1
00:00:05,800 --> 00:00:08,720
Speaker 1: Welcome to the Bloomberg p m L Podcast. I'm pim Fox.

2
00:00:08,760 --> 00:00:11,520
Speaker 1: Along with my co host Lisa Bramowitz. Each day we

3
00:00:11,640 --> 00:00:15,120
Speaker 1: bring you the most important, noteworthy, and useful interviews for

4
00:00:15,200 --> 00:00:17,840
Speaker 1: you and your money, whether you're at the grocery store

5
00:00:17,960 --> 00:00:20,720
Speaker 1: or the trading floor. Find the Bloomberg p m L

6
00:00:20,840 --> 00:00:32,240
Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. Well.

7
00:00:32,400 --> 00:00:36,160
Speaker 1: Robin Hood is known from taking from wealthy in order

8
00:00:36,240 --> 00:00:38,880
Speaker 1: to give to the poor. A new version of robin

9
00:00:38,960 --> 00:00:41,840
Speaker 1: Hood today is it comes in the form of robin Hood,

10
00:00:41,880 --> 00:00:45,360
Speaker 1: the company that's based in Palo Alto, California that provides

11
00:00:45,440 --> 00:00:48,879
Speaker 1: a commission free app for trading stocks and with us

12
00:00:48,880 --> 00:00:51,960
Speaker 1: here as the co founder and co chief executive officer

13
00:00:52,080 --> 00:00:55,120
Speaker 1: of the company, Beiju Bot Beiji, thank you so much

14
00:00:55,200 --> 00:00:57,240
Speaker 1: for joining us. So can you just give us a

15
00:00:57,320 --> 00:01:02,760
Speaker 1: sense of what the company does and how you make money? Absolutely,

16
00:01:02,840 --> 00:01:05,600
Speaker 1: and thanks for having me on robin Hood. Our mission

17
00:01:05,680 --> 00:01:08,240
Speaker 1: is to make the markets more accessible for everyone in

18
00:01:08,280 --> 00:01:11,640
Speaker 1: the US. UM what we do is we have an

19
00:01:11,840 --> 00:01:14,880
Speaker 1: an app for Iowa's and Android, and we recently around

20
00:01:15,319 --> 00:01:18,440
Speaker 1: a desktop version of robin Hood as well that lets

21
00:01:18,440 --> 00:01:21,920
Speaker 1: consumers across the country invest in the stock market, and

22
00:01:21,920 --> 00:01:25,600
Speaker 1: we cut out the things that really create a difference

23
00:01:25,600 --> 00:01:27,880
Speaker 1: between the rich and the poor when it comes to investing.

24
00:01:28,040 --> 00:01:31,520
Speaker 1: So there's no minimums to get started, there's no five

25
00:01:31,560 --> 00:01:34,800
Speaker 1: to ten dollar commission every time you trade, and it's

26
00:01:34,920 --> 00:01:37,280
Speaker 1: it's made to be really easy for people that are

27
00:01:37,280 --> 00:01:39,880
Speaker 1: getting started and also really powerful for people that have

28
00:01:40,000 --> 00:01:43,320
Speaker 1: been using it for a long time. And we announced

29
00:01:43,360 --> 00:01:45,760
Speaker 1: it about two years ago. It's been growing very quickly.

30
00:01:46,040 --> 00:01:49,240
Speaker 1: Um we've crossed now over three billion users in the

31
00:01:49,320 --> 00:01:53,880
Speaker 1: US UM and we've transacted over a hundred billion dollars

32
00:01:53,920 --> 00:01:56,960
Speaker 1: in the last two years, saving our consumers over a

33
00:01:57,040 --> 00:02:00,840
Speaker 1: million or over a billion excuse me dollar in commissions

34
00:02:00,840 --> 00:02:03,640
Speaker 1: in total. So the business has been growing, you know,

35
00:02:03,720 --> 00:02:06,720
Speaker 1: really well. And uh, it's it's really introduced just a

36
00:02:06,760 --> 00:02:09,480
Speaker 1: brand new generation of consumers. And in the stock market.

37
00:02:09,639 --> 00:02:12,480
Speaker 1: How do you make money? The way we make money

38
00:02:12,600 --> 00:02:14,840
Speaker 1: is we've got a paid version of robin Hood, which

39
00:02:14,880 --> 00:02:18,720
Speaker 1: lets consumers borrow from US. So for as little as

40
00:02:18,760 --> 00:02:20,800
Speaker 1: ten dollars a month, you can get up to an

41
00:02:20,840 --> 00:02:23,880
Speaker 1: additional two thousand dollars in your account, and we also

42
00:02:24,040 --> 00:02:27,880
Speaker 1: collect interest on the cash and stocks in your account,

43
00:02:28,000 --> 00:02:31,040
Speaker 1: and we we split that with the consumer, And just

44
00:02:31,120 --> 00:02:34,200
Speaker 1: to follow up on that, why do you believe that

45
00:02:34,280 --> 00:02:38,280
Speaker 1: a five dollar or six dollar commission is what's keeping

46
00:02:38,320 --> 00:02:43,760
Speaker 1: the majority of non wealthy investors from participating in the

47
00:02:43,840 --> 00:02:47,120
Speaker 1: stock market, because that kind of speaks to the issue

48
00:02:47,120 --> 00:02:51,040
Speaker 1: of trading in and out and that somehow the commissions

49
00:02:51,040 --> 00:02:55,919
Speaker 1: are what are the obstacle rather than a viable long

50
00:02:56,040 --> 00:02:59,520
Speaker 1: term strategy to make money. Well, I think it's I

51
00:02:59,520 --> 00:03:03,400
Speaker 1: think it's commissions are a much bigger deal for consumers

52
00:03:03,639 --> 00:03:06,800
Speaker 1: than you know, as your I that kind of are in.

53
00:03:07,120 --> 00:03:08,880
Speaker 1: But why, I mean, if you're not going to trade

54
00:03:08,880 --> 00:03:12,040
Speaker 1: a lot, why would let's say, you know, buying shares

55
00:03:12,080 --> 00:03:15,919
Speaker 1: and paying your commission and holding the stock for whatever

56
00:03:16,040 --> 00:03:19,040
Speaker 1: period of time. Why is that the barrier? Isn't that

57
00:03:19,080 --> 00:03:22,440
Speaker 1: the cost of doing business? No, it's really not. I

58
00:03:22,440 --> 00:03:24,799
Speaker 1: think we've got a generation of consumers that are not

59
00:03:25,080 --> 00:03:27,800
Speaker 1: that are not really willing to pay for digital services.

60
00:03:27,840 --> 00:03:31,200
Speaker 1: And when you look at what goes into facilitating a

61
00:03:31,240 --> 00:03:35,520
Speaker 1: stock transaction, it's purely electronic and there's really no difference

62
00:03:35,560 --> 00:03:38,119
Speaker 1: between that and sending an email. So I think it's

63
00:03:38,200 --> 00:03:40,040
Speaker 1: it's one of those things that when it once it

64
00:03:40,120 --> 00:03:44,480
Speaker 1: goes away, it gets a lot of people to start thinking, oh, well,

65
00:03:44,520 --> 00:03:46,960
Speaker 1: maybe I'll try this and I'll kind of see how

66
00:03:47,000 --> 00:03:49,880
Speaker 1: this works, um and it just it feels like a

67
00:03:50,000 --> 00:03:54,120
Speaker 1: less cumbersome commitment to make. So are most of the

68
00:03:54,200 --> 00:03:56,160
Speaker 1: clients that you have, do you have a sense of

69
00:03:56,200 --> 00:03:59,440
Speaker 1: how old they are? Yeah? Absolutely, So. This is one

70
00:03:59,480 --> 00:04:02,480
Speaker 1: of the interesting things about our company is that the

71
00:04:02,520 --> 00:04:04,520
Speaker 1: majority of people that use robin Hood and I think

72
00:04:04,560 --> 00:04:08,200
Speaker 1: it's being like of our consumers are under the age

73
00:04:08,200 --> 00:04:11,279
Speaker 1: of forty and so it's this new generation of consumers

74
00:04:11,320 --> 00:04:17,039
Speaker 1: that sort of commonly called millennials um that that they're

75
00:04:17,120 --> 00:04:19,279
Speaker 1: using this as kind of their first entry point into

76
00:04:19,279 --> 00:04:22,279
Speaker 1: investing in the stock market, and they're they're getting started

77
00:04:22,600 --> 00:04:25,880
Speaker 1: today when they're a lot younger, rather than waiting until

78
00:04:26,320 --> 00:04:28,400
Speaker 1: they're a little bit later in life in their fifties

79
00:04:28,480 --> 00:04:30,960
Speaker 1: or sixties. When you know, I think a lot of

80
00:04:31,120 --> 00:04:34,320
Speaker 1: companies like The Trade and and tdumer Trade see their

81
00:04:34,320 --> 00:04:38,320
Speaker 1: consumers getting online. You know, there's this sort of stereotype

82
00:04:38,480 --> 00:04:42,440
Speaker 1: that millennials aren't as interested in stock trading and see

83
00:04:42,480 --> 00:04:46,200
Speaker 1: more value and say bitcoin, uh than stocks, especially because

84
00:04:46,240 --> 00:04:49,479
Speaker 1: they grew up with the very present who thousand and

85
00:04:49,520 --> 00:04:53,160
Speaker 1: eight experience and I'm just wondering how much truth there

86
00:04:53,320 --> 00:04:55,480
Speaker 1: is to you that based on your findings. In other words,

87
00:04:55,640 --> 00:04:59,039
Speaker 1: is there a big group of millennials who are interested

88
00:04:59,040 --> 00:05:02,760
Speaker 1: in trading individuals stacks? You know, that's really what we're seeing.

89
00:05:02,800 --> 00:05:05,160
Speaker 1: I mean, if you take a look at our our numbers, right,

90
00:05:05,200 --> 00:05:08,000
Speaker 1: We've we've got over three million people that have AC

91
00:05:08,000 --> 00:05:09,880
Speaker 1: counts with us that have found up in just the

92
00:05:09,960 --> 00:05:12,400
Speaker 1: last two years. And you can put that side by

93
00:05:12,440 --> 00:05:15,080
Speaker 1: side with the trade which has about three points six

94
00:05:15,120 --> 00:05:19,760
Speaker 1: million consumers UM. So we're we're becoming, you know, a

95
00:05:19,839 --> 00:05:25,039
Speaker 1: very sizeable market participant, and largely on you know, on

96
00:05:25,120 --> 00:05:28,080
Speaker 1: the grounds that young people have chosen us, Robinson. So,

97
00:05:28,120 --> 00:05:31,120
Speaker 1: I think that the market demand is certainly there UM,

98
00:05:31,200 --> 00:05:33,279
Speaker 1: And I think it's it's one of those things that's

99
00:05:33,320 --> 00:05:37,360
Speaker 1: happening relatively quickly. And I think when we look at

100
00:05:37,400 --> 00:05:39,240
Speaker 1: this in a couple of years, we'll see that the

101
00:05:39,320 --> 00:05:43,479
Speaker 1: younger generation of consumers UM will actually amount for I

102
00:05:43,520 --> 00:05:45,640
Speaker 1: think it will be a bigger audience than than the

103
00:05:45,720 --> 00:05:49,200
Speaker 1: last generation. And what about backing, who are some of

104
00:05:49,279 --> 00:05:54,279
Speaker 1: your investors? Yeah, so we've raised over I think a

105
00:05:54,320 --> 00:05:57,400
Speaker 1: hundred and seventy six million dollars over the last few years.

106
00:05:57,480 --> 00:06:02,280
Speaker 1: We're backed by top tier investors like uh any A,

107
00:06:03,000 --> 00:06:08,400
Speaker 1: uh Google Ventures, and Trees and Harwood Ventures. Indeed also

108
00:06:09,080 --> 00:06:13,000
Speaker 1: some some celebrities like Ashton Kutcher and Snoop dogg Um

109
00:06:13,120 --> 00:06:15,279
Speaker 1: in the Gang from It's Always in Sunny in Philadelphia

110
00:06:15,279 --> 00:06:19,320
Speaker 1: if you ever watched that show. So all right, well,

111
00:06:19,760 --> 00:06:24,000
Speaker 1: thanks for enlightening us on robin Hood much appreciated Buys

112
00:06:24,040 --> 00:06:26,760
Speaker 1: You Bought is the co founder and the co chief

113
00:06:26,800 --> 00:06:30,760
Speaker 1: executive of robin Hood. They're based in Palo Alto, offering

114
00:06:30,920 --> 00:06:46,920
Speaker 1: what they described as commission free trading in equities. Artificial

115
00:06:47,000 --> 00:06:51,160
Speaker 1: intelligence and the use of artificial intelligence in the economy

116
00:06:51,240 --> 00:06:56,360
Speaker 1: is expected to reach about thirty six billion dollars by

117
00:06:56,360 --> 00:07:01,359
Speaker 1: this according to a report of from a eligence marketer.

118
00:07:01,520 --> 00:07:04,400
Speaker 1: This is a grand View research and here to help

119
00:07:04,480 --> 00:07:07,680
Speaker 1: us understand how this uh new technology will be used

120
00:07:07,720 --> 00:07:10,480
Speaker 1: in industries such as the restaurant business as well as

121
00:07:10,520 --> 00:07:13,080
Speaker 1: retail is Shing Tao. He is the chairman and the

122
00:07:13,160 --> 00:07:16,480
Speaker 1: chief executive of Remark Holdings, and he joins us now

123
00:07:16,600 --> 00:07:19,320
Speaker 1: on the phone line from Las Vegas. Shing, thank you

124
00:07:19,400 --> 00:07:22,360
Speaker 1: very much for being with us. Maybe just begin by

125
00:07:22,360 --> 00:07:26,720
Speaker 1: talking about UH sort of subsidiary of yours called can Can.

126
00:07:26,800 --> 00:07:30,160
Speaker 1: That's k A N k A N What is it

127
00:07:30,240 --> 00:07:34,400
Speaker 1: and how does that fold into the overall business of

128
00:07:34,480 --> 00:07:39,520
Speaker 1: Remark Holdings. Great, thank you for having me. Um. We uh,

129
00:07:39,560 --> 00:07:42,320
Speaker 1: we had built it's been about three years since we

130
00:07:42,440 --> 00:07:44,920
Speaker 1: kind of laid up division to build can Can, which

131
00:07:44,960 --> 00:07:48,560
Speaker 1: is a big data artificial intelligence platform, and that was

132
00:07:48,640 --> 00:07:51,720
Speaker 1: really to marry all the different assets that we had

133
00:07:51,720 --> 00:07:54,920
Speaker 1: currently owned and had you know, looked to potentially acquire

134
00:07:54,960 --> 00:07:58,400
Speaker 1: in the future. Uh, you know, going going into really

135
00:07:58,480 --> 00:08:01,240
Speaker 1: two thousand and thirteen, and we found that, you know,

136
00:08:01,320 --> 00:08:03,120
Speaker 1: the business that we were in, it was very tough

137
00:08:03,200 --> 00:08:05,680
Speaker 1: to compete and if we didn't have you know, if

138
00:08:05,720 --> 00:08:07,920
Speaker 1: we didn't care about the profits and all that. So

139
00:08:08,320 --> 00:08:10,560
Speaker 1: it was pretty important at that time to really make

140
00:08:10,720 --> 00:08:14,320
Speaker 1: a a big bet at that time, uh, to see

141
00:08:14,320 --> 00:08:17,720
Speaker 1: how do we create a technology platform or technology that

142
00:08:18,000 --> 00:08:20,960
Speaker 1: you can't just throw cash at the problem, uh, in

143
00:08:21,040 --> 00:08:23,800
Speaker 1: order to enter it. And and that's really how we

144
00:08:24,720 --> 00:08:28,160
Speaker 1: first um you know, kind of made the move into

145
00:08:28,200 --> 00:08:31,480
Speaker 1: that field. And we realized that in the beginning was like, look,

146
00:08:31,520 --> 00:08:34,080
Speaker 1: you know, with at that time, you know, the social

147
00:08:34,080 --> 00:08:38,000
Speaker 1: media accounts, we weren't really very well integrated um. You know,

148
00:08:38,080 --> 00:08:41,719
Speaker 1: now three years later you see a kind of a

149
00:08:42,000 --> 00:08:44,199
Speaker 1: there there's a need for a break between the data

150
00:08:44,240 --> 00:08:46,559
Speaker 1: that you see in China and and and other parts

151
00:08:46,559 --> 00:08:49,240
Speaker 1: of the world, given that China is closed to the

152
00:08:49,320 --> 00:08:51,960
Speaker 1: kind of foreign groups going into there. Uh, And so

153
00:08:52,000 --> 00:08:55,600
Speaker 1: we created a platform to marry the data from both

154
00:08:55,600 --> 00:08:59,600
Speaker 1: sides where um it would serve two purposes. One is

155
00:08:59,600 --> 00:09:02,800
Speaker 1: to help, you know, obviously help our own businesses, but

156
00:09:02,960 --> 00:09:06,800
Speaker 1: number two create solutions for business that are trying to

157
00:09:07,559 --> 00:09:12,200
Speaker 1: enter into China or Chinese business trying to enter outside

158
00:09:12,200 --> 00:09:15,080
Speaker 1: of China into the US. So shan just sort of

159
00:09:15,120 --> 00:09:18,560
Speaker 1: to zoom out a little bit. So Remark is a

160
00:09:18,679 --> 00:09:21,640
Speaker 1: data collection company, right, You collect certain data and this

161
00:09:21,760 --> 00:09:24,280
Speaker 1: partnership with ken Ken allows you to analyze it and

162
00:09:24,760 --> 00:09:28,240
Speaker 1: use it more for your clients. Is that accurate? No?

163
00:09:29,840 --> 00:09:32,320
Speaker 1: Is our AI platform that we've built from the ground up. Okay?

164
00:09:32,400 --> 00:09:35,840
Speaker 1: And then with respect to Remark, it's you get data

165
00:09:35,920 --> 00:09:39,120
Speaker 1: from a number of different sources that you subscribers can

166
00:09:39,320 --> 00:09:44,080
Speaker 1: use to analyze consumer habits, credit, etcetera. Right, So what

167
00:09:44,200 --> 00:09:46,840
Speaker 1: we do is we create we create a technology to

168
00:09:46,880 --> 00:09:49,440
Speaker 1: be able to scrape all the data from you know,

169
00:09:49,480 --> 00:09:52,400
Speaker 1: all the major social media sites around the world. Number one.

170
00:09:52,480 --> 00:09:55,240
Speaker 1: Number two really any site with a lot of consumer information.

171
00:09:55,679 --> 00:09:58,080
Speaker 1: Now obviously that has a lot of data, as we

172
00:09:58,160 --> 00:10:02,400
Speaker 1: have over one point three billion unique user profiles, and

173
00:10:02,440 --> 00:10:05,040
Speaker 1: then there needs to be, uh, you know, something to

174
00:10:05,080 --> 00:10:07,480
Speaker 1: really connect the dots. And so that's what we've built

175
00:10:07,800 --> 00:10:11,600
Speaker 1: as the AI platform basically is trained by the data

176
00:10:11,679 --> 00:10:14,400
Speaker 1: that we've have, because you know, strong AI is driven

177
00:10:14,400 --> 00:10:18,320
Speaker 1: by strong data. So, um, it's fascinating and I'm wondering

178
00:10:18,400 --> 00:10:22,400
Speaker 1: what the challenges for you to gain access to that

179
00:10:22,520 --> 00:10:24,960
Speaker 1: data you're scraping from all these other sites. I know

180
00:10:25,040 --> 00:10:28,760
Speaker 1: that this has been held up as the greatest value

181
00:10:28,880 --> 00:10:32,160
Speaker 1: of the big companies is really their data, right, so

182
00:10:32,480 --> 00:10:34,640
Speaker 1: it's hard for you to get access to it. And

183
00:10:34,640 --> 00:10:36,520
Speaker 1: what's your business model? I mean you get subscription fees

184
00:10:36,559 --> 00:10:38,760
Speaker 1: and you use part of it to uh to license

185
00:10:38,800 --> 00:10:41,320
Speaker 1: the data. How does it work? Yeah, So, so we

186
00:10:41,320 --> 00:10:44,600
Speaker 1: we have a number of different partnerships, some some we

187
00:10:45,000 --> 00:10:48,240
Speaker 1: you know, it's it's primarily just a scraping technology that

188
00:10:48,280 --> 00:10:51,960
Speaker 1: we're able to get public available, publicly available data better

189
00:10:52,000 --> 00:10:54,880
Speaker 1: than others. That's number one. Number two. We have data

190
00:10:54,920 --> 00:10:59,800
Speaker 1: partnerships with both Ali Baba intencent where we open up

191
00:10:59,800 --> 00:11:02,720
Speaker 1: our data and they do as well, and we basically

192
00:11:02,760 --> 00:11:06,240
Speaker 1: utilize the three groups different data to train to AI models,

193
00:11:06,280 --> 00:11:08,840
Speaker 1: which is why it works so well from a business

194
00:11:08,880 --> 00:11:10,960
Speaker 1: standpoint um. You know, as as you know, there are

195
00:11:10,960 --> 00:11:13,000
Speaker 1: a lot of different AI companies out there, and that's

196
00:11:13,040 --> 00:11:15,680
Speaker 1: the buzzword. You know. We're able to monetize very quickly

197
00:11:15,720 --> 00:11:18,960
Speaker 1: because we are providing solutions in a number of different areas.

198
00:11:19,640 --> 00:11:23,520
Speaker 1: The first in the biggest isn't isn't fintech in China,

199
00:11:23,679 --> 00:11:26,000
Speaker 1: which is the first market that we launched in less

200
00:11:26,040 --> 00:11:28,640
Speaker 1: than the third of the people they're actually have a

201
00:11:28,679 --> 00:11:31,320
Speaker 1: credit history, as we know in the US, so the

202
00:11:31,360 --> 00:11:35,520
Speaker 1: way the financial institutions are doing their their lending is

203
00:11:35,559 --> 00:11:38,400
Speaker 1: based on your behavioral history. Presumably we have the most

204
00:11:38,400 --> 00:11:41,199
Speaker 1: of it, given all the social media history, uh that

205
00:11:41,240 --> 00:11:43,440
Speaker 1: we have, and a big target for us are the

206
00:11:43,480 --> 00:11:47,439
Speaker 1: millennials and and sort of the the younger demographic from

207
00:11:47,520 --> 00:11:49,920
Speaker 1: and because because we have so much data and because

208
00:11:49,960 --> 00:11:53,160
Speaker 1: that's really what creates the foundation for the AI platform,

209
00:11:53,160 --> 00:11:55,400
Speaker 1: we're able to move into a number of different areas

210
00:11:55,440 --> 00:11:58,360
Speaker 1: as well. It's not just fintech, but it's also in

211
00:11:58,440 --> 00:12:02,520
Speaker 1: for example, like food safety. So one of the one

212
00:12:02,520 --> 00:12:04,400
Speaker 1: of the projects that we're working on right now is

213
00:12:04,400 --> 00:12:07,920
Speaker 1: with the Shanghainese government where the where the China government

214
00:12:07,920 --> 00:12:10,840
Speaker 1: has mandated where you have to wear a mask and

215
00:12:10,880 --> 00:12:14,199
Speaker 1: a hat uh in order to prepare food. Uh. So

216
00:12:14,440 --> 00:12:18,079
Speaker 1: using our technology, being able to monitor a whole area

217
00:12:18,080 --> 00:12:21,240
Speaker 1: and be able to identify who's who, be able to

218
00:12:21,280 --> 00:12:24,000
Speaker 1: decipher if a person is wearing a mask, a hat

219
00:12:24,240 --> 00:12:27,280
Speaker 1: or both. Uh. You know, our AI allows us to

220
00:12:27,320 --> 00:12:29,680
Speaker 1: do that and to pick up where there might be

221
00:12:29,880 --> 00:12:33,080
Speaker 1: a violation of that policy, and then it sends directly

222
00:12:33,120 --> 00:12:36,080
Speaker 1: to the health agencies will make sure there's um you know,

223
00:12:36,160 --> 00:12:40,080
Speaker 1: actually integrity in that in that data. So from that too,

224
00:12:40,880 --> 00:12:44,360
Speaker 1: um you know. Uh. In hunsl right now where Ali

225
00:12:44,400 --> 00:12:46,760
Speaker 1: Baba is based there, they're going through a mandate where

226
00:12:46,800 --> 00:12:51,880
Speaker 1: they're banning motorcycles on the street to prevent new slow

227
00:12:51,920 --> 00:12:54,360
Speaker 1: down pollution. But how do you know the difference between

228
00:12:54,640 --> 00:12:57,679
Speaker 1: a motorcycle and electric bikes? So are AI because it's

229
00:12:57,720 --> 00:13:01,000
Speaker 1: been trained by so many images, uh you know, over

230
00:13:01,040 --> 00:13:05,240
Speaker 1: ten billion images, allows us to actually be very accurate

231
00:13:05,280 --> 00:13:08,400
Speaker 1: how we decipher who's who, what's what. Shanta, thank you

232
00:13:08,440 --> 00:13:11,240
Speaker 1: so much for joining ours Shingtau, chairman and chief executive

233
00:13:11,280 --> 00:13:14,880
Speaker 1: officer of Remark Holdings based in Shanghai, China. But he

234
00:13:14,920 --> 00:13:32,280
Speaker 1: was coming to us from Las Vegas taxes and debate

235
00:13:32,400 --> 00:13:36,679
Speaker 1: in Washington over changes in tax policy. Let's find out

236
00:13:36,720 --> 00:13:39,760
Speaker 1: more from Wayne wine Garden. He is a senior Fellow

237
00:13:39,800 --> 00:13:44,560
Speaker 1: in Business and Economics at the Pacific Research Institute. Wayne Uh,

238
00:13:44,720 --> 00:13:49,040
Speaker 1: let's just begin by maybe laying out from your perspective

239
00:13:49,480 --> 00:13:53,480
Speaker 1: what needs to change for tax policy to help the

240
00:13:53,520 --> 00:13:58,040
Speaker 1: economy grow. Well, if we're looking at the proposal that's

241
00:13:58,080 --> 00:14:01,240
Speaker 1: out there, the most important art is the corporate income

242
00:14:01,280 --> 00:14:05,640
Speaker 1: tax reform. The US corporate tax system is so out

243
00:14:05,679 --> 00:14:09,080
Speaker 1: of balance and so uncompetitive compared to all of our

244
00:14:09,120 --> 00:14:13,280
Speaker 1: global global competitors. I think that's something that's very well known.

245
00:14:13,600 --> 00:14:15,959
Speaker 1: So the idea that we're bringing that tax rate down

246
00:14:16,280 --> 00:14:21,000
Speaker 1: from thirty and the fact that we're going from this

247
00:14:21,120 --> 00:14:24,280
Speaker 1: global tax system that nobody else has to the territorial

248
00:14:24,320 --> 00:14:29,200
Speaker 1: tax system brings our our corporate tax system into ligne

249
00:14:29,320 --> 00:14:33,840
Speaker 1: with the global competitors. And importantly, what it's also going

250
00:14:33,880 --> 00:14:35,600
Speaker 1: to do for us is it's going to stop this

251
00:14:35,760 --> 00:14:39,800
Speaker 1: kind of movement of inversions and companies moving overseas, that's

252
00:14:39,800 --> 00:14:41,760
Speaker 1: gonna all stop, and the incentive is going to be

253
00:14:42,000 --> 00:14:45,080
Speaker 1: to set up corporations here in the US. Wayne, how

254
00:14:45,080 --> 00:14:49,560
Speaker 1: do you feel about the healthcare angle that's been pulled

255
00:14:49,600 --> 00:14:54,520
Speaker 1: into this tax debate with the Senate provision removing the

256
00:14:54,720 --> 00:15:01,280
Speaker 1: Obamacare payments that are required. That's it's it's a it's

257
00:15:01,280 --> 00:15:04,400
Speaker 1: a bet that a more complex bill is going to

258
00:15:04,440 --> 00:15:07,360
Speaker 1: be easier to get through than something perhaps more simple.

259
00:15:07,840 --> 00:15:11,640
Speaker 1: And you know, perhaps that bet is right, perhaps it's wrong. Individually,

260
00:15:11,680 --> 00:15:14,680
Speaker 1: each makes sense, right that the mandate doesn't make sense.

261
00:15:14,680 --> 00:15:18,040
Speaker 1: It forces people to buy insurance at a cost that

262
00:15:18,120 --> 00:15:21,440
Speaker 1: they don't believe is worthwhile for them. Wait, hold away,

263
00:15:21,960 --> 00:15:23,640
Speaker 1: Hold on a second. I mean the idea was to

264
00:15:23,680 --> 00:15:27,480
Speaker 1: sort of sustain Obamacare. But I guess what I want is,

265
00:15:27,920 --> 00:15:30,160
Speaker 1: do you think that this is a prudent move? And

266
00:15:30,240 --> 00:15:34,560
Speaker 1: do you think that pulling out this Obamacare mandate is

267
00:15:34,600 --> 00:15:39,080
Speaker 1: the right move? It's right moves if it can pass, Yes,

268
00:15:39,720 --> 00:15:42,000
Speaker 1: I don't believe. I believe it makes it a little

269
00:15:42,000 --> 00:15:44,040
Speaker 1: bit more difficult to pass because of bringing in the

270
00:15:44,080 --> 00:15:47,000
Speaker 1: Affordable Care Act issues into the tax reform, which is

271
00:15:47,040 --> 00:15:50,800
Speaker 1: already complex enough. But if if it does pass, we

272
00:15:50,800 --> 00:15:53,520
Speaker 1: were going to have based on the budget scoring window

273
00:15:53,920 --> 00:15:56,000
Speaker 1: that they have to kind of fit this bill through.

274
00:15:56,360 --> 00:15:59,400
Speaker 1: It helps move tax reform along in terms of the

275
00:15:59,440 --> 00:16:03,160
Speaker 1: deficit constraints that it faces, and it's in itself it's

276
00:16:03,160 --> 00:16:06,280
Speaker 1: a good policy. So combine those two things together, Yes,

277
00:16:06,360 --> 00:16:09,240
Speaker 1: it's a good move politically, though you're bringing in the

278
00:16:09,280 --> 00:16:11,600
Speaker 1: host of other issues and that needs to be kind

279
00:16:11,600 --> 00:16:15,040
Speaker 1: of worked out. When can we just go back to

280
00:16:15,080 --> 00:16:17,920
Speaker 1: the corporate tax reformation for just a moment? Is is

281
00:16:17,920 --> 00:16:21,320
Speaker 1: there any evidence that lower in corporate taxes will lead

282
00:16:21,360 --> 00:16:25,640
Speaker 1: to greater economic growth? Yes, And if you look at

283
00:16:25,640 --> 00:16:29,640
Speaker 1: the economics literature, undoubtedly the corporate income tax has a

284
00:16:29,760 --> 00:16:34,960
Speaker 1: very large impact on kind of how much capital companies

285
00:16:34,960 --> 00:16:36,800
Speaker 1: are going to invest, how much they can reinvest in

286
00:16:36,840 --> 00:16:40,320
Speaker 1: their workers. So there's a lot of connection between how

287
00:16:40,400 --> 00:16:43,080
Speaker 1: high or how burdens them your corporate income taxt rate

288
00:16:43,200 --> 00:16:47,520
Speaker 1: is and how much economic growth the country can can sustain. Okay,

289
00:16:47,600 --> 00:16:50,480
Speaker 1: So then so I was gonna say, so okay, So

290
00:16:50,640 --> 00:16:54,400
Speaker 1: having said that, why or do you believe it would

291
00:16:54,440 --> 00:16:59,000
Speaker 1: be relevant to in a sense have a test. For example,

292
00:16:59,040 --> 00:17:03,080
Speaker 1: if you do off for this tax reduction to corporations,

293
00:17:03,640 --> 00:17:06,720
Speaker 1: they are only able to claim it if indeed they

294
00:17:06,800 --> 00:17:10,520
Speaker 1: follow through on what you just described, which is spending

295
00:17:10,560 --> 00:17:15,920
Speaker 1: more on research and development, spending more on hiring. Well,

296
00:17:16,240 --> 00:17:19,080
Speaker 1: you don't necessarily want every corporation spending more on R

297
00:17:19,119 --> 00:17:21,880
Speaker 1: and DM. And this just gets to what drives growth

298
00:17:22,200 --> 00:17:24,439
Speaker 1: and for us to try to determine what's the right

299
00:17:24,520 --> 00:17:27,880
Speaker 1: investment for every corporation through the tax code, I mean,

300
00:17:27,920 --> 00:17:30,679
Speaker 1: that's how we end up with this complex you know,

301
00:17:31,160 --> 00:17:34,800
Speaker 1: tens of thousands of pages of tax code to what

302
00:17:34,840 --> 00:17:37,920
Speaker 1: we want is a ideally that the much more simple

303
00:17:38,200 --> 00:17:42,399
Speaker 1: flat tax structure that makes it kind of easier for

304
00:17:42,560 --> 00:17:45,080
Speaker 1: corporations to spend their time talking about how do we

305
00:17:45,119 --> 00:17:48,120
Speaker 1: get the best products to customers and not worrying about

306
00:17:48,160 --> 00:17:50,480
Speaker 1: how do we kind of gain the tax system to

307
00:17:50,680 --> 00:17:53,919
Speaker 1: maximize profits. If we do that, we'll get stronger growth.

308
00:17:54,160 --> 00:17:57,760
Speaker 1: Some companies we may want to contract because they're our

309
00:17:57,760 --> 00:18:00,920
Speaker 1: growth industries. We want more investment in areas matter, so

310
00:18:01,280 --> 00:18:04,080
Speaker 1: as a kind of from a government perspective, you don't

311
00:18:04,080 --> 00:18:05,679
Speaker 1: know that, and so you want that to be as

312
00:18:05,720 --> 00:18:08,159
Speaker 1: neutral as possible so that we can kind of sustain

313
00:18:08,240 --> 00:18:12,160
Speaker 1: the most dynamic economy we can create. When is there

314
00:18:12,200 --> 00:18:16,440
Speaker 1: anything in either the Senate or the House bill as

315
00:18:16,480 --> 00:18:21,320
Speaker 1: they currently stand that concerns you. There's there's several things.

316
00:18:21,320 --> 00:18:23,960
Speaker 1: I mean, certainly on the personal income tax inside, there's

317
00:18:24,640 --> 00:18:28,240
Speaker 1: a lot of kind of trying to bang square pegs

318
00:18:28,240 --> 00:18:30,840
Speaker 1: into round holes. I mean, the big problem. I would

319
00:18:30,880 --> 00:18:33,800
Speaker 1: have loved to have seen the payroll tax be included

320
00:18:33,840 --> 00:18:37,399
Speaker 1: in the tax reform because when you're talking about middle

321
00:18:37,440 --> 00:18:41,080
Speaker 1: class tax relief, class and lower income people don't pay

322
00:18:41,119 --> 00:18:44,679
Speaker 1: income taxes, they pay payroll taxes, and so there's drive

323
00:18:44,800 --> 00:18:48,679
Speaker 1: to try to create lower income and middle income tax relief,

324
00:18:48,960 --> 00:18:52,600
Speaker 1: but not including the most important taxes to those individuals

325
00:18:52,880 --> 00:18:56,240
Speaker 1: has has led to a lot of unnecessary complexities and

326
00:18:56,280 --> 00:19:00,960
Speaker 1: backward bending, kind of strange provisions. So I would have

327
00:19:01,000 --> 00:19:04,520
Speaker 1: ideally like to have seen those removed and actually to

328
00:19:04,560 --> 00:19:07,280
Speaker 1: start addressing how can we fold the payroll tax into

329
00:19:07,320 --> 00:19:10,359
Speaker 1: this broader tax reform. Are you concerned at all about

330
00:19:10,440 --> 00:19:13,800
Speaker 1: the fact that the deficit is poised to expand by

331
00:19:13,800 --> 00:19:18,359
Speaker 1: one and a half trillion dollars with a conservative estimate. Yes, Uh,

332
00:19:18,760 --> 00:19:21,919
Speaker 1: the depth is definitely a problem. But the problem with

333
00:19:21,960 --> 00:19:24,920
Speaker 1: the deficit is it's a spending problem, and that's something

334
00:19:24,960 --> 00:19:29,240
Speaker 1: that with or without tax reform, we need to address. Uh,

335
00:19:29,280 --> 00:19:32,560
Speaker 1: and that needs to be addressed to fundamental budget process reform,

336
00:19:32,920 --> 00:19:35,960
Speaker 1: fundamental spending reform, so that we can bring kind of

337
00:19:35,960 --> 00:19:40,520
Speaker 1: our expenditures into line with what we're able to kind

338
00:19:40,560 --> 00:19:42,800
Speaker 1: of generate from a tax revenue. If you look at

339
00:19:42,800 --> 00:19:45,879
Speaker 1: how much revenue the tax system raises kind of relative

340
00:19:45,920 --> 00:19:49,760
Speaker 1: to the size of the economy, that's been fairly constant,

341
00:19:50,080 --> 00:19:53,120
Speaker 1: even though we've had major changes in terms of tax revenue.

342
00:19:53,440 --> 00:19:56,240
Speaker 1: But we look at the expenditures and that keeps rising,

343
00:19:56,320 --> 00:19:59,639
Speaker 1: you know, long term, there's lots of wiggles around it,

344
00:19:59,720 --> 00:20:03,119
Speaker 1: but rising long term relative to the size of the economy.

345
00:20:03,560 --> 00:20:05,840
Speaker 1: That's the problem with the deficit, and we need to

346
00:20:05,880 --> 00:20:08,680
Speaker 1: bring this spending under control. Otherwise we're not going to

347
00:20:08,760 --> 00:20:11,600
Speaker 1: get control the deficity no matter what we do in taxes. Wait,

348
00:20:11,760 --> 00:20:14,240
Speaker 1: just quickly. We were speaking yesterday with a group of

349
00:20:14,280 --> 00:20:18,399
Speaker 1: real estate executives at the Burden Executive Summit here at

350
00:20:18,440 --> 00:20:20,960
Speaker 1: Bloomberg and one of the things they talked about was

351
00:20:21,000 --> 00:20:25,080
Speaker 1: the deductibility of state and local taxes. Uh. Do you

352
00:20:25,160 --> 00:20:28,639
Speaker 1: believe that that will be erased in this in this

353
00:20:28,720 --> 00:20:33,720
Speaker 1: tax bill? Um? No, I think if you're gonna it

354
00:20:33,760 --> 00:20:36,040
Speaker 1: should be erased. Let me start with that that it's

355
00:20:36,160 --> 00:20:38,639
Speaker 1: the right policy to get rid of it, but you

356
00:20:38,720 --> 00:20:41,960
Speaker 1: have strong interests behind it. And when you start looking

357
00:20:41,960 --> 00:20:44,520
Speaker 1: at how do we get the bill a bill past,

358
00:20:45,040 --> 00:20:48,040
Speaker 1: it seems that some kind of measured like the house

359
00:20:48,080 --> 00:20:51,320
Speaker 1: where they have the property taxes can still be included

360
00:20:51,400 --> 00:20:53,480
Speaker 1: up to I think it's ten thousand dollars. I think

361
00:20:53,480 --> 00:20:56,280
Speaker 1: you're going to have some sort of compromise on that

362
00:20:56,520 --> 00:21:01,040
Speaker 1: simply because there's too much interest to keep it going.

363
00:21:01,400 --> 00:21:03,879
Speaker 1: But under an ideal tax REFILM, absolutely you would give

364
00:21:03,920 --> 00:21:07,040
Speaker 1: it of that. You need to broaden the base to

365
00:21:07,119 --> 00:21:10,040
Speaker 1: get the tax weight down, and that's an important area.

366
00:21:10,359 --> 00:21:12,280
Speaker 1: Wayne wine Garden, thank you so much for joining us.

367
00:21:12,280 --> 00:21:15,479
Speaker 1: Wayne wide Garden, Senior Fellow in Business and Economics at

368
00:21:15,520 --> 00:21:31,080
Speaker 1: the Pacific Research Institute and Falls Church, Virginia. Let's turn

369
00:21:31,119 --> 00:21:33,720
Speaker 1: our attention now to a piece of paper, a very

370
00:21:33,720 --> 00:21:36,719
Speaker 1: expensive piece of paper, indeed, a four hundred and fifty

371
00:21:36,760 --> 00:21:40,000
Speaker 1: million dollar piece of paper that just happens to have

372
00:21:40,240 --> 00:21:43,560
Speaker 1: the painting. I guess it may even be a canvas, uh,

373
00:21:43,640 --> 00:21:47,199
Speaker 1: that is m Leonardo da Vinci. Work by Leonardo da

374
00:21:47,240 --> 00:21:49,920
Speaker 1: Vinci sold for four hundred and fifty million dollars at

375
00:21:49,960 --> 00:21:53,080
Speaker 1: Christie's auction House yesterday evening, and here to tell us

376
00:21:53,080 --> 00:21:57,600
Speaker 1: more about it is Bloomberg's art reporter Katya Kazakia. And

377
00:21:57,600 --> 00:21:59,359
Speaker 1: I usually get your name right, I beg your pardon.

378
00:21:59,560 --> 00:22:01,560
Speaker 1: I guess I was stunned by the four and fifty

379
00:22:01,560 --> 00:22:04,720
Speaker 1: million dollar price. Tech tell me tell us about Salvatore

380
00:22:04,800 --> 00:22:08,679
Speaker 1: Mundi or Savior of the World. And why would anyone

381
00:22:08,720 --> 00:22:13,880
Speaker 1: pay four hundred and fifty million dollars for a single painting? Well,

382
00:22:13,920 --> 00:22:20,080
Speaker 1: it's the last Leonardo. So that's that was how Christie's

383
00:22:20,160 --> 00:22:22,560
Speaker 1: promoted it. And it's actually true, you know, Leonardo. There

384
00:22:22,560 --> 00:22:25,800
Speaker 1: only less than twenty Leonardo paintings in the world right now.

385
00:22:25,840 --> 00:22:30,000
Speaker 1: And I was rediscovered in two thousand eleven. It had

386
00:22:30,040 --> 00:22:35,560
Speaker 1: been really restored, pretty hands on restoration. So the condition

387
00:22:35,640 --> 00:22:38,680
Speaker 1: is not great, but it has this magic about it, right,

388
00:22:38,720 --> 00:22:41,320
Speaker 1: it has Leonardo, but one of the greatest, if not

389
00:22:41,359 --> 00:22:44,080
Speaker 1: the greatest artists of all times. And um, it's the

390
00:22:44,160 --> 00:22:46,919
Speaker 1: last one. And so for someone there's so much wealth,

391
00:22:46,960 --> 00:22:48,720
Speaker 1: and you know, we've talked about it a lot. You know,

392
00:22:48,800 --> 00:22:52,000
Speaker 1: in the growth of wealth has been unprecedented, and so

393
00:22:52,280 --> 00:22:54,840
Speaker 1: there are a lot of billionaires in Asia and in America,

394
00:22:54,840 --> 00:22:57,840
Speaker 1: and in Europe and emerging economists who are building their museums,

395
00:22:57,840 --> 00:23:00,680
Speaker 1: who need you know, they need a show upper And

396
00:23:00,680 --> 00:23:03,440
Speaker 1: and we've seen over the past months. It's only been

397
00:23:03,520 --> 00:23:06,760
Speaker 1: on tour by Christie's four months and thirty thousand people.

398
00:23:07,000 --> 00:23:08,679
Speaker 1: They showed it in Hong Kong, they showed it in

399
00:23:08,720 --> 00:23:11,400
Speaker 1: London and San Francisco and in in New York. And during

400
00:23:11,440 --> 00:23:14,640
Speaker 1: the last week people in the rain waited to get

401
00:23:14,640 --> 00:23:16,960
Speaker 1: in and to see this work. And Christie's they're they're

402
00:23:16,960 --> 00:23:20,240
Speaker 1: really smart marketers. They put together this video of people

403
00:23:20,680 --> 00:23:24,159
Speaker 1: looking at the painting in all transfixed, some with tears

404
00:23:24,160 --> 00:23:27,439
Speaker 1: in their eyes. Leonardo Dicapriism on them appears suddenly, you know,

405
00:23:27,640 --> 00:23:31,080
Speaker 1: and so the profound effect that this painting, whether or

406
00:23:31,119 --> 00:23:34,000
Speaker 1: not you know it is the real Leonardo or not,

407
00:23:34,080 --> 00:23:36,399
Speaker 1: like it kind of be on the pail, it doesn't matter,

408
00:23:36,520 --> 00:23:38,840
Speaker 1: you know. Well, God, yeah, I want to pick up

409
00:23:38,840 --> 00:23:40,800
Speaker 1: on a point that you mentioned here, which is this

410
00:23:40,880 --> 00:23:43,680
Speaker 1: highlights just how much wealth is slashing around. I mean,

411
00:23:43,720 --> 00:23:47,640
Speaker 1: we're talking nearly half a billion dollars here for a painting. Yes,

412
00:23:47,760 --> 00:23:50,080
Speaker 1: it is rare. A lot of things are rare in

413
00:23:50,080 --> 00:23:52,399
Speaker 1: this world. It doesn't mean that there is a willing

414
00:23:52,920 --> 00:23:56,960
Speaker 1: buyer for nearly half a billion dollars, especially considering that

415
00:23:57,040 --> 00:23:59,760
Speaker 1: it's way more than twice as much as the last

416
00:24:00,200 --> 00:24:02,679
Speaker 1: mark of hundred and seventy nine point four million dollars

417
00:24:02,840 --> 00:24:06,080
Speaker 1: for a Pablo Picasso painting. Do you have a sense

418
00:24:06,160 --> 00:24:08,399
Speaker 1: of how small the group is of people who not

419
00:24:08,440 --> 00:24:10,959
Speaker 1: only could afford this, but who would be interested in

420
00:24:11,040 --> 00:24:14,720
Speaker 1: spending this for like a vanity project or what else

421
00:24:14,720 --> 00:24:18,120
Speaker 1: could it be? It's, um, I don't think it's it's

422
00:24:18,440 --> 00:24:20,320
Speaker 1: the group is not small at all. And that was

423
00:24:20,400 --> 00:24:24,040
Speaker 1: so striking is because, uh, in three hundred up to

424
00:24:24,160 --> 00:24:26,719
Speaker 1: three hundred and seventy million, there were three people who

425
00:24:27,000 --> 00:24:30,480
Speaker 1: were doggedly competing for it. Um, So there were three

426
00:24:30,480 --> 00:24:32,960
Speaker 1: people in the world who could afford it at that level.

427
00:24:33,320 --> 00:24:35,919
Speaker 1: And you know, you mentioned the Picasso of course because

428
00:24:35,920 --> 00:24:38,000
Speaker 1: it was hundred and eighty million dollars, but they have

429
00:24:38,119 --> 00:24:40,640
Speaker 1: been private sales a lot higher up to like three

430
00:24:40,720 --> 00:24:43,520
Speaker 1: hundred million, that would know. But we've never even heard

431
00:24:43,560 --> 00:24:46,320
Speaker 1: those numbers in the sales room. We've never you know,

432
00:24:46,920 --> 00:24:50,800
Speaker 1: two hundred million, three hundred million dollars in the context

433
00:24:50,840 --> 00:24:53,800
Speaker 1: of an auction. It it's it sounded surreal, you know,

434
00:24:53,800 --> 00:24:56,760
Speaker 1: it never happened, and so I think that, yeah, I

435
00:24:56,760 --> 00:24:58,879
Speaker 1: mean the money, the money is not an issue for

436
00:24:59,040 --> 00:25:01,680
Speaker 1: people of that elibor. I think of that wealth, so,

437
00:25:02,080 --> 00:25:04,639
Speaker 1: you know, and it's not a rare just a rare thing.

438
00:25:04,680 --> 00:25:08,160
Speaker 1: There a lot of rare things, you know this. You know, well,

439
00:25:08,240 --> 00:25:10,760
Speaker 1: if we accept that this is the last Leonardo, it

440
00:25:10,840 --> 00:25:13,960
Speaker 1: is the last Leonardo. There's nothing else like it. So

441
00:25:14,200 --> 00:25:17,520
Speaker 1: I think for people, you know, and an interestingly, you know,

442
00:25:17,560 --> 00:25:20,720
Speaker 1: I spoke before the sale with the specialist at Christie's

443
00:25:20,800 --> 00:25:22,359
Speaker 1: and I don't think they didn't know it's going to

444
00:25:22,440 --> 00:25:24,960
Speaker 1: go that high. They kind of were thinking it's either

445
00:25:25,000 --> 00:25:27,119
Speaker 1: going to sell on one bed to the guaranteur of

446
00:25:27,160 --> 00:25:29,679
Speaker 1: the work, or you know, maybe it will go to

447
00:25:29,760 --> 00:25:34,800
Speaker 1: like one fifty. But you know, I think it's surprised everyone. Well,

448
00:25:34,840 --> 00:25:38,000
Speaker 1: I wonder if it's surprised the owner or the previous owner,

449
00:25:38,240 --> 00:25:43,840
Speaker 1: the Russian billionaire Dmitri rub Lovelev right, probably he bought

450
00:25:43,920 --> 00:25:46,520
Speaker 1: the painting in tween. He paid about a hundred and

451
00:25:46,520 --> 00:25:49,399
Speaker 1: twenty eight million dollars. It was a private sale, so

452
00:25:49,440 --> 00:25:52,760
Speaker 1: a hundred and twenty eight million dollars in becomes four

453
00:25:52,840 --> 00:25:56,520
Speaker 1: hundred and fifty million dollars that's correct. But you know

454
00:25:56,560 --> 00:26:01,120
Speaker 1: also remember that um, yes, but tube was he got

455
00:26:01,119 --> 00:26:04,000
Speaker 1: it four seven a half, but the person who sold

456
00:26:04,000 --> 00:26:06,439
Speaker 1: it to him got it for a d like literally

457
00:26:06,480 --> 00:26:08,520
Speaker 1: like a few days before. It's a good it's a

458
00:26:08,560 --> 00:26:10,960
Speaker 1: good way to show just how much the wealthy are

459
00:26:11,000 --> 00:26:13,719
Speaker 1: are getting wealthier right now. And sixty Fo'm serrying sixty four.

460
00:26:13,840 --> 00:26:16,120
Speaker 1: Just an interesting factor it that sixty four years ago

461
00:26:16,200 --> 00:26:18,440
Speaker 1: the same work sold it in the States, sale forty

462
00:26:18,520 --> 00:26:25,440
Speaker 1: five pounds million dollars in sixty or four years. Katya Kazakina,

463
00:26:25,760 --> 00:26:31,600
Speaker 1: Art market reporter for Bloomberg News. Thank you. Thanks for

464
00:26:31,640 --> 00:26:34,280
Speaker 1: listening to the Bloomberg P and L podcast. You can

465
00:26:34,320 --> 00:26:38,159
Speaker 1: subscribe and listen to interviews at Apple Podcasts, SoundCloud, or

466
00:26:38,200 --> 00:26:41,679
Speaker 1: whatever podcast platform you prefer. I'm pim Fox. I'm on

467
00:26:41,720 --> 00:26:45,560
Speaker 1: Twitter at pim Fox. I'm on Twitter at Lisa Abramo.

468
00:26:45,680 --> 00:26:48,280
Speaker 1: It's one before the podcast. You can always catch us

469
00:26:48,320 --> 00:26:49,880
Speaker 1: worldwide on Bloomberg Radio.