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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.

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Speaker 3: Manchesk, Milk, thank you, thank you agree today me too.

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Speaker 3: I have a few case studies on my portfolio, just

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

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

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

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Speaker 3: I did 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 4: Since graduating college. I would say I've applied to around

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Speaker 4: 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 5: This is essentially the first time on a ongoing basis

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Speaker 5: of this has occurred in the forty five plus years

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Speaker 5: 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 5: So really this is pointing towards a lot of people

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Speaker 5: who have that higher educational attainment are actually worse off

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Speaker 5: than many of their peers who have lower educational attainment.

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Speaker 5: That's an interesting dynamic to have in a market where

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Speaker 5: you're told you go to college, you work hard, you'll

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Speaker 5: have a job after graduation, and unfortunately for many at

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Speaker 5: 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 5: Tech is one of those that has been really slowed

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Speaker 5: down from a lot of that comes down to the

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Speaker 5: hiring boom that they did post pandemic and then looking

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Speaker 5: to the right size. You also have other mathematical occupations

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Speaker 5: and finance, legal. Some of these fields that you would

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Speaker 5: again have been in high demands over the last couple

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Speaker 5: 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 5: I think there is definitely a link between the two.

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Speaker 5: It's hard to very parse through and just say, you know,

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Speaker 5: X amount of this issue is because of AI, X

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Speaker 5: amount is because of just a slower demand overall. But

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Speaker 5: when we look at the numbers, there definitely could be

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Speaker 5: a connection there. So one of the things we're seeing

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Speaker 5: is that a the tech sector is one of the

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Speaker 5: industries with the highest adoption rates AI. They have around

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Speaker 5: twenty five percent adoption rates for all businesses using it

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Speaker 5: in their day to day operations, whereas the national average

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Speaker 5: is around five percent. So clearly the sector is just

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Speaker 5: using it more in general. And then when we look

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Speaker 5: into the specifically the recent college graduate data, we see

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Speaker 5: that in the last years employment for recent college grads

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Speaker 5: twenty two to twenty seven, they've seen a decrease in

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Speaker 5: employment opportunities overall, whereas if you look in the same

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Speaker 5: fields for those twenty eight onwards, they've actually seen slightly

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Speaker 5: 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. We see an awful

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

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

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Speaker 1: to work?

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Speaker 6: Now?

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Speaker 1: Obviously there are different ways it could work high, medium, low,

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Speaker 1: But do we know it's fundamently going to work that

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Speaker 1: There's no chance this is a bubble.

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Speaker 7: So it's not a bubble. I am always a believer

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Speaker 7: in progress. AI is progress over the long haul. It

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Speaker 7: will be good in the short term. 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 5: While the recent college graduate unemployment has raised overall, it's

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Speaker 5: really been concentrated in males specifically. A lot of that

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Speaker 5: comes down to what industries are they looking to work in.

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Speaker 5: If we do a quick breakout of the industries of

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Speaker 5: recent college graduates, females tend to move towards healthcare services

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Speaker 5: and educational services or demands quite high. I think between

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Speaker 5: those two they account for over forty percent of recent

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Speaker 5: college graduates for females, whereas males specifically in healthcare services

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Speaker 5: only counts around five percent. So it's an imbalance of

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Speaker 5: what positions are these males versus females going into. Males

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Speaker 5: tend to skew towards these computer science professions, whether that's

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Speaker 5: programming or the tech sector just in general. That's closer

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Speaker 5: to twenty twenty five percent for them. So you just

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Speaker 5: have this imbalance of males tending to want to go

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Speaker 5: to these industries where demand is really low at the moment,

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Speaker 5: whereas females are entering industries where demand remigins 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 8: 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.

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Speaker 8: 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.

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Speaker 8: Judgment's not going out 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 that 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. Now,

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Speaker 7: what is that about for the most part the US economy?

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Speaker 7: In my world, the big company CEOs, big company senior executives,

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Speaker 7: they've learned to one operate quite apart from policy in Washington,

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Speaker 7: and two they have been operating a period of volatility

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Speaker 7: for so many years now they've just gotten much better

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Speaker 7: about how to remove uncertainty in their own businesses. However,

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Speaker 7: coos certainly talk about long term impacts of policy, and

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Speaker 7: what I hear about most more than anything is our

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Speaker 7: federal debt situation, which is clearly dramatic and clearly something

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Speaker 7: that at some point will create 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.

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Speaker 8: Being spent by the government.

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Speaker 7: That's almost twenty percent of what they do, more than

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Speaker 7: they do on defense. That is something that CEO's are

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Speaker 7: attuned to. 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. I think you have to start

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Speaker 8: with what.

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Speaker 7: Is the most secure way to help your supply chain,

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Speaker 7: and that certainly can be on shore. It certainly can

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

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Speaker 7: be to Mexico. It's been going both ways. I know

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Speaker 7: of a company that services Canada with its plants in Michigan.

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Speaker 7: It's thinking about putting capital in Canada because of what's

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Speaker 7: going on, so they can produce locally, so it actually

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Speaker 7: goes both ways. And the kind of manufacturing we're talking about,

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Speaker 7: for the most part, is a very advanced manufacturing, so

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

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Speaker 7: it's the equipment. So I think that on balance, it's

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Speaker 7: hard to talk to any CEO who thinks that tariffs

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Speaker 7: are a good thing and that high tariffs more't heavy

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Speaker 7: 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 8: I can make a case for dark clouds well down

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Speaker 8: 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 or whether he must be.

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Speaker 8: 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 creatus 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 he is the

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Speaker 7: mayor will appreciate that he needs to be the mayor

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Speaker 7: 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 8: I would say I don't know. However, I'm going to

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Speaker 8: 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 8: It's going to be very clear.

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Speaker 7: I don't know yet or does anyone what he's learning,

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Speaker 7: But make no mistake, rent control will reduce housing stock.

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Speaker 8: Putting at grocery store in each borough.

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Speaker 7: That's five relative to the twenty five hundred grocery stores

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Speaker 7: we have in the five boroughs doesn't do much. Let's

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Speaker 7: be clear, anyone here is already paying a lot of tax.

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Speaker 7: It's a privilege to live in New York. There is

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Speaker 7: a point where, and we sit in studies, it has

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Speaker 7: an impact if it gets out of line with other states.

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Speaker 7: So the point is we all need to focus on

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

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

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

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Speaker 7: 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 allowed to talk about

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

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Speaker 1: real issue for New York City?

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Speaker 8: Is it a real issue? Absolutely? Have you risen to

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Speaker 8: 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 8: 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.

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Speaker 8: And I believe that the party always should.

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Speaker 7: Focus on all ends of the spectrum, but I do

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Speaker 7: believe it will end up unifying on what matters most,

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Speaker 7: solving people's issues.

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

445
00:23:39,720 --> 00:23:44,000
Speaker 1: America's largest companies to one specific, modest sized company that

446
00:23:44,119 --> 00:23:47,840
Speaker 1: depends on imports and exports for its textile business. We

447
00:23:47,920 --> 00:23:50,760
Speaker 1: go to Bend, Oregon and hear from Jeff Bowman, CEO

448
00:23:50,920 --> 00:24:07,720
Speaker 1: of Cocona Labs. This is a story about friendly fire.

449
00:24:08,320 --> 00:24:11,080
Speaker 1: President Trump has embraced tariffs as a way to protect

450
00:24:11,080 --> 00:24:14,199
Speaker 1: domestic US businesses from unfair foreign competition.

451
00:24:15,480 --> 00:24:17,240
Speaker 9: I might go up with that tariff in the not

452
00:24:17,280 --> 00:24:18,320
Speaker 9: too distant future.

453
00:24:18,359 --> 00:24:20,080
Speaker 7: The higher you go, the more likely it is they

454
00:24:20,119 --> 00:24:21,080
Speaker 7: build a plant here.

455
00:24:21,880 --> 00:24:24,000
Speaker 1: Last week, we've brought you the story of a music

456
00:24:24,040 --> 00:24:26,919
Speaker 1: accessories company that is finding new ways of getting the

457
00:24:26,920 --> 00:24:31,080
Speaker 1: supplies it needs and maintaining its export business despite those tariffs.

458
00:24:31,480 --> 00:24:35,400
Speaker 1: But for some industries that's easier said than done.

459
00:24:37,040 --> 00:24:42,119
Speaker 2: Already, it's impacted our business because it's reduced the amount

460
00:24:42,200 --> 00:24:43,920
Speaker 2: of volume of business that's out there.

461
00:24:44,280 --> 00:24:47,600
Speaker 1: Jeff Bowman is the CEO of Cocona Labs, which makes

462
00:24:47,640 --> 00:24:49,360
Speaker 1: a compound used in fabric.

463
00:24:49,880 --> 00:24:53,160
Speaker 2: We have a technology called thirty seven point five technology.

464
00:24:53,560 --> 00:24:56,480
Speaker 2: The purpose of it is to remove humidity from the

465
00:24:56,520 --> 00:24:59,600
Speaker 2: microclimate next to your skin. That helps you stay more

466
00:24:59,640 --> 00:25:04,480
Speaker 2: comfortab in both warm and cold weather conditions, and it

467
00:25:04,560 --> 00:25:07,920
Speaker 2: helps you operate at a peak performance. If you're an athlete,

468
00:25:08,160 --> 00:25:11,399
Speaker 2: you can operate in peak performance longer. And what we

469
00:25:11,480 --> 00:25:14,440
Speaker 2: do is we mine a mineral in the United States.

470
00:25:14,680 --> 00:25:18,640
Speaker 2: Essentially it's a volcanic sand. We convert that into what's

471
00:25:18,680 --> 00:25:22,080
Speaker 2: called master batch, which is a concentration of that mineral

472
00:25:22,359 --> 00:25:26,639
Speaker 2: that's then put into the spinning operations for things like

473
00:25:26,800 --> 00:25:32,920
Speaker 2: nylon and polypropylene and polyester. They make filaments and yarns

474
00:25:32,920 --> 00:25:36,520
Speaker 2: out of that. Those yarns and filaments are then made

475
00:25:36,520 --> 00:25:37,719
Speaker 2: into fabrics.

476
00:25:38,119 --> 00:25:39,720
Speaker 6: Those fabrics are.

477
00:25:39,960 --> 00:25:45,080
Speaker 2: Made into clothing or bedding or footwear, and we make

478
00:25:45,160 --> 00:25:47,639
Speaker 2: money by selling the master batch, which we make in

479
00:25:47,680 --> 00:25:52,280
Speaker 2: the United States. Primarily we export that because most yarn

480
00:25:52,359 --> 00:25:55,560
Speaker 2: spinners are outside of the United States. And then we

481
00:25:55,640 --> 00:25:59,600
Speaker 2: also license the technology to brands that use it. Sleep

482
00:25:59,680 --> 00:26:04,800
Speaker 2: Numbers for betting, the Men's Warehouse for clothing, Solomon for

483
00:26:04,880 --> 00:26:09,600
Speaker 2: technical clothing. If they're hunters, they know first light. So

484
00:26:09,760 --> 00:26:12,679
Speaker 2: there's well over one hundred brands that we license the

485
00:26:12,720 --> 00:26:13,560
Speaker 2: technology to.

486
00:26:13,920 --> 00:26:15,760
Speaker 1: And that's a proprietary process.

487
00:26:16,080 --> 00:26:16,480
Speaker 6: It is.

488
00:26:16,560 --> 00:26:21,040
Speaker 2: It's very difficult to take a water loving mineral and

489
00:26:21,119 --> 00:26:25,560
Speaker 2: put it into manufacturing operations at hate water, So we

490
00:26:25,840 --> 00:26:28,959
Speaker 2: have patents on it and a ton of trade secrets.

491
00:26:29,160 --> 00:26:32,640
Speaker 2: You know, we're very cognizant of keeping those trade secrets secret.

492
00:26:33,680 --> 00:26:37,240
Speaker 1: The United States is the world's second largest exporter of textiles.

493
00:26:37,520 --> 00:26:39,840
Speaker 1: It used to be first, employing one and a half

494
00:26:39,960 --> 00:26:43,520
Speaker 1: million workers, but now it employs only about four hundred

495
00:26:43,560 --> 00:26:46,800
Speaker 1: and seventy thousand. As China has risen to become the

496
00:26:46,880 --> 00:26:48,800
Speaker 1: number one producer, we.

497
00:26:48,720 --> 00:26:52,800
Speaker 2: Do have customers in the United States. Generally, the spinners

498
00:26:52,840 --> 00:26:55,119
Speaker 2: outside of the United States are more competitive from a

499
00:26:55,160 --> 00:27:00,760
Speaker 2: price standpoint, and they've got more capacity, more infrastructure. Decades ago,

500
00:27:00,880 --> 00:27:03,359
Speaker 2: most of that did reside in the United States and

501
00:27:03,480 --> 00:27:06,719
Speaker 2: eventually moved outside of the United States. China at one

502
00:27:06,800 --> 00:27:09,320
Speaker 2: point in time decided that it was a strategic industry,

503
00:27:09,720 --> 00:27:13,760
Speaker 2: so they worked with their business people to develop that infrastructure,

504
00:27:14,160 --> 00:27:16,320
Speaker 2: and now that's primarily where.

505
00:27:15,760 --> 00:27:17,480
Speaker 6: The most of it's done today.

506
00:27:17,560 --> 00:27:21,000
Speaker 2: India is also a big export country for US as well.

507
00:27:21,080 --> 00:27:23,960
Speaker 1: The tariffs being imposed by the Trump administration affect a

508
00:27:24,000 --> 00:27:27,359
Speaker 1: textile business like Cocona Labse both in the cost of

509
00:27:27,400 --> 00:27:31,240
Speaker 1: its raw products and ironically in its exports to places

510
00:27:31,359 --> 00:27:32,119
Speaker 1: like China.

511
00:27:32,760 --> 00:27:37,040
Speaker 2: People are not placing orders because they're uncertain in terms

512
00:27:37,080 --> 00:27:39,639
Speaker 2: of what the ultimate pricing is going to be. I

513
00:27:39,680 --> 00:27:42,800
Speaker 2: spent this morning all morning talking to my team about

514
00:27:43,040 --> 00:27:45,200
Speaker 2: a business that we're working on right now with one

515
00:27:45,240 --> 00:27:48,720
Speaker 2: of the largest hotel chains in the United States. They're

516
00:27:48,720 --> 00:27:52,520
Speaker 2: looking to incorporate our technology in their bedding, and one

517
00:27:52,560 --> 00:27:55,760
Speaker 2: of the questions that we're getting from our Chinese partners

518
00:27:55,760 --> 00:27:58,520
Speaker 2: who would be spinning the yarn for that is, how

519
00:27:58,560 --> 00:28:02,879
Speaker 2: should they price the yarn based on the tariffs, And

520
00:28:02,920 --> 00:28:05,440
Speaker 2: of course that's a very difficult question for us to answer.

521
00:28:05,680 --> 00:28:10,320
Speaker 2: We did exports some product of China last week. We

522
00:28:10,520 --> 00:28:14,199
Speaker 2: ate the tariff ourself of about sixteen and a half percent.

523
00:28:14,880 --> 00:28:16,480
Speaker 6: We actually ate ten percent of that.

524
00:28:16,680 --> 00:28:19,000
Speaker 2: Previously there was six and a half percent tariff in

525
00:28:19,040 --> 00:28:21,880
Speaker 2: place already, So it's kind of a wait and see

526
00:28:21,960 --> 00:28:26,080
Speaker 2: right now. But in the short term, it is reducing

527
00:28:26,080 --> 00:28:28,800
Speaker 2: the amount of business that's out there, and it's reducing

528
00:28:28,840 --> 00:28:31,160
Speaker 2: the amount of investment that people are willing to make

529
00:28:31,280 --> 00:28:34,600
Speaker 2: in new products. Specific to us, because we're a small

530
00:28:34,640 --> 00:28:38,440
Speaker 2: company that exports as opposed to imports, you know, from

531
00:28:38,600 --> 00:28:43,080
Speaker 2: China and Indian those other places, the reciprocal tariffs also

532
00:28:43,560 --> 00:28:48,400
Speaker 2: impact us. And when Trump first announced, you know, the

533
00:28:48,520 --> 00:28:52,200
Speaker 2: large tariffs on China, China retaliated with one hundred and

534
00:28:52,200 --> 00:28:54,600
Speaker 2: forty five percent tariff. That would have been a killer

535
00:28:54,600 --> 00:28:56,400
Speaker 2: for us, It would have killed our business. We just

536
00:28:56,480 --> 00:28:58,880
Speaker 2: happened to have a bunch of our product in our

537
00:28:58,920 --> 00:29:02,880
Speaker 2: Shanghai warehouse the time, so within thirty six hours we

538
00:29:02,880 --> 00:29:05,480
Speaker 2: were able to sell that to our customers, move it

539
00:29:05,480 --> 00:29:08,200
Speaker 2: out of the warehouse, and that brought us a few

540
00:29:08,240 --> 00:29:10,960
Speaker 2: months traff free. We're waiting to see what's going to

541
00:29:11,000 --> 00:29:14,800
Speaker 2: happen with the tariffs. It impacts our business in several ways.

542
00:29:15,200 --> 00:29:19,760
Speaker 2: We're also trying to develop a domestic business in those countries.

543
00:29:20,320 --> 00:29:23,440
Speaker 2: So this shirt is an example. It's a shirt made

544
00:29:23,440 --> 00:29:26,880
Speaker 2: in China and it's sold to Chinese customers. And when

545
00:29:26,920 --> 00:29:31,560
Speaker 2: the price of the yarns increase, then it impacts the

546
00:29:31,600 --> 00:29:34,560
Speaker 2: price of the finished products, and it impacts our business.

547
00:29:34,840 --> 00:29:35,680
Speaker 1: They charge us.

548
00:29:35,720 --> 00:29:36,440
Speaker 6: We judge him.

549
00:29:36,680 --> 00:29:39,280
Speaker 1: In pursuing his approach to tariffs, President Trump has said

550
00:29:39,320 --> 00:29:41,600
Speaker 1: again and again one of his main goals is to

551
00:29:41,640 --> 00:29:45,360
Speaker 1: bring manufacturing back on shore in the United States. Would

552
00:29:45,360 --> 00:29:47,880
Speaker 1: that work out that way in your business.

553
00:29:47,760 --> 00:29:48,800
Speaker 6: No, definitely not.

554
00:29:49,200 --> 00:29:53,040
Speaker 2: You'd have to cut and sew this garment or bedding

555
00:29:53,400 --> 00:29:57,280
Speaker 2: or any other garment. You'd have to make that garment

556
00:29:57,280 --> 00:30:01,000
Speaker 2: in the United States, and people just don't want those jobs.

557
00:30:01,120 --> 00:30:04,440
Speaker 2: Number one, and number two, the cost of labor here

558
00:30:04,520 --> 00:30:07,080
Speaker 2: is so much higher that the price of those goods

559
00:30:07,120 --> 00:30:10,880
Speaker 2: would dramatically increase if they're made in the United States.

560
00:30:10,960 --> 00:30:14,040
Speaker 2: So it would make everything much more expensive. You know,

561
00:30:14,080 --> 00:30:16,040
Speaker 2: even the deal with Vietnam is a case in point.

562
00:30:16,160 --> 00:30:19,760
Speaker 2: I don't think most people understand that those goods coming

563
00:30:19,800 --> 00:30:23,040
Speaker 2: into the United States already had a tariff on them,

564
00:30:23,360 --> 00:30:26,600
Speaker 2: And the case of clothing coming from Vietnam as an example,

565
00:30:26,680 --> 00:30:27,640
Speaker 2: there was a sixteen and.

566
00:30:27,600 --> 00:30:29,680
Speaker 6: A half percent tariff before Trump.

567
00:30:29,840 --> 00:30:32,880
Speaker 2: With the additional twenty percent that was just negotiated, that

568
00:30:32,960 --> 00:30:34,880
Speaker 2: means that there's a thirty six and a half percent

569
00:30:34,960 --> 00:30:39,200
Speaker 2: tariff on apparel now coming into the United States from Vietnam.

570
00:30:39,440 --> 00:30:41,040
Speaker 2: Some of that's going to get passed on, there's no

571
00:30:41,160 --> 00:30:44,240
Speaker 2: question about it. So the price of goods will go up.

572
00:30:44,640 --> 00:30:46,680
Speaker 2: With the increase in the price of goods, the amount

573
00:30:46,680 --> 00:30:49,280
Speaker 2: of business will go down. I mean, Trump and I

574
00:30:49,320 --> 00:30:52,760
Speaker 2: both went to the same business school, Wharton, and apparently

575
00:30:52,920 --> 00:30:57,640
Speaker 2: he didn't take the tariff class, because certainly the teachings

576
00:30:58,200 --> 00:31:02,200
Speaker 2: and the empirical history showed is that when you impose tariff,

577
00:31:02,480 --> 00:31:04,680
Speaker 2: the net effect is that you reduce the amount of

578
00:31:04,720 --> 00:31:07,120
Speaker 2: business that takes place, and we're seeing that happen.

579
00:31:07,160 --> 00:31:09,120
Speaker 6: Already, costs go up.

580
00:31:09,360 --> 00:31:14,280
Speaker 2: Consumers pay the price eventually, and there's just less business

581
00:31:14,280 --> 00:31:14,960
Speaker 2: being conducted.

582
00:31:15,320 --> 00:31:19,080
Speaker 1: Whatever proves out from President Trump's tterarf at this point,

583
00:31:19,120 --> 00:31:21,440
Speaker 1: it appeers there are going to be tariffs. We already

584
00:31:21,520 --> 00:31:24,640
Speaker 1: see fifteen percent effective and think in some place it

585
00:31:24,680 --> 00:31:28,320
Speaker 1: could be higher than that. So assuming that happens, just

586
00:31:28,360 --> 00:31:31,240
Speaker 1: let's assume from it's fifteen to twenty percent effective tariff.

587
00:31:32,040 --> 00:31:34,720
Speaker 1: What do you do to change your business to make

588
00:31:34,760 --> 00:31:36,720
Speaker 1: sure it's still a good business, it's viable.

589
00:31:37,000 --> 00:31:37,760
Speaker 6: Well, what we have.

590
00:31:37,800 --> 00:31:40,160
Speaker 2: Been doing is number one, making sure that our customers

591
00:31:40,200 --> 00:31:43,520
Speaker 2: have options, So we ship master Match around the world.

592
00:31:43,880 --> 00:31:50,400
Speaker 2: We ship it to Turkey, we ship it to India, Taiwan, China, Korea,

593
00:31:50,520 --> 00:31:53,440
Speaker 2: et cetera. Making sure that they have options so that

594
00:31:53,480 --> 00:31:58,840
Speaker 2: there's so we can mitigate the tariffs hopefully in any

595
00:31:58,920 --> 00:31:59,640
Speaker 2: one country.

596
00:32:00,200 --> 00:32:01,560
Speaker 6: That's the first thing that we're doing.

597
00:32:01,680 --> 00:32:06,200
Speaker 2: And second is, you know, we're looking at in some

598
00:32:06,280 --> 00:32:09,800
Speaker 2: cases potentially moving some of our manufacturing out of the

599
00:32:09,920 --> 00:32:13,440
Speaker 2: United States into China, something that we're loath to do,

600
00:32:13,840 --> 00:32:17,360
Speaker 2: just to reduce the reciprocal tariffs that are being placed

601
00:32:17,360 --> 00:32:19,600
Speaker 2: on our goods. You know, ultimately it will help with

602
00:32:19,760 --> 00:32:22,080
Speaker 2: the cost of goods coming back to the United States.

603
00:32:22,520 --> 00:32:25,040
Speaker 2: It will certainly help even more with the goods that

604
00:32:25,080 --> 00:32:28,840
Speaker 2: are being sold into those countries we're worried about tariffs.

605
00:32:28,880 --> 00:32:32,320
Speaker 2: I mean, candidly, we shipped a master batch to China

606
00:32:32,360 --> 00:32:34,840
Speaker 2: two weeks ago. We paid sixteen and a half percent

607
00:32:35,080 --> 00:32:38,400
Speaker 2: tariff on it. We eight ten percent of that. That's

608
00:32:38,440 --> 00:32:40,920
Speaker 2: not sustainable for us long term. You know, we'd have

609
00:32:40,960 --> 00:32:43,920
Speaker 2: to raise our prices. When we raise our prices, it

610
00:32:43,960 --> 00:32:46,560
Speaker 2: will ultimately affect the amount of volume that's being sold.

611
00:32:46,680 --> 00:32:47,840
Speaker 2: So it's not good.

612
00:32:48,200 --> 00:32:51,520
Speaker 1: How elastic or inelastic is that demand for your product?

613
00:32:51,880 --> 00:32:54,200
Speaker 1: I mean, how what pricing power do you have to

614
00:32:54,240 --> 00:32:56,680
Speaker 1: pass along some or all of those increases?

615
00:32:56,880 --> 00:32:58,720
Speaker 2: You know, we have to pass on some otherwise we'll

616
00:32:58,720 --> 00:33:01,240
Speaker 2: go out of business. And there's trade off. And that's

617
00:33:01,280 --> 00:33:04,040
Speaker 2: the fact what I was talking about this morning. Our

618
00:33:04,120 --> 00:33:08,480
Speaker 2: Chinese partners are trying to figure out how to price

619
00:33:08,560 --> 00:33:12,560
Speaker 2: their yarn for this very large hotel chain opportunity, and

620
00:33:12,560 --> 00:33:14,720
Speaker 2: they want to know how to account for the tariffs,

621
00:33:15,200 --> 00:33:17,520
Speaker 2: and we don't have an answer form candidly, other than

622
00:33:17,560 --> 00:33:20,480
Speaker 2: to tell them that we're not going to eat that tariff.

623
00:33:21,240 --> 00:33:22,800
Speaker 6: We may eat a portion of it.

624
00:33:23,520 --> 00:33:26,680
Speaker 2: I think we're waiting to see what ultimately happens and

625
00:33:26,760 --> 00:33:30,520
Speaker 2: what the tariffs actually end up being, and then we'll adjust,

626
00:33:31,120 --> 00:33:34,320
Speaker 2: you know, accordingly as best we can there will be collaboration,

627
00:33:34,400 --> 00:33:38,320
Speaker 2: I'm sure throughout the supply chain. Ultimately you'll see higher

628
00:33:38,360 --> 00:33:39,840
Speaker 2: prices at the consumer level.

629
00:33:40,000 --> 00:33:42,680
Speaker 1: And if in fact these tariffs continue, what's going to

630
00:33:42,680 --> 00:33:44,880
Speaker 1: happen to your employment situation in general?

631
00:33:45,080 --> 00:33:47,880
Speaker 2: You know, we don't lay anybody off. We were actually

632
00:33:47,960 --> 00:33:50,520
Speaker 2: voted one of the top fifty small companies in the

633
00:33:50,680 --> 00:33:54,040
Speaker 2: United States to work for last year, so you know,

634
00:33:54,120 --> 00:33:56,800
Speaker 2: we value our employees greatly and try to hold on

635
00:33:56,840 --> 00:34:00,520
Speaker 2: to them. Definitely, during COVID, everybody took a pay cut

636
00:34:00,600 --> 00:34:02,880
Speaker 2: for a period of time. It's just so that we

637
00:34:02,920 --> 00:34:05,480
Speaker 2: can survive. So we'll do what we have to do

638
00:34:05,560 --> 00:34:08,000
Speaker 2: to survive. We'll do it we have to do to

639
00:34:08,080 --> 00:34:11,239
Speaker 2: hold on to our employees. Ideally, we'll do we have

640
00:34:11,280 --> 00:34:13,880
Speaker 2: to do to continue manufacturing in the United States.

641
00:34:14,080 --> 00:34:15,160
Speaker 6: We're very adaptable.

642
00:34:15,960 --> 00:34:19,040
Speaker 1: As difficult as the tariffs may be for kcona Lab's business,

643
00:34:19,440 --> 00:34:22,080
Speaker 1: Bowman does believe they deserve to have a role in

644
00:34:22,160 --> 00:34:25,839
Speaker 1: economic policy, just not quite as broad a role as

645
00:34:25,880 --> 00:34:28,200
Speaker 1: they are being given. If you got to sit down

646
00:34:28,200 --> 00:34:32,399
Speaker 1: with your fellow Wharton graduate in the White House and

647
00:34:32,760 --> 00:34:34,400
Speaker 1: give him a piece of your mind about what he

648
00:34:34,440 --> 00:34:36,320
Speaker 1: should be doing, what would you advise him.

649
00:34:36,160 --> 00:34:39,919
Speaker 2: Well, I think tariffs are a strategic tool. I think

650
00:34:39,960 --> 00:34:46,319
Speaker 2: if they're used surgically, they can help. I think broadbrushed

651
00:34:46,400 --> 00:34:50,320
Speaker 2: tariffs don't work in my opinion, so I would encourage

652
00:34:50,400 --> 00:34:52,960
Speaker 2: him to be surgical about it. We're a small American

653
00:34:53,000 --> 00:34:57,480
Speaker 2: company trying to do business in China, where historically there

654
00:34:57,520 --> 00:35:00,760
Speaker 2: has been very little tariff of our goods go into China.

655
00:35:01,080 --> 00:35:04,000
Speaker 2: It's still very difficult to do business in China because

656
00:35:04,040 --> 00:35:07,880
Speaker 2: of the non tariff barriers to doing business, things like

657
00:35:07,920 --> 00:35:10,960
Speaker 2: the banking laws and the marketing laws and the administrative

658
00:35:11,480 --> 00:35:13,840
Speaker 2: issues that you have to deal with on a daily basis.

659
00:35:14,480 --> 00:35:15,200
Speaker 6: And it's not.

660
00:35:15,239 --> 00:35:17,680
Speaker 2: Clear to me that this tariff war that we're in

661
00:35:17,800 --> 00:35:20,439
Speaker 2: is going to impact any of that. All I see

662
00:35:20,480 --> 00:35:22,719
Speaker 2: happening is that we're going to increase the price of

663
00:35:22,760 --> 00:35:26,080
Speaker 2: goods coming into the United States. We're not going to

664
00:35:26,120 --> 00:35:30,040
Speaker 2: address the fundamental issues that are making it different difficult

665
00:35:30,120 --> 00:35:33,640
Speaker 2: for companies like mine to do business in places like China.

666
00:35:34,000 --> 00:35:36,960
Speaker 2: And that's those non tariff barriers to doing.

667
00:35:36,760 --> 00:35:40,360
Speaker 1: Business, whether it's to break down barriers to US exports

668
00:35:40,560 --> 00:35:44,000
Speaker 1: or to discourage production abroad. One of President Trump's main

669
00:35:44,120 --> 00:35:49,080
Speaker 1: goals is increasing US manufacturing overall something special contributor to

670
00:35:49,120 --> 00:35:52,440
Speaker 1: Larry Summers of Harvard says, tariffs will not accomplish.

671
00:35:52,840 --> 00:35:56,520
Speaker 10: This isn't a debate about whether manufacturing is important or not.

672
00:35:56,680 --> 00:36:01,120
Speaker 10: That's an important debate to have as we go forward.

673
00:36:01,520 --> 00:36:06,000
Speaker 10: But even if you take as given the central importance

674
00:36:06,080 --> 00:36:17,880
Speaker 10: of cutting edge manufacturing, taxing inputs, alienating partners, and enraging

675
00:36:19,120 --> 00:36:24,960
Speaker 10: those who control the external markets can't possibly be the

676
00:36:25,080 --> 00:36:32,960
Speaker 10: right strategy for rebuilding American manufacturers. Look, I take another

677
00:36:33,000 --> 00:36:37,080
Speaker 10: way of looking at this. We think of ourselves. We

678
00:36:37,120 --> 00:36:41,040
Speaker 10: think of automobiles as like a traditional industry that we're

679
00:36:41,120 --> 00:36:49,080
Speaker 10: trying to protect in a good way. But this is

680
00:36:49,640 --> 00:36:55,360
Speaker 10: slashing general motors profits. This is slash all the automobile

681
00:36:55,440 --> 00:37:00,680
Speaker 10: companies are complaining about our tariff policies because what they

682
00:37:00,800 --> 00:37:08,120
Speaker 10: mean for integrated production in North America. So even for

683
00:37:08,239 --> 00:37:12,720
Speaker 10: the people who were the traditional constituency for this kind

684
00:37:12,800 --> 00:37:21,480
Speaker 10: of policy, they're not seeing it as as a positive.

685
00:37:22,080 --> 00:37:29,560
Speaker 10: So this is not some debate between the coastal people

686
00:37:29,600 --> 00:37:33,760
Speaker 10: who like finance and movies as businesses and the people

687
00:37:33,760 --> 00:37:36,480
Speaker 10: in the middle of the country who are focused on

688
00:37:36,560 --> 00:37:41,799
Speaker 10: the heartland of manufacturing. Even if you just look at

689
00:37:41,960 --> 00:37:49,239
Speaker 10: what you're doing for the heartland of manufacturing higher priced inputs,

690
00:37:50,120 --> 00:37:58,800
Speaker 10: much more uncertainty for investors and alienating of customers can't

691
00:37:58,880 --> 00:38:03,680
Speaker 10: be the right strategy. There's a winner here. There really

692
00:38:03,719 --> 00:38:07,080
Speaker 10: is a winner here in the strategy that we are pursuing.

693
00:38:07,880 --> 00:38:11,960
Speaker 10: His name is she Jinping, and I hope we'll stop.

694
00:38:13,880 --> 00:38:16,400
Speaker 1: Up next. Data centers may be the path to an

695
00:38:16,440 --> 00:38:19,600
Speaker 1: AI future. But what if a community isn't sure they

696
00:38:19,680 --> 00:38:23,120
Speaker 1: want more data centers? We go to Warrenton, Virginia to

697
00:38:23,160 --> 00:38:36,080
Speaker 1: find out. This is a story about needs and wants.

698
00:38:36,719 --> 00:38:39,640
Speaker 1: Sometimes what we need may not be what we want.

699
00:38:40,280 --> 00:38:43,640
Speaker 1: We need more productivity from AI, but we don't want

700
00:38:43,840 --> 00:38:47,440
Speaker 1: young college graduates going unemployed. And at least some of

701
00:38:47,480 --> 00:38:50,880
Speaker 1: us may not want our local communities transformed by the

702
00:38:50,880 --> 00:38:59,040
Speaker 1: building of all those data centers we need for AI.

703
00:39:00,040 --> 00:39:05,680
Speaker 11: Warrenton is a tiny town ten thousand people. Picture Norman

704
00:39:05,800 --> 00:39:10,000
Speaker 11: Rockwell Pictures picture Mayberry and Andy Griffith.

705
00:39:10,560 --> 00:39:14,000
Speaker 1: Cindy Burbank lives in Warrenton, Virginia. She took us to

706
00:39:14,040 --> 00:39:16,960
Speaker 1: what would have been a data center down This.

707
00:39:17,320 --> 00:39:21,160
Speaker 11: We did pathway is the forty two acre site that

708
00:39:21,320 --> 00:39:23,920
Speaker 11: Amazon purchased to build a data center.

709
00:39:23,680 --> 00:39:28,480
Speaker 1: On without data centers to process, store, and distribute information.

710
00:39:28,800 --> 00:39:32,040
Speaker 1: We don't have AI with everything from internet search.

711
00:39:32,400 --> 00:39:35,600
Speaker 8: We are on an exponential curve and a relatively steep.

712
00:39:35,360 --> 00:39:37,360
Speaker 1: One to drug discovery and approval.

713
00:39:37,719 --> 00:39:43,200
Speaker 12: Is going to propel the biological research AI in unprecedented ways.

714
00:39:43,320 --> 00:39:46,000
Speaker 1: To reap all the benefits AI has to offer. Global

715
00:39:46,080 --> 00:39:48,759
Speaker 1: data center capacity is expected to grow at a rate

716
00:39:48,800 --> 00:39:52,080
Speaker 1: of fifteen percent per year, and even that may not

717
00:39:52,120 --> 00:39:55,160
Speaker 1: be enough to keep up with demand. Christine Wood is

718
00:39:55,160 --> 00:39:59,040
Speaker 1: the data center practice lead at Burns and MacDonald and

719
00:39:59,080 --> 00:40:01,440
Speaker 1: consults with company building data centers.

720
00:40:01,840 --> 00:40:04,719
Speaker 13: Power is becoming the long pole in the tent to

721
00:40:04,840 --> 00:40:07,840
Speaker 13: be able to find a data center site, and so

722
00:40:07,920 --> 00:40:11,239
Speaker 13: it really starts with that is the power puzzle that

723
00:40:11,280 --> 00:40:13,920
Speaker 13: we're seeing associated with the data center site.

724
00:40:14,719 --> 00:40:17,600
Speaker 1: But supplying all that power can cause disruption to the

725
00:40:17,640 --> 00:40:21,480
Speaker 1: communities where data centers want to be located. Not everyone

726
00:40:21,640 --> 00:40:23,560
Speaker 1: wants a data center for a neighbor.

727
00:40:23,960 --> 00:40:27,160
Speaker 9: We're situated about forty five miles to the west of Washington. D. C.

728
00:40:28,480 --> 00:40:31,920
Speaker 1: Carter Neville is a native of Warrenton, Virginia. He has

729
00:40:31,960 --> 00:40:34,520
Speaker 1: been the mayor of the town since twenty nineteen.

730
00:40:34,920 --> 00:40:37,759
Speaker 9: We are heavily depended upon our meal taxes, and our

731
00:40:37,840 --> 00:40:41,080
Speaker 9: largest meal sax provider is Chick fil A. We have

732
00:40:41,280 --> 00:40:44,480
Speaker 9: very fortunate to have as residents very low taxes in

733
00:40:44,520 --> 00:40:47,360
Speaker 9: terms of real estate taxes, and so pretty much our

734
00:40:47,440 --> 00:40:51,880
Speaker 9: largest employer is the school board, followed by the hospital

735
00:40:51,880 --> 00:40:52,480
Speaker 9: in Walmart.

736
00:40:53,120 --> 00:40:56,680
Speaker 1: Warrenton's data center journey began in twenty twenty three when

737
00:40:56,680 --> 00:40:59,560
Speaker 1: the town council voted to consider an Amazon data center

738
00:40:59,600 --> 00:41:01,880
Speaker 1: on a forty two acre site.

739
00:41:01,560 --> 00:41:03,840
Speaker 9: On the up side to provide a significant amount of revenue,

740
00:41:03,840 --> 00:41:06,400
Speaker 9: which we are desperately in need of. Our infrastructure is aging.

741
00:41:06,800 --> 00:41:08,359
Speaker 9: We're behind the eight ball and a lot of our

742
00:41:08,360 --> 00:41:12,040
Speaker 9: improvements needed to our water treatment and wastewater treatments especially.

743
00:41:13,080 --> 00:41:14,719
Speaker 9: You know, we have parts going back that can't even

744
00:41:14,760 --> 00:41:17,480
Speaker 9: be produced anymore, you know, parts of dating back to

745
00:41:17,480 --> 00:41:19,840
Speaker 9: the nineteen fifties, and we're desperate need of upgrading that

746
00:41:19,920 --> 00:41:22,279
Speaker 9: facility for the safety of our community.

747
00:41:22,480 --> 00:41:26,440
Speaker 1: But even the economic benefit couldn't outweigh the town's concerns.

748
00:41:26,920 --> 00:41:30,799
Speaker 11: Our frustration was the fact that we were being kept

749
00:41:30,840 --> 00:41:34,000
Speaker 11: in the dark. Then finally a year and a half later,

750
00:41:35,000 --> 00:41:38,080
Speaker 11: we learn about it because Dominion is proposing these massive

751
00:41:38,200 --> 00:41:42,200
Speaker 11: transmission lines, and that is what was like throwing a

752
00:41:42,280 --> 00:41:48,760
Speaker 11: match onto a powder keg. The citizens exploded and first

753
00:41:48,800 --> 00:41:53,000
Speaker 11: against the transmission lines and dominion, and then they learned

754
00:41:53,000 --> 00:41:55,440
Speaker 11: it was tied to the data center. Then we learned

755
00:41:55,480 --> 00:41:59,720
Speaker 11: from our neighboring jurisdictions what data centers were and the impacts.

756
00:41:59,760 --> 00:42:05,280
Speaker 11: They had a lot of noise, huge energy generation, monster

757
00:42:05,520 --> 00:42:11,799
Speaker 11: size to dominate your landscape and bringing transmission lines and

758
00:42:11,840 --> 00:42:18,000
Speaker 11: other problems. We did a Freedom of information Act for

759
00:42:18,120 --> 00:42:22,200
Speaker 11: the town records how many people spoke up or wrote

760
00:42:22,440 --> 00:42:27,400
Speaker 11: in support of the Amazon data center, how many opposed it?

761
00:42:27,800 --> 00:42:32,960
Speaker 11: And the result was, I'll show you this number, two thousand,

762
00:42:33,239 --> 00:42:37,400
Speaker 11: three hundred and eighty nine of our citizens, our residents,

763
00:42:38,520 --> 00:42:41,800
Speaker 11: took the time to oppose the data center. They came

764
00:42:41,880 --> 00:42:45,360
Speaker 11: to the town council meetings, they wrote emails to the

765
00:42:45,400 --> 00:42:51,600
Speaker 11: town council, and so forth. Eleven, only eleven people here

766
00:42:52,239 --> 00:42:55,359
Speaker 11: wanted that data center to happen, and yet we were

767
00:42:55,480 --> 00:43:00,480
Speaker 11: ignored because Amazon had cut a deal with our elected

768
00:43:00,520 --> 00:43:02,799
Speaker 11: officials before we even knew about it.

769
00:43:03,000 --> 00:43:06,680
Speaker 9: As the opposition grew, it got very very heated, it

770
00:43:06,719 --> 00:43:10,000
Speaker 9: got very very emotional. I think that there was a

771
00:43:10,040 --> 00:43:12,640
Speaker 9: lot of information that was selectively put out to the

772
00:43:12,680 --> 00:43:15,840
Speaker 9: community and so by the time it came to a vote,

773
00:43:15,880 --> 00:43:19,520
Speaker 9: it was a very divided community, and I'm grateful for

774
00:43:19,560 --> 00:43:21,360
Speaker 9: the council members that had the courage to do what

775
00:43:21,400 --> 00:43:22,319
Speaker 9: they believe was right.

776
00:43:23,239 --> 00:43:27,240
Speaker 1: Last month, Warrenton's City Council voted unanimously on a zoning

777
00:43:27,320 --> 00:43:31,880
Speaker 1: change that effectively bans data centers altogether. Among the citizens'

778
00:43:31,960 --> 00:43:34,800
Speaker 1: concerns is the amount of power a data center needs,

779
00:43:35,080 --> 00:43:37,040
Speaker 1: which would eat up their own access.

780
00:43:37,320 --> 00:43:37,520
Speaker 12: Yeah.

781
00:43:37,520 --> 00:43:39,080
Speaker 9: I think the biggest concern, and the one that the

782
00:43:39,120 --> 00:43:41,600
Speaker 9: industry itself really needs to address, is the energy usage.

783
00:43:41,600 --> 00:43:44,279
Speaker 9: That is just huge, and that is something as I've

784
00:43:44,280 --> 00:43:46,320
Speaker 9: pointed out to many people, the data centers are coming.

785
00:43:46,600 --> 00:43:49,520
Speaker 9: Whether one community says yes or no, does not ultimately

786
00:43:49,520 --> 00:43:52,480
Speaker 9: impact them to demand on energy. The industry and the

787
00:43:52,600 --> 00:43:54,839
Speaker 9: energy producers have got to figure that out. They've got

788
00:43:54,840 --> 00:43:57,680
Speaker 9: to find a better way of providing of that energy

789
00:43:58,040 --> 00:44:00,040
Speaker 9: without hitting the rate payers like you and me.

790
00:44:01,000 --> 00:44:04,520
Speaker 13: Fifteen years ago, a twelve megawatt data center was a

791
00:44:04,920 --> 00:44:08,319
Speaker 13: huge data center. Now with AI, what we're seeing on

792
00:44:08,360 --> 00:44:12,040
Speaker 13: the development side is more than a gigawatt.

793
00:44:11,480 --> 00:44:12,160
Speaker 6: On the campus.

794
00:44:12,200 --> 00:44:15,200
Speaker 13: And to put that in real terms for you, I'm

795
00:44:15,239 --> 00:44:21,000
Speaker 13: sitting in Dallas Dallas takes about three gigawatts to five gigawats,

796
00:44:21,040 --> 00:44:24,720
Speaker 13: depending on the time of year, to power the entire city.

797
00:44:25,920 --> 00:44:28,680
Speaker 1: One alternative to drawing the needed power from the grid

798
00:44:28,880 --> 00:44:32,319
Speaker 1: is small modular nuclear reactors, but even they may not

799
00:44:32,440 --> 00:44:33,640
Speaker 1: be enough to keep up.

800
00:44:33,920 --> 00:44:37,359
Speaker 12: When it comes to AI data centers, they are an

801
00:44:37,560 --> 00:44:42,560
Speaker 12: order of magnitude more power hungry than the data centers

802
00:44:42,600 --> 00:44:44,920
Speaker 12: we are used to since the Internet age.

803
00:44:45,520 --> 00:44:48,960
Speaker 1: Kr street Ar is the founder and CEO of Bloom Energy,

804
00:44:49,280 --> 00:44:51,720
Speaker 1: which just signed a deal to provide power to Oracle

805
00:44:51,800 --> 00:44:56,040
Speaker 1: for its infrastructure data centers in the United States. Is

806
00:44:56,080 --> 00:45:01,400
Speaker 1: there an overall approach to addressing that enormous demand.

807
00:45:01,040 --> 00:45:02,560
Speaker 8: For power today?

808
00:45:03,000 --> 00:45:07,200
Speaker 12: It is a scramble for that AI data centers. Collectively,

809
00:45:07,320 --> 00:45:11,879
Speaker 12: they're investing more than six hundred billion dollars just this year.

810
00:45:12,120 --> 00:45:17,440
Speaker 12: That's the publicly uttered commitments. That's mind boggling, right, almost

811
00:45:17,480 --> 00:45:20,600
Speaker 12: two billion dollars a day to feed this beast.

812
00:45:21,360 --> 00:45:25,440
Speaker 1: Sweetheart's. Bloom Energy was created to do just that, provide

813
00:45:25,480 --> 00:45:29,600
Speaker 1: clean energy on site, eliminating the need for transmission lines

814
00:45:29,640 --> 00:45:31,320
Speaker 1: and traditional power generators.

815
00:45:31,840 --> 00:45:38,240
Speaker 12: The world is completely digital dependent. Not having power even

816
00:45:38,400 --> 00:45:43,160
Speaker 12: for a millisecond is not an option because critical life

817
00:45:43,160 --> 00:45:46,840
Speaker 12: functions depend on it, like a surgery happening in a hospital,

818
00:45:46,920 --> 00:45:47,640
Speaker 12: for example.

819
00:45:48,000 --> 00:45:49,120
Speaker 8: So we said.

820
00:45:49,800 --> 00:45:53,399
Speaker 12: Power needs to be extremely reliable. You cannot get that

821
00:45:53,680 --> 00:45:58,880
Speaker 12: redundant reliability at very high nines unless you have on

822
00:45:59,000 --> 00:46:02,760
Speaker 12: site power. If you're going to have on site power,

823
00:46:03,719 --> 00:46:07,120
Speaker 12: because you're going to be breathing that air when you're

824
00:46:07,160 --> 00:46:10,520
Speaker 12: sitting in your office or when you're in your bedroom,

825
00:46:10,840 --> 00:46:14,000
Speaker 12: by definition, it needs to be clean, and it needs

826
00:46:14,000 --> 00:46:16,760
Speaker 12: to be very efficient, and it needs to be quiet.

827
00:46:16,960 --> 00:46:19,400
Speaker 12: So this is the principle on which we found a

828
00:46:19,480 --> 00:46:20,600
Speaker 12: bloom energy.

829
00:46:21,239 --> 00:46:25,080
Speaker 1: For the citizens of Warrenton, Virginia. Finding that optimal solution

830
00:46:25,280 --> 00:46:28,279
Speaker 1: of providing data center power in ways the community can

831
00:46:28,320 --> 00:46:32,920
Speaker 1: tolerate has been elusive, but that has real fiscal consequences

832
00:46:32,960 --> 00:46:33,520
Speaker 1: for the town.

833
00:46:33,960 --> 00:46:36,759
Speaker 9: We are so revenue strapped, but we're facing about ninety

834
00:46:36,800 --> 00:46:38,759
Speaker 9: one million dollars of investment in our waste water and

835
00:46:38,760 --> 00:46:41,239
Speaker 9: water treatment plants, and so you know that money is

836
00:46:41,239 --> 00:46:42,680
Speaker 9: just going to have to come out of debt unless

837
00:46:42,680 --> 00:46:45,960
Speaker 9: we find revenue streams. Our streets are cracked, you know,

838
00:46:45,960 --> 00:46:47,360
Speaker 9: we have things that are falling apart, and we're just

839
00:46:47,400 --> 00:46:49,239
Speaker 9: kind of keeping trying to keep the lights on.

840
00:46:49,600 --> 00:46:52,080
Speaker 1: Amazon declined to comment on the status of the warrant

841
00:46:52,160 --> 00:46:54,760
Speaker 1: and data center. For now, the fate of the project

842
00:46:54,800 --> 00:46:56,560
Speaker 1: will have to be decided by the courts.

843
00:46:56,880 --> 00:46:59,520
Speaker 9: There's a March hearing data on that. There's a citizen

844
00:46:59,560 --> 00:47:03,640
Speaker 9: group that challenged it in court, and so we are

845
00:47:03,680 --> 00:47:05,600
Speaker 9: waiting to see what that decision is in March. And

846
00:47:05,680 --> 00:47:08,640
Speaker 9: I believe that Amazon is probably moved on, but I

847
00:47:08,680 --> 00:47:12,040
Speaker 9: do expect that, you know, if they remain, if the

848
00:47:12,040 --> 00:47:15,960
Speaker 9: courts favor the town and Amazon, that they will eventually

849
00:47:15,960 --> 00:47:19,160
Speaker 9: build there. But as of now, I'm not sure where

850
00:47:19,160 --> 00:47:19,640
Speaker 9: it stands.

851
00:47:20,560 --> 00:47:24,120
Speaker 1: The transformative power of AI means that we need to

852
00:47:24,200 --> 00:47:27,000
Speaker 1: have it, and that means we need to have data

853
00:47:27,000 --> 00:47:31,040
Speaker 1: centers even with their enormous power demands. The question now

854
00:47:31,200 --> 00:47:34,040
Speaker 1: is where they will go and whether we can find

855
00:47:34,080 --> 00:47:36,600
Speaker 1: ways to make them the good neighbors that people in

856
00:47:36,640 --> 00:47:41,920
Speaker 1: towns like Warranton, Virginia will welcome. That does it for

857
00:47:42,000 --> 00:47:44,399
Speaker 1: us Here at Wall Street Week, I'm David Weston. See

858
00:47:44,400 --> 00:48:00,000
Speaker 1: you next week for more stories of capitalism.