WEBVTT - Bloomberg Businessweek Weekend - December 22nd, 2023

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<v Speaker 1>This is Bloomberg business Week Inside from the reporters and

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<v Speaker 1>editors who bring you America's most trusted business magazine, plus.

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<v Speaker 2>Global business, finance and tech news.

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<v Speaker 1>The Bloomberg Business Week Podcast with Carol Messer and Tim

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<v Speaker 1>Stenebeck from Bloomberg Radio.

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<v Speaker 3>Hi everyone, Merry Christmas, and welcome to the Bloomberg Business

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<v Speaker 3>Week Weekend Podcast. Carol is off this week, so we'll

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<v Speaker 3>have Bloomberg News Equities reporter Bailey Lipschultz and Cross Asset

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<v Speaker 3>reporter Emily Grafeo filling in as we celebrate the season

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<v Speaker 3>of giving. This hour, we'll learn about the windfall for

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<v Speaker 3>Florida based charities thanks to the state's recent wealth boom,

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<v Speaker 3>and why dozens of smaller American colleges and universities find

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<v Speaker 3>themselves in need as falling enrollments along with expiring government

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<v Speaker 3>aid push their finances to the brink. All of that

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<v Speaker 3>and more to come, We begin with a look at

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<v Speaker 3>a special edition of Bloomberg Business Week magazine. It's the

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<v Speaker 3>Longevity Issue. It tells some of the stories of the

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<v Speaker 3>billions of dollars that are being to live longer or

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<v Speaker 3>age slower in the never ending quest for the Fountain

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<v Speaker 3>of youth or to be forever young. Stories in there

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<v Speaker 3>also delve into the science behind the concept and how

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<v Speaker 3>researchers are making headway when it comes to longevity. And

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<v Speaker 3>that's where the cover story comes in. It's by Kristin V. Brown,

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<v Speaker 3>healthcare reporter for Bloomberg News. She writes about how the

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<v Speaker 3>biggest breakthrough in longevity may start with menopause. The story

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<v Speaker 3>starts with mice. With mice, yes, how do they play

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<v Speaker 3>into this?

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<v Speaker 4>Well, So, mice, as many people may know, are a

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<v Speaker 4>very common model animal in scientific research, and this scientist

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<v Speaker 4>in particular was giving mice ovarian tumors to try and

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<v Speaker 4>figure out whether a hormone called AMH that both men

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<v Speaker 4>and women produce lots of, whether that hormone could treat

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<v Speaker 4>the ovarian tumors. And when he did that, he gave

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<v Speaker 4>them this AMH gene therapy. And when he did that,

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<v Speaker 4>he realized something incredible was happening. Whether they actually treated

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<v Speaker 4>the tumors or not, I don't know. But one thing

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<v Speaker 4>they did do is make the ovaries of the mice shrink.

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<v Speaker 4>He said they looked like the ovaries of newborn mice.

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<v Speaker 4>And what that suggested to him was that it was

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<v Speaker 4>almost like a reverse aging process, that this hormone reversed

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<v Speaker 4>the aging of the mice's ovaries. And as any one

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<v Speaker 4>with ovaries nose, as your ovaries age, they age much

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<v Speaker 4>faster than the rest of your body, and eventually that

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<v Speaker 4>means you can't have a baby, eventually go through menopause,

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<v Speaker 4>which is horrible for many women. So this indicated that

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<v Speaker 4>he might be onto something huge, something that could really

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<v Speaker 4>solve a lot of problems that women face as the

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<v Speaker 4>reproductive system ages.

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<v Speaker 5>And are we seeing this AMH or other types of

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<v Speaker 5>science being used in more advanced animals, like more similar

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<v Speaker 5>animals cats, dogs.

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<v Speaker 4>Cats, Yeah, you must have read the story. I was

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<v Speaker 4>very excited to get a cat reference into the story

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<v Speaker 4>as a crazy cat lady. Yeah, so AMAH could have

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<v Speaker 4>a lot of potential uses because it does have this

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<v Speaker 4>really powerful sway over the whole reproductive system. And so

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<v Speaker 4>his first thought was, actually, well, maybe I could make

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<v Speaker 4>a better birth control with less side effects, And so

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<v Speaker 4>he decided to test that in cats and cats it's

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<v Speaker 4>a permanent birth control instead of removing their reproductive organs.

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<v Speaker 4>They worked with the Cincinnati Zoo and they gave cats

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<v Speaker 4>this AMH gene therapies similar to what he gave the mice,

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<v Speaker 4>and the cats were no longer able to get pregnant

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<v Speaker 4>without having to lose their reproductive organs, which is not

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<v Speaker 4>a pleasant experience.

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<v Speaker 3>So how can scientists and researchers extrapolate what's happening in

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<v Speaker 3>the ovaries and apply that across a broad spectrum for longevity?

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<v Speaker 6>Right?

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<v Speaker 4>So what's really interesting about the ovaries beyond just the

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<v Speaker 4>idea that you might be able to slow menopause or

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<v Speaker 4>extend a woman's number of fertile years, which would be

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<v Speaker 4>incredible on their own. Incredible on their own. Yeah, But

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<v Speaker 4>so it's really hot to study aging in general because

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<v Speaker 4>there's not a great you know, we can study it

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<v Speaker 4>in mice, but how similar are mice related to humans?

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<v Speaker 2>Right?

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<v Speaker 4>We can only learn so much about aging in humans

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<v Speaker 4>by studying animals that age much faster. Right, A mouse

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<v Speaker 4>has a much faster life cycle. So it's better to

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<v Speaker 4>study these things, But it's not similar enough to humans

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<v Speaker 4>to really know how it would do in humans, how

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<v Speaker 4>these therapies would do in humans. But the ovaries, they

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<v Speaker 4>are a human organ, and they happen to be a

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<v Speaker 4>human organ that ages at twice the rate of the

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<v Speaker 4>rest of the body. But it also ages in a

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<v Speaker 4>very short period of time. When a woman hits her thirties,

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<v Speaker 4>her reproductive system starts all of a sudden. Her ovaries

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<v Speaker 4>in particular start aging very quickly. So you could test

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<v Speaker 4>these therapies, these longevity therapies that people think might intervene

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<v Speaker 4>in aging broadly, more broadly, right, not just in one

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<v Speaker 4>organ system like wrapamycin. You could see if it has

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<v Speaker 4>an impact on the ovaries, and that's a much better

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<v Speaker 4>indication in some ways of whether it might have an

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<v Speaker 4>indication in humans for longevity than by studying my So

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<v Speaker 4>it could be a really great sort of testing proving

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<v Speaker 4>ground for other therapies.

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<v Speaker 5>What's the funding dynamic right now? I know it's been

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<v Speaker 5>a long story about women's health not receiving the funding

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<v Speaker 5>or their share of the share of funding that you

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<v Speaker 5>would expect. How does longevity and the potential for longevity

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<v Speaker 5>play into that?

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<v Speaker 4>Oh my gosh, this is one of those things that

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<v Speaker 4>any woman who reads this figure are like, what that

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<v Speaker 4>if women were not forced to be included in clinical

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<v Speaker 4>trials in the United States until like, very very recently.

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<v Speaker 7>I think it's so crazy.

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<v Speaker 4>Yeah, I think it was two thousand and nineteen ninety three,

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<v Speaker 4>nineteen ninety nin eighty three. Thank you, I don't have

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<v Speaker 4>it in front of me. Yeah, that's so recent. Was

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<v Speaker 4>I was in kindergarten then before they started including women

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<v Speaker 4>in clinical trials. And that's because there hasn't been interest

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<v Speaker 4>in women's health. But there's a lot of interest in

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<v Speaker 4>longevity in you know, lots of people in Silicon Valley

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<v Speaker 4>wanting to live forever. And so once you cast the

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<v Speaker 4>problem of the ovaries as a problem not just of

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<v Speaker 4>women's health, but of health for everyone. All of the

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<v Speaker 4>major diseases are diseases of aging, right heart disease, dementia.

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<v Speaker 4>They're all diseases brought on by agents. So once you

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<v Speaker 4>cast the problem of women's health, it's one that's related

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<v Speaker 4>to that. There's been a lot more interest in funding

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<v Speaker 4>startups and funding scientists who are doing.

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<v Speaker 3>This work based on your reporting and what you saw

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<v Speaker 3>reporting out this piece help promising is the research that's

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<v Speaker 3>being done.

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<v Speaker 4>Well, it's promising enough that I have convinced myself will

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<v Speaker 4>never have to go through menopause. I'm thirty seven. I

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<v Speaker 4>don't know how realistic that is. I don't know how

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<v Speaker 4>realistic that is. But I do think that there's really

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<v Speaker 4>really promising work that is starting to enter human clinical trials.

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<v Speaker 4>Not the mouth stuff that's not quite there yet, that's

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<v Speaker 4>a couple of years away. But there's some researchers at

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<v Speaker 4>Columbia who are doing something with a popular anti aging

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<v Speaker 4>drug called rapamycin that is entering trials now unrolling people.

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<v Speaker 4>And so I think that while we may not have

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<v Speaker 4>a way to pause menopause sorry anytime soon, I do

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<v Speaker 4>think that we will very soon out of this research

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<v Speaker 4>have better indications of how to treat infertility, of how

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<v Speaker 4>to treat the symptoms of menopause when people are going

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<v Speaker 4>through it. So I think that this work is very

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<v Speaker 4>promising in that regard, in that for the first time

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<v Speaker 4>in a very long time, we're having interest in just

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<v Speaker 4>looking at the basic biology of female reproduction and understanding

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<v Speaker 4>how it works. And I think that's going to lead

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<v Speaker 4>to many very big breakthroughs.

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<v Speaker 5>And Christ And this may be a dumb question in

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<v Speaker 5>terms of a clinical trial, though, if you're treating cancer,

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<v Speaker 5>you can at least compare a placebo versus someone who's

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<v Speaker 5>on a drug and how much longer they live. How

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<v Speaker 5>do you study the reproduction like that women's reproduction and

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<v Speaker 5>whether these work or not.

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<v Speaker 4>That's a good question. Well, so, in previous studies they

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<v Speaker 4>have looked at some of the signaling in the ovaries

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<v Speaker 4>of women who are slightly older, right like forties, late

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<v Speaker 4>thirties versus younger women, and you can see the rapid decline.

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<v Speaker 4>So if you know what to expect at different ages,

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<v Speaker 4>and you looked at a large group of people, then

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<v Speaker 4>I think you could see whether you're obviously there's variation

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<v Speaker 4>from person to person, but I think that you can

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<v Speaker 4>see whether it's looking more like what you would expect

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<v Speaker 4>of a younger ovary for example, and.

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<v Speaker 5>You you'll go ahead, No, I guess coming back to

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<v Speaker 5>the timeline, drug development's risky, it's expensive. Are there companies

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<v Speaker 5>that are funding or a lot of these studies being

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<v Speaker 5>done at universities?

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<v Speaker 8>Yeah, both both.

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<v Speaker 4>There are some companies in the space. One of the

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<v Speaker 4>big companies that I write about that came out of

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<v Speaker 4>the guy doing the amazing mousework, Aviva. They say that

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<v Speaker 4>their first therapy, which will be for infertility, is a

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<v Speaker 4>couple of years away from clinical trials, and their second

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<v Speaker 4>therapy is you know, maybe a couple of years behind

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<v Speaker 4>not so, I mean, which which is not a long

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<v Speaker 4>time in the time time scale of clinical trials. So

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<v Speaker 4>there are companies that are working on bringing this stuff

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<v Speaker 4>to the clinics soon.

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<v Speaker 3>Christian, you made this comment that stuck with me, which

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<v Speaker 3>is in Silicon Valley, a lot of people are interested

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<v Speaker 3>in longevity, and that's certainly what we see, and we

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<v Speaker 3>talked to actually Advance yesterday, who you know, had the

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<v Speaker 3>chance to go and visit this lab that and startup

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<v Speaker 3>that you know has one hundred and eighty million dollars

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<v Speaker 3>in funding from Sam Waltman, a huge figure in Silicon

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<v Speaker 3>Valley right now, do you think we're to the point

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<v Speaker 3>where the idea of longevity, the study of longevity, the

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<v Speaker 3>search for the fountain of youth has gone beyond you know,

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<v Speaker 3>these tech billionaires, yes, and is now more widespread.

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<v Speaker 4>Yeah, I definitely do. And I think that you have

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<v Speaker 4>to separate the people who want to you know, put

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<v Speaker 4>their brain in some kind of freezer box, in a

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<v Speaker 4>computer leader or something. Those guys, this sort of transhumanists,

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<v Speaker 4>from the people who really want to understand the biological

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<v Speaker 4>mechanisms at the root of aging, because we really don't

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<v Speaker 4>understand so much of why aging occurs. Right, Obviously, time environment,

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<v Speaker 4>it wears on the body. But once you begin to

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<v Speaker 4>unpack at a molecular level what's happening, then you can

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<v Speaker 4>certainly intervene in it in some way. How how much

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<v Speaker 4>effect we can have, who knows, But I think that

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<v Speaker 4>we're starting to see interest in not just the sort

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<v Speaker 4>of pie and the sky transhumanist, Silicon Valley billionaire stuff,

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<v Speaker 4>but the stuff that's doing the basic science that will

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<v Speaker 4>one day lead to better drugs, better therapies.

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<v Speaker 3>Well, it makes me have the question about the way

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<v Speaker 3>that drug companies approach this, which is treating things that

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<v Speaker 3>lead to death, things that you mentioned in your article cancer,

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<v Speaker 3>heart disease, trying to solve for those things rather than

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<v Speaker 3>solving for preventing those things. It's like two different approach

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<v Speaker 3>is here?

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<v Speaker 4>Yeah, it's two different approaches And I think a lot

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<v Speaker 4>of that comes down to it. It's hard to know,

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<v Speaker 4>Like we talked about why the ovary is such a

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<v Speaker 4>good model for this, right, how do you know if

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<v Speaker 4>your aging therapy is working? You have to follow people

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<v Speaker 4>for a really long time, which doesn't mean they won't

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<v Speaker 4>happen eventually, right, But in drug development, a lot of

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<v Speaker 4>what happens is we figure out something works, but we

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<v Speaker 4>don't know why it works. With aging research, we're trying

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<v Speaker 4>to figure out why do things stop working, and then

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<v Speaker 4>how do we intervene in that. So it is fundamentally

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<v Speaker 4>two different approaches, and I think that they're both valid, right,

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<v Speaker 4>But to intervene in aging, I think we're really going

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<v Speaker 4>to have to understand the fundamental basic biology here, which

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<v Speaker 4>is stuff that takes a lot of time and a

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<v Speaker 4>lot of money.

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<v Speaker 5>Well, one of the numbers that stood out to me

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<v Speaker 5>was in the story you have It, the market for

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<v Speaker 5>longevity drugs could reach more than forty four billion dollars

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<v Speaker 5>within the next decade. I look at that. Everyone wants

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<v Speaker 5>to talk about GLP ones and that's hundred billion dollar

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<v Speaker 5>market value. So I'm surprised that there's not more funding

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<v Speaker 5>and more of an advancement from private equity venture capital

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<v Speaker 5>given that. I mean, it's more than double. But that's

0:12:10.840 --> 0:12:13.360
<v Speaker 5>not a small pool of money, right, Yeah, it's not off.

0:12:14.200 --> 0:12:16.120
<v Speaker 4>It's not a small pool of money. But I think

0:12:16.120 --> 0:12:17.960
<v Speaker 4>that the time scale and how hard it is to

0:12:18.000 --> 0:12:21.080
<v Speaker 4>know whether it's working does make it a riskier intervention

0:12:21.240 --> 0:12:23.480
<v Speaker 4>versus weightless. You know, you either lose the weight or

0:12:23.480 --> 0:12:24.319
<v Speaker 4>you don't sell.

0:12:25.120 --> 0:12:26.800
<v Speaker 5>It's light easier. Everyone's on it.

0:12:27.200 --> 0:12:30.040
<v Speaker 7>Everyone's on it, so we're told. So I hear.

0:12:31.040 --> 0:12:33.040
<v Speaker 3>Okay, we only have a couple of minutes left, but

0:12:33.040 --> 0:12:34.760
<v Speaker 3>I wanted to make sure to get to other stories

0:12:34.760 --> 0:12:37.800
<v Speaker 3>that you've written recently, because you've been very busy. It's

0:12:37.800 --> 0:12:39.800
<v Speaker 3>not just the cover story of Bloomberg Business. You also

0:12:39.840 --> 0:12:42.360
<v Speaker 3>have a story out about sick shaming, pushing Americans to

0:12:42.400 --> 0:12:45.000
<v Speaker 3>take too much cold medicine. We're talking about this during

0:12:45.040 --> 0:12:48.000
<v Speaker 3>the break, and it's like, what's going on here? Because

0:12:49.000 --> 0:12:51.240
<v Speaker 3>when we're in you know, quote unquote lockdowns, when we're

0:12:51.280 --> 0:12:54.560
<v Speaker 3>all working from home, we were never sick. Now everyone's

0:12:54.559 --> 0:12:57.320
<v Speaker 3>coming back to the office, kids are back in school,

0:12:57.360 --> 0:13:01.480
<v Speaker 3>in daycare. I'm sick all the time, and we're just

0:13:01.559 --> 0:13:03.319
<v Speaker 3>like mainlining suit of fed.

0:13:03.480 --> 0:13:04.120
<v Speaker 7>Is that what's going on?

0:13:04.200 --> 0:13:04.360
<v Speaker 9>Yeah?

0:13:04.440 --> 0:13:06.840
<v Speaker 4>Okay, So this story actually came out of a personal

0:13:06.840 --> 0:13:09.640
<v Speaker 4>experience of my own. I've started just carrying around cough

0:13:09.720 --> 0:13:12.360
<v Speaker 4>drops and I take them all the time, even if

0:13:12.400 --> 0:13:15.760
<v Speaker 4>the reason I have a cough is because I swallowed

0:13:15.760 --> 0:13:18.320
<v Speaker 4>something weird, or you know, it's wintertime. It's very dry

0:13:18.360 --> 0:13:21.240
<v Speaker 4>in the Bloomberg offices, right now, so my makes my

0:13:21.280 --> 0:13:24.560
<v Speaker 4>throat always dry, so I just pop cough drops even

0:13:24.600 --> 0:13:25.520
<v Speaker 4>when I'm not sick.

0:13:25.640 --> 0:13:27.319
<v Speaker 7>You can know that I can go to you for coffee.

0:13:27.400 --> 0:13:29.560
<v Speaker 4>Yeah yeah, I have. I have a huge stash in

0:13:29.600 --> 0:13:31.839
<v Speaker 4>my desk, so come on by h And I started

0:13:31.840 --> 0:13:33.760
<v Speaker 4>to wonder are other people doing this?

0:13:34.800 --> 0:13:35.960
<v Speaker 10>Is it good for you?

0:13:36.000 --> 0:13:37.720
<v Speaker 4>It can't be good for you to take this main

0:13:37.760 --> 0:13:40.079
<v Speaker 4>cough drops. I mean the calories alone, right, got to

0:13:40.120 --> 0:13:44.839
<v Speaker 4>add up. That's why you need the GLP ones anyways,

0:13:44.960 --> 0:13:47.720
<v Speaker 4>So so we looked into it. I looked into it

0:13:47.720 --> 0:13:53.000
<v Speaker 4>with our great reporter Kaye Lapara, and we found that absolutely,

0:13:53.200 --> 0:13:55.320
<v Speaker 4>you can take too much cough and cold medicine. It

0:13:55.400 --> 0:13:57.040
<v Speaker 4>is not a good idea, and that this is a

0:13:57.080 --> 0:13:59.960
<v Speaker 4>thing that is happening. People feel pressure to go to work,

0:14:00.480 --> 0:14:03.079
<v Speaker 4>to go to Thanksgiving dinner, to go to happy hours

0:14:04.400 --> 0:14:07.240
<v Speaker 4>even though they're not feeling great. But they don't want

0:14:07.320 --> 0:14:09.320
<v Speaker 4>to have people looking at them giving them that look

0:14:09.400 --> 0:14:11.240
<v Speaker 4>like why are you here even though you're sick. So

0:14:11.280 --> 0:14:14.160
<v Speaker 4>people are just doing what I'm doing and taking tons

0:14:14.320 --> 0:14:15.960
<v Speaker 4>of cough medicine and.

0:14:16.080 --> 0:14:17.640
<v Speaker 5>Just looking at some of the numbers, I mean US

0:14:17.720 --> 0:14:20.920
<v Speaker 5>sales of upper respiratory over the counter medications of twenty

0:14:20.960 --> 0:14:23.320
<v Speaker 5>three percent to almost twelve billion dollars in the fifty

0:14:23.360 --> 0:14:24.720
<v Speaker 5>two weeks through early December.

0:14:25.960 --> 0:14:27.840
<v Speaker 4>Great for them, bad for us.

0:14:27.920 --> 0:14:28.920
<v Speaker 8>It's bad for us.

0:14:29.040 --> 0:14:31.800
<v Speaker 7>Yeah, totally. I mean, I feel like I'm coming down

0:14:31.840 --> 0:14:33.200
<v Speaker 7>to something just talking about this.

0:14:33.920 --> 0:14:36.400
<v Speaker 4>Probably got it from me. Yeah, just kidding.

0:14:36.720 --> 0:14:38.440
<v Speaker 7>Well, I'll get some cough drops from me. Okay.

0:14:38.520 --> 0:14:38.680
<v Speaker 1>Hey.

0:14:38.720 --> 0:14:41.320
<v Speaker 3>Check out all of Christian's stories available on the Bloomberg

0:14:41.400 --> 0:14:43.520
<v Speaker 3>Terminal and at Bloomberg dot com, and pick up the

0:14:43.600 --> 0:14:46.720
<v Speaker 3>new issue of Bloomberg BusinessWeek. It's the Longevity issue on Newstance.

0:14:46.720 --> 0:14:49.200
<v Speaker 3>It's already online at Bloomberg dot com, slash BusinessWeek and

0:14:49.320 --> 0:14:52.120
<v Speaker 3>on the Bloomberg Terminal. Kristin B. Brown is healthcare reporter

0:14:52.160 --> 0:14:54.920
<v Speaker 3>for Bloomberg News. Joining us this afternoon and the Bloomberg

0:14:54.960 --> 0:14:56.120
<v Speaker 3>Interactive broke their studio.

0:14:56.320 --> 0:15:03.840
<v Speaker 1>This is Business Week. You're listening to the Bloomberg Business

0:15:03.840 --> 0:15:07.480
<v Speaker 1>Week podcast. Catch us live weekday afternoons from three to

0:15:07.520 --> 0:15:08.920
<v Speaker 1>six Eastern Listen.

0:15:08.720 --> 0:15:11.640
<v Speaker 2>On Bloomberg dot com, the iHeartRadio app, and.

0:15:11.560 --> 0:15:15.000
<v Speaker 1>The Bloomberg Business App, or watch us live on YouTube.

0:15:15.920 --> 0:15:19.120
<v Speaker 3>The title of managing director at Wall Street Firms is

0:15:19.200 --> 0:15:23.040
<v Speaker 3>typically the most senior rank in finance apart from those

0:15:23.400 --> 0:15:26.240
<v Speaker 3>who are executives. It's a position that comes with enhanced

0:15:26.240 --> 0:15:30.800
<v Speaker 3>compensation and presumably power, and women at that level are

0:15:30.840 --> 0:15:33.760
<v Speaker 3>often touted by banks as examples of how gender is

0:15:33.840 --> 0:15:38.000
<v Speaker 3>no bar to advancement. But Ardith Lindsay, who joined City

0:15:38.080 --> 0:15:40.200
<v Speaker 3>Bank back in two thousand and seven, is now suing

0:15:40.240 --> 0:15:43.560
<v Speaker 3>the bank, accusing it of tolerating years of sexual harassment

0:15:43.560 --> 0:15:47.400
<v Speaker 3>and assault against her. Says seniority didn't protect her, nor

0:15:47.480 --> 0:15:50.400
<v Speaker 3>did the elevation of another woman, Jane Fraser, to be

0:15:50.440 --> 0:15:53.960
<v Speaker 3>Citygroup's chief executive officer. This is one of the most

0:15:54.000 --> 0:15:57.160
<v Speaker 3>read stories on the Bloomberg terminal page. Smith wrote it.

0:15:57.160 --> 0:15:59.960
<v Speaker 3>She's consumer finance reporter at Bloomberg News and she's here

0:16:00.240 --> 0:16:03.760
<v Speaker 3>in the Bloomberg Interactive broker's studio page. Good to see you.

0:16:03.800 --> 0:16:06.920
<v Speaker 3>This is a really powerful and important story. Tell us

0:16:07.600 --> 0:16:08.720
<v Speaker 3>who Artith Lindsay is.

0:16:09.200 --> 0:16:11.880
<v Speaker 9>Hey, folks, thanks for having me, and this is a

0:16:11.920 --> 0:16:16.040
<v Speaker 9>really important story. And Ardith sat down with me and

0:16:16.400 --> 0:16:19.560
<v Speaker 9>recently and told me sort of her side of the story.

0:16:19.600 --> 0:16:23.400
<v Speaker 9>And we have poured over the complaint that she filed

0:16:23.400 --> 0:16:28.040
<v Speaker 9>against City on November twentieth, and as you sort of said,

0:16:28.080 --> 0:16:31.760
<v Speaker 9>you know, the one thing that really struck a number

0:16:31.840 --> 0:16:33.960
<v Speaker 9>of folks who have heard her story as the fact

0:16:34.000 --> 0:16:38.000
<v Speaker 9>that Ardith sort of rose through the ranks within city

0:16:38.120 --> 0:16:43.280
<v Speaker 9>and was, you know, reached that very you know, it's

0:16:43.280 --> 0:16:46.640
<v Speaker 9>a it's a significant achievement on Wall Street to have

0:16:46.720 --> 0:16:51.800
<v Speaker 9>the title of managing director and Artith Artith achieved that

0:16:52.320 --> 0:16:54.880
<v Speaker 9>achieved that goal during her period and during her tenure

0:16:54.920 --> 0:16:58.360
<v Speaker 9>and career at the bank. And she's not alone. She's

0:16:58.400 --> 0:17:02.040
<v Speaker 9>not the first managing director to come forward with allegations

0:17:02.080 --> 0:17:06.879
<v Speaker 9>of toxic workplace culture and harassment and just kind of

0:17:06.920 --> 0:17:10.440
<v Speaker 9>horrific mistreatment across the board. She's joined by a number

0:17:10.480 --> 0:17:13.960
<v Speaker 9>of other women who have come forward to tell their

0:17:14.000 --> 0:17:17.640
<v Speaker 9>stories from places like Goldman Sachs and other Wall Street

0:17:17.720 --> 0:17:19.480
<v Speaker 9>institutions as well.

0:17:19.520 --> 0:17:23.480
<v Speaker 6>In the story, there are a few people quoted who

0:17:23.520 --> 0:17:26.480
<v Speaker 6>said it gets worse. There was a subhead in your

0:17:26.520 --> 0:17:29.879
<v Speaker 6>story just it gets worse. Can you talk about why

0:17:30.280 --> 0:17:33.160
<v Speaker 6>that is and what these people that you interviewed actually

0:17:33.400 --> 0:17:36.000
<v Speaker 6>said that they're working at a firm, they're getting more

0:17:36.040 --> 0:17:39.120
<v Speaker 6>and more senior, but they're women, and so somehow their

0:17:39.160 --> 0:17:42.520
<v Speaker 6>experience at the firm is getting more unpleasant.

0:17:42.920 --> 0:17:46.320
<v Speaker 9>Certainly, so one person that I interviewed for this story

0:17:46.480 --> 0:17:50.400
<v Speaker 9>was Jamie Fiori Higgins. She's a former Goldman Sachs managing

0:17:50.440 --> 0:17:53.879
<v Speaker 9>director and in twenty twenty two she actually wrote a

0:17:53.880 --> 0:17:55.680
<v Speaker 9>book about her experience called.

0:17:55.520 --> 0:17:57.680
<v Speaker 3>Bully Market had on the program.

0:17:57.440 --> 0:18:00.879
<v Speaker 9>Yeah, and it was a very very compelling story. And

0:18:01.359 --> 0:18:04.000
<v Speaker 9>you know, I interviewed her and she had the chance

0:18:04.040 --> 0:18:07.119
<v Speaker 9>to review artist's complaints in her allegations mentioned in the

0:18:07.160 --> 0:18:10.080
<v Speaker 9>lawsuit against city. And you know, she said, based on

0:18:10.160 --> 0:18:13.880
<v Speaker 9>her experience at Goldman Sachs that there, as you sort

0:18:13.880 --> 0:18:17.800
<v Speaker 9>of ascend that corporate ladder, if you will, the stakes

0:18:17.840 --> 0:18:20.800
<v Speaker 9>get higher as you get high. As you get higher,

0:18:20.880 --> 0:18:25.600
<v Speaker 9>essentially with the hierarchy, the compensation is much greater. There's

0:18:25.720 --> 0:18:28.560
<v Speaker 9>more leverage, and as she said, banks sort of have

0:18:28.680 --> 0:18:31.159
<v Speaker 9>the leverage to say, well, where else are you going

0:18:31.240 --> 0:18:33.600
<v Speaker 9>to go that pays you this much? And you know,

0:18:33.680 --> 0:18:37.960
<v Speaker 9>for women on Wall Street, it's there have been many

0:18:38.240 --> 0:18:41.560
<v Speaker 9>claims and stories and allegations of this sort of so

0:18:41.640 --> 0:18:45.159
<v Speaker 9>called boys culture or locker room culture on places like

0:18:45.200 --> 0:18:48.800
<v Speaker 9>training room floors and places that have been historically occupied

0:18:48.880 --> 0:18:54.560
<v Speaker 9>by men. And it's extremely challenging for women still in

0:18:54.600 --> 0:18:57.920
<v Speaker 9>today's day and age to be promoted to managing directors.

0:18:58.320 --> 0:19:01.400
<v Speaker 3>So you were able to speak with artists at length

0:19:01.480 --> 0:19:03.800
<v Speaker 3>about this, you were able to look at the lawsuit

0:19:03.800 --> 0:19:06.840
<v Speaker 3>and the allegations contained in the lawsuit for its part,

0:19:06.840 --> 0:19:09.399
<v Speaker 3>What did City say when you presented them with what

0:19:09.440 --> 0:19:09.960
<v Speaker 3>you've learned?

0:19:10.119 --> 0:19:13.240
<v Speaker 9>So City referred us to a sort of a previous

0:19:13.280 --> 0:19:18.560
<v Speaker 9>statement that they that they mentioned. But you know, City

0:19:19.160 --> 0:19:23.120
<v Speaker 9>did call one of the named individuals and artists complaint,

0:19:23.480 --> 0:19:28.320
<v Speaker 9>They did call the conduct deplorable, and they did have

0:19:28.359 --> 0:19:31.920
<v Speaker 9>the they have had had this time to review the

0:19:32.200 --> 0:19:37.520
<v Speaker 9>allegations contained in artist's complaint. But you know, it was

0:19:37.560 --> 0:19:41.360
<v Speaker 9>also interesting that after the fact, City came forward and

0:19:41.680 --> 0:19:46.840
<v Speaker 9>actually issued a memo that essentially advised traders to anyone

0:19:46.840 --> 0:19:50.159
<v Speaker 9>within se City actually to come forward and sort of

0:19:50.840 --> 0:19:53.720
<v Speaker 9>raise a flag if they see anything along the lines

0:19:53.760 --> 0:19:55.920
<v Speaker 9>of mistreatment or misconduct in the workplace.

0:19:56.200 --> 0:19:59.399
<v Speaker 3>And she's right now on leave from city, correct, So

0:19:59.440 --> 0:20:02.080
<v Speaker 3>we know, we don't know how this plays out, if

0:20:02.080 --> 0:20:05.880
<v Speaker 3>she will actually return in her previous capacity to the firm.

0:20:06.040 --> 0:20:10.040
<v Speaker 9>Indeed, and I think that it's also very important to

0:20:10.040 --> 0:20:12.199
<v Speaker 9>note that these are allegations. This is a lawsuit that's

0:20:12.280 --> 0:20:15.520
<v Speaker 9>run forward and been brought forward, and we have to

0:20:15.560 --> 0:20:18.880
<v Speaker 9>sort of let the legal process proceed as it may.

0:20:19.400 --> 0:20:23.440
<v Speaker 9>But it is worth noting that, just to step back here,

0:20:22.840 --> 0:20:26.960
<v Speaker 9>there have been multiple lawsuits brought against other banks as well,

0:20:27.080 --> 0:20:30.240
<v Speaker 9>not just City Group. Smith Barney settled for one hundred

0:20:30.240 --> 0:20:33.800
<v Speaker 9>and fifty million. That was the infamous nineteen nineties Boom

0:20:33.840 --> 0:20:37.840
<v Speaker 9>Boom room lawsuit. But there have been many, many, other,

0:20:39.160 --> 0:20:43.879
<v Speaker 9>many other instances describing workplace misconduct on Wall Street firms.

0:20:43.920 --> 0:20:45.440
<v Speaker 9>Within Wall Street firms.

0:20:45.200 --> 0:20:49.119
<v Speaker 6>You mentioned City kind of telling employees they should raise

0:20:49.160 --> 0:20:53.080
<v Speaker 6>a flag. What exactly does that mean, you know, raise

0:20:53.080 --> 0:20:55.960
<v Speaker 6>a flag to other misconduct. Are the big banks actually

0:20:56.240 --> 0:21:00.880
<v Speaker 6>doing anything else to try and combat this kind of

0:21:01.560 --> 0:21:02.520
<v Speaker 6>workplace culture.

0:21:03.320 --> 0:21:05.440
<v Speaker 9>I think that's a good question, and I think frankly

0:21:05.600 --> 0:21:08.159
<v Speaker 9>it will be at least in this manner. We'll have

0:21:08.200 --> 0:21:11.200
<v Speaker 9>to sort of let the judge and review the case

0:21:11.280 --> 0:21:13.840
<v Speaker 9>and sort of make make a claim in that in

0:21:13.840 --> 0:21:17.919
<v Speaker 9>that front, But there have been, you know, to the banks,

0:21:18.040 --> 0:21:20.199
<v Speaker 9>to the bank's credit, there have been efforts made to

0:21:20.840 --> 0:21:25.000
<v Speaker 9>boost representation at sort of all tiers of the firms.

0:21:25.440 --> 0:21:29.439
<v Speaker 9>There have been efforts to promote more women in managing

0:21:29.480 --> 0:21:32.640
<v Speaker 9>director classes. There have been also more diversity and inclusion

0:21:32.640 --> 0:21:38.960
<v Speaker 9>efforts kind of throughout Wall Street. But in terms of

0:21:39.000 --> 0:21:41.480
<v Speaker 9>these specific allegations, we'll have to see how they kind

0:21:41.480 --> 0:21:42.440
<v Speaker 9>of play out over time.

0:21:43.119 --> 0:21:47.159
<v Speaker 3>Is how should we read into the fact that this

0:21:47.200 --> 0:21:50.960
<v Speaker 3>is happening at City and it's the only big bank

0:21:51.040 --> 0:21:53.320
<v Speaker 3>that is led by a woman right now, It's a

0:21:53.320 --> 0:21:55.200
<v Speaker 3>point that you made in this story, but I want

0:21:55.240 --> 0:21:56.480
<v Speaker 3>you to talk a little bit about that.

0:21:58.200 --> 0:22:02.760
<v Speaker 9>I will say that that is a fact there that

0:22:02.840 --> 0:22:07.600
<v Speaker 9>there are a number of women occupying sort of the

0:22:07.680 --> 0:22:12.359
<v Speaker 9>upper nearly the uppermost echelons of these banks, but not.

0:22:12.400 --> 0:22:14.640
<v Speaker 3>And we just spoke to Hannah Levit earlier who talked about,

0:22:14.720 --> 0:22:17.920
<v Speaker 3>you know, the JP Morgan who could succeed Jamie Diamond

0:22:17.960 --> 0:22:19.840
<v Speaker 3>excuse me, yes, and two women are.

0:22:19.720 --> 0:22:22.080
<v Speaker 9>On the list there, and that is you know there,

0:22:22.800 --> 0:22:25.000
<v Speaker 9>that is a fact that Jane Fraser of them among

0:22:25.440 --> 0:22:29.040
<v Speaker 9>is the only CEO among the biggest of the biggest

0:22:29.359 --> 0:22:34.679
<v Speaker 9>banks in the world. And there, like I said, there

0:22:34.720 --> 0:22:39.480
<v Speaker 9>are managing director sewer women, but in terms of the

0:22:39.520 --> 0:22:42.240
<v Speaker 9>C suite, the Jane is the only one.

0:22:42.520 --> 0:22:46.080
<v Speaker 3>Hey, a lot of people are reading this story. Can

0:22:46.080 --> 0:22:47.600
<v Speaker 3>you talk just a little bit about any of the

0:22:47.600 --> 0:22:49.600
<v Speaker 3>reaction that you've gotten since it's been published.

0:22:50.640 --> 0:22:53.520
<v Speaker 9>Well, I'll say actually the reaction that Artith has received,

0:22:53.560 --> 0:22:55.560
<v Speaker 9>which she shared with me when when I sat down

0:22:55.600 --> 0:22:58.040
<v Speaker 9>with her for her interview. She said that she's received

0:22:58.440 --> 0:23:02.240
<v Speaker 9>dozens of messages from not just women, but men within

0:23:02.240 --> 0:23:04.320
<v Speaker 9>the firm as well, who have come forward to just

0:23:04.760 --> 0:23:08.040
<v Speaker 9>kind of express their support for her claims, but also

0:23:08.200 --> 0:23:11.359
<v Speaker 9>just to say that she is sort of sharing things

0:23:11.359 --> 0:23:13.600
<v Speaker 9>that they have observed perhaps in the past as well

0:23:13.840 --> 0:23:18.600
<v Speaker 9>based on her allegations. And I will say too, Jamie

0:23:19.000 --> 0:23:22.000
<v Speaker 9>Higgins had the same reaction after her story was published.

0:23:22.040 --> 0:23:23.840
<v Speaker 3>Well, everybody check out this story. It's among the most

0:23:23.840 --> 0:23:27.560
<v Speaker 3>read on the Bloomberg Terminal page. Smith is here with us.

0:23:27.600 --> 0:23:30.119
<v Speaker 3>She is consumer finance reporter at Bloomberg News.

0:23:33.119 --> 0:23:36.720
<v Speaker 1>You're listening to the Bloomberg Business Week podcast. Catch us

0:23:36.720 --> 0:23:40.760
<v Speaker 1>live weekday afternoons from three to six Easter on Bloomberg Radio,

0:23:40.960 --> 0:23:44.240
<v Speaker 1>the Bloomberg Business app, and YouTube. You can also listen

0:23:44.320 --> 0:23:47.440
<v Speaker 1>live on Amazon Alexa from our flagship New York station,

0:23:47.880 --> 0:23:50.679
<v Speaker 1>Just say Alexa play Bloomberg eleven thirty.

0:23:51.960 --> 0:23:53.840
<v Speaker 3>Well, like I said, it's today's big take. It is

0:23:53.880 --> 0:23:56.080
<v Speaker 3>one of the most read stories on the Bloomberg terminal.

0:23:56.119 --> 0:24:00.560
<v Speaker 3>It's about the crisis unfolding in higher education. The pressure

0:24:00.600 --> 0:24:04.000
<v Speaker 3>that small nonprofit colleges and universities are facing as they

0:24:04.040 --> 0:24:08.080
<v Speaker 3>bear the brunt of a massive shift in demographics. We're

0:24:08.080 --> 0:24:12.480
<v Speaker 3>talking falling enrollment, persistent operating losses, and that's led to

0:24:12.520 --> 0:24:16.040
<v Speaker 3>about ten four year institutions to close or announce plans to.

0:24:15.960 --> 0:24:17.320
<v Speaker 7>Close their doors this year.

0:24:18.480 --> 0:24:21.560
<v Speaker 3>Fitch Ratings actually predicts twenty to twenty five schools will

0:24:21.560 --> 0:24:24.719
<v Speaker 3>close each year going forward. That's roughly double the annual

0:24:24.760 --> 0:24:28.400
<v Speaker 3>average of closures at private, nonprofit four year institutions over

0:24:28.480 --> 0:24:31.160
<v Speaker 3>the last decade. Well, the team here at Bloomberg News

0:24:31.160 --> 0:24:34.399
<v Speaker 3>did an exhaustive analysis of one hundred thousand data points

0:24:34.600 --> 0:24:37.240
<v Speaker 3>from about one thousand US schools. These schools had fewer

0:24:37.280 --> 0:24:40.960
<v Speaker 3>than five thousand students, and they had some pretty surprising findings.

0:24:40.960 --> 0:24:44.520
<v Speaker 3>About one hundred and seventy small nonprofit colleges and universities

0:24:44.720 --> 0:24:48.280
<v Speaker 3>are facing increasing pressure. Nick Corolo is part of that

0:24:48.359 --> 0:24:51.399
<v Speaker 3>team here at Bloomberg News. He's municipal finance and schools reporter.

0:24:51.960 --> 0:24:55.280
<v Speaker 3>He joins us here in the Bloomberg Interactive broker Studios.

0:24:55.600 --> 0:24:58.720
<v Speaker 3>Nick a Jarring story. I couldn't put it down, partly

0:24:58.760 --> 0:25:01.400
<v Speaker 3>because I had to search for the name of my small, private,

0:25:01.440 --> 0:25:04.679
<v Speaker 3>nonprofit Alma Mater, which fortunately, to my relief, was not

0:25:04.720 --> 0:25:06.480
<v Speaker 3>on there. But take us through what you and the

0:25:06.520 --> 0:25:08.320
<v Speaker 3>team did in this huge data analysis.

0:25:08.720 --> 0:25:09.080
<v Speaker 5>Sure.

0:25:09.800 --> 0:25:12.119
<v Speaker 8>Yeah, So, like you mentioned, I'm a reporter on the

0:25:12.200 --> 0:25:15.360
<v Speaker 8>municipal finance team and we cover the debt that these

0:25:15.400 --> 0:25:17.680
<v Speaker 8>schools sell in the bond market. And for a long

0:25:17.720 --> 0:25:22.280
<v Speaker 8>time we had been hearing investors growing increasingly concerned about

0:25:22.320 --> 0:25:25.840
<v Speaker 8>this industry. So we wanted to write a story in

0:25:25.880 --> 0:25:28.400
<v Speaker 8>a very data driven way to understand, you know, how

0:25:28.600 --> 0:25:33.040
<v Speaker 8>have schools in this world changed over the last ten

0:25:33.160 --> 0:25:37.520
<v Speaker 8>fifteen years. And the federal government has a very comprehensive

0:25:37.600 --> 0:25:42.480
<v Speaker 8>database on schools called ipeds, So we pulled as much

0:25:42.560 --> 0:25:45.600
<v Speaker 8>data as we could that could be uniformly tracked over

0:25:45.680 --> 0:25:49.280
<v Speaker 8>this whole universe, going back to at least two thousand

0:25:49.280 --> 0:25:51.720
<v Speaker 8>and eight, a little bit further back, and we found

0:25:51.880 --> 0:25:54.959
<v Speaker 8>just that, like you mentioned, because of these demographic changes,

0:25:55.000 --> 0:25:58.800
<v Speaker 8>there's fewer students enrolling in college, there's fewer students graduating

0:25:58.800 --> 0:26:00.920
<v Speaker 8>from high school, and that's projected to worsen over the

0:26:01.000 --> 0:26:03.720
<v Speaker 8>coming years too. Schools are starting to see a lot

0:26:03.760 --> 0:26:04.280
<v Speaker 8>more stress.

0:26:05.240 --> 0:26:08.040
<v Speaker 6>Can you talk about some of the examples of the

0:26:08.160 --> 0:26:11.479
<v Speaker 6>signs of stress. What does it actually look like when

0:26:11.520 --> 0:26:14.040
<v Speaker 6>a school is under stress? According to Bloomberg's analysis.

0:26:14.280 --> 0:26:17.680
<v Speaker 8>Sure, so we wanted to create some metrics that could

0:26:17.720 --> 0:26:21.720
<v Speaker 8>be uniformly tracked, and we consulted with six different experts

0:26:21.760 --> 0:26:24.760
<v Speaker 8>to help us kind of create these metrics. And this

0:26:24.840 --> 0:26:28.360
<v Speaker 8>is what we landed on. So a high acceptance rate,

0:26:28.440 --> 0:26:31.000
<v Speaker 8>you know, school is accepting on average over eighty percent

0:26:31.040 --> 0:26:34.360
<v Speaker 8>of its applicants over the last three years on average.

0:26:34.720 --> 0:26:37.040
<v Speaker 8>And then similarly, a low yield, so you're giving out

0:26:37.080 --> 0:26:39.320
<v Speaker 8>a lot of offers, but not a lot of students

0:26:39.359 --> 0:26:42.399
<v Speaker 8>are accepting those offers and coming to campus. A pretty

0:26:42.400 --> 0:26:46.280
<v Speaker 8>clear one is just consecutive enrollment declines. Your student body

0:26:46.320 --> 0:26:49.600
<v Speaker 8>is shrinking year after year after year. And this one's

0:26:49.600 --> 0:26:53.080
<v Speaker 8>a little more complicated, something called rising institutional aid, which

0:26:53.119 --> 0:26:56.000
<v Speaker 8>is basically how much the school is discounting the tuition

0:26:56.160 --> 0:26:59.840
<v Speaker 8>sticker price. And finally we looked at the core of

0:27:00.080 --> 0:27:02.760
<v Speaker 8>breeding losses. You know, is the school making money or

0:27:02.800 --> 0:27:05.920
<v Speaker 8>losing money from its core operations when you strip out

0:27:05.920 --> 0:27:08.679
<v Speaker 8>any of the money it makes on its endowment or investments.

0:27:08.960 --> 0:27:10.959
<v Speaker 3>Nick, what's so interesting about this story is it kind

0:27:11.000 --> 0:27:12.960
<v Speaker 3>of runs counter to the narrative that I think a

0:27:12.960 --> 0:27:15.600
<v Speaker 3>lot of people have about higher education in this country,

0:27:15.640 --> 0:27:19.359
<v Speaker 3>which is that it's becoming increasingly competitive, it's becoming increasingly expensive,

0:27:19.520 --> 0:27:22.199
<v Speaker 3>and it's harder and harder for you know, people's kids

0:27:22.560 --> 0:27:25.720
<v Speaker 3>to actually get into school. But not all colleges and

0:27:25.800 --> 0:27:28.199
<v Speaker 3>universities are created equal here, So talk a little bit

0:27:28.240 --> 0:27:32.040
<v Speaker 3>about sort of the differences and the types of schools

0:27:32.040 --> 0:27:33.720
<v Speaker 3>that are under pressure right now.

0:27:34.040 --> 0:27:36.159
<v Speaker 8>Sure, the way the experts think about this, and I

0:27:36.160 --> 0:27:38.359
<v Speaker 8>think a lot of school leadership thinks about this, is

0:27:38.400 --> 0:27:42.560
<v Speaker 8>there is a massive bifurcation happening in the higher education market.

0:27:42.960 --> 0:27:47.400
<v Speaker 8>The absolute strongest schools think you know, your name brand schools,

0:27:47.440 --> 0:27:51.320
<v Speaker 8>your top fifty, those are getting much stronger year after year.

0:27:51.480 --> 0:27:55.360
<v Speaker 8>They have massive endowments north of tens of billions of dollars,

0:27:56.560 --> 0:28:00.000
<v Speaker 8>and you know, some schools have issued one hundred year bond.

0:28:00.160 --> 0:28:02.640
<v Speaker 8>In that category, there's no reason for us to think

0:28:02.680 --> 0:28:06.000
<v Speaker 8>that they're going anywhere anytime soon. But on the flip side,

0:28:06.240 --> 0:28:08.840
<v Speaker 8>they have these smaller, you know, they tend to be

0:28:10.680 --> 0:28:14.520
<v Speaker 8>maybe more regional schools that are seeing the pressure that

0:28:14.560 --> 0:28:17.960
<v Speaker 8>we're talking about, And I think we've seen a number

0:28:18.000 --> 0:28:20.880
<v Speaker 8>of closures already and Fitch, like you mentioned, is predicting

0:28:20.880 --> 0:28:23.040
<v Speaker 8>more closures in this space, and part of that is

0:28:23.080 --> 0:28:26.000
<v Speaker 8>just because the demographics are changing. There's more seats than

0:28:26.000 --> 0:28:26.800
<v Speaker 8>there are students.

0:28:27.680 --> 0:28:31.840
<v Speaker 6>What happens when a college closes, Where do the students go?

0:28:31.920 --> 0:28:33.200
<v Speaker 6>What happens to all the jobs?

0:28:33.800 --> 0:28:35.800
<v Speaker 8>It can kind of be case by case. We've written

0:28:35.880 --> 0:28:40.080
<v Speaker 8>some individual stories about this. One great example is Casanovia

0:28:40.160 --> 0:28:41.520
<v Speaker 8>College in New York.

0:28:41.560 --> 0:28:42.480
<v Speaker 7>I have a friend who went there.

0:28:42.840 --> 0:28:47.040
<v Speaker 8>We wrote about them, and the school had some dead

0:28:47.080 --> 0:28:50.320
<v Speaker 8>out standing, which makes it really complicated. If a school

0:28:50.320 --> 0:28:52.640
<v Speaker 8>has an endowment, for instance, that can also be complicated.

0:28:52.680 --> 0:28:55.280
<v Speaker 8>Where does that endowment go? In some cases it has

0:28:55.280 --> 0:28:56.560
<v Speaker 8>to go all the way up to the state attorney

0:28:56.560 --> 0:28:58.640
<v Speaker 8>general to decide what to do with the money, because

0:28:58.720 --> 0:29:01.360
<v Speaker 8>donors gave that an ear marked it for a specific purpose.

0:29:02.720 --> 0:29:05.320
<v Speaker 8>My understanding was that in Casanovia they were able to

0:29:05.400 --> 0:29:08.200
<v Speaker 8>arrange some transfer agreements for students. Usually they get absorbed

0:29:08.200 --> 0:29:13.320
<v Speaker 8>by nearby schools. The real estate often gets sold at

0:29:13.360 --> 0:29:18.040
<v Speaker 8>auction or buy some outside company, and the money, well,

0:29:18.320 --> 0:29:20.760
<v Speaker 8>depending on the school has debt, maybe we'll go back

0:29:20.800 --> 0:29:24.400
<v Speaker 8>to pay some debt holders. But it's very complicated and

0:29:24.680 --> 0:29:26.800
<v Speaker 8>even some lawyers I talked to for this story talked

0:29:26.800 --> 0:29:30.040
<v Speaker 8>about how closing the school is not an easy business.

0:29:30.960 --> 0:29:34.800
<v Speaker 3>Nick, this story has serious economic implications as well. I

0:29:34.840 --> 0:29:37.960
<v Speaker 3>remember when I was in school at my small college

0:29:38.200 --> 0:29:42.080
<v Speaker 3>in central Maine was in a small town, but the

0:29:42.160 --> 0:29:45.000
<v Speaker 3>economic effect that the school has on the town is

0:29:45.120 --> 0:29:47.320
<v Speaker 3>just massive. It's one of the biggest employers there, along

0:29:47.360 --> 0:29:50.600
<v Speaker 3>with the hospital. Students spend a lot of money their

0:29:50.600 --> 0:29:53.239
<v Speaker 3>parents when they come and visit, stay at the hotels there,

0:29:53.280 --> 0:29:55.200
<v Speaker 3>and need at the restaurants in the town. What are

0:29:55.240 --> 0:29:58.680
<v Speaker 3>the economic effects on a surrounding area when a college closes.

0:29:58.720 --> 0:30:01.000
<v Speaker 8>It can be quite significant. I think, like you mentioned

0:30:01.200 --> 0:30:04.920
<v Speaker 8>hospitals and institutions of higher education or what are known

0:30:04.920 --> 0:30:08.240
<v Speaker 8>as anchor institutions. You know, especially in rural places, they

0:30:08.240 --> 0:30:11.200
<v Speaker 8>can often be the largest employer. You think about not

0:30:11.240 --> 0:30:14.360
<v Speaker 8>only losing the faculty, but all the staff, the people

0:30:14.400 --> 0:30:18.640
<v Speaker 8>who maintain the grounds, and this huge flow of students

0:30:18.640 --> 0:30:21.440
<v Speaker 8>in their families that's coming to the town every year

0:30:21.880 --> 0:30:24.880
<v Speaker 8>and spending money. If that goes away, that is a

0:30:25.040 --> 0:30:26.880
<v Speaker 8>sizeable economic hit to a region.

0:30:28.600 --> 0:30:31.720
<v Speaker 6>In your reporting, you spoke to some students who attend

0:30:31.840 --> 0:30:35.000
<v Speaker 6>colleges that are under stress. What were some takeaways from

0:30:35.040 --> 0:30:36.040
<v Speaker 6>those conversations.

0:30:36.440 --> 0:30:39.000
<v Speaker 8>Sure, I talked to so one of the main schools

0:30:39.040 --> 0:30:42.200
<v Speaker 8>in our story as a school called Rider University. I

0:30:42.240 --> 0:30:45.680
<v Speaker 8>interviewed their president and quite a few students and faculty members,

0:30:46.200 --> 0:30:49.800
<v Speaker 8>and they are definitely facing some stress. They hit about

0:30:49.800 --> 0:30:53.840
<v Speaker 8>four flags in our study, and some students talked about,

0:30:53.920 --> 0:30:56.280
<v Speaker 8>you know, this kind of chilling effect that happens on campus.

0:30:57.040 --> 0:31:00.440
<v Speaker 8>Writer has seen some improvement lately. And I also want

0:31:00.480 --> 0:31:02.520
<v Speaker 8>to be clear, like schools are coming out of the pandemic,

0:31:02.600 --> 0:31:07.000
<v Speaker 8>that was an extremely difficult period for them, But when

0:31:07.040 --> 0:31:12.160
<v Speaker 8>students are going through this experience, it doesn't I think

0:31:12.160 --> 0:31:14.280
<v Speaker 8>it's hard on them. It's hard on this community. You know,

0:31:14.280 --> 0:31:16.440
<v Speaker 8>that's in part what schools are selling is an experience

0:31:16.480 --> 0:31:19.000
<v Speaker 8>in addition to an education. And if there's a possibility

0:31:19.040 --> 0:31:21.120
<v Speaker 8>that your school may not be here in ten fifteen years,

0:31:21.240 --> 0:31:23.760
<v Speaker 8>it's hard for you to really build that level of

0:31:23.800 --> 0:31:26.200
<v Speaker 8>connection that I think schools really pride themselves.

0:31:25.880 --> 0:31:27.560
<v Speaker 6>On and get excited about your school.

0:31:27.720 --> 0:31:29.760
<v Speaker 3>Absolutely, Nick, in the last minute that we have with you,

0:31:29.760 --> 0:31:31.760
<v Speaker 3>talk a little bit about the way the schools responded

0:31:31.800 --> 0:31:33.959
<v Speaker 3>to the analysis here, and just to you know, emphasize

0:31:34.000 --> 0:31:36.800
<v Speaker 3>just because the schools on this list or ident you

0:31:36.880 --> 0:31:39.080
<v Speaker 3>and the team identified it as being under pressure, has

0:31:39.160 --> 0:31:42.000
<v Speaker 3>no bearing or indication that it will actually close.

0:31:42.360 --> 0:31:42.680
<v Speaker 5>Correct.

0:31:42.760 --> 0:31:46.320
<v Speaker 8>It's almost impossible to predict a school closure based off

0:31:46.360 --> 0:31:49.720
<v Speaker 8>of metrics like these. Like I mentioned, a big part

0:31:49.720 --> 0:31:51.480
<v Speaker 8>of that is because of endowments. If a school has

0:31:51.480 --> 0:31:53.280
<v Speaker 8>a big endowment, they're going to have a much easier

0:31:53.320 --> 0:31:56.000
<v Speaker 8>time surviving a period of hardship than a school that doesn't.

0:31:56.360 --> 0:31:59.160
<v Speaker 8>But we reached out to every school that had three

0:31:59.240 --> 0:32:04.040
<v Speaker 8>or more flags, and we got a vast array of responses.

0:32:04.440 --> 0:32:07.120
<v Speaker 8>Some schools made the case that, you know, their acceptance

0:32:07.360 --> 0:32:09.800
<v Speaker 8>is high by design, they're trying to be accessible to,

0:32:09.800 --> 0:32:12.200
<v Speaker 8>you know, low income students, and that's why they're increasing

0:32:12.240 --> 0:32:14.120
<v Speaker 8>their aid. But then on the flip side, there were

0:32:14.160 --> 0:32:17.000
<v Speaker 8>some schools who totally agreed. I mean, they openly acknowledged

0:32:17.040 --> 0:32:20.320
<v Speaker 8>that it's a very difficult landscape for small institutions, and

0:32:20.360 --> 0:32:22.600
<v Speaker 8>they talked about some of the big changes that they're

0:32:22.600 --> 0:32:23.120
<v Speaker 8>having to make.

0:32:23.400 --> 0:32:26.040
<v Speaker 3>Well, it's a really, really powerful piece. I encourage everybody

0:32:26.040 --> 0:32:27.280
<v Speaker 3>to check it out. There's a reason it's among the

0:32:27.320 --> 0:32:29.680
<v Speaker 3>most read on the Bloomberg terminal. Nick Corolo part of

0:32:29.680 --> 0:32:32.120
<v Speaker 3>the team here at Bloomberg News. Who wrote it Municipal

0:32:32.120 --> 0:32:34.000
<v Speaker 3>Finance and Schools reporter at Bloomberg.

0:32:34.280 --> 0:32:37.840
<v Speaker 1>You're listening to the Bloomberg Business Week podcast. Catch us

0:32:37.880 --> 0:32:41.239
<v Speaker 1>live weekday afternoons from three to six Eastern Listen on

0:32:41.280 --> 0:32:45.320
<v Speaker 1>Bloomberg dot com, the iHeartRadio app, and the Bloomberg Business App,

0:32:45.600 --> 0:32:47.400
<v Speaker 1>or watch us live on YouTube.

0:32:48.680 --> 0:32:52.400
<v Speaker 3>Well before finance professionals began flocking to West Palm Beach,

0:32:52.720 --> 0:32:55.560
<v Speaker 3>the biggest gift that the city's Cox Science Center and

0:32:55.600 --> 0:33:00.360
<v Speaker 3>Aquarium ever received was nine hundred thousand dollars in twenty

0:33:00.400 --> 0:33:02.240
<v Speaker 3>twenty one. It's set out to raise funds for a

0:33:02.240 --> 0:33:04.320
<v Speaker 3>new building that's expected to cost one hundred and five

0:33:04.320 --> 0:33:07.800
<v Speaker 3>million dollars. Ken Griffin a name familiar with our audience,

0:33:07.840 --> 0:33:11.160
<v Speaker 3>the billionaire founder of the Citadel Financial Empire. He moved

0:33:11.160 --> 0:33:15.320
<v Speaker 3>there to Florida from Chicago last year. He gave eight

0:33:15.440 --> 0:33:18.760
<v Speaker 3>million dollars. This is just one example of Florida's new

0:33:18.760 --> 0:33:22.320
<v Speaker 3>donor class. Recently transplanted financial professionals are forging a new

0:33:22.320 --> 0:33:25.840
<v Speaker 3>era of philanthropy across the Sunstein State, from Tampa to

0:33:25.920 --> 0:33:30.240
<v Speaker 3>Miami and Sarasota to Boca Raton affluent young people and families.

0:33:30.320 --> 0:33:32.880
<v Speaker 3>Rooted in Wall Street careers have helped drive a boom

0:33:33.040 --> 0:33:36.720
<v Speaker 3>in giving. Amanda Gordon writes all about Florida's recent giving boom.

0:33:36.760 --> 0:33:39.160
<v Speaker 3>She's Wealth Team reporter for Bloomberg News, and she joins

0:33:39.200 --> 0:33:41.440
<v Speaker 3>us not from Florida this afternoon, but here in the

0:33:41.440 --> 0:33:45.400
<v Speaker 3>Bloomberg Interactive Brokers studio in New York. What's going on

0:33:45.440 --> 0:33:46.280
<v Speaker 3>in Florida.

0:33:45.960 --> 0:33:49.440
<v Speaker 11>Amanda, It is a saying for you give while you live,

0:33:49.680 --> 0:33:55.840
<v Speaker 11>and give where you live. So what I documented going

0:33:55.880 --> 0:34:00.240
<v Speaker 11>around Miami, the West coast of Florida, in this West

0:34:00.280 --> 0:34:03.680
<v Speaker 11>Palm Beach area is that new people come in and

0:34:03.800 --> 0:34:07.160
<v Speaker 11>they need to make a down payment for being a

0:34:07.200 --> 0:34:12.200
<v Speaker 11>member of the community, and so they're reaching out. What

0:34:12.239 --> 0:34:15.440
<v Speaker 11>they find in Florida that's so different from the places

0:34:15.440 --> 0:34:19.000
<v Speaker 11>they came is that it's easy access.

0:34:19.200 --> 0:34:20.400
<v Speaker 7>What do you mean easy access?

0:34:20.600 --> 0:34:23.400
<v Speaker 11>Well, Florida is super happy to have people that have

0:34:23.480 --> 0:34:26.680
<v Speaker 11>a culture of giving, that come from cities that have

0:34:26.760 --> 0:34:31.440
<v Speaker 11>been built with philanthropy. It's literally, you know, on engraved

0:34:31.480 --> 0:34:35.560
<v Speaker 11>on the walls of museums and libraries, that strong, strong tradition.

0:34:36.160 --> 0:34:39.600
<v Speaker 11>And in a younger state that's always had a more

0:34:39.600 --> 0:34:45.080
<v Speaker 11>transient population and an older population that had done their

0:34:45.120 --> 0:34:50.160
<v Speaker 11>giving already. Now they're looking to build a culture that's

0:34:50.320 --> 0:34:53.400
<v Speaker 11>just as vital in the prime of your life for

0:34:54.320 --> 0:34:57.960
<v Speaker 11>refashioning the community to their needs and interests.

0:34:58.239 --> 0:35:00.279
<v Speaker 3>Yeah, you quoted somebody in the story, and I'm you know,

0:35:00.440 --> 0:35:01.719
<v Speaker 3>not going to get the quote exactly right because I

0:35:01.760 --> 0:35:03.080
<v Speaker 3>don't have it in front of me. But basically who

0:35:03.120 --> 0:35:04.960
<v Speaker 3>basically said, if you wanted to have the impact that

0:35:05.360 --> 0:35:08.560
<v Speaker 3>people are having here in Florida, you'd have to in

0:35:08.640 --> 0:35:10.880
<v Speaker 3>New York, for example, you'd have to take a time machine,

0:35:11.000 --> 0:35:13.040
<v Speaker 3>go back in time and add a few zeros to.

0:35:13.080 --> 0:35:17.720
<v Speaker 11>It, exactly. And it's true that Ken Griffin gave eight million,

0:35:17.760 --> 0:35:20.280
<v Speaker 11>which seems like a lot compared to nine hundred thousand,

0:35:20.719 --> 0:35:24.280
<v Speaker 11>but he's given several bigger gifts of twenty five million,

0:35:24.560 --> 0:35:27.520
<v Speaker 11>and he's probably at over one hundred million so far

0:35:27.800 --> 0:35:33.439
<v Speaker 11>in Florida. But there is this sense that people talk

0:35:33.480 --> 0:35:35.799
<v Speaker 11>about it in this way, we can have more of

0:35:35.840 --> 0:35:41.040
<v Speaker 11>an impact with our giving. There's less clutter, there's less bureaucracy,

0:35:41.080 --> 0:35:43.520
<v Speaker 11>there's less of a weight to get onto the board.

0:35:44.280 --> 0:35:49.279
<v Speaker 11>And they they love, you know, the freedom that they're

0:35:49.480 --> 0:35:54.920
<v Speaker 11>they're finding there. To parrot some of the DeSantis rhetoricans.

0:35:54.960 --> 0:35:58.440
<v Speaker 3>Say, Florida, and that sounds like you've been watching some

0:35:58.480 --> 0:36:02.080
<v Speaker 3>of those debates over the last few months. So what

0:36:02.120 --> 0:36:06.680
<v Speaker 3>are the causes that the Wall Street class are finding

0:36:07.200 --> 0:36:09.920
<v Speaker 3>in Florida and are they different at all than the

0:36:09.960 --> 0:36:14.320
<v Speaker 3>causes they supported in Chicago and New York, in San Francisco,

0:36:14.480 --> 0:36:16.880
<v Speaker 3>for example, for Orlando Bravo for example.

0:36:17.360 --> 0:36:21.640
<v Speaker 11>Well, Orlando Bravo is an interesting case. So he didn't

0:36:21.640 --> 0:36:24.000
<v Speaker 11>really he said to me, he didn't know anyone in

0:36:24.040 --> 0:36:24.880
<v Speaker 11>Miami when.

0:36:24.719 --> 0:36:27.680
<v Speaker 3>He picked it, when he picked it to move there,

0:36:27.680 --> 0:36:27.960
<v Speaker 3>when he.

0:36:27.960 --> 0:36:31.400
<v Speaker 11>Picked it to move there, whereas Ken Griffin had time

0:36:31.440 --> 0:36:33.560
<v Speaker 11>as a child going through school there. He went to

0:36:33.640 --> 0:36:38.160
<v Speaker 11>high school in Boca Ratone, So he talked about how

0:36:38.239 --> 0:36:44.080
<v Speaker 11>people were just so welcoming, and he really envisions Miami

0:36:44.239 --> 0:36:47.680
<v Speaker 11>as a meeting place for tech and finance, which is

0:36:47.760 --> 0:36:52.640
<v Speaker 11>kind of what his firm does. So he's taking a

0:36:52.680 --> 0:36:56.440
<v Speaker 11>program that he knows from San Francisco and has funded

0:36:56.920 --> 0:37:02.319
<v Speaker 11>and opening up a branch in Miami. That's about some

0:37:02.440 --> 0:37:06.680
<v Speaker 11>of the issues are national issue science education, access to

0:37:06.800 --> 0:37:11.000
<v Speaker 11>higher education, and there have been programs that have been

0:37:11.160 --> 0:37:13.960
<v Speaker 11>sort of become ingrained in the culture in these older

0:37:14.000 --> 0:37:18.440
<v Speaker 11>cities and that kind of program is expanding, often with

0:37:18.520 --> 0:37:22.279
<v Speaker 11>an eye on developing talent, local talent that can be

0:37:22.400 --> 0:37:25.920
<v Speaker 11>hired by a Wall Street firm who's moved south. So

0:37:25.960 --> 0:37:29.840
<v Speaker 11>there's a college in West Palm Beach downtown that's raising

0:37:29.920 --> 0:37:33.920
<v Speaker 11>money to build a trading floor experience as part of

0:37:33.960 --> 0:37:37.080
<v Speaker 11>their business school. And literally, you know, zero point seventy

0:37:37.120 --> 0:37:41.080
<v Speaker 11>two and Elliott Management or across the street and would

0:37:41.120 --> 0:37:43.040
<v Speaker 11>be you could be ready, but you could walk across

0:37:43.080 --> 0:37:48.959
<v Speaker 11>the street onto their onto their terminals. So one thing

0:37:49.200 --> 0:37:54.000
<v Speaker 11>is culture. We take it for granted that we have it,

0:37:54.239 --> 0:37:58.080
<v Speaker 11>and we have these august buildings that house it, and

0:37:58.960 --> 0:38:01.600
<v Speaker 11>some of the younger people I talk to want more

0:38:01.640 --> 0:38:04.680
<v Speaker 11>of it and are investing in that too.

0:38:04.840 --> 0:38:06.960
<v Speaker 3>Well, that's what I was going to ask, is you know,

0:38:07.040 --> 0:38:10.719
<v Speaker 3>and it's kind of a weird way to talk about it,

0:38:10.760 --> 0:38:13.600
<v Speaker 3>but there is a prestige that comes with having your

0:38:13.680 --> 0:38:17.120
<v Speaker 3>name on the New York Public Library or having your

0:38:17.200 --> 0:38:22.319
<v Speaker 3>name adorned some institution that's been around forever that you know,

0:38:22.600 --> 0:38:28.960
<v Speaker 3>houses the New York Philharmonic or you know, an orchestra

0:38:29.080 --> 0:38:32.160
<v Speaker 3>here in the city. Is there that same prestige? Are

0:38:32.160 --> 0:38:34.240
<v Speaker 3>they finding that same prestige in Florida?

0:38:34.920 --> 0:38:39.880
<v Speaker 11>That's a good question. There are names on buildings. Adrian

0:38:40.040 --> 0:38:42.880
<v Speaker 11>Arst gave a gift for the Performing Arts Center in Miami.

0:38:43.600 --> 0:38:45.839
<v Speaker 11>Philip Frost gave a big gift for the and name

0:38:45.960 --> 0:38:49.960
<v Speaker 11>the Science center there, George Perez for the museum. I

0:38:50.000 --> 0:38:53.360
<v Speaker 11>think that, you know, we're all we've all become a

0:38:53.400 --> 0:38:57.480
<v Speaker 11>little skeptical about what that name gives you, Like, is

0:38:57.520 --> 0:38:59.719
<v Speaker 11>it a way just to get yourself out there? It

0:38:59.880 --> 0:39:04.320
<v Speaker 11>is they're more personal gain or is it about setting

0:39:04.360 --> 0:39:08.200
<v Speaker 11>an example and then having other people aspire to be

0:39:08.360 --> 0:39:12.880
<v Speaker 11>that person who gets engraved on the wall. So I

0:39:12.960 --> 0:39:17.120
<v Speaker 11>think that it's much more dynamic, and there aren't there

0:39:18.160 --> 0:39:24.279
<v Speaker 11>really aren't like the sort of clear like Rockefellers in

0:39:24.320 --> 0:39:29.160
<v Speaker 11>every town. It's more people talk about it being more open.

0:39:29.239 --> 0:39:31.280
<v Speaker 11>When I say that, it's not just that the people

0:39:31.360 --> 0:39:34.439
<v Speaker 11>moving in feel that way, it's that the people who

0:39:34.520 --> 0:39:41.680
<v Speaker 11>are at the nonprofits are really interested in welcoming new people.

0:39:41.920 --> 0:39:44.240
<v Speaker 3>And yeah, it seems like there's this idea of accessibility

0:39:44.239 --> 0:39:46.520
<v Speaker 3>that doesn't necessarily exist in other cities.

0:39:46.680 --> 0:39:49.960
<v Speaker 11>Well, they also say that because they're younger, they can

0:39:50.040 --> 0:39:53.680
<v Speaker 11>experiment in a new way, So we'll be seeing some

0:39:53.719 --> 0:39:54.440
<v Speaker 11>different models.

0:39:54.480 --> 0:39:57.360
<v Speaker 7>It's a really cool story. Check it out. It's Amanda Gordon.

0:39:57.480 --> 0:40:00.640
<v Speaker 3>She's Bloomberg Wealth Team reporter here and this is on

0:40:00.680 --> 0:40:03.240
<v Speaker 3>the Bloomberg terminal, of course, and also at Bloomberg dot com.

0:40:03.239 --> 0:40:04.360
<v Speaker 7>This is BusinessWeek.

0:40:05.680 --> 0:40:09.239
<v Speaker 1>You're listening to the Bloomberg Business Week podcast. Catch us

0:40:09.280 --> 0:40:13.400
<v Speaker 1>live weekday afternoons from three to six Easter on Bloomberg Radio,

0:40:13.480 --> 0:40:16.760
<v Speaker 1>the Bloomberg Business app, and YouTube. You can also listen

0:40:16.880 --> 0:40:19.959
<v Speaker 1>live on Amazon Alexa from our flagship New York station

0:40:20.400 --> 0:40:23.200
<v Speaker 1>Just Say Alexa play Bloomberg eleven thirty.

0:40:24.760 --> 0:40:26.959
<v Speaker 3>Plenty ahead in our second hour of the weekend edition

0:40:26.960 --> 0:40:30.360
<v Speaker 3>of Bloomberg Business Week, including the CEO of publicly traded

0:40:30.400 --> 0:40:33.680
<v Speaker 3>Secure works on artificial intelligence, along with the set of

0:40:33.800 --> 0:40:37.360
<v Speaker 3>new data breach reporting rules for public companies. And speaking

0:40:37.440 --> 0:40:39.760
<v Speaker 3>of AI, we'll hear from the head of a startup

0:40:39.800 --> 0:40:42.600
<v Speaker 3>called neat Leaf, who's using cutting edge tech to help

0:40:42.680 --> 0:40:47.000
<v Speaker 3>revolutionize cultivation of cannabis, and so much more, Plus why

0:40:47.040 --> 0:40:49.920
<v Speaker 3>toy prices are falling dramatically at the most wonderful time

0:40:49.920 --> 0:40:52.680
<v Speaker 3>of the year. First up this hour, Emily Graffeo and

0:40:52.719 --> 0:40:55.360
<v Speaker 3>I dive back into the special double issue of Bloomberg

0:40:55.400 --> 0:40:58.560
<v Speaker 3>BusinessWeek magazine for thousands of years. Finding the so called

0:40:58.560 --> 0:41:00.920
<v Speaker 3>a fountain of youth has really only been the stuff

0:41:00.920 --> 0:41:03.680
<v Speaker 3>of legend and fairy tales, and Silicon Valley more than

0:41:03.680 --> 0:41:06.480
<v Speaker 3>anywhere else, has for decades been you know, hoping not

0:41:06.560 --> 0:41:09.040
<v Speaker 3>only to slow aging, but also to find an actual

0:41:09.160 --> 0:41:12.239
<v Speaker 3>cure for death. This goal has made the region and

0:41:12.320 --> 0:41:14.759
<v Speaker 3>its techno files the butt of many jokes and the

0:41:14.760 --> 0:41:17.080
<v Speaker 3>target of criticism. I mean, just think back to the

0:41:17.160 --> 0:41:19.799
<v Speaker 3>blood Boys who provided their blood to Gavin Belson in

0:41:19.840 --> 0:41:21.920
<v Speaker 3>the HBO show Silicon Valley.

0:41:21.640 --> 0:41:22.399
<v Speaker 7>A few years ago.

0:41:22.840 --> 0:41:25.600
<v Speaker 3>But there's some serious money going into anti aging startups

0:41:25.600 --> 0:41:29.400
<v Speaker 3>in the valley, among them retro Biosciences, a startup with

0:41:29.480 --> 0:41:31.799
<v Speaker 3>one hundred and eighty million dollars from none other than

0:41:31.920 --> 0:41:34.920
<v Speaker 3>open Ai Sam Altman. The company is a simple and

0:41:35.040 --> 0:41:38.719
<v Speaker 3>audacious goal, add ten good years to your life. And

0:41:38.800 --> 0:41:41.759
<v Speaker 3>until now we haven't actually had a glimpse inside of

0:41:41.800 --> 0:41:44.879
<v Speaker 3>retro bias Sciences, But thanks to Ashley Vance, features writer

0:41:44.920 --> 0:41:47.799
<v Speaker 3>for Bloomberg BusinessWeek, now we do. Joel Weber is the

0:41:47.920 --> 0:41:50.400
<v Speaker 3>editor of Bloomberg BusinessWeek. He joins us here in the

0:41:50.440 --> 0:41:55.560
<v Speaker 3>Bloomberg Interactive Broker's Studio Joel. This story is part of

0:41:55.600 --> 0:41:59.879
<v Speaker 3>the Longevity Issue I'm obsessed with longevity, not just because

0:41:59.880 --> 0:42:01.920
<v Speaker 3>I would like to live longer that yeah you're not

0:42:01.960 --> 0:42:03.440
<v Speaker 3>alone though, yeah I'm not alone.

0:42:03.880 --> 0:42:08.520
<v Speaker 12>But Ashley has just had his pulse. He had his

0:42:08.560 --> 0:42:11.200
<v Speaker 12>finger on the pulse of Silicon Valley for everything forever.

0:42:11.760 --> 0:42:15.880
<v Speaker 12>And what we appreciated was that Silicon Valley is obsessed

0:42:16.120 --> 0:42:19.040
<v Speaker 12>with this puzzle of how do we live longer? How

0:42:19.040 --> 0:42:23.400
<v Speaker 12>do we live better as we age. There's some very

0:42:23.680 --> 0:42:28.239
<v Speaker 12>interesting individuals who have novel approaches to that. There's also

0:42:28.400 --> 0:42:30.120
<v Speaker 12>just like people who have a lot of money and

0:42:30.160 --> 0:42:31.600
<v Speaker 12>are willing to throw it at the problem and see

0:42:31.600 --> 0:42:34.520
<v Speaker 12>where that ends up. So we started talking about this

0:42:34.560 --> 0:42:36.360
<v Speaker 12>issue kind of earlier in the year and we just

0:42:36.360 --> 0:42:38.239
<v Speaker 12>started to try and figure out the stories that we

0:42:38.280 --> 0:42:40.040
<v Speaker 12>wanted to do, and this began as like an open

0:42:40.080 --> 0:42:43.760
<v Speaker 12>invite to Ashley. There are a number of labs obsessed

0:42:43.800 --> 0:42:46.520
<v Speaker 12>with longevity. Ashley told us about the one that we

0:42:46.560 --> 0:42:47.320
<v Speaker 12>decided to feature.

0:42:48.239 --> 0:42:51.600
<v Speaker 13>Yeah, well, thank you guys for all the kai words. Well,

0:42:51.680 --> 0:42:55.640
<v Speaker 13>this is retro Bio. Like you guys said, it's funded

0:42:55.680 --> 0:43:04.799
<v Speaker 13>by Sam Altman. Perhaps you know that name, yes, but

0:43:05.239 --> 0:43:07.799
<v Speaker 13>you know retro is one. What we point out in

0:43:07.840 --> 0:43:11.239
<v Speaker 13>the story that I think is particularly interesting in the

0:43:11.239 --> 0:43:15.160
<v Speaker 13>field of longevity is the science kind of comes and

0:43:15.200 --> 0:43:17.640
<v Speaker 13>goes that people get excited about. But there's a thing

0:43:17.719 --> 0:43:22.160
<v Speaker 13>called cellular reprogramming, which which is the current as far

0:43:22.200 --> 0:43:25.759
<v Speaker 13>as like real longevity science goes. It's it's sort of

0:43:25.760 --> 0:43:29.279
<v Speaker 13>what people are most excited about. Retro is one of

0:43:29.280 --> 0:43:32.800
<v Speaker 13>a handful of companies that have received hundreds of millions

0:43:32.800 --> 0:43:36.320
<v Speaker 13>and some of these billions of dollars to further this technology.

0:43:36.320 --> 0:43:40.320
<v Speaker 13>And it's basically about taking the cells in your body,

0:43:41.719 --> 0:43:46.080
<v Speaker 13>hitting them with proteins or small molecules, and taking the

0:43:46.120 --> 0:43:49.440
<v Speaker 13>cells back to a younger, healthier state. It's stuff that

0:43:49.480 --> 0:43:52.680
<v Speaker 13>we know we can do in animals and now we're

0:43:52.719 --> 0:43:54.840
<v Speaker 13>trying to find out if we can do this safely

0:43:55.000 --> 0:43:58.320
<v Speaker 13>in humans. And Retro is one of these companies going after.

0:43:58.280 --> 0:44:00.640
<v Speaker 12>What does that actually look like and how much money

0:44:00.800 --> 0:44:04.000
<v Speaker 12>do you need to do this because there are labs

0:44:04.200 --> 0:44:06.759
<v Speaker 12>longevity centers who have a lot more money than what

0:44:06.880 --> 0:44:09.240
<v Speaker 12>Retros actually even though they have a lot of money too.

0:44:09.719 --> 0:44:11.960
<v Speaker 13>Yeah, I mean they have one hundred eighty million dollars.

0:44:11.960 --> 0:44:16.680
<v Speaker 13>They're weird because they're chasing five different types of longevity

0:44:16.760 --> 0:44:19.480
<v Speaker 13>technology at the same time. You know, the table stakes

0:44:19.600 --> 0:44:22.800
<v Speaker 13>here appear to be at least one hundred million dollars.

0:44:22.840 --> 0:44:27.000
<v Speaker 13>There's a company called Alto's Labs that's backed by Jeff Bezos, and.

0:44:27.000 --> 0:44:27.640
<v Speaker 1>You're a miller.

0:44:27.719 --> 0:44:30.520
<v Speaker 13>They have three billion dollars, and so there's something of

0:44:30.760 --> 0:44:34.799
<v Speaker 13>a race going on where again, we've for about, you know,

0:44:34.920 --> 0:44:38.440
<v Speaker 13>fifteen years, maybe even a little more, we've done all

0:44:38.440 --> 0:44:42.040
<v Speaker 13>these experiments on animals where you essentially take one of

0:44:42.040 --> 0:44:44.839
<v Speaker 13>their old cells. You can take a skin cell off

0:44:44.880 --> 0:44:48.040
<v Speaker 13>of a mouse and you can revert that all the

0:44:48.040 --> 0:44:50.360
<v Speaker 13>way back to a stem cell, kind of that blank

0:44:50.680 --> 0:44:54.640
<v Speaker 13>slate where all life begins, and then advance it again

0:44:54.719 --> 0:44:57.920
<v Speaker 13>and create a whole new animal. In this case, we

0:44:57.960 --> 0:45:00.440
<v Speaker 13>want to take something like a liver cell that has

0:45:00.480 --> 0:45:02.960
<v Speaker 13>its identity as a liver cell, and we want to

0:45:03.000 --> 0:45:05.600
<v Speaker 13>reverse it, but but not to that blank slate. We

0:45:05.640 --> 0:45:08.919
<v Speaker 13>want to just reverse it back to a healthier liver cell.

0:45:08.960 --> 0:45:11.440
<v Speaker 13>And this this is sort of the trick that everyone's

0:45:11.440 --> 0:45:12.200
<v Speaker 13>trying to figure out.

0:45:12.280 --> 0:45:15.000
<v Speaker 12>Emily, you're gonna have to pick a horse one hundred

0:45:15.000 --> 0:45:18.799
<v Speaker 12>and eighty million dollars with Sam Altman, three billion dollars

0:45:18.840 --> 0:45:23.520
<v Speaker 12>with Jeff Bezos. Go, which one do you pick? Oh, Okay,

0:45:23.760 --> 0:45:26.680
<v Speaker 12>well that was the wrong answer.

0:45:26.440 --> 0:45:30.120
<v Speaker 6>But no, I just picked more money, honestly.

0:45:30.239 --> 0:45:32.879
<v Speaker 13>Yeah it might be right, it might be right.

0:45:33.640 --> 0:45:34.879
<v Speaker 7>Well, who knows, right?

0:45:34.880 --> 0:45:38.880
<v Speaker 12>But okay, so this walk us through this partial cell

0:45:39.200 --> 0:45:42.279
<v Speaker 12>reprogramming Ashley, And what does that actually mean? Because like

0:45:42.520 --> 0:45:44.640
<v Speaker 12>that's a frame, like the thing about longevity. You start

0:45:44.680 --> 0:45:46.840
<v Speaker 12>like like getting into the space and all of a

0:45:46.880 --> 0:45:49.920
<v Speaker 12>sudden there's this whole other world of vocabulary that no

0:45:49.920 --> 0:45:52.680
<v Speaker 12>one's ever familiar with. So what does that actually mean?

0:45:52.719 --> 0:45:54.759
<v Speaker 12>And can I do it now?

0:45:55.560 --> 0:45:55.799
<v Speaker 7>Yeah?

0:45:55.960 --> 0:45:58.200
<v Speaker 13>Okay, I'm gonna nerd out on everyone for a second.

0:45:58.280 --> 0:46:04.239
<v Speaker 13>But uh, around owned two thousand and six, a Japanese researcher,

0:46:04.280 --> 0:46:08.520
<v Speaker 13>a doctor named Yamanaka, he discovered that there were these

0:46:08.560 --> 0:46:12.240
<v Speaker 13>they called them transcription factors. They're basically proteins. They're present

0:46:12.280 --> 0:46:15.279
<v Speaker 13>in our body. We have between fifteen hundred and two

0:46:15.320 --> 0:46:19.080
<v Speaker 13>thousand transcription factors in our body that perform various functions.

0:46:19.120 --> 0:46:22.600
<v Speaker 13>But he discovered you could take a cell from a

0:46:22.680 --> 0:46:27.800
<v Speaker 13>mouse and hit it with these these four transcription factors

0:46:27.800 --> 0:46:30.680
<v Speaker 13>that sort of turned on some of the genes that

0:46:30.719 --> 0:46:34.680
<v Speaker 13>are present in our youngest state when we're closest to

0:46:34.760 --> 0:46:38.160
<v Speaker 13>a stem cell, and those cells would in fact start

0:46:38.239 --> 0:46:41.640
<v Speaker 13>reverting back to their stem cell state. And so at

0:46:41.640 --> 0:46:45.399
<v Speaker 13>the beginning people just thought, oh, this is interesting. Stem

0:46:45.440 --> 0:46:49.799
<v Speaker 13>cells were very controversial. You have to harvest them from embryos,

0:46:49.840 --> 0:46:54.680
<v Speaker 13>and this was now a way to manufacture stem cells artificially.

0:46:55.200 --> 0:46:59.239
<v Speaker 13>And then in twenty sixteen, another researcher named Alex o'campo.

0:46:59.800 --> 0:47:02.600
<v Speaker 13>He was the one who discovered in mice again that

0:47:03.160 --> 0:47:06.440
<v Speaker 13>if you finessed this process just right and you didn't

0:47:06.440 --> 0:47:08.759
<v Speaker 13>turn these genes on all the time, you just turned

0:47:08.800 --> 0:47:11.480
<v Speaker 13>them on for a couple of days, that the mice

0:47:11.680 --> 0:47:15.640
<v Speaker 13>suddenly started living thirty percent longer than all of their peers.

0:47:15.640 --> 0:47:18.480
<v Speaker 13>And this is when the longevity field got excited about

0:47:18.520 --> 0:47:19.239
<v Speaker 13>this technology.

0:47:19.560 --> 0:47:23.880
<v Speaker 6>Tell Us about the founder, Joe bets Lacroix, I think.

0:47:23.719 --> 0:47:27.200
<v Speaker 13>I'm saying that, yeah, he's a character and a half.

0:47:28.160 --> 0:47:31.000
<v Speaker 13>He grew up in Oregon with these kind of like

0:47:31.120 --> 0:47:36.560
<v Speaker 13>counterculture parents during the sixties. He's got a very unconventional background.

0:47:36.600 --> 0:47:39.360
<v Speaker 13>He had ad average in high school and then somehow

0:47:39.680 --> 0:47:42.319
<v Speaker 13>got into Harvard from what he says was a very

0:47:42.360 --> 0:47:48.400
<v Speaker 13>creative essay and transfer application from a community college and

0:47:48.840 --> 0:47:52.440
<v Speaker 13>has had his interesting career. He's been a serial entrepreneur

0:47:52.520 --> 0:47:56.360
<v Speaker 13>in Silicon Valley. He's been an angel investor tied to

0:47:56.719 --> 0:48:00.120
<v Speaker 13>y Comminator, and he's done a number of startups of

0:48:00.120 --> 0:48:04.720
<v Speaker 13>the years. Some of the older people listening will remember OQO.

0:48:04.840 --> 0:48:08.360
<v Speaker 13>They made this tiny computer that they caught everyone's attention

0:48:08.520 --> 0:48:12.279
<v Speaker 13>around the two thousands, and you know, he's he was

0:48:12.800 --> 0:48:16.320
<v Speaker 13>basically he's an autodiete act who is not a trained

0:48:16.560 --> 0:48:19.480
<v Speaker 13>biologist or anything like it, but he got interested in

0:48:19.520 --> 0:48:22.360
<v Speaker 13>the longevity field and learned all of this science and

0:48:22.360 --> 0:48:25.280
<v Speaker 13>then put this team together. The thing that stands out

0:48:25.400 --> 0:48:30.120
<v Speaker 13>about Retro is that it's a very unconventional biotech firm.

0:48:30.160 --> 0:48:33.759
<v Speaker 13>All their mice live in these shipping containers that Joe

0:48:33.840 --> 0:48:36.480
<v Speaker 13>and a handful of people built by hand inside of

0:48:36.480 --> 0:48:41.120
<v Speaker 13>this retail buildings. Is not like your standard laboratory, and they,

0:48:41.360 --> 0:48:44.680
<v Speaker 13>you know, built the whole air filtration system, the HVAC system,

0:48:44.719 --> 0:48:46.160
<v Speaker 13>everything by hand.

0:48:47.080 --> 0:48:51.040
<v Speaker 3>Hey, Ashley, you've done a good portion of reporting this year,

0:48:51.040 --> 0:48:55.000
<v Speaker 3>and gosh, even last year on folks who are looking

0:48:55.080 --> 0:48:58.120
<v Speaker 3>into anti aging, folks who are trying to reverse aging,

0:48:58.160 --> 0:48:59.520
<v Speaker 3>Folks were trying to add good.

0:48:59.440 --> 0:49:00.000
<v Speaker 7>Years to life.

0:49:01.600 --> 0:49:05.120
<v Speaker 3>Brian Johnson's documenting this on Twitter now quite a bit

0:49:06.040 --> 0:49:09.239
<v Speaker 3>based on your reporting, and I'm just curious if you

0:49:09.320 --> 0:49:12.719
<v Speaker 3>think like, within our lifetimes we are actually going to

0:49:12.760 --> 0:49:15.040
<v Speaker 3>be able to take advantage of this type of technology.

0:49:15.520 --> 0:49:17.520
<v Speaker 13>I mean, you know, you look at what Brian's doing.

0:49:17.560 --> 0:49:20.839
<v Speaker 13>He's basically testing every single substance that's ever been in

0:49:20.880 --> 0:49:24.680
<v Speaker 13>a paper that's shown some sort of benefit for longevity.

0:49:24.719 --> 0:49:29.400
<v Speaker 13>And there's interesting things there. There's people doing stuff like autophogy,

0:49:29.440 --> 0:49:32.040
<v Speaker 13>which is basically calorie restriction, which a lot of people

0:49:32.040 --> 0:49:34.720
<v Speaker 13>have heard of, where your body seems to repair itself.

0:49:34.760 --> 0:49:37.239
<v Speaker 13>All those things you know are real and seem to

0:49:37.600 --> 0:49:41.440
<v Speaker 13>contribute in different ways, but it's this cell reprogramming stuff

0:49:41.440 --> 0:49:44.920
<v Speaker 13>that really seems to have the most promise, and they

0:49:45.000 --> 0:49:47.400
<v Speaker 13>really did get me excited. If you talk to the

0:49:47.480 --> 0:49:51.280
<v Speaker 13>researchers in this field, they will tell you what I said,

0:49:51.320 --> 0:49:54.880
<v Speaker 13>that we've seen this work in animals. That what happens

0:49:54.920 --> 0:49:59.359
<v Speaker 13>is sometimes when this process gets overactivated, it causes your

0:49:59.400 --> 0:50:03.160
<v Speaker 13>body to row tumors all over the body. And so

0:50:03.239 --> 0:50:05.560
<v Speaker 13>this is why we have not tried this in humans.

0:50:05.640 --> 0:50:06.280
<v Speaker 7>That's not good.

0:50:06.400 --> 0:50:09.080
<v Speaker 13>If that's not good. If we can figure this out,

0:50:09.120 --> 0:50:11.160
<v Speaker 13>it's a huge if. But if we can figure this

0:50:11.200 --> 0:50:13.360
<v Speaker 13>out and all these sciences really think this is like

0:50:13.400 --> 0:50:17.760
<v Speaker 13>an engineering problem. They think the science is solved. Yes,

0:50:17.880 --> 0:50:19.879
<v Speaker 13>I think, you know, this is the most encouraged I've

0:50:19.880 --> 0:50:22.000
<v Speaker 13>been by anything in a long time. And you have

0:50:22.120 --> 0:50:24.959
<v Speaker 13>you have incredibly smart people and tons of money being

0:50:24.960 --> 0:50:27.440
<v Speaker 13>thrown at this, and it's our it's our best bet.

0:50:27.560 --> 0:50:27.840
<v Speaker 7>Okay.

0:50:27.880 --> 0:50:29.279
<v Speaker 12>So what does ten years look like?

0:50:29.360 --> 0:50:29.520
<v Speaker 2>Then?

0:50:29.640 --> 0:50:30.920
<v Speaker 7>Like, what do I what do I have to do

0:50:30.960 --> 0:50:32.120
<v Speaker 7>to get ten more years?

0:50:32.360 --> 0:50:35.680
<v Speaker 3>Well, you know, I would like I would like twenty

0:50:35.719 --> 0:50:37.279
<v Speaker 3>I would like twenty six to thirty six.

0:50:38.880 --> 0:50:41.080
<v Speaker 7>Okay, you we pick up like that, ash, Yeah, those

0:50:41.120 --> 0:50:42.080
<v Speaker 7>are the years that I want back.

0:50:42.360 --> 0:50:44.680
<v Speaker 13>When you talk to these researchers. The way this is

0:50:44.719 --> 0:50:46.799
<v Speaker 13>probably if it works, the way this is probably going

0:50:46.880 --> 0:50:49.080
<v Speaker 13>to play out first is that somebody would come in

0:50:49.280 --> 0:50:53.680
<v Speaker 13>with with liver disease and you would repair the liver,

0:50:53.920 --> 0:50:56.920
<v Speaker 13>or you would have heart problems and you would repair

0:50:57.000 --> 0:50:58.520
<v Speaker 13>the heart, and it would be this kind of like

0:50:58.680 --> 0:51:03.080
<v Speaker 13>organ by organ base is where you're fixing pretty severe problems.

0:51:03.960 --> 0:51:08.440
<v Speaker 13>The promise is this more like generalized use of this technology,

0:51:08.640 --> 0:51:13.200
<v Speaker 13>where you really would repair the entire body and all

0:51:13.320 --> 0:51:16.360
<v Speaker 13>the cells within it. I mean that is far off,

0:51:16.440 --> 0:51:18.920
<v Speaker 13>but like I've asked these people. I'm like, so you're

0:51:18.960 --> 0:51:21.239
<v Speaker 13>telling me I'm going to get an injection and then

0:51:21.239 --> 0:51:25.399
<v Speaker 13>my face is going to like revert back to my twenty.

0:51:25.080 --> 0:51:26.720
<v Speaker 7>Five year old face.

0:51:26.880 --> 0:51:29.680
<v Speaker 13>And they will tell you quite point blank that yes,

0:51:29.760 --> 0:51:31.960
<v Speaker 13>that is kind of the goal that over a period

0:51:32.040 --> 0:51:35.120
<v Speaker 13>of months, that is what would happen to you.

0:51:35.880 --> 0:51:39.240
<v Speaker 3>I mean I cared less about, like I guess, reversing

0:51:39.239 --> 0:51:43.160
<v Speaker 3>the aging process from a bhysical perspective like looks, and

0:51:43.239 --> 0:51:46.000
<v Speaker 3>more like feeling like I don't want to be sore anymore.

0:51:46.600 --> 0:51:47.880
<v Speaker 3>I want to be able to just go on a

0:51:47.920 --> 0:51:49.840
<v Speaker 3>ski trip and not train for it. Yeah you know

0:51:49.880 --> 0:51:51.319
<v Speaker 3>what I mean, like things you could do when you

0:51:51.320 --> 0:51:53.959
<v Speaker 3>were like a lot younger, and that was totally fine.

0:51:54.000 --> 0:51:57.239
<v Speaker 12>I can't wait to hear how this plays out for you.

0:51:58.360 --> 0:52:02.040
<v Speaker 7>Here's the problem. Here's the problem. I'm not a billionaire. Okay,

0:52:02.320 --> 0:52:04.360
<v Speaker 7>that's the problem. I have no blood boys. Put the

0:52:04.360 --> 0:52:06.160
<v Speaker 7>shot right here. I have zero blood boys. Put the

0:52:06.400 --> 0:52:07.680
<v Speaker 7>right here. Yeah.

0:52:07.840 --> 0:52:10.799
<v Speaker 12>Okay, Actually, what what about the competition?

0:52:11.000 --> 0:52:11.160
<v Speaker 7>Right?

0:52:11.160 --> 0:52:15.080
<v Speaker 12>We mentioned three billion dollars in bezos? Uh, what what's

0:52:15.120 --> 0:52:18.640
<v Speaker 12>the pro What are the different approaches that you know,

0:52:19.280 --> 0:52:21.160
<v Speaker 12>kind of round out this field? And Retro's got this

0:52:21.239 --> 0:52:23.640
<v Speaker 12>way how are other people going to go about solving it?

0:52:24.160 --> 0:52:26.359
<v Speaker 13>Yeah, so retros. Retros kind of do it. All these

0:52:26.360 --> 0:52:29.120
<v Speaker 13>experiments on these these mice. They have tens of thousands

0:52:29.160 --> 0:52:32.480
<v Speaker 13>of mice. Altos Labs, the company backed by by Bezos

0:52:32.480 --> 0:52:36.120
<v Speaker 13>and Miller. What they've essentially done is hired the whole

0:52:36.160 --> 0:52:40.080
<v Speaker 13>world's top academics in this field away from their university

0:52:40.120 --> 0:52:43.160
<v Speaker 13>positions and given them money and their own laboratories. Have

0:52:43.200 --> 0:52:45.600
<v Speaker 13>been like, you know, let's see how fast you can run,

0:52:46.120 --> 0:52:48.600
<v Speaker 13>and if you don't have to, you know, teach and

0:52:48.719 --> 0:52:51.360
<v Speaker 13>write papers and all that. And so that that's another approach.

0:52:51.680 --> 0:52:54.640
<v Speaker 13>There's a company called New Limit, which is funded by

0:52:54.719 --> 0:53:00.239
<v Speaker 13>the coinbase founder Brian Armstrong. They are essentially doing talked

0:53:00.239 --> 0:53:04.680
<v Speaker 13>about those transcription factors, those four proteins that have unlocked

0:53:05.400 --> 0:53:08.960
<v Speaker 13>this process so far. They're doing a massive study of

0:53:09.000 --> 0:53:14.320
<v Speaker 13>all two thousand transcription factors, essentially a huge computing project

0:53:14.360 --> 0:53:16.719
<v Speaker 13>where they take these samples and they analyze them, and

0:53:16.719 --> 0:53:20.480
<v Speaker 13>then they're trying to find the best combination of these

0:53:20.520 --> 0:53:23.560
<v Speaker 13>factors that will affect the body. And so everybody's coming

0:53:23.600 --> 0:53:26.800
<v Speaker 13>at this from like a slightly different angle, and it's

0:53:26.880 --> 0:53:30.560
<v Speaker 13>just not clear which approach will be first. I'm actually

0:53:30.719 --> 0:53:34.080
<v Speaker 13>kind of shocked there's really not that many startups beyond

0:53:34.080 --> 0:53:35.280
<v Speaker 13>those three.

0:53:35.719 --> 0:53:36.680
<v Speaker 7>I mean, you can.

0:53:36.640 --> 0:53:39.600
<v Speaker 13>Argue pretty easily that this could be the best business

0:53:39.719 --> 0:53:43.239
<v Speaker 13>of all time. I'm sort of surprised there's not twenty thirty,

0:53:43.320 --> 0:53:45.680
<v Speaker 13>forty fifty startups in the space.

0:53:45.920 --> 0:53:46.960
<v Speaker 12>Well, there's still time.

0:53:47.000 --> 0:53:49.840
<v Speaker 3>And there's also still time to check out this story

0:53:49.920 --> 0:53:51.960
<v Speaker 3>on the Bloomberg Terminal. It's going to be available in

0:53:52.000 --> 0:53:54.640
<v Speaker 3>the upcoming new issue of Bloomberg BusinessWeek on newstands later

0:53:54.680 --> 0:53:57.160
<v Speaker 3>this week, already online at Bloomberg dot com, Slash business Week,

0:53:57.200 --> 0:54:00.000
<v Speaker 3>and of course on the Bloomberg Terminal. Big thank you

0:54:00.320 --> 0:54:05.000
<v Speaker 3>two Ashley Vance, he is features writer for Bloomberg business Week,

0:54:05.040 --> 0:54:06.960
<v Speaker 3>and also big thank you to Joe Weber, the editor

0:54:07.360 --> 0:54:09.040
<v Speaker 3>of Bloomberg BusinessWeek.

0:54:10.480 --> 0:54:14.040
<v Speaker 1>You're listening to the Bloomberg Business Week podcast. Catch us

0:54:14.080 --> 0:54:17.560
<v Speaker 1>live weekday afternoons from three to six Eastern Listen.

0:54:17.320 --> 0:54:20.800
<v Speaker 2>On Bloomberg dot com, the iHeartRadio app, and the Bloomberg

0:54:20.840 --> 0:54:24.320
<v Speaker 2>Business App, or watch us live on YouTube.

0:54:24.800 --> 0:54:27.160
<v Speaker 3>Well, some public companies are still trying to figure out

0:54:27.440 --> 0:54:30.000
<v Speaker 3>exactly how to comply with new rules from the US

0:54:30.160 --> 0:54:35.840
<v Speaker 3>Securities and Exchange Commission requiring speedy disclosure of significant cyber attacks.

0:54:36.200 --> 0:54:39.280
<v Speaker 3>Those rules, which kicked in Monday, require companies to report

0:54:39.360 --> 0:54:42.440
<v Speaker 3>cyber incidents within four business days of determining that they

0:54:42.480 --> 0:54:46.720
<v Speaker 3>are quote material to shareholders. The SEC previously required firms

0:54:46.719 --> 0:54:49.800
<v Speaker 3>to disclose major events that would be of shareholder interest,

0:54:49.880 --> 0:54:52.400
<v Speaker 3>but didn't specify cyber events.

0:54:52.440 --> 0:54:52.560
<v Speaker 7>Well.

0:54:52.680 --> 0:54:55.040
<v Speaker 3>Next guest knows a thing or two about cybersecurity. Wendy

0:54:55.040 --> 0:54:59.200
<v Speaker 3>Thomas is president and CEO of Secure Works, the cybersecurity company.

0:54:59.280 --> 0:55:01.960
<v Speaker 3>She's back with us. It's a little different environment than

0:55:02.000 --> 0:55:04.479
<v Speaker 3>when we last spoke to you just a few months ago.

0:55:04.760 --> 0:55:10.400
<v Speaker 3>Specifically when it comes to this new SEC requiring ruling

0:55:10.440 --> 0:55:13.720
<v Speaker 3>that requires speedy disclosure of significant cyber attacks. It seems

0:55:13.760 --> 0:55:17.719
<v Speaker 3>like there's a lot of confusion with what exactly companies

0:55:17.760 --> 0:55:21.439
<v Speaker 3>now have to disclose to their shareholders when it comes

0:55:21.480 --> 0:55:22.280
<v Speaker 3>to cyber attacks.

0:55:22.320 --> 0:55:23.319
<v Speaker 7>What can you tell us about it?

0:55:24.960 --> 0:55:29.600
<v Speaker 10>Sure, glad to share the emerging insights into the new regulations.

0:55:30.040 --> 0:55:31.520
<v Speaker 10>I think about it in sort of the front end

0:55:31.520 --> 0:55:34.520
<v Speaker 10>and the back end. On the back end, as you mentioned,

0:55:35.160 --> 0:55:38.759
<v Speaker 10>public companies now have to report cyber incidents that are

0:55:38.800 --> 0:55:41.799
<v Speaker 10>determined by the company themselves to be material, which is

0:55:41.960 --> 0:55:46.120
<v Speaker 10>a discussion in and of itself within four business days,

0:55:46.440 --> 0:55:49.799
<v Speaker 10>and so that's the primary back end requirement. But on

0:55:49.840 --> 0:55:55.200
<v Speaker 10>the front end, the disclosures around everything from policies governing

0:55:55.280 --> 0:56:01.480
<v Speaker 10>cybersecurity risks, the companies risk management processes, processes whereby management

0:56:01.520 --> 0:56:05.560
<v Speaker 10>is informed about cyber incidents, and the board's oversight of that.

0:56:05.800 --> 0:56:08.480
<v Speaker 10>All of those are now required to be disclosed in

0:56:08.920 --> 0:56:12.200
<v Speaker 10>annual ten K filings by public companies, and so the

0:56:12.560 --> 0:56:16.200
<v Speaker 10>scrutiny of whether we are pairing sufficiently for cyber incidents,

0:56:16.560 --> 0:56:18.640
<v Speaker 10>as well as disclosure about them on the back end

0:56:18.760 --> 0:56:20.400
<v Speaker 10>is significantly ramped up.

0:56:20.520 --> 0:56:22.799
<v Speaker 3>I want to talk about this just because this one's

0:56:22.840 --> 0:56:25.040
<v Speaker 3>in the news, but I mean, you could just replace

0:56:25.440 --> 0:56:28.600
<v Speaker 3>VF Corp. With any given company at any given time.

0:56:30.000 --> 0:56:34.440
<v Speaker 3>VF Corp. Suffered this cyber attack at pretty much the

0:56:34.440 --> 0:56:38.480
<v Speaker 3>worst possible time and the most important quarter of the company,

0:56:38.520 --> 0:56:42.160
<v Speaker 3>and it has hurt orders for Vans, north Face and

0:56:42.280 --> 0:56:46.200
<v Speaker 3>other brands that the company has. I'm wondering how this

0:56:46.239 --> 0:56:49.000
<v Speaker 3>stuff continues to happen when we understand that the threats

0:56:49.200 --> 0:56:51.680
<v Speaker 3>are out there and so many companies are spending so

0:56:51.760 --> 0:56:54.640
<v Speaker 3>much money on keeping their network safe.

0:56:55.000 --> 0:56:58.680
<v Speaker 10>They are spending significant amounts of money. But as you

0:56:58.719 --> 0:57:01.920
<v Speaker 10>can see, the cyber criminal only has to be right once,

0:57:02.440 --> 0:57:04.520
<v Speaker 10>and we have to be right in and out every day.

0:57:04.760 --> 0:57:08.000
<v Speaker 10>And that's why it's so important for companies to have

0:57:08.280 --> 0:57:11.719
<v Speaker 10>a security expert partner to help them prepare for and

0:57:11.840 --> 0:57:15.840
<v Speaker 10>navigate situations like this to minimize the damage. There's nothing

0:57:16.600 --> 0:57:19.440
<v Speaker 10>worse for a company than their high season to be

0:57:19.480 --> 0:57:22.560
<v Speaker 10>impacted by this type of a cyber attack. And to

0:57:22.600 --> 0:57:24.880
<v Speaker 10>be frank, the reason these regulations are in place is

0:57:24.920 --> 0:57:29.120
<v Speaker 10>that when things like this hit the headlines, regulators feel

0:57:29.520 --> 0:57:35.680
<v Speaker 10>impelled to incent companies to protect themselves proactively and then

0:57:35.720 --> 0:57:39.520
<v Speaker 10>to be straightforward with shareholders or investors around what may

0:57:39.560 --> 0:57:42.560
<v Speaker 10>be impacting their business in terms of its operations or

0:57:42.640 --> 0:57:45.760
<v Speaker 10>financial performance. And this is a case in point of

0:57:45.760 --> 0:57:47.920
<v Speaker 10>why we see new regulations coming out around this.

0:57:48.200 --> 0:57:50.800
<v Speaker 3>In your opinion, do the regulations go far enough?

0:57:51.000 --> 0:57:54.480
<v Speaker 10>The regulations, I think are a good first step in

0:57:54.880 --> 0:57:57.400
<v Speaker 10>many ways, and I have no doubt we'll continue to

0:57:57.440 --> 0:58:01.240
<v Speaker 10>see them evolve like many other new forms of rulemaking

0:58:01.280 --> 0:58:03.640
<v Speaker 10>over the years. But what I will tell you is

0:58:03.680 --> 0:58:07.440
<v Speaker 10>these regulations are not intended to punish. They are really

0:58:07.480 --> 0:58:12.360
<v Speaker 10>intended to create the right incentives. Look twenty twenty three.

0:58:12.440 --> 0:58:14.880
<v Speaker 10>I think it's on track to be the most prolific

0:58:14.960 --> 0:58:17.560
<v Speaker 10>year for ransomware ever and to have one of the

0:58:17.640 --> 0:58:21.520
<v Speaker 10>highest economic costs of security breaches today. Right this cyber

0:58:21.560 --> 0:58:26.600
<v Speaker 10>crime is an ongoing, continual transfer of wealth to criminal enterprises,

0:58:27.080 --> 0:58:30.200
<v Speaker 10>and so I think that the SEC feels compelled to

0:58:30.240 --> 0:58:34.480
<v Speaker 10>provide guidance around how to better prepare for these types

0:58:34.520 --> 0:58:38.640
<v Speaker 10>of situations going forward. And frankly, they've raised the bar

0:58:38.800 --> 0:58:44.600
<v Speaker 10>for CEOs, CSOs and boards of directors around preparing for

0:58:44.640 --> 0:58:46.480
<v Speaker 10>these types of incidents.

0:58:46.840 --> 0:58:49.240
<v Speaker 3>What, in your opinion is the right amount of money

0:58:49.280 --> 0:58:52.720
<v Speaker 3>for a company to actually spend on keeping their company

0:58:52.760 --> 0:58:57.720
<v Speaker 3>secure from cyber attacks question? But not a single figure,

0:58:57.840 --> 0:58:59.520
<v Speaker 3>not a single figure, but a certain like a portion

0:58:59.600 --> 0:59:02.360
<v Speaker 3>of repid for example, or portion of expenses.

0:59:02.920 --> 0:59:05.800
<v Speaker 10>That does vary a bit, and I'm glad to provide

0:59:05.840 --> 0:59:08.200
<v Speaker 10>some ranges there. It does vary a bit depending on

0:59:08.280 --> 0:59:12.800
<v Speaker 10>whether the organization is in a highly attacked and frankly,

0:59:12.880 --> 0:59:15.840
<v Speaker 10>highly regulated industry.

0:59:15.400 --> 0:59:18.040
<v Speaker 3>Eating like a healthcare or financial services for example.

0:59:18.400 --> 0:59:20.760
<v Speaker 10>Exactly, they have a much higher standard. Now what we

0:59:20.800 --> 0:59:23.680
<v Speaker 10>see is damages to companies that are more in the

0:59:23.720 --> 0:59:27.120
<v Speaker 10>manufacturing in the retail space. So it does matter from

0:59:27.360 --> 0:59:31.440
<v Speaker 10>a business up time and financial performance perspective, But generally

0:59:31.480 --> 0:59:36.520
<v Speaker 10>we expect to see companies spending somewhere in the four

0:59:36.560 --> 0:59:41.120
<v Speaker 10>to five percent range. It can sort of merge into

0:59:41.160 --> 0:59:44.120
<v Speaker 10>other technology spend a little bit because you can have

0:59:44.200 --> 0:59:48.960
<v Speaker 10>secure by design technology deployments, but somewhere between that and

0:59:49.600 --> 0:59:54.320
<v Speaker 10>twelve to fifteen percent of your total technology spend is

0:59:54.360 --> 0:59:57.840
<v Speaker 10>what we see typically in place. Again, for businesses that

0:59:57.840 --> 1:00:00.800
<v Speaker 10>aren't necessarily in a highly regulated industry, do.

1:00:00.760 --> 1:00:02.360
<v Speaker 7>You see that portion moving up?

1:00:02.800 --> 1:00:04.680
<v Speaker 10>I do see that portion moving up, but I also

1:00:04.840 --> 1:00:08.840
<v Speaker 10>see it moving in terms of mix. So while you

1:00:08.920 --> 1:00:12.480
<v Speaker 10>want to invest in preventing as much as you possibly can,

1:00:13.080 --> 1:00:15.480
<v Speaker 10>what these regulations are showing is that you have to

1:00:15.560 --> 1:00:20.080
<v Speaker 10>invest in you aren't able to prevent everything. So you

1:00:20.280 --> 1:00:23.600
<v Speaker 10>absolutely have to be able to detect quickly. Because with

1:00:23.680 --> 1:00:27.840
<v Speaker 10>these regulations, the ability to assess the impact of an

1:00:27.880 --> 1:00:30.680
<v Speaker 10>incident in terms of what it's going to do to

1:00:30.720 --> 1:00:35.680
<v Speaker 10>your business operations and the financial outcomes of that. You

1:00:35.720 --> 1:00:40.040
<v Speaker 10>absolutely cannot do that with humans alone. It requires technology

1:00:40.120 --> 1:00:44.640
<v Speaker 10>investments and investments in awareness and preparation of your team.

1:00:44.800 --> 1:00:47.240
<v Speaker 10>So I do think that the mix is going to

1:00:47.280 --> 1:00:52.320
<v Speaker 10>shift quite a bit towards the detection and preparation aspect

1:00:52.320 --> 1:00:52.600
<v Speaker 10>of this.

1:00:52.920 --> 1:00:56.320
<v Speaker 3>Wendy, We've talked a lot about AI in the context

1:00:56.360 --> 1:01:02.760
<v Speaker 3>of making companies more productive and being able to.

1:01:01.840 --> 1:01:02.680
<v Speaker 7>Be more efficient.

1:01:02.760 --> 1:01:05.640
<v Speaker 3>We've talked about it too in the concept of people

1:01:05.640 --> 1:01:08.040
<v Speaker 3>being worried about AI, sort of like the way that

1:01:08.080 --> 1:01:14.560
<v Speaker 3>Elon musks concerned about AGI. I'm wondering how you've seen

1:01:14.720 --> 1:01:17.880
<v Speaker 3>or how you could see threat actors use AI to

1:01:18.080 --> 1:01:22.280
<v Speaker 3>pose as humans to actually gain access to networks. What's

1:01:22.320 --> 1:01:24.560
<v Speaker 3>worst case scenario here that you guys are thinking about

1:01:24.560 --> 1:01:25.520
<v Speaker 3>over its secure works.

1:01:25.960 --> 1:01:28.840
<v Speaker 10>Sure, as we've talked about before, the power in the

1:01:28.880 --> 1:01:32.800
<v Speaker 10>peril of AI is very real for cybersecurity, and so

1:01:32.880 --> 1:01:35.080
<v Speaker 10>while the power of it to be able to detect

1:01:35.560 --> 1:01:39.880
<v Speaker 10>certain behavioral techniques of the adversary are incredibly important for

1:01:39.960 --> 1:01:43.280
<v Speaker 10>how we operate as secure works, we also see the

1:01:43.320 --> 1:01:47.200
<v Speaker 10>adversary taking advantage of these tools and techniques as well.

1:01:47.720 --> 1:01:50.200
<v Speaker 10>And I think the most near term one that we

1:01:50.280 --> 1:01:53.800
<v Speaker 10>have seen and increasingly concerns us in terms of its

1:01:53.840 --> 1:01:58.520
<v Speaker 10>accelerated use, is around deep fakes, with the ability to

1:01:58.760 --> 1:02:02.400
<v Speaker 10>impersonate a CEO not just in an email, but in

1:02:02.440 --> 1:02:06.040
<v Speaker 10>a video, and to do that at scale with you know,

1:02:06.120 --> 1:02:10.720
<v Speaker 10>large language models like chat GBT. It just has put

1:02:10.840 --> 1:02:16.520
<v Speaker 10>a pace of deployment and a fidelity of believability in

1:02:16.560 --> 1:02:21.200
<v Speaker 10>what they're using to deceive, just taking it to another level.

1:02:21.760 --> 1:02:24.800
<v Speaker 10>That's the primary area where we're focused on. The ability

1:02:24.800 --> 1:02:25.240
<v Speaker 10>to detect.

1:02:25.600 --> 1:02:26.960
<v Speaker 3>Yeah, one hard thing is you mean you have to

1:02:26.960 --> 1:02:30.680
<v Speaker 3>remain sovid vigilant regardless of who's you know in your organization,

1:02:30.840 --> 1:02:34.400
<v Speaker 3>from person with access to certain systems versus the person

1:02:34.400 --> 1:02:38.880
<v Speaker 3>whose password or login credentials could be compromised. Wendy, we

1:02:38.920 --> 1:02:40.960
<v Speaker 3>love it when you join us. Wendy Thomas is President

1:02:41.000 --> 1:02:43.800
<v Speaker 3>and chief executive officer of Secure Works. Back with us

1:02:44.080 --> 1:02:47.480
<v Speaker 3>on a zoom from Atlanta, Georgia.

1:02:50.360 --> 1:02:53.920
<v Speaker 1>You're listening to the Bloomberg Business Week podcast. Catch us

1:02:53.960 --> 1:02:57.919
<v Speaker 1>live weekday afternoons from three to six Easter on Bloomberg Radio,

1:02:58.160 --> 1:03:01.840
<v Speaker 1>the Bloomberg Business App, and you can also listen live

1:03:01.920 --> 1:03:05.200
<v Speaker 1>on Amazon Alexa from our flagship New York station, Just

1:03:05.240 --> 1:03:07.880
<v Speaker 1>say Alexa play Bloomberg eleven thirty.

1:03:09.200 --> 1:03:09.320
<v Speaker 11>Well.

1:03:09.360 --> 1:03:11.480
<v Speaker 3>Here on Bloomberg Business Week, we talk a lot about

1:03:11.640 --> 1:03:14.200
<v Speaker 3>feeding the world and what that takes and how so

1:03:14.240 --> 1:03:16.440
<v Speaker 3>many different people from all over are trying to tackle

1:03:16.480 --> 1:03:20.040
<v Speaker 3>the problem of food insecurity. From plant based meats and

1:03:20.080 --> 1:03:25.360
<v Speaker 3>proteins to increasing the sustainability and efficiency of crops. One

1:03:25.360 --> 1:03:28.400
<v Speaker 3>person trying to do that is Almar Meyer, the co

1:03:28.480 --> 1:03:29.960
<v Speaker 3>founder and CEO of Neatly.

1:03:30.000 --> 1:03:30.520
<v Speaker 7>If it's a.

1:03:30.520 --> 1:03:34.760
<v Speaker 3>Company that says it's using robotics and AI to transform farming.

1:03:35.200 --> 1:03:38.200
<v Speaker 3>Almar Meyer joins us on zoom from Santa Cruz, California.

1:03:38.240 --> 1:03:41.360
<v Speaker 3>It's good to have you on Bloomberg Business Week. Almar

1:03:41.720 --> 1:03:45.560
<v Speaker 3>explain what exactly neat Leaf is because when I hear platform,

1:03:46.160 --> 1:03:49.960
<v Speaker 3>I think of software. I think of something that can

1:03:50.000 --> 1:03:53.280
<v Speaker 3>be that's like virtual, that's moved from place to place.

1:03:53.320 --> 1:03:54.760
<v Speaker 3>How do you apply that to farming.

1:03:55.360 --> 1:03:59.440
<v Speaker 14>What they basically do is there basically are extensive data

1:03:59.480 --> 1:04:03.560
<v Speaker 14>collection tool for cultivators. One thing which we have realized

1:04:03.800 --> 1:04:07.680
<v Speaker 14>is that cultivation is far from optimal. There's like ten

1:04:07.680 --> 1:04:10.600
<v Speaker 14>to fifteen even twenty percent crop loss every citele depending

1:04:10.640 --> 1:04:13.240
<v Speaker 14>on the crop. But it really is something which is

1:04:13.280 --> 1:04:16.280
<v Speaker 14>not optimized and that it's basically a lack of data collection.

1:04:16.800 --> 1:04:18.640
<v Speaker 14>So it Neatly if we came up with a system

1:04:18.680 --> 1:04:21.280
<v Speaker 14>it's called a Neatly spider, which is a cable based

1:04:21.280 --> 1:04:24.200
<v Speaker 14>system like a football stadium camera which operates in between

1:04:24.240 --> 1:04:27.520
<v Speaker 14>the canopy and any ceiling infrastructure in greenhouses and other

1:04:27.520 --> 1:04:31.000
<v Speaker 14>indoor facilities, and collects a lot of data. It collects

1:04:31.040 --> 1:04:35.160
<v Speaker 14>camera images, It collects temperature, humidity, CO two lend height,

1:04:35.640 --> 1:04:38.720
<v Speaker 14>leaf temperature, which is key for cultivators and provides a

1:04:38.760 --> 1:04:42.840
<v Speaker 14>comprehensive picture of what's going on while feeding an AI,

1:04:42.920 --> 1:04:46.240
<v Speaker 14>which picks up on blend stress and identifies issues as

1:04:46.280 --> 1:04:49.080
<v Speaker 14>the system is moving around transparently for the cultivator.

1:04:49.320 --> 1:04:53.960
<v Speaker 6>I'm curious specifically for cannabis cultivation, how exactly does the

1:04:54.040 --> 1:04:57.600
<v Speaker 6>platform help cannabis forming.

1:04:58.040 --> 1:05:01.120
<v Speaker 14>Right, So it's a crop stick system. So we are

1:05:01.160 --> 1:05:03.760
<v Speaker 14>currently like starting with cannabis, but we're also talking to

1:05:03.840 --> 1:05:08.000
<v Speaker 14>leafy greens, ornamentals, nurseries and any other crop which is

1:05:08.040 --> 1:05:12.240
<v Speaker 14>cultivated in indoors in caa controlled environment and your culture facilities.

1:05:12.800 --> 1:05:17.800
<v Speaker 14>So for cannabis, we basically capture all these environmental factors

1:05:18.040 --> 1:05:22.400
<v Speaker 14>throughout this environment to provide a consistent understanding of the microclimates,

1:05:22.440 --> 1:05:25.240
<v Speaker 14>how the environment changes throughout the day, throughout the night,

1:05:25.560 --> 1:05:28.680
<v Speaker 14>throughout the full growth cycle. But then we also pick

1:05:28.800 --> 1:05:33.160
<v Speaker 14>up on blend stress, We detect yellowing, necrosis, leave folding

1:05:33.600 --> 1:05:39.600
<v Speaker 14>and quantify that. So the cultivator gets the blend stress

1:05:39.640 --> 1:05:42.080
<v Speaker 14>in numbers. It knows like, oh I have five percent

1:05:42.200 --> 1:05:45.680
<v Speaker 14>more yellowing today, I have ten percent less falling, And

1:05:45.720 --> 1:05:49.400
<v Speaker 14>it knows whether the changes they make, like nutrients changes,

1:05:49.880 --> 1:05:54.160
<v Speaker 14>irrigation changes, prey applications, had a pocity for negative impact.

1:05:54.640 --> 1:05:58.080
<v Speaker 14>We also measure the actual crop itself, like the butts

1:05:58.080 --> 1:06:01.080
<v Speaker 14>for cannabis. We detect the sizes. You count them and

1:06:01.120 --> 1:06:03.480
<v Speaker 14>so you can forecast a yield. You know that whether

1:06:03.520 --> 1:06:06.919
<v Speaker 14>a certain microclimate resulted in a specific you know, yield

1:06:07.000 --> 1:06:09.880
<v Speaker 14>increase and the outther one was suffering, and you get

1:06:09.880 --> 1:06:13.000
<v Speaker 14>a better understanding what's happening and how to optimize your cultivation,

1:06:13.120 --> 1:06:15.640
<v Speaker 14>making it more sustainable, making it more efficient.

1:06:16.360 --> 1:06:19.520
<v Speaker 3>But we do know that it's not just for cannabis,

1:06:19.560 --> 1:06:22.080
<v Speaker 3>and I know that you're focused on cannabis right now,

1:06:22.120 --> 1:06:27.960
<v Speaker 3>but you can apply this to any indoor farming right totally.

1:06:27.720 --> 1:06:31.360
<v Speaker 14>Though it's not limited to cannabis at all. It's crop

1:06:31.360 --> 1:06:34.479
<v Speaker 14>agnostic as it's that and the whole idea is really

1:06:34.520 --> 1:06:37.280
<v Speaker 14>like if any any technology development, you start with the

1:06:37.360 --> 1:06:38.840
<v Speaker 14>highest marching crop first.

1:06:38.560 --> 1:06:40.680
<v Speaker 7>And that's cannabis right now, and that's kind of it.

1:06:40.800 --> 1:06:42.800
<v Speaker 14>Yeah, cannabis has the highest margins right now. It's like

1:06:42.880 --> 1:06:45.080
<v Speaker 14>Tesla also started with the roads turning up with the

1:06:45.160 --> 1:06:47.920
<v Speaker 14>models free and you develop your technology and you make

1:06:47.920 --> 1:06:51.320
<v Speaker 14>it more applicable for like crops with smaller margins, and

1:06:51.360 --> 1:06:54.560
<v Speaker 14>it's totally possible. Like this cable based technology, it really

1:06:54.640 --> 1:06:58.160
<v Speaker 14>leans itself to like a large application in large cultivation environments,

1:06:58.200 --> 1:07:00.840
<v Speaker 14>like a large base of crow Why can't you.

1:07:00.800 --> 1:07:02.919
<v Speaker 3>Do this extravers Why can't this Why is this only

1:07:02.920 --> 1:07:04.000
<v Speaker 3>for indoor farming right now?

1:07:04.440 --> 1:07:06.919
<v Speaker 14>That's a really good question. So as we capture this data,

1:07:06.960 --> 1:07:11.080
<v Speaker 14>we captured the understanding how to optimize these environments. Now indoors,

1:07:11.120 --> 1:07:14.600
<v Speaker 14>in controlled environment ariculture environments, you can control these barometers.

1:07:14.640 --> 1:07:17.320
<v Speaker 14>You can actually leverage all this knowledge put it to

1:07:17.360 --> 1:07:20.520
<v Speaker 14>the best use. However, it's not limited, Like you can

1:07:20.560 --> 1:07:23.600
<v Speaker 14>imagine putting like four poles outside of a vineyard and

1:07:23.720 --> 1:07:26.880
<v Speaker 14>operate the same system outdoors and capture the same information.

1:07:27.240 --> 1:07:29.680
<v Speaker 14>You might not be able to change everything, but you

1:07:29.840 --> 1:07:32.320
<v Speaker 14>are still able to understand the correlations and how to

1:07:32.400 --> 1:07:35.000
<v Speaker 14>optimize depending on what parameters you can change and whatnot.

1:07:35.240 --> 1:07:39.240
<v Speaker 6>How does AI work here, Because this is a huge

1:07:39.400 --> 1:07:43.240
<v Speaker 6>buzzword of twenty twenty three, artificial intelligence. It seems like

1:07:43.480 --> 1:07:47.880
<v Speaker 6>every business in every industry is saying that they're leveraging

1:07:48.360 --> 1:07:53.320
<v Speaker 6>AI to increase their scale or increase their efficiency. So

1:07:53.520 --> 1:07:55.680
<v Speaker 6>can you talk a little bit more exactly about the

1:07:55.800 --> 1:07:58.360
<v Speaker 6>artificial intelligence aspect totally.

1:07:58.480 --> 1:08:02.760
<v Speaker 14>Yeah, it's a huge So cultivation is a huge black box.

1:08:03.080 --> 1:08:06.440
<v Speaker 14>My background is not cultivation or blend science, but like

1:08:06.560 --> 1:08:09.040
<v Speaker 14>learning about it space. I got so much respect for

1:08:09.120 --> 1:08:13.320
<v Speaker 14>cultivators who have to navigate all these different barometers and

1:08:13.400 --> 1:08:14.880
<v Speaker 14>factors which are correlated.

1:08:14.920 --> 1:08:15.720
<v Speaker 15>You know, you have to change.

1:08:15.720 --> 1:08:18.160
<v Speaker 14>If you change one barameter, you have to change all

1:08:18.200 --> 1:08:21.400
<v Speaker 14>the other barometers as well, And that's really the challenge there.

1:08:21.439 --> 1:08:23.559
<v Speaker 14>And that's where we have seen that the I and

1:08:23.640 --> 1:08:27.760
<v Speaker 14>machine learning really how performs human and captures these correlations,

1:08:27.840 --> 1:08:31.120
<v Speaker 14>understands them and knows how to optimize the space to

1:08:31.200 --> 1:08:33.880
<v Speaker 14>really navigate it. It's obviously it's probably one of the

1:08:33.920 --> 1:08:37.160
<v Speaker 14>reasons why we don't see so much adoption in cultivation

1:08:37.360 --> 1:08:39.880
<v Speaker 14>is because farmers know if they introduce a change, if

1:08:39.920 --> 1:08:42.559
<v Speaker 14>you change the way how they cultivate, they have to

1:08:42.640 --> 1:08:46.120
<v Speaker 14>relearn everything and it's only only that many iterations you

1:08:46.160 --> 1:08:48.280
<v Speaker 14>can do in a lifetime of a cultivator.

1:08:48.840 --> 1:08:51.640
<v Speaker 3>I'm wondering how if you can give us some numbers

1:08:52.280 --> 1:08:55.080
<v Speaker 3>that show how much it increases yield, how much it

1:08:55.120 --> 1:08:58.719
<v Speaker 3>increases efficiency. If people people who use the platform, growers

1:08:58.760 --> 1:09:02.240
<v Speaker 3>who use the platform, what are the results that they get?

1:09:02.680 --> 1:09:06.200
<v Speaker 14>So we are still trying to capture our solid baseline

1:09:06.240 --> 1:09:09.280
<v Speaker 14>around like what these numbers are. We have seen runs

1:09:09.320 --> 1:09:11.920
<v Speaker 14>where we perform ten percent better, runs very from twenty

1:09:11.960 --> 1:09:14.759
<v Speaker 14>percent better, even like one run very well, like seventy

1:09:14.800 --> 1:09:17.280
<v Speaker 14>percent better. But like again I don't I wouldn't say

1:09:17.280 --> 1:09:20.040
<v Speaker 14>that these are like the numbers as a baseline is

1:09:20.040 --> 1:09:23.360
<v Speaker 14>are like single examples and statistically not really like a

1:09:23.439 --> 1:09:26.640
<v Speaker 14>significantly say, But we are still capturing it and it

1:09:26.760 --> 1:09:28.800
<v Speaker 14>just requires a lot of data for us to come

1:09:28.880 --> 1:09:31.760
<v Speaker 14>up with a statistically significant number as a baseline for

1:09:31.840 --> 1:09:34.240
<v Speaker 14>what we can do with the system. What we don't

1:09:34.280 --> 1:09:38.000
<v Speaker 14>have been doing has been like calling out issues earlier,

1:09:38.240 --> 1:09:41.120
<v Speaker 14>like way earlier than a human can basically pick them up.

1:09:41.320 --> 1:09:45.920
<v Speaker 14>In a regular cultivation environment, a cultivator has to watch

1:09:46.120 --> 1:09:48.800
<v Speaker 14>fifteen thousand plants, you know, like depending on the crop

1:09:48.840 --> 1:09:51.519
<v Speaker 14>againd like in cannabis, like fifteen thousand plants per person.

1:09:51.840 --> 1:09:53.440
<v Speaker 5>Obviously that's not possible.

1:09:53.800 --> 1:09:57.200
<v Speaker 14>Our system can keep an eye on each blend, basically

1:09:57.200 --> 1:10:00.720
<v Speaker 14>on each leave multiple times a day, like constantly and

1:10:00.760 --> 1:10:04.400
<v Speaker 14>basically capture what happens, what that it changes, and pick

1:10:04.600 --> 1:10:07.519
<v Speaker 14>them up as early as possible before you can potentially

1:10:07.520 --> 1:10:10.000
<v Speaker 14>even see them. We're using technology which goes beyond the

1:10:10.120 --> 1:10:14.639
<v Speaker 14>human eye, and that's obviously a huge win. Now, the

1:10:14.400 --> 1:10:17.080
<v Speaker 14>really the reason why the baseline is challenging is it's

1:10:17.080 --> 1:10:19.439
<v Speaker 14>hard to say, like, what would have happened if you

1:10:19.479 --> 1:10:21.800
<v Speaker 14>would not have called that out? You know, it's hard

1:10:21.840 --> 1:10:24.840
<v Speaker 14>to only go down one path in time, and so

1:10:24.880 --> 1:10:28.439
<v Speaker 14>it's difficult to like to compare it. But get capturing

1:10:28.439 --> 1:10:31.200
<v Speaker 14>more data it will allow us to generalize it and

1:10:31.280 --> 1:10:35.160
<v Speaker 14>create these numbers which will help statistically significance.

1:10:35.960 --> 1:10:39.320
<v Speaker 6>So you co found a neatleaf in twenty twenty. What

1:10:39.560 --> 1:10:42.800
<v Speaker 6>drew you to this space indoor farming? Because I know

1:10:42.840 --> 1:10:47.280
<v Speaker 6>that you worked at Lucid Motors the Moonshot factory. Why

1:10:47.280 --> 1:10:48.559
<v Speaker 6>pivot to farming?

1:10:48.920 --> 1:10:51.680
<v Speaker 14>That's a really good question. I originally come from the

1:10:51.720 --> 1:10:54.439
<v Speaker 14>countries like the Delian Alps, and I spent my summers

1:10:54.479 --> 1:10:56.480
<v Speaker 14>on my aunt's farm, so I have like some emotional

1:10:56.560 --> 1:10:59.479
<v Speaker 14>ties to cultivation, and then I live here and I

1:10:59.720 --> 1:11:02.559
<v Speaker 14>send them cruise for many many years, and I started

1:11:02.600 --> 1:11:05.840
<v Speaker 14>talking to cultivators, and everyone kept talking about data driven

1:11:05.880 --> 1:11:10.120
<v Speaker 14>cultivation and that's the new agricultural revolution. We have to change,

1:11:10.720 --> 1:11:14.160
<v Speaker 14>to change the way how we cultivate. But then when

1:11:14.160 --> 1:11:17.759
<v Speaker 14>I looked at their greenhouses, I saw these dangling sensors

1:11:17.760 --> 1:11:21.080
<v Speaker 14>from the ceiling which capture temperature and humidity. And you know,

1:11:21.200 --> 1:11:25.360
<v Speaker 14>I spent my whole career in sensor fusion, sensor understanding

1:11:25.400 --> 1:11:29.320
<v Speaker 14>machine learning, and I really understood, like, I know what's possible,

1:11:29.360 --> 1:11:31.960
<v Speaker 14>but I knew that these single sensors are not going

1:11:32.040 --> 1:11:33.800
<v Speaker 14>to capture it. It's not going to be able to

1:11:34.000 --> 1:11:37.479
<v Speaker 14>provide this revolution which everyone kept talking about. And I

1:11:37.520 --> 1:11:39.960
<v Speaker 14>looked at well, how cultivators operate in that space, and

1:11:40.000 --> 1:11:42.080
<v Speaker 14>they go around and they look at each plant, you know,

1:11:42.120 --> 1:11:44.720
<v Speaker 14>like they use the visual cues. They they learn the

1:11:44.840 --> 1:11:48.360
<v Speaker 14>language of the plants, how they the plant expresses stress

1:11:48.600 --> 1:11:52.880
<v Speaker 14>and singles other invests and issues. And I know that

1:11:52.920 --> 1:11:56.400
<v Speaker 14>it's also possible with AI and cameras, And that's why

1:11:56.439 --> 1:11:58.080
<v Speaker 14>I was like, well, we just need a camera which

1:11:58.080 --> 1:12:01.080
<v Speaker 14>allows us to move around the plant. How I cultivated

1:12:01.200 --> 1:12:03.639
<v Speaker 14>us so that we can learn models to pick up

1:12:03.640 --> 1:12:07.200
<v Speaker 14>on this language on the signals and how ultimate that process.

1:12:07.360 --> 1:12:09.600
<v Speaker 3>It's really really cool stuff and we really appreciate you

1:12:09.680 --> 1:12:11.960
<v Speaker 3>taking the time in joining us this afternoon. That's Elmer

1:12:12.080 --> 1:12:15.000
<v Speaker 3>Meyer joining us on Zoom from Santa Cruz, California, the

1:12:15.040 --> 1:12:17.840
<v Speaker 3>co founder and CEO of Neatly, the company that uses

1:12:17.920 --> 1:12:20.400
<v Speaker 3>robotics and AI to help farmers.

1:12:20.840 --> 1:12:24.439
<v Speaker 1>You're listening to the Bloomberg Business Week podcast. Catch us

1:12:24.439 --> 1:12:27.920
<v Speaker 1>live weekday afternoons from three to six Eastern Listen.

1:12:27.720 --> 1:12:30.720
<v Speaker 2>On Bloomberg dot com, the iHeartRadio app, and the.

1:12:30.640 --> 1:12:34.240
<v Speaker 1>Bloomberg Business App, or watch us live on YouTube.

1:12:34.720 --> 1:12:38.280
<v Speaker 3>Well, Bailey, something weird is happening to toy prices. They're

1:12:38.280 --> 1:12:40.800
<v Speaker 3>actually falling this holiday season. I don't know if you

1:12:40.840 --> 1:12:42.840
<v Speaker 3>saw this story from Bloomberg News yesterday.

1:12:42.840 --> 1:12:43.320
<v Speaker 7>Did you see this?

1:12:43.439 --> 1:12:45.000
<v Speaker 5>I saw it, but I'm not buying toys.

1:12:45.000 --> 1:12:47.599
<v Speaker 7>Are you buying toys? Yeah? Yeah, you know, Santa Claus

1:12:47.640 --> 1:12:49.120
<v Speaker 7>has got to go shopping somewhere.

1:12:49.120 --> 1:12:52.800
<v Speaker 3>Okay, reported yesterday Bloomberg News here reporting yesterday the toy

1:12:52.840 --> 1:12:56.040
<v Speaker 3>makers are actually slashing prices, with toy promotions during the

1:12:56.040 --> 1:12:59.599
<v Speaker 3>holiday season expected to peak at thirty five percent. Okay,

1:12:59.640 --> 1:13:02.720
<v Speaker 3>that's just toys. It's certainly a unique retail segment and

1:13:03.120 --> 1:13:05.840
<v Speaker 3>one that saw a huge shift during the pandemic. So

1:13:05.840 --> 1:13:07.960
<v Speaker 3>what about other types of retail? Well, we've got a

1:13:07.960 --> 1:13:10.760
<v Speaker 3>great guest to talk all things retail as we do

1:13:10.960 --> 1:13:14.920
<v Speaker 3>get through the holiday season. Nicole Larson is National Research

1:13:14.960 --> 1:13:18.320
<v Speaker 3>manager for Retail Services at Calliers. She's on the retail

1:13:18.360 --> 1:13:21.719
<v Speaker 3>team there, manages a portfolio of over two billion square

1:13:21.720 --> 1:13:25.679
<v Speaker 3>feet in the US. They got storefronts, malls, grocery stores,

1:13:25.840 --> 1:13:28.920
<v Speaker 3>restaurants and more. Nicole joining us this afternoon on Zoom

1:13:28.920 --> 1:13:30.479
<v Speaker 3>from Fort Lauderdale, Florida.

1:13:30.560 --> 1:13:31.800
<v Speaker 7>Nicole, how are you good?

1:13:31.840 --> 1:13:32.599
<v Speaker 15>How are you guys?

1:13:32.600 --> 1:13:34.360
<v Speaker 16>I'm excited to talk all things retail.

1:13:34.520 --> 1:13:36.599
<v Speaker 3>Yeah, well, what are you seeing right now? I mean,

1:13:36.680 --> 1:13:40.280
<v Speaker 3>you guys have incredible real time data that shows who's

1:13:40.360 --> 1:13:44.479
<v Speaker 3>shopping where, who's eating where, and who's spending money where.

1:13:44.520 --> 1:13:45.960
<v Speaker 7>What does the environment look like?

1:13:46.280 --> 1:13:48.400
<v Speaker 16>I mean, first of all, let's start off with the

1:13:48.439 --> 1:13:51.599
<v Speaker 16>holiday shopping season has really been off to a great start.

1:13:51.920 --> 1:13:56.560
<v Speaker 16>I mean, obviously, consumers are remaining pretty cautious and selective

1:13:56.560 --> 1:13:59.680
<v Speaker 16>about what they're buying. They're focusing, they're purchasing power more

1:13:59.680 --> 1:14:02.759
<v Speaker 16>on their needs rather than they're once. They're not buying

1:14:02.760 --> 1:14:06.439
<v Speaker 16>on impulse, and they're really not splurging on products that

1:14:06.479 --> 1:14:09.920
<v Speaker 16>don't wow them. So we actually saw nearly sixty percent

1:14:09.960 --> 1:14:12.960
<v Speaker 16>of consumers said that they bought less on Impulse this

1:14:13.080 --> 1:14:16.479
<v Speaker 16>Black Friday than they did last year. But also about

1:14:16.520 --> 1:14:19.800
<v Speaker 16>forty percent of consumers thought that retailers were more generous

1:14:19.840 --> 1:14:22.080
<v Speaker 16>this year with their discounts than they were last year.

1:14:22.120 --> 1:14:25.200
<v Speaker 16>So that goes to your point of toy prices as.

1:14:25.040 --> 1:14:28.519
<v Speaker 5>Well, Nicole. One thing that has definitely been top of

1:14:28.600 --> 1:14:31.479
<v Speaker 5>mine for me has been this kind of rush of

1:14:31.560 --> 1:14:32.439
<v Speaker 5>buy now, pay later.

1:14:32.520 --> 1:14:33.840
<v Speaker 7>I thought you were gonna say buying toys.

1:14:33.880 --> 1:14:35.840
<v Speaker 5>I know I don't have you know, I don't know,

1:14:35.920 --> 1:14:37.320
<v Speaker 5>and to buy toys, I don't have anyone to buy

1:14:37.360 --> 1:14:37.760
<v Speaker 5>toys for.

1:14:38.120 --> 1:14:39.479
<v Speaker 7>So buy toys for my kids.

1:14:39.600 --> 1:14:41.799
<v Speaker 5>Yeah, exactly. You know it'll be a Christmas surprise.

1:14:41.840 --> 1:14:43.760
<v Speaker 7>Sounds a Christmas miracle.

1:14:43.520 --> 1:14:46.920
<v Speaker 5>A Christmas miracle brought to you by Bailey Lipschiltz, Nicole.

1:14:47.000 --> 1:14:50.200
<v Speaker 5>So we saw a firm with a partnership with Walmart

1:14:50.240 --> 1:14:52.720
<v Speaker 5>to drive for their volume. How does buy now pay

1:14:52.760 --> 1:14:55.480
<v Speaker 5>later play into the retail landscape.

1:14:55.600 --> 1:14:57.080
<v Speaker 15>So we actually took a survey.

1:14:57.160 --> 1:15:01.040
<v Speaker 16>We found one in five consumers planned to heavily leverage

1:15:01.080 --> 1:15:04.439
<v Speaker 16>the buy now, pay later program payment plans during the

1:15:04.479 --> 1:15:08.599
<v Speaker 16>holiday season. I think really too spreading out the holidays

1:15:08.640 --> 1:15:11.320
<v Speaker 16>spend so you know how we actually or Amazon had

1:15:11.320 --> 1:15:14.679
<v Speaker 16>the deal days Target best by Walmart. I think really

1:15:14.720 --> 1:15:19.080
<v Speaker 16>stretching out their spending period has also really helped consumers

1:15:19.680 --> 1:15:21.719
<v Speaker 16>get them to open their wallets. I mean, we're already

1:15:21.760 --> 1:15:23.640
<v Speaker 16>starting to see it now with other holidays. If you

1:15:23.680 --> 1:15:26.640
<v Speaker 16>go into Target, you can already start to see Valentine's

1:15:26.720 --> 1:15:27.240
<v Speaker 16>Day items.

1:15:27.320 --> 1:15:28.160
<v Speaker 15>So I think that's going.

1:15:28.120 --> 1:15:30.880
<v Speaker 16>To be a trend not only just with Christmas and

1:15:30.920 --> 1:15:32.720
<v Speaker 16>Black Friday spend, but you're going to see it with

1:15:32.760 --> 1:15:36.120
<v Speaker 16>other holidays too. In order to get those consumers to spend,

1:15:36.800 --> 1:15:38.760
<v Speaker 16>you know, start selling the products a little bit.

1:15:38.640 --> 1:15:39.559
<v Speaker 15>Earlier than usual.

1:15:39.800 --> 1:15:41.640
<v Speaker 3>Does that I mean, Bailey, I don't know about you,

1:15:41.680 --> 1:15:45.360
<v Speaker 3>but that Walmart firm news yesterday that sent news of

1:15:45.560 --> 1:15:49.240
<v Speaker 3>that sent shares of a firm up double digits. That

1:15:49.600 --> 1:15:52.800
<v Speaker 3>really got me thinking, Wait a second, Nicole, is it

1:15:52.880 --> 1:15:57.920
<v Speaker 3>the best thing? If consumers are at Walmart paying for

1:15:57.960 --> 1:16:00.880
<v Speaker 3>Walmart purchases over time, what's lad does that tell about

1:16:00.880 --> 1:16:01.839
<v Speaker 3>the American consumer?

1:16:02.320 --> 1:16:05.040
<v Speaker 16>I mean, one trend we're looking at for twenty twenty

1:16:05.080 --> 1:16:07.840
<v Speaker 16>four is that our consumers are going to be a

1:16:07.880 --> 1:16:10.639
<v Speaker 16>lot more considerate in their perching habits.

1:16:11.000 --> 1:16:12.680
<v Speaker 15>I definitely don't think.

1:16:12.880 --> 1:16:15.920
<v Speaker 16>Retail spend is going to increase by very much next year.

1:16:15.920 --> 1:16:18.439
<v Speaker 16>If anything, it's going to remain either the same it

1:16:18.600 --> 1:16:21.400
<v Speaker 16>was this year or maybe a few basis points higher.

1:16:21.640 --> 1:16:24.240
<v Speaker 16>But really there's going to be a big stabilization, as

1:16:24.320 --> 1:16:26.720
<v Speaker 16>I mentioned, because consumers are just being a little bit

1:16:26.760 --> 1:16:27.320
<v Speaker 16>more picky.

1:16:27.560 --> 1:16:30.000
<v Speaker 15>I mean, we have so much information out there.

1:16:30.040 --> 1:16:32.760
<v Speaker 16>If we do want to buy a TV or some

1:16:32.800 --> 1:16:35.360
<v Speaker 16>sort of appliance, We're not just going to one retailer

1:16:35.560 --> 1:16:37.439
<v Speaker 16>to look and click and automatically buy.

1:16:37.479 --> 1:16:38.920
<v Speaker 15>We're looking at all platforms.

1:16:38.920 --> 1:16:41.320
<v Speaker 16>We're going in store, We're going in line really to

1:16:41.400 --> 1:16:44.720
<v Speaker 16>do our due diligence on where is the best price available.

1:16:44.920 --> 1:16:48.360
<v Speaker 5>Well, Nicole, again with your portfolio of storefronts and malls,

1:16:48.479 --> 1:16:50.720
<v Speaker 5>how much of this foot track is foot traffic is

1:16:50.760 --> 1:16:53.519
<v Speaker 5>actually delivering sales? Because you mentioned that if I walk

1:16:53.520 --> 1:16:55.720
<v Speaker 5>into a store and see that there's a TV that

1:16:55.760 --> 1:16:57.880
<v Speaker 5>I want, I immediately google it and see what kind

1:16:57.880 --> 1:17:00.799
<v Speaker 5>of price I can get elsewhere that'll probably have free shipping.

1:17:01.040 --> 1:17:04.639
<v Speaker 16>Yeah, so surprisingly, you know, I think the pandemic definitely,

1:17:05.640 --> 1:17:09.360
<v Speaker 16>you know, surged online sales that went up very quickly,

1:17:09.400 --> 1:17:12.120
<v Speaker 16>a lot faster than we probably expected for that to

1:17:12.439 --> 1:17:14.040
<v Speaker 16>come up over the next few years.

1:17:14.400 --> 1:17:15.880
<v Speaker 15>But despite all of this e.

1:17:15.800 --> 1:17:19.280
<v Speaker 16>Commerce growth, brick and mortar stores really remains the preferred

1:17:19.320 --> 1:17:22.760
<v Speaker 16>shopping center or the preferred shopping experience, I should say

1:17:22.760 --> 1:17:25.800
<v Speaker 16>by consumers. We work with a lot of retailers, and

1:17:26.000 --> 1:17:28.880
<v Speaker 16>a lot of them have already really perfected their online

1:17:28.920 --> 1:17:31.960
<v Speaker 16>and omni channel strategy, so now they're actually looking to

1:17:32.040 --> 1:17:36.679
<v Speaker 16>really perfect and right size their physical storefront. Maybe get

1:17:36.680 --> 1:17:40.880
<v Speaker 16>that store size right, maybe experience with experiment with new formats.

1:17:41.120 --> 1:17:43.120
<v Speaker 16>So that's really going to be one big thing you're

1:17:43.120 --> 1:17:44.800
<v Speaker 16>going to see in the new years, a lot of

1:17:44.840 --> 1:17:48.080
<v Speaker 16>retailers putting their focus back into the physical space.

1:17:48.720 --> 1:17:51.320
<v Speaker 3>That's so interesting to hear because it feels like to

1:17:51.439 --> 1:17:57.360
<v Speaker 3>me physical apart from groceries, and even groceries like physical

1:17:57.439 --> 1:18:00.240
<v Speaker 3>stores are like a last resort for me. There for

1:18:00.600 --> 1:18:02.160
<v Speaker 3>when I want to try on a pair of shoes

1:18:02.280 --> 1:18:05.920
<v Speaker 3>or try on something but I don't know what, Nicole,

1:18:05.960 --> 1:18:09.519
<v Speaker 3>I don't want to be bothered to leave, you know.

1:18:09.520 --> 1:18:10.040
<v Speaker 7>My house.

1:18:10.400 --> 1:18:12.439
<v Speaker 5>Well, we even buy I feel like we buy stuff.

1:18:12.439 --> 1:18:14.400
<v Speaker 5>My fiance buy stuff to try it on and if

1:18:14.439 --> 1:18:15.240
<v Speaker 5>she doesn't.

1:18:14.920 --> 1:18:17.599
<v Speaker 3>Send it send it back, which is just cost terrible.

1:18:17.800 --> 1:18:19.679
<v Speaker 5>Yeah, and it's also bad for the bad for the environment.

1:18:19.720 --> 1:18:21.800
<v Speaker 3>But I mean, Nicole, how does that gel with what

1:18:21.840 --> 1:18:22.879
<v Speaker 3>you're seeing at Colliers.

1:18:23.320 --> 1:18:27.439
<v Speaker 16>Yeah, I mean or the foot traffic definitely shows that

1:18:27.560 --> 1:18:31.519
<v Speaker 16>foot traffic is up, and it's really for these desired centers.

1:18:31.560 --> 1:18:33.760
<v Speaker 16>I mean, you're starting to see a lot higher foot

1:18:33.800 --> 1:18:37.360
<v Speaker 16>traffic in strip centers where they're closer to the suburbs.

1:18:37.400 --> 1:18:39.320
<v Speaker 16>Like you mentioned, you don't want to drive far to

1:18:39.400 --> 1:18:42.679
<v Speaker 16>have to return something or to purchase something. So sometimes

1:18:42.680 --> 1:18:44.400
<v Speaker 16>you know, if it is more convenient for you to

1:18:44.439 --> 1:18:47.080
<v Speaker 16>just hop in your car and head onto the nearest

1:18:47.080 --> 1:18:49.400
<v Speaker 16>strip center close by your house, that might be the

1:18:49.479 --> 1:18:53.479
<v Speaker 16>easier alternative to offer a return as opposed to sending

1:18:53.479 --> 1:18:55.920
<v Speaker 16>it in mail. So that will also drive foot traffic

1:18:55.960 --> 1:18:56.360
<v Speaker 16>as well.

1:18:56.520 --> 1:18:58.600
<v Speaker 3>I do also think that it has to do with

1:18:58.640 --> 1:19:02.000
<v Speaker 3>the experiences that these retail spaces are increasingly offering. Right,

1:19:02.840 --> 1:19:04.920
<v Speaker 3>It's not just you know, it's the stuff that you

1:19:04.960 --> 1:19:08.080
<v Speaker 3>can't do online, and you know, it's like the Lego

1:19:08.160 --> 1:19:10.960
<v Speaker 3>Lands or you know what Mattel's trying to do with

1:19:11.120 --> 1:19:14.080
<v Speaker 3>like Pepa pig World, and these experiences and stuff just

1:19:14.120 --> 1:19:15.880
<v Speaker 3>in the last thirty seconds that we have, Betina Cole,

1:19:15.960 --> 1:19:17.080
<v Speaker 3>is that something we're going to see more of?

1:19:17.280 --> 1:19:21.520
<v Speaker 16>Yeah, definitely. I mean landlords should definitely try to monetize

1:19:21.600 --> 1:19:23.800
<v Speaker 16>their space as much as they can. There's so much

1:19:23.840 --> 1:19:27.720
<v Speaker 16>they can do with these events, any sort of experiences

1:19:27.720 --> 1:19:30.080
<v Speaker 16>like you're mentioning, We're also seeing retailers do that, you know,

1:19:30.200 --> 1:19:32.599
<v Speaker 16>even if they have a store in a location already,

1:19:32.920 --> 1:19:35.200
<v Speaker 16>adding some sort of pop up at the other side

1:19:35.240 --> 1:19:37.439
<v Speaker 16>of the mall. So maybe if your consumer doesn't go

1:19:37.479 --> 1:19:39.400
<v Speaker 16>to your side of the mall, maybe the ones who

1:19:39.439 --> 1:19:40.760
<v Speaker 16>do go to the other side of the mall can

1:19:41.280 --> 1:19:43.600
<v Speaker 16>figure out that you're there as well. So lots of

1:19:43.640 --> 1:19:47.720
<v Speaker 16>different options for landlords to get the space activated.

1:19:47.960 --> 1:19:51.719
<v Speaker 3>Nicole Larson, national research manager for retail Services at Callers,

1:19:52.040 --> 1:19:54.280
<v Speaker 3>joining us on Zoom from Fort Lauderdale, Florida, Bailey, are

1:19:54.320 --> 1:19:55.880
<v Speaker 3>you done with all holiday shopping?

1:19:55.960 --> 1:19:56.360
<v Speaker 5>I'm done?

1:19:56.360 --> 1:19:56.519
<v Speaker 6>Well.

1:19:56.520 --> 1:19:58.400
<v Speaker 5>I was going to say, you're thinking about Lego land

1:19:58.439 --> 1:20:00.760
<v Speaker 5>and Peppa Pake. I just got a new smoker for Christmas, Bok,

1:20:01.160 --> 1:20:02.080
<v Speaker 5>So different stages of.

1:20:02.080 --> 1:20:03.960
<v Speaker 3>All, right, So fourth of July we're coming over.

1:20:04.200 --> 1:20:05.599
<v Speaker 5>I'll be over there in January two.

1:20:05.600 --> 1:20:08.599
<v Speaker 7>Oh wow, that sounds good. Hey, you're listening to Bloomberg

1:20:08.640 --> 1:20:10.000
<v Speaker 7>Business Week. This is Bloomberg.

1:20:10.360 --> 1:20:15.000
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1:20:15.160 --> 1:20:18.879
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