1
00:00:01,280 --> 00:00:05,920
Speaker 1: From Marhart where Innovation, money and power Collie in Silicon

2
00:00:06,000 --> 00:00:10,440
Speaker 1: Vallet NBN. This is Bloomberg Technology with Caroline Hyde and

3
00:00:10,680 --> 00:00:11,760
Speaker 1: Ed Ludlow.

4
00:00:25,480 --> 00:00:26,920
Speaker 2: Live from New York and San Francisco.

5
00:00:27,120 --> 00:00:30,960
Speaker 3: This is Bloomberg Technology coming up and Vidio needs benchmarks

6
00:00:31,000 --> 00:00:33,559
Speaker 3: in a big week for markets as investors, a weight

7
00:00:33,640 --> 00:00:36,440
Speaker 3: economic data and a fed Pal speech.

8
00:00:36,360 --> 00:00:37,280
Speaker 2: Out of Jackson Hole.

9
00:00:38,440 --> 00:00:42,000
Speaker 4: Chinese tech stock sink overnight the latest and why Walmart's

10
00:00:42,080 --> 00:00:45,120
Speaker 4: three point six billion dollar JD dot com steak sale

11
00:00:45,440 --> 00:00:48,600
Speaker 4: has put e commerce companies under pressure, and Ford pulls.

12
00:00:48,320 --> 00:00:51,040
Speaker 3: Back further from vs scrapping an all electric suv and

13
00:00:51,080 --> 00:00:55,360
Speaker 3: taking a one point nine billion dollar charge to shift strategies. Again,

14
00:00:55,920 --> 00:00:58,160
Speaker 3: that's all to come a first as checking on these markets.

15
00:00:58,400 --> 00:01:00,800
Speaker 3: Some economic data that we are currently digesting is their

16
00:01:00,800 --> 00:01:04,160
Speaker 3: revisions to the jobs data. And yes they're more than

17
00:01:04,160 --> 00:01:06,840
Speaker 3: eight hundred thousand, but not enough to really change the

18
00:01:06,920 --> 00:01:09,200
Speaker 3: dynamic move of the markets right now. Are still holding

19
00:01:09,200 --> 00:01:11,039
Speaker 3: onto our gains up a tenth of a percent, yes,

20
00:01:11,080 --> 00:01:13,440
Speaker 3: and come off the highs, but still managing to cling

21
00:01:13,520 --> 00:01:15,440
Speaker 3: on to an upcycle when it comes to the technology

22
00:01:15,520 --> 00:01:18,040
Speaker 3: name stock six hundred over in Europe, also powering ahead,

23
00:01:18,200 --> 00:01:21,880
Speaker 3: Rock six tens percent. Interestingly, a rebound in Chinese tech

24
00:01:22,000 --> 00:01:23,800
Speaker 3: names as well. I shine a light on the Nasdak

25
00:01:23,840 --> 00:01:25,679
Speaker 3: Golden Dragon, which I know you're going to dig into

26
00:01:25,680 --> 00:01:28,480
Speaker 3: some individual moves of ED, but yesterday under pressure, today's

27
00:01:28,480 --> 00:01:30,680
Speaker 3: still some individual names we're going to be shining a

28
00:01:30,760 --> 00:01:32,880
Speaker 3: light on and just take talk us through what you're

29
00:01:32,920 --> 00:01:33,360
Speaker 3: watching ED.

30
00:01:34,080 --> 00:01:36,880
Speaker 4: Yeah, I mean overnight, a lot of pressure on Hong

31
00:01:37,000 --> 00:01:41,000
Speaker 4: Kong listed tech shares. Why Walmart made three point six

32
00:01:41,040 --> 00:01:44,720
Speaker 4: billion dollars selling out of its stake in JD dot

33
00:01:44,760 --> 00:01:47,960
Speaker 4: Com actually JD confirming through the Hong Kong Exchange that

34
00:01:48,000 --> 00:01:49,240
Speaker 4: wal might no longer hold a steak.

35
00:01:49,320 --> 00:01:50,040
Speaker 5: We're going to hit that.

36
00:01:50,040 --> 00:01:53,720
Speaker 4: Story hard throughout the hour with our reporters who are

37
00:01:53,760 --> 00:01:55,840
Speaker 4: on it. But it's interesting to see, at least with

38
00:01:55,920 --> 00:02:00,200
Speaker 4: JD dot COM's ADRs. The pressure in videos top of mind.

39
00:02:00,000 --> 00:02:02,240
Speaker 4: And again we're at like one hundred and twenty nine

40
00:02:02,280 --> 00:02:04,320
Speaker 4: dollars a share, one hundred and twenty seven dollars a share,

41
00:02:04,400 --> 00:02:06,320
Speaker 4: not far off that one hundred and thirty five dollars

42
00:02:06,360 --> 00:02:08,720
Speaker 4: a share record we hit in June. There is a

43
00:02:08,720 --> 00:02:12,360
Speaker 4: lot of anticipation ahead of next week's earnings. August twenty

44
00:02:12,400 --> 00:02:15,239
Speaker 4: eight far after market in whether in video will get

45
00:02:15,280 --> 00:02:18,560
Speaker 4: back to that record level record heights led the market

46
00:02:18,639 --> 00:02:21,720
Speaker 4: right in this rebound we've seen since August or the

47
00:02:21,720 --> 00:02:24,920
Speaker 4: beginning of August, there's a lot of hope where the

48
00:02:24,960 --> 00:02:26,760
Speaker 4: hope translates to confidence.

49
00:02:26,760 --> 00:02:29,160
Speaker 3: Who knows, Carol, Yeah, and let's dig in to one

50
00:02:29,160 --> 00:02:31,040
Speaker 3: of the key stories being written on the terminal today

51
00:02:31,080 --> 00:02:33,720
Speaker 3: by our own round Lostellica joining us to already pass

52
00:02:33,800 --> 00:02:36,960
Speaker 3: through what the market sentiment is around in video whether

53
00:02:37,000 --> 00:02:40,480
Speaker 3: it can bring this macro level event come August the

54
00:02:40,520 --> 00:02:41,200
Speaker 3: twenty eighth.

55
00:02:41,320 --> 00:02:43,000
Speaker 2: And you talked to some key balls.

56
00:02:42,720 --> 00:02:46,040
Speaker 6: Out there, Yes, good morning, thanks for having me. So

57
00:02:46,080 --> 00:02:48,679
Speaker 6: it does seem like there is growing confidence that the

58
00:02:48,760 --> 00:02:51,720
Speaker 6: spending on AI, which has been really the central ball

59
00:02:51,840 --> 00:02:55,040
Speaker 6: thesis behind in video and really a major driver behind

60
00:02:55,240 --> 00:02:58,519
Speaker 6: overall tech games. People are pretty confident that spending is

61
00:02:58,560 --> 00:03:01,680
Speaker 6: going to remain pretty consistent. There were some concerns earlier

62
00:03:01,720 --> 00:03:05,320
Speaker 6: this month about how much ROI are these companies seeing

63
00:03:05,320 --> 00:03:05,840
Speaker 6: out of their.

64
00:03:05,720 --> 00:03:07,040
Speaker 5: AI investments so far.

65
00:03:07,520 --> 00:03:10,400
Speaker 6: The general thesis seems to be that even if the

66
00:03:10,520 --> 00:03:12,680
Speaker 6: ROI is a little bit delayed and it doesn't start

67
00:03:12,680 --> 00:03:16,200
Speaker 6: showing up in growth and improved efficiency for maybe a

68
00:03:16,200 --> 00:03:19,400
Speaker 6: few more quarters or even longer than that. There's still

69
00:03:19,400 --> 00:03:21,320
Speaker 6: going to be a ton of demand for these AI

70
00:03:21,480 --> 00:03:24,720
Speaker 6: chips and hardware and infrastructure, and that kind of tail

71
00:03:24,760 --> 00:03:27,920
Speaker 6: one is going to continue supporting Nvidia at least in

72
00:03:27,960 --> 00:03:28,760
Speaker 6: terms of its growth.

73
00:03:29,880 --> 00:03:30,040
Speaker 1: Right.

74
00:03:30,080 --> 00:03:31,000
Speaker 5: It's what for counting.

75
00:03:31,120 --> 00:03:33,960
Speaker 4: What's happened just in the last couple of weeks sold

76
00:03:33,960 --> 00:03:37,240
Speaker 4: me through the rally, the rebound in Nvidia shares.

77
00:03:37,920 --> 00:03:38,119
Speaker 1: Yeah.

78
00:03:38,160 --> 00:03:38,840
Speaker 5: Absolutely so.

79
00:03:38,880 --> 00:03:41,680
Speaker 6: We did see some pretty broad based weakness earlier in

80
00:03:41,680 --> 00:03:44,120
Speaker 6: this month. Some of that was related to the end

81
00:03:44,120 --> 00:03:46,240
Speaker 6: and just something else, you know, just other factors like that,

82
00:03:46,400 --> 00:03:48,680
Speaker 6: some concerns about valuation, but we did see a pretty

83
00:03:49,040 --> 00:03:53,080
Speaker 6: dramatic rebound. We also had the megacap results that we

84
00:03:53,120 --> 00:03:59,880
Speaker 6: saw out last month. All the major Nvidia customers like Microsoft, Amazon, Alphabet, Metay,

85
00:04:00,000 --> 00:04:01,640
Speaker 6: we know, really kind of reagread.

86
00:04:01,240 --> 00:04:02,800
Speaker 5: That they're going to continue spending on this.

87
00:04:02,880 --> 00:04:05,160
Speaker 6: And I think those two things just people looking for

88
00:04:05,200 --> 00:04:08,200
Speaker 6: a dip to buy and then that sort of spending tailwind.

89
00:04:08,480 --> 00:04:11,000
Speaker 6: The confluence of those two things really, you know, spurred

90
00:04:11,000 --> 00:04:13,360
Speaker 6: investors to buy the dip on in video and you've

91
00:04:13,360 --> 00:04:15,360
Speaker 6: seen the stock just jump up I think about thirty

92
00:04:15,400 --> 00:04:17,440
Speaker 6: percent or so off that low earlier this month.

93
00:04:18,279 --> 00:04:23,239
Speaker 3: It's interesting that chip makers have been what of dictated where.

94
00:04:23,040 --> 00:04:25,400
Speaker 2: The nazag one hunter is gone and broad on.

95
00:04:25,560 --> 00:04:27,640
Speaker 3: Many on this show call it sort of the poor

96
00:04:27,680 --> 00:04:30,120
Speaker 3: man's in video, and people have been buying into that name.

97
00:04:30,200 --> 00:04:33,000
Speaker 3: I'm looking at Texas Instruments today and actually here's a

98
00:04:33,000 --> 00:04:36,440
Speaker 3: company that's curtailing some of its capital expenditure, which people

99
00:04:36,480 --> 00:04:39,000
Speaker 3: are worrying about too much of in the hyperscaler's area.

100
00:04:39,040 --> 00:04:41,880
Speaker 2: What do you make of TI today, Yeah.

101
00:04:41,640 --> 00:04:43,640
Speaker 6: So they are kind of maybe pulling back in their capex.

102
00:04:43,720 --> 00:04:45,760
Speaker 6: There's some optimism that that's going to lead to some

103
00:04:45,839 --> 00:04:47,960
Speaker 6: improved free cash flow for them. So a little bit

104
00:04:47,960 --> 00:04:50,080
Speaker 6: of a different kind of player. They're not as active

105
00:04:50,080 --> 00:04:52,119
Speaker 6: in AI, certainly not to the extent that in video

106
00:04:52,240 --> 00:04:54,200
Speaker 6: or AMD or some of these other major players are,

107
00:04:54,440 --> 00:04:56,120
Speaker 6: but it does seem like people are looking to them

108
00:04:56,120 --> 00:04:57,800
Speaker 6: just as a way to improve their free cash flow.

109
00:04:57,839 --> 00:04:59,040
Speaker 6: And that's why we're seeing a little bit of a

110
00:04:59,080 --> 00:05:01,360
Speaker 6: lift in that stock today. And we saw that city

111
00:05:01,440 --> 00:05:03,720
Speaker 6: upgrade of the stock kind of expecting improvement in margins,

112
00:05:03,720 --> 00:05:06,440
Speaker 6: improvement in cash flow, all related to its capex comments

113
00:05:07,000 --> 00:05:07,599
Speaker 6: Ran it's.

114
00:05:07,480 --> 00:05:09,960
Speaker 3: A great story on Nvidia talking to so many money

115
00:05:09,960 --> 00:05:12,719
Speaker 3: managers on both sides, and indeed, the LATESTU on Texas Instruments,

116
00:05:12,760 --> 00:05:15,200
Speaker 3: we thank you Ron for SELICA. Now let's dig more

117
00:05:15,200 --> 00:05:17,320
Speaker 3: into some of the individual names and indeed the broader

118
00:05:17,360 --> 00:05:20,200
Speaker 3: market with Kevin Welkosh. He's the portfolio manager of it

119
00:05:20,279 --> 00:05:22,760
Speaker 3: GENS and Investment Management covering tech, AI and chips, who

120
00:05:22,800 --> 00:05:24,320
Speaker 3: are the perfect sort of person that we need to

121
00:05:24,320 --> 00:05:27,520
Speaker 3: speak to when many are wondering what the return on

122
00:05:27,720 --> 00:05:32,000
Speaker 3: investment of AI is for you? Is infrastructure picks and

123
00:05:32,000 --> 00:05:34,640
Speaker 3: shovels still the good place to be allocating.

124
00:05:35,480 --> 00:05:39,200
Speaker 7: Right when we think about it. So what with GENS

125
00:05:39,240 --> 00:05:42,320
Speaker 7: in investment management, we're focused on quality investing, We're focused

126
00:05:42,320 --> 00:05:44,599
Speaker 7: on a long term we think right now sort of

127
00:05:44,600 --> 00:05:47,159
Speaker 7: the AI attention certainly is more in the short term

128
00:05:47,200 --> 00:05:50,920
Speaker 7: in terms of CAPEX band and demand for Nvidia chips,

129
00:05:52,000 --> 00:05:55,640
Speaker 7: but historically that the chip industry is very cyclical and

130
00:05:55,680 --> 00:05:58,599
Speaker 7: we're looking for sort of those platform players that can

131
00:05:58,680 --> 00:06:02,240
Speaker 7: kind of continue to monetize consistently over the long term.

132
00:06:02,320 --> 00:06:05,600
Speaker 7: So when we think about today, certainly the intensions on Nvidia,

133
00:06:06,520 --> 00:06:08,680
Speaker 7: but we're really looking more towards the long term, So

134
00:06:08,720 --> 00:06:12,160
Speaker 7: that would be the Microsoft's really establishing themselves with that

135
00:06:12,200 --> 00:06:16,120
Speaker 7: massive CAPEC spend. We're also seeing it on alphabet side,

136
00:06:16,440 --> 00:06:19,440
Speaker 7: the alphabet side where you know, again that investment's building

137
00:06:19,440 --> 00:06:21,400
Speaker 7: that beachhead for long term monetization.

138
00:06:22,560 --> 00:06:23,600
Speaker 1: We also like Accentrire.

139
00:06:23,720 --> 00:06:25,960
Speaker 7: We think Accenture from an AI story has sort of

140
00:06:25,960 --> 00:06:28,960
Speaker 7: been really misunderstood by the market in our opinion. When

141
00:06:28,960 --> 00:06:31,920
Speaker 7: we look at Accenture right now, we think the mart

142
00:06:32,040 --> 00:06:34,120
Speaker 7: the market, which we continue to think as a long

143
00:06:34,200 --> 00:06:35,800
Speaker 7: term investor, we tend to think of it as very

144
00:06:35,800 --> 00:06:38,359
Speaker 7: short term focused, tends to focus on a little bit

145
00:06:38,360 --> 00:06:40,760
Speaker 7: of slowdown in it. Spend has sort of, you know,

146
00:06:41,320 --> 00:06:44,719
Speaker 7: unduly hammered the stock in our opinion, And when we

147
00:06:44,760 --> 00:06:48,720
Speaker 7: look at Accenture, they've committed to the spending three billion

148
00:06:48,920 --> 00:06:51,680
Speaker 7: to tool up for AI. They were a market leader

149
00:06:51,720 --> 00:06:54,440
Speaker 7: in terms of cloud build significant share when they did

150
00:06:54,480 --> 00:06:57,240
Speaker 7: a large investment like that as well, and we would

151
00:06:57,279 --> 00:06:59,400
Speaker 7: expect them as sort of a picks and shovels type

152
00:07:00,400 --> 00:07:02,800
Speaker 7: company to really benefit in a long term from AI

153
00:07:03,000 --> 00:07:05,120
Speaker 7: as an implementer in that they don't really have to

154
00:07:05,160 --> 00:07:07,120
Speaker 7: pick the winner, they just have to continue to be

155
00:07:07,240 --> 00:07:08,520
Speaker 7: very good at implementing the tech.

156
00:07:09,720 --> 00:07:11,480
Speaker 4: Kevin I didn't expect us to jump straight in on

157
00:07:11,480 --> 00:07:14,080
Speaker 4: an accenture. I'll be honest with you. It's a pretty

158
00:07:14,120 --> 00:07:17,960
Speaker 4: big week economic data, power speaks and everyone has a

159
00:07:18,000 --> 00:07:21,360
Speaker 4: lot of anxiety about in Vidia on August twenty eighth,

160
00:07:21,400 --> 00:07:23,120
Speaker 4: which is the most important for you.

161
00:07:24,400 --> 00:07:25,280
Speaker 5: I mean for us.

162
00:07:25,800 --> 00:07:28,360
Speaker 7: When we look at Nvidia, it's interesting, but we don't

163
00:07:28,400 --> 00:07:30,920
Speaker 7: own in video, and when we think about the market,

164
00:07:30,960 --> 00:07:33,760
Speaker 7: you know, it's been very binary, handful of AI driven

165
00:07:33,760 --> 00:07:37,480
Speaker 7: stocks and really sort of missing sort of the broader

166
00:07:37,560 --> 00:07:41,720
Speaker 7: value creation across the market as a whole. So in

167
00:07:41,760 --> 00:07:44,440
Speaker 7: this case, you know, we certainly acknowledge in VideA. We'll

168
00:07:44,480 --> 00:07:48,320
Speaker 7: understand how it contextually sort of can impact our portfolio.

169
00:07:48,880 --> 00:07:50,880
Speaker 7: What we're sort of keyed in on the short term

170
00:07:50,960 --> 00:07:53,120
Speaker 7: is just sort of continuation of that sort of trend

171
00:07:53,160 --> 00:07:57,440
Speaker 7: and migration from a long term perspective towards AI, but

172
00:07:57,520 --> 00:08:00,120
Speaker 7: also broader tech and seeing how healthy it is in

173
00:08:00,200 --> 00:08:00,800
Speaker 7: terms of growing.

174
00:08:01,920 --> 00:08:03,600
Speaker 5: So that's that's about it.

175
00:08:03,600 --> 00:08:05,640
Speaker 7: It's sort of contextually looking at the short term, but

176
00:08:06,080 --> 00:08:07,720
Speaker 7: really with an eye towards the long term.

177
00:08:07,800 --> 00:08:10,760
Speaker 3: I'm really interested in that that you're not in in video,

178
00:08:10,840 --> 00:08:15,000
Speaker 3: but you are in alphabet, Microsoft TSMC. The likes of

179
00:08:15,160 --> 00:08:18,520
Speaker 3: Eccentia Texas Instruments, maybe less so on the AI side,

180
00:08:18,600 --> 00:08:20,280
Speaker 3: but the others are AI names.

181
00:08:20,560 --> 00:08:21,760
Speaker 2: Why are you in Nvidia?

182
00:08:22,720 --> 00:08:27,000
Speaker 7: So what's interesting about our strategy very inherent in terms

183
00:08:27,000 --> 00:08:27,360
Speaker 7: of us.

184
00:08:27,400 --> 00:08:29,800
Speaker 5: In our sort of quest for.

185
00:08:29,760 --> 00:08:33,400
Speaker 7: Long term quality growth businesses, we look for companies that

186
00:08:34,000 --> 00:08:36,880
Speaker 7: have high returns on capital and consistently, so we're looking

187
00:08:36,880 --> 00:08:39,839
Speaker 7: for companies that have return and excess of fifteen percent

188
00:08:39,920 --> 00:08:42,280
Speaker 7: return on equity for ten years. So it's not an

189
00:08:42,280 --> 00:08:45,160
Speaker 7: average every year as to hit it's supposed to the

190
00:08:45,200 --> 00:08:48,400
Speaker 7: ten years. It is supposed to denote an all sort

191
00:08:48,440 --> 00:08:52,520
Speaker 7: of market type environment of high capital returns. And Nvidia

192
00:08:52,920 --> 00:08:56,360
Speaker 7: had not up until this most recent fiscal year qualified

193
00:08:57,400 --> 00:08:59,440
Speaker 7: and so in that case it does qualify. Now we

194
00:08:59,480 --> 00:09:03,240
Speaker 7: are actually researching it. Our sense when we look at

195
00:09:03,320 --> 00:09:07,920
Speaker 7: the valuation would be that from a fundamental standpoint, we'll

196
00:09:07,920 --> 00:09:10,239
Speaker 7: probably like it. But I think from a valuation standpoint

197
00:09:10,480 --> 00:09:13,080
Speaker 7: for us too rich to sort of enter position.

198
00:09:14,160 --> 00:09:17,600
Speaker 4: Right now, Kevin, The nvidious side of the equation on

199
00:09:17,679 --> 00:09:21,719
Speaker 4: AI is easy to understand. The capex investment on airastructures

200
00:09:21,760 --> 00:09:24,120
Speaker 4: there if you go back to your accenture example, I

201
00:09:24,120 --> 00:09:27,160
Speaker 4: find really interesting where people struggle is to see the

202
00:09:27,480 --> 00:09:30,240
Speaker 4: top line growth coming out the other side, those that

203
00:09:30,320 --> 00:09:34,920
Speaker 4: are working on AI services and products, do you see it.

204
00:09:34,920 --> 00:09:36,199
Speaker 7: It's early days we've seen.

205
00:09:36,840 --> 00:09:38,280
Speaker 8: What's interesting is is you look.

206
00:09:38,120 --> 00:09:41,640
Speaker 7: At the growth in terms of AI projects and spend

207
00:09:41,960 --> 00:09:47,320
Speaker 7: for Accenture has been growing dramatically and so but relative

208
00:09:47,360 --> 00:09:49,520
Speaker 7: to total revenue it's not as large yet. And that's

209
00:09:49,520 --> 00:09:52,000
Speaker 7: typically what we've see in these sort of early adoptions

210
00:09:52,200 --> 00:09:56,280
Speaker 7: of new, newer or new technologies. You sort of see

211
00:09:56,360 --> 00:10:00,600
Speaker 7: small base, large growth, and right now, relative to the picture,

212
00:10:00,840 --> 00:10:03,280
Speaker 7: it looks small. And I think the market, you know,

213
00:10:03,440 --> 00:10:05,280
Speaker 7: from our perspective and we look at it, you know,

214
00:10:05,320 --> 00:10:08,440
Speaker 7: the market's so focused on short term gains that in

215
00:10:08,559 --> 00:10:10,160
Speaker 7: essence sort of looking at the trees, and we're trying

216
00:10:10,200 --> 00:10:12,640
Speaker 7: to look at the forest. And we like the trends,

217
00:10:12,679 --> 00:10:15,520
Speaker 7: we like the direction, and having been invested in this

218
00:10:15,520 --> 00:10:17,360
Speaker 7: company for a long time and seeing this sort of

219
00:10:17,360 --> 00:10:21,320
Speaker 7: play out before in other tech trends, we're incredibly excited

220
00:10:21,320 --> 00:10:23,240
Speaker 7: about the opportunities that had for Accenture.

221
00:10:24,160 --> 00:10:26,760
Speaker 3: Kevin Welcos great to have some time with you. Thank you,

222
00:10:26,840 --> 00:10:31,199
Speaker 3: portfolio manager. Thanks and Jensen making investment management. Now we

223
00:10:31,280 --> 00:10:34,280
Speaker 3: shift gears a little here because Italian divers have now

224
00:10:34,320 --> 00:10:37,320
Speaker 3: been joined by an underwater robot as the search and

225
00:10:37,360 --> 00:10:39,800
Speaker 3: rescue efforts resumed for a third day around the sunken

226
00:10:39,880 --> 00:10:43,079
Speaker 3: yacht off Sicily. A British tech entrepreneur, Mike Lynch will

227
00:10:43,120 --> 00:10:45,960
Speaker 3: we stand the international chairman, Jonathan Bloomer, and four others

228
00:10:46,360 --> 00:10:49,000
Speaker 3: are feared to have died earlier this week. The robot

229
00:10:49,040 --> 00:10:51,400
Speaker 3: is capable of operating on the seabed at a depth

230
00:10:51,440 --> 00:10:53,560
Speaker 3: of up to three hundred meters for between six and

231
00:10:53,600 --> 00:10:57,240
Speaker 3: seven hours as his authorities continue to investigate just how

232
00:10:57,320 --> 00:10:59,880
Speaker 3: the luxury yacht sank in the early hours of Monday morning.

233
00:11:00,320 --> 00:11:02,040
Speaker 3: Will keep you up to date on the latest from

234
00:11:02,080 --> 00:11:02,640
Speaker 3: this tragedy.

235
00:11:03,320 --> 00:11:05,280
Speaker 5: Ed okay coming out.

236
00:11:05,280 --> 00:11:08,600
Speaker 4: Walmart ends its partnership with JD dot Com in China.

237
00:11:08,679 --> 00:11:13,040
Speaker 4: Details on how Walmot's refining its strategy in that country.

238
00:11:13,080 --> 00:11:15,040
Speaker 5: Next, this is Bloomberg Technology.

239
00:11:25,640 --> 00:11:28,120
Speaker 3: Let's return to JD dot Com and the pressure there

240
00:11:28,160 --> 00:11:31,520
Speaker 3: because Walmart actually sold out, raising about three point six

241
00:11:31,559 --> 00:11:33,720
Speaker 3: billion dollars by selling its stake in the Chinese e

242
00:11:33,720 --> 00:11:36,959
Speaker 3: commerce firm, and the move winds down an eight year

243
00:11:37,040 --> 00:11:40,079
Speaker 3: partnership that appears to be paying diminishing returns and pretty

244
00:11:40,160 --> 00:11:41,959
Speaker 3: challenging landscape for Chinese tech giants.

245
00:11:42,320 --> 00:11:44,560
Speaker 2: Bloomberg's Amy Or has more of the details.

246
00:11:44,960 --> 00:11:48,160
Speaker 3: A lot of pressure on the shares of JD dot

247
00:11:48,160 --> 00:11:51,080
Speaker 3: Com for obvious reasons, but it's really sending slight shot

248
00:11:51,120 --> 00:11:54,240
Speaker 3: waves across the entire industry group. People are worried about

249
00:11:54,240 --> 00:11:56,240
Speaker 3: what the signals for JD dot com and its growth.

250
00:11:56,520 --> 00:12:00,680
Speaker 9: Yeah, well, especially at a time when Chinese economy is slowing,

251
00:12:00,720 --> 00:12:04,400
Speaker 9: and also like it's been reflected in e commerce, and

252
00:12:04,480 --> 00:12:07,760
Speaker 9: also it really shows that US companies can actually go

253
00:12:07,800 --> 00:12:08,400
Speaker 9: their own way.

254
00:12:08,640 --> 00:12:10,280
Speaker 2: It's not like eight years ago.

255
00:12:10,280 --> 00:12:12,920
Speaker 9: When the partnership started that you really need a local

256
00:12:12,960 --> 00:12:17,960
Speaker 9: partnership to really gain popularity among Chinese consumers. And now

257
00:12:18,320 --> 00:12:21,800
Speaker 9: Sam's Club has actually got really good recognition amound Chinese

258
00:12:21,880 --> 00:12:22,760
Speaker 9: customers as well.

259
00:12:24,080 --> 00:12:24,680
Speaker 5: This is good point.

260
00:12:24,720 --> 00:12:25,000
Speaker 1: Amy.

261
00:12:25,200 --> 00:12:27,480
Speaker 4: You know, in your reporting you give the backstory of

262
00:12:27,480 --> 00:12:30,840
Speaker 4: Walmart in China, which is about eight years of partnership

263
00:12:30,840 --> 00:12:34,040
Speaker 4: with JD dot Com, but their kind of hypermart business

264
00:12:34,160 --> 00:12:34,640
Speaker 4: is strong.

265
00:12:34,760 --> 00:12:36,280
Speaker 5: Sources talk to us about that.

266
00:12:36,600 --> 00:12:41,520
Speaker 9: What did you learn, So essentially the partnership in the past,

267
00:12:41,559 --> 00:12:44,680
Speaker 9: it was great. It started off as trying to get

268
00:12:44,760 --> 00:12:49,080
Speaker 9: Chinese customers to actually recognize is brands and try to

269
00:12:49,240 --> 00:12:52,400
Speaker 9: gain shares that it might not have been able to

270
00:12:52,559 --> 00:12:55,400
Speaker 9: without a Chinese partner. But now that kind of growth

271
00:12:55,440 --> 00:12:58,880
Speaker 9: has slowed together with the Chinese economy slowing as well.

272
00:12:59,080 --> 00:13:01,920
Speaker 9: And then Sam's Club essentially is the only one of

273
00:13:02,000 --> 00:13:05,800
Speaker 9: the top five e commerce platforms in China that recorded

274
00:13:05,840 --> 00:13:08,440
Speaker 9: growth that really shows that it can actually go alone,

275
00:13:08,480 --> 00:13:11,160
Speaker 9: and it probably should go alone. And essentially the three

276
00:13:11,200 --> 00:13:14,160
Speaker 9: point six billion that it got can actually put to

277
00:13:14,280 --> 00:13:14,839
Speaker 9: better use.

278
00:13:15,840 --> 00:13:18,360
Speaker 4: YEP, that'll help Bloomberg's Amy or who broke the story

279
00:13:18,440 --> 00:13:21,480
Speaker 4: last night, Thank you. Another one that we're watching Ford

280
00:13:21,600 --> 00:13:26,400
Speaker 4: recalibrating its EV strategy again. This time you also makers

281
00:13:26,440 --> 00:13:29,640
Speaker 4: canceling plans for his fully electric suv, a move that

282
00:13:29,679 --> 00:13:32,840
Speaker 4: could cost the company nearly two billion dollars Bloombos.

283
00:13:32,880 --> 00:13:34,720
Speaker 5: Keith Norton is here with more.

284
00:13:34,880 --> 00:13:37,800
Speaker 4: Oh, Keith, it feels just like yesterday that you and

285
00:13:37,840 --> 00:13:41,679
Speaker 4: I were reporting that Ford was going to focus its

286
00:13:41,720 --> 00:13:44,800
Speaker 4: EV units separate away from everything else, and now it's

287
00:13:44,840 --> 00:13:47,800
Speaker 4: a change. I think you spoke to the CEO, Jim Farley,

288
00:13:48,679 --> 00:13:49,960
Speaker 4: what's the rationale here?

289
00:13:51,280 --> 00:13:51,600
Speaker 10: Well?

290
00:13:51,640 --> 00:13:55,080
Speaker 11: And they are trying to find profitability for that EV

291
00:13:55,280 --> 00:13:58,439
Speaker 11: unit that they broke off. Two years ago when we

292
00:13:58,480 --> 00:14:02,200
Speaker 11: reported on that they're expecting that unit known as MODELI

293
00:14:02,320 --> 00:14:05,200
Speaker 11: to lose as much as five point five billion dollars.

294
00:14:05,679 --> 00:14:08,160
Speaker 10: This three row suv that they've canceled.

295
00:14:08,200 --> 00:14:10,520
Speaker 11: They said that Jim Farley told me they just couldn't

296
00:14:10,559 --> 00:14:13,200
Speaker 11: find a way to make it profitable. It requires a

297
00:14:13,360 --> 00:14:16,920
Speaker 11: very large battery, which is high cost, and they can't

298
00:14:16,920 --> 00:14:18,920
Speaker 11: put it out there with all the competition in the

299
00:14:18,960 --> 00:14:20,880
Speaker 11: market requiring price cuts.

300
00:14:20,560 --> 00:14:22,960
Speaker 10: On e these, so they canceled it.

301
00:14:22,880 --> 00:14:25,960
Speaker 11: And they're looking at perhaps doing an extended range electric

302
00:14:26,040 --> 00:14:27,560
Speaker 11: vehicle technology on that.

303
00:14:28,080 --> 00:14:30,120
Speaker 3: Yeah, Jim Vinie actually saying that we loved our three

304
00:14:30,200 --> 00:14:32,680
Speaker 3: road crossover. He was so excited to show it to everyone,

305
00:14:32,680 --> 00:14:35,120
Speaker 3: but they couldn't get it to be profitable. It feels

306
00:14:35,160 --> 00:14:37,320
Speaker 3: as though once again hybrid is where it's at.

307
00:14:38,480 --> 00:14:41,200
Speaker 11: Yes, they're seeing a lot of growth in hybrid, and

308
00:14:41,680 --> 00:14:46,240
Speaker 11: Farley in particular is really enthused by eravs, this extended

309
00:14:46,320 --> 00:14:48,680
Speaker 11: range electric vehicle, which is where you have a gas

310
00:14:48,720 --> 00:14:52,320
Speaker 11: engine that sole purpose is to just recharge the battery.

311
00:14:52,360 --> 00:14:55,760
Speaker 11: It doesn't drive the wheels, so you're really in electric

312
00:14:55,840 --> 00:14:58,080
Speaker 11: mode most of the time. He saw those of her

313
00:14:58,120 --> 00:15:00,440
Speaker 11: in China. He really wants to get them in the

314
00:15:00,440 --> 00:15:02,920
Speaker 11: Ford lineup, and we may see it as that three

315
00:15:03,080 --> 00:15:04,320
Speaker 11: row suv eventually.

316
00:15:05,560 --> 00:15:07,800
Speaker 4: That's something that's popular in China, and a number of

317
00:15:07,840 --> 00:15:12,400
Speaker 4: the Chinese domestic manufacturers have a thought about that. I mean,

318
00:15:12,400 --> 00:15:17,200
Speaker 4: what I don't understand, Keith, is whether this will ever

319
00:15:17,280 --> 00:15:19,720
Speaker 4: be a place where Ford just does evs. I thought

320
00:15:19,720 --> 00:15:22,760
Speaker 4: that that was the transition plan, right, profit from pickups

321
00:15:23,240 --> 00:15:26,160
Speaker 4: funds the transition long term to evs. Now it seems

322
00:15:26,200 --> 00:15:29,920
Speaker 4: like that's not as clear cut, right it.

323
00:15:29,960 --> 00:15:33,560
Speaker 11: Is not ed, and the new plan really is leans

324
00:15:33,600 --> 00:15:36,360
Speaker 11: on hybrids more, which Ford has been in the business

325
00:15:36,360 --> 00:15:39,880
Speaker 11: of doing for a long time. In the EV space.

326
00:15:40,000 --> 00:15:43,640
Speaker 11: They're looking to do smaller evs and they're really looking

327
00:15:43,680 --> 00:15:48,680
Speaker 11: at advances in battery technology. They have LFP batteries coming

328
00:15:48,680 --> 00:15:52,040
Speaker 11: out of plant in Michigan that will power a new

329
00:15:52,280 --> 00:15:54,000
Speaker 11: medium size pickup truck.

330
00:15:54,320 --> 00:15:56,600
Speaker 10: Ed pickup truck will have in twenty twenty seven.

331
00:15:57,240 --> 00:15:59,960
Speaker 11: Jim Farley told me that the total cost of owner

332
00:16:00,360 --> 00:16:03,640
Speaker 11: of that truck with those lower cost batteries will actually

333
00:16:03,680 --> 00:16:05,640
Speaker 11: be cheaper than traditional vehicles.

334
00:16:06,520 --> 00:16:10,520
Speaker 2: He called us a tremendous pivot. Is this an embarrassment, Keith?

335
00:16:10,720 --> 00:16:15,640
Speaker 3: Is this something that Jim's worried about investor reactions one Tim.

336
00:16:15,440 --> 00:16:18,760
Speaker 11: Well, so far investors have reacted positively. The stock is

337
00:16:18,840 --> 00:16:19,560
Speaker 11: up today.

338
00:16:20,080 --> 00:16:22,359
Speaker 10: I guess there's a couple of ways to look at it.

339
00:16:22,360 --> 00:16:25,400
Speaker 11: It's certainly embarrassing that everyone got so caught up in

340
00:16:25,440 --> 00:16:28,840
Speaker 11: the hype that they started building plants and building battery

341
00:16:28,880 --> 00:16:31,680
Speaker 11: factories everywhere and now have to scale back.

342
00:16:31,960 --> 00:16:34,640
Speaker 10: But for it at least has acknowledge the need to

343
00:16:34,680 --> 00:16:35,120
Speaker 10: do that.

344
00:16:35,240 --> 00:16:38,280
Speaker 11: They've been sort of out front saying no, we need

345
00:16:38,320 --> 00:16:41,040
Speaker 11: to change, and they've been sort of chipping away at it,

346
00:16:41,080 --> 00:16:43,840
Speaker 11: and now they've done this whole plan that he says

347
00:16:44,320 --> 00:16:46,880
Speaker 11: he thinks will be the final move.

348
00:16:47,040 --> 00:16:47,800
Speaker 10: We'll see.

349
00:16:48,400 --> 00:16:50,160
Speaker 3: It's a big cost to have to sucker once again,

350
00:16:50,240 --> 00:16:52,680
Speaker 3: Keith Norton quite importing me. Thank you so much for

351
00:16:52,760 --> 00:16:53,360
Speaker 3: joining us on.

352
00:16:53,320 --> 00:17:04,520
Speaker 4: It time for AI in Action, and today we're looking

353
00:17:04,560 --> 00:17:07,640
Speaker 4: at the rising risks of AI in cybersecurity and how

354
00:17:07,640 --> 00:17:11,119
Speaker 4: one company aims to fight fire with fire in combating

355
00:17:11,119 --> 00:17:14,560
Speaker 4: the threats. Joining us now is Evan Reiser, Abnormal Security CEO.

356
00:17:15,000 --> 00:17:17,440
Speaker 4: A Normal recently hit a five point one billion dollar

357
00:17:17,560 --> 00:17:20,800
Speaker 4: valuation and it's latest Series D fundraise, and I think

358
00:17:20,840 --> 00:17:23,959
Speaker 4: that the story here is actually pretty clear that the

359
00:17:24,000 --> 00:17:28,280
Speaker 4: tools available to your industry in defense are also available

360
00:17:28,320 --> 00:17:31,040
Speaker 4: to the threat actors. Right that AI has given them

361
00:17:31,480 --> 00:17:33,880
Speaker 4: something to work with, and that's kind of what you're

362
00:17:33,880 --> 00:17:34,520
Speaker 4: trying to combat.

363
00:17:34,520 --> 00:17:38,320
Speaker 12: Evan, That's absolutely right. You know, AI is an extremely

364
00:17:38,359 --> 00:17:41,040
Speaker 12: powerful technology. Like all technology, it's a tool, and tool

365
00:17:41,040 --> 00:17:43,679
Speaker 12: could be used for good or evil. Unfortunately, you know,

366
00:17:43,760 --> 00:17:46,840
Speaker 12: criminals are faster adopting technology, and they're using new AI

367
00:17:46,920 --> 00:17:49,680
Speaker 12: technologies to do more bad things.

368
00:17:50,320 --> 00:17:54,000
Speaker 2: You've got twenty four hundred plus customers. How are they

369
00:17:54,119 --> 00:17:54,439
Speaker 2: using you?

370
00:17:54,560 --> 00:17:58,400
Speaker 3: Because ultimately you're trying to predict me, the human and

371
00:17:58,480 --> 00:18:01,560
Speaker 3: when I start to act in a strange way, right.

372
00:18:02,200 --> 00:18:04,800
Speaker 5: Yeah, in an abnormal way. So that that's correct.

373
00:18:04,800 --> 00:18:06,840
Speaker 12: So you know, if you look, you know, decade ago,

374
00:18:07,160 --> 00:18:10,200
Speaker 12: criminals are very focused on attach infrastructure. Now they've moved

375
00:18:10,200 --> 00:18:12,359
Speaker 12: on to the more vulnerable target, which is people, and

376
00:18:12,640 --> 00:18:15,320
Speaker 12: we see you know, social enduring, fraud, fishing, those are

377
00:18:15,359 --> 00:18:17,920
Speaker 12: the biggest forms of cyber attacks, number one cause of breaches.

378
00:18:18,600 --> 00:18:21,960
Speaker 12: Just like you said, abnormals focused on understanding human behavior,

379
00:18:22,000 --> 00:18:23,879
Speaker 12: you know, better than humans, so we can protect humans.

380
00:18:26,040 --> 00:18:30,040
Speaker 4: What I understand Evan is you've got a really impressive

381
00:18:30,040 --> 00:18:33,600
Speaker 4: annual revenue run rate two hundred million dollars. Right, Why

382
00:18:33,680 --> 00:18:35,840
Speaker 4: is it such a vacuum for you to jump into?

383
00:18:36,400 --> 00:18:38,760
Speaker 4: It seems like we've been talking about the AI story

384
00:18:38,800 --> 00:18:40,920
Speaker 4: for a few years, let's say two or three years,

385
00:18:41,280 --> 00:18:43,400
Speaker 4: and now everyone's like, Okay, I need to think about

386
00:18:43,400 --> 00:18:44,280
Speaker 4: the threat before me.

387
00:18:44,560 --> 00:18:45,800
Speaker 5: Why not do it earlier?

388
00:18:47,040 --> 00:18:47,240
Speaker 1: Yeah?

389
00:18:47,320 --> 00:18:49,560
Speaker 12: I think there's two reasons. One is that with the

390
00:18:49,640 --> 00:18:52,440
Speaker 12: rise of these new technologies, it's very easy and accessible

391
00:18:52,480 --> 00:18:56,200
Speaker 12: for criminals to you know, supercharge their attacks. We've seen

392
00:18:56,200 --> 00:18:58,560
Speaker 12: this rise of social enduring and fishing over the last

393
00:18:58,600 --> 00:19:02,240
Speaker 12: couple of years. As Ano there's still the number one cybercrime,

394
00:19:02,760 --> 00:19:05,320
Speaker 12: but now any petty criminal can use chat GBT. They

395
00:19:05,320 --> 00:19:08,399
Speaker 12: can send emails in perfect English, with perfect grammar, with

396
00:19:08,480 --> 00:19:12,200
Speaker 12: context about their victims. And these new technologies allow criminals

397
00:19:12,280 --> 00:19:14,879
Speaker 12: to personalize at scale, and that is very hard to

398
00:19:14,880 --> 00:19:17,400
Speaker 12: defend against. The reason it's really hard is that conventional

399
00:19:17,400 --> 00:19:19,960
Speaker 12: cybersecurits focus on threat intelligence, kind of studying all the

400
00:19:20,000 --> 00:19:23,520
Speaker 12: known bad attacks blocking the existing attacks. And so this

401
00:19:23,560 --> 00:19:25,800
Speaker 12: is why it requires a new technology that's not focused

402
00:19:25,800 --> 00:19:28,400
Speaker 12: on known bad but instead of really good at understanding

403
00:19:28,400 --> 00:19:31,040
Speaker 12: known good, understanding human behavior and then looking for those

404
00:19:31,040 --> 00:19:33,479
Speaker 12: anomalies and abbormalities the basis for threat detection.

405
00:19:33,760 --> 00:19:35,960
Speaker 3: Can you explain a little bit of the underlying technology

406
00:19:35,960 --> 00:19:38,200
Speaker 3: of how you're working that, So, what are you applying

407
00:19:38,680 --> 00:19:41,200
Speaker 3: and ultimately why just raising more money help you.

408
00:19:41,280 --> 00:19:41,960
Speaker 2: Beef that up.

409
00:19:43,160 --> 00:19:43,400
Speaker 1: Yeah.

410
00:19:43,440 --> 00:19:45,440
Speaker 12: So at the core, you know, we're really an artificial

411
00:19:45,440 --> 00:19:48,760
Speaker 12: intelligence company that builds cybersecurity products, and the core technology

412
00:19:49,040 --> 00:19:52,000
Speaker 12: is focused on really understanding human behavior. By integrating all

413
00:19:52,080 --> 00:19:56,399
Speaker 12: kinds of enterprise technology systems, we understand the baseline activity,

414
00:19:56,720 --> 00:19:58,520
Speaker 12: who talks to who about what types of things, what

415
00:19:58,560 --> 00:20:00,800
Speaker 12: business processes. We can map out the supply chain all

416
00:20:00,840 --> 00:20:04,159
Speaker 12: thesing artificial intelligence. We then take that behavioral baseline it

417
00:20:04,160 --> 00:20:07,360
Speaker 12: like for anomalies. And so that's a very's very effective

418
00:20:07,359 --> 00:20:11,000
Speaker 12: at stopping these personalized social engineering phishing attacks I've never

419
00:20:11,080 --> 00:20:12,919
Speaker 12: been seen before, and so it's a kind of a

420
00:20:12,920 --> 00:20:14,360
Speaker 12: really new paradigm.

421
00:20:13,960 --> 00:20:16,119
Speaker 10: For how to do cybersecurity.

422
00:20:16,359 --> 00:20:17,800
Speaker 12: And then I think that the final reason is that

423
00:20:17,840 --> 00:20:20,560
Speaker 12: conventional cybersecurity is very reliant on you know, kind of

424
00:20:20,560 --> 00:20:23,600
Speaker 12: a human powered paradigm, right, lots of human analytic analyzing,

425
00:20:23,640 --> 00:20:26,280
Speaker 12: lots of data. Obviously, machine learning and AI is you know,

426
00:20:26,320 --> 00:20:28,919
Speaker 12: far superior, faster and more effective at doing that, and

427
00:20:28,960 --> 00:20:31,240
Speaker 12: so AI allows us to make you know, superhuman judgments,

428
00:20:31,280 --> 00:20:35,320
Speaker 12: you know, faster than faster than conventional security operations. So

429
00:20:35,480 --> 00:20:37,000
Speaker 12: not only is it a bigger need in the market,

430
00:20:37,040 --> 00:20:39,359
Speaker 12: but new tools allow you know, organizations like us to

431
00:20:39,720 --> 00:20:41,840
Speaker 12: you know, become way, you know, an order magnitude more

432
00:20:41,840 --> 00:20:43,400
Speaker 12: effective at stopping these cyber attacks.

433
00:20:43,760 --> 00:20:47,000
Speaker 3: So to become ever more effective, do you need more people,

434
00:20:47,040 --> 00:20:49,760
Speaker 3: more talent, or do you need more compute more money

435
00:20:49,760 --> 00:20:50,120
Speaker 3: for that?

436
00:20:51,680 --> 00:20:53,359
Speaker 12: It's a bit of everything, right, you know, we just

437
00:20:53,440 --> 00:20:55,119
Speaker 12: raised this big ground. As you mentioned, there's really kind

438
00:20:55,160 --> 00:20:57,320
Speaker 12: of three ways we're planning on doing what you're planning

439
00:20:57,320 --> 00:21:00,240
Speaker 12: on growing the business. One is just you know, growing

440
00:21:00,240 --> 00:21:02,840
Speaker 12: into new markets where you're very West based focus. We're

441
00:21:02,840 --> 00:21:05,760
Speaker 12: explading international in Asia and Europe. The second is we

442
00:21:05,800 --> 00:21:07,280
Speaker 12: do need to grow our team. We want to go

443
00:21:07,320 --> 00:21:11,040
Speaker 12: get the world's best you know leaders in technology and beyond.

444
00:21:11,320 --> 00:21:13,200
Speaker 12: And the third is you know, making more investments in

445
00:21:13,240 --> 00:21:16,080
Speaker 12: that core end platform and then using that to build

446
00:21:16,119 --> 00:21:18,919
Speaker 12: you know, expand our product based beyond you know email security,

447
00:21:18,920 --> 00:21:21,600
Speaker 12: which are known for it protect other surface areas across

448
00:21:21,600 --> 00:21:22,240
Speaker 12: the organization.

449
00:21:22,480 --> 00:21:23,119
Speaker 2: I'm a riser.

450
00:21:23,560 --> 00:21:26,360
Speaker 3: Great to have some time with you I'm Normal Security CEO.

451
00:21:34,200 --> 00:21:36,399
Speaker 3: Welcome back to Bloomberg Technology. I'm Carolin Hyde in New

452
00:21:36,440 --> 00:21:37,440
Speaker 3: York and I'm.

453
00:21:37,440 --> 00:21:38,800
Speaker 5: Ed Ludlow in San Francisco.

454
00:21:39,160 --> 00:21:41,560
Speaker 3: Quick check on the sentiment of these markets right now, ed,

455
00:21:41,600 --> 00:21:43,199
Speaker 3: because we are coming off of what have been a

456
00:21:43,240 --> 00:21:47,320
Speaker 3: slightly stronger rally prior to the jobs revisions data that

457
00:21:47,320 --> 00:21:50,520
Speaker 3: we eventually got more than eight hundred thousand jobs, fewer

458
00:21:50,560 --> 00:21:53,040
Speaker 3: than we had previously been seeing. Yes, we knew that

459
00:21:53,080 --> 00:21:55,160
Speaker 3: revisions were to come, but this is being seen as

460
00:21:55,160 --> 00:21:58,360
Speaker 3: a soft data point. The Federal Reserve therefore probably can

461
00:21:58,400 --> 00:22:00,479
Speaker 3: still cut into this. I'm looking at to is our

462
00:22:00,520 --> 00:22:02,440
Speaker 3: sentiment gage, and we're up four tens percent, but we're

463
00:22:02,480 --> 00:22:05,199
Speaker 3: down of our hives. Some interesting stories also around a

464
00:22:05,200 --> 00:22:09,439
Speaker 3: pretty crowded trade and trade in derivatives, basically showing that

465
00:22:09,560 --> 00:22:11,919
Speaker 3: maybe we're going to could get a short squeeze if

466
00:22:11,920 --> 00:22:14,119
Speaker 3: we've got any more volatility in this market as people

467
00:22:14,119 --> 00:22:16,080
Speaker 3: do go short the asset. Let's move on to some

468
00:22:16,119 --> 00:22:19,400
Speaker 3: of the individual movers in the stocks perspective. Microship Technology

469
00:22:19,440 --> 00:22:22,639
Speaker 3: individual mover here on the back of a cyber attack,

470
00:22:22,840 --> 00:22:26,000
Speaker 3: the microchip company, which basically supplies the US defense interestry,

471
00:22:26,040 --> 00:22:28,520
Speaker 3: saying they were hit. The servers were hit by a

472
00:22:28,600 --> 00:22:31,080
Speaker 3: cyber attack. City saying, look, this is only gonna be temporary.

473
00:22:31,160 --> 00:22:33,600
Speaker 3: Nature are actually up more than two percent. Intuitive Machines.

474
00:22:34,320 --> 00:22:37,439
Speaker 3: This was up more than forty percent yesterday. Remember the

475
00:22:37,520 --> 00:22:40,040
Speaker 3: rover that they had been sending to the moon. This

476
00:22:40,240 --> 00:22:43,320
Speaker 3: space based company is currently down, just as we perhaps

477
00:22:43,359 --> 00:22:45,040
Speaker 3: see a bit of profit taking. I'm looking at dd

478
00:22:45,119 --> 00:22:46,960
Speaker 3: Global as well, just up two and a half percent

479
00:22:47,000 --> 00:22:47,480
Speaker 3: in the moment.

480
00:22:47,800 --> 00:22:49,879
Speaker 2: Well, they come out and finally turn a profit.

481
00:22:49,920 --> 00:22:52,480
Speaker 3: It's small, one hundred ninety six million dollars, but better

482
00:22:52,560 --> 00:22:54,159
Speaker 3: as revenue climbs more than four percent end.

483
00:22:54,200 --> 00:22:55,119
Speaker 2: But we got more on China.

484
00:22:56,000 --> 00:22:59,320
Speaker 4: Yeah, as we said, tech stock slumped after Walmarts sold

485
00:22:59,359 --> 00:23:01,200
Speaker 4: its stake in JD dot Com.

486
00:23:01,240 --> 00:23:02,280
Speaker 5: Also, some key.

487
00:23:02,400 --> 00:23:05,480
Speaker 4: Tech players in China have posted port earnings of late

488
00:23:05,720 --> 00:23:08,359
Speaker 4: Let's get the details with Bloomberg's Isabelle Lee. And it

489
00:23:08,400 --> 00:23:11,920
Speaker 4: was interesting when the Bloomberg reporting hit on the Walmart

490
00:23:12,040 --> 00:23:16,240
Speaker 4: JD steak because actually ripples were felt very quickly across

491
00:23:16,320 --> 00:23:19,760
Speaker 4: Chinese or at least Hong Kong listed equity markets for China.

492
00:23:19,840 --> 00:23:20,000
Speaker 13: Tech.

493
00:23:20,080 --> 00:23:21,440
Speaker 5: Good morning, what did you see?

494
00:23:21,680 --> 00:23:23,720
Speaker 14: We have shares of Jada dot com slumping and it's

495
00:23:23,720 --> 00:23:26,120
Speaker 14: not a surprise because this is an eight year partnership

496
00:23:26,200 --> 00:23:28,880
Speaker 14: between Walmart and JD dot Com, and when Walmart's sold,

497
00:23:29,080 --> 00:23:32,560
Speaker 14: it's roughly three point six billion steak that's at an

498
00:23:32,600 --> 00:23:35,600
Speaker 14: eleven percent discounts, so it seems to be paying diminishing returns.

499
00:23:35,600 --> 00:23:38,000
Speaker 14: This began in twenty sixteen when we have JDA dot

500
00:23:38,080 --> 00:23:40,720
Speaker 14: Com taking over one of Walmart's online marketplace, so this

501
00:23:41,160 --> 00:23:44,800
Speaker 14: focused on selling groceries to hire end shoppers, mostly female

502
00:23:44,800 --> 00:23:47,720
Speaker 14: in major Chinese cities, and Citygroup said this was a surprise.

503
00:23:47,920 --> 00:23:50,320
Speaker 14: We have Bloomberg Intelligence saying that even if they renew

504
00:23:50,400 --> 00:23:53,119
Speaker 14: this eight year non compete contract that is existing right

505
00:23:53,160 --> 00:23:55,440
Speaker 14: now and it ends in twenty twenty four, the terms

506
00:23:55,440 --> 00:23:57,879
Speaker 14: for JD dot Com may not be as favorable. And

507
00:23:57,960 --> 00:23:59,719
Speaker 14: Jeffries also said that you see the drop in JD

508
00:23:59,760 --> 00:24:02,720
Speaker 14: dot Com mainly because of the unexpected of it all.

509
00:24:02,760 --> 00:24:05,520
Speaker 14: And to your point, it's really been a tough year

510
00:24:05,720 --> 00:24:08,479
Speaker 14: or few years for China tech because mostly all of them,

511
00:24:08,480 --> 00:24:09,720
Speaker 14: I don't want to say all of them, most of

512
00:24:09,760 --> 00:24:12,880
Speaker 14: them are really struggling, and particularly.

513
00:24:12,320 --> 00:24:16,280
Speaker 3: When those are exposed to consumption in China. Whip Shop,

514
00:24:16,320 --> 00:24:19,600
Speaker 3: for example, rebounds today yesterday sunk hard on the back

515
00:24:19,600 --> 00:24:20,400
Speaker 3: of its earnings.

516
00:24:20,880 --> 00:24:23,920
Speaker 14: Yes, exactly because it as a broader economy.

517
00:24:24,000 --> 00:24:26,120
Speaker 2: China is still struggling. We have job.

518
00:24:25,920 --> 00:24:29,000
Speaker 14: Prospects, especially for the youth, still uncertain, the property crisis

519
00:24:29,080 --> 00:24:31,000
Speaker 14: day in and day out, it's still there. And we

520
00:24:31,040 --> 00:24:33,040
Speaker 14: also have just the economy. It's not doing well. And

521
00:24:33,119 --> 00:24:35,840
Speaker 14: so this all affects consumer consumption. And we had a

522
00:24:35,840 --> 00:24:38,040
Speaker 14: slew of earnings from Ali Baba, JD dot Com and

523
00:24:38,080 --> 00:24:40,840
Speaker 14: even PDD. And I must know that last week Ali Baba,

524
00:24:40,880 --> 00:24:43,120
Speaker 14: which is long seen as a barometer for Chinese health,

525
00:24:43,240 --> 00:24:45,640
Speaker 14: it's a price investors, when it said its main business,

526
00:24:45,640 --> 00:24:48,320
Speaker 14: commerce shrunk last quarter. So what does that tell you?

527
00:24:48,359 --> 00:24:51,440
Speaker 14: And it really shows the divergence between what we see

528
00:24:51,480 --> 00:24:53,080
Speaker 14: in the US and what we see in China. And

529
00:24:53,359 --> 00:24:55,840
Speaker 14: Walmart in a statement, said that they want to focus

530
00:24:56,040 --> 00:24:58,960
Speaker 14: on their own business and want to reallocate the other

531
00:24:59,200 --> 00:25:02,240
Speaker 14: to other funds, likely the Sam's Club franchise they have,

532
00:25:02,280 --> 00:25:04,520
Speaker 14: which is doing well in China. But Walmart did call

533
00:25:04,680 --> 00:25:06,960
Speaker 14: the partnership with JD dot Com pressure, so there's that,

534
00:25:07,040 --> 00:25:08,840
Speaker 14: and it said it will continue to cooperate.

535
00:25:09,480 --> 00:25:11,320
Speaker 5: I mean, is weel you know me? I love a chart.

536
00:25:11,520 --> 00:25:13,879
Speaker 4: I think that chart on your screen is one of

537
00:25:13,920 --> 00:25:17,280
Speaker 4: the stories, at least for this sort of second quarter

538
00:25:17,359 --> 00:25:20,080
Speaker 4: of this calendar year. Look at the Nasdaq and then

539
00:25:20,119 --> 00:25:23,160
Speaker 4: look at Hanseng Tech. Just go a bit deeper into

540
00:25:23,359 --> 00:25:26,639
Speaker 4: into the outperformance of US listed technology shares and some

541
00:25:26,680 --> 00:25:29,760
Speaker 4: of the struggles of China or at least Hong Kong

542
00:25:29,840 --> 00:25:30,520
Speaker 4: listed tech.

543
00:25:31,119 --> 00:25:34,160
Speaker 14: We have the Chinese Golden Dragon Index down twelve percent

544
00:25:34,240 --> 00:25:36,960
Speaker 14: year today, down fourteen percent in the past twelve months,

545
00:25:37,000 --> 00:25:38,760
Speaker 14: and compare that with the Nasdaq and the S and

546
00:25:38,840 --> 00:25:41,399
Speaker 14: p's double digit game this year. Whether it's seventeen percent

547
00:25:41,720 --> 00:25:45,439
Speaker 14: twenty percent, it's really a story of how US technology

548
00:25:45,720 --> 00:25:48,520
Speaker 14: companies are outperforming the rest of the world. And I

549
00:25:48,520 --> 00:25:51,000
Speaker 14: think it's not even a China story where China is

550
00:25:51,000 --> 00:25:53,480
Speaker 14: stuck in their doll drums, although that's one thing. It's

551
00:25:53,560 --> 00:25:55,600
Speaker 14: just really how this is a US story at the

552
00:25:55,680 --> 00:25:58,720
Speaker 14: end of the day, even Europe can't compete, even Asia,

553
00:25:58,760 --> 00:26:01,480
Speaker 14: it's US, and guest companies in the world are in

554
00:26:01,520 --> 00:26:04,080
Speaker 14: the US. Whether that rally will continue as we know

555
00:26:04,119 --> 00:26:07,480
Speaker 14: that the AI halo is fading sort of for a

556
00:26:07,520 --> 00:26:11,760
Speaker 14: while now, but still you cannot deny that US is

557
00:26:11,880 --> 00:26:14,200
Speaker 14: really the bright spot in the rest of the world,

558
00:26:14,240 --> 00:26:15,320
Speaker 14: at least the tech companies.

559
00:26:15,800 --> 00:26:18,440
Speaker 2: It's WELLI great to have you on. We thank you.

560
00:26:18,720 --> 00:26:21,160
Speaker 3: We're gonna look at China in a different perspective right

561
00:26:21,160 --> 00:26:24,159
Speaker 3: now because we're looking at the release of China's biggest

562
00:26:24,200 --> 00:26:27,320
Speaker 3: PC game to date, Black Myth Wukong, the game about

563
00:26:27,359 --> 00:26:30,080
Speaker 3: by ten Cent Remember, has become a bit of a sensation,

564
00:26:30,400 --> 00:26:32,560
Speaker 3: more than two million players trying out the game in

565
00:26:32,600 --> 00:26:35,560
Speaker 3: the first day alone in its global launch. It's called

566
00:26:35,600 --> 00:26:38,359
Speaker 3: in to stream dB. It's a strong first day performance

567
00:26:38,440 --> 00:26:40,840
Speaker 3: and of course it could shore up expectations for China's

568
00:26:41,000 --> 00:26:45,240
Speaker 3: forty billion dollar plus gaming arena after years of regulatory scrutiny.

569
00:26:45,480 --> 00:26:48,400
Speaker 3: Let's bring in Nissa Christmas Hansen, CEO of Nico Partners

570
00:26:48,640 --> 00:26:52,119
Speaker 3: for more you really understand and deep dive on the

571
00:26:52,200 --> 00:26:56,320
Speaker 3: China element of gaming. What is the China consumer showing

572
00:26:56,359 --> 00:26:58,560
Speaker 3: you when it comes to a desire to get into gaming.

573
00:26:58,600 --> 00:27:00,200
Speaker 2: They don't seem to be reticent there.

574
00:27:01,760 --> 00:27:04,880
Speaker 13: Hi, Yeah, the Chinese consumer has always been.

575
00:27:04,960 --> 00:27:09,440
Speaker 8: Avid for gaming. They are They find that gaming.

576
00:27:09,280 --> 00:27:14,040
Speaker 13: Is an inexpensive form of digital entertainment. It's very social

577
00:27:14,680 --> 00:27:18,160
Speaker 13: and the higher the quality of the games, the more

578
00:27:18,240 --> 00:27:21,800
Speaker 13: these high demanding gamers will throw themselves into them and

579
00:27:21,840 --> 00:27:24,720
Speaker 13: spend a lot of money, and Black Myth Wukong is

580
00:27:24,880 --> 00:27:26,440
Speaker 13: just a perfect example of that.

581
00:27:26,720 --> 00:27:28,240
Speaker 8: It's a perfect example.

582
00:27:27,920 --> 00:27:32,800
Speaker 13: Of lots of things like Chinese game development prowess, how

583
00:27:32,960 --> 00:27:37,879
Speaker 13: Chinese game developers are really world class, and how a

584
00:27:37,960 --> 00:27:40,880
Speaker 13: relatively unknown company like Game Science.

585
00:27:40,480 --> 00:27:43,160
Speaker 8: Which is the developer of Black Myth Wukong.

586
00:27:43,160 --> 00:27:46,920
Speaker 13: Backed by Tencent, backed by Hero Entertainment, how it could

587
00:27:47,040 --> 00:27:49,439
Speaker 13: just have developed this game for years on end and

588
00:27:49,480 --> 00:27:51,840
Speaker 13: then have it come out at the top of.

589
00:27:51,840 --> 00:27:54,920
Speaker 5: Every chart, Okay, well every chart.

590
00:27:55,000 --> 00:27:57,639
Speaker 4: The other data set I was tracking was Sony PlayStation

591
00:27:57,840 --> 00:28:02,200
Speaker 4: five sales on Ali Barba the seven days through August twentieth,

592
00:28:02,200 --> 00:28:05,359
Speaker 4: and it was top of the consumer Electronics tree playerborn

593
00:28:05,440 --> 00:28:08,320
Speaker 4: PS five and PC. What does that tell us about

594
00:28:08,359 --> 00:28:11,639
Speaker 4: the Chinese game player right now, the Chinese consumer that

595
00:28:11,680 --> 00:28:15,480
Speaker 4: loves video games, Lisa, Well, it.

596
00:28:15,440 --> 00:28:17,360
Speaker 8: Tells us that they are discerning.

597
00:28:17,520 --> 00:28:19,680
Speaker 13: It tells us that they have money to spend when

598
00:28:19,680 --> 00:28:21,840
Speaker 13: they want to spend it, and it tells us that

599
00:28:21,920 --> 00:28:23,800
Speaker 13: they will spend that money on things that.

600
00:28:23,760 --> 00:28:25,080
Speaker 10: Are high quality.

601
00:28:25,320 --> 00:28:29,080
Speaker 13: So I think that for other game developers worldwide looking

602
00:28:29,280 --> 00:28:29,960
Speaker 13: to tackle this.

603
00:28:30,040 --> 00:28:33,359
Speaker 8: Market, there is opportunity there right now.

604
00:28:33,520 --> 00:28:37,080
Speaker 13: Blackmith Wukong does have one of the coveted government issued

605
00:28:37,160 --> 00:28:41,800
Speaker 13: ISBN licenses permitting it to have domestic distribution, but its

606
00:28:41,960 --> 00:28:47,680
Speaker 13: primary source of distribution in China today is Steam International,

607
00:28:48,040 --> 00:28:52,520
Speaker 13: and for Steam International, the ISBN is not really relevant,

608
00:28:52,880 --> 00:28:56,520
Speaker 13: so this is still a way for game companies to

609
00:28:56,600 --> 00:28:59,400
Speaker 13: distribute games in China.

610
00:29:00,080 --> 00:29:03,640
Speaker 3: Therefore, is a government going to assess what seems to

611
00:29:03,640 --> 00:29:05,400
Speaker 3: be a phenomenal first day.

612
00:29:07,480 --> 00:29:09,480
Speaker 13: Well, I think the government will assess this as a

613
00:29:09,560 --> 00:29:14,000
Speaker 13: victory for a Chinese based developer and one that is

614
00:29:14,760 --> 00:29:19,600
Speaker 13: going to certainly be a leader among the expul pack

615
00:29:19,960 --> 00:29:23,240
Speaker 13: of games that are being exported from China to the

616
00:29:23,280 --> 00:29:26,440
Speaker 13: rest of the world. This is a single player PC

617
00:29:26,800 --> 00:29:30,120
Speaker 13: offline game, meaning it's a premium game. You pay for

618
00:29:30,200 --> 00:29:33,000
Speaker 13: it up front and there are no in game purchases.

619
00:29:33,120 --> 00:29:36,400
Speaker 13: So this is something really amazing because most of the

620
00:29:36,440 --> 00:29:38,960
Speaker 13: success of games in China has.

621
00:29:38,800 --> 00:29:41,960
Speaker 8: Been for free to play or with in app.

622
00:29:41,800 --> 00:29:44,760
Speaker 13: Purchases this type of thing, and most of the hits

623
00:29:44,760 --> 00:29:47,080
Speaker 13: have been on mobile. This is a PC based single

624
00:29:47,120 --> 00:29:51,000
Speaker 13: player game, and just to give you some point of reference,

625
00:29:51,640 --> 00:29:55,400
Speaker 13: by the NIQUE China Games and Streaming Tracker, one of

626
00:29:55,440 --> 00:29:57,240
Speaker 13: the tools that we use to measure the market.

627
00:29:57,320 --> 00:29:59,600
Speaker 8: It's our proprietary tool.

628
00:30:00,120 --> 00:30:03,480
Speaker 13: We track streams on dou Huya and Billy Billy and

629
00:30:03,520 --> 00:30:08,280
Speaker 13: there were twenty nine million viewers by Nico's algorithmic approximation

630
00:30:08,360 --> 00:30:12,600
Speaker 13: of viewers of this game out of one hundred and

631
00:30:12,640 --> 00:30:18,080
Speaker 13: thirty million viewers of all streamed games on those platforms

632
00:30:18,200 --> 00:30:21,400
Speaker 13: on August twentieth, So that was really remarkable. You know,

633
00:30:21,480 --> 00:30:24,840
Speaker 13: almost twenty five percent of all gamers who are watching

634
00:30:25,000 --> 00:30:27,440
Speaker 13: streams of games are watching this.

635
00:30:27,560 --> 00:30:33,320
Speaker 4: Game, Lisa, developed by Game Science and Hung Joe. I

636
00:30:33,440 --> 00:30:36,360
Speaker 4: got so much marketing for this, particularly through the PS app.

637
00:30:38,120 --> 00:30:41,360
Speaker 4: Do you think, just to extend Caroline's question, this is

638
00:30:41,600 --> 00:30:44,600
Speaker 4: going to be a national champion for China that they'll say,

639
00:30:44,680 --> 00:30:46,760
Speaker 4: what a success you've got here on our hands?

640
00:30:48,640 --> 00:30:48,880
Speaker 5: Yeah?

641
00:30:48,920 --> 00:30:49,120
Speaker 2: Sure.

642
00:30:49,160 --> 00:30:51,120
Speaker 13: I think this is one of the big successes not

643
00:30:51,160 --> 00:30:54,320
Speaker 13: only of China but of PC offline games kind of globally,

644
00:30:54,640 --> 00:30:56,920
Speaker 13: and it happens to be from China, and it happens

645
00:30:56,960 --> 00:31:00,000
Speaker 13: to be from a relatively unknown studio called Game Science.

646
00:31:00,000 --> 00:31:03,440
Speaker 8: And similarly, Genchin Impact.

647
00:31:03,160 --> 00:31:06,040
Speaker 13: Had been released by Mijoyo a couple of years back,

648
00:31:06,080 --> 00:31:09,920
Speaker 13: and it, too, Blue Doors passed everyone's expectations and was

649
00:31:10,040 --> 00:31:14,600
Speaker 13: a national you know, big hit that went global and

650
00:31:14,720 --> 00:31:19,440
Speaker 13: lots of people love the game. Genshin Impact, so Mijoyo

651
00:31:19,560 --> 00:31:22,360
Speaker 13: also came out with that game as their first big hit.

652
00:31:23,080 --> 00:31:25,400
Speaker 13: And now we see game Science doing the same thing.

653
00:31:25,480 --> 00:31:28,440
Speaker 13: We've seen lots of tencent games do this. We've seen

654
00:31:28,480 --> 00:31:31,520
Speaker 13: lots of Netti's games do this. And speaking of Nettie's,

655
00:31:31,600 --> 00:31:34,600
Speaker 13: their big long standing hit of twenty years is Fantasy

656
00:31:34,600 --> 00:31:39,600
Speaker 13: Westward Journey. And Fantasy Westward Journey is a cultural mythical

657
00:31:39,720 --> 00:31:43,520
Speaker 13: history based game about you know, journey to the West

658
00:31:43,600 --> 00:31:44,280
Speaker 13: and legend of.

659
00:31:44,240 --> 00:31:44,920
Speaker 8: The Monkey King.

660
00:31:45,240 --> 00:31:47,960
Speaker 13: And that is the same theme that we see here

661
00:31:48,080 --> 00:31:51,360
Speaker 13: in Black Myth Wukong. And this theme has been very

662
00:31:51,400 --> 00:31:56,040
Speaker 13: popular among Chinese gamers forever and now we see that again.

663
00:31:57,040 --> 00:31:59,800
Speaker 13: It doesn't get tired that the game gamers don't get

664
00:31:59,800 --> 00:32:00,560
Speaker 13: tied of it.

665
00:32:00,600 --> 00:32:02,520
Speaker 8: This is something that they love to play.

666
00:32:03,760 --> 00:32:06,680
Speaker 4: If I can find just a couple of hours this week,

667
00:32:06,880 --> 00:32:10,800
Speaker 4: I will play. Lisa Costmas Hansen, CEO NICO Partners, thank

668
00:32:10,840 --> 00:32:22,720
Speaker 4: you so much for coming back. Barck and Michelle Obama

669
00:32:22,800 --> 00:32:25,440
Speaker 4: made their appearance on the second night of the Democratic

670
00:32:25,520 --> 00:32:30,560
Speaker 4: National Convention, blasting Republican nominee Donald Trump while painting Vice

671
00:32:30,560 --> 00:32:34,960
Speaker 4: President Kamala Harris is the heir of their historic political legacy.

672
00:32:35,000 --> 00:32:36,960
Speaker 4: I want to bring in Bloombose Kadie Liones for more.

673
00:32:37,000 --> 00:32:39,640
Speaker 4: And no matter which social media platform you looked on

674
00:32:39,720 --> 00:32:41,880
Speaker 4: the clips were shared, a lot of people had a

675
00:32:41,920 --> 00:32:44,520
Speaker 4: lot of comments. Is there any summary of what was

676
00:32:44,520 --> 00:32:45,480
Speaker 4: said on stage?

677
00:32:47,880 --> 00:32:49,360
Speaker 15: Well, we have to keep in mind here ed that

678
00:32:49,400 --> 00:32:51,720
Speaker 15: the Obamas, both of them, are some of the most

679
00:32:51,720 --> 00:32:55,800
Speaker 15: popular figures in modern democratic politics. Their approval reading among

680
00:32:55,960 --> 00:32:58,719
Speaker 15: those in the Democratic Party is north of ninety percent.

681
00:32:58,800 --> 00:33:01,280
Speaker 15: Michelle Obama, in Particulkiller is kind of more so even

682
00:33:01,320 --> 00:33:04,200
Speaker 15: a cultural figure than she was a former First Lady,

683
00:33:04,240 --> 00:33:06,400
Speaker 15: given the resonance of her book that published a few

684
00:33:06,480 --> 00:33:08,320
Speaker 15: years ago, in the tour that came along with thousands

685
00:33:08,360 --> 00:33:10,920
Speaker 15: of people, she certainly has an ability to kind of

686
00:33:10,960 --> 00:33:13,280
Speaker 15: resonate with the American populace, and of course as a

687
00:33:13,320 --> 00:33:16,120
Speaker 15: hometown girl in Chicago, her speech was very much one

688
00:33:16,120 --> 00:33:19,000
Speaker 15: that seemed targeted at getting people to actually.

689
00:33:18,640 --> 00:33:19,400
Speaker 2: Turn out to vote.

690
00:33:19,440 --> 00:33:22,200
Speaker 15: Both she and her husband, the former President Barack Obama,

691
00:33:22,360 --> 00:33:25,200
Speaker 15: kind of warned about these ideas of complacency, essentially saying

692
00:33:25,240 --> 00:33:28,360
Speaker 15: that this is going to be a tough fight. Michelle Obama,

693
00:33:28,440 --> 00:33:31,760
Speaker 15: calling on voters to do something, while Barack Obama, for

694
00:33:31,840 --> 00:33:35,000
Speaker 15: his part, talked more about his relationship with Joe Biden.

695
00:33:35,080 --> 00:33:38,320
Speaker 15: Of course, has Biden kind of gave his farewell address

696
00:33:38,360 --> 00:33:41,200
Speaker 15: on night one of the convention that went acknowledge and

697
00:33:41,200 --> 00:33:44,080
Speaker 15: Biden also. Obama rather had some pretty sharp attacks for

698
00:33:44,120 --> 00:33:46,800
Speaker 15: Donald Trump, talking about what he dubbed his weird obsession

699
00:33:47,240 --> 00:33:50,440
Speaker 15: with crowd size, talking about how he's been whining for

700
00:33:50,560 --> 00:33:53,040
Speaker 15: years and that it's getting stale for American voters. So

701
00:33:53,080 --> 00:33:55,120
Speaker 15: certainly it was fiery, And of course the Obamas have

702
00:33:55,120 --> 00:33:58,200
Speaker 15: had this kind of social media power that we have seen.

703
00:33:58,280 --> 00:34:01,080
Speaker 15: They really were the first kind of internet campaign, if

704
00:34:01,120 --> 00:34:03,120
Speaker 15: you will, back in two thousand and eight, and so

705
00:34:03,160 --> 00:34:05,680
Speaker 15: some of these resonating moments on social media we've certainly

706
00:34:06,000 --> 00:34:07,760
Speaker 15: seen playing out. And keeping in mind as well that

707
00:34:07,840 --> 00:34:11,520
Speaker 15: in that convention hall there are hundreds of social media influencers,

708
00:34:11,520 --> 00:34:14,880
Speaker 15: some of which have gotten actual speaking slots at this convention.

709
00:34:15,960 --> 00:34:19,040
Speaker 3: Katie lyones this like tech nuance on what was a

710
00:34:19,080 --> 00:34:22,560
Speaker 3: big night for the DNC. Now, let's discuss the impact

711
00:34:22,640 --> 00:34:25,120
Speaker 3: of the election on the tech landscape on bench capital

712
00:34:25,120 --> 00:34:27,800
Speaker 3: in particular, Marlin Nichols is with US Managing General partner

713
00:34:27,800 --> 00:34:30,520
Speaker 3: at mac Bench Capital, one of the signatories of VC's

714
00:34:30,560 --> 00:34:33,120
Speaker 3: for Kamala, and I mean just quickly to start us

715
00:34:33,160 --> 00:34:35,400
Speaker 3: off with the DNC and the energy there at the moment,

716
00:34:35,440 --> 00:34:37,760
Speaker 3: what are you seeing? How is it you hearing anything

717
00:34:37,800 --> 00:34:40,560
Speaker 3: from a policy perspective that speaks to your industry.

718
00:34:41,960 --> 00:34:44,960
Speaker 16: Yeah, I think there's still a lot to be said

719
00:34:45,680 --> 00:34:50,000
Speaker 16: and disclose on the actual policy. But my perspective is

720
00:34:50,080 --> 00:34:53,239
Speaker 16: Kamala is from the Bay Area, she was Aga California.

721
00:34:53,640 --> 00:34:56,480
Speaker 16: She knows tech. She's been an advocate and a supporter

722
00:34:56,560 --> 00:34:58,719
Speaker 16: of tech for a long time, and I'll expect that

723
00:34:58,760 --> 00:34:59,759
Speaker 16: to change anytime soon.

724
00:35:01,440 --> 00:35:03,719
Speaker 4: Modern A lot has been made of the idea that

725
00:35:04,000 --> 00:35:06,360
Speaker 4: Kamala Harris is from from the Bay We also had

726
00:35:06,360 --> 00:35:08,200
Speaker 4: a lot of people on the show that that say, well,

727
00:35:08,280 --> 00:35:11,040
Speaker 4: jd Vance was a bench Capitalist. You know, here's the

728
00:35:11,120 --> 00:35:14,879
Speaker 4: experience of backing entrepreneurs. Therefore might be better for a

729
00:35:14,920 --> 00:35:17,680
Speaker 4: startup leader for for an entrepreneurial environment.

730
00:35:17,880 --> 00:35:18,960
Speaker 5: How would you respond to that?

731
00:35:20,080 --> 00:35:24,880
Speaker 16: I'll say, this election to me is it's it's about

732
00:35:24,920 --> 00:35:28,359
Speaker 16: more than just you know, what's going to happen for tech.

733
00:35:28,400 --> 00:35:30,239
Speaker 16: I think tech is going to be okay, right. This

734
00:35:30,360 --> 00:35:35,680
Speaker 16: election to me is about human rights and freedom in

735
00:35:35,800 --> 00:35:39,040
Speaker 16: unifying the country, and I feel like Kamala is a

736
00:35:39,040 --> 00:35:42,200
Speaker 16: better choice in that respect. I also think she's the

737
00:35:42,200 --> 00:35:45,759
Speaker 16: most qualified candidate running, right. I mentioned she was an

738
00:35:45,760 --> 00:35:48,479
Speaker 16: ag she's part of the Senate, and she's been vice

739
00:35:48,520 --> 00:35:52,480
Speaker 16: president in a you know, in an administration that had

740
00:35:52,520 --> 00:35:54,200
Speaker 16: to do a lot of tournaments from a lot of

741
00:35:54,239 --> 00:35:57,759
Speaker 16: harm that was done from the administration prior. So that's

742
00:35:57,800 --> 00:36:00,920
Speaker 16: why I was I'm supportive of Kamala and why I

743
00:36:00,960 --> 00:36:03,400
Speaker 16: was a signer of the petition.

744
00:36:04,160 --> 00:36:07,440
Speaker 3: That human rights element speaks a lot to ultimately what

745
00:36:07,560 --> 00:36:11,160
Speaker 3: the work you've been doing within MAC Venture Capital because

746
00:36:11,840 --> 00:36:15,799
Speaker 3: you are backing founders from diverse backgrounds, you are doing

747
00:36:15,800 --> 00:36:19,200
Speaker 3: the work to not only be a majority black led team,

748
00:36:19,400 --> 00:36:21,319
Speaker 3: but also then put your money to work there too.

749
00:36:21,480 --> 00:36:24,440
Speaker 3: What sort of outside performance do you therefore get from that?

750
00:36:24,520 --> 00:36:27,520
Speaker 3: As you speak to you're putting your money and vote

751
00:36:27,560 --> 00:36:30,279
Speaker 3: behind Kamala not just because of what she looks like

752
00:36:30,320 --> 00:36:33,360
Speaker 3: and how she identifies, but also the experience she has.

753
00:36:34,880 --> 00:36:35,080
Speaker 5: Yeah.

754
00:36:35,200 --> 00:36:38,080
Speaker 16: I mean, for us, we've always tried to build a

755
00:36:38,160 --> 00:36:41,680
Speaker 16: fund that was one hundred percent meritocratic, right, and I

756
00:36:41,680 --> 00:36:44,640
Speaker 16: think that's the way that the world should work. Right,

757
00:36:44,760 --> 00:36:48,440
Speaker 16: despite your skin color, your gender, et cetera. If you

758
00:36:48,480 --> 00:36:52,360
Speaker 16: are the most capable and you're building the thing that

759
00:36:52,760 --> 00:36:57,760
Speaker 16: matters the most for the majority of the world, then

760
00:36:57,920 --> 00:37:00,000
Speaker 16: you should get the funding. You should have the opportunity

761
00:37:01,120 --> 00:37:02,120
Speaker 16: to build that company.

762
00:37:02,280 --> 00:37:02,759
Speaker 5: And that's the.

763
00:37:02,680 --> 00:37:05,640
Speaker 16: Philosophy that we've employed at at MAC from the beginning

764
00:37:05,920 --> 00:37:07,160
Speaker 16: and it served us pretty well.

765
00:37:09,040 --> 00:37:10,960
Speaker 4: Marlon and I want to just ask how things are

766
00:37:10,960 --> 00:37:14,040
Speaker 4: going at the firm to twenty twenty two. You have

767
00:37:14,120 --> 00:37:17,160
Speaker 4: your sophomore fund double your inaugural fund. You've had two

768
00:37:17,239 --> 00:37:20,080
Speaker 4: years to invest, go and see what's out there.

769
00:37:20,120 --> 00:37:21,040
Speaker 5: How's that gone?

770
00:37:21,800 --> 00:37:24,040
Speaker 16: Yeah, I mean I've put our track rocket up against

771
00:37:24,040 --> 00:37:27,040
Speaker 16: any other fund. You know, we're investing out of our

772
00:37:27,080 --> 00:37:31,080
Speaker 16: third fund now, all in for managing just around six

773
00:37:31,200 --> 00:37:33,880
Speaker 16: hundred million, which I think makes us the largest seat

774
00:37:34,680 --> 00:37:38,840
Speaker 16: seat stage fund in Los Angeles and one of the

775
00:37:38,920 --> 00:37:43,120
Speaker 16: largest in the country. I think things are going pretty well.

776
00:37:43,200 --> 00:37:45,120
Speaker 3: Let's talk a little bit about being in LA and

777
00:37:45,160 --> 00:37:48,320
Speaker 3: therefore some of the areas you've been allocating and first

778
00:37:48,360 --> 00:37:50,319
Speaker 3: comes to mind as the entertainment space, and they're like,

779
00:37:50,360 --> 00:37:52,719
Speaker 3: but I'm sure that you've also been looking at artificial intelligence.

780
00:37:53,160 --> 00:37:56,000
Speaker 3: What are valuations like when you're taking those two separate

781
00:37:56,000 --> 00:37:57,080
Speaker 3: areas in industry groups.

782
00:37:58,080 --> 00:38:02,240
Speaker 16: Yeah, you know, fortunately at the stage, valuations haven't jumped

783
00:38:03,160 --> 00:38:06,520
Speaker 16: or or declined too much. It's been pretty steady. You're

784
00:38:06,640 --> 00:38:09,400
Speaker 16: the range is roughly you know, ten million post money

785
00:38:09,400 --> 00:38:12,880
Speaker 16: to about twenty five and it's it's pretty it stayed

786
00:38:12,920 --> 00:38:16,360
Speaker 16: pretty flat there, even if it's an AI company. So

787
00:38:16,360 --> 00:38:21,640
Speaker 16: so we hadn't we weren't really too affected by the

788
00:38:22,320 --> 00:38:24,560
Speaker 16: major jumps in valuation that you're seeing at Series A

789
00:38:24,719 --> 00:38:25,879
Speaker 16: and beyond.

790
00:38:26,680 --> 00:38:28,480
Speaker 10: AI is a hot area for.

791
00:38:28,520 --> 00:38:31,839
Speaker 16: Everyone right now, and obviously we're we're investing that. We've

792
00:38:31,880 --> 00:38:34,480
Speaker 16: been investing in that area for over ten years now.

793
00:38:34,920 --> 00:38:38,200
Speaker 16: I have over ten years now the fund since our inception.

794
00:38:38,960 --> 00:38:40,600
Speaker 10: So yeah, you.

795
00:38:40,560 --> 00:38:45,400
Speaker 16: Know, markets, uh, you know, rise and fall and we

796
00:38:45,520 --> 00:38:46,440
Speaker 16: just try to be steady.

797
00:38:47,880 --> 00:38:50,879
Speaker 4: Marlon Nichols, Managing general partner at matt Bench Capital. Great

798
00:38:50,880 --> 00:38:52,600
Speaker 4: to have you here on Bloomberg Technology.

799
00:38:52,640 --> 00:38:53,000
Speaker 5: Thank you.

800
00:38:53,000 --> 00:38:56,399
Speaker 4: Now. Coming out, Serious XM signs a multi year deal

801
00:38:56,480 --> 00:39:00,399
Speaker 4: for the cool Her Daddy podcast. Interesting conversation coming out.

802
00:39:00,400 --> 00:39:09,520
Speaker 4: This is Bloomberg Technology.

803
00:39:11,280 --> 00:39:13,600
Speaker 3: Let's just talk about Serious XM after it signed a

804
00:39:13,680 --> 00:39:16,880
Speaker 3: multi year deal for Alex Cooper's Cooler Daddy podcast and

805
00:39:17,000 --> 00:39:19,959
Speaker 3: network of shows. Now it will give the satellite radio

806
00:39:20,000 --> 00:39:23,040
Speaker 3: company the exclusive rights to sell ads on the audio

807
00:39:23,040 --> 00:39:24,839
Speaker 3: and the video as you see here versions of her show,

808
00:39:24,960 --> 00:39:27,120
Speaker 3: as well as bonus content and events, and not only

809
00:39:27,120 --> 00:39:29,400
Speaker 3: her show, but those of other contributors to the Unwell

810
00:39:29,480 --> 00:39:33,000
Speaker 3: network like Alex Earl as well Ashley Carmen joins us

811
00:39:33,080 --> 00:39:35,480
Speaker 3: more for a few and our audience they'll be like,

812
00:39:35,560 --> 00:39:37,839
Speaker 3: who's Alex Cooper, Who's Alex el What are we talking

813
00:39:37,880 --> 00:39:40,239
Speaker 3: about here? This is a really gen z demographic, But

814
00:39:40,280 --> 00:39:42,120
Speaker 3: it means she's got one of what the seventh most

815
00:39:42,160 --> 00:39:44,040
Speaker 3: listened to podcast in the US.

816
00:39:44,120 --> 00:39:46,319
Speaker 17: Yes, this first quarter she's had a huge show call.

817
00:39:46,360 --> 00:39:48,960
Speaker 17: Her Daddy's been around for years. She had an exclusive

818
00:39:48,960 --> 00:39:50,799
Speaker 17: deal with Spotify that had been going on for the

819
00:39:50,800 --> 00:39:54,160
Speaker 17: past four years, I believe, And recently she took some

820
00:39:54,239 --> 00:39:56,480
Speaker 17: of the money she made in Spotify and started her

821
00:39:56,520 --> 00:39:59,520
Speaker 17: own network with a lot of TikTok YouTube stars.

822
00:39:59,520 --> 00:40:01,120
Speaker 2: So again, a young demographic.

823
00:40:02,800 --> 00:40:05,279
Speaker 4: What I'm fascinated about in the world of podcasts and

824
00:40:05,320 --> 00:40:08,160
Speaker 4: podcasts is if we use cool her daddy as the

825
00:40:08,200 --> 00:40:10,640
Speaker 4: example is, who are the power brokers that get the

826
00:40:10,680 --> 00:40:13,440
Speaker 4: deals done? But how does something like this come together?

827
00:40:15,160 --> 00:40:18,719
Speaker 17: Yeah, that's a great question. Well, Alex Cooper's agent is

828
00:40:18,800 --> 00:40:22,840
Speaker 17: Aren Rosenbaum from UTA. He is definitely one of the

829
00:40:22,880 --> 00:40:26,880
Speaker 17: power brokers in podcasting. He has signed deals for Alex Cooper,

830
00:40:27,200 --> 00:40:30,839
Speaker 17: Ashley Flowers of Crime Junkie, also with SYRIASXM, and many

831
00:40:30,840 --> 00:40:33,640
Speaker 17: many other podcasters like Guy raz of how I built this,

832
00:40:34,040 --> 00:40:36,440
Speaker 17: so many others who work with UTA, and of course

833
00:40:36,520 --> 00:40:38,799
Speaker 17: all the agencies have podcast agents as well.

834
00:40:39,600 --> 00:40:42,720
Speaker 3: What's wild is that, Yeah, his a one year performers

835
00:40:42,719 --> 00:40:45,279
Speaker 3: a SYRIXXM. It's down twenty four percent, but yesterday I

836
00:40:45,320 --> 00:40:47,600
Speaker 3: added basically more than eight hundred million dollars in market

837
00:40:47,640 --> 00:40:51,480
Speaker 3: capitalization through this one deal. Syrus has been aggressive and

838
00:40:51,600 --> 00:40:55,800
Speaker 3: also got into smartness another one hundred million dollar amount written.

839
00:40:55,960 --> 00:40:58,040
Speaker 2: Is this the new price tag for a successful podcast?

840
00:40:58,719 --> 00:41:01,480
Speaker 17: It was really the way the business was operating actually

841
00:41:01,520 --> 00:41:04,200
Speaker 17: like four years ago, and Spotify got into the space,

842
00:41:04,560 --> 00:41:07,800
Speaker 17: But recently we haven't seen as many of these splashy deals.

843
00:41:07,800 --> 00:41:10,520
Speaker 17: Serious XM is really the one out there that's being aggressive,

844
00:41:10,760 --> 00:41:15,080
Speaker 17: paying these big bills and focusing on the star talent

845
00:41:15,120 --> 00:41:18,080
Speaker 17: because I think they really see an opportunity where Howard

846
00:41:18,080 --> 00:41:21,719
Speaker 17: Stearns's contract is coming up they're losing subscribers. They really

847
00:41:21,800 --> 00:41:24,200
Speaker 17: need to bring in a younger demographic, and I think

848
00:41:24,239 --> 00:41:26,120
Speaker 17: the way they see that they're able to do that

849
00:41:26,239 --> 00:41:27,800
Speaker 17: is through these younger podcast stars.

850
00:41:28,560 --> 00:41:30,080
Speaker 5: So these are high profile very quickly.

851
00:41:30,120 --> 00:41:35,120
Speaker 4: Actually examples of leaving Spotify is that a worry in exodus, So.

852
00:41:35,120 --> 00:41:38,279
Speaker 17: Yeah, some of these folks have left Spotify. Spotify has

853
00:41:38,320 --> 00:41:42,080
Speaker 17: really just changed its operating mission. I mean, they were

854
00:41:42,520 --> 00:41:45,200
Speaker 17: very very very aggressive in the podcast space, but since

855
00:41:45,239 --> 00:41:49,400
Speaker 17: then they've now really focused on efficiency and being profitable

856
00:41:49,480 --> 00:41:52,520
Speaker 17: and having a better margin, and so we've seen their

857
00:41:52,560 --> 00:41:55,480
Speaker 17: business actually do very well as they've backed away from

858
00:41:55,480 --> 00:41:57,400
Speaker 17: some of these high profile podcast deals.

859
00:41:58,160 --> 00:42:00,840
Speaker 4: Bloomberg's actually common terrific sporting as always.

860
00:42:00,880 --> 00:42:03,879
Speaker 3: Thank you some big deals to finish off this edition

861
00:42:03,920 --> 00:42:05,320
Speaker 3: of the Bloomberg Technology.

862
00:42:04,920 --> 00:42:07,879
Speaker 4: Yet don't forget recap on the podcast. You can find

863
00:42:07,920 --> 00:42:10,000
Speaker 4: it on the Bloomberg terminal as well as on Apple,

864
00:42:10,080 --> 00:42:12,480
Speaker 4: Spotify and iHeart that does it.

865
00:42:12,760 --> 00:42:14,000
Speaker 5: This is Bloomberg Technology