1
00:00:01,360 --> 00:00:05,680
Speaker 1: This is Bloomberg Business Wait inside from the reporters and

2
00:00:05,880 --> 00:00:09,440
Speaker 1: editors who bring you America's most trusted business magazine, plus

3
00:00:09,520 --> 00:00:13,680
Speaker 1: global business finance and tech news. The Bloomberg Business Week

4
00:00:13,720 --> 00:00:19,000
Speaker 1: podcast with Carol Messer and Tim Stenebeck from Bloomberg Radio.

5
00:00:20,320 --> 00:00:22,640
Speaker 2: Among those stops on the decline today two point six

6
00:00:22,680 --> 00:00:26,680
Speaker 2: percent lower. In fact, is Walmart's staying at its worst levels.

7
00:00:26,880 --> 00:00:30,600
Speaker 2: That despite the world's largest retailer lifting its annual profit

8
00:00:30,640 --> 00:00:34,400
Speaker 2: outlook again. But you know what else was with it? Tim,

9
00:00:34,560 --> 00:00:35,720
Speaker 2: a bit of a cautious tone.

10
00:00:35,840 --> 00:00:37,839
Speaker 3: Yeah, it's always good to look at the commentary, and

11
00:00:37,880 --> 00:00:40,960
Speaker 3: perhaps this is why the stock is lower. The cautious

12
00:00:40,960 --> 00:00:43,240
Speaker 3: tone on consumers in the US economy. Walmart said that

13
00:00:43,520 --> 00:00:46,000
Speaker 3: the rising borrowing costs, Okay, we know that those are

14
00:00:46,080 --> 00:00:48,880
Speaker 3: hitting consumers. Look at what's happening to you know, credit

15
00:00:48,920 --> 00:00:50,800
Speaker 3: card loans, people having to pay off those, and then

16
00:00:50,840 --> 00:00:53,159
Speaker 3: of course mortgage rates as well, and the resumption of

17
00:00:53,159 --> 00:00:56,160
Speaker 3: student loan repayments will add to the strain on US

18
00:00:56,160 --> 00:00:58,840
Speaker 3: household budgets in the coming months. We've got a great

19
00:00:58,840 --> 00:01:00,640
Speaker 3: guest with us this afternoon. It is with more on

20
00:01:00,680 --> 00:01:03,200
Speaker 3: the results outlook and on the company that is the

21
00:01:03,200 --> 00:01:06,560
Speaker 3: world's largest retailer. We got Bloomberg Intelligence senior retail staples

22
00:01:06,560 --> 00:01:09,920
Speaker 3: and packaged food analysts Jen bartashis. She is joining us

23
00:01:10,040 --> 00:01:13,080
Speaker 3: from New Jersey this afternoon. So why are shares of

24
00:01:13,080 --> 00:01:13,680
Speaker 3: Walmart lower?

25
00:01:13,760 --> 00:01:16,440
Speaker 4: Jen? Well, I think you nailed it when you were

26
00:01:16,440 --> 00:01:18,600
Speaker 4: talking about this in the intro, and it's really just

27
00:01:18,680 --> 00:01:21,800
Speaker 4: that conservative outlook. And you have to remember, for retail,

28
00:01:22,000 --> 00:01:25,720
Speaker 4: back to school and holiday are the most important seasons

29
00:01:25,720 --> 00:01:29,600
Speaker 4: of the year. So if Walmart's striking a cautious tone

30
00:01:29,760 --> 00:01:33,440
Speaker 4: headed into those key seasons, that's probably what's pulling the

31
00:01:33,440 --> 00:01:34,920
Speaker 4: stock down a little bit this afternoon.

32
00:01:34,959 --> 00:01:37,480
Speaker 2: It's like one of those like, uh, what was the

33
00:01:37,480 --> 00:01:39,199
Speaker 2: cautious Was the cautionary?

34
00:01:39,360 --> 00:01:41,880
Speaker 3: Was the caution with the actual outlook when it came

35
00:01:41,920 --> 00:01:44,800
Speaker 3: to numbers, or was it the commentary that we repeated

36
00:01:44,800 --> 00:01:47,600
Speaker 3: there about student loan repayments and borrowing costs.

37
00:01:48,280 --> 00:01:50,720
Speaker 4: It's more about the commentary about the state of the consumer.

38
00:01:51,160 --> 00:01:55,040
Speaker 4: Because Walmart's numbers were great, and you know, the appeal

39
00:01:55,120 --> 00:01:58,400
Speaker 4: of their value proposition is continuing to grow. So when

40
00:01:58,440 --> 00:02:01,120
Speaker 4: you listen to what management's talking about their gaining share

41
00:02:01,240 --> 00:02:05,040
Speaker 4: in some critical areas like grocery, and you know, but

42
00:02:05,360 --> 00:02:08,480
Speaker 4: it's that cautious tone. And the problem is that grocery

43
00:02:08,480 --> 00:02:10,840
Speaker 4: is a great business, but it's not a high margin business.

44
00:02:10,960 --> 00:02:13,760
Speaker 4: And so for Walmart to really be successful, they want

45
00:02:13,800 --> 00:02:16,560
Speaker 4: to incorporate some of more of that general merchandise sales.

46
00:02:16,600 --> 00:02:18,160
Speaker 4: And those are the things that are important to back

47
00:02:18,200 --> 00:02:19,120
Speaker 4: to school and holidays.

48
00:02:19,160 --> 00:02:20,760
Speaker 2: And for those who missed, and I bet Jen heard

49
00:02:20,760 --> 00:02:24,720
Speaker 2: at first hand the CEO Doug McMillan of Walmart said

50
00:02:24,760 --> 00:02:27,799
Speaker 2: on that conference call with investors and analysts, the good stuff, jobs, wages,

51
00:02:27,800 --> 00:02:30,720
Speaker 2: and pockets of a disinflation are helping our customers. That's

52
00:02:30,760 --> 00:02:33,840
Speaker 2: the good stuff. The bad stuff, but rising energy prices,

53
00:02:33,880 --> 00:02:37,160
Speaker 2: resuming student loan payments, higher borrowing costs and tightening lending

54
00:02:37,200 --> 00:02:40,920
Speaker 2: standards and a draw down in excess savings mean that

55
00:02:40,960 --> 00:02:43,280
Speaker 2: household budgets are still under pressure. I mean, Jen, that's

56
00:02:43,320 --> 00:02:47,160
Speaker 2: a big laundry list. And especially for customers that typically

57
00:02:47,160 --> 00:02:49,639
Speaker 2: shop at Walmart, I mean, you're talking about a lot

58
00:02:49,639 --> 00:02:53,400
Speaker 2: of Americans. This is where they shop and that's a big,

59
00:02:53,639 --> 00:02:54,880
Speaker 2: you know, stress list, if you.

60
00:02:54,880 --> 00:02:58,760
Speaker 4: Will, It really is. And what we're seeing and what

61
00:02:58,880 --> 00:03:01,400
Speaker 4: the patterns so far the year has been is that

62
00:03:01,480 --> 00:03:04,040
Speaker 4: people do want to spend, but they're being super selective

63
00:03:04,080 --> 00:03:06,160
Speaker 4: on where they're doing it, which means that they're saving

64
00:03:06,280 --> 00:03:09,920
Speaker 4: everywhere else that they can. And so where you know,

65
00:03:10,040 --> 00:03:12,480
Speaker 4: you do see that people are still going on trips,

66
00:03:12,520 --> 00:03:14,920
Speaker 4: they're still going on vacation, they're still going out to

67
00:03:14,919 --> 00:03:18,600
Speaker 4: dinner for special occasions. That's where they're choosing to do

68
00:03:18,639 --> 00:03:20,640
Speaker 4: that little bit of splurge. But they're making up for

69
00:03:20,720 --> 00:03:24,360
Speaker 4: that splurge by saving and saving, saving, cutting down on

70
00:03:24,440 --> 00:03:28,120
Speaker 4: other expenditures. And that's part of what the behavior that

71
00:03:28,120 --> 00:03:29,600
Speaker 4: we're seeing happen at Walmart as well.

72
00:03:30,040 --> 00:03:32,679
Speaker 2: Go ahead, Carol, Well, Tim and I constantly talk about, well,

73
00:03:32,760 --> 00:03:35,520
Speaker 2: you know Amazon, I need something, go to Amazon. I

74
00:03:35,560 --> 00:03:37,120
Speaker 2: just did a big thing last night because I know

75
00:03:37,120 --> 00:03:38,800
Speaker 2: it's easy to return and stuff.

76
00:03:39,240 --> 00:03:41,080
Speaker 3: But tell us what, Well, I was going to say,

77
00:03:41,120 --> 00:03:43,160
Speaker 3: I heard Jen, I heard you this morning on surveillance

78
00:03:43,160 --> 00:03:44,800
Speaker 3: and you were like speaking to me. I think you

79
00:03:44,840 --> 00:03:47,320
Speaker 3: said everybody goes to Walmart for something? Is that what

80
00:03:47,360 --> 00:03:47,760
Speaker 3: you said?

81
00:03:48,480 --> 00:03:48,840
Speaker 4: It is?

82
00:03:49,040 --> 00:03:49,320
Speaker 2: It is?

83
00:03:49,360 --> 00:03:51,440
Speaker 3: And it's true, so true, Carol, because I do the

84
00:03:51,480 --> 00:03:55,800
Speaker 3: vast majority of our shopping at Amazon. But because I

85
00:03:55,840 --> 00:03:58,360
Speaker 3: have American Express, I'm able to join like the Walmart

86
00:03:58,880 --> 00:04:01,360
Speaker 3: Amazon Prime thing whatever it's called for free, like you

87
00:04:01,360 --> 00:04:04,040
Speaker 3: can join that for free, and if we need something

88
00:04:04,040 --> 00:04:06,560
Speaker 3: that's not available on Amazon, we do it at Walmart.

89
00:04:06,600 --> 00:04:07,640
Speaker 3: And it's like the same thing as Prime.

90
00:04:07,800 --> 00:04:09,920
Speaker 2: So Jen talked to us about Walmart Plus, how big

91
00:04:10,000 --> 00:04:11,600
Speaker 2: a deal is it and how much of a growth

92
00:04:11,600 --> 00:04:15,040
Speaker 2: engine it potentially is or will be for Walmart.

93
00:04:15,840 --> 00:04:18,600
Speaker 4: So Walmart Plus started about two years ago, so it's

94
00:04:18,600 --> 00:04:20,880
Speaker 4: still a pretty new program, but it does compete with

95
00:04:20,920 --> 00:04:23,640
Speaker 4: Amazon Prime, and I think it is going to really

96
00:04:23,680 --> 00:04:26,800
Speaker 4: become a very powerful driver for both sales and profit

97
00:04:26,839 --> 00:04:29,360
Speaker 4: for the company over the next five years. We actually

98
00:04:29,360 --> 00:04:32,880
Speaker 4: published a research piece on this earlier this week where

99
00:04:32,920 --> 00:04:34,719
Speaker 4: we think over the next five years it can add

100
00:04:34,720 --> 00:04:37,520
Speaker 4: one hundred to one hundred and sixty billion dollars in

101
00:04:37,600 --> 00:04:41,880
Speaker 4: revenue for Walmart. And that's just on that's just on

102
00:04:41,920 --> 00:04:46,280
Speaker 4: that on that program, and if you put it in perspective,

103
00:04:46,360 --> 00:04:50,479
Speaker 4: that's the size of target, right. So it has the

104
00:04:50,520 --> 00:04:54,400
Speaker 4: potential to really be a differentiator for Walmart. And part

105
00:04:54,400 --> 00:04:57,200
Speaker 4: of the reason for that is that it appeals exactly

106
00:04:57,240 --> 00:05:00,960
Speaker 4: to what people want, which is convenience and access to

107
00:05:01,000 --> 00:05:04,200
Speaker 4: low prices. And so the fact that you can have

108
00:05:04,279 --> 00:05:07,000
Speaker 4: deliveries of groceries or things that are in store to

109
00:05:07,040 --> 00:05:10,039
Speaker 4: your house on the same day with no fee associated

110
00:05:10,080 --> 00:05:12,919
Speaker 4: with it is a pretty powerful engine for them to

111
00:05:12,920 --> 00:05:13,479
Speaker 4: build off of.

112
00:05:14,160 --> 00:05:17,000
Speaker 3: Can they do it in a way that gives them margins?

113
00:05:18,520 --> 00:05:21,359
Speaker 4: Yeah? And you know, if you think about the way

114
00:05:21,600 --> 00:05:25,080
Speaker 4: a retailer makes the most margin, it's when somebody comes

115
00:05:25,080 --> 00:05:27,039
Speaker 4: into the store, buy something and takes it out to

116
00:05:27,040 --> 00:05:30,080
Speaker 4: their current takes it home themselves. The next most profitable

117
00:05:30,120 --> 00:05:32,480
Speaker 4: thing is when somebody comes and they pick it up

118
00:05:32,480 --> 00:05:35,159
Speaker 4: at the store curbside and then take it home. And

119
00:05:35,200 --> 00:05:37,960
Speaker 4: then the third is one it's shipped to your house. Right,

120
00:05:38,040 --> 00:05:41,360
Speaker 4: So what Walmart has done is that part of the

121
00:05:41,480 --> 00:05:45,320
Speaker 4: value proposition is that curbside pickup and so that business

122
00:05:45,320 --> 00:05:48,560
Speaker 4: has been growing very quickly for Walmart and is getting

123
00:05:48,560 --> 00:05:52,560
Speaker 4: a lot of adoption. So that's actually a pretty a

124
00:05:52,600 --> 00:05:55,800
Speaker 4: pretty good way to engage digital customers without having a

125
00:05:55,839 --> 00:05:58,400
Speaker 4: margin drag that you would associate with things that have

126
00:05:58,440 --> 00:05:59,440
Speaker 4: to be shipped to your home.

127
00:05:59,640 --> 00:06:02,880
Speaker 3: We live so Caroly and I live in Carroll's in

128
00:06:02,880 --> 00:06:06,160
Speaker 3: New Jersey. I'm in Brooklyn, which just outside, so you're

129
00:06:06,160 --> 00:06:07,599
Speaker 3: in the sun. What I'm saying is you're in the city,

130
00:06:07,640 --> 00:06:10,360
Speaker 3: You're not like driving to these places to actually pick

131
00:06:10,360 --> 00:06:13,320
Speaker 3: stuff up. Now, when I was on printal leave while

132
00:06:13,320 --> 00:06:16,040
Speaker 3: we were visiting my parents in California, and my sister

133
00:06:16,200 --> 00:06:18,680
Speaker 3: was like ordering stuff from Target and we'd go, we'd

134
00:06:18,760 --> 00:06:20,800
Speaker 3: drive there, they would I mean, anyone listening who has

135
00:06:20,839 --> 00:06:22,560
Speaker 3: a car is actually laughing at me the fact that

136
00:06:22,560 --> 00:06:24,720
Speaker 3: I'm like talking about this. But it's such a we're

137
00:06:24,720 --> 00:06:26,960
Speaker 3: such strange animals in New York City because we don't

138
00:06:27,000 --> 00:06:29,400
Speaker 3: take advantage of these programs. But I will tell you

139
00:06:29,560 --> 00:06:32,320
Speaker 3: it is so easy to order something on one of

140
00:06:32,320 --> 00:06:34,839
Speaker 3: these apps and then drive up to the curb and

141
00:06:34,880 --> 00:06:36,400
Speaker 3: they literally bring it out to your car.

142
00:06:36,560 --> 00:06:38,760
Speaker 2: I will tell you my daughter who goes to school

143
00:06:39,120 --> 00:06:43,120
Speaker 2: six hours outside of New York City in a southern state,

144
00:06:43,440 --> 00:06:46,559
Speaker 2: it's much more like residential and so and so forth.

145
00:06:46,760 --> 00:06:48,600
Speaker 2: I remember one of the years we were driving to

146
00:06:48,640 --> 00:06:51,440
Speaker 2: her school and Jen along the way, we kept ordering

147
00:06:51,480 --> 00:06:53,039
Speaker 2: from Target, like she's like, oh, I need this, So

148
00:06:53,080 --> 00:06:54,520
Speaker 2: we'll be like, okay, well in an hour, we'll be

149
00:06:54,560 --> 00:06:57,720
Speaker 2: at this Target and we would just pick up curbside,

150
00:06:57,800 --> 00:06:59,000
Speaker 2: and it was incredible.

151
00:07:00,320 --> 00:07:04,200
Speaker 4: And it's that convenience that's really driving the success of

152
00:07:04,279 --> 00:07:08,400
Speaker 4: these retailers because they're not only offering value, but it's convenience.

153
00:07:08,520 --> 00:07:11,880
Speaker 4: And this is where for Walmart's perspective. You know, Amazon

154
00:07:12,000 --> 00:07:14,880
Speaker 4: is a behemoth and they're very good at what they do.

155
00:07:15,480 --> 00:07:18,600
Speaker 4: But Walmart has been building out the size of its marketplace.

156
00:07:18,640 --> 00:07:22,280
Speaker 4: The number of sellers on its marketplace is becoming more competitive,

157
00:07:22,440 --> 00:07:24,840
Speaker 4: and that powerful engine of being able to pick things

158
00:07:24,880 --> 00:07:27,280
Speaker 4: up at the store at your convenience or have it

159
00:07:27,280 --> 00:07:31,119
Speaker 4: delivered to your home same day is a pretty good

160
00:07:31,160 --> 00:07:32,400
Speaker 4: strategy that they're following.

161
00:07:32,480 --> 00:07:35,080
Speaker 3: What's that stat Jen with the X percentage of the

162
00:07:35,240 --> 00:07:37,640
Speaker 3: US population lives within you know, just a couple miles

163
00:07:37,680 --> 00:07:38,680
Speaker 3: of a Walmart.

164
00:07:38,960 --> 00:07:42,080
Speaker 4: It's like over eighty five percent of the US population

165
00:07:42,200 --> 00:07:44,560
Speaker 4: lives within a ten mile radius of a Walmart.

166
00:07:44,600 --> 00:07:46,560
Speaker 3: I believe is what incredible the company has said.

167
00:07:46,640 --> 00:07:48,720
Speaker 2: All right, so you were sitting down with Doug McMillan

168
00:07:48,800 --> 00:07:51,000
Speaker 2: and you wanted to I don't know, you had two

169
00:07:51,080 --> 00:07:53,040
Speaker 2: minutes with him. What would be the question you would

170
00:07:53,080 --> 00:07:54,240
Speaker 2: ask him?

171
00:07:54,880 --> 00:07:57,160
Speaker 4: Well, I would ask him about things for the company

172
00:07:57,200 --> 00:07:59,320
Speaker 4: hasn't really given a lot of disclosure. I would ask

173
00:07:59,360 --> 00:08:02,000
Speaker 4: him about the size of their membership for Walmart. Plus,

174
00:08:02,120 --> 00:08:04,560
Speaker 4: I would ask him about the current profitability level of

175
00:08:04,640 --> 00:08:07,520
Speaker 4: their of their online business. These are things that we

176
00:08:07,600 --> 00:08:10,400
Speaker 4: know they're building, things that we know that they're working on,

177
00:08:11,120 --> 00:08:13,520
Speaker 4: but we don't have a lot of detail behind it

178
00:08:13,600 --> 00:08:16,360
Speaker 4: yet to understand how profitable it is or will be

179
00:08:16,440 --> 00:08:17,600
Speaker 4: for the company long term.

180
00:08:17,680 --> 00:08:19,440
Speaker 2: Ye, and it's matth I'm like, look at your research

181
00:08:19,520 --> 00:08:23,080
Speaker 2: to about you know, advertising revenue at Walmart. Like it's

182
00:08:23,280 --> 00:08:26,880
Speaker 2: just this company that the broad reach by just evolving.

183
00:08:27,080 --> 00:08:29,560
Speaker 3: Jen, you're the best you've been up since, like you know,

184
00:08:29,720 --> 00:08:31,920
Speaker 3: the crack of dawn covering this stuff. We love it

185
00:08:31,920 --> 00:08:34,079
Speaker 3: when you join us and we get to talk. Also,

186
00:08:34,200 --> 00:08:35,839
Speaker 3: just be honest, do Carol and I just sound like

187
00:08:35,840 --> 00:08:38,000
Speaker 3: complete luttites the fact that we just found out about

188
00:08:38,000 --> 00:08:39,400
Speaker 3: this curb side pickup.

189
00:08:39,040 --> 00:08:41,119
Speaker 2: At a little while.

190
00:08:41,120 --> 00:08:41,800
Speaker 4: Not in the least.

191
00:08:41,880 --> 00:08:44,599
Speaker 2: Okay, goody, Jen, And no worry, it's just back to

192
00:08:44,640 --> 00:08:47,480
Speaker 2: school shopping is next and oh yeah the holiday shopping season,

193
00:08:47,559 --> 00:08:51,600
Speaker 2: so you'll get plenty of rest or not listen, we do.

194
00:08:52,040 --> 00:08:53,640
Speaker 4: I think it's going to be a busy second half

195
00:08:53,679 --> 00:08:54,880
Speaker 4: of the year, let's put it that way.

196
00:08:54,960 --> 00:08:56,719
Speaker 2: Well, we'll be checking in with you, no doubt about it.

197
00:08:56,760 --> 00:08:59,760
Speaker 2: Jen Bartasha's she's Bloomberg Intelligence Senior Retail Stables and package

198
00:08:59,760 --> 00:09:03,320
Speaker 2: food analysts really a must read among our Bloomberg Intelligence team,

199
00:09:03,320 --> 00:09:05,880
Speaker 2: and so appreciate her finding some time Walmart chairs, though

200
00:09:06,000 --> 00:09:07,880
Speaker 2: still under pressure. In today's straight.

201
00:09:08,080 --> 00:09:11,640
Speaker 1: You're listening to the Bloomberg Business Week podcast. Catch us

202
00:09:11,679 --> 00:09:15,000
Speaker 1: Live weekday afternoons from three to six Eastern Listen on

203
00:09:15,080 --> 00:09:19,120
Speaker 1: Bloomberg dot com, the iHeartRadio app, and the Bloomberg Business App,

204
00:09:19,400 --> 00:09:21,720
Speaker 1: or watch us Live on YouTube.

205
00:09:22,360 --> 00:09:24,079
Speaker 2: I don't know if he would pall, but Bill Gates

206
00:09:24,080 --> 00:09:29,040
Speaker 2: back in March talked about seeing an artificial intelligence system

207
00:09:29,080 --> 00:09:33,160
Speaker 2: ac tricky medical examination and how it represented the most

208
00:09:33,240 --> 00:09:37,080
Speaker 2: important advance in technology since the graphical user interface. The

209
00:09:37,120 --> 00:09:40,840
Speaker 2: guy uh. He said that software that, of course lets

210
00:09:40,880 --> 00:09:43,240
Speaker 2: people to more easily interact with computers, and in the

211
00:09:43,320 --> 00:09:47,120
Speaker 2: nineteen eighty set the standard for modern operating systems, including

212
00:09:47,160 --> 00:09:48,120
Speaker 2: Microsoft's Windows.

213
00:09:48,200 --> 00:09:49,839
Speaker 3: That's the gooey Carol, that gooey gewey.

214
00:09:49,920 --> 00:09:51,719
Speaker 2: I said that, I know, gueye wooey.

215
00:09:51,600 --> 00:09:54,679
Speaker 3: Gooey wooey, but keep it in mind. Incredible statement.

216
00:09:54,920 --> 00:09:57,400
Speaker 2: Well, it is right you think about how game changing

217
00:09:57,440 --> 00:10:00,319
Speaker 2: that was. Keep in mind, Microsoft has an app. It's

218
00:10:00,360 --> 00:10:03,240
Speaker 2: called the DAX app. It's powered by AI. It's from

219
00:10:03,240 --> 00:10:07,040
Speaker 2: the company's Nuanced division, and it transcribes doctors and patients comments,

220
00:10:07,280 --> 00:10:10,360
Speaker 2: then creates a clinical physician summary formatted for an electronic

221
00:10:10,440 --> 00:10:13,560
Speaker 2: health record. So AI in helping out doctors.

222
00:10:13,920 --> 00:10:16,760
Speaker 3: It's an episode actually of the Bloomer Originals video series

223
00:10:16,800 --> 00:10:19,120
Speaker 3: AI I RL earlier this year. It can be found

224
00:10:19,280 --> 00:10:22,240
Speaker 3: on our streaming surface service. With more on the topic

225
00:10:22,360 --> 00:10:24,960
Speaker 3: and curious if AI is yet penetrating his world, We

226
00:10:25,000 --> 00:10:27,760
Speaker 3: got back with us doctor Ian Luspader. He's clinical professor

227
00:10:27,760 --> 00:10:30,600
Speaker 3: of Medicine at NYU Lango and Medical Center. He joins

228
00:10:30,679 --> 00:10:33,120
Speaker 3: us on Zoom from New York City, Doctor Lusbater, How

229
00:10:33,160 --> 00:10:33,560
Speaker 3: are you.

230
00:10:35,160 --> 00:10:39,120
Speaker 5: Great to see you guys, Carol and Tim. Tim, congratulations

231
00:10:39,160 --> 00:10:41,440
Speaker 5: on the new born. Hope ball is going well.

232
00:10:41,520 --> 00:10:43,760
Speaker 3: Yeah, all is going well. Thank you very much. Hey,

233
00:10:43,840 --> 00:10:46,480
Speaker 3: how is that AI hitting your world? I mean, the

234
00:10:46,520 --> 00:10:48,480
Speaker 3: medical community is one where I got to tell you

235
00:10:49,080 --> 00:10:50,920
Speaker 3: sometimes I'm still required to send faxes.

236
00:10:52,960 --> 00:10:53,160
Speaker 2: Right.

237
00:10:53,720 --> 00:10:57,840
Speaker 5: Well, well, we're not there yet, but AI has tremendous potential,

238
00:10:58,360 --> 00:11:02,839
Speaker 5: and AI functions in many ways like a person or

239
00:11:02,880 --> 00:11:07,360
Speaker 5: a doctorate takes a lot of data points, analyzes it

240
00:11:07,600 --> 00:11:12,320
Speaker 5: and process it and comes up with some conclusions. We're

241
00:11:12,400 --> 00:11:16,080
Speaker 5: still not quite there integrating in every way. But the

242
00:11:16,160 --> 00:11:22,480
Speaker 5: potential is enormous, from scheduling appointments, to pre interviewing patients

243
00:11:22,480 --> 00:11:26,800
Speaker 5: to find out what they're coming in for, assisting with

244
00:11:26,880 --> 00:11:28,840
Speaker 5: a diagnosis in the exam room.

245
00:11:28,880 --> 00:11:31,640
Speaker 3: Well, talk about that part, assisting with the diagnosis. That's

246
00:11:31,640 --> 00:11:34,240
Speaker 3: that's really interesting to me because what you guys as

247
00:11:34,280 --> 00:11:36,120
Speaker 3: doctors try to do. And look, I didn't go to

248
00:11:36,160 --> 00:11:39,520
Speaker 3: medical school, but I've been to plenty of doctors. You

249
00:11:39,640 --> 00:11:43,839
Speaker 3: ask so many questions about symptoms, and each time somebody

250
00:11:43,840 --> 00:11:47,960
Speaker 3: answers a question about that symptom, you know, there's sort

251
00:11:47,960 --> 00:11:50,600
Speaker 3: of this calculation that goes on inside a doctor's head. Okay,

252
00:11:50,640 --> 00:11:51,840
Speaker 3: it could be this, or it could be this, and

253
00:11:51,880 --> 00:11:54,680
Speaker 3: you keep investigating. So how do you use how do

254
00:11:54,720 --> 00:11:58,560
Speaker 3: you use you know, first the internet or databases right now?

255
00:11:58,800 --> 00:12:00,559
Speaker 3: And how do you think that can be aided AI.

256
00:12:02,720 --> 00:12:06,079
Speaker 5: So we, in a very similar way, extract information. A

257
00:12:06,160 --> 00:12:09,000
Speaker 5: patient comes in with a complaint or an issue. We

258
00:12:09,080 --> 00:12:12,839
Speaker 5: review a variety of questions that helps us hone in

259
00:12:13,120 --> 00:12:17,400
Speaker 5: eliminate certain things and gives us the most likely diagnosis

260
00:12:17,400 --> 00:12:20,560
Speaker 5: based on history, and then we get additional information from

261
00:12:20,640 --> 00:12:24,080
Speaker 5: tests or labs to try and make a specific diagnosis.

262
00:12:24,080 --> 00:12:27,800
Speaker 5: And then a specific treatment. AI can be very helpful

263
00:12:27,840 --> 00:12:31,360
Speaker 5: because it can also process a lot of questions, either

264
00:12:32,200 --> 00:12:38,600
Speaker 5: interrogate the patient or review lab data and kind of

265
00:12:38,640 --> 00:12:44,120
Speaker 5: synthesize the information. And we see this most helpfully for example,

266
00:12:44,160 --> 00:12:47,560
Speaker 5: with X rays. If you're comparing a series of cat scans,

267
00:12:47,880 --> 00:12:52,200
Speaker 5: tremendous amount of data and subtle abnormalities can be picked up.

268
00:12:52,720 --> 00:12:57,400
Speaker 5: Same for lab data we're using it for example with colonoscopy,

269
00:12:57,800 --> 00:13:01,240
Speaker 5: where AI picks up subtle abnormality such as a polyp.

270
00:13:01,640 --> 00:13:04,880
Speaker 5: Now it can't really tell if that is a polyp

271
00:13:05,040 --> 00:13:09,160
Speaker 5: or necessarily maybe a clump of mucus. The doctor still

272
00:13:09,200 --> 00:13:12,360
Speaker 5: has to irrigate it away and say, oh, yes, you're right,

273
00:13:12,600 --> 00:13:16,040
Speaker 5: and is pursuing it. So AI can be very helpful

274
00:13:16,080 --> 00:13:19,839
Speaker 5: to say, hey, have you thought about this? Is this relevant?

275
00:13:20,720 --> 00:13:23,640
Speaker 5: Do we need to explore it further? And whether it's

276
00:13:23,679 --> 00:13:27,240
Speaker 5: developing new drugs, whether it's processing a lot of lab

277
00:13:27,360 --> 00:13:30,240
Speaker 5: data to say, hey, it looks like this blood count

278
00:13:30,280 --> 00:13:33,400
Speaker 5: has slowly been trickling down. Do we have to worry

279
00:13:33,400 --> 00:13:37,400
Speaker 5: about bleeding somewhere? Could there be a cancer? So it's

280
00:13:37,480 --> 00:13:41,520
Speaker 5: not really integrated yet into the medical record system. It's

281
00:13:41,600 --> 00:13:45,000
Speaker 5: not in the office helping us. Yet it's really more

282
00:13:45,400 --> 00:13:49,920
Speaker 5: scheduling appointments. It's more helping with cat scans or diagnostic tests,

283
00:13:50,160 --> 00:13:53,960
Speaker 5: even robotic surgery. But eventually, I think it will come

284
00:13:53,960 --> 00:14:00,000
Speaker 5: into the office to really help extract important questions from patients,

285
00:14:00,000 --> 00:14:03,360
Speaker 5: tact important data, and help us make a diagnosis and

286
00:14:03,440 --> 00:14:04,240
Speaker 5: a treatment plan.

287
00:14:04,760 --> 00:14:06,520
Speaker 2: Ian how long does it take for it to come

288
00:14:06,520 --> 00:14:08,640
Speaker 2: in you think in a really significant way.

289
00:14:11,320 --> 00:14:15,040
Speaker 5: You know, a lot of that is based on doing trials,

290
00:14:15,080 --> 00:14:17,360
Speaker 5: A lot of it is based on the developer of

291
00:14:17,440 --> 00:14:22,360
Speaker 5: the electronic medical record. We spend probably fifty percent of

292
00:14:22,400 --> 00:14:27,760
Speaker 5: our time documenting, so if we can expand our patient

293
00:14:27,840 --> 00:14:33,080
Speaker 5: contact time and thinking time and reducing our documentation time,

294
00:14:33,520 --> 00:14:38,040
Speaker 5: that will definitely improve our ability to get information. I

295
00:14:38,080 --> 00:14:40,280
Speaker 5: think within the next few years it's going to be

296
00:14:40,320 --> 00:14:43,600
Speaker 5: in the office, but it's not certainly next month.

297
00:14:44,160 --> 00:14:47,960
Speaker 3: Do you see this from the incumbent players in medicine

298
00:14:48,000 --> 00:14:49,880
Speaker 3: right now? And I think of one, I mean Epic

299
00:14:49,960 --> 00:14:53,640
Speaker 3: Medical Systems is like, if you go anywhere EPIC owns,

300
00:14:53,800 --> 00:14:56,840
Speaker 3: the room is are they going to do it?

301
00:14:56,920 --> 00:14:57,240
Speaker 6: Epic?

302
00:14:57,240 --> 00:14:59,560
Speaker 3: It is the behemor it's the behemor well.

303
00:14:59,400 --> 00:15:03,000
Speaker 5: They're talking about it, yep, it's the behemoth. All the

304
00:15:03,040 --> 00:15:06,000
Speaker 5: major medical centers. They've been able in a few short

305
00:15:06,040 --> 00:15:09,520
Speaker 5: years to really dominate it, and basically everyone has to

306
00:15:09,640 --> 00:15:11,920
Speaker 5: join now because all the big players are on it

307
00:15:12,240 --> 00:15:15,520
Speaker 5: and it is actually a pretty good medical record system.

308
00:15:15,960 --> 00:15:18,760
Speaker 5: They are working on it and we're working with them

309
00:15:19,280 --> 00:15:24,120
Speaker 5: to develop different algorithms. But it is not yet interviewing

310
00:15:24,160 --> 00:15:28,160
Speaker 5: the patient. It is not yet documenting helping us document.

311
00:15:28,240 --> 00:15:30,800
Speaker 5: We still have to dictate the charter or type the note,

312
00:15:31,200 --> 00:15:32,840
Speaker 5: and it is not at the end of the day

313
00:15:32,880 --> 00:15:36,680
Speaker 5: helping us process the information and say, hey, you missed

314
00:15:36,720 --> 00:15:40,440
Speaker 5: this low blood count or high liver enzyme. Maybe we

315
00:15:40,480 --> 00:15:43,120
Speaker 5: should look into it. I think that's going to happen,

316
00:15:44,000 --> 00:15:46,240
Speaker 5: but I don't think it's going to happen in the

317
00:15:46,280 --> 00:15:46,800
Speaker 5: next year.

318
00:15:47,000 --> 00:15:49,960
Speaker 2: Do we need one system though, for this to really work.

319
00:15:52,240 --> 00:15:55,520
Speaker 5: Yes, we've talked about this before for many years, having

320
00:15:55,560 --> 00:15:58,760
Speaker 5: a national system where if someone goes to a hospital

321
00:15:58,800 --> 00:16:01,920
Speaker 5: across the street or us the country, we have all

322
00:16:01,960 --> 00:16:06,120
Speaker 5: the information integrated so we can do less tests and

323
00:16:06,320 --> 00:16:09,200
Speaker 5: have to be less redundant in what we do. But

324
00:16:09,280 --> 00:16:15,200
Speaker 5: that is a problem with administration and hip and privacy violations.

325
00:16:15,480 --> 00:16:17,760
Speaker 5: We really would need the government to say, look, we're

326
00:16:17,800 --> 00:16:22,760
Speaker 5: mandating that everyone have one system that puts everyone on

327
00:16:22,760 --> 00:16:25,960
Speaker 5: one platform. So that's also a few years away. But

328
00:16:26,040 --> 00:16:28,160
Speaker 5: we should have done that years ago. It's really been

329
00:16:28,200 --> 00:16:30,840
Speaker 5: a waste of time having all these separate systems that

330
00:16:30,920 --> 00:16:32,320
Speaker 5: don't really talk to each other.

331
00:16:32,760 --> 00:16:35,120
Speaker 3: So, doctor Lespater, how does this end up in the

332
00:16:35,520 --> 00:16:36,800
Speaker 3: operating room in the future.

333
00:16:38,600 --> 00:16:41,600
Speaker 5: Well, we're already doing it for robotic surgery, which is

334
00:16:41,600 --> 00:16:45,840
Speaker 5: a tremendous advance small incisions. The robots really help us

335
00:16:45,840 --> 00:16:50,480
Speaker 5: do very technically delicate procedures. There are better outcomes. It's

336
00:16:50,520 --> 00:16:54,400
Speaker 5: helping with drug development, it's helping with analyzing studies, so

337
00:16:54,560 --> 00:16:56,280
Speaker 5: it can take all our information.

338
00:16:56,680 --> 00:17:00,680
Speaker 3: So Carol, go back to what you were saying. When

339
00:17:00,720 --> 00:17:03,320
Speaker 3: it comes to medical imaging, right, you know, it's only

340
00:17:03,320 --> 00:17:05,600
Speaker 3: as perfect as the person reading the image.

341
00:17:05,680 --> 00:17:07,879
Speaker 2: Right, What if somebody has a bad day or is tired,

342
00:17:08,600 --> 00:17:10,600
Speaker 2: or they just miss it. It's not judge. I'm not

343
00:17:10,640 --> 00:17:11,200
Speaker 2: being judgy.

344
00:17:11,320 --> 00:17:11,880
Speaker 3: It happens.

345
00:17:12,000 --> 00:17:12,520
Speaker 2: It's life.

346
00:17:13,000 --> 00:17:16,200
Speaker 3: It happens. Happens to people in my family, like and

347
00:17:16,440 --> 00:17:19,520
Speaker 3: you know what happens is there can be serious consequences

348
00:17:19,560 --> 00:17:21,280
Speaker 3: to that. What if you were able to use AI

349
00:17:21,400 --> 00:17:22,719
Speaker 3: and I know they're working on this. There was an

350
00:17:22,760 --> 00:17:24,960
Speaker 3: article in times just a few months ago when it

351
00:17:25,000 --> 00:17:27,760
Speaker 3: comes to mammograms using AI technology to try to find

352
00:17:28,440 --> 00:17:30,080
Speaker 3: masses and mammograms.

353
00:17:29,520 --> 00:17:31,159
Speaker 2: And just if it's a case of able to go

354
00:17:31,200 --> 00:17:32,919
Speaker 2: through stuff and then just say listen, we got some

355
00:17:33,000 --> 00:17:36,040
Speaker 2: questions about this, and then a radiologist or a doctor

356
00:17:36,040 --> 00:17:38,520
Speaker 2: comes in and says, okay, let me take another look

357
00:17:38,560 --> 00:17:41,840
Speaker 2: at this, and just it's another layer of really making

358
00:17:41,880 --> 00:17:46,000
Speaker 2: sure that you don't miss something that's truly truly important.

359
00:17:46,000 --> 00:17:49,560
Speaker 3: I mean it's in a hybrid way, right, working hand

360
00:17:49,560 --> 00:17:50,399
Speaker 3: in hand, right.

361
00:17:50,240 --> 00:17:53,360
Speaker 2: And in terms of medicine, right, it's never necessarily an

362
00:17:53,359 --> 00:17:55,800
Speaker 2: exact science. So if you are able to have some

363
00:17:55,880 --> 00:17:58,320
Speaker 2: kind of database that pulls in all the kind of

364
00:17:58,320 --> 00:18:01,639
Speaker 2: different variations, if either an A or reading on a

365
00:18:01,720 --> 00:18:04,840
Speaker 2: scan that shows when there could be potential problems, I think,

366
00:18:04,960 --> 00:18:07,240
Speaker 2: think about how valuable and important that could be. Ian

367
00:18:07,280 --> 00:18:09,239
Speaker 2: ls Beta, We always look forward to it. Doctor Ian

368
00:18:09,280 --> 00:18:11,760
Speaker 2: las Beta over at NYU Lango Medical Center.

369
00:18:13,280 --> 00:18:16,840
Speaker 1: You're listening to the Bloomberg Business Week podcast. Catch us

370
00:18:16,880 --> 00:18:20,879
Speaker 1: live weekday afternoons from three to six Eastern on Bloomberg Radio,

371
00:18:21,080 --> 00:18:24,359
Speaker 1: the Bloomberg Business app, and YouTube. You can also listen

372
00:18:24,480 --> 00:18:27,560
Speaker 1: live on Amazon Alexa from our flagship New York station

373
00:18:28,040 --> 00:18:30,760
Speaker 1: just say Alexa Play Bloomberg eleven.

374
00:18:30,520 --> 00:18:35,000
Speaker 2: Thirty tim Some data last month a came out from

375
00:18:35,040 --> 00:18:38,479
Speaker 2: the research from Pitchbook. It showed VC venture capital funding

376
00:18:38,600 --> 00:18:41,520
Speaker 2: globally almost halved in the first six months of twenty

377
00:18:41,560 --> 00:18:45,399
Speaker 2: twenty three, so showing investors just not interested. Also some

378
00:18:45,520 --> 00:18:48,760
Speaker 2: less demand amid sharply higher interest rates. And this is though,

379
00:18:48,800 --> 00:18:52,720
Speaker 2: despite huge interest in AI startups sparked by the success

380
00:18:52,720 --> 00:18:55,200
Speaker 2: of open AI's Chatchipe. That's where money is going.

381
00:18:55,320 --> 00:18:58,160
Speaker 3: Yeah, Investors report more than forty billion dollars into AI

382
00:18:58,200 --> 00:19:00,440
Speaker 3: startups in the past six months. That's according to the data,

383
00:19:00,480 --> 00:19:03,080
Speaker 3: including the ten billion dollar investment by Microsoft and open

384
00:19:03,119 --> 00:19:05,840
Speaker 3: Ai and one point three billion dollars in funding for

385
00:19:05,960 --> 00:19:07,399
Speaker 3: rival Inflection AI.

386
00:19:08,119 --> 00:19:10,520
Speaker 2: All right, so let's get to it. Because back with

387
00:19:10,600 --> 00:19:13,200
Speaker 2: us to talk about what's going on when it comes

388
00:19:13,280 --> 00:19:15,159
Speaker 2: to the VC world. And we're delighted to have her

389
00:19:15,160 --> 00:19:17,800
Speaker 2: back with us. Shila Patel. She's vice chairgeneral partner of

390
00:19:17,800 --> 00:19:21,320
Speaker 2: b Capital. She's former chairman of Goldman Saccesset Management. She's

391
00:19:21,320 --> 00:19:23,840
Speaker 2: with us on Zoom from Park City, Utah. Sheila, Nice

392
00:19:23,880 --> 00:19:25,760
Speaker 2: to be talking with you again. Last time I think

393
00:19:25,800 --> 00:19:31,000
Speaker 2: it was at the Milken Institute Global Conference in May.

394
00:19:31,160 --> 00:19:32,840
Speaker 2: How are you and talk to us about your world

395
00:19:32,880 --> 00:19:34,080
Speaker 2: and how things have changed.

396
00:19:35,680 --> 00:19:37,640
Speaker 7: I'm great, Thank you so much, and it's so great

397
00:19:37,640 --> 00:19:40,040
Speaker 7: to chat with you again. You know, I think the

398
00:19:40,080 --> 00:19:43,080
Speaker 7: world since Milkin has had some good news, had some

399
00:19:43,160 --> 00:19:46,399
Speaker 7: rougher news, but overall, I think it's been a fascinating

400
00:19:46,440 --> 00:19:51,399
Speaker 7: period to see how investor sentiment hasn't necessarily responded to

401
00:19:51,440 --> 00:19:53,480
Speaker 7: some of the good news we're seeing on the economy

402
00:19:53,840 --> 00:19:57,479
Speaker 7: and particularly among public companies on profits and so on.

403
00:19:57,560 --> 00:19:59,639
Speaker 7: So there's a lot out there to look to. But

404
00:19:59,680 --> 00:20:02,560
Speaker 7: as you pointed out in opening this, when it comes

405
00:20:02,560 --> 00:20:05,720
Speaker 7: to venture there are definitely some challenges and it'll be

406
00:20:05,720 --> 00:20:07,000
Speaker 7: fun to chat about them with you.

407
00:20:07,080 --> 00:20:09,000
Speaker 3: Well, let's talk about the challenges first, and then I

408
00:20:09,040 --> 00:20:11,520
Speaker 3: want to talk about the opportunities. So it's been choppy

409
00:20:11,560 --> 00:20:15,040
Speaker 3: waters certainly given higher interest rates and pullback on funding,

410
00:20:15,040 --> 00:20:19,280
Speaker 3: and we've seen some venture backed companies wealth cease to exist,

411
00:20:19,280 --> 00:20:21,000
Speaker 3: cease to operate. It's a different world than it was

412
00:20:21,040 --> 00:20:23,520
Speaker 3: in twenty twenty and twenty twenty one. What are the

413
00:20:23,600 --> 00:20:25,400
Speaker 3: challenges that you're seeing at the capital.

414
00:20:26,840 --> 00:20:30,840
Speaker 7: Well, I think you know, along with the entire VC community.

415
00:20:31,119 --> 00:20:34,840
Speaker 7: We certainly see challenges as you mentioned, in terms of

416
00:20:35,119 --> 00:20:39,639
Speaker 7: investor interest, and I think institutional investors data a survey

417
00:20:39,680 --> 00:20:43,960
Speaker 7: of about forty four LPs show that almost three quarters

418
00:20:44,000 --> 00:20:46,800
Speaker 7: would allocate less to VC next year. Now, many of

419
00:20:46,840 --> 00:20:50,000
Speaker 7: them have been strong allocators and supporters, and I think

420
00:20:50,040 --> 00:20:53,840
Speaker 7: they'll be back, but I think there's definitely concern over valuations,

421
00:20:53,880 --> 00:20:57,200
Speaker 7: and that's where sometimes challenging news can end up being

422
00:20:57,200 --> 00:20:59,760
Speaker 7: good news. We've been very fortunate in being able to

423
00:21:00,640 --> 00:21:03,880
Speaker 7: raise our latest fun Growth fund three and beat our

424
00:21:03,920 --> 00:21:07,520
Speaker 7: goals with that, and you have seen several VC firms,

425
00:21:07,680 --> 00:21:11,000
Speaker 7: particularly some of the largest, have some success raising money.

426
00:21:11,720 --> 00:21:14,160
Speaker 7: I think because of those other themes that you mentioned,

427
00:21:14,359 --> 00:21:17,600
Speaker 7: the interest in AI, the focus on areas where tech

428
00:21:18,000 --> 00:21:21,240
Speaker 7: really can be additive in up and down markets, and

429
00:21:21,280 --> 00:21:24,040
Speaker 7: that in many cases is enterprise and fintech, which are

430
00:21:24,080 --> 00:21:27,159
Speaker 7: areas in which the capital operates. So I think there

431
00:21:27,160 --> 00:21:30,560
Speaker 7: are some high points, but in particular, investors are looking

432
00:21:30,800 --> 00:21:33,840
Speaker 7: to really make sure that the people they give money

433
00:21:33,840 --> 00:21:37,040
Speaker 7: too are focused on these questions of valuations, and that's

434
00:21:37,080 --> 00:21:39,879
Speaker 7: where the data has really been stark and is the

435
00:21:39,960 --> 00:21:43,919
Speaker 7: opportunity maybe the silver lining to the cloud, so to speak,

436
00:21:43,960 --> 00:21:46,199
Speaker 7: because valuations have come in and it means there are

437
00:21:46,240 --> 00:21:49,600
Speaker 7: great opportunities to find good tech companies at much more

438
00:21:49,640 --> 00:21:51,800
Speaker 7: reasonable and appropriate valuation.

439
00:21:51,960 --> 00:21:55,680
Speaker 2: Hey, CILA, is AI actually helping you guys find better

440
00:21:55,720 --> 00:21:58,359
Speaker 2: opportunities right now and able to go through maybe a

441
00:21:58,440 --> 00:22:00,840
Speaker 2: lot more deals and figure out what really makes sense.

442
00:22:02,240 --> 00:22:02,400
Speaker 8: Yeah.

443
00:22:02,440 --> 00:22:06,040
Speaker 7: I think applying AI to deal flow and to analysis

444
00:22:06,320 --> 00:22:09,480
Speaker 7: goes hand in hand with looking at how our companies

445
00:22:09,520 --> 00:22:13,080
Speaker 7: are using AI. And certainly I would say it's been

446
00:22:13,080 --> 00:22:17,520
Speaker 7: an amazing partnership talking about AI with our strategic partner BCG.

447
00:22:18,240 --> 00:22:20,160
Speaker 7: When you look at the work that BCG has put

448
00:22:20,160 --> 00:22:24,280
Speaker 7: in at all levels of Corporate America and global corporates

449
00:22:24,520 --> 00:22:27,600
Speaker 7: and thought about how to apply AI to solving some

450
00:22:27,640 --> 00:22:30,359
Speaker 7: of the problems they're facing, it has really helped us

451
00:22:30,960 --> 00:22:33,000
Speaker 7: take it in house and think about ways to apply

452
00:22:33,040 --> 00:22:36,520
Speaker 7: it in our own investing, in our application of analyzing

453
00:22:36,560 --> 00:22:38,200
Speaker 7: potential portfolio companies.

454
00:22:38,480 --> 00:22:40,560
Speaker 2: But I guess what I'm also wondering too, has something

455
00:22:40,600 --> 00:22:43,800
Speaker 2: fundamentally It's interesting to hear what you say about, you know,

456
00:22:44,080 --> 00:22:46,639
Speaker 2: investor interest when it comes to VC. Is there something

457
00:22:46,720 --> 00:22:49,679
Speaker 2: fundamentally structurally going on? We talk a lot about I know,

458
00:22:49,720 --> 00:22:51,640
Speaker 2: I think we did it milk, you know, the private

459
00:22:51,680 --> 00:22:53,640
Speaker 2: credit markets and the amount of money that's out there.

460
00:22:53,680 --> 00:22:57,639
Speaker 2: I mean, is there something structurally fundamentally going on that's

461
00:22:57,880 --> 00:23:01,520
Speaker 2: changing the VC world like it was, like it's always been.

462
00:23:01,680 --> 00:23:06,320
Speaker 7: Yeah, Look, I actually I guess I'll I'll put my

463
00:23:06,440 --> 00:23:09,919
Speaker 7: longer term hat on it. To me, is the maturation

464
00:23:10,240 --> 00:23:12,960
Speaker 7: of a segment of investing, just like we've seen other

465
00:23:13,000 --> 00:23:16,160
Speaker 7: areas go through. There was a moment when index investing

466
00:23:16,560 --> 00:23:19,879
Speaker 7: was young and new, and some crazy you know, evaluation,

467
00:23:20,080 --> 00:23:23,359
Speaker 7: some crazy structures, lots of things went on, and now

468
00:23:23,480 --> 00:23:26,719
Speaker 7: it's a typical part of people's investment in portfolios. I

469
00:23:26,760 --> 00:23:28,960
Speaker 7: think when I look at venture and I compare it

470
00:23:29,560 --> 00:23:32,280
Speaker 7: to the history my twenty years at Goldman, my thirty

471
00:23:32,359 --> 00:23:35,640
Speaker 7: years in the industry, and watching various areas come into

472
00:23:35,640 --> 00:23:40,480
Speaker 7: their own such as quant even hedge funds and private equity.

473
00:23:41,040 --> 00:23:43,400
Speaker 7: They go through a maturation process, and I think it's

474
00:23:43,400 --> 00:23:46,679
Speaker 7: a natural evolution to me, you know, I look at

475
00:23:46,720 --> 00:23:48,880
Speaker 7: venture and maybe it's because I'm one of the old

476
00:23:48,920 --> 00:23:52,000
Speaker 7: people in venture. That's how young an industry it is.

477
00:23:53,000 --> 00:23:57,480
Speaker 7: It's filled with amazing technology experts, but maybe you haven't

478
00:23:57,480 --> 00:23:59,960
Speaker 7: seen as many market cycles and at the end of

479
00:24:00,160 --> 00:24:03,800
Speaker 7: the day BC has to perform the same as other areas,

480
00:24:04,080 --> 00:24:07,040
Speaker 7: and that maturation is part of why you see some

481
00:24:07,080 --> 00:24:10,040
Speaker 7: funds able to cope with the changing environment, the same

482
00:24:10,080 --> 00:24:12,919
Speaker 7: as you see some portfolio companies coping better with a

483
00:24:12,920 --> 00:24:14,560
Speaker 7: more difficult funding environment.

484
00:24:14,920 --> 00:24:18,439
Speaker 3: Okay, you know, we only have a couple of minutes left,

485
00:24:18,480 --> 00:24:20,120
Speaker 3: and I wanted to get to the opportunities that you're

486
00:24:20,119 --> 00:24:22,280
Speaker 3: seeing right now. Sheila's so, so talk to me about

487
00:24:22,280 --> 00:24:25,880
Speaker 3: two opportunities in two senses of the word. One geographically,

488
00:24:25,880 --> 00:24:28,120
Speaker 3: where are you deploying money? And then also what types

489
00:24:28,119 --> 00:24:29,440
Speaker 3: of companies excite you right now?

490
00:24:30,680 --> 00:24:33,120
Speaker 7: Sure, well, maybe I'll start with the last first, because

491
00:24:33,160 --> 00:24:36,119
Speaker 7: I think it ties into that question of AI. I

492
00:24:36,160 --> 00:24:38,520
Speaker 7: think that we spend so much time and maybe this

493
00:24:38,680 --> 00:24:42,160
Speaker 7: is a trend in general on the downside of certain

494
00:24:42,200 --> 00:24:46,200
Speaker 7: technologies or of things in general, that the upsides sometimes

495
00:24:46,200 --> 00:24:48,960
Speaker 7: get missed. And when I look at AI, I look

496
00:24:49,000 --> 00:24:51,560
Speaker 7: at the way a can turbo boost drug discovery and

497
00:24:51,640 --> 00:24:54,080
Speaker 7: research in areas that will be really important in a

498
00:24:54,119 --> 00:24:57,720
Speaker 7: climate changing world, like crop protection. So a company like

499
00:24:57,760 --> 00:25:00,520
Speaker 7: atom Wise, which is one of our portfolio company, a

500
00:25:00,560 --> 00:25:06,280
Speaker 7: small molecule expert partnering with the world's leading pharmaceuticals, biotechs,

501
00:25:06,600 --> 00:25:11,000
Speaker 7: agrochemical companies to discover new ways and new treatments, whether

502
00:25:11,080 --> 00:25:14,679
Speaker 7: drug treatments or whether crop protection, et cetera. So I

503
00:25:14,680 --> 00:25:17,800
Speaker 7: think that's an incredible area for focus for AI is

504
00:25:17,880 --> 00:25:22,240
Speaker 7: drug discovery, small molecule work that can go beyond medications

505
00:25:22,240 --> 00:25:27,639
Speaker 7: into other applications, industrial applications. That's amazing to me. And

506
00:25:27,680 --> 00:25:32,600
Speaker 7: then when I think about that broader question on opportunity,

507
00:25:33,440 --> 00:25:36,600
Speaker 7: I think that what we have to look to is

508
00:25:37,119 --> 00:25:39,920
Speaker 7: the longer term prospect for some of these companies when

509
00:25:39,960 --> 00:25:42,280
Speaker 7: you have a lower valuation to start with, and that

510
00:25:42,359 --> 00:25:44,800
Speaker 7: means from an investor perspective, you want to be sure

511
00:25:44,800 --> 00:25:48,560
Speaker 7: where the challenging valuations, you're challenging companies on the levels

512
00:25:48,600 --> 00:25:49,560
Speaker 7: they're looking to raise.

513
00:25:50,400 --> 00:25:54,840
Speaker 2: Super stuff. So appreciated. Shila Patel, Vice Chair, General Partner

514
00:25:54,880 --> 00:25:57,160
Speaker 2: be Capital, joining us on Zoom from Park City, Utah.

515
00:25:57,160 --> 00:25:59,679
Speaker 2: As we said from a chairman of Goldman Sachs Asset

516
00:25:59,720 --> 00:26:02,000
Speaker 2: Manage talking about the VC space.

517
00:26:02,280 --> 00:26:05,760
Speaker 1: If you're listening to the Bloomberg Business Week podcast, catch

518
00:26:05,840 --> 00:26:09,200
Speaker 1: us live weekday afternoons from three to six Eastern Listen

519
00:26:09,240 --> 00:26:12,720
Speaker 1: on Bloomberg dot com, the iHeartRadio app, and the Bloomberg

520
00:26:12,760 --> 00:26:17,080
Speaker 1: Business app or watch us live on YouTube.

521
00:26:17,000 --> 00:26:19,520
Speaker 3: Carol Ifuber get those textounts to just from unknown numbers.

522
00:26:19,560 --> 00:26:22,480
Speaker 3: Sometimes they say just hello, or maybe they have something

523
00:26:22,480 --> 00:26:25,479
Speaker 3: more like, Hi David, I'm Vicky Hoe. Don't you remember me?

524
00:26:25,560 --> 00:26:26,560
Speaker 2: I run in the other direction.

525
00:26:26,680 --> 00:26:28,240
Speaker 3: Okay, well that's probably a pretty smart thing to do.

526
00:26:28,480 --> 00:26:30,200
Speaker 3: I mean I get them all the time, but unlike Bloomberg,

527
00:26:30,280 --> 00:26:32,480
Speaker 3: Zeke Fox, and probably like most people who are just

528
00:26:32,480 --> 00:26:35,879
Speaker 3: a bit technologically savvy, I ignore them. We're grateful that

529
00:26:35,960 --> 00:26:37,879
Speaker 3: Zeke didn't, though, because even if it meant there was

530
00:26:37,920 --> 00:26:41,119
Speaker 3: a risk he'd be led to what's known as pig butchering.

531
00:26:41,560 --> 00:26:43,600
Speaker 3: If you're a little confused, don't worry. This is all

532
00:26:43,640 --> 00:26:46,240
Speaker 3: part of Zeke's brand new book. It's called Number Go Up.

533
00:26:46,400 --> 00:26:49,960
Speaker 3: Inside Crypto's Wild Rise and Staggering Fall, in which he

534
00:26:50,040 --> 00:26:53,840
Speaker 3: uncovers a crypto powered human trafficking ring in Cambodia.

535
00:26:53,960 --> 00:26:56,920
Speaker 2: Yeah, Zeke's new book coming out September twelfth. The excerpt

536
00:26:57,000 --> 00:26:59,440
Speaker 2: is the domestic cover story of the new issue of Bloomberg

537
00:26:59,400 --> 00:27:02,200
Speaker 2: Business Week on stands right as we speak online a

538
00:27:02,200 --> 00:27:04,880
Speaker 2: Bloomberg dot com slash businessweeknd on the Bloomberg with more

539
00:27:05,240 --> 00:27:07,919
Speaker 2: Zeke is with us. He is financial investigations reporter at

540
00:27:07,920 --> 00:27:10,120
Speaker 2: Bloomberg News. He's on Zoom in New York City. Also

541
00:27:10,160 --> 00:27:12,720
Speaker 2: here the editor of Bloomberg BusinessWeek, Jill Weber. Here in

542
00:27:12,760 --> 00:27:16,440
Speaker 2: our Bloomberg Interactive Brokers studio. Hi, Joel High, Vicky Hei Zee.

543
00:27:18,320 --> 00:27:21,479
Speaker 9: Only two of those three characters are real. So so

544
00:27:22,800 --> 00:27:26,960
Speaker 9: this is an amazing excerpt. I'm so excited for Zeke

545
00:27:27,040 --> 00:27:27,600
Speaker 9: in this book.

546
00:27:27,720 --> 00:27:28,480
Speaker 3: Number go up.

547
00:27:29,359 --> 00:27:31,800
Speaker 9: You can pre order your copy right now on Amazon

548
00:27:31,840 --> 00:27:35,399
Speaker 9: dot com. Right Zeke, And uh, this is a little

549
00:27:35,440 --> 00:27:39,400
Speaker 9: sneak peak of where some of the book goes. And

550
00:27:40,119 --> 00:27:42,520
Speaker 9: we're just so tickled to have it as an excerpt

551
00:27:42,520 --> 00:27:45,640
Speaker 9: in this current issue and as our cover story. And

552
00:27:45,800 --> 00:27:48,639
Speaker 9: you know we're talking about Zeke here and uh, you

553
00:27:48,640 --> 00:27:52,119
Speaker 9: know it all begins with this text that he got,

554
00:27:52,520 --> 00:27:56,680
Speaker 9: uh from a person named quote unquote Vicky. Who was

555
00:27:57,000 --> 00:27:58,680
Speaker 9: Who is Vicky? And what text did you get?

556
00:27:58,760 --> 00:27:59,000
Speaker 3: Zeke?

557
00:28:00,840 --> 00:28:03,800
Speaker 6: Yeah, so Vicky gave me and sent me a text

558
00:28:03,960 --> 00:28:06,640
Speaker 6: out of nowhere saying like Hi, David, how's it going

559
00:28:06,760 --> 00:28:09,920
Speaker 6: or something like that, and like I'm not David obviously,

560
00:28:10,240 --> 00:28:12,760
Speaker 6: So I've been getting a lot of these. I'm been

561
00:28:12,840 --> 00:28:15,520
Speaker 6: kind of curious about that I decided to me.

562
00:28:15,640 --> 00:28:19,680
Speaker 9: Yeah, Tim says he gets like Carol, get them, Yeah,

563
00:28:19,720 --> 00:28:22,320
Speaker 9: I ignored delete reporter of spam if you can, right,

564
00:28:22,400 --> 00:28:24,600
Speaker 9: but you you wrote.

565
00:28:24,440 --> 00:28:28,120
Speaker 6: Back, yeah, And the first part of this, like, it's

566
00:28:28,160 --> 00:28:31,800
Speaker 6: probably about what you would assume. Where Vicky. She tries

567
00:28:31,880 --> 00:28:34,520
Speaker 6: to make friends with me. She's flirting a little bit,

568
00:28:34,600 --> 00:28:40,280
Speaker 6: she's sending me suggestive pictures and she's like a beautiful,

569
00:28:40,400 --> 00:28:44,480
Speaker 6: heavily airshed young woman who claims to be in New

570
00:28:44,520 --> 00:28:46,479
Speaker 6: York like me, but does not want to meet up,

571
00:28:46,520 --> 00:28:48,560
Speaker 6: and none of her pictures actually look like she's in

572
00:28:48,600 --> 00:28:54,440
Speaker 6: New York. And after I'm waiting for her to try

573
00:28:54,480 --> 00:28:56,440
Speaker 6: to scam me because I wanted to know how it works,

574
00:28:56,720 --> 00:28:59,520
Speaker 6: and it takes like days before she's very patient before

575
00:29:01,040 --> 00:29:03,240
Speaker 6: her true mission comes out, and she starts telling me

576
00:29:03,280 --> 00:29:06,480
Speaker 6: about how good she is at bitcoin trading and that

577
00:29:07,080 --> 00:29:09,440
Speaker 6: I gotta try to well. She doesn't even say like

578
00:29:09,480 --> 00:29:11,800
Speaker 6: you got a trade like me. She really plays it slow.

579
00:29:11,840 --> 00:29:14,240
Speaker 6: She's like, you know, I'm just just real busy today.

580
00:29:14,240 --> 00:29:16,200
Speaker 6: I can't talk. I got to make like thousands of

581
00:29:16,240 --> 00:29:20,760
Speaker 6: dollars on my bitcoin trades. And then finally, after like

582
00:29:20,800 --> 00:29:23,440
Speaker 6: a lot of trying, she's like Okay, I'll let you

583
00:29:23,480 --> 00:29:28,800
Speaker 6: in on it, download this app and send me some

584
00:29:28,960 --> 00:29:32,000
Speaker 6: of this cryptocurrency called Tetor. And that's what I was

585
00:29:32,000 --> 00:29:32,840
Speaker 6: really waiting to hear.

586
00:29:33,360 --> 00:29:35,959
Speaker 9: What I love about it, though, is how how slow

587
00:29:36,080 --> 00:29:39,560
Speaker 9: she is. It's like, I mean, and this comes through

588
00:29:39,560 --> 00:29:42,280
Speaker 9: in the excerpt, is like you're like encouraging her along

589
00:29:42,400 --> 00:29:44,120
Speaker 9: to like, come on, get to the point where you're

590
00:29:44,160 --> 00:29:45,080
Speaker 9: trying me.

591
00:29:46,440 --> 00:29:48,640
Speaker 6: I even I had to tell her that, like I

592
00:29:48,680 --> 00:29:51,280
Speaker 6: really wanted a tesla, like I need to make money,

593
00:29:51,760 --> 00:29:54,400
Speaker 6: like she just she was really wanted to make sure

594
00:29:54,440 --> 00:29:58,640
Speaker 6: I was hooked before she went for the scam. And

595
00:29:59,600 --> 00:30:03,440
Speaker 6: once so I sent her Tether is like it's a

596
00:30:03,440 --> 00:30:07,440
Speaker 6: stable coin. It always costs a dollar because it's supposedly

597
00:30:07,480 --> 00:30:09,160
Speaker 6: backed by real dollars in the bank. And you can

598
00:30:09,200 --> 00:30:12,280
Speaker 6: go on any of your normal crypto apps like coinbase

599
00:30:12,400 --> 00:30:15,040
Speaker 6: or crypto dot com or whatever, you can buy tether

600
00:30:15,640 --> 00:30:19,480
Speaker 6: and then you send them to Vicky at her supposedly

601
00:30:19,560 --> 00:30:24,640
Speaker 6: like magiccoin trading app. And so all that involves is

602
00:30:24,720 --> 00:30:27,000
Speaker 6: getting Vicky said. Vicky gave me her address. I mean,

603
00:30:27,040 --> 00:30:29,240
Speaker 6: she walked me through it very carefully, and I sent

604
00:30:29,320 --> 00:30:32,880
Speaker 6: her a hundred tethers from like a normal trading app

605
00:30:33,320 --> 00:30:36,640
Speaker 6: to her special app where I was going to get

606
00:30:36,680 --> 00:30:41,360
Speaker 6: make this like big score. But as soon as I

607
00:30:41,400 --> 00:30:43,520
Speaker 6: sent it, she started telling me like I needed to

608
00:30:43,560 --> 00:30:45,960
Speaker 6: send more, at which point I felt like it had

609
00:30:46,000 --> 00:30:48,400
Speaker 6: gone far enough. I told her I was a reporter,

610
00:30:49,000 --> 00:30:54,920
Speaker 6: and she disappeared. But the part where the story gets

611
00:30:55,440 --> 00:30:58,760
Speaker 6: really dark and really weird is when I tried to

612
00:30:58,800 --> 00:31:01,640
Speaker 6: figure out who is on the other end of these messages,

613
00:31:01,720 --> 00:31:02,760
Speaker 6: like who could Vicky b.

614
00:31:04,600 --> 00:31:04,719
Speaker 3: So?

615
00:31:04,920 --> 00:31:06,000
Speaker 6: And it turns out.

616
00:31:05,920 --> 00:31:08,000
Speaker 9: That that I want to I want to slow you

617
00:31:08,040 --> 00:31:09,080
Speaker 9: down before I let.

618
00:31:08,960 --> 00:31:13,160
Speaker 2: You go there, Uh the great revel Yeah, Which that's it, right.

619
00:31:13,200 --> 00:31:16,640
Speaker 9: You know part part of what the book is so

620
00:31:16,720 --> 00:31:19,000
Speaker 9: much bigger than just this, right. Keep in mind this

621
00:31:19,080 --> 00:31:22,360
Speaker 9: is an excerpt, and Zeke, I do want to just

622
00:31:22,440 --> 00:31:25,440
Speaker 9: like back up for a second and talk about, uh,

623
00:31:25,680 --> 00:31:28,560
Speaker 9: how you ended up having this book at all?

624
00:31:28,800 --> 00:31:31,040
Speaker 6: Well, I was I was hoping that I was hoping

625
00:31:31,040 --> 00:31:35,760
Speaker 6: that you would because good this really started. This started like, uh,

626
00:31:36,400 --> 00:31:39,280
Speaker 6: you know, it's almost two years ago, maybe even a

627
00:31:39,280 --> 00:31:41,440
Speaker 6: little more than two years ago. Joel came by my

628
00:31:41,560 --> 00:31:44,800
Speaker 6: desk and I had not been reporting on crypto at all.

629
00:31:45,800 --> 00:31:48,520
Speaker 6: I tend to write about like weird stuff going on

630
00:31:48,520 --> 00:31:51,960
Speaker 6: on Wall Street. But I've sort of resisted crypto as

631
00:31:51,960 --> 00:31:54,800
Speaker 6: a topic. I like most of you listening to this,

632
00:31:54,880 --> 00:31:57,400
Speaker 6: you probably feel like I'm never going to get it,

633
00:31:57,480 --> 00:32:00,600
Speaker 6: and like the constant like boosterisms of a turn off.

634
00:32:02,440 --> 00:32:06,080
Speaker 6: But Joel said, hey, what do you know about stable

635
00:32:06,120 --> 00:32:11,160
Speaker 6: coins like Tether? And I thought, huh, I mean, maybe

636
00:32:11,200 --> 00:32:13,160
Speaker 6: like this wants I mean, if Joel wants the story,

637
00:32:13,200 --> 00:32:15,920
Speaker 6: it might be kind of fun to look into it.

638
00:32:17,640 --> 00:32:21,959
Speaker 6: And but I mean, I don't think I'm sure that

639
00:32:22,160 --> 00:32:25,080
Speaker 6: neither of us had any idea where looking into Tether

640
00:32:25,280 --> 00:32:27,280
Speaker 6: was was going to take us now.

641
00:32:28,280 --> 00:32:31,120
Speaker 9: Certainly not me. And we've had you know, great reporting

642
00:32:31,120 --> 00:32:34,080
Speaker 9: from Zeke on on Tether and more crypto along the way.

643
00:32:35,160 --> 00:32:37,360
Speaker 9: So yeah, just a little pop up my own car

644
00:32:37,440 --> 00:32:40,040
Speaker 9: there for helping Zeke get inspired to write and get

645
00:32:40,040 --> 00:32:44,800
Speaker 9: interested in Tether, because the Tether part is ultimately where

646
00:32:44,840 --> 00:32:48,840
Speaker 9: this this other side of your your story about you know,

647
00:32:48,920 --> 00:32:52,560
Speaker 9: quote unquote Vicky takes you. And I will say, this

648
00:32:52,600 --> 00:32:55,400
Speaker 9: is where it gets dark. We've been kind of laughing,

649
00:32:55,440 --> 00:33:00,160
Speaker 9: ha ha, it gets dark really quickly here, Zeke. And

650
00:33:00,160 --> 00:33:03,080
Speaker 9: why don't you take us to this human rights catastrophe

651
00:33:03,120 --> 00:33:05,560
Speaker 9: that you discovered yeah, on the other side of the world.

652
00:33:06,960 --> 00:33:12,280
Speaker 6: I mean, the truth, as I learned through activists and

653
00:33:12,560 --> 00:33:15,840
Speaker 6: reporters on the ground in Southeast Asia, is that the

654
00:33:15,880 --> 00:33:20,520
Speaker 6: people who send these text messages are themselves often victims

655
00:33:20,880 --> 00:33:27,400
Speaker 6: of human trafficking. And there's whole office buildings of people

656
00:33:27,400 --> 00:33:30,920
Speaker 6: who've been learned to say, like Cambodia is a popular

657
00:33:30,960 --> 00:33:33,520
Speaker 6: spot for this, and they've been learned there with the

658
00:33:33,560 --> 00:33:37,440
Speaker 6: promise of a good job. Once they arrive, they're trapped

659
00:33:37,800 --> 00:33:41,320
Speaker 6: and they're forced to scam people under with the threat

660
00:33:41,320 --> 00:33:45,400
Speaker 6: of beatings or torture or even worse. And it sounds

661
00:33:45,440 --> 00:33:48,720
Speaker 6: like some sort of like QAnon conspiracy thing, but like

662
00:33:48,800 --> 00:33:52,560
Speaker 6: it's real. These messages are often coming from people who

663
00:33:52,600 --> 00:33:56,720
Speaker 6: are suffering like horrible abuses and can't leave unless they

664
00:33:56,760 --> 00:34:00,320
Speaker 6: pay essentially like a large ransom to the gangs that

665
00:34:00,400 --> 00:34:01,760
Speaker 6: run these scam compounds.

666
00:34:02,040 --> 00:34:05,120
Speaker 3: Can you Zeke talk about who you met in Ho

667
00:34:05,240 --> 00:34:08,640
Speaker 3: Chi Minh City and how he was able to escape

668
00:34:09,200 --> 00:34:13,120
Speaker 3: from this one of these uh, one of these sort

669
00:34:13,160 --> 00:34:15,719
Speaker 3: of prison like structures.

670
00:34:18,360 --> 00:34:22,040
Speaker 6: So over a video chat with the translator, I started

671
00:34:22,040 --> 00:34:27,000
Speaker 6: interviewing people who had escaped from these compounds. One of

672
00:34:27,040 --> 00:34:32,319
Speaker 6: them his name is twee. He's twenty nine years old.

673
00:34:32,400 --> 00:34:35,840
Speaker 6: The first time we speak on Zoom's it's like a

674
00:34:35,920 --> 00:34:39,600
Speaker 6: rainy night in ho Chi Minh city. He's walking down

675
00:34:39,600 --> 00:34:42,480
Speaker 6: the street smoking a cigarette. I can see that he's

676
00:34:42,480 --> 00:34:45,000
Speaker 6: missing quite a few teeth, and he tells me that

677
00:34:45,000 --> 00:34:49,520
Speaker 6: these were he lost them in like a horrible beating

678
00:34:49,600 --> 00:34:53,520
Speaker 6: by his captors. And it was truly a desperate situation.

679
00:34:53,640 --> 00:35:00,040
Speaker 6: He'd been tricked into, tricked into going to Cambodia, and

680
00:35:01,320 --> 00:35:08,320
Speaker 6: he was only able to escape by stealing a guard's

681
00:35:08,360 --> 00:35:13,880
Speaker 6: phone then hiding it in his rectum, which was a

682
00:35:13,920 --> 00:35:18,000
Speaker 6: trick that he'd learned in prison. Then and like he's

683
00:35:18,040 --> 00:35:20,240
Speaker 6: telling me this stuff and it's it's pretty hard to believe.

684
00:35:22,719 --> 00:35:26,000
Speaker 6: He told me that he was able to take the

685
00:35:26,040 --> 00:35:28,919
Speaker 6: phone apart with no tools, and then since he didn't

686
00:35:28,920 --> 00:35:32,719
Speaker 6: have a charger, he hot wired the battery to a

687
00:35:33,040 --> 00:35:35,919
Speaker 6: fluorescent light fixture in the ceiling of the room where

688
00:35:35,920 --> 00:35:39,600
Speaker 6: he's being held. So he had been a great source

689
00:35:39,640 --> 00:35:44,800
Speaker 6: of information about these schemes and about these scam compounds

690
00:35:44,800 --> 00:35:47,239
Speaker 6: and Cambodia. But I wasn't quite sure about this part

691
00:35:47,280 --> 00:35:50,640
Speaker 6: of the story. So I went to Hochi min the

692
00:35:50,640 --> 00:35:53,799
Speaker 6: city I met hup with him. I told him like

693
00:35:53,840 --> 00:35:56,359
Speaker 6: tweet like, I'm so sorry for what's happened to you.

694
00:35:57,120 --> 00:35:59,120
Speaker 6: I hate to say this though, but like, is that

695
00:35:59,200 --> 00:36:03,160
Speaker 6: even really possible to charge an iPhone like that? And

696
00:36:03,320 --> 00:36:07,560
Speaker 6: with no hesitation? Well he said yeah, he said, I'll

697
00:36:07,600 --> 00:36:11,480
Speaker 6: show you right now, went to the store, bought like

698
00:36:11,560 --> 00:36:14,759
Speaker 6: a old iPhone for about fifty bucks, and with no

699
00:36:14,800 --> 00:36:17,840
Speaker 6: hesitation he took it apart. Took apart a lamp at

700
00:36:18,280 --> 00:36:21,440
Speaker 6: an LED light bulb in my room hotel room, wired

701
00:36:21,440 --> 00:36:24,480
Speaker 6: it to the battery and got should turn on.

702
00:36:25,120 --> 00:36:28,239
Speaker 3: So it was so you can do that?

703
00:36:28,280 --> 00:36:29,280
Speaker 6: Incredibly impressed.

704
00:36:29,760 --> 00:36:35,719
Speaker 9: Yes, so, and so bring it back to to so

705
00:36:35,880 --> 00:36:38,640
Speaker 9: we if we if you have firsthand experience from these

706
00:36:38,680 --> 00:36:42,840
Speaker 9: people and what they've endured and what they've endured to

707
00:36:42,920 --> 00:36:45,720
Speaker 9: get their freedom back, what did you learn from talking

708
00:36:45,760 --> 00:36:49,320
Speaker 9: to them about what was on the other side of

709
00:36:49,520 --> 00:36:55,120
Speaker 9: these let's call it like a modified a hotel, shall

710
00:36:55,160 --> 00:36:56,160
Speaker 9: we say?

711
00:36:56,840 --> 00:37:01,239
Speaker 6: Well, we had been held in se Anookville, which is

712
00:37:01,280 --> 00:37:07,960
Speaker 6: a city in southwestern Cambodia that had an incredible casino

713
00:37:08,000 --> 00:37:11,600
Speaker 6: building boom fueled by Chinese investors in recent years. Then

714
00:37:11,920 --> 00:37:16,959
Speaker 6: like a horrible bust win the during COVID and also

715
00:37:17,040 --> 00:37:20,640
Speaker 6: due to a change in the law about online gambling

716
00:37:20,680 --> 00:37:23,600
Speaker 6: in Cambodia, so there was now all this empty space,

717
00:37:23,760 --> 00:37:28,799
Speaker 6: all these people who specialized in gam online gambling, and

718
00:37:29,040 --> 00:37:32,640
Speaker 6: he'd been held in this one area called Chinatown, which

719
00:37:32,680 --> 00:37:36,360
Speaker 6: is like the most like evil office park on Earth,

720
00:37:36,440 --> 00:37:42,560
Speaker 6: like twenty maybe like fifty towers, where there were credible

721
00:37:42,600 --> 00:37:45,560
Speaker 6: reports and also at local media and other press that

722
00:37:45,600 --> 00:37:49,000
Speaker 6: were saying that these were sites of where there were

723
00:37:49,640 --> 00:37:52,879
Speaker 6: thousands and thousands of people who were forced to scam.

724
00:37:53,320 --> 00:37:55,080
Speaker 6: But I felt like this was one of those stories

725
00:37:55,120 --> 00:38:00,640
Speaker 6: that you had to see to believe. And also the

726
00:38:00,680 --> 00:38:04,200
Speaker 6: people who covered it, understandably were not that interested in

727
00:38:04,239 --> 00:38:08,960
Speaker 6: the in the crypto angle, but I felt like crypto

728
00:38:09,480 --> 00:38:11,960
Speaker 6: was I wanted to know how crypto was really fueling

729
00:38:12,040 --> 00:38:15,360
Speaker 6: these these scams, because at the heart of it, like

730
00:38:15,440 --> 00:38:18,680
Speaker 6: people in the US or other well off countries need

731
00:38:18,760 --> 00:38:23,960
Speaker 6: to send money to like Chinese gangsters in Cambodia, And

732
00:38:24,000 --> 00:38:26,080
Speaker 6: if you tried to do that with like your visa card,

733
00:38:26,719 --> 00:38:28,600
Speaker 6: like that's not going to work for very long, Like

734
00:38:28,640 --> 00:38:30,480
Speaker 6: there's going to be charge. Visa would cut you off

735
00:38:30,480 --> 00:38:33,480
Speaker 6: in a minute, and uh Bank of America is going

736
00:38:33,560 --> 00:38:36,759
Speaker 6: to like flag that transfer, but if you do it

737
00:38:36,840 --> 00:38:43,200
Speaker 6: using crypto, it's instant, it's irreversible. And it's also it's

738
00:38:43,280 --> 00:38:45,320
Speaker 6: kind of plausible that there could be this like great

739
00:38:45,920 --> 00:38:49,160
Speaker 6: bitcoin trading strategy, I mean plausible for if you're like

740
00:38:49,200 --> 00:38:55,440
Speaker 6: not that well informed. So and what I saw when

741
00:38:55,480 --> 00:38:59,440
Speaker 6: I went to Cambodia was that at I went to

742
00:38:59,520 --> 00:39:02,439
Speaker 6: Chinatown and it was just as twe and the other

743
00:39:02,600 --> 00:39:06,480
Speaker 6: escapees had described and right although by the time that

744
00:39:06,600 --> 00:39:09,319
Speaker 6: I went to visit, it had largely been shut down

745
00:39:09,719 --> 00:39:14,759
Speaker 6: by become like an international embarrassment and Camponian authorities had

746
00:39:15,360 --> 00:39:20,279
Speaker 6: closed down most of the scam compounds there. But right

747
00:39:20,320 --> 00:39:25,240
Speaker 6: at the entrance, there was a store that had closed.

748
00:39:25,280 --> 00:39:27,720
Speaker 6: There's a money changing store, and it had a sign

749
00:39:28,160 --> 00:39:31,920
Speaker 6: on its billboard advertising that you could change your tether

750
00:39:32,080 --> 00:39:36,000
Speaker 6: for US dollars there, which is not something I'd seen

751
00:39:36,040 --> 00:39:43,400
Speaker 6: like anywhere else in the real world. So that was

752
00:39:43,440 --> 00:39:44,319
Speaker 6: a big shock to me.

753
00:39:44,760 --> 00:39:47,120
Speaker 2: So we have about a minute left here, Zeke, and

754
00:39:47,160 --> 00:39:49,160
Speaker 2: there's we know so much more in your book, but

755
00:39:49,280 --> 00:39:53,239
Speaker 2: this particular excerpt, I mean, how does it make you

756
00:39:53,280 --> 00:39:56,240
Speaker 2: think kind of about you know, this the crypto world

757
00:39:56,239 --> 00:39:58,479
Speaker 2: where we are a lot of questions out there.

758
00:40:00,960 --> 00:40:04,000
Speaker 6: I mean, to me, this is like the one I

759
00:40:04,040 --> 00:40:08,480
Speaker 6: looked all over the world from like El Salvador, Philippines, Bahamas,

760
00:40:08,520 --> 00:40:11,839
Speaker 6: everywhere I went. Crypto was not really living up to

761
00:40:11,880 --> 00:40:16,920
Speaker 6: the hype here. Sadly, it was extremely useful for these scammers,

762
00:40:17,239 --> 00:40:21,000
Speaker 6: and to me it explained, uh it could. These types

763
00:40:21,040 --> 00:40:24,840
Speaker 6: of illicit uses could explain a big part of Crypto's

764
00:40:24,840 --> 00:40:28,440
Speaker 6: continued popularity in the face of like so many reasons

765
00:40:28,480 --> 00:40:29,040
Speaker 6: to avoid it.

766
00:40:29,040 --> 00:40:33,719
Speaker 2: Now, all right, we're gonna leave it on that note. Unbelievable, Jill,

767
00:40:33,800 --> 00:40:34,040
Speaker 2: what are.

768
00:40:34,000 --> 00:40:36,680
Speaker 9: Your I'm just so proud of proud of the zequ

769
00:40:36,719 --> 00:40:40,520
Speaker 9: Fox for this, for this book, and grateful for this excerpt.

770
00:40:40,640 --> 00:40:44,920
Speaker 9: And just remember to pre order your copy of Number

771
00:40:44,960 --> 00:40:47,560
Speaker 9: Go up now so that you you get one of

772
00:40:47,560 --> 00:40:48,680
Speaker 9: the first ones off the press.

773
00:40:48,960 --> 00:40:50,440
Speaker 2: And all we're going to say is there's more of

774
00:40:50,520 --> 00:40:53,160
Speaker 2: Zeke Fox. We're going to cover this book certainly again

775
00:40:53,239 --> 00:40:55,959
Speaker 2: in September and cover more of it, So looking forward

776
00:40:55,960 --> 00:40:58,279
Speaker 2: to that. Ze Fox check out his book. As Jiell

777
00:40:58,440 --> 00:41:01,120
Speaker 2: just mentioned, he is financial invest gations reporter at Bloomberg,

778
00:41:01,160 --> 00:41:03,279
Speaker 2: and of course Jill Webber the editor of Bloomberg Business Week.

779
00:41:03,360 --> 00:41:05,680
Speaker 2: This is the domestic cover of Business Week.

780
00:41:07,480 --> 00:41:10,640
Speaker 1: M a journal.

781
00:41:11,640 --> 00:41:12,640
Speaker 4: Now about you let me drive?

782
00:41:12,920 --> 00:41:17,320
Speaker 8: Oh no, no, no, no, please, honey, please, I'll do the

783
00:41:17,440 --> 00:41:18,240
Speaker 8: riding gravel.

784
00:41:18,800 --> 00:41:20,160
Speaker 7: Let's wat I want to drive.

785
00:41:20,160 --> 00:41:23,360
Speaker 2: It's a good question.

786
00:41:27,160 --> 00:41:30,359
Speaker 3: This is the drive to the globe down thing.

787
00:41:30,480 --> 00:41:33,560
Speaker 1: We'll buyer on Bloomberg Radio.

788
00:41:33,880 --> 00:41:36,560
Speaker 2: All right, TikTok, everybody, About eighteen minutes left in today's

789
00:41:36,560 --> 00:41:39,600
Speaker 2: trading session. It's been safe to say for at least

790
00:41:39,719 --> 00:41:42,320
Speaker 2: the second half of the day decidedly lower. As you

791
00:41:42,480 --> 00:41:45,600
Speaker 2: just heard from Bill and Charlie tracking the trade here.

792
00:41:45,760 --> 00:41:48,400
Speaker 2: Really the Nasdaq down the most on a percentage basis.

793
00:41:48,400 --> 00:41:50,560
Speaker 2: So stocks are down, bonds are down two as yield

794
00:41:50,600 --> 00:41:51,160
Speaker 2: have moved up to.

795
00:41:51,360 --> 00:41:54,240
Speaker 3: Nasdak is down seven percent just this month, Carol.

796
00:41:54,280 --> 00:41:56,640
Speaker 2: Yeah, August has been a bummer. Yeah, it is, except

797
00:41:56,640 --> 00:41:58,880
Speaker 2: if you're a mayor then you're like, hey, finally my

798
00:41:58,960 --> 00:41:59,440
Speaker 2: time has come.

799
00:41:59,560 --> 00:42:03,319
Speaker 3: Weather's pretty good. No, okay, that's true.

800
00:42:03,360 --> 00:42:05,160
Speaker 2: All right, So let's get to it. Let's talk about

801
00:42:05,160 --> 00:42:07,960
Speaker 2: the trade. Back with us is John Augustine. He is

802
00:42:08,040 --> 00:42:11,799
Speaker 2: CIO of Huntington Private Bank, joining us on zoom from Cincinnati, Ohio. John, Hey,

803
00:42:12,000 --> 00:42:14,560
Speaker 2: nice to have you back here with Tim and myself.

804
00:42:15,400 --> 00:42:17,680
Speaker 2: If we may first up because you are at Huntington

805
00:42:17,719 --> 00:42:21,080
Speaker 2: Private Bank. You are bank. Talk to us though about

806
00:42:21,320 --> 00:42:24,800
Speaker 2: the banking trends and what you guys are seeing stress points.

807
00:42:25,600 --> 00:42:27,320
Speaker 2: I don't know what you can tell us about loan demand.

808
00:42:27,360 --> 00:42:30,560
Speaker 2: What are you seeing just in general about the lending environment,

809
00:42:30,560 --> 00:42:32,960
Speaker 2: what it tells you about the overall macro environment.

810
00:42:34,120 --> 00:42:39,279
Speaker 8: Well, from my perspective, at least healthy bank the environment is.

811
00:42:39,960 --> 00:42:43,200
Speaker 8: You know, business spend on capex in the second quarter.

812
00:42:43,320 --> 00:42:45,719
Speaker 8: I don't have any numbers for the third quarter so far,

813
00:42:46,560 --> 00:42:49,680
Speaker 8: but one of the surprises in the overall economy been

814
00:42:49,719 --> 00:42:52,960
Speaker 8: around the Great Lakes. Businesses were spending on capex in

815
00:42:53,000 --> 00:42:56,200
Speaker 8: the second quarter, and arguably that's good news for the economy.

816
00:42:57,480 --> 00:42:58,600
Speaker 3: What do you think it's going to look like this

817
00:42:58,719 --> 00:42:59,400
Speaker 3: quarter though.

818
00:43:00,920 --> 00:43:03,200
Speaker 8: Probably slows down a little bit. I mean, they go

819
00:43:03,239 --> 00:43:06,280
Speaker 8: through cycles, just like you and I would buy endurable goods.

820
00:43:06,960 --> 00:43:10,960
Speaker 8: But it'll probably slow down. But so far, not a

821
00:43:11,000 --> 00:43:13,600
Speaker 8: lot based on the economic reports this week.

822
00:43:13,760 --> 00:43:16,279
Speaker 3: So the people who want to go out and borrow money,

823
00:43:16,280 --> 00:43:18,880
Speaker 3: the business owners who need to go out and borrow money,

824
00:43:18,920 --> 00:43:21,359
Speaker 3: even at these high interest rates, they're still doing it.

825
00:43:23,080 --> 00:43:26,160
Speaker 8: There's probably more cash. They're using more cash, just like

826
00:43:26,239 --> 00:43:29,520
Speaker 8: you and I. Consumers are using more cash, paying cash

827
00:43:29,520 --> 00:43:32,239
Speaker 8: for high dollar items. Businesses are doing the same thing.

828
00:43:32,880 --> 00:43:36,160
Speaker 8: We're still, you know, three and a half trillion dollars

829
00:43:36,760 --> 00:43:40,200
Speaker 8: in cash levels above where we were pre COVID.

830
00:43:40,640 --> 00:43:41,040
Speaker 3: Wow.

831
00:43:41,800 --> 00:43:44,320
Speaker 8: Well as a country, as a country.

832
00:43:44,000 --> 00:43:46,239
Speaker 3: Yeah, not just you and Mary.

833
00:43:47,040 --> 00:43:49,040
Speaker 8: Yeah, Carroll might be.

834
00:43:48,920 --> 00:43:49,680
Speaker 3: But not you and I.

835
00:43:50,840 --> 00:43:52,960
Speaker 2: Hey, so, John, you showed with our producer Paul Reddan

836
00:43:53,000 --> 00:43:55,120
Speaker 2: a top list of items on your mind. Of them,

837
00:43:55,160 --> 00:43:57,600
Speaker 2: what is the one thing that you think and talk

838
00:43:57,640 --> 00:43:59,680
Speaker 2: about the most and that you think we should all

839
00:43:59,719 --> 00:44:04,080
Speaker 2: be paying the most at attention to as investors?

840
00:44:04,120 --> 00:44:07,799
Speaker 8: The Fed? You know, we thought, we erroneously thought the

841
00:44:07,880 --> 00:44:11,239
Speaker 8: Fed would be done at their June twentieth meeting, and

842
00:44:11,280 --> 00:44:14,520
Speaker 8: they're not. They'll probably skip in September, but probably raising

843
00:44:15,000 --> 00:44:19,440
Speaker 8: in November. So it's the Fed that's still on our mind.

844
00:44:19,600 --> 00:44:22,040
Speaker 8: We thought they'd be off our mind by now, but

845
00:44:22,080 --> 00:44:22,920
Speaker 8: that's not the case.

846
00:44:23,440 --> 00:44:25,080
Speaker 3: Why do you think they'll raise again in November?

847
00:44:26,680 --> 00:44:30,480
Speaker 8: GDP, you know, inflation is probably going their way enough,

848
00:44:31,000 --> 00:44:33,520
Speaker 8: even though we get another report in August before excuse me,

849
00:44:33,520 --> 00:44:38,200
Speaker 8: in September, before they meet, but GDP is not going

850
00:44:38,239 --> 00:44:41,719
Speaker 8: their way. In other words, GDP maybe according to the

851
00:44:41,800 --> 00:44:45,440
Speaker 8: Atlanta Fed accelerating in the third quarter. That will come

852
00:44:45,440 --> 00:44:48,440
Speaker 8: out about a week before they meet in November. That's

853
00:44:48,440 --> 00:44:49,920
Speaker 8: probably why they raise in November.

854
00:44:50,120 --> 00:44:52,960
Speaker 3: You're talking about the Just so everyone is aware, you're

855
00:44:52,960 --> 00:44:55,319
Speaker 3: talking about the Atlanta Fed's GDP now index, right.

856
00:44:56,000 --> 00:44:59,120
Speaker 8: Yeah, Yeah, it rose now tracker with over five percent.

857
00:44:59,239 --> 00:45:01,839
Speaker 3: It rose yesterday a five point seven six percent, Carol,

858
00:45:01,880 --> 00:45:04,200
Speaker 3: from five point oh three percent in third quarter versus

859
00:45:04,400 --> 00:45:05,359
Speaker 3: it's previous release on.

860
00:45:05,280 --> 00:45:07,920
Speaker 8: All that's impressive. That is impressive.

861
00:45:09,040 --> 00:45:11,160
Speaker 2: It can move around, though, John, Right.

862
00:45:11,520 --> 00:45:15,280
Speaker 8: Oh, no, it's just this week. Think about this week though, Carol.

863
00:45:15,280 --> 00:45:19,760
Speaker 8: We were three for three. This week was activity economic reports,

864
00:45:20,080 --> 00:45:22,799
Speaker 8: and we were three for three that those came in

865
00:45:22,840 --> 00:45:23,800
Speaker 8: higher than expected.

866
00:45:24,360 --> 00:45:27,960
Speaker 2: Yeah, you know, it's it's kind of the good conundrum,

867
00:45:28,000 --> 00:45:29,839
Speaker 2: if you will, if you're the Fed, right, you're kind

868
00:45:29,840 --> 00:45:33,279
Speaker 2: of glad that the economy hasn't fallen apart and people

869
00:45:33,360 --> 00:45:35,839
Speaker 2: are still working. But it also means that they're still

870
00:45:35,880 --> 00:45:38,960
Speaker 2: momentum for people to go out and buy or invest

871
00:45:39,160 --> 00:45:42,440
Speaker 2: and continue to kind of keep inflation either at these

872
00:45:42,520 --> 00:45:45,319
Speaker 2: levels or even potentially higher. That's the conundrum we live in.

873
00:45:46,719 --> 00:45:49,799
Speaker 8: Yeah, I mean, and you see that in markets, right.

874
00:45:50,200 --> 00:45:53,080
Speaker 8: The story about in August so far has been about

875
00:45:53,160 --> 00:45:56,239
Speaker 8: yields moving back up, and the economy is part of that.

876
00:45:56,320 --> 00:45:58,320
Speaker 8: We would say, the economy is part of that. Stocks

877
00:45:58,320 --> 00:46:01,600
Speaker 8: aren't quite sure what to do with it yet. But

878
00:46:02,160 --> 00:46:05,520
Speaker 8: good news, we would say, we agree with you. Good news.

879
00:46:05,600 --> 00:46:09,960
Speaker 8: Employment is staying high and GDP growth is arguably staying high.

880
00:46:10,080 --> 00:46:14,120
Speaker 8: But the Fed keeps referencing it and they're going to

881
00:46:14,120 --> 00:46:16,440
Speaker 8: probably talk about it again at their September meeting.

882
00:46:16,960 --> 00:46:19,799
Speaker 2: So, John, you've got new money. Tim O's always asked us,

883
00:46:19,840 --> 00:46:21,279
Speaker 2: and I think it's a good question when we have

884
00:46:21,320 --> 00:46:23,120
Speaker 2: folks like you on But if you've got new money,

885
00:46:23,120 --> 00:46:24,880
Speaker 2: do you commit to the fixed income world where you

886
00:46:24,920 --> 00:46:28,640
Speaker 2: can get decent yields or the equity world where there

887
00:46:28,640 --> 00:46:31,920
Speaker 2: are questions about valuations, but if you think the outlook

888
00:46:31,960 --> 00:46:34,720
Speaker 2: is good and growth momentum continues, that kind of makes sense,

889
00:46:34,880 --> 00:46:37,080
Speaker 2: or would you prefer alternatives of some sort.

890
00:46:38,480 --> 00:46:42,680
Speaker 8: Well, what we talked about internally with our portfolio managers yesterday.

891
00:46:42,680 --> 00:46:46,240
Speaker 8: We have about seventy portfolio managers through the Great Lakes

892
00:46:46,280 --> 00:46:50,040
Speaker 8: region and then out west, and what we said was

893
00:46:50,200 --> 00:46:55,280
Speaker 8: probably buy bonds now and do three months to step

894
00:46:55,320 --> 00:47:00,200
Speaker 8: into stocks. We're in the scary season for stocks August, September, October,

895
00:47:00,280 --> 00:47:03,240
Speaker 8: So step into stocks, be a little bit more aggressive

896
00:47:03,239 --> 00:47:07,080
Speaker 8: buying bonds right now, and so far that's that's working.

897
00:47:07,719 --> 00:47:09,440
Speaker 2: How far out though, in terms of bonds are you

898
00:47:09,480 --> 00:47:10,319
Speaker 2: comfortable to go?

899
00:47:11,520 --> 00:47:12,000
Speaker 7: Not far.

900
00:47:12,160 --> 00:47:15,399
Speaker 8: We have a short term bias, so we're generally think

901
00:47:15,440 --> 00:47:17,279
Speaker 8: of the three to five year space. Think of the

902
00:47:17,320 --> 00:47:18,400
Speaker 8: three to five year space.

903
00:47:19,800 --> 00:47:22,440
Speaker 3: So going back to the Fed, is there a chance

904
00:47:22,480 --> 00:47:24,239
Speaker 3: that the Fed isn't done after November?

905
00:47:26,320 --> 00:47:28,719
Speaker 8: Yeah, I mean there's always a chance. We hope they're

906
00:47:28,719 --> 00:47:33,000
Speaker 8: done after November. Obviously the futures market thinks they're going

907
00:47:33,040 --> 00:47:38,160
Speaker 8: to be done. But if the economy, what they're worried about,

908
00:47:38,239 --> 00:47:43,160
Speaker 8: let me back up is the seventies where inflation decelerated

909
00:47:43,280 --> 00:47:46,160
Speaker 8: but then everything's sped back up and they had to

910
00:47:46,239 --> 00:47:49,840
Speaker 8: raise rates again. And that's what they're fixated on right now,

911
00:47:50,239 --> 00:47:52,920
Speaker 8: even though we're obviously in a much different economic situation.

912
00:47:53,160 --> 00:47:55,239
Speaker 3: But what that could mean is on the seventies. But

913
00:47:55,280 --> 00:47:57,400
Speaker 3: maybe we can interpret that as holding rates higher for

914
00:47:57,480 --> 00:47:59,799
Speaker 3: longer rather than cutting or rather than raising again.

915
00:48:00,000 --> 00:48:04,120
Speaker 8: We hope, Yeah, we don't know, raising again, but holding

916
00:48:04,200 --> 00:48:06,799
Speaker 8: higher for longer. They're finally getting to by the way

917
00:48:06,880 --> 00:48:08,240
Speaker 8: the futures market.

918
00:48:07,800 --> 00:48:11,279
Speaker 2: With that thought, Hey, John, just got about forty five

919
00:48:11,320 --> 00:48:15,240
Speaker 2: seconds left here. You guys recently edited Adobe, Amex, American Express,

920
00:48:15,320 --> 00:48:19,920
Speaker 2: Rockwell Automation, Parkerhannafen, T Mobile, Netflix, Tarbuck Salesforce. It's a

921
00:48:20,000 --> 00:48:23,640
Speaker 2: variety of companies. Is there any kind of commonality meaning?

922
00:48:23,719 --> 00:48:26,799
Speaker 2: Was it a fundamental play, valuation play, technical plane a

923
00:48:26,840 --> 00:48:29,120
Speaker 2: play or was it kind of a stock pickers market?

924
00:48:29,160 --> 00:48:31,080
Speaker 2: And again got about forty seconds.

925
00:48:31,560 --> 00:48:34,960
Speaker 8: Sure, stock pickers market. What our equity team generally does

926
00:48:35,880 --> 00:48:39,440
Speaker 8: is make their buy and sell decisions after earning season.

927
00:48:40,120 --> 00:48:42,759
Speaker 8: So there's something in that, either in the conference call

928
00:48:42,840 --> 00:48:46,160
Speaker 8: or in the earnings report that triggers them. So that

929
00:48:46,760 --> 00:48:49,239
Speaker 8: those were some names there that you talked about, but

930
00:48:49,320 --> 00:48:52,280
Speaker 8: they'll generally do their trims and ads after earning season.

931
00:48:52,360 --> 00:48:55,200
Speaker 8: We like that. Actually, that gives a little bit more

932
00:48:55,480 --> 00:48:59,279
Speaker 8: let's say, conviction to our thoughts there, even though they

933
00:48:59,280 --> 00:49:01,840
Speaker 8: may not work out the very short term. We like

934
00:49:01,920 --> 00:49:03,040
Speaker 8: what the actuity team does.

935
00:49:03,400 --> 00:49:06,279
Speaker 2: All right, great conversation, John, Thank you so much. You well,

936
00:49:06,440 --> 00:49:09,719
Speaker 2: John Augustine, He's CIO over at Huntington Private Bank. Joinning

937
00:49:09,760 --> 00:49:11,480
Speaker 2: us on zoom from Cincinnati, Ohio.

938
00:49:12,560 --> 00:49:17,200
Speaker 1: This is the Bloomberg Business Week podcast, available on Apple, Spotify,

939
00:49:17,320 --> 00:49:21,040
Speaker 1: and anywhere else you get your podcast. Listen live weekday

940
00:49:21,080 --> 00:49:24,680
Speaker 1: afternoons from three to six Eastern on Bloomberg dot com,

941
00:49:24,719 --> 00:49:28,040
Speaker 1: the iHeartRadio app tune In, and the Bloomberg Business App.

942
00:49:28,120 --> 00:49:31,200
Speaker 1: You can also watch us live every weekday on YouTube

943
00:49:31,280 --> 00:49:33,240
Speaker 1: and always on the Bloomberg terminal