1
00:00:00,040 --> 00:00:10,719
Speaker 1: Bloomberg Audio Studios, Podcasts, radio news.

2
00:00:11,680 --> 00:00:14,840
Speaker 2: Welcome to the Daybreak Asia podcast. I'm Doug Chrissner. We

3
00:00:14,960 --> 00:00:18,479
Speaker 2: begin today with Nvidia. After the US closed, the company

4
00:00:18,520 --> 00:00:22,560
Speaker 2: issued revenue guidance for the current quarter above estimates. Sales

5
00:00:22,560 --> 00:00:25,400
Speaker 2: could be as much as seventy eight billion dollars plus

6
00:00:25,480 --> 00:00:28,640
Speaker 2: or minus two percent, and in Vidia CEO Jensen Wong

7
00:00:28,760 --> 00:00:33,600
Speaker 2: said the enterprise adoption of AI agents is skyrocketing. For

8
00:00:33,640 --> 00:00:36,000
Speaker 2: a closer look, now, I'm joined by Daniel Newman. He

9
00:00:36,120 --> 00:00:39,199
Speaker 2: is the CEO at the Futurum Group. Daniel, thank you

10
00:00:39,240 --> 00:00:41,839
Speaker 2: for being here. Give me your take on what we

11
00:00:41,880 --> 00:00:42,760
Speaker 2: heard from Nvidia.

12
00:00:43,479 --> 00:00:46,519
Speaker 3: I think we knew coming into this print that the

13
00:00:47,080 --> 00:00:49,680
Speaker 3: numbers both for this quarter and the outlook, we're going

14
00:00:49,760 --> 00:00:52,640
Speaker 3: to be very positive. I think the market has been

15
00:00:52,720 --> 00:00:56,560
Speaker 3: in this bit of an uncertain space, almost in between

16
00:00:56,600 --> 00:01:00,000
Speaker 3: whether AI is a bubble or AI is so valued

17
00:01:00,240 --> 00:01:04,680
Speaker 3: that it is going to disrupt and basically eliminate entire industries.

18
00:01:04,880 --> 00:01:07,560
Speaker 3: But in Vidio sits in this really good position where

19
00:01:07,600 --> 00:01:13,080
Speaker 3: it's providing the quote unquote arms for this revolution. And

20
00:01:13,840 --> 00:01:16,880
Speaker 3: with those CAPEX numbers, we knew forty fifty percent of

21
00:01:16,880 --> 00:01:19,400
Speaker 3: those numbers would go to Nvidia. We're seeing it show

22
00:01:19,440 --> 00:01:22,240
Speaker 3: up in this big data center number and this forward

23
00:01:22,280 --> 00:01:27,400
Speaker 3: guidance that was almost ten percent above expectations. Just further

24
00:01:27,480 --> 00:01:29,880
Speaker 3: is that this isn't a short term There isn't some

25
00:01:30,080 --> 00:01:33,720
Speaker 3: near term pullback in neither capex or demand for its products.

26
00:01:34,000 --> 00:01:37,039
Speaker 3: It shows that not only the hyperscalers, but these next

27
00:01:37,040 --> 00:01:42,119
Speaker 3: generation clouds enterprises, sovereign nations are all continuing to bet

28
00:01:42,160 --> 00:01:45,440
Speaker 3: big on building out their AI futures with Nvidia haarmwork.

29
00:01:45,640 --> 00:01:48,560
Speaker 2: So we also heard from Salesforce after the balance. Kind

30
00:01:48,560 --> 00:01:51,640
Speaker 2: of interesting because this company faced a lot of scrutiny

31
00:01:52,280 --> 00:01:57,320
Speaker 2: when the conversation focused on disruption from artificial intelligence, and

32
00:01:57,400 --> 00:02:01,520
Speaker 2: the outlook from Salesforce was a little under whelming. Which

33
00:02:01,600 --> 00:02:04,520
Speaker 2: companies do you think are most at risk of being

34
00:02:04,600 --> 00:02:07,240
Speaker 2: threatened by some of the advancements that we're seeing in

35
00:02:07,320 --> 00:02:08,200
Speaker 2: AI right now?

36
00:02:09,120 --> 00:02:12,440
Speaker 3: Yeah, So Salesforce is a great example of a company

37
00:02:12,480 --> 00:02:15,040
Speaker 3: that's kind of into being the baby being thrown out

38
00:02:15,040 --> 00:02:15,880
Speaker 3: with the bathwater.

39
00:02:17,480 --> 00:02:21,000
Speaker 4: CRM, Salesforce Oracle Service.

40
00:02:21,040 --> 00:02:24,560
Speaker 3: Now, some of these big enterprise software companies that have

41
00:02:24,960 --> 00:02:29,560
Speaker 3: massive data modes and deep deeply entrenched inside of enterprises,

42
00:02:29,960 --> 00:02:32,640
Speaker 3: we just aren't seeing that the CIOs we're talking to

43
00:02:32,800 --> 00:02:36,560
Speaker 3: aren't seriously considering any sort of replacement, and basically their

44
00:02:36,639 --> 00:02:39,200
Speaker 3: numbers have been more of a continuation of the trend

45
00:02:39,200 --> 00:02:43,320
Speaker 3: lines that preceded this AI revolution. Having said that there

46
00:02:43,440 --> 00:02:45,760
Speaker 3: is some software that seems to be at risk, we

47
00:02:46,080 --> 00:02:48,840
Speaker 3: see the software companies that were sort of features that

48
00:02:48,960 --> 00:02:52,760
Speaker 3: ended up becoming large SaaS platforms that ended up going

49
00:02:52,800 --> 00:02:55,160
Speaker 3: public at high valuations with high multiples.

50
00:02:55,400 --> 00:02:58,959
Speaker 4: Say a software company like expensify that just does expenses.

51
00:02:59,000 --> 00:03:03,240
Speaker 3: It's one feature that can be augmented by an AI

52
00:03:03,520 --> 00:03:07,320
Speaker 3: vibe coded tool. Some of the design platforms like Canva,

53
00:03:07,400 --> 00:03:09,440
Speaker 3: we think are at risk when you see what Google

54
00:03:09,520 --> 00:03:12,440
Speaker 3: is doing with Nana Banana and other AI platforms that

55
00:03:12,480 --> 00:03:15,880
Speaker 3: are making design really easy. So there are softwares that

56
00:03:15,919 --> 00:03:19,160
Speaker 3: are at risk of being disrupted meaningfully by AI. We

57
00:03:19,200 --> 00:03:21,760
Speaker 3: look at the ones that have significant data modes, that

58
00:03:21,880 --> 00:03:25,520
Speaker 3: have large entrenched enterprise utilization, and of course those that

59
00:03:25,600 --> 00:03:31,600
Speaker 3: have like cross border transaction, major governance compliance. Those softwares

60
00:03:31,639 --> 00:03:33,639
Speaker 3: don't seem to be in trouble. Also, some of the

61
00:03:33,720 --> 00:03:37,440
Speaker 3: key technologies that are like in the design of semiconductors

62
00:03:37,440 --> 00:03:41,040
Speaker 3: and chips, we saw companies like Synopsis being sold on this.

63
00:03:41,240 --> 00:03:44,120
Speaker 3: I don't see them being disrupted. And also the security companies.

64
00:03:44,120 --> 00:03:47,800
Speaker 3: We see security and AI as symbiotic, and Thropic had

65
00:03:47,840 --> 00:03:51,480
Speaker 3: a major breach by China. They need CrowdStrike, they need

66
00:03:51,520 --> 00:03:54,320
Speaker 3: Palo Alto, so some software at risk. But I think

67
00:03:54,760 --> 00:03:56,560
Speaker 3: the selling has been largely overblown.

68
00:03:56,760 --> 00:04:00,160
Speaker 2: So I'm glad that you mentioned Anthropic, Daniel, because today

69
00:04:00,200 --> 00:04:04,440
Speaker 2: the company indicated that it's basically loosening its commitment to

70
00:04:04,520 --> 00:04:08,760
Speaker 2: managing its guardrails where AI is concerned. Some people were

71
00:04:08,760 --> 00:04:10,720
Speaker 2: saying this is one of the most dramatic shifts that

72
00:04:10,760 --> 00:04:13,360
Speaker 2: we have seen lately in the industry. Back in twenty three,

73
00:04:14,040 --> 00:04:18,400
Speaker 2: Anthropics that it would delay AI development that might be dangerous.

74
00:04:18,440 --> 00:04:21,279
Speaker 2: But we just learned yesterday that the company will no

75
00:04:21,400 --> 00:04:25,520
Speaker 2: longer do so if it believes it lacks a significant

76
00:04:25,600 --> 00:04:28,520
Speaker 2: lead over a competitor. And in the context of this,

77
00:04:28,720 --> 00:04:32,640
Speaker 2: earlier in the week, Defense Secretary Pete Hegseth was giving

78
00:04:32,720 --> 00:04:37,440
Speaker 2: Anthropic until Friday to open its AI technology for unrestricted

79
00:04:37,880 --> 00:04:42,040
Speaker 2: military use or risk losing its government contracts. So talk

80
00:04:42,080 --> 00:04:46,760
Speaker 2: to me about regulatory risk here, or at the very least,

81
00:04:47,080 --> 00:04:50,720
Speaker 2: how the government may get involved to kind of influence

82
00:04:50,720 --> 00:04:52,679
Speaker 2: where this industry moves going forward.

83
00:04:53,560 --> 00:04:57,440
Speaker 3: And this is a really difficult situation for Dario and

84
00:04:57,520 --> 00:05:02,240
Speaker 3: Anthropic because if Kiyak, if he ACQUIESCES, he looks like

85
00:05:02,520 --> 00:05:05,000
Speaker 3: sort of his long term position of being the more

86
00:05:05,040 --> 00:05:09,479
Speaker 3: ethical AI platform, the one that would move more slowly,

87
00:05:09,560 --> 00:05:13,400
Speaker 3: that would potentially be the protector of this innovation. But

88
00:05:13,480 --> 00:05:15,720
Speaker 3: on the other side of this, if he doesn't do it,

89
00:05:16,839 --> 00:05:22,080
Speaker 3: Open Ai, Google, you know, many of the other companies

90
00:05:22,200 --> 00:05:26,479
Speaker 3: that are that are pursuing leadership in this space likely

91
00:05:26,560 --> 00:05:30,719
Speaker 3: will And so the question mark is by potentially loosening

92
00:05:30,800 --> 00:05:33,880
Speaker 3: some guardrails, can he still be influential in trying to

93
00:05:34,000 --> 00:05:38,320
Speaker 3: drive some policy, some level of guardrails, some awareness to

94
00:05:38,360 --> 00:05:42,320
Speaker 3: the public markets of what is at stake as we

95
00:05:42,400 --> 00:05:46,480
Speaker 3: continue to see the incredible power of its technology and

96
00:05:46,920 --> 00:05:49,320
Speaker 3: the others that are in this space. I don't think

97
00:05:49,320 --> 00:05:52,360
Speaker 3: there's a win for Anthropic here. If they hold their ground,

98
00:05:52,560 --> 00:05:54,960
Speaker 3: they're going to lose significant business. They're going to be

99
00:05:54,960 --> 00:05:57,640
Speaker 3: at risk of being under immense pressure. We've seen how

100
00:05:57,760 --> 00:06:00,720
Speaker 3: you know, the various administrations can use new comer's tactics

101
00:06:00,720 --> 00:06:03,919
Speaker 3: to create pressure on companies, and of course their ability

102
00:06:03,960 --> 00:06:06,560
Speaker 3: to influence the future becomes less than.

103
00:06:06,480 --> 00:06:09,880
Speaker 4: It is today. So I'm to some extent I.

104
00:06:09,880 --> 00:06:12,640
Speaker 3: Doubt this is what he truly wants, But I think

105
00:06:12,680 --> 00:06:16,720
Speaker 3: his choices be at the table potentially with maybe not

106
00:06:17,120 --> 00:06:22,440
Speaker 3: completely living his ideal, or maybe be taken away from

107
00:06:22,440 --> 00:06:24,279
Speaker 3: the opportunity to be at the table and have no

108
00:06:24,400 --> 00:06:27,440
Speaker 3: influence at all on a really important shift into the future.

109
00:06:27,640 --> 00:06:31,320
Speaker 2: We've seen a lot of volatility in the equity market

110
00:06:31,440 --> 00:06:34,000
Speaker 2: around some of these AI names this week after that

111
00:06:34,120 --> 00:06:38,920
Speaker 2: report from a little known firm called Saittrini Research. It

112
00:06:39,000 --> 00:06:43,800
Speaker 2: seemed to outline some potential downside risk from AI on

113
00:06:43,839 --> 00:06:48,400
Speaker 2: a number of industries. And this report used several hypothetical

114
00:06:48,440 --> 00:06:52,560
Speaker 2: scenarios to kind of set the future. Future is in

115
00:06:52,640 --> 00:06:54,839
Speaker 2: the name of your firm. So if you had to

116
00:06:54,920 --> 00:06:58,719
Speaker 2: kind of forecast, Daniel, what the next five years may

117
00:06:58,760 --> 00:07:02,479
Speaker 2: look like as the artificial intelligence and the advancements that

118
00:07:02,520 --> 00:07:04,839
Speaker 2: we're talking about, what do you come away with?

119
00:07:06,000 --> 00:07:09,920
Speaker 3: Yeah, that report was quite the Doom's Day report. However,

120
00:07:09,920 --> 00:07:13,200
Speaker 3: there were iterations that were created that had greater levels

121
00:07:13,200 --> 00:07:17,920
Speaker 3: of optimism. We see many trillions of dollars of economic

122
00:07:17,960 --> 00:07:21,440
Speaker 3: growth that will come with the efficiencies and productivity that

123
00:07:21,600 --> 00:07:22,760
Speaker 3: will be generated by AI.

124
00:07:23,360 --> 00:07:25,600
Speaker 4: Where we see some potential.

125
00:07:25,160 --> 00:07:29,280
Speaker 3: Risk is the digestion period that we've historically seen during

126
00:07:29,440 --> 00:07:32,400
Speaker 3: massive transformative periods, you know, whether it's been steam or

127
00:07:32,400 --> 00:07:36,400
Speaker 3: electricity or the Internet, generally took place over a much

128
00:07:36,560 --> 00:07:41,320
Speaker 3: longer time. This particular innovation cycle has happened in a

129
00:07:41,440 --> 00:07:44,520
Speaker 3: very short period of time. And so many things, many

130
00:07:44,880 --> 00:07:48,320
Speaker 3: many roles in jobs and careers. You hear, you know,

131
00:07:48,360 --> 00:07:50,800
Speaker 3: will we need lawyers, Will we need doctors, Will we

132
00:07:50,840 --> 00:07:56,160
Speaker 3: need engineers, marketers, designers, salespeople. All these things have never

133
00:07:56,200 --> 00:07:58,080
Speaker 3: been put into question the same way. But if you

134
00:07:58,160 --> 00:08:00,160
Speaker 3: do look at history, and I think it's a and

135
00:08:00,200 --> 00:08:03,440
Speaker 3: the people do. With every revolution, there were roles that

136
00:08:03,440 --> 00:08:06,400
Speaker 3: people said, wow, will that go away? Just like the

137
00:08:06,400 --> 00:08:08,640
Speaker 3: people that used to walk the streets of small towns

138
00:08:08,840 --> 00:08:12,240
Speaker 3: and light the gas lamps at night, the original gas lighters.

139
00:08:12,280 --> 00:08:14,320
Speaker 3: Right with electricity, there would no longer be roles, or

140
00:08:14,320 --> 00:08:17,760
Speaker 3: they would no longer need people to chisel wheels out

141
00:08:17,800 --> 00:08:20,240
Speaker 3: of stone, Doug. But the net of it is is

142
00:08:20,280 --> 00:08:23,320
Speaker 3: that we ended up building industries that were exponentially larger.

143
00:08:24,160 --> 00:08:25,640
Speaker 4: Manufacturing at scale.

144
00:08:26,400 --> 00:08:30,360
Speaker 3: The Internet created entire new industries in marketplaces that did

145
00:08:30,360 --> 00:08:32,240
Speaker 3: not exist in the largest companies in the world that

146
00:08:32,280 --> 00:08:34,480
Speaker 3: employ the most people were, many of which were.

147
00:08:34,400 --> 00:08:35,440
Speaker 4: Created in that era.

148
00:08:35,640 --> 00:08:38,160
Speaker 3: So I think the hardest part for most people is

149
00:08:38,200 --> 00:08:41,560
Speaker 3: to visualize the unknown. And while I can't concretely say

150
00:08:41,600 --> 00:08:43,920
Speaker 3: what will be created, what I could say is this

151
00:08:44,000 --> 00:08:47,040
Speaker 3: innovation is creating an immense amount of additional productivity. It's

152
00:08:47,040 --> 00:08:49,760
Speaker 3: giving humans the potential to five and ten x what

153
00:08:49,800 --> 00:08:55,079
Speaker 3: they're able to output, and new roles, new opportunities will emerge.

154
00:08:55,760 --> 00:08:58,080
Speaker 3: But there is a risk for a short period of

155
00:08:58,160 --> 00:09:01,280
Speaker 3: time that there will be downside because how fast this

156
00:09:01,360 --> 00:09:04,680
Speaker 3: innovation has happened, and they know the humanity has never

157
00:09:04,720 --> 00:09:06,199
Speaker 3: seen anything change in this quick.

158
00:09:06,320 --> 00:09:09,880
Speaker 2: So as I'm listening to I'm thinking of Amara's law.

159
00:09:10,160 --> 00:09:13,760
Speaker 2: It says that people tend to overestimate the short term

160
00:09:13,800 --> 00:09:19,280
Speaker 2: impact of new technologies and underestimate their longer term effects.

161
00:09:19,800 --> 00:09:21,640
Speaker 2: Is that what you're saying, does it really apply in

162
00:09:21,720 --> 00:09:22,199
Speaker 2: this case?

163
00:09:22,920 --> 00:09:24,640
Speaker 3: There's a little bit of that, and there's a little

164
00:09:24,679 --> 00:09:27,600
Speaker 3: bit of Jevin's paradox in the sense that as we

165
00:09:27,679 --> 00:09:31,280
Speaker 3: continue to become more efficient as human beings, we will

166
00:09:31,280 --> 00:09:34,760
Speaker 3: continue to scale industries at a great rate. You know,

167
00:09:34,800 --> 00:09:37,640
Speaker 3: we've talked about numbers of GDP growth in the twenties

168
00:09:37,640 --> 00:09:40,520
Speaker 3: and thirties of trillion. There will be work to support

169
00:09:40,520 --> 00:09:43,080
Speaker 3: that that will not all be done by robots in

170
00:09:43,120 --> 00:09:46,280
Speaker 3: by AI. We need multi sided marketplaces. We need to

171
00:09:46,280 --> 00:09:49,680
Speaker 3: fulfill goods and services. People will still travel, people will

172
00:09:49,679 --> 00:09:52,760
Speaker 3: still get into autonomous vehicles, people will still want to

173
00:09:52,840 --> 00:09:56,360
Speaker 3: eat people. You know, the things that we do that

174
00:09:56,480 --> 00:10:00,520
Speaker 3: creates economic value will still be done and consumed. However,

175
00:10:00,800 --> 00:10:04,559
Speaker 3: some of that monotonous work, some of those low less

176
00:10:04,679 --> 00:10:08,319
Speaker 3: value processes that people are grinding away at their desk,

177
00:10:08,440 --> 00:10:11,720
Speaker 3: moving data from one cell to the next, or moving

178
00:10:11,760 --> 00:10:15,560
Speaker 3: papers from one email inbox to another, sending documents to

179
00:10:15,559 --> 00:10:18,120
Speaker 3: be signed, things like that will be automated. But people

180
00:10:18,160 --> 00:10:21,920
Speaker 3: will continue to evolve like we have all throughout history.

181
00:10:22,000 --> 00:10:25,000
Speaker 3: But the speed is the risk. But I think in

182
00:10:25,040 --> 00:10:28,120
Speaker 3: good time what you mentioned with the Mar's law does

183
00:10:28,200 --> 00:10:28,839
Speaker 3: become the truth.

184
00:10:29,120 --> 00:10:31,800
Speaker 2: So Daniel, before i'd let you go, give me your

185
00:10:31,920 --> 00:10:35,360
Speaker 2: sense of where investors can find the best opportunities right

186
00:10:35,400 --> 00:10:36,960
Speaker 2: now in the info tech space.

187
00:10:37,840 --> 00:10:41,559
Speaker 3: Yeah, there's really two places that we are focusing on.

188
00:10:41,559 --> 00:10:45,439
Speaker 3: One is we look at where everything remains constrained compute

189
00:10:45,920 --> 00:10:49,520
Speaker 3: memory power. These things are a multi year trend and

190
00:10:49,559 --> 00:10:51,720
Speaker 3: while there has been a lot of volatility. We've seen

191
00:10:51,880 --> 00:10:55,400
Speaker 3: Nvidia settle a little bit, We've seen Micron move quite

192
00:10:55,440 --> 00:10:58,640
Speaker 3: a bit up. We're still seeing the need for power

193
00:10:59,040 --> 00:11:01,640
Speaker 3: and rare earth. All of those areas are going to

194
00:11:01,640 --> 00:11:03,520
Speaker 3: continue to see growth as we see a number of

195
00:11:03,559 --> 00:11:06,560
Speaker 3: as high as four trillion dollars in data center infrastructure

196
00:11:06,840 --> 00:11:09,280
Speaker 3: by the end of the decade. On the other hand,

197
00:11:09,320 --> 00:11:11,920
Speaker 3: there is some opportunity in the arbitrage and in the

198
00:11:12,320 --> 00:11:15,000
Speaker 3: hard selloff that a number of companies have faced. We

199
00:11:15,400 --> 00:11:19,360
Speaker 3: do not believe companies like IBM, like I mentioned, Service

200
00:11:19,440 --> 00:11:22,200
Speaker 3: Now Salesforce, they have been sold at a rate that

201
00:11:22,400 --> 00:11:25,720
Speaker 3: is not appropriate based on the business and metrics that

202
00:11:25,760 --> 00:11:29,199
Speaker 3: they have delivered. There is some risk of AI and disruption,

203
00:11:29,559 --> 00:11:32,160
Speaker 3: but we think the names I mentioned and several others

204
00:11:32,200 --> 00:11:35,840
Speaker 3: of those deep moat enterprise software companies will continue to

205
00:11:35,880 --> 00:11:38,440
Speaker 3: perform at a high level despite the fact that the

206
00:11:38,520 --> 00:11:41,800
Speaker 3: rerating may be real, but the upside still exists.

207
00:11:41,920 --> 00:11:43,880
Speaker 2: Daniel Bell, leave it. They are always a pleasure. Thanks

208
00:11:43,880 --> 00:11:46,360
Speaker 2: so much. Daniel Newman there, he is the CEO at

209
00:11:46,440 --> 00:11:50,040
Speaker 2: the Futureum Group, joining us here on the Daybreak Asia podcast.

210
00:11:57,360 --> 00:11:59,800
Speaker 2: Welcome back to the Daybreak Asia Podcast. I'm Doug, Chris

211
00:12:00,200 --> 00:12:02,839
Speaker 2: and as I mentioned a moment ago, in Vidia gave

212
00:12:02,880 --> 00:12:06,840
Speaker 2: another bullish forecast for quarterly revenue. This would suggest the

213
00:12:06,880 --> 00:12:10,720
Speaker 2: massive buildout in AI compute remains on track now. This

214
00:12:10,800 --> 00:12:15,520
Speaker 2: could underpin earnings expectations across the Asian semiconductor supply chain,

215
00:12:15,679 --> 00:12:19,640
Speaker 2: especially where high bandwidth memory chip makers are concerned. And

216
00:12:19,679 --> 00:12:23,160
Speaker 2: that's where we begin our conversation with Karen Calder. Kieren

217
00:12:23,400 --> 00:12:26,959
Speaker 2: is head of equity research for Asia at UBP. He

218
00:12:27,040 --> 00:12:30,440
Speaker 2: spoke earlier with Bloomberg TV host Heidi Stroud Watts and

219
00:12:30,480 --> 00:12:30,960
Speaker 2: Sherry on.

220
00:12:31,120 --> 00:12:32,680
Speaker 5: We have this situation where, of course all of the

221
00:12:32,720 --> 00:12:36,480
Speaker 5: macro factors are still at play, ranging from everything to

222
00:12:36,480 --> 00:12:41,000
Speaker 5: you have political related to tariffs to central banks globally,

223
00:12:41,520 --> 00:12:43,800
Speaker 5: but at the moment it is really all about that

224
00:12:43,920 --> 00:12:46,200
Speaker 5: tech space. What do you make of the price action

225
00:12:46,320 --> 00:12:49,720
Speaker 5: that we've seen under the numbers from nvidio take away

226
00:12:50,360 --> 00:12:54,160
Speaker 5: the fear that clearly was dominating the training up until

227
00:12:54,200 --> 00:12:55,720
Speaker 5: now for the last few days.

228
00:12:58,160 --> 00:12:58,680
Speaker 4: Good morning.

229
00:12:58,760 --> 00:13:01,560
Speaker 6: So obviously the the numbers from a video are are

230
00:13:01,600 --> 00:13:05,000
Speaker 6: a big relief as a bellweather of you know, the

231
00:13:05,040 --> 00:13:09,920
Speaker 6: AI trade, at least on the infrastructure CAPEX build out side,

232
00:13:11,400 --> 00:13:15,520
Speaker 6: Nvidia needs to keep delivering and and and having a

233
00:13:15,520 --> 00:13:18,760
Speaker 6: strong outlook, So that's pretty positive if you look at

234
00:13:18,920 --> 00:13:20,200
Speaker 6: you know, over the course of this week. Of course,

235
00:13:20,240 --> 00:13:23,559
Speaker 6: we had this Centreny report over the weekend talking about

236
00:13:23,559 --> 00:13:25,920
Speaker 6: a hypothetical scenario a couple of years down the road

237
00:13:25,960 --> 00:13:30,920
Speaker 6: where we've had, you know, the AI build out and

238
00:13:31,000 --> 00:13:35,360
Speaker 6: the implementation. It basically takes out a lot of different sectors,

239
00:13:35,360 --> 00:13:40,920
Speaker 6: and I think probably we've passed peak worry about about

240
00:13:40,920 --> 00:13:44,040
Speaker 6: that sort of scenario. In fact, if you put that

241
00:13:44,080 --> 00:13:46,960
Speaker 6: report into chat GPT itself, it really says, don't worry

242
00:13:47,000 --> 00:13:49,320
Speaker 6: too much about this. This is a hypothetical scenario looking

243
00:13:49,360 --> 00:13:54,400
Speaker 6: at identifying risks going forward. So we think peak fear

244
00:13:54,640 --> 00:13:58,320
Speaker 6: has probably passed, and it's an opportunity, we think to

245
00:13:58,480 --> 00:14:04,520
Speaker 6: recalibrate in the names that have a decent near term outlook.

246
00:14:04,559 --> 00:14:07,720
Speaker 6: And for us, it's really the sort of Capex AI

247
00:14:07,880 --> 00:14:11,920
Speaker 6: CAPEX build out. So the names like in Nvidia as

248
00:14:11,920 --> 00:14:14,800
Speaker 6: well as some of the semiconductor manufacturers I think are

249
00:14:15,480 --> 00:14:16,720
Speaker 6: a good place to be at the moment.

250
00:14:20,680 --> 00:14:24,680
Speaker 7: Karin, how long do you think we sort of should

251
00:14:24,680 --> 00:14:26,720
Speaker 7: be looking at in terms of the time for that

252
00:14:26,800 --> 00:14:30,600
Speaker 7: separation of the sustainable gainers within that AI space. And

253
00:14:30,640 --> 00:14:33,640
Speaker 7: those you know clearly, you know, particularly in the likes

254
00:14:33,640 --> 00:14:36,000
Speaker 7: of Jamie Diamond's World, are something that you should be

255
00:14:36,040 --> 00:14:37,200
Speaker 7: a lot more cautious about.

256
00:14:40,160 --> 00:14:42,600
Speaker 6: Well, I mean think the timeline is now if you look,

257
00:14:42,640 --> 00:14:45,200
Speaker 6: you know, particularly JP Morgamy. They had a mini investor

258
00:14:45,280 --> 00:14:46,960
Speaker 6: day this week and they highlighted that they're going to

259
00:14:46,960 --> 00:14:50,080
Speaker 6: be spending almost twenty billion dollars this year, about half

260
00:14:50,120 --> 00:14:54,280
Speaker 6: of which is investment in platforms and AI capabilities. You know,

261
00:14:54,360 --> 00:14:58,840
Speaker 6: big corporates, including financials, which are able to spend and

262
00:14:58,840 --> 00:15:02,200
Speaker 6: implement AI will will benefit from that, and they'll be

263
00:15:02,200 --> 00:15:05,000
Speaker 6: able to help their clients and names that are a

264
00:15:05,000 --> 00:15:07,680
Speaker 6: little bit more exposed, you know, we'll have some work

265
00:15:07,720 --> 00:15:11,040
Speaker 6: to do. So obviously there's a sharp contrast between in

266
00:15:11,160 --> 00:15:14,000
Speaker 6: video results and Salesforce results, so Salesforce has a bit

267
00:15:14,040 --> 00:15:17,560
Speaker 6: of work to do to you know, adapt their business

268
00:15:17,560 --> 00:15:20,080
Speaker 6: model to you know, the new environment.

269
00:15:22,280 --> 00:15:24,320
Speaker 8: Karen, can you have this point also tell who the

270
00:15:24,320 --> 00:15:27,880
Speaker 8: winners or losers could be across Asia as well?

271
00:15:30,480 --> 00:15:33,200
Speaker 6: I wish I could. I mean, obviously I think that

272
00:15:33,280 --> 00:15:36,720
Speaker 6: we uh, we think that the semiconductor names are an

273
00:15:36,760 --> 00:15:39,320
Speaker 6: obvious place to be, so, you know, the big ones

274
00:15:39,520 --> 00:15:43,320
Speaker 6: T SMC and Samsung, which of course have have done well.

275
00:15:44,040 --> 00:15:47,840
Speaker 6: We think there's some critical companies in Japan in the

276
00:15:47,960 --> 00:15:51,920
Speaker 6: in the supply chain, which which we think at this

277
00:15:51,960 --> 00:15:54,040
Speaker 6: stage is also a place to be. I think it's

278
00:15:54,040 --> 00:15:58,280
Speaker 6: probably too it's difficult now to pick the winners on

279
00:15:58,320 --> 00:16:01,840
Speaker 6: the implementation side. So you know, while software names have

280
00:16:01,920 --> 00:16:04,640
Speaker 6: been really taken to the cleaners, I think it's maybe

281
00:16:04,680 --> 00:16:07,120
Speaker 6: a little bit too early to start to pick through

282
00:16:07,120 --> 00:16:10,120
Speaker 6: there and try to identify winners as we're still at

283
00:16:10,120 --> 00:16:11,000
Speaker 6: the build out stage.

284
00:16:12,440 --> 00:16:15,920
Speaker 8: Yeah, lots of component makers also chemical suppliers across Japan,

285
00:16:16,000 --> 00:16:19,320
Speaker 8: right Kieran. But here we also do have the idiosyncratic

286
00:16:19,360 --> 00:16:22,320
Speaker 8: story around the Bank of Japan, what privates Attakaichi wants

287
00:16:22,320 --> 00:16:25,680
Speaker 8: when it comes to those rate hikes, the weakness of

288
00:16:25,760 --> 00:16:29,720
Speaker 8: the Japanese yend and the acceleration of the losses. Is

289
00:16:29,720 --> 00:16:32,160
Speaker 8: this a risk or is this just more upside for

290
00:16:32,200 --> 00:16:33,160
Speaker 8: those exporters.

291
00:16:36,600 --> 00:16:39,640
Speaker 6: Well, so, okay, the end is a difficult issue for

292
00:16:40,920 --> 00:16:45,840
Speaker 6: Japan obviously. You know, the Bank of Japan is obviously

293
00:16:45,920 --> 00:16:49,080
Speaker 6: very cautious and with the comments and the new appointments

294
00:16:50,080 --> 00:16:53,760
Speaker 6: from the comments from the Prime Minister, a rate hike,

295
00:16:54,280 --> 00:16:56,560
Speaker 6: a further rate hike is probably pretty unlikely.

296
00:16:56,280 --> 00:16:56,840
Speaker 4: At this point.

297
00:16:57,920 --> 00:17:00,800
Speaker 6: On the other hand, the weekend is all very problematic,

298
00:17:00,920 --> 00:17:05,520
Speaker 6: especially for Japanese households, because so much of uh, you know,

299
00:17:05,640 --> 00:17:09,400
Speaker 6: the household basket is imported all mostly all energy is important,

300
00:17:09,440 --> 00:17:14,720
Speaker 6: lots of food is imported so important. Inflation is a

301
00:17:14,720 --> 00:17:18,320
Speaker 6: big problem, not only for the economy but specifically for households.

302
00:17:19,480 --> 00:17:22,520
Speaker 6: This is likely to continue, and I think, you know,

303
00:17:22,560 --> 00:17:25,040
Speaker 6: the export is obviously benefit from translation of earnings, but

304
00:17:25,320 --> 00:17:28,920
Speaker 6: at some point it's just too weak for the domestic economy.

305
00:17:29,280 --> 00:17:32,399
Speaker 6: She's got, she's bought herself four years. Hopefully there'll be,

306
00:17:33,080 --> 00:17:35,399
Speaker 6: you know, some measures taken sooner.

307
00:17:35,640 --> 00:17:38,479
Speaker 2: That was Karen Calder, head of Equity Research for Asia

308
00:17:38,520 --> 00:17:42,480
Speaker 2: at UBP, speaking with Bloomberg TV host Heidi Stroud, Watts

309
00:17:42,520 --> 00:17:45,520
Speaker 2: and Sherry On bringing you their conversation here on the

310
00:17:45,600 --> 00:17:51,000
Speaker 2: Daybreak Asia podcast. Thanks for listening to today's episode of

311
00:17:51,040 --> 00:17:55,160
Speaker 2: the Bloomberg Daybreak Asia Edition podcast. Each weekday, we look

312
00:17:55,160 --> 00:17:58,960
Speaker 2: at the story shaping markets, finance, and geopolitics in the

313
00:17:59,000 --> 00:18:02,080
Speaker 2: Asia Pacific. If you can find us on Apple, Spotify,

314
00:18:02,200 --> 00:18:05,720
Speaker 2: the Bloomberg Podcast YouTube channel, or anywhere else you listen,

315
00:18:06,119 --> 00:18:09,000
Speaker 2: join us again tomorrow for insight on the market moves

316
00:18:09,080 --> 00:18:13,600
Speaker 2: from Hong Kong to Singapore and Australia. I'm Doug Prisoner,

317
00:18:13,760 --> 00:18:15,200
Speaker 2: and this is Bloomberg