1
00:00:02,759 --> 00:00:10,000
Speaker 1: Bloomberg Audio Studios, podcasts, radio news from the heart of

2
00:00:10,080 --> 00:00:15,800
Speaker 1: where innovation, money and power collide in Silicon Valley and beyond.

3
00:00:16,239 --> 00:00:40,520
Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow.

4
00:00:35,240 --> 00:00:39,400
Speaker 2: Live from New York. I'm Caroline Hyde.

5
00:00:38,000 --> 00:00:41,400
Speaker 3: And I'm Mike Sheppard in Washington. This is Bloomberg Technology.

6
00:00:41,640 --> 00:00:42,159
Speaker 2: Coming up.

7
00:00:42,280 --> 00:00:46,280
Speaker 4: Reports of Nvidia's biggest customers facing further Blackwell delays, adding

8
00:00:46,320 --> 00:00:47,720
Speaker 4: to those concerns.

9
00:00:47,200 --> 00:00:49,440
Speaker 2: Of export limits from the White House.

10
00:00:49,800 --> 00:00:53,320
Speaker 4: Plus iPhone anxiety as new research shows a sales drop

11
00:00:53,479 --> 00:00:57,160
Speaker 4: globally and loss of ground Chinese rivals and the LA

12
00:00:57,280 --> 00:01:00,640
Speaker 4: wildfires they rage on with high winds predict for this week.

13
00:01:00,680 --> 00:01:02,120
Speaker 2: We talk to Lift.

14
00:01:01,880 --> 00:01:04,480
Speaker 4: About how the company is helping in relief efforts.

15
00:01:04,760 --> 00:01:05,560
Speaker 2: But first we.

16
00:01:05,600 --> 00:01:08,240
Speaker 4: Check in on these markets and we are seeing some

17
00:01:08,520 --> 00:01:11,080
Speaker 4: risk off tone across the markets. The NASAC is off

18
00:01:11,080 --> 00:01:13,440
Speaker 4: by about a percentage point. We've got key inflation data.

19
00:01:13,600 --> 00:01:15,600
Speaker 4: We of course got the start to earning season, but

20
00:01:15,720 --> 00:01:18,160
Speaker 4: really big tech is being drag lower and I signified

21
00:01:18,200 --> 00:01:21,120
Speaker 4: two key point drags, one of them Apple down two

22
00:01:21,160 --> 00:01:24,520
Speaker 4: point four percent. Counterpoint research coming out with the latest

23
00:01:24,600 --> 00:01:27,120
Speaker 4: numbers saying look for the last quarter of the year

24
00:01:27,200 --> 00:01:29,360
Speaker 4: we're probably seeing sales down by some five percent. They

25
00:01:29,360 --> 00:01:31,200
Speaker 4: are losing market shared to the tune of about just

26
00:01:31,280 --> 00:01:33,880
Speaker 4: eighteen percent globally. We'll dig into that with Mark German

27
00:01:33,920 --> 00:01:35,679
Speaker 4: in a moment, and plenty more to do with Apple,

28
00:01:35,920 --> 00:01:38,319
Speaker 4: but let's just dig into Nvidia, of course, the most

29
00:01:38,400 --> 00:01:40,560
Speaker 4: valuable company out there, off by three percent?

30
00:01:41,000 --> 00:01:42,240
Speaker 2: Is it interest rate focus?

31
00:01:42,440 --> 00:01:45,160
Speaker 4: Is it the White House in particular, finally unfolding what

32
00:01:45,280 --> 00:01:47,880
Speaker 4: are yet key implementations that will come in in one

33
00:01:47,960 --> 00:01:50,960
Speaker 4: year's time to the compute power that Nvidia and other

34
00:01:51,000 --> 00:01:55,320
Speaker 4: AI chip makers can export globally. But then to add

35
00:01:55,320 --> 00:01:57,880
Speaker 4: to this is the latest out of the information saying

36
00:01:57,920 --> 00:02:00,800
Speaker 4: that yet more delays potentially surround in Blackwell and the

37
00:02:00,880 --> 00:02:03,800
Speaker 4: rats that of course house their latest and greatest chips

38
00:02:04,160 --> 00:02:06,800
Speaker 4: in King has more, And just very briefly with the

39
00:02:06,800 --> 00:02:10,080
Speaker 4: information report here in is there anything new? We know

40
00:02:10,280 --> 00:02:14,000
Speaker 4: that the latest innovations take time to roll out, and

41
00:02:14,040 --> 00:02:16,200
Speaker 4: perhaps it's not surprising that you get these sort of

42
00:02:16,200 --> 00:02:18,600
Speaker 4: snaffoos when it comes to actual glitches in the system.

43
00:02:19,480 --> 00:02:21,800
Speaker 5: Yeah, I mean, we don't know whether the reports are

44
00:02:21,840 --> 00:02:25,840
Speaker 5: based upon fact or just speculation at this particular point.

45
00:02:26,320 --> 00:02:29,120
Speaker 5: What we can say in fact is that the senior

46
00:02:29,160 --> 00:02:33,360
Speaker 5: executive Jensen Wang and also his CFO as recently as

47
00:02:33,440 --> 00:02:36,440
Speaker 5: last week said no problems. The issues that we had

48
00:02:36,680 --> 00:02:39,280
Speaker 5: trying to get broad well to market up behind us.

49
00:02:39,520 --> 00:02:42,960
Speaker 5: Everything's going full tilt. A very complicated system, but we

50
00:02:43,000 --> 00:02:45,079
Speaker 5: are making a lot of progress, is what they said.

51
00:02:45,440 --> 00:02:48,280
Speaker 3: Ian. There is a big head when coming in videos

52
00:02:48,320 --> 00:02:51,600
Speaker 3: way from the direction of Washington, and that is in

53
00:02:51,639 --> 00:02:54,520
Speaker 3: the form of these new AI chips curves that the

54
00:02:54,560 --> 00:02:56,720
Speaker 3: Biden administration announced this morning.

55
00:02:56,919 --> 00:02:57,640
Speaker 2: The company has.

56
00:02:57,560 --> 00:03:02,560
Speaker 3: Already expressed unusually local opposition to this. Where do you

57
00:03:02,639 --> 00:03:05,079
Speaker 3: see this headed? Is this something that Jensen Wan could

58
00:03:05,080 --> 00:03:08,760
Speaker 3: perhaps persuade Donald Trump once he takes office to roll

59
00:03:08,800 --> 00:03:09,680
Speaker 3: back or change.

60
00:03:10,320 --> 00:03:11,640
Speaker 5: Yeah, I mean there's a lot of to and fro

61
00:03:11,760 --> 00:03:13,040
Speaker 5: on this, and now, as you say, it's been an

62
00:03:13,120 --> 00:03:15,640
Speaker 5: unusual situation and that a company has got out ahead

63
00:03:15,680 --> 00:03:19,040
Speaker 5: of the actual announcement and started saying things. What I

64
00:03:19,080 --> 00:03:20,799
Speaker 5: think is key here and what you're hinting at is

65
00:03:20,840 --> 00:03:24,320
Speaker 5: obviously whether this initiative, whether this new such of rules

66
00:03:24,360 --> 00:03:29,640
Speaker 5: can survive the transition and administrations. Who knows you're in Washington,

67
00:03:29,680 --> 00:03:32,400
Speaker 5: you probably know better than we do, Mike, But clearly

68
00:03:32,480 --> 00:03:35,600
Speaker 5: this is another restriction that investors do not want to see.

69
00:03:35,600 --> 00:03:37,960
Speaker 5: This is another limit to what they can do and

70
00:03:38,160 --> 00:03:41,080
Speaker 5: coming in another direction which would potentially hurt them.

71
00:03:41,440 --> 00:03:45,840
Speaker 3: Bloombergy and King thank you now. Apple sol five percent

72
00:03:45,920 --> 00:03:49,440
Speaker 3: fewer iPhones globally and lost ground to Chinese rivals in

73
00:03:49,480 --> 00:03:54,040
Speaker 3: the final quarter of last year, according to Counterpoint Research data.

74
00:03:54,240 --> 00:03:57,680
Speaker 3: Let's break it down with Bloomberg's Mark German. Mark tell

75
00:03:57,760 --> 00:04:01,600
Speaker 3: us what the backstory and the under into this all is.

76
00:04:01,720 --> 00:04:04,960
Speaker 3: It seems that artificial intelligence seems to be a thread

77
00:04:05,360 --> 00:04:08,480
Speaker 3: holding this all together. Investors don't seem to be and

78
00:04:08,760 --> 00:04:11,600
Speaker 3: consumers don't seem to be satisfied with what they are seeing.

79
00:04:11,600 --> 00:04:14,440
Speaker 3: In terms of Apple's AI offerings today.

80
00:04:14,480 --> 00:04:16,520
Speaker 6: Yeah, Mike, thank you so much for having me so

81
00:04:16,680 --> 00:04:19,800
Speaker 6: Back in June, Apple announced Apple Intelligence, right, this is

82
00:04:19,839 --> 00:04:21,320
Speaker 6: their new AI platform.

83
00:04:21,640 --> 00:04:23,960
Speaker 7: The good news is is that Apple is here too

84
00:04:24,000 --> 00:04:24,480
Speaker 7: with AI.

85
00:04:24,600 --> 00:04:27,200
Speaker 6: The bad news is their AI is really not up

86
00:04:27,240 --> 00:04:30,760
Speaker 6: to snuff in comparison to the competition. You have Samsung,

87
00:04:30,920 --> 00:04:33,080
Speaker 6: which has Google Gemini deeply integrated.

88
00:04:33,440 --> 00:04:34,760
Speaker 7: You have Microsoft products.

89
00:04:34,800 --> 00:04:38,920
Speaker 6: Obviously they're not mobile, but they have open AI deeply integrated.

90
00:04:39,000 --> 00:04:41,159
Speaker 7: Right at the core of their AI stack.

91
00:04:41,800 --> 00:04:45,400
Speaker 6: You see meta with Lama integrating their AI into different

92
00:04:45,400 --> 00:04:49,000
Speaker 6: devices and different applications, and so the industry knows that

93
00:04:49,080 --> 00:04:50,800
Speaker 6: Apple is behind, right.

94
00:04:51,120 --> 00:04:52,760
Speaker 7: But here's the other side of the coin.

95
00:04:53,120 --> 00:04:56,080
Speaker 6: I don't think that ninety five percent of consumers really

96
00:04:56,080 --> 00:04:58,960
Speaker 6: care about AI. I'm not sure consumers are making their

97
00:04:58,960 --> 00:05:03,040
Speaker 6: purchasing decisions based on Apple Intelligence, based on Opening, based

98
00:05:03,080 --> 00:05:04,000
Speaker 6: on Gemini, etc.

99
00:05:04,480 --> 00:05:04,920
Speaker 7: Right.

100
00:05:05,760 --> 00:05:08,360
Speaker 6: I think consumers are making their buying decisions still very

101
00:05:08,440 --> 00:05:12,080
Speaker 6: much because of design, hardware features, and what we've seen

102
00:05:12,080 --> 00:05:14,799
Speaker 6: from Apple over the past few years is no major

103
00:05:14,839 --> 00:05:17,440
Speaker 6: hardware upgrades, So I think that is what's holding back

104
00:05:17,520 --> 00:05:20,320
Speaker 6: iPhone sales. I think a lot of people are anticipating

105
00:05:20,839 --> 00:05:23,920
Speaker 6: in the market at least big iPhone redesigns in the

106
00:05:23,960 --> 00:05:26,280
Speaker 6: fall of this year, right, So there's probably a lot

107
00:05:26,279 --> 00:05:28,840
Speaker 6: of consumers holding off on their purchases till the end

108
00:05:28,880 --> 00:05:31,480
Speaker 6: of this year, wanting something that's quite a bit thinner,

109
00:05:31,640 --> 00:05:35,400
Speaker 6: better camera capabilities, new designs, new display So I think

110
00:05:35,400 --> 00:05:39,480
Speaker 6: it's really the design stagnation that's driving a little bit

111
00:05:39,480 --> 00:05:41,600
Speaker 6: of this pessimistem on the iPhone in particular.

112
00:05:42,240 --> 00:05:46,599
Speaker 4: Interesting time, given that potential pessimism around the iPhone to

113
00:05:46,720 --> 00:05:48,719
Speaker 4: be seeing a big paying as lease go to the CEO.

114
00:05:49,240 --> 00:05:51,120
Speaker 7: Yeah. I mean, if you look at it comparison to

115
00:05:51,200 --> 00:05:51,919
Speaker 7: a few years.

116
00:05:51,720 --> 00:05:54,159
Speaker 6: Ago, Tim Cook was getting paid nearly one hundred million

117
00:05:54,200 --> 00:05:57,839
Speaker 6: dollars per year, right, his page did go up about

118
00:05:57,880 --> 00:06:01,000
Speaker 6: eighteen percent in twenty twenty four, went up to something

119
00:06:01,000 --> 00:06:04,279
Speaker 6: north of seventy million dollars. Base salary is the same

120
00:06:04,880 --> 00:06:07,400
Speaker 6: the RSUs, the shares were the same, but because of

121
00:06:07,440 --> 00:06:09,520
Speaker 6: the stock growth in the past year or so, you

122
00:06:09,560 --> 00:06:12,360
Speaker 6: saw the stock go up, so you saw his pay

123
00:06:13,040 --> 00:06:15,640
Speaker 6: increase with a little bit more share count as well.

124
00:06:15,760 --> 00:06:18,600
Speaker 6: So I think that's the driving force there in terms

125
00:06:18,600 --> 00:06:19,600
Speaker 6: of how Apple's.

126
00:06:19,200 --> 00:06:20,320
Speaker 7: Going to fare this year.

127
00:06:21,080 --> 00:06:24,359
Speaker 6: In my column yesterday in power On, I gave a

128
00:06:24,400 --> 00:06:26,400
Speaker 6: look at the roadmap for the Apple product line for

129
00:06:26,440 --> 00:06:29,560
Speaker 6: twenty twenty five, and what you're seeing are a lot

130
00:06:29,560 --> 00:06:32,280
Speaker 6: of devices. This is going to be an extraordinarily jam

131
00:06:32,320 --> 00:06:35,000
Speaker 6: packed here in terms of new products for Apple, but

132
00:06:35,080 --> 00:06:38,760
Speaker 6: nothing particularly revolutionary or innovative. But I still think that

133
00:06:38,760 --> 00:06:41,520
Speaker 6: it's going to drive a good chunk of sales. I

134
00:06:41,560 --> 00:06:44,000
Speaker 6: think that Apple engineers and people who work on the

135
00:06:44,000 --> 00:06:47,600
Speaker 6: iPhone in particular are pretty excited about the iPhone roadmap.

136
00:06:47,680 --> 00:06:50,000
Speaker 6: I mean, this year you're going to see a new

137
00:06:50,040 --> 00:06:53,160
Speaker 6: iPhone seventeen Air model, much center device.

138
00:06:53,240 --> 00:06:54,599
Speaker 7: They think that's going to do well.

139
00:06:54,720 --> 00:06:56,240
Speaker 6: And then in the years to come, you're going to

140
00:06:56,240 --> 00:06:58,560
Speaker 6: see foldable iPhones and other new types of phone. So

141
00:06:59,240 --> 00:07:01,800
Speaker 6: I think they're quite about the future.

142
00:07:02,360 --> 00:07:02,600
Speaker 8: Mark.

143
00:07:03,080 --> 00:07:05,680
Speaker 3: Moving away from Apple, we'd like to ask about news

144
00:07:05,680 --> 00:07:10,520
Speaker 3: from Sonos today. The audio technology provider changed its CEO.

145
00:07:11,040 --> 00:07:15,040
Speaker 3: It promoted Tom Conrad to replace Patrick Spence, and this

146
00:07:15,080 --> 00:07:18,000
Speaker 3: is in part due to troubles with their new app.

147
00:07:18,320 --> 00:07:21,200
Speaker 3: What sort of challenge will Tom Conrad face as he

148
00:07:21,320 --> 00:07:22,320
Speaker 3: takes this new job.

149
00:07:22,520 --> 00:07:25,080
Speaker 6: Yeah, I would say this is almost entirely or it

150
00:07:25,120 --> 00:07:26,800
Speaker 6: is entirely because of the new app.

151
00:07:26,840 --> 00:07:28,040
Speaker 7: So it's just some background here.

152
00:07:28,400 --> 00:07:31,760
Speaker 6: About a year ago last May, Sonos revamped its app,

153
00:07:31,800 --> 00:07:34,800
Speaker 6: and so the Sonos user experience, the app is really

154
00:07:35,000 --> 00:07:37,320
Speaker 6: the center of everything. It's how you control your speakers,

155
00:07:37,360 --> 00:07:38,920
Speaker 6: it's how you set up your speakers, and so if

156
00:07:38,920 --> 00:07:42,080
Speaker 6: the app doesn't work right, your ecosystem and in some cases,

157
00:07:42,160 --> 00:07:44,480
Speaker 6: you know, tens of thousands of dollars of speakers in

158
00:07:44,520 --> 00:07:46,840
Speaker 6: your home theater in your house, right, it's not going

159
00:07:46,880 --> 00:07:47,640
Speaker 6: to work properly.

160
00:07:47,840 --> 00:07:52,080
Speaker 7: Right, This was driving people really mad. Right.

161
00:07:52,360 --> 00:07:55,440
Speaker 6: Not Able to control volume, not able to control music properly,

162
00:07:55,480 --> 00:07:57,360
Speaker 6: not able to play the songs they want, not able

163
00:07:57,400 --> 00:07:59,480
Speaker 6: to search alarm's not going off, the sleep type are

164
00:07:59,520 --> 00:08:02,800
Speaker 6: not working. This really led to a sales decline for

165
00:08:02,840 --> 00:08:04,800
Speaker 6: someone else, You're going to see another fifteen percent decline

166
00:08:04,760 --> 00:08:05,560
Speaker 6: in the current border.

167
00:08:06,360 --> 00:08:07,320
Speaker 7: They had to hold time.

168
00:08:07,240 --> 00:08:09,920
Speaker 6: Responsible, and they chose the CEO the New Guys has

169
00:08:09,960 --> 00:08:13,000
Speaker 6: been a MORE member for several years and helped create Pandora.

170
00:08:13,440 --> 00:08:15,680
Speaker 4: Mark German with the latest and greatest reporting me thank

171
00:08:15,720 --> 00:08:26,960
Speaker 4: you so much. Meta CEO Mark Zuckerberg joined the Joe

172
00:08:27,080 --> 00:08:30,840
Speaker 4: Rogan podcast on Friday to lament the rise of culturally

173
00:08:30,920 --> 00:08:31,880
Speaker 4: newted companies.

174
00:08:32,160 --> 00:08:32,800
Speaker 2: Just take a listen.

175
00:08:33,800 --> 00:08:36,840
Speaker 9: The kind of masculine energy I think is good. I

176
00:08:36,840 --> 00:08:39,800
Speaker 9: think corporate culture was really like trying to get away

177
00:08:39,840 --> 00:08:43,960
Speaker 9: from it. Having a culture that celebrates the aggression a

178
00:08:44,000 --> 00:08:46,160
Speaker 9: bit more has its own merits.

179
00:08:47,240 --> 00:08:50,520
Speaker 4: Let's bring in Bloomberg's Riley Griffin for more. Riley, you

180
00:08:50,640 --> 00:08:54,800
Speaker 4: go back in history and Mark Zuckerberg's first business he

181
00:08:54,920 --> 00:08:58,280
Speaker 4: built was about rating women's attractiveness at Harvard University.

182
00:08:58,480 --> 00:09:00,480
Speaker 2: Look, he's progressed.

183
00:09:00,120 --> 00:09:03,160
Speaker 4: And changed and become the CEO of a massive company.

184
00:09:03,720 --> 00:09:07,840
Speaker 4: So too his changes around culture. Eban Flow it seems.

185
00:09:07,120 --> 00:09:09,920
Speaker 10: No doubt, and he said it in his own words there.

186
00:09:10,679 --> 00:09:14,280
Speaker 10: The use of that very strong language culturally neutered companies

187
00:09:14,640 --> 00:09:18,240
Speaker 10: has drawn the attention of several and he talked about

188
00:09:18,280 --> 00:09:20,840
Speaker 10: feminine energy. He's a man who grew up around women

189
00:09:21,040 --> 00:09:24,600
Speaker 10: and has three daughters himself. But those comments have been

190
00:09:24,679 --> 00:09:27,720
Speaker 10: controversial over the weekend, to say the least riley.

191
00:09:28,040 --> 00:09:30,800
Speaker 3: We have to ask also about another comment that he

192
00:09:30,880 --> 00:09:33,840
Speaker 3: made during this exchange with Joe Rogan, and that was

193
00:09:33,880 --> 00:09:38,520
Speaker 3: about the company's relationship with the Biden administration. It sounded

194
00:09:38,559 --> 00:09:40,800
Speaker 3: like it was not a very fruitful and it might

195
00:09:40,840 --> 00:09:43,760
Speaker 3: even have been hostile. Tell us about that and tell

196
00:09:43,840 --> 00:09:46,520
Speaker 3: us what sort of change in direction he is looking

197
00:09:46,600 --> 00:09:48,679
Speaker 3: for as the Trump team comes in.

198
00:09:49,480 --> 00:09:50,800
Speaker 2: It's a great question, Mike.

199
00:09:51,280 --> 00:09:55,000
Speaker 10: The three hour interview with Rogan was wide ranging, and

200
00:09:55,080 --> 00:09:58,360
Speaker 10: much of the conversation was spent talking about the Biden

201
00:09:58,400 --> 00:10:03,400
Speaker 10: administration and specifically content moderation in.

202
00:10:02,640 --> 00:10:03,839
Speaker 2: The COVID era.

203
00:10:04,240 --> 00:10:07,520
Speaker 10: He described the Biden administration and staffers as hostile to

204
00:10:07,559 --> 00:10:12,560
Speaker 10: the company, shouting, cursing, and it was a tone that

205
00:10:12,640 --> 00:10:15,800
Speaker 10: shifted when he then talked about Donald Trump and the

206
00:10:15,840 --> 00:10:19,080
Speaker 10: incoming administration. He described a lot of optimism there.

207
00:10:19,679 --> 00:10:20,559
Speaker 2: And it's no.

208
00:10:21,280 --> 00:10:24,880
Speaker 10: Surprise that this podcast has come at a time in

209
00:10:24,920 --> 00:10:29,480
Speaker 10: which Zuckerberg is positioning himself as a closer ally to

210
00:10:29,520 --> 00:10:33,200
Speaker 10: the administration. Just last week he actually went upon Beach,

211
00:10:33,679 --> 00:10:34,840
Speaker 10: reportedly to mar A.

212
00:10:34,840 --> 00:10:35,640
Speaker 2: Lago as well.

213
00:10:35,920 --> 00:10:38,440
Speaker 10: And it was a big week for Meta one that

214
00:10:38,520 --> 00:10:43,640
Speaker 10: began with news of appointing UFC chief Dana White, who

215
00:10:43,679 --> 00:10:46,760
Speaker 10: is an ally to Trump, and then ending third party

216
00:10:46,760 --> 00:10:49,880
Speaker 10: fact checking here in the United States, changing its hateful

217
00:10:49,920 --> 00:10:54,760
Speaker 10: conduct policies, ending some D and I programs. So what

218
00:10:54,880 --> 00:10:57,160
Speaker 10: a week it was for Meta and all in this

219
00:10:57,240 --> 00:11:00,559
Speaker 10: theme of moving closer to the Trump administration.

220
00:11:01,040 --> 00:11:05,440
Speaker 3: Bloomberg's Riley Griffin thank you. In other news, O'Leary Venture's

221
00:11:05,520 --> 00:11:09,240
Speaker 3: chairman and Shark Tanks star Kevin O'Leary met with President

222
00:11:09,280 --> 00:11:12,240
Speaker 3: elect Donald Trump at mar A Lago over the weekend

223
00:11:12,280 --> 00:11:16,160
Speaker 3: to discuss his plans to buy TikTok. This after the

224
00:11:16,240 --> 00:11:19,600
Speaker 3: US Supreme Court appeared likely to uphold the law on Friday,

225
00:11:19,840 --> 00:11:22,959
Speaker 3: which would ban TikTok on January nineteenth, less than a

226
00:11:23,000 --> 00:11:25,520
Speaker 3: week from now, unless it's a vest from its Chinese

227
00:11:25,600 --> 00:11:29,600
Speaker 3: owner byt Dance. Let's bring in Jennifer Huddleston now she

228
00:11:29,720 --> 00:11:32,439
Speaker 3: is a Senior Fellow in Technology Policy at the Cato

229
00:11:32,559 --> 00:11:36,559
Speaker 3: Institute For More Jennifer, where do we go from here?

230
00:11:36,720 --> 00:11:39,760
Speaker 3: Really looks like the High Court is going to uphold

231
00:11:39,800 --> 00:11:43,080
Speaker 3: this law? What are the options? The road is running out?

232
00:11:43,120 --> 00:11:46,400
Speaker 3: Do we see a purchase happening? And do we see

233
00:11:46,400 --> 00:11:49,280
Speaker 3: Donald Trump enforcing this law with any vigor in the

234
00:11:49,320 --> 00:11:51,160
Speaker 3: event a transaction doesn't happen.

235
00:11:51,520 --> 00:11:53,679
Speaker 11: Well, Notably, what we see is there's a deadline of

236
00:11:53,760 --> 00:11:56,120
Speaker 11: January nineteenth, which would be the last day of the

237
00:11:56,160 --> 00:12:00,720
Speaker 11: Biden administration before the Trump administration took office. There is

238
00:12:00,760 --> 00:12:03,320
Speaker 11: in the law an option of a ninety day extension,

239
00:12:03,360 --> 00:12:06,080
Speaker 11: so we could see that happen. That would then kick

240
00:12:06,080 --> 00:12:10,000
Speaker 11: any further conversations around a potential investment or around other

241
00:12:10,040 --> 00:12:13,199
Speaker 11: options into the Trump administration. We also could see the

242
00:12:13,240 --> 00:12:16,439
Speaker 11: Supreme Court act either issue a final decision on the

243
00:12:16,480 --> 00:12:19,920
Speaker 11: constitutionality of the law or provide some sort of preliminary

244
00:12:19,920 --> 00:12:23,200
Speaker 11: injunction or even an administrative stay, as was discussed during

245
00:12:23,320 --> 00:12:26,760
Speaker 11: oral arguments, That could extend that timeline a bit further.

246
00:12:27,120 --> 00:12:29,840
Speaker 11: For what in any case, even if a divestment were

247
00:12:29,880 --> 00:12:32,040
Speaker 11: to be found to be possible, would be a quite

248
00:12:32,120 --> 00:12:34,360
Speaker 11: significant transaction, and.

249
00:12:34,280 --> 00:12:38,319
Speaker 4: How unprecedented would a stay a delay by the Supreme

250
00:12:38,360 --> 00:12:41,800
Speaker 4: Court be. Aroun Blue Bag Intelligence analysis says that basically

251
00:12:41,880 --> 00:12:44,199
Speaker 4: there's only a thirty percent chance here of TikTok winning

252
00:12:44,200 --> 00:12:45,079
Speaker 4: this well.

253
00:12:45,120 --> 00:12:47,240
Speaker 11: I think some of the questions there is what does

254
00:12:47,280 --> 00:12:49,560
Speaker 11: the court see in terms of is there enough of

255
00:12:49,559 --> 00:12:51,960
Speaker 11: a possibility? Is this considered enough of a risk. Do

256
00:12:52,040 --> 00:12:55,800
Speaker 11: they need more time to really analyze the underlying constitutional

257
00:12:55,840 --> 00:12:58,120
Speaker 11: issues of the case such that they want to issue

258
00:12:58,120 --> 00:13:02,920
Speaker 11: that preliminary injunction in the likelihood that it might happen again.

259
00:13:03,000 --> 00:13:06,000
Speaker 11: There is also that option of the Biden administration doing

260
00:13:06,000 --> 00:13:10,440
Speaker 11: something to extend that deadline slightly. But I think it

261
00:13:10,480 --> 00:13:13,040
Speaker 11: remains to be seen whether or not, given that this

262
00:13:13,080 --> 00:13:15,880
Speaker 11: would be a very quick turnaround, and we've seen that

263
00:13:15,920 --> 00:13:19,640
Speaker 11: things in this particular issue have moved very quickly, almost

264
00:13:19,720 --> 00:13:23,480
Speaker 11: unprecedently quickly at times, could we see a bit longer

265
00:13:23,480 --> 00:13:25,400
Speaker 11: of a timeline just to make sure that those deep

266
00:13:25,480 --> 00:13:30,120
Speaker 11: underlying issues around free speech, around executive power, around what

267
00:13:30,200 --> 00:13:34,560
Speaker 11: this means about balancing national security versus free speech. Some

268
00:13:34,600 --> 00:13:37,319
Speaker 11: of the questions around the appropriate levels of scrutiny can

269
00:13:37,360 --> 00:13:39,959
Speaker 11: really be analyzed the way the Court wants Jennifer.

270
00:13:40,000 --> 00:13:43,360
Speaker 4: We had Frank McCourt junior, who alongside Kevin O'Leary, is

271
00:13:43,400 --> 00:13:45,760
Speaker 4: presenting this offer to byte Dance as it stands.

272
00:13:46,320 --> 00:13:47,960
Speaker 2: We had them on the show Friday.

273
00:13:48,400 --> 00:13:50,640
Speaker 4: His perspective was, this is a win for the consumers

274
00:13:50,720 --> 00:13:52,640
Speaker 4: to keep TikTok in the US, It's a win for

275
00:13:52,840 --> 00:13:56,240
Speaker 4: potentially investors in the overall parent company byte Dance because

276
00:13:56,240 --> 00:13:58,720
Speaker 4: at least they get some value. And more broadly, this

277
00:13:58,760 --> 00:14:01,560
Speaker 4: is a win for national security with a different tech stack,

278
00:14:01,960 --> 00:14:06,000
Speaker 4: with moving to decentralization, would it solve some of these

279
00:14:06,040 --> 00:14:07,120
Speaker 4: national security concents?

280
00:14:07,120 --> 00:14:10,120
Speaker 11: From real perspective, I'm an online speech expert and a

281
00:14:10,160 --> 00:14:13,080
Speaker 11: technology policy expert, not a national security expert. But I

282
00:14:13,080 --> 00:14:14,960
Speaker 11: think there are a lot of questions around what a

283
00:14:15,000 --> 00:14:18,560
Speaker 11: devestment looks like. There's the underlying question of what exactly

284
00:14:18,640 --> 00:14:21,520
Speaker 11: are you divesting? Is it the branding, is it the algorithm?

285
00:14:21,600 --> 00:14:23,960
Speaker 11: Is that the data? So some of these questions about

286
00:14:24,000 --> 00:14:28,160
Speaker 11: what a satisfactory devestment looks like remains a bit unknown,

287
00:14:28,600 --> 00:14:31,400
Speaker 11: and that's been one of the underlying issues. The government

288
00:14:31,440 --> 00:14:35,200
Speaker 11: will have to approve any potential investment as alleviating the

289
00:14:35,400 --> 00:14:38,760
Speaker 11: concerns about potential foreign influence, and of course that remains

290
00:14:38,760 --> 00:14:41,320
Speaker 11: to be seen what that would look like, both in

291
00:14:41,360 --> 00:14:44,200
Speaker 11: a Biden administration before the nineteenth as well as in

292
00:14:44,240 --> 00:14:45,760
Speaker 11: an incoming Trump administration.

293
00:14:46,080 --> 00:14:48,920
Speaker 3: Jennifer, let's pull the lens back a little bit on

294
00:14:49,120 --> 00:14:52,720
Speaker 3: this whole TikTok story. What are the broader implications of

295
00:14:52,760 --> 00:14:57,000
Speaker 3: the government singling out the ownership of this app? What

296
00:14:57,520 --> 00:15:01,560
Speaker 3: happens in other areas where could we see some ripple effects?

297
00:15:01,880 --> 00:15:03,480
Speaker 11: You know, at the end of the day, this underlying

298
00:15:03,480 --> 00:15:06,680
Speaker 11: discussion is much bigger than just TikTok itself. A lot

299
00:15:06,680 --> 00:15:09,120
Speaker 11: of the questions that we saw raised at the Supreme

300
00:15:09,160 --> 00:15:11,800
Speaker 11: Court were not just about this particular app and this

301
00:15:11,880 --> 00:15:17,040
Speaker 11: particular data use or this particular relationship to the parent company.

302
00:15:17,240 --> 00:15:20,080
Speaker 11: They were underlying questions about what does it mean for

303
00:15:20,200 --> 00:15:22,840
Speaker 11: a divest or band bill to be potentially a less

304
00:15:22,840 --> 00:15:26,600
Speaker 11: restrictive means? Is this something that actually implicates speech in

305
00:15:26,880 --> 00:15:30,600
Speaker 11: forcing a parent company or a US subsidiary to divest

306
00:15:30,640 --> 00:15:33,680
Speaker 11: from a parent company, or is that a secondary effect

307
00:15:33,960 --> 00:15:36,200
Speaker 11: and that's going to have a much broader impact in

308
00:15:36,200 --> 00:15:38,840
Speaker 11: the future if we see this come up as it

309
00:15:38,880 --> 00:15:41,360
Speaker 11: relates to other apps. There also were a lot of

310
00:15:41,440 --> 00:15:44,800
Speaker 11: questions about what does this mean for the underlying user's speech.

311
00:15:44,840 --> 00:15:48,720
Speaker 11: So we focused a lot on TikTok and TikTok speech

312
00:15:48,800 --> 00:15:51,640
Speaker 11: as it relates to their choice of algorithms. But there's

313
00:15:51,680 --> 00:15:54,960
Speaker 11: also a combined case that involves TikTok users, the millions

314
00:15:55,000 --> 00:15:57,720
Speaker 11: of Americans who have chosen this to be their preferred

315
00:15:57,760 --> 00:16:01,480
Speaker 11: platform for content creation. Could the Supreme Court rule on

316
00:16:01,600 --> 00:16:05,040
Speaker 11: how the government impacts those users' choice of venue?

317
00:16:05,240 --> 00:16:07,800
Speaker 3: And it also puts a burden, a specific burden on

318
00:16:07,840 --> 00:16:11,280
Speaker 3: companies like Apple and Google and even the Internet service

319
00:16:11,320 --> 00:16:15,480
Speaker 3: providers to ensure that this law is upheld. Where does

320
00:16:15,520 --> 00:16:18,480
Speaker 3: it leave them and do they have any say in this?

321
00:16:18,720 --> 00:16:21,400
Speaker 3: The heads of the US of the House China Committee

322
00:16:21,600 --> 00:16:25,480
Speaker 3: even wrote to those companies ahead of time last month

323
00:16:25,520 --> 00:16:27,440
Speaker 3: to warrant they expect full enforcement.

324
00:16:27,520 --> 00:16:28,400
Speaker 8: Where does it leave them?

325
00:16:28,480 --> 00:16:30,520
Speaker 11: So I think this is a very interesting and often

326
00:16:30,680 --> 00:16:33,440
Speaker 11: underappreciated element of the law. What the law actually does

327
00:16:33,600 --> 00:16:38,520
Speaker 11: is it impacts the updating, maintenance, and distribution of this app,

328
00:16:38,720 --> 00:16:41,160
Speaker 11: or of other apps that may find themselves subject to

329
00:16:41,200 --> 00:16:44,760
Speaker 11: the law. That means that ultimately the enforcement, as you mentioned,

330
00:16:44,960 --> 00:16:45,720
Speaker 11: comes down to.

331
00:16:45,920 --> 00:16:47,760
Speaker 2: App stores like Google and Apple.

332
00:16:48,080 --> 00:16:52,000
Speaker 11: It comes down to various ISPs or data storage providers.

333
00:16:52,320 --> 00:16:54,520
Speaker 11: That does mean there is an option where in some

334
00:16:54,680 --> 00:16:58,680
Speaker 11: cases TikTok could move the data to continue to allow

335
00:16:58,760 --> 00:17:02,200
Speaker 11: people to access it potentially, but that would be quite cumbersome,

336
00:17:02,240 --> 00:17:05,200
Speaker 11: and then that raises its own data security issues for

337
00:17:05,720 --> 00:17:08,399
Speaker 11: potential users. There are a lot of questions for the

338
00:17:08,520 --> 00:17:11,200
Speaker 11: users of the app what this means if on January

339
00:17:11,240 --> 00:17:13,640
Speaker 11: twentieth they wake up and the law.

340
00:17:13,440 --> 00:17:14,840
Speaker 2: Has gone into effect.

341
00:17:14,880 --> 00:17:18,040
Speaker 11: But this does impact the American companies just like it

342
00:17:18,119 --> 00:17:21,000
Speaker 11: impacts the American users, and I think we have to

343
00:17:21,359 --> 00:17:24,400
Speaker 11: pay serious attention to what that means as well.

344
00:17:24,560 --> 00:17:26,560
Speaker 4: Of Course, the principle of the Cato Institute in many

345
00:17:26,560 --> 00:17:29,840
Speaker 4: ways are libertarian individual liberty, limiting government, and the likes.

346
00:17:30,119 --> 00:17:32,880
Speaker 4: Can you broaden it out to what has been seen

347
00:17:32,920 --> 00:17:35,440
Speaker 4: to be done elsewhere other than in the United States.

348
00:17:35,760 --> 00:17:39,520
Speaker 4: TikTok is in Europe, but Europe has far strict rules

349
00:17:39,640 --> 00:17:41,000
Speaker 4: around content moderation, for.

350
00:17:40,960 --> 00:17:46,239
Speaker 11: Example, so we've seen a broader ecosystem around conversations as

351
00:17:46,280 --> 00:17:48,960
Speaker 11: it relates to tech and online speech more generally.

352
00:17:49,280 --> 00:17:49,960
Speaker 2: Here in the US.

353
00:17:50,000 --> 00:17:51,960
Speaker 11: When we're looking at this particular issue, we're going to

354
00:17:52,000 --> 00:17:55,520
Speaker 11: be looking at the potential First Amendment issues here as

355
00:17:55,560 --> 00:17:59,399
Speaker 11: well as the broader free speech environment in general. So,

356
00:17:59,480 --> 00:18:01,320
Speaker 11: of course one of the concerns is if we start

357
00:18:01,320 --> 00:18:05,520
Speaker 11: to see the US create divestor ban requirements around this

358
00:18:05,600 --> 00:18:08,280
Speaker 11: app or around other app, could we see other countries

359
00:18:08,320 --> 00:18:11,719
Speaker 11: start to grafter some of those leading American platforms, platforms

360
00:18:11,880 --> 00:18:16,960
Speaker 11: like Facebook or x around actions that they don't like

361
00:18:17,119 --> 00:18:19,640
Speaker 11: that those platforms are taking when it comes to their

362
00:18:19,720 --> 00:18:23,520
Speaker 11: content moderation. And what kind of principles does that give

363
00:18:23,600 --> 00:18:26,680
Speaker 11: the US and its leadership to push back against potentially

364
00:18:26,760 --> 00:18:29,560
Speaker 11: some of those attacks. If we see the US restricting

365
00:18:30,000 --> 00:18:33,439
Speaker 11: speech because of how it feels, and algorithm is choosing

366
00:18:33,440 --> 00:18:36,199
Speaker 11: to portray that speech in its own way, this is.

367
00:18:36,200 --> 00:18:36,760
Speaker 2: A key test.

368
00:18:37,000 --> 00:18:40,639
Speaker 4: Jeniph Halsten, Senior Fellow at the Cato Institute, Thank you.

369
00:18:41,080 --> 00:18:44,320
Speaker 4: Coming up Waimo's expansion plans and competition with Tesla.

370
00:18:44,720 --> 00:18:46,040
Speaker 2: That's next. This is Blue Med.

371
00:18:45,880 --> 00:19:01,640
Speaker 3: Technology, Waimo's CEO, to qidramwa Khana, plans to expand testing

372
00:19:01,680 --> 00:19:04,920
Speaker 3: of autonomous driving in the US and abroad this year.

373
00:19:05,320 --> 00:19:09,119
Speaker 3: She sat down with Bloomberg's Ed Ludlow at CEES last week,

374
00:19:09,240 --> 00:19:12,320
Speaker 3: where she claimed that competition with Tesla is a good thing.

375
00:19:12,720 --> 00:19:15,400
Speaker 3: That's because safe driving is a big goal that will

376
00:19:15,400 --> 00:19:17,240
Speaker 3: take more than one company to meet.

377
00:19:17,640 --> 00:19:21,400
Speaker 12: Take a listen, we welcome competition in the space, right

378
00:19:21,480 --> 00:19:24,719
Speaker 12: like making the road safer is an important mission and

379
00:19:24,720 --> 00:19:27,920
Speaker 12: it's too big for one company. So that's a great thing.

380
00:19:28,119 --> 00:19:28,439
Speaker 8: Okay.

381
00:19:28,520 --> 00:19:31,440
Speaker 12: As far as a national framework, that would be great.

382
00:19:31,640 --> 00:19:34,719
Speaker 12: It's just that that framework should require people to demonstrate

383
00:19:34,720 --> 00:19:35,639
Speaker 12: their safety record.

384
00:19:36,440 --> 00:19:39,040
Speaker 9: Do you have faith that Elon Musk, if he were

385
00:19:39,080 --> 00:19:40,920
Speaker 9: to influence that process.

386
00:19:40,480 --> 00:19:42,800
Speaker 13: Would do it in a way that is fair to

387
00:19:43,480 --> 00:19:45,560
Speaker 13: companies pursuing the same goal as his.

388
00:19:47,000 --> 00:19:50,000
Speaker 12: I'm not going to talk either about my faith or

389
00:19:52,000 --> 00:19:59,240
Speaker 12: Elon Musk in that context. I have a lot of faith,

390
00:19:59,320 --> 00:20:01,520
Speaker 12: though I do have a lot of faith as a person,

391
00:20:01,920 --> 00:20:06,680
Speaker 12: as a human in humanity. And no, I mean I

392
00:20:06,720 --> 00:20:10,480
Speaker 12: think it's a I think I think you know. I've

393
00:20:10,480 --> 00:20:13,919
Speaker 12: been at WEIMO for almost eight years. When I joined WEAMO,

394
00:20:14,320 --> 00:20:18,520
Speaker 12: Donald Trump was president Secretary Chow was Transportation Secretary. Yes,

395
00:20:18,800 --> 00:20:23,280
Speaker 12: the administration was very forward leaning on advancing AVIS. I

396
00:20:23,400 --> 00:20:28,240
Speaker 12: feel really strongly that there's an opportunity to do so

397
00:20:28,680 --> 00:20:33,879
Speaker 12: in a very different context, which globally, the race around

398
00:20:33,920 --> 00:20:37,400
Speaker 12: autonomous vehicles has matured, and so this is a real

399
00:20:37,440 --> 00:20:43,200
Speaker 12: opportunity for US leadership and so enabling you know, safe

400
00:20:43,840 --> 00:20:48,760
Speaker 12: sustainable transportation that's autonomous is very aligned with what I

401
00:20:48,800 --> 00:20:51,159
Speaker 12: think this administration will want to do.

402
00:20:52,119 --> 00:20:55,800
Speaker 3: That was that was Waymo's CEO at CES Caroline.

403
00:20:56,280 --> 00:20:57,680
Speaker 2: It's time now for talking tech Mike.

404
00:20:57,720 --> 00:21:00,840
Speaker 4: First up, Jeff Bezos is Blue Origin has delayed its

405
00:21:00,840 --> 00:21:03,080
Speaker 4: inaugural launch of its new Glen Rocket.

406
00:21:03,240 --> 00:21:05,959
Speaker 2: The company says the temporary pause was due to an

407
00:21:06,000 --> 00:21:07,639
Speaker 2: unspecified issue with a vehicle now.

408
00:21:07,680 --> 00:21:10,359
Speaker 4: Blue Origin did not provide a new launch date, but

409
00:21:10,480 --> 00:21:13,320
Speaker 4: says they're reviewing opportunities for its next attempt.

410
00:21:13,800 --> 00:21:15,440
Speaker 2: Plus investors in mainland.

411
00:21:15,160 --> 00:21:19,240
Speaker 4: China are doubling down on ten cent ownership by Onshore

412
00:21:19,320 --> 00:21:21,520
Speaker 4: Chinese Traders has actually swelled to an all time high,

413
00:21:21,680 --> 00:21:24,800
Speaker 4: climbing to ten point eight percent following the Biden administration's

414
00:21:24,840 --> 00:21:28,119
Speaker 4: moved to blacklist the company over alleged Chinese military ties

415
00:21:28,760 --> 00:21:31,960
Speaker 4: and CATL is planning a second Hong Kong listing and

416
00:21:32,080 --> 00:21:33,879
Speaker 4: is looking to raise as much as five billion dollars,

417
00:21:34,040 --> 00:21:37,240
Speaker 4: escording to sources, The world's top EV battery maker is

418
00:21:37,280 --> 00:21:39,600
Speaker 4: said to be hiring banks including Bag of America, China

419
00:21:39,640 --> 00:21:43,440
Speaker 4: International Capitals, CSC Financial, and JP Worgan Chase to arrange

420
00:21:43,480 --> 00:21:45,480
Speaker 4: what could be the biggest Hong Kong.

421
00:21:45,320 --> 00:21:46,800
Speaker 2: Offering in recent years.

422
00:21:47,800 --> 00:21:51,560
Speaker 4: Coming up, how Snowflake plans to meet the growing demand

423
00:21:51,640 --> 00:21:55,440
Speaker 4: for AI skills. Company CEO Shido Ramaswami is joining us. Next,

424
00:21:56,119 --> 00:22:08,240
Speaker 4: this is Bloomberg Technology. Welcome back to Blue meg Technology

425
00:22:08,320 --> 00:22:09,960
Speaker 4: and Karen Hide in New York. A quick check on

426
00:22:10,000 --> 00:22:12,120
Speaker 4: these markets for you. We're in sell off mode when

427
00:22:12,119 --> 00:22:15,119
Speaker 4: it comes to the NASDAK. All of their Magnificent seven

428
00:22:15,160 --> 00:22:17,520
Speaker 4: as they're known, are in the red, so no wonder

429
00:22:17,520 --> 00:22:18,800
Speaker 4: from a point's perspective, we're being.

430
00:22:18,800 --> 00:22:20,959
Speaker 2: Drag lower on the Nasdaq. We've got worries about inflation

431
00:22:21,000 --> 00:22:21,920
Speaker 2: prints coming this week.

432
00:22:22,000 --> 00:22:24,280
Speaker 4: We've also got the start to the earning season and

433
00:22:24,359 --> 00:22:26,919
Speaker 4: where inflation rates and interest rates are therefore going what

434
00:22:27,000 --> 00:22:28,520
Speaker 4: it means for big tech. But move on and have

435
00:22:28,520 --> 00:22:31,240
Speaker 4: a look at some key movers. The one I choose

436
00:22:31,560 --> 00:22:33,960
Speaker 4: is in Nvidia. It's actually second now in terms of

437
00:22:33,960 --> 00:22:36,879
Speaker 4: market capitalization on the Nasdaq, one hundred below that of

438
00:22:36,880 --> 00:22:39,240
Speaker 4: Apple three point two trillion. We've wiped off all of

439
00:22:39,240 --> 00:22:43,080
Speaker 4: twenty twenty five gains. This latest issue is of course,

440
00:22:43,080 --> 00:22:46,840
Speaker 4: surrounding the White House implementation from at least this current administration,

441
00:22:46,920 --> 00:22:49,040
Speaker 4: there'll be limitations on the ability to sell their very

442
00:22:49,119 --> 00:22:52,000
Speaker 4: high ENDAI chips to certain countries around the world. Of course,

443
00:22:52,000 --> 00:22:54,239
Speaker 4: export limitation is going to hit the revenue growth. But

444
00:22:54,400 --> 00:22:57,800
Speaker 4: then there's also this new headline coming from the information

445
00:22:57,880 --> 00:23:01,480
Speaker 4: that yet there are still these delays affecting Blackwell in particular,

446
00:23:01,600 --> 00:23:03,840
Speaker 4: we're off by three percent. But we want to talk

447
00:23:03,840 --> 00:23:06,679
Speaker 4: about generative AI and the implications of it for you

448
00:23:06,760 --> 00:23:09,040
Speaker 4: and I when it comes to our own skill base. Now,

449
00:23:09,240 --> 00:23:12,879
Speaker 4: Snowflake it wants to upskill one million people in the

450
00:23:12,920 --> 00:23:15,639
Speaker 4: next four years to meet the growing demand for AI across.

451
00:23:15,359 --> 00:23:17,280
Speaker 2: The tech industry. The first phase of.

452
00:23:17,240 --> 00:23:19,919
Speaker 4: The plan being announce today is it will train one

453
00:23:19,960 --> 00:23:23,359
Speaker 4: hundred thousand uses on the Snowflake AI date cloud and

454
00:23:23,440 --> 00:23:23,919
Speaker 4: no cost.

455
00:23:24,280 --> 00:23:24,800
Speaker 2: Here for more.

456
00:23:24,880 --> 00:23:28,159
Speaker 4: Snowflake CEO shude Al Ramaswami and so good to have

457
00:23:28,240 --> 00:23:31,960
Speaker 4: you on this show, Shradia. This focus on well skills,

458
00:23:32,080 --> 00:23:33,200
Speaker 4: it's global in nature.

459
00:23:33,200 --> 00:23:34,640
Speaker 2: Where are you focusing the investment?

460
00:23:35,960 --> 00:23:38,760
Speaker 8: Well, thank you, Carlin, excited to be here.

461
00:23:39,240 --> 00:23:43,639
Speaker 13: We all know this data and AI are transforming the

462
00:23:43,640 --> 00:23:46,359
Speaker 13: world and Snowflake is right at the center of it.

463
00:23:46,520 --> 00:23:49,320
Speaker 13: It is the most consequential platform for data in AI,

464
00:23:50,440 --> 00:23:53,720
Speaker 13: and we thought that it was really important that we

465
00:23:53,880 --> 00:23:55,080
Speaker 13: help upskill people.

466
00:23:55,520 --> 00:23:57,639
Speaker 8: So we at investing a lot into this.

467
00:23:57,640 --> 00:24:01,280
Speaker 13: This is the one million Minds on a Single Platform program.

468
00:24:01,440 --> 00:24:06,040
Speaker 13: A lot of it is dedicated to emerging markets, but

469
00:24:06,200 --> 00:24:10,120
Speaker 13: it also benefits companies that are in the US because

470
00:24:10,160 --> 00:24:13,439
Speaker 13: they have competency centers in India.

471
00:24:13,520 --> 00:24:14,000
Speaker 8: As you were.

472
00:24:13,880 --> 00:24:16,280
Speaker 13: Saying, we want to get one hundred thousand people trained

473
00:24:16,400 --> 00:24:19,440
Speaker 13: right this year, but the ambitious plan is to train

474
00:24:19,600 --> 00:24:23,120
Speaker 13: a million people in data and AI. Yes, partly Snowflake,

475
00:24:23,160 --> 00:24:25,800
Speaker 13: but more generally data and how AI can be used

476
00:24:25,800 --> 00:24:26,760
Speaker 13: over the next four.

477
00:24:26,680 --> 00:24:28,560
Speaker 2: Years penanthropic in nature.

478
00:24:28,960 --> 00:24:31,359
Speaker 4: But what about the skills shortage right here in the

479
00:24:31,440 --> 00:24:34,879
Speaker 4: United States where Snowflake is based. How confident are you

480
00:24:34,960 --> 00:24:36,520
Speaker 4: that you could get the right talent in the door

481
00:24:36,560 --> 00:24:39,280
Speaker 4: to help build your generative by offerings right now?

482
00:24:40,080 --> 00:24:43,000
Speaker 13: Well, so we've been We've been at this for several years.

483
00:24:43,040 --> 00:24:47,760
Speaker 13: We have an amazing team. We also work extensively with

484
00:24:47,920 --> 00:24:49,240
Speaker 13: all of our customers here.

485
00:24:49,280 --> 00:24:50,520
Speaker 8: In fact, one of.

486
00:24:50,520 --> 00:24:53,560
Speaker 13: The programs that beat on with our customers here are

487
00:24:53,560 --> 00:24:58,560
Speaker 13: hands on workshops that our sales engineers around with our customers.

488
00:24:59,240 --> 00:25:01,560
Speaker 8: And this is it's great for them.

489
00:25:01,400 --> 00:25:03,560
Speaker 13: Because they get to learn about new technology, how they

490
00:25:03,600 --> 00:25:07,200
Speaker 13: can be more efficient with Snowflake, it raises the general

491
00:25:07,280 --> 00:25:10,760
Speaker 13: level of awareness, and so we are doing this across

492
00:25:10,800 --> 00:25:14,200
Speaker 13: the board, both within Snowflake but also with our customers

493
00:25:14,240 --> 00:25:18,560
Speaker 13: that are here and in emerging markets like India and others.

494
00:25:18,880 --> 00:25:21,840
Speaker 4: Talking of India, I mean the US gets an awful

495
00:25:21,880 --> 00:25:24,359
Speaker 4: lot of talent from India via H one B visas,

496
00:25:24,440 --> 00:25:25,080
Speaker 4: for example.

497
00:25:25,359 --> 00:25:26,640
Speaker 2: Is that something that you use a lot?

498
00:25:26,680 --> 00:25:29,880
Speaker 4: How confident are you of getting the right talent from abroad?

499
00:25:29,960 --> 00:25:30,359
Speaker 2: Right now?

500
00:25:30,359 --> 00:25:33,200
Speaker 4: Considering the next administration, it seems as though Trump thus

501
00:25:33,240 --> 00:25:35,760
Speaker 4: far is pretty pro the H one B visa scheme.

502
00:25:37,720 --> 00:25:41,280
Speaker 8: Look, I came as a student, I was on in

503
00:25:41,680 --> 00:25:42,280
Speaker 8: H one visa.

504
00:25:42,560 --> 00:25:46,160
Speaker 13: A lot of people you know, have had amazing opportunity

505
00:25:46,280 --> 00:25:49,000
Speaker 13: and impact in areas like high.

506
00:25:48,760 --> 00:25:52,879
Speaker 8: Tech where there is a real shortage, so we use it.

507
00:25:52,920 --> 00:25:55,760
Speaker 13: But we also hired pretty you know a lot of

508
00:25:55,800 --> 00:25:57,320
Speaker 13: folks from our own.

509
00:25:57,320 --> 00:26:01,440
Speaker 8: Universities here in the in the United States.

510
00:26:01,560 --> 00:26:04,080
Speaker 13: We're fairly confident that the program is a win win

511
00:26:04,200 --> 00:26:06,960
Speaker 13: and that it will and that it will continue.

512
00:26:07,160 --> 00:26:09,600
Speaker 4: I think the thing we talk to most with leaders

513
00:26:09,680 --> 00:26:13,080
Speaker 4: such as yourself is an anxiety over talent. But there's

514
00:26:13,119 --> 00:26:15,760
Speaker 4: also currently an anxiety over infrastructure when it comes to

515
00:26:15,760 --> 00:26:17,919
Speaker 4: the future of Generator AI. You just heard the latest

516
00:26:17,960 --> 00:26:21,520
Speaker 4: and worries of a slow down or at least some

517
00:26:21,680 --> 00:26:25,240
Speaker 4: contailments in getting the latest black Well chip into well

518
00:26:25,320 --> 00:26:26,760
Speaker 4: the data centers that are so needed.

519
00:26:26,800 --> 00:26:28,680
Speaker 2: Are you hearing that from clients.

520
00:26:28,280 --> 00:26:31,040
Speaker 4: Worries about access to chips and ones that ultimately work

521
00:26:31,080 --> 00:26:32,080
Speaker 4: within their server acts.

522
00:26:33,480 --> 00:26:36,160
Speaker 13: I mean, there's a lot of attention that is going

523
00:26:36,200 --> 00:26:38,199
Speaker 13: into this space in terms of how do you do

524
00:26:39,200 --> 00:26:40,920
Speaker 13: you know, what's called inference, how do you use these

525
00:26:40,960 --> 00:26:44,280
Speaker 13: AI marvels at run time? And for a company like Snowflake,

526
00:26:45,000 --> 00:26:49,000
Speaker 13: we care a little bit more about doing inference right

527
00:26:49,080 --> 00:26:51,560
Speaker 13: so that our customers can use these mardels than we

528
00:26:51,640 --> 00:26:54,240
Speaker 13: do overout training. When it comes to training, absolutely, we

529
00:26:54,280 --> 00:26:57,159
Speaker 13: work with wonderful partners like Anthropic, which went live on

530
00:26:57,320 --> 00:26:58,720
Speaker 13: Snowflake last week, by.

531
00:26:58,560 --> 00:27:02,520
Speaker 8: The way, to help them train great models.

532
00:27:03,480 --> 00:27:07,639
Speaker 13: And while there's some shortages in some areas, but there

533
00:27:07,680 --> 00:27:10,639
Speaker 13: are also lots of great new things that are coming,

534
00:27:10,640 --> 00:27:13,200
Speaker 13: new kinds of chips, lots lots of new innovations, new

535
00:27:13,240 --> 00:27:14,680
Speaker 13: models that are smaller.

536
00:27:15,000 --> 00:27:17,040
Speaker 8: We feel pretty confident about.

537
00:27:16,720 --> 00:27:19,280
Speaker 13: Being able to meet demand for the kind of use

538
00:27:19,320 --> 00:27:23,640
Speaker 13: cases that our customers want to use on top of Snowflake.

539
00:27:23,520 --> 00:27:25,600
Speaker 4: There's been a lot of confidence for your own investors.

540
00:27:25,760 --> 00:27:28,240
Speaker 4: It's been building in terms of the product pipeline that

541
00:27:28,240 --> 00:27:30,800
Speaker 4: you'll bring in the generative AI innovations. Just tell us

542
00:27:30,800 --> 00:27:33,080
Speaker 4: through the product roadmap at the moment as.

543
00:27:32,960 --> 00:27:35,840
Speaker 2: To whether or not your meeting demand.

544
00:27:36,200 --> 00:27:38,720
Speaker 4: What does demand feel like right now, because for many

545
00:27:38,800 --> 00:27:41,399
Speaker 4: it felt like a height cycle in terms of actually

546
00:27:41,480 --> 00:27:43,840
Speaker 4: being able to be productive with some of these tools.

547
00:27:44,400 --> 00:27:45,800
Speaker 8: That's right, that's right. Yeah.

548
00:27:45,840 --> 00:27:49,359
Speaker 13: So you know, I've been CEO for a year and

549
00:27:49,800 --> 00:27:54,359
Speaker 13: a lot of my attention has gone into accelerating product velocity,

550
00:27:54,440 --> 00:27:57,760
Speaker 13: product delivery, and I'm pretty happy with what we have

551
00:27:57,840 --> 00:28:01,119
Speaker 13: done over twenty five products that we made available to

552
00:28:01,200 --> 00:28:02,600
Speaker 13: all of our customers.

553
00:28:02,160 --> 00:28:04,679
Speaker 8: Including many in AI.

554
00:28:05,800 --> 00:28:09,040
Speaker 13: And what is unique about Snowflake is that we work

555
00:28:09,280 --> 00:28:13,160
Speaker 13: very very closely with our customers to help them realize value.

556
00:28:13,160 --> 00:28:15,600
Speaker 13: We work on water coil use cases, which is how

557
00:28:15,640 --> 00:28:18,200
Speaker 13: they think about a business function that we can help

558
00:28:18,280 --> 00:28:22,159
Speaker 13: them fulfill. And in AI, we have over a thousand

559
00:28:22,280 --> 00:28:24,960
Speaker 13: use cases that are in production, thousands of customers that

560
00:28:25,160 --> 00:28:29,400
Speaker 13: are using our AI products. A lot of strength in

561
00:28:29,680 --> 00:28:33,240
Speaker 13: the core Snowflake product which is really around analytics plus

562
00:28:33,280 --> 00:28:35,240
Speaker 13: momentum around the new things that we are doing with

563
00:28:35,320 --> 00:28:38,400
Speaker 13: data engineering and AI. And it's really this combination of

564
00:28:38,400 --> 00:28:41,600
Speaker 13: course strength, new products doing well that makes me feel

565
00:28:41,640 --> 00:28:46,600
Speaker 13: pretty optimistic about twenty twenty five and what we are.

566
00:28:46,520 --> 00:28:49,440
Speaker 8: Able to do. Absolutely, we are meeting demand.

567
00:28:49,520 --> 00:28:51,560
Speaker 13: We're also hiring a lot of people because we think

568
00:28:51,600 --> 00:28:53,200
Speaker 13: there's a lot more business to be created.

569
00:28:54,200 --> 00:28:58,959
Speaker 4: Where are most of the imbounds coming from? Is it

570
00:28:59,160 --> 00:29:02,800
Speaker 4: brand new wins that you're making people who are adopting

571
00:29:02,840 --> 00:29:04,840
Speaker 4: Generator AI and wanting to use it for the first case?

572
00:29:05,040 --> 00:29:07,080
Speaker 2: Are you managing to take from competitors?

573
00:29:07,120 --> 00:29:09,760
Speaker 4: Look, we've talked often about data breaks, ramping up its

574
00:29:09,760 --> 00:29:12,440
Speaker 4: own private numbers and money. Thus farm Microsoft as a

575
00:29:12,520 --> 00:29:15,120
Speaker 4: key competitor, how are you managing to make inroad streater?

576
00:29:17,120 --> 00:29:21,640
Speaker 13: You know, Snowflake is the is the easiest to use,

577
00:29:22,080 --> 00:29:26,040
Speaker 13: fastest time to value, and most efficient data platform. That

578
00:29:26,040 --> 00:29:29,880
Speaker 13: that is a lot of our competitors are still implementing

579
00:29:29,920 --> 00:29:32,880
Speaker 13: features that we had five, six, seven years ago.

580
00:29:33,240 --> 00:29:35,680
Speaker 8: So it is a world class product. And what we

581
00:29:35,760 --> 00:29:37,320
Speaker 8: did with AI was make.

582
00:29:37,200 --> 00:29:42,400
Speaker 13: It a natural complement to how people use existing Snowflake products.

583
00:29:42,520 --> 00:29:45,440
Speaker 13: This is why you have customers you know, like Disney

584
00:29:45,480 --> 00:29:49,080
Speaker 13: and Hire use things, for example in machine learning on

585
00:29:49,240 --> 00:29:54,640
Speaker 13: top of Snowflake, and so when it comes to incremental

586
00:29:54,640 --> 00:29:58,560
Speaker 13: revenue that is being generated for Snowflake, it typically comes

587
00:29:58,600 --> 00:30:02,240
Speaker 13: from existing customers those first deals. Most new relationships that

588
00:30:02,280 --> 00:30:06,760
Speaker 13: Snowflake has with companies tend to start small, like fifty

589
00:30:06,800 --> 00:30:09,600
Speaker 13: thousand dollars one hundred thousand dollars, but they can rapidly

590
00:30:09,720 --> 00:30:12,760
Speaker 13: ramp up to millions of dollars in areas like AI.

591
00:30:13,440 --> 00:30:17,320
Speaker 13: What we are very clear is in our ability to

592
00:30:17,760 --> 00:30:21,640
Speaker 13: enhance the value of data that people already have. In Snowflake.

593
00:30:21,800 --> 00:30:23,680
Speaker 13: We are not an AI research lab. We don't compete

594
00:30:23,720 --> 00:30:26,800
Speaker 13: with open AIA Oranthropic. We partner with them. It is

595
00:30:26,840 --> 00:30:30,800
Speaker 13: that combination that unlocks value very very quickly for our

596
00:30:30,880 --> 00:30:31,840
Speaker 13: existing customers.

597
00:30:32,480 --> 00:30:34,560
Speaker 2: You're such a great mind to talk about this.

598
00:30:34,600 --> 00:30:36,720
Speaker 4: We showt up because not only you offering the latest

599
00:30:36,920 --> 00:30:39,840
Speaker 4: in generative AI products to be used in the real.

600
00:30:39,640 --> 00:30:41,720
Speaker 2: World rather than hypothetical.

601
00:30:41,160 --> 00:30:44,760
Speaker 4: But you are busy building well an application previously that

602
00:30:45,000 --> 00:30:47,640
Speaker 4: use large language models. So I just want to understand

603
00:30:47,840 --> 00:30:50,600
Speaker 4: the future twenty twenty five, Will we get the reality

604
00:30:50,640 --> 00:30:52,400
Speaker 4: of return on AI investments?

605
00:30:52,480 --> 00:30:55,520
Speaker 8: You think, well, I think.

606
00:30:55,400 --> 00:31:00,080
Speaker 13: The reality of AI is here when it comes to

607
00:31:00,200 --> 00:31:02,320
Speaker 13: any kind of content that even you and I want

608
00:31:02,360 --> 00:31:05,000
Speaker 13: to generate, whether it's a new blog that we want

609
00:31:05,000 --> 00:31:07,840
Speaker 13: to write, or better formats and existing thoughts that we have,

610
00:31:08,320 --> 00:31:11,720
Speaker 13: or even generate a fun new image, we are already

611
00:31:11,760 --> 00:31:15,520
Speaker 13: turning to AI. And that was the promise that my

612
00:31:15,560 --> 00:31:18,320
Speaker 13: previous company, Neiva, which was a search engine, saw and

613
00:31:18,360 --> 00:31:21,000
Speaker 13: that was the main reason why Snowflake acquired Neiva, because

614
00:31:21,000 --> 00:31:23,640
Speaker 13: we were able to see into the future of making

615
00:31:23,720 --> 00:31:28,560
Speaker 13: AI easy to use as well as powerful. I think

616
00:31:28,600 --> 00:31:31,120
Speaker 13: this is absolutely going to be the year where you're

617
00:31:31,160 --> 00:31:35,600
Speaker 13: going to see broader and broader applications of AI. I

618
00:31:35,720 --> 00:31:38,840
Speaker 13: roughly tell people any visual interface that we have used

619
00:31:38,840 --> 00:31:43,280
Speaker 13: so far probably has a better version powered by AI.

620
00:31:43,640 --> 00:31:46,400
Speaker 13: Information is going to be a lot easier. Data transformations

621
00:31:46,520 --> 00:31:49,000
Speaker 13: are easy when you use language models. This is what

622
00:31:49,440 --> 00:31:53,080
Speaker 13: people use Snowflake AI Cortex AI fought because somebody that's

623
00:31:53,120 --> 00:31:55,400
Speaker 13: just an analyst that knows how to write sq queries

624
00:31:55,680 --> 00:31:58,640
Speaker 13: is now an AI analyst because they can use the

625
00:31:58,640 --> 00:32:00,400
Speaker 13: power of these AI models in.

626
00:32:00,480 --> 00:32:02,840
Speaker 8: Their day to day work. I think, similar to fons,

627
00:32:02,840 --> 00:32:04,400
Speaker 8: it is just going to be a technology that.

628
00:32:04,360 --> 00:32:06,800
Speaker 13: Permeates everything we do, and we are right at the

629
00:32:06,840 --> 00:32:09,640
Speaker 13: forefront of how our customers are making this happen.

630
00:32:10,320 --> 00:32:13,440
Speaker 4: Almost a year into the job of CEO of Snowflake

631
00:32:13,720 --> 00:32:16,920
Speaker 4: after acquiring Neva Shida Ramaswami is great to speak with you.

632
00:32:17,400 --> 00:32:18,080
Speaker 2: Thanks so much.

633
00:32:19,040 --> 00:32:22,320
Speaker 4: Now coming up, as California wildfires continue to rage, social

634
00:32:22,360 --> 00:32:27,000
Speaker 4: media is battling fast spreading conspiracy theories related to this disaster.

635
00:32:27,480 --> 00:32:43,440
Speaker 4: More on that next, this is Bloomberg Technology. As California's

636
00:32:43,480 --> 00:32:47,080
Speaker 4: deadly fires look to reignite, some social media users are

637
00:32:47,160 --> 00:32:50,280
Speaker 4: using the disaster to push political agendas as well as

638
00:32:50,280 --> 00:32:53,560
Speaker 4: conspiracy theories. For more, Bloomberg's Jeff Stone has the latest

639
00:32:53,600 --> 00:32:58,080
Speaker 4: on political agendas being one thing, disinformation another. How does

640
00:32:58,120 --> 00:33:01,480
Speaker 4: this compare the moment Jeff to via such events.

641
00:33:01,640 --> 00:33:03,320
Speaker 14: One of the things that we're seeing, Caroline, is this

642
00:33:03,480 --> 00:33:06,800
Speaker 14: really become a normal part of these disasters. If you

643
00:33:06,840 --> 00:33:09,640
Speaker 14: remember back in twenty twenty three, there were some suspected

644
00:33:09,720 --> 00:33:14,320
Speaker 14: Chinese operatives that were spreading conspiracy theories around the wildfires

645
00:33:14,320 --> 00:33:18,200
Speaker 14: that struck in Maui. As social media has become the

646
00:33:18,240 --> 00:33:21,760
Speaker 14: way that people consume news instantaneously, a lot of these

647
00:33:21,800 --> 00:33:26,680
Speaker 14: conspiracies really unfounded. In some cases, bizarre conspiracy theories gain

648
00:33:26,760 --> 00:33:28,080
Speaker 14: a lot of attention very quickly.

649
00:33:28,880 --> 00:33:31,239
Speaker 3: Jeff, you and I have worked on a number of

650
00:33:31,280 --> 00:33:35,880
Speaker 3: disinformation stories over the past year, including on natural disasters,

651
00:33:35,880 --> 00:33:40,280
Speaker 3: but in other areas too, including online extremism. But one

652
00:33:40,320 --> 00:33:42,640
Speaker 3: thing that we have noted in our work is that

653
00:33:42,760 --> 00:33:46,120
Speaker 3: it doesn't stay online. What are some of the real

654
00:33:46,240 --> 00:33:49,200
Speaker 3: world impacts of these kinds of bogus claims?

655
00:33:49,720 --> 00:33:50,400
Speaker 7: That's right, Mike.

656
00:33:51,200 --> 00:33:54,120
Speaker 14: Last year, around the time that some of the hurricanes

657
00:33:54,160 --> 00:33:57,640
Speaker 14: were really hammering the southeast United States, we saw the

658
00:33:57,640 --> 00:34:02,280
Speaker 14: biggest example. Some are militia groups, really some extremist groups

659
00:34:02,280 --> 00:34:05,480
Speaker 14: showing up in some of the worst hit areas where

660
00:34:05,520 --> 00:34:09,400
Speaker 14: FEMA and other US federal agencies were trying to support

661
00:34:09,480 --> 00:34:13,520
Speaker 14: communities as they were rebuilding their homes picking up some

662
00:34:13,640 --> 00:34:16,000
Speaker 14: of the mess. Instead of being able to respond to

663
00:34:16,040 --> 00:34:19,440
Speaker 14: those areas, they were in fact turned away as some

664
00:34:19,480 --> 00:34:24,359
Speaker 14: of these again armed groups were trying to stop them

665
00:34:24,400 --> 00:34:26,480
Speaker 14: from getting involved. It got to the point where FIMA

666
00:34:26,520 --> 00:34:30,080
Speaker 14: administrators needed to give a press conference and send instructions

667
00:34:30,080 --> 00:34:32,080
Speaker 14: that in fact they were there to help rather than

668
00:34:32,640 --> 00:34:34,359
Speaker 14: try to exploit the situation in some way.

669
00:34:34,680 --> 00:34:38,160
Speaker 3: Bloomberg, Jeff Stone, thanks and for more in the LA

670
00:34:38,239 --> 00:34:41,200
Speaker 3: wildfires and what some companies are doing to help in

671
00:34:41,280 --> 00:34:45,680
Speaker 3: relief efforts. Were joined by LIFT Chief policy officer Jerry

672
00:34:45,680 --> 00:34:47,320
Speaker 3: Golden here in Washington.

673
00:34:47,600 --> 00:34:48,440
Speaker 8: Thank you, Jerry.

674
00:34:49,400 --> 00:34:52,759
Speaker 3: I've wanted to ask you about in light of this

675
00:34:53,120 --> 00:34:56,879
Speaker 3: immense strategy that we're seeing unfold even now, about your

676
00:34:56,960 --> 00:35:00,120
Speaker 3: company's efforts and what is being done and how much

677
00:35:00,160 --> 00:35:04,200
Speaker 3: more frequently you and other companies are finding yourselves having

678
00:35:04,239 --> 00:35:07,080
Speaker 3: to step in to pitch in in times of crisis.

679
00:35:07,120 --> 00:35:08,719
Speaker 15: You know, thanks so much for the question and for

680
00:35:08,719 --> 00:35:10,400
Speaker 15: the opportunity to be here to talk to you about this.

681
00:35:11,200 --> 00:35:12,400
Speaker 7: LIFT tries to show up.

682
00:35:12,320 --> 00:35:14,480
Speaker 15: In a very LIFT way when there are times like this,

683
00:35:14,560 --> 00:35:17,480
Speaker 15: big crises, natural disasters and the like. And in this

684
00:35:17,520 --> 00:35:21,560
Speaker 15: particular situation, we're providing the ride Code ca Fire Relief

685
00:35:21,600 --> 00:35:25,359
Speaker 15: twenty five to enable anyone who's affected by the wildfires

686
00:35:25,400 --> 00:35:27,600
Speaker 15: to get free your discounted rides through LIFT.

687
00:35:27,640 --> 00:35:28,560
Speaker 2: It's a way that we can.

688
00:35:28,480 --> 00:35:31,040
Speaker 15: Contribute to the drivers, to the riders, and to the

689
00:35:31,040 --> 00:35:31,879
Speaker 15: communities we serve.

690
00:35:32,400 --> 00:35:35,440
Speaker 2: How many drivers are continuing to do their role at

691
00:35:35,480 --> 00:35:38,080
Speaker 2: the moment, Jerry, pardon me, how many.

692
00:35:37,920 --> 00:35:40,480
Speaker 4: Drivers are able to fulfill their role at the moment

693
00:35:40,560 --> 00:35:43,279
Speaker 4: in La more broadly, you.

694
00:35:43,239 --> 00:35:45,920
Speaker 15: Know, drivers, just like any anyone else in the community,

695
00:35:46,400 --> 00:35:48,600
Speaker 15: are affected by the wildfires. And the first thing we

696
00:35:48,640 --> 00:35:50,759
Speaker 15: always tell drivers is safety first. Make sure you're not

697
00:35:50,920 --> 00:35:54,040
Speaker 15: entering into dangerous places. Turn to nine point one before

698
00:35:54,080 --> 00:35:55,640
Speaker 15: you turn to your LIFT app if you're in a

699
00:35:55,680 --> 00:35:58,400
Speaker 15: dangerous situation. But what we have seen in these circumstances

700
00:35:58,440 --> 00:36:01,840
Speaker 15: already is more than fifteen thousand riders have taken the

701
00:36:01,880 --> 00:36:04,880
Speaker 15: ride claim codes already, and we see that number continuing

702
00:36:04,920 --> 00:36:07,839
Speaker 15: to grow. So I think both writers and drivers who

703
00:36:07,840 --> 00:36:10,120
Speaker 15: are either on the driving side or sometimes the drivers

704
00:36:10,160 --> 00:36:13,319
Speaker 15: themselves become writers in these situations, are hopefully benefiting from

705
00:36:13,360 --> 00:36:13,800
Speaker 15: the service.

706
00:36:14,000 --> 00:36:18,200
Speaker 4: You are a long serving policy leader across various industries.

707
00:36:18,239 --> 00:36:20,400
Speaker 4: I mean, one of them being the insurance industry. Interestingly,

708
00:36:20,480 --> 00:36:24,080
Speaker 4: but Jerry, how is it able? How are you able

709
00:36:24,080 --> 00:36:27,360
Speaker 4: at this moment to speak to leadership, to governments, to

710
00:36:27,400 --> 00:36:31,040
Speaker 4: those in California, to those currently in the White House.

711
00:36:31,200 --> 00:36:32,160
Speaker 2: How easy has it been?

712
00:36:33,920 --> 00:36:36,160
Speaker 15: You know, we've been trying to keep open lines of communication.

713
00:36:36,280 --> 00:36:39,480
Speaker 15: We've found those lines of communication very positive in the

714
00:36:39,520 --> 00:36:41,080
Speaker 15: sense that LIFT is trying to show up in a

715
00:36:41,120 --> 00:36:46,120
Speaker 15: way that's in spirit of partnership. So whether it's local officials, writers, drivers,

716
00:36:46,120 --> 00:36:49,600
Speaker 15: community leaders in these areas or our partnerships. One thing

717
00:36:49,640 --> 00:36:52,319
Speaker 15: that anyone can do, Caroline, whether you're in the LA

718
00:36:52,400 --> 00:36:54,840
Speaker 15: area or not, is in your Lift app. Go to

719
00:36:54,880 --> 00:36:58,800
Speaker 15: the donate feature click within the lift app, click for

720
00:36:58,880 --> 00:37:02,560
Speaker 15: the American Red Cross for your roundup and donate opportunity

721
00:37:02,880 --> 00:37:05,480
Speaker 15: for someone like me who's a lift rider all the time.

722
00:37:05,520 --> 00:37:06,160
Speaker 7: Besides being the.

723
00:37:06,160 --> 00:37:08,560
Speaker 15: Chief policy officer, I really love the fact that if

724
00:37:08,600 --> 00:37:10,960
Speaker 15: a ride costs me nine dollars and two cents, I

725
00:37:11,040 --> 00:37:12,960
Speaker 15: use the round up and donate. It's set it and

726
00:37:12,960 --> 00:37:15,520
Speaker 15: forget it. Ninety eight cents rounded up to ten dollars

727
00:37:15,520 --> 00:37:18,680
Speaker 15: goes to the American Red Cross in this situation to

728
00:37:18,760 --> 00:37:21,160
Speaker 15: serve those in need for the wildfires. But that's something

729
00:37:21,200 --> 00:37:23,960
Speaker 15: that's been really sort of important for us and high impact.

730
00:37:24,840 --> 00:37:25,120
Speaker 8: Jerry.

731
00:37:25,160 --> 00:37:28,160
Speaker 3: I wanted to ask about coordination with other businesses that

732
00:37:28,200 --> 00:37:31,040
Speaker 3: are also seeking to help victims in this area.

733
00:37:31,120 --> 00:37:33,160
Speaker 2: How is that happening and who is.

734
00:37:33,160 --> 00:37:36,960
Speaker 3: Actually doing the coordination, not only with Lyft, but others

735
00:37:37,000 --> 00:37:39,080
Speaker 3: like Amazon that may be trying to provide help.

736
00:37:39,400 --> 00:37:42,520
Speaker 15: We've been really finding it really positive, Mike to see

737
00:37:42,600 --> 00:37:45,000
Speaker 15: all of the efforts that have been coalescing around rising

738
00:37:45,000 --> 00:37:47,800
Speaker 15: to this occasion, I know that for LYFT, our direct

739
00:37:47,840 --> 00:37:50,560
Speaker 15: lines of communication have really been with the elected officials

740
00:37:50,560 --> 00:37:53,840
Speaker 15: and with the transit transit people and the governments.

741
00:37:53,480 --> 00:37:54,680
Speaker 7: That we're working with.

742
00:37:55,040 --> 00:37:58,480
Speaker 15: I think fundamentally our opportunity has been to make sure

743
00:37:58,800 --> 00:38:02,680
Speaker 15: that we're keeping safety as one guidepost and opportunities for

744
00:38:02,760 --> 00:38:05,480
Speaker 15: both riders and drivers through round Up and Donate and

745
00:38:05,520 --> 00:38:07,120
Speaker 15: through the ride code itself.

746
00:38:07,560 --> 00:38:10,120
Speaker 3: Now, Jerry is a point person on public policy. We'd

747
00:38:10,160 --> 00:38:14,400
Speaker 3: be remiss if we didn't ask you how this seeming

748
00:38:14,520 --> 00:38:19,520
Speaker 3: unending wave of disasters, the hurricanes is full and the wildfires, now,

749
00:38:19,800 --> 00:38:23,399
Speaker 3: how this shapes what you are asking the government to do.

750
00:38:24,000 --> 00:38:27,080
Speaker 15: You know, we have found more and more need for

751
00:38:27,239 --> 00:38:30,440
Speaker 15: natural disaster support in our time. Lift Up, our program

752
00:38:30,480 --> 00:38:33,840
Speaker 15: that provides disaster relief MIC, began in twenty seventeen and

753
00:38:33,920 --> 00:38:37,320
Speaker 15: since that the origin we have found thirty three million

754
00:38:37,400 --> 00:38:40,560
Speaker 15: dollars contributed to twenty two nonprofits throughout the United States

755
00:38:40,600 --> 00:38:43,880
Speaker 15: and Canada by very generous round Up and Donate support

756
00:38:44,760 --> 00:38:47,920
Speaker 15: from our riders. All that we've been asking for governments

757
00:38:47,920 --> 00:38:50,880
Speaker 15: to do is to turn to LIFT and other partners

758
00:38:51,440 --> 00:38:53,360
Speaker 15: in a spirit of partnership to make sure the private

759
00:38:53,400 --> 00:38:56,200
Speaker 15: sector and the public sector together rise to this occasion

760
00:38:56,800 --> 00:38:59,040
Speaker 15: and recognize that we're all in this together.

761
00:38:59,080 --> 00:39:03,480
Speaker 3: Right, Okay, Chief policy Officer Jerry Golden, we thank you.

762
00:39:11,440 --> 00:39:14,080
Speaker 4: A win for Elon Musk amid his ongoing fights with

763
00:39:14,120 --> 00:39:17,080
Speaker 4: open AI, the US Justice Department and the Federal Trade

764
00:39:17,080 --> 00:39:20,759
Speaker 4: Commission have sided with the tech billionaire, arguing that overlapping

765
00:39:20,800 --> 00:39:23,960
Speaker 4: board directors at tech giants could harm competition even if

766
00:39:23,960 --> 00:39:26,920
Speaker 4: the people then resign. In Musk's lawsuit, open AI and

767
00:39:27,040 --> 00:39:30,720
Speaker 4: Microsoft are claimed to have violated antitrust laws by allowing

768
00:39:30,800 --> 00:39:33,640
Speaker 4: LinkedIn co founder Reid Hoffman to serve on the boards

769
00:39:33,680 --> 00:39:37,040
Speaker 4: of both companies from twenty seventeen to twenty twenty three.

770
00:39:37,800 --> 00:39:39,279
Speaker 2: Meanwhile, open ai.

771
00:39:39,239 --> 00:39:42,640
Speaker 4: Is focused on bolstering support for investment in artificial intelligence

772
00:39:42,719 --> 00:39:43,040
Speaker 4: in the.

773
00:39:43,040 --> 00:39:44,520
Speaker 2: Changing political landscape.

774
00:39:44,600 --> 00:39:46,799
Speaker 4: The company is planning to host events in Washington, DC

775
00:39:46,920 --> 00:39:48,799
Speaker 4: and two other key swing states for more.

776
00:39:48,840 --> 00:39:50,440
Speaker 2: Bloombergitary and Gafari.

777
00:39:50,200 --> 00:39:53,080
Speaker 4: Joins US now and well, why the what is there

778
00:39:53,400 --> 00:39:57,680
Speaker 4: anxiety around future commitments to AI investment as the administration's shift.

779
00:39:58,120 --> 00:40:00,440
Speaker 16: I think open ai is really making its key and

780
00:40:00,480 --> 00:40:05,400
Speaker 16: framing their policy recommendations under a sort of US interest

781
00:40:05,520 --> 00:40:09,400
Speaker 16: first angle and saying that if the USA wants to

782
00:40:09,440 --> 00:40:12,720
Speaker 16: win this AI arms race with China, that the government

783
00:40:12,800 --> 00:40:15,880
Speaker 16: needs to support open ai in its request for support

784
00:40:15,920 --> 00:40:19,120
Speaker 16: to build out AI infrastructure things like data centers, as

785
00:40:19,160 --> 00:40:23,120
Speaker 16: well as taking investment from foreign investors.

786
00:40:23,400 --> 00:40:26,880
Speaker 4: It's interesting that once again China is almost put up

787
00:40:26,880 --> 00:40:29,279
Speaker 4: as the bogaman as to why we need to have

788
00:40:29,360 --> 00:40:32,040
Speaker 4: this commitment to investment. We heard that from a meta

789
00:40:32,120 --> 00:40:36,080
Speaker 4: playback previously. When it comes to talks with Washington, what

790
00:40:36,160 --> 00:40:37,759
Speaker 4: do you think these events will actually stir?

791
00:40:38,280 --> 00:40:42,160
Speaker 16: I think that this is some sort of testing ground

792
00:40:42,200 --> 00:40:45,880
Speaker 16: to see how a new Washington DC reacts to open

793
00:40:45,960 --> 00:40:49,080
Speaker 16: ai suggestions. And I think that when CEO C Molmon

794
00:40:49,200 --> 00:40:51,879
Speaker 16: goes to to see leader this month, and as we broke,

795
00:40:52,440 --> 00:40:55,640
Speaker 16: he will also be attending Trump's inauguration, I think we're

796
00:40:55,640 --> 00:40:58,040
Speaker 16: going to see how receptive this new audience will be.

797
00:40:59,160 --> 00:41:01,640
Speaker 4: You've been speaking to the now Vice president for Global Affairs,

798
00:41:01,840 --> 00:41:04,600
Speaker 4: that's Chris Lahane, who's well known over in the cryptos

799
00:41:04,600 --> 00:41:08,120
Speaker 4: space before that and Airbnb. What is he seeing as

800
00:41:08,560 --> 00:41:10,880
Speaker 4: the narrative with the new administration? And then what have

801
00:41:10,960 --> 00:41:13,959
Speaker 4: been some tensions between Elon Musk, for example, and Open

802
00:41:14,000 --> 00:41:16,680
Speaker 4: AI's leadership and its birth in fact, and how close

803
00:41:16,719 --> 00:41:20,480
Speaker 4: Elon Musk is to well Trump and the President elect.

804
00:41:20,840 --> 00:41:23,040
Speaker 16: Right. So I spoke with Chris Lean ahead of this

805
00:41:23,600 --> 00:41:26,600
Speaker 16: blueprints released, and what he told me is that he

806
00:41:26,800 --> 00:41:32,719
Speaker 16: has seen the administration being receptive to this US interest

807
00:41:33,040 --> 00:41:37,040
Speaker 16: in AI and understands or of the economic stakes here,

808
00:41:37,120 --> 00:41:40,719
Speaker 16: So you know, they're hopeful, he's hopeful that they will

809
00:41:40,760 --> 00:41:43,359
Speaker 16: find a receptive audience for this message.

810
00:41:44,640 --> 00:41:48,560
Speaker 4: And the money, what sort of money do they seem

811
00:41:48,680 --> 00:41:51,400
Speaker 4: necessary at the moment to come from the administration in

812
00:41:51,440 --> 00:41:51,880
Speaker 4: the future.

813
00:41:52,480 --> 00:41:54,799
Speaker 16: Well, we don't know specifics about how much or if

814
00:41:54,800 --> 00:41:56,880
Speaker 16: for exactly when they're trying to fundraise, but what we

815
00:41:56,960 --> 00:41:59,520
Speaker 16: know in AI is that if you're a private company,

816
00:41:59,560 --> 00:42:02,320
Speaker 16: you're sort of always fundraising, right. That's a common refrainer

817
00:42:02,400 --> 00:42:07,200
Speaker 16: here from AI CEOs. So at some level there sort

818
00:42:07,239 --> 00:42:09,360
Speaker 16: of is an upper limit to how much domestic investment

819
00:42:09,400 --> 00:42:11,479
Speaker 16: you can even take sometimes, right, And so that's why

820
00:42:11,560 --> 00:42:14,800
Speaker 16: we see foreign interest in investment and some of the

821
00:42:14,800 --> 00:42:16,880
Speaker 16: most important AI companies.

822
00:42:16,680 --> 00:42:18,839
Speaker 4: And they're wanting to invest in open AI like we've

823
00:42:18,840 --> 00:42:21,040
Speaker 4: seen from Asiyoshi Sun, but he's also wanted to invest

824
00:42:21,080 --> 00:42:23,040
Speaker 4: in the infrastructure in the United States and the data

825
00:42:23,080 --> 00:42:26,400
Speaker 4: centers too, so plenty of red across there. Shrien KAfari,

826
00:42:26,440 --> 00:42:28,480
Speaker 4: it's a great interview. I urgually to go and read

827
00:42:28,520 --> 00:42:31,120
Speaker 4: it on the Bloomberg and online. Meanwhile, let's get to

828
00:42:31,120 --> 00:42:34,120
Speaker 4: the infrastructure story of open AI and of generator of AI.

829
00:42:34,120 --> 00:42:34,720
Speaker 2: Writ large.

830
00:42:34,840 --> 00:42:37,640
Speaker 4: Nvidia on the downside, down by three percent. It is

831
00:42:37,680 --> 00:42:41,080
Speaker 4: now only the second most valuable company on the Nasdaq

832
00:42:41,200 --> 00:42:44,720
Speaker 4: after Apple. Once again concerns about policy. This is Bloomberg

833
00:42:44,760 --> 00:42:45,280
Speaker 4: Technology