1
00:00:02,920 --> 00:00:10,840
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the

2
00:00:10,840 --> 00:00:15,000
Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am

3
00:00:15,040 --> 00:00:18,040
Speaker 1: Eastern on Affo, Cardplay and Android Auto with the Bloomberg

4
00:00:18,079 --> 00:00:21,400
Speaker 1: Business App. Listen on demand wherever you get your podcasts,

5
00:00:21,640 --> 00:00:23,360
Speaker 1: or watch us live on YouTube.

6
00:00:23,440 --> 00:00:27,200
Speaker 2: But it's hard to understand, at least for me. The

7
00:00:27,240 --> 00:00:29,760
Speaker 2: ISM Services index. John was just saying comes in a

8
00:00:29,760 --> 00:00:33,240
Speaker 2: bit light. Price is paid coming in light, employment coming

9
00:00:33,320 --> 00:00:35,519
Speaker 2: in light, and new orders coming in light, but like

10
00:00:35,600 --> 00:00:37,400
Speaker 2: some of them are still over expansion. So we've got

11
00:00:37,440 --> 00:00:39,720
Speaker 2: to get to the guy behind the data. Anthony Yavis

12
00:00:39,800 --> 00:00:42,960
Speaker 2: is chair of the Ism Services Business Committee, and he

13
00:00:43,080 --> 00:00:45,040
Speaker 2: is here with us to help break down this data.

14
00:00:45,080 --> 00:00:47,839
Speaker 2: Is this a good report or is this a slowing

15
00:00:48,000 --> 00:00:48,680
Speaker 2: bad report?

16
00:00:51,000 --> 00:00:52,960
Speaker 3: I look at it as being a good report, and

17
00:00:53,000 --> 00:00:55,960
Speaker 3: the reason being, as we know the baseline being fifty,

18
00:00:56,040 --> 00:00:59,080
Speaker 3: we're still experiencing growth month over months. So even though

19
00:00:59,120 --> 00:01:01,600
Speaker 3: we had this pulled back of one point two percentage

20
00:01:01,640 --> 00:01:04,240
Speaker 3: points to the fifty one point four from fifty two

21
00:01:04,280 --> 00:01:08,119
Speaker 3: point six, activity still looks fairly strong. It's up point

22
00:01:08,120 --> 00:01:11,520
Speaker 3: two percentage points and new orders, which tells us what's

23
00:01:11,520 --> 00:01:14,800
Speaker 3: in the pipeline. Did come down one point seven percent,

24
00:01:14,920 --> 00:01:17,200
Speaker 3: but again we're north of the fifty baseline. It's a

25
00:01:17,280 --> 00:01:20,880
Speaker 3: fifty four point four, which indicates that we'll have continued growth.

26
00:01:21,720 --> 00:01:24,759
Speaker 3: As you mentioned in the prelude, that we see employment

27
00:01:25,440 --> 00:01:28,800
Speaker 3: and deliveries of what's pulling down the composite index here.

28
00:01:29,680 --> 00:01:32,120
Speaker 4: So again, as we think about the services part of

29
00:01:32,160 --> 00:01:36,000
Speaker 4: the economy, the major part of the US economy, again

30
00:01:36,080 --> 00:01:38,640
Speaker 4: just for our listeners and our viewers, anything above fifty

31
00:01:38,800 --> 00:01:42,000
Speaker 4: is expansion, So that part of the economy is still expanding,

32
00:01:42,120 --> 00:01:43,280
Speaker 4: is that the way that you think about it.

33
00:01:45,440 --> 00:01:48,320
Speaker 3: Absolutely, And we're measuring change month over a month, So

34
00:01:48,600 --> 00:01:50,640
Speaker 3: what we're seeing is just a slowing in the rate

35
00:01:50,680 --> 00:01:54,480
Speaker 3: of growth. And the employment picture is continues. As I've

36
00:01:54,480 --> 00:01:56,400
Speaker 3: been saying for several months now, it's a mixed bag.

37
00:01:56,600 --> 00:01:59,560
Speaker 3: Forty eight point five, it's down point five percentage points.

38
00:02:00,120 --> 00:02:03,160
Speaker 3: And what the respondents are indicating is that, you know,

39
00:02:03,200 --> 00:02:06,560
Speaker 3: they're very cautious and it's a controllable expense for them,

40
00:02:06,760 --> 00:02:10,320
Speaker 3: and so interesting some of the respondents indicated in their

41
00:02:10,360 --> 00:02:15,120
Speaker 3: comments that even though business activity is still going good,

42
00:02:15,200 --> 00:02:17,720
Speaker 3: that they're not hiring. They're holding back, waiting to see

43
00:02:17,760 --> 00:02:21,560
Speaker 3: how things materialize. In the upcoming months, because as you

44
00:02:21,639 --> 00:02:26,160
Speaker 3: mentioned about pricing, pricing still shows increases month over month.

45
00:02:26,240 --> 00:02:29,440
Speaker 3: It's strong, but it's somewhat stabilizing from where it was

46
00:02:29,480 --> 00:02:32,639
Speaker 3: in the past. And well we see the most volatility

47
00:02:32,720 --> 00:02:34,440
Speaker 3: continues to be in the food items.

48
00:02:34,680 --> 00:02:36,440
Speaker 2: Do you get the impression when you talk to the

49
00:02:36,440 --> 00:02:39,480
Speaker 2: respondents that they're going to have to look at layoffs

50
00:02:40,240 --> 00:02:42,320
Speaker 2: or is it just more about hiring more.

51
00:02:44,160 --> 00:02:46,640
Speaker 3: It's a combination, and it depends on the industry. It

52
00:02:46,720 --> 00:02:50,320
Speaker 3: varies by industry within the sectors within this sector, i

53
00:02:50,360 --> 00:02:54,359
Speaker 3: should say, and you know, certain industries like accommodation and

54
00:02:54,440 --> 00:02:57,960
Speaker 3: food services is continuing to hire, whereas we know with

55
00:02:58,080 --> 00:03:00,680
Speaker 3: the slowdown that we experienced in the past with real

56
00:03:00,760 --> 00:03:03,239
Speaker 3: estate rental and leasing, there you're not hiring.

57
00:03:04,160 --> 00:03:06,200
Speaker 2: All right, Anthony, thanks a lot, We really appreciate it.

58
00:03:06,200 --> 00:03:08,600
Speaker 2: It's always good to get the details here. Anthony Javis

59
00:03:08,840 --> 00:03:11,720
Speaker 2: share of the Ism Business Services Committee again that overall

60
00:03:11,720 --> 00:03:15,880
Speaker 2: index still an expansionary territory, but definitely still slowing down just.

61
00:03:15,840 --> 00:03:16,320
Speaker 5: A little bit.

62
00:03:17,880 --> 00:03:21,760
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

63
00:03:21,840 --> 00:03:25,359
Speaker 1: weekdays at ten am Eastern on applecar Play and Android

64
00:03:25,400 --> 00:03:28,160
Speaker 1: Auto with the Bloomberg Business app. You can also listen

65
00:03:28,280 --> 00:03:31,360
Speaker 1: live on Amazon Alexa from our flagship New York station.

66
00:03:31,760 --> 00:03:34,520
Speaker 1: Just say Alexa play Bloomberg eleven thirty.

67
00:03:35,840 --> 00:03:39,360
Speaker 2: This is Bloomberg Intelligence Radio. We bring you all the

68
00:03:39,400 --> 00:03:42,280
Speaker 2: amazing work from our Bloomberg Intelligence analysts. They cover over

69
00:03:42,320 --> 00:03:45,400
Speaker 2: two thousand companies and one hundred and forty industries all

70
00:03:45,520 --> 00:03:48,200
Speaker 2: around the world. And one of them is named Tim

71
00:03:48,200 --> 00:03:53,280
Speaker 2: Craighead Bloomberg Intelligence Research Director for content fancy title. And

72
00:03:53,320 --> 00:03:55,440
Speaker 2: the reason why is because he came up with a

73
00:03:55,520 --> 00:03:59,560
Speaker 2: really great list along with his teammates about fifty companies

74
00:03:59,560 --> 00:04:01,880
Speaker 2: to why in January they came up with that and

75
00:04:01,920 --> 00:04:03,760
Speaker 2: that was the ones you got to watch for this year.

76
00:04:03,800 --> 00:04:07,200
Speaker 2: And they're now back with ten specifically just for the

77
00:04:07,280 --> 00:04:11,240
Speaker 2: second quarter, based on scenarios from Bloomberg Intelligence. You want

78
00:04:11,240 --> 00:04:14,040
Speaker 2: to get the good apples from the bad apples, cross sector,

79
00:04:14,160 --> 00:04:16,680
Speaker 2: cross regions, and he's here now to help us break

80
00:04:16,680 --> 00:04:19,440
Speaker 2: it down. Hey, Tim, thanks for joining. Okay, so what

81
00:04:19,480 --> 00:04:20,200
Speaker 2: are the top ten?

82
00:04:21,440 --> 00:04:25,480
Speaker 6: So Alex, thanks for having me on a couple of

83
00:04:25,560 --> 00:04:29,240
Speaker 6: things to keep in mind here. Top ten for two Q.

84
00:04:30,920 --> 00:04:33,240
Speaker 6: These are part of a broader set of focus ideas

85
00:04:33,240 --> 00:04:35,880
Speaker 6: that we've got there's roughly seventy five to eighty of

86
00:04:35,920 --> 00:04:40,440
Speaker 6: them right now, across sectors, across regions. These specifically have

87
00:04:40,600 --> 00:04:44,360
Speaker 6: important catalysts coming up into Q that we think can

88
00:04:44,440 --> 00:04:47,520
Speaker 6: bring the market around to our perspective. All of these

89
00:04:47,520 --> 00:04:52,000
Speaker 6: focus ideas have really strong, high conviction fundamental views that

90
00:04:52,040 --> 00:04:54,440
Speaker 6: we think are different from the market, and these catalysts

91
00:04:55,240 --> 00:05:00,919
Speaker 6: should bring that awareness around. It's it's a group of

92
00:05:01,720 --> 00:05:05,799
Speaker 6: US companies. There's four of them. Uh, there's three in Europe,

93
00:05:05,960 --> 00:05:10,520
Speaker 6: three in in Asia, and they span the gamut from healthcare,

94
00:05:11,120 --> 00:05:18,120
Speaker 6: med tech products to big financials to tires. So interesting group.

95
00:05:18,000 --> 00:05:20,480
Speaker 4: And Tim one of the ones that you know, I've

96
00:05:20,480 --> 00:05:22,919
Speaker 4: been kind of thinking about. I just think these Olympics

97
00:05:22,920 --> 00:05:25,200
Speaker 4: in Parish are going to be just huge, and I'm

98
00:05:25,200 --> 00:05:27,520
Speaker 4: thinking that ways to play it. You guys have one

99
00:05:27,560 --> 00:05:29,599
Speaker 4: of them in the lodging space. Talk to us about

100
00:05:29,960 --> 00:05:30,720
Speaker 4: that opportunity.

101
00:05:31,320 --> 00:05:36,679
Speaker 6: Yeah, we do. Acor it's a big French hotel company.

102
00:05:36,720 --> 00:05:40,400
Speaker 6: You might not know Acor, but you probably know some

103
00:05:40,480 --> 00:05:45,640
Speaker 6: of their underlying brands. And you know, anybody who's made

104
00:05:45,640 --> 00:05:49,320
Speaker 6: a hotel booking as of late knows that the prices

105
00:05:49,360 --> 00:05:56,680
Speaker 6: seem to be really high. Surprisingly so that's feeding Acor's

106
00:05:57,279 --> 00:06:00,479
Speaker 6: rev par They're they're pricing, which is playing through into

107
00:06:00,480 --> 00:06:04,719
Speaker 6: profit margins, and the catalyst for these guys is, in fact,

108
00:06:06,680 --> 00:06:09,520
Speaker 6: the Paris Olympics. We should start to see early booking

109
00:06:09,600 --> 00:06:13,039
Speaker 6: data over the course of two Q heading into Summer Olympics.

110
00:06:13,080 --> 00:06:15,520
Speaker 6: Now Paris is only one part of their portfolio. They're

111
00:06:15,520 --> 00:06:18,599
Speaker 6: across Europe and Asia, and we're seeing a strong sustained

112
00:06:18,640 --> 00:06:22,120
Speaker 6: travel recovery story that we think is better than consensus expects.

113
00:06:22,360 --> 00:06:24,040
Speaker 2: But apparently they have to like clean out the send.

114
00:06:24,080 --> 00:06:25,840
Speaker 2: Did you read that that there's so much to you,

115
00:06:27,040 --> 00:06:29,080
Speaker 2: Like can you imagine being an Olympic swimmer and like

116
00:06:29,080 --> 00:06:30,600
Speaker 2: there's garbage hitting you in the send? I mean they're

117
00:06:30,640 --> 00:06:31,000
Speaker 2: working on it.

118
00:06:31,040 --> 00:06:31,960
Speaker 6: They're definitely working on it.

119
00:06:32,560 --> 00:06:32,839
Speaker 5: Okay.

120
00:06:32,880 --> 00:06:35,720
Speaker 2: Your second one is in retail chow Tai Fuk. No

121
00:06:35,720 --> 00:06:37,560
Speaker 2: one's going to know this company here in the US,

122
00:06:37,600 --> 00:06:39,719
Speaker 2: So tell us about it and why this makes the outlook.

123
00:06:40,400 --> 00:06:40,640
Speaker 1: Yep.

124
00:06:40,760 --> 00:06:43,560
Speaker 6: So that and the next one on the list from

125
00:06:43,560 --> 00:06:48,080
Speaker 6: an alphabetical perspective, are both sort of interesting twists on

126
00:06:48,120 --> 00:06:52,280
Speaker 6: the same underlying theme, and that's China. We all know

127
00:06:52,400 --> 00:06:55,039
Speaker 6: that there's problems in China. From the standpoint that the

128
00:06:55,040 --> 00:06:59,120
Speaker 6: property market, and now that's playing through in terms of sentiment,

129
00:07:00,360 --> 00:07:03,320
Speaker 6: uh with the wealth effect on spending plans, et cetera.

130
00:07:03,880 --> 00:07:09,200
Speaker 6: Child Tai Fook is arguably the best known Chinese retailer

131
00:07:09,240 --> 00:07:11,960
Speaker 6: when it comes to jewelry, and it's a big market

132
00:07:12,080 --> 00:07:16,320
Speaker 6: historically for tourists and travelers coming into Hong Kong and

133
00:07:16,360 --> 00:07:19,720
Speaker 6: to make those purchases. In fact, what we're seeing right

134
00:07:19,760 --> 00:07:23,360
Speaker 6: now is Hong kongers are going out of Hong Kong

135
00:07:23,520 --> 00:07:26,240
Speaker 6: across the border in the mainland China to shop for

136
00:07:26,360 --> 00:07:31,280
Speaker 6: pretty much everything because it's cheaper. So you combine that

137
00:07:31,520 --> 00:07:34,360
Speaker 6: with negative sentiment, we think Hong Kong retail sales are

138
00:07:34,360 --> 00:07:37,400
Speaker 6: going to disappoint, and Child Tai Fuk is a poster child.

139
00:07:37,760 --> 00:07:42,760
Speaker 6: The other one, CSC Financial is one of the largest

140
00:07:42,840 --> 00:07:47,560
Speaker 6: Chinese brokerages you know, good old fashioned, you know, wealth management,

141
00:07:47,960 --> 00:07:50,520
Speaker 6: and they are the investment banking business we think is

142
00:07:50,520 --> 00:07:53,760
Speaker 6: set for disappointment because I POS and other new new

143
00:07:53,840 --> 00:07:59,800
Speaker 6: stock listings are way under way, underwater, and you know,

144
00:07:59,840 --> 00:08:03,320
Speaker 6: we think you're going to see investment banking revenue disappoint

145
00:08:03,720 --> 00:08:03,920
Speaker 6: You know.

146
00:08:03,920 --> 00:08:05,600
Speaker 4: I was looking through the list here, Tim, and I

147
00:08:05,640 --> 00:08:08,280
Speaker 4: was looking for a name from Michael Dean and the

148
00:08:08,320 --> 00:08:10,680
Speaker 4: European Autos team, and I think I found one in

149
00:08:10,760 --> 00:08:12,240
Speaker 4: Perelli Tires.

150
00:08:13,760 --> 00:08:17,280
Speaker 6: Yes, indeed, I started to say, if you're an F

151
00:08:17,360 --> 00:08:20,200
Speaker 6: one fan, but that's more from a European perspective than

152
00:08:20,320 --> 00:08:25,040
Speaker 6: NASCAR in the US. But Perelli is a well known

153
00:08:25,080 --> 00:08:29,920
Speaker 6: tire maker and what they're doing is twofold Number one,

154
00:08:30,560 --> 00:08:32,720
Speaker 6: upgrading their mix. They're getting rid of some of their

155
00:08:32,720 --> 00:08:36,679
Speaker 6: lower end products. And number two, they've taken on a

156
00:08:36,679 --> 00:08:41,040
Speaker 6: pretty big initiative to shift production from core Europe to

157
00:08:41,160 --> 00:08:45,480
Speaker 6: places like Romania over here in Mexico over there, all

158
00:08:45,520 --> 00:08:48,640
Speaker 6: of which is driving better margins. And we think that

159
00:08:48,679 --> 00:08:51,520
Speaker 6: this is going to continue more than what consensus currently

160
00:08:51,559 --> 00:08:51,960
Speaker 6: bakes in.

161
00:08:52,280 --> 00:08:54,560
Speaker 4: Plus, it's from one of my favorite analysts over in

162
00:08:54,559 --> 00:08:57,600
Speaker 4: our London office, Gillian Davis, doing some good work there,

163
00:08:57,679 --> 00:08:58,360
Speaker 4: so good stuff.

164
00:08:58,640 --> 00:09:01,200
Speaker 7: Home Building Morrison made the list.

165
00:09:01,240 --> 00:09:02,960
Speaker 2: That's not normally the company you think of when you

166
00:09:02,960 --> 00:09:05,880
Speaker 2: think of like a homebuilder, right, you're thinking Lenar, KB homes,

167
00:09:05,880 --> 00:09:07,720
Speaker 2: et cetera. How does one make a list?

168
00:09:08,280 --> 00:09:12,360
Speaker 6: Yeah, I mean they're they're continuing to expand, specifically in

169
00:09:12,440 --> 00:09:16,600
Speaker 6: some larger developments in Florida and Texas, and it just

170
00:09:16,679 --> 00:09:21,080
Speaker 6: seems to be a case of good old fashioned running

171
00:09:21,120 --> 00:09:26,559
Speaker 6: the numbers with those expansion plans feeding through and their

172
00:09:26,679 --> 00:09:30,080
Speaker 6: order book coming through better than anticipated, where we think

173
00:09:30,120 --> 00:09:33,920
Speaker 6: their positive surprises d but it certainly is you know,

174
00:09:34,640 --> 00:09:38,120
Speaker 6: it is a classic story from the standpoint of expanding

175
00:09:38,240 --> 00:09:40,160
Speaker 6: order books, expanding volumes.

176
00:09:40,800 --> 00:09:43,480
Speaker 4: You know, one name that's onnor that surprising, but I

177
00:09:43,480 --> 00:09:47,600
Speaker 4: think it's a potential rule opportunity. Is Tencent talks about

178
00:09:47,679 --> 00:09:49,839
Speaker 4: big Chinese e commerce because for a lot of people

179
00:09:49,840 --> 00:09:52,640
Speaker 4: tim as you well know, China's kind of taboot right now.

180
00:09:52,679 --> 00:09:54,240
Speaker 4: From an investment perspective.

181
00:09:54,360 --> 00:09:58,360
Speaker 6: It is, especially when it comes to technology and regulatory concerns,

182
00:09:58,640 --> 00:10:02,040
Speaker 6: and you know, beij focusing in on you know, what

183
00:10:02,120 --> 00:10:05,400
Speaker 6: the big tech companies are doing. You know, historically these

184
00:10:05,480 --> 00:10:11,600
Speaker 6: have ten cents driven by online gaming and that growth

185
00:10:11,679 --> 00:10:16,400
Speaker 6: rate is still somewhat muted. But there's more video that's

186
00:10:16,440 --> 00:10:21,959
Speaker 6: coming through. There's AI assisted advertising that is that is growing.

187
00:10:22,040 --> 00:10:26,160
Speaker 6: Both of those are drivers of their income statement that

188
00:10:26,200 --> 00:10:30,200
Speaker 6: we think are underappreciated. We think it's mid teams of

189
00:10:30,200 --> 00:10:32,720
Speaker 6: growth going forward. But if you do the math, the

190
00:10:32,760 --> 00:10:37,360
Speaker 6: market seems to be discounting basically static to maybe low

191
00:10:37,440 --> 00:10:40,320
Speaker 6: single digit growth rates and so there it's just a

192
00:10:40,480 --> 00:10:43,560
Speaker 6: it's just a case where we think the market doesn't

193
00:10:43,600 --> 00:10:47,360
Speaker 6: realize that they're back to having good, solid forward growth.

194
00:10:47,520 --> 00:10:48,960
Speaker 4: Yeah, it's gonna be I mean it could be a

195
00:10:49,000 --> 00:10:51,240
Speaker 4: really interesting story. A lot of folks again really not

196
00:10:51,240 --> 00:10:53,360
Speaker 4: sure how to play that part of the market. Tim Craig,

197
00:10:53,559 --> 00:10:55,760
Speaker 4: thanks for joining us. Always appreciate getting your thoughts. Tim

198
00:10:55,840 --> 00:10:58,520
Speaker 4: craigett Research director for Content. He has also got a

199
00:10:58,559 --> 00:11:01,760
Speaker 4: day job European Equity Streates just and we're appreciating some

200
00:11:01,800 --> 00:11:04,480
Speaker 4: of his times from Bloomberg Intelligence. He's based in our

201
00:11:04,559 --> 00:11:07,280
Speaker 4: London office, the Queen Victoria Street offices, which are just

202
00:11:07,400 --> 00:11:10,600
Speaker 4: awesome right in the City of London, right near the

203
00:11:10,640 --> 00:11:13,200
Speaker 4: Bank of England. You can't get more city of London

204
00:11:13,240 --> 00:11:16,200
Speaker 4: and heart of the financial system in London than the

205
00:11:16,240 --> 00:11:19,040
Speaker 4: Bank of England, and that's where Bloomberg has their offices.

206
00:11:21,400 --> 00:11:25,280
Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live

207
00:11:25,360 --> 00:11:28,440
Speaker 1: weekdays at ten am Eastern on applecar Playing and broud

208
00:11:28,480 --> 00:11:31,560
Speaker 1: Otto with the Bloomberg Business app. Listen on demand wherever

209
00:11:31,640 --> 00:11:35,240
Speaker 1: you get your podcasts, or watch us live on YouTube.

210
00:11:36,000 --> 00:11:39,680
Speaker 4: Intel disappointing outlooks, dock down seven percent. It's one of

211
00:11:39,720 --> 00:11:42,160
Speaker 4: those chip makers that's really bucking the trend here. It's

212
00:11:42,160 --> 00:11:45,360
Speaker 4: down nineteen percent a year to date, while you got

213
00:11:45,400 --> 00:11:49,080
Speaker 4: some of the others, Nvidia Broadcom putting up just extraordinary gains.

214
00:11:49,080 --> 00:11:51,439
Speaker 4: So it feels like it's Intel's missing that. So let's

215
00:11:51,480 --> 00:11:53,280
Speaker 4: break it down a little bit more for Intel, the

216
00:11:53,360 --> 00:11:56,000
Speaker 4: chip names, tech, the markets overall. We can do that

217
00:11:56,040 --> 00:11:58,120
Speaker 4: on our next guest, Kim Forest. She's the founder and

218
00:11:58,160 --> 00:12:03,640
Speaker 4: chief investment officer at Boca Partners, I believe, located in Pittsburgh,

219
00:12:03,760 --> 00:12:07,720
Speaker 4: lovely city, big fan of Pittsburgh. All right, Kim, on

220
00:12:07,760 --> 00:12:10,880
Speaker 4: your list on your notes, top picks continue to be Intel,

221
00:12:10,920 --> 00:12:13,800
Speaker 4: A and D mu Let's start with Intel. What is

222
00:12:13,880 --> 00:12:16,640
Speaker 4: Intel just not getting right here?

223
00:12:17,920 --> 00:12:20,040
Speaker 5: Sure? Well, a couple of things.

224
00:12:20,440 --> 00:12:24,040
Speaker 8: They are coming up a very steep hill from where

225
00:12:24,200 --> 00:12:28,120
Speaker 8: prior CEOs left them, and what happened was they kind

226
00:12:28,160 --> 00:12:32,400
Speaker 8: of fell behind the curve in making sure that they

227
00:12:32,440 --> 00:12:36,839
Speaker 8: could make the very fastest, best chips they could, and

228
00:12:36,960 --> 00:12:38,719
Speaker 8: their technology just floundered.

229
00:12:38,920 --> 00:12:41,160
Speaker 5: That's pretty much the short answer.

230
00:12:41,720 --> 00:12:46,720
Speaker 8: Now. Pat Gelsinger was a long time Intel employee, went

231
00:12:46,720 --> 00:12:50,200
Speaker 8: over to run VMware, came back to Intel, and he's

232
00:12:50,240 --> 00:12:52,679
Speaker 8: been trying to write the ship. But more than that,

233
00:12:52,800 --> 00:12:55,000
Speaker 8: he's been trying to make them into a foundry for

234
00:12:55,160 --> 00:13:00,440
Speaker 8: other company companies that designed chips. So this is a

235
00:13:00,480 --> 00:13:05,160
Speaker 8: long and arduous process. Becoming a foundry is they've they've

236
00:13:05,200 --> 00:13:08,600
Speaker 8: done that for specific companies in the past, but they

237
00:13:08,640 --> 00:13:12,120
Speaker 8: want to make it more broad like akin to what

238
00:13:12,480 --> 00:13:15,520
Speaker 8: Taiwan's Semiconductor can do. And they're just going to have

239
00:13:15,559 --> 00:13:18,000
Speaker 8: to come up the curve and spend some money. So

240
00:13:18,480 --> 00:13:20,880
Speaker 8: they have the one two Pine ship spending money and

241
00:13:20,920 --> 00:13:24,560
Speaker 8: then not having that hot AI chip, which is what

242
00:13:24,640 --> 00:13:27,839
Speaker 8: is driving everybody else in the world apparently.

243
00:13:27,640 --> 00:13:28,560
Speaker 5: Yeah, exactly.

244
00:13:28,559 --> 00:13:31,520
Speaker 4: So let's I love to just get your your AI

245
00:13:31,600 --> 00:13:34,360
Speaker 4: AI call if you will. How much do you believe

246
00:13:34,400 --> 00:13:37,720
Speaker 4: in it? How big can it be? And how are

247
00:13:37,720 --> 00:13:38,679
Speaker 4: you guys maybe playing it?

248
00:13:39,679 --> 00:13:41,160
Speaker 5: Sure? Well? A couple of things.

249
00:13:41,200 --> 00:13:43,480
Speaker 8: You have to understand that before I came over to

250
00:13:43,520 --> 00:13:46,040
Speaker 8: this side of the world finance, I was a software

251
00:13:46,080 --> 00:13:47,559
Speaker 8: engineer doing AI.

252
00:13:48,200 --> 00:13:49,840
Speaker 5: Like how lucky is that? Okay?

253
00:13:49,960 --> 00:13:54,719
Speaker 8: I know how Right's super Yes, it's nerd girl right

254
00:13:54,760 --> 00:13:57,959
Speaker 8: here anyhow. But moving on, So I believe in AI

255
00:13:58,120 --> 00:14:01,040
Speaker 8: and I think a couple of things that are going

256
00:14:01,120 --> 00:14:02,120
Speaker 8: to drive it forward.

257
00:14:02,160 --> 00:14:03,360
Speaker 5: First, we have enough data.

258
00:14:03,400 --> 00:14:05,360
Speaker 8: We didn't have enough data in the nineties when I

259
00:14:05,440 --> 00:14:09,000
Speaker 8: was doing it to be able to meaningfully understand the

260
00:14:09,040 --> 00:14:13,520
Speaker 8: world through the lens of AI problems. The second thing

261
00:14:13,640 --> 00:14:17,440
Speaker 8: is we have cheap fast computers. Again, we're spending a

262
00:14:17,440 --> 00:14:20,680
Speaker 8: lot of money on these computers, but for the compute

263
00:14:20,720 --> 00:14:24,360
Speaker 8: power back in the day. These are incredibly fast machines

264
00:14:24,640 --> 00:14:27,840
Speaker 8: that are incredibly cheap. And the third thing is, and

265
00:14:27,880 --> 00:14:31,480
Speaker 8: I always look through technologies with technology with this lens,

266
00:14:32,040 --> 00:14:39,120
Speaker 8: is we can actually make AI be productive, right like

267
00:14:39,280 --> 00:14:42,640
Speaker 8: help humans out. And I think putting those three things together,

268
00:14:42,720 --> 00:14:46,040
Speaker 8: I'm a strong believer in it. But the caveat is this,

269
00:14:46,200 --> 00:14:48,280
Speaker 8: it's not going to happen in the next fifteen minutes

270
00:14:48,400 --> 00:14:51,640
Speaker 8: or even next year. It's probably going to take seven

271
00:14:51,680 --> 00:14:54,640
Speaker 8: to ten years for this really to play out and

272
00:14:54,680 --> 00:14:58,280
Speaker 8: to change the world in fundamental ways, much like the

273
00:14:58,400 --> 00:15:00,600
Speaker 8: rollout of the Internet was so kim.

274
00:15:00,640 --> 00:15:01,320
Speaker 7: How do you play that?

275
00:15:01,320 --> 00:15:01,480
Speaker 5: Then?

276
00:15:01,920 --> 00:15:03,680
Speaker 2: Like, is it not an Nvidia play? Is it going

277
00:15:03,760 --> 00:15:05,480
Speaker 2: to be a different kind of play?

278
00:15:06,560 --> 00:15:08,280
Speaker 8: Well, I think you're going to have to cast your

279
00:15:08,320 --> 00:15:11,280
Speaker 8: net a little wider than in Nvidia. I love Nvidia,

280
00:15:11,440 --> 00:15:14,640
Speaker 8: but they do have a reputation for making the fastest

281
00:15:14,680 --> 00:15:18,160
Speaker 8: chips at the highest prices, and that's why I'm a

282
00:15:18,200 --> 00:15:21,960
Speaker 8: believer still in Intel, even as it rolls that rock

283
00:15:22,040 --> 00:15:25,160
Speaker 8: up the hill to correct itself. I think that it

284
00:15:25,400 --> 00:15:28,120
Speaker 8: can do that. And I also think AMD is a

285
00:15:28,160 --> 00:15:32,240
Speaker 8: good supplier as well, and people building aid data centers

286
00:15:32,280 --> 00:15:36,160
Speaker 8: aren't going to want to spend the bucks for Nvidia

287
00:15:36,280 --> 00:15:40,920
Speaker 8: in perpetuity, and I think that's just what is going

288
00:15:40,960 --> 00:15:43,800
Speaker 8: to drive those two names.

289
00:15:43,800 --> 00:15:47,920
Speaker 2: Micron, Micron, go ahead, go ahead, go ahead.

290
00:15:48,400 --> 00:15:51,240
Speaker 8: Okay, yeah, and I love to be cool like nerd girl,

291
00:15:51,320 --> 00:15:54,560
Speaker 8: cool but still cool. Okay, but I'm back so Micron,

292
00:15:54,840 --> 00:15:59,320
Speaker 8: I can't under estimate or understate this or overstate this enough.

293
00:15:59,360 --> 00:16:02,520
Speaker 8: That's what I mean is that AI needs a lot

294
00:16:02,560 --> 00:16:05,440
Speaker 8: of data, like tons and tons of data, and I

295
00:16:05,480 --> 00:16:08,480
Speaker 8: won't get into it. That's beyond here. There are really

296
00:16:08,480 --> 00:16:13,160
Speaker 8: good reasons why. But you're gonna need tons of data

297
00:16:13,200 --> 00:16:16,560
Speaker 8: and you're gonna need to store it in duplicative ways,

298
00:16:16,880 --> 00:16:20,160
Speaker 8: and so any bet on storing data is a good

299
00:16:20,240 --> 00:16:23,000
Speaker 8: bet right now. And I think Micron is among the

300
00:16:23,080 --> 00:16:28,280
Speaker 8: leaders of land storage, which is kind of non volatile

301
00:16:28,720 --> 00:16:31,200
Speaker 8: chip storage and not a spinning disk.

302
00:16:31,520 --> 00:16:34,760
Speaker 5: So that's the shortest answer. I can get for that one.

303
00:16:34,680 --> 00:16:37,080
Speaker 2: Kim, I have an ancillary This is totally talking my

304
00:16:37,120 --> 00:16:40,440
Speaker 2: own book here. What about power plays in relation to AI?

305
00:16:41,600 --> 00:16:43,480
Speaker 2: Just because if you want to run all the stuff,

306
00:16:43,520 --> 00:16:46,040
Speaker 2: you need a ton of power and the amount of

307
00:16:46,040 --> 00:16:47,880
Speaker 2: money that these companies are gonna be willing to pay

308
00:16:47,920 --> 00:16:49,920
Speaker 2: for it has got to be astronomical concerning that we

309
00:16:49,960 --> 00:16:52,360
Speaker 2: haven't seen power demand grow at all in twenty years.

310
00:16:52,680 --> 00:16:54,440
Speaker 7: Are you playing this derivative play at all?

311
00:16:55,520 --> 00:16:58,200
Speaker 8: Not yet, because I don't really see a good way

312
00:16:58,200 --> 00:17:00,640
Speaker 8: to do it. You know, there you can by like

313
00:17:03,360 --> 00:17:07,760
Speaker 8: certain power companies that are probably physically well positioned, that

314
00:17:07,920 --> 00:17:11,960
Speaker 8: have assets that are somewhat cheaper where the companies will

315
00:17:12,000 --> 00:17:15,840
Speaker 8: build their data centers. That's one way to do it.

316
00:17:15,840 --> 00:17:18,399
Speaker 8: It's too long of a play, Like, I'd rather stay

317
00:17:18,480 --> 00:17:21,960
Speaker 8: back in the AI kind of direct space rather than

318
00:17:22,520 --> 00:17:27,000
Speaker 8: that or even you know, the the data center providers.

319
00:17:27,200 --> 00:17:30,520
Speaker 8: You could play it that way too, But again, I

320
00:17:30,640 --> 00:17:34,520
Speaker 8: like my margins a little higher than those end markets.

321
00:17:34,760 --> 00:17:39,720
Speaker 4: Yeah, hey, it's interesting Kim thinking about Tesla. I'm just

322
00:17:40,600 --> 00:17:42,080
Speaker 4: I want to want to get your opinion on if

323
00:17:42,119 --> 00:17:43,680
Speaker 4: you have one. I mean a stocks down thirty two

324
00:17:43,680 --> 00:17:47,159
Speaker 4: percent year to date. Boy, has the tide turn on

325
00:17:47,320 --> 00:17:49,080
Speaker 4: that name, the sentiment turned on that name. Do you

326
00:17:49,119 --> 00:17:50,560
Speaker 4: have an opinion on Tesla here?

327
00:17:52,880 --> 00:17:56,400
Speaker 8: I've never invested in Tesla because I like my CEO

328
00:17:56,600 --> 00:18:01,560
Speaker 8: is more focused than Elon must But that being said,

329
00:18:02,160 --> 00:18:07,280
Speaker 8: like it is a battery storage play, and your other question,

330
00:18:07,480 --> 00:18:11,159
Speaker 8: Alex was on power plays. Maybe batteries are going to

331
00:18:11,200 --> 00:18:15,800
Speaker 8: be one of the ways that we allow for, you know,

332
00:18:15,840 --> 00:18:18,120
Speaker 8: the build out of electric power. So I think they

333
00:18:18,119 --> 00:18:23,320
Speaker 8: have more than one product there. I think evs have

334
00:18:23,480 --> 00:18:28,600
Speaker 8: also been overrated and people didn't do enough use case scenarios,

335
00:18:28,640 --> 00:18:31,520
Speaker 8: Like I have family that lives in North Dakota. There's

336
00:18:31,680 --> 00:18:34,880
Speaker 8: no way they could ever have an EV it gets

337
00:18:34,920 --> 00:18:38,600
Speaker 8: to negative twenty three. There a lot, right, you can't

338
00:18:38,720 --> 00:18:43,160
Speaker 8: charge them. There aren't enough towns where you could pull

339
00:18:43,200 --> 00:18:47,520
Speaker 8: over and you know, recharge. It's just like it's like

340
00:18:47,560 --> 00:18:49,399
Speaker 8: I look at use cases like that, and I know

341
00:18:49,480 --> 00:18:52,040
Speaker 8: that not everybody lives in North Dakota, but enough people,

342
00:18:52,080 --> 00:18:55,760
Speaker 8: do you know, like, and enough people live in the

343
00:18:55,800 --> 00:18:59,720
Speaker 8: North that these things are not necessarily ready for it

344
00:18:59,800 --> 00:19:03,159
Speaker 8: to be your only car. And I think that's a

345
00:19:03,160 --> 00:19:06,280
Speaker 8: little bit behind it. I think they make a good automobile,

346
00:19:06,560 --> 00:19:08,760
Speaker 8: and I'm not sure, people are just dying for that

347
00:19:09,359 --> 00:19:12,960
Speaker 8: Chevy Vault or whatever they're making, So I think they

348
00:19:13,400 --> 00:19:16,240
Speaker 8: they still have a product, and they still have a

349
00:19:16,280 --> 00:19:20,480
Speaker 8: certain cachet, and they were a good first mover in evs,

350
00:19:20,560 --> 00:19:23,000
Speaker 8: so I wouldn't throw them out necessarily.

351
00:19:23,240 --> 00:19:26,440
Speaker 2: But funnily enough, my work car's recently all been Teslas.

352
00:19:26,720 --> 00:19:29,439
Speaker 7: I don't know what that means, but that I mean something.

353
00:19:29,600 --> 00:19:32,120
Speaker 2: Maybe they're as you're talking about Ed Ludlow, like, there's

354
00:19:32,160 --> 00:19:34,320
Speaker 2: actually more teslas now because no one's actually buying them.

355
00:19:34,320 --> 00:19:37,560
Speaker 7: So then you can tree hugger capital of the world.

356
00:19:37,840 --> 00:19:42,440
Speaker 2: Yes, I live in parks that, yeah, but not necessarily.

357
00:19:42,440 --> 00:19:44,240
Speaker 2: My drivers don't necessarily live there.

358
00:19:44,520 --> 00:19:47,640
Speaker 4: Bar's not taking a EV from eastern Ohio here.

359
00:19:47,920 --> 00:19:55,440
Speaker 1: I'm from Pennsylvania. We are a lot of pickups stack Yeah.

360
00:19:54,160 --> 00:19:55,919
Speaker 2: Kim, before we let you go, do you care if

361
00:19:55,960 --> 00:19:58,919
Speaker 2: the FED cuts rates or not? And when they do it?

362
00:19:59,119 --> 00:19:59,800
Speaker 5: Oh? Sure I do.

363
00:20:00,240 --> 00:20:03,600
Speaker 8: Heck yeah, I mean I think we're signed up for

364
00:20:04,119 --> 00:20:08,080
Speaker 8: a rate cut, and I'll say it even crazy relatively soon,

365
00:20:08,200 --> 00:20:12,600
Speaker 8: I'd say before July. And I do think that they

366
00:20:12,640 --> 00:20:15,919
Speaker 8: need to cut. The dollar is awfully strong relative to

367
00:20:15,960 --> 00:20:19,000
Speaker 8: other currencies, and that might be able to cool us

368
00:20:19,040 --> 00:20:20,760
Speaker 8: off a little bit more on that, and I think

369
00:20:20,760 --> 00:20:25,120
Speaker 8: that's important because even though I'm a generalist, I love technology.

370
00:20:25,600 --> 00:20:28,200
Speaker 8: We sell technology to the rest of the world at

371
00:20:28,240 --> 00:20:31,359
Speaker 8: the price of a dollar, and we have to have

372
00:20:31,480 --> 00:20:32,200
Speaker 8: that in mind.

373
00:20:33,200 --> 00:20:35,400
Speaker 4: Kim, thank you for joining us, as I always appreciate

374
00:20:35,440 --> 00:20:38,200
Speaker 4: getting your views there. Kim Farest. She is to founder

375
00:20:38,320 --> 00:20:42,439
Speaker 4: and chief investment officer Boca Capital Partners in Pittsburgh, a

376
00:20:42,560 --> 00:20:45,920
Speaker 4: former software person, a tech geek by her own admissions,

377
00:20:45,920 --> 00:20:49,239
Speaker 4: So good stuff there talking about AI and boy that

378
00:20:49,280 --> 00:20:52,520
Speaker 4: Intel news that John was reporting earlier having problems with

379
00:20:52,560 --> 00:20:55,920
Speaker 4: this the founder here, it's one of those chip stocks

380
00:20:55,960 --> 00:20:59,800
Speaker 4: that is not participating in the whole AI rising tide

381
00:21:00,119 --> 00:21:02,320
Speaker 4: thing like it is for Nvidia and Broadcommon others.

382
00:21:02,400 --> 00:21:04,040
Speaker 2: We'll talk about a long play, right, because if they

383
00:21:04,040 --> 00:21:05,720
Speaker 2: can get the founder of business really going, it could

384
00:21:05,760 --> 00:21:08,080
Speaker 2: really help them either make chips for themselves and then

385
00:21:08,080 --> 00:21:09,639
Speaker 2: also make it for others, and it could be a

386
00:21:09,720 --> 00:21:11,440
Speaker 2: nice driver. I mean, just look at the revenue that

387
00:21:11,480 --> 00:21:14,880
Speaker 2: TSM is minting. But you got to get it there, right,

388
00:21:15,359 --> 00:21:17,720
Speaker 2: and it's just not quite there yet.

389
00:21:18,920 --> 00:21:22,800
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us Live

390
00:21:22,880 --> 00:21:26,440
Speaker 1: weekdays at ten Am Eastern on Applecar Play and Android

391
00:21:26,440 --> 00:21:29,240
Speaker 1: Auto with the Bloomberg Business Act. You can also listen

392
00:21:29,359 --> 00:21:32,440
Speaker 1: live on Amazon Alexa from our flagship New York station,

393
00:21:32,800 --> 00:21:35,560
Speaker 1: Just Say Alexa playing Bloomberg eleven thirty.

394
00:21:36,960 --> 00:21:39,520
Speaker 4: Matt Winkler joins us here in studio. Matt Winkler is

395
00:21:39,680 --> 00:21:44,080
Speaker 4: the founder of Bloomberg News, as he editor at Meritis

396
00:21:44,160 --> 00:21:45,879
Speaker 4: right now Bloomberg News. He joins us here in our

397
00:21:45,920 --> 00:21:48,360
Speaker 4: studio and he's been doing a series of opinion pieces

398
00:21:48,880 --> 00:21:52,560
Speaker 4: about key swing states in the upcoming election and give

399
00:21:52,680 --> 00:21:54,880
Speaker 4: us the latest on county how the economies are doing

400
00:21:55,480 --> 00:21:57,639
Speaker 4: in those key states. We started with Michigan ethic and

401
00:21:57,640 --> 00:22:00,720
Speaker 4: then we did California by looking at La San Francisco.

402
00:22:00,800 --> 00:22:04,000
Speaker 4: Now heading down to the great state of Georgia. Matt,

403
00:22:04,000 --> 00:22:05,000
Speaker 4: what did you find in Georgia.

404
00:22:05,040 --> 00:22:05,720
Speaker 1: I love Georgia.

405
00:22:05,720 --> 00:22:08,000
Speaker 4: It seems like it's been growing for I don't know,

406
00:22:08,160 --> 00:22:09,200
Speaker 4: twenty thirty years.

407
00:22:09,280 --> 00:22:10,080
Speaker 5: It's a great story.

408
00:22:10,680 --> 00:22:14,000
Speaker 9: Well, great to be with you, and you're right. Georgia

409
00:22:14,240 --> 00:22:16,800
Speaker 9: population of more than eleven million makes it the eighth

410
00:22:16,960 --> 00:22:21,040
Speaker 9: largest state, and it's booming right now like it never

411
00:22:21,240 --> 00:22:24,520
Speaker 9: did before. Joe Biden became the forty sixth president and

412
00:22:24,560 --> 00:22:29,280
Speaker 9: we know this because not since data was initially collected

413
00:22:29,320 --> 00:22:31,920
Speaker 9: in nineteen ninety has there been a three year period

414
00:22:32,640 --> 00:22:36,439
Speaker 9: when growth in the Peach States manufacturing payrolls came close

415
00:22:36,520 --> 00:22:41,159
Speaker 9: to matching the eleven point nine percent increase in jobs

416
00:22:41,160 --> 00:22:45,800
Speaker 9: since twenty twenty one, or its rate of employment gains

417
00:22:45,800 --> 00:22:49,480
Speaker 9: compared with the US overall. And that's according to data

418
00:22:49,480 --> 00:22:53,600
Speaker 9: compiled by Bloomberg. So we are seeing a manufacturing boom

419
00:22:53,840 --> 00:22:57,439
Speaker 9: job boom in Georgia the likes of which Georgia has

420
00:22:57,800 --> 00:23:01,439
Speaker 9: never seen. And by the way, this is counter to

421
00:23:01,520 --> 00:23:05,960
Speaker 9: the trend in the twenty first century. Every presidency in

422
00:23:06,000 --> 00:23:09,960
Speaker 9: the New century except Biden's has seen a decline in

423
00:23:10,080 --> 00:23:12,399
Speaker 9: manufacturing jobs. So this is a departure.

424
00:23:12,880 --> 00:23:15,159
Speaker 7: So how much of that do you attribute to the

425
00:23:15,240 --> 00:23:17,760
Speaker 7: Chips Act and the Inflation Reduction Act, Because something that

426
00:23:17,800 --> 00:23:19,840
Speaker 7: gets lost in all this messaging is with the IRA

427
00:23:19,960 --> 00:23:22,320
Speaker 7: for example, So much of that money is actually going

428
00:23:22,359 --> 00:23:26,400
Speaker 7: to Red States because they have the space to build stuff.

429
00:23:27,200 --> 00:23:30,920
Speaker 9: Well, there's no question that actually Red states are a

430
00:23:30,960 --> 00:23:34,760
Speaker 9: big beneficiary. But it's not just the American Rescue Act

431
00:23:34,840 --> 00:23:39,600
Speaker 9: of twenty twenty one. It's also the Infrastructure Investment Jobs Act,

432
00:23:39,680 --> 00:23:43,520
Speaker 9: the Chips and Science Act again, the Inflation Reduction Act,

433
00:23:45,000 --> 00:23:51,359
Speaker 9: all of which have helped Biden make a legitimate claim,

434
00:23:51,480 --> 00:23:55,200
Speaker 9: which is he created close to eight hundred thousand manufacturing

435
00:23:55,280 --> 00:23:57,600
Speaker 9: jobs since he took office. And we know this is

436
00:23:57,640 --> 00:24:02,280
Speaker 9: not an idle boast because those very careful fact checkers

437
00:24:02,280 --> 00:24:06,160
Speaker 9: called PolitiFact actually took a look at this claim and

438
00:24:06,320 --> 00:24:11,400
Speaker 9: they showed that the nation's manufacturing rebound is the strongest

439
00:24:11,440 --> 00:24:15,560
Speaker 9: at this point after a recession since nineteen fifty one.

440
00:24:15,680 --> 00:24:16,439
Speaker 10: You weren't around.

441
00:24:16,440 --> 00:24:19,080
Speaker 7: There wasn't either.

442
00:24:20,560 --> 00:24:21,199
Speaker 5: I'll leave it there.

443
00:24:21,200 --> 00:24:21,760
Speaker 10: I'm not sure.

444
00:24:23,600 --> 00:24:28,919
Speaker 4: So is Georgia per se doing anything right versus I mean,

445
00:24:29,000 --> 00:24:31,679
Speaker 4: is this all just the largest of the federal government

446
00:24:31,720 --> 00:24:35,240
Speaker 4: met or does Georgia have just better incentives, better tax

447
00:24:35,280 --> 00:24:35,679
Speaker 4: and centives?

448
00:24:35,680 --> 00:24:36,359
Speaker 5: Are they doing something?

449
00:24:36,359 --> 00:24:38,040
Speaker 4: Maybe some other states aren't doing well.

450
00:24:37,960 --> 00:24:43,000
Speaker 9: Well, There's no question that some of the credit has

451
00:24:43,080 --> 00:24:46,119
Speaker 9: to go to, as I said, these acts, because the

452
00:24:46,200 --> 00:24:53,280
Speaker 9: investments include, for example, South Korea's Hanwa Solutions Corp. Expansion

453
00:24:53,320 --> 00:24:56,600
Speaker 9: of solar panel and manufacturing. They were already there. They

454
00:24:56,640 --> 00:24:59,080
Speaker 9: got incentives to do a lot more than what they

455
00:24:59,119 --> 00:25:05,879
Speaker 9: were doing. The same thing with Hyundai with EV vehicles,

456
00:25:06,280 --> 00:25:09,400
Speaker 9: They're already there. They're doing more. And then there's this

457
00:25:09,600 --> 00:25:14,159
Speaker 9: battery company from Norway, freyer Or Battery, which is going

458
00:25:14,240 --> 00:25:20,320
Speaker 9: to make clean battery manufacturing in Koeda County in Georgia.

459
00:25:20,480 --> 00:25:25,000
Speaker 9: So those things are definitely tied to the incentives that

460
00:25:25,080 --> 00:25:29,359
Speaker 9: come out of these acts. There's no question state and

461
00:25:29,440 --> 00:25:34,000
Speaker 9: local officials have been supportive. But interestingly enough, the governor

462
00:25:34,040 --> 00:25:38,040
Speaker 9: of Georgia voted against or would have voted against, the

463
00:25:38,040 --> 00:25:42,399
Speaker 9: Inflation Reduction Act. All of the Republicans in Congress voted

464
00:25:42,440 --> 00:25:45,440
Speaker 9: against the Inflation Reduction Act, which is a big part

465
00:25:45,480 --> 00:25:48,960
Speaker 9: of why George is doing well. But they're not ready

466
00:25:48,960 --> 00:25:52,440
Speaker 9: to give the money back. They're very happy that it's

467
00:25:52,480 --> 00:25:53,080
Speaker 9: all happening.

468
00:25:53,480 --> 00:25:56,760
Speaker 2: We have seen with some of these acts that we

469
00:25:56,800 --> 00:25:59,879
Speaker 2: get all excited about the money that's being spent, but

470
00:26:00,119 --> 00:26:02,360
Speaker 2: then getting the shovel in the ground and actually doing

471
00:26:02,400 --> 00:26:05,400
Speaker 2: the hiring and the manufacturing gets delayed a little bit

472
00:26:05,480 --> 00:26:08,840
Speaker 2: and gets pushed out, or the plans get scaled back

473
00:26:08,880 --> 00:26:11,600
Speaker 2: a bit. Are we seeing any of this in Georgia

474
00:26:11,800 --> 00:26:14,000
Speaker 2: or is the outlook particularly rosy.

475
00:26:15,040 --> 00:26:17,879
Speaker 9: It's more of the latter, you know. And then again

476
00:26:17,920 --> 00:26:21,240
Speaker 9: the proof of that is that if you look at

477
00:26:21,280 --> 00:26:27,520
Speaker 9: the unemployment rate, uh in. In Georgia it actually declined

478
00:26:27,640 --> 00:26:31,919
Speaker 9: most recently and is several percentage points, you know, less

479
00:26:31,960 --> 00:26:36,000
Speaker 9: than the national average. So you know, unemployment in the

480
00:26:36,119 --> 00:26:39,200
Speaker 9: US actually is ticked up a bit. In Georgia it's

481
00:26:39,240 --> 00:26:42,960
Speaker 9: gone the other way. And that's an example that the

482
00:26:43,000 --> 00:26:47,600
Speaker 9: boom is very much underway and shows no signs of abating.

483
00:26:48,200 --> 00:26:50,560
Speaker 4: You know, you have to remind yourself, I mean, how

484
00:26:50,600 --> 00:26:54,119
Speaker 4: the economy in the South has really changed from you know,

485
00:26:54,240 --> 00:26:59,680
Speaker 4: agricultural textiles, you know, very low technology touch types of

486
00:26:59,760 --> 00:27:02,639
Speaker 4: venggees to now it seems like every new auto plant

487
00:27:02,760 --> 00:27:07,639
Speaker 4: that you know, international automakers, they want to build them in.

488
00:27:07,000 --> 00:27:08,280
Speaker 7: The movie studios.

489
00:27:09,320 --> 00:27:11,520
Speaker 4: So much investments going to the Southeast. And that's again

490
00:27:11,600 --> 00:27:15,600
Speaker 4: been a twenty third thirty year story. What's next on

491
00:27:15,880 --> 00:27:16,720
Speaker 4: your state?

492
00:27:16,920 --> 00:27:17,080
Speaker 5: Yeah?

493
00:27:17,119 --> 00:27:17,720
Speaker 6: What state is?

494
00:27:17,800 --> 00:27:18,200
Speaker 1: Next year?

495
00:27:18,280 --> 00:27:25,280
Speaker 9: We'll probably get to Nevada. So we've done Pennsylvania and Michigan,

496
00:27:26,240 --> 00:27:29,359
Speaker 9: now Georgia, and we're working through the list.

497
00:27:30,200 --> 00:27:35,120
Speaker 4: So if you were advising President Biden his campaign about

498
00:27:35,440 --> 00:27:37,840
Speaker 4: the messaging here, what would you say in terms of

499
00:27:37,920 --> 00:27:40,920
Speaker 4: because I would argue that the messaging has not been effective,

500
00:27:40,960 --> 00:27:45,000
Speaker 4: that this message of economic success has not been told well.

501
00:27:44,840 --> 00:27:47,240
Speaker 9: I'd say actually their messaging has been pretty good. A

502
00:27:47,280 --> 00:27:51,000
Speaker 9: lot of this data that we've compiled at Bloomberg is

503
00:27:51,200 --> 00:27:56,240
Speaker 9: available in various announcements from either the White House or

504
00:27:56,280 --> 00:27:59,760
Speaker 9: the Treasury. But the saying is, you know, you can

505
00:27:59,840 --> 00:28:02,320
Speaker 9: lead a horse to water, but you can't get the

506
00:28:02,320 --> 00:28:05,800
Speaker 9: horse to drink. The horse in this case is our profession,

507
00:28:05,920 --> 00:28:09,600
Speaker 9: the media, and it's pretty much ignored what's in front

508
00:28:09,640 --> 00:28:13,240
Speaker 9: of its nose, which are the fact. So unless reporters

509
00:28:14,440 --> 00:28:17,520
Speaker 9: find this relevant, I do.

510
00:28:19,640 --> 00:28:19,800
Speaker 4: It.

511
00:28:19,960 --> 00:28:23,120
Speaker 9: You know, they'd rather focus on poles. And so that's

512
00:28:23,200 --> 00:28:24,600
Speaker 9: perception not reality.

513
00:28:24,720 --> 00:28:26,639
Speaker 4: Before we went on the area, you made a comment

514
00:28:26,640 --> 00:28:29,679
Speaker 4: about California. How big is that economy now? Because I

515
00:28:29,760 --> 00:28:31,080
Speaker 4: used to to me it was the seventh or ay

516
00:28:31,119 --> 00:28:31,760
Speaker 4: largest economy.

517
00:28:31,760 --> 00:28:35,080
Speaker 9: It's number four. It's now numbers and it's poised to

518
00:28:35,119 --> 00:28:39,080
Speaker 9: overtake Germany. And that's because you know, you keep talking

519
00:28:39,080 --> 00:28:42,640
Speaker 9: about the Magnificent seven on this show. If you look

520
00:28:42,640 --> 00:28:48,560
Speaker 9: at corporate California, Corporate California just dwarfs corporate Anywhere, dwarfs

521
00:28:48,560 --> 00:28:54,800
Speaker 9: Corporate Texas, It dwarfs corporate Germany. Corporate California is still

522
00:28:54,840 --> 00:28:58,280
Speaker 9: a juggernaut. And you know who those companies are, but

523
00:28:58,320 --> 00:29:02,480
Speaker 9: it's not just the big companies, big and small companies alike.

524
00:29:02,520 --> 00:29:06,320
Speaker 9: In the Russell three thousand, Yeah, that make California unique.

525
00:29:06,360 --> 00:29:08,800
Speaker 4: It's yeah, it's just extraordinary, all right, Matt, thank you

526
00:29:08,800 --> 00:29:10,800
Speaker 4: so much for joining us. Matt Winkler, he's the editor

527
00:29:10,840 --> 00:29:13,840
Speaker 4: in chief emeritus. He is the founder of Bloomberg News

528
00:29:14,720 --> 00:29:18,160
Speaker 4: for and he's now part of Bloomberg Opinion, and he

529
00:29:18,240 --> 00:29:20,560
Speaker 4: joins us here in our office talking about the great

530
00:29:20,640 --> 00:29:25,000
Speaker 4: state of Georgia and the strong economic performance our friends

531
00:29:25,080 --> 00:29:28,760
Speaker 4: down there are seeing, particularly you know, manufacturing and employment,

532
00:29:28,800 --> 00:29:31,280
Speaker 4: and for my media business, they've long been a destination.

533
00:29:33,960 --> 00:29:37,800
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

534
00:29:37,920 --> 00:29:41,440
Speaker 1: weekdays at ten am Eastern on applecar Play and Android

535
00:29:41,480 --> 00:29:44,600
Speaker 1: Auto with the Bloomberg Business. You can also listen live

536
00:29:44,760 --> 00:29:47,880
Speaker 1: on Amazon Alexa from our flagship New York station, Just

537
00:29:47,960 --> 00:29:50,600
Speaker 1: say Alexa playing Bloomberg eleven thirty.

538
00:29:52,320 --> 00:29:52,800
Speaker 5: So here's a.

539
00:29:52,800 --> 00:29:53,360
Speaker 7: Stop for you.

540
00:29:53,720 --> 00:29:57,400
Speaker 2: At Vander it's up about ten percent so far this year,

541
00:29:57,760 --> 00:30:00,840
Speaker 2: done pretty well. The top on the street is that

542
00:30:00,920 --> 00:30:04,000
Speaker 2: it wants to be really a mini Thermo Fisher. So

543
00:30:04,240 --> 00:30:07,120
Speaker 2: part of its business is a supplier of R and

544
00:30:07,240 --> 00:30:09,880
Speaker 2: D labs for the global farmer industry, and now it's

545
00:30:09,960 --> 00:30:12,680
Speaker 2: trying to make a big push into bioproductions. That's basically

546
00:30:12,720 --> 00:30:17,200
Speaker 2: the equipment of manufacturing biologic and biotech drugs. Luckily, we

547
00:30:17,280 --> 00:30:19,640
Speaker 2: have the CEO and president with us to help understand

548
00:30:19,680 --> 00:30:22,120
Speaker 2: what this company does, what's the opportunity, what's the margin,

549
00:30:22,160 --> 00:30:25,320
Speaker 2: what's the growth. Michael Stubblefield is president and CEO of

550
00:30:25,400 --> 00:30:29,320
Speaker 2: A vand Or and he joins us now from Allentown, Pennsylvania. Michael,

551
00:30:29,320 --> 00:30:32,160
Speaker 2: thanks for joining walk us through dumb down what you

552
00:30:32,240 --> 00:30:33,640
Speaker 2: guys do for people like me and Paul.

553
00:30:34,640 --> 00:30:36,440
Speaker 10: Yeah, Alex and Paul, thanks for having me. Really happy

554
00:30:36,480 --> 00:30:38,400
Speaker 10: to be here today now. I think it's a great

555
00:30:38,400 --> 00:30:41,880
Speaker 10: place for us to start the conversation today. We're probably

556
00:30:41,920 --> 00:30:43,360
Speaker 10: one of the bigger companies that a lot of people

557
00:30:43,400 --> 00:30:46,120
Speaker 10: have never heard of, and we are really ubiquitous in

558
00:30:46,320 --> 00:30:51,520
Speaker 10: the biopharma space. I would say we enable innovation across

559
00:30:52,080 --> 00:30:55,360
Speaker 10: the life sciences and events technology industries, and we really

560
00:30:55,400 --> 00:30:58,920
Speaker 10: embed ourselves with our customers. We support them every step

561
00:30:58,960 --> 00:31:02,400
Speaker 10: of their scientific journey. We were in virtually every research

562
00:31:02,480 --> 00:31:05,000
Speaker 10: lab around the world, more than three hundred thousand locations,

563
00:31:06,120 --> 00:31:10,320
Speaker 10: and we have a really robust portfolio, an e commerce platform,

564
00:31:10,400 --> 00:31:13,840
Speaker 10: and a whole host of productivity enhancing services that really

565
00:31:13,880 --> 00:31:17,240
Speaker 10: help our the scientists that our customers, you know, develop

566
00:31:17,320 --> 00:31:21,640
Speaker 10: the new therapies that you're referencing, and as those innovations

567
00:31:21,800 --> 00:31:26,320
Speaker 10: advance to development and production, our materials and technologies become

568
00:31:26,360 --> 00:31:29,880
Speaker 10: embedded into our customers applications, everything from biological therapies to

569
00:31:29,960 --> 00:31:32,080
Speaker 10: medical plants to electronics.

570
00:31:32,440 --> 00:31:34,160
Speaker 4: So, Michael, give us a sense of maybe who you're

571
00:31:34,760 --> 00:31:39,320
Speaker 4: you know, a common customers, an average customer of your company.

572
00:31:39,600 --> 00:31:42,200
Speaker 4: Who are your customer rebase and what did they really

573
00:31:42,280 --> 00:31:43,080
Speaker 4: look to you guys for.

574
00:31:44,240 --> 00:31:45,680
Speaker 10: You're really not going to be able to find too

575
00:31:45,720 --> 00:31:48,640
Speaker 10: many customers that are doing work in the biopharma or

576
00:31:48,640 --> 00:31:51,920
Speaker 10: the biotech space that we wouldn't be serving in some capacity.

577
00:31:52,440 --> 00:31:54,640
Speaker 10: I think that's one of the more interesting aspects of

578
00:31:54,720 --> 00:31:56,680
Speaker 10: our of our business model, is that we are deeply

579
00:31:56,720 --> 00:32:01,400
Speaker 10: embedded across this space. So it's everything from you know,

580
00:32:01,480 --> 00:32:04,280
Speaker 10: the traditional large pharma you know, the Johnson and Johnson's

581
00:32:04,280 --> 00:32:07,800
Speaker 10: amjen No Artist's of the world all the way through

582
00:32:07,880 --> 00:32:10,400
Speaker 10: to you know a lot of the startups and you

583
00:32:10,480 --> 00:32:13,920
Speaker 10: know biotech companies that are out there. We have an

584
00:32:13,960 --> 00:32:17,800
Speaker 10: exclusive relationship with one of the bioconsortiums here in the

585
00:32:17,920 --> 00:32:21,240
Speaker 10: in the US that you know, literally thousands of how

586
00:32:21,360 --> 00:32:24,920
Speaker 10: tech startups you know, uh, access our products and services

587
00:32:24,960 --> 00:32:29,160
Speaker 10: across that platform. So we're extremely well positioned to serve

588
00:32:29,240 --> 00:32:29,680
Speaker 10: this space.

589
00:32:30,160 --> 00:32:32,520
Speaker 2: And so basically they come to you for stuff, right like, Hey,

590
00:32:32,720 --> 00:32:34,520
Speaker 2: I need this ingredient do this thing, or I need

591
00:32:34,560 --> 00:32:35,960
Speaker 2: this piece of equipment to do this thing.

592
00:32:36,120 --> 00:32:39,200
Speaker 10: Right Yeah, I'm not sure we would call it stuff, but.

593
00:32:40,760 --> 00:32:42,960
Speaker 5: We break it down for you here, so talk about it.

594
00:32:43,120 --> 00:32:45,800
Speaker 10: So we have you know, you can almost think about,

595
00:32:46,640 --> 00:32:49,040
Speaker 10: you know, there's nothing in a laboratory that a scientist

596
00:32:49,120 --> 00:32:50,960
Speaker 10: would need that we don't supply. We have over six

597
00:32:51,040 --> 00:32:54,640
Speaker 10: million products. That's everything from chemicals and reagents, to equipment

598
00:32:54,720 --> 00:32:58,040
Speaker 10: and instruments to you know, plastic wear and glassware consumables.

599
00:32:58,080 --> 00:33:00,920
Speaker 10: Think about just what a scientist needs in order to

600
00:33:01,000 --> 00:33:03,960
Speaker 10: conduct their research in a laboratory they would be able

601
00:33:04,000 --> 00:33:07,280
Speaker 10: to procure from us. We look to be a one

602
00:33:07,360 --> 00:33:10,800
Speaker 10: stop shop to meet all of our customers needs within

603
00:33:10,880 --> 00:33:14,080
Speaker 10: that laboratory environment. And one of the things that we

604
00:33:14,160 --> 00:33:17,480
Speaker 10: really focus on is you know, bringing efficiency, you know,

605
00:33:17,560 --> 00:33:20,440
Speaker 10: to our customers. A lot of the discussion in our

606
00:33:20,480 --> 00:33:23,000
Speaker 10: space is how do we return time to the scientists

607
00:33:23,400 --> 00:33:25,840
Speaker 10: so that they can really focus on, you know, pushing

608
00:33:25,960 --> 00:33:29,160
Speaker 10: forward these breakthrough therapies that are really game changing for

609
00:33:29,280 --> 00:33:32,280
Speaker 10: patients around the world. And so we have, in addition

610
00:33:32,360 --> 00:33:35,040
Speaker 10: to the products, a whole host of services and digital

611
00:33:35,080 --> 00:33:38,600
Speaker 10: capabilities that we deploy really all meant to improve the

612
00:33:38,600 --> 00:33:40,080
Speaker 10: efficiency of how a lab works.

613
00:33:41,040 --> 00:33:42,600
Speaker 4: I like to ask, you know, when we talk to

614
00:33:42,800 --> 00:33:45,840
Speaker 4: healthcare companies anywhere within the space, I like to ask

615
00:33:46,040 --> 00:33:49,960
Speaker 4: this following question, how is your business impacted by the pandemic?

616
00:33:50,080 --> 00:33:52,520
Speaker 4: And maybe how is it different today than it was before,

617
00:33:52,560 --> 00:33:54,920
Speaker 4: because it just seems like that whole corner of the

618
00:33:55,080 --> 00:33:57,959
Speaker 4: you know, that the healthcare space really had to adjust.

619
00:33:59,000 --> 00:34:02,240
Speaker 10: Yeah, certainly interesting time from a lot of different aspects.

620
00:34:02,320 --> 00:34:04,000
Speaker 10: And you know, I think it's important to point out

621
00:34:04,040 --> 00:34:06,520
Speaker 10: that we did play a really important role in you know,

622
00:34:06,640 --> 00:34:09,000
Speaker 10: helping address you know, the pandemic. A lot of our

623
00:34:09,040 --> 00:34:12,839
Speaker 10: materials were being used to produce you know, the vaccines

624
00:34:13,000 --> 00:34:16,120
Speaker 10: and as well as a lot of the materials that

625
00:34:16,160 --> 00:34:18,800
Speaker 10: needed to go into produce the tests to detect COVID.

626
00:34:19,960 --> 00:34:22,240
Speaker 10: But you know, there's probably a couple of different dynamics

627
00:34:22,280 --> 00:34:24,440
Speaker 10: that that I would point out that we experienced during

628
00:34:24,480 --> 00:34:29,279
Speaker 10: the pandemic, first, really unpredestant, unprecedented supply chain constraints. There

629
00:34:29,400 --> 00:34:32,239
Speaker 10: was shortages you know, throughout the value chain that made

630
00:34:32,360 --> 00:34:35,799
Speaker 10: executing our supply chain you know, rather challenging. We had

631
00:34:35,920 --> 00:34:38,360
Speaker 10: you know, significant step up in demand to support you know,

632
00:34:38,440 --> 00:34:41,560
Speaker 10: some of these COVID based workflows. And then as things

633
00:34:41,600 --> 00:34:43,840
Speaker 10: started to normalize, of course, you know, our industry has

634
00:34:43,880 --> 00:34:47,359
Speaker 10: been facing some of the headwindes associated with just uh

635
00:34:47,520 --> 00:34:51,560
Speaker 10: normalization of procurement and supply chain trends. So it's been

636
00:34:51,600 --> 00:34:54,800
Speaker 10: a really dynamic time. But I think we've we've weathered

637
00:34:54,840 --> 00:34:56,560
Speaker 10: the storm well, and you know, we've offered a lot

638
00:34:56,600 --> 00:35:00,279
Speaker 10: of value you know, during that process to focus on

639
00:35:00,400 --> 00:35:03,799
Speaker 10: executing our growth strategy, and we're really pleased with how

640
00:35:03,840 --> 00:35:05,040
Speaker 10: we're positioned coming out of all that.

641
00:35:05,280 --> 00:35:07,080
Speaker 2: So, as I mentioned, the stock up ten percent so

642
00:35:07,200 --> 00:35:11,160
Speaker 2: far this year, zero cell ratings, six holds and fifteen buys,

643
00:35:11,239 --> 00:35:14,320
Speaker 2: with a twelve month price target of about twenty seven,

644
00:35:14,440 --> 00:35:16,440
Speaker 2: we're almost there, or like a stone's throw away from that.

645
00:35:16,560 --> 00:35:16,680
Speaker 10: Now.

646
00:35:17,000 --> 00:35:19,480
Speaker 2: I was talking to Jonathan Palmer, he's our Bloomberg intelligence

647
00:35:19,520 --> 00:35:21,400
Speaker 2: guy who covers your company, and he was saying that

648
00:35:21,680 --> 00:35:24,839
Speaker 2: the debate now on the street is when you can

649
00:35:24,920 --> 00:35:29,000
Speaker 2: really return to growth and show some profitability improvements.

650
00:35:29,360 --> 00:35:29,800
Speaker 6: When is that?

651
00:35:31,320 --> 00:35:33,440
Speaker 10: Yeah, So, you know, one of the things that I

652
00:35:33,520 --> 00:35:35,759
Speaker 10: really like about our business is just the exposure we

653
00:35:35,880 --> 00:35:38,560
Speaker 10: have to some really attractive end markets. You've reught ferenced

654
00:35:38,560 --> 00:35:41,920
Speaker 10: bile pharma and biotech and healthcare space, which is more

655
00:35:41,960 --> 00:35:45,359
Speaker 10: than sixty percent of our revenues. And when I look

656
00:35:45,400 --> 00:35:47,480
Speaker 10: at the health of those end markets, you know, I

657
00:35:47,520 --> 00:35:51,279
Speaker 10: think we're quite excited about the long term positioning of

658
00:35:52,120 --> 00:35:56,840
Speaker 10: the space, really driven by record levels of new drug approvals,

659
00:35:57,160 --> 00:36:00,440
Speaker 10: pipelines that are as robust as they've ever been, and

660
00:36:00,600 --> 00:36:04,160
Speaker 10: new therapeutic platforms, whether that be you know, selling gene therapy,

661
00:36:04,400 --> 00:36:06,400
Speaker 10: you know, GLP ones that have been getting a lot

662
00:36:06,440 --> 00:36:09,279
Speaker 10: of attention. And so I would say the fundamentals for

663
00:36:09,360 --> 00:36:12,359
Speaker 10: our space are are really robust, and you know, as

664
00:36:12,480 --> 00:36:15,120
Speaker 10: some of these headwinds that have been plaguing the industry

665
00:36:15,160 --> 00:36:16,919
Speaker 10: that we just talked about, whether it be supply chain

666
00:36:17,160 --> 00:36:20,080
Speaker 10: or you know, just some of the normalization coming out

667
00:36:20,120 --> 00:36:23,239
Speaker 10: of covid is, as those things subside, you know, I

668
00:36:23,280 --> 00:36:25,680
Speaker 10: think we're really well positioned to, you know, for a

669
00:36:25,760 --> 00:36:28,280
Speaker 10: strong setup and to be able to leverage the momentum

670
00:36:28,360 --> 00:36:29,760
Speaker 10: that's that's in our end markets.

671
00:36:30,280 --> 00:36:34,759
Speaker 4: So one of the I guess, just what's one of

672
00:36:34,800 --> 00:36:37,680
Speaker 4: the bigger headwinds that you are facing here that you

673
00:36:37,800 --> 00:36:41,320
Speaker 4: need to get you know, your your hands around to

674
00:36:41,400 --> 00:36:43,880
Speaker 4: kind of move this forward here. I mean, again, Stock's

675
00:36:43,920 --> 00:36:46,879
Speaker 4: done well, but what's a key challenge for you guys

676
00:36:47,040 --> 00:36:47,480
Speaker 4: these days.

677
00:36:48,480 --> 00:36:50,640
Speaker 10: Yes, when we looked at, you know, some of the

678
00:36:50,920 --> 00:36:53,920
Speaker 10: things that have impacted our growth in our industry over

679
00:36:53,960 --> 00:36:57,799
Speaker 10: the last years. So it's things like inventory d stockings

680
00:36:57,880 --> 00:37:01,000
Speaker 10: as our customers are normalizing their inventory, is you know,

681
00:37:01,080 --> 00:37:03,840
Speaker 10: some of the supply chain constraints, whether it be access

682
00:37:03,880 --> 00:37:06,480
Speaker 10: to raw materials or even just our own production capacity

683
00:37:06,520 --> 00:37:08,920
Speaker 10: that we've been able to you know deep bottleneck with

684
00:37:09,120 --> 00:37:13,080
Speaker 10: with investments, or you know some of the funding constraints

685
00:37:13,200 --> 00:37:17,279
Speaker 10: in biotech and things as the capital markets have you know,

686
00:37:17,400 --> 00:37:19,360
Speaker 10: been a bit challenged here over the last year. So

687
00:37:19,920 --> 00:37:22,319
Speaker 10: you know, those things have largely normalized. When I look

688
00:37:22,360 --> 00:37:24,560
Speaker 10: at the instability of the business in the back half

689
00:37:24,640 --> 00:37:28,000
Speaker 10: of that last year and into the early you know

690
00:37:28,719 --> 00:37:31,680
Speaker 10: quarter here in twenty twenty four, you know, I would

691
00:37:31,719 --> 00:37:34,880
Speaker 10: say that things have largely stabilized and you know, the

692
00:37:35,680 --> 00:37:38,439
Speaker 10: business I think is poised for you know, a nice

693
00:37:38,480 --> 00:37:40,719
Speaker 10: recovery trajectory here as we move, you know, through this

694
00:37:40,880 --> 00:37:41,759
Speaker 10: year and into next year.

695
00:37:42,120 --> 00:37:44,319
Speaker 2: Before I let you go, we got jobs Friday coming up.

696
00:37:44,400 --> 00:37:47,320
Speaker 2: You got a blood ADP. We had a super strong

697
00:37:47,400 --> 00:37:50,919
Speaker 2: manufacturing ism data. We got a good jolt number. How's

698
00:37:50,960 --> 00:37:51,799
Speaker 2: your hiring front?

699
00:37:51,880 --> 00:37:52,600
Speaker 6: Are you hiring?

700
00:37:52,760 --> 00:37:53,160
Speaker 3: Are you not?

701
00:37:53,440 --> 00:37:55,960
Speaker 7: What is it like to get and retain talent?

702
00:37:57,239 --> 00:37:57,439
Speaker 5: Yeah?

703
00:37:57,440 --> 00:37:59,120
Speaker 10: I mean you mentioned a lot of you know, leading

704
00:37:59,160 --> 00:38:01,680
Speaker 10: indicators there that that I think we look at, and

705
00:38:01,840 --> 00:38:03,719
Speaker 10: you know, we also look at a whole host of

706
00:38:03,800 --> 00:38:07,960
Speaker 10: other you know, levels of you know, activity and laboratories, funding,

707
00:38:08,120 --> 00:38:11,800
Speaker 10: capital markets, you know, the pipelines. I think we just

708
00:38:11,880 --> 00:38:14,200
Speaker 10: kind of put all that together and it does certainly

709
00:38:14,320 --> 00:38:17,680
Speaker 10: signal you know, I think strong momentum in our in

710
00:38:17,760 --> 00:38:20,959
Speaker 10: our space, and you know, has this incredibly optimistic about

711
00:38:20,960 --> 00:38:24,319
Speaker 10: where things are headed. Talent is is critical to us,

712
00:38:24,440 --> 00:38:26,840
Speaker 10: and you know, we're we're proud of the fourteen thousand

713
00:38:26,920 --> 00:38:29,359
Speaker 10: plus associates that we have around the world that are

714
00:38:29,360 --> 00:38:32,279
Speaker 10: focused on, you know, growing our business and serving our

715
00:38:32,800 --> 00:38:36,400
Speaker 10: customers and helping enable some of these breakthrough therapies that

716
00:38:36,440 --> 00:38:39,440
Speaker 10: are that are being developed, and you know, We're continuously

717
00:38:39,560 --> 00:38:42,200
Speaker 10: in the market, you know, adding capabilities, whether that be

718
00:38:43,440 --> 00:38:45,480
Speaker 10: you know, new new capabilities to serve some of the

719
00:38:45,560 --> 00:38:48,160
Speaker 10: new modalities like selling gene therapy or you know, some

720
00:38:48,239 --> 00:38:52,640
Speaker 10: of the digital capabilities around AI and automation. But you know,

721
00:38:52,719 --> 00:38:54,480
Speaker 10: we've got a global footprint. We're in one hundred eighty

722
00:38:54,520 --> 00:38:58,200
Speaker 10: countries around the world, and so you know, this is

723
00:38:58,239 --> 00:39:01,320
Speaker 10: the heart of our business ours and we continue to

724
00:39:01,400 --> 00:39:03,279
Speaker 10: lean in to make sure that we have the right

725
00:39:03,320 --> 00:39:05,200
Speaker 10: capabilities to serve our customers.

726
00:39:05,360 --> 00:39:06,279
Speaker 2: All right, Michael, thanks lot.

727
00:39:06,400 --> 00:39:07,880
Speaker 7: We really appreciate Michael S. Doublefield.

728
00:39:07,920 --> 00:39:12,279
Speaker 2: He's a ban Tour CEO joining us from Allentown, Pennsylvania.

729
00:39:12,440 --> 00:39:14,520
Speaker 2: That stock is up by one point two percent today,

730
00:39:14,680 --> 00:39:17,800
Speaker 2: up ten percent plus for the year. As I mentioned,

731
00:39:17,960 --> 00:39:22,120
Speaker 2: zero cells and five hold with seventeen buys, sixteen buys

732
00:39:22,200 --> 00:39:23,880
Speaker 2: something like that, a lot of buys there on the

733
00:39:23,920 --> 00:39:25,560
Speaker 2: stock with an average price target of the next twelve

734
00:39:25,560 --> 00:39:27,400
Speaker 2: months or about twenty seven dollars.

735
00:39:27,640 --> 00:39:32,120
Speaker 1: This is the Bloomberg Intelligence podcast, available on apples, Spotify,

736
00:39:32,360 --> 00:39:35,520
Speaker 1: and anywhere else you get your podcasts. Listen live each

737
00:39:35,560 --> 00:39:38,719
Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com,

738
00:39:39,040 --> 00:39:42,399
Speaker 1: Thehart Radio app Tune in and the Bloomberg Business App.

739
00:39:42,560 --> 00:39:45,520
Speaker 1: You can also watch us live every weekday on YouTube

740
00:39:45,800 --> 00:39:47,560
Speaker 1: and always on the Bloomberg terminal