1
00:00:02,520 --> 00:00:14,400
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is live

2
00:00:14,520 --> 00:00:18,000
Speaker 1: from the heart of Silicon Valley with ed la Though

3
00:00:18,160 --> 00:00:19,240
Speaker 1: in San Francisco.

4
00:00:22,200 --> 00:00:23,800
Speaker 2: This is Bloomberg Tech coming up.

5
00:00:23,840 --> 00:00:27,400
Speaker 3: SpaceX is selling investment grade bonds for the first time

6
00:00:27,560 --> 00:00:29,200
Speaker 3: following its record breaking IPO.

7
00:00:29,480 --> 00:00:30,560
Speaker 2: We'll have the details.

8
00:00:30,760 --> 00:00:34,879
Speaker 3: Plus Micron strikes an AI infrastructure agreement with Anthropics to

9
00:00:34,960 --> 00:00:38,199
Speaker 3: drive enterprise AI adoption and a lot more. And Chevron

10
00:00:38,280 --> 00:00:41,879
Speaker 3: signs a twenty year deal with Microsoft to power a

11
00:00:41,960 --> 00:00:45,000
Speaker 3: West Texas data center could be one of the biggest

12
00:00:45,440 --> 00:00:46,400
Speaker 3: in US.

13
00:00:46,520 --> 00:00:48,400
Speaker 2: Good Morning, Monday.

14
00:00:48,080 --> 00:00:53,559
Speaker 3: June twenty second, twenty twenty six, SpaceX's inaugural bond sale.

15
00:00:53,800 --> 00:00:58,040
Speaker 3: This is about repaying their existing debt funding corporate needs,

16
00:00:58,400 --> 00:01:01,360
Speaker 3: and Bloomberg's reporting has been spot on. What we understand

17
00:01:01,440 --> 00:01:03,640
Speaker 3: is that right now you have the same banks they've

18
00:01:03,680 --> 00:01:07,440
Speaker 3: been working with on their existing financing going out, holding

19
00:01:07,480 --> 00:01:12,240
Speaker 3: investor meetings, gauging appetite. But we believe that they'll look

20
00:01:12,280 --> 00:01:15,280
Speaker 3: at maybe raising around twenty billion dollars through this mechanism.

21
00:01:15,360 --> 00:01:17,720
Speaker 3: By the way, the stock is down for a third

22
00:01:17,760 --> 00:01:21,399
Speaker 3: straight session, down ten percent this Monday, trading below one

23
00:01:21,480 --> 00:01:24,039
Speaker 3: hundred and seventy dollars a share. Remember this is an

24
00:01:24,040 --> 00:01:30,640
Speaker 3: IPO that priced on June twelfth, June eleventh Trader June twelve,

25
00:01:30,840 --> 00:01:32,800
Speaker 3: one hundred and thirty five dollars a share. Now, why

26
00:01:32,840 --> 00:01:35,920
Speaker 3: would a company sitting on one hundred billion dollars and

27
00:01:36,000 --> 00:01:39,679
Speaker 3: more of cash need to tap the corporate debt market?

28
00:01:39,720 --> 00:01:44,720
Speaker 3: Bloomberg Intelligence writing that quote, despite operational questions and considerable

29
00:01:44,800 --> 00:01:48,800
Speaker 3: key man risk, they expect SpaceX's debt issue to be

30
00:01:49,080 --> 00:01:50,160
Speaker 3: very warmly received.

31
00:01:50,200 --> 00:01:51,440
Speaker 2: There's only one man for the job.

32
00:01:51,480 --> 00:01:55,840
Speaker 3: Bloomberg Intelligence senior credit analyst, Robert Schiffman. That's the story, right,

33
00:01:56,160 --> 00:01:58,680
Speaker 3: Why would a company that has one hundred billion dollars

34
00:01:59,120 --> 00:02:01,200
Speaker 3: of cash on hand and do this?

35
00:02:01,440 --> 00:02:01,840
Speaker 2: Explain?

36
00:02:02,440 --> 00:02:04,160
Speaker 4: Well, I didn't think I could be more bullish, but

37
00:02:04,440 --> 00:02:06,720
Speaker 4: every day I wake up and show up at Bloomberg,

38
00:02:06,800 --> 00:02:09,480
Speaker 4: I'm more and more bullish on AI, and I think

39
00:02:09,520 --> 00:02:12,160
Speaker 4: this deal is just going to support that. You know,

40
00:02:12,280 --> 00:02:14,280
Speaker 4: why do they need to raise more money. It's because

41
00:02:14,320 --> 00:02:16,680
Speaker 4: over the next handful of years they're probably going to

42
00:02:16,720 --> 00:02:19,839
Speaker 4: spend a lot more than one hundred billion dollars. In fact,

43
00:02:19,840 --> 00:02:21,440
Speaker 4: the free cash flow is probably going to be a

44
00:02:21,440 --> 00:02:25,520
Speaker 4: lot more than one hundred billion dollars. On the negative side.

45
00:02:25,600 --> 00:02:28,960
Speaker 4: So they're going to need money because they're building the

46
00:02:29,000 --> 00:02:30,920
Speaker 4: next hyperscaler business in space.

47
00:02:32,080 --> 00:02:34,800
Speaker 3: Let's go to the investor demand or appetite for this.

48
00:02:35,360 --> 00:02:39,120
Speaker 3: We had reported even prior to the IPO that SpaceX

49
00:02:39,160 --> 00:02:41,720
Speaker 3: had managed to get investment grade ratings from the three

50
00:02:41,760 --> 00:02:45,840
Speaker 3: main agencies, and in your react you basically say, this

51
00:02:45,880 --> 00:02:48,480
Speaker 3: is how we think they'll stack up relative to their

52
00:02:48,560 --> 00:02:52,079
Speaker 3: hyperscale piers. You have a greater rating. Still, why does

53
00:02:52,120 --> 00:02:53,000
Speaker 3: all of that matter?

54
00:02:53,520 --> 00:02:56,080
Speaker 4: Well, I think listen, nobody owns SpaceX right now from

55
00:02:56,120 --> 00:02:58,600
Speaker 4: the bond side, so there's going to be enormous demand

56
00:02:58,800 --> 00:03:01,280
Speaker 4: when you have an inaugural offering like this, it's going

57
00:03:01,320 --> 00:03:03,679
Speaker 4: to be a feeding frenzy. And quite frankly, I think

58
00:03:03,720 --> 00:03:05,200
Speaker 4: three quarters of the people that are going to buy

59
00:03:05,200 --> 00:03:07,400
Speaker 4: the bonds aren't even going to know what this company does.

60
00:03:07,720 --> 00:03:10,200
Speaker 4: It's going to be very much of a buy now,

61
00:03:10,320 --> 00:03:13,280
Speaker 4: ask questions later. But what do they have supporting them?

62
00:03:13,560 --> 00:03:17,360
Speaker 4: They've got very strong, stable investment grade ratings. You do

63
00:03:17,400 --> 00:03:19,200
Speaker 4: not have to worry about this name going to jump.

64
00:03:19,680 --> 00:03:24,120
Speaker 4: You've got perhaps the greatest investor of our generation, Elon Musk,

65
00:03:24,160 --> 00:03:29,280
Speaker 4: who I think has replaced Warren Buffett, and you have

66
00:03:29,280 --> 00:03:31,280
Speaker 4: one hundred billion dollars of cash sitting on the books,

67
00:03:31,520 --> 00:03:36,360
Speaker 4: plus tremendous upside across a diversity of businesses. My sense is,

68
00:03:36,400 --> 00:03:38,880
Speaker 4: if they raise twenty odd billion dollars, you're going to

69
00:03:38,880 --> 00:03:41,440
Speaker 4: have a book size that's well north of one hundred

70
00:03:41,480 --> 00:03:42,080
Speaker 4: billion dollars.

71
00:03:43,200 --> 00:03:46,040
Speaker 3: You mean the demand, the one hundred billion dollars demand

72
00:03:46,040 --> 00:03:49,000
Speaker 3: for twenty billion dollars of supply, just real quick like,

73
00:03:49,320 --> 00:03:50,680
Speaker 3: trying to compare and contrast.

74
00:03:50,440 --> 00:03:51,160
Speaker 2: The equity story.

75
00:03:51,160 --> 00:03:53,600
Speaker 3: This stocks down nine percent one hundred and sixty seven

76
00:03:53,640 --> 00:03:57,880
Speaker 3: dollars a share for the equity investor. They read the prospectives,

77
00:03:57,920 --> 00:04:01,520
Speaker 3: they listen to Elon Musk, and everything that's being pitched

78
00:04:01,600 --> 00:04:04,360
Speaker 3: is very far into the future, but is the kind

79
00:04:04,360 --> 00:04:07,320
Speaker 3: of common denominator between the equity investors' approach and the

80
00:04:07,320 --> 00:04:11,160
Speaker 3: credit investors approach to SpaceX. Trying to understand the path

81
00:04:11,160 --> 00:04:13,640
Speaker 3: of CAPEX. Does it work the same way?

82
00:04:13,880 --> 00:04:16,719
Speaker 4: Listen? Equity and credit are different. Equity is based on

83
00:04:16,920 --> 00:04:20,360
Speaker 4: hope and future cash flows. Credit generally is based upon

84
00:04:20,480 --> 00:04:24,359
Speaker 4: present cash flows and what leverage looks like right now.

85
00:04:24,600 --> 00:04:26,640
Speaker 4: I actually do think that those are going to somewhat

86
00:04:26,640 --> 00:04:29,760
Speaker 4: come together over time. This company is committed to reasonably

87
00:04:29,800 --> 00:04:33,119
Speaker 4: low leverage below three times. They're committing to having twenty

88
00:04:33,120 --> 00:04:35,800
Speaker 4: five billion dollars of cash on the books for the

89
00:04:35,839 --> 00:04:40,239
Speaker 4: foreseeable future, and there's a hope of just absolute tremendous growth.

90
00:04:40,320 --> 00:04:43,440
Speaker 4: Quite frankly, it's not so different than what Microsoft or

91
00:04:43,480 --> 00:04:46,200
Speaker 4: Alphabet or Amazon is doing. And just remember in the

92
00:04:46,279 --> 00:04:48,280
Speaker 4: early days of names like Amazon, they lost a lot

93
00:04:48,279 --> 00:04:51,080
Speaker 4: of money too. You have to spend money to make money.

94
00:04:51,080 --> 00:04:52,680
Speaker 4: This is what this company is doing. I think the

95
00:04:52,720 --> 00:04:54,760
Speaker 4: bond market's going to be there to fund it, and

96
00:04:54,800 --> 00:04:57,839
Speaker 4: I think the equity market when you start thinking about valuations,

97
00:04:57,920 --> 00:05:01,279
Speaker 4: all these AI revenues in particular come in. Remember this

98
00:05:01,360 --> 00:05:05,800
Speaker 4: company is going to transition from what is a satellite

99
00:05:06,520 --> 00:05:10,400
Speaker 4: data business and aerospace and defense business to what's going

100
00:05:10,480 --> 00:05:13,640
Speaker 4: to be a hyperscaler in the sky. I think there's

101
00:05:13,839 --> 00:05:16,560
Speaker 4: nothing but upside when it comes to revenues and casualows

102
00:05:16,600 --> 00:05:19,560
Speaker 4: for this business. So bondholders and credit holders will sort

103
00:05:19,560 --> 00:05:21,120
Speaker 4: of all be looking at the same thing a few

104
00:05:21,160 --> 00:05:22,000
Speaker 4: years from now.

105
00:05:22,640 --> 00:05:23,040
Speaker 2: Doing both.

106
00:05:23,120 --> 00:05:25,880
Speaker 3: Robert Schiffman, senior credit analysts of Bloomberg Intelligence, thank you

107
00:05:26,000 --> 00:05:28,120
Speaker 3: very much. We talked a lot about SpaceX so far,

108
00:05:28,160 --> 00:05:31,800
Speaker 3: this morning, But the bigger market story is this return

109
00:05:31,839 --> 00:05:35,480
Speaker 3: of tech leadership. Right, chip stocks outperforming and leading again.

110
00:05:35,920 --> 00:05:38,719
Speaker 3: Software continues to lag. There's a lot of focus on

111
00:05:38,760 --> 00:05:42,599
Speaker 3: lower energy prices, what's happening in the golf, and that's

112
00:05:42,600 --> 00:05:44,000
Speaker 3: supporting the growth trade.

113
00:05:44,279 --> 00:05:45,240
Speaker 2: Joining US senior.

114
00:05:44,960 --> 00:05:48,920
Speaker 3: Portfolio manager Tiffany Wade at Columbia Frednedle, there's a lot

115
00:05:48,960 --> 00:05:51,919
Speaker 3: to kind of put together in this morning session. But

116
00:05:52,080 --> 00:05:55,480
Speaker 3: again technology continues to be the story in this market.

117
00:05:55,839 --> 00:05:56,599
Speaker 2: Is that true for you?

118
00:05:57,120 --> 00:05:59,600
Speaker 5: Yes, that's absolutely true. We did see tech stocks take

119
00:05:59,600 --> 00:06:02,120
Speaker 5: a bit of a breather, including semi stocks last week,

120
00:06:02,160 --> 00:06:04,760
Speaker 5: but they're after the races again this week and that

121
00:06:04,760 --> 00:06:06,200
Speaker 5: continuously the market higher.

122
00:06:07,200 --> 00:06:11,760
Speaker 3: Of course, for investors of all types, the outlook for

123
00:06:11,800 --> 00:06:14,839
Speaker 3: monetary policy and rates is important. It was really interesting

124
00:06:15,000 --> 00:06:19,520
Speaker 3: Noria Rabini on Bloomberg Television earlier basically saying, nah, it's

125
00:06:19,520 --> 00:06:20,000
Speaker 3: still tech.

126
00:06:20,120 --> 00:06:23,400
Speaker 2: Just listen to this real quick. People obsessed with the FED.

127
00:06:23,720 --> 00:06:26,560
Speaker 2: Whether there's fifty businesspose higher, where's al right? Now, what's

128
00:06:26,560 --> 00:06:29,719
Speaker 2: the difference. The key story is tech boom and that's

129
00:06:29,720 --> 00:06:31,720
Speaker 2: going to be the most important first order I feel

130
00:06:31,800 --> 00:06:32,360
Speaker 2: anything else.

131
00:06:33,920 --> 00:06:36,800
Speaker 3: You know, Tiffany, I grew up in the Bloomberg Newsroom

132
00:06:36,920 --> 00:06:39,640
Speaker 3: school of Well, you got to understand the rate environment

133
00:06:39,720 --> 00:06:42,839
Speaker 3: because higher rates discount the present value of future cash flows,

134
00:06:43,000 --> 00:06:45,120
Speaker 3: and future cash flows are one way that we measure

135
00:06:45,160 --> 00:06:48,320
Speaker 3: the health and valuation of technology companies big and small.

136
00:06:48,960 --> 00:06:50,320
Speaker 2: Does the FED really matter at all?

137
00:06:50,960 --> 00:06:52,479
Speaker 5: I think the FED still matters a lot, and it

138
00:06:52,520 --> 00:06:54,760
Speaker 5: matters a lot for tech stocks as well. It matters

139
00:06:54,760 --> 00:06:57,039
Speaker 5: a lot for high growth stocks. We did see a

140
00:06:57,080 --> 00:07:01,039
Speaker 5: slightly more hawkish FED meeting last week, Kevin Worsh's first

141
00:07:01,040 --> 00:07:04,160
Speaker 5: FED meeting. However, you know, quite a bit has changed

142
00:07:04,200 --> 00:07:06,359
Speaker 5: since the data they were looking at last week, including

143
00:07:06,640 --> 00:07:08,680
Speaker 5: the MoU between the US and Iran. So I think

144
00:07:08,720 --> 00:07:11,080
Speaker 5: we might see, you know, potentially some different news coming

145
00:07:11,080 --> 00:07:13,320
Speaker 5: out of the next FED meeting. Off oil prices continue

146
00:07:13,320 --> 00:07:15,200
Speaker 5: to normalize, and.

147
00:07:15,240 --> 00:07:17,480
Speaker 3: In the background, of course, for the Bloomberg Tech audience,

148
00:07:17,560 --> 00:07:20,240
Speaker 3: you know that there are headlines coming from the Vice

149
00:07:20,280 --> 00:07:24,320
Speaker 3: President and his participation in the talks between the United

150
00:07:24,360 --> 00:07:27,920
Speaker 3: States in Iran. We're very soon going to start talking

151
00:07:27,920 --> 00:07:31,440
Speaker 3: about earnings again, and I just wondered Tipany from Columbia

152
00:07:31,480 --> 00:07:36,000
Speaker 3: thread Neils perspective, how important earning's momentum is going to

153
00:07:36,000 --> 00:07:39,440
Speaker 3: be for a market where technology continues to dominate.

154
00:07:39,960 --> 00:07:41,920
Speaker 5: We're going to get a first read on that from

155
00:07:42,000 --> 00:07:43,960
Speaker 5: Micron this Wednesday. I think that's going to be very

156
00:07:43,960 --> 00:07:46,840
Speaker 5: important to see if the momentum from these stocks can continue.

157
00:07:47,480 --> 00:07:50,000
Speaker 5: We're going to want to see continued revenue upside from

158
00:07:50,040 --> 00:07:52,400
Speaker 5: a lot of the semiconductor stocks, and we're also probably

159
00:07:52,400 --> 00:07:56,000
Speaker 5: going to want to see continued CAEPEX positive capex comments

160
00:07:56,000 --> 00:07:58,280
Speaker 5: from all of the hyperscalers. That will help keep the

161
00:07:58,320 --> 00:08:00,880
Speaker 5: spending party going. For all of the ass cated infrastructure

162
00:08:00,920 --> 00:08:02,600
Speaker 5: names related to AI build out.

163
00:08:03,440 --> 00:08:06,640
Speaker 3: It's really hard in aggregate to kind of say where's

164
00:08:06,680 --> 00:08:08,720
Speaker 3: the beginning, middle, and end here? You know, I think

165
00:08:08,760 --> 00:08:10,600
Speaker 3: you'd note right, tiffanyed it last week some of the

166
00:08:10,680 --> 00:08:12,920
Speaker 3: chip stocks, the picks and shovels, took a bit of

167
00:08:12,920 --> 00:08:15,760
Speaker 3: a breather, and off we are again today with the

168
00:08:15,760 --> 00:08:20,080
Speaker 3: Philadelphia Semiconductor Index outperforming and engauge that's up ninety percent

169
00:08:20,160 --> 00:08:20,760
Speaker 3: year to date.

170
00:08:21,440 --> 00:08:22,840
Speaker 2: How long can that carry on for?

171
00:08:24,000 --> 00:08:26,880
Speaker 5: I think we're going to see this continue for another

172
00:08:26,960 --> 00:08:29,800
Speaker 5: couple of quarters. The outlook for these stocks related to

173
00:08:29,840 --> 00:08:34,040
Speaker 5: AI infrastructure spending is tremendous, and the revenue and earning

174
00:08:34,160 --> 00:08:36,160
Speaker 5: estimates continue to be revised higher, and I think as

175
00:08:36,200 --> 00:08:38,800
Speaker 5: long as that continues, you'll continue to see these stocks working.

176
00:08:39,559 --> 00:08:41,520
Speaker 5: And some of the announcement we've seen, you know, with

177
00:08:41,600 --> 00:08:46,320
Speaker 5: Micronanthropic and then also around you know, Microsoft and you

178
00:08:46,360 --> 00:08:51,840
Speaker 5: know more data center spending in Texas continues to reinforce

179
00:08:51,880 --> 00:08:54,079
Speaker 5: the story that this spending is going to continue for

180
00:08:54,160 --> 00:08:56,360
Speaker 5: another couple of years and probably continue at a faster

181
00:08:56,440 --> 00:08:58,800
Speaker 5: pace than it has been for the last one or

182
00:08:58,840 --> 00:09:00,640
Speaker 5: two years. So I think we'll cantinue to see those

183
00:09:00,679 --> 00:09:03,640
Speaker 5: positive earnings revisions that will continue to push the stockshire.

184
00:09:04,720 --> 00:09:09,720
Speaker 3: We started the program today talking about SpaceX's inaugural bond sale,

185
00:09:10,160 --> 00:09:12,520
Speaker 3: but I just use that as a case study that

186
00:09:13,120 --> 00:09:17,280
Speaker 3: with Capex, both in equity, the market's being flooded, right,

187
00:09:17,360 --> 00:09:22,800
Speaker 3: Alphabet doing its offering, the use of corporate credit to

188
00:09:22,880 --> 00:09:27,079
Speaker 3: fund what's going on. How closely are you tracking these

189
00:09:27,120 --> 00:09:30,160
Speaker 3: companies going to market to get the capital.

190
00:09:29,800 --> 00:09:32,160
Speaker 2: They need to fund these big commitments.

191
00:09:33,040 --> 00:09:36,160
Speaker 5: We're certainly focused on the outcome of these bond sales

192
00:09:36,160 --> 00:09:38,120
Speaker 5: because I think it's an indicator of the appetite for

193
00:09:38,160 --> 00:09:40,480
Speaker 5: the market for the amount of Capex spending that the

194
00:09:40,480 --> 00:09:41,320
Speaker 5: companies are doing.

195
00:09:41,960 --> 00:09:42,640
Speaker 2: It's also non.

196
00:09:42,520 --> 00:09:46,320
Speaker 5: Indicator of future capex expectations. So as those bond sales

197
00:09:46,360 --> 00:09:50,240
Speaker 5: continue to increase in size, it can you know, it

198
00:09:50,280 --> 00:09:53,040
Speaker 5: means that capex spending intentions continue to rise. So it's

199
00:09:53,040 --> 00:09:55,800
Speaker 5: certainly something that we're watching to indicate the health of

200
00:09:55,840 --> 00:09:58,480
Speaker 5: the capex spending cycle and also the intention of a

201
00:09:58,480 --> 00:10:02,400
Speaker 5: lot of the large companies around additional spending for AI infrastructure.

202
00:10:03,640 --> 00:10:05,840
Speaker 3: Tiffany, real quick, what's the next thing you're watching for

203
00:10:05,920 --> 00:10:06,640
Speaker 3: on your calendar?

204
00:10:07,679 --> 00:10:10,240
Speaker 5: I think it's probably earnings Micron this Wednesday is certainly

205
00:10:10,280 --> 00:10:11,800
Speaker 5: a big one that we'll be keeping an eye out for.

206
00:10:12,679 --> 00:10:14,680
Speaker 3: And there's been some news this morning about Micron and

207
00:10:14,679 --> 00:10:16,360
Speaker 3: a deal with Anthropic, which we'll get to you later

208
00:10:16,400 --> 00:10:19,040
Speaker 3: in the program. Tiffany Wade of Columbia, Fred needle Back on.

209
00:10:18,960 --> 00:10:20,559
Speaker 2: Bloomberg Tech, thank you very much.

210
00:10:20,720 --> 00:10:24,040
Speaker 3: Now coming up, Meta invests nine hundred million dollars into

211
00:10:24,080 --> 00:10:27,959
Speaker 3: an Indian fintech startup called Cred, with plans to appoint

212
00:10:28,000 --> 00:10:31,760
Speaker 3: its founder as the new leader of WhatsApp. We've got

213
00:10:31,760 --> 00:10:34,000
Speaker 3: more on that story next, and sticking with big tech names,

214
00:10:34,240 --> 00:10:37,760
Speaker 3: also taking a look at shares of Alphabet, parent of Google,

215
00:10:38,160 --> 00:10:42,119
Speaker 3: down six percent in the session, biggest drop since February,

216
00:10:42,200 --> 00:10:44,719
Speaker 3: as it stands, a lot in the newsflow has been

217
00:10:44,720 --> 00:10:48,960
Speaker 3: about the departures from various parts of Google's AI initiatives,

218
00:10:48,960 --> 00:10:52,280
Speaker 3: the latest being a prominent AI research or Google DeepMind.

219
00:10:52,360 --> 00:10:57,599
Speaker 3: Vice president John Jumper said Friday he was leaving for Anthropic.

220
00:10:58,000 --> 00:11:00,679
Speaker 3: That seems to be weighing on this stock. Keep tracking it.

221
00:11:00,720 --> 00:11:09,800
Speaker 3: This is Bloomberg Tech, all right. Some headlines have just

222
00:11:09,840 --> 00:11:12,560
Speaker 3: crossed the Bloomberg terminal. SpaceX appears to have signed a

223
00:11:12,600 --> 00:11:16,760
Speaker 3: deal with Reflection AI worth about six point three billion dollars.

224
00:11:17,040 --> 00:11:18,440
Speaker 2: That's according to some reports out there.

225
00:11:18,440 --> 00:11:21,080
Speaker 3: I think the company is also just put headlines out

226
00:11:21,200 --> 00:11:24,560
Speaker 3: of its own. SpaceX can end the deal after three months,

227
00:11:24,720 --> 00:11:27,440
Speaker 3: and Reflection is set to pay SpaceX one hundred and

228
00:11:27,440 --> 00:11:31,480
Speaker 3: fifty million dollars per month for that compute as part

229
00:11:31,480 --> 00:11:33,920
Speaker 3: of the deal. It didn't really do much to ease

230
00:11:33,920 --> 00:11:35,760
Speaker 3: the pressure on the stock, which is again down for

231
00:11:35,760 --> 00:11:38,880
Speaker 3: a third straight day, but still above its IPO price

232
00:11:38,920 --> 00:11:41,480
Speaker 3: one hundred and thirty five dollars a share. Big Tech

233
00:11:41,600 --> 00:11:45,360
Speaker 3: is turning to big oil to meet the electricity demand

234
00:11:45,360 --> 00:11:48,440
Speaker 3: of the AI boom. Microsoft has signed a twenty year

235
00:11:48,559 --> 00:11:52,400
Speaker 3: deal with Chevron to build a massive natural gas fire

236
00:11:52,440 --> 00:11:56,760
Speaker 3: generating station for a proposed data center in West Texas,

237
00:11:57,000 --> 00:12:01,480
Speaker 3: dubbed Project Kilby. The power once expected to power up

238
00:12:01,960 --> 00:12:04,440
Speaker 3: by twenty twenty eight. Bloomers Kevin Crowley joins US for

239
00:12:04,480 --> 00:12:09,040
Speaker 3: more from Houston. The scale of this is on paper

240
00:12:09,080 --> 00:12:11,520
Speaker 3: pretty big, right, So they're talking about ramping up to

241
00:12:11,960 --> 00:12:14,240
Speaker 3: about two point six two point seven gigawatts.

242
00:12:14,320 --> 00:12:15,400
Speaker 2: What do we need to know about this?

243
00:12:15,520 --> 00:12:18,480
Speaker 3: And like the natural gas part of it, the Permian

244
00:12:18,640 --> 00:12:21,080
Speaker 3: West Texas very interesting.

245
00:12:22,040 --> 00:12:24,160
Speaker 2: Yes, exactly. Well, that's the key part of this.

246
00:12:24,240 --> 00:12:27,360
Speaker 6: It's a huge power power plant to fund an enormous

247
00:12:27,440 --> 00:12:30,319
Speaker 6: data center. But the really important part is it's right

248
00:12:30,360 --> 00:12:32,880
Speaker 6: in the middle of the Permian Basin, which which many

249
00:12:32,920 --> 00:12:33,560
Speaker 6: people know is the.

250
00:12:34,280 --> 00:12:36,760
Speaker 2: Biggest oil field here in the US.

251
00:12:36,840 --> 00:12:38,960
Speaker 6: But with all the oil that comes up, there's an

252
00:12:39,040 --> 00:12:41,840
Speaker 6: enormous amount of natural gas that also comes and there's

253
00:12:41,880 --> 00:12:44,520
Speaker 6: so much natural gas within the basin that it actually

254
00:12:44,559 --> 00:12:48,079
Speaker 6: often overwhelms pipeline capacity and has to be either burned

255
00:12:48,080 --> 00:12:51,760
Speaker 6: off or companies have to pay people to take it away.

256
00:12:51,760 --> 00:12:54,520
Speaker 6: So there is abundance of natural gas and that is

257
00:12:54,559 --> 00:12:57,480
Speaker 6: what Chevron and Microsoft and they're partner Engine Number one,

258
00:12:58,080 --> 00:13:01,000
Speaker 6: are looking to take advantage of here got incredibly cheap

259
00:13:01,080 --> 00:13:05,079
Speaker 6: natural gas that can that can fuel the power station

260
00:13:05,240 --> 00:13:08,760
Speaker 6: that will then power this this big data center that

261
00:13:09,000 --> 00:13:10,199
Speaker 6: Microsoft plans to build.

262
00:13:12,400 --> 00:13:16,040
Speaker 3: Discuss in your report the bigger picture of what Microsoft's

263
00:13:16,040 --> 00:13:18,559
Speaker 3: trying to do right. Microsoft has said it wants to

264
00:13:18,600 --> 00:13:20,959
Speaker 3: double data center capacity of its own over the next

265
00:13:21,040 --> 00:13:23,880
Speaker 3: two years, and in aggregate, that's the direction that the

266
00:13:23,960 --> 00:13:27,480
Speaker 3: United States is going in over the next decade. But

267
00:13:27,480 --> 00:13:30,760
Speaker 3: they'd also had these like prior commitments to match the

268
00:13:30,880 --> 00:13:34,160
Speaker 3: sourcing of their power in terms of their commitments from

269
00:13:34,200 --> 00:13:38,280
Speaker 3: renewable sources, this is natural gas. So how are they

270
00:13:38,320 --> 00:13:39,319
Speaker 3: managing all of that?

271
00:13:41,000 --> 00:13:43,560
Speaker 6: Microsoft have said that they're they're pursuing kind of a

272
00:13:43,600 --> 00:13:48,960
Speaker 6: diversification strategy, So they want they want diversification both in

273
00:13:49,000 --> 00:13:52,360
Speaker 6: terms of geography but also in terms of fuel sources

274
00:13:52,440 --> 00:13:56,760
Speaker 6: to renewables and natural gas. And I think they're pursuing

275
00:13:57,440 --> 00:14:00,160
Speaker 6: nuclear as well. I mean, obviously the natural gas as

276
00:14:01,320 --> 00:14:03,160
Speaker 6: is on is on the fossil fuel side, so it

277
00:14:03,160 --> 00:14:05,359
Speaker 6: does does create a lot more a lot more emissions.

278
00:14:06,240 --> 00:14:09,400
Speaker 6: We are seeing some of these big tech companies UH

279
00:14:09,440 --> 00:14:13,920
Speaker 6: sort of diluting or walking away some of these prior

280
00:14:13,960 --> 00:14:18,280
Speaker 6: commitments to two one hundred percent renewable energy, just because

281
00:14:18,320 --> 00:14:21,320
Speaker 6: of the sheer scale of the of the demand and

282
00:14:21,360 --> 00:14:25,680
Speaker 6: the ambitions that AI in AI that they have.

283
00:14:27,320 --> 00:14:28,480
Speaker 2: There is in this project.

284
00:14:28,520 --> 00:14:34,560
Speaker 6: There are there are guardrails to mitigate the emissions, and

285
00:14:34,600 --> 00:14:38,080
Speaker 6: they do have plans to to maybe build in some

286
00:14:38,400 --> 00:14:40,920
Speaker 6: build in some solar, maybe some batteries at some point

287
00:14:41,000 --> 00:14:43,160
Speaker 6: in the in the future. But but the end of

288
00:14:43,200 --> 00:14:45,640
Speaker 6: the day, it's an enormous natural gas plants, so the

289
00:14:45,640 --> 00:14:48,840
Speaker 6: emissions are going to are going to be sizable associated

290
00:14:48,880 --> 00:14:49,120
Speaker 6: with that.

291
00:14:50,600 --> 00:14:54,360
Speaker 3: Chevron's shares modestly higher. Microsoft shares modestly lower after still

292
00:14:54,360 --> 00:14:57,000
Speaker 3: was announced. Bloombers Kevin Crowley in Houston, thank you well.

293
00:14:57,120 --> 00:14:57,560
Speaker 2: Modestly so.

294
00:14:57,640 --> 00:15:01,360
Speaker 3: Misoft down two percent, turning out to news in big tech,

295
00:15:01,560 --> 00:15:05,720
Speaker 3: Meta is shaking up the executive suite at WhatsApp and

296
00:15:05,880 --> 00:15:09,080
Speaker 3: is in part a massive nine hundred million dollar investment

297
00:15:09,520 --> 00:15:14,080
Speaker 3: into an Indian fintech startup, Cred. So Meta is tapping

298
00:15:14,160 --> 00:15:18,400
Speaker 3: Cred's founder Canal Shah to step in is the new

299
00:15:18,440 --> 00:15:21,680
Speaker 3: global chief of WhatsApp to build out new revenue streams

300
00:15:21,720 --> 00:15:24,720
Speaker 3: for the mesaging platform. Ms Kat Wagner joins us with

301
00:15:24,760 --> 00:15:28,440
Speaker 3: the story, it's not straightforward, this okay, so basically the

302
00:15:28,480 --> 00:15:31,840
Speaker 3: news is there's a new head of WhatsApp. But the

303
00:15:32,240 --> 00:15:35,280
Speaker 3: mechanism that Metas used to get there is also interesting.

304
00:15:36,480 --> 00:15:38,880
Speaker 7: It is and you're right, there's two parts of this,

305
00:15:38,880 --> 00:15:42,840
Speaker 7: the investment part in credit. There's the new executive for

306
00:15:42,920 --> 00:15:47,280
Speaker 7: WhatsApp in Kunal Shah. And this is a strategy that

307
00:15:47,320 --> 00:15:50,840
Speaker 7: you may be somewhat familiar with from Meta. With this

308
00:15:50,920 --> 00:15:53,800
Speaker 7: sort of like invest in recruit at the same time.

309
00:15:54,080 --> 00:15:56,880
Speaker 7: This is how they got Alexander Wang from Scaleai.

310
00:15:57,000 --> 00:15:57,600
Speaker 2: Last summer.

311
00:15:57,680 --> 00:16:01,680
Speaker 7: They invested heavily in Scale and recruited alex Wang at

312
00:16:01,720 --> 00:16:05,280
Speaker 7: the same time. This is a very similar approach smaller scale.

313
00:16:05,880 --> 00:16:09,080
Speaker 7: This is a smaller investment than last summers, but this

314
00:16:09,280 --> 00:16:12,040
Speaker 7: is sort of their way of bringing entrepreneurs who have

315
00:16:12,120 --> 00:16:14,880
Speaker 7: their own businesses, who have you know, a financial stake

316
00:16:14,920 --> 00:16:17,880
Speaker 7: in a startup and enticing them to come work at

317
00:16:18,080 --> 00:16:20,640
Speaker 7: a more established company like Meta.

318
00:16:21,800 --> 00:16:23,880
Speaker 2: How's Meta sort of explaining this.

319
00:16:24,680 --> 00:16:26,160
Speaker 3: You know, we put a lot of emphasis on the

320
00:16:26,200 --> 00:16:30,960
Speaker 3: idea that WhatsApp can monetize in different ways future revenue streams.

321
00:16:31,120 --> 00:16:32,920
Speaker 2: Is there any kind of hint of what that looks like.

322
00:16:34,080 --> 00:16:37,840
Speaker 7: Well, they're doing a handful of things right now, including ads, subscriptions,

323
00:16:37,880 --> 00:16:40,480
Speaker 7: and business messaging. Those are sort of the three things

324
00:16:40,520 --> 00:16:43,600
Speaker 7: that are going for revenue. What I think is important

325
00:16:43,640 --> 00:16:47,560
Speaker 7: here is that Khunaalshab being from India and having an

326
00:16:47,600 --> 00:16:49,560
Speaker 7: Indian startup is not a coincidence.

327
00:16:50,160 --> 00:16:51,560
Speaker 2: WhatsApp is huge in India.

328
00:16:51,840 --> 00:16:54,720
Speaker 7: They see a ton of potential there from a business standpoint,

329
00:16:54,960 --> 00:16:57,440
Speaker 7: and I think they thought that it was important to,

330
00:16:58,320 --> 00:17:00,880
Speaker 7: you know, have someone very familiar with market and very

331
00:17:00,880 --> 00:17:03,680
Speaker 7: familiar with how WhatsApp is used in that region coming

332
00:17:03,680 --> 00:17:06,400
Speaker 7: in and taking over. And so the hope is certainly

333
00:17:06,480 --> 00:17:09,439
Speaker 7: that he can take those revenue streams that are sort

334
00:17:09,440 --> 00:17:13,320
Speaker 7: of already in place but grow them considerably in India,

335
00:17:13,359 --> 00:17:17,560
Speaker 7: but also in other markets outside of North America.

336
00:17:17,840 --> 00:17:20,640
Speaker 3: Bloomberg text cut Wagner with the meadow WhatsApp story, thank

337
00:17:20,640 --> 00:17:23,280
Speaker 3: you very much. I want to go to China's battery

338
00:17:23,280 --> 00:17:27,600
Speaker 3: metals market. Chinese lithium futures have plunged nearly nine percent

339
00:17:27,720 --> 00:17:32,360
Speaker 3: over the last two days over speculation that caatl could

340
00:17:32,400 --> 00:17:35,879
Speaker 3: soon restart, one of the world's largest lithium mines. A

341
00:17:35,960 --> 00:17:40,199
Speaker 3: preliminary provincial land notice has some investors' bedding that the

342
00:17:40,240 --> 00:17:43,440
Speaker 3: site may reopen in the second half of twenty twenty six.

343
00:17:43,520 --> 00:17:45,280
Speaker 2: Now that supply.

344
00:17:45,040 --> 00:17:50,360
Speaker 3: Volatility comes as Beijing tries to sharpen its geopolitical leverage

345
00:17:50,440 --> 00:17:55,040
Speaker 3: over other critical minerals integral to making everything from iPhones

346
00:17:55,320 --> 00:17:59,679
Speaker 3: to missiles. China has imposed new export controls on major

347
00:18:00,160 --> 00:18:04,200
Speaker 3: US rare earth producers, MPEd Materials and USA rare Earth.

348
00:18:04,480 --> 00:18:08,720
Speaker 3: The retaliatory move comes off to the Pentagon blacklisted some

349
00:18:08,760 --> 00:18:12,719
Speaker 3: of China's biggest firms earlier this month. Okay, coming up,

350
00:18:12,760 --> 00:18:17,800
Speaker 3: Apple's design revival incoming, CEO John Turnas looks to restore

351
00:18:17,880 --> 00:18:20,840
Speaker 3: the creative edge that defined the iPhone era.

352
00:18:21,480 --> 00:18:23,240
Speaker 2: That's sex, this is Bloomberg tech.

353
00:18:31,440 --> 00:18:34,600
Speaker 8: I mean hara anod, and it's time now for talking tech.

354
00:18:34,680 --> 00:18:38,200
Speaker 8: First up, ten Cents is giving we Chat an AI upgrade.

355
00:18:38,280 --> 00:18:41,199
Speaker 8: The company is testing a new assistant called Shaowei with

356
00:18:41,320 --> 00:18:44,359
Speaker 8: a small group of users, allowing them to complete tasks

357
00:18:44,359 --> 00:18:47,359
Speaker 8: through text or voice. The move could bring AI to

358
00:18:47,440 --> 00:18:50,200
Speaker 8: weed Chat more than one billion users and help ten

359
00:18:50,240 --> 00:18:53,359
Speaker 8: cents compete with rivals like Ali Baba and byte Edance

360
00:18:53,720 --> 00:18:55,200
Speaker 8: in China's AI race.

361
00:18:55,320 --> 00:18:55,760
Speaker 2: Plus.

362
00:18:56,119 --> 00:18:58,960
Speaker 8: Robinhood is looking to raise two billion dollars through a

363
00:18:58,960 --> 00:19:03,600
Speaker 8: convertible bond off, tapping into a booming market for corporate fundraising.

364
00:19:03,600 --> 00:19:06,199
Speaker 8: The company says about three hundred million dollars of the

365
00:19:06,240 --> 00:19:10,680
Speaker 8: proceeds will go towards share buybacks, with additional funds earmarks

366
00:19:10,720 --> 00:19:14,719
Speaker 8: for hedging transactions tied to the deal end. Climb is

367
00:19:14,760 --> 00:19:18,760
Speaker 8: gearing up for its Wall Street debut. The electric bike

368
00:19:18,880 --> 00:19:21,520
Speaker 8: and scooter rental company is looking to raise up to

369
00:19:21,560 --> 00:19:24,360
Speaker 8: one hundred and eighty one million dollars in its USIPO,

370
00:19:24,520 --> 00:19:28,400
Speaker 8: offering six point seven million shares at between twenty four

371
00:19:28,720 --> 00:19:31,280
Speaker 8: and twenty six dollars a piece. At the top end

372
00:19:31,280 --> 00:19:34,240
Speaker 8: of that range, that uberbacked company would be valued at

373
00:19:34,240 --> 00:19:36,960
Speaker 8: about one point seven billion dollars ed.

374
00:19:37,800 --> 00:19:39,240
Speaker 2: Thank you very much, you hire right now.

375
00:19:39,280 --> 00:19:43,240
Speaker 3: Apple built its reputation on iconic product design, but the

376
00:19:43,280 --> 00:19:46,560
Speaker 3: influence of the team behind the iPhone and iPad has

377
00:19:46,560 --> 00:19:50,040
Speaker 3: faded over the past decade. Bloombo's Mark German writes in

378
00:19:50,040 --> 00:19:53,960
Speaker 3: this week's power On newsletter that the incoming CEO knows

379
00:19:54,040 --> 00:19:57,439
Speaker 3: a major design shakeup is needed and is getting ready

380
00:19:57,480 --> 00:20:01,560
Speaker 3: to put his firm imprint on the tea. Mark joins us, now,

381
00:20:01,680 --> 00:20:05,320
Speaker 3: and this is about the industrial design studio that in

382
00:20:05,400 --> 00:20:08,080
Speaker 3: the era of first Steve Jobs and then Johnny Ive

383
00:20:08,520 --> 00:20:11,760
Speaker 3: for a very long time held a lot of power

384
00:20:12,400 --> 00:20:16,520
Speaker 3: within the organization at Apple. What are you writing about

385
00:20:16,560 --> 00:20:19,359
Speaker 3: empower on that's changed and why do you think that

386
00:20:19,680 --> 00:20:23,359
Speaker 3: the incoming CEO takes us back to that place of power.

387
00:20:23,720 --> 00:20:23,920
Speaker 2: Yeah.

388
00:20:23,960 --> 00:20:26,720
Speaker 9: Over the past decade or so, you've seen the industrial

389
00:20:26,760 --> 00:20:30,800
Speaker 9: design studio go from where exactly the future products are

390
00:20:30,800 --> 00:20:33,399
Speaker 9: is dreamed up, where the whole company would come in

391
00:20:33,440 --> 00:20:36,200
Speaker 9: to rally around the next big thing, leading to products

392
00:20:36,200 --> 00:20:39,960
Speaker 9: like the iPhone, the iPad, the iMac, the iPod, the

393
00:20:40,000 --> 00:20:42,159
Speaker 9: Apple Watch, you name it. And it's now more of

394
00:20:42,200 --> 00:20:47,320
Speaker 9: a service organization where different teams, operations, engineering, they come in,

395
00:20:47,600 --> 00:20:49,480
Speaker 9: they get what they need, and they get out. It's

396
00:20:49,520 --> 00:20:53,080
Speaker 9: no longer the central hub, the influential center of innovation

397
00:20:53,680 --> 00:20:56,760
Speaker 9: for the company, and John Turnus knows that things need

398
00:20:56,800 --> 00:20:59,600
Speaker 9: to be shaken up there. The head of the industrial

399
00:20:59,640 --> 00:21:04,560
Speaker 9: design team Molly Anderson. She's known as a great individual designer.

400
00:21:05,160 --> 00:21:08,119
Speaker 9: She's known as someone who's beloved by her team. But

401
00:21:08,280 --> 00:21:10,640
Speaker 9: it's not like Apple went out to look for the

402
00:21:10,680 --> 00:21:14,400
Speaker 9: best industrial design leader in the world to take that position.

403
00:21:14,720 --> 00:21:18,240
Speaker 9: They made that choice from a place of defense, not

404
00:21:18,280 --> 00:21:21,679
Speaker 9: wanting more people to leave the team. Versus the perspective

405
00:21:21,720 --> 00:21:24,600
Speaker 9: of a company that's worth three trillion dollars plus that

406
00:21:24,640 --> 00:21:26,760
Speaker 9: could go out and get anyone they want to lead

407
00:21:26,840 --> 00:21:29,719
Speaker 9: design in this post Johnny ive era that's already been

408
00:21:29,760 --> 00:21:31,160
Speaker 9: going on for nearly a decade.

409
00:21:32,240 --> 00:21:35,879
Speaker 3: Mark you've detailed recently the kind of roadmap on the

410
00:21:35,920 --> 00:21:38,040
Speaker 3: hardware side for not just the rest of this year,

411
00:21:38,080 --> 00:21:41,080
Speaker 3: but beyond that. Is there a case study of where

412
00:21:41,119 --> 00:21:43,840
Speaker 3: this kind of return to design focus plays out.

413
00:21:43,840 --> 00:21:45,280
Speaker 2: We just have about thirty seconds.

414
00:21:45,640 --> 00:21:48,960
Speaker 9: Yeah, I mean we would like to see bigger design changes,

415
00:21:49,080 --> 00:21:52,600
Speaker 9: right for the first time in twenty years. You're going

416
00:21:52,640 --> 00:21:55,560
Speaker 9: to have a meaningful shakeup to the iPhone over the

417
00:21:55,600 --> 00:21:58,360
Speaker 9: next few months with the fold wall phone, which I'm

418
00:21:58,359 --> 00:22:01,399
Speaker 9: calling the iPhone Ultra. But there's a lot more to

419
00:22:01,480 --> 00:22:05,399
Speaker 9: be done. There's more product categories. Major changes are necessary

420
00:22:05,440 --> 00:22:08,520
Speaker 9: to freshen up the AirPods and the album watch. Those

421
00:22:08,520 --> 00:22:12,479
Speaker 9: are categories that have essentially looked the same since twenty sixteen.

422
00:22:13,359 --> 00:22:16,200
Speaker 3: Right, Blue Moos Mark German who puts power on out

423
00:22:16,200 --> 00:22:19,000
Speaker 3: on Sunday mornings and then comes on Bloomberg Tech Monday

424
00:22:19,240 --> 00:22:19,760
Speaker 3: to go through it.

425
00:22:19,760 --> 00:22:20,480
Speaker 2: Thank you so much.

426
00:22:20,480 --> 00:22:23,880
Speaker 3: Now, coming up, Micron lands a key deal with anthropic

427
00:22:24,160 --> 00:22:28,520
Speaker 3: as AI demand keeps surging. It's so interesting to see

428
00:22:28,800 --> 00:22:33,080
Speaker 3: Micron and Memory name go direct to a frontier lab.

429
00:22:33,119 --> 00:22:35,080
Speaker 2: We're going to go through that. This is what your

430
00:22:35,080 --> 00:22:35,760
Speaker 2: markets look like.

431
00:22:35,880 --> 00:22:39,080
Speaker 3: Kind of muted, but outperformance in chips, which has been

432
00:22:39,080 --> 00:22:40,120
Speaker 3: the story of the year.

433
00:22:44,080 --> 00:22:45,720
Speaker 2: Welcome back to Bloomberg Tech.

434
00:22:45,840 --> 00:22:50,360
Speaker 3: Our top stories today, SpaceX's bond sale and Micron striking

435
00:22:50,359 --> 00:22:54,520
Speaker 3: a partnership with Anthropic. Bloomberg executive reporter Kim Ragki here

436
00:22:54,640 --> 00:22:55,680
Speaker 3: with details.

437
00:22:55,240 --> 00:22:55,920
Speaker 2: On both coming.

438
00:22:56,440 --> 00:22:58,360
Speaker 10: Yeah, thanks, Ed. So we're seeing a lot of red

439
00:22:58,359 --> 00:23:00,440
Speaker 10: on the screen today here with SpaceX share the are

440
00:23:00,440 --> 00:23:02,960
Speaker 10: down about eight percent off the lows of the day.

441
00:23:03,080 --> 00:23:05,760
Speaker 10: But if this continues, this will notch a third straight

442
00:23:05,880 --> 00:23:09,160
Speaker 10: day of selling and the name after its record breaking IPO.

443
00:23:09,680 --> 00:23:11,960
Speaker 10: So what we're seeing here this morning is that investors

444
00:23:12,040 --> 00:23:14,159
Speaker 10: might be reacting to a few different things. One is

445
00:23:14,200 --> 00:23:17,640
Speaker 10: this investment grade bond sale the company looking to raise

446
00:23:17,680 --> 00:23:20,840
Speaker 10: even more money to spend on AI funded sort of

447
00:23:20,840 --> 00:23:24,280
Speaker 10: future growth after raising a record seventy five billion in

448
00:23:24,320 --> 00:23:26,919
Speaker 10: an IPO. The other thing we're seeing here is, you know,

449
00:23:27,000 --> 00:23:30,840
Speaker 10: classic volatility that we see from IPOs, especially ones that

450
00:23:30,880 --> 00:23:33,560
Speaker 10: have low float, like we know is the case here

451
00:23:33,600 --> 00:23:37,760
Speaker 10: for SpaceX. We also are looking today at shares of Micron,

452
00:23:38,040 --> 00:23:39,920
Speaker 10: so this stock is looking a little bit better today,

453
00:23:40,000 --> 00:23:43,920
Speaker 10: up about four point four percent. It's bucking the broader

454
00:23:44,000 --> 00:23:47,560
Speaker 10: trend down in the market today. What's happening this morning

455
00:23:47,640 --> 00:23:50,840
Speaker 10: is that the company said that it has a strategic

456
00:23:50,920 --> 00:23:54,159
Speaker 10: agreement with Anthropic, so it's going to work together on

457
00:23:54,240 --> 00:23:59,080
Speaker 10: things like memory and storage architecture for AI, and also

458
00:23:59,119 --> 00:24:02,560
Speaker 10: gave some money to to one of the Anthropics funding rounds.

459
00:24:02,680 --> 00:24:06,240
Speaker 10: This company has been on an absolutely massive stock rally.

460
00:24:06,240 --> 00:24:07,879
Speaker 10: The shares are up more than two hundred and sixty

461
00:24:07,920 --> 00:24:11,320
Speaker 10: percent from a March thirtieth trough here, and we're going

462
00:24:11,359 --> 00:24:13,640
Speaker 10: to be watching this one really closely because it reports

463
00:24:13,720 --> 00:24:15,720
Speaker 10: earnings on Wednesday after market.

464
00:24:15,760 --> 00:24:19,560
Speaker 3: Clothes the most common rhinicky, Thank you very much, Let's

465
00:24:19,600 --> 00:24:23,719
Speaker 3: stay with Micron. Jake Silverman of Bloomberg Intelligence says Micron's

466
00:24:23,920 --> 00:24:27,720
Speaker 3: new supply deal with Anthropic highlights just how tight memory

467
00:24:27,800 --> 00:24:30,680
Speaker 3: chip capacity has become. He says major AI data center

468
00:24:30,720 --> 00:24:34,840
Speaker 3: customers are increasingly locking in long term agreements to secure

469
00:24:34,880 --> 00:24:37,440
Speaker 3: that supply, and Jake joins us, now, this is the thing, though,

470
00:24:38,000 --> 00:24:43,119
Speaker 3: it's a deal between Micron and a frontier lab direct right,

471
00:24:43,280 --> 00:24:47,760
Speaker 3: not not the middleman that we call hyperscale. How unusual

472
00:24:47,840 --> 00:24:49,719
Speaker 3: is that? How surprised were you to see it?

473
00:24:50,480 --> 00:24:53,480
Speaker 11: It's definitely not typical for memory makers themselves to sign

474
00:24:53,560 --> 00:24:56,560
Speaker 11: these kind of deals, but it's not necessarily uncommon for

475
00:24:58,119 --> 00:25:02,600
Speaker 11: big customers to look for supply assurances. Hyperscalers themselves have

476
00:25:02,720 --> 00:25:04,840
Speaker 11: often done this in terms of how you think about

477
00:25:05,200 --> 00:25:07,800
Speaker 11: wayfer allocation from someone like TSMC, for.

478
00:25:07,760 --> 00:25:13,080
Speaker 3: Example, in your react, you're talking about it being reflective

479
00:25:13,160 --> 00:25:16,000
Speaker 3: of the situation, the state of play, and how tight

480
00:25:16,000 --> 00:25:18,760
Speaker 3: supply is. But this is good news for Micron, right

481
00:25:18,960 --> 00:25:21,119
Speaker 3: if you just go on the sheer reaction alone.

482
00:25:21,600 --> 00:25:23,639
Speaker 11: Yeah, this is good for Micron in the sense that

483
00:25:23,680 --> 00:25:25,720
Speaker 11: it gives them a little bit more visibility into the

484
00:25:25,760 --> 00:25:29,000
Speaker 11: actual downstream demand, at least theoretically based on what we're

485
00:25:29,000 --> 00:25:32,280
Speaker 11: seeing from the agreement that they actually put out in

486
00:25:32,280 --> 00:25:36,280
Speaker 11: their press release. So you know, Micron needs to build

487
00:25:36,320 --> 00:25:40,120
Speaker 11: out capacity, right, The entire industry is supply constrained, and

488
00:25:40,359 --> 00:25:42,960
Speaker 11: this allows them to potentially build it out in a

489
00:25:43,000 --> 00:25:46,720
Speaker 11: responsible way so that when Anthropic is serving their customers,

490
00:25:46,720 --> 00:25:50,320
Speaker 11: increasing their capacity not specifically of memory, but the chips

491
00:25:50,920 --> 00:25:55,119
Speaker 11: to allow people to use their models. So now theoretically

492
00:25:55,840 --> 00:25:59,000
Speaker 11: Micron can get an idea of exactly how much chip

493
00:25:59,000 --> 00:25:59,960
Speaker 11: demand there actually is.

494
00:26:01,160 --> 00:26:04,840
Speaker 3: You write in the react note that this sets them

495
00:26:04,920 --> 00:26:09,119
Speaker 3: up well for the next down cycle situation. You know,

496
00:26:09,359 --> 00:26:13,680
Speaker 3: historically memory markets have been cyclical, right, boom and bust.

497
00:26:13,760 --> 00:26:15,800
Speaker 3: We talk about that a lot on the show, But

498
00:26:15,840 --> 00:26:17,560
Speaker 3: there are loads of people that don't think there'll be

499
00:26:17,560 --> 00:26:21,480
Speaker 3: a downcycle, you know, in the traditional sense, because of

500
00:26:21,560 --> 00:26:25,840
Speaker 3: the trajectory of data center building an investment. Just explain

501
00:26:25,920 --> 00:26:27,400
Speaker 3: your point of view on that.

502
00:26:28,000 --> 00:26:30,159
Speaker 11: Yeah, I think just if you look at historical trends,

503
00:26:30,200 --> 00:26:33,439
Speaker 11: Having covered memory for a while, we've always seen up

504
00:26:33,520 --> 00:26:36,199
Speaker 11: cycles and down cycles, and I don't think that just

505
00:26:36,240 --> 00:26:39,240
Speaker 11: because we're seeing very robust demand right now, it doesn't

506
00:26:39,280 --> 00:26:41,800
Speaker 11: mean that we won't see price declines on say a

507
00:26:41,840 --> 00:26:44,640
Speaker 11: sequential or you're over your basis in the future. What

508
00:26:44,680 --> 00:26:46,879
Speaker 11: these agreements might do, though, on the other hand, is

509
00:26:46,920 --> 00:26:49,920
Speaker 11: it might reduce some of the volatility. We've seen massive

510
00:26:49,960 --> 00:26:53,200
Speaker 11: price decreases in the past during down cycles, such as

511
00:26:53,400 --> 00:26:56,760
Speaker 11: you know, fifty percent or so quarter over quarter, So

512
00:26:56,840 --> 00:26:59,760
Speaker 11: we might see a reduction in some of that volatility.

513
00:27:00,720 --> 00:27:02,440
Speaker 11: And at the same time, if you look at past

514
00:27:02,440 --> 00:27:06,040
Speaker 11: cloud cycles, there are periods where capex if flattens or

515
00:27:06,200 --> 00:27:09,160
Speaker 11: declined slightly, and so that might be an opportunity for

516
00:27:09,800 --> 00:27:12,920
Speaker 11: hyperscalers to get slightly better deals on the actual memory

517
00:27:12,960 --> 00:27:15,600
Speaker 11: that they're purchasing. So we don't necessarily think that the

518
00:27:15,720 --> 00:27:19,320
Speaker 11: cycle is not going to happen again or downcycle, but

519
00:27:19,400 --> 00:27:22,840
Speaker 11: we do think that we might see less volatility, more

520
00:27:22,960 --> 00:27:26,000
Speaker 11: of a straightforward idea of what the demand levels will

521
00:27:26,040 --> 00:27:27,480
Speaker 11: be over the next say five years.

522
00:27:28,640 --> 00:27:31,600
Speaker 3: Jacob, As you know, Micro reports fiscal third quarter earnings

523
00:27:31,600 --> 00:27:32,960
Speaker 3: after market closed Wednesday.

524
00:27:33,040 --> 00:27:35,120
Speaker 2: What are your expectations got.

525
00:27:34,960 --> 00:27:39,400
Speaker 11: Yeah, yeah, yeah, we think they're definitely going to offer

526
00:27:40,000 --> 00:27:43,879
Speaker 11: guidance results that are above consensus right now.

527
00:27:44,119 --> 00:27:44,800
Speaker 2: We do think that.

528
00:27:44,760 --> 00:27:47,359
Speaker 11: They'll talk a little bit about some of their contracts.

529
00:27:47,359 --> 00:27:50,000
Speaker 11: But I think one of the reasons that they make

530
00:27:50,040 --> 00:27:52,520
Speaker 11: this announcement today with Anthropic is it's something that they

531
00:27:52,520 --> 00:27:56,280
Speaker 11: can highlight to investors to give everyone idea of what

532
00:27:56,400 --> 00:28:00,600
Speaker 11: types of customers that they're signing to these agreements. But

533
00:28:00,880 --> 00:28:02,600
Speaker 11: we don't necessarily think that they're going to give a

534
00:28:02,640 --> 00:28:04,720
Speaker 11: lot of details in terms of you know, we don't

535
00:28:04,720 --> 00:28:06,640
Speaker 11: think they're going to give anything in terms of pricing.

536
00:28:06,800 --> 00:28:08,560
Speaker 11: They might not say anything in terms of the length

537
00:28:08,600 --> 00:28:11,919
Speaker 11: of these contracts or a lot of the very intricate details.

538
00:28:13,040 --> 00:28:16,119
Speaker 11: But that said, I think they'll probably give a longer

539
00:28:16,200 --> 00:28:18,320
Speaker 11: term view of how they see the market, given the

540
00:28:18,359 --> 00:28:21,199
Speaker 11: fact that there are more of these contracts being signed

541
00:28:21,720 --> 00:28:24,240
Speaker 11: and memory prices continue to be very strong, and the

542
00:28:24,320 --> 00:28:27,040
Speaker 11: environment is very favorable for them at least through this year,

543
00:28:27,040 --> 00:28:29,720
Speaker 11: and we think at least into next year as well.

544
00:28:29,920 --> 00:28:31,960
Speaker 3: And to recap micro on not four percent on this

545
00:28:32,080 --> 00:28:35,560
Speaker 3: supply agreement with Anthropic, but up more than three hundred percent.

546
00:28:36,040 --> 00:28:39,440
Speaker 3: Here today Jake Silverman of Bloomberg Intelligence, with the analysis

547
00:28:39,480 --> 00:28:42,160
Speaker 3: now coming up. The CEO of Base ten and one

548
00:28:42,200 --> 00:28:44,840
Speaker 3: of its lead investors, is coming on the show to

549
00:28:44,880 --> 00:28:48,840
Speaker 3: talk about the inference startup's latest one point five billion

550
00:28:48,880 --> 00:28:51,640
Speaker 3: dollar fundraise. But this is really a conversation about the

551
00:28:51,680 --> 00:28:57,520
Speaker 3: economics of AI, and particularly as we transition increasingly to inference,

552
00:28:57,840 --> 00:29:01,120
Speaker 3: the use of open source and getting the best cost

553
00:29:01,640 --> 00:29:02,479
Speaker 3: dollar per token.

554
00:29:03,080 --> 00:29:05,680
Speaker 2: Really really looking forward to this one. It's next. Swim

555
00:29:05,800 --> 00:29:06,400
Speaker 2: Bog Tech.

556
00:29:21,440 --> 00:29:24,320
Speaker 3: In Video is developing chips and software that will make

557
00:29:24,400 --> 00:29:28,720
Speaker 3: humanoid robots safer around people. Humanoid robotics are expected to

558
00:29:28,760 --> 00:29:31,720
Speaker 3: generate two hundred billion dollars in revenues by twenty thirty five,

559
00:29:32,000 --> 00:29:35,680
Speaker 3: according to Barkley's estimates, but current safety systems can make

560
00:29:35,720 --> 00:29:39,440
Speaker 3: the machines less productive. In Vidia's Halo software will give

561
00:29:39,520 --> 00:29:42,400
Speaker 3: robots a much better sense of their surroundings so they

562
00:29:42,400 --> 00:29:45,240
Speaker 3: can interact and even make contact with people.

563
00:29:45,280 --> 00:29:46,600
Speaker 2: We're going to talk with a little bit more about

564
00:29:46,600 --> 00:29:47,400
Speaker 2: that later in the week.

565
00:29:48,080 --> 00:29:51,080
Speaker 3: Aim ferance startup Base ten has raised one point five

566
00:29:51,120 --> 00:29:54,800
Speaker 3: billion dollars across two tranchers, one an eleven billion dollar valuation,

567
00:29:54,920 --> 00:29:58,400
Speaker 3: the other at thirteen billion dollars. Base ten specializes in

568
00:29:58,440 --> 00:30:02,880
Speaker 3: delivering software and computing capacity to companies tapping open source,

569
00:30:03,160 --> 00:30:07,040
Speaker 3: typically lower cost AI models. The firm's co founder and

570
00:30:07,160 --> 00:30:10,560
Speaker 3: CEO two instead of US that joins along with one

571
00:30:10,600 --> 00:30:13,760
Speaker 3: of its investors, Ultimated Capital partner Paul of Agrowaile, who's

572
00:30:13,800 --> 00:30:16,600
Speaker 3: firm co led the round and actually was really interesting

573
00:30:16,640 --> 00:30:21,200
Speaker 3: the list of participants that back to you. Let's start

574
00:30:21,240 --> 00:30:24,240
Speaker 3: with the basic questions that the audience always has, which is, wow,

575
00:30:24,520 --> 00:30:26,000
Speaker 3: you know, one point five billions doll a round to

576
00:30:26,040 --> 00:30:28,600
Speaker 3: a lot of funds, but the stories seem to not

577
00:30:28,760 --> 00:30:32,719
Speaker 3: change right. Compute as a constraint talent is a competitive

578
00:30:32,720 --> 00:30:36,080
Speaker 3: marketplace and assuming that's where those those proceeds go.

579
00:30:36,280 --> 00:30:39,480
Speaker 12: Yeah, absolutely, Look we're with inference right now. What we

580
00:30:39,560 --> 00:30:42,760
Speaker 12: are seeing is there's a lot of demand for inserting

581
00:30:42,800 --> 00:30:46,320
Speaker 12: intelligence everywhere we possibly can, because open source models are

582
00:30:46,320 --> 00:30:49,880
Speaker 12: getting very good, right and post training techniques to specialize

583
00:30:49,880 --> 00:30:52,600
Speaker 12: those models for specific tasks are getting very good. And

584
00:30:52,640 --> 00:30:55,600
Speaker 12: as the app player comes online, you know, there's an

585
00:30:55,760 --> 00:30:59,560
Speaker 12: enormous surge of demand for us. What that means is, look,

586
00:30:59,600 --> 00:31:02,920
Speaker 12: when you're procure a lot of compute to be able

587
00:31:02,960 --> 00:31:08,000
Speaker 12: to fulfill that demand and also hire amazing infrastructure engineers

588
00:31:08,040 --> 00:31:10,920
Speaker 12: and research engineers to be able to build the software

589
00:31:11,000 --> 00:31:11,840
Speaker 12: later on top of that too.

590
00:31:12,040 --> 00:31:15,880
Speaker 3: Just real quick, what's that like trying to acquire that

591
00:31:16,160 --> 00:31:19,680
Speaker 3: the compute? Like, do you have any leverage in that market?

592
00:31:19,680 --> 00:31:23,959
Speaker 3: If everyone is supply constrained? How is the experience of

593
00:31:24,000 --> 00:31:26,760
Speaker 3: being a company of base tends, age and size going

594
00:31:26,840 --> 00:31:29,520
Speaker 3: to in video as an example, saying we kind of

595
00:31:29,560 --> 00:31:29,920
Speaker 3: need this.

596
00:31:30,240 --> 00:31:32,120
Speaker 12: Yeah, Look, we have a very strong relationship with the

597
00:31:32,200 --> 00:31:34,600
Speaker 12: Video and you know, they've been very supportive of us

598
00:31:34,640 --> 00:31:38,760
Speaker 12: over the years. Look, we one thing that we realize

599
00:31:38,800 --> 00:31:41,280
Speaker 12: is that you cannot be reliant on one compute lost.

600
00:31:41,720 --> 00:31:45,880
Speaker 12: You have to, you know, work to diversify, not necessarily

601
00:31:45,920 --> 00:31:48,560
Speaker 12: from a chip perspective, from a cloud perspective. So you know,

602
00:31:48,280 --> 00:31:52,080
Speaker 12: we acquire compute from eighteen different clouds. Now we sit

603
00:31:52,400 --> 00:31:56,160
Speaker 12: in around nineteen different clusters, and that's the flexibility we

604
00:31:56,160 --> 00:31:58,600
Speaker 12: need to be able to acquire compute and fulfill the demand.

605
00:31:58,840 --> 00:32:01,280
Speaker 3: I posted on x that you're both coming on the

606
00:32:01,280 --> 00:32:03,080
Speaker 3: show and poor I kind of put this quession out

607
00:32:03,080 --> 00:32:05,880
Speaker 3: there that if you could only invest in one part

608
00:32:05,920 --> 00:32:08,040
Speaker 3: of the five layer cake, as Jensen calls it, what

609
00:32:08,040 --> 00:32:12,280
Speaker 3: would it be? But Altimus has been so busy, you know,

610
00:32:12,520 --> 00:32:16,080
Speaker 3: with the frontier labs at one end and other parts

611
00:32:16,120 --> 00:32:19,479
Speaker 3: of the five layer cake at the other end. What

612
00:32:19,600 --> 00:32:22,400
Speaker 3: was the thesis around base ten and why you wanted

613
00:32:22,440 --> 00:32:24,080
Speaker 3: to get in here to co lead the.

614
00:32:24,120 --> 00:32:26,560
Speaker 2: Round ed It's good to be here.

615
00:32:27,200 --> 00:32:29,880
Speaker 13: Some troops cannot be said enough times, and one of

616
00:32:29,880 --> 00:32:32,040
Speaker 13: those is that Inference is going to be one of

617
00:32:32,080 --> 00:32:35,120
Speaker 13: the largest, if not the largest markets not an AI

618
00:32:35,360 --> 00:32:38,120
Speaker 13: in the world. And why is that the case? You know,

619
00:32:38,640 --> 00:32:41,960
Speaker 13: the early innings of AI were Q and A tell

620
00:32:41,960 --> 00:32:44,680
Speaker 13: me about my trip. We've come a long way since then.

621
00:32:44,920 --> 00:32:47,640
Speaker 13: You know, it's multi agent they've gotten longer context. You

622
00:32:47,680 --> 00:32:51,800
Speaker 13: go retrieve, analyze, synthesize, verifying, then you go again, and

623
00:32:51,920 --> 00:32:54,440
Speaker 13: each request is kicking off hundreds, if not thousands of

624
00:32:54,440 --> 00:32:58,160
Speaker 13: inference requests. The second thing is, you know I might

625
00:32:58,200 --> 00:33:01,960
Speaker 13: correct something you said ed. It's not just it's capability

626
00:33:02,840 --> 00:33:08,640
Speaker 13: control and cost frontier models. The open s post train

627
00:33:08,680 --> 00:33:11,400
Speaker 13: open source model comes very close to frontier model performance,

628
00:33:11,400 --> 00:33:15,240
Speaker 13: sometimes even better for specific workflows control. As satan Adilla

629
00:33:15,280 --> 00:33:18,040
Speaker 13: said last weekend, you know, you've got to compound your

630
00:33:18,240 --> 00:33:21,240
Speaker 13: unique advantages as an enterprise, your data, your knowledge or

631
00:33:21,280 --> 00:33:23,800
Speaker 13: know how in a way that you're not giving away

632
00:33:23,840 --> 00:33:27,880
Speaker 13: your intelligence to on rent. And that's what BASM allows.

633
00:33:28,400 --> 00:33:30,240
Speaker 13: And so to your question from this morning is like

634
00:33:30,280 --> 00:33:32,120
Speaker 13: where does value your crew? Is it at the app layer?

635
00:33:32,200 --> 00:33:33,480
Speaker 13: Is it at the model air? You know, the way

636
00:33:33,520 --> 00:33:35,640
Speaker 13: I like to frame it is, Look, the model air

637
00:33:35,880 --> 00:33:39,040
Speaker 13: is a phenomenal business for very few people. It's a

638
00:33:39,040 --> 00:33:41,360
Speaker 13: game of emperors. You've got to be at the frontier

639
00:33:41,520 --> 00:33:43,400
Speaker 13: to accrue a lot of value. And it's it's a

640
00:33:43,480 --> 00:33:46,000
Speaker 13: knife fight. As you know, I've started joking, there's four

641
00:33:46,040 --> 00:33:49,920
Speaker 13: seasons to the year. It's anthropic open EI SpaceX Google

642
00:33:50,040 --> 00:33:52,160
Speaker 13: with an evergreen of open source, and.

643
00:33:52,200 --> 00:33:55,120
Speaker 3: So that's that part's so fascinating, right, if we could

644
00:33:55,400 --> 00:33:58,760
Speaker 3: just go to open source. When this story started for us,

645
00:33:58,880 --> 00:34:02,160
Speaker 3: let's say twenty two, but for you guys probably before that,

646
00:34:02,680 --> 00:34:06,360
Speaker 3: it was this idea that it was only the model,

647
00:34:06,440 --> 00:34:09,680
Speaker 3: the largest models with the hundreds of billions of parameters

648
00:34:09,719 --> 00:34:12,840
Speaker 3: that mattered, and in the world of open source that

649
00:34:12,880 --> 00:34:17,400
Speaker 3: the issue was that that just didn't work. People couldn't

650
00:34:17,400 --> 00:34:20,000
Speaker 3: make any real use of them. I go back to

651
00:34:20,000 --> 00:34:23,200
Speaker 3: what Paul just said. You know, you are making utility

652
00:34:23,239 --> 00:34:26,680
Speaker 3: out of open source models, but probably also a slightly

653
00:34:26,719 --> 00:34:27,880
Speaker 3: smaller scale of model.

654
00:34:28,239 --> 00:34:29,319
Speaker 2: Is that fair? Yes?

655
00:34:29,680 --> 00:34:32,319
Speaker 12: Look, I think if you look at you know, when

656
00:34:32,320 --> 00:34:34,120
Speaker 12: you think about open source has kind of gone through

657
00:34:34,280 --> 00:34:36,520
Speaker 12: a bit of a journey. You know, there's LAMA three,

658
00:34:37,600 --> 00:34:39,799
Speaker 12: maybe two two and a half years ago, and then

659
00:34:39,840 --> 00:34:41,600
Speaker 12: there was a bit of a winter, and then I

660
00:34:41,680 --> 00:34:44,000
Speaker 12: think the deep Seek moment last year really put it

661
00:34:44,040 --> 00:34:46,400
Speaker 12: back on the map, and there was definitely the capability

662
00:34:46,400 --> 00:34:50,040
Speaker 12: gap shrunk. I think this is happening again just over

663
00:34:50,080 --> 00:34:52,239
Speaker 12: the last couple of weeks with GLM five Print two,

664
00:34:52,960 --> 00:34:56,600
Speaker 12: which is i'd say a frontier level model that is

665
00:34:56,680 --> 00:34:59,319
Speaker 12: actually usable and it isn't just doing well on the benchmarks.

666
00:34:59,480 --> 00:35:02,840
Speaker 12: When people we're using it, they are feeling that, you know,

667
00:35:02,960 --> 00:35:05,920
Speaker 12: at the frontier capability. For us, it's just like, how

668
00:35:05,920 --> 00:35:07,960
Speaker 12: do we make these models very very easy to use it?

669
00:35:08,000 --> 00:35:10,160
Speaker 12: And it doesn't really matter what size they are that

670
00:35:10,280 --> 00:35:14,080
Speaker 12: we work with small models, big models, massive models. What

671
00:35:14,200 --> 00:35:17,000
Speaker 12: we are trying to give our customers the ability to

672
00:35:17,040 --> 00:35:20,279
Speaker 12: run these models when they don't necessarily have access to

673
00:35:20,320 --> 00:35:23,279
Speaker 12: the infrastructured teams of the frontier labs.

674
00:35:23,040 --> 00:35:23,840
Speaker 2: Doing it for them.

675
00:35:24,000 --> 00:35:26,720
Speaker 3: So let's go back to what we were talking about cost,

676
00:35:26,840 --> 00:35:32,040
Speaker 3: but also giveability and capability. You know, the other case

677
00:35:32,080 --> 00:35:34,160
Speaker 3: study in this open source debate was Meta kind of

678
00:35:34,239 --> 00:35:38,120
Speaker 3: changing its narrative saying, well, actually, we are committed to

679
00:35:38,160 --> 00:35:40,360
Speaker 3: open source, but we you know, at some point we

680
00:35:40,440 --> 00:35:44,120
Speaker 3: need to be realistic about about the benefit of closed models.

681
00:35:45,840 --> 00:35:49,040
Speaker 3: Is what is the point on the capability part and

682
00:35:49,600 --> 00:35:52,560
Speaker 3: actually take a moment explain what base ten does, why

683
00:35:52,600 --> 00:35:55,520
Speaker 3: it's why it is such an important player in that respect.

684
00:35:55,680 --> 00:36:01,000
Speaker 13: Sure, look at yesterday it was Deepseek, today it's GLM.

685
00:36:01,160 --> 00:36:02,280
Speaker 2: It'll be something tomorrow.

686
00:36:02,400 --> 00:36:06,120
Speaker 13: And as I said, it's seasonal, and that battle at

687
00:36:06,120 --> 00:36:09,040
Speaker 13: the model here is not we're nowhere near the endgame.

688
00:36:09,320 --> 00:36:12,520
Speaker 13: What base tend does is it helps customers like cursor,

689
00:36:12,719 --> 00:36:15,600
Speaker 13: like a bridge, like open evidence, harness the power of

690
00:36:16,280 --> 00:36:19,640
Speaker 13: the model underneath, combine that with your unique advantage as

691
00:36:19,680 --> 00:36:23,240
Speaker 13: an enterprise, your workflow, and deliver something that is best

692
00:36:23,280 --> 00:36:24,360
Speaker 13: for that specific workflow.

693
00:36:24,360 --> 00:36:25,520
Speaker 2: Now it's not just one model.

694
00:36:25,840 --> 00:36:28,200
Speaker 13: It's typically a collection of portfolio of models that are

695
00:36:28,200 --> 00:36:31,440
Speaker 13: fine tuned to deliver that and that compounds over time,

696
00:36:32,120 --> 00:36:35,399
Speaker 13: and so based in is successful. As these models get better,

697
00:36:35,680 --> 00:36:38,000
Speaker 13: it benefits us and ultimately in the service of the

698
00:36:38,000 --> 00:36:40,520
Speaker 13: app layer, which is you know, the customers that we discussed,

699
00:36:41,760 --> 00:36:43,960
Speaker 13: who are delivering AI to the customer.

700
00:36:44,760 --> 00:36:48,080
Speaker 3: Jensen's been repeating a lot recently, just among the CEO

701
00:36:48,120 --> 00:36:50,840
Speaker 3: of Nvidia. Sometimes you have to remind the audience testing case.

702
00:36:52,040 --> 00:36:56,280
Speaker 3: You know, he wants to see his teams burning tokens.

703
00:36:58,680 --> 00:37:00,840
Speaker 3: How does that apply to your custom is right? So

704
00:37:01,600 --> 00:37:05,120
Speaker 3: pouls just explain really clearly the value that you're adding.

705
00:37:05,200 --> 00:37:09,759
Speaker 3: But the economics are really really important to some of

706
00:37:09,760 --> 00:37:10,480
Speaker 3: these companies.

707
00:37:11,360 --> 00:37:13,120
Speaker 2: Yeah, Look, I think.

708
00:37:14,800 --> 00:37:18,759
Speaker 12: Using tokens is obviously just what's synonymous with, Hey, I

709
00:37:18,800 --> 00:37:21,400
Speaker 12: want us to be using AI everywhere.

710
00:37:22,040 --> 00:37:24,080
Speaker 2: It's in their interest for that to be the story

711
00:37:24,160 --> 00:37:24,560
Speaker 2: right now.

712
00:37:25,000 --> 00:37:27,479
Speaker 12: And I think for us, you know, what we see

713
00:37:27,560 --> 00:37:29,960
Speaker 12: is that customers kind of go through the same curve

714
00:37:30,040 --> 00:37:32,600
Speaker 12: over and over again. Is that, you know, they go

715
00:37:32,800 --> 00:37:35,920
Speaker 12: very aggressively, they start using AI everywhere, they start to

716
00:37:35,960 --> 00:37:39,680
Speaker 12: see gains, but they don't necessarily do it profitably, and

717
00:37:39,719 --> 00:37:41,640
Speaker 12: then they need to figure out how to do it profitably,

718
00:37:42,040 --> 00:37:43,920
Speaker 12: and that's when they come to open source models. That's

719
00:37:43,920 --> 00:37:45,520
Speaker 12: when they come to Base ten. That's when they come

720
00:37:45,560 --> 00:37:48,960
Speaker 12: to you know, post train models on Base ten to

721
00:37:49,000 --> 00:37:51,359
Speaker 12: be able to do it better, faster and cheaper. And

722
00:37:51,400 --> 00:37:55,400
Speaker 12: that's when you decide to get both you know, intelligence everywhere,

723
00:37:55,600 --> 00:37:58,000
Speaker 12: but also unit economics that makes sense for your business.

724
00:37:58,680 --> 00:38:01,520
Speaker 2: What I've been trying to reconcile is.

725
00:38:03,360 --> 00:38:06,680
Speaker 3: Ultimita is invested in in the big frontier labs as well,

726
00:38:07,600 --> 00:38:10,320
Speaker 3: and those big frontier labs plus S FACEX, depending on

727
00:38:10,360 --> 00:38:11,640
Speaker 3: how you define what they are.

728
00:38:11,880 --> 00:38:14,279
Speaker 2: Would also say that they have a role to play

729
00:38:14,320 --> 00:38:15,080
Speaker 2: in that market.

730
00:38:15,880 --> 00:38:18,040
Speaker 3: Where are we in this cycle of where they're just

731
00:38:18,080 --> 00:38:20,160
Speaker 3: in the NBA, very big players.

732
00:38:20,280 --> 00:38:22,480
Speaker 2: Where does Space ten fit in that? Look?

733
00:38:22,520 --> 00:38:25,360
Speaker 13: A lot of ink has been spilled on this debate

734
00:38:25,400 --> 00:38:26,480
Speaker 13: of open versus closed.

735
00:38:26,920 --> 00:38:28,120
Speaker 2: Let me just take that head on.

736
00:38:28,719 --> 00:38:31,680
Speaker 13: The closed models of Frontier models are very very good,

737
00:38:32,040 --> 00:38:34,680
Speaker 13: and they're very good for the use case that require

738
00:38:34,760 --> 00:38:39,319
Speaker 13: highest intelligenced, highest reasoning, new use case discovery. If you

739
00:38:39,320 --> 00:38:41,280
Speaker 13: didn't want to think about just one model as a service,

740
00:38:41,400 --> 00:38:43,800
Speaker 13: they're very good and they will always be very good.

741
00:38:43,719 --> 00:38:47,920
Speaker 2: For that for a class of customers. That's not a

742
00:38:47,920 --> 00:38:48,440
Speaker 2: long list.

743
00:38:48,480 --> 00:38:50,520
Speaker 13: You know, as Jonathan Ross said, there's about thirty five

744
00:38:50,560 --> 00:38:53,640
Speaker 13: companies that drive ninety nine percent of all inference. The

745
00:38:53,680 --> 00:38:56,360
Speaker 13: top five are the labs that you just listed, top four, top.

746
00:38:56,160 --> 00:38:57,760
Speaker 2: Five, the next thirty.

747
00:38:57,800 --> 00:39:00,120
Speaker 13: You know, that's people like Cursor, like open Evidence, like

748
00:39:00,160 --> 00:39:04,200
Speaker 13: a bridge, like Harvey, and for them, if you if

749
00:39:04,200 --> 00:39:05,839
Speaker 13: you're not playing the game of the Emperors and you're

750
00:39:05,840 --> 00:39:08,680
Speaker 13: you know, you're thinking about how do I deliver something

751
00:39:08,719 --> 00:39:11,440
Speaker 13: that is not rented from somebody else but mine that

752
00:39:11,480 --> 00:39:15,160
Speaker 13: I can compound with. That's where that's where post trained,

753
00:39:15,200 --> 00:39:18,000
Speaker 13: open source can help. And so you know, we posted

754
00:39:18,000 --> 00:39:21,400
Speaker 13: this start this morning about how you know, Harvey, for example,

755
00:39:21,480 --> 00:39:25,880
Speaker 13: delivery achieved Frontier capabilities post training and open source model,

756
00:39:26,320 --> 00:39:29,520
Speaker 13: but obviously with a much better cost and control. Frontier

757
00:39:30,000 --> 00:39:32,840
Speaker 13: as an example of what the next thirty are thinking about.

758
00:39:32,880 --> 00:39:34,840
Speaker 13: And then the next thousand were thinking.

759
00:39:34,640 --> 00:39:40,000
Speaker 3: About two hinges for Vestava back on Bloomberg Tech, found

760
00:39:40,000 --> 00:39:43,320
Speaker 3: a CEO based ten and Paul Varruaal of Ultimata. Back

761
00:39:43,760 --> 00:39:45,480
Speaker 3: again on Bloomberg Tech. It's really great to have you

762
00:39:45,560 --> 00:39:48,160
Speaker 3: here in person. Thank you very much. Indeed, coming up

763
00:39:48,160 --> 00:39:52,960
Speaker 3: on the show, Amazon is pairing Prime Day with pizza delivery.

764
00:39:53,600 --> 00:39:55,760
Speaker 3: We're going to talk about what's behind the e commerce giants,

765
00:39:55,840 --> 00:39:56,800
Speaker 3: fast food pivot.

766
00:39:56,840 --> 00:39:58,760
Speaker 2: Next, this is Bloomberg Tech.

767
00:40:03,680 --> 00:40:07,080
Speaker 3: Today's big number, twenty six point three billion dollars. That's

768
00:40:07,080 --> 00:40:10,560
Speaker 3: how much US consumers are expected to spend on Amazon

769
00:40:10,680 --> 00:40:13,400
Speaker 3: and other retailers during the four day sale that we

770
00:40:13,480 --> 00:40:16,759
Speaker 3: all know as Prime Day. That figure comes from Adobe.

771
00:40:16,960 --> 00:40:21,080
Speaker 3: Amazon's facing headwinds though consumer confidence to competition for the

772
00:40:21,080 --> 00:40:23,200
Speaker 3: e commence giant has a few tricks up its sleeve

773
00:40:23,280 --> 00:40:28,399
Speaker 3: to encourage shoppers to spend, including pizza delivery, Bluebog'spenser Sofa

774
00:40:28,520 --> 00:40:31,600
Speaker 3: here with the latest. Okay, we're getting ready for Prime Day.

775
00:40:31,920 --> 00:40:35,120
Speaker 3: Adobe Analytics always puts out numbers like this. Put that

776
00:40:35,160 --> 00:40:38,000
Speaker 3: twenty six point three billion into context for us. That's big,

777
00:40:38,040 --> 00:40:40,000
Speaker 3: it's small, it's growth, it's not.

778
00:40:41,680 --> 00:40:42,200
Speaker 2: It's big.

779
00:40:42,239 --> 00:40:45,359
Speaker 14: I mean Adobe's predicting nine percent growth from the four

780
00:40:45,440 --> 00:40:48,520
Speaker 14: day Prime Day period last year, which was July. You know,

781
00:40:48,760 --> 00:40:51,960
Speaker 14: it's always tricky to compare Prime Day to the year

782
00:40:52,000 --> 00:40:54,080
Speaker 14: earlier because the date moves around, and this is going

783
00:40:54,120 --> 00:40:55,520
Speaker 14: to be the second year that they do a four

784
00:40:55,600 --> 00:40:59,120
Speaker 14: day sale. But Adobe is predicting a nine percent lift,

785
00:40:59,120 --> 00:41:01,279
Speaker 14: which is pretty size bull and a lot of that

786
00:41:01,320 --> 00:41:05,000
Speaker 14: can be not you know, it's counterintuitive. Even though there's

787
00:41:05,000 --> 00:41:07,480
Speaker 14: a lot of things weighing on consumers, sometimes that compels

788
00:41:07,520 --> 00:41:10,080
Speaker 14: them to kind of save up money and hold off

789
00:41:10,080 --> 00:41:13,000
Speaker 14: a big ticket purchase for a big deal, big deal period,

790
00:41:13,360 --> 00:41:14,840
Speaker 14: which can which can raise spending.

791
00:41:14,880 --> 00:41:15,759
Speaker 2: And Walmart and.

792
00:41:15,760 --> 00:41:18,920
Speaker 14: Target both have overlapping sales, so there's going to be

793
00:41:19,000 --> 00:41:20,920
Speaker 14: just a lot of a lot of deals going on

794
00:41:20,960 --> 00:41:21,720
Speaker 14: those four days.

795
00:41:22,560 --> 00:41:25,879
Speaker 3: Okay, the latest tech in depth, Amazon offers.

796
00:41:25,560 --> 00:41:32,520
Speaker 2: Pizza in Prime Day experiment. Explain So this is the

797
00:41:32,520 --> 00:41:33,480
Speaker 2: first year I've seen this.

798
00:41:33,640 --> 00:41:38,880
Speaker 14: The Amazon's offering five dollars pizza from Little Caesars. So basically,

799
00:41:38,920 --> 00:41:41,040
Speaker 14: if you go to the site, you'll see this pretty

800
00:41:41,040 --> 00:41:45,120
Speaker 14: prominently a Little Caesar's emblem on the site. You can

801
00:41:45,120 --> 00:41:47,839
Speaker 14: click over there to get to redeem a five five

802
00:41:47,880 --> 00:41:50,839
Speaker 14: dollars pizza. And they're running that every day of Prime Day,

803
00:41:50,840 --> 00:41:54,080
Speaker 14: even a couple of days before it, and it seems

804
00:41:54,120 --> 00:41:56,040
Speaker 14: to be something to you know, get people to come

805
00:41:56,040 --> 00:41:59,040
Speaker 14: to the site, you know, just another another sweetener to

806
00:41:59,040 --> 00:42:00,480
Speaker 14: get people that come to the site and check out

807
00:42:00,520 --> 00:42:03,520
Speaker 14: the deals. Because Amazon really doesn't have anything in hot

808
00:42:03,520 --> 00:42:07,160
Speaker 14: food delivery. You know, they had an Amazon Restaurants thing

809
00:42:07,239 --> 00:42:09,719
Speaker 14: years ago that they shuddered. They now have kind of

810
00:42:09,719 --> 00:42:12,600
Speaker 14: a tie in with grub Hub. But you know, when

811
00:42:12,640 --> 00:42:15,560
Speaker 14: you think about grocery and Amazon really is its eye

812
00:42:15,560 --> 00:42:18,320
Speaker 14: on the grocery business. A lot of people couple grocery

813
00:42:18,360 --> 00:42:21,120
Speaker 14: and dinner, you know, especially when it's hot in the summer,

814
00:42:21,160 --> 00:42:23,239
Speaker 14: you don't get the kitchen hot. So this might be

815
00:42:23,280 --> 00:42:25,960
Speaker 14: a way to just you know, get people to check

816
00:42:25,960 --> 00:42:27,799
Speaker 14: out their deals and also cross something off the list

817
00:42:27,800 --> 00:42:30,600
Speaker 14: that they got to get done done that night.

818
00:42:31,880 --> 00:42:35,200
Speaker 3: Bloomberg'spenser Sopa with a look ahead to Prime Day. But

819
00:42:35,280 --> 00:42:36,879
Speaker 3: go and read the Tech in depth that went out

820
00:42:36,880 --> 00:42:39,400
Speaker 3: this morning as well, Thank you very much, before you go.

821
00:42:39,760 --> 00:42:44,000
Speaker 3: Disney's Toy Story five has gone to infinity and beyond.

822
00:42:44,160 --> 00:42:47,479
Speaker 3: On its opening weekend, Woody Buzz and their friends brought

823
00:42:47,480 --> 00:42:50,000
Speaker 3: in one hundred and sixty million dollars in US box

824
00:42:50,000 --> 00:42:53,880
Speaker 3: office sales, marking the best launch in that franchise's history,

825
00:42:54,239 --> 00:42:57,080
Speaker 3: and lastly, the title of best twenty twenty six debut

826
00:42:57,440 --> 00:42:59,880
Speaker 3: from the Super Mario Galaxy movie The Whole Was It.

827
00:43:00,000 --> 00:43:03,319
Speaker 3: The low end of Bloomberg Intelligence is lofty expectations for

828
00:43:03,400 --> 00:43:07,560
Speaker 3: the film and enthusiasm for blockbusters including Toy Story Spider

829
00:43:07,600 --> 00:43:11,160
Speaker 3: Man The Odyssey is anticipated if you're a ten percent

830
00:43:11,239 --> 00:43:14,360
Speaker 3: year over year growth rate and the best summer for

831
00:43:14,440 --> 00:43:19,239
Speaker 3: cinemas since the pandemic. All right, that does it for

832
00:43:19,280 --> 00:43:21,839
Speaker 3: this addition of Bloomberg Tech. From here in San Francisco,

833
00:43:21,880 --> 00:43:24,320
Speaker 3: this is what markets look like. A little soft on

834
00:43:24,360 --> 00:43:27,000
Speaker 3: than has that one hundred down a percentage point, half

835
00:43:27,000 --> 00:43:30,440
Speaker 3: a percentage point, but still outperformance in semiconductors that socks

836
00:43:30,520 --> 00:43:33,560
Speaker 3: up almost percent, a lot of that being the newsflow

837
00:43:33,560 --> 00:43:36,600
Speaker 3: around micron and Anthropics deal. SpaceX down for a third

838
00:43:36,640 --> 00:43:40,000
Speaker 3: straight session, but well above it's IPO price as it

839
00:43:40,040 --> 00:43:42,520
Speaker 3: goes to the bomb market for the first time. Check

840
00:43:42,520 --> 00:43:44,839
Speaker 3: out the pod. You know where to find it. This

841
00:43:44,880 --> 00:43:45,680
Speaker 3: is Bloomberg Tech.