1
00:00:01,400 --> 00:00:04,120
Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney. Along

2
00:00:04,160 --> 00:00:06,240
Speaker 1: with my co host of Bonnie Quinn. Every business day

3
00:00:06,240 --> 00:00:10,360
Speaker 1: we bring you interviews from CEOs, market pros, and Bloomberg experts,

4
00:00:10,400 --> 00:00:13,560
Speaker 1: along with essential market moving news. Kind the Bloomberg Markets

5
00:00:13,600 --> 00:00:17,000
Speaker 1: Podcast on Apple podcast or wherever you listen to podcasts,

6
00:00:17,000 --> 00:00:20,919
Speaker 1: and on Bloomberg dot com. Well, we've been keeping a

7
00:00:20,920 --> 00:00:24,000
Speaker 1: close eye on real estate, particularly commercial real estate. Or

8
00:00:24,079 --> 00:00:26,840
Speaker 1: next to guest is the leader of b d os

9
00:00:27,080 --> 00:00:31,640
Speaker 1: us site selection team. So has he been busy? My suspicion,

10
00:00:31,680 --> 00:00:34,760
Speaker 1: as he'll say, yes, let's ask him directly. Tom Stringer

11
00:00:34,880 --> 00:00:38,160
Speaker 1: joins us. Tom, thanks for joining. What's happening out there

12
00:00:38,280 --> 00:00:44,919
Speaker 1: are companies still looking for sites, making plans, changing strategies. Well,

13
00:00:44,960 --> 00:00:47,559
Speaker 1: first of all, good morning, Fanny, thanks for having me.

14
00:00:47,560 --> 00:00:50,440
Speaker 1: It's great to be back. And yes, you are right,

15
00:00:50,520 --> 00:00:52,400
Speaker 1: we are busy. I mean, I think for the first

16
00:00:52,400 --> 00:00:55,320
Speaker 1: two months of the pandemic, folks were in shutdown and

17
00:00:55,320 --> 00:00:58,600
Speaker 1: wait and see mode and kind of a springtime turned

18
00:00:58,600 --> 00:01:01,920
Speaker 1: into summer, the wheel to commerce luckily started turning again,

19
00:01:02,040 --> 00:01:05,240
Speaker 1: certainly on on large Catholic investment projects, and you started

20
00:01:05,280 --> 00:01:07,520
Speaker 1: to see that. Obviously, today we have some great news

21
00:01:07,520 --> 00:01:09,880
Speaker 1: in New York City with what Facebook announced with the

22
00:01:10,160 --> 00:01:13,960
Speaker 1: Farley Post Office building taking a substantial block of space

23
00:01:13,959 --> 00:01:16,319
Speaker 1: in the Central Business District, which I think is really

24
00:01:16,319 --> 00:01:19,800
Speaker 1: probably great news for the brokerage communities across the country.

25
00:01:20,040 --> 00:01:22,040
Speaker 1: And in the last week or so, you've had some

26
00:01:22,040 --> 00:01:26,440
Speaker 1: some major investments in manufacturing, certainly with Tesla and Nicolo

27
00:01:26,520 --> 00:01:29,119
Speaker 1: with the groundbreaking on their factories and the automotive sector,

28
00:01:29,240 --> 00:01:32,559
Speaker 1: so wheels are starting to move, which is great news.

29
00:01:33,160 --> 00:01:36,520
Speaker 1: Is your client, we should we should mention exactly so.

30
00:01:36,520 --> 00:01:39,440
Speaker 1: So Tom, you know, I guess the debate is still

31
00:01:39,480 --> 00:01:44,040
Speaker 1: open about, you know, where people will work going forward.

32
00:01:44,360 --> 00:01:45,840
Speaker 1: A lot of us, most of us, a lot of

33
00:01:45,920 --> 00:01:47,680
Speaker 1: us are working from home right now, we have been

34
00:01:47,720 --> 00:01:50,800
Speaker 1: since March. Some folks unfortunately are unable to do that,

35
00:01:51,200 --> 00:01:54,240
Speaker 1: and there are on on the front lines. But how

36
00:01:54,400 --> 00:01:56,960
Speaker 1: is it the thought process evolving about how this is

37
00:01:57,000 --> 00:01:58,280
Speaker 1: going to play out? Are we ever gonna go back

38
00:01:58,320 --> 00:02:01,600
Speaker 1: to the office full force? It's a great an'swer, a

39
00:02:01,640 --> 00:02:04,440
Speaker 1: great question. I think the answer to that is probably not.

40
00:02:04,920 --> 00:02:07,080
Speaker 1: I think that any time you can take right the

41
00:02:07,160 --> 00:02:10,080
Speaker 1: two biggest costs on corporate balance sheets or people, we

42
00:02:10,120 --> 00:02:12,440
Speaker 1: all get that and then obviously real estates number two.

43
00:02:13,000 --> 00:02:16,160
Speaker 1: And anytime you can take your second leading cost and

44
00:02:16,160 --> 00:02:18,280
Speaker 1: and maybe half it or reduce it by even as

45
00:02:18,360 --> 00:02:21,800
Speaker 1: much as the third um, that's a huge difference. And

46
00:02:21,919 --> 00:02:24,400
Speaker 1: it also takes an account of people like working at

47
00:02:24,440 --> 00:02:27,040
Speaker 1: home to some degree. Some jobs people would certainly prefer

48
00:02:27,080 --> 00:02:29,240
Speaker 1: to be back to the office. But that has really

49
00:02:29,240 --> 00:02:31,680
Speaker 1: been injected into the thought process on the corporate real

50
00:02:31,800 --> 00:02:33,600
Speaker 1: estate side that hey, we're going to have to tack

51
00:02:33,639 --> 00:02:36,760
Speaker 1: a little bit here at you know, try to reconfigure

52
00:02:36,800 --> 00:02:38,800
Speaker 1: how we utilize space. Is it going to be that

53
00:02:38,840 --> 00:02:42,000
Speaker 1: big open concept anymore? Will that work? Do we need

54
00:02:42,040 --> 00:02:44,800
Speaker 1: a smaller footprint that's just going to be more hotel

55
00:02:44,880 --> 00:02:47,880
Speaker 1: and meeting space and and let most people office out

56
00:02:47,880 --> 00:02:51,200
Speaker 1: of their homes. So those discussions are really taking place,

57
00:02:51,240 --> 00:02:53,760
Speaker 1: and I think we're probably going to see a reduction

58
00:02:53,919 --> 00:02:57,680
Speaker 1: in overall office usage. But that's being picked up pretty

59
00:02:57,720 --> 00:03:00,760
Speaker 1: aggressively on the manufacturing and warehousing side right now. I mean,

60
00:03:01,000 --> 00:03:05,440
Speaker 1: you can't find warehousing space really anywhere around the country

61
00:03:05,440 --> 00:03:08,720
Speaker 1: in sizeable blocks anymore, which is is indicative of kind

62
00:03:08,720 --> 00:03:10,639
Speaker 1: of the rise of e commerce. Well, so you'd be

63
00:03:10,639 --> 00:03:13,120
Speaker 1: in a great position to tell us are those all

64
00:03:13,280 --> 00:03:16,760
Speaker 1: for server farms and for computers or the actual people

65
00:03:16,800 --> 00:03:18,840
Speaker 1: that are Like, I'm fascinated by this for NATO stories

66
00:03:18,880 --> 00:03:22,079
Speaker 1: seven square feet in the middle of New York City

67
00:03:22,160 --> 00:03:24,480
Speaker 1: right by Pen I mean it is Penn Station basically,

68
00:03:24,960 --> 00:03:27,640
Speaker 1: and uh, you know that's ten million square feet in total.

69
00:03:27,680 --> 00:03:31,280
Speaker 1: But is Facebook going to move people into that building

70
00:03:31,440 --> 00:03:34,520
Speaker 1: or is this another server farm? Yeah, no, that's a

71
00:03:34,560 --> 00:03:37,600
Speaker 1: good question. Um, we don't know what their internal plants

72
00:03:37,600 --> 00:03:40,120
Speaker 1: are yet. Focus has really been on the block of space.

73
00:03:40,240 --> 00:03:43,960
Speaker 1: But given where it is, given the locality, given frankly,

74
00:03:44,000 --> 00:03:46,280
Speaker 1: the utility rates and the costs of utilities in New

75
00:03:46,360 --> 00:03:49,840
Speaker 1: York versus putting servers in in more cost effective jurisdictions

76
00:03:49,880 --> 00:03:52,240
Speaker 1: or even upstate New York to take advantage of things

77
00:03:52,280 --> 00:03:55,400
Speaker 1: like Niagara Mohawk power, the odds are that this is

78
00:03:55,440 --> 00:03:58,560
Speaker 1: really going to be a people dominated facility, which is

79
00:03:58,600 --> 00:04:01,280
Speaker 1: great news, but also are around the country there has

80
00:04:01,320 --> 00:04:03,880
Speaker 1: been a lot of pick up really, as you point out,

81
00:04:03,920 --> 00:04:08,520
Speaker 1: in computer capacity in server farms coming online to handle

82
00:04:08,720 --> 00:04:10,920
Speaker 1: just the demand of e commerce and the fact that

83
00:04:11,640 --> 00:04:16,039
Speaker 1: really we're moving our infrastructure and architecture into the digital

84
00:04:16,040 --> 00:04:18,880
Speaker 1: world as opposed to physical plan But it's also being

85
00:04:18,920 --> 00:04:21,680
Speaker 1: picked up by warehouse I mean actual good old fashioned

86
00:04:21,720 --> 00:04:26,120
Speaker 1: inventory as really unsexy as that is, companies are building

87
00:04:26,120 --> 00:04:28,640
Speaker 1: that into their models now because the consumer markets are

88
00:04:28,720 --> 00:04:32,159
Speaker 1: driving Hey, you need to have these types of prescriptions

89
00:04:32,200 --> 00:04:35,240
Speaker 1: on hand, these consumer products on hand, so that that

90
00:04:35,360 --> 00:04:37,400
Speaker 1: kind of the death of the just in time inventory

91
00:04:37,440 --> 00:04:40,800
Speaker 1: models really taking hold. Hey, Tom, you know, let's talk

92
00:04:40,839 --> 00:04:43,000
Speaker 1: about New York City in particular. A couple of weeks ago,

93
00:04:43,000 --> 00:04:44,719
Speaker 1: I came into the city for the first time since

94
00:04:44,760 --> 00:04:46,640
Speaker 1: early March, and I was just shocked at the number

95
00:04:46,640 --> 00:04:50,880
Speaker 1: of vacancies, commercial vacancies along blocks that I'm very familiar

96
00:04:50,880 --> 00:04:53,719
Speaker 1: with where I know they were vibrant businesses just several

97
00:04:53,720 --> 00:04:55,760
Speaker 1: months ago. Where are we in terms of kind of

98
00:04:56,000 --> 00:05:02,440
Speaker 1: commercial vacancies and maybe even rates um in New York City. Well,

99
00:05:02,560 --> 00:05:05,440
Speaker 1: you asked a really good question, because even though office

100
00:05:05,560 --> 00:05:08,320
Speaker 1: and we're housing an industrial may be doing very well,

101
00:05:08,600 --> 00:05:11,039
Speaker 1: um certainly in the New York City marketplace right now,

102
00:05:11,560 --> 00:05:14,080
Speaker 1: and Facebook being a big help to that. The retail

103
00:05:14,200 --> 00:05:17,080
Speaker 1: and street level retail is a big issue, and that's

104
00:05:17,080 --> 00:05:19,320
Speaker 1: been an issue that's really been going on for two

105
00:05:19,320 --> 00:05:22,600
Speaker 1: and three years prior to the pandemic with landlords just

106
00:05:22,680 --> 00:05:26,200
Speaker 1: kind of holding out for higher price rents and small

107
00:05:26,279 --> 00:05:28,520
Speaker 1: mom and pop type of shops and able to make

108
00:05:28,560 --> 00:05:31,280
Speaker 1: it and make it just on a commercial lease standpoint

109
00:05:31,320 --> 00:05:34,039
Speaker 1: that couldn't afford the rent. This is, along with the

110
00:05:34,080 --> 00:05:38,320
Speaker 1: lockdown and shutting them out of any revenue, has really

111
00:05:38,360 --> 00:05:42,360
Speaker 1: been a significant problem for the city and it probably

112
00:05:42,360 --> 00:05:45,240
Speaker 1: will be for the foreseeable future. Um. You know, the

113
00:05:45,279 --> 00:05:47,240
Speaker 1: programs that have come out of the Federal Reserve, the

114
00:05:47,279 --> 00:05:50,560
Speaker 1: p PP, the extensions of it right have been helpful,

115
00:05:50,600 --> 00:05:53,520
Speaker 1: but but it doesn't replace real revenue. It doesn't replace

116
00:05:53,600 --> 00:05:56,680
Speaker 1: real consumer demand. It's it's a band aid, um that

117
00:05:56,720 --> 00:05:59,760
Speaker 1: that was designed to get us to right market demand

118
00:05:59,760 --> 00:06:02,880
Speaker 1: again that still has not come back yet. So I

119
00:06:02,920 --> 00:06:06,279
Speaker 1: think you point out a really good issue that that

120
00:06:06,400 --> 00:06:09,719
Speaker 1: retail and main street related issues for real estate not

121
00:06:09,800 --> 00:06:11,680
Speaker 1: just in the cities, but in the suburbs. I mean

122
00:06:11,720 --> 00:06:14,360
Speaker 1: that they're going through the same issues right now. That's

123
00:06:14,360 --> 00:06:17,120
Speaker 1: a problem. That's a problem that economic development is going

124
00:06:17,160 --> 00:06:19,719
Speaker 1: to have to deal with. That that legislatures at the

125
00:06:19,760 --> 00:06:21,800
Speaker 1: both the federal, state local are going to have to

126
00:06:21,839 --> 00:06:27,160
Speaker 1: deal with and we've never done that for that. Yeah,

127
00:06:27,600 --> 00:06:29,640
Speaker 1: it's interesting to see. It's gonna be a big turn

128
00:06:29,720 --> 00:06:32,320
Speaker 1: on Tom as you're suggesting. Tom Stringer, Corporate real Estate

129
00:06:32,320 --> 00:06:36,080
Speaker 1: Advisory Managing Director for accounting firm b DEO, giving us

130
00:06:36,120 --> 00:06:38,800
Speaker 1: his thoughts on the commercial real estate business again. The

131
00:06:38,839 --> 00:06:42,080
Speaker 1: big news in New York City was um Facebook taking

132
00:06:42,160 --> 00:06:44,719
Speaker 1: a huge piece of commercial real estate, the Farley Building,

133
00:06:44,720 --> 00:06:50,120
Speaker 1: which is right across street from Penn Station in Midtown Manhattan. Well.

134
00:06:50,120 --> 00:06:53,320
Speaker 1: Bloomberg Markets magazine is out with a special issue focused

135
00:06:53,360 --> 00:06:56,640
Speaker 1: on diversity, where black men and women share their experience

136
00:06:56,800 --> 00:06:59,240
Speaker 1: on Wall Street. Lauren Simmons is one of the contributors.

137
00:06:59,240 --> 00:07:01,520
Speaker 1: She's the youngest black woman ever to work on the

138
00:07:01,520 --> 00:07:03,719
Speaker 1: New York Stock Exchange and she joins us now, Lauren,

139
00:07:03,880 --> 00:07:06,880
Speaker 1: thank you so much. We really appreciate it. Entrepreneur found

140
00:07:06,880 --> 00:07:09,680
Speaker 1: their CEO of Lauren Simmons, We really appreciate you joining

141
00:07:09,720 --> 00:07:12,840
Speaker 1: us here, Lauren, Wall Street. Why can't they seem to

142
00:07:12,920 --> 00:07:17,640
Speaker 1: make any steady progress at all on diversity? You know,

143
00:07:17,760 --> 00:07:20,040
Speaker 1: I think it has to be an initiative that people

144
00:07:20,400 --> 00:07:24,360
Speaker 1: want to care about. And if you have people sitting

145
00:07:24,360 --> 00:07:27,240
Speaker 1: at the top that don't make this an importance, it's

146
00:07:27,320 --> 00:07:30,560
Speaker 1: not going to be an importance. UM. I know that

147
00:07:30,680 --> 00:07:33,560
Speaker 1: in two thousand and twenty these issues have been brought

148
00:07:33,640 --> 00:07:37,360
Speaker 1: up again. And once these issues are brought up, it's

149
00:07:37,400 --> 00:07:39,880
Speaker 1: no reason to turn a blind light, like you should

150
00:07:39,880 --> 00:07:43,160
Speaker 1: be going above and beyond to to do better and

151
00:07:43,200 --> 00:07:45,800
Speaker 1: to be better and to have more diversity inclusion within

152
00:07:45,840 --> 00:07:49,400
Speaker 1: your organization. But two thousand and twenties, since the first

153
00:07:49,400 --> 00:07:52,720
Speaker 1: time that these issues have been brought up in seventeen,

154
00:07:52,840 --> 00:07:54,520
Speaker 1: while I was still on the trading floor, it was

155
00:07:54,560 --> 00:08:00,120
Speaker 1: the year of the Woman, and since up until we

156
00:08:00,160 --> 00:08:04,440
Speaker 1: have made marginalized efforts, as you know, bringing more women

157
00:08:04,480 --> 00:08:09,520
Speaker 1: into this space, let alone bringing more minorities. So, Lauren,

158
00:08:09,600 --> 00:08:11,360
Speaker 1: how long did you work on Wall Street and did

159
00:08:11,400 --> 00:08:14,280
Speaker 1: you feel like there was any progress at all between

160
00:08:14,360 --> 00:08:20,040
Speaker 1: arriving and leaving. I worked there for two years, and uh,

161
00:08:20,120 --> 00:08:24,520
Speaker 1: there was one other lady who eventually joined the trading floor,

162
00:08:24,600 --> 00:08:28,480
Speaker 1: and she was much older, had been on the trading floor,

163
00:08:28,960 --> 00:08:31,360
Speaker 1: you know, back in the nineties, early two thousand and

164
00:08:31,680 --> 00:08:36,239
Speaker 1: and had came back. So from my experience, no, specifically

165
00:08:36,320 --> 00:08:38,960
Speaker 1: to the New York Stock Exchange, No, I don't think

166
00:08:39,000 --> 00:08:42,920
Speaker 1: there's been much improvement. So it's interesting, Lauren. You know,

167
00:08:43,040 --> 00:08:44,760
Speaker 1: I've worked on Wall Street for about thirty years and

168
00:08:44,760 --> 00:08:47,560
Speaker 1: what I noticed and this is probably has some relation

169
00:08:47,640 --> 00:08:50,959
Speaker 1: to corporate America as well as the incoming classes, whether

170
00:08:51,160 --> 00:08:54,439
Speaker 1: at a college, whether their investment banking analysts or their

171
00:08:54,480 --> 00:08:58,199
Speaker 1: new sales and people are traders. There's a fair amount

172
00:08:58,240 --> 00:09:02,680
Speaker 1: of diversity there, both gender diversity and all other types

173
00:09:02,720 --> 00:09:06,080
Speaker 1: of diversity. Yet when you get ten years forward, seven eight, nine,

174
00:09:06,080 --> 00:09:09,800
Speaker 1: ten years forward, when there's managing director decisions, partnership decisions,

175
00:09:09,880 --> 00:09:12,959
Speaker 1: those ranks are really thinned out and it does look

176
00:09:13,200 --> 00:09:16,280
Speaker 1: much less diverse. What do you think the industry has

177
00:09:16,360 --> 00:09:21,080
Speaker 1: to do to kind of keep those people in the workforce. Yeah,

178
00:09:21,160 --> 00:09:23,719
Speaker 1: so I don't think that there should be sponsorships. There

179
00:09:23,720 --> 00:09:28,640
Speaker 1: should be allies UM people actively involved in trying to

180
00:09:28,679 --> 00:09:32,360
Speaker 1: progress individuals careers. When I was on the trading floor,

181
00:09:32,880 --> 00:09:35,720
Speaker 1: I love that the men and it was mutual on

182
00:09:35,760 --> 00:09:38,640
Speaker 1: both sides me. But then we were coming together, what

183
00:09:38,640 --> 00:09:42,080
Speaker 1: can we do to help you with your career? You know,

184
00:09:42,240 --> 00:09:44,439
Speaker 1: I'm sure you don't want to stay on the trading floor.

185
00:09:44,520 --> 00:09:46,199
Speaker 1: What do you want to do in finance? And they

186
00:09:46,240 --> 00:09:50,160
Speaker 1: introduced me to many different people and they were definitely

187
00:09:50,480 --> 00:09:52,920
Speaker 1: helpful and facilitating that, But I know that that's not

188
00:09:53,000 --> 00:09:58,040
Speaker 1: the experience UM with throughout all of corporate America or

189
00:09:58,080 --> 00:10:00,800
Speaker 1: even within the financial industry. So I think there needs

190
00:10:00,800 --> 00:10:05,880
Speaker 1: to be a bridge and to go from there. Lauren,

191
00:10:05,960 --> 00:10:08,400
Speaker 1: what did you learn from Wall Street and would you

192
00:10:08,440 --> 00:10:14,920
Speaker 1: ever work on Wall Street again? I learned so many things. Um. Obviously,

193
00:10:14,920 --> 00:10:17,559
Speaker 1: working on the train floor was a very fast pace

194
00:10:18,200 --> 00:10:22,040
Speaker 1: alpha mel environment and I was the only woman on

195
00:10:22,080 --> 00:10:24,800
Speaker 1: the floor. Um. I think one of the best devices

196
00:10:24,840 --> 00:10:27,640
Speaker 1: that Richard Rose and Wise It could give me while

197
00:10:27,640 --> 00:10:30,960
Speaker 1: I worked at Rose bat Securities is you know people

198
00:10:31,000 --> 00:10:34,360
Speaker 1: are going to notice you regardless. And he didn't mean

199
00:10:34,400 --> 00:10:37,640
Speaker 1: anything by it, but he just said that, because I

200
00:10:37,720 --> 00:10:41,760
Speaker 1: am the other in the room, and you know I'm

201
00:10:41,880 --> 00:10:44,520
Speaker 1: essentially the elephant the room, make sure that I have

202
00:10:44,800 --> 00:10:47,840
Speaker 1: something to say, something of substance to say when people

203
00:10:47,880 --> 00:10:51,480
Speaker 1: approached me, because people are going to notice me either way,

204
00:10:51,520 --> 00:10:56,040
Speaker 1: and there's no way of hiding, um and encountering behind

205
00:10:56,080 --> 00:10:58,280
Speaker 1: other people. So I thought that was the best device

206
00:10:58,360 --> 00:11:00,480
Speaker 1: that I would give. As far as you running back

207
00:11:00,480 --> 00:11:02,959
Speaker 1: to Wall Street, I I don't think so. I very

208
00:11:03,040 --> 00:11:06,960
Speaker 1: much enjoyed my experience there, um, but I have been

209
00:11:07,000 --> 00:11:10,400
Speaker 1: showed off into doing uh many more things that I'm

210
00:11:10,440 --> 00:11:15,680
Speaker 1: passionate about that are making a much brander impact. Lauren Simmons,

211
00:11:15,720 --> 00:11:17,960
Speaker 1: thanks so much for joining us. We really appreciate your thoughts.

212
00:11:18,000 --> 00:11:21,000
Speaker 1: A fascinating story. Lauren Simmons, entrepreneur, founder and see of

213
00:11:21,080 --> 00:11:24,199
Speaker 1: Lauren Simmons l l C. Featured it in the Bluemore

214
00:11:24,240 --> 00:11:27,200
Speaker 1: Markets magazine. They have a special issue focused on diversity. Really,

215
00:11:27,760 --> 00:11:29,760
Speaker 1: uh suggest you take a look at that is where

216
00:11:29,800 --> 00:11:32,080
Speaker 1: black men and women they really share their experiences on

217
00:11:32,120 --> 00:11:35,880
Speaker 1: Wall Street and again, Vannie, it's just you know, my

218
00:11:36,000 --> 00:11:39,160
Speaker 1: sense that, um, there's just not enough support to get

219
00:11:39,160 --> 00:11:41,240
Speaker 1: them through the ranks. Again, I think they do a

220
00:11:41,240 --> 00:11:44,240
Speaker 1: pretty decent job getting people in the door, but keeping

221
00:11:44,280 --> 00:11:47,440
Speaker 1: them as a whole another story. Well, it's definitely a

222
00:11:47,520 --> 00:11:51,480
Speaker 1: question of willingness. And Lauren at twenty six, with that

223
00:11:51,600 --> 00:11:55,280
Speaker 1: kind of experience under her belt, is you know, a

224
00:11:55,280 --> 00:11:57,280
Speaker 1: great ambassador for somebody who went out there and did

225
00:11:57,320 --> 00:11:59,240
Speaker 1: that in spite of all the odds. I had a

226
00:11:59,240 --> 00:12:01,040
Speaker 1: conversation with her esterday and she was telling me that

227
00:12:01,080 --> 00:12:03,760
Speaker 1: she was the second black person to ever have worked

228
00:12:03,760 --> 00:12:05,960
Speaker 1: on the New York Stock Exchange, which, really, when you

229
00:12:06,000 --> 00:12:08,920
Speaker 1: think about it, the amount of people that have worked

230
00:12:08,920 --> 00:12:11,839
Speaker 1: down there over the year is the entire history of

231
00:12:11,880 --> 00:12:15,360
Speaker 1: the New York Stock Change heard the second black equity

232
00:12:15,360 --> 00:12:18,200
Speaker 1: trader down there is really quite something phenomenal, I think,

233
00:12:18,440 --> 00:12:22,600
Speaker 1: And if you read the Diversity Issue magazine, you'll see

234
00:12:22,640 --> 00:12:24,560
Speaker 1: that all of these people seem to be one off

235
00:12:24,679 --> 00:12:26,880
Speaker 1: and that's just not good enough. No, it's not And

236
00:12:26,920 --> 00:12:30,480
Speaker 1: as as she was mentioning, um, as Lauren was mentioning here,

237
00:12:30,600 --> 00:12:32,840
Speaker 1: there really has to be support. And we've heard a

238
00:12:32,840 --> 00:12:34,760
Speaker 1: lot of talk not just from the senior folks on

239
00:12:34,800 --> 00:12:37,440
Speaker 1: Wall Street, but just senior management is c suite in general,

240
00:12:37,640 --> 00:12:40,480
Speaker 1: you know, over the last decade plus about commitment to diversity,

241
00:12:41,120 --> 00:12:43,520
Speaker 1: but there it just doesn't bear out in the numbers,

242
00:12:43,520 --> 00:12:44,920
Speaker 1: and we see that time and time again. And so

243
00:12:44,920 --> 00:12:47,160
Speaker 1: that's why I think this Bloomberg Markets issue, there's a

244
00:12:47,200 --> 00:12:49,840
Speaker 1: really good job that goes below the numbers and really

245
00:12:49,840 --> 00:12:51,959
Speaker 1: talks to the people, interview some people that have worked

246
00:12:51,960 --> 00:12:53,920
Speaker 1: on Wall Street black men, men and women, and they

247
00:12:53,920 --> 00:12:56,679
Speaker 1: share their personal experiences and kind of really brings at home.

248
00:12:56,720 --> 00:12:59,520
Speaker 1: So it's a good job by the team of Bloomberg Markets.

249
00:12:59,640 --> 00:13:02,320
Speaker 1: And I'm gonna be looking forward to seeing what Lauren

250
00:13:02,360 --> 00:13:05,920
Speaker 1: Simmons does next because she's clearly a trailblazer in her fields.

251
00:13:05,920 --> 00:13:08,280
Speaker 1: So Thank you to Lauren Simmons for joining us and

252
00:13:08,360 --> 00:13:14,720
Speaker 1: speaking about her experience. It's time for Bloomberg Opinion. We

253
00:13:14,800 --> 00:13:18,280
Speaker 1: are joined this morning by Rachel Rosenthal, editor Bloomberg Opinion

254
00:13:18,360 --> 00:13:21,120
Speaker 1: based in Singapore, out with a fascinating column, and it

255
00:13:21,160 --> 00:13:24,920
Speaker 1: goes to that whole H one B visa issue that

256
00:13:25,080 --> 00:13:27,480
Speaker 1: is making. It's always a political issue, but it's even

257
00:13:27,480 --> 00:13:29,720
Speaker 1: a bigger one this year with President Trump looking to

258
00:13:29,960 --> 00:13:34,719
Speaker 1: rein that in and uh, interesting column. Um, Rachel's out

259
00:13:34,720 --> 00:13:37,160
Speaker 1: with this column, Big Tech wants you to believe in

260
00:13:37,200 --> 00:13:41,200
Speaker 1: a skills gap. Titles suggest that maybe Rachel doesn't necessarily

261
00:13:41,200 --> 00:13:43,520
Speaker 1: believe that. Rachel, thanks so much for joining us here.

262
00:13:43,960 --> 00:13:47,439
Speaker 1: I've always heard from Silicon Valley that these H one

263
00:13:47,480 --> 00:13:51,080
Speaker 1: B VISs are critical to their businesses because the US

264
00:13:51,160 --> 00:13:55,600
Speaker 1: does not produce enough skilled labor. What's the real take there?

265
00:13:55,640 --> 00:13:58,800
Speaker 1: Do you believe that? Um? Well, you know what, I

266
00:14:00,000 --> 00:14:02,200
Speaker 1: into Stanford, so in the heart of Silicon Valley, and

267
00:14:02,200 --> 00:14:04,360
Speaker 1: I very much believe the same. And you know, I

268
00:14:04,400 --> 00:14:07,000
Speaker 1: think I would have agreed with you until about about

269
00:14:07,360 --> 00:14:10,920
Speaker 1: feed or ten weeks ago when I started researching this piece. Um.

270
00:14:10,960 --> 00:14:13,320
Speaker 1: You know, you start looking at the data and it

271
00:14:13,440 --> 00:14:16,720
Speaker 1: just doesn't really add up. UM. And you know, if

272
00:14:16,760 --> 00:14:19,320
Speaker 1: you look at the number of graduates that we have

273
00:14:19,400 --> 00:14:21,480
Speaker 1: in computer science and engineering, if you look at the

274
00:14:21,560 --> 00:14:24,360
Speaker 1: number of them graduates overall, and then you look at

275
00:14:24,360 --> 00:14:27,240
Speaker 1: the number of openings, the number of vacancies, patterns of

276
00:14:27,240 --> 00:14:29,160
Speaker 1: wage growth, and you look at a whole host of

277
00:14:29,160 --> 00:14:32,480
Speaker 1: other things. Um, you start to see some that the

278
00:14:32,640 --> 00:14:35,800
Speaker 1: thread really starts to unravel there, and you start you

279
00:14:35,840 --> 00:14:37,920
Speaker 1: have to kind of ask yourself the bigger question. You

280
00:14:37,960 --> 00:14:41,720
Speaker 1: know what, um, what do you tech companies want? And

281
00:14:41,760 --> 00:14:45,640
Speaker 1: it's not just tech companies, it's companies across the tech industry.

282
00:14:45,760 --> 00:14:48,400
Speaker 1: You know, they're tech workers and all kinds of fields

283
00:14:48,400 --> 00:14:50,040
Speaker 1: that you know, I see you know, every kind of

284
00:14:50,080 --> 00:14:53,440
Speaker 1: company has I F workers for example, so um, they

285
00:14:53,480 --> 00:14:57,160
Speaker 1: could be in in retail, to restaurants. So UM. There

286
00:14:57,280 --> 00:15:00,560
Speaker 1: is a lot of incentive to claim the there are

287
00:15:00,800 --> 00:15:03,960
Speaker 1: enough skilled workers because you can get a pretty serious

288
00:15:04,040 --> 00:15:10,040
Speaker 1: discount on on foreign labor um. And these are guest workers. UM.

289
00:15:10,120 --> 00:15:12,880
Speaker 1: So I think. You know, there's a really fascinating paper

290
00:15:13,280 --> 00:15:16,040
Speaker 1: upon which a lot of my research was based, UM,

291
00:15:16,160 --> 00:15:20,800
Speaker 1: out of the Economic Policy Institute. It came out in May, UM,

292
00:15:20,840 --> 00:15:23,560
Speaker 1: indicating that you know, there are four wage levels for

293
00:15:23,640 --> 00:15:26,280
Speaker 1: each one vs UM, and the two lowest ones companies

294
00:15:26,320 --> 00:15:29,360
Speaker 1: can get a discount. UM. It was up to you know,

295
00:15:29,560 --> 00:15:32,840
Speaker 1: thirty six percent for the lowest wage bracket and eighteen

296
00:15:32,880 --> 00:15:35,280
Speaker 1: percent for the second lowest. Now that was that that

297
00:15:35,320 --> 00:15:37,480
Speaker 1: they had to look at a narrowest place within the

298
00:15:37,560 --> 00:15:41,000
Speaker 1: DC metro area in a certain computer occupation. UM. But

299
00:15:41,080 --> 00:15:43,640
Speaker 1: it was pretty telling to me that this kind of UM,

300
00:15:43,720 --> 00:15:45,560
Speaker 1: this kind of discount can really add up and create

301
00:15:45,680 --> 00:15:49,120
Speaker 1: huge savings and create incentives for this narrative to persist.

302
00:15:50,280 --> 00:15:54,600
Speaker 1: So Rachel talked to us about degrees earned versus openings.

303
00:15:54,840 --> 00:15:57,120
Speaker 1: So in the United States, for example, you know what

304
00:15:57,240 --> 00:16:02,480
Speaker 1: degrees are earned versus openings? Sure, um, So if you know,

305
00:16:02,560 --> 00:16:04,800
Speaker 1: I went through a lot of data which is UM

306
00:16:04,840 --> 00:16:09,280
Speaker 1: you know, and they're about uh, roughly five thousand stem

307
00:16:09,320 --> 00:16:12,360
Speaker 1: degrees UM in in the U s UM in the

308
00:16:12,680 --> 00:16:15,200
Speaker 1: latest year that they attracted, which was twenty eight keen UM.

309
00:16:15,240 --> 00:16:18,200
Speaker 1: So that's up six from a decade earlier. So people

310
00:16:18,240 --> 00:16:21,280
Speaker 1: are you know, American students are getting lots and lots

311
00:16:21,280 --> 00:16:24,320
Speaker 1: of degrees in in quote unquote STEM fields. And you know,

312
00:16:24,440 --> 00:16:26,840
Speaker 1: I think you know those knees europe impuls would be like, oh, well,

313
00:16:27,120 --> 00:16:30,240
Speaker 1: aren't all those graduates you know from abroad, and they're not.

314
00:16:30,440 --> 00:16:32,600
Speaker 1: You know, what I found was that actually north of

315
00:16:32,720 --> 00:16:35,640
Speaker 1: nine d son of them were actually American citizens and

316
00:16:35,680 --> 00:16:38,840
Speaker 1: permanent residents. UM. Now that picture can change as you

317
00:16:38,880 --> 00:16:41,880
Speaker 1: go up the scale in terms of UM graduate degrees,

318
00:16:41,920 --> 00:16:43,720
Speaker 1: and there's that's a little bit of a different metrics.

319
00:16:43,760 --> 00:16:46,120
Speaker 1: But UM, you know, when you look at the bachelor's

320
00:16:46,160 --> 00:16:50,200
Speaker 1: degrees that are earned, a whole lot of them are Americans,

321
00:16:50,720 --> 00:16:54,280
Speaker 1: Americans and permanent residents. And if you look at openings,

322
00:16:54,440 --> 00:16:57,120
Speaker 1: you know, you have to consider that there are certain

323
00:16:57,160 --> 00:17:02,160
Speaker 1: openings that go that typically would go to UM someone

324
00:17:02,160 --> 00:17:04,639
Speaker 1: who had a specific type of degree that's called computer

325
00:17:04,680 --> 00:17:08,560
Speaker 1: science degree or related computer science engineering field. UM. But

326
00:17:08,640 --> 00:17:13,040
Speaker 1: that you know, not everyone in these fields necessarily has

327
00:17:13,240 --> 00:17:15,680
Speaker 1: to have a computer science degree. I'm sure. I mean

328
00:17:16,080 --> 00:17:19,159
Speaker 1: Steve Jobs themselves is a very good example. UM. But

329
00:17:19,280 --> 00:17:21,240
Speaker 1: I know, you know, just from my time at Stanford,

330
00:17:21,280 --> 00:17:23,080
Speaker 1: I know a number of people who ended up in

331
00:17:23,119 --> 00:17:25,760
Speaker 1: tech who did not make during computer science UM. And

332
00:17:25,920 --> 00:17:28,199
Speaker 1: it was actually pretty interesting that you know, two thirds

333
00:17:28,320 --> 00:17:32,359
Speaker 1: of um I T occupation new entrants do not have

334
00:17:32,400 --> 00:17:35,200
Speaker 1: a computer science degree. So then you have to consider

335
00:17:35,280 --> 00:17:39,040
Speaker 1: the entire pool of UM American bachelor's degree owners as

336
00:17:39,080 --> 00:17:42,639
Speaker 1: potential people who could be trained. And I think UM

337
00:17:42,840 --> 00:17:45,800
Speaker 1: for for this type of work. And I think that

338
00:17:46,200 --> 00:17:48,520
Speaker 1: you know, that is really a key feature in some

339
00:17:48,600 --> 00:17:50,720
Speaker 1: of the research that I've done, is you know, why,

340
00:17:50,880 --> 00:17:53,439
Speaker 1: what's the incentive to train someone when you've got you know,

341
00:17:53,600 --> 00:17:56,800
Speaker 1: ready to work cheaper labor that's coming in from abroad.

342
00:17:57,760 --> 00:18:01,840
Speaker 1: What do the tech companies say, UM, in response to

343
00:18:01,920 --> 00:18:05,480
Speaker 1: kind of the economics of this the cheaper nature of

344
00:18:05,520 --> 00:18:09,680
Speaker 1: some of these foreign workers is what's typically the response? Sure,

345
00:18:10,040 --> 00:18:12,359
Speaker 1: I mean, I think the typical response would never be

346
00:18:12,440 --> 00:18:14,359
Speaker 1: to say they are cheaper. And I think that there

347
00:18:14,400 --> 00:18:17,200
Speaker 1: there is a tremendous backlash UM when Trump is issued

348
00:18:17,240 --> 00:18:20,479
Speaker 1: is executive were in June? Um, you know, And and

349
00:18:20,520 --> 00:18:23,520
Speaker 1: there are a lot of like really legitimate claims to

350
00:18:23,640 --> 00:18:25,639
Speaker 1: be made that you know, a lot of there's a

351
00:18:25,640 --> 00:18:29,240
Speaker 1: lot of intelligence and innovation and entrepreneurships UM that and

352
00:18:29,320 --> 00:18:31,640
Speaker 1: that America is built in the foundation of immigrants, which

353
00:18:31,640 --> 00:18:34,000
Speaker 1: I a hundred percent beliefs. But you have to remember

354
00:18:34,000 --> 00:18:36,640
Speaker 1: the hm vvs as holders are not immigrants, I mean

355
00:18:36,680 --> 00:18:39,880
Speaker 1: their guest workers, and and they are you know, get

356
00:18:39,920 --> 00:18:42,960
Speaker 1: trapped in what's a lot of people called some version

357
00:18:43,040 --> 00:18:46,720
Speaker 1: of indenture labor because they are tied UM, they're sponsored

358
00:18:46,720 --> 00:18:49,239
Speaker 1: by an employer UM, and it's really difficult to move

359
00:18:49,240 --> 00:18:51,400
Speaker 1: from one employer to another. So they you know, they

360
00:18:51,480 --> 00:18:54,000
Speaker 1: they're they're they're kind of stuck and you know, they

361
00:18:54,040 --> 00:18:56,280
Speaker 1: don't have UM. You know, they could be here for

362
00:18:56,280 --> 00:18:58,119
Speaker 1: three years, they could be here for six years. Sometimes

363
00:18:58,119 --> 00:19:01,240
Speaker 1: that six years gets extended, sometimes it doesn't UM. So

364
00:19:01,400 --> 00:19:04,040
Speaker 1: you know, it's they're they're they're really suck. And I

365
00:19:04,080 --> 00:19:09,200
Speaker 1: think that you know, they because their company sponsors their

366
00:19:09,320 --> 00:19:11,879
Speaker 1: visa and because their visas side of their immigration status,

367
00:19:11,920 --> 00:19:15,080
Speaker 1: and because their immigration status enables them to stay in

368
00:19:15,080 --> 00:19:18,000
Speaker 1: the US, pay taxes, have families, and be part of

369
00:19:18,160 --> 00:19:20,320
Speaker 1: you know, be part of the communities that they're part of.

370
00:19:20,720 --> 00:19:23,880
Speaker 1: You know, they can't whistle blow on their companies. So

371
00:19:23,920 --> 00:19:26,280
Speaker 1: I think, you know, it's very easy to paint this

372
00:19:26,640 --> 00:19:30,400
Speaker 1: picture is UM, you know, anti immigrant UM, but it's

373
00:19:30,400 --> 00:19:33,080
Speaker 1: really not, because I think the system actually really hurts

374
00:19:33,119 --> 00:19:35,080
Speaker 1: not only American workers but which want to be these

375
00:19:35,080 --> 00:19:38,919
Speaker 1: the holders as well. What happens to the American workers

376
00:19:38,960 --> 00:19:43,359
Speaker 1: that don't get those jobs then? So you know, there's

377
00:19:43,359 --> 00:19:45,840
Speaker 1: a number of things. You know, I think you could look. Um,

378
00:19:45,880 --> 00:19:49,040
Speaker 1: you know, I've often casually wondered to myself like, why

379
00:19:49,080 --> 00:19:53,040
Speaker 1: are there so many you know, computer engineers and finance

380
00:19:53,080 --> 00:19:55,560
Speaker 1: and you know, it was always just sort of a

381
00:19:55,560 --> 00:19:57,760
Speaker 1: curiosity to me that I never really thought twice about.

382
00:19:57,800 --> 00:20:00,080
Speaker 1: But you know, if you think about it, when you

383
00:20:00,080 --> 00:20:04,439
Speaker 1: know you're not seeing um wage growth, um, you know

384
00:20:04,520 --> 00:20:07,480
Speaker 1: that I should mention that the wages in the I

385
00:20:07,600 --> 00:20:10,879
Speaker 1: T sector, for example, are higher than the median UM

386
00:20:10,880 --> 00:20:15,040
Speaker 1: wages in the US broadly, but they're not necessarily growing.

387
00:20:15,040 --> 00:20:17,080
Speaker 1: I mean, I think there's this idea that you know,

388
00:20:17,600 --> 00:20:20,080
Speaker 1: if you get a computer science degree or programming, you're

389
00:20:20,400 --> 00:20:22,800
Speaker 1: you know, you know how to code, that you know,

390
00:20:22,880 --> 00:20:26,119
Speaker 1: you're you're you've got a ticket to success. And I just,

391
00:20:26,200 --> 00:20:28,720
Speaker 1: you know, I don't necessarily know that that's that that's

392
00:20:28,760 --> 00:20:32,560
Speaker 1: the case. Um, So you know, I think that, UM,

393
00:20:33,040 --> 00:20:35,520
Speaker 1: you know, this is this is something that you know,

394
00:20:35,600 --> 00:20:38,920
Speaker 1: you just have to consider all aspects of Rachel. It

395
00:20:39,040 --> 00:20:42,040
Speaker 1: is a fascinating bloombergiminion base, and we always love to

396
00:20:42,320 --> 00:20:45,200
Speaker 1: focus on these because they tend to put out a

397
00:20:45,920 --> 00:20:50,120
Speaker 1: little viewpoint that might differ slightly or even entirely with

398
00:20:50,400 --> 00:20:53,120
Speaker 1: what's out there sort of in the ether. So Rachel's

399
00:20:53,119 --> 00:20:55,840
Speaker 1: thanks for that. Rachel Wilson's Hall is editor at Zloomberg

400
00:20:56,000 --> 00:20:59,159
Speaker 1: Opinion and her piece today, big tech wants you to

401
00:20:59,200 --> 00:21:02,320
Speaker 1: believe in a bills gap subtitle, but what they really

402
00:21:02,359 --> 00:21:06,720
Speaker 1: want is a steady supply of cheap, dependent I T workers.

403
00:21:07,040 --> 00:21:10,400
Speaker 1: I think that might be called capitalism poll. Yes, exactly,

404
00:21:10,400 --> 00:21:14,080
Speaker 1: and it's certainly different from the narrative. What is the

405
00:21:14,160 --> 00:21:17,879
Speaker 1: latest between TikTok and the United States? What company is

406
00:21:17,920 --> 00:21:21,199
Speaker 1: interested in buying TikTok and will they be able to

407
00:21:21,320 --> 00:21:26,320
Speaker 1: Let's bring in Andrew Brown, Bloomberg New Economy editor, and

408
00:21:26,440 --> 00:21:29,280
Speaker 1: in fact he is the editorial director of New Economy. Andy,

409
00:21:29,560 --> 00:21:31,639
Speaker 1: very great to have you. We've had so many reports

410
00:21:31,680 --> 00:21:34,240
Speaker 1: over the last couple of days. Fox reporting today that

411
00:21:34,480 --> 00:21:37,720
Speaker 1: several companies, including Facebook might actually be interested Axio, saying

412
00:21:38,080 --> 00:21:40,480
Speaker 1: other companies are interested in the one we knew about

413
00:21:40,560 --> 00:21:44,160
Speaker 1: yesterday Microsoft to any of these companies have a better

414
00:21:44,240 --> 00:21:48,440
Speaker 1: chance than any other. Look, I'm not I'm not really

415
00:21:48,440 --> 00:21:51,600
Speaker 1: in a position of of weighing the merits of the

416
00:21:51,640 --> 00:21:56,760
Speaker 1: deal itself. Um. You know what, what what you have

417
00:21:56,920 --> 00:22:01,400
Speaker 1: here is a US president, um who has weighted into

418
00:22:01,440 --> 00:22:07,000
Speaker 1: this incredibly complicated corporate situation that touches on very fundamental

419
00:22:07,080 --> 00:22:10,520
Speaker 1: issues for the United States around freedom of speech, the

420
00:22:10,560 --> 00:22:13,760
Speaker 1: operations of a market economy, and he seems to have

421
00:22:13,840 --> 00:22:17,639
Speaker 1: decided in a in a peremptory, in almost arbitrary way,

422
00:22:17,760 --> 00:22:20,760
Speaker 1: that a deal, some deal, whether it's with Microsoft or

423
00:22:20,800 --> 00:22:22,919
Speaker 1: some of Facebook or some of the other actors that

424
00:22:22,960 --> 00:22:29,199
Speaker 1: you've you've mentioned, must occur by September fifteen. Otherwise one

425
00:22:29,280 --> 00:22:32,840
Speaker 1: of the most popular apps in the United States right now,

426
00:22:32,920 --> 00:22:35,360
Speaker 1: an app that's been downloaded a hundred and sixty five

427
00:22:35,720 --> 00:22:41,320
Speaker 1: million times. Um an app that gen z uh folks

428
00:22:41,520 --> 00:22:44,320
Speaker 1: live by. It's gonna be it's gonna be close, it's

429
00:22:44,320 --> 00:22:48,639
Speaker 1: gonna be closed down. And needless to say, um, Chinese

430
00:22:48,720 --> 00:22:54,440
Speaker 1: media absolutely outraged by what's going on. So Andy, let's

431
00:22:54,480 --> 00:22:56,199
Speaker 1: get to the heart of the matter. I think the

432
00:22:56,240 --> 00:22:59,639
Speaker 1: heart of the matter might be h security, national security,

433
00:22:59,680 --> 00:23:03,119
Speaker 1: and is there any evidence to suggest that there that

434
00:23:03,200 --> 00:23:07,880
Speaker 1: TikTok does represent a risk to the United States. Look,

435
00:23:08,119 --> 00:23:13,639
Speaker 1: there is no direct evidence that um uh, you know,

436
00:23:13,720 --> 00:23:19,280
Speaker 1: TikTok is giving us data to the Chinese government. There

437
00:23:19,440 --> 00:23:22,679
Speaker 1: is circumstantial evidence there are sort of there is what

438
00:23:22,880 --> 00:23:26,560
Speaker 1: ifs right. So what if the Chinese government said to

439
00:23:26,760 --> 00:23:30,440
Speaker 1: to Bike Dance, which owns TikTok, you have to hand

440
00:23:30,520 --> 00:23:34,240
Speaker 1: over this data. Would would TikTok to well they the

441
00:23:34,240 --> 00:23:39,000
Speaker 1: company insists that it wouldn't. Um, it's actually highly unlikely

442
00:23:39,080 --> 00:23:41,600
Speaker 1: that any company would say no to the to the

443
00:23:41,680 --> 00:23:44,280
Speaker 1: Chinese government to such a request. So it's all it's

444
00:23:44,320 --> 00:23:47,680
Speaker 1: it's all hypothetical, I mean, and that that hardly represents

445
00:23:47,760 --> 00:23:51,440
Speaker 1: smoking gun evidence that TikTok is a national security But

446
00:23:51,720 --> 00:23:57,959
Speaker 1: having said that, there are really genuine questions, uh, and fears, um,

447
00:23:58,000 --> 00:24:01,320
Speaker 1: you know about data security and the Chinese government. I mean,

448
00:24:01,359 --> 00:24:03,639
Speaker 1: and these go back years. In two thousand and fifteen,

449
00:24:03,640 --> 00:24:06,000
Speaker 1: you remember there was a there was the Chinese state

450
00:24:06,040 --> 00:24:07,960
Speaker 1: hack has got into the office of the U S

451
00:24:07,960 --> 00:24:12,040
Speaker 1: Officer of Personnel Managers Management and installed data of four

452
00:24:12,119 --> 00:24:17,280
Speaker 1: million you know, US government federal government workers. More recently,

453
00:24:17,280 --> 00:24:19,879
Speaker 1: these accusations of state hackers have been getting into labs

454
00:24:19,920 --> 00:24:23,119
Speaker 1: and stealing trying to steal secrets on the development of

455
00:24:23,160 --> 00:24:28,000
Speaker 1: COVID nineteen vaccines. So you know, there are legitimate concerns.

456
00:24:28,000 --> 00:24:30,280
Speaker 1: The question is is this the right way to address

457
00:24:30,359 --> 00:24:33,480
Speaker 1: these concerns exactly? And also you know, can the US

458
00:24:33,560 --> 00:24:36,480
Speaker 1: take a fee for doing so? I mean that's that's

459
00:24:36,520 --> 00:24:39,040
Speaker 1: the part that you know, almost becomes a funny at

460
00:24:39,040 --> 00:24:41,480
Speaker 1: that point, and Narricolo is just telling reporters right now

461
00:24:41,480 --> 00:24:43,680
Speaker 1: that is not your TikTok deal. Fee to the United

462
00:24:43,680 --> 00:24:47,120
Speaker 1: States is a quote key stipulation. I mean, where did

463
00:24:47,119 --> 00:24:51,320
Speaker 1: that come out of? Yeah, I I don't think the

464
00:24:51,720 --> 00:24:54,159
Speaker 1: the stipulations in this. I mean this is this is

465
00:24:54,200 --> 00:24:58,639
Speaker 1: this goes back to the sort of the this whole arbitrariness. Um,

466
00:24:58,680 --> 00:25:02,320
Speaker 1: you know there are presidents, right, so some years ago, um,

467
00:25:02,840 --> 00:25:07,359
Speaker 1: the Chinese owner of grind this this you know dating app,

468
00:25:07,480 --> 00:25:10,480
Speaker 1: was forced to sell after a review by Sippius, which

469
00:25:10,480 --> 00:25:13,119
Speaker 1: of course the U. S. Treasury body that that that

470
00:25:13,240 --> 00:25:16,560
Speaker 1: screens foreign investments. But but you know that this was

471
00:25:16,680 --> 00:25:18,879
Speaker 1: there was a process there. I mean it was it

472
00:25:18,920 --> 00:25:22,720
Speaker 1: was as all as all cipious processes are. It was secretive,

473
00:25:23,000 --> 00:25:27,040
Speaker 1: but nevertheless there was a process and companies could strategize

474
00:25:27,080 --> 00:25:31,320
Speaker 1: around that process. I mean, here you have, um, you know,

475
00:25:32,240 --> 00:25:35,280
Speaker 1: essentially it seems like a decision coming straight out of

476
00:25:35,320 --> 00:25:39,199
Speaker 1: the the the West wing. The owner of the company,

477
00:25:39,280 --> 00:25:42,480
Speaker 1: John Yeming, he's been telling employees he has no idea

478
00:25:42,600 --> 00:25:44,920
Speaker 1: really what's going on? But he thinks the ultimate aim

479
00:25:44,960 --> 00:25:48,080
Speaker 1: here is to close down, um, his app and that

480
00:25:48,200 --> 00:25:51,119
Speaker 1: that's what he thinks. The end game is so andy.

481
00:25:51,119 --> 00:25:53,800
Speaker 1: I know you have your ear close to uh, what's

482
00:25:53,800 --> 00:25:56,280
Speaker 1: happening on the ground in China. What's the response there?

483
00:25:56,320 --> 00:25:59,080
Speaker 1: I know the Chinese media has had some response here,

484
00:25:59,080 --> 00:26:01,320
Speaker 1: But if you heard some other people that you trust

485
00:26:01,359 --> 00:26:04,600
Speaker 1: your sources about kind of how they view the Chinese

486
00:26:04,640 --> 00:26:07,560
Speaker 1: view of what's going on here. Yeah, I mean, well

487
00:26:07,680 --> 00:26:10,520
Speaker 1: we we we've all seen the Chinese you know, state

488
00:26:10,560 --> 00:26:13,240
Speaker 1: media reaction, China Daily going one about the smash and

489
00:26:13,840 --> 00:26:16,280
Speaker 1: grab rate and who Hi Jin, who's the editor in

490
00:26:16,359 --> 00:26:19,000
Speaker 1: chief of the Global Times, is saying this is this

491
00:26:19,080 --> 00:26:23,040
Speaker 1: is open open robbery. I mean, um, look, this is

492
00:26:23,720 --> 00:26:26,359
Speaker 1: in many ways, TikTok is the avatar of of of

493
00:26:26,480 --> 00:26:30,240
Speaker 1: Chinese tech ambitions. I mean, Chinese people are incredibly proud

494
00:26:30,359 --> 00:26:33,920
Speaker 1: of of of what it's achieved. It's the first Chinese

495
00:26:33,960 --> 00:26:37,760
Speaker 1: internet company, social media company to come into the United

496
00:26:37,760 --> 00:26:42,399
Speaker 1: States take on the huge these gigantic incumbents and when

497
00:26:42,480 --> 00:26:44,320
Speaker 1: you know, I mean this this is it is the

498
00:26:44,480 --> 00:26:48,840
Speaker 1: essence of the of the capitalist success story. And you

499
00:26:48,880 --> 00:26:52,560
Speaker 1: know in China. Essentially, the view is they're they're trying

500
00:26:52,560 --> 00:26:56,560
Speaker 1: to trying to you know, close close down UM and

501
00:26:56,720 --> 00:26:59,760
Speaker 1: and an app that UM has taken on, and just

502
00:27:00,000 --> 00:27:05,120
Speaker 1: beta American companies that you know, this is basically trade protectionism.

503
00:27:05,160 --> 00:27:09,840
Speaker 1: If this happens, you know, kind of new company keep

504
00:27:09,840 --> 00:27:13,439
Speaker 1: it as successful. Will will people just be fine with

505
00:27:13,520 --> 00:27:16,959
Speaker 1: new ownership or you know, the people that have been

506
00:27:17,000 --> 00:27:19,200
Speaker 1: on TikTok, especially the younger fans, have shown themselves to

507
00:27:19,200 --> 00:27:23,000
Speaker 1: be pretty politically active. Will they sort of boycott TikTok

508
00:27:23,080 --> 00:27:26,040
Speaker 1: if Microsoft buyser? I mean, you can't answer that, I guess,

509
00:27:26,040 --> 00:27:29,560
Speaker 1: but it's an interesting question, right, Yeah, I'm I'm not

510
00:27:29,640 --> 00:27:32,280
Speaker 1: the expert on that. I'm not even the expert on

511
00:27:32,040 --> 00:27:34,440
Speaker 1: on on TikTok. I don't I don't use it myself,

512
00:27:34,520 --> 00:27:38,040
Speaker 1: but um, you know, it seems it seems to me

513
00:27:38,240 --> 00:27:41,760
Speaker 1: that the concept is brilliant and simple. I mean, this

514
00:27:41,920 --> 00:27:46,480
Speaker 1: is sort of sharing goofy video, it's family fund UM

515
00:27:46,760 --> 00:27:50,960
Speaker 1: very much now a part of the lifestyle of a

516
00:27:51,040 --> 00:27:54,439
Speaker 1: generation of young Americans, and they're not understanding why this

517
00:27:54,520 --> 00:27:56,680
Speaker 1: app needs to be closed down either. I mean, this

518
00:27:56,760 --> 00:27:59,399
Speaker 1: is why you need to have a national debate, why

519
00:27:59,680 --> 00:28:01,760
Speaker 1: you know, this is a serious issue. How are we

520
00:28:01,800 --> 00:28:06,280
Speaker 1: going to handle in the long run, um, Chinese investments

521
00:28:06,400 --> 00:28:08,359
Speaker 1: in this area? How how are we going to deal

522
00:28:08,480 --> 00:28:13,800
Speaker 1: with with with Chinese companies that that access US data? Um?

523
00:28:13,840 --> 00:28:16,159
Speaker 1: You know, the the the the approach now which is

524
00:28:16,440 --> 00:28:19,159
Speaker 1: is close it down? Um. I mean this is what

525
00:28:19,200 --> 00:28:23,919
Speaker 1: the Chinese would do. I mean, you know, if the idea, um,

526
00:28:24,040 --> 00:28:27,560
Speaker 1: you know, is to preserve freedom of speech in the

527
00:28:27,600 --> 00:28:30,480
Speaker 1: United States, to oppose the Chinese style of doing these, well,

528
00:28:30,560 --> 00:28:34,679
Speaker 1: you're actually acting rather like the Chinese would do exactly right.

529
00:28:34,760 --> 00:28:37,720
Speaker 1: Always fascinating talking to Andy Brown. He has a great

530
00:28:37,720 --> 00:28:40,800
Speaker 1: perspective all things China. Andy Brown, editorial director for Bloomberg

531
00:28:40,800 --> 00:28:44,640
Speaker 1: New Economy, talking about that crazy news about TikTok? Is

532
00:28:44,640 --> 00:28:46,120
Speaker 1: it going to be shut down? Is it going to

533
00:28:46,200 --> 00:28:47,840
Speaker 1: be sold? If it's gonna be sold, who's going to

534
00:28:47,960 --> 00:28:50,760
Speaker 1: buy it? Does President Trump and the government get an

535
00:28:50,760 --> 00:28:53,520
Speaker 1: investment banking fee? All kinds of things we are following

536
00:28:53,640 --> 00:28:55,440
Speaker 1: right now. We'll have that more for you coming up.

537
00:28:56,840 --> 00:28:59,680
Speaker 1: Thanks for listening to Bloomberg Markets podcast. You can sub

538
00:28:59,680 --> 00:29:02,840
Speaker 1: school ribe and listen to interviews at Apple Podcasts or

539
00:29:02,880 --> 00:29:06,240
Speaker 1: whatever a podcast platform you prefer. I'm Bonnie Quinn, I'm

540
00:29:06,280 --> 00:29:08,880
Speaker 1: on Twitter at Bonnie Quinn, and I'm Paul Sweeney. I'm

541
00:29:08,880 --> 00:29:11,520
Speaker 1: on Twitter at pt Sweeney. Before the podcast, you can

542
00:29:11,560 --> 00:29:13,800
Speaker 1: always catch us worldwide at Bloomberg Radio