1
00:00:01,600 --> 00:00:05,280
Speaker 1: From the Heart where Innovation, Money and power Collie in

2
00:00:05,360 --> 00:00:06,880
Speaker 1: Silicon Valley, NBN.

3
00:00:07,200 --> 00:00:11,280
Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Love Love.

4
00:00:24,560 --> 00:00:27,680
Speaker 3: I'm Paradine Heider Bloomberg's world headquarters in New York, ALANM.

5
00:00:27,800 --> 00:00:30,920
Speaker 4: Ed Lovelow in San Francisco. This is Bloomberg Technology coming up.

6
00:00:30,960 --> 00:00:34,760
Speaker 3: Apple, it scrats its EV ambitions after a decade long

7
00:00:34,800 --> 00:00:37,440
Speaker 3: effort as the company focuses instead on AI.

8
00:00:37,920 --> 00:00:39,160
Speaker 5: We'll have full coverage ahead.

9
00:00:39,840 --> 00:00:43,559
Speaker 4: Plus Google CEO Sunderpitch Eye blasting the failures of the

10
00:00:43,560 --> 00:00:47,360
Speaker 4: Gemini image generation feature as the company looks to remedy

11
00:00:47,400 --> 00:00:49,640
Speaker 4: the situation. We have all of the details and.

12
00:00:49,560 --> 00:00:52,239
Speaker 3: We sit down with the CEO of data automation firm Clavio.

13
00:00:52,440 --> 00:00:54,639
Speaker 3: As a company reports earnings for the second times it's

14
00:00:54,680 --> 00:00:58,400
Speaker 3: going public, and as they announced, guess what new AI products?

15
00:00:58,400 --> 00:00:59,920
Speaker 5: All that and so much more coming up.

16
00:01:00,040 --> 00:01:02,320
Speaker 3: So let's check in on these markets because there was

17
00:01:02,360 --> 00:01:04,640
Speaker 3: a torrent of economic data, not much to catch the

18
00:01:04,640 --> 00:01:07,479
Speaker 3: attention today. Mixed data means that we're currently off about

19
00:01:07,480 --> 00:01:09,560
Speaker 3: four tens percent on the Nasdaq. As we really care

20
00:01:09,680 --> 00:01:13,800
Speaker 3: about the PCEE number, the favored inflation data by the

21
00:01:13,800 --> 00:01:15,479
Speaker 3: federal reserve that comes out tomorrow.

22
00:01:15,520 --> 00:01:16,240
Speaker 5: All eyes on that.

23
00:01:16,319 --> 00:01:17,959
Speaker 3: We see a little bit of a sell off ahead

24
00:01:17,959 --> 00:01:20,880
Speaker 3: of that number Golden Dragon index over in China. Of course,

25
00:01:20,880 --> 00:01:23,000
Speaker 3: this is the US traded version of some of these

26
00:01:23,040 --> 00:01:25,679
Speaker 3: Chinese big names, down one and a half percent, so

27
00:01:25,800 --> 00:01:27,960
Speaker 3: really some weakness coming over from what was a pretty

28
00:01:28,040 --> 00:01:30,160
Speaker 3: ugly day in China trading. I'm looking at what's happening

29
00:01:30,160 --> 00:01:32,800
Speaker 3: in the ten year yield, nothing burger, nothing really moving.

30
00:01:32,800 --> 00:01:35,560
Speaker 5: We're off just about one basis point where there is action.

31
00:01:36,080 --> 00:01:38,200
Speaker 3: And on the macro perspective of we're looking at one

32
00:01:38,240 --> 00:01:40,400
Speaker 3: particular asset, claus A choice Bitcoin.

33
00:01:40,440 --> 00:01:41,440
Speaker 5: Look at this one point.

34
00:01:41,280 --> 00:01:44,640
Speaker 3: Seven point seven percent higher. We are encroaching on that

35
00:01:44,720 --> 00:01:48,480
Speaker 3: all important sixty nine thousand dollars record level ed. We

36
00:01:48,600 --> 00:01:50,960
Speaker 3: know the sixty one thousand has already been eclipsed. Why,

37
00:01:51,440 --> 00:01:54,600
Speaker 3: good old supply demand dynamics. There's more demand from the ets.

38
00:01:54,600 --> 00:01:56,240
Speaker 3: The supply side we know is going to be halved

39
00:01:56,280 --> 00:01:58,840
Speaker 3: in terms of amount of bitcoins going to be mined

40
00:01:59,080 --> 00:02:01,600
Speaker 3: come end of April. And of course, well not many

41
00:02:01,600 --> 00:02:03,240
Speaker 3: holders are selling at the moment, but ed, what are

42
00:02:03,280 --> 00:02:03,680
Speaker 3: you watching?

43
00:02:04,720 --> 00:02:10,320
Speaker 4: One story Apple Apple is shutting down its car project

44
00:02:10,720 --> 00:02:14,880
Speaker 4: after a decade of work. Full kudos to Bloomberg's Mark German.

45
00:02:15,200 --> 00:02:17,720
Speaker 4: He's going to join us in just a few moments.

46
00:02:17,760 --> 00:02:20,200
Speaker 4: He broke that story. But this is a two day chart.

47
00:02:20,560 --> 00:02:23,600
Speaker 4: That is the moment that Mark broke the story. The

48
00:02:23,639 --> 00:02:26,080
Speaker 4: stock would have been a little bit lower Tuesday, and

49
00:02:26,120 --> 00:02:28,240
Speaker 4: look at the gain on it. Okay with flat now,

50
00:02:28,639 --> 00:02:33,000
Speaker 4: But the reaction globally to this has been pretty profound. Actually,

51
00:02:33,280 --> 00:02:35,680
Speaker 4: we know that some of the two thousand people working

52
00:02:35,720 --> 00:02:38,240
Speaker 4: on the Apple car project will lose their job, some

53
00:02:38,280 --> 00:02:41,520
Speaker 4: of them will be shifted into Apple's work on generative AI,

54
00:02:41,600 --> 00:02:45,880
Speaker 4: according to Mark's reporting. But for me, this story was

55
00:02:45,919 --> 00:02:48,880
Speaker 4: about ten years where Apple did not know what that

56
00:02:49,120 --> 00:02:53,400
Speaker 4: end car product ev product, robotaxi product would eventually look like.

57
00:02:54,000 --> 00:02:57,040
Speaker 4: And you've seen it all over social media overnight. In

58
00:02:57,040 --> 00:02:59,880
Speaker 4: this morning, Caroline, it's the one thing everyone's talking about,

59
00:03:00,080 --> 00:03:01,200
Speaker 4: a big backtrack.

60
00:03:02,160 --> 00:03:05,320
Speaker 3: Well, they talked about it over that decod long process.

61
00:03:05,520 --> 00:03:07,680
Speaker 3: Here's actually Tim Cook and what he had to say

62
00:03:07,680 --> 00:03:09,680
Speaker 3: about the plans all the way back in twenty seventeen.

63
00:03:09,760 --> 00:03:10,280
Speaker 5: Take a listen.

64
00:03:11,760 --> 00:03:14,200
Speaker 6: We sort of see it as the mother of all

65
00:03:14,280 --> 00:03:19,880
Speaker 6: AI projects. It's probably one of the most difficult AI projects. Actually,

66
00:03:20,280 --> 00:03:24,320
Speaker 6: to work on. And so autonomy is something that's incredibly

67
00:03:25,200 --> 00:03:30,119
Speaker 6: exciting for us, but we'll see where it takes.

68
00:03:29,960 --> 00:03:32,480
Speaker 5: Is and Mark German joins us.

69
00:03:32,480 --> 00:03:35,480
Speaker 3: Now, isn't that interesting the fact that Tim Cook talked

70
00:03:35,520 --> 00:03:38,720
Speaker 3: to it as the mother of all AI projects and

71
00:03:38,800 --> 00:03:40,280
Speaker 3: that's where the talent now goes.

72
00:03:40,600 --> 00:03:45,839
Speaker 7: But to generative AI, it's certainly an AI project right

73
00:03:45,880 --> 00:03:48,440
Speaker 7: at the very core of an autonomous self driving system

74
00:03:48,480 --> 00:03:54,640
Speaker 7: as artificial intelligence using cloud, using onboard software, using onboard

75
00:03:54,680 --> 00:03:58,400
Speaker 7: hardware to understand what the vehicle is, do live processing

76
00:03:58,840 --> 00:04:02,680
Speaker 7: and make those decisions using an AI engine on behalf

77
00:04:02,800 --> 00:04:03,600
Speaker 7: the user.

78
00:04:03,640 --> 00:04:06,480
Speaker 8: Whether to stop, whether to make that turn, whether to

79
00:04:06,560 --> 00:04:10,120
Speaker 8: change lanes, to understand the environment, to drive in the

80
00:04:10,200 --> 00:04:12,800
Speaker 8: snow or drive into the rain. Right, those are all

81
00:04:12,840 --> 00:04:15,520
Speaker 8: decisions that are made by an AI processor, And so

82
00:04:15,600 --> 00:04:19,520
Speaker 8: clearly Apple does have some AI talent there that they're

83
00:04:19,560 --> 00:04:23,160
Speaker 8: able to relocate to their other AI initiatives that have

84
00:04:23,240 --> 00:04:24,799
Speaker 8: nothing to do with the car.

85
00:04:25,640 --> 00:04:28,440
Speaker 4: Mark, let's go deep into the details that you reported.

86
00:04:28,680 --> 00:04:33,320
Speaker 4: So there was a meeting held by two high level

87
00:04:33,320 --> 00:04:37,640
Speaker 4: Apple executives where they informed the two thousand or so

88
00:04:38,360 --> 00:04:41,400
Speaker 4: staff working on the Apple car project that it was

89
00:04:41,440 --> 00:04:43,040
Speaker 4: being shut down. What else do we know.

90
00:04:44,480 --> 00:04:47,160
Speaker 8: Yeah, there was a meeting Tuesday morning at ten am

91
00:04:47,279 --> 00:04:50,640
Speaker 8: where Jeff Williams, Apple COO and Kevin Lynn Chappels, VP

92
00:04:50,760 --> 00:04:53,800
Speaker 8: of Technology, who's been in charge of the car project

93
00:04:53,839 --> 00:04:57,400
Speaker 8: known as Titan inside the company since twenty twenty one.

94
00:04:57,760 --> 00:05:00,400
Speaker 8: They informed the team, like you said, the it would

95
00:05:00,440 --> 00:05:04,919
Speaker 8: be winding down immediately. Really three main groups there. You

96
00:05:05,000 --> 00:05:07,960
Speaker 8: have the hardware engineers. You know this is simplification, but

97
00:05:08,000 --> 00:05:10,440
Speaker 8: you have the hardware side, you have the software side,

98
00:05:10,440 --> 00:05:12,960
Speaker 8: and you have the AI side. So the AI side

99
00:05:12,960 --> 00:05:17,080
Speaker 8: of the project will be shifting towards Apple's AI and

100
00:05:17,240 --> 00:05:21,120
Speaker 8: mL division under John g and Andrea. The software side,

101
00:05:21,160 --> 00:05:24,719
Speaker 8: most of those folks will be moved to Craig federigi's

102
00:05:24,800 --> 00:05:28,760
Speaker 8: operating systems organization, and then the hardware team a lot

103
00:05:28,800 --> 00:05:31,760
Speaker 8: of those people, unfortunately, are being laid off. It's one

104
00:05:31,760 --> 00:05:35,640
Speaker 8: of the biggest layoffs, i'd say, in Apple's modern day

105
00:05:35,839 --> 00:05:42,400
Speaker 8: history since certainly since Tim Cook became CEO in twenty eleven.

106
00:05:43,200 --> 00:05:45,359
Speaker 8: And then some of the hardware engineers will have the

107
00:05:45,360 --> 00:05:49,120
Speaker 8: opportunity to apply to other jobs within the company. Obviously

108
00:05:49,200 --> 00:05:52,000
Speaker 8: Apple's a hardware company, and perhaps some of those people

109
00:05:52,040 --> 00:05:55,159
Speaker 8: will find roles on other teams, whether that's for the iPhone,

110
00:05:55,680 --> 00:05:58,760
Speaker 8: the Vision Pro, the Apple Watch, you name it. But

111
00:05:58,880 --> 00:06:03,280
Speaker 8: this is a bombshell development for Apple. This is something

112
00:06:03,320 --> 00:06:06,039
Speaker 8: that Apple just doesn't do, throwing in the towel on

113
00:06:06,120 --> 00:06:09,359
Speaker 8: a major project in such a public fashion. You know,

114
00:06:09,400 --> 00:06:13,000
Speaker 8: the Apple car started in twenty fourteen, ten years ago,

115
00:06:13,360 --> 00:06:16,120
Speaker 8: and Apple is such a secret company, but everyone knows

116
00:06:16,160 --> 00:06:18,359
Speaker 8: they've been working on this, so this has been a

117
00:06:18,360 --> 00:06:20,720
Speaker 8: really public failure for them.

118
00:06:21,240 --> 00:06:25,880
Speaker 4: Being Bo's Mark German, terrific reporting, impactful reporting. Thank you

119
00:06:25,880 --> 00:06:27,560
Speaker 4: for joining us on the show. What does it mean

120
00:06:27,600 --> 00:06:29,680
Speaker 4: for Apple? But what does it mean for the ev

121
00:06:29,839 --> 00:06:32,920
Speaker 4: industry at large? Let's bring in Cities Global Head of

122
00:06:32,960 --> 00:06:36,960
Speaker 4: Auto's Itai mcayley to get into what Apple winding down

123
00:06:37,000 --> 00:06:40,160
Speaker 4: its electric car effort means for names like Tesla. You

124
00:06:40,160 --> 00:06:42,400
Speaker 4: look at Tesla shares up two percent in the session

125
00:06:43,120 --> 00:06:46,839
Speaker 4: it Your thesis seems to be that if this is

126
00:06:46,920 --> 00:06:50,680
Speaker 4: good for anyone, Apple canceling its car project, it's good

127
00:06:50,720 --> 00:06:51,760
Speaker 4: for Tesla.

128
00:06:51,800 --> 00:06:55,440
Speaker 9: Why absolutely, Yeah, yeah, So you know we always thought

129
00:06:55,480 --> 00:06:58,320
Speaker 9: that Apple would have been most likely to compete directly

130
00:06:58,440 --> 00:07:01,880
Speaker 9: against Tesla, and too a lesser extent companies like GM

131
00:07:02,000 --> 00:07:04,520
Speaker 9: and Ford. You know, a lot is changing with electric

132
00:07:04,800 --> 00:07:07,000
Speaker 9: and software and autonomous of course, But one thing that

133
00:07:07,040 --> 00:07:09,280
Speaker 9: we're learning, not just from this news but even other

134
00:07:09,320 --> 00:07:12,440
Speaker 9: developments recently, is that the barriers to enter and succeed

135
00:07:12,480 --> 00:07:15,240
Speaker 9: in scale in the EV and particularly also in the

136
00:07:15,240 --> 00:07:18,080
Speaker 9: AV the autonomous side, are still pretty high. And so

137
00:07:18,080 --> 00:07:20,160
Speaker 9: I think this is another data point kind of supporting

138
00:07:20,160 --> 00:07:22,680
Speaker 9: that those barriers still do exist, and we think it

139
00:07:22,680 --> 00:07:25,360
Speaker 9: does bold well competitively, maybe mostly in our coverage for

140
00:07:25,440 --> 00:07:27,640
Speaker 9: Tesla and to a lesser extent, GM and Ford.

141
00:07:28,720 --> 00:07:32,600
Speaker 4: It's how you lead auto's coverage at City as opposed

142
00:07:32,640 --> 00:07:35,280
Speaker 4: to Apple. But what I always think about when I've

143
00:07:35,280 --> 00:07:37,240
Speaker 4: covered this story of Mark over the last six years

144
00:07:37,320 --> 00:07:40,240
Speaker 4: or so is like Apple is used to margins for

145
00:07:40,320 --> 00:07:43,320
Speaker 4: consumer electronics, and even if you get to like Tesla

146
00:07:43,440 --> 00:07:47,760
Speaker 4: level margins, it's not even close. Did you have any

147
00:07:47,760 --> 00:07:50,280
Speaker 4: sense of what you think Apple was trying to get

148
00:07:50,280 --> 00:07:51,480
Speaker 4: out of a car project?

149
00:07:52,360 --> 00:07:52,600
Speaker 10: Sure?

150
00:07:52,640 --> 00:07:52,840
Speaker 1: Yeah.

151
00:07:53,320 --> 00:07:56,160
Speaker 9: Broadly for the industry, we see the biggest opportunity for

152
00:07:56,320 --> 00:08:00,440
Speaker 9: higher margins is in software services and particularly around the economy.

153
00:08:00,720 --> 00:08:02,920
Speaker 9: And if you look at the average automaker today in

154
00:08:02,960 --> 00:08:05,640
Speaker 9: the US, we've estimated it from a lifetime revenue, a

155
00:08:05,680 --> 00:08:09,080
Speaker 9: lifetime revenue of a car. Today, those automakers only generate

156
00:08:09,120 --> 00:08:12,080
Speaker 9: maybe forty percent of the lifetime revenue of the car.

157
00:08:12,160 --> 00:08:14,760
Speaker 9: There's no a whole other sixty percent out there that

158
00:08:14,800 --> 00:08:16,800
Speaker 9: there tends to be much higher margin that you could

159
00:08:16,840 --> 00:08:20,840
Speaker 9: begin to tackle with software services think about autonomous vehicle

160
00:08:20,840 --> 00:08:23,680
Speaker 9: subscription models in the future, and so we do thee

161
00:08:23,680 --> 00:08:26,240
Speaker 9: as part of our industry thesis that the future of

162
00:08:26,280 --> 00:08:29,040
Speaker 9: this business model isn't just about selling a car, making

163
00:08:29,200 --> 00:08:31,160
Speaker 9: a kind of money on the one time sale, but

164
00:08:31,280 --> 00:08:34,160
Speaker 9: really thinking about the entire lifetime revenue that a vehicle

165
00:08:34,160 --> 00:08:37,480
Speaker 9: can generate, plus incremental revenue from services you peer to

166
00:08:37,520 --> 00:08:42,960
Speaker 9: peer sharing deliveries that really autonomous technology can unlock over time.

167
00:08:43,200 --> 00:08:45,480
Speaker 9: But it is a difficult challenge. You seen a lot

168
00:08:45,520 --> 00:08:47,800
Speaker 9: of companies, you know, kind of take longer to develop

169
00:08:47,880 --> 00:08:51,000
Speaker 9: level three and level four technology. You could argue that,

170
00:08:51,080 --> 00:08:53,280
Speaker 9: you know, Apple kind of shutting down that the program

171
00:08:53,679 --> 00:08:55,640
Speaker 9: suggests that, you know, maybe more negative view on the

172
00:08:55,640 --> 00:08:58,160
Speaker 9: long term potential. But then you could also argue that

173
00:08:58,200 --> 00:09:00,240
Speaker 9: the companies who are kind of leading the way from

174
00:09:00,280 --> 00:09:04,199
Speaker 9: level four will ultimately build much bigger competitive mode just

175
00:09:04,240 --> 00:09:07,200
Speaker 9: because of how difficult it is to ultimately achieve that

176
00:09:07,320 --> 00:09:08,960
Speaker 9: degree of technology and capability.

177
00:09:09,520 --> 00:09:12,560
Speaker 3: What was so interesting was obviously the price point in

178
00:09:12,559 --> 00:09:14,480
Speaker 3: which Apple was originally going to be targeting. I mean,

179
00:09:14,520 --> 00:09:16,800
Speaker 3: there was talk of a one hundred thousand dollars car

180
00:09:17,120 --> 00:09:20,280
Speaker 3: that immedia made me think of the BYD news earlier

181
00:09:20,280 --> 00:09:22,679
Speaker 3: this week, that they're going to be having some supercar

182
00:09:23,040 --> 00:09:26,520
Speaker 3: coming onto the market more than one hundred thousand dollars

183
00:09:26,720 --> 00:09:29,760
Speaker 3: in terms of a price point. Is that where the

184
00:09:29,760 --> 00:09:32,560
Speaker 3: competition now lies for a Tesla, for a FOURD, for

185
00:09:32,600 --> 00:09:36,160
Speaker 3: a GM. It's not actually homegrown Apple, it's looking over

186
00:09:36,240 --> 00:09:37,480
Speaker 3: what China is doing in a BYD.

187
00:09:39,200 --> 00:09:39,440
Speaker 5: Broadly.

188
00:09:39,480 --> 00:09:40,920
Speaker 9: Yeah, there's a lot a lot of competition, of course,

189
00:09:41,160 --> 00:09:43,640
Speaker 9: broadly coming out of China. I think every automaker is

190
00:09:44,000 --> 00:09:48,040
Speaker 9: keenly focused on it. There's different degrees of EP penetration

191
00:09:48,240 --> 00:09:51,000
Speaker 9: around the world, the US of course being smaller, but

192
00:09:51,000 --> 00:09:52,840
Speaker 9: we actually do need to think in the US more

193
00:09:52,880 --> 00:09:56,360
Speaker 9: EV product particularly and more affordable price segments they have

194
00:09:56,480 --> 00:09:59,559
Speaker 9: not yet been penetrated. It is going to be a

195
00:09:59,559 --> 00:10:03,079
Speaker 9: competitive market going forward. Yeah, I do think that even

196
00:10:03,160 --> 00:10:06,440
Speaker 9: with the slowdown today in ev adoption, of course, the

197
00:10:06,440 --> 00:10:08,640
Speaker 9: competitive threats global, we are still quite there. I think

198
00:10:08,679 --> 00:10:11,360
Speaker 9: automakers are certainly thinking about that, not only again when

199
00:10:11,400 --> 00:10:15,800
Speaker 9: thinking about the EV platforms, but also incremental services revenue

200
00:10:15,840 --> 00:10:19,000
Speaker 9: from autonomous and software of course being a big part

201
00:10:19,240 --> 00:10:21,960
Speaker 9: of ultimately making the economics work, particularly at those lower

202
00:10:22,000 --> 00:10:22,560
Speaker 9: price points.

203
00:10:23,160 --> 00:10:25,720
Speaker 3: And this is where I suppose the starkness of the

204
00:10:25,800 --> 00:10:28,560
Speaker 3: data is so raw. The fact that we're expecting an

205
00:10:28,600 --> 00:10:31,920
Speaker 3: eleven percent increase in sales of evs in the US

206
00:10:31,960 --> 00:10:34,560
Speaker 3: this year compared to a more than forty percent increase

207
00:10:34,640 --> 00:10:37,280
Speaker 3: in the year of twenty twenty three. I mean, ultimately,

208
00:10:37,400 --> 00:10:40,920
Speaker 3: is this the only way that these companies can distinguish themselves.

209
00:10:40,440 --> 00:10:40,920
Speaker 5: Can survive.

210
00:10:40,960 --> 00:10:43,400
Speaker 3: Perhaps to tap some of the talent that's about to

211
00:10:43,400 --> 00:10:45,920
Speaker 3: be let go at Apple is by driving the services,

212
00:10:46,280 --> 00:10:48,679
Speaker 3: because Apple's got car play. But obviously there's much more

213
00:10:48,720 --> 00:10:51,679
Speaker 3: margin generation to be done some of the other auto sectors.

214
00:10:52,000 --> 00:10:55,160
Speaker 9: Yeah, truly, all the above. Every auto company is trying

215
00:10:55,200 --> 00:10:59,160
Speaker 9: to work the numbers to that creates some delays, frankly

216
00:10:59,240 --> 00:11:02,000
Speaker 9: in new product introductions to make the P and L

217
00:11:02,040 --> 00:11:04,480
Speaker 9: and unit economics work for EV's, but there's no question

218
00:11:04,559 --> 00:11:08,160
Speaker 9: over time that software and services is key to towards

219
00:11:08,160 --> 00:11:11,480
Speaker 9: making those economics even stronger and again unlocking longer term

220
00:11:11,520 --> 00:11:14,360
Speaker 9: a lot of revenue opportunities I spoke about a bit before.

221
00:11:15,160 --> 00:11:17,280
Speaker 9: But you know, in the US, we're not as negative

222
00:11:17,400 --> 00:11:20,319
Speaker 9: on EV adoption as consensus is today. We actually think

223
00:11:20,320 --> 00:11:24,640
Speaker 9: the US does need more product to drive better coverage,

224
00:11:24,640 --> 00:11:26,760
Speaker 9: and we've seen a very uneven market in terms of

225
00:11:26,800 --> 00:11:29,520
Speaker 9: the number of products today, and of course auto companies

226
00:11:29,520 --> 00:11:32,079
Speaker 9: with evs, we've seen a very uneven market even when

227
00:11:32,080 --> 00:11:34,920
Speaker 9: you look at the geographical distribution of electric vehicle sales

228
00:11:34,960 --> 00:11:36,880
Speaker 9: in the US, and so you know, we think it

229
00:11:36,880 --> 00:11:38,720
Speaker 9: will be slow and steady growth. There's not been a

230
00:11:38,800 --> 00:11:42,400
Speaker 9: disruptive of course transition, which again is good for the

231
00:11:42,440 --> 00:11:45,320
Speaker 9: so called legacy automakers. It sort of supports this notion

232
00:11:45,360 --> 00:11:47,160
Speaker 9: we've been writing about about the sort of comeback of

233
00:11:47,200 --> 00:11:50,560
Speaker 9: the legacy automakers in some degree. But ultimately, you know,

234
00:11:50,600 --> 00:11:53,240
Speaker 9: EV's we still think are the future and we're done

235
00:11:53,320 --> 00:11:56,000
Speaker 9: well and right. They're very compelling products and we think

236
00:11:56,000 --> 00:11:58,640
Speaker 9: that you know, automakers are still going to be invecting

237
00:11:58,640 --> 00:12:00,080
Speaker 9: pretty aggressively into them and.

238
00:12:00,080 --> 00:12:01,640
Speaker 5: When the infrastructure is there to support it.

239
00:12:01,679 --> 00:12:05,400
Speaker 3: To City Global Head of Auto Sector Ittai McCurley, it's

240
00:12:05,440 --> 00:12:06,520
Speaker 3: so great to have you on the show.

241
00:12:06,559 --> 00:12:07,360
Speaker 5: We appreciate it.

242
00:12:07,400 --> 00:12:09,599
Speaker 3: Meanwhile, that there's more news coming out of Apple, in

243
00:12:09,640 --> 00:12:11,960
Speaker 3: particular with the latest on allegations of the company has

244
00:12:12,000 --> 00:12:15,720
Speaker 3: imposed software and hardware limitations on its iPhones and iPads

245
00:12:15,840 --> 00:12:19,360
Speaker 3: that actually impede rivals from effectively competing. Now, representatives in

246
00:12:19,440 --> 00:12:22,080
Speaker 3: the company met with the Justice Department last week in

247
00:12:22,120 --> 00:12:24,400
Speaker 3: one is a final effort to persuade the agency not

248
00:12:24,640 --> 00:12:27,280
Speaker 3: to file an antitrust suit against Apple.

249
00:12:27,400 --> 00:12:29,719
Speaker 5: It's all according to sources, the suit is expected to

250
00:12:29,760 --> 00:12:31,520
Speaker 5: be coming in the next few weeks, lu likely by

251
00:12:31,520 --> 00:12:32,160
Speaker 5: the end of March.

252
00:12:32,400 --> 00:12:41,920
Speaker 3: So the sources say, we've got to check in on

253
00:12:42,000 --> 00:12:45,000
Speaker 3: bitcoin today because we are training ever so close to

254
00:12:45,080 --> 00:12:46,640
Speaker 3: the record high that we saw all the way back

255
00:12:46,640 --> 00:12:48,959
Speaker 3: in November twenty twenty one. We're back at a sixty

256
00:12:49,000 --> 00:12:51,480
Speaker 3: one thousand hand or remember sixty nine thousand is the

257
00:12:51,520 --> 00:12:53,280
Speaker 3: record high that we saw for bitcoin.

258
00:12:53,040 --> 00:12:55,679
Speaker 5: R up another seven point seven percent on the day.

259
00:12:56,280 --> 00:12:58,679
Speaker 3: This is notable given risk assets are actually selling off

260
00:12:58,679 --> 00:13:00,839
Speaker 3: more broadly today and tech hasn't got much love, and

261
00:13:00,880 --> 00:13:03,720
Speaker 3: we're more focused on a federal reserve. But Bitcoin manages

262
00:13:03,760 --> 00:13:06,240
Speaker 3: to push higher despite that. Let's stick in to risk

263
00:13:06,240 --> 00:13:09,160
Speaker 3: assets across the board. IPEC Oscar desh Gaya is with

264
00:13:09,240 --> 00:13:11,679
Speaker 3: US senior market analysts over at Swiss Code. We're going

265
00:13:11,720 --> 00:13:13,680
Speaker 3: to be getting your tech markets feel out here and

266
00:13:13,720 --> 00:13:15,960
Speaker 3: more broadly, I mean, when you're looking at a risk

267
00:13:15,960 --> 00:13:18,559
Speaker 3: asset of bitcoin, is the story more about mass adoption,

268
00:13:18,720 --> 00:13:22,280
Speaker 3: about that being a meaningful part of a general portfolio.

269
00:13:22,840 --> 00:13:25,119
Speaker 3: As to why we're seeing a run up at the moment.

270
00:13:25,160 --> 00:13:25,960
Speaker 5: Well exactly.

271
00:13:26,040 --> 00:13:29,439
Speaker 11: I mean bitcoin has become like an important thing for

272
00:13:29,600 --> 00:13:32,400
Speaker 11: the financial industry and we think that's got a great

273
00:13:32,480 --> 00:13:36,520
Speaker 11: future in terms of finance and the centralized finance, and

274
00:13:36,720 --> 00:13:39,120
Speaker 11: while the next couple of years and the fact that

275
00:13:39,200 --> 00:13:41,800
Speaker 11: we also saw the ETF seeing the daylight is very

276
00:13:41,800 --> 00:13:45,400
Speaker 11: important in terms of adoption. And what's interesting is if

277
00:13:45,400 --> 00:13:48,840
Speaker 11: bitcoin could actually break that poor relation that it has

278
00:13:48,920 --> 00:13:51,760
Speaker 11: with the traditional risk assets, then it would be just

279
00:13:52,000 --> 00:13:56,440
Speaker 11: a very interesting asset that someone should hold in peers

280
00:13:56,559 --> 00:14:00,160
Speaker 11: or her portfolio because it's just a different thing that

281
00:14:00,280 --> 00:14:03,679
Speaker 11: is moving on different fundamentals and it's really a great

282
00:14:03,720 --> 00:14:06,520
Speaker 11: alternative for portfolio diversification.

283
00:14:07,920 --> 00:14:12,199
Speaker 4: Let's go to earning season. It's a game of artificial

284
00:14:12,240 --> 00:14:18,200
Speaker 4: intelligence or artificial sweetener, because everyone's doing buybacks and if

285
00:14:18,240 --> 00:14:20,040
Speaker 4: you look at like names like eBay. I think back

286
00:14:20,040 --> 00:14:23,720
Speaker 4: to like Disney, Mercedes Benz. In Europe, everyone's doing buybacks

287
00:14:23,720 --> 00:14:27,600
Speaker 4: and it kind of makes earning season seem like reallyuthoric. Well,

288
00:14:27,640 --> 00:14:28,640
Speaker 4: how do you feel about that?

289
00:14:29,640 --> 00:14:32,800
Speaker 11: Well, the earning season for big technology companies has been

290
00:14:32,960 --> 00:14:37,080
Speaker 11: good beyond the buybacks, because if we're looking at Magnificent

291
00:14:37,200 --> 00:14:41,640
Speaker 11: seven Socks, well they eat out some fifty five percent earnings.

292
00:14:41,240 --> 00:14:42,480
Speaker 5: Growth and that's a big deal.

293
00:14:42,480 --> 00:14:44,560
Speaker 11: And it's even a bigger deal when you think that

294
00:14:44,680 --> 00:14:47,520
Speaker 11: expectations have gone just through the roof. So we think

295
00:14:47,560 --> 00:14:50,720
Speaker 11: that there is something fundamentally positive there in terms of

296
00:14:51,000 --> 00:14:55,120
Speaker 11: well development. And AI was obviously the major takeaway of

297
00:14:55,200 --> 00:14:58,560
Speaker 11: this earning season because what we see is well AI

298
00:14:58,720 --> 00:15:02,440
Speaker 11: investments are really pouring in, and when we talk with

299
00:15:02,560 --> 00:15:05,840
Speaker 11: industry heads, well, we also realize that the investment decisions

300
00:15:05,880 --> 00:15:09,200
Speaker 11: in AI seems to be well quicker than other investment

301
00:15:09,200 --> 00:15:14,160
Speaker 11: decisions because in industries and companies seem to understand.

302
00:15:13,840 --> 00:15:15,760
Speaker 5: Very well how AI is going to.

303
00:15:15,680 --> 00:15:19,560
Speaker 11: Increase their productivity, decrease their costs, and improve their profit

304
00:15:19,600 --> 00:15:22,680
Speaker 11: and their profit margins. And they also have quite a

305
00:15:22,720 --> 00:15:26,680
Speaker 11: short payoff period. So investments are actually pouring in and

306
00:15:26,720 --> 00:15:30,280
Speaker 11: that's absolutely helping the big technology sucks, especially those who

307
00:15:30,320 --> 00:15:33,240
Speaker 11: are related to AI or just eat out some mind

308
00:15:33,240 --> 00:15:34,200
Speaker 11: blowing results.

309
00:15:35,160 --> 00:15:39,480
Speaker 4: The main beneficiary of this story continues to be in Vidia, right,

310
00:15:40,000 --> 00:15:44,600
Speaker 4: the hardware provider of the underlying technology. The thesis you

311
00:15:44,760 --> 00:15:47,600
Speaker 4: just outlined is more about the end use, so the

312
00:15:47,720 --> 00:15:51,400
Speaker 4: end case. Do you still think it's important to stay

313
00:15:51,400 --> 00:15:55,080
Speaker 4: closely aligned with names like Nvidia, AMD and some of

314
00:15:55,120 --> 00:15:56,080
Speaker 4: the hyperscalers.

315
00:15:57,160 --> 00:15:59,600
Speaker 11: Well, I think that yes, because those are the early

316
00:15:59,640 --> 00:16:03,040
Speaker 11: commerce and they are the pillars of this AI revolution.

317
00:16:03,200 --> 00:16:06,040
Speaker 11: What we see in AI today is a little bit

318
00:16:06,160 --> 00:16:10,840
Speaker 11: like the digital version, the digital equivalent of the industrial revolution.

319
00:16:11,040 --> 00:16:15,280
Speaker 11: So the potential is absolutely huge. Now, looking at the valiations, yes,

320
00:16:15,680 --> 00:16:19,800
Speaker 11: Nvidia's valuation has gone through the roof, but not the

321
00:16:19,920 --> 00:16:23,360
Speaker 11: valiations as of today are not that shocking because Nvidia's

322
00:16:23,480 --> 00:16:26,680
Speaker 11: valuation in terms of PE ratio is lower today than

323
00:16:26,720 --> 00:16:31,080
Speaker 11: it was at last year's twenty twenty three peak. So yes,

324
00:16:31,160 --> 00:16:34,480
Speaker 11: the Nvidia stock price is going higher exponentially, but the

325
00:16:34,520 --> 00:16:37,960
Speaker 11: earnings follow as well, So we think that Nvidia is

326
00:16:38,000 --> 00:16:41,640
Speaker 11: a very interesting stock to hold in an AI portfolio.

327
00:16:41,680 --> 00:16:45,640
Speaker 11: And zooming out of Nvidia, the global technology stocks are

328
00:16:45,720 --> 00:16:49,720
Speaker 11: also trading at valiations which are lower than their twenty

329
00:16:49,880 --> 00:16:54,760
Speaker 11: twenty one peak levels. So by historical terms, the valuations

330
00:16:54,760 --> 00:16:55,880
Speaker 11: that we have today.

331
00:16:55,600 --> 00:16:57,120
Speaker 5: Are not that shocking.

332
00:16:57,520 --> 00:17:01,240
Speaker 3: Yeah, if you're looking at forward p well, the ratio

333
00:17:01,280 --> 00:17:04,080
Speaker 3: there is like a thirty or thereabouts for an nvideo,

334
00:17:04,119 --> 00:17:06,480
Speaker 3: It's only sixteen for a Qualcom. And I bring up

335
00:17:06,520 --> 00:17:09,280
Speaker 3: Qualcom because actually the CEO of that company was joining

336
00:17:09,320 --> 00:17:11,639
Speaker 3: Bloomberg a little bit earlier, and well guess what he

337
00:17:11,680 --> 00:17:12,399
Speaker 3: was excited about.

338
00:17:12,400 --> 00:17:13,040
Speaker 5: Just take a listen.

339
00:17:14,160 --> 00:17:18,800
Speaker 1: Why we cannot predict when there's the next cycle. What

340
00:17:19,359 --> 00:17:22,199
Speaker 1: I can tell you right now with precision is AI

341
00:17:22,480 --> 00:17:25,600
Speaker 1: is changing how we interact, in how we use our phones,

342
00:17:26,040 --> 00:17:28,680
Speaker 1: and if eventually, if consumers field that they need to

343
00:17:28,720 --> 00:17:33,320
Speaker 1: have an aiphone, that will create this new growth momentum

344
00:17:33,320 --> 00:17:34,080
Speaker 1: for the industry.

345
00:17:36,400 --> 00:17:38,720
Speaker 3: We've of course heard Christiano i'm on talk a lot

346
00:17:38,800 --> 00:17:41,920
Speaker 3: about AI applications and of course the AI use within

347
00:17:42,160 --> 00:17:46,159
Speaker 3: our smart devices. But should we broaden our remit of

348
00:17:46,200 --> 00:17:49,520
Speaker 3: investment opportunities at this point? Should we look less at

349
00:17:49,720 --> 00:17:52,440
Speaker 3: perhaps the pixel shovels. Where are the applications you're looking

350
00:17:52,440 --> 00:17:53,440
Speaker 3: at other industry groups?

351
00:17:53,520 --> 00:17:53,680
Speaker 12: Now?

352
00:17:54,720 --> 00:17:56,960
Speaker 11: Well, absolutely, I mean you can growth in this to

353
00:17:57,160 --> 00:18:00,399
Speaker 11: chip makers and especially you can also growthen your vision

354
00:18:00,440 --> 00:18:06,400
Speaker 11: to geographically other diversification opportunities, and for example, Japanese chip

355
00:18:06,400 --> 00:18:09,280
Speaker 11: makers or Japanese companies in the chip sector are also

356
00:18:09,359 --> 00:18:13,560
Speaker 11: looking very interesting to us in terms of in terms

357
00:18:13,600 --> 00:18:18,400
Speaker 11: of good diversification opportunities while having an exposure to AI. Now,

358
00:18:18,560 --> 00:18:22,399
Speaker 11: obviously other technology areas are very interesting as well because

359
00:18:22,480 --> 00:18:28,280
Speaker 11: technology as by nature, is very adaptable to well AI revolutions.

360
00:18:28,320 --> 00:18:32,520
Speaker 11: So every company it has to do that has potential

361
00:18:32,600 --> 00:18:36,280
Speaker 11: to improve their products and services with AI are interesting

362
00:18:36,320 --> 00:18:39,040
Speaker 11: to you know, having a AI proatfolio.

363
00:18:40,240 --> 00:18:42,719
Speaker 4: Any guest that comes on the show and says something

364
00:18:42,800 --> 00:18:46,960
Speaker 4: like technology by its very nature can come on the

365
00:18:46,960 --> 00:18:50,440
Speaker 4: show again anytime. E Pepvsco. Desh Gaya, senior market analysts

366
00:18:50,440 --> 00:18:53,359
Speaker 4: at Swisco, just quiit a terrific conversation. Thank you. Back

367
00:18:53,400 --> 00:18:56,439
Speaker 4: to bitcoin really quickly, hitting sixty thousand dollars for the

368
00:18:56,440 --> 00:18:58,960
Speaker 4: first time in more than two years. This comes as

369
00:18:59,080 --> 00:19:01,800
Speaker 4: demand for the TOE and is widening, being not beyond

370
00:19:01,880 --> 00:19:05,880
Speaker 4: committed digital assets enthusiasts. That's been the story. Meanwhile, US

371
00:19:05,960 --> 00:19:10,040
Speaker 4: Senator Elizabeth Warren sat down with Bloomberg yesterday a wide

372
00:19:10,119 --> 00:19:12,879
Speaker 4: ranging interview on the looming government shut down the path

373
00:19:12,920 --> 00:19:15,320
Speaker 4: ahead for rates, but also cryptoregulation.

374
00:19:15,359 --> 00:19:22,000
Speaker 10: Have listen, in our financial system, pretty much everybody follows

375
00:19:22,040 --> 00:19:25,439
Speaker 10: the same set of rules. I'm talking banks and credit

376
00:19:25,560 --> 00:19:30,560
Speaker 10: unions and credit card companies, gold traders and stockbrokers.

377
00:19:31,200 --> 00:19:32,840
Speaker 5: Private equity now.

378
00:19:32,800 --> 00:19:38,320
Speaker 10: Has to follow the rules precious metal dealers, Venmo, Western Union,

379
00:19:39,520 --> 00:19:43,520
Speaker 10: but not crypto. My view of the world is same

380
00:19:43,600 --> 00:19:47,879
Speaker 10: kind of activity, same common risk, should have the same regulation.

381
00:19:57,840 --> 00:20:00,800
Speaker 3: Google, we have a response from the Sea Sono Pitchy

382
00:20:00,920 --> 00:20:03,760
Speaker 3: sending an email to staff what has been, of course,

383
00:20:03,800 --> 00:20:07,399
Speaker 3: the problematic responses from Google's Gemini AI engine, describing them

384
00:20:07,400 --> 00:20:11,000
Speaker 3: as quote completely unacceptable, according to a note that teams

385
00:20:11,040 --> 00:20:14,040
Speaker 3: are now working around the clock to rectify the issues.

386
00:20:14,520 --> 00:20:17,600
Speaker 5: More, let's bring in Blue Meg, Seth Figermann and Seth.

387
00:20:18,480 --> 00:20:21,879
Speaker 3: I mean to put it lightly problematic, but is this

388
00:20:22,119 --> 00:20:24,520
Speaker 3: enough of this sort of like mere cull per moment,

389
00:20:24,680 --> 00:20:28,399
Speaker 3: This is not good enough enough to for ultimately the

390
00:20:28,480 --> 00:20:29,040
Speaker 3: damage to the.

391
00:20:29,040 --> 00:20:29,840
Speaker 5: Brand that this is done.

392
00:20:30,240 --> 00:20:31,800
Speaker 13: Yeah, I mean, at some level of the question is

393
00:20:32,000 --> 00:20:35,000
Speaker 13: is this even a fixable problem? And it's very unclear

394
00:20:35,119 --> 00:20:36,960
Speaker 13: right now that is. They're saying they're going to work

395
00:20:36,960 --> 00:20:39,560
Speaker 13: around the clock, test out different prompts and try to

396
00:20:39,680 --> 00:20:41,560
Speaker 13: weak out bad cases like what we saw over the

397
00:20:41,640 --> 00:20:44,840
Speaker 13: last week. But the technology itself is fundamentally flawed. The

398
00:20:44,880 --> 00:20:47,439
Speaker 13: data it's trained on, it's fundamentally biased, and they are

399
00:20:47,440 --> 00:20:50,119
Speaker 13: attempting band aid measures here just to get the product

400
00:20:50,200 --> 00:20:52,360
Speaker 13: out there. So even if they wait two weeks, three

401
00:20:52,400 --> 00:20:55,040
Speaker 13: weeks a month, we may still see users effectively troubleshoot

402
00:20:55,040 --> 00:20:56,840
Speaker 13: it in the wild and find other issues.

403
00:20:57,320 --> 00:20:59,320
Speaker 3: When we've actually gone to spokespeople at Google, they say

404
00:20:59,320 --> 00:21:02,000
Speaker 3: Gemini is built on creativity and productivity tool It may

405
00:21:02,080 --> 00:21:04,480
Speaker 3: not always be accurate or reliable, but as you say,

406
00:21:04,520 --> 00:21:09,280
Speaker 3: this is an over correction of previous lackings in other

407
00:21:09,400 --> 00:21:12,239
Speaker 3: AI generators, and I'm interested as to what really has

408
00:21:12,280 --> 00:21:13,000
Speaker 3: gone round wrong.

409
00:21:13,080 --> 00:21:14,640
Speaker 5: From a technical perspective.

410
00:21:14,359 --> 00:21:16,720
Speaker 13: Yes, what we understand is that behind the scenes, Google

411
00:21:16,880 --> 00:21:19,320
Speaker 13: has effectively traed a technical fix here. They have done

412
00:21:19,359 --> 00:21:22,119
Speaker 13: what we call prompt engineering. They are when you enter

413
00:21:22,200 --> 00:21:24,960
Speaker 13: a prompt and say give me a picture of a nurse. Ordinarily,

414
00:21:25,119 --> 00:21:27,560
Speaker 13: on certain image generators will show you a woman by default,

415
00:21:27,720 --> 00:21:30,680
Speaker 13: or often a woman, but maybe now without even knowing it,

416
00:21:30,720 --> 00:21:32,920
Speaker 13: it'll add show me a male nurse and a woman nurse,

417
00:21:32,960 --> 00:21:35,679
Speaker 13: and a nurse of this adversity and that the problem

418
00:21:35,800 --> 00:21:38,159
Speaker 13: is users don't know that's happening behind the scenes and

419
00:21:38,480 --> 00:21:40,920
Speaker 13: over correcting forgating that they almost prevented you from be

420
00:21:40,960 --> 00:21:43,200
Speaker 13: able to see an image of a white person in

421
00:21:43,320 --> 00:21:45,359
Speaker 13: that role or any other And so it just speaks

422
00:21:45,400 --> 00:21:48,080
Speaker 13: to the urgency right now. These companies feel to deploy

423
00:21:48,160 --> 00:21:51,320
Speaker 13: these products even if they can't properly safeguard for all

424
00:21:51,400 --> 00:21:52,159
Speaker 13: bad use cases.

425
00:21:52,480 --> 00:21:55,400
Speaker 3: MENTA has a white paper just on trying to explain

426
00:21:55,520 --> 00:22:05,879
Speaker 3: this phenomenon. Cethiman, great to have you on, Welcome back

427
00:22:05,920 --> 00:22:07,679
Speaker 3: to blow Meg Technology and Caroline had in.

428
00:22:07,680 --> 00:22:10,040
Speaker 4: New York and Mamed love Loow in San Francisco. A

429
00:22:10,119 --> 00:22:11,880
Speaker 4: quick check in on the markets. I guess we're kind

430
00:22:11,880 --> 00:22:15,679
Speaker 4: of like treading water right now. We get core PC Thursday.

431
00:22:16,280 --> 00:22:19,600
Speaker 4: There is still a micro focus on what economic data

432
00:22:19,960 --> 00:22:22,000
Speaker 4: will lead the market to believe the FED is going

433
00:22:22,080 --> 00:22:24,440
Speaker 4: to do. And you see US tenure yield around four

434
00:22:24,440 --> 00:22:27,080
Speaker 4: point two nine percent where it's been equity market it's

435
00:22:27,080 --> 00:22:29,920
Speaker 4: a little softer three tens percent on the Nasdaq one hundred.

436
00:22:29,920 --> 00:22:32,600
Speaker 4: It's a very tech heavy index with sw I always

437
00:22:32,720 --> 00:22:34,720
Speaker 4: check that we showed you once, we showed you twice.

438
00:22:34,720 --> 00:22:37,560
Speaker 4: I should show you a third time Bitcoin above sixty

439
00:22:37,600 --> 00:22:40,440
Speaker 4: one thousand US dollars par token. Go back two years

440
00:22:40,960 --> 00:22:42,960
Speaker 4: when it was at that level. What happened in the

441
00:22:43,000 --> 00:22:45,760
Speaker 4: same month as it reached sixty nine thousand dollars per token?

442
00:22:46,480 --> 00:22:49,159
Speaker 4: Where will we go next? It's exciting. We'll keep on

443
00:22:49,240 --> 00:22:51,200
Speaker 4: track of it. I do want to go back to Alphabet,

444
00:22:51,359 --> 00:22:53,520
Speaker 4: parent of Google. I know we just had seth orII

445
00:22:53,760 --> 00:23:00,800
Speaker 4: editor explaining sunderpitch eyes reaction to what are accuracies and

446
00:23:00,880 --> 00:23:04,200
Speaker 4: bias problems in Gemini, And you can go on bloomberg

447
00:23:04,240 --> 00:23:06,680
Speaker 4: dot com see that full memo that Bloomberg's published he

448
00:23:06,760 --> 00:23:09,000
Speaker 4: sent staff. But we're down another two percent in the

449
00:23:09,080 --> 00:23:12,719
Speaker 4: session and trading at one point in the session at

450
00:23:12,760 --> 00:23:15,600
Speaker 4: the lowest level for Alphabet since mid December. So there's

451
00:23:15,640 --> 00:23:17,520
Speaker 4: a clear reaction here. We're going to keep on top

452
00:23:17,520 --> 00:23:20,119
Speaker 4: of the story because there is a debate carry you

453
00:23:20,119 --> 00:23:22,000
Speaker 4: and I have been talking about it all morning. How

454
00:23:22,040 --> 00:23:25,520
Speaker 4: do they actually fix this technically or is it not

455
00:23:25,600 --> 00:23:27,680
Speaker 4: as straightforward as that. It's one to watch certainly, and

456
00:23:27,720 --> 00:23:30,240
Speaker 4: we're down five percent in the last or four and

457
00:23:30,400 --> 00:23:31,800
Speaker 4: percent in the last five sessions or so.

458
00:23:32,520 --> 00:23:35,560
Speaker 3: I mean, all of this brought on by prompt transformation,

459
00:23:36,040 --> 00:23:38,640
Speaker 3: something that we'll get far more used to. Meanwhile, yesterday

460
00:23:38,760 --> 00:23:41,040
Speaker 3: and Sony of course announced that it will be laying

461
00:23:41,080 --> 00:23:43,800
Speaker 3: off some nine hundred employees in its gaming division and

462
00:23:44,280 --> 00:23:47,080
Speaker 3: we'll completely shutter PlayStation London. That brings the amount of

463
00:23:47,160 --> 00:23:49,560
Speaker 3: video game industry workers who have lost their jobs this

464
00:23:49,720 --> 00:23:51,280
Speaker 3: year to more than six thousand.

465
00:23:51,640 --> 00:23:53,520
Speaker 5: Let's bring in an expert in the feed field.

466
00:23:53,720 --> 00:23:56,159
Speaker 3: Jason Chapman joins US co founder and managing partner over

467
00:23:56,160 --> 00:23:56,960
Speaker 3: at Convoy Ventures.

468
00:23:56,960 --> 00:23:59,760
Speaker 5: It's a firm that actually invests in platforms and technologies

469
00:23:59,800 --> 00:24:00,359
Speaker 5: in gaming.

470
00:24:00,880 --> 00:24:04,879
Speaker 3: And from the perspective of the industry here, Jason, is

471
00:24:04,960 --> 00:24:06,760
Speaker 3: this something we're going to see across the board? Because

472
00:24:06,840 --> 00:24:09,080
Speaker 3: for Microsoft, when they made layoffs, we sort of thought, oh,

473
00:24:09,119 --> 00:24:11,359
Speaker 3: it's the M and A deal. But more broadly, is

474
00:24:11,400 --> 00:24:14,359
Speaker 3: this another industry group that got too big during COVID

475
00:24:14,400 --> 00:24:15,480
Speaker 3: and now has to slim back down.

476
00:24:17,160 --> 00:24:20,639
Speaker 14: Thank you for having me. While these layoffs were significant,

477
00:24:20,640 --> 00:24:23,200
Speaker 14: they're not surprising. We're going to continue to see more

478
00:24:23,280 --> 00:24:26,840
Speaker 14: layoffs throughout twenty twenty four. For context, we saw about

479
00:24:26,880 --> 00:24:29,720
Speaker 14: three percent of the global gaming workforce laid off in

480
00:24:29,800 --> 00:24:32,480
Speaker 14: twenty twenty three. We're in February and we've already seen

481
00:24:32,520 --> 00:24:35,200
Speaker 14: two percent of that workforce laid off. We expect that

482
00:24:35,280 --> 00:24:37,119
Speaker 14: number to continue to rise, both with groups that have

483
00:24:37,240 --> 00:24:40,879
Speaker 14: already done rounds of layoffs like Microsoft, Gaming, Unity, Riot

484
00:24:41,000 --> 00:24:45,200
Speaker 14: and others, and also some newer companies that haven't yet announced.

485
00:24:45,880 --> 00:24:49,919
Speaker 4: Why, Jason, why are the companies doing this belt tightening,

486
00:24:50,119 --> 00:24:50,880
Speaker 4: It's a great question.

487
00:24:51,280 --> 00:24:54,000
Speaker 14: It's because money is not cheap anymore. We were all

488
00:24:54,040 --> 00:24:56,520
Speaker 14: bloated and we got way too comfortable, hired way too

489
00:24:56,560 --> 00:25:00,960
Speaker 14: many people. In the post COVID boom, company including in

490
00:25:01,280 --> 00:25:03,399
Speaker 14: games as well as in tech, is having to right

491
00:25:03,520 --> 00:25:05,920
Speaker 14: size their industry. We need to do more with less.

492
00:25:06,040 --> 00:25:08,480
Speaker 14: We need to get more capital efficient, and I think

493
00:25:08,520 --> 00:25:10,760
Speaker 14: you're seeing that hit gaming in full force in twenty

494
00:25:10,800 --> 00:25:11,959
Speaker 14: twenty three and twenty twenty four.

495
00:25:12,400 --> 00:25:14,920
Speaker 3: Okay, so the narrative back in early twenty twenty three

496
00:25:14,960 --> 00:25:17,960
Speaker 3: when the big tech companies were belt tightening, was well,

497
00:25:18,080 --> 00:25:19,960
Speaker 3: we'll leave some of this talent and go.

498
00:25:20,000 --> 00:25:22,879
Speaker 5: And build their own ventures. But is this also happening

499
00:25:22,920 --> 00:25:23,800
Speaker 5: at the startup level?

500
00:25:23,920 --> 00:25:26,639
Speaker 3: Are you seeing belt tightening cut layoffs happening there.

501
00:25:26,520 --> 00:25:28,440
Speaker 5: Too, and ultimately what does it mean for your part

502
00:25:28,480 --> 00:25:29,080
Speaker 5: of the industry.

503
00:25:30,040 --> 00:25:32,600
Speaker 14: Absolutely, it's happening both with the big players and also

504
00:25:32,640 --> 00:25:34,879
Speaker 14: the small players. So you know, the startups are not

505
00:25:36,200 --> 00:25:39,560
Speaker 14: isolated from these concerns either. You know, we've seen across

506
00:25:39,640 --> 00:25:41,880
Speaker 14: many of our companies in average of about thirty percent

507
00:25:41,960 --> 00:25:45,200
Speaker 14: pullbacks and staff and we expect that to continue. And

508
00:25:45,560 --> 00:25:47,800
Speaker 14: all those companies are either doing the same on a

509
00:25:47,880 --> 00:25:50,360
Speaker 14: top line basis or actually better. And so I think

510
00:25:50,400 --> 00:25:52,600
Speaker 14: that just shows you that we got inefficient. And I

511
00:25:52,640 --> 00:25:54,720
Speaker 14: think that's a lesson learned in games, it's a lesson

512
00:25:54,800 --> 00:25:58,080
Speaker 14: learned in tech. And as I said before, money is

513
00:25:58,200 --> 00:26:00,760
Speaker 14: not cheap anymore, and people it's very hard for them

514
00:26:00,800 --> 00:26:03,080
Speaker 14: to raise both in public and also in private settings

515
00:26:03,119 --> 00:26:03,600
Speaker 14: for capital.

516
00:26:04,760 --> 00:26:08,160
Speaker 4: Yeah, I think long term we're talking about generative AI

517
00:26:08,440 --> 00:26:12,200
Speaker 4: being a tool to accelerate game development. But right now,

518
00:26:12,400 --> 00:26:15,520
Speaker 4: I mean, I'm playing PlayStation. Right now, I'm playing Switch.

519
00:26:15,960 --> 00:26:18,480
Speaker 4: I'm enjoying the games, but there isn't sort of one

520
00:26:18,600 --> 00:26:23,200
Speaker 4: title or trend or thing that I'm really excited about

521
00:26:23,240 --> 00:26:26,320
Speaker 4: in twenty twenty four. Maybe Star Wars Outlaws shout out

522
00:26:26,680 --> 00:26:28,680
Speaker 4: that game, it's going to be incredible, But do you

523
00:26:28,720 --> 00:26:30,600
Speaker 4: see what I mean? There isn't a sort of short

524
00:26:30,720 --> 00:26:34,520
Speaker 4: term catalyst to drive a spending cycle. I wondered if

525
00:26:34,560 --> 00:26:36,040
Speaker 4: you agree with that, Jason.

526
00:26:37,119 --> 00:26:39,240
Speaker 14: I think you're gonna have to look to the UGC platforms.

527
00:26:39,280 --> 00:26:41,040
Speaker 14: You know, you're gonna have to look to Roadblocks. You're

528
00:26:41,040 --> 00:26:43,320
Speaker 14: gonna have to look to UFN with Epic and Fortnite

529
00:26:43,400 --> 00:26:46,960
Speaker 14: obviously huge announcements there with the Disney investment, I think

530
00:26:47,000 --> 00:26:49,160
Speaker 14: you're gonna see a lot of new titles come from

531
00:26:49,320 --> 00:26:53,040
Speaker 14: those those regions versus you know, kind of you standalone

532
00:26:53,160 --> 00:26:57,600
Speaker 14: huge titles that you see more historically announced by the

533
00:26:57,880 --> 00:27:00,560
Speaker 14: major groups. So I would tell you go on Rodblocks,

534
00:27:00,600 --> 00:27:03,200
Speaker 14: let's go plan on Fortnite together. There's some exciting things

535
00:27:03,240 --> 00:27:07,360
Speaker 14: coming down the pipeline there, but definitely noted. Also, the big,

536
00:27:07,480 --> 00:27:09,760
Speaker 14: big disappointment in the games industry obviously is that Grand

537
00:27:09,800 --> 00:27:12,920
Speaker 14: Theft Auto got delayed to twenty twenty five. That was

538
00:27:12,960 --> 00:27:15,960
Speaker 14: a huge disappointment for many and probably sounds like for

539
00:27:16,080 --> 00:27:19,320
Speaker 14: you at as well. So yes, while in the games

540
00:27:19,320 --> 00:27:21,440
Speaker 14: industry we're gonna have to make do with UGC for

541
00:27:21,480 --> 00:27:23,760
Speaker 14: a little bit, there are some other exciting titles coming

542
00:27:23,840 --> 00:27:25,080
Speaker 14: out here, and I think we're gonna have to make

543
00:27:25,160 --> 00:27:27,920
Speaker 14: do with some of the older content for the rest

544
00:27:27,960 --> 00:27:28,280
Speaker 14: of the year.

545
00:27:29,160 --> 00:27:32,480
Speaker 4: Okay. One, let's play Fortnite. Two roadblocks okay, three, Yes,

546
00:27:32,760 --> 00:27:38,600
Speaker 4: very sad about DJs niver A managing partner, Jason Chapman,

547
00:27:38,640 --> 00:27:41,000
Speaker 4: you bring it all. Thank you so much, Okay. I've

548
00:27:41,000 --> 00:27:42,600
Speaker 4: been waiting to show you this for a long time.

549
00:27:42,760 --> 00:27:46,840
Speaker 4: In twenty twenty three, San Francisco positioned itself as the

550
00:27:46,960 --> 00:27:49,720
Speaker 4: place to be when it came to AI and investors

551
00:27:49,840 --> 00:27:52,840
Speaker 4: back that title. But what happens next? I looked at

552
00:27:52,880 --> 00:27:56,760
Speaker 4: what's really going on in our city. For Bloomberg regionals,

553
00:27:56,800 --> 00:28:03,120
Speaker 4: welcome to the real Cerebral Valley. This is the Hayes

554
00:28:03,240 --> 00:28:07,800
Speaker 4: Valley neighborhood of San Francisco, is characterized by the painted ladies,

555
00:28:08,160 --> 00:28:11,800
Speaker 4: hip shops and restaurants, and it's fairly diminutive size.

556
00:28:12,359 --> 00:28:15,640
Speaker 2: Blink. I mean you miss it, really, But tech industry

557
00:28:15,720 --> 00:28:18,359
Speaker 2: local sea Hayes Valley, or Cerebral Valley as they like

558
00:28:18,440 --> 00:28:21,800
Speaker 2: to call it. As a metaphor for what's happening in

559
00:28:21,920 --> 00:28:27,720
Speaker 2: San Francisco, it became the epicenter for new and growing

560
00:28:27,840 --> 00:28:31,120
Speaker 2: AI companies, part of a vanguard that could help SF

561
00:28:31,200 --> 00:28:34,280
Speaker 2: bring back business that slipped out to Silicon Valley or

562
00:28:34,320 --> 00:28:37,119
Speaker 2: even out of state. I talked to a couple of

563
00:28:37,200 --> 00:28:39,920
Speaker 2: local tech luminaries to see how much of this is

564
00:28:40,040 --> 00:28:42,280
Speaker 2: real and how much of this is hype.

565
00:28:42,840 --> 00:28:45,280
Speaker 15: That every revolution in Silicon Valley sort of happens in

566
00:28:45,400 --> 00:28:48,479
Speaker 15: waves like that. There's personal computer as the Internet, your

567
00:28:48,600 --> 00:28:52,160
Speaker 15: mobile phones, and I think that this is as big

568
00:28:52,360 --> 00:28:56,520
Speaker 15: or bigger, because what you see is now software being

569
00:28:56,880 --> 00:29:00,520
Speaker 15: able to reason, and that's transforming not just one or

570
00:29:00,560 --> 00:29:03,280
Speaker 15: two industries, but absolutely all of them at the same time.

571
00:29:03,800 --> 00:29:07,440
Speaker 4: So what happens in the next twelve months with artifisial

572
00:29:07,480 --> 00:29:08,560
Speaker 4: intelligence in this city.

573
00:29:08,840 --> 00:29:10,320
Speaker 15: One of the big things that we talk about in

574
00:29:10,360 --> 00:29:13,520
Speaker 15: San Francisco is the doom loop. The commercial buildings are

575
00:29:13,600 --> 00:29:18,120
Speaker 15: completely empty, prices for real estate have completely crashed. But

576
00:29:18,800 --> 00:29:22,080
Speaker 15: the silver lining here is we're going to fill every

577
00:29:22,160 --> 00:29:25,840
Speaker 15: one of those skyscrapers with thousands of very high paying

578
00:29:25,960 --> 00:29:30,920
Speaker 15: jobs that then create software and products that basically serve

579
00:29:31,040 --> 00:29:32,360
Speaker 15: billions of people out there.

580
00:29:32,800 --> 00:29:35,000
Speaker 4: And some folks on there that don't agree with that

581
00:29:35,120 --> 00:29:37,719
Speaker 4: view as well. Check out the full documentary on Bloomberger Regionals,

582
00:29:37,760 --> 00:29:40,160
Speaker 4: Bloomberg dot Com and all the social platforms.

583
00:29:40,240 --> 00:29:44,440
Speaker 3: Caroline meanwhile ed look vc Bakklana shrank It's net losses

584
00:29:44,760 --> 00:29:46,920
Speaker 3: I seventy six percent in twenty twenty three. We're talking

585
00:29:46,920 --> 00:29:50,240
Speaker 3: about a fintech buy now, pay later firm over there

586
00:29:50,280 --> 00:29:53,240
Speaker 3: in Europe, not instant people vary that makes preparations for

587
00:29:53,320 --> 00:29:53,560
Speaker 3: one of.

588
00:29:53,560 --> 00:29:55,120
Speaker 5: Its biggest IPOs of the year.

589
00:29:55,200 --> 00:29:58,080
Speaker 3: Now the fintech company is even briefly moving into profit

590
00:29:58,200 --> 00:30:01,000
Speaker 3: that happened in the previous three months, accelerating its faith

591
00:30:01,000 --> 00:30:04,120
Speaker 3: it grows as we've seen the targeted this further expansion

592
00:30:04,160 --> 00:30:06,440
Speaker 3: here right here in the United States, which is now

593
00:30:06,600 --> 00:30:08,000
Speaker 3: as big as market now.

594
00:30:08,280 --> 00:30:09,400
Speaker 5: Interestingly, part of.

595
00:30:09,440 --> 00:30:12,000
Speaker 3: Its growth has actually been thanks to open AI powered

596
00:30:12,080 --> 00:30:15,400
Speaker 3: AI Assistant, which Klara says is doing the equivalent get

597
00:30:15,480 --> 00:30:18,880
Speaker 3: this of seven hundred full time agents and now's two

598
00:30:18,920 --> 00:30:22,600
Speaker 3: point three million conversations with the first month of being deployed. Now,

599
00:30:22,680 --> 00:30:25,480
Speaker 3: that announcement was enough to send the shares as you'll

600
00:30:25,480 --> 00:30:30,120
Speaker 3: see of Teleperformance down twenty nine percent, hitting their lowest

601
00:30:30,200 --> 00:30:31,760
Speaker 3: level since late twenty sixteen.

602
00:30:32,040 --> 00:30:34,920
Speaker 5: We say, of course, the anountsourcing company that is all about.

603
00:30:35,240 --> 00:30:38,120
Speaker 3: We're on the phone and people, let's see what wells,

604
00:30:38,240 --> 00:30:40,880
Speaker 3: what's coming up when it comes to AI disruption. A

605
00:30:41,000 --> 00:30:44,360
Speaker 3: new AI image generator is in town, and we've just

606
00:30:44,480 --> 00:30:48,520
Speaker 3: got the backing of Andrewson Horowitz. More on that one next. Meanwhile,

607
00:30:48,560 --> 00:30:49,880
Speaker 3: let's just keep an eye on some of the public

608
00:30:49,920 --> 00:30:51,600
Speaker 3: P trader companies that we've got to be looking at.

609
00:30:51,640 --> 00:30:55,400
Speaker 3: Applied materials. This one down, as you'll see, just some

610
00:30:55,640 --> 00:30:57,960
Speaker 3: two point three percent. This is more of a legal

611
00:30:58,320 --> 00:31:01,680
Speaker 3: ramification for this company. The US is renewing an inquiry

612
00:31:01,760 --> 00:31:05,400
Speaker 3: into applying materials as Chinese business. They're underscoring Washing his

613
00:31:05,480 --> 00:31:08,000
Speaker 3: efforts thrilly to curb chips being sent.

614
00:31:07,920 --> 00:31:10,040
Speaker 5: To that particular company or off by two point four percent.

615
00:31:10,480 --> 00:31:11,640
Speaker 5: This is blue their technology.

616
00:31:26,240 --> 00:31:29,520
Speaker 4: New funding for AI companies keeps pouring in, and this

617
00:31:29,720 --> 00:31:33,680
Speaker 4: time it's Ideogram announcing eighty million dollars in a series

618
00:31:33,760 --> 00:31:36,840
Speaker 4: A funding round led by Andres and Horowitz and launching

619
00:31:37,240 --> 00:31:40,920
Speaker 4: a new version of their image generation model, joining us

620
00:31:41,000 --> 00:31:44,040
Speaker 4: with the Deets Bloom both Rachel Metz The timing of

621
00:31:44,160 --> 00:31:49,080
Speaker 4: this is astonishing. But let's focus first on Ideogram. What

622
00:31:49,320 --> 00:31:51,120
Speaker 4: is it that they do? Who are they? What can

623
00:31:51,200 --> 00:31:51,960
Speaker 4: you tell us about them?

624
00:31:53,000 --> 00:31:56,600
Speaker 12: Sure so, Idea Graham was formed by some people who

625
00:31:56,960 --> 00:32:01,400
Speaker 12: worked on Google's image generation technology in this and they

626
00:32:01,520 --> 00:32:04,600
Speaker 12: launched their first version of their software last year, and

627
00:32:05,160 --> 00:32:08,600
Speaker 12: basically they are concentrating on making it easy for people

628
00:32:08,720 --> 00:32:12,720
Speaker 12: to come up with really good AI generated images without

629
00:32:12,760 --> 00:32:15,080
Speaker 12: having to put in like a really leathy written prompt.

630
00:32:15,480 --> 00:32:17,960
Speaker 12: And also typography. They're really focused on making it so

631
00:32:18,040 --> 00:32:20,360
Speaker 12: you can have legible words in your images.

632
00:32:21,120 --> 00:32:25,960
Speaker 3: Ironically, it was led by several former Google employees. That

633
00:32:26,120 --> 00:32:29,200
Speaker 3: timing therefore juxtaposed with the issues going on with Google,

634
00:32:29,280 --> 00:32:34,560
Speaker 3: but also the exuberance around video generation and more image

635
00:32:34,600 --> 00:32:36,600
Speaker 3: based focus or ZA for example.

636
00:32:37,200 --> 00:32:39,280
Speaker 5: This is why we're governering this sort of valuation.

637
00:32:39,440 --> 00:32:43,640
Speaker 12: Rachel, Yeah, I mean, well, I think that the funding

638
00:32:43,680 --> 00:32:46,200
Speaker 12: that they're getting this time around is it's a lot

639
00:32:46,240 --> 00:32:47,920
Speaker 12: of money for a company that's not very old. But

640
00:32:47,960 --> 00:32:50,280
Speaker 12: I think it's really reflective in part of how much

641
00:32:50,720 --> 00:32:54,160
Speaker 12: computing power you need, which is very expensive. In order

642
00:32:54,280 --> 00:32:57,760
Speaker 12: to train these models and to run these models. You know,

643
00:32:57,800 --> 00:32:59,800
Speaker 12: they need to hire some more employees. I think they're

644
00:32:59,800 --> 00:33:03,040
Speaker 12: still a pretty lean stirrup over there. But as you said,

645
00:33:03,080 --> 00:33:05,640
Speaker 12: the timing, yes, very interesting. In fact, one of the things,

646
00:33:05,800 --> 00:33:07,920
Speaker 12: one of the things that's very interesting in addition to

647
00:33:08,360 --> 00:33:10,840
Speaker 12: this funding that they're getting, they have this new model.

648
00:33:11,040 --> 00:33:14,360
Speaker 12: One of the new features they have allows them to

649
00:33:14,960 --> 00:33:18,320
Speaker 12: people to turn on or off automatically lengthening their prompts,

650
00:33:18,520 --> 00:33:20,400
Speaker 12: which is something that some companies do but they don't

651
00:33:20,440 --> 00:33:22,920
Speaker 12: always talk about to users. So this way users will

652
00:33:23,000 --> 00:33:25,720
Speaker 12: it'll be very obvious to users whether that's happening or not.

653
00:33:26,120 --> 00:33:29,680
Speaker 3: Prompt transformation something we're talking about several times today that

654
00:33:29,720 --> 00:33:32,560
Speaker 3: hasn't really been discussed before. And also I mean talking

655
00:33:32,560 --> 00:33:36,320
Speaker 3: at timing stability AI's latest image generator as well, got

656
00:33:36,360 --> 00:33:37,520
Speaker 3: a lot of people talking Rachel.

657
00:33:37,560 --> 00:33:39,960
Speaker 5: Met's absolutely brilliant to have you on as always. Thank you.

658
00:33:40,720 --> 00:33:43,240
Speaker 3: Now let's stick on the world of venture capital and

659
00:33:43,320 --> 00:33:45,120
Speaker 3: our VC spotlight today. We want to take a look

660
00:33:45,200 --> 00:33:48,760
Speaker 3: at Black Tech Nation Ventures, so Pittsburg based venture firm

661
00:33:49,000 --> 00:33:51,240
Speaker 3: which just announced the close of a fifteen million dollar

662
00:33:51,320 --> 00:33:54,800
Speaker 3: inaugural fund to back software startups led by founders from

663
00:33:54,880 --> 00:33:58,600
Speaker 3: diverse backgrounds. Let's bring in general partner David Motley for more.

664
00:33:58,600 --> 00:34:00,880
Speaker 3: Who's staying up late for us while traveling in Hong Kong?

665
00:34:01,040 --> 00:34:03,120
Speaker 3: But where will your money be deployed?

666
00:34:03,200 --> 00:34:05,719
Speaker 5: David? Is it more US focus? Is it Pittsburgh focused?

667
00:34:05,840 --> 00:34:05,960
Speaker 14: Is it?

668
00:34:06,880 --> 00:34:07,720
Speaker 5: Are you agnostic?

669
00:34:09,600 --> 00:34:10,040
Speaker 16: Caroline?

670
00:34:10,080 --> 00:34:10,320
Speaker 9: Thank you?

671
00:34:10,440 --> 00:34:12,920
Speaker 16: Let me start bus saying we really appreciate the opportunity

672
00:34:13,080 --> 00:34:16,120
Speaker 16: on your program. Excited about the attention that our fund

673
00:34:16,239 --> 00:34:20,120
Speaker 16: is getting Fund one launched honest way, and we're looking

674
00:34:20,120 --> 00:34:22,320
Speaker 16: forward to earning the right to launch a much larger

675
00:34:22,480 --> 00:34:22,960
Speaker 16: fund too.

676
00:34:23,239 --> 00:34:24,640
Speaker 9: To follow with.

677
00:34:26,160 --> 00:34:28,440
Speaker 16: With respect to where we are investing, we are investing

678
00:34:28,560 --> 00:34:32,640
Speaker 16: nationally and technology funds that are led by black and

679
00:34:33,000 --> 00:34:38,600
Speaker 16: diverse founders with an eye towards delivering outsize returns to

680
00:34:38,920 --> 00:34:39,680
Speaker 16: our founders.

681
00:34:40,960 --> 00:34:43,640
Speaker 3: Thinking of your founders, you've already invested in a whole

682
00:34:43,640 --> 00:34:45,960
Speaker 3: gamut of like the future of finance with MTech. You're

683
00:34:45,960 --> 00:34:48,319
Speaker 3: looking at good find which is helping sort of well

684
00:34:48,360 --> 00:34:50,759
Speaker 3: food trucks and the like. But I'm interested, David, you

685
00:34:50,840 --> 00:34:54,120
Speaker 3: say about the attention your fund is garnered. I'm going

686
00:34:54,160 --> 00:34:57,120
Speaker 3: to be brutally honest. Fifty million dollars isn't that much

687
00:34:57,200 --> 00:34:59,279
Speaker 3: in the grand scheme of things at VC. But what's

688
00:34:59,320 --> 00:35:01,960
Speaker 3: sad is it's large when it comes to backing black

689
00:35:02,040 --> 00:35:06,040
Speaker 3: founders and raised by black vcs. Why is it still

690
00:35:06,640 --> 00:35:08,920
Speaker 3: only a fifty million if you can, dare say only

691
00:35:09,640 --> 00:35:11,319
Speaker 3: and what are some of the headwinds that you've found

692
00:35:11,400 --> 00:35:12,359
Speaker 3: trying to raise this fund?

693
00:35:13,960 --> 00:35:16,520
Speaker 16: Caroline, your point is well made. Fifty million dollars in

694
00:35:16,600 --> 00:35:19,200
Speaker 16: the scheme of venture is a very small fund, we

695
00:35:19,320 --> 00:35:21,800
Speaker 16: do have the opportunity to leverage what we're doing with

696
00:35:21,920 --> 00:35:25,040
Speaker 16: our fund with other like minded funds and deliver much

697
00:35:25,160 --> 00:35:28,040
Speaker 16: larger scale relevance for the companies in which we invest.

698
00:35:28,840 --> 00:35:34,920
Speaker 16: One of the reasons why venture is retreated for black

699
00:35:35,000 --> 00:35:38,120
Speaker 16: lead funds is that the overall venture market declined in

700
00:35:38,320 --> 00:35:44,239
Speaker 16: twenty two twenty three, after watershed years, if you will,

701
00:35:44,280 --> 00:35:47,400
Speaker 16: in twenty and twenty one. But in particular, now that

702
00:35:47,600 --> 00:35:51,759
Speaker 16: the attention that was garnered on the backside of the

703
00:35:51,960 --> 00:35:54,480
Speaker 16: tragedy with George Floyd is retreating in the rearview, mirror

704
00:35:55,160 --> 00:35:57,240
Speaker 16: funds are also moving away from this sector.

705
00:36:00,040 --> 00:36:02,720
Speaker 4: David, I'm looking at this data point. Around seven hundred

706
00:36:02,760 --> 00:36:07,600
Speaker 4: million dollars of venture money went to blackfounded startups in

707
00:36:07,680 --> 00:36:11,080
Speaker 4: the US last year, below a billion for the first

708
00:36:11,120 --> 00:36:14,600
Speaker 4: time in a long time. What's the trajectory looking forward?

709
00:36:14,840 --> 00:36:16,560
Speaker 4: You know, thank you for your time on the show.

710
00:36:17,040 --> 00:36:19,000
Speaker 4: We you know, we're very interesting talk about the fund.

711
00:36:19,080 --> 00:36:21,160
Speaker 4: But I'm assuming that you feel like you can't do

712
00:36:21,280 --> 00:36:24,160
Speaker 4: this on your own. Are you getting support from other

713
00:36:24,760 --> 00:36:27,080
Speaker 4: firms who are raising funds with the same intent.

714
00:36:30,200 --> 00:36:33,239
Speaker 16: So the answer to your question is yes, we are

715
00:36:33,360 --> 00:36:36,160
Speaker 16: getting a lot of support and interest from other like

716
00:36:36,280 --> 00:36:40,040
Speaker 16: minded funds. We have in that convened a forum for

717
00:36:40,640 --> 00:36:43,840
Speaker 16: black led funds across the country. When we take a

718
00:36:43,920 --> 00:36:45,840
Speaker 16: look at what's it going to take to try and

719
00:36:45,920 --> 00:36:49,000
Speaker 16: move the needle, it's going to be funds like ours

720
00:36:49,160 --> 00:36:52,440
Speaker 16: proving out that there is alpha and the sector there

721
00:36:52,520 --> 00:36:56,560
Speaker 16: is the opportunity to deliver success from funds like ours,

722
00:36:56,600 --> 00:36:59,399
Speaker 16: from founders like the ones we're investing in, and prove

723
00:36:59,440 --> 00:37:02,000
Speaker 16: out that this not just an initiative, but an investment

724
00:37:02,080 --> 00:37:05,800
Speaker 16: category that can provide venture like returns.

725
00:37:07,000 --> 00:37:10,440
Speaker 5: Well, for now, like sorry, Ed, oh, no, Cara, go

726
00:37:10,520 --> 00:37:10,680
Speaker 5: for it.

727
00:37:11,560 --> 00:37:13,359
Speaker 3: Well, I mean it's notable that for now you got

728
00:37:13,360 --> 00:37:15,920
Speaker 3: back in from alphabet First, National Bank, Mark, Cuban to

729
00:37:16,000 --> 00:37:16,480
Speaker 3: name a few.

730
00:37:16,640 --> 00:37:20,640
Speaker 4: Ed all right, Black Technicians, General Partner David Mottley.

731
00:37:20,880 --> 00:37:21,560
Speaker 12: We got to go.

732
00:37:21,760 --> 00:37:23,960
Speaker 4: But by the way, super grateful for you joining us

733
00:37:24,000 --> 00:37:25,480
Speaker 4: in Hong Kong. Very late there.

734
00:37:25,760 --> 00:37:26,840
Speaker 5: We'll have you back in the future.

735
00:37:26,840 --> 00:37:38,320
Speaker 4: You appreciate it. Date automation and marketing firm clavi o

736
00:37:38,440 --> 00:37:41,080
Speaker 4: out with four quarter earnings after the bell last night.

737
00:37:41,160 --> 00:37:44,400
Speaker 4: It's only the second report they've done as a public company.

738
00:37:44,800 --> 00:37:47,400
Speaker 4: Revenue beat estimates in the final few months of the year,

739
00:37:47,480 --> 00:37:51,680
Speaker 4: Johnny us Now Clavier CEO, Andrew Bioleki Andrew, they talk

740
00:37:51,760 --> 00:37:55,680
Speaker 4: about that period of the year being seasonally strong. But

741
00:37:55,800 --> 00:37:57,480
Speaker 4: if I'm trying to pick out a story, it's that

742
00:37:57,560 --> 00:38:00,799
Speaker 4: you added a lot of new customers, and I wonder

743
00:38:00,880 --> 00:38:03,680
Speaker 4: if that was a direct result of the IPO. You know,

744
00:38:03,800 --> 00:38:06,600
Speaker 4: one rationale for going public is let's get our name

745
00:38:06,640 --> 00:38:06,960
Speaker 4: out there.

746
00:38:07,280 --> 00:38:10,239
Speaker 17: Yeah, well, first, thanks for having me. You know, we're

747
00:38:10,239 --> 00:38:13,520
Speaker 17: really excited about our results in the fourth quarter. You know,

748
00:38:13,680 --> 00:38:15,400
Speaker 17: as you mentioned, we work with a lot of retail

749
00:38:15,440 --> 00:38:18,120
Speaker 17: and e commerce businesses and brands over one hundred and

750
00:38:18,160 --> 00:38:22,480
Speaker 17: forty thousand of them in Q four. Is there time

751
00:38:22,520 --> 00:38:25,319
Speaker 17: to shine and we're really excited that at last year

752
00:38:26,160 --> 00:38:29,160
Speaker 17: we help those businesses generate over fifty billion dollars in sales.

753
00:38:29,320 --> 00:38:32,120
Speaker 17: You know, Chlaviya, We're all about helping businesses and brands

754
00:38:32,200 --> 00:38:36,200
Speaker 17: build first party relationships with their customers, with then consumers,

755
00:38:36,800 --> 00:38:38,880
Speaker 17: and then building great experiences for them that helps them

756
00:38:38,960 --> 00:38:39,600
Speaker 17: drive real growth.

757
00:38:40,440 --> 00:38:42,600
Speaker 3: Let's just talk a little bit about the where the

758
00:38:42,680 --> 00:38:45,239
Speaker 3: headwinds are or indeed why the share price is down.

759
00:38:45,280 --> 00:38:46,920
Speaker 3: I know, risk assets are down across the board at

760
00:38:46,960 --> 00:38:48,680
Speaker 3: the moment. Andrew and I'm sure we don't look at

761
00:38:48,680 --> 00:38:50,759
Speaker 3: the day to day vagaries of your share price, but

762
00:38:50,840 --> 00:38:53,160
Speaker 3: it is lower, perhaps more on our earnings for share perspective,

763
00:38:53,239 --> 00:38:56,080
Speaker 3: and maybe Morgan Stanley coming out with a price target downgrade.

764
00:38:56,360 --> 00:38:59,360
Speaker 3: I'm interested as to what, if anything, you're having to

765
00:38:59,520 --> 00:39:01,520
Speaker 3: spend a lot of money on even as you're driving

766
00:39:01,600 --> 00:39:03,280
Speaker 3: up revenue and growth in terms of users.

767
00:39:03,719 --> 00:39:03,919
Speaker 9: Sure.

768
00:39:04,040 --> 00:39:05,440
Speaker 17: Yeah, I mean we don't spend too much time thinking

769
00:39:05,440 --> 00:39:06,799
Speaker 17: about the stock price day over day. I mean, I'll

770
00:39:06,800 --> 00:39:09,360
Speaker 17: tell you what I'm excited about. Yeah, yesterday we launched

771
00:39:09,920 --> 00:39:14,279
Speaker 17: Klavyo AI, which is our artificial intelligence toolkit for all

772
00:39:14,320 --> 00:39:17,520
Speaker 17: of our businesses and brands. You know, as we look forward,

773
00:39:17,880 --> 00:39:21,640
Speaker 17: we build claveo is a great platform for any consumer

774
00:39:21,719 --> 00:39:25,800
Speaker 17: business to build direct relationships with their end customers. We

775
00:39:25,880 --> 00:39:28,880
Speaker 17: think the future of marketing, and really CRM is all

776
00:39:28,920 --> 00:39:33,400
Speaker 17: about autonomous. Autonomous doesn't mean that marketers are out of jobs.

777
00:39:33,480 --> 00:39:36,840
Speaker 17: It means that they're back to doing strategy and creative

778
00:39:36,840 --> 00:39:39,920
Speaker 17: work work on the brands and out of the minutia. So,

779
00:39:40,920 --> 00:39:42,880
Speaker 17: you know, we launched a series of features that I

780
00:39:42,960 --> 00:39:45,120
Speaker 17: think are going to becomes table stakes, things like the

781
00:39:45,200 --> 00:39:49,440
Speaker 17: ability to create email content from a prompts or automatically

782
00:39:49,640 --> 00:39:55,040
Speaker 17: optimize that email or SMS or your website without a

783
00:39:55,120 --> 00:39:57,080
Speaker 17: marketer having to do all the editing and testing. The

784
00:39:57,160 --> 00:39:59,760
Speaker 17: AI can do it for you. And we're really excited

785
00:39:59,760 --> 00:40:02,680
Speaker 17: about the results that that's driving for customers. You know,

786
00:40:02,880 --> 00:40:06,600
Speaker 17: just as an example, willow Tree start using some of

787
00:40:06,680 --> 00:40:09,560
Speaker 17: our predictive analytics, some of our machine learning based artificial

788
00:40:09,600 --> 00:40:14,440
Speaker 17: intelligence based tooling, and that increased their revenue from email

789
00:40:14,719 --> 00:40:17,880
Speaker 17: by over fifty percent. You know what's great is I

790
00:40:17,920 --> 00:40:20,320
Speaker 17: think artificial intelligence is going to allow a lot of

791
00:40:20,400 --> 00:40:23,280
Speaker 17: marketers to not only free up time be more productive,

792
00:40:23,360 --> 00:40:26,200
Speaker 17: but it's going to drive better results. And you know

793
00:40:26,239 --> 00:40:28,759
Speaker 17: that's we're spending a lot of time and you know,

794
00:40:28,840 --> 00:40:32,000
Speaker 17: frankly human capital investing in building out that functionality.

795
00:40:32,160 --> 00:40:34,640
Speaker 5: Okay, so you hiring on that front. Therefore, Andrew are you?

796
00:40:35,120 --> 00:40:37,080
Speaker 5: Are you going to do more with less when it

797
00:40:37,160 --> 00:40:38,239
Speaker 5: comes to your own people power?

798
00:40:39,560 --> 00:40:42,200
Speaker 17: Yeah, well we've already been you know, we founded Klavyo,

799
00:40:42,960 --> 00:40:45,080
Speaker 17: we bootstrapped our business, so we've always been a very

800
00:40:45,120 --> 00:40:48,840
Speaker 17: efficient company. But yeah, we're hiring a lot of folks

801
00:40:49,400 --> 00:40:52,600
Speaker 17: on the artificial intelligence and product and engineering side. We're

802
00:40:52,600 --> 00:40:56,920
Speaker 17: also seeing great demand from larger brands, brands like Mattel

803
00:40:57,239 --> 00:41:00,399
Speaker 17: Good American Marine Layer that are choosing Clay. So we're

804
00:41:00,400 --> 00:41:03,359
Speaker 17: also investing in increasing our sales capacity as we see

805
00:41:03,400 --> 00:41:05,640
Speaker 17: a lot of demand from mid market and enterprise brands

806
00:41:05,680 --> 00:41:07,200
Speaker 17: that want to move on to art.

807
00:41:07,560 --> 00:41:09,560
Speaker 4: Andrew, we just have fifteen seconds. How much do you

808
00:41:09,680 --> 00:41:13,680
Speaker 4: think you spent R and D wise and launching clavy

809
00:41:13,719 --> 00:41:14,080
Speaker 4: o AI.

810
00:41:14,600 --> 00:41:17,520
Speaker 17: Yeah, we've beneficient at it. Actually, our costs to build

811
00:41:17,560 --> 00:41:19,480
Speaker 17: that have not been as large as some of the

812
00:41:19,560 --> 00:41:22,800
Speaker 17: foundational large language models you see out there. And the

813
00:41:22,880 --> 00:41:24,880
Speaker 17: other really great part for us is because we're so

814
00:41:25,040 --> 00:41:27,800
Speaker 17: close to driving value, we can show our customers the

815
00:41:27,880 --> 00:41:30,800
Speaker 17: actual revenue that artificial intelligence is creating for them. I

816
00:41:30,840 --> 00:41:32,719
Speaker 17: think there's going to be opportunities for us that as

817
00:41:32,760 --> 00:41:35,480
Speaker 17: our customers, you know, see more of that value that

818
00:41:35,600 --> 00:41:38,480
Speaker 17: we can also partake in, you know, that value creation,

819
00:41:38,600 --> 00:41:39,239
Speaker 17: that monetization.

820
00:41:40,200 --> 00:41:42,680
Speaker 3: Well, enjoy that sunny day behind you. We appreciate you

821
00:41:42,760 --> 00:41:45,080
Speaker 3: coming on talking us through the numbers and the AI focus.

822
00:41:45,440 --> 00:41:48,760
Speaker 3: Glavy O CEO Andrew Vaileki of course on the second

823
00:41:48,880 --> 00:41:50,960
Speaker 3: set of earnings as a public company.

824
00:41:51,040 --> 00:41:53,000
Speaker 5: And meanwhile, that does it for this edition of Bloomberg

825
00:41:53,000 --> 00:41:54,440
Speaker 5: Technology A.

826
00:41:54,560 --> 00:41:57,279
Speaker 4: Huge show, a huge week. Check out the podcast. You

827
00:41:57,360 --> 00:42:00,399
Speaker 4: know where to find it Bloomberg, Apple, Spotify, and also

828
00:42:00,480 --> 00:42:03,680
Speaker 4: on iHeart. It feels like earning seasons, kinda in the

829
00:42:03,760 --> 00:42:07,640
Speaker 4: rear view mirror, but AI just doesn't go away from

830
00:42:07,680 --> 00:42:11,440
Speaker 4: New York City and San Francisco. This is bloomboat technology.