WEBVTT - Millennial Consumers Aren't Willing to Pay for Digital Transactions, Bhatt Says

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<v Speaker 1>Welcome to the Bloomberg p m L Podcast. I'm pim Fox.

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<v Speaker 1>Along with my co host Lisa Bramowitz. Each day we

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<v Speaker 1>bring you the most important, noteworthy, and useful interviews for

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<v Speaker 1>you and your money, whether you're at the grocery store

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<v Speaker 1>or the trading floor. Find the Bloomberg p m L

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<v Speaker 1>Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. Well.

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<v Speaker 1>Robin Hood is known from taking from wealthy in order

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<v Speaker 1>to give to the poor. A new version of robin

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<v Speaker 1>Hood today is it comes in the form of robin Hood,

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<v Speaker 1>the company that's based in Palo Alto, California that provides

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<v Speaker 1>a commission free app for trading stocks and with us

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<v Speaker 1>here as the co founder and co chief executive officer

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<v Speaker 1>of the company, Beiju Bot Beiji, thank you so much

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<v Speaker 1>for joining us. So can you just give us a

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<v Speaker 1>sense of what the company does and how you make money? Absolutely,

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<v Speaker 1>and thanks for having me on robin Hood. Our mission

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<v Speaker 1>is to make the markets more accessible for everyone in

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<v Speaker 1>the US. UM what we do is we have an

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<v Speaker 1>an app for Iowa's and Android, and we recently around

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<v Speaker 1>a desktop version of robin Hood as well that lets

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<v Speaker 1>consumers across the country invest in the stock market, and

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<v Speaker 1>we cut out the things that really create a difference

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<v Speaker 1>between the rich and the poor when it comes to investing.

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<v Speaker 1>So there's no minimums to get started, there's no five

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<v Speaker 1>to ten dollar commission every time you trade, and it's

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<v Speaker 1>it's made to be really easy for people that are

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<v Speaker 1>getting started and also really powerful for people that have

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<v Speaker 1>been using it for a long time. And we announced

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<v Speaker 1>it about two years ago. It's been growing very quickly.

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<v Speaker 1>Um we've crossed now over three billion users in the

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<v Speaker 1>US UM and we've transacted over a hundred billion dollars

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<v Speaker 1>in the last two years, saving our consumers over a

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<v Speaker 1>million or over a billion excuse me dollar in commissions

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<v Speaker 1>in total. So the business has been growing, you know,

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<v Speaker 1>really well. And uh, it's it's really introduced just a

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<v Speaker 1>brand new generation of consumers. And in the stock market.

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<v Speaker 1>How do you make money? The way we make money

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<v Speaker 1>is we've got a paid version of robin Hood, which

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<v Speaker 1>lets consumers borrow from US. So for as little as

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<v Speaker 1>ten dollars a month, you can get up to an

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<v Speaker 1>additional two thousand dollars in your account, and we also

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<v Speaker 1>collect interest on the cash and stocks in your account,

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<v Speaker 1>and we we split that with the consumer, And just

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<v Speaker 1>to follow up on that, why do you believe that

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<v Speaker 1>a five dollar or six dollar commission is what's keeping

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<v Speaker 1>the majority of non wealthy investors from participating in the

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<v Speaker 1>stock market, because that kind of speaks to the issue

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<v Speaker 1>of trading in and out and that somehow the commissions

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<v Speaker 1>are what are the obstacle rather than a viable long

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<v Speaker 1>term strategy to make money. Well, I think it's I

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<v Speaker 1>think it's commissions are a much bigger deal for consumers

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<v Speaker 1>than you know, as your I that kind of are in.

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<v Speaker 1>But why, I mean, if you're not going to trade

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<v Speaker 1>a lot, why would let's say, you know, buying shares

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<v Speaker 1>and paying your commission and holding the stock for whatever

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<v Speaker 1>period of time. Why is that the barrier? Isn't that

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<v Speaker 1>the cost of doing business? No, it's really not. I

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<v Speaker 1>think we've got a generation of consumers that are not

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<v Speaker 1>that are not really willing to pay for digital services.

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<v Speaker 1>And when you look at what goes into facilitating a

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<v Speaker 1>stock transaction, it's purely electronic and there's really no difference

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<v Speaker 1>between that and sending an email. So I think it's

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<v Speaker 1>it's one of those things that when it once it

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<v Speaker 1>goes away, it gets a lot of people to start thinking, oh, well,

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<v Speaker 1>maybe I'll try this and I'll kind of see how

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<v Speaker 1>this works, um and it just it feels like a

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<v Speaker 1>less cumbersome commitment to make. So are most of the

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<v Speaker 1>clients that you have, do you have a sense of

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<v Speaker 1>how old they are? Yeah? Absolutely, So. This is one

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<v Speaker 1>of the interesting things about our company is that the

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<v Speaker 1>majority of people that use robin Hood and I think

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<v Speaker 1>it's being like of our consumers are under the age

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<v Speaker 1>of forty and so it's this new generation of consumers

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<v Speaker 1>that sort of commonly called millennials um that that they're

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<v Speaker 1>using this as kind of their first entry point into

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<v Speaker 1>investing in the stock market, and they're they're getting started

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<v Speaker 1>today when they're a lot younger, rather than waiting until

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<v Speaker 1>they're a little bit later in life in their fifties

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<v Speaker 1>or sixties. When you know, I think a lot of

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<v Speaker 1>companies like The Trade and and tdumer Trade see their

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<v Speaker 1>consumers getting online. You know, there's this sort of stereotype

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<v Speaker 1>that millennials aren't as interested in stock trading and see

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<v Speaker 1>more value and say bitcoin, uh than stocks, especially because

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<v Speaker 1>they grew up with the very present who thousand and

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<v Speaker 1>eight experience and I'm just wondering how much truth there

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<v Speaker 1>is to you that based on your findings. In other words,

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<v Speaker 1>is there a big group of millennials who are interested

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<v Speaker 1>in trading individuals stacks? You know, that's really what we're seeing.

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<v Speaker 1>I mean, if you take a look at our our numbers, right,

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<v Speaker 1>We've we've got over three million people that have AC

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<v Speaker 1>counts with us that have found up in just the

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<v Speaker 1>last two years. And you can put that side by

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<v Speaker 1>side with the trade which has about three points six

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<v Speaker 1>million consumers UM. So we're we're becoming, you know, a

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<v Speaker 1>very sizeable market participant, and largely on you know, on

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<v Speaker 1>the grounds that young people have chosen us, Robinson. So,

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<v Speaker 1>I think that the market demand is certainly there UM,

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<v Speaker 1>And I think it's it's one of those things that's

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<v Speaker 1>happening relatively quickly. And I think when we look at

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<v Speaker 1>this in a couple of years, we'll see that the

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<v Speaker 1>younger generation of consumers UM will actually amount for I

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<v Speaker 1>think it will be a bigger audience than than the

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<v Speaker 1>last generation. And what about backing, who are some of

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<v Speaker 1>your investors? Yeah, so we've raised over I think a

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<v Speaker 1>hundred and seventy six million dollars over the last few years.

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<v Speaker 1>We're backed by top tier investors like uh any A,

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<v Speaker 1>uh Google Ventures, and Trees and Harwood Ventures. Indeed also

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<v Speaker 1>some some celebrities like Ashton Kutcher and Snoop dogg Um

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<v Speaker 1>in the Gang from It's Always in Sunny in Philadelphia

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<v Speaker 1>if you ever watched that show. So all right, well,

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<v Speaker 1>thanks for enlightening us on robin Hood much appreciated Buys

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<v Speaker 1>You Bought is the co founder and the co chief

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<v Speaker 1>executive of robin Hood. They're based in Palo Alto, offering

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<v Speaker 1>what they described as commission free trading in equities. Artificial

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<v Speaker 1>intelligence and the use of artificial intelligence in the economy

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<v Speaker 1>is expected to reach about thirty six billion dollars by

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<v Speaker 1>this according to a report of from a eligence marketer.

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<v Speaker 1>This is a grand View research and here to help

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<v Speaker 1>us understand how this uh new technology will be used

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<v Speaker 1>in industries such as the restaurant business as well as

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<v Speaker 1>retail is Shing Tao. He is the chairman and the

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<v Speaker 1>chief executive of Remark Holdings, and he joins us now

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<v Speaker 1>on the phone line from Las Vegas. Shing, thank you

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<v Speaker 1>very much for being with us. Maybe just begin by

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<v Speaker 1>talking about UH sort of subsidiary of yours called can Can.

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<v Speaker 1>That's k A N k A N What is it

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<v Speaker 1>and how does that fold into the overall business of

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<v Speaker 1>Remark Holdings. Great, thank you for having me. Um. We uh,

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<v Speaker 1>we had built it's been about three years since we

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<v Speaker 1>kind of laid up division to build can Can, which

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<v Speaker 1>is a big data artificial intelligence platform, and that was

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<v Speaker 1>really to marry all the different assets that we had

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<v Speaker 1>currently owned and had you know, looked to potentially acquire

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<v Speaker 1>in the future. Uh, you know, going going into really

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<v Speaker 1>two thousand and thirteen, and we found that, you know,

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<v Speaker 1>the business that we were in, it was very tough

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<v Speaker 1>to compete and if we didn't have you know, if

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<v Speaker 1>we didn't care about the profits and all that. So

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<v Speaker 1>it was pretty important at that time to really make

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<v Speaker 1>a a big bet at that time, uh, to see

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<v Speaker 1>how do we create a technology platform or technology that

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<v Speaker 1>you can't just throw cash at the problem, uh, in

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<v Speaker 1>order to enter it. And and that's really how we

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<v Speaker 1>first um you know, kind of made the move into

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<v Speaker 1>that field. And we realized that in the beginning was like, look,

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<v Speaker 1>you know, with at that time, you know, the social

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<v Speaker 1>media accounts, we weren't really very well integrated um. You know,

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<v Speaker 1>now three years later you see a kind of a

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<v Speaker 1>there there's a need for a break between the data

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<v Speaker 1>that you see in China and and and other parts

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<v Speaker 1>of the world, given that China is closed to the

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<v Speaker 1>kind of foreign groups going into there. Uh, And so

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<v Speaker 1>we created a platform to marry the data from both

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<v Speaker 1>sides where um it would serve two purposes. One is

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<v Speaker 1>to help, you know, obviously help our own businesses, but

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<v Speaker 1>number two create solutions for business that are trying to

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<v Speaker 1>enter into China or Chinese business trying to enter outside

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<v Speaker 1>of China into the US. So shan just sort of

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<v Speaker 1>to zoom out a little bit. So Remark is a

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<v Speaker 1>data collection company, right, You collect certain data and this

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<v Speaker 1>partnership with ken Ken allows you to analyze it and

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<v Speaker 1>use it more for your clients. Is that accurate? No?

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<v Speaker 1>Is our AI platform that we've built from the ground up. Okay?

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<v Speaker 1>And then with respect to Remark, it's you get data

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<v Speaker 1>from a number of different sources that you subscribers can

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<v Speaker 1>use to analyze consumer habits, credit, etcetera. Right, So what

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<v Speaker 1>we do is we create we create a technology to

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<v Speaker 1>be able to scrape all the data from you know,

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<v Speaker 1>all the major social media sites around the world. Number one.

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<v Speaker 1>Number two really any site with a lot of consumer information.

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<v Speaker 1>Now obviously that has a lot of data, as we

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<v Speaker 1>have over one point three billion unique user profiles, and

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<v Speaker 1>then there needs to be, uh, you know, something to

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<v Speaker 1>really connect the dots. And so that's what we've built

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<v Speaker 1>as the AI platform basically is trained by the data

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<v Speaker 1>that we've have, because you know, strong AI is driven

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<v Speaker 1>by strong data. So, um, it's fascinating and I'm wondering

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<v Speaker 1>what the challenges for you to gain access to that

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<v Speaker 1>data you're scraping from all these other sites. I know

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<v Speaker 1>that this has been held up as the greatest value

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<v Speaker 1>of the big companies is really their data, right, so

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<v Speaker 1>it's hard for you to get access to it. And

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<v Speaker 1>what's your business model? I mean you get subscription fees

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<v Speaker 1>and you use part of it to uh to license

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<v Speaker 1>the data. How does it work? Yeah, So, so we

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<v Speaker 1>we have a number of different partnerships, some some we

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<v Speaker 1>you know, it's it's primarily just a scraping technology that

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<v Speaker 1>we're able to get public available, publicly available data better

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<v Speaker 1>than others. That's number one. Number two. We have data

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<v Speaker 1>partnerships with both Ali Baba intencent where we open up

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<v Speaker 1>our data and they do as well, and we basically

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<v Speaker 1>utilize the three groups different data to train to AI models,

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<v Speaker 1>which is why it works so well from a business

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<v Speaker 1>standpoint um. You know, as as you know, there are

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<v Speaker 1>a lot of different AI companies out there, and that's

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<v Speaker 1>the buzzword. You know. We're able to monetize very quickly

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<v Speaker 1>because we are providing solutions in a number of different areas.

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<v Speaker 1>The first in the biggest isn't isn't fintech in China,

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<v Speaker 1>which is the first market that we launched in less

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<v Speaker 1>than the third of the people they're actually have a

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<v Speaker 1>credit history, as we know in the US, so the

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<v Speaker 1>way the financial institutions are doing their their lending is

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<v Speaker 1>based on your behavioral history. Presumably we have the most

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<v Speaker 1>of it, given all the social media history, uh that

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<v Speaker 1>we have, and a big target for us are the

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<v Speaker 1>millennials and and sort of the the younger demographic from

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<v Speaker 1>and because because we have so much data and because

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<v Speaker 1>that's really what creates the foundation for the AI platform,

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<v Speaker 1>we're able to move into a number of different areas

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<v Speaker 1>as well. It's not just fintech, but it's also in

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<v Speaker 1>for example, like food safety. So one of the one

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<v Speaker 1>of the projects that we're working on right now is

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<v Speaker 1>with the Shanghainese government where the where the China government

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<v Speaker 1>has mandated where you have to wear a mask and

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<v Speaker 1>a hat uh in order to prepare food. Uh. So

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<v Speaker 1>using our technology, being able to monitor a whole area

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<v Speaker 1>and be able to identify who's who, be able to

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<v Speaker 1>decipher if a person is wearing a mask, a hat

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<v Speaker 1>or both. Uh. You know, our AI allows us to

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<v Speaker 1>do that and to pick up where there might be

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<v Speaker 1>a violation of that policy, and then it sends directly

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<v Speaker 1>to the health agencies will make sure there's um you know,

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<v Speaker 1>actually integrity in that in that data. So from that too,

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<v Speaker 1>um you know. Uh. In hunsl right now where Ali

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<v Speaker 1>Baba is based there, they're going through a mandate where

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<v Speaker 1>they're banning motorcycles on the street to prevent new slow

0:12:51.920 --> 0:12:54.360
<v Speaker 1>down pollution. But how do you know the difference between

0:12:54.640 --> 0:12:57.679
<v Speaker 1>a motorcycle and electric bikes? So are AI because it's

0:12:57.720 --> 0:13:01.000
<v Speaker 1>been trained by so many images, uh you know, over

0:13:01.040 --> 0:13:05.240
<v Speaker 1>ten billion images, allows us to actually be very accurate

0:13:05.280 --> 0:13:08.400
<v Speaker 1>how we decipher who's who, what's what. Shanta, thank you

0:13:08.440 --> 0:13:11.240
<v Speaker 1>so much for joining ours Shingtau, chairman and chief executive

0:13:11.280 --> 0:13:14.880
<v Speaker 1>officer of Remark Holdings based in Shanghai, China. But he

0:13:14.920 --> 0:13:32.280
<v Speaker 1>was coming to us from Las Vegas taxes and debate

0:13:32.400 --> 0:13:36.679
<v Speaker 1>in Washington over changes in tax policy. Let's find out

0:13:36.720 --> 0:13:39.760
<v Speaker 1>more from Wayne wine Garden. He is a senior Fellow

0:13:39.800 --> 0:13:44.560
<v Speaker 1>in Business and Economics at the Pacific Research Institute. Wayne Uh,

0:13:44.720 --> 0:13:49.040
<v Speaker 1>let's just begin by maybe laying out from your perspective

0:13:49.480 --> 0:13:53.480
<v Speaker 1>what needs to change for tax policy to help the

0:13:53.520 --> 0:13:58.040
<v Speaker 1>economy grow. Well, if we're looking at the proposal that's

0:13:58.080 --> 0:14:01.240
<v Speaker 1>out there, the most important art is the corporate income

0:14:01.280 --> 0:14:05.640
<v Speaker 1>tax reform. The US corporate tax system is so out

0:14:05.679 --> 0:14:09.080
<v Speaker 1>of balance and so uncompetitive compared to all of our

0:14:09.120 --> 0:14:13.280
<v Speaker 1>global global competitors. I think that's something that's very well known.

0:14:13.600 --> 0:14:15.959
<v Speaker 1>So the idea that we're bringing that tax rate down

0:14:16.280 --> 0:14:21.000
<v Speaker 1>from thirty and the fact that we're going from this

0:14:21.120 --> 0:14:24.280
<v Speaker 1>global tax system that nobody else has to the territorial

0:14:24.320 --> 0:14:29.200
<v Speaker 1>tax system brings our our corporate tax system into ligne

0:14:29.320 --> 0:14:33.840
<v Speaker 1>with the global competitors. And importantly, what it's also going

0:14:33.880 --> 0:14:35.600
<v Speaker 1>to do for us is it's going to stop this

0:14:35.760 --> 0:14:39.800
<v Speaker 1>kind of movement of inversions and companies moving overseas, that's

0:14:39.800 --> 0:14:41.760
<v Speaker 1>gonna all stop, and the incentive is going to be

0:14:42.000 --> 0:14:45.080
<v Speaker 1>to set up corporations here in the US. Wayne, how

0:14:45.080 --> 0:14:49.560
<v Speaker 1>do you feel about the healthcare angle that's been pulled

0:14:49.600 --> 0:14:54.520
<v Speaker 1>into this tax debate with the Senate provision removing the

0:14:54.720 --> 0:15:01.280
<v Speaker 1>Obamacare payments that are required. That's it's it's a it's

0:15:01.280 --> 0:15:04.400
<v Speaker 1>a bet that a more complex bill is going to

0:15:04.440 --> 0:15:07.360
<v Speaker 1>be easier to get through than something perhaps more simple.

0:15:07.840 --> 0:15:11.640
<v Speaker 1>And you know, perhaps that bet is right, perhaps it's wrong. Individually,

0:15:11.680 --> 0:15:14.680
<v Speaker 1>each makes sense, right that the mandate doesn't make sense.

0:15:14.680 --> 0:15:18.040
<v Speaker 1>It forces people to buy insurance at a cost that

0:15:18.120 --> 0:15:21.440
<v Speaker 1>they don't believe is worthwhile for them. Wait, hold away,

0:15:21.960 --> 0:15:23.640
<v Speaker 1>Hold on a second. I mean the idea was to

0:15:23.680 --> 0:15:27.480
<v Speaker 1>sort of sustain Obamacare. But I guess what I want is,

0:15:27.920 --> 0:15:30.160
<v Speaker 1>do you think that this is a prudent move? And

0:15:30.240 --> 0:15:34.560
<v Speaker 1>do you think that pulling out this Obamacare mandate is

0:15:34.600 --> 0:15:39.080
<v Speaker 1>the right move? It's right moves if it can pass, Yes,

0:15:39.720 --> 0:15:42.000
<v Speaker 1>I don't believe. I believe it makes it a little

0:15:42.000 --> 0:15:44.040
<v Speaker 1>bit more difficult to pass because of bringing in the

0:15:44.080 --> 0:15:47.000
<v Speaker 1>Affordable Care Act issues into the tax reform, which is

0:15:47.040 --> 0:15:50.800
<v Speaker 1>already complex enough. But if if it does pass, we

0:15:50.800 --> 0:15:53.520
<v Speaker 1>were going to have based on the budget scoring window

0:15:53.920 --> 0:15:56.000
<v Speaker 1>that they have to kind of fit this bill through.

0:15:56.360 --> 0:15:59.400
<v Speaker 1>It helps move tax reform along in terms of the

0:15:59.440 --> 0:16:03.160
<v Speaker 1>deficit constraints that it faces, and it's in itself it's

0:16:03.160 --> 0:16:06.280
<v Speaker 1>a good policy. So combine those two things together, Yes,

0:16:06.360 --> 0:16:09.240
<v Speaker 1>it's a good move politically, though you're bringing in the

0:16:09.280 --> 0:16:11.600
<v Speaker 1>host of other issues and that needs to be kind

0:16:11.600 --> 0:16:15.040
<v Speaker 1>of worked out. When can we just go back to

0:16:15.080 --> 0:16:17.920
<v Speaker 1>the corporate tax reformation for just a moment? Is is

0:16:17.920 --> 0:16:21.320
<v Speaker 1>there any evidence that lower in corporate taxes will lead

0:16:21.360 --> 0:16:25.640
<v Speaker 1>to greater economic growth? Yes, And if you look at

0:16:25.640 --> 0:16:29.640
<v Speaker 1>the economics literature, undoubtedly the corporate income tax has a

0:16:29.760 --> 0:16:34.960
<v Speaker 1>very large impact on kind of how much capital companies

0:16:34.960 --> 0:16:36.800
<v Speaker 1>are going to invest, how much they can reinvest in

0:16:36.840 --> 0:16:40.320
<v Speaker 1>their workers. So there's a lot of connection between how

0:16:40.400 --> 0:16:43.080
<v Speaker 1>high or how burdens them your corporate income taxt rate

0:16:43.200 --> 0:16:47.520
<v Speaker 1>is and how much economic growth the country can can sustain. Okay,

0:16:47.600 --> 0:16:50.480
<v Speaker 1>So then so I was gonna say, so okay, So

0:16:50.640 --> 0:16:54.400
<v Speaker 1>having said that, why or do you believe it would

0:16:54.440 --> 0:16:59.000
<v Speaker 1>be relevant to in a sense have a test. For example,

0:16:59.040 --> 0:17:03.080
<v Speaker 1>if you do off for this tax reduction to corporations,

0:17:03.640 --> 0:17:06.720
<v Speaker 1>they are only able to claim it if indeed they

0:17:06.800 --> 0:17:10.520
<v Speaker 1>follow through on what you just described, which is spending

0:17:10.560 --> 0:17:15.920
<v Speaker 1>more on research and development, spending more on hiring. Well,

0:17:16.240 --> 0:17:19.080
<v Speaker 1>you don't necessarily want every corporation spending more on R

0:17:19.119 --> 0:17:21.880
<v Speaker 1>and DM. And this just gets to what drives growth

0:17:22.200 --> 0:17:24.439
<v Speaker 1>and for us to try to determine what's the right

0:17:24.520 --> 0:17:27.880
<v Speaker 1>investment for every corporation through the tax code, I mean,

0:17:27.920 --> 0:17:30.679
<v Speaker 1>that's how we end up with this complex you know,

0:17:31.160 --> 0:17:34.800
<v Speaker 1>tens of thousands of pages of tax code to what

0:17:34.840 --> 0:17:37.920
<v Speaker 1>we want is a ideally that the much more simple

0:17:38.200 --> 0:17:42.399
<v Speaker 1>flat tax structure that makes it kind of easier for

0:17:42.560 --> 0:17:45.080
<v Speaker 1>corporations to spend their time talking about how do we

0:17:45.119 --> 0:17:48.120
<v Speaker 1>get the best products to customers and not worrying about

0:17:48.160 --> 0:17:50.480
<v Speaker 1>how do we kind of gain the tax system to

0:17:50.680 --> 0:17:53.919
<v Speaker 1>maximize profits. If we do that, we'll get stronger growth.

0:17:54.160 --> 0:17:57.760
<v Speaker 1>Some companies we may want to contract because they're our

0:17:57.760 --> 0:18:00.920
<v Speaker 1>growth industries. We want more investment in areas matter, so

0:18:01.280 --> 0:18:04.080
<v Speaker 1>as a kind of from a government perspective, you don't

0:18:04.080 --> 0:18:05.679
<v Speaker 1>know that, and so you want that to be as

0:18:05.720 --> 0:18:08.159
<v Speaker 1>neutral as possible so that we can kind of sustain

0:18:08.240 --> 0:18:12.160
<v Speaker 1>the most dynamic economy we can create. When is there

0:18:12.200 --> 0:18:16.440
<v Speaker 1>anything in either the Senate or the House bill as

0:18:16.480 --> 0:18:21.320
<v Speaker 1>they currently stand that concerns you. There's there's several things.

0:18:21.320 --> 0:18:23.960
<v Speaker 1>I mean, certainly on the personal income tax inside, there's

0:18:24.640 --> 0:18:28.240
<v Speaker 1>a lot of kind of trying to bang square pegs

0:18:28.240 --> 0:18:30.840
<v Speaker 1>into round holes. I mean, the big problem. I would

0:18:30.880 --> 0:18:33.800
<v Speaker 1>have loved to have seen the payroll tax be included

0:18:33.840 --> 0:18:37.399
<v Speaker 1>in the tax reform because when you're talking about middle

0:18:37.440 --> 0:18:41.080
<v Speaker 1>class tax relief, class and lower income people don't pay

0:18:41.119 --> 0:18:44.679
<v Speaker 1>income taxes, they pay payroll taxes, and so there's drive

0:18:44.800 --> 0:18:48.679
<v Speaker 1>to try to create lower income and middle income tax relief,

0:18:48.960 --> 0:18:52.600
<v Speaker 1>but not including the most important taxes to those individuals

0:18:52.880 --> 0:18:56.240
<v Speaker 1>has has led to a lot of unnecessary complexities and

0:18:56.280 --> 0:19:00.960
<v Speaker 1>backward bending, kind of strange provisions. So I would have

0:19:01.000 --> 0:19:04.520
<v Speaker 1>ideally like to have seen those removed and actually to

0:19:04.560 --> 0:19:07.280
<v Speaker 1>start addressing how can we fold the payroll tax into

0:19:07.320 --> 0:19:10.359
<v Speaker 1>this broader tax reform. Are you concerned at all about

0:19:10.440 --> 0:19:13.800
<v Speaker 1>the fact that the deficit is poised to expand by

0:19:13.800 --> 0:19:18.359
<v Speaker 1>one and a half trillion dollars with a conservative estimate. Yes, Uh,

0:19:18.760 --> 0:19:21.919
<v Speaker 1>the depth is definitely a problem. But the problem with

0:19:21.960 --> 0:19:24.920
<v Speaker 1>the deficit is it's a spending problem, and that's something

0:19:24.960 --> 0:19:29.240
<v Speaker 1>that with or without tax reform, we need to address. Uh,

0:19:29.280 --> 0:19:32.560
<v Speaker 1>and that needs to be addressed to fundamental budget process reform,

0:19:32.920 --> 0:19:35.960
<v Speaker 1>fundamental spending reform, so that we can bring kind of

0:19:35.960 --> 0:19:40.520
<v Speaker 1>our expenditures into line with what we're able to kind

0:19:40.560 --> 0:19:42.800
<v Speaker 1>of generate from a tax revenue. If you look at

0:19:42.800 --> 0:19:45.879
<v Speaker 1>how much revenue the tax system raises kind of relative

0:19:45.920 --> 0:19:49.760
<v Speaker 1>to the size of the economy, that's been fairly constant,

0:19:50.080 --> 0:19:53.120
<v Speaker 1>even though we've had major changes in terms of tax revenue.

0:19:53.440 --> 0:19:56.240
<v Speaker 1>But we look at the expenditures and that keeps rising,

0:19:56.320 --> 0:19:59.639
<v Speaker 1>you know, long term, there's lots of wiggles around it,

0:19:59.720 --> 0:20:03.119
<v Speaker 1>but rising long term relative to the size of the economy.

0:20:03.560 --> 0:20:05.840
<v Speaker 1>That's the problem with the deficit, and we need to

0:20:05.880 --> 0:20:08.680
<v Speaker 1>bring this spending under control. Otherwise we're not going to

0:20:08.760 --> 0:20:11.600
<v Speaker 1>get control the deficity no matter what we do in taxes. Wait,

0:20:11.760 --> 0:20:14.240
<v Speaker 1>just quickly. We were speaking yesterday with a group of

0:20:14.280 --> 0:20:18.399
<v Speaker 1>real estate executives at the Burden Executive Summit here at

0:20:18.440 --> 0:20:20.960
<v Speaker 1>Bloomberg and one of the things they talked about was

0:20:21.000 --> 0:20:25.080
<v Speaker 1>the deductibility of state and local taxes. Uh. Do you

0:20:25.160 --> 0:20:28.639
<v Speaker 1>believe that that will be erased in this in this

0:20:28.720 --> 0:20:33.720
<v Speaker 1>tax bill? Um? No, I think if you're gonna it

0:20:33.760 --> 0:20:36.040
<v Speaker 1>should be erased. Let me start with that that it's

0:20:36.160 --> 0:20:38.639
<v Speaker 1>the right policy to get rid of it, but you

0:20:38.720 --> 0:20:41.960
<v Speaker 1>have strong interests behind it. And when you start looking

0:20:41.960 --> 0:20:44.520
<v Speaker 1>at how do we get the bill a bill past,

0:20:45.040 --> 0:20:48.040
<v Speaker 1>it seems that some kind of measured like the house

0:20:48.080 --> 0:20:51.320
<v Speaker 1>where they have the property taxes can still be included

0:20:51.400 --> 0:20:53.480
<v Speaker 1>up to I think it's ten thousand dollars. I think

0:20:53.480 --> 0:20:56.280
<v Speaker 1>you're going to have some sort of compromise on that

0:20:56.520 --> 0:21:01.040
<v Speaker 1>simply because there's too much interest to keep it going.

0:21:01.400 --> 0:21:03.879
<v Speaker 1>But under an ideal tax REFILM, absolutely you would give

0:21:03.920 --> 0:21:07.040
<v Speaker 1>it of that. You need to broaden the base to

0:21:07.119 --> 0:21:10.040
<v Speaker 1>get the tax weight down, and that's an important area.

0:21:10.359 --> 0:21:12.280
<v Speaker 1>Wayne wine Garden, thank you so much for joining us.

0:21:12.280 --> 0:21:15.479
<v Speaker 1>Wayne wide Garden, Senior Fellow in Business and Economics at

0:21:15.520 --> 0:21:31.080
<v Speaker 1>the Pacific Research Institute and Falls Church, Virginia. Let's turn

0:21:31.119 --> 0:21:33.720
<v Speaker 1>our attention now to a piece of paper, a very

0:21:33.720 --> 0:21:36.719
<v Speaker 1>expensive piece of paper, indeed, a four hundred and fifty

0:21:36.760 --> 0:21:40.000
<v Speaker 1>million dollar piece of paper that just happens to have

0:21:40.240 --> 0:21:43.560
<v Speaker 1>the painting. I guess it may even be a canvas, uh,

0:21:43.640 --> 0:21:47.199
<v Speaker 1>that is m Leonardo da Vinci. Work by Leonardo da

0:21:47.240 --> 0:21:49.920
<v Speaker 1>Vinci sold for four hundred and fifty million dollars at

0:21:49.960 --> 0:21:53.080
<v Speaker 1>Christie's auction House yesterday evening, and here to tell us

0:21:53.080 --> 0:21:57.600
<v Speaker 1>more about it is Bloomberg's art reporter Katya Kazakia. And

0:21:57.600 --> 0:21:59.359
<v Speaker 1>I usually get your name right, I beg your pardon.

0:21:59.560 --> 0:22:01.560
<v Speaker 1>I guess I was stunned by the four and fifty

0:22:01.560 --> 0:22:04.720
<v Speaker 1>million dollar price. Tech tell me tell us about Salvatore

0:22:04.800 --> 0:22:08.679
<v Speaker 1>Mundi or Savior of the World. And why would anyone

0:22:08.720 --> 0:22:13.880
<v Speaker 1>pay four hundred and fifty million dollars for a single painting? Well,

0:22:13.920 --> 0:22:20.080
<v Speaker 1>it's the last Leonardo. So that's that was how Christie's

0:22:20.160 --> 0:22:22.560
<v Speaker 1>promoted it. And it's actually true, you know, Leonardo. There

0:22:22.560 --> 0:22:25.800
<v Speaker 1>only less than twenty Leonardo paintings in the world right now.

0:22:25.840 --> 0:22:30.000
<v Speaker 1>And I was rediscovered in two thousand eleven. It had

0:22:30.040 --> 0:22:35.560
<v Speaker 1>been really restored, pretty hands on restoration. So the condition

0:22:35.640 --> 0:22:38.680
<v Speaker 1>is not great, but it has this magic about it, right,

0:22:38.720 --> 0:22:41.320
<v Speaker 1>it has Leonardo, but one of the greatest, if not

0:22:41.359 --> 0:22:44.080
<v Speaker 1>the greatest artists of all times. And um, it's the

0:22:44.160 --> 0:22:46.919
<v Speaker 1>last one. And so for someone there's so much wealth,

0:22:46.960 --> 0:22:48.720
<v Speaker 1>and you know, we've talked about it a lot. You know,

0:22:48.800 --> 0:22:52.000
<v Speaker 1>in the growth of wealth has been unprecedented, and so

0:22:52.280 --> 0:22:54.840
<v Speaker 1>there are a lot of billionaires in Asia and in America,

0:22:54.840 --> 0:22:57.840
<v Speaker 1>and in Europe and emerging economists who are building their museums,

0:22:57.840 --> 0:23:00.680
<v Speaker 1>who need you know, they need a show upper And

0:23:00.680 --> 0:23:03.440
<v Speaker 1>and we've seen over the past months. It's only been

0:23:03.520 --> 0:23:06.760
<v Speaker 1>on tour by Christie's four months and thirty thousand people.

0:23:07.000 --> 0:23:08.679
<v Speaker 1>They showed it in Hong Kong, they showed it in

0:23:08.720 --> 0:23:11.400
<v Speaker 1>London and San Francisco and in in New York. And during

0:23:11.440 --> 0:23:14.640
<v Speaker 1>the last week people in the rain waited to get

0:23:14.640 --> 0:23:16.960
<v Speaker 1>in and to see this work. And Christie's they're they're

0:23:16.960 --> 0:23:20.240
<v Speaker 1>really smart marketers. They put together this video of people

0:23:20.680 --> 0:23:24.159
<v Speaker 1>looking at the painting in all transfixed, some with tears

0:23:24.160 --> 0:23:27.439
<v Speaker 1>in their eyes. Leonardo Dicapriism on them appears suddenly, you know,

0:23:27.640 --> 0:23:31.080
<v Speaker 1>and so the profound effect that this painting, whether or

0:23:31.119 --> 0:23:34.000
<v Speaker 1>not you know it is the real Leonardo or not,

0:23:34.080 --> 0:23:36.399
<v Speaker 1>like it kind of be on the pail, it doesn't matter,

0:23:36.520 --> 0:23:38.840
<v Speaker 1>you know. Well, God, yeah, I want to pick up

0:23:38.840 --> 0:23:40.800
<v Speaker 1>on a point that you mentioned here, which is this

0:23:40.880 --> 0:23:43.680
<v Speaker 1>highlights just how much wealth is slashing around. I mean,

0:23:43.720 --> 0:23:47.640
<v Speaker 1>we're talking nearly half a billion dollars here for a painting. Yes,

0:23:47.760 --> 0:23:50.080
<v Speaker 1>it is rare. A lot of things are rare in

0:23:50.080 --> 0:23:52.399
<v Speaker 1>this world. It doesn't mean that there is a willing

0:23:52.920 --> 0:23:56.960
<v Speaker 1>buyer for nearly half a billion dollars, especially considering that

0:23:57.040 --> 0:23:59.760
<v Speaker 1>it's way more than twice as much as the last

0:24:00.200 --> 0:24:02.679
<v Speaker 1>mark of hundred and seventy nine point four million dollars

0:24:02.840 --> 0:24:06.080
<v Speaker 1>for a Pablo Picasso painting. Do you have a sense

0:24:06.160 --> 0:24:08.399
<v Speaker 1>of how small the group is of people who not

0:24:08.440 --> 0:24:10.959
<v Speaker 1>only could afford this, but who would be interested in

0:24:11.040 --> 0:24:14.720
<v Speaker 1>spending this for like a vanity project or what else

0:24:14.720 --> 0:24:18.120
<v Speaker 1>could it be? It's, um, I don't think it's it's

0:24:18.440 --> 0:24:20.320
<v Speaker 1>the group is not small at all. And that was

0:24:20.400 --> 0:24:24.040
<v Speaker 1>so striking is because, uh, in three hundred up to

0:24:24.160 --> 0:24:26.719
<v Speaker 1>three hundred and seventy million, there were three people who

0:24:27.000 --> 0:24:30.480
<v Speaker 1>were doggedly competing for it. Um, So there were three

0:24:30.480 --> 0:24:32.960
<v Speaker 1>people in the world who could afford it at that level.

0:24:33.320 --> 0:24:35.919
<v Speaker 1>And you know, you mentioned the Picasso of course because

0:24:35.920 --> 0:24:38.000
<v Speaker 1>it was hundred and eighty million dollars, but they have

0:24:38.119 --> 0:24:40.640
<v Speaker 1>been private sales a lot higher up to like three

0:24:40.720 --> 0:24:43.520
<v Speaker 1>hundred million, that would know. But we've never even heard

0:24:43.560 --> 0:24:46.320
<v Speaker 1>those numbers in the sales room. We've never you know,

0:24:46.920 --> 0:24:50.800
<v Speaker 1>two hundred million, three hundred million dollars in the context

0:24:50.840 --> 0:24:53.800
<v Speaker 1>of an auction. It it's it sounded surreal, you know,

0:24:53.800 --> 0:24:56.760
<v Speaker 1>it never happened, and so I think that, yeah, I

0:24:56.760 --> 0:24:58.879
<v Speaker 1>mean the money, the money is not an issue for

0:24:59.040 --> 0:25:01.680
<v Speaker 1>people of that elibor. I think of that wealth, so,

0:25:02.080 --> 0:25:04.639
<v Speaker 1>you know, and it's not a rare just a rare thing.

0:25:04.680 --> 0:25:08.160
<v Speaker 1>There a lot of rare things, you know this. You know, well,

0:25:08.240 --> 0:25:10.760
<v Speaker 1>if we accept that this is the last Leonardo, it

0:25:10.840 --> 0:25:13.960
<v Speaker 1>is the last Leonardo. There's nothing else like it. So

0:25:14.200 --> 0:25:17.520
<v Speaker 1>I think for people, you know, and an interestingly, you know,

0:25:17.560 --> 0:25:20.720
<v Speaker 1>I spoke before the sale with the specialist at Christie's

0:25:20.800 --> 0:25:22.359
<v Speaker 1>and I don't think they didn't know it's going to

0:25:22.440 --> 0:25:24.960
<v Speaker 1>go that high. They kind of were thinking it's either

0:25:25.000 --> 0:25:27.119
<v Speaker 1>going to sell on one bed to the guaranteur of

0:25:27.160 --> 0:25:29.679
<v Speaker 1>the work, or you know, maybe it will go to

0:25:29.760 --> 0:25:34.800
<v Speaker 1>like one fifty. But you know, I think it's surprised everyone. Well,

0:25:34.840 --> 0:25:38.000
<v Speaker 1>I wonder if it's surprised the owner or the previous owner,

0:25:38.240 --> 0:25:43.840
<v Speaker 1>the Russian billionaire Dmitri rub Lovelev right, probably he bought

0:25:43.920 --> 0:25:46.520
<v Speaker 1>the painting in tween. He paid about a hundred and

0:25:46.520 --> 0:25:49.399
<v Speaker 1>twenty eight million dollars. It was a private sale, so

0:25:49.440 --> 0:25:52.760
<v Speaker 1>a hundred and twenty eight million dollars in becomes four

0:25:52.840 --> 0:25:56.520
<v Speaker 1>hundred and fifty million dollars that's correct. But you know

0:25:56.560 --> 0:26:01.120
<v Speaker 1>also remember that um, yes, but tube was he got

0:26:01.119 --> 0:26:04.000
<v Speaker 1>it four seven a half, but the person who sold

0:26:04.000 --> 0:26:06.439
<v Speaker 1>it to him got it for a d like literally

0:26:06.480 --> 0:26:08.520
<v Speaker 1>like a few days before. It's a good it's a

0:26:08.560 --> 0:26:10.960
<v Speaker 1>good way to show just how much the wealthy are

0:26:11.000 --> 0:26:13.719
<v Speaker 1>are getting wealthier right now. And sixty Fo'm serrying sixty four.

0:26:13.840 --> 0:26:16.120
<v Speaker 1>Just an interesting factor it that sixty four years ago

0:26:16.200 --> 0:26:18.440
<v Speaker 1>the same work sold it in the States, sale forty

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<v Speaker 1>five pounds million dollars in sixty or four years. Katya Kazakina,

0:26:25.760 --> 0:26:31.600
<v Speaker 1>Art market reporter for Bloomberg News. Thank you. Thanks for

0:26:31.640 --> 0:26:34.280
<v Speaker 1>listening to the Bloomberg P and L podcast. You can

0:26:34.320 --> 0:26:38.159
<v Speaker 1>subscribe and listen to interviews at Apple Podcasts, SoundCloud, or

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<v Speaker 1>whatever podcast platform you prefer. I'm pim Fox. I'm on

0:26:41.720 --> 0:26:45.560
<v Speaker 1>Twitter at pim Fox. I'm on Twitter at Lisa Abramo.

0:26:45.680 --> 0:26:48.280
<v Speaker 1>It's one before the podcast. You can always catch us

0:26:48.320 --> 0:26:49.880
<v Speaker 1>worldwide on Bloomberg Radio.