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Speaker 1: This is Wall Street Week. I'm David Weston bringing you

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Speaker 1: stories of capitalism. President Trump managed to get a slew

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Speaker 1: of tariffs in place, either unilaterally or by agreement before

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Speaker 1: his August one deadline, but at least so far, we

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Speaker 1: haven't seen them do much to the economy. We explore

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Speaker 1: what CEOs of major companies are doing to make sure

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Speaker 1: their business stays on track despite the tariffs, and we

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Speaker 1: get a somewhat different perspective from the head of a

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Speaker 1: modest sized textile company in Colorado that counts heavily on

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Speaker 1: imports and exports. Plus, not everyone is welcoming all those

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Speaker 1: data centers with open arms. We travel to Warranton, Virginia,

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Speaker 1: a town where some are concerned about what they will

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Speaker 1: mean for their community. But we start with another comfortable

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Speaker 1: truths linked to AI, the possibility that this time it

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Speaker 1: really is different. Different in what technological change is doing

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Speaker 1: to recent college graduates trying to start their careers. There

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Speaker 1: is no question that AI is here to stay. Just

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Speaker 1: last month, we reported that Anthropic, a company that didn't

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Speaker 1: exist six years ago, could be worth more than Nike

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Speaker 1: or Starbucks.

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Speaker 2: Anthropic one hundred and seventy billion, XAI and talks for

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Speaker 2: two hundred.

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Speaker 3: Billion, then I think there's nowhere to go about up.

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Speaker 1: And there's no question that at least some employees stand

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Speaker 1: to make a great deal of money for their ride

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Speaker 1: on the AI train. Apple's top executive in charge of

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Speaker 1: AI models is leaving for meta platforms.

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Speaker 2: I'm told this pay package is probably somewhere north of

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Speaker 2: two hundred million dollars.

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Speaker 1: We have never seen anything like this before, and there's

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Speaker 1: a lot we don't know about what it will do

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Speaker 1: for business and the economy, but we do know that

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Speaker 1: leaders of the world's largest and most powerful firms expect

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Speaker 1: a title wave, are preparing for it, and already some

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Speaker 1: people are feeling the effects on the ground right now,

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Speaker 1: people not at the very top of the corporate ladder,

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Speaker 1: but those trying to get a step on the bottom rung.

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Speaker 1: This year, Jacob Ayub and Tiffany Lee are joining millions

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Speaker 1: of their fellow college graduates trying to get their career started.

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Speaker 4: This is just one of the case studies that I've done,

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Speaker 4: but it's pretty representative of what you can expect if

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Speaker 4: you're applying for any full time role at a private

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Speaker 4: equity company. Luckily, Boston College does a really good job

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Speaker 4: of setting that financial background. We've done in class DCFS

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Speaker 4: and Class LBOs Manchester Milk.

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Speaker 3: Thank you, thank you agree today me too. I have

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Speaker 3: a few case studies on my portfolio, just showcasing the

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Speaker 3: work that I've done at school for the companies and

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Speaker 3: nonprofits that I've worked for. The last experience I have

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Speaker 3: is another contract role with a startup that I did

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Speaker 3: through a club at Cornell as well.

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Speaker 1: They have studied different subjects at different schools, Tiffany information

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Speaker 1: science and psychology at Cornell, Jacob economics and finance at

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Speaker 1: Boston College, but the two graduates have a lot in common.

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Speaker 1: They both got good grades, networked widely, and lended prestigious internships,

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Speaker 1: Tiffany at Apple and Jacob at a boutique private equity firm,

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Speaker 1: and for both it has yet to add up to

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Speaker 1: a job.

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Speaker 5: Since graduating college.

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Speaker 4: I would say I've applied to around two hundred jobs.

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Speaker 3: I would say I've applied to about one hundred and

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Speaker 3: fifty maybe more now.

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Speaker 4: Some of the interview processes consist of constructing models and valuations.

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Speaker 4: Some of them are pitch decks.

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Speaker 3: I made it to the final round for one like

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Speaker 3: big tech company didn't know getting it. I passed round

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Speaker 3: two for another one, and the other ones I either

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Speaker 3: haven't heard back or just like ghosted.

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Speaker 1: It's a story that's becoming more common, and it's being

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Speaker 1: reflected in an unusual new trend in the economic data.

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Speaker 1: As far back as the numbers go, the unemployment rate

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Speaker 1: for recent college graduates has been below the rate for

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Speaker 1: all US workers. But over the past few years, unemployment

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Speaker 1: among recent grads climbed above the rate for the broader population.

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Speaker 6: This is essentially the first time on a ongoing basis

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Speaker 6: of this has occurred in the forty five plus years

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Speaker 6: that we have data going back.

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Speaker 1: To Matthew Martin is a senior economist at Oxford Economics.

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Speaker 6: So really this is pointing towards a lot of people

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Speaker 6: who have that higher educational attainment are actually worse off

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Speaker 6: than many of their peers who have lower educational attainment.

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Speaker 6: That's an interesting dynamic to have in a market where

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Speaker 6: you're told you go to college, you work hard, you'll

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Speaker 6: have a job after graduation, and unfortunately for many at

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Speaker 6: the moment, that's just not happening.

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Speaker 1: Recent college grads may be having a harder time right

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Speaker 1: now finding their first job but it depends in part

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Speaker 1: on what kind of job they're seeking. Entry level jobs

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Speaker 1: in the areas Tiffany and Jacob want, tech and finance, respectively,

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Speaker 1: turn out to be particularly hard to find.

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Speaker 4: A lot of my peers who were in years advance

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Speaker 4: for me and trying to give me insights on how

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Speaker 4: the process worked, all either had had internships or were

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Speaker 4: in the process of getting them, or had full time offers.

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Speaker 4: And these are sometimes people that are just juniors who

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Speaker 4: have a full time offer at a bank or different firm.

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Speaker 4: And I think with my graduation class there's been a

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Speaker 4: pretty drastic change. I'd say about fifty percent of my

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Speaker 4: graduating class is unemployed.

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Speaker 3: I see a lot of job descriptions that are asking

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Speaker 3: for two years of experience or like three years of experience.

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Speaker 3: And in design specifically, it's been pretty hard. I think

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Speaker 3: it's just a very saturated field and there's a lot

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Speaker 3: of talent out there.

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Speaker 6: Tech is one of those that has been really slowed

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Speaker 6: down from a lot of that comes down to the

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Speaker 6: hiring boom that they did post pandemic and then looking

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Speaker 6: to the right size. You also have other mathematical occupations

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Speaker 6: and finance, legal. Some of these fields that you would

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Speaker 6: again have been in high demands over the last couple

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Speaker 6: of years that just aren't seeing as much.

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Speaker 1: Economists say the reasons can be difficult to trace. Some

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Speaker 1: of it is likely the overall labor market cooling after

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Speaker 1: the post pandemic hiring surge, But now there's a new

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Speaker 1: cause coming on the scene. Artificial intelligence AI will leave

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Speaker 1: a lot of white color people behind. All junior level

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Speaker 1: jobs in any industry are at high risk.

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Speaker 3: This is like a tsunami hitting the labor market.

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Speaker 1: For months, economists and executives have been raising concerns about

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Speaker 1: AI or publicly embracing it, sometimes both. Amazon CEO Andy

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Speaker 1: Jesse wrote a note to employees in June saying that

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Speaker 1: AI would change the way work is done at the

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Speaker 1: tech giant and that jobs would be cut as AI

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Speaker 1: increased efficiency, and Thropic CEO Dario Amoday told one news

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Speaker 1: outlet that AI could potentially wipe out half of all

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Speaker 1: entry level white collar jobs and send unemployment rates surging.

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Speaker 1: We may not be seeing anything quite that bad yet,

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Speaker 1: but AI's impact does seem to be showing up in

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Speaker 1: unemployment figures for recent college graduates.

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Speaker 6: I think there is definitely a link between the two.

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Speaker 6: It's hard to very parse through and just say, you know,

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Speaker 6: X amount of this issue is because of AI, X

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Speaker 6: amount is because of just a slower demand overall. But

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Speaker 6: when we look at the numbers, there definitely could be

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Speaker 6: a connection there. So one of the things we're seeing

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Speaker 6: is that a the tech sector is one of the

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Speaker 6: industries with the highest adoption rates AI. They have around

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Speaker 6: twenty five percent adoption rates for all businesses using it

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Speaker 6: in their day to day operations, whereas the national average

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Speaker 6: is around five percent. So clearly this sector is just

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Speaker 6: using it more in general. And then when we look

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Speaker 6: into the specifically the recent college graduate data, we see

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Speaker 6: that in the last years employment for recent college grads

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Speaker 6: twenty two to twenty seven, they've seen a decrease in

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Speaker 6: employment opportunities overall, whereas if you look in the same

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Speaker 6: fields for those twenty eight onwards, they've actually seen slightly

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Speaker 6: flat to maybe a little bit up overall.

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Speaker 1: A similar story appears in the latest company hiring data.

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Speaker 1: An analysis by Indeed conducted for Wall Street Week shows

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Speaker 1: junior level job postings have tumbled over the last two

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Speaker 1: years and are now twenty one percent below their pre

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Speaker 1: pandemic level, even as senior level openings are up. Although

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Speaker 1: tech maybe one area where AI is particularly affecting entry

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Speaker 1: level positions, it's not the only one. As Martin says,

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Speaker 1: they see vulnerability in finance jobs like the one that

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Speaker 1: Jacob is seeking. To learn more, we sat down with

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Speaker 1: Blair Ephron, co founder of center View Partners, one of

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Speaker 1: the world's most prestigious investment banks.

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Speaker 5: We see an.

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Speaker 1: Awful lot of money being invested in AI. Are we

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Speaker 1: far enough into the process now to know whether it's

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Speaker 1: going to work? Now? Obviously there are different ways it

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Speaker 1: could work high, medium, low, But do we know it's

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Speaker 1: fundamently going to work that There's no chance this.

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Speaker 7: Is a bubble, so it's not a bubble. I am

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Speaker 7: always a believer in progress. AI is progress over the

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Speaker 7: long haul. It'll be good in the short term.

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Speaker 8: I have big concerns.

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Speaker 7: I believe the impact on employment is going to be

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Speaker 7: dramatic sooner than we think now.

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Speaker 8: With that set, AI for us as an example, is

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Speaker 8: a positive.

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Speaker 7: We're less focused on productivity, more focused on freeing up

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Speaker 7: someone's time so that they can use that time more productively.

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Speaker 7: But given all the positive, we have to absolutely look

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Speaker 7: after the watchout sign.

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Speaker 8: What is going to go on with employment.

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Speaker 1: One outcome could be a workforce that looks very different

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Speaker 1: than it does today, fewer jobs in computer science, finance,

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Speaker 1: or law, and more jobs that benefit from interpersonal interaction.

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Speaker 1: Matthew Martin says these trends are already showing up in

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Speaker 1: the data, whether or not they can be traced to AI,

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Speaker 1: and they are affecting men and women differently.

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Speaker 6: While the recent college graduate unemployment has raised overall, it's

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Speaker 6: really been concentrated in males specifically. A lot of that

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Speaker 6: comes down to what industries are they looking to work in.

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Speaker 6: If we do a quick breakout of the industries of

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Speaker 6: recent college graduates, females tend to move towards healthcare services

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Speaker 6: and educational services or demands quite high. I think between

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Speaker 6: those two they account for over forty percent of recent

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Speaker 6: college graduates for females, whereas males specifically in healthcare services

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Speaker 6: only counts around five percent. So it's an imbalance of

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Speaker 6: what positions are these males versus females going into. Males

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Speaker 6: tend to skew towards these computer science professions, whether that's

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Speaker 6: programming or the tech sector just in general. That's closer

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Speaker 6: to twenty twenty five percent for them. So you just

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Speaker 6: have this imbalance of males tending to want to go

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Speaker 6: to these industries where demand is really low at the moment,

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Speaker 6: whereas females are entering industries where demand remains quite high.

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Speaker 1: Although a picture of AI's economic impact may be just

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Speaker 1: starting to come into focus, we can be sure that

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Speaker 1: the best and the worst are yet to come. Some

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Speaker 1: advice from the C suite to help the next generation prepare,

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Speaker 1: stay flexible, and develop the skills that remain so far

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Speaker 1: uniquely human.

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Speaker 9: Broad based thinking, deep thinking.

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Speaker 7: And there are just so many different fields and so

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Speaker 7: many different kinds of enterprises, big and small, that will

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Speaker 7: help you do that wide in the aperture of what

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Speaker 7: you think might be a fit, taking a lot of

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Speaker 7: writing courses, a lot of humanities, learning how to think, critically,

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Speaker 7: learning the art of learning, curiosity, thinking about judgment, thinking

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Speaker 7: about coming out with an output and answer based on

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Speaker 7: facts that is contrary to what a model may show you.

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Speaker 7: This is what we need, because this is what I

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Speaker 7: think will never got a style. Judgment's not going out

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Speaker 7: of style.

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Speaker 1: The benefits of AI in the long run may well

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Speaker 1: dwarf the problems. But when you're in the middle of

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Speaker 1: that first job search. The long run may seem very

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Speaker 1: far off.

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Speaker 4: You put in a lot of work starting from high school.

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Speaker 4: I get the right SAT score, go to the right college,

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Speaker 4: get internships, get experience. You come out with kind of

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Speaker 4: an uncertainty.

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Speaker 3: It kind of makes me want to consider a master's

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Speaker 3: but I know that it's really expensive, and I just

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Speaker 3: don't know if it's worth all that money just to

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Speaker 3: get an entry level job.

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Speaker 4: It's more so frustration towards the system and overall process,

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Speaker 4: but you also start questioning your underlying abilities.

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Speaker 1: Jacob and Tiffany, it seems did everything right, and it's

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Speaker 1: not quite that they're falling behind, but that the entire

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Speaker 1: world is shifting under their feet. The rules have suddenly changed,

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Speaker 1: and what are those new rules for all of us?

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Speaker 1: That still remains to be seen. Coming up, we hear

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Speaker 1: more from Blair Ephron, this time not on what AI

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Speaker 1: means for corporate leaders, but on what those leaders are

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Speaker 1: doing to make sure that Trump tariffs don't disrupt their businesses.

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Speaker 1: This is a story about adapting. Charles Darwin taught us

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Speaker 1: that it is not the smartest or strongest that survive,

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Speaker 1: but those who are best able to adapt to the

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Speaker 1: changing environment. And when it comes to CEOs of major companies,

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Speaker 1: there's no question the President Trump's approach has changed their environment.

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Speaker 1: Blair Ephron, co founder of Centerview Partners, has watched them adapt.

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Speaker 1: President Trump came to office saying I'm going to change

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Speaker 1: the economy in fundamental ways. Has it changed the lives

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Speaker 1: of American.

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Speaker 7: CEOs much less than you think. And the fact of the

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Speaker 7: matter is they're growing well under Biden. They're going well.

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Speaker 7: Now what is that about for the most part the

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Speaker 7: US economy? In my world, the big company CEOs, big

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Speaker 7: company senior executives, they've learned to one operate quite apart

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Speaker 7: from policy in Washington, and two they have been operating

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Speaker 7: a period of volatility for so many years now they've

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Speaker 7: just gotten much better about how to remove uncertainty in

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Speaker 7: their own businesses. However, CEOs certainly talk about long term

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Speaker 7: impacts of policy, and what I hear about most more

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Speaker 7: than anything, is our federal debt situation, which is clearly

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Speaker 7: dramatic and clearly something that at some point will create

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Speaker 7: a problem.

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Speaker 1: CEOs may have the tools to adapt to short term

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Speaker 1: changes in trade policy, but they may not have the

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Speaker 1: same ability to do anything about the US fiscal imbalance

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Speaker 1: over the long term.

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Speaker 7: Twenty ten, Simpson Bowls put together talk about the debt.

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Speaker 7: We had sixteen trillion dollars of debt. Thet we're heading

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Speaker 7: towards forty three times what we had. Then trillion dollars

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Speaker 7: of interest expense being spent by the government. That's almost

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Speaker 7: twenty percent of what they do, more than they do

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Speaker 7: on defense. That is something that CEO's are attuned to.

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Speaker 7: They know that it wasn't this crowding out effect.

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Speaker 8: You could have even more growth. They would benefit.

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Speaker 1: President Trump's goal in imposing substantial tariffs on imports is

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Speaker 1: to move manufacturing back on shore, and so far EFRON

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Speaker 1: sees them as having mixed results.

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Speaker 8: It goes two ways.

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Speaker 7: I think you have to start with what is the

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Speaker 7: most secure way to help your supply chain, and that

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Speaker 7: certainly can be on shore. It certainly can be to

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Speaker 7: more friendly markets in Southeast Asia, can certainly be to Mexico.

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Speaker 7: It's been going both ways. I know of a company

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Speaker 7: that services Canada with its plants in Michigan. It's thinking

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Speaker 7: about putting capital in Canada because of what's going on,

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Speaker 7: so they can produce locally, so it actually goes both ways.

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Speaker 7: And the kind of manufacturing we're talking about, for the

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Speaker 7: most part, is a very advanced manufacturing, so it's not

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Speaker 7: labor intensive, it's not people in a plant, it's the equipment.

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Speaker 7: So I think that on balance, it's hard to talk

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Speaker 7: to any CEO who thinks that tariffs are a good

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Speaker 7: thing and that high tariffs more't heavy dramatic negative impact.

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Speaker 1: There's a good deal to talk about uncertainty, and the

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Speaker 1: uncertainty over everything from tariffs to who's going to be

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Speaker 1: the FED share the interest rates. Now, does that really

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Speaker 1: affect the real economy?

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Speaker 7: The answer is clearly not, okay, And CEOs have gotten

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Speaker 7: so good about blocking all noise, all noise from all

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Speaker 7: political quarters. They focus on facts, substance, real policy. They

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Speaker 7: know that something said today by any elected official doesn't

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Speaker 7: mean much unless there's consisting to it and staying with it,

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Speaker 7: and they just block out the noise.

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Speaker 8: That CEOs for the most part are really.

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Speaker 7: Experienced, really talented, and have been through the volatility wars.

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Speaker 7: But the reason I have been, and we've talked about it,

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Speaker 7: more optimistic about the economy is because of that, and

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Speaker 7: I believe.

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Speaker 9: I can make a case for dark clouds well down

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Speaker 9: the road.

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Speaker 7: But barring some unforced error, which certainly we are prone

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Speaker 7: to make, I think the economy will continue to show

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Speaker 7: tailwind and resilience.

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Speaker 1: That tailwind may look a bit different in New York City,

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Speaker 1: where New York State Assemblyman Zorian Mamdani is in a

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Speaker 1: strong position to become its next mayor and is running

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Speaker 1: on policies not generally seen as pro business.

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Speaker 7: So let's first talk about New York trillion dollar economy,

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Speaker 7: sixty three million visitors, ten percent of every student graduating

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Speaker 7: college comes to the city.

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Speaker 8: New York will be fine.

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Speaker 7: That said, the mayor, whoever that person is, needs to

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Speaker 7: be mayor for eight and a half million people in

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Speaker 7: every borough. It has to be a mayor who appreciates

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Speaker 7: what it means to attract business formation, what attracts capital formation,

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Speaker 7: what it means to keep people in the city and thriving.

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Speaker 7: For example, Zoramndani, he's going to have to make the

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Speaker 7: choice whether he must be mayor.

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Speaker 9: Or whether he must be an effective mayor.

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Speaker 7: And I believe to be an effective mayor, a talented

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Speaker 7: political official, and he's talented will have to be attuned

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Speaker 7: to that a lot of us met with him in

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Speaker 7: the business community the other.

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Speaker 8: Day, and clearly one it was worthwhile meeting.

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Speaker 7: He listened curatous for him to come to this group.

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Speaker 7: The questions were pointed, and my goal is that the

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Speaker 7: more he hears from the business community, and now the

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Speaker 7: business community helps drive the engine of New York along

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Speaker 7: with many other communities here, the more.

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Speaker 9: If he is the mayor, will appreciate that he needs

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Speaker 9: to be the mayor for everyone.

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Speaker 1: Having heard from him directly, do you have a sense

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Speaker 1: that he knows what he doesn't know? Because that may

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Speaker 1: be one of the most important things in life. To

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Speaker 1: really say there are things I don't know I need

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Speaker 1: to learn.

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Speaker 9: I would say I don't know. However, I'm going to

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Speaker 9: make a judgment.

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Speaker 7: He has said publicly, publicly informs where I've been just

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Speaker 7: that that he wants to get the best people in

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Speaker 7: every position, people who know more about that ptic area

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Speaker 7: than he does. He strikes me as someone who is

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Speaker 7: absolutely willing to learn, and I don't think you can

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Speaker 7: do as well as he's done as a candidate without

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Speaker 7: that ability. Whoever the mayor is, I hope, really appreciates

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Speaker 7: that the skill set of a mayor, as opposed to

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Speaker 7: somebody in Congress, is less about policy and more about

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Speaker 7: making sure day to day the city is affordable and livable.

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Speaker 1: It's hard to be against being affordable and livable. But

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Speaker 1: as far as I can tell, an essential work of

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Speaker 1: governance is unintended consequences. For example, we can say we're

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Speaker 1: going to freeze rents, which sounds good for people who

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Speaker 1: are renting, but it doesn't necessarily give incentive for building

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Speaker 1: a lot more units. We will bring prices down. Is

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Speaker 1: he learning? Can he learn those sort of understanded consequences that,

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Speaker 1: in fact, the best way to make things more affordable

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Speaker 1: may not be the most obvious way.

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Speaker 7: It's let me be very clear, I don't know yet

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Speaker 7: or does anyone what he's learning, But make no mistake,

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Speaker 7: rent control will reduce housing stock. Putting at grocery store

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Speaker 7: in each borough that's five relative to the twenty five

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Speaker 7: hundred grocery stores we have.

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Speaker 8: In the five boroughs doesn't do much.

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Speaker 7: Let's be clear, anyone here is already paying a lot

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Speaker 7: of tax. It's a privilege to live in New York.

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Speaker 7: There is a point where, and we sit in studies,

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Speaker 7: it has an impact if it gets out of line

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Speaker 7: with other states. So the point is we all need

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Speaker 7: to focus on a probability. I think he is spot on,

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Speaker 7: But to your point, there are many ways to attack it.

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Speaker 7: I think we need to be much more focused as

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Speaker 7: a city to do just that.

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Speaker 1: You make a powerful case for why New York will

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Speaker 1: be just fine in terms of the economy overall. But

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Speaker 1: do we need in New York City to be more

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Speaker 1: concerned with competing with other centers around the country, around

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Speaker 1: the world, but particularly around the country than we have

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Speaker 1: it in the past. We've heard a lot to talk

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Speaker 1: about moving to Dallas, about moving to Miami. Is that

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Speaker 1: a real issue for New York City?

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Speaker 9: Is it a real issue?

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Speaker 8: Absolutely? Have you risen to the challenge? You bet?

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Speaker 7: New York is longer today than it was four years ago.

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Speaker 7: We have more people coming in than not. We have

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Speaker 7: the second biggest tech hub in New York City. We

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Speaker 7: have more drugs and healthcare coming out of here than

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Speaker 7: any other city.

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Speaker 8: But for Boston, we're doing fine.

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Speaker 1: Where is the Democratic Party right now and what do

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Speaker 1: you see as a forward path?

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Speaker 8: Great question.

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Speaker 7: I think the Democratic Party is a work in progress,

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Speaker 7: and the next test.

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Speaker 9: Will obviously be in a few quarters November twenty six.

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Speaker 7: What I can tell you is as a party, Democrats

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Speaker 7: ought to have the base case that the economy will

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Speaker 7: have a tailwind. I ought to have his base case. Therefore,

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Speaker 7: it has to be much more about what the party's for,

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Speaker 7: not for the other guys for. And when I think

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Speaker 7: about that, the party has to do a much better

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Speaker 7: job diagnosing what the issues are, coming up with solutions

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Speaker 7: that meet the needs of all Americans. I don't think

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Speaker 7: we've done a very good job focusing on affordability in

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Speaker 7: a very practical way, understanding that policy should be driven

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Speaker 7: from people up, not from thinkers down, and has to

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Speaker 7: clearly be much more unified about how that's going to happen.

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Speaker 8: I think that has been an issue for us. I

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Speaker 8: do find.

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Speaker 7: Parties in general are pretty nimble about course correcting, whether

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Speaker 7: a Republican or Democrat. I assume by the time midterms

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Speaker 7: come around, Democrats, well, I figured out how Court's correct.

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Speaker 7: But it better be not just an agenda based on

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Speaker 7: fairness as opposed to fairness and growth.

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Speaker 1: Who will help guide the Democrat Party away from a

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Speaker 1: very obvious alternative, which is to go to the other extreme.

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Speaker 1: I mean, some people think we're seeing that right now.

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Speaker 1: With you could name names aoc or Bernie Sanders or

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Speaker 1: even Mamdani. There's a natural reaction to say, Okay, we've

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Speaker 1: got to compensate. If we've got right wing populism over here,

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Speaker 1: let's go left from podcism. How will the Democratic Party

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Speaker 1: avoid that? If in fact it should so.

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Speaker 7: I'm quite confident that the Democratic Party has enough voices

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Speaker 7: across the spectrum that by the time.

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Speaker 8: We get to.

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Speaker 7: The election, we will have a strong group of candidates

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Speaker 7: will reflect the thinking of most Americans, which is less

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Speaker 7: about political ends of the spectrum more about just getting

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Speaker 7: tomorrow put on the table, getting a good job, raising

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Speaker 7: your family the right way. And I believe that the

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Speaker 7: party always should focus on all ends of the spectrum,

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Speaker 7: but I do believe it will end up unifying on

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Speaker 7: what matters most, solving people's issues.

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Speaker 1: Next, we turn from the c suites of some of

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Speaker 1: America's largest companies to one specific, modest sized company that

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Speaker 1: depends on imports and exports for its textile business. We

444
00:23:47,920 --> 00:23:50,760
Speaker 1: go to Bend, Oregon and hear from Jeff Bowman, CEO

445
00:23:50,920 --> 00:24:07,720
Speaker 1: of Cocona Labs. This is a story about friendly fire.

446
00:24:08,320 --> 00:24:11,080
Speaker 1: President Trump has embraced tariffs as a way to protect

447
00:24:11,080 --> 00:24:14,199
Speaker 1: domestic US businesses from unfair foreign competition.

448
00:24:15,480 --> 00:24:17,240
Speaker 10: I might go up with that tariff in the not

449
00:24:17,280 --> 00:24:18,320
Speaker 10: too distant future.

450
00:24:18,359 --> 00:24:20,080
Speaker 5: The higher you go, the more likely it is they

451
00:24:20,119 --> 00:24:21,080
Speaker 5: build a plant here.

452
00:24:21,880 --> 00:24:24,000
Speaker 1: Last week, we've brought you the story of a music

453
00:24:24,040 --> 00:24:26,919
Speaker 1: accessories company that is finding new ways of getting the

454
00:24:26,920 --> 00:24:31,080
Speaker 1: supplies it needs and maintaining its export business despite those tariffs.

455
00:24:31,480 --> 00:24:35,400
Speaker 1: But for some industries that's easier said than done.

456
00:24:37,040 --> 00:24:42,119
Speaker 2: Already, it's impacted our business because it's reduced the amount

457
00:24:42,200 --> 00:24:43,920
Speaker 2: of volume of business that's out there.

458
00:24:44,280 --> 00:24:47,600
Speaker 1: Jeff Bowman is the CEO of Cocona Labs, which makes

459
00:24:47,640 --> 00:24:49,360
Speaker 1: a compound used in fabric.

460
00:24:49,880 --> 00:24:53,160
Speaker 2: We have a technology called thirty seven point five technology.

461
00:24:53,560 --> 00:24:56,480
Speaker 2: The purpose of it is to remove humidity from the

462
00:24:56,520 --> 00:24:59,600
Speaker 2: microclimate next to your skin. That helps you stay more

463
00:24:59,640 --> 00:25:04,480
Speaker 2: comfortab in both warm and cold weather conditions, and it

464
00:25:04,560 --> 00:25:07,960
Speaker 2: helps you operate at a peak performance. If you're an athlete,

465
00:25:08,160 --> 00:25:11,399
Speaker 2: you can operate in peak performance longer. And what we

466
00:25:11,480 --> 00:25:14,440
Speaker 2: do is we mine a mineral in the United States.

467
00:25:14,680 --> 00:25:18,640
Speaker 2: Essentially it's a volcanic sand. We convert that into what's

468
00:25:18,680 --> 00:25:22,080
Speaker 2: called master batch, which is a concentration of that mineral

469
00:25:22,359 --> 00:25:26,639
Speaker 2: that's then put into the spinning operations for things like

470
00:25:26,800 --> 00:25:32,920
Speaker 2: nylon and polypropylene and polyester. They make filaments and yarns

471
00:25:32,920 --> 00:25:36,520
Speaker 2: out of that. Those yarns and filaments are then made

472
00:25:36,520 --> 00:25:42,240
Speaker 2: into fabrics. Those fabrics are made into clothing or bedding

473
00:25:42,600 --> 00:25:47,000
Speaker 2: or footwear. And we make money by selling the master batch,

474
00:25:47,000 --> 00:25:50,560
Speaker 2: which we make in the United States. Primarily we export

475
00:25:50,640 --> 00:25:54,360
Speaker 2: that because most yarn spinners are outside of the United States.

476
00:25:55,080 --> 00:25:58,399
Speaker 2: And then we also license the technology to brands that

477
00:25:58,560 --> 00:26:03,639
Speaker 2: use it. Sleep Numbers for betting, the Men's Warehouse for clothing,

478
00:26:04,119 --> 00:26:08,960
Speaker 2: Solomon for technical clothing. If they're hunters, they know first light.

479
00:26:09,480 --> 00:26:12,560
Speaker 2: So there's well over one hundred brands that we license

480
00:26:12,600 --> 00:26:13,560
Speaker 2: the technology to.

481
00:26:13,920 --> 00:26:15,760
Speaker 1: And that's a proprietary process.

482
00:26:16,080 --> 00:26:16,480
Speaker 5: It is.

483
00:26:16,560 --> 00:26:21,040
Speaker 2: It's very difficult to take a water loving mineral and

484
00:26:21,119 --> 00:26:25,560
Speaker 2: put it into manufacturing operations at hate water. So we

485
00:26:25,840 --> 00:26:28,959
Speaker 2: have patents on it and a ton of trade secrets.

486
00:26:29,160 --> 00:26:32,640
Speaker 2: You know, we're very cognizant of keeping those trade secrets secret.

487
00:26:33,680 --> 00:26:37,240
Speaker 1: The United States is the world's second largest exporter of textiles.

488
00:26:37,520 --> 00:26:39,840
Speaker 1: It used to be first, employing one and a half

489
00:26:39,960 --> 00:26:43,520
Speaker 1: million workers, but now it employs only about four hundred

490
00:26:43,560 --> 00:26:46,800
Speaker 1: and seventy thousand. As China has risen to become the

491
00:26:46,880 --> 00:26:48,800
Speaker 1: number one producer, we.

492
00:26:48,720 --> 00:26:52,800
Speaker 2: Do have customers in the United States. Generally, the spinners

493
00:26:52,840 --> 00:26:55,119
Speaker 2: outside of the United States are more competitive from a

494
00:26:55,160 --> 00:27:00,760
Speaker 2: price standpoint, and they've got more capacity, more infrastructure. Decades ago,

495
00:27:00,880 --> 00:27:03,359
Speaker 2: most of that did reside in the United States and

496
00:27:03,480 --> 00:27:06,760
Speaker 2: eventually moved outside of the United States. China at one

497
00:27:06,800 --> 00:27:09,320
Speaker 2: point in time decided that it was a strategic industry,

498
00:27:09,720 --> 00:27:13,760
Speaker 2: so they worked with their business people to develop that infrastructure,

499
00:27:14,160 --> 00:27:16,320
Speaker 2: and now that's primarily where.

500
00:27:15,760 --> 00:27:17,480
Speaker 5: The most of it's done today.

501
00:27:17,560 --> 00:27:21,000
Speaker 2: India is also a big export country for US as well.

502
00:27:21,080 --> 00:27:23,960
Speaker 1: The tariffs being imposed by the Trump administration affect a

503
00:27:24,000 --> 00:27:27,359
Speaker 1: textile business like Cocona Labse both in the cost of

504
00:27:27,400 --> 00:27:31,240
Speaker 1: its raw products and ironically in its exports to places

505
00:27:31,359 --> 00:27:32,119
Speaker 1: like China.

506
00:27:32,760 --> 00:27:37,040
Speaker 2: People are not placing orders because they're uncertain in terms

507
00:27:37,080 --> 00:27:39,639
Speaker 2: of what the ultimate pricing is going to be. I

508
00:27:39,680 --> 00:27:42,800
Speaker 2: spent this morning all morning talking to my team about

509
00:27:43,040 --> 00:27:45,200
Speaker 2: a business that we're working on right now with one

510
00:27:45,240 --> 00:27:48,720
Speaker 2: of the largest hotel chains in the United States. They're

511
00:27:48,720 --> 00:27:52,520
Speaker 2: looking to incorporate our technology in their bedding, and one

512
00:27:52,560 --> 00:27:55,760
Speaker 2: of the questions that we're getting from our Chinese partners

513
00:27:55,760 --> 00:27:58,520
Speaker 2: who would be spinning the yarn for that is, how

514
00:27:58,560 --> 00:28:02,879
Speaker 2: should they price the yarn based on the tariffs, And

515
00:28:02,920 --> 00:28:05,440
Speaker 2: of course that's a very difficult question for us to answer.

516
00:28:05,680 --> 00:28:10,320
Speaker 2: We did exports some product of China last week. We

517
00:28:10,520 --> 00:28:14,199
Speaker 2: ate the tariff ourself of about sixteen and a half percent.

518
00:28:14,880 --> 00:28:16,480
Speaker 5: We actually ate ten percent of that.

519
00:28:16,680 --> 00:28:19,000
Speaker 2: Previously there was six and a half percent tariff in

520
00:28:19,040 --> 00:28:21,880
Speaker 2: place already, So it's kind of a wait and see

521
00:28:21,960 --> 00:28:22,240
Speaker 2: right now.

522
00:28:22,280 --> 00:28:26,240
Speaker 5: But in the short term, it is reducing the.

523
00:28:26,200 --> 00:28:28,919
Speaker 2: Amount of business that's out there, and it's reducing the

524
00:28:28,920 --> 00:28:31,440
Speaker 2: amount of investment that people are willing to make in

525
00:28:31,480 --> 00:28:35,040
Speaker 2: new products. Specific to us, because we're a small company

526
00:28:35,040 --> 00:28:38,959
Speaker 2: that exports as opposed to imports, you know, from China

527
00:28:38,960 --> 00:28:44,360
Speaker 2: and Indian those other places, the reciprocal tariffs also impact us.

528
00:28:45,040 --> 00:28:49,360
Speaker 2: And when Trump first announced, you know, the large tariffs

529
00:28:49,400 --> 00:28:52,600
Speaker 2: on China, China retaliated with one hundred and forty five

530
00:28:52,600 --> 00:28:54,840
Speaker 2: percent tariff. That would have been a killer for us,

531
00:28:54,840 --> 00:28:56,880
Speaker 2: It would have killed our business. We just happened to

532
00:28:56,920 --> 00:28:59,880
Speaker 2: have a bunch of our product in our Shanghai warehouse

533
00:29:00,160 --> 00:29:03,280
Speaker 2: the time, so within thirty six hours we were able

534
00:29:03,320 --> 00:29:05,720
Speaker 2: to sell that to our customers, move it out of

535
00:29:05,760 --> 00:29:09,560
Speaker 2: the warehouse, and that brought us a few months traff free.

536
00:29:09,720 --> 00:29:11,960
Speaker 2: We're waiting to see what's going to happen with the tariffs.

537
00:29:12,480 --> 00:29:16,080
Speaker 2: It impacts our business in several ways. We're also trying

538
00:29:16,080 --> 00:29:19,760
Speaker 2: to develop a domestic business in those countries.

539
00:29:20,320 --> 00:29:21,880
Speaker 5: So this shirt is an example.

540
00:29:21,960 --> 00:29:24,640
Speaker 2: It's a shirt made in China and it's sold to

541
00:29:24,720 --> 00:29:28,960
Speaker 2: Chinese customers, and when the price of the yarns increase,

542
00:29:29,840 --> 00:29:33,320
Speaker 2: then it impacts the price of the finished products, and

543
00:29:33,440 --> 00:29:34,560
Speaker 2: it impacts our business.

544
00:29:34,840 --> 00:29:36,440
Speaker 3: They charge us we judge him.

545
00:29:36,680 --> 00:29:39,280
Speaker 1: In pursuing his approach to tariffs, President Trump has said

546
00:29:39,320 --> 00:29:41,600
Speaker 1: again and again one of his main goals is to

547
00:29:41,640 --> 00:29:45,360
Speaker 1: bring manufacturing back on shore in the United States. Would

548
00:29:45,360 --> 00:29:47,880
Speaker 1: that work out that way in your business.

549
00:29:47,760 --> 00:29:48,800
Speaker 5: No, definitely not.

550
00:29:49,200 --> 00:29:53,040
Speaker 2: You'd have to cut and sew this garment or bedding

551
00:29:53,400 --> 00:29:57,280
Speaker 2: or any other garment. You'd have to make that garment

552
00:29:57,280 --> 00:30:01,000
Speaker 2: in the United States, and people just don't want those jobs.

553
00:30:01,120 --> 00:30:04,440
Speaker 2: Number one, and number two, the cost of labor here

554
00:30:04,520 --> 00:30:07,080
Speaker 2: is so much higher that the price of those goods

555
00:30:07,120 --> 00:30:10,880
Speaker 2: would dramatically increase if they're made in the United States.

556
00:30:10,960 --> 00:30:14,040
Speaker 2: So it would make everything much more expensive. You know,

557
00:30:14,080 --> 00:30:16,040
Speaker 2: even the deal with Vietnam is a case in point.

558
00:30:16,160 --> 00:30:19,760
Speaker 2: I don't think most people understand that those goods coming

559
00:30:19,800 --> 00:30:21,320
Speaker 2: into the United States.

560
00:30:21,280 --> 00:30:23,400
Speaker 5: Already had a tariff on them.

561
00:30:23,360 --> 00:30:26,600
Speaker 2: And the case of clothing coming from Vietnam as an example,

562
00:30:26,680 --> 00:30:27,640
Speaker 2: there was a sixteen and.

563
00:30:27,600 --> 00:30:29,680
Speaker 5: A half percent tariff before Trump.

564
00:30:29,840 --> 00:30:32,880
Speaker 2: With the additional twenty percent that was just negotiated, that

565
00:30:32,960 --> 00:30:34,880
Speaker 2: means that there's a thirty six and a half percent

566
00:30:34,960 --> 00:30:39,200
Speaker 2: tariff on apparel now coming into the United States from Vietnam.

567
00:30:39,440 --> 00:30:41,040
Speaker 2: Some of that's going to get passed on, there's no

568
00:30:41,160 --> 00:30:44,240
Speaker 2: question about it. So the price of goods will go up.

569
00:30:44,640 --> 00:30:46,680
Speaker 2: With the increase in the price of goods, the amount

570
00:30:46,680 --> 00:30:49,280
Speaker 2: of business will go down. I mean, Trump and I

571
00:30:49,320 --> 00:30:52,760
Speaker 2: both went to the same business school, Wharton, and apparently

572
00:30:52,920 --> 00:30:57,640
Speaker 2: he didn't take the tariff class, because certainly the teachings

573
00:30:58,200 --> 00:31:02,200
Speaker 2: and the empirical history showed is that when you impose tariff,

574
00:31:02,480 --> 00:31:04,680
Speaker 2: the net effect is that you reduce the amount of

575
00:31:04,720 --> 00:31:05,800
Speaker 2: business that takes place.

576
00:31:06,040 --> 00:31:09,120
Speaker 5: And we're seeing that happen. Already, Costs go up.

577
00:31:09,360 --> 00:31:14,280
Speaker 2: Consumers pay the price eventually, and there's just less business

578
00:31:14,280 --> 00:31:14,960
Speaker 2: being conducted.

579
00:31:15,320 --> 00:31:19,080
Speaker 1: Whatever proves out from President Trump's tterarf. At this point,

580
00:31:19,120 --> 00:31:21,440
Speaker 1: it appeers there are going to be tariffs. We already

581
00:31:21,520 --> 00:31:24,640
Speaker 1: see fifteen percent effective and think in some place it

582
00:31:24,680 --> 00:31:28,320
Speaker 1: could be higher than that. So assuming that happens, just

583
00:31:28,360 --> 00:31:31,240
Speaker 1: let's assume from it's fifteen to twenty percent effective tariff.

584
00:31:32,040 --> 00:31:34,720
Speaker 1: What do you do to change your business to make

585
00:31:34,760 --> 00:31:36,720
Speaker 1: sure it's still a good business, it's viable.

586
00:31:37,000 --> 00:31:37,760
Speaker 5: Well, what we have.

587
00:31:37,800 --> 00:31:40,160
Speaker 2: Been doing is number one, making sure that our customers

588
00:31:40,200 --> 00:31:43,520
Speaker 2: have options, So we ship master Match around the world.

589
00:31:43,880 --> 00:31:50,400
Speaker 2: We ship it to Turkey, we ship it to India, Taiwan, China, Korea,

590
00:31:50,520 --> 00:31:53,440
Speaker 2: et cetera. Making sure that they have options so that

591
00:31:53,480 --> 00:31:58,840
Speaker 2: there's so we can mitigate the tariffs hopefully in any

592
00:31:58,920 --> 00:31:59,640
Speaker 2: one country.

593
00:32:00,200 --> 00:32:01,560
Speaker 5: That's the first thing that we're doing.

594
00:32:01,680 --> 00:32:06,200
Speaker 2: And second is, you know, we're looking at in some

595
00:32:06,280 --> 00:32:09,800
Speaker 2: cases potentially moving some of our manufacturing out of the

596
00:32:09,920 --> 00:32:13,440
Speaker 2: United States into China, something that we're loath to do,

597
00:32:13,840 --> 00:32:17,360
Speaker 2: just to reduce the reciprocal tariffs that are being placed

598
00:32:17,360 --> 00:32:19,600
Speaker 2: on our goods. You know, ultimately it will help with

599
00:32:19,760 --> 00:32:22,080
Speaker 2: the cost of goods coming back to the United States.

600
00:32:22,520 --> 00:32:25,040
Speaker 2: It will certainly help even more with the goods that

601
00:32:25,080 --> 00:32:26,920
Speaker 2: are being sold into those countries.

602
00:32:27,720 --> 00:32:28,840
Speaker 5: We're worried about tariffs.

603
00:32:28,880 --> 00:32:32,320
Speaker 2: I mean, candidly, we shipped a master batch to China

604
00:32:32,360 --> 00:32:34,840
Speaker 2: two weeks ago. We paid sixteen and a half percent

605
00:32:35,080 --> 00:32:38,400
Speaker 2: tariff on it. We eight ten percent of that. That's

606
00:32:38,440 --> 00:32:40,920
Speaker 2: not sustainable for us long term. You know, we'd have

607
00:32:40,960 --> 00:32:43,920
Speaker 2: to raise our prices. When we raise our prices, it

608
00:32:43,960 --> 00:32:46,560
Speaker 2: will ultimately affect the amount of volume that's being sold.

609
00:32:46,680 --> 00:32:47,840
Speaker 2: So it's not good.

610
00:32:48,200 --> 00:32:51,520
Speaker 1: How elastic or inelastic is that demand for your product?

611
00:32:51,880 --> 00:32:54,200
Speaker 1: I mean, how what pricing power do you have to

612
00:32:54,240 --> 00:32:56,680
Speaker 1: pass along some or all of those increases?

613
00:32:56,880 --> 00:32:58,720
Speaker 2: You know, we have to pass on some otherwise we'll

614
00:32:58,720 --> 00:33:01,240
Speaker 2: go out of business. And there's trade off. And that's

615
00:33:01,280 --> 00:33:04,040
Speaker 2: the fact what I was talking about this morning. Our

616
00:33:04,120 --> 00:33:08,480
Speaker 2: Chinese partners are trying to figure out how to price

617
00:33:08,560 --> 00:33:12,560
Speaker 2: their yarn for this very large hotel chain opportunity, and

618
00:33:12,560 --> 00:33:14,720
Speaker 2: they want to know how to account for the tariffs,

619
00:33:15,200 --> 00:33:17,520
Speaker 2: and we don't have an answer form candidly, other than

620
00:33:17,560 --> 00:33:20,480
Speaker 2: to tell them that we're not going to eat that tariff.

621
00:33:21,240 --> 00:33:22,800
Speaker 5: We may eat a portion of it.

622
00:33:23,520 --> 00:33:26,680
Speaker 2: I think we're waiting to see what ultimately happens and

623
00:33:26,760 --> 00:33:30,520
Speaker 2: what the tariffs actually end up being, and then we'll adjust,

624
00:33:31,120 --> 00:33:33,520
Speaker 2: you know, accordingly as best we can. There will be

625
00:33:33,680 --> 00:33:37,960
Speaker 2: collaboration I'm sure throughout the supply chain. Ultimately you'll see

626
00:33:38,040 --> 00:33:39,840
Speaker 2: higher prices at the consumer level.

627
00:33:40,000 --> 00:33:42,680
Speaker 1: And if in fact these tariffs continue, what's going to

628
00:33:42,680 --> 00:33:44,880
Speaker 1: happen to your employment situation in general?

629
00:33:45,080 --> 00:33:47,880
Speaker 2: You know, we don't lay anybody off. We were actually

630
00:33:47,960 --> 00:33:50,520
Speaker 2: voted one of the top fifty small companies in the

631
00:33:50,680 --> 00:33:54,040
Speaker 2: United States to work for last year, so you know,

632
00:33:54,120 --> 00:33:56,800
Speaker 2: we value our employees greatly and try to hold on

633
00:33:56,840 --> 00:34:00,520
Speaker 2: to them. Definitely, during COVID, everybody took a pay cut

634
00:34:00,600 --> 00:34:03,520
Speaker 2: for a period of time just so that we can survive.

635
00:34:04,120 --> 00:34:06,160
Speaker 2: So we'll do what we have to do to survive.

636
00:34:07,040 --> 00:34:08,400
Speaker 2: We'll do it we have to do to hold on

637
00:34:08,440 --> 00:34:11,480
Speaker 2: to our employees. Ideally, we'll do we have to do

638
00:34:11,560 --> 00:34:13,880
Speaker 2: to continue manufacturing in the United States.

639
00:34:14,080 --> 00:34:15,160
Speaker 5: We're very adaptable.

640
00:34:15,960 --> 00:34:19,040
Speaker 1: As difficult as the tariffs may be for Kokona Lab's business,

641
00:34:19,440 --> 00:34:22,080
Speaker 1: Bowman does believe they deserve to have a role in

642
00:34:22,160 --> 00:34:25,839
Speaker 1: economic policy, just not quite as broad a role as

643
00:34:25,880 --> 00:34:28,200
Speaker 1: they are being given. If you got to sit down

644
00:34:28,200 --> 00:34:32,399
Speaker 1: with your fellow Wharton graduate in the White House and

645
00:34:32,760 --> 00:34:34,400
Speaker 1: give him a piece of your mind about what he

646
00:34:34,440 --> 00:34:36,320
Speaker 1: should be doing, what would you advise him.

647
00:34:36,160 --> 00:34:39,919
Speaker 2: Well, I think tariffs are a strategic tool. I think

648
00:34:39,960 --> 00:34:46,319
Speaker 2: if they're used surgically, they can help. I think broadbrushed

649
00:34:46,400 --> 00:34:50,320
Speaker 2: tariffs don't work in my opinion, so I would encourage

650
00:34:50,400 --> 00:34:52,960
Speaker 2: him to be surgical about it. We're a small American

651
00:34:53,000 --> 00:34:57,480
Speaker 2: company trying to do business in China, where historically there

652
00:34:57,520 --> 00:35:00,760
Speaker 2: has been very little tariff of our goods go into China.

653
00:35:01,080 --> 00:35:04,000
Speaker 2: It's still very difficult to do business in China because

654
00:35:04,040 --> 00:35:07,880
Speaker 2: of the non tariff barriers to doing business, things like

655
00:35:07,920 --> 00:35:10,960
Speaker 2: the banking laws and the marketing laws and the administrative

656
00:35:11,480 --> 00:35:13,840
Speaker 2: issues that you have to deal with on a daily basis.

657
00:35:14,480 --> 00:35:17,120
Speaker 2: And it's not clear to me that this tariff war

658
00:35:17,200 --> 00:35:19,400
Speaker 2: that we're in is going to impact any of that.

659
00:35:19,760 --> 00:35:22,080
Speaker 2: All I see happening is that we're going to increase

660
00:35:22,120 --> 00:35:25,640
Speaker 2: the price of goods coming into the United States. We're

661
00:35:25,680 --> 00:35:28,839
Speaker 2: not going to address the fundamental issues that are making

662
00:35:28,920 --> 00:35:32,520
Speaker 2: it different difficult for companies like mine to do business

663
00:35:32,520 --> 00:35:35,960
Speaker 2: in places like China, and that's those non tariff barriers

664
00:35:36,000 --> 00:35:36,960
Speaker 2: to doing.

665
00:35:36,760 --> 00:35:40,360
Speaker 1: Business, whether it's to break down barriers to US exports

666
00:35:40,560 --> 00:35:44,000
Speaker 1: or to discourage production abroad. One of President Trump's main

667
00:35:44,120 --> 00:35:49,080
Speaker 1: goals is increasing US manufacturing overall something special contributor to

668
00:35:49,120 --> 00:35:52,440
Speaker 1: Larry Summers of Harvard says, tariffs will not accomplish.

669
00:35:52,840 --> 00:35:56,520
Speaker 11: This isn't a debate about whether manufacturing is important or not.

670
00:35:56,680 --> 00:36:01,120
Speaker 11: That's an important debate to have as we go forward.

671
00:36:01,520 --> 00:36:06,000
Speaker 11: But even if you take as given the central importance

672
00:36:06,080 --> 00:36:17,880
Speaker 11: of cutting edge manufacturing, taxing inputs, alienating partners, and enraging

673
00:36:19,120 --> 00:36:24,960
Speaker 11: those who control the external markets can't possibly be the

674
00:36:25,080 --> 00:36:32,960
Speaker 11: right strategy for rebuilding American manufacturers. Look, I take another

675
00:36:33,000 --> 00:36:37,080
Speaker 11: way of looking at this. We think of ourselves. We

676
00:36:37,120 --> 00:36:41,040
Speaker 11: think of automobiles as like a traditional industry that we're

677
00:36:41,120 --> 00:36:49,080
Speaker 11: trying to protect in a good way. But this is

678
00:36:49,640 --> 00:36:55,360
Speaker 11: slashing general motors profits. This is slash all the automobile

679
00:36:55,440 --> 00:37:00,680
Speaker 11: companies are complaining about our tariff policies because what they

680
00:37:00,800 --> 00:37:08,120
Speaker 11: mean for integrated production in North America. So even for

681
00:37:08,239 --> 00:37:12,720
Speaker 11: the people who were the traditional constituency for this kind

682
00:37:12,800 --> 00:37:21,480
Speaker 11: of policy, they're not seeing it as as a positive.

683
00:37:22,080 --> 00:37:29,560
Speaker 11: So this is not some debate between the coastal people

684
00:37:29,600 --> 00:37:33,760
Speaker 11: who like finance and movies as businesses and the people

685
00:37:33,760 --> 00:37:36,480
Speaker 11: in the middle of the country who are focused on

686
00:37:36,560 --> 00:37:41,799
Speaker 11: the heartland of manufacturing. Even if you just look at

687
00:37:41,960 --> 00:37:49,239
Speaker 11: what you're doing for the heartland of manufacturing higher priced inputs,

688
00:37:50,120 --> 00:37:58,800
Speaker 11: much more uncertainty for investors and alienating of customers can't

689
00:37:58,880 --> 00:38:03,680
Speaker 11: be the right strategy. There's a winner here. There really

690
00:38:03,719 --> 00:38:07,080
Speaker 11: is a winner here in the strategy that we are pursuing.

691
00:38:07,880 --> 00:38:11,960
Speaker 11: His name is she Jinping, and I hope we'll stop.

692
00:38:13,880 --> 00:38:14,360
Speaker 5: Up next.

693
00:38:14,600 --> 00:38:17,240
Speaker 1: Data centers may be the path to an AI future,

694
00:38:17,560 --> 00:38:20,319
Speaker 1: But what if a community isn't sure they want more

695
00:38:20,400 --> 00:38:23,680
Speaker 1: data centers? We go to Warrenton, Virginia to find out.

696
00:38:33,600 --> 00:38:37,400
Speaker 1: This is a story about needs and wants. Sometimes what

697
00:38:37,440 --> 00:38:40,880
Speaker 1: we need may not be what we want. We need

698
00:38:40,920 --> 00:38:44,560
Speaker 1: more productivity from AI, but we don't want young college

699
00:38:44,600 --> 00:38:47,879
Speaker 1: graduates going unemployed. And at least some of us may

700
00:38:47,920 --> 00:38:51,400
Speaker 1: not want our local communities transformed by the building of

701
00:38:51,480 --> 00:38:59,040
Speaker 1: all those data centers we need for AI.

702
00:39:00,040 --> 00:39:05,680
Speaker 12: Warrenton is a tiny town ten thousand people. Picture Norman

703
00:39:05,800 --> 00:39:10,000
Speaker 12: Rockwell Pictures picture Mayberry and Andy Griffith.

704
00:39:10,560 --> 00:39:14,000
Speaker 1: Cindy Burbank lives in Warrenton, Virginia. She took us to

705
00:39:14,040 --> 00:39:16,960
Speaker 1: what would have been a data center down This.

706
00:39:17,320 --> 00:39:21,160
Speaker 12: We did Pathway is the forty two acre site that

707
00:39:21,320 --> 00:39:23,759
Speaker 12: Amazon purchased to build a data center on.

708
00:39:25,120 --> 00:39:28,920
Speaker 1: Without data centers to process, store and distribute information, we

709
00:39:28,960 --> 00:39:32,040
Speaker 1: don't have AI with everything from Internet search.

710
00:39:32,400 --> 00:39:35,600
Speaker 9: We are on an exponential curve and a relatively steep.

711
00:39:35,360 --> 00:39:37,360
Speaker 1: One to drug discovery and approval.

712
00:39:37,719 --> 00:39:43,200
Speaker 13: Is going to propel the biological research AI in unprecedented ways.

713
00:39:43,320 --> 00:39:46,000
Speaker 1: To reap all the benefits AI has to offer. Global

714
00:39:46,080 --> 00:39:48,759
Speaker 1: data center capacity is expected to grow at a rate

715
00:39:48,800 --> 00:39:52,080
Speaker 1: of fifteen percent per year, and even that may not

716
00:39:52,120 --> 00:39:55,160
Speaker 1: be enough to keep up with demand. Christine Wood is

717
00:39:55,160 --> 00:39:59,040
Speaker 1: the data center practice lead at Burns and MacDonald and

718
00:39:59,080 --> 00:40:01,440
Speaker 1: consults with company building data centers.

719
00:40:01,840 --> 00:40:04,719
Speaker 14: Power is becoming the long pole in the tent to

720
00:40:04,840 --> 00:40:07,840
Speaker 14: be able to find a data center site, and so

721
00:40:07,920 --> 00:40:11,239
Speaker 14: it really starts with that is the power puzzle that

722
00:40:11,280 --> 00:40:13,920
Speaker 14: we're seeing associated with the data center site.

723
00:40:14,719 --> 00:40:17,600
Speaker 1: But supplying all that power can cause disruption to the

724
00:40:17,640 --> 00:40:21,480
Speaker 1: communities where data centers want to be located. Not everyone

725
00:40:21,640 --> 00:40:23,560
Speaker 1: wants a data center for a neighbor.

726
00:40:23,960 --> 00:40:27,160
Speaker 10: We're situated about forty five miles to the west of Washington. D. C.

727
00:40:28,480 --> 00:40:31,920
Speaker 1: Carter Neville is a native of Warrenton, Virginia. He has

728
00:40:31,960 --> 00:40:34,520
Speaker 1: been the mayor of the town since twenty nineteen.

729
00:40:34,920 --> 00:40:37,759
Speaker 10: We are heavily depended upon our meal taxes, and our

730
00:40:37,840 --> 00:40:41,080
Speaker 10: largest meal sax provider is Chick fil A. We have

731
00:40:41,280 --> 00:40:44,480
Speaker 10: very fortunate to have as residents very low taxes in

732
00:40:44,520 --> 00:40:47,360
Speaker 10: terms of real estate taxes, and so pretty much our

733
00:40:47,440 --> 00:40:51,880
Speaker 10: largest employer is the school board, followed by the hospital

734
00:40:51,880 --> 00:40:52,480
Speaker 10: in Walmart.

735
00:40:53,120 --> 00:40:56,680
Speaker 1: Warrenton's data center journey began in twenty twenty three when

736
00:40:56,680 --> 00:40:59,560
Speaker 1: the town council voted to consider an Amazon data center

737
00:40:59,600 --> 00:41:01,880
Speaker 1: on a forty two acre site.

738
00:41:01,560 --> 00:41:03,840
Speaker 10: On the up side to provide a significant amount of revenue,

739
00:41:03,840 --> 00:41:06,400
Speaker 10: which we are desperately in need of. Our infrastructure is aging.

740
00:41:06,800 --> 00:41:08,359
Speaker 10: We're behind the eight ball and a lot of our

741
00:41:08,360 --> 00:41:12,040
Speaker 10: improvements needed to our water treatment and wastewater treatments especially.

742
00:41:13,080 --> 00:41:14,719
Speaker 10: You know, we have parts going back that can't even

743
00:41:14,760 --> 00:41:17,480
Speaker 10: be produced anymore, you know, parts of dating back to

744
00:41:17,480 --> 00:41:19,840
Speaker 10: the nineteen fifties, and we're desperate need of upgrading that

745
00:41:19,920 --> 00:41:22,279
Speaker 10: facility for the safety of our community.

746
00:41:22,480 --> 00:41:26,440
Speaker 1: But even the economic benefit couldn't outweigh the town's concerns.

747
00:41:26,920 --> 00:41:30,799
Speaker 12: Our frustration was the fact that we were being kept

748
00:41:30,840 --> 00:41:34,000
Speaker 12: in the dark. Then finally a year and a half later,

749
00:41:35,000 --> 00:41:38,080
Speaker 12: we learn about it because Dominion is proposing these massive

750
00:41:38,200 --> 00:41:42,200
Speaker 12: transmission lines, and that is what was like throwing a

751
00:41:42,280 --> 00:41:48,760
Speaker 12: match onto a powder keg. The citizens exploded and first

752
00:41:48,800 --> 00:41:53,000
Speaker 12: against the transmission lines and dominion, and then they learned

753
00:41:53,000 --> 00:41:55,440
Speaker 12: it was tied to the data center. Then we learned

754
00:41:55,480 --> 00:41:59,720
Speaker 12: from our neighboring jurisdictions what data centers were and the impacts.

755
00:41:59,760 --> 00:42:05,280
Speaker 12: They had a lot of noise, huge energy generation, monster

756
00:42:05,520 --> 00:42:11,799
Speaker 12: size to dominate your landscape and bringing transmission lines and

757
00:42:11,840 --> 00:42:18,000
Speaker 12: other problems. We did a Freedom of information Act for

758
00:42:18,120 --> 00:42:22,200
Speaker 12: the town records how many people spoke up or wrote

759
00:42:22,440 --> 00:42:27,400
Speaker 12: in support of the Amazon data center, how many opposed it,

760
00:42:27,800 --> 00:42:32,960
Speaker 12: And the result was, I'll show you this number, two thousand,

761
00:42:33,239 --> 00:42:37,400
Speaker 12: three hundred and eighty nine of our citizens, our residents,

762
00:42:38,520 --> 00:42:41,759
Speaker 12: took the time to oppose the data center. They came

763
00:42:41,880 --> 00:42:45,360
Speaker 12: to the town council meetings, they wrote emails to the

764
00:42:45,400 --> 00:42:51,600
Speaker 12: town council, and so forth. Eleven, only eleven people here

765
00:42:52,239 --> 00:42:55,359
Speaker 12: wanted that data center to happen, and yet we were

766
00:42:55,480 --> 00:43:00,480
Speaker 12: ignored because Amazon had cut a deal with our elected

767
00:43:00,520 --> 00:43:02,799
Speaker 12: officials before we even knew about it.

768
00:43:03,000 --> 00:43:06,680
Speaker 10: As the opposition grew, it got very very heated, it

769
00:43:06,719 --> 00:43:10,000
Speaker 10: got very very emotional. I think that there was a

770
00:43:10,040 --> 00:43:13,200
Speaker 10: lot of information that was selectively put out to the community,

771
00:43:14,080 --> 00:43:15,840
Speaker 10: and so by the time it came to a vote,

772
00:43:15,880 --> 00:43:19,520
Speaker 10: it was a very divided community, and I'm grateful for

773
00:43:19,560 --> 00:43:21,360
Speaker 10: the council members that had the courage to do what

774
00:43:21,400 --> 00:43:22,319
Speaker 10: they believed was right.

775
00:43:23,239 --> 00:43:27,240
Speaker 1: Last month, Warrenton's City Council voted unanimously on a zoning

776
00:43:27,320 --> 00:43:31,880
Speaker 1: change that effectively bans data centers altogether. Among the citizens'

777
00:43:31,960 --> 00:43:34,840
Speaker 1: concerns is the amount of power a data center needs,

778
00:43:35,080 --> 00:43:37,040
Speaker 1: which would eat up their own access.

779
00:43:37,320 --> 00:43:37,520
Speaker 13: Yeah.

780
00:43:37,520 --> 00:43:39,080
Speaker 10: I think the biggest concern, and the one that the

781
00:43:39,120 --> 00:43:41,600
Speaker 10: industry itself really needs to address, is the energy usage.

782
00:43:41,600 --> 00:43:44,279
Speaker 10: That is just huge, and that is something as I've

783
00:43:44,280 --> 00:43:46,320
Speaker 10: pointed out to many people, the data centers are coming.

784
00:43:46,600 --> 00:43:49,520
Speaker 10: Whether one community says yes or no, does not ultimately

785
00:43:49,520 --> 00:43:52,480
Speaker 10: impact them to demand on energy. The industry and the

786
00:43:52,600 --> 00:43:54,839
Speaker 10: energy producers have got to figure that out. They've got

787
00:43:54,840 --> 00:43:57,680
Speaker 10: to find a better way of providing of that energy

788
00:43:58,040 --> 00:44:00,040
Speaker 10: without hitting the rate payers like you and me.

789
00:44:01,000 --> 00:44:04,520
Speaker 14: Fifteen years ago, a twelve megawatt data center was a

790
00:44:04,920 --> 00:44:08,319
Speaker 14: huge data center. Now with AI, what we're seeing on

791
00:44:08,360 --> 00:44:12,160
Speaker 14: the development side is more than a gigawatt on the campus.

792
00:44:12,200 --> 00:44:15,200
Speaker 14: And to put that in real terms for you, I'm

793
00:44:15,239 --> 00:44:21,000
Speaker 14: sitting in Dallas. Dallas takes about three gigawatts to five gigawats,

794
00:44:21,040 --> 00:44:24,720
Speaker 14: depending on the time of year, to power the entire city.

795
00:44:25,920 --> 00:44:28,680
Speaker 1: One alternative to drawing the needed power from the grid

796
00:44:28,880 --> 00:44:32,319
Speaker 1: is small modular nuclear reactors, but even they may not

797
00:44:32,440 --> 00:44:33,640
Speaker 1: be enough to keep up.

798
00:44:33,920 --> 00:44:37,359
Speaker 13: When it comes to AI data centers, they are an

799
00:44:37,560 --> 00:44:42,560
Speaker 13: order of magnitude more power hungry than the data centers

800
00:44:42,600 --> 00:44:44,960
Speaker 13: were used to since the Internet age.

801
00:44:45,520 --> 00:44:48,960
Speaker 1: Kr street Ar is the founder and CEO of Bloom Energy,

802
00:44:49,280 --> 00:44:51,720
Speaker 1: which just signed a deal to provide power to Oracle

803
00:44:51,800 --> 00:44:56,040
Speaker 1: for its infrastructure data centers in the United States. Is

804
00:44:56,080 --> 00:45:01,400
Speaker 1: there an overall approach to addressing that enormous demand.

805
00:45:01,040 --> 00:45:04,760
Speaker 13: For power today? It is a scramble for that AI

806
00:45:04,840 --> 00:45:10,360
Speaker 13: data centers. Collectively, they're investing more than six hundred billion

807
00:45:10,440 --> 00:45:15,439
Speaker 13: dollars just this year. That's the publicly uttered commitments. That's

808
00:45:15,560 --> 00:45:19,680
Speaker 13: mind boggling, right, almost two billion dollars a day to

809
00:45:19,760 --> 00:45:20,600
Speaker 13: feed this beast.

810
00:45:21,320 --> 00:45:25,440
Speaker 1: Sweetheart's. Bloom Energy was created to do just that, provide

811
00:45:25,480 --> 00:45:29,600
Speaker 1: clean energy on site, eliminating the need for transmission lines

812
00:45:29,640 --> 00:45:31,320
Speaker 1: and traditional power generators.

813
00:45:31,840 --> 00:45:38,240
Speaker 13: The world is completely digital dependent. Not having power even

814
00:45:38,400 --> 00:45:43,160
Speaker 13: for a millisecond is not an option because critical life

815
00:45:43,160 --> 00:45:46,840
Speaker 13: functions depend on it, like a surgery happening in a hospital,

816
00:45:46,920 --> 00:45:47,680
Speaker 13: for example.

817
00:45:48,000 --> 00:45:49,120
Speaker 8: So we said.

818
00:45:49,800 --> 00:45:53,399
Speaker 13: Power needs to be extremely reliable. You cannot get that

819
00:45:53,680 --> 00:45:58,880
Speaker 13: redundant reliability at very high nines unless you have on

820
00:45:59,000 --> 00:46:02,760
Speaker 13: site power. If you're going to have on site power,

821
00:46:03,719 --> 00:46:07,120
Speaker 13: because you're going to be breathing that air when you're

822
00:46:07,160 --> 00:46:10,520
Speaker 13: sitting in your office or when you're in your bedroom,

823
00:46:10,840 --> 00:46:14,000
Speaker 13: by definition, it needs to be clean, and it needs

824
00:46:14,000 --> 00:46:16,760
Speaker 13: to be very efficient, and it needs to be quiet.

825
00:46:16,960 --> 00:46:19,400
Speaker 13: So this is the principle on which we found a

826
00:46:19,480 --> 00:46:20,600
Speaker 13: bloom energy.

827
00:46:21,239 --> 00:46:25,080
Speaker 1: For the citizens of Warrenton, Virginia. Finding that optimal solution

828
00:46:25,280 --> 00:46:28,279
Speaker 1: of providing data center power in ways the community can

829
00:46:28,320 --> 00:46:32,920
Speaker 1: tolerate has been elusive, but that has real fiscal consequences

830
00:46:32,960 --> 00:46:33,520
Speaker 1: for the town.

831
00:46:33,960 --> 00:46:36,759
Speaker 10: We are so revenue strapped, but we're facing about ninety

832
00:46:36,800 --> 00:46:38,759
Speaker 10: one million dollars of investment in our waste water and

833
00:46:38,760 --> 00:46:41,239
Speaker 10: water treatment plants, and so you know that money is

834
00:46:41,239 --> 00:46:42,680
Speaker 10: just going to have to come out of debt unless

835
00:46:42,680 --> 00:46:45,960
Speaker 10: we find revenue streams. Our streets are cracked, you know,

836
00:46:45,960 --> 00:46:47,360
Speaker 10: we have things that are falling apart, and we're just

837
00:46:47,400 --> 00:46:49,239
Speaker 10: kind of keeping trying to keep the lights on.

838
00:46:49,600 --> 00:46:52,200
Speaker 1: Amazon declined to comment on the status of the Warranton

839
00:46:52,280 --> 00:46:55,000
Speaker 1: Data center. For now, the fate of the project will

840
00:46:55,000 --> 00:46:56,560
Speaker 1: have to be decided by the courts.

841
00:46:56,880 --> 00:46:59,520
Speaker 10: There's a March hearing data on that. There's a citizen

842
00:46:59,560 --> 00:47:03,640
Speaker 10: group that challenged it in court, and so we are

843
00:47:03,680 --> 00:47:05,640
Speaker 10: waiting to see what that decision is in March, and

844
00:47:05,680 --> 00:47:08,640
Speaker 10: I believe that Amazon is probably moved on, but I

845
00:47:08,680 --> 00:47:12,040
Speaker 10: do expect that, you know, if they remain, if the

846
00:47:12,040 --> 00:47:15,960
Speaker 10: courts favor the town and Amazon, that they will eventually

847
00:47:15,960 --> 00:47:19,160
Speaker 10: build there. But as of now, I'm not sure where

848
00:47:19,160 --> 00:47:19,640
Speaker 10: it stands.

849
00:47:20,560 --> 00:47:24,120
Speaker 1: The transformative power of AI means that we need to

850
00:47:24,200 --> 00:47:27,400
Speaker 1: have it, and that means we need to have data centers,

851
00:47:27,520 --> 00:47:31,279
Speaker 1: even with their enormous power demands. The question now is

852
00:47:31,360 --> 00:47:34,400
Speaker 1: where they will go and whether we can find ways

853
00:47:34,440 --> 00:47:36,960
Speaker 1: to make them the good neighbors that people in towns

854
00:47:37,040 --> 00:47:42,120
Speaker 1: like Warranton, Virginia will welcome. That does it for us

855
00:47:42,120 --> 00:47:44,480
Speaker 1: Here at Wall Street Week, I'm David Weston. See you

856
00:47:44,640 --> 00:48:00,000
Speaker 1: next week for more stories of capitalism.