the truck.

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Speaker 4: He puts on a headset with a mic and what

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Speaker 4: we call guided repair, and that guided repair takes him

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Speaker 4: through the whole maintenance process on the trucks. And then

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Speaker 4: that data, because it's live, goes into our SOS center,

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Speaker 4: which is our call center for breakdown this whole avenue,

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Speaker 4: downloading the data, taking it, making it real. Then we

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Speaker 4: take it and for the customer, we'll go back to

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Speaker 4: you as a fleet and we'll take and give you

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Speaker 4: your data live and how we compare location to location

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Speaker 4: you have and we can look at that and then

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Speaker 4: determine what are the things that we have to do

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Speaker 4: take action. We have fifteen thousand customers and many of

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Speaker 4: them don't have the opportunity to have this kind of data.

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Speaker 4: So it's sticky, it's important, and it's taking all the

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Speaker 4: technology that we can get.

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Speaker 2: Whenever we begin to talk about automation and AI, people

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Speaker 2: raise questions about jobs. What's the effect on jobs? Are

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Speaker 2: you going to eliminate a lot of jobs in supply chains.

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Speaker 8: It's a how job, and many folks, many young folks,

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Speaker 8: do not want they take on this job, being three

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Speaker 8: hundred days on the road, not being able to raise

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Speaker 8: a family, not being able to have predictable access to

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Speaker 8: sleep and food. I think for the foresible future, as

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Speaker 8: you start rolling self driving, gradually you augment those jobs,

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Speaker 8: You fulfill the empty spots in the demand for those jobs.

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Speaker 8: You don't replace those jobs. There are gonna be plenty

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Speaker 8: of time for the existing population to retire over the

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Speaker 8: next few decades. As the truck replacement cycleed against like

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Speaker 8: four million trucks in the United States, it's going to

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Speaker 8: take a while until self diving will actually sort of

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Speaker 8: make an economical dent to the driving population. So I

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Speaker 8: see that as a gradual job transition on self diving.

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Speaker 8: I think if you look at knowledge workers, I think

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Speaker 8: we'll see a faster transition because the digital AI will

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Speaker 8: is even more prevalent and will scale potentially even faster.

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Speaker 8: I think there it's about an opportunity to up level

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Speaker 8: so a little bit freight. We've automated a bunch of

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Speaker 8: those manual, repetitive tasks, but then those folks have been

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Speaker 8: up leveled and they are now most sort of an

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Speaker 8: orchestrators of an orchestra. They are the brain behind actually

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Speaker 8: orcheslating all those agents because those AI agents can now

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Speaker 8: do this job and that job and that job, and

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Speaker 8: I'm sort of more the integrator. There is no replacement

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Speaker 8: for the human connectivity, so I can spend my time

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Speaker 8: focusing on customer needs.

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Speaker 11: I think in the idea that people think that jobs

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Speaker 11: will disappear, that it will be just talkue to AI,

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Speaker 11: I think it's overhyped.

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Speaker 2: Chris Kaplis is the executive director of MIT's Center for

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Speaker 2: Logistics and Transportation.

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Speaker 11: I think there's a lot of overhype in that it

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Speaker 11: can truly take over a human's full job, when in

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Speaker 11: fact jobs are tasks, and it'll take over some tasks.

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Speaker 11: And so then there are certain tasks that each one

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Speaker 11: of us in our jobs that cannot be done by AI,

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Speaker 11: to include the person filming us right now, it's more

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Speaker 11: the mundane things that you get automated and then the

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Speaker 11: things that can help enhance what a human does. I

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Speaker 11: think there's a continuum on how you automate things. We've

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Speaker 11: been automating stuff forever, right, but there's a line of

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Speaker 11: what this is mundane and over here I still need

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Speaker 11: a human. That line keeps moving as AI gets smarter.

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Speaker 11: I don't think it'll go all the way.

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Speaker 2: Artificial intelligence may well transform our supply chains, but to

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Speaker 2: get there, it's not enough to have AI alone. It

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Speaker 2: has to be linked to autonomy. What is AI without

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Speaker 2: autonomous vehicles? What are autonomous vehicles without AI?

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Speaker 8: I fundamentally see the next decade really about building the

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Speaker 8: autonomous infrastructure over the industry, and I view self driving

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Speaker 8: as the most profound help in driving efficiency and safety

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Speaker 8: into supply chain. The whole digital infrastructure, in the end

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Speaker 8: of the day can drive ten fifteen percent optimization of

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Speaker 8: the network when you start actually moving assets in a

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Speaker 8: self driving way, When you can move an asset from

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Speaker 8: six hours a day of being utilized with a human

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Speaker 8: drive to twenty hours twenty four hours, when you can

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Speaker 8: drive safety into our roads. With thousands of unnecessary days

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Speaker 8: and half a million related incidents in the US every

443
00:23:18,640 --> 00:23:22,960
Speaker 8: year on trucks. You can make a profound change and

444
00:23:23,000 --> 00:23:27,080
Speaker 8: a profound help to the industry. This is the time.

445
00:23:28,119 --> 00:23:31,520
Speaker 8: I think it's a confluence of many things really coming

446
00:23:31,560 --> 00:23:34,960
Speaker 8: together to make the next year, not even five years,

447
00:23:34,960 --> 00:23:39,320
Speaker 8: the next yeal transformative from seldriving in industry like this

448
00:23:39,359 --> 00:23:42,360
Speaker 8: is game time, This is really now. It's happening.

449
00:23:45,400 --> 00:23:48,320
Speaker 2: Up next to Lesuto. Why not come to everyone's mind

450
00:23:48,320 --> 00:23:51,640
Speaker 2: as a textile exporter, but it was on President Trump's

451
00:23:51,720 --> 00:23:55,159
Speaker 2: list of terror recipients for not playing fair. We go

452
00:23:55,240 --> 00:23:57,439
Speaker 2: to the country in southern Africa to see what a

453
00:23:57,520 --> 00:24:00,359
Speaker 2: small kingdom can do to stand up to the world's

454
00:24:00,440 --> 00:24:15,879
Speaker 2: largest economy. This is a story of David and Goliath.

455
00:24:16,359 --> 00:24:19,359
Speaker 2: President Trump's biggest tariff moves have been against countries of

456
00:24:19,400 --> 00:24:23,080
Speaker 2: similar stature to the United States, places like China and Europe,

457
00:24:23,240 --> 00:24:25,679
Speaker 2: but he's also hit some of the smallest economies in

458
00:24:25,720 --> 00:24:29,040
Speaker 2: the world. Our colleague David Goura has the story of Lusutu,

459
00:24:29,359 --> 00:24:31,960
Speaker 2: a country out of sight for some, but one that's

460
00:24:31,960 --> 00:24:34,719
Speaker 2: been hit particularly hard.

461
00:24:36,280 --> 00:24:40,000
Speaker 4: The African nation of Lesuto, which nobody has ever heard.

462
00:24:44,960 --> 00:24:47,760
Speaker 3: The sun is rising over the Tatsani district of Maseru,

463
00:24:47,880 --> 00:24:51,280
Speaker 3: the capital of Lsutu. The smoke rising from the textile

464
00:24:51,320 --> 00:24:54,760
Speaker 3: factory signals the workday has begun, but only for the

465
00:24:54,840 --> 00:24:59,359
Speaker 3: lucky few, cutting fabric, sewing seams, making clothes for the

466
00:24:59,359 --> 00:25:02,320
Speaker 3: Western world world. Many of these workers make as little

467
00:25:02,359 --> 00:25:08,400
Speaker 3: as eighteen hundred US dollars a year. Outside the factory,

468
00:25:08,520 --> 00:25:12,040
Speaker 3: a crowd is building of locals hoping to find work.

469
00:25:12,320 --> 00:25:14,720
Speaker 6: Any kind of job in the factory.

470
00:25:14,800 --> 00:25:14,960
Speaker 3: There.

471
00:25:15,000 --> 00:25:18,720
Speaker 12: I lived up ticket and President Trump should show mercy

472
00:25:18,760 --> 00:25:21,480
Speaker 12: to Lusutu because the demand for jobs is high in

473
00:25:21,480 --> 00:25:21,959
Speaker 12: the country.

474
00:25:23,760 --> 00:25:27,080
Speaker 3: It's been like this since June, when Lusutu's largest industry

475
00:25:27,119 --> 00:25:30,480
Speaker 3: began to feel the effects of President Trump's tariffs first

476
00:25:30,600 --> 00:25:33,679
Speaker 3: fifty percent, among the highest rates in the world at

477
00:25:33,720 --> 00:25:34,159
Speaker 3: the time.

478
00:25:35,920 --> 00:25:39,840
Speaker 13: It give us a shock to us. Luckily, later on

479
00:25:39,960 --> 00:25:42,560
Speaker 13: it was revised to fifteen percent.

480
00:25:44,160 --> 00:25:47,760
Speaker 3: Sam Matakane is the Prime Minister of Lusutu. He declared

481
00:25:47,800 --> 00:25:50,440
Speaker 3: a two year state of disaster for his country's economy

482
00:25:50,640 --> 00:25:52,080
Speaker 3: after the tariffs were put in place.

483
00:25:52,680 --> 00:25:56,280
Speaker 13: We see in negotiations with the US government or a

484
00:25:56,280 --> 00:26:00,240
Speaker 13: federal reduction maybe to ten percent or to zero where

485
00:26:00,280 --> 00:26:05,520
Speaker 13: we were before. We would appreciate the reduction and as

486
00:26:05,560 --> 00:26:08,520
Speaker 13: well as the renewal of al GOA because our GOA

487
00:26:08,560 --> 00:26:09,840
Speaker 13: has just come to an end now.

488
00:26:10,400 --> 00:26:13,439
Speaker 2: The African Growth and Opportunity.

489
00:26:12,840 --> 00:26:16,919
Speaker 3: Act a GOA, the African Growth and Opportunity Act, signed

490
00:26:16,960 --> 00:26:20,120
Speaker 3: in two thousand, gave some nations in Africa duty free

491
00:26:20,160 --> 00:26:23,840
Speaker 3: access to the US market. That deal expired last month,

492
00:26:24,080 --> 00:26:26,280
Speaker 3: and while the Trump administration has said it would support

493
00:26:26,280 --> 00:26:30,280
Speaker 3: a one year extension, that hasn't happened. The Prime Minister

494
00:26:30,320 --> 00:26:33,560
Speaker 3: says agoa's expiration and the higher tariffs have had a

495
00:26:33,600 --> 00:26:35,080
Speaker 3: crushing effect on Lesutu.

496
00:26:36,720 --> 00:26:39,840
Speaker 13: Well, we are hoping for the best. Negotiations are still ongoing,

497
00:26:40,000 --> 00:26:41,720
Speaker 13: but do we are hoping for the best of the best.

498
00:26:43,560 --> 00:26:46,359
Speaker 3: Dubbed the denim capital of the World, there are two

499
00:26:46,440 --> 00:26:50,280
Speaker 3: industries that drive Lsutu's economy, a landlocked country of just

500
00:26:50,320 --> 00:26:54,240
Speaker 3: over two million people surrounded by South Africa. Those industries

501
00:26:54,480 --> 00:26:58,440
Speaker 3: are diamonds and textiles. Diamond prices have been in free

502
00:26:58,480 --> 00:27:01,360
Speaker 3: fall since twenty twenty two, and that's led to layoffs

503
00:27:01,359 --> 00:27:05,560
Speaker 3: in Lesutu's mines. Now it's textiles that have been hit hard.

504
00:27:06,240 --> 00:27:10,119
Speaker 3: As recently as January, Lsutu's Central Bank expected the sector

505
00:27:10,200 --> 00:27:13,480
Speaker 3: to grow in twenty twenty five, but by June it

506
00:27:13,520 --> 00:27:16,880
Speaker 3: had downgraded its forecast by more than ten percentage points,

507
00:27:16,920 --> 00:27:21,480
Speaker 3: saying textile and clothing manufacturing would quote shrink significantly due

508
00:27:21,520 --> 00:27:25,719
Speaker 3: to the negative impact of newly imposed tariffs. Afra Expo

509
00:27:25,800 --> 00:27:29,399
Speaker 3: Textiles is feeling that pressure. To Bojo Kabelli is the

510
00:27:29,400 --> 00:27:31,600
Speaker 3: company's founder, but ed of the the day.

511
00:27:32,400 --> 00:27:33,840
Speaker 10: One must specialize on something.

512
00:27:34,520 --> 00:27:37,560
Speaker 14: If it was a big shock, even know what happened

513
00:27:37,560 --> 00:27:40,200
Speaker 14: with Kovid, it was something similar to that because it

514
00:27:40,960 --> 00:27:44,360
Speaker 14: meant shut in daw and when you shot, you mean

515
00:27:45,000 --> 00:27:50,119
Speaker 14: thirty forty thousand jobs.

516
00:27:50,200 --> 00:27:52,800
Speaker 3: The US imported more than two hundred and thirty five

517
00:27:52,880 --> 00:27:55,920
Speaker 3: million dollars worth of goods from LSUTU last year, about

518
00:27:55,920 --> 00:28:00,239
Speaker 3: eleven percent of lsutu's total GDP. That represents just a

519
00:28:00,280 --> 00:28:04,960
Speaker 3: tiny fraction of Americas. Forty seven percent of lsutu's exports

520
00:28:05,000 --> 00:28:07,760
Speaker 3: are clothing that will end up being sold by retailers

521
00:28:07,880 --> 00:28:11,679
Speaker 3: such as Walmart and Levi's. Some of President Trump's branded

522
00:28:11,720 --> 00:28:15,719
Speaker 3: golf shirts are made in Lsutu, but the trade relationship

523
00:28:15,840 --> 00:28:19,800
Speaker 3: has been unbalanced. Last year, LISUTU imported less than three

524
00:28:19,840 --> 00:28:23,080
Speaker 3: million dollars in goods from the US, and although Trump

525
00:28:23,160 --> 00:28:25,400
Speaker 3: might have been unfamiliar with the country, a few months

526
00:28:25,440 --> 00:28:29,000
Speaker 3: ago that imbalance was enough to make Lusutu a target

527
00:28:29,080 --> 00:28:29,720
Speaker 3: for tariffs.

528
00:28:29,840 --> 00:28:34,560
Speaker 4: Such horrendous imbalances have devastated our industrial base and put

529
00:28:34,560 --> 00:28:37,120
Speaker 4: our national security at risk.

530
00:28:37,160 --> 00:28:40,480
Speaker 5: They've ripped us off left and right. But now it's

531
00:28:40,520 --> 00:28:43,040
Speaker 5: our turn to do the Rippin'.

532
00:28:44,280 --> 00:28:47,880
Speaker 3: Kabelli didn't imagine the American President was talking about him.

533
00:28:48,160 --> 00:28:50,840
Speaker 3: His factory is one of only a few that's locally

534
00:28:50,880 --> 00:28:51,640
Speaker 3: owned in Masero.

535
00:28:52,200 --> 00:28:57,760
Speaker 14: The biggest parts was onder foreign directing missed us. Who

536
00:28:57,840 --> 00:29:01,560
Speaker 14: are the biggest and where are the hero visis?

537
00:29:01,800 --> 00:29:02,760
Speaker 5: They're not the listened to.

538
00:29:03,880 --> 00:29:07,520
Speaker 14: He's just the awakers here who are producing employment for

539
00:29:08,160 --> 00:29:11,640
Speaker 14: our people, and therefore our people are out of work

540
00:29:12,240 --> 00:29:13,080
Speaker 14: and now we suffer.

541
00:29:13,800 --> 00:29:14,320
Speaker 5: You see.

542
00:29:15,440 --> 00:29:18,440
Speaker 14: But if we could have taken it up to our hands,

543
00:29:18,880 --> 00:29:23,160
Speaker 14: then would be now knowledgeable of how do we talk

544
00:29:23,240 --> 00:29:29,200
Speaker 14: to how I might get easily. But we never take

545
00:29:29,240 --> 00:29:32,840
Speaker 14: that opportunity. The government failed to proude their own.

546
00:29:35,240 --> 00:29:38,600
Speaker 3: The unemployment rate in LSUTU in twenty twenty four was

547
00:29:38,680 --> 00:29:42,840
Speaker 3: thirty percent. That's the most recent official data collected before

548
00:29:42,880 --> 00:29:46,920
Speaker 3: the tariff induced layoffs began. Say Pug Macacolay is the

549
00:29:46,960 --> 00:29:50,040
Speaker 3: General Secretary of the Economic Freedom Trade Union.

550
00:29:51,880 --> 00:29:57,200
Speaker 15: Very inverastating. We are in a bad situation now. Our

551
00:29:57,240 --> 00:30:03,320
Speaker 15: economy is so ailing. The problem because the effectory workers

552
00:30:03,560 --> 00:30:07,880
Speaker 15: are the ones who are contributing a lot in our economy.

553
00:30:08,080 --> 00:30:11,640
Speaker 15: So if the factorists are not engaging a lot of workers,

554
00:30:12,200 --> 00:30:14,960
Speaker 15: then it means we have a big problem as a country.

555
00:30:15,640 --> 00:30:18,840
Speaker 3: What if anything is the government doing to improve the

556
00:30:18,880 --> 00:30:19,600
Speaker 3: economy here?

557
00:30:19,720 --> 00:30:24,000
Speaker 15: Not doing anything. We don't see anything yet.

558
00:30:26,000 --> 00:30:29,040
Speaker 3: But the government says Lsutu is not competing on a

559
00:30:29,120 --> 00:30:30,040
Speaker 3: level playing field.

560
00:30:30,920 --> 00:30:35,040
Speaker 13: The introduction of the tariffs have put us in the

561
00:30:35,080 --> 00:30:41,600
Speaker 13: disadvantaged positions because now the tariffs are not equaled. You know,

562
00:30:42,120 --> 00:30:44,920
Speaker 13: in many countries you'll find that they've got lower tariffs,

563
00:30:45,000 --> 00:30:48,920
Speaker 13: others have got higher tariffs. So the ones that we

564
00:30:50,040 --> 00:30:53,800
Speaker 13: has got the lower tariff have got advantages over the

565
00:30:53,800 --> 00:30:58,400
Speaker 13: ones that have got higher tariff because the business can

566
00:30:58,440 --> 00:31:01,600
Speaker 13: be able to move from other countries to other countries

567
00:31:01,920 --> 00:31:02,520
Speaker 13: because of that.

568
00:31:04,880 --> 00:31:08,320
Speaker 3: Kenya's tariff RAID, for example, is lower than Lasuitus at

569
00:31:08,400 --> 00:31:11,680
Speaker 3: just ten percent, which makes it more attractive to foreign

570
00:31:11,680 --> 00:31:15,640
Speaker 3: investors looking for a manufacturing hub. Given that, what can

571
00:31:15,680 --> 00:31:20,600
Speaker 3: a country like Lasutu do. Trudy Makayah is a Johannesburg

572
00:31:20,640 --> 00:31:23,800
Speaker 3: based consultant who works on trade and geopolitics at the

573
00:31:23,840 --> 00:31:24,920
Speaker 3: Boston Consulting Group.

574
00:31:25,360 --> 00:31:28,520
Speaker 6: It is devastating in the short term, and to pivot

575
00:31:29,360 --> 00:31:32,720
Speaker 6: is not always easy. So the idea that you know,

576
00:31:32,880 --> 00:31:36,680
Speaker 6: workers can be retrained do new things is difficult. So

577
00:31:37,000 --> 00:31:40,440
Speaker 6: to that extent, you know, the preferences and the access

578
00:31:40,480 --> 00:31:42,960
Speaker 6: to the market, just the pure access to the market

579
00:31:43,040 --> 00:31:47,720
Speaker 6: is important. I think where a different argument can be

580
00:31:47,800 --> 00:31:50,720
Speaker 6: made is that if you look globally at the level

581
00:31:50,760 --> 00:31:54,440
Speaker 6: of US trade, we still see, you know, from WTO

582
00:31:54,560 --> 00:31:59,600
Speaker 6: figures that eighty percent of trade is still happening outside

583
00:31:59,640 --> 00:32:04,160
Speaker 6: of the US contribution still happening on kind of regular

584
00:32:04,920 --> 00:32:08,760
Speaker 6: trade rules, on most favored nation principles. So there is

585
00:32:08,800 --> 00:32:11,640
Speaker 6: a sense that there is a world out there that continues.

586
00:32:13,040 --> 00:32:17,160
Speaker 6: But it would be folly to underestimate the second order

587
00:32:17,200 --> 00:32:20,520
Speaker 6: and the third order effects that come from this, because

588
00:32:20,880 --> 00:32:23,880
Speaker 6: once we're seeing all of these production shutdowns, we see

589
00:32:23,880 --> 00:32:29,000
Speaker 6: investments being pulled out. So I think it becomes very

590
00:32:29,000 --> 00:32:33,960
Speaker 6: difficult for a small, landlocked economy with significant development challenges

591
00:32:34,440 --> 00:32:39,000
Speaker 6: to find other ways to grow itself out of this situation.

592
00:32:39,120 --> 00:32:41,440
Speaker 6: And I think that's why you know, trade policy, like

593
00:32:41,480 --> 00:32:45,760
Speaker 6: I've go and also just general market access has been

594
00:32:45,800 --> 00:32:48,760
Speaker 6: an important part of the economic growth story of the

595
00:32:48,840 --> 00:32:52,080
Speaker 6: past thirty years, and now countries no longer have that option.

596
00:32:52,400 --> 00:32:54,880
Speaker 6: I mean, even if you look at Asia, for instance,

597
00:32:54,880 --> 00:32:57,400
Speaker 6: and you look at the development story in Asia, a

598
00:32:57,400 --> 00:33:00,000
Speaker 6: lot of it was driven by trade and the ability

599
00:33:00,320 --> 00:33:04,440
Speaker 6: to not only sell product, but trade also brings expertise.

600
00:33:05,160 --> 00:33:08,760
Speaker 6: You know, having to meet those senates, having to be

601
00:33:08,960 --> 00:33:12,959
Speaker 6: export ready does something to the productivity of an economy

602
00:33:13,560 --> 00:33:15,800
Speaker 6: that needs to develop that now, as you know, their

603
00:33:15,880 --> 00:33:17,920
Speaker 6: right is being pulled from under their feet.

604
00:33:19,760 --> 00:33:23,360
Speaker 3: Makaia says that while the trade story has devastated Lasuda's economy,

605
00:33:23,680 --> 00:33:27,920
Speaker 3: the ramifications of President Trump's trade policy go beyond exports

606
00:33:27,960 --> 00:33:28,600
Speaker 3: and imports.

607
00:33:29,840 --> 00:33:34,120
Speaker 6: These are small economies, yet you know, to think about

608
00:33:34,760 --> 00:33:38,360
Speaker 6: the gains that they've been able to make in terms

609
00:33:38,400 --> 00:33:41,520
Speaker 6: of taking people out of poverty. It means it rewires

610
00:33:41,520 --> 00:33:44,840
Speaker 6: the global economy in a different way. It resets their relationships.

611
00:33:44,960 --> 00:33:50,640
Speaker 6: It also has meant barely chains that support American industry.

612
00:33:51,400 --> 00:33:55,280
Speaker 6: So in many instances, you know, you'd have investors that

613
00:33:55,400 --> 00:33:58,959
Speaker 6: come from the United States, not always you would have

614
00:33:59,040 --> 00:34:05,320
Speaker 6: capabilities that are delivered by American institutions. So you know,

615
00:34:05,720 --> 00:34:09,719
Speaker 6: to the extent that you create a market. Creating a

616
00:34:09,760 --> 00:34:11,640
Speaker 6: market also means that the people that you want to

617
00:34:11,680 --> 00:34:14,120
Speaker 6: sell things to should also be able to sell things

618
00:34:14,120 --> 00:34:16,799
Speaker 6: to you and develop their purchasing power. So to the

619
00:34:16,840 --> 00:34:19,840
Speaker 6: extent that you open up your markets, you're developing that

620
00:34:19,920 --> 00:34:22,960
Speaker 6: purchasing power in other parts of the world. It also

621
00:34:23,040 --> 00:34:29,759
Speaker 6: means strengthening relations between countries. We do see generally that

622
00:34:30,000 --> 00:34:33,719
Speaker 6: there's greater trade within the South and that that is

623
00:34:33,760 --> 00:34:37,440
Speaker 6: going to happen, but it doesn't mean that therefore the

624
00:34:37,520 --> 00:34:42,640
Speaker 6: West should, you know, step back, because by losing economic relationships,

625
00:34:42,719 --> 00:34:46,640
Speaker 6: it also, you know, weakens the geopolitical ties that have

626
00:34:46,680 --> 00:34:50,160
Speaker 6: been so important for global stability. So even from that perspective,

627
00:34:50,160 --> 00:34:52,240
Speaker 6: if you want to look at it from a security lens,

628
00:34:52,920 --> 00:34:55,880
Speaker 6: there are benefits to ensure in that countries are able

629
00:34:55,920 --> 00:34:58,920
Speaker 6: to attain some level of economic self sufficiency and are

630
00:34:58,960 --> 00:35:03,120
Speaker 6: not beholden to other influences which might also take them

631
00:35:03,480 --> 00:35:09,840
Speaker 6: in ways that are hostile to interests in countries like America.

632
00:35:12,080 --> 00:35:16,120
Speaker 3: Since taking office, President Trump has rewired the global economy

633
00:35:16,640 --> 00:35:19,719
Speaker 3: for now, countries like Lusutu appear to be losing out.

634
00:35:20,160 --> 00:35:24,160
Speaker 3: But Tobojo Cabelli says it's also given his country an opportunity.

635
00:35:24,840 --> 00:35:27,400
Speaker 3: I asked him what he'd say to Trump, the American President,

636
00:35:27,440 --> 00:35:30,640
Speaker 3: who claimed he'd never heard of Lasutu. If the two ever.

637
00:35:30,480 --> 00:35:33,920
Speaker 14: Met, I would say thank you, President Trump, thank you

638
00:35:33,960 --> 00:35:39,960
Speaker 14: for making Lesuto. Nor for a president of a powerful

639
00:35:40,239 --> 00:35:46,200
Speaker 14: country like us to mention in his congress you know

640
00:35:46,480 --> 00:35:50,239
Speaker 14: is a powerful might get into because then nobody knows

641
00:35:50,239 --> 00:35:52,920
Speaker 14: well esuit is and everybody else now is that I'm

642
00:35:52,960 --> 00:35:56,839
Speaker 14: traveling into Lsutu. Thank you, President Trump, and also the.

643
00:35:56,840 --> 00:35:57,919
Speaker 10: World will now.

644
00:35:58,160 --> 00:36:02,239
Speaker 14: I tend to asked what is less so to good? In?

645
00:36:03,160 --> 00:36:06,319
Speaker 14: Is good in producing higher quality comments to the US,

646
00:36:06,400 --> 00:36:08,960
Speaker 14: my kid? That is why I said thank you President Trump.

647
00:36:09,960 --> 00:36:11,680
Speaker 14: If it was possible for me, I would shake your

648
00:36:11,680 --> 00:36:14,560
Speaker 14: hand and said thank you for making us wake up,

649
00:36:15,840 --> 00:36:17,080
Speaker 14: because that was a wake.

650
00:36:17,000 --> 00:36:17,560
Speaker 10: Up call.

651
00:36:18,960 --> 00:36:23,480
Speaker 3: In the biblical story David beat Goliath. But Tobojo Cabelli says,

652
00:36:23,680 --> 00:36:27,440
Speaker 3: it's not about winning and losing, but about finding common ground.

653
00:36:28,440 --> 00:36:31,680
Speaker 14: President Trump is a business man, and my prime Minister

654
00:36:31,800 --> 00:36:34,400
Speaker 14: is a businessman. What they have to do is just

655
00:36:34,400 --> 00:36:38,120
Speaker 14: to talk business, President Trump. I've got this and that

656
00:36:38,239 --> 00:36:41,200
Speaker 14: and that. I can supploy your good, high quality goods.

657
00:36:41,239 --> 00:36:42,080
Speaker 14: What do you want from me.

658
00:36:42,440 --> 00:36:44,080
Speaker 5: Let's changed the way we.

659
00:36:43,960 --> 00:36:47,759
Speaker 14: Were doing things because the giant has dictated that.

660
00:36:51,280 --> 00:36:54,000
Speaker 2: Coming up, Why some of our biggest and most innovative

661
00:36:54,000 --> 00:36:56,719
Speaker 2: companies won't let us own a piece of them, and

662
00:36:56,840 --> 00:37:12,400
Speaker 2: whether we should be concerned. This is a story about privacy,

663
00:37:12,840 --> 00:37:16,600
Speaker 2: specifically the privacy of keeping a company's ownership and operations

664
00:37:16,640 --> 00:37:19,840
Speaker 2: out of the public domain. It seems like every day

665
00:37:19,920 --> 00:37:23,520
Speaker 2: we hear about another so called unicorn, a private company

666
00:37:23,600 --> 00:37:27,040
Speaker 2: valued at over a billion dollars. It's not just the number,

667
00:37:27,200 --> 00:37:31,200
Speaker 2: it's the size and how long they've maintained their privacy.

668
00:37:32,600 --> 00:37:36,560
Speaker 5: I'm in a rush to a public The public is,

669
00:37:36,840 --> 00:37:37,880
Speaker 5: I guess, a way to.

670
00:37:39,840 --> 00:37:43,200
Speaker 4: Inter potential to make more money, but at the expense

671
00:37:43,280 --> 00:37:46,839
Speaker 4: of a lot of public company overhead and inevitably.

672
00:37:47,920 --> 00:37:49,960
Speaker 5: How much lawsuits, which are very annoying.

673
00:37:50,440 --> 00:37:53,480
Speaker 2: There seems to be broad agreement that more early stage

674
00:37:53,480 --> 00:37:56,160
Speaker 2: companies are in no great hurry to go public.

675
00:37:56,440 --> 00:37:58,520
Speaker 16: It's roughly that it used to take seven years and

676
00:37:58,560 --> 00:38:00,920
Speaker 16: now and takes eleven. That's a big change, and that

677
00:38:00,960 --> 00:38:03,759
Speaker 16: doesn't count off the companies that failed and didn't get.

678
00:38:03,680 --> 00:38:04,240
Speaker 5: To go public.

679
00:38:04,360 --> 00:38:08,480
Speaker 12: There's no doubt that many companies now here are staying

680
00:38:08,719 --> 00:38:11,440
Speaker 12: you know, private, more than ten years, even which used

681
00:38:11,480 --> 00:38:14,480
Speaker 12: to be the typical maximum time new'd stay private.

682
00:38:15,719 --> 00:38:18,600
Speaker 2: This reluctance to rush into the public markets has meant

683
00:38:18,680 --> 00:38:22,120
Speaker 2: we've seen an explosion in the number of so called unicorns,

684
00:38:22,520 --> 00:38:26,160
Speaker 2: private companies reaching valuations of one billion dollars or more.

685
00:38:26,719 --> 00:38:30,160
Speaker 2: According to Pitchbook, we've gone from virtually no unicorns in

686
00:38:30,200 --> 00:38:34,200
Speaker 2: twenty ten to over one thousand, four hundred today, worth

687
00:38:34,320 --> 00:38:39,560
Speaker 2: over five trillion dollars combined. The reasons are straightforward. First

688
00:38:39,600 --> 00:38:42,839
Speaker 2: of all, being a public company can be expensive and

689
00:38:43,160 --> 00:38:44,520
Speaker 2: just a pain in the neck.

690
00:38:45,120 --> 00:38:47,600
Speaker 16: I have several friends who are CEOs of public companies.

691
00:38:47,719 --> 00:38:49,279
Speaker 16: You know, the worst part of their life is the

692
00:38:49,320 --> 00:38:52,239
Speaker 16: week before earnings day or the week after earnings day.

693
00:38:52,840 --> 00:38:57,640
Speaker 16: You know, it's incredibly painful, expensive to have a public company.

694
00:38:58,040 --> 00:39:01,880
Speaker 2: Jonathan Foster is president and CEO of Angelus Wealth Management,

695
00:39:02,200 --> 00:39:05,880
Speaker 2: advising high net worth individuals on their investments.

696
00:39:06,239 --> 00:39:09,160
Speaker 16: There's not a public company alive that doesn't pay at

697
00:39:09,239 --> 00:39:12,320
Speaker 16: least five million and up for being public, and that

698
00:39:12,360 --> 00:39:15,520
Speaker 16: would be from microcap right, So you imagine what the

699
00:39:15,880 --> 00:39:17,160
Speaker 16: large companies are paying.

700
00:39:17,320 --> 00:39:20,680
Speaker 5: And the scrutiny that you have from the public market.

701
00:39:22,760 --> 00:39:25,759
Speaker 2: It's not just the cost and hassle that can discourage

702
00:39:25,760 --> 00:39:29,040
Speaker 2: growth companies from going public. Gordon Phillips is a professor

703
00:39:29,040 --> 00:39:32,120
Speaker 2: at Dartmouth's Tuch School of Business who has studied the

704
00:39:32,200 --> 00:39:34,640
Speaker 2: reasons behind the decision to stay private.

705
00:39:35,000 --> 00:39:37,080
Speaker 12: When you go public, you release a lot of information,

706
00:39:37,600 --> 00:39:40,680
Speaker 12: so there's sometimes proprietary information that you don't want to convey.

707
00:39:40,960 --> 00:39:44,040
Speaker 2: Does that drive the impetus to stay private longer, in

708
00:39:44,080 --> 00:39:47,520
Speaker 2: part because you can't necessarily patent the relationship between what

709
00:39:47,560 --> 00:39:50,320
Speaker 2: you know and what the capital provides.

710
00:39:50,200 --> 00:39:52,680
Speaker 12: Right, I think it does. I think then you're waiting.

711
00:39:52,920 --> 00:39:55,640
Speaker 12: You're waiting, David to go to go public until you've

712
00:39:55,719 --> 00:39:58,560
Speaker 12: established a brand name that's well recognized. You can think

713
00:39:58,600 --> 00:40:01,480
Speaker 12: of Uber and Lyft wanted to wait to go public

714
00:40:01,880 --> 00:40:04,040
Speaker 12: until there was enough consumers signed up.

715
00:40:04,480 --> 00:40:07,920
Speaker 2: And then there's perhaps the biggest reason that large, successful

716
00:40:07,960 --> 00:40:12,000
Speaker 2: tech companies stay private for longer, simply because they can.

717
00:40:12,560 --> 00:40:14,920
Speaker 12: One of the reasons is because of the availability of

718
00:40:14,920 --> 00:40:18,160
Speaker 12: private capital and the venture capital money and growth equity

719
00:40:18,200 --> 00:40:20,759
Speaker 12: money that's available. So there's tons of money, so you

720
00:40:20,760 --> 00:40:23,040
Speaker 12: can get a lot of money from private capital. You

721
00:40:23,120 --> 00:40:25,480
Speaker 12: don't need to go to the public market as quickly.

722
00:40:25,600 --> 00:40:28,960
Speaker 2: But Not all private companies are created equal, and most

723
00:40:28,960 --> 00:40:31,440
Speaker 2: of them are certainly not unicorns.

724
00:40:31,880 --> 00:40:34,439
Speaker 7: They're not your typical private company. I think when most

725
00:40:34,520 --> 00:40:38,880
Speaker 7: people and regulators and others historically have thought of private companies,

726
00:40:39,080 --> 00:40:42,320
Speaker 7: they've historically thought of a family company or a small

727
00:40:42,360 --> 00:40:45,759
Speaker 7: business or something along those lines. The companies that you're

728
00:40:45,760 --> 00:40:48,880
Speaker 7: talking about are nothing like that. Those are real companies

729
00:40:48,880 --> 00:40:50,880
Speaker 7: that are operating on a global scale.

730
00:40:51,360 --> 00:40:55,880
Speaker 2: Sarah Cohan Williamson is the CEO of FCLT Global and

731
00:40:56,040 --> 00:40:59,000
Speaker 2: author of the new book The CEO's Guide to the

732
00:40:59,040 --> 00:40:59,960
Speaker 2: Investment Galaxy.

733
00:41:00,960 --> 00:41:05,200
Speaker 7: What's interesting about private markets is that the ultimate owner

734
00:41:05,640 --> 00:41:08,400
Speaker 7: sets the price. So if you think about in private markets,

735
00:41:08,640 --> 00:41:11,480
Speaker 7: who actually if a company goes private, for example, whoever

736
00:41:11,520 --> 00:41:13,600
Speaker 7: pays for that sort of sets the price, and then

737
00:41:13,640 --> 00:41:16,719
Speaker 7: that's what the price is. In public markets, the marginal

738
00:41:16,840 --> 00:41:19,480
Speaker 7: trader sets the price, so the price changes every day,

739
00:41:19,520 --> 00:41:22,279
Speaker 7: every minute perhaps. And so I think that one of

740
00:41:22,320 --> 00:41:26,399
Speaker 7: the really important differences is how good are the valuations

741
00:41:26,440 --> 00:41:28,600
Speaker 7: on different sides. And so you mentioned some of the

742
00:41:28,600 --> 00:41:31,920
Speaker 7: companies that are valued very highly, and those may be

743
00:41:32,080 --> 00:41:35,080
Speaker 7: very accurate valuations, but a lot of times the difference

744
00:41:35,120 --> 00:41:38,520
Speaker 7: is between a mark and an actual trade.

745
00:41:38,680 --> 00:41:42,480
Speaker 2: Being a public company has its share of costs and disadvantages,

746
00:41:42,960 --> 00:41:46,600
Speaker 2: but staying private has its own drawbacks. Professor Phillips has

747
00:41:46,640 --> 00:41:50,600
Speaker 2: compared some similarly situated companies that have gone public with

748
00:41:50,760 --> 00:41:51,640
Speaker 2: those that have not.

749
00:41:52,320 --> 00:41:55,680
Speaker 12: In a research paper. We looked at companies which went public,

750
00:41:55,719 --> 00:41:57,960
Speaker 12: and then we looked at companies which pulled their ipl

751
00:41:58,040 --> 00:42:00,160
Speaker 12: They filed to go public, and then they decided, hey,

752
00:42:00,160 --> 00:42:03,360
Speaker 12: the price isn't good enough, I'm gonna walk back. So

753
00:42:03,400 --> 00:42:06,080
Speaker 12: we look at those who filed. They both had the incidents,

754
00:42:06,440 --> 00:42:09,920
Speaker 12: and we found those that went they still got substantial benefits.

755
00:42:10,440 --> 00:42:13,960
Speaker 12: The benefits are the liquidity we talked about, but really

756
00:42:14,000 --> 00:42:17,880
Speaker 12: the second benefit also is acquisition currency. When you're public,

757
00:42:18,640 --> 00:42:21,480
Speaker 12: you can actually buy other companies with stock and you

758
00:42:21,520 --> 00:42:24,160
Speaker 12: don't need necessarily cash to make that purchase or debt.

759
00:42:24,320 --> 00:42:27,239
Speaker 12: One example is, you know, Apple has done lots of acquisitions,

760
00:42:27,280 --> 00:42:30,080
Speaker 12: including beats by doctor Dre. They bought that back a

761
00:42:30,160 --> 00:42:33,279
Speaker 12: lot while back, and they paid two billion dollars, but

762
00:42:33,360 --> 00:42:36,640
Speaker 12: it was again they had enough cash and shares that

763
00:42:36,680 --> 00:42:38,839
Speaker 12: they could easily make that purchase even that was two

764
00:42:38,880 --> 00:42:39,600
Speaker 12: billion dollars.

765
00:42:40,520 --> 00:42:44,200
Speaker 2: Having publicly traded stock also allows management to give incentives

766
00:42:44,200 --> 00:42:48,239
Speaker 2: to employees through stock options and similar compensation arrangements.

767
00:42:48,520 --> 00:42:50,640
Speaker 12: A lot of times is the employees want to be

768
00:42:50,640 --> 00:42:53,480
Speaker 12: able to sell. Now, there are some private internal markets

769
00:42:53,480 --> 00:42:56,840
Speaker 12: that have developed to allow employees to cash out before

770
00:42:56,840 --> 00:42:59,680
Speaker 12: they go public. However those markets are not as liquid.

771
00:42:59,719 --> 00:43:01,160
Speaker 12: Maybe price is art is good.

772
00:43:01,760 --> 00:43:04,640
Speaker 2: There are also some benefits in going public that can't

773
00:43:04,680 --> 00:43:05,240
Speaker 2: be measured.

774
00:43:05,800 --> 00:43:09,160
Speaker 7: There is credibility to being a public company, whether that

775
00:43:09,440 --> 00:43:14,000
Speaker 7: is to suppliers or other business partners, or employees or others.

776
00:43:14,440 --> 00:43:17,360
Speaker 7: And then I think you really opens up all of

777
00:43:17,400 --> 00:43:21,319
Speaker 7: these opportunities like issuing more stock to purchase companies. It

778
00:43:21,440 --> 00:43:23,719
Speaker 7: just opens up a whole new world. So there are

779
00:43:23,880 --> 00:43:27,839
Speaker 7: very good reasons for companies to go public and that's

780
00:43:27,880 --> 00:43:29,920
Speaker 7: why many many do still.

781
00:43:30,000 --> 00:43:32,120
Speaker 2: But what does it all mean for the rest of us?

782
00:43:32,440 --> 00:43:34,560
Speaker 2: For those of us not lucky enough to get in

783
00:43:34,640 --> 00:43:37,360
Speaker 2: on the ground floor. First of all, it means that

784
00:43:37,400 --> 00:43:40,799
Speaker 2: there are just fewer public companies for us to invest in.

785
00:43:41,280 --> 00:43:44,480
Speaker 16: Twenty five years ago, there were north of seven thousand

786
00:43:44,520 --> 00:43:47,239
Speaker 16: public companies, maybe eight thousand public companies. Now there are

787
00:43:47,239 --> 00:43:50,200
Speaker 16: only four thousand public companies, So there's been a real

788
00:43:50,280 --> 00:43:52,920
Speaker 16: shrinkage of opportunities to invest in equities.

789
00:43:52,920 --> 00:43:53,840
Speaker 5: In public markets.

790
00:43:54,440 --> 00:43:56,919
Speaker 2: And then there's the prospect that by the time those

791
00:43:56,960 --> 00:44:01,880
Speaker 2: big growth companies go public, they've already reared their explosive growth.

792
00:44:02,440 --> 00:44:04,719
Speaker 12: When Apple went public, it only had like one round

793
00:44:04,719 --> 00:44:07,719
Speaker 12: of venture capital. People went and bought Apple stock. You

794
00:44:07,840 --> 00:44:10,440
Speaker 12: got to participate in. All the upside is the stock

795
00:44:10,560 --> 00:44:13,360
Speaker 12: was rising in the public markets. If you're going public

796
00:44:13,400 --> 00:44:15,680
Speaker 12: and it's already worth you know, one hundred billion dollars

797
00:44:15,680 --> 00:44:17,840
Speaker 12: at the time of the IPO or twenty billion, whatever,

798
00:44:17,840 --> 00:44:21,439
Speaker 12: the number is still a big number already, then you've

799
00:44:21,480 --> 00:44:23,400
Speaker 12: already baked in a lot of the gains, and so

800
00:44:23,440 --> 00:44:26,680
Speaker 12: the gains still get distributed is much broadly to the

801
00:44:27,200 --> 00:44:29,920
Speaker 12: you know, to the population. Retail investors are not getting

802
00:44:29,920 --> 00:44:33,279
Speaker 12: that period of heavy growth at the beginning. Now we're

803
00:44:33,320 --> 00:44:35,640
Speaker 12: seeing you know, open Ai staying private and they are

804
00:44:35,680 --> 00:44:37,399
Speaker 12: going to be pretty big. And then you know, other

805
00:44:37,440 --> 00:44:41,080
Speaker 12: companies like SpaceX, you know, again staying private. You can't

806
00:44:41,160 --> 00:44:44,880
Speaker 12: yet participate until they go public. As a retail investor.

807
00:44:45,120 --> 00:44:49,040
Speaker 2: The pendulum has definitely swung toward companies maintaining their privacy

808
00:44:49,080 --> 00:44:53,560
Speaker 2: for longer or even indefinitely. But as so often happens

809
00:44:53,560 --> 00:44:56,240
Speaker 2: in the markets, what looks like a long term trend

810
00:44:56,400 --> 00:44:59,000
Speaker 2: may turn out to be more of a cycle, in

811
00:44:59,080 --> 00:45:02,240
Speaker 2: no small part because coming into the sunlight over time

812
00:45:02,560 --> 00:45:05,799
Speaker 2: has given investors some pretty favorable returns.

813
00:45:06,080 --> 00:45:09,680
Speaker 16: I asked chat GPT a great question a few weeks ago,

814
00:45:09,760 --> 00:45:12,400
Speaker 16: what is the relative return between putting a million dollars

815
00:45:12,400 --> 00:45:17,680
Speaker 16: into residential real estate on an unleveraged basis in zip code.

816
00:45:17,480 --> 00:45:18,960
Speaker 5: Nine zero zero four nine.

817
00:45:19,000 --> 00:45:22,120
Speaker 16: So that's you know, in Los Angeles, which is where

818
00:45:22,120 --> 00:45:23,360
Speaker 16: I happened to have a home, and that's been one

819
00:45:23,400 --> 00:45:26,759
Speaker 16: of the great residential markets in the America. Said doing

820
00:45:26,800 --> 00:45:29,640
Speaker 16: that versus one million dollars in the stock market from

821
00:45:29,760 --> 00:45:33,279
Speaker 16: nineteen sixty to today, And it took it like a

822
00:45:33,400 --> 00:45:35,319
Speaker 16: minute to figure it out, and it said, if you

823
00:45:35,360 --> 00:45:38,000
Speaker 16: put one million dollars in the residential real estate unlevered

824
00:45:38,000 --> 00:45:40,240
Speaker 16: in that zip code in nineteen sixty, it's worth seven

825
00:45:40,280 --> 00:45:43,000
Speaker 16: point seven million today, and in the S and P

826
00:45:43,080 --> 00:45:46,000
Speaker 16: five hundred with dividends reinvested is worth one point one

827
00:45:46,239 --> 00:45:51,120
Speaker 16: billion dollars. The public markets are great over a long

828
00:45:51,239 --> 00:45:55,399
Speaker 16: term basis, so I think people should not feel they're

829
00:45:55,440 --> 00:45:56,200
Speaker 16: being left out.

830
00:45:56,800 --> 00:45:59,680
Speaker 2: The biggest consideration of all might be the value of

831
00:45:59,680 --> 00:46:02,520
Speaker 2: public companies to the depth of the capital markets.

832
00:46:02,800 --> 00:46:05,000
Speaker 7: Well, if you look at economic history, you will see

833
00:46:05,040 --> 00:46:07,840
Speaker 7: that the most vibrant economies over time have had a

834
00:46:07,920 --> 00:46:11,720
Speaker 7: vibrant public market. So I think there is public value

835
00:46:11,800 --> 00:46:14,640
Speaker 7: in vibrant public markets. And of course that's where we

836
00:46:14,719 --> 00:46:17,319
Speaker 7: come back to this level playing field argument, which is

837
00:46:17,680 --> 00:46:20,040
Speaker 7: it used to be ten or twenty years ago that

838
00:46:20,600 --> 00:46:24,120
Speaker 7: a regular retail individual, just you know, a regular citizen

839
00:46:24,320 --> 00:46:26,840
Speaker 7: would be very unlikely to have much exposure to the

840
00:46:26,880 --> 00:46:30,440
Speaker 7: private markets. But today, because there's such a large portion

841
00:46:30,640 --> 00:46:34,839
Speaker 7: of pension plans, define benefit plans in particular, and other

842
00:46:34,920 --> 00:46:38,520
Speaker 7: investment vehicles, individuals do have a lot of exposure to

843
00:46:39,200 --> 00:46:42,600
Speaker 7: private markets if they are are participant in a pension plan.

844
00:46:43,040 --> 00:46:46,400
Speaker 2: With all the pluses and minuses of going public or

845
00:46:46,440 --> 00:46:49,719
Speaker 2: staying private, in the end, it comes down to more

846
00:46:49,760 --> 00:46:53,839
Speaker 2: than just capital structure and protecting your trade secrets and

847
00:46:53,920 --> 00:46:55,239
Speaker 2: incentive compensation.

848
00:46:55,840 --> 00:46:59,080
Speaker 7: So I think as a business leader, the question is

849
00:46:59,239 --> 00:47:01,880
Speaker 7: really what kind of company do you want to be

850
00:47:02,760 --> 00:47:04,879
Speaker 7: as much as what kind of capital do you want

851
00:47:04,920 --> 00:47:08,440
Speaker 7: to get? The best company leaders think about their investor

852
00:47:08,480 --> 00:47:11,920
Speaker 7: strategy just like they think about a customer strategy. So

853
00:47:11,960 --> 00:47:14,640
Speaker 7: if you ask a CEO what their customer strategy is

854
00:47:14,960 --> 00:47:17,919
Speaker 7: you can almost always get a very clear answer about

855
00:47:17,960 --> 00:47:21,800
Speaker 7: what kind of customer they're targeting, in what location why.

856
00:47:22,000 --> 00:47:24,359
Speaker 7: If you ask a lot of leaders what is their

857
00:47:24,480 --> 00:47:28,040
Speaker 7: investor strategy, you don't get nearly as clear an answer,

858
00:47:28,800 --> 00:47:32,000
Speaker 7: And it's really important to have that kind of clarity

859
00:47:32,400 --> 00:47:37,080
Speaker 7: because those investors will end up dictating or at least

860
00:47:37,680 --> 00:47:40,440
Speaker 7: nudging the company in certain directions, whether it be a

861
00:47:40,440 --> 00:47:43,960
Speaker 7: shorter term or longer term. So have an investor strategy

862
00:47:44,040 --> 00:47:46,240
Speaker 7: that just is robust as a customer.

863
00:47:45,880 --> 00:47:49,879
Speaker 2: Strategy that does it for us. Here at Wall Street Week,

864
00:47:50,080 --> 00:47:53,200
Speaker 2: I'm David Weston. See you next week for more stories

865
00:47:53,280 --> 00:48:03,760
Speaker 2: of capitalism.