1
00:00:13,240 --> 00:00:16,479
Speaker 1: I'm Caroline hired Bloomberg's World headquarters in New York, and

2
00:00:16,520 --> 00:00:19,759
Speaker 1: I'm Lovo in San Francisco. This is Bloomberg Technology risk

3
00:00:19,840 --> 00:00:22,599
Speaker 1: On in the markets AI A driver and no surprise

4
00:00:22,680 --> 00:00:26,799
Speaker 1: Caroline from Power No surprise markets risk on. But having

5
00:00:26,840 --> 00:00:30,360
Speaker 1: noticed all these chatbots, from Bernie to Being to of

6
00:00:30,360 --> 00:00:32,760
Speaker 1: course barred over at Google, they're all named for a

7
00:00:32,800 --> 00:00:34,560
Speaker 1: B we get into that in a moment. But let's

8
00:00:34,560 --> 00:00:36,880
Speaker 1: get into the race first and foremost, the AI race.

9
00:00:37,000 --> 00:00:39,240
Speaker 1: It is on end from Microsoft to Google to Baidu.

10
00:00:39,560 --> 00:00:42,519
Speaker 1: We've been in the latest global generative AI developments and

11
00:00:42,560 --> 00:00:45,360
Speaker 1: speak to a kid executive from Microsoft on its big

12
00:00:45,600 --> 00:00:49,479
Speaker 1: being announcement and honing in on the chip industry arm

13
00:00:49,560 --> 00:00:53,760
Speaker 1: posting increase in revenue in its latest quarter. We speak

14
00:00:53,800 --> 00:00:56,520
Speaker 1: with the CEO of the company as it prepares what

15
00:00:56,640 --> 00:00:59,560
Speaker 1: parents soft Bank hopes will be the largest ever I

16
00:00:59,640 --> 00:01:03,200
Speaker 1: p O for a chip maker, plus zoom slashing of

17
00:01:03,200 --> 00:01:06,200
Speaker 1: its workforce while metal ones managers to get back to

18
00:01:06,319 --> 00:01:09,679
Speaker 1: making things or leave all that and so much more

19
00:01:09,760 --> 00:01:12,720
Speaker 1: coming up at first. Soft Bank get posted its earnings,

20
00:01:12,760 --> 00:01:15,480
Speaker 1: losing nearly six billion dollars in the most recent quarter,

21
00:01:15,760 --> 00:01:18,280
Speaker 1: failed to start up bets and its Vision Fund segment.

22
00:01:18,600 --> 00:01:22,080
Speaker 1: But the silver lining the conglomerate has in its arsenal,

23
00:01:22,120 --> 00:01:25,200
Speaker 1: it's its ownership of ARM. The UK based company's chip

24
00:01:25,280 --> 00:01:29,039
Speaker 1: designs and its technology are present throughout consumer electronics. Just

25
00:01:29,080 --> 00:01:32,080
Speaker 1: think the chips that are powering almost every single smartphone

26
00:01:32,080 --> 00:01:34,280
Speaker 1: on the planet, or the designs come from ARM And

27
00:01:34,319 --> 00:01:36,800
Speaker 1: just reported a twenty percent increase in revenue for the

28
00:01:36,880 --> 00:01:40,360
Speaker 1: latest quarter. It's preparing, we understand, for a highly anticipated

29
00:01:40,400 --> 00:01:42,520
Speaker 1: I p O this year. That's why we are so

30
00:01:42,560 --> 00:01:45,080
Speaker 1: pleased to welcome the CEO. Ren A House is joy

31
00:01:45,120 --> 00:01:47,000
Speaker 1: to have you with us. Just talk to us about

32
00:01:47,000 --> 00:01:50,680
Speaker 1: this twenty eight percent increase in revenue because we've seen

33
00:01:50,920 --> 00:01:54,760
Speaker 1: overall the chip sector be somewhat decimated, the demand supply

34
00:01:54,920 --> 00:01:58,680
Speaker 1: flip reversing, and yet you manage to be pushing higher

35
00:01:58,760 --> 00:02:02,280
Speaker 1: when your clients are four. What's happening. We're very happy

36
00:02:02,320 --> 00:02:05,320
Speaker 1: with the quarter that we've just had. Our revenue was up.

37
00:02:06,560 --> 00:02:10,440
Speaker 1: We saw an increase of our licensing business and twelve

38
00:02:10,440 --> 00:02:14,120
Speaker 1: percent and royalties. But more importantly, we've seen a significant

39
00:02:14,160 --> 00:02:19,160
Speaker 1: diversification in our business. Caroline are business around the cloud

40
00:02:20,040 --> 00:02:23,600
Speaker 1: has been up double double digits. Our business and automotive

41
00:02:23,960 --> 00:02:27,919
Speaker 1: probably triple digits. Uh. This diversification strategy is not new.

42
00:02:28,000 --> 00:02:30,400
Speaker 1: We started a number of years ago, but we're just

43
00:02:30,480 --> 00:02:32,560
Speaker 1: now starting to see the fruits of that labor as

44
00:02:33,160 --> 00:02:35,200
Speaker 1: the power efficiency at which ARM is known for is

45
00:02:35,240 --> 00:02:37,640
Speaker 1: stinding its way into the data center and all evs.

46
00:02:38,080 --> 00:02:40,160
Speaker 1: Can you give us some targets around the focus on

47
00:02:40,280 --> 00:02:44,400
Speaker 1: data center's automotive pecs rather than just a smartphone sector.

48
00:02:45,720 --> 00:02:49,480
Speaker 1: You know, these areas are places where the growth has

49
00:02:49,520 --> 00:02:51,359
Speaker 1: been very significant in the last number of years, and

50
00:02:51,400 --> 00:02:53,919
Speaker 1: we made a very conscious decision to focus on these

51
00:02:53,919 --> 00:02:57,640
Speaker 1: new markets, particularly with new products are Neo verse flying

52
00:02:57,720 --> 00:03:01,560
Speaker 1: for servers, adding functional safety to our automotive products, and

53
00:03:01,680 --> 00:03:03,760
Speaker 1: as a result, we've seen a huge benefit from that.

54
00:03:04,480 --> 00:03:07,600
Speaker 1: Every major cloud provider now has ARM instances in the

55
00:03:07,720 --> 00:03:11,679
Speaker 1: cloud aws forty eight out of their top fifty customers

56
00:03:11,800 --> 00:03:15,440
Speaker 1: are able to use ARM and electronic vehicles. We're seeing

57
00:03:15,480 --> 00:03:19,880
Speaker 1: companies such as st Renaissance, Mobile, I and Video all

58
00:03:19,919 --> 00:03:23,720
Speaker 1: shifting silicon into automotive Renne, good afternoon to you. Look,

59
00:03:23,800 --> 00:03:26,120
Speaker 1: as Caroline said, you bucked the trend. I want to

60
00:03:26,160 --> 00:03:28,519
Speaker 1: dig into this idea of how you continue to grow

61
00:03:29,160 --> 00:03:31,519
Speaker 1: when your customers don't is this an issue of lag

62
00:03:32,000 --> 00:03:33,959
Speaker 1: that what the rest of the chip sector is going

63
00:03:34,000 --> 00:03:37,960
Speaker 1: through you are not yet experiencing, but will experience later

64
00:03:38,080 --> 00:03:40,360
Speaker 1: on in the year. We're no different than the rest

65
00:03:40,400 --> 00:03:43,160
Speaker 1: of the industry. We certainly feel the effects of the

66
00:03:43,200 --> 00:03:47,040
Speaker 1: slowdown in certain markets, but we are buffered to some extent.

67
00:03:47,160 --> 00:03:50,000
Speaker 1: As I said, we've seen significant growth by gaining market

68
00:03:50,120 --> 00:03:55,320
Speaker 1: share in the cloud, gaining market share in automotive, in smartphones,

69
00:03:55,440 --> 00:03:57,560
Speaker 1: where it's been well documented there's been a slowdown of

70
00:03:57,600 --> 00:04:00,520
Speaker 1: that industry, we've actually seen some very good results, and

71
00:04:00,640 --> 00:04:03,280
Speaker 1: that's been due to kind of two main factors. One

72
00:04:03,440 --> 00:04:06,480
Speaker 1: is our version nine architecture, which carries a higher royalty rate,

73
00:04:06,520 --> 00:04:09,640
Speaker 1: which needs more value for us. But also, more importantly,

74
00:04:09,760 --> 00:04:13,680
Speaker 1: we're just seeing more technology inside these smartphones. More processors

75
00:04:13,800 --> 00:04:15,480
Speaker 1: that find their ways into all different areas of the

76
00:04:15,520 --> 00:04:19,800
Speaker 1: application space, means more CPUs inside the smartphone, which again

77
00:04:19,920 --> 00:04:22,520
Speaker 1: means more royalties for us. So we've been able to

78
00:04:22,560 --> 00:04:24,920
Speaker 1: be buffered to some extent in that space and seeing

79
00:04:24,960 --> 00:04:29,680
Speaker 1: growth really across all our markets. Okay, how confident are

80
00:04:29,760 --> 00:04:34,000
Speaker 1: you that those outliers of performance that you have just

81
00:04:34,120 --> 00:04:39,280
Speaker 1: flags can continue throughout calendar twenty three? You know who

82
00:04:39,400 --> 00:04:42,000
Speaker 1: to be really competent these days in terms of predicting

83
00:04:42,040 --> 00:04:43,720
Speaker 1: where the market is going to go at that we're

84
00:04:43,920 --> 00:04:47,040
Speaker 1: very confident in the long term secular trends that are

85
00:04:47,080 --> 00:04:50,040
Speaker 1: good for ARM and that is number one, more compute

86
00:04:50,720 --> 00:04:54,159
Speaker 1: and number two that is more power efficient compute. Again,

87
00:04:54,200 --> 00:04:55,960
Speaker 1: when you look at the data center, when you look

88
00:04:55,960 --> 00:04:58,720
Speaker 1: at automotive, these are areas that really need a high

89
00:04:58,760 --> 00:05:01,520
Speaker 1: degree of compute, They need a high degree of power efficiency.

90
00:05:01,960 --> 00:05:04,400
Speaker 1: You know, another number that really surprised folks last quarter

91
00:05:04,680 --> 00:05:08,159
Speaker 1: was a record number of shipments by the ARMED partners.

92
00:05:08,360 --> 00:05:11,800
Speaker 1: Eight billion ships that were shipped by our partners, and

93
00:05:11,920 --> 00:05:14,040
Speaker 1: that feeds a cycle of more and more people who

94
00:05:14,040 --> 00:05:17,160
Speaker 1: are developing software on those eight billion ships, which feeds

95
00:05:17,200 --> 00:05:20,159
Speaker 1: into more demand. So I think the quarter to quarter

96
00:05:20,200 --> 00:05:22,560
Speaker 1: of proturbations, you're always going to see those relative to

97
00:05:22,600 --> 00:05:25,680
Speaker 1: what's going on with inventory and such. But we are

98
00:05:26,160 --> 00:05:28,520
Speaker 1: very confident that the long term secular trends that we're

99
00:05:28,520 --> 00:05:31,840
Speaker 1: seeing in the industry are very good for us. Someone

100
00:05:31,880 --> 00:05:35,160
Speaker 1: else is confident around you on its soft bank, the

101
00:05:35,279 --> 00:05:37,080
Speaker 1: confident that the I p O will be done by

102
00:05:37,160 --> 00:05:40,440
Speaker 1: this year. Are you doing a jewel approaches in London?

103
00:05:40,520 --> 00:05:42,640
Speaker 1: New York. Do you have a time frame for US?

104
00:05:43,920 --> 00:05:45,839
Speaker 1: I can say that we are fully committed to an

105
00:05:45,880 --> 00:05:48,480
Speaker 1: I p O in There has been a lot of

106
00:05:48,520 --> 00:05:50,680
Speaker 1: planning underway already on this. We've been doing a lot

107
00:05:50,720 --> 00:05:55,480
Speaker 1: of work regarding location, listening venue. I can't talk about that.

108
00:05:55,600 --> 00:05:58,600
Speaker 1: Noticisions have been made there yet. What needs to be

109
00:05:58,720 --> 00:06:02,600
Speaker 1: cleared up? Tease crossed, eyes dotted when it comes to

110
00:06:02,640 --> 00:06:05,120
Speaker 1: a relationship, for example, with Qualcom ahead of the initial

111
00:06:05,160 --> 00:06:07,640
Speaker 1: public offering. Of course, there's been dispute there one of

112
00:06:07,680 --> 00:06:10,400
Speaker 1: your key clients. How is that progressing? You know, I

113
00:06:10,480 --> 00:06:13,320
Speaker 1: can't comment on the litigation there. Carolina had to lead

114
00:06:13,360 --> 00:06:15,280
Speaker 1: it up to our legal team. You know. I can

115
00:06:15,400 --> 00:06:17,680
Speaker 1: say though that we are very competent in the case

116
00:06:18,520 --> 00:06:23,080
Speaker 1: and we think the court will ultimately agree with our position. Ready,

117
00:06:23,080 --> 00:06:25,200
Speaker 1: I want to talk about China a little bit. You know,

118
00:06:25,480 --> 00:06:29,520
Speaker 1: ARM is all about the underlying technology, the intellectual property

119
00:06:29,680 --> 00:06:34,000
Speaker 1: chip designs. How impacted are you by US and Allied

120
00:06:34,120 --> 00:06:38,480
Speaker 1: efforts to cut China off to access to the latest

121
00:06:38,600 --> 00:06:44,680
Speaker 1: in chip technology? Like any other company, we comply with

122
00:06:44,960 --> 00:06:48,520
Speaker 1: whatever the US government comes down with visa the export controls,

123
00:06:49,480 --> 00:06:52,600
Speaker 1: because we actually don't make anything physical that is what

124
00:06:52,760 --> 00:06:55,960
Speaker 1: we deliver as intellectual property. I either the license rates

125
00:06:56,040 --> 00:06:58,760
Speaker 1: to build a product with our designs were now so

126
00:06:58,960 --> 00:07:01,839
Speaker 1: directly impacted by some of the recent rulings which really

127
00:07:01,920 --> 00:07:05,680
Speaker 1: impact more people who are actually building the chips, either

128
00:07:05,839 --> 00:07:09,520
Speaker 1: chip companies or or fab equipment makers or the fabs themselves.

129
00:07:09,640 --> 00:07:12,840
Speaker 1: So we have some level of indirect impact, and we

130
00:07:12,920 --> 00:07:15,080
Speaker 1: comply whenever there's something that we need to comply with

131
00:07:15,200 --> 00:07:18,200
Speaker 1: these a lat of course, but given where we should

132
00:07:18,240 --> 00:07:20,640
Speaker 1: in the value chain, not so directly impacted some of

133
00:07:20,680 --> 00:07:25,040
Speaker 1: the other companies might be. As we discussed, your technology

134
00:07:25,160 --> 00:07:28,920
Speaker 1: is present through so much that goes into consumer electronics.

135
00:07:29,280 --> 00:07:31,720
Speaker 1: Your designs are sort of integral to the chips are

136
00:07:31,720 --> 00:07:35,400
Speaker 1: going smartphones. But the hot topic the area in vogue

137
00:07:35,480 --> 00:07:39,160
Speaker 1: is artificial intelligence. Is ARM competitive in that field in

138
00:07:39,320 --> 00:07:42,120
Speaker 1: terms of the chips that it's designing, and I know

139
00:07:42,280 --> 00:07:44,720
Speaker 1: that you work closely with some big tech names that

140
00:07:44,800 --> 00:07:48,080
Speaker 1: are doing more in house chips in this area. Yeah,

141
00:07:48,640 --> 00:07:50,880
Speaker 1: I'd like to say we're far more than competitive. You know,

142
00:07:50,920 --> 00:07:53,800
Speaker 1: there's a lot of AI that runs on ARM already.

143
00:07:55,120 --> 00:07:57,680
Speaker 1: When you think about inference at the edge and any

144
00:07:57,760 --> 00:08:00,680
Speaker 1: kind of artificial intelligence that's being run there that's all

145
00:08:00,760 --> 00:08:04,120
Speaker 1: being running armed. They're being running armed CPUs. Uh. There's

146
00:08:04,160 --> 00:08:05,720
Speaker 1: been a lot of buzz you know, in the industry

147
00:08:05,760 --> 00:08:10,000
Speaker 1: and certainly even today regarding generative AI and chat GPT

148
00:08:10,160 --> 00:08:14,080
Speaker 1: and such. That's gonna mean even more demand for armed technology.

149
00:08:14,200 --> 00:08:15,880
Speaker 1: You know, when you look at the trade offs between

150
00:08:15,880 --> 00:08:18,400
Speaker 1: the workloads that run on the CPU and the workloads

151
00:08:18,440 --> 00:08:21,360
Speaker 1: that run on accelerators, there's room for some of this

152
00:08:21,440 --> 00:08:24,760
Speaker 1: acceleration to take place in both areas. I think the

153
00:08:24,840 --> 00:08:26,840
Speaker 1: area where we're going to really perform well in this

154
00:08:26,920 --> 00:08:31,360
Speaker 1: space again, it's around power efficiency. These new AI algorithms

155
00:08:31,360 --> 00:08:34,400
Speaker 1: are incredibly compute intensive, and in a world where we

156
00:08:34,480 --> 00:08:36,559
Speaker 1: just don't have that much energy to throw with the problem,

157
00:08:36,960 --> 00:08:39,880
Speaker 1: having energy efficient processors to assist and offload is going

158
00:08:39,960 --> 00:08:43,480
Speaker 1: to be critically important. Rene haasom CEO. We look forward

159
00:08:43,520 --> 00:08:45,800
Speaker 1: to that i p O that Jill committed to at

160
00:08:45,880 --> 00:08:57,920
Speaker 1: some point this year. The big reveal today Microsoft announcing

161
00:08:57,960 --> 00:09:02,000
Speaker 1: it's utilizing open AI technology power new versions of it's

162
00:09:02,040 --> 00:09:05,360
Speaker 1: being Search Engine and Edge Browser. But pleased to dig

163
00:09:05,440 --> 00:09:08,040
Speaker 1: into this now use of medies with us Microsoft Corporate

164
00:09:08,160 --> 00:09:10,640
Speaker 1: vice president of Modern Life and Devices Group, and has

165
00:09:10,679 --> 00:09:12,920
Speaker 1: been a busy day and just how busy has it

166
00:09:13,000 --> 00:09:15,040
Speaker 1: been in terms of sign ups? There's a wait list

167
00:09:15,120 --> 00:09:18,520
Speaker 1: to access to start using being in this way. What's

168
00:09:18,559 --> 00:09:21,200
Speaker 1: the reaction been like use? Yeah, it's it's early days,

169
00:09:21,240 --> 00:09:23,400
Speaker 1: but it's already or early hour, as I should say.

170
00:09:23,800 --> 00:09:25,599
Speaker 1: There's a lot of sign up interests. I'm getting a

171
00:09:25,640 --> 00:09:27,280
Speaker 1: lot of emails from people who want to get in

172
00:09:27,360 --> 00:09:29,400
Speaker 1: front of the wait list. But I think we've struck

173
00:09:29,440 --> 00:09:31,719
Speaker 1: a nerve. I think we actually have um kind of

174
00:09:31,800 --> 00:09:34,560
Speaker 1: lit up people's imagination about what's possible with a new

175
00:09:34,600 --> 00:09:38,760
Speaker 1: generation of search by infusing AI and chat and then

176
00:09:39,000 --> 00:09:41,240
Speaker 1: getting to a place where you don't just get search results,

177
00:09:41,280 --> 00:09:43,760
Speaker 1: but you can actually get answers to your questions. And

178
00:09:43,840 --> 00:09:45,679
Speaker 1: I think it's struck a chord so far. So we're

179
00:09:45,679 --> 00:09:48,520
Speaker 1: pretty excited about it. Struck a nerve as an interesting,

180
00:09:49,040 --> 00:09:51,480
Speaker 1: interesting way of saying it, because it feels like you've

181
00:09:51,480 --> 00:09:54,720
Speaker 1: struck a nerve lit far underneath Google as well, because

182
00:09:54,920 --> 00:09:58,280
Speaker 1: it's talking up it's barred at the moment. Just discuss

183
00:09:58,480 --> 00:10:01,120
Speaker 1: why there seems to be the sudden flurry of announcements

184
00:10:01,200 --> 00:10:06,079
Speaker 1: this race upon us for this generative AI within search. Well,

185
00:10:06,240 --> 00:10:07,959
Speaker 1: we've been working at it for a little while and

186
00:10:08,920 --> 00:10:11,080
Speaker 1: and we've been excited about. The thing that has motivated

187
00:10:11,160 --> 00:10:13,520
Speaker 1: us is today there's like ten billion queries that happened

188
00:10:13,520 --> 00:10:16,760
Speaker 1: on any given day, and by our estimation, roughly half

189
00:10:16,800 --> 00:10:19,839
Speaker 1: go unanswered, and that to us is a huge opportunity

190
00:10:20,240 --> 00:10:23,120
Speaker 1: to sort of reinvent search. The other big dynamic is

191
00:10:23,160 --> 00:10:25,440
Speaker 1: that AI in the last really in the last number

192
00:10:25,440 --> 00:10:28,520
Speaker 1: of months, has seen an inflection. Both the technical capability

193
00:10:28,520 --> 00:10:30,959
Speaker 1: has gotten so much better, but the user experienced this

194
00:10:31,120 --> 00:10:34,040
Speaker 1: idea of being able to chat. The combination of those

195
00:10:34,080 --> 00:10:36,040
Speaker 1: two things has really come together to make for a

196
00:10:36,080 --> 00:10:40,240
Speaker 1: big opportunity use of good off need to you busy days.

197
00:10:40,320 --> 00:10:43,959
Speaker 1: Caroline said, My question is why now you know? How

198
00:10:44,120 --> 00:10:48,360
Speaker 1: ready is it? How convinced are you that being with

199
00:10:48,520 --> 00:10:53,800
Speaker 1: AI is ready? I thought the timing was was interesting. Yeah,

200
00:10:54,040 --> 00:10:55,800
Speaker 1: as I mentioned, we've been working at for a while,

201
00:10:55,920 --> 00:10:58,199
Speaker 1: we've been at it for for many months. We do

202
00:10:58,360 --> 00:11:00,400
Speaker 1: feel like we're ready to go to the limit preview

203
00:11:00,440 --> 00:11:02,400
Speaker 1: and we're happy to have shipped in that state. And

204
00:11:02,520 --> 00:11:04,679
Speaker 1: the reason for that is that in the evolution of

205
00:11:04,720 --> 00:11:06,640
Speaker 1: the technology, there's only so much you can do in

206
00:11:06,679 --> 00:11:08,960
Speaker 1: the lab, and then eventually you get to a place

207
00:11:09,000 --> 00:11:11,440
Speaker 1: where you need to get that you know and user feedback.

208
00:11:11,520 --> 00:11:13,120
Speaker 1: You need to have that to come and improve the product.

209
00:11:13,520 --> 00:11:16,360
Speaker 1: We feel confident today that we've got a we've got

210
00:11:16,400 --> 00:11:19,480
Speaker 1: a wow idea in terms of how chat and search

211
00:11:19,679 --> 00:11:22,160
Speaker 1: and the brows are all come together and uh, and

212
00:11:22,240 --> 00:11:24,400
Speaker 1: we've put in all of the good work to make

213
00:11:24,440 --> 00:11:26,840
Speaker 1: sure it is a stable system. And so we're looking

214
00:11:26,880 --> 00:11:30,320
Speaker 1: forward now at getting feedback and then improving. You se

215
00:11:30,400 --> 00:11:34,040
Speaker 1: if open ai has a closed profit model, it's been

216
00:11:34,160 --> 00:11:37,959
Speaker 1: licensing access to chat, GPT and other tools to developers.

217
00:11:38,480 --> 00:11:42,240
Speaker 1: Is there any element of exclusivity in this arrangement with

218
00:11:42,440 --> 00:11:45,719
Speaker 1: open ai that gives you an advantage over others in

219
00:11:45,880 --> 00:11:51,480
Speaker 1: utilizing the underlying technology, either for search or other applications. Um,

220
00:11:51,920 --> 00:11:54,360
Speaker 1: the open could work with money companies. The opportunity we

221
00:11:54,480 --> 00:11:57,240
Speaker 1: have is that we've worked with them together so so closely.

222
00:11:57,640 --> 00:12:00,880
Speaker 1: So this latest ai model on any the covers, which

223
00:12:00,920 --> 00:12:02,840
Speaker 1: we announced today as a brand new one, this one

224
00:12:02,960 --> 00:12:05,920
Speaker 1: is much more powerful than chat, GPT and it's tuned

225
00:12:06,000 --> 00:12:08,480
Speaker 1: for search. We've worked together to make that happen. That's

226
00:12:08,480 --> 00:12:11,559
Speaker 1: pretty unique. Certainly, other companies can work with the technology,

227
00:12:11,640 --> 00:12:14,400
Speaker 1: but I think we have a great collaboration there and

228
00:12:14,840 --> 00:12:17,800
Speaker 1: we're making that across because we that cloud of Azure

229
00:12:18,120 --> 00:12:19,959
Speaker 1: powers the back in. We've worked with them on the

230
00:12:20,000 --> 00:12:22,800
Speaker 1: AI supercomputer UH and we work with them on the

231
00:12:22,840 --> 00:12:26,040
Speaker 1: modernization model as well. So across the board we have

232
00:12:26,080 --> 00:12:30,280
Speaker 1: a very deep partnership. Talk to us about monetization such

233
00:12:30,320 --> 00:12:32,400
Speaker 1: in a data you're CEO saying the technology is going

234
00:12:32,440 --> 00:12:35,840
Speaker 1: to recipe basically pretty much every software category. What does

235
00:12:35,880 --> 00:12:37,920
Speaker 1: that mean in terms of revenues, particularly when it comes

236
00:12:37,960 --> 00:12:41,400
Speaker 1: to boosting search for example. Well, the way to think

237
00:12:41,400 --> 00:12:46,560
Speaker 1: about this is um search is the largest software category

238
00:12:46,600 --> 00:12:50,040
Speaker 1: and software that's out there. Advertising as a whole as

239
00:12:50,040 --> 00:12:53,600
Speaker 1: a six D billion dollar business, and searches the predominant

240
00:12:53,640 --> 00:12:56,280
Speaker 1: part of that. We have an opportunity to reshape that,

241
00:12:56,679 --> 00:12:59,160
Speaker 1: to to bring new value to people, as we talked

242
00:12:59,160 --> 00:13:01,760
Speaker 1: about in terms of being able to answer questions, but

243
00:13:01,840 --> 00:13:04,800
Speaker 1: also new value to advertisers who within that experience that

244
00:13:04,920 --> 00:13:07,880
Speaker 1: can bring more targeted advertising and get better R o I.

245
00:13:08,280 --> 00:13:10,800
Speaker 1: So the opportunity to improve that is is something that

246
00:13:10,880 --> 00:13:13,959
Speaker 1: we think is going to benefit everybody. It's interesting we

247
00:13:14,040 --> 00:13:17,000
Speaker 1: went to our own audience via Twitter to ask them

248
00:13:17,720 --> 00:13:20,640
Speaker 1: where they feel the winnings will be made now, not

249
00:13:20,800 --> 00:13:24,840
Speaker 1: just talking about Google versus by do versus yourself at

250
00:13:24,880 --> 00:13:29,559
Speaker 1: the moment, but China versus the US, and notably interesting me,

251
00:13:29,640 --> 00:13:33,280
Speaker 1: they didn't think well thought that US technology companies are

252
00:13:33,280 --> 00:13:36,360
Speaker 1: going to win the AI race less likely China, but

253
00:13:36,440 --> 00:13:39,120
Speaker 1: the robots win is actually where they came out with you.

254
00:13:39,240 --> 00:13:42,280
Speaker 1: So that speaks to a point of the power of

255
00:13:42,440 --> 00:13:46,800
Speaker 1: artificial intelligence and at that point, to recorrect itself, to

256
00:13:46,960 --> 00:13:49,480
Speaker 1: improve upon itself. And to that end, I asked you

257
00:13:49,559 --> 00:13:52,600
Speaker 1: about the concerns, the safety, the issues, the biases. How

258
00:13:52,679 --> 00:13:54,640
Speaker 1: do you ensure that we're at the right place there

259
00:13:55,080 --> 00:13:58,880
Speaker 1: to unleash this. Yeah, that's a great question, Caroline, that's

260
00:13:58,920 --> 00:14:02,040
Speaker 1: top of mind for us. We um We have focused

261
00:14:02,120 --> 00:14:05,320
Speaker 1: now for many years on responsible AI. We've put forth

262
00:14:05,720 --> 00:14:08,599
Speaker 1: principles and a framework, and we've done a lot of

263
00:14:08,679 --> 00:14:11,800
Speaker 1: engineering in that regard. We've learned a lot from our

264
00:14:11,880 --> 00:14:15,480
Speaker 1: past releases. So what we released today has a bunch

265
00:14:15,520 --> 00:14:17,880
Speaker 1: of guard rails and safety in there for things like

266
00:14:18,000 --> 00:14:21,440
Speaker 1: hate speech and violence and self harm. We you know,

267
00:14:21,520 --> 00:14:23,480
Speaker 1: we catch the quarries when people type them in, and

268
00:14:23,560 --> 00:14:26,240
Speaker 1: we can determine before it gets to the model, before

269
00:14:26,240 --> 00:14:29,360
Speaker 1: it generates any content. We've trained the model specifically to

270
00:14:29,520 --> 00:14:32,080
Speaker 1: catch these things and then again before answers come out.

271
00:14:32,400 --> 00:14:34,640
Speaker 1: So we've done a lot of work that said. You know,

272
00:14:34,680 --> 00:14:36,920
Speaker 1: it's been clear there's definitely bad actors out there that

273
00:14:37,040 --> 00:14:38,960
Speaker 1: like to hack the system, and we're gonna have to

274
00:14:39,000 --> 00:14:41,520
Speaker 1: stay vigilant and on top of it. Um. We feel

275
00:14:41,560 --> 00:14:43,360
Speaker 1: that today is an important part because part of how

276
00:14:43,400 --> 00:14:45,280
Speaker 1: you get better and better is you have to go

277
00:14:45,360 --> 00:14:47,440
Speaker 1: to market. You actually have to ship. You can't just

278
00:14:47,520 --> 00:14:49,760
Speaker 1: solve these problems in the lab, and today is an

279
00:14:49,800 --> 00:14:52,560
Speaker 1: important step to continue to improve on that for a

280
00:14:52,760 --> 00:14:55,400
Speaker 1: I being something that can work for everybody. Yes, if

281
00:14:55,600 --> 00:14:58,360
Speaker 1: we talk about Generative AI day in day out in

282
00:14:58,440 --> 00:15:00,960
Speaker 1: the show, it's great to have you thank you yourself

283
00:15:01,040 --> 00:15:04,200
Speaker 1: many of microslot and and you didn't talk about it

284
00:15:04,280 --> 00:15:06,440
Speaker 1: day in day out, not just from Microsoft's perspective, but

285
00:15:06,520 --> 00:15:09,280
Speaker 1: some of the other competitors, right. Yeah, So by do

286
00:15:09,560 --> 00:15:12,760
Speaker 1: the big name coming out overnight out of China confirming

287
00:15:12,800 --> 00:15:15,400
Speaker 1: a Bloomberg scoop that as soon as March it will

288
00:15:15,560 --> 00:15:20,760
Speaker 1: roll out in English. Ernie it's chat GPT competitor in

289
00:15:20,840 --> 00:15:24,440
Speaker 1: the former Generative AI, a chat bot. And it's interesting

290
00:15:24,520 --> 00:15:27,160
Speaker 1: because they didn't do a big onstage splash with the

291
00:15:27,440 --> 00:15:30,560
Speaker 1: press and with analysts gathered, they just made a statement

292
00:15:30,600 --> 00:15:33,040
Speaker 1: and we're out on Twitter kind of flexing a little

293
00:15:33,080 --> 00:15:36,400
Speaker 1: bit about the roadmap for Ernie. We've been reporting around

294
00:15:36,400 --> 00:15:38,760
Speaker 1: the work they've done around this, Carol. But you know,

295
00:15:38,920 --> 00:15:41,600
Speaker 1: by Do is the Google of China right dominates the

296
00:15:41,680 --> 00:15:44,760
Speaker 1: search market there, and they plan to integrate that tool

297
00:15:44,800 --> 00:15:47,600
Speaker 1: into their own search capabilities. And as we've discussed, the

298
00:15:47,680 --> 00:15:50,680
Speaker 1: shares absolutely surging both in Hong Kong and in the

299
00:15:50,840 --> 00:15:54,960
Speaker 1: US in response. Yeah, and and just the element here

300
00:15:55,000 --> 00:15:58,800
Speaker 1: that we've talked time and time again about US versus China,

301
00:15:58,920 --> 00:16:01,280
Speaker 1: and just at one point, you think of the books

302
00:16:01,440 --> 00:16:04,080
Speaker 1: have been written, the focus from Kaifu Lee and many

303
00:16:04,160 --> 00:16:07,000
Speaker 1: others that China really managed to get into the home

304
00:16:07,160 --> 00:16:09,880
Speaker 1: so much faster, the use of you know, the basic

305
00:16:10,000 --> 00:16:12,520
Speaker 1: form of generative language AI that we're all used to have,

306
00:16:12,600 --> 00:16:14,960
Speaker 1: basically asking alex or or some sort of piece of

307
00:16:15,000 --> 00:16:16,920
Speaker 1: equipment in our home. China led the way in that.

308
00:16:17,080 --> 00:16:19,040
Speaker 1: And I wonder how much further ahead by Do will

309
00:16:19,120 --> 00:16:21,320
Speaker 1: end up becoming and how many checks and balances it

310
00:16:21,360 --> 00:16:24,440
Speaker 1: will have itself. Right, And what's so interesting is Ernie

311
00:16:24,480 --> 00:16:27,920
Speaker 1: is so similar to chat GBT gives a conversational style response.

312
00:16:28,240 --> 00:16:31,440
Speaker 1: They're just handling it slightly differently. Sorry, well, of course,

313
00:16:31,480 --> 00:16:33,680
Speaker 1: continues track. Now coming up, we're going to turn back

314
00:16:33,760 --> 00:16:35,920
Speaker 1: to soft Bank and take a look at maybe a

315
00:16:36,080 --> 00:16:39,520
Speaker 1: Vision Fund three plus a new fund from Goldman Sachs

316
00:16:39,600 --> 00:16:41,960
Speaker 1: has its eyes set on more tech. We'll bring all

317
00:16:42,000 --> 00:16:59,800
Speaker 1: of you that that next. This is Bloomberg time for

318
00:17:00,000 --> 00:17:03,880
Speaker 1: talking tech. Soft Bank may consider launching a third Vision

319
00:17:03,960 --> 00:17:07,480
Speaker 1: Fund after it exhausts its available capital, and the executive

320
00:17:07,520 --> 00:17:10,400
Speaker 1: has told Bloomberg the Vision Fund too still has six

321
00:17:10,440 --> 00:17:14,320
Speaker 1: point five billion dollars for fresh investments. But SoftBank's Marquee

322
00:17:14,359 --> 00:17:17,840
Speaker 1: funds lost five billion dollars in value in the December quarter,

323
00:17:18,000 --> 00:17:21,639
Speaker 1: dragged down by those slumping valuations across private startups. It

324
00:17:21,760 --> 00:17:25,600
Speaker 1: was the segments fourth straight quarter of losses and more.

325
00:17:25,680 --> 00:17:27,680
Speaker 1: Of course, in the world of AI, the Apple Dabby

326
00:17:27,760 --> 00:17:31,160
Speaker 1: based AI company G forty two hiring dozens of people

327
00:17:31,240 --> 00:17:34,760
Speaker 1: in Singapore, Jakarta, Shanghai, and Tel Aviv to scout for

328
00:17:34,840 --> 00:17:38,600
Speaker 1: opportunities for a ten billion dollar tech fund. That's, according

329
00:17:38,680 --> 00:17:42,959
Speaker 1: to sources, the Fungal target technology investments across those countries,

330
00:17:43,000 --> 00:17:46,000
Speaker 1: as well as markets in Saudi Arabia and in Egypt.

331
00:17:52,760 --> 00:17:55,320
Speaker 1: Welcome back to Bluemo Technology. M Carraine Hide in New York,

332
00:17:55,400 --> 00:17:58,600
Speaker 1: and lawmakers are targeting TikTok during a markup of three

333
00:17:58,680 --> 00:18:01,840
Speaker 1: bipartisan proposals. According to reporting from Bring the Government tech

334
00:18:01,920 --> 00:18:05,960
Speaker 1: reporter Maria Curie, she enjoys us now from Capital Hill. Maria,

335
00:18:06,040 --> 00:18:08,480
Speaker 1: the Senate Judiciary Committee is going to hold this hearing

336
00:18:08,560 --> 00:18:11,959
Speaker 1: on protecting kids online next week. Sources tell us how

337
00:18:12,080 --> 00:18:14,399
Speaker 1: much is this a long term goal for Biden. I

338
00:18:14,440 --> 00:18:17,760
Speaker 1: think he's discussed this in speeches before relative to try

339
00:18:17,840 --> 00:18:22,560
Speaker 1: and put pressure on Congress to do something about it. Sure,

340
00:18:22,720 --> 00:18:24,840
Speaker 1: so this is definitely an issue that can be tackled

341
00:18:24,880 --> 00:18:28,680
Speaker 1: from multiple angles, not just from Congress, but from relevant

342
00:18:28,720 --> 00:18:31,720
Speaker 1: federal agencies like a Federal Trade Commission. UM. And so

343
00:18:32,040 --> 00:18:35,240
Speaker 1: it certainly can be a long term goal on various fronts.

344
00:18:35,320 --> 00:18:38,359
Speaker 1: But the Senate Judiciary Committee next week, we'll put a

345
00:18:38,440 --> 00:18:41,359
Speaker 1: focus on it early on in the eighth Congress. The

346
00:18:41,440 --> 00:18:43,960
Speaker 1: witnesses have yet to be determined, but we can expect

347
00:18:44,000 --> 00:18:47,560
Speaker 1: some conversations. They're on key legislation that um did not

348
00:18:47,680 --> 00:18:50,160
Speaker 1: make it across the finish lying in the last Congress,

349
00:18:50,240 --> 00:18:54,560
Speaker 1: but did come close. UM. So, yeah, it was interesting

350
00:18:54,640 --> 00:18:59,600
Speaker 1: when we've had guests on in particular well FCC commissioners,

351
00:18:59,640 --> 00:19:02,720
Speaker 1: and then they've talked about perhaps that this should be driven,

352
00:19:02,760 --> 00:19:06,360
Speaker 1: the TikTok focus should be coming from the administration rather

353
00:19:06,440 --> 00:19:10,280
Speaker 1: than from Congress. When is there any update on Saphius

354
00:19:10,400 --> 00:19:13,399
Speaker 1: or in general where the administration stands on TikTok and

355
00:19:13,440 --> 00:19:16,760
Speaker 1: our access to it. Well, we heard recently that the

356
00:19:16,800 --> 00:19:19,480
Speaker 1: President said he's not sure if TikTok should be banned

357
00:19:19,480 --> 00:19:23,760
Speaker 1: in the country. But interestingly, Congress's approach would be giving

358
00:19:23,800 --> 00:19:27,120
Speaker 1: the President more authority to limit TikTok, so it would

359
00:19:27,119 --> 00:19:31,480
Speaker 1: still end up being UM an executive branch effort UM. Importantly,

360
00:19:31,680 --> 00:19:35,600
Speaker 1: Senator Warner is working on a bill that UM would

361
00:19:35,680 --> 00:19:40,080
Speaker 1: prevent giving other countries a platform for retaliating against US companies,

362
00:19:40,119 --> 00:19:42,920
Speaker 1: So that's certainly a consideration moving forward as we see

363
00:19:43,000 --> 00:19:46,119
Speaker 1: more and more bills crop up. Today's markup of the

364
00:19:46,240 --> 00:19:49,639
Speaker 1: three UM TikTok bills were different in the sense that

365
00:19:49,680 --> 00:19:54,040
Speaker 1: they would empower consumers to have more knowledge about their app.

366
00:19:54,520 --> 00:19:58,359
Speaker 1: They would UM app distributors would be required to tell

367
00:19:58,880 --> 00:20:02,520
Speaker 1: consumers that it is is UM owned by a company

368
00:20:02,600 --> 00:20:05,080
Speaker 1: that is based in China, and that their data is

369
00:20:05,160 --> 00:20:07,520
Speaker 1: being collected, and so this is taking on a different approach.

370
00:20:07,520 --> 00:20:09,359
Speaker 1: Instead of banning or limiting the app, it would just

371
00:20:09,440 --> 00:20:13,880
Speaker 1: be empowering consumers with more information. Career Curi of Bloomberg

372
00:20:13,920 --> 00:20:17,760
Speaker 1: Government now staying in Washington to u S. Senators express

373
00:20:17,840 --> 00:20:21,560
Speaker 1: their concern to Meta CEO Mark Zuckerberg over the risk

374
00:20:21,600 --> 00:20:25,040
Speaker 1: of developers in China and Russia having access to use

375
00:20:25,119 --> 00:20:28,000
Speaker 1: a data they say metas Facebook unit new in two

376
00:20:28,080 --> 00:20:31,520
Speaker 1: thousand eighteen that quote, hundreds of thousands of developers in

377
00:20:31,640 --> 00:20:35,800
Speaker 1: countries characterized as high risk had access to significant amounts

378
00:20:35,960 --> 00:20:39,200
Speaker 1: of sensitive user data. The senators said their staff held

379
00:20:39,280 --> 00:20:42,359
Speaker 1: meetings with company execs to determine who could get the

380
00:20:42,520 --> 00:20:45,760
Speaker 1: data and what Facebook planned to do about it, particularly

381
00:20:46,119 --> 00:20:50,800
Speaker 1: with regards to protecting users information. But stick with Meta,

382
00:20:50,880 --> 00:20:54,920
Speaker 1: the company is asking many of its managers and directors

383
00:20:55,280 --> 00:21:00,320
Speaker 1: to transition to individual contributor jobs or leave the penny

384
00:21:00,600 --> 00:21:03,600
Speaker 1: as it tries to become more efficient. Joining us for

385
00:21:03,720 --> 00:21:08,439
Speaker 1: more is Bloomberg Sarah Fryer who broke this story. I mean, Sarah,

386
00:21:08,880 --> 00:21:11,959
Speaker 1: since we reported the kind of mass round of layoffs

387
00:21:11,960 --> 00:21:14,640
Speaker 1: at Meta, I imagine there are a number of employees

388
00:21:14,680 --> 00:21:17,680
Speaker 1: who have been waiting for the acts to drop. But

389
00:21:17,800 --> 00:21:21,920
Speaker 1: in this case, it's very specific roles that Mark Zuckerberg

390
00:21:21,920 --> 00:21:24,400
Speaker 1: and co. Appear to be going after. Right if you're

391
00:21:24,440 --> 00:21:27,879
Speaker 1: a Meta employee, Ever since those cuts in November, this

392
00:21:28,080 --> 00:21:32,520
Speaker 1: historic time for the company, You've seen a number of

393
00:21:32,560 --> 00:21:34,879
Speaker 1: other rounds of layoffs at other companies and you're thinking,

394
00:21:35,720 --> 00:21:37,320
Speaker 1: you know what's coming next? Are we going to have

395
00:21:37,400 --> 00:21:40,639
Speaker 1: another round? Amazon had another round? And what it turns

396
00:21:40,680 --> 00:21:43,119
Speaker 1: out from our sources is there is a thinning of

397
00:21:43,280 --> 00:21:46,720
Speaker 1: the ranks happening at Meta as we speak, that managers

398
00:21:46,760 --> 00:21:51,119
Speaker 1: are getting told by their supervisors, Um, you need to

399
00:21:51,920 --> 00:21:55,280
Speaker 1: get to a point where you're doing individual contributor work,

400
00:21:55,359 --> 00:22:00,320
Speaker 1: whether that's coding, research, design, um, whatever it is that

401
00:22:00,400 --> 00:22:03,359
Speaker 1: you can do. That is part of building as opposed

402
00:22:03,400 --> 00:22:06,240
Speaker 1: to managing. That is what Meta values right now, that's

403
00:22:06,280 --> 00:22:07,720
Speaker 1: where we want to push it forward. And if and

404
00:22:07,760 --> 00:22:09,600
Speaker 1: if you can't do that, if you don't find yourself

405
00:22:09,640 --> 00:22:11,720
Speaker 1: in a role like that, we'll have to ask you

406
00:22:11,800 --> 00:22:15,399
Speaker 1: to leave. And here's here's here's a nice package to

407
00:22:15,560 --> 00:22:18,120
Speaker 1: that point, the nice package. But Sarah, if you're being

408
00:22:18,400 --> 00:22:21,880
Speaker 1: asked to pull back on managerial role but do more yourself,

409
00:22:22,640 --> 00:22:24,920
Speaker 1: is that more valuable or less in terms of recompense

410
00:22:25,000 --> 00:22:28,840
Speaker 1: to you. Well, it's unclear whether people are being asked

411
00:22:29,280 --> 00:22:32,119
Speaker 1: as they make those transitions to take lower salaries. I

412
00:22:32,119 --> 00:22:35,000
Speaker 1: imagine that would be hard to swallow if if you're

413
00:22:35,240 --> 00:22:39,320
Speaker 1: a Meta employee. But I think the important factor here

414
00:22:39,400 --> 00:22:42,040
Speaker 1: is that the company is trying to become more efficient.

415
00:22:42,280 --> 00:22:45,520
Speaker 1: Zuckerberg is called the Year of Efficiency. They're calling this

416
00:22:45,720 --> 00:22:48,280
Speaker 1: move the flattening um. They're trying to make sure that

417
00:22:48,359 --> 00:22:51,320
Speaker 1: there are fewer steps to getting things done and trying

418
00:22:51,359 --> 00:22:54,120
Speaker 1: to make sure that products are better, profits are better,

419
00:22:54,520 --> 00:22:57,719
Speaker 1: all things across the board can in theory, be solved

420
00:22:57,800 --> 00:23:00,720
Speaker 1: by having fewer middle managers m R. I p to

421
00:23:00,800 --> 00:23:03,480
Speaker 1: all of us who who have that role. Apparently, so

422
00:23:03,920 --> 00:23:07,240
Speaker 1: I think that that is that is the takeaway. Whether

423
00:23:07,359 --> 00:23:10,240
Speaker 1: Meta will actually be more efficient as a result of

424
00:23:10,560 --> 00:23:13,280
Speaker 1: of this thinning, I'm I don't know. I mean, I

425
00:23:13,359 --> 00:23:16,200
Speaker 1: think that there there are, you know, roles that have

426
00:23:16,280 --> 00:23:18,200
Speaker 1: been created for a reason. But what I have heard

427
00:23:18,680 --> 00:23:22,480
Speaker 1: in talking to employees at the company is there is

428
00:23:22,600 --> 00:23:26,560
Speaker 1: some some encouraging science here. People who have noticed that

429
00:23:26,680 --> 00:23:29,280
Speaker 1: there are some managers that only are in charge of

430
00:23:29,359 --> 00:23:32,720
Speaker 1: one or two employees, that there are some teams that

431
00:23:32,840 --> 00:23:36,920
Speaker 1: are tasked with doing jobs that are in conflict or

432
00:23:37,240 --> 00:23:40,760
Speaker 1: in competition with what other teams are doing. So certainly

433
00:23:40,880 --> 00:23:43,639
Speaker 1: those things can be sorted out and fixed in this

434
00:23:43,840 --> 00:23:46,440
Speaker 1: next round. And maybe the fact that they're not doing

435
00:23:46,480 --> 00:23:49,480
Speaker 1: it all at once UM and more on an individual

436
00:23:49,560 --> 00:23:52,080
Speaker 1: basis gives them time to be more thoughtful, since I

437
00:23:52,160 --> 00:23:55,760
Speaker 1: think there was a lot of of tension following the

438
00:23:55,880 --> 00:23:58,600
Speaker 1: last layoffs that people didn't hear about until the last minute.

439
00:23:59,040 --> 00:24:01,880
Speaker 1: Every single person wants to be more productive, more effective

440
00:24:01,920 --> 00:24:05,040
Speaker 1: at work, have more impact. Do we know if there's

441
00:24:05,200 --> 00:24:07,200
Speaker 1: bits of the business that this is going to really

442
00:24:07,280 --> 00:24:11,560
Speaker 1: affect Where the bloating really got from a managerial perspective, Well,

443
00:24:11,600 --> 00:24:14,320
Speaker 1: I think that the company wants to really focus on

444
00:24:14,880 --> 00:24:17,359
Speaker 1: UM some of their their core initiatives. We know that

445
00:24:17,440 --> 00:24:21,640
Speaker 1: they want to improve their artificial intelligence for better ad targeting,

446
00:24:21,800 --> 00:24:28,040
Speaker 1: for better algorithmic sharing, on on their real products, trying

447
00:24:28,119 --> 00:24:30,280
Speaker 1: to make sure that people get the right videos. They're

448
00:24:30,320 --> 00:24:33,040
Speaker 1: trying to ensure that UM you know, some of the

449
00:24:33,200 --> 00:24:36,560
Speaker 1: work they're doing on Horizon World can translate to a

450
00:24:36,680 --> 00:24:40,080
Speaker 1: mobile phone user. So there's some really key initiatives that

451
00:24:40,119 --> 00:24:44,199
Speaker 1: they're working towards here that are focused on profits. One

452
00:24:44,240 --> 00:24:46,720
Speaker 1: of them that was mentioned on the earnings call that

453
00:24:46,840 --> 00:24:51,400
Speaker 1: caught my eye was this transition to making money in messaging,

454
00:24:51,520 --> 00:24:53,679
Speaker 1: which is something that people have been asking that about

455
00:24:53,760 --> 00:24:56,640
Speaker 1: for years. In Zuckerberg SAIDs are now at ten billion

456
00:24:57,280 --> 00:24:59,639
Speaker 1: annual run rate and revenue there. So I think that

457
00:24:59,680 --> 00:25:01,679
Speaker 1: we're going in to see the company get a lot

458
00:25:01,800 --> 00:25:05,720
Speaker 1: more focus, a lot less experimental throwing spaghetti at the

459
00:25:05,760 --> 00:25:08,480
Speaker 1: world to see what six and more thoughtful about where

460
00:25:08,520 --> 00:25:11,840
Speaker 1: they spend their time going forward to try to to

461
00:25:12,080 --> 00:25:16,040
Speaker 1: bring you know, this sense of efficiency and energy in

462
00:25:16,359 --> 00:25:18,800
Speaker 1: the next few months. So far, the music to some

463
00:25:18,960 --> 00:25:21,800
Speaker 1: investors is at least Springberg. Sarah Fry, thank you so much.

464
00:25:21,880 --> 00:25:25,440
Speaker 1: Great reporting, even though she's of course senior editor of

465
00:25:25,520 --> 00:25:27,800
Speaker 1: that team. Meanwhile, coming up, we'll talk about the future

466
00:25:27,840 --> 00:25:30,960
Speaker 1: of mobility from e VS here on Earth two planes

467
00:25:31,080 --> 00:25:34,240
Speaker 1: to the moon on that next with up Partners co

468
00:25:34,359 --> 00:25:37,520
Speaker 1: founder Sarah Sagary. It's just published a key report for

469
00:25:37,600 --> 00:25:40,680
Speaker 1: full insights on all of this, and you're keeping a

470
00:25:40,760 --> 00:25:44,159
Speaker 1: keen eye on certain shares right now. Yeah, let's continue

471
00:25:44,160 --> 00:25:46,200
Speaker 1: to look at Microsoft, as I said, to stop moving

472
00:25:46,240 --> 00:25:48,440
Speaker 1: in lockstep with the SMP five. A lot of this

473
00:25:48,600 --> 00:25:51,560
Speaker 1: about fed chair pals comments, but there is some buoyancy

474
00:25:52,000 --> 00:25:56,320
Speaker 1: around using open aiyes tech with being with the edge

475
00:25:57,080 --> 00:26:00,000
Speaker 1: browser and what they can do to boost their competitor

476
00:26:00,000 --> 00:26:02,680
Speaker 1: ativeness against Google and particular in the world of search.

477
00:26:02,760 --> 00:26:23,040
Speaker 1: Investors clearly liking what they see. This is Bloomberg. Later tonight,

478
00:26:23,119 --> 00:26:26,919
Speaker 1: President Biden is expected to discuss his economic progress so far,

479
00:26:27,040 --> 00:26:30,760
Speaker 1: including how the Inflation Reduction Act will drive manufacturing and

480
00:26:30,920 --> 00:26:34,159
Speaker 1: supply chain improvements for the adoption of electric fields and

481
00:26:34,320 --> 00:26:38,240
Speaker 1: by extension, cut US emissions. Speaking of which, UP Partners

482
00:26:38,400 --> 00:26:41,280
Speaker 1: just published its own Moving World Report, which looks at

483
00:26:41,359 --> 00:26:44,480
Speaker 1: all of the macro and micro trends across mobility and

484
00:26:44,560 --> 00:26:48,520
Speaker 1: transport in the venture firms. Report is based on hundreds

485
00:26:48,720 --> 00:26:52,800
Speaker 1: of research studies, dozens of interviews and draws some critical conclusions.

486
00:26:52,880 --> 00:26:56,920
Speaker 1: Firstly that funding for mobility has outpaced most tech sectors.

487
00:26:57,280 --> 00:27:01,280
Speaker 1: But secondly, mobility has become the most important segment when

488
00:27:01,359 --> 00:27:04,080
Speaker 1: it comes to looking at how we combat emissions here

489
00:27:04,080 --> 00:27:07,159
Speaker 1: in North American beyond joining us to discuss Sorry Cigary,

490
00:27:07,240 --> 00:27:10,560
Speaker 1: managing partner and co founder of UP Partners, and sorry's

491
00:27:10,600 --> 00:27:12,639
Speaker 1: before you you joined us here in San Francisco for

492
00:27:12,680 --> 00:27:15,520
Speaker 1: the show You posted on linked In about the report

493
00:27:15,560 --> 00:27:18,320
Speaker 1: coming out, and hundreds of people engage with you and

494
00:27:18,359 --> 00:27:20,720
Speaker 1: said I'd like to see it. But those are the

495
00:27:20,800 --> 00:27:24,359
Speaker 1: two key conclusions. More money from vcs into mobility than

496
00:27:24,400 --> 00:27:28,840
Speaker 1: any other subsector, and also the mobility counts for more emissions.

497
00:27:28,880 --> 00:27:31,920
Speaker 1: What is it that you've discovered or found? Yea, thanks

498
00:27:31,960 --> 00:27:35,520
Speaker 1: for having us. The big takeaway is that mobility is

499
00:27:35,600 --> 00:27:39,800
Speaker 1: growing rapidly thirty x increase and venture investment and mobility,

500
00:27:40,160 --> 00:27:44,400
Speaker 1: and it represents about of all CEO two emissions as

501
00:27:44,440 --> 00:27:46,200
Speaker 1: a ten trillion dollar market. And so we have this

502
00:27:46,280 --> 00:27:48,680
Speaker 1: convergence of perhaps one of the most important thing for

503
00:27:48,760 --> 00:27:51,280
Speaker 1: society today, which is how do we reduce carbon missions

504
00:27:51,320 --> 00:27:54,080
Speaker 1: with how we move things on the ground, air, see

505
00:27:54,119 --> 00:27:56,920
Speaker 1: in space, people and packages. And we're see an opportunity

506
00:27:56,920 --> 00:27:59,800
Speaker 1: for real economic development as a result of it. So

507
00:28:00,400 --> 00:28:03,440
Speaker 1: I guess the conclusion to clear this is where the

508
00:28:03,480 --> 00:28:06,639
Speaker 1: money is going. This is what's the biggest contributors to

509
00:28:06,680 --> 00:28:09,639
Speaker 1: emissions backward looking when you think about the nobility your

510
00:28:09,640 --> 00:28:12,440
Speaker 1: avenge capitalists, you know you, and I've spent some time

511
00:28:12,520 --> 00:28:15,040
Speaker 1: with some of your portfolio companies and areas you invest.

512
00:28:15,560 --> 00:28:18,399
Speaker 1: What did the findings of the report push you to

513
00:28:18,520 --> 00:28:21,040
Speaker 1: do with your own investment thesis and where you're looking

514
00:28:21,320 --> 00:28:23,159
Speaker 1: to make change. But we just released it, so now

515
00:28:23,200 --> 00:28:24,959
Speaker 1: we get to actually go make some decisions based off

516
00:28:25,000 --> 00:28:26,520
Speaker 1: the data. And we made it open source, so other

517
00:28:26,560 --> 00:28:29,320
Speaker 1: skin as well. A couple of key takeaways. One is

518
00:28:29,720 --> 00:28:34,240
Speaker 1: in the aviation industry, we are facing a major pilot shortage.

519
00:28:34,480 --> 00:28:36,920
Speaker 1: Will be at sixty five thousand pilots short by you.

520
00:28:38,000 --> 00:28:40,040
Speaker 1: That is a huge impact our economy if we don't

521
00:28:40,080 --> 00:28:42,880
Speaker 1: fix so we're looking to invest in technologies and businesses

522
00:28:42,920 --> 00:28:45,280
Speaker 1: that can solve that. Two is, as we go towards

523
00:28:45,320 --> 00:28:50,040
Speaker 1: electrification of everything, a massive material shortage. We're looking at

524
00:28:50,080 --> 00:28:52,719
Speaker 1: a ten x increase in lithium ion batteries. We already

525
00:28:52,720 --> 00:28:56,760
Speaker 1: saw close to increase in the lithium ion price index.

526
00:28:57,680 --> 00:29:01,400
Speaker 1: That entire ecosystem for technology used to help create lithium

527
00:29:01,440 --> 00:29:05,560
Speaker 1: ion batteries cleaner, faster, cheaper, more accessible is a huge

528
00:29:05,600 --> 00:29:07,800
Speaker 1: area of continued investment for us. What are the charts

529
00:29:07,840 --> 00:29:09,680
Speaker 1: that jumped out from me at the report was the

530
00:29:09,760 --> 00:29:14,040
Speaker 1: kind of demand curve for batteries going through an area

531
00:29:14,080 --> 00:29:17,720
Speaker 1: Bloomberg U Energy finances looked at as well. When you

532
00:29:17,840 --> 00:29:20,080
Speaker 1: discuss with your portfolio companies in this aero you go

533
00:29:20,240 --> 00:29:24,240
Speaker 1: out is with making an investment in mind, how do

534
00:29:24,320 --> 00:29:26,160
Speaker 1: you fix the problem? You know, which areas do you

535
00:29:26,240 --> 00:29:28,880
Speaker 1: invest in that there are offering solutions to fight this

536
00:29:29,040 --> 00:29:31,840
Speaker 1: shortage of supply. Yeah, so we've made a couple of

537
00:29:31,920 --> 00:29:34,400
Speaker 1: direct investments in this space just recently, company called a

538
00:29:34,520 --> 00:29:37,720
Speaker 1: on X which is using AI to help understand what

539
00:29:37,880 --> 00:29:40,080
Speaker 1: materials can help solve another AI compare there you go.

540
00:29:40,200 --> 00:29:42,120
Speaker 1: So it's AI and energy put together. So it's very

541
00:29:42,160 --> 00:29:44,600
Speaker 1: relevant based off what's going on. We also have investment

542
00:29:44,600 --> 00:29:47,360
Speaker 1: a company called unit x which is using UM computer

543
00:29:47,520 --> 00:29:50,640
Speaker 1: Vision AI in factories to help look at batteries for

544
00:29:50,640 --> 00:29:53,760
Speaker 1: their faults. You know, battery fires are a big big deal.

545
00:29:54,040 --> 00:29:55,600
Speaker 1: Can be up to forty five minutes put out a

546
00:29:55,600 --> 00:29:58,080
Speaker 1: battery fire for an e V. So ensuring you've got

547
00:29:58,200 --> 00:30:00,960
Speaker 1: high quality of product going out the door using technology

548
00:30:01,000 --> 00:30:06,240
Speaker 1: do that big areasm of investment. What about the focus federally?

549
00:30:06,600 --> 00:30:11,320
Speaker 1: What about a desire for lowering emissions and seeing obviously

550
00:30:11,480 --> 00:30:15,160
Speaker 1: a labor shortage alleviated coming from an administration? Is that

551
00:30:15,480 --> 00:30:18,360
Speaker 1: head wind or tail wind for you at the moment? Well,

552
00:30:18,400 --> 00:30:22,040
Speaker 1: the Inflation Reduction Act has created some pretty significant tail

553
00:30:22,040 --> 00:30:24,080
Speaker 1: ones for this whole industry. We're looking at close to

554
00:30:24,120 --> 00:30:26,480
Speaker 1: a hundred and fifty billion dollars allocated out of the

555
00:30:26,720 --> 00:30:30,720
Speaker 1: Inflation Reduction Act into hydrogen, into clean energy, into transportation,

556
00:30:31,040 --> 00:30:33,200
Speaker 1: which is going to create all sorts of new activity

557
00:30:33,240 --> 00:30:36,360
Speaker 1: in and around mobility. Specifically here in the United States.

558
00:30:36,680 --> 00:30:39,600
Speaker 1: One big piece of news is the the upcoming Green Deal.

559
00:30:39,800 --> 00:30:42,640
Speaker 1: In Europe, they're looking at close to a trillion dollars

560
00:30:42,640 --> 00:30:45,960
Speaker 1: to be allocated towards going net zero. By this is

561
00:30:46,040 --> 00:30:50,160
Speaker 1: a huge secular change in all things moving people in goods,

562
00:30:50,480 --> 00:30:53,200
Speaker 1: on the ground, air scene, and space. And so it's

563
00:30:53,600 --> 00:30:56,720
Speaker 1: having literally an Act of Congress, be it here or

564
00:30:56,760 --> 00:30:59,480
Speaker 1: in Europe or elsewhere, is going to be required to

565
00:30:59,520 --> 00:31:02,080
Speaker 1: see these ajor major changes and how we move about

566
00:31:02,120 --> 00:31:04,160
Speaker 1: this planet. I mean, you're a man who is so

567
00:31:04,280 --> 00:31:07,760
Speaker 1: passionate about how you move out this planet, in particular flying.

568
00:31:07,960 --> 00:31:11,920
Speaker 1: You've from a very young age understand how to navigate

569
00:31:12,240 --> 00:31:14,960
Speaker 1: the skies. Talk to us about there are other people

570
00:31:15,080 --> 00:31:17,440
Speaker 1: who navigate the skies, who run these businesses, I think

571
00:31:17,480 --> 00:31:19,600
Speaker 1: the Delta CEO, and many have come on very passionately

572
00:31:19,640 --> 00:31:22,040
Speaker 1: wanting to bring down their own emissions curve and and

573
00:31:22,120 --> 00:31:24,520
Speaker 1: ways in which to do that. Are you seeing the

574
00:31:24,600 --> 00:31:28,240
Speaker 1: sort of focus on real innovation here coming from the

575
00:31:28,280 --> 00:31:30,600
Speaker 1: big companies are having doesn't have to come from the

576
00:31:30,640 --> 00:31:33,680
Speaker 1: smaller companies that you've got. I think it's really a

577
00:31:33,720 --> 00:31:36,640
Speaker 1: partnership between entrepreneurs and the largest companies in the world

578
00:31:36,920 --> 00:31:39,720
Speaker 1: to really affect major change and how we move people

579
00:31:39,760 --> 00:31:42,200
Speaker 1: and goods around the world. A company like zip zip Line,

580
00:31:42,200 --> 00:31:45,080
Speaker 1: which is using drones to do package delivery in partnership

581
00:31:45,160 --> 00:31:47,920
Speaker 1: with Walmart and bent Vial Arkansas today, where they did

582
00:31:47,960 --> 00:31:51,640
Speaker 1: over five thousand delivery flights last year, we've seen two

583
00:31:52,040 --> 00:31:54,840
Speaker 1: x increase in the number of drone delivery flights from

584
00:31:54,880 --> 00:31:57,240
Speaker 1: last year, which doubled the previous year, which doubled the

585
00:31:57,280 --> 00:32:00,040
Speaker 1: year before. So I think to your specific question, you

586
00:32:00,080 --> 00:32:02,479
Speaker 1: got to get the entrepreneurs partner with the largest companies

587
00:32:02,480 --> 00:32:04,400
Speaker 1: in the world to really collectively transform the way we

588
00:32:04,480 --> 00:32:07,600
Speaker 1: move things. So you're very able pilots across a number

589
00:32:07,640 --> 00:32:10,800
Speaker 1: of classes of aircraft. As you know, my head is

590
00:32:10,840 --> 00:32:13,200
Speaker 1: often up in space, and I was really interested to

591
00:32:13,280 --> 00:32:17,760
Speaker 1: see a focus of your report and the opportunity outside

592
00:32:17,800 --> 00:32:21,400
Speaker 1: of this atmosphere. Why go there? Why look at that? Well,

593
00:32:21,520 --> 00:32:23,960
Speaker 1: it's really the last frontier for us is space, and

594
00:32:24,040 --> 00:32:27,360
Speaker 1: I think it's so important to recognize the consequences of

595
00:32:27,440 --> 00:32:30,240
Speaker 1: what Ellen is doing with Starship. The way people should

596
00:32:30,240 --> 00:32:33,400
Speaker 1: think about Starship is like the railway to space. It's

597
00:32:33,400 --> 00:32:36,200
Speaker 1: effectively making almost free to get a pound of payload

598
00:32:36,520 --> 00:32:38,880
Speaker 1: to orbit. And when you make it practically free to

599
00:32:38,920 --> 00:32:40,800
Speaker 1: get a pound of palot to orbit, you can't begin

600
00:32:40,880 --> 00:32:43,400
Speaker 1: to understand what the consequences will be a new business.

601
00:32:43,400 --> 00:32:45,360
Speaker 1: As an opportunity, he is like a base on the

602
00:32:45,440 --> 00:32:47,480
Speaker 1: Moon or Mars or whatever it might be. So this

603
00:32:47,600 --> 00:32:50,040
Speaker 1: is a huge area of investment and interest I think collectively.

604
00:32:50,400 --> 00:32:54,240
Speaker 1: All right, sorry Cigary managing partner, co founder out Partners,

605
00:32:54,280 --> 00:32:56,400
Speaker 1: Good to catch up. You know what's so interesting? Care

606
00:32:56,480 --> 00:32:58,960
Speaker 1: for me as well as the opportunity as an investor.

607
00:32:59,320 --> 00:33:01,040
Speaker 1: You know, we see so many of these reports, but

608
00:33:01,160 --> 00:33:05,360
Speaker 1: Sara saying they're actually sustainability offers an attractive return down

609
00:33:05,400 --> 00:33:17,880
Speaker 1: the road. Sticking with sustainability from investment to reality, Amazon

610
00:33:17,920 --> 00:33:21,040
Speaker 1: has started selling lettuce leaves through an investment made two

611
00:33:21,120 --> 00:33:23,920
Speaker 1: years ago in the startup Hippo Harvest. The leaves are

612
00:33:23,960 --> 00:33:30,320
Speaker 1: produced using less water, less fertilizer than conventionally produced crops.

613
00:33:30,360 --> 00:33:33,720
Speaker 1: According to Hippo. Nike Ellis from the Amazon Climate Pledge

614
00:33:33,760 --> 00:33:36,680
Speaker 1: Fund joined me here in San Francisco. That's the story.

615
00:33:37,040 --> 00:33:40,240
Speaker 1: How did this investment become a real product for Amazon.

616
00:33:41,040 --> 00:33:43,680
Speaker 1: It was a collaborative effort between Amazon and Hippo Harvests

617
00:33:43,760 --> 00:33:46,640
Speaker 1: over the last fifteen months. When we first diligence and

618
00:33:46,720 --> 00:33:49,600
Speaker 1: sourced the deal. We sold two really compelling features about

619
00:33:49,600 --> 00:33:52,400
Speaker 1: the opportunity. One was to drive down the price and

620
00:33:52,480 --> 00:33:55,040
Speaker 1: the second was to increase the quality of the investment rate.

621
00:33:55,160 --> 00:33:57,200
Speaker 1: And so we've worked for fifteen months now hand in

622
00:33:57,480 --> 00:33:59,640
Speaker 1: hand to bring this product in the market in our

623
00:33:59,680 --> 00:34:03,959
Speaker 1: fresh doors, iterating on the timing, the packaging, the quality

624
00:34:04,000 --> 00:34:06,320
Speaker 1: of the produce, the price point, and we're finally able

625
00:34:06,360 --> 00:34:07,760
Speaker 1: to get it over the line and proud to announce

626
00:34:07,760 --> 00:34:09,520
Speaker 1: it here today. Let's talk about the economics of it day.

627
00:34:09,520 --> 00:34:11,600
Speaker 1: And I'm understanding you to have a pretty small footprint

628
00:34:11,680 --> 00:34:15,719
Speaker 1: down their Santa Clara or Santa Cruz. H Is this

629
00:34:15,800 --> 00:34:18,200
Speaker 1: a money maker? Is this a viable business long term

630
00:34:18,239 --> 00:34:22,960
Speaker 1: for Amazon to make these investments and convert them to product. Absolutely,

631
00:34:23,520 --> 00:34:25,480
Speaker 1: From the Amazon Climate Pludge Fund point of view, we

632
00:34:25,600 --> 00:34:28,160
Speaker 1: have a dual mandate of both reducing carbon emissions for

633
00:34:28,200 --> 00:34:31,520
Speaker 1: Amazon and also looking to do it profitably. So for US,

634
00:34:31,600 --> 00:34:34,040
Speaker 1: hip hop Harvest really represents the best of both of

635
00:34:34,080 --> 00:34:36,520
Speaker 1: those cases, and I think they're already demonstrating that down

636
00:34:36,560 --> 00:34:38,399
Speaker 1: there in Puscadero, and the hope is that that scales

637
00:34:38,440 --> 00:34:40,239
Speaker 1: throughout the network and around the world here in the

638
00:34:40,320 --> 00:34:43,480
Speaker 1: years ahead. Can give us a sense some timing of scaling.

639
00:34:43,560 --> 00:34:45,279
Speaker 1: When I get it in New York, when can you

640
00:34:45,360 --> 00:34:47,799
Speaker 1: get it abroad? It's gonna take time, Caroline. We're at

641
00:34:47,840 --> 00:34:51,680
Speaker 1: a crawl, walk, run experimentation phase. The first step is

642
00:34:51,719 --> 00:34:53,880
Speaker 1: just to get it into the market and validate customers

643
00:34:53,920 --> 00:34:56,680
Speaker 1: are interested in purchasing it. Our expectation is we will

644
00:34:56,719 --> 00:35:00,200
Speaker 1: scale out throughout the Amazon and Whole Foods ecosystem, and

645
00:35:00,280 --> 00:35:02,480
Speaker 1: then we're going to look to support Hippo Harvest as

646
00:35:02,560 --> 00:35:05,759
Speaker 1: they try and expand both domestically and internationally. But we

647
00:35:05,800 --> 00:35:07,560
Speaker 1: would expect that's going to take a couple of years

648
00:35:07,600 --> 00:35:10,200
Speaker 1: before we finally hit that stride. We've been looking at

649
00:35:10,320 --> 00:35:12,320
Speaker 1: some of the pictures of how this will works. I

650
00:35:12,400 --> 00:35:14,880
Speaker 1: can see your robots that are there, and I know

651
00:35:14,960 --> 00:35:17,279
Speaker 1: you're a man who talks about startups about climate, but

652
00:35:17,360 --> 00:35:21,160
Speaker 1: you also talk about workforce. How is this sort of focus,

653
00:35:21,280 --> 00:35:25,200
Speaker 1: this efficiency going to be impacting blue collar workers an

654
00:35:25,280 --> 00:35:29,520
Speaker 1: inability to need that sort of labor. Well, we're just

655
00:35:29,600 --> 00:35:31,799
Speaker 1: beginning to learn how these robots are going to change

656
00:35:31,800 --> 00:35:33,960
Speaker 1: the dynamics on the farm. I think one thing we've

657
00:35:34,000 --> 00:35:37,280
Speaker 1: seen early on is that Hippo Harvest is still employing

658
00:35:37,360 --> 00:35:40,440
Speaker 1: local employees and bringing their farm expertise to bear to

659
00:35:40,560 --> 00:35:43,399
Speaker 1: help produce this produce. And then we're also seeing new

660
00:35:43,520 --> 00:35:48,000
Speaker 1: jobs emerge, obviously servicing and supporting the robots, but also

661
00:35:48,120 --> 00:35:52,000
Speaker 1: doing quality assurance and control and supply chain management to

662
00:35:52,160 --> 00:35:55,359
Speaker 1: modernize these farms. And so my suspicion is the net

663
00:35:55,440 --> 00:35:57,520
Speaker 1: of it's going to be we see job growth in

664
00:35:57,600 --> 00:35:59,680
Speaker 1: both areas as we look to secure our food supply

665
00:35:59,760 --> 00:36:02,040
Speaker 1: chain or Nick, I know you're you're an Amazon Climate

666
00:36:02,120 --> 00:36:06,080
Speaker 1: Pledge Fund principle, that's your job. My question, can hipp

667
00:36:06,080 --> 00:36:08,920
Speaker 1: Ho move as fast as it's done without Amazon behind it?

668
00:36:09,040 --> 00:36:11,560
Speaker 1: Does it take an Amazon to bring this kind of

669
00:36:11,680 --> 00:36:16,320
Speaker 1: rapid success? Certainly having Amazon behind you as a key catalyst.

670
00:36:16,480 --> 00:36:18,200
Speaker 1: I think one of the things we've looked for in

671
00:36:18,280 --> 00:36:23,280
Speaker 1: our investment floor proven entrepreneurs that can reliably deliver innovations

672
00:36:23,280 --> 00:36:26,160
Speaker 1: to market, certainly with hippop Harvest. We have serial entrepreneurs

673
00:36:26,239 --> 00:36:29,000
Speaker 1: here and they've done it again, so our expectitioners will

674
00:36:29,000 --> 00:36:31,560
Speaker 1: see more of the same and it doesn't necessarily require Amazon.

675
00:36:31,640 --> 00:36:33,879
Speaker 1: Very very quickly, Have you eaten the lettuce? Is it good?

676
00:36:34,080 --> 00:36:37,240
Speaker 1: I have? It's fantastic. Highly recommend everybody give it a trial. Okay,

677
00:36:37,800 --> 00:36:40,760
Speaker 1: it's quite the array from remain to all types of letters.

678
00:36:40,800 --> 00:36:42,399
Speaker 1: We thank you so much for coming on and talking

679
00:36:42,440 --> 00:36:46,320
Speaker 1: all about the Amazon Climate Pledge Fund principle. Nick Ellis, Meanwhile,

680
00:36:46,400 --> 00:36:48,640
Speaker 1: that does it for this edition of blomg Technology. We're

681
00:36:48,680 --> 00:36:51,880
Speaker 1: gonna talk about key earnings coming thinking vast on Wednesday's

682
00:36:51,960 --> 00:36:55,160
Speaker 1: uber On CEO Dara Kososha he joins Bloomberg and then

683
00:36:55,239 --> 00:36:59,200
Speaker 1: thirty a m Eastern eight thirty am Pacific. Of course,

684
00:36:59,239 --> 00:37:02,000
Speaker 1: don't want to miss that sort of conversation from New

685
00:37:02,080 --> 00:37:11,680
Speaker 1: York from San Francisco, this is Bloombergh