1
00:00:02,720 --> 00:00:10,520
Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. You're listening to the

2
00:00:10,560 --> 00:00:14,480
Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am

3
00:00:14,560 --> 00:00:17,840
Speaker 1: Eastern on Apple Corplay and Android Auto with the Bloomberg

4
00:00:17,880 --> 00:00:21,000
Speaker 1: Business App. Listen on demand wherever you get your podcasts,

5
00:00:21,320 --> 00:00:23,040
Speaker 1: or watch us live on YouTube.

6
00:00:23,720 --> 00:00:26,320
Speaker 2: Alex deal At, Paul Sweeny, John Tucker, this is Bloomberg

7
00:00:26,360 --> 00:00:28,280
Speaker 2: Intelligence Radio. I want to get back to this data

8
00:00:28,320 --> 00:00:30,200
Speaker 2: because it is moving the bond market. In particular, the

9
00:00:30,240 --> 00:00:32,880
Speaker 2: ten year yields now down by full ten basis points,

10
00:00:32,960 --> 00:00:35,560
Speaker 2: kind of encroaching on the two hundred day moving average.

11
00:00:35,680 --> 00:00:38,479
Speaker 2: All that consumer confidence data following the most since August

12
00:00:38,479 --> 00:00:42,120
Speaker 2: of twenty twenty one, joining us now as Stefani Guie Schard,

13
00:00:42,120 --> 00:00:46,199
Speaker 2: a senior economist at the Conference Board, what led this

14
00:00:46,320 --> 00:00:47,440
Speaker 2: decline in confidence?

15
00:00:49,040 --> 00:00:53,320
Speaker 3: So, first, this decline of confidence was broad based across

16
00:00:53,320 --> 00:00:57,520
Speaker 3: income and edge groups, and it was ready driven by

17
00:00:58,240 --> 00:01:03,880
Speaker 3: a return to pasimes in terms of expectations for future

18
00:01:04,800 --> 00:01:06,880
Speaker 3: business condition and future employment.

19
00:01:07,920 --> 00:01:10,759
Speaker 4: Stephanie, it seems like when we had the election, there

20
00:01:10,840 --> 00:01:14,200
Speaker 4: was this burst of optimism across the number of fronts,

21
00:01:14,200 --> 00:01:18,040
Speaker 4: including the economic front, that perhaps the Trump administration would

22
00:01:18,080 --> 00:01:22,399
Speaker 4: be very pro business, pro growth, less regulation, lower taxes,

23
00:01:22,440 --> 00:01:24,960
Speaker 4: all that kind of the data kind of suggests that

24
00:01:24,959 --> 00:01:26,440
Speaker 4: that is fading a little bit.

25
00:01:26,480 --> 00:01:28,920
Speaker 5: Is that Is that how you interpret a data point

26
00:01:28,959 --> 00:01:29,160
Speaker 5: like this?

27
00:01:30,440 --> 00:01:34,600
Speaker 3: So I think what you were mentioning before was reflected

28
00:01:34,720 --> 00:01:39,279
Speaker 3: in our CEO Confidence Index that search over the last quarter.

29
00:01:40,680 --> 00:01:44,800
Speaker 3: So we published this last week. In terms of consumers,

30
00:01:44,800 --> 00:01:47,760
Speaker 3: we are really focused on the short term, on what's

31
00:01:47,800 --> 00:01:52,160
Speaker 3: happening right now, to inflation, to the job market. We

32
00:01:52,280 --> 00:01:55,280
Speaker 3: saw some increase in the price of some key stapers

33
00:01:55,280 --> 00:01:58,360
Speaker 3: for consumers, like the price of X, and this is

34
00:01:58,400 --> 00:02:01,560
Speaker 3: really affecting their mood and how they think about the economy.

35
00:02:03,560 --> 00:02:06,360
Speaker 2: Is this data bifurcated in terms of party lines, because

36
00:02:06,480 --> 00:02:09,160
Speaker 2: we typically see, say in the Umish surveys, the Republicans

37
00:02:09,200 --> 00:02:13,120
Speaker 2: feeling good, Democrats feeling bad, particularly well under President Trump.

38
00:02:14,520 --> 00:02:17,520
Speaker 3: Yeah, so that's still the case. I mean, Republicans are

39
00:02:17,560 --> 00:02:20,480
Speaker 3: feeling much better than Democrats. But what we saw this

40
00:02:20,680 --> 00:02:27,120
Speaker 3: month is a decline across the board, strong decline for

41
00:02:27,560 --> 00:02:32,680
Speaker 3: independents and Democrat consumers, and a slight decline also for Republicans.

42
00:02:33,480 --> 00:02:35,600
Speaker 5: Stephanie, thank you so much for joining us. Really appreciate it.

43
00:02:35,600 --> 00:02:39,799
Speaker 4: Stephanie Gisha Senior Commerce for the Conference Board. Joining us

44
00:02:39,960 --> 00:02:42,720
Speaker 4: from New York via zoom.

45
00:02:43,080 --> 00:02:46,760
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

46
00:02:46,840 --> 00:02:49,960
Speaker 1: weekdays at ten am Eastern on Apple Coarclay, and Android

47
00:02:49,960 --> 00:02:53,240
Speaker 1: Otto with the Bloomberg Business App. Listen on demand wherever

48
00:02:53,320 --> 00:02:56,440
Speaker 1: you get your podcasts, or watch us live on YouTube.

49
00:02:57,040 --> 00:02:59,400
Speaker 2: We bring you all the top news in business, economics

50
00:02:59,400 --> 00:03:02,239
Speaker 2: and finance, broadcasting to live from our interactive broker studio

51
00:03:02,320 --> 00:03:05,000
Speaker 2: right here in midtown Manhattan. Just taking a look care

52
00:03:05,040 --> 00:03:06,799
Speaker 2: at the markets. As John Tucker was just talking about,

53
00:03:06,800 --> 00:03:08,519
Speaker 2: we're around the loads of the session. The NASDAG is

54
00:03:08,520 --> 00:03:10,359
Speaker 2: now out by almost full two percentage points, the S

55
00:03:10,400 --> 00:03:12,919
Speaker 2: and P off by one percent. The semis are also

56
00:03:12,960 --> 00:03:15,120
Speaker 2: getting hit quite hard. But really the rollover picked up

57
00:03:15,120 --> 00:03:17,840
Speaker 2: a little bit of steam after that. US consumer confidence

58
00:03:17,880 --> 00:03:20,239
Speaker 2: that have fell the most since August of twenty twenty one.

59
00:03:20,360 --> 00:03:24,040
Speaker 2: Carolshlife is chief investment Officer at BIMO Family Office and

60
00:03:24,200 --> 00:03:26,320
Speaker 2: joins US now. Carol, I know you look longer term,

61
00:03:26,840 --> 00:03:28,680
Speaker 2: but what do you make of that market action we've

62
00:03:28,720 --> 00:03:31,000
Speaker 2: seen over the last three days. It feels like the

63
00:03:31,040 --> 00:03:33,280
Speaker 2: buy the dip is sort of losing some steam here.

64
00:03:34,440 --> 00:03:37,600
Speaker 6: Well, maybe by the dip is But it's important to

65
00:03:37,600 --> 00:03:40,000
Speaker 6: remember too, we hit all time highs a week ago,

66
00:03:40,640 --> 00:03:44,200
Speaker 6: so and markets have gone a very long period of

67
00:03:44,240 --> 00:03:48,240
Speaker 6: time without the normal level of volatility that you'd see.

68
00:03:48,280 --> 00:03:50,440
Speaker 6: You would expect a five to ten percent kind of

69
00:03:50,440 --> 00:03:53,640
Speaker 6: pullback in the normal range of things. We haven't really

70
00:03:53,760 --> 00:03:57,480
Speaker 6: seen that, and so for markets to come a bit

71
00:03:57,600 --> 00:04:00,640
Speaker 6: off that US exceptionalism story. And look at there are

72
00:04:00,680 --> 00:04:03,560
Speaker 6: a lot of moving parts going in the economy right now,

73
00:04:03,960 --> 00:04:07,720
Speaker 6: but consumer confidence, business confidence, some of that stuff is

74
00:04:07,720 --> 00:04:11,200
Speaker 6: starting to waiver. We came off of the elections with

75
00:04:11,240 --> 00:04:15,240
Speaker 6: a lot of exuberance in both places, and it's important

76
00:04:15,240 --> 00:04:18,160
Speaker 6: to look at those numbers and realize that people are

77
00:04:18,200 --> 00:04:21,520
Speaker 6: just kind of like it's been an awful, big barrage

78
00:04:21,520 --> 00:04:23,919
Speaker 6: of news in the last forty five days, and so

79
00:04:24,080 --> 00:04:26,960
Speaker 6: taking a step back to take a breath and process

80
00:04:27,000 --> 00:04:28,280
Speaker 6: some of that is pretty normal.

81
00:04:28,760 --> 00:04:31,880
Speaker 4: So, Karl, I know you folks over there at BIMO

82
00:04:32,160 --> 00:04:35,760
Speaker 4: Family Office take a longer term view here. But putting

83
00:04:35,760 --> 00:04:38,120
Speaker 4: in the context that we've had investors in twenty three

84
00:04:38,160 --> 00:04:40,840
Speaker 4: and twenty four had really really really good equity returns

85
00:04:40,880 --> 00:04:44,160
Speaker 4: north of twenty percent, how do you kind of reset

86
00:04:44,200 --> 00:04:46,600
Speaker 4: here for twenty twenty five. What are the conversations you're

87
00:04:46,600 --> 00:04:48,320
Speaker 4: having most often with your clients.

88
00:04:48,760 --> 00:04:50,760
Speaker 6: Well, I think a piece of it too is to

89
00:04:50,839 --> 00:04:54,760
Speaker 6: realize that having balanced in the portfolio and having balanced

90
00:04:54,760 --> 00:04:58,440
Speaker 6: portfolios didn't work in twenty two, but it definitely has

91
00:04:58,560 --> 00:05:00,960
Speaker 6: worked some in twenty three and twenty four. And so

92
00:05:01,520 --> 00:05:04,719
Speaker 6: having some high quality fixed income in the portfolio being

93
00:05:04,760 --> 00:05:08,120
Speaker 6: willing to take some trims in there instead and in

94
00:05:08,440 --> 00:05:11,760
Speaker 6: cash that's fielding four and a half five five percent

95
00:05:12,080 --> 00:05:14,640
Speaker 6: is a pretty nice way to balance some of this

96
00:05:14,800 --> 00:05:17,400
Speaker 6: off in the short run. Now, not trying to time

97
00:05:17,440 --> 00:05:18,880
Speaker 6: it all the way in or all the way out,

98
00:05:18,920 --> 00:05:21,680
Speaker 6: but there is an ability to be able to have

99
00:05:21,800 --> 00:05:24,720
Speaker 6: some of that dry powder, if you will, on the sidelines.

100
00:05:24,800 --> 00:05:27,400
Speaker 6: And we wouldn't be alone because clearly Warren Buffett has

101
00:05:27,440 --> 00:05:28,760
Speaker 6: a lot of dry powder as well.

102
00:05:28,960 --> 00:05:31,680
Speaker 7: Oh yeah, it was eleven billion an interest in funny

103
00:05:31,680 --> 00:05:32,000
Speaker 7: market for.

104
00:05:34,279 --> 00:05:34,720
Speaker 5: Sitting there.

105
00:05:34,760 --> 00:05:38,120
Speaker 7: Yeah, I'll take that. What we have seen though, yeah, right.

106
00:05:38,200 --> 00:05:41,120
Speaker 2: Is money kind of flows starting to move out of

107
00:05:41,160 --> 00:05:43,039
Speaker 2: the US and into Europe. Do you think that that

108
00:05:43,200 --> 00:05:44,919
Speaker 2: actually is a trend that had legs?

109
00:05:45,560 --> 00:05:48,720
Speaker 6: Yeah, we do think that You've got that. You know,

110
00:05:48,880 --> 00:05:53,119
Speaker 6: you've had a lot of conversation around Trump tariff's notwithstanding

111
00:05:53,200 --> 00:05:57,719
Speaker 6: that there is an appreciation for Chairman Chief, for example,

112
00:05:57,920 --> 00:06:01,320
Speaker 6: and there's a suspicion that all the tariff talk is

113
00:06:01,360 --> 00:06:03,240
Speaker 6: because he wants to cut a deal there. So we

114
00:06:03,279 --> 00:06:06,440
Speaker 6: do suspect that you've got a lot going on. And

115
00:06:06,480 --> 00:06:09,359
Speaker 6: it's interesting because I saw some stats yesterday that talked

116
00:06:09,400 --> 00:06:14,279
Speaker 6: about the performance in ETFs since inauguration day, and when

117
00:06:14,279 --> 00:06:16,640
Speaker 6: you look at that, the US is flat, China's up

118
00:06:16,680 --> 00:06:20,520
Speaker 6: close to twenty percent, And so you've got a lot

119
00:06:20,520 --> 00:06:24,359
Speaker 6: of different interesting things going on there. And we have

120
00:06:24,560 --> 00:06:29,880
Speaker 6: recently just actually taken a country position in China specific

121
00:06:30,320 --> 00:06:32,720
Speaker 6: as well as the Japan specific one that we've had

122
00:06:32,760 --> 00:06:35,480
Speaker 6: on from a house view standpoint for a very long

123
00:06:35,520 --> 00:06:36,239
Speaker 6: period of time.

124
00:06:36,400 --> 00:06:38,479
Speaker 4: Well, talk to us about that China call, because we

125
00:06:38,600 --> 00:06:41,080
Speaker 4: just had a segment we were talking with Bloomberg economists

126
00:06:41,120 --> 00:06:44,440
Speaker 4: just about the tariffs and the trade situation with China.

127
00:06:44,920 --> 00:06:47,000
Speaker 4: So what is your view on China these days?

128
00:06:47,520 --> 00:06:49,920
Speaker 6: Well, I think another piece of it is is when

129
00:06:49,920 --> 00:06:52,880
Speaker 6: you look at the technology leadership that they've gotten, not

130
00:06:53,080 --> 00:06:56,680
Speaker 6: just deepseek, but you go back to twenty fifteen when

131
00:06:56,720 --> 00:07:00,520
Speaker 6: they first initiated the China twenty twenty five plan industries

132
00:07:00,560 --> 00:07:03,600
Speaker 6: they wanted to own and the ten years they've had

133
00:07:03,640 --> 00:07:06,640
Speaker 6: to really focus on them. There's a lot of technological

134
00:07:06,800 --> 00:07:10,880
Speaker 6: innovation and work around and the more we restrict advanced

135
00:07:10,880 --> 00:07:13,360
Speaker 6: technology from them, the more they try to figure out

136
00:07:13,360 --> 00:07:16,560
Speaker 6: how to make it work. And so between that and

137
00:07:16,600 --> 00:07:19,080
Speaker 6: the beaten up valuations, not just in China but in

138
00:07:19,160 --> 00:07:23,520
Speaker 6: Southeast Asia in particular, there's a lot of interesting things

139
00:07:23,600 --> 00:07:25,960
Speaker 6: going on there from trying to balance off the world

140
00:07:26,120 --> 00:07:30,760
Speaker 6: and it's always been a challenge. We've always led with,

141
00:07:30,960 --> 00:07:34,320
Speaker 6: if you will, with globally diversified portfolios, but that's been

142
00:07:34,320 --> 00:07:37,320
Speaker 6: a really hard argument to make in the last few years,

143
00:07:37,440 --> 00:07:43,320
Speaker 6: as as the US markets have so vastly outperformed so

144
00:07:43,400 --> 00:07:46,000
Speaker 6: many others. But we think you're seeing a broadening out

145
00:07:46,040 --> 00:07:49,680
Speaker 6: of that. So having balanced portfolios. And the key challenge

146
00:07:49,720 --> 00:07:52,800
Speaker 6: is for long term investors is never knowing exactly when

147
00:07:52,840 --> 00:07:55,440
Speaker 6: those when those non US markets are.

148
00:07:55,320 --> 00:07:56,360
Speaker 5: Going to move interesting.

149
00:07:56,360 --> 00:07:58,000
Speaker 4: All right, Kenl, thank you so much for a few

150
00:07:58,000 --> 00:08:00,040
Speaker 4: minutes your time. We really appreciate a couch life. The

151
00:08:00,080 --> 00:08:02,960
Speaker 4: Chief Investment Officer BIMO Family Office.

152
00:08:03,000 --> 00:08:04,280
Speaker 5: Here.

153
00:08:04,960 --> 00:08:08,640
Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us Live

154
00:08:08,720 --> 00:08:12,239
Speaker 1: weekdays at ten am Eastern on Applecarclay, and Android Auto

155
00:08:12,240 --> 00:08:15,280
Speaker 1: with the Bloomberg Business app. Listen on demand wherever you

156
00:08:15,320 --> 00:08:18,679
Speaker 1: get your podcasts, or watch us live on YouTube.

157
00:08:18,880 --> 00:08:22,200
Speaker 4: One of the other big global economic issues out there

158
00:08:22,720 --> 00:08:27,040
Speaker 4: for global trade is the US relationship with China. It

159
00:08:27,120 --> 00:08:30,600
Speaker 4: got tough under Trump's first administration, President Biden kind of

160
00:08:30,840 --> 00:08:33,800
Speaker 4: keeping some of that rhetoric up as well, and now

161
00:08:33,920 --> 00:08:36,160
Speaker 4: the second Trump administration trying to take up another level.

162
00:08:36,200 --> 00:08:37,400
Speaker 4: So we want to get a sense of kind of

163
00:08:37,400 --> 00:08:40,679
Speaker 4: where that this may go. And there's no one better

164
00:08:40,720 --> 00:08:42,960
Speaker 4: to speak to than Tom Orlick. He's a senior economist.

165
00:08:43,040 --> 00:08:46,640
Speaker 4: I mean, he is the Bloomberg Economics chief economist, lived

166
00:08:46,640 --> 00:08:48,720
Speaker 4: and worked in Beijing for many years, so we really

167
00:08:48,720 --> 00:08:52,720
Speaker 4: really understand what's going on over there. Tom, give us

168
00:08:52,720 --> 00:08:54,320
Speaker 4: a sense of kind of how you think the second

169
00:08:54,360 --> 00:08:58,559
Speaker 4: Trump administration is going to approach China and maybe how

170
00:08:58,640 --> 00:08:59,680
Speaker 4: China will react.

171
00:09:01,480 --> 00:09:04,280
Speaker 8: Well, it's great to be here, Paul. So I think

172
00:09:04,320 --> 00:09:06,440
Speaker 8: it's interesting. I think there's going to be two rival

173
00:09:06,520 --> 00:09:09,680
Speaker 8: groups who are kind of jockeying for position in terms

174
00:09:09,720 --> 00:09:15,120
Speaker 8: of shaping Trump's second term China policy. The first group, well,

175
00:09:15,120 --> 00:09:18,959
Speaker 8: that's the China Hawks. That's the folks who think China's

176
00:09:19,000 --> 00:09:22,280
Speaker 8: catching up with the US way too fast. It's playing

177
00:09:22,320 --> 00:09:24,840
Speaker 8: fast and loose with the rules on global trade, on

178
00:09:24,880 --> 00:09:28,320
Speaker 8: intellectual property, and the US needs to take a really

179
00:09:28,400 --> 00:09:32,920
Speaker 8: really strict stand on this. And the second group, and

180
00:09:33,000 --> 00:09:36,600
Speaker 8: you saw them on stage at the inauguration, well that's

181
00:09:36,640 --> 00:09:40,600
Speaker 8: the tech titans. It's folks like Tim Cook who have

182
00:09:41,040 --> 00:09:43,559
Speaker 8: the entire supply chain or a substantial part of their

183
00:09:43,559 --> 00:09:49,640
Speaker 8: supply chain located in China. It's folks like Nvidia who

184
00:09:50,679 --> 00:09:55,000
Speaker 8: have a huge part of their business depending on China's

185
00:09:55,080 --> 00:09:59,680
Speaker 8: electronics supply chain. So of course that group would very

186
00:09:59,760 --> 00:10:04,160
Speaker 8: much like to see something which looks more predictable, more stable,

187
00:10:04,520 --> 00:10:08,680
Speaker 8: more open in terms of US China relations. Early days,

188
00:10:09,280 --> 00:10:13,280
Speaker 8: but we've got some reporting from Bloomberg News saying that

189
00:10:13,679 --> 00:10:16,960
Speaker 8: Trump officials are speaking with the Netherlands and speaking with

190
00:10:17,240 --> 00:10:23,080
Speaker 8: Japan about further toughening controls on China's access to semiconductors.

191
00:10:23,600 --> 00:10:26,640
Speaker 8: So what that suggests is that the China Hawks, at

192
00:10:26,720 --> 00:10:29,040
Speaker 8: least for now, are trying to set the agenda.

193
00:10:30,400 --> 00:10:32,480
Speaker 2: So based on that, we're taking a look at the

194
00:10:32,480 --> 00:10:35,400
Speaker 2: socks INDECKS semi index down over one and a half percent.

195
00:10:35,440 --> 00:10:37,120
Speaker 2: So let's just go with the idea that we'll see

196
00:10:37,559 --> 00:10:39,080
Speaker 2: stricter curbs on chips.

197
00:10:39,440 --> 00:10:41,599
Speaker 7: Will it work? I mean, we saw what happened with

198
00:10:41,640 --> 00:10:42,760
Speaker 7: deep seek for example.

199
00:10:44,200 --> 00:10:48,600
Speaker 8: Yeah, it's a great point, Alex. So I think if

200
00:10:48,640 --> 00:10:51,040
Speaker 8: you were speaking with one of the folks that did

201
00:10:51,080 --> 00:10:55,040
Speaker 8: the export controls in Biden's first term, they'd probably make

202
00:10:55,080 --> 00:10:59,080
Speaker 8: a couple of points. So the first is that the

203
00:10:59,080 --> 00:11:03,600
Speaker 8: first round of export controls, the Biden administration told everyone

204
00:11:03,679 --> 00:11:06,600
Speaker 8: about it six months before it came into force, right,

205
00:11:07,200 --> 00:11:09,559
Speaker 8: And that's a kind of fairly obvious misstep.

206
00:11:09,800 --> 00:11:10,000
Speaker 5: Right.

207
00:11:10,280 --> 00:11:12,840
Speaker 8: If you tell China in six months time you can't

208
00:11:12,880 --> 00:11:16,560
Speaker 8: buy any advanced semiconductors, well, of course China is going

209
00:11:16,600 --> 00:11:22,080
Speaker 8: to spend six months assiduously stockpiling those advanced semiconductors. And

210
00:11:22,160 --> 00:11:25,760
Speaker 8: then the second thing I think those China hawks would

211
00:11:25,800 --> 00:11:29,600
Speaker 8: say would be, well, we always knew China was going

212
00:11:29,640 --> 00:11:34,160
Speaker 8: to catch up pretty quick on the large language models.

213
00:11:35,040 --> 00:11:38,200
Speaker 8: We knew they were only a few months behind chat GPT,

214
00:11:38,880 --> 00:11:42,440
Speaker 8: so deepseak wasn't the surprise. The export controls are really

215
00:11:42,480 --> 00:11:46,959
Speaker 8: aimed at preventing China from taking that next step towards

216
00:11:47,120 --> 00:11:49,320
Speaker 8: autonomous general intelligence.

217
00:11:49,840 --> 00:11:52,560
Speaker 4: TOM based upon your experience in China, how do you

218
00:11:52,720 --> 00:11:58,240
Speaker 4: think presidency and the Chinese government will I guess respond

219
00:11:58,600 --> 00:11:59,520
Speaker 4: and deal.

220
00:11:59,320 --> 00:12:00,480
Speaker 5: With this trumpet ininustration?

221
00:12:00,679 --> 00:12:04,120
Speaker 4: Did they understand how transactional President Trump can be and

222
00:12:04,160 --> 00:12:06,800
Speaker 4: therefore maybe there is a quote unquote deal to be

223
00:12:06,840 --> 00:12:10,200
Speaker 4: worked out to maybe just avert some of the bigger issues.

224
00:12:11,720 --> 00:12:16,440
Speaker 8: So we've had this news overnight from Bloomberg reporters on

225
00:12:16,520 --> 00:12:20,000
Speaker 8: the on the Trump team speaking with their counterparts in

226
00:12:20,040 --> 00:12:25,360
Speaker 8: the Netherlands and Tokyo. So clearly there's not nothing going

227
00:12:25,400 --> 00:12:29,600
Speaker 8: on on US China policy. At the same time, if

228
00:12:29,600 --> 00:12:32,120
Speaker 8: we think about where the kind of the major focus

229
00:12:32,160 --> 00:12:37,400
Speaker 8: of attention for Trump has been in his first few weeks, well,

230
00:12:39,120 --> 00:12:42,439
Speaker 8: it's been on Ukraine, it's been on Gaza in the

231
00:12:42,480 --> 00:12:46,520
Speaker 8: Middle East. There have been tariffs on China, certainly, but

232
00:12:46,559 --> 00:12:51,479
Speaker 8: there's also been tariff threats against Canada, tariff threats against Mexico.

233
00:12:52,400 --> 00:12:56,240
Speaker 8: So China, relative to where I think we could have been,

234
00:12:57,200 --> 00:13:00,000
Speaker 8: doesn't appear to be the kind of top of mind

235
00:13:00,040 --> 00:13:04,200
Speaker 8: gender item for the Trump administration. Now, I don't have,

236
00:13:04,280 --> 00:13:08,000
Speaker 8: you know, a hotline into China's leadership compound. I don't

237
00:13:08,200 --> 00:13:12,400
Speaker 8: I'm not privy to chi jianpings inner thoughts. But my

238
00:13:12,520 --> 00:13:17,600
Speaker 8: guests would be that's kind of good news from their perspective, right,

239
00:13:18,240 --> 00:13:20,800
Speaker 8: and they'd be pretty happy if Trump expends all of

240
00:13:20,840 --> 00:13:24,920
Speaker 8: his energy focused on Ukraine and the Middle East and

241
00:13:25,280 --> 00:13:29,120
Speaker 8: near neighbors and perhaps doesn't spend so much time thinking

242
00:13:29,120 --> 00:13:32,040
Speaker 8: about China and China's rise as a geopolitical challenger.

243
00:13:32,960 --> 00:13:34,280
Speaker 7: All right, Tom, I always appreciate it.

244
00:13:34,280 --> 00:13:36,440
Speaker 2: Thank you so much, Tom, Or like Bloomberg Economics chief

245
00:13:36,480 --> 00:13:39,600
Speaker 2: economists joining us on potential chip controls over China and

246
00:13:39,600 --> 00:13:41,000
Speaker 2: then the ramifications of that.

247
00:13:42,880 --> 00:13:46,559
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

248
00:13:46,640 --> 00:13:49,720
Speaker 1: weekdays at ten am Eastern on Apple Coarclay and Android

249
00:13:49,720 --> 00:13:53,040
Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever

250
00:13:53,120 --> 00:13:56,200
Speaker 1: you get your podcasts, or watch us live on YouTube.

251
00:13:57,120 --> 00:13:57,920
Speaker 5: Steal and Paul Swinging.

252
00:13:57,960 --> 00:14:00,600
Speaker 4: We're live here on Bloomberg Interactive Broker Studio in New

253
00:14:00,679 --> 00:14:01,040
Speaker 4: York City.

254
00:14:01,080 --> 00:14:02,760
Speaker 5: We're also streaming live on YouTube.

255
00:14:02,920 --> 00:14:05,280
Speaker 4: Head over to YouTube dot com and search Bloomberg Podcast

256
00:14:05,360 --> 00:14:07,400
Speaker 4: Live and that's where you find us. Well after the

257
00:14:07,440 --> 00:14:11,840
Speaker 4: close tomorrow in Nvidia reports, and that is always a

258
00:14:11,960 --> 00:14:15,199
Speaker 4: big day for in Nvidia shareholders, I think for technology

259
00:14:15,280 --> 00:14:16,640
Speaker 4: shareholders as well, and as.

260
00:14:16,520 --> 00:14:17,839
Speaker 5: The markets overall.

261
00:14:18,440 --> 00:14:20,400
Speaker 4: Really, for the last couple of years, in Vidia has

262
00:14:20,440 --> 00:14:22,480
Speaker 4: been a market moving event. So we want to get

263
00:14:22,480 --> 00:14:24,520
Speaker 4: a little bit of your preview there, and then we're

264
00:14:24,520 --> 00:14:25,800
Speaker 4: also going to talk Hamburgers.

265
00:14:26,200 --> 00:14:27,720
Speaker 5: How we make the connection here, I don't know, but

266
00:14:27,760 --> 00:14:28,360
Speaker 5: we'll try to do it.

267
00:14:28,440 --> 00:14:28,880
Speaker 7: I like it.

268
00:14:29,000 --> 00:14:33,000
Speaker 4: Kim Farst, founder and chief investment officer Boquet Capital Partners

269
00:14:33,080 --> 00:14:36,320
Speaker 4: in Pittsburgh. So, Kim, I'd love to take advantage of

270
00:14:36,360 --> 00:14:40,320
Speaker 4: your technology, chops. What should we be looking for when

271
00:14:41,320 --> 00:14:42,440
Speaker 4: in Vidia reports tomorrow?

272
00:14:43,520 --> 00:14:47,400
Speaker 9: Sure, well, I'll say what I say for absolutely every

273
00:14:47,440 --> 00:14:50,480
Speaker 9: company who really cares what happened last quarter. I want

274
00:14:50,520 --> 00:14:52,120
Speaker 9: to know what's going to happen next quarter in the

275
00:14:52,200 --> 00:14:55,760
Speaker 9: quarter after that. And I think never has this been

276
00:14:55,840 --> 00:14:59,720
Speaker 9: more important than for a company like Nvidia. Who is

277
00:14:59,720 --> 00:15:03,840
Speaker 9: it the forefront of a very new technology that you know,

278
00:15:04,480 --> 00:15:07,560
Speaker 9: literally hundreds of billions of dollars are being thrown at.

279
00:15:08,000 --> 00:15:11,000
Speaker 9: So the question that we have is this is it

280
00:15:11,040 --> 00:15:13,600
Speaker 9: for real? Are people still going to pay up? Are

281
00:15:13,680 --> 00:15:19,000
Speaker 9: they still going to you know, build these huge data

282
00:15:19,040 --> 00:15:23,400
Speaker 9: centers to run something that we don't have a problem

283
00:15:23,440 --> 00:15:27,760
Speaker 9: that we can identify that that the money is fixing

284
00:15:28,440 --> 00:15:30,080
Speaker 9: that is the question.

285
00:15:30,760 --> 00:15:33,120
Speaker 7: And what do you think that's going to be best?

286
00:15:33,160 --> 00:15:35,840
Speaker 7: Guess yes, I.

287
00:15:37,400 --> 00:15:41,240
Speaker 9: Well for a couple of reasons. Firstly, you know, I'm

288
00:15:41,240 --> 00:15:46,320
Speaker 9: a nerd. I'm a proud nerd was I was a

289
00:15:46,360 --> 00:15:49,920
Speaker 9: software engineer in AI doing stuff with neural networks. How

290
00:15:50,000 --> 00:15:52,480
Speaker 9: lucky are we that like this all has come up

291
00:15:52,640 --> 00:15:58,960
Speaker 9: and I know about it? Okay, But anyhow, here's the problem.

292
00:15:59,000 --> 00:16:00,960
Speaker 9: The only thing that I can and see that large

293
00:16:01,040 --> 00:16:04,680
Speaker 9: language models really solve is the ability to get information

294
00:16:04,880 --> 00:16:09,280
Speaker 9: in and out from humans to computers a little bit better.

295
00:16:09,800 --> 00:16:12,120
Speaker 9: And who's going to benefit? I say, right now it

296
00:16:12,160 --> 00:16:15,800
Speaker 9: looks like search that people are using it to search,

297
00:16:15,920 --> 00:16:19,120
Speaker 9: they love it, and all these other kind of higher

298
00:16:19,280 --> 00:16:23,160
Speaker 9: order smarter things we're not really able to pull off, like,

299
00:16:23,680 --> 00:16:29,720
Speaker 9: you know, take care of my travel to go to Sydney, Australia,

300
00:16:30,040 --> 00:16:32,360
Speaker 9: right and here are the dates and get me the

301
00:16:32,480 --> 00:16:36,920
Speaker 9: cheapest fastest airline there. It can't do that, not yet,

302
00:16:37,160 --> 00:16:39,960
Speaker 9: maybe not ever. We don't know who cares. But right

303
00:16:40,000 --> 00:16:42,440
Speaker 9: now I think the companies that are putting money into

304
00:16:42,480 --> 00:16:45,800
Speaker 9: this are those that have some sort of search that

305
00:16:45,800 --> 00:16:50,080
Speaker 9: they're trying to maintain their market share. So Google, I'm

306
00:16:50,080 --> 00:16:53,520
Speaker 9: talking to you and to a certain extent meta right,

307
00:16:53,600 --> 00:16:56,680
Speaker 9: and Microsoft is the third one. So those people are

308
00:16:56,680 --> 00:16:58,840
Speaker 9: going to continue or those companies are going to continue

309
00:16:58,840 --> 00:17:02,080
Speaker 9: to pour money into this area for the foreseeable future.

310
00:17:02,200 --> 00:17:05,439
Speaker 4: All Right, here's the hard pivot from AI to hamburgers.

311
00:17:05,440 --> 00:17:09,000
Speaker 4: And here's my take on the hamburger business. Wendy's. I

312
00:17:09,000 --> 00:17:13,520
Speaker 4: will stack up a good old Wendy's burger against a

313
00:17:13,560 --> 00:17:15,560
Speaker 4: lot of the new ones out there, whether it's the Shakeshack,

314
00:17:15,600 --> 00:17:17,280
Speaker 4: the five Guys, all that kind of stuff that Wendy's

315
00:17:17,280 --> 00:17:20,760
Speaker 4: burger hangs in there. How about the stock here, Kim,

316
00:17:20,840 --> 00:17:23,320
Speaker 4: what do you guys think about Wendy's here?

317
00:17:24,240 --> 00:17:27,440
Speaker 9: It has not been hanging in That's the first thing, right,

318
00:17:27,560 --> 00:17:30,040
Speaker 9: New is always better. People go hey, let me try

319
00:17:30,040 --> 00:17:32,280
Speaker 9: that shake Shack, and then they're going to drop a

320
00:17:32,280 --> 00:17:34,480
Speaker 9: lot of money and go, well, that was an okay burger,

321
00:17:34,520 --> 00:17:37,560
Speaker 9: But okay, next time, maybe I want to do something different.

322
00:17:38,119 --> 00:17:41,000
Speaker 9: That's one of our theories is the growth of Shakeshack

323
00:17:41,240 --> 00:17:45,840
Speaker 9: is going to reintroduce people to a good hamburger. Like McDonald's,

324
00:17:45,840 --> 00:17:49,800
Speaker 9: has its own flavor profile. It's not like a regular

325
00:17:49,840 --> 00:17:52,280
Speaker 9: hamburger that you'd make it home and I guess Wendy's

326
00:17:52,280 --> 00:17:55,280
Speaker 9: isn't either because it's square. But we move on from that,

327
00:17:55,440 --> 00:18:00,320
Speaker 9: right anyhow, back to it now, the company's been trying

328
00:18:00,320 --> 00:18:04,359
Speaker 9: to grow in breakfast and that has impacted its earnings,

329
00:18:04,880 --> 00:18:07,760
Speaker 9: and we're looking past that, saying, hey, they've got a

330
00:18:07,800 --> 00:18:13,320
Speaker 9: good idea. They're mostly a franchise e chain. The company owns,

331
00:18:13,480 --> 00:18:17,800
Speaker 9: you know, some of the restaurants. This is a good model.

332
00:18:18,200 --> 00:18:20,240
Speaker 9: And you know, we're hanging in there thinking they have

333
00:18:20,280 --> 00:18:23,159
Speaker 9: a pretty good product and it might be time to

334
00:18:23,200 --> 00:18:25,120
Speaker 9: look at it because it's sold off recently.

335
00:18:25,880 --> 00:18:28,760
Speaker 2: So how do you pick between all the fast food guys?

336
00:18:28,800 --> 00:18:31,919
Speaker 2: It's sort of like, you know, can you own a

337
00:18:31,960 --> 00:18:34,000
Speaker 2: lot of them at this point when you have exposure

338
00:18:34,000 --> 00:18:36,440
Speaker 2: to inflation, when you have exposure to the consumer.

339
00:18:38,480 --> 00:18:42,200
Speaker 9: Well, I kind of go on my own, right, because

340
00:18:42,200 --> 00:18:45,000
Speaker 9: I'm a consumer and I'm kind of a picky I

341
00:18:45,080 --> 00:18:47,040
Speaker 9: like really high quality stuff, but I don't want to

342
00:18:47,080 --> 00:18:50,160
Speaker 9: pay a lot for it, and Wendy's kind of fits

343
00:18:50,200 --> 00:18:52,919
Speaker 9: that profile in many ways. First of all, they skew

344
00:18:53,040 --> 00:18:57,119
Speaker 9: to a higher income buyer than a lot of the

345
00:18:57,160 --> 00:18:59,800
Speaker 9: other chains, So that's something that I like. And I

346
00:19:00,080 --> 00:19:05,359
Speaker 9: also like that in any sort of consumer oriented pick

347
00:19:05,480 --> 00:19:08,720
Speaker 9: because as the I think there was news out yesterday

348
00:19:08,720 --> 00:19:12,120
Speaker 9: maybe by Bloomberg that the top ten percent is spending

349
00:19:12,160 --> 00:19:15,040
Speaker 9: more than they ever have, and they spend on things

350
00:19:15,119 --> 00:19:18,800
Speaker 9: like hamburgers, right, So we like that, we like their

351
00:19:18,880 --> 00:19:22,480
Speaker 9: salads in the summertime, they get a lot of traffic

352
00:19:22,520 --> 00:19:25,679
Speaker 9: away from Panera again because it's a value option and

353
00:19:25,720 --> 00:19:28,800
Speaker 9: it's convenient. So that's kind of my view. I don't

354
00:19:28,800 --> 00:19:30,800
Speaker 9: want to own the whole lot of them. I want

355
00:19:30,840 --> 00:19:34,080
Speaker 9: to own companies that are poised to beat the competition.

356
00:19:34,640 --> 00:19:35,080
Speaker 5: How about them?

357
00:19:35,119 --> 00:19:37,440
Speaker 4: I think, how about on the cost side, the costs

358
00:19:37,480 --> 00:19:39,879
Speaker 4: of beef. I know that's been an issue for cost of.

359
00:19:39,960 --> 00:19:43,840
Speaker 9: Beef is And here's the problem. They're fresh nut frozen,

360
00:19:43,880 --> 00:19:46,680
Speaker 9: so they're buying from America. They don't really have the

361
00:19:46,880 --> 00:19:49,120
Speaker 9: luxury that a lot of the other burger chains who

362
00:19:49,119 --> 00:19:53,159
Speaker 9: are buying from South America and Australia. So that is

363
00:19:53,200 --> 00:19:55,199
Speaker 9: an issue and that's something that we're all going to

364
00:19:55,240 --> 00:19:58,280
Speaker 9: have to suffer through for a little while here. But hey,

365
00:19:58,320 --> 00:19:59,880
Speaker 9: I hear they're these things called.

366
00:19:59,720 --> 00:20:03,879
Speaker 7: Tear Yes truth. I hate Kim always good to catch up.

367
00:20:03,920 --> 00:20:06,719
Speaker 2: Appreciate you at Kim forrest overbook Capital partners.

368
00:20:06,760 --> 00:20:07,640
Speaker 7: Thank you very much.

369
00:20:08,280 --> 00:20:12,960
Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify,

370
00:20:13,160 --> 00:20:17,120
Speaker 1: and anywhere else you get your podcasts. Listen live each weekday,

371
00:20:17,359 --> 00:20:20,639
Speaker 1: ten am to noon Eastern on Bloomberg dot com, the

372
00:20:20,720 --> 00:20:24,560
Speaker 1: iHeartRadio app, tune In, and the Bloomberg Business app. You

373
00:20:24,600 --> 00:20:27,879
Speaker 1: can also watch us live every weekday on YouTube and

374
00:20:28,119 --> 00:20:30,880
Speaker 1: always on the Bloomberg terminal