1
00:00:02,720 --> 00:00:10,600
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the

2
00:00:10,600 --> 00:00:14,560
Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am

3
00:00:14,640 --> 00:00:17,880
Speaker 1: Eastern on Apple, Coarclay, and Android Auto with the Bloomberg

4
00:00:17,920 --> 00:00:21,000
Speaker 1: Business App. Listen on demand wherever you get your podcasts,

5
00:00:21,360 --> 00:00:23,520
Speaker 1: or watch us live on YouTube.

6
00:00:24,320 --> 00:00:27,440
Speaker 2: On this Friday after Thanksgiving, Jeff Chang joins us. He's

7
00:00:27,480 --> 00:00:31,920
Speaker 2: a president of vest Financial. You know, it's great, Jeff

8
00:00:31,960 --> 00:00:33,320
Speaker 2: to have you here. I'm looking at the S and

9
00:00:33,320 --> 00:00:35,919
Speaker 2: P five hundred year to date up fifteen percent. I've

10
00:00:35,960 --> 00:00:40,240
Speaker 2: got the NASHTAC up twenty percent. Boy, that is solid performance.

11
00:00:40,400 --> 00:00:42,199
Speaker 2: I don't care where you are, who you are, What

12
00:00:42,240 --> 00:00:43,360
Speaker 2: do you do for twenty twenty six?

13
00:00:43,400 --> 00:00:43,800
Speaker 3: Do you think?

14
00:00:45,200 --> 00:00:46,200
Speaker 4: Yeah, great to be here.

15
00:00:46,560 --> 00:00:49,120
Speaker 5: You know, I still think the biggest driver of growth

16
00:00:49,240 --> 00:00:52,400
Speaker 5: is going to be AI. It's also what's interesting now,

17
00:00:52,400 --> 00:00:55,200
Speaker 5: it's also going to be the greatest source of volatility.

18
00:00:55,480 --> 00:00:57,640
Speaker 5: I mean, you can even see today with the CMB

19
00:00:57,760 --> 00:01:00,200
Speaker 5: and data centers like this is really going to be

20
00:01:00,400 --> 00:01:03,760
Speaker 5: what's going to be driving the market overall into the

21
00:01:03,800 --> 00:01:06,480
Speaker 5: next year. I think also, you know, on a larger basis,

22
00:01:06,520 --> 00:01:11,160
Speaker 5: you're kind of weighing between kind of the macro factors, cooling,

23
00:01:11,160 --> 00:01:14,840
Speaker 5: inflation AI, but also you know, earnings catching up to

24
00:01:15,240 --> 00:01:18,640
Speaker 5: valuations what we've seen today, you know, with multiples being

25
00:01:18,720 --> 00:01:20,520
Speaker 5: on the higher end of the spectrum.

26
00:01:21,080 --> 00:01:23,800
Speaker 6: Yeah, so this AI trade, I mean it's definitely changing.

27
00:01:23,800 --> 00:01:26,319
Speaker 7: There's rotation involved, right, I mean, do you still stick

28
00:01:26,360 --> 00:01:28,520
Speaker 7: with the nvideos or do you start looking at some

29
00:01:28,600 --> 00:01:30,680
Speaker 7: of the other companies like the alphabets now that are

30
00:01:30,800 --> 00:01:32,760
Speaker 7: very involved with our TPUs.

31
00:01:33,520 --> 00:01:37,319
Speaker 5: Yeah. I think there's definitely growing competition, but I think

32
00:01:37,360 --> 00:01:40,160
Speaker 5: when you look at Nvidia, it's still the bell weather

33
00:01:40,319 --> 00:01:44,319
Speaker 5: for AI hardware. You know, from there because when you

34
00:01:44,360 --> 00:01:46,560
Speaker 5: look at AI and the foundational parts of it, you're

35
00:01:46,560 --> 00:01:49,480
Speaker 5: looking at you know, compute, data centers, networking and cloud

36
00:01:49,560 --> 00:01:53,520
Speaker 5: and cooling. Isn't that as well? But I think when

37
00:01:53,560 --> 00:01:56,240
Speaker 5: you look at Nvidia, it's sits very kind of right

38
00:01:56,240 --> 00:01:59,760
Speaker 5: in the center as far as foundational parts of a

39
00:02:00,720 --> 00:02:04,440
Speaker 5: given that you know, one thing is that they've also

40
00:02:04,680 --> 00:02:06,640
Speaker 5: if you look at the numbers and four guidance has

41
00:02:06,680 --> 00:02:09,040
Speaker 5: been pretty strong. I do think where you start to

42
00:02:09,040 --> 00:02:10,920
Speaker 5: see a little bit of volatility is the idea that

43
00:02:10,919 --> 00:02:13,040
Speaker 5: competition is coming in. And so if you start to

44
00:02:13,040 --> 00:02:15,840
Speaker 5: look at you know, the likes of Microsoft really planting

45
00:02:15,880 --> 00:02:18,480
Speaker 5: the flag and cloud, and then like you said, Google

46
00:02:18,480 --> 00:02:21,440
Speaker 5: and Alphabet coming in into the AI chip space as well,

47
00:02:21,480 --> 00:02:24,840
Speaker 5: So we can't see some volatility there, but mostly the

48
00:02:24,880 --> 00:02:27,640
Speaker 5: dominance of Nvidia we'll see is going to be pretty

49
00:02:27,639 --> 00:02:28,800
Speaker 5: strong into twenty twenty six.

50
00:02:29,320 --> 00:02:31,680
Speaker 2: Fanny, I think I'm oh for a lifetime trying to

51
00:02:31,720 --> 00:02:32,920
Speaker 2: call the top of a bubble.

52
00:02:33,160 --> 00:02:35,000
Speaker 3: You know, I just never.

53
00:02:35,240 --> 00:02:37,080
Speaker 6: Possibly won't even try doing that both.

54
00:02:37,120 --> 00:02:41,480
Speaker 2: I was selling stock Boy cranking at IPO's March of

55
00:02:41,520 --> 00:02:44,240
Speaker 2: two thousand with a vengeance and no idea. Yeah, like

56
00:02:44,520 --> 00:02:48,200
Speaker 2: five minutes later it was over. So how do you

57
00:02:48,280 --> 00:02:50,520
Speaker 2: and how do investors in general? Jeff do you think?

58
00:02:50,800 --> 00:02:53,320
Speaker 2: How do you think about a bubble? And are we

59
00:02:53,560 --> 00:02:55,280
Speaker 2: in a bubble? Are we at the top of the bubble?

60
00:02:55,280 --> 00:02:56,240
Speaker 2: How do you think about that?

61
00:02:57,960 --> 00:03:00,960
Speaker 5: I don't think as far as like you've probably seen

62
00:03:01,080 --> 00:03:03,720
Speaker 5: recently in the news of soft Bank and the like

63
00:03:03,800 --> 00:03:06,840
Speaker 5: Superior Teal selling their positions in Nvidia, I actually don't

64
00:03:06,880 --> 00:03:09,400
Speaker 5: think that's an indication of people getting out due to

65
00:03:09,480 --> 00:03:12,400
Speaker 5: a bubble. I think, sure, are the are the AI

66
00:03:12,440 --> 00:03:15,520
Speaker 5: stocks in hyper skillers expensive? Yeah? I could you can

67
00:03:15,520 --> 00:03:17,880
Speaker 5: make an argument for that, But when you're thinking about

68
00:03:18,000 --> 00:03:20,720
Speaker 5: AI and the idea that it might be a bubble.

69
00:03:20,800 --> 00:03:23,600
Speaker 5: This is very different than something like you know, if

70
00:03:23,600 --> 00:03:26,320
Speaker 5: you were to look at crypto, I would say that

71
00:03:26,760 --> 00:03:31,000
Speaker 5: this is a structural shift. Now, selectivity matters where you're

72
00:03:31,040 --> 00:03:33,280
Speaker 5: actually going to be in AI. But I think what

73
00:03:33,280 --> 00:03:36,280
Speaker 5: we're seeing is a generational shift as far as you know,

74
00:03:36,320 --> 00:03:39,720
Speaker 5: because AI is going to diffuse into mainstream as far

75
00:03:39,720 --> 00:03:45,080
Speaker 5: as anywhere from logistics to enterprise software, you know, healthcare,

76
00:03:45,160 --> 00:03:45,840
Speaker 5: defense is.

77
00:03:45,840 --> 00:03:47,000
Speaker 4: Going to go everywhere there.

78
00:03:47,040 --> 00:03:49,960
Speaker 5: I think where you're seeing a lot of people misunderstanding

79
00:03:50,080 --> 00:03:52,720
Speaker 5: is and when we've seen this story play out before,

80
00:03:53,280 --> 00:03:56,200
Speaker 5: is the idea that, okay, we've seen a huge amount

81
00:03:56,200 --> 00:03:59,280
Speaker 5: of investment into AI, uh you know, and you know,

82
00:03:59,400 --> 00:04:02,480
Speaker 5: hundreds of bill dollars, where as in the actual earnings

83
00:04:02,520 --> 00:04:05,360
Speaker 5: you're talking to twenty you know, twenty five thirty billion

84
00:04:05,960 --> 00:04:08,240
Speaker 5: in actual earnings. But this is where you start to

85
00:04:09,200 --> 00:04:11,040
Speaker 5: if you pull back the curtain a little bit. Keep

86
00:04:11,080 --> 00:04:13,920
Speaker 5: in mind your earnings is based off of accounting depreciation,

87
00:04:14,080 --> 00:04:18,120
Speaker 5: which tends to front load your expenses, and it doesn't

88
00:04:18,320 --> 00:04:20,599
Speaker 5: account for the earnings potential over the long term at

89
00:04:20,680 --> 00:04:23,320
Speaker 5: least seen this play out before. If you look at

90
00:04:23,320 --> 00:04:26,080
Speaker 5: going back, like fiber optics, there are billions of dollars

91
00:04:26,080 --> 00:04:30,120
Speaker 5: on front end investment, but we hadn't seen the trillions

92
00:04:30,120 --> 00:04:32,560
Speaker 5: of dollars of earnings potential that came out in many

93
00:04:32,640 --> 00:04:35,320
Speaker 5: years afterwards. So you know, like they always say, you know,

94
00:04:35,440 --> 00:04:37,680
Speaker 5: history doesn't repeat itself, but it does rhyme. I think

95
00:04:37,720 --> 00:04:40,480
Speaker 5: what you're seeing here is kind of the fiber optic

96
00:04:40,520 --> 00:04:44,200
Speaker 5: Internet type situation. We're really fundamentally changing, you know, how

97
00:04:44,240 --> 00:04:45,359
Speaker 5: we do business going forward.

98
00:04:45,400 --> 00:04:47,840
Speaker 7: So you say, the foundation of long term AI is sound,

99
00:04:47,880 --> 00:04:49,720
Speaker 7: but where do the revenues come from? And what are

100
00:04:49,760 --> 00:04:52,160
Speaker 7: you projecting out in terms of revenue for these companies

101
00:04:52,200 --> 00:04:54,040
Speaker 7: that are spending a fortune on AI.

102
00:04:55,400 --> 00:05:00,400
Speaker 5: Yeah, So it like they said, like selectivity matters. So

103
00:05:00,480 --> 00:05:04,200
Speaker 5: if you're talking about you know, the Microsoft metals, Metas,

104
00:05:04,240 --> 00:05:07,359
Speaker 5: the Googles, and the videos of the world, those they

105
00:05:07,400 --> 00:05:10,800
Speaker 5: are going to touch every part of AI, whether or not,

106
00:05:10,880 --> 00:05:12,839
Speaker 5: whether or not the earnings are going to be generated

107
00:05:12,880 --> 00:05:15,960
Speaker 5: from industrials, whether you know, industrial animation, whether it's going

108
00:05:16,000 --> 00:05:19,040
Speaker 5: to be generated from healthcare, they're going to be at

109
00:05:19,080 --> 00:05:24,200
Speaker 5: the center. Given that the core foundation being cloud, compute, data.

110
00:05:23,960 --> 00:05:25,040
Speaker 4: Centers, and networking.

111
00:05:25,160 --> 00:05:27,839
Speaker 5: So that's really where if you're looking as a long

112
00:05:27,960 --> 00:05:31,839
Speaker 5: term investor, who are where the earnings are more sustainably

113
00:05:32,040 --> 00:05:37,040
Speaker 5: and more higher probability of actually realization is.

114
00:05:37,000 --> 00:05:38,080
Speaker 4: Being at kind of that center.

115
00:05:38,160 --> 00:05:39,880
Speaker 5: I think where you start to get a little bit dicey,

116
00:05:40,000 --> 00:05:43,279
Speaker 5: is you know, trading on the hype, going for these

117
00:05:43,320 --> 00:05:47,159
Speaker 5: types of moonshot type trades where you're looking at small

118
00:05:47,240 --> 00:05:51,280
Speaker 5: cap stocks and maybe in small robotics or so on

119
00:05:51,279 --> 00:05:52,800
Speaker 5: and so forth. That's where you start to get a

120
00:05:52,839 --> 00:05:55,279
Speaker 5: little bit of dicey as far as will we see

121
00:05:55,320 --> 00:05:58,159
Speaker 5: that earnings realization to justify the valuations.

122
00:05:58,440 --> 00:06:00,840
Speaker 2: You know, Vonnie, I remember the beginning of this whole

123
00:06:01,400 --> 00:06:04,800
Speaker 2: AI discussion maybe three years ago. Gene Monster from deep

124
00:06:04,800 --> 00:06:07,440
Speaker 2: Water Asset Management, to me is one of the best

125
00:06:07,480 --> 00:06:10,880
Speaker 2: technology analysts on Wall Street. He said, Hey, think about

126
00:06:10,880 --> 00:06:14,160
Speaker 2: a continuum of what's important. Let's put electricity like from

127
00:06:14,279 --> 00:06:16,920
Speaker 2: zero to one hundred. Electricity he puts it ninety five.

128
00:06:17,440 --> 00:06:21,120
Speaker 2: The Internet he puts at seventy AI he put it

129
00:06:21,240 --> 00:06:24,160
Speaker 2: like ninety right there. I mean that, So every time

130
00:06:24,160 --> 00:06:26,800
Speaker 2: I think about, jeez, maybe we're kind of in a ball.

131
00:06:26,920 --> 00:06:30,440
Speaker 3: I know. He was so right. So, Jeff, where do

132
00:06:30,480 --> 00:06:32,800
Speaker 3: we go from here? What's the next way to play it.

133
00:06:32,839 --> 00:06:35,680
Speaker 2: If I've played out my Nvidia trade, maybe I've played

134
00:06:35,680 --> 00:06:39,000
Speaker 2: out my Microsoft Microsoft trade on AI. What's the next

135
00:06:39,880 --> 00:06:42,520
Speaker 2: leg do you think for this whole investment thesis.

136
00:06:43,920 --> 00:06:47,120
Speaker 5: Yeah, so this is very similar to let's say, in

137
00:06:47,200 --> 00:06:49,960
Speaker 5: nineteen ninety nine and two thousand in the tech boom,

138
00:06:50,040 --> 00:06:52,080
Speaker 5: that a lot of these pumply traded companies people were

139
00:06:52,080 --> 00:06:55,640
Speaker 5: taking kind of bets here and there. I actually believe

140
00:06:55,640 --> 00:06:58,120
Speaker 5: if you then really kind of fast forward the tape,

141
00:06:58,120 --> 00:07:01,600
Speaker 5: you'll notice that the real winners, the companies that really

142
00:07:01,600 --> 00:07:03,440
Speaker 5: want to kind of the Internet boom. The facebooks of

143
00:07:03,480 --> 00:07:06,000
Speaker 5: the world, the Netflixes of the world, the Airbnb's of

144
00:07:06,000 --> 00:07:08,359
Speaker 5: the world, they weren't around. They were in startup mode.

145
00:07:08,400 --> 00:07:11,800
Speaker 5: So I think a lot of folks that are trying

146
00:07:11,800 --> 00:07:14,640
Speaker 5: to play the AI trade, a lot of the facebooks

147
00:07:14,680 --> 00:07:18,120
Speaker 5: in AI of tomorrow are going to be in venture

148
00:07:18,200 --> 00:07:21,480
Speaker 5: They're going to be in early stage or middle stage

149
00:07:21,480 --> 00:07:25,240
Speaker 5: startups that are really being able to innovate, move fast,

150
00:07:25,400 --> 00:07:28,320
Speaker 5: kind of break things kind of mentality. I think, like

151
00:07:28,440 --> 00:07:32,440
Speaker 5: I said, the next wave, it's not publicly traded, especially

152
00:07:32,440 --> 00:07:34,400
Speaker 5: in the AI space. I think, like I said, the

153
00:07:34,440 --> 00:07:37,640
Speaker 5: foundational parts, like you know, the hyperscalers and the videos

154
00:07:37,680 --> 00:07:39,920
Speaker 5: of the world are publicly traded, but I think a

155
00:07:39,960 --> 00:07:43,640
Speaker 5: lot of the next wave of software and usage cases

156
00:07:43,880 --> 00:07:47,440
Speaker 5: across all industries are actually in private markets right now.

157
00:07:47,520 --> 00:07:49,200
Speaker 7: All right, well, Jeff, we have to leave it there,

158
00:07:49,280 --> 00:07:51,480
Speaker 7: but thank you so much for joining us today. And

159
00:07:51,520 --> 00:07:53,720
Speaker 7: I know you're in Las Vegas, so if you're putting

160
00:07:53,720 --> 00:07:58,480
Speaker 7: anything putting on black today that is best Financial President

161
00:07:58,560 --> 00:07:59,600
Speaker 7: and co founder.

162
00:07:59,400 --> 00:08:02,880
Speaker 3: Jeff, stay with us. We're from Bloomberg Intelligence coming.

163
00:08:02,800 --> 00:08:03,600
Speaker 8: Up there for this.

164
00:08:07,200 --> 00:08:10,920
Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live

165
00:08:11,000 --> 00:08:14,040
Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android

166
00:08:14,080 --> 00:08:17,400
Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever

167
00:08:17,440 --> 00:08:20,560
Speaker 1: you get your podcasts, or watch us live on YouTube.

168
00:08:21,360 --> 00:08:22,840
Speaker 7: So let's bring in our next guest to talk a

169
00:08:22,880 --> 00:08:26,160
Speaker 7: little shopping. We have Julie Bornstein. She is the CEO

170
00:08:26,360 --> 00:08:29,360
Speaker 7: of Daydream, which is an AI agent for fashion retail

171
00:08:29,400 --> 00:08:29,960
Speaker 7: in particular.

172
00:08:30,080 --> 00:08:31,440
Speaker 6: Julie, thank you so much for joining.

173
00:08:31,480 --> 00:08:34,440
Speaker 7: First, just explain a little bit what a fashion agent

174
00:08:34,480 --> 00:08:35,480
Speaker 7: for retail can do.

175
00:08:35,480 --> 00:08:37,760
Speaker 6: For me when I go online.

176
00:08:38,000 --> 00:08:40,520
Speaker 9: Yes, well, ultimately the goal is to be a personal

177
00:08:40,559 --> 00:08:42,080
Speaker 9: stylist in your pocket.

178
00:08:42,520 --> 00:08:45,199
Speaker 10: Today. What we do is we allow you to let.

179
00:08:45,080 --> 00:08:47,960
Speaker 9: Us know what you're looking for using whatever you want to. Say,

180
00:08:48,120 --> 00:08:52,800
Speaker 9: you have a Christmas party with your office in a

181
00:08:53,080 --> 00:08:55,560
Speaker 9: bar in New York City, and we can help you

182
00:08:55,600 --> 00:08:59,360
Speaker 9: find what you're looking for. We make it easier to shop,

183
00:08:59,640 --> 00:09:02,720
Speaker 9: and we also allow you to post a picture and

184
00:09:02,760 --> 00:09:04,640
Speaker 9: then we can help you find something that looks like that.

185
00:09:05,559 --> 00:09:08,400
Speaker 3: So I don't know where to go here, Julie.

186
00:09:08,559 --> 00:09:13,560
Speaker 2: But of shoppers say they're using AI tools for holiday shopping.

187
00:09:13,600 --> 00:09:16,520
Speaker 2: That's according to a horse pull for MasterCard.

188
00:09:16,920 --> 00:09:19,839
Speaker 3: Are retailers are they ready for that? They are they

189
00:09:19,920 --> 00:09:22,280
Speaker 3: trying to take advantage of that? How are they performing here?

190
00:09:22,720 --> 00:09:26,080
Speaker 9: It's early days for the retailers, but they are starting

191
00:09:26,120 --> 00:09:29,800
Speaker 9: to learn how to both appear through some of these

192
00:09:30,760 --> 00:09:34,600
Speaker 9: different chat surfaces and they are also trying to figure

193
00:09:34,600 --> 00:09:36,960
Speaker 9: out how to use AI to make the customer experience

194
00:09:37,000 --> 00:09:39,400
Speaker 9: better on their sites. Some of them are doing interesting

195
00:09:39,440 --> 00:09:42,680
Speaker 9: things get up, H and M. They're also participating with

196
00:09:42,760 --> 00:09:45,360
Speaker 9: us Daydreams. So because we're sort of the first AI

197
00:09:45,800 --> 00:09:49,920
Speaker 9: shopping platform, they can partner with us and their show

198
00:09:50,000 --> 00:09:52,600
Speaker 9: up as customers are explaining what it is that they're

199
00:09:52,640 --> 00:09:54,679
Speaker 9: looking for in a whole new way. Julie.

200
00:09:54,720 --> 00:09:56,520
Speaker 7: We always hear when it comes to AI that is

201
00:09:57,040 --> 00:09:59,720
Speaker 7: all about the prompt, right, So what are customers doing

202
00:09:59,760 --> 00:10:01,280
Speaker 7: and how should we prompt better?

203
00:10:02,400 --> 00:10:03,320
Speaker 10: It's a great question.

204
00:10:03,400 --> 00:10:06,160
Speaker 9: I mean we see prompts that are everything from I'm

205
00:10:06,200 --> 00:10:08,080
Speaker 9: going to a wedding and I want to wear a

206
00:10:08,080 --> 00:10:11,079
Speaker 9: pink dress, but all look like a cupcake too. I'm

207
00:10:11,120 --> 00:10:13,320
Speaker 9: going to a wedding and I want to wear a

208
00:10:13,360 --> 00:10:17,440
Speaker 9: revenge dress in the selt burn. So we have lots

209
00:10:17,440 --> 00:10:18,040
Speaker 9: of different kinds.

210
00:10:18,080 --> 00:10:19,760
Speaker 6: I want to see what the hallucinations are for those

211
00:10:19,800 --> 00:10:20,160
Speaker 6: of users.

212
00:10:20,400 --> 00:10:23,800
Speaker 10: Yeah, exactly what the cupcake dress looks like.

213
00:10:24,120 --> 00:10:27,240
Speaker 9: People are very creative and the truth is that every

214
00:10:27,480 --> 00:10:29,840
Speaker 9: consumers are just learning how to prompt, and so part

215
00:10:29,880 --> 00:10:32,200
Speaker 9: of what we do is also give them ideas so

216
00:10:32,200 --> 00:10:34,000
Speaker 9: that if they're starting to look, and then once they

217
00:10:34,040 --> 00:10:36,720
Speaker 9: get used to it, people will write very long things

218
00:10:36,720 --> 00:10:38,880
Speaker 9: with very descriptive details of what they're looking for.

219
00:10:39,480 --> 00:10:43,280
Speaker 2: I've seen some technology use some apps on the iPhone,

220
00:10:43,720 --> 00:10:47,079
Speaker 2: take a screenshot of something and then it brings you

221
00:10:47,240 --> 00:10:49,480
Speaker 2: right into a shopping environment.

222
00:10:49,920 --> 00:10:52,480
Speaker 3: Talk to us about where that is right now, where

223
00:10:52,520 --> 00:10:52,960
Speaker 3: that's going.

224
00:10:53,640 --> 00:10:55,440
Speaker 10: Yeah, that's going to just get better and better.

225
00:10:55,480 --> 00:10:58,640
Speaker 9: I think the idea of being able to screenshot something

226
00:10:58,760 --> 00:11:01,880
Speaker 9: on the new iOS allows you to instantly you don't

227
00:11:01,880 --> 00:11:04,400
Speaker 9: even have to open an app bring up results from

228
00:11:04,520 --> 00:11:07,160
Speaker 9: any of the apps that you have already downloaded. So

229
00:11:07,200 --> 00:11:10,000
Speaker 9: if you have Daydream downloaded on your phone and you

230
00:11:10,080 --> 00:11:13,280
Speaker 9: screenshot anything from Instagram or as you're walking around and

231
00:11:13,320 --> 00:11:15,960
Speaker 9: you see someone wearing something you like, you have the

232
00:11:16,000 --> 00:11:19,640
Speaker 9: ability instantly to pull up the results that are closest

233
00:11:19,640 --> 00:11:22,880
Speaker 9: to that product, and if that product still exists, you

234
00:11:22,880 --> 00:11:24,080
Speaker 9: can get that exact product.

235
00:11:24,080 --> 00:11:26,360
Speaker 10: So it's pretty cool and it's a pretty quick way.

236
00:11:26,480 --> 00:11:29,520
Speaker 9: And what we're seeing is younger generation gen Z is

237
00:11:29,760 --> 00:11:33,559
Speaker 9: very higher use case for that. They're just used to

238
00:11:33,640 --> 00:11:36,000
Speaker 9: being able to snap anything and then be able to

239
00:11:36,320 --> 00:11:37,400
Speaker 9: find what they're looking for.

240
00:11:38,280 --> 00:11:42,760
Speaker 7: So if I screenshot something, it sends a whole load

241
00:11:42,800 --> 00:11:44,480
Speaker 7: of information into the background of my phone.

242
00:11:44,559 --> 00:11:44,960
Speaker 6: Is that right?

243
00:11:45,000 --> 00:11:46,520
Speaker 7: That sounds a little dangerous to me. It means I

244
00:11:46,520 --> 00:11:48,000
Speaker 7: should probably stop screenshotting things.

245
00:11:49,200 --> 00:11:52,360
Speaker 9: No, it's really if you choose to then use the

246
00:11:52,480 --> 00:11:56,360
Speaker 9: search mechanism for it. So in general, screenshots are not

247
00:11:56,600 --> 00:11:59,520
Speaker 9: you know, nothing's happening with them until you decide to

248
00:11:59,559 --> 00:12:02,000
Speaker 9: do something with that screenshot. So you know, if you're

249
00:12:02,040 --> 00:12:03,720
Speaker 9: looking for a product, it's pretty safe.

250
00:12:04,880 --> 00:12:09,360
Speaker 2: So all right, how about this warm but not bulky

251
00:12:09,400 --> 00:12:14,160
Speaker 2: coat under three hundred dollars. Where is the AI thingamajig

252
00:12:14,280 --> 00:12:14,880
Speaker 2: going to take me?

253
00:12:16,200 --> 00:12:18,480
Speaker 9: Well, on daydream, it's going to take you to a

254
00:12:18,480 --> 00:12:21,800
Speaker 9: set of results that match that kind of query, and

255
00:12:22,120 --> 00:12:25,079
Speaker 9: then we'll see lots of results from around the web

256
00:12:25,440 --> 00:12:28,000
Speaker 9: and we will once you find what you're looking for,

257
00:12:28,040 --> 00:12:31,120
Speaker 9: you link out to the brand or the retailer that

258
00:12:31,200 --> 00:12:33,920
Speaker 9: sells that product. So similar to the way Google works,

259
00:12:34,160 --> 00:12:36,840
Speaker 9: we're basically sort of a search and discovery platform that

260
00:12:36,880 --> 00:12:39,160
Speaker 9: then helps you find the retailer to buy from.

261
00:12:39,200 --> 00:12:41,800
Speaker 2: All right, well, in the world of search, it's search

262
00:12:42,360 --> 00:12:46,160
Speaker 2: optimization type thing. Is that is there a similar type

263
00:12:46,240 --> 00:12:47,640
Speaker 2: thing here in the world of AI.

264
00:12:48,800 --> 00:12:51,760
Speaker 9: So it can depend on what the business model is.

265
00:12:51,800 --> 00:12:54,040
Speaker 9: In our case, we are totally focused on finding you

266
00:12:54,040 --> 00:12:57,040
Speaker 9: the best product for you that matches both the query

267
00:12:57,320 --> 00:13:00,160
Speaker 9: and your personal preferences as we learn about you, and

268
00:13:00,200 --> 00:13:04,240
Speaker 9: so it's not an issue of whatever you know, whoever

269
00:13:04,320 --> 00:13:08,000
Speaker 9: pays the most gets the way to show you what

270
00:13:08,080 --> 00:13:10,360
Speaker 9: their product is. But in some cases, you know, there's

271
00:13:10,400 --> 00:13:11,240
Speaker 9: an ad base model.

272
00:13:11,280 --> 00:13:11,760
Speaker 10: Ours is not.

273
00:13:12,520 --> 00:13:15,280
Speaker 7: Julie Open ai on Amazon both made headlines this week

274
00:13:15,280 --> 00:13:18,880
Speaker 7: with shopping launches. How are their products different to yours?

275
00:13:19,000 --> 00:13:21,200
Speaker 7: Are they compatible to use together or how are you

276
00:13:21,200 --> 00:13:22,280
Speaker 7: dealing with that competition.

277
00:13:23,640 --> 00:13:24,840
Speaker 10: We're really excited about it.

278
00:13:24,880 --> 00:13:26,840
Speaker 9: I think in general shopping, you know, this is the

279
00:13:26,880 --> 00:13:31,000
Speaker 9: first season where AI is really going to improve the

280
00:13:31,040 --> 00:13:33,280
Speaker 9: shopping experience, and it's only going to get better. I

281
00:13:33,360 --> 00:13:35,040
Speaker 9: think the fact that they're both doing this is very

282
00:13:35,120 --> 00:13:38,080
Speaker 9: validating to us. They're separate, but ours is totally focused

283
00:13:38,120 --> 00:13:40,880
Speaker 9: on fashion, and so if you go to either of

284
00:13:40,920 --> 00:13:44,080
Speaker 9: those platforms, you'll find that you were kind of generally

285
00:13:44,120 --> 00:13:47,120
Speaker 9: across all categories, but a little bit less focused on

286
00:13:47,160 --> 00:13:50,320
Speaker 9: a single category. And so shopping and fashion is so

287
00:13:50,440 --> 00:13:53,280
Speaker 9: nuanced that, you know, daydream is better for that.

288
00:13:54,000 --> 00:13:56,880
Speaker 2: So, Julie, what are your clients telling you here about

289
00:13:57,000 --> 00:14:00,400
Speaker 2: this holiday shopping season. What's the level of optimism because

290
00:14:00,400 --> 00:14:03,040
Speaker 2: there are a lot of economic cross currents out there.

291
00:14:03,679 --> 00:14:06,040
Speaker 9: Yeah, that really are I think that this year is

292
00:14:06,080 --> 00:14:09,360
Speaker 9: going to be bigger than last year, but probably not

293
00:14:09,480 --> 00:14:12,600
Speaker 9: by a huge amount. What we're seeing is that there's

294
00:14:12,640 --> 00:14:14,680
Speaker 9: a lot of excitement for all of the sales that

295
00:14:14,720 --> 00:14:16,920
Speaker 9: are going on this week for Cyber Week, and so

296
00:14:17,440 --> 00:14:21,560
Speaker 9: what we have is a daydream broadcast on It's a

297
00:14:21,560 --> 00:14:24,520
Speaker 9: broadcast channel on Instagram where you can actually see all

298
00:14:24,560 --> 00:14:27,240
Speaker 9: the brands and all of the offers that they're giving you.

299
00:14:27,720 --> 00:14:29,280
Speaker 9: What we know is that a lot of people are

300
00:14:29,320 --> 00:14:31,760
Speaker 9: saving the things that they want to buy and coming

301
00:14:31,800 --> 00:14:35,040
Speaker 9: back today to go see if they're on sale.

302
00:14:35,160 --> 00:14:38,040
Speaker 7: You have a lot of experience raising money and bringing

303
00:14:38,040 --> 00:14:40,200
Speaker 7: companies to market and so on. You sold your previous

304
00:14:40,200 --> 00:14:43,640
Speaker 7: company to Pinterest actually, and you've been on the boards

305
00:14:43,640 --> 00:14:47,480
Speaker 7: of you know, Sweet Green, Redfin Weight Watchers, so much experience.

306
00:14:47,680 --> 00:14:50,560
Speaker 7: What is the environment like now for consumer companies and

307
00:14:50,640 --> 00:14:52,120
Speaker 7: raising money.

308
00:14:52,720 --> 00:14:53,360
Speaker 10: It's interesting.

309
00:14:53,400 --> 00:14:56,640
Speaker 9: It's a very strange environment because the role of AI

310
00:14:56,840 --> 00:14:59,680
Speaker 9: has kind of thrown everything off, and so I think

311
00:14:59,760 --> 00:15:04,920
Speaker 9: every everybody who has an interesting angle with AI is

312
00:15:04,960 --> 00:15:08,640
Speaker 9: definitely able to get conversations going. I think that if

313
00:15:08,680 --> 00:15:11,200
Speaker 9: you're not doing something with AI right now, it needs

314
00:15:11,240 --> 00:15:14,720
Speaker 9: to be a very unique proposition. It is a very

315
00:15:14,760 --> 00:15:18,400
Speaker 9: AI focused fundraising environment. Doesn't mean that there aren't other

316
00:15:18,680 --> 00:15:21,760
Speaker 9: ideas that are great and out there, but certainly the

317
00:15:21,840 --> 00:15:24,680
Speaker 9: fact is that AI can help pretty much every aspect

318
00:15:24,760 --> 00:15:27,160
Speaker 9: of building a business right now, and so you want

319
00:15:27,160 --> 00:15:29,680
Speaker 9: to be able to show how you have an advantage

320
00:15:29,760 --> 00:15:32,280
Speaker 9: leveraging AI that other people don't have, so you have

321
00:15:32,320 --> 00:15:33,720
Speaker 9: to figure out what your moat is.

322
00:15:33,920 --> 00:15:37,280
Speaker 7: Julie is partially the reason it's such an AI type

323
00:15:37,280 --> 00:15:42,840
Speaker 7: of fundraising environment because all these dollar donors are invested

324
00:15:42,880 --> 00:15:43,920
Speaker 7: in AI themselves.

325
00:15:46,440 --> 00:15:49,560
Speaker 9: No, I think it's more because everyone believes it's going

326
00:15:49,640 --> 00:15:52,800
Speaker 9: to shape the future, and it's still early, so it's

327
00:15:52,880 --> 00:15:55,680
Speaker 9: hard to know exactly how. But if you're not actually

328
00:15:55,680 --> 00:15:58,480
Speaker 9: taking advantage of it, it seems like a really missed

329
00:15:58,520 --> 00:16:01,560
Speaker 9: opportunity because it will change to the way everyone does work,

330
00:16:01,600 --> 00:16:04,400
Speaker 9: from the way employees work, to the way products are made,

331
00:16:04,560 --> 00:16:06,960
Speaker 9: to the way supply chains run, to the way consumers

332
00:16:07,040 --> 00:16:07,880
Speaker 9: experience a HI.

333
00:16:09,280 --> 00:16:12,280
Speaker 2: So what's the next step here for AI? Do you think, Julie,

334
00:16:12,360 --> 00:16:14,040
Speaker 2: is there another natural progression?

335
00:16:15,240 --> 00:16:17,920
Speaker 9: I think that there will be a lot of evolutions

336
00:16:17,920 --> 00:16:20,400
Speaker 9: in the models. I also think we'll start to see

337
00:16:20,440 --> 00:16:23,760
Speaker 9: over the next year or two a lot more consumer

338
00:16:23,840 --> 00:16:26,040
Speaker 9: experience is changing. So we haven't seen a lot of

339
00:16:26,080 --> 00:16:28,800
Speaker 9: that yet other than going directly to some of these

340
00:16:28,880 --> 00:16:29,720
Speaker 9: new chatbots.

341
00:16:30,040 --> 00:16:32,160
Speaker 10: But if you think about.

342
00:16:31,880 --> 00:16:33,880
Speaker 9: All the things you do in your day to day life,

343
00:16:34,040 --> 00:16:36,880
Speaker 9: things will get a little bit smoother and smarter. As

344
00:16:36,920 --> 00:16:39,360
Speaker 9: a result of AI, and there will be new businesses

345
00:16:39,400 --> 00:16:44,280
Speaker 9: that come along, like Daydream that just make shopping, for example, easier, smoother,

346
00:16:44,360 --> 00:16:45,120
Speaker 9: and more personal.

347
00:16:45,840 --> 00:16:47,560
Speaker 2: All right, great stuff, Thank you so much. We really

348
00:16:47,600 --> 00:16:52,840
Speaker 2: appreciate Julie Bornstein. She's the founder and CEO at Daydream.

349
00:16:52,400 --> 00:16:54,920
Speaker 3: And stay with us. More from Bloomberg Intelligence coming up

350
00:16:55,160 --> 00:16:55,800
Speaker 3: after this.

351
00:17:00,200 --> 00:17:03,880
Speaker 1: To the Bloomberg Intelligence Podcast. Catch us live weekdays at

352
00:17:03,880 --> 00:17:06,879
Speaker 1: ten am Eastern on Apple, Cocklay and Android Auto with

353
00:17:06,920 --> 00:17:10,040
Speaker 1: the Bloomberg Business App. Listen on demand wherever you get

354
00:17:10,080 --> 00:17:14,160
Speaker 1: your podcasts, or watch us live on YouTube Now.

355
00:17:14,200 --> 00:17:15,919
Speaker 2: For more on the state of the consumer on this

356
00:17:15,960 --> 00:17:20,000
Speaker 2: Black Friday, is Andrea Barnardo, a seasoned business leader and

357
00:17:20,040 --> 00:17:23,919
Speaker 2: board director in consumer brands and retail Andrew, thanks so

358
00:17:23,960 --> 00:17:29,000
Speaker 2: much for joining us here. What's the mood of retailers

359
00:17:29,000 --> 00:17:31,919
Speaker 2: here going into this holiday shopping season? There certainly are

360
00:17:31,920 --> 00:17:34,240
Speaker 2: a lot of economic crosswinds out there.

361
00:17:35,440 --> 00:17:39,000
Speaker 11: The retailer is actually quite optimistic, especially compared to where

362
00:17:39,040 --> 00:17:41,640
Speaker 11: they were last year. At the beginning of this year,

363
00:17:41,680 --> 00:17:44,640
Speaker 11: we thought tariffs and tax cuts would drive consumer behavior,

364
00:17:45,040 --> 00:17:49,000
Speaker 11: and while tariffs still remain a big concern, consumers are

365
00:17:49,040 --> 00:17:52,560
Speaker 11: also absorbing something much more subtle. The ripple effects of

366
00:17:52,720 --> 00:17:56,879
Speaker 11: a shutdown, fewer government services, a cold housing market, and

367
00:17:56,960 --> 00:18:01,640
Speaker 11: large company layoffs. So frankly, the eccount of feels contradictory.

368
00:18:02,040 --> 00:18:04,520
Speaker 11: But spending is holding, as we've just seen in Q

369
00:18:04,720 --> 00:18:08,320
Speaker 11: the Q three earnings reports, they are holding. Households are

370
00:18:08,320 --> 00:18:11,440
Speaker 11: more cautious, but customers are still spending in a really

371
00:18:11,480 --> 00:18:12,320
Speaker 11: selective way.

372
00:18:13,080 --> 00:18:14,000
Speaker 6: What are they buying.

373
00:18:14,119 --> 00:18:16,720
Speaker 7: Andrea Deloyde is seeing a lower average spend for the

374
00:18:16,720 --> 00:18:20,080
Speaker 7: Black Friday Cyber Monday. But you know, these statistics sometimes

375
00:18:20,119 --> 00:18:23,040
Speaker 7: can be misleading in the sense that people will continue

376
00:18:23,080 --> 00:18:24,639
Speaker 7: to spend or they've already started spending.

377
00:18:25,680 --> 00:18:27,960
Speaker 11: That's such a great question because when we talk about

378
00:18:27,960 --> 00:18:32,040
Speaker 11: the contradiction of seeing spending the customers being more selective,

379
00:18:32,480 --> 00:18:35,480
Speaker 11: I like to define it as the customer isn't stepping back,

380
00:18:35,600 --> 00:18:39,960
Speaker 11: they're really reprioritizing and they're doing a purposeful reallocation. So

381
00:18:40,040 --> 00:18:42,199
Speaker 11: when you think about what they're spending on, they're kind

382
00:18:42,240 --> 00:18:45,520
Speaker 11: of three big shifts that are happening. One, customers are

383
00:18:45,520 --> 00:18:49,600
Speaker 11: moving towards intelligent value. They're looking for things that have function,

384
00:18:49,920 --> 00:18:53,280
Speaker 11: meaning utility, longevity, so they can feel like they're really

385
00:18:53,280 --> 00:18:55,080
Speaker 11: getting a return on their investment.

386
00:18:55,560 --> 00:18:57,720
Speaker 4: Two their consolidating purchases.

387
00:18:58,040 --> 00:18:59,960
Speaker 11: And so one of the interesting things we've seen is

388
00:19:00,359 --> 00:19:02,960
Speaker 11: in some of these QB earnings reports.

389
00:19:03,000 --> 00:19:05,640
Speaker 4: Is where some of the values are going down.

390
00:19:05,760 --> 00:19:09,480
Speaker 11: We are seeing higher ticket prices and higher order values

391
00:19:09,560 --> 00:19:12,880
Speaker 11: on top of lower order numbers. And so this means

392
00:19:12,920 --> 00:19:17,200
Speaker 11: customers are consolidating purchases, they're sticking with fewer retailers, They're

393
00:19:17,280 --> 00:19:22,320
Speaker 11: leaning heavily into loyalty programs and also convenience. And then finally,

394
00:19:22,440 --> 00:19:24,440
Speaker 11: customers are shopping to reduce stress.

395
00:19:24,760 --> 00:19:26,440
Speaker 4: They're choosing products.

396
00:19:25,960 --> 00:19:29,960
Speaker 11: And services that save time, that simplify life and add comfort.

397
00:19:30,840 --> 00:19:32,520
Speaker 3: How about availability inventory?

398
00:19:32,560 --> 00:19:33,840
Speaker 2: If I'm going to go out and get my kid

399
00:19:33,880 --> 00:19:35,840
Speaker 2: the Gi Joe with the Kung Fu grip, is it

400
00:19:35,880 --> 00:19:37,240
Speaker 2: going to be in the stores?

401
00:19:38,440 --> 00:19:39,000
Speaker 4: It will be.

402
00:19:39,200 --> 00:19:41,600
Speaker 11: That's one of the really interesting things that happened this

403
00:19:41,720 --> 00:19:45,000
Speaker 11: year with tariffs, a lot of companies pulled.

404
00:19:44,720 --> 00:19:45,919
Speaker 4: Their inventory forward.

405
00:19:46,160 --> 00:19:48,919
Speaker 11: So whether you were planning on a shorter cycle as usual,

406
00:19:49,200 --> 00:19:52,000
Speaker 11: they actually said, let's pull inventory for the next year

407
00:19:52,040 --> 00:19:55,560
Speaker 11: so we can get goods into stores and online. So yes,

408
00:19:55,640 --> 00:19:58,520
Speaker 11: hopefully your kid's toy will be their in store, and

409
00:19:58,600 --> 00:20:02,040
Speaker 11: if not, I can tell you. With the improvement in

410
00:20:02,119 --> 00:20:04,960
Speaker 11: AI and technology, they are surely going to offer you

411
00:20:05,040 --> 00:20:07,320
Speaker 11: something that they think is just as compatible.

412
00:20:08,160 --> 00:20:10,640
Speaker 7: I was speaking to the CEO of a toy company

413
00:20:10,720 --> 00:20:12,919
Speaker 7: just on Wednesday, I guess, and he was saying that

414
00:20:13,000 --> 00:20:16,160
Speaker 7: a toy that was maybe twenty dollars before the pandemic

415
00:20:16,720 --> 00:20:19,119
Speaker 7: is now forty dollars for various reasons. So there was

416
00:20:19,160 --> 00:20:22,640
Speaker 7: a pandemic right the supply chain snawfoods, and then inflation

417
00:20:22,800 --> 00:20:25,640
Speaker 7: thanks to that, and then inflation from you know, fiscal

418
00:20:26,359 --> 00:20:28,720
Speaker 7: spending and so on, and now we're right up to

419
00:20:28,720 --> 00:20:30,560
Speaker 7: almost double what it was before.

420
00:20:30,720 --> 00:20:32,399
Speaker 6: Are we seeing that? Are we seeing that kind of

421
00:20:32,400 --> 00:20:33,520
Speaker 6: inflation across the board?

422
00:20:34,520 --> 00:20:37,480
Speaker 11: Yes, we are, and it's happening in more categories than other.

423
00:20:37,560 --> 00:20:41,479
Speaker 11: So you're mentioning toys, disposables, groceries. We are seeing that

424
00:20:41,560 --> 00:20:44,600
Speaker 11: inflation across the board. And to your point, it has

425
00:20:44,600 --> 00:20:46,760
Speaker 11: to do with the general inflation. It has to do

426
00:20:46,840 --> 00:20:49,720
Speaker 11: with terraiffs because while companies have absorbed some of the

427
00:20:49,760 --> 00:20:52,440
Speaker 11: tariff costs, many of them have actually passed.

428
00:20:52,200 --> 00:20:53,600
Speaker 4: It right on to consumers.

429
00:20:53,840 --> 00:20:55,960
Speaker 11: And so this is why consumers are being much more

430
00:20:56,000 --> 00:21:00,359
Speaker 11: selective about where they purchase, and related to that, to

431
00:21:00,400 --> 00:21:04,240
Speaker 11: trade down or explore alternative options. If it's a commodity item,

432
00:21:04,320 --> 00:21:06,480
Speaker 11: they might actually say, let me go explore this other

433
00:21:06,520 --> 00:21:08,840
Speaker 11: brand that might be a little bit cheaper, or buy

434
00:21:08,840 --> 00:21:11,119
Speaker 11: the generic version of that to help capture some of

435
00:21:11,160 --> 00:21:12,000
Speaker 11: that value back.

436
00:21:13,160 --> 00:21:17,000
Speaker 2: What's promotional environment out there these days? Andrea, Am I

437
00:21:17,040 --> 00:21:19,280
Speaker 2: going to get deals when I go to the store,

438
00:21:19,280 --> 00:21:21,320
Speaker 2: assuming I actually do go shopping.

439
00:21:22,440 --> 00:21:25,280
Speaker 11: You won't get deals. They will be very selective deals.

440
00:21:25,280 --> 00:21:28,760
Speaker 11: So as retailers and businesses are looking at their inventory,

441
00:21:28,880 --> 00:21:31,760
Speaker 11: they want to be much more precise about what they're promoting.

442
00:21:32,000 --> 00:21:34,320
Speaker 11: So they're going to be focused on where do they

443
00:21:34,320 --> 00:21:37,000
Speaker 11: have a lot of inventory, what can they replace fast,

444
00:21:37,359 --> 00:21:39,920
Speaker 11: what's going discontinued, where.

445
00:21:39,680 --> 00:21:41,280
Speaker 4: Are you going to get that value?

446
00:21:41,359 --> 00:21:44,320
Speaker 11: And even in some areas, they are going to price

447
00:21:44,359 --> 00:21:46,640
Speaker 11: promote on items where they know you will add on.

448
00:21:46,960 --> 00:21:49,879
Speaker 11: So you won't find full deals across the board. You

449
00:21:49,920 --> 00:21:52,479
Speaker 11: won't see fifty percent off side wide as much as

450
00:21:52,520 --> 00:21:54,919
Speaker 11: you used to, But what you will see is on

451
00:21:54,960 --> 00:21:59,040
Speaker 11: specific categories they're certainly discounting, but at the same time

452
00:21:59,359 --> 00:22:02,000
Speaker 11: they will bemting those discounts because they know this is

453
00:22:02,040 --> 00:22:05,359
Speaker 11: the holiday season, they know customers are focused on value,

454
00:22:05,440 --> 00:22:07,760
Speaker 11: so they're still going to put those select deals right

455
00:22:07,760 --> 00:22:09,560
Speaker 11: in front of you, So it feels like the same

456
00:22:09,640 --> 00:22:11,600
Speaker 11: velocity as it did in previous years.

457
00:22:11,720 --> 00:22:15,000
Speaker 7: What's popular when it comes to fashion and retail and

458
00:22:15,040 --> 00:22:18,560
Speaker 7: department store retail in that sense. Coles for example, reporting

459
00:22:18,600 --> 00:22:21,520
Speaker 7: the other day and their strategy under the previous CEO

460
00:22:21,600 --> 00:22:24,760
Speaker 7: who was ousted was to focus on fragrances and petits,

461
00:22:24,840 --> 00:22:26,800
Speaker 7: and that strategy seems to be working even for the

462
00:22:26,840 --> 00:22:30,520
Speaker 7: incoming CEO who's now been made permanent. But it struck

463
00:22:30,560 --> 00:22:33,040
Speaker 7: me as odd that suddenly fragrances are very popular.

464
00:22:34,040 --> 00:22:37,080
Speaker 11: They are, and fragrances have to do with that longevity

465
00:22:37,119 --> 00:22:39,800
Speaker 11: and value. Customers want something that they know they can

466
00:22:39,920 --> 00:22:42,760
Speaker 11: use over time, and so what you'll see is right now,

467
00:22:42,800 --> 00:22:45,960
Speaker 11: you'll see purchases in what I call functional tech. So

468
00:22:46,000 --> 00:22:48,280
Speaker 11: if you need a new laptop, if you need a TV,

469
00:22:48,440 --> 00:22:50,920
Speaker 11: things that you need that will have a long term

470
00:22:51,000 --> 00:22:53,960
Speaker 11: value to you, also fragrances and well, and it's something

471
00:22:54,000 --> 00:22:56,240
Speaker 11: where you feel like you'll get your return, it will

472
00:22:56,320 --> 00:22:58,760
Speaker 11: last over a period of time as well as you

473
00:22:58,800 --> 00:23:01,719
Speaker 11: will feel good about yourself. And then finally, there are

474
00:23:01,720 --> 00:23:05,040
Speaker 11: other areas in beauty and in luxury, and many of

475
00:23:05,080 --> 00:23:08,919
Speaker 11: these retailers, especially those that have multi brand portfolios. We

476
00:23:09,040 --> 00:23:12,720
Speaker 11: have seen their luxury brands, whether it's home furnishings or apparel,

477
00:23:13,119 --> 00:23:16,720
Speaker 11: actually outperform. And that still goes to the value, which

478
00:23:16,760 --> 00:23:19,479
Speaker 11: you don't often see value in luxury. But what happens

479
00:23:19,520 --> 00:23:22,439
Speaker 11: is customers think, Okay, at least if nothing else, I

480
00:23:22,480 --> 00:23:24,960
Speaker 11: have longevity and I have resale value.

481
00:23:25,080 --> 00:23:28,080
Speaker 2: Hey, Andrew, you say that share of wallet is the

482
00:23:28,119 --> 00:23:30,400
Speaker 2: real battle this season, not just traffic.

483
00:23:30,400 --> 00:23:33,119
Speaker 11: What do you mean by that, Well, what we're seeing

484
00:23:33,240 --> 00:23:36,119
Speaker 11: is that with these average order values going up, what

485
00:23:36,320 --> 00:23:38,480
Speaker 11: customers want is they want to go to a one

486
00:23:38,520 --> 00:23:41,800
Speaker 11: stop shop. And so where retailers are competing is they're

487
00:23:41,800 --> 00:23:45,199
Speaker 11: adding services, they're adding shipping, they're making sure they're inventory

488
00:23:45,200 --> 00:23:46,639
Speaker 11: and logistics are there.

489
00:23:46,560 --> 00:23:49,480
Speaker 4: So that you can add more to your purchase in

490
00:23:49,520 --> 00:23:50,159
Speaker 4: one stop.

491
00:23:50,480 --> 00:23:53,520
Speaker 11: One of the big opportunities that I'm seeing is bundling.

492
00:23:53,600 --> 00:23:56,840
Speaker 11: We've often heard about bundling when it comes to travel

493
00:23:56,920 --> 00:23:59,399
Speaker 11: and dieting, but when it comes to bundling, if you

494
00:23:59,440 --> 00:24:01,600
Speaker 11: were able to to talk about a gift set, or

495
00:24:01,680 --> 00:24:04,280
Speaker 11: add the pants with the shirt, or combine a whole

496
00:24:04,320 --> 00:24:07,199
Speaker 11: set of items, customers always see that as an opportunity.

497
00:24:07,480 --> 00:24:09,679
Speaker 11: And so you see that retailers are using that to

498
00:24:09,840 --> 00:24:11,680
Speaker 11: increase their ticket values right now.

499
00:24:12,000 --> 00:24:14,080
Speaker 7: Yeah, I love all the words associated with a bundling

500
00:24:14,160 --> 00:24:17,879
Speaker 7: and a whole, yeah and unlox my whole today. I

501
00:24:17,960 --> 00:24:19,840
Speaker 7: love getting holes, but I never I never seem to

502
00:24:19,840 --> 00:24:22,159
Speaker 7: feel to afford them. Andrea, thank you so much for

503
00:24:22,240 --> 00:24:24,560
Speaker 7: joining us today. A fun conversation there, and a serious

504
00:24:24,600 --> 00:24:29,320
Speaker 7: conversation too with Andrea Vornado, much appreciated consumer brands and

505
00:24:29,640 --> 00:24:31,119
Speaker 7: retail experts.

506
00:24:31,359 --> 00:24:33,720
Speaker 3: Stay with us or from Bloomberg Intelligence coming.

507
00:24:33,640 --> 00:24:41,000
Speaker 1: Up there for this, you're listening to the Bloomberg Intelligence podcast.

508
00:24:41,320 --> 00:24:44,240
Speaker 1: Catch us live weekdays at ten am Eastern on Apple,

509
00:24:44,280 --> 00:24:47,600
Speaker 1: Cocklay and Android Auto with the Bloomberg Business app. Listen

510
00:24:47,640 --> 00:24:50,760
Speaker 1: on demand wherever you get your podcasts, or watch us

511
00:24:50,800 --> 00:24:52,160
Speaker 1: live on YouTube.

512
00:24:52,920 --> 00:24:55,280
Speaker 7: Let's talk something that maybe isn't going to be so

513
00:24:55,359 --> 00:24:57,560
Speaker 7: calm though in the coming weeks, tariffs taking their toll

514
00:24:57,760 --> 00:25:01,240
Speaker 7: on consumers and companies alike. Our next guest says tariffs

515
00:25:01,240 --> 00:25:03,960
Speaker 7: are now one of the biggest variables in retail margins.

516
00:25:04,040 --> 00:25:06,439
Speaker 7: Joining us to discuss as Ryan Peterson, founder and CEO

517
00:25:06,520 --> 00:25:10,320
Speaker 7: of flex Sport, a leader in global supply chain technology. Ryan,

518
00:25:10,400 --> 00:25:13,200
Speaker 7: thank you so much for joining I would have thought

519
00:25:13,200 --> 00:25:15,119
Speaker 7: that things would have calmed down a little bit. We

520
00:25:15,160 --> 00:25:18,040
Speaker 7: have at least some kind of certainty, albeit we're waiting

521
00:25:18,080 --> 00:25:19,879
Speaker 7: for a Scolds decision, but we have some kind of

522
00:25:19,880 --> 00:25:21,879
Speaker 7: certainty over tariffs at the moment.

523
00:25:22,520 --> 00:25:24,840
Speaker 6: What are supply chains like? Is there's still chaos out

524
00:25:24,880 --> 00:25:25,440
Speaker 6: there or are we.

525
00:25:25,400 --> 00:25:29,840
Speaker 7: Getting to some kind of ability to see the future.

526
00:25:31,240 --> 00:25:31,919
Speaker 8: Somewhere in between.

527
00:25:31,960 --> 00:25:33,880
Speaker 12: I think people are getting more used to the fact

528
00:25:33,880 --> 00:25:35,520
Speaker 12: that you have to operate and a lot of uncertainty,

529
00:25:35,680 --> 00:25:38,320
Speaker 12: and brands are figuring out how to be agile about

530
00:25:38,359 --> 00:25:40,040
Speaker 12: it and a lot of different strategies where.

531
00:25:39,920 --> 00:25:40,480
Speaker 8: They can do that.

532
00:25:40,600 --> 00:25:43,399
Speaker 12: But there's still a lot of you mentioned scotis that

533
00:25:43,560 --> 00:25:46,359
Speaker 12: that big core case is coming. People are hoping and

534
00:25:46,359 --> 00:25:50,720
Speaker 12: praying for a refund and it's actually you know, there's

535
00:25:50,760 --> 00:25:52,320
Speaker 12: a lot of variables here. You have to figure out

536
00:25:52,320 --> 00:25:54,119
Speaker 12: how much of the duties, how much of the tariffs

537
00:25:54,119 --> 00:25:56,280
Speaker 12: do you want to pass through to your customer. Goldman

538
00:25:56,359 --> 00:25:58,919
Speaker 12: Sachs reporting about thirty seven percent of the duties are

539
00:25:58,920 --> 00:26:04,639
Speaker 12: getting passed through, with nine percent getting forced on the supplier,

540
00:26:04,760 --> 00:26:07,240
Speaker 12: and the balance is just hitting earnings for these companies.

541
00:26:07,280 --> 00:26:10,240
Speaker 12: So there's that and then even just calculating the duties

542
00:26:10,320 --> 00:26:15,000
Speaker 12: is harder than ever before the nature of the stacking tariffs,

543
00:26:15,000 --> 00:26:16,840
Speaker 12: where there's all these different tariffs, they just.

544
00:26:16,880 --> 00:26:18,160
Speaker 8: Keep getting layered on each other.

545
00:26:18,520 --> 00:26:21,560
Speaker 12: We've seen more traffic to our tariff simulator on our

546
00:26:21,600 --> 00:26:23,920
Speaker 12: website than to the overall website.

547
00:26:24,920 --> 00:26:29,959
Speaker 2: So it's interesting, Ryan, do we think that companies are

548
00:26:29,960 --> 00:26:32,600
Speaker 2: going to try to push more of those costs through

549
00:26:33,080 --> 00:26:36,320
Speaker 2: going forward as opposed to absorbing them into their P

550
00:26:36,400 --> 00:26:37,360
Speaker 2: and L statement.

551
00:26:38,000 --> 00:26:40,040
Speaker 12: You only have two choices kind of. I mean, I

552
00:26:40,040 --> 00:26:42,560
Speaker 12: get you can renegotiate things with your factory. That's not

553
00:26:42,600 --> 00:26:44,560
Speaker 12: a choice, that's something you're going to always try to do.

554
00:26:45,240 --> 00:26:47,480
Speaker 12: And then you have two choices. It's do you pass

555
00:26:47,520 --> 00:26:50,160
Speaker 12: it through and or do you eat the costs?

556
00:26:50,200 --> 00:26:52,120
Speaker 8: And the good businesses will pass it through.

557
00:26:52,160 --> 00:26:53,760
Speaker 12: I think that's you know, one of the hallmarks of

558
00:26:53,800 --> 00:26:56,919
Speaker 12: a good business is your ability to pass through price increases,

559
00:26:57,240 --> 00:27:00,000
Speaker 12: but a lot of companies can't. So may not even

560
00:27:00,080 --> 00:27:02,119
Speaker 12: be a choice as much as you'd like to. The

561
00:27:02,160 --> 00:27:04,359
Speaker 12: moment you raise your prices, you start to lose demand.

562
00:27:04,520 --> 00:27:07,359
Speaker 12: So yeah, then longer term you have some more options

563
00:27:07,359 --> 00:27:10,600
Speaker 12: around moving supply chains, but it's become so difficult because

564
00:27:11,440 --> 00:27:13,600
Speaker 12: you know it's not just hey, exit China, move your

565
00:27:13,640 --> 00:27:15,960
Speaker 12: factory somewhere else. Tariffs on India are just about the

566
00:27:16,000 --> 00:27:18,439
Speaker 12: same as China, and they might pop up anywhere in

567
00:27:18,440 --> 00:27:20,119
Speaker 12: the world that with the way things have been.

568
00:27:19,960 --> 00:27:22,800
Speaker 7: Going, ryan, are we done with all the avoiding certain

569
00:27:22,920 --> 00:27:25,639
Speaker 7: areas because you know, there's a drought in one canal,

570
00:27:25,720 --> 00:27:28,480
Speaker 7: and there's war and potentially in another canal, and there's

571
00:27:28,680 --> 00:27:33,080
Speaker 7: you know, terrorism in another part of the globe, or you.

572
00:27:32,960 --> 00:27:35,680
Speaker 6: Know, are we back to everyone can ship everywhere?

573
00:27:37,160 --> 00:27:39,760
Speaker 12: I know we're definitely not back to normalcy there. And

574
00:27:39,920 --> 00:27:44,399
Speaker 12: the container volumes going through the Suez Canal are still

575
00:27:44,480 --> 00:27:50,000
Speaker 12: down about seventy five percent since the HOUTI started to

576
00:27:50,040 --> 00:27:53,040
Speaker 12: cut that channel off at the end of a couple

577
00:27:53,119 --> 00:27:56,639
Speaker 12: of years ago, and actually just last week created a

578
00:27:56,640 --> 00:27:58,719
Speaker 12: poly market on this. Now you can bet on anything

579
00:28:00,080 --> 00:28:03,159
Speaker 12: out bet on whether when traffic will return to normal

580
00:28:03,160 --> 00:28:06,359
Speaker 12: through the Suez Canal. It's not very it's pretty thinly

581
00:28:06,400 --> 00:28:07,719
Speaker 12: traded right now. We'll see if we get some more

582
00:28:07,720 --> 00:28:10,119
Speaker 12: signal out of that. But there are some rumors that

583
00:28:10,160 --> 00:28:12,000
Speaker 12: will start to come back to life next year and

584
00:28:12,240 --> 00:28:14,200
Speaker 12: ships will return, but for now they're going all the

585
00:28:14,240 --> 00:28:17,800
Speaker 12: way around Africa and polymarket has me at thirty seven

586
00:28:17,840 --> 00:28:20,760
Speaker 12: percent to return in Key one. I think that's free

587
00:28:20,800 --> 00:28:22,920
Speaker 12: money I'm taking. That won't happen side of that shore.

588
00:28:23,000 --> 00:28:25,240
Speaker 6: Hey, we're ignoring that part of the conversation. What about

589
00:28:25,240 --> 00:28:28,480
Speaker 6: the Panama Canal, Well, Panama's been.

590
00:28:28,440 --> 00:28:30,879
Speaker 8: There ongoing thing ever since they re engineered it.

591
00:28:30,920 --> 00:28:34,000
Speaker 12: So they widened the canal in back in twenty fifteen.

592
00:28:34,240 --> 00:28:36,359
Speaker 12: And you know, the Panama Canal runs on fresh water,

593
00:28:36,760 --> 00:28:38,880
Speaker 12: so if you widen the canal, more fresh water flows

594
00:28:38,880 --> 00:28:41,280
Speaker 12: out of the canal into the ocean than when it

595
00:28:41,320 --> 00:28:45,040
Speaker 12: was narrower. And so ever since they've done that with

596
00:28:45,120 --> 00:28:48,080
Speaker 12: the same amount of rain, you've now had these periodic

597
00:28:48,160 --> 00:28:50,719
Speaker 12: moments where there's just not enough water to operate the canal.

598
00:28:51,440 --> 00:28:54,400
Speaker 12: We're currently in a normal period, but a couple of

599
00:28:54,440 --> 00:28:56,480
Speaker 12: two years ago it was really bad. They could only

600
00:28:56,480 --> 00:28:58,680
Speaker 12: operate about two thirds capacity on that. That's going to

601
00:28:58,760 --> 00:29:01,800
Speaker 12: keep coming back unless they spend tens of billions of

602
00:29:01,800 --> 00:29:04,080
Speaker 12: dollars re engineering the water flows in the canal, which

603
00:29:04,080 --> 00:29:05,440
Speaker 12: I don't I don't think they're gonna be able to do.

604
00:29:06,440 --> 00:29:08,600
Speaker 3: That's on my bucket list to go through the Panama Canal,

605
00:29:09,080 --> 00:29:09,560
Speaker 3: it really is.

606
00:29:10,160 --> 00:29:12,520
Speaker 7: I think you're containership wisely then, Paul, because you're not

607
00:29:12,520 --> 00:29:13,360
Speaker 7: getting on all of them.

608
00:29:13,440 --> 00:29:16,600
Speaker 2: I know exactly, but I'm ready to do that. Talk

609
00:29:16,600 --> 00:29:21,240
Speaker 2: to us about inventory levels, Ryan, I mean, are retailers

610
00:29:21,240 --> 00:29:24,840
Speaker 2: are they stocking their usual amount of retail inventory or

611
00:29:24,840 --> 00:29:26,520
Speaker 2: are they going a little bit light or what are

612
00:29:26,560 --> 00:29:26,880
Speaker 2: they doing?

613
00:29:27,400 --> 00:29:29,280
Speaker 8: No, you could argue they're going the opposite.

614
00:29:29,320 --> 00:29:32,280
Speaker 12: They're going heavy on us here because because not so

615
00:29:32,360 --> 00:29:35,840
Speaker 12: much because of the uncertainty of demand, although that always exists,

616
00:29:36,000 --> 00:29:37,120
Speaker 12: but because of the tariffs.

617
00:29:37,120 --> 00:29:38,560
Speaker 8: So everybody front loaded.

618
00:29:38,680 --> 00:29:42,200
Speaker 12: You know, these reciprocal tariffs on restive world ended August

619
00:29:42,280 --> 00:29:44,640
Speaker 12: twenty ninth, and you had a while, a couple of

620
00:29:44,640 --> 00:29:47,360
Speaker 12: months to know that that was coming, and so everybody

621
00:29:47,360 --> 00:29:50,920
Speaker 12: brought all their inventory in early. And what it's going

622
00:29:51,000 --> 00:29:53,440
Speaker 12: to look like is you can never really predict what's

623
00:29:53,480 --> 00:29:55,760
Speaker 12: going to be a hit product and what's not. And

624
00:29:55,840 --> 00:29:58,080
Speaker 12: so you're gonna have You're gonna have stock outs on

625
00:29:58,160 --> 00:30:00,720
Speaker 12: certain hit products where because it's not much hard to replenish,

626
00:30:00,760 --> 00:30:04,720
Speaker 12: you're paying higher duties, and so you're gonna have stockouts

627
00:30:04,720 --> 00:30:07,640
Speaker 12: on some of the hit products. And then I suspect

628
00:30:07,680 --> 00:30:09,240
Speaker 12: you're going to have quite a bit of overhead of

629
00:30:09,280 --> 00:30:12,040
Speaker 12: products that didn't move, and you'll probably see some good

630
00:30:12,080 --> 00:30:12,880
Speaker 12: deals coming.

631
00:30:12,680 --> 00:30:14,000
Speaker 8: In the beginning of next year.

632
00:30:14,880 --> 00:30:16,840
Speaker 12: I mean, we may see some right now with Black Friday,

633
00:30:16,880 --> 00:30:19,520
Speaker 12: but usually these things play out into the Q one

634
00:30:19,760 --> 00:30:21,240
Speaker 12: period once the holidays.

635
00:30:20,840 --> 00:30:21,560
Speaker 8: Are over all.

636
00:30:21,640 --> 00:30:23,720
Speaker 2: Right, Fannie, here's the book recommendation of the day for

637
00:30:23,800 --> 00:30:27,560
Speaker 2: you about the Panama Canal, The Path between the Seas

638
00:30:27,640 --> 00:30:28,520
Speaker 2: David McCullough.

639
00:30:28,720 --> 00:30:30,200
Speaker 3: Phenomenal book about the history of the.

640
00:30:30,160 --> 00:30:32,200
Speaker 6: Panama and a great writer. I will definitely read that.

641
00:30:32,280 --> 00:30:34,400
Speaker 7: I'm on my twenty second book of the year, which

642
00:30:34,520 --> 00:30:35,360
Speaker 7: I guess is good, but.

643
00:30:37,560 --> 00:30:38,440
Speaker 6: So Ryan.

644
00:30:38,760 --> 00:30:43,120
Speaker 7: You know, in terms of shipping companies and em and A,

645
00:30:43,640 --> 00:30:47,280
Speaker 7: there was plenty of speculation that ZIM, for example, was

646
00:30:47,320 --> 00:30:47,800
Speaker 7: going to.

647
00:30:47,720 --> 00:30:48,800
Speaker 6: Get bought out or whatever.

648
00:30:48,840 --> 00:30:50,560
Speaker 7: That happened a couple of times this year, and some

649
00:30:50,640 --> 00:30:52,520
Speaker 7: of these shipping companies have been really having a hard

650
00:30:52,560 --> 00:30:54,480
Speaker 7: time of it trying to deal with all of this uncertainty.

651
00:30:54,800 --> 00:30:56,680
Speaker 7: Do you have any forecast for us and what we

652
00:30:56,720 --> 00:30:58,480
Speaker 7: might see maybe next year in terms of m and

653
00:30:58,480 --> 00:30:59,320
Speaker 7: A and consolidation.

654
00:31:00,920 --> 00:31:03,720
Speaker 8: No, I don't really know. We'll see it.

655
00:31:04,040 --> 00:31:06,400
Speaker 12: You know, we see a few of the big companies

656
00:31:06,600 --> 00:31:09,720
Speaker 12: executing that strategy. DSV is the biggest right forwarder in

657
00:31:09,720 --> 00:31:12,280
Speaker 12: the world. They've been the most inquisitive. But they just

658
00:31:12,320 --> 00:31:15,080
Speaker 12: bought dB Schenker and that deals closed, and.

659
00:31:15,520 --> 00:31:16,800
Speaker 8: That is a lot to digest.

660
00:31:16,840 --> 00:31:19,040
Speaker 12: They're going to be you know, it basically doubles their

661
00:31:19,080 --> 00:31:22,120
Speaker 12: number of offices around the world, almost doubles their volumes.

662
00:31:22,520 --> 00:31:25,000
Speaker 12: So the main acquire is probably off the table for

663
00:31:25,040 --> 00:31:27,080
Speaker 12: the next couple of years as they two on that one,

664
00:31:27,880 --> 00:31:29,479
Speaker 12: and we'll see. There's you know, there's been a lot

665
00:31:29,520 --> 00:31:32,840
Speaker 12: of consolidation. When I started Flexport, there were twenty three

666
00:31:32,920 --> 00:31:38,840
Speaker 12: ocean carriers, major ones were down to eleven, and I

667
00:31:38,840 --> 00:31:41,480
Speaker 12: don't know there's there may be some mergers, but they've

668
00:31:41,480 --> 00:31:44,040
Speaker 12: all in the past that was done out of financial

669
00:31:44,120 --> 00:31:46,480
Speaker 12: weakness where they were all kind of struggling.

670
00:31:46,560 --> 00:31:48,080
Speaker 8: Well, they've all put a lot of money on their

671
00:31:48,120 --> 00:31:48,800
Speaker 8: balance sheet.

672
00:31:48,640 --> 00:31:50,719
Speaker 12: Over the last few years when the rates were going crazy,

673
00:31:50,800 --> 00:31:52,280
Speaker 12: So there's not going to be a lot of the

674
00:31:52,320 --> 00:31:54,920
Speaker 12: forced m and a that we saw that drove that consolidation.

675
00:31:55,440 --> 00:31:58,120
Speaker 12: We'll see though, maybe there's some interesting strategic opportunities.

676
00:31:58,320 --> 00:32:01,040
Speaker 2: Well, we do have little kasa or pending consolidation in

677
00:32:01,080 --> 00:32:03,720
Speaker 2: the railroad industry. Norfolk Southern and Union Pacific do you

678
00:32:03,760 --> 00:32:10,040
Speaker 2: think shippers want a transcontinental railroad, Ryan, probably.

679
00:32:09,600 --> 00:32:11,880
Speaker 12: But I don't think they just want one. You get

680
00:32:11,960 --> 00:32:15,000
Speaker 12: monopoly powers. They'd like to see more than one. And

681
00:32:15,040 --> 00:32:17,440
Speaker 12: that's the challenge. We've only got four railroads in the US,

682
00:32:17,480 --> 00:32:19,560
Speaker 12: and if you merged the two and you got two

683
00:32:19,600 --> 00:32:21,320
Speaker 12: on the west and two on the east, so you

684
00:32:21,320 --> 00:32:23,000
Speaker 12: could end up in a position where you've got two

685
00:32:23,160 --> 00:32:25,719
Speaker 12: end to end railroads crossing the country.

686
00:32:25,760 --> 00:32:26,880
Speaker 8: I mean, it makes a lot of sense.

687
00:32:26,880 --> 00:32:29,800
Speaker 12: It's sort of silly to transfer to a different railroad

688
00:32:29,840 --> 00:32:32,640
Speaker 12: just because you're crossing the country. But you will see

689
00:32:32,880 --> 00:32:35,360
Speaker 12: probably who knows how we'll play out, if the efficiencies

690
00:32:35,360 --> 00:32:38,000
Speaker 12: are passed through to the brands, to the people that

691
00:32:38,040 --> 00:32:40,560
Speaker 12: are shipping cargo or not, or does it get absorbed

692
00:32:40,560 --> 00:32:42,520
Speaker 12: in sort of oligopoly pricing.

693
00:32:42,560 --> 00:32:45,840
Speaker 7: Ryan, When it comes to the different modes of transportation,

694
00:32:45,920 --> 00:32:49,440
Speaker 7: so poor its trains, you know, rail shipping, and even

695
00:32:49,760 --> 00:32:52,360
Speaker 7: at one point people were flying their cargoes right for

696
00:32:52,360 --> 00:32:56,560
Speaker 7: a huge premia. Who are facing the highest premia or

697
00:32:56,640 --> 00:33:01,440
Speaker 7: is it the same for everybody across the board?

698
00:33:01,920 --> 00:33:02,240
Speaker 3: Not sure?

699
00:33:02,240 --> 00:33:03,640
Speaker 8: Answer the question in terms of what.

700
00:33:04,080 --> 00:33:08,960
Speaker 7: If you're booking travel for your inventory, does it matter

701
00:33:09,040 --> 00:33:12,280
Speaker 7: if you're a toy retailer, or if you're you know,

702
00:33:13,080 --> 00:33:16,920
Speaker 7: a clothing retailer, or if you ship furniture, is it

703
00:33:17,000 --> 00:33:18,880
Speaker 7: the same price or does it depend on ways?

704
00:33:18,920 --> 00:33:20,640
Speaker 6: And how does that all work? Do you bid on it?

705
00:33:20,880 --> 00:33:21,400
Speaker 7: Is it an auction?

706
00:33:21,680 --> 00:33:23,200
Speaker 12: Your pricing will be about the same no matter what

707
00:33:23,240 --> 00:33:25,000
Speaker 12: you're shipping these days, that's the beauty of the container.

708
00:33:25,080 --> 00:33:26,680
Speaker 12: Nobody cares what's inside of it as long as it

709
00:33:26,680 --> 00:33:29,320
Speaker 12: doesn't you know, catch on fire or anything. But but

710
00:33:29,440 --> 00:33:31,880
Speaker 12: the tariffs will be quite different based on the different

711
00:33:32,040 --> 00:33:35,600
Speaker 12: uh based on the value of the goods and what's

712
00:33:35,640 --> 00:33:39,320
Speaker 12: being shipped. And so there you see big category shifts

713
00:33:40,000 --> 00:33:42,320
Speaker 12: where certain products are just really hard.

714
00:33:42,200 --> 00:33:43,840
Speaker 8: Hitting others are totally xempted.

715
00:33:44,280 --> 00:33:47,080
Speaker 12: And the other factor here is if shipping prices go

716
00:33:47,200 --> 00:33:50,600
Speaker 12: way up, like a container of furniture only holds about

717
00:33:50,640 --> 00:33:53,920
Speaker 12: eight couches something, so a high shipping price there, all right,

718
00:33:54,040 --> 00:33:55,280
Speaker 12: hits the pretty hard.

719
00:33:55,160 --> 00:33:56,760
Speaker 2: Rough Leave it there, Ryan, thanks so much for joining

720
00:33:56,800 --> 00:33:59,040
Speaker 2: us at Ryan Peterson Founder and see you a flexport.

721
00:33:59,240 --> 00:34:03,920
Speaker 1: This is the Boomberg Intelligence podcast, available on Apple, Spotify,

722
00:34:04,120 --> 00:34:07,560
Speaker 1: and anywhere else you get your podcasts. Listen live each

723
00:34:07,600 --> 00:34:11,360
Speaker 1: weekday ten am to noon Eastern on bloomberg dot com,

724
00:34:11,480 --> 00:34:15,000
Speaker 1: the iHeartRadio app tune In, and the Bloomberg Business app.

725
00:34:15,440 --> 00:34:18,360
Speaker 1: You can also watch us live every weekday on YouTube

726
00:34:18,760 --> 00:34:21,000
Speaker 1: and always on the Bloomberg terminal