1
00:00:02,400 --> 00:00:07,680
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Let's talk about this

2
00:00:07,720 --> 00:00:09,920
Speaker 1: markets because things have been going crazy. We talk about

3
00:00:10,039 --> 00:00:12,280
Speaker 1: an SMP in an ASDAC right now around session lows

4
00:00:12,280 --> 00:00:15,040
Speaker 1: I was looking at the Philadelphia Semiconductor Index down seven

5
00:00:15,080 --> 00:00:15,880
Speaker 1: percent on the deck.

6
00:00:16,000 --> 00:00:18,160
Speaker 2: Yeah, it's pretty brutal out there. It's not just chips,

7
00:00:18,160 --> 00:00:20,240
Speaker 2: it's also some of the AI plays. We were speaking

8
00:00:20,239 --> 00:00:22,400
Speaker 2: earlier about some of those AI darlings getting hit with

9
00:00:22,440 --> 00:00:25,239
Speaker 2: some short reports. So investors coming out and saying, I'm

10
00:00:25,239 --> 00:00:26,640
Speaker 2: sure at the stock and this is why, and that

11
00:00:26,760 --> 00:00:29,800
Speaker 2: has lumen, for example, has gotten hidden in part of that?

12
00:00:30,080 --> 00:00:30,280
Speaker 3: Yeah?

13
00:00:30,320 --> 00:00:33,240
Speaker 1: Absolutely, And it raises the question super micro, symbiotic, a

14
00:00:33,280 --> 00:00:34,920
Speaker 1: lot of these other names here. I don't know. Is

15
00:00:34,960 --> 00:00:36,400
Speaker 1: this just a symptom of the fact that these were

16
00:00:36,400 --> 00:00:38,479
Speaker 1: the high flyers, So hey, if you made a lot

17
00:00:38,479 --> 00:00:40,680
Speaker 1: of money off them and you're stealing skittish, why not

18
00:00:40,800 --> 00:00:43,240
Speaker 1: just cash out? Or is this more symptomatic of people

19
00:00:43,479 --> 00:00:44,839
Speaker 1: rethinking that HOLYI.

20
00:00:44,600 --> 00:00:46,560
Speaker 2: Trade or with the idea of say super micro, is

21
00:00:46,560 --> 00:00:48,080
Speaker 2: that now it's not in the small caps, it's in

22
00:00:48,080 --> 00:00:50,120
Speaker 2: the SMP. So does that change who covers it and

23
00:00:50,120 --> 00:00:52,320
Speaker 2: how they look at it? And that changes how you

24
00:00:52,440 --> 00:00:54,520
Speaker 2: view the numbers. But either way you're laughing at me.

25
00:00:54,600 --> 00:00:56,360
Speaker 1: I'm laughing because it's some day we have to talk

26
00:00:56,360 --> 00:00:59,560
Speaker 1: about why super Micro is even in the SMP five hundred.

27
00:00:59,600 --> 00:01:02,000
Speaker 1: That's a whole on the topics, right, But.

28
00:01:01,880 --> 00:01:03,640
Speaker 2: That's also why the analysts can now look at it

29
00:01:03,640 --> 00:01:05,360
Speaker 2: in a different way, and then you have different analysts

30
00:01:05,600 --> 00:01:07,560
Speaker 2: covering that stock, and that winds up hurting. It does

31
00:01:07,640 --> 00:01:10,160
Speaker 2: raise the question in terms of the AI trade, what

32
00:01:10,240 --> 00:01:12,240
Speaker 2: a cyclical what a structural role? And then how do

33
00:01:12,240 --> 00:01:14,760
Speaker 2: you invest in that? Sam palm Asano, chairman of the

34
00:01:14,800 --> 00:01:17,920
Speaker 2: Center for Global Enterprise and former CEO over at IBM,

35
00:01:18,000 --> 00:01:20,360
Speaker 2: it joins us. Now, Sam, it's really good to get

36
00:01:20,360 --> 00:01:23,000
Speaker 2: your perspective. We very much appreciate this. How do you

37
00:01:23,120 --> 00:01:25,080
Speaker 2: how does someone who's been in the technology industry for

38
00:01:25,120 --> 00:01:29,520
Speaker 2: decades and understands innovation and understands a business cycle, what

39
00:01:29,720 --> 00:01:30,759
Speaker 2: is AI right now?

40
00:01:31,720 --> 00:01:33,759
Speaker 3: Well, that's a great question, Alex, and remain good being

41
00:01:33,760 --> 00:01:35,480
Speaker 3: with you guys. Again, it's always a pleasure to be

42
00:01:35,520 --> 00:01:38,360
Speaker 3: with you, especially in a beautiful afternoon. It's great to

43
00:01:38,360 --> 00:01:40,800
Speaker 3: be in my office not outside playing golf or something.

44
00:01:40,840 --> 00:01:43,520
Speaker 3: But on a more serious note, now, I think that

45
00:01:43,640 --> 00:01:45,319
Speaker 3: the best way to think about this that I can

46
00:01:45,360 --> 00:01:47,920
Speaker 3: draw parallels to the Internet if you like, but when

47
00:01:48,000 --> 00:01:50,280
Speaker 3: they're still in the early stations of AI. And if

48
00:01:50,320 --> 00:01:52,600
Speaker 3: you go back to the early days of the Internet,

49
00:01:52,840 --> 00:01:55,960
Speaker 3: what happens in phases. It begins with the enablers, and

50
00:01:55,960 --> 00:01:58,480
Speaker 3: that would have been back then that's scage on Cisco

51
00:01:58,520 --> 00:02:01,120
Speaker 3: and AT and T. Today you would put the enablers

52
00:02:01,240 --> 00:02:04,559
Speaker 3: is the Microsoft and videos, AMD, anthropics, the chip guys,

53
00:02:04,560 --> 00:02:08,160
Speaker 3: et cetera, the foundational model guys. And then it moves

54
00:02:08,200 --> 00:02:11,440
Speaker 3: to the adopters over time. But that's time and that

55
00:02:11,680 --> 00:02:13,239
Speaker 3: if they go back to the Internet, that would have

56
00:02:13,280 --> 00:02:17,600
Speaker 3: been Meta Amazon, DoorDash, Netflix, and then the next phase

57
00:02:17,639 --> 00:02:20,679
Speaker 3: with the people people that apply the innovation to the technology,

58
00:02:20,800 --> 00:02:23,520
Speaker 3: and that would be the ubers, the Netflix of the

59
00:02:23,560 --> 00:02:26,080
Speaker 3: Airbnb's et cetera, et cetera, in door dash, you know.

60
00:02:26,560 --> 00:02:29,280
Speaker 3: So that's the phases of this thing and so far

61
00:02:29,360 --> 00:02:32,360
Speaker 3: to repeating itself. And we are in the early phase,

62
00:02:32,600 --> 00:02:34,640
Speaker 3: there's no doubt about it. So the enablers are going

63
00:02:34,680 --> 00:02:37,280
Speaker 3: to continue, to my opinion, anyway, do well now, I mean,

64
00:02:37,680 --> 00:02:40,160
Speaker 3: you guys know markets. I can't speak to evaluations of

65
00:02:40,200 --> 00:02:43,280
Speaker 3: what happens day to day but long term trends. I've

66
00:02:43,320 --> 00:02:45,799
Speaker 3: seen this thing. I saw in the early days of

67
00:02:45,800 --> 00:02:48,040
Speaker 3: Client So I've been at this industry a bout fifty years,

68
00:02:48,080 --> 00:02:50,240
Speaker 3: so this isn't my first time to kind of view

69
00:02:50,280 --> 00:02:51,200
Speaker 3: these transitions.

70
00:02:51,400 --> 00:02:53,919
Speaker 2: So, Sam, is the real boost and efficiency going to

71
00:02:54,000 --> 00:02:55,880
Speaker 2: come from Romaine and I using it on our phones,

72
00:02:56,040 --> 00:02:58,600
Speaker 2: or is it going to come from like entirely new

73
00:02:58,639 --> 00:03:02,520
Speaker 2: business lines and re thinking the way we fundamentally do.

74
00:03:02,560 --> 00:03:06,040
Speaker 3: Things, Alex, I tell you that's a very interesting insight

75
00:03:06,160 --> 00:03:08,640
Speaker 3: because if you look at what's going to happen short term,

76
00:03:08,639 --> 00:03:11,720
Speaker 3: if I quote the Kinsey estimates of this sort of thing,

77
00:03:12,120 --> 00:03:14,760
Speaker 3: they value the GENAI that's got the adoption in the

78
00:03:14,800 --> 00:03:18,560
Speaker 3: early stages of it in four key areas. That's customer operations,

79
00:03:18,639 --> 00:03:20,880
Speaker 3: marketing and sales, software engineering in R and D. I

80
00:03:20,919 --> 00:03:23,919
Speaker 3: ete productivity in the word, so they're saying the first

81
00:03:23,919 --> 00:03:26,840
Speaker 3: phase will be productivity. That also could be productivity to

82
00:03:26,880 --> 00:03:28,520
Speaker 3: the user on the phone, by the way, as far

83
00:03:28,560 --> 00:03:32,280
Speaker 3: as their user experience booking, reservations or whatever it happens

84
00:03:32,280 --> 00:03:35,360
Speaker 3: to be, it could be simplified by the application of ANI.

85
00:03:35,520 --> 00:03:37,880
Speaker 3: But in the enterprise itself, it's going to be more

86
00:03:37,880 --> 00:03:42,520
Speaker 3: around productivity Now having said that, the real innovation comes,

87
00:03:42,520 --> 00:03:45,680
Speaker 3: real value creation comes in the next phase. And I

88
00:03:45,760 --> 00:03:47,760
Speaker 3: go back to my analogy to the Internet. That's what

89
00:03:47,880 --> 00:03:51,880
Speaker 3: I mentioned earlier. Uber, Airbnb, Spotify, Netflix, That is the

90
00:03:51,920 --> 00:03:54,840
Speaker 3: next phase where this thing is going to take off.

91
00:03:56,040 --> 00:03:57,840
Speaker 1: Go ahead, Yes, so you're seeing a lot of paly.

92
00:03:57,880 --> 00:03:59,440
Speaker 1: I didn't mean to cut you off, but I want

93
00:03:59,480 --> 00:04:01,119
Speaker 1: you to kind of on that because, I mean, your

94
00:04:01,280 --> 00:04:04,200
Speaker 1: experience is relatively unique. I mean, you came of age

95
00:04:04,200 --> 00:04:06,560
Speaker 1: at least share in your career at IBM at a

96
00:04:06,600 --> 00:04:11,000
Speaker 1: time when corporate computing was really taking off, then personal computing.

97
00:04:11,040 --> 00:04:13,280
Speaker 1: You were still there when the sort of the Internet

98
00:04:13,320 --> 00:04:15,960
Speaker 1: age came about, and now you're not with IBM anymore,

99
00:04:16,000 --> 00:04:18,080
Speaker 1: but you're still around here investing in what a lot

100
00:04:18,120 --> 00:04:19,960
Speaker 1: of people think is going to be that next big

101
00:04:20,000 --> 00:04:23,200
Speaker 1: new technology. I don't think anyone is doubting that this

102
00:04:23,320 --> 00:04:25,160
Speaker 1: might be the next big thing. I think people are

103
00:04:25,240 --> 00:04:27,719
Speaker 1: kind of doubting who the winners and losers are going

104
00:04:27,800 --> 00:04:29,680
Speaker 1: to be. And right now all the bets seem to

105
00:04:29,720 --> 00:04:31,480
Speaker 1: be with such a small core the companies.

106
00:04:32,279 --> 00:04:34,400
Speaker 3: Well that's I mean, that's exactly right, and that's how

107
00:04:34,440 --> 00:04:35,880
Speaker 3: it was. To go back to the Internet, it's a

108
00:04:35,920 --> 00:04:38,920
Speaker 3: small core that benefited initially and then it broadened out,

109
00:04:39,200 --> 00:04:41,360
Speaker 3: and it broadened out when it got to the adoption phase,

110
00:04:41,440 --> 00:04:45,080
Speaker 3: not the enabling, I mean the infrastructure build out. That's

111
00:04:45,080 --> 00:04:47,800
Speaker 3: why these guys are sold out, because everybody's chasing the

112
00:04:47,839 --> 00:04:51,080
Speaker 3: infrastructure buildout, I EI the hyper scalers, and they're ordering

113
00:04:51,120 --> 00:04:53,720
Speaker 3: GPUs and chips lay crazy because they want to be

114
00:04:53,760 --> 00:04:56,799
Speaker 3: ahead of the curve as this adoption phase or demand

115
00:04:56,839 --> 00:05:00,880
Speaker 3: increases on the cloud providers Amazon, Google, Microsoft Is or

116
00:05:00,920 --> 00:05:03,240
Speaker 3: et cetera, et cetera. That's kind of where we are

117
00:05:03,279 --> 00:05:06,479
Speaker 3: in the phase. I really do think that the hard

118
00:05:06,560 --> 00:05:09,560
Speaker 3: thing to predict, I mean go back in time. Even

119
00:05:09,640 --> 00:05:12,360
Speaker 3: in the early days of Internet when companies were digitizing,

120
00:05:12,600 --> 00:05:15,160
Speaker 3: did people think it will be impacting how you take

121
00:05:15,200 --> 00:05:17,359
Speaker 3: a ride in a car a uber or get a

122
00:05:17,440 --> 00:05:20,880
Speaker 3: room someplace, or get videos media to place and all

123
00:05:20,920 --> 00:05:23,680
Speaker 3: that sort of stuff from music? Nobody really conceived in

124
00:05:23,680 --> 00:05:25,880
Speaker 3: the early days of the Internet. It was more about

125
00:05:25,880 --> 00:05:28,479
Speaker 3: digitizing things and putting your information up from the web

126
00:05:28,640 --> 00:05:31,080
Speaker 3: and maybe doing a little bit of commerce. Did anybody

127
00:05:31,120 --> 00:05:32,960
Speaker 3: think that a little bit of commerce would be Amazon?

128
00:05:33,640 --> 00:05:37,200
Speaker 3: So the point is now your point is predicting where

129
00:05:37,200 --> 00:05:39,160
Speaker 3: it will go. I'd tell you that's really I'm not

130
00:05:39,279 --> 00:05:42,760
Speaker 3: an investor in this space, and you know, we're focused

131
00:05:42,760 --> 00:05:45,640
Speaker 3: primarily in areas around healthcare and around cyber and those

132
00:05:45,720 --> 00:05:46,280
Speaker 3: sorts of things.

133
00:05:46,520 --> 00:05:50,560
Speaker 1: And you're seeing the substantive use or potential use of

134
00:05:50,560 --> 00:05:52,560
Speaker 1: this technology. Because even when you go back to the

135
00:05:52,600 --> 00:05:53,760
Speaker 1: Internet age, and I know there was a lot of

136
00:05:53,800 --> 00:05:55,760
Speaker 1: hype and a lot of folks who were predicting things

137
00:05:55,760 --> 00:05:57,600
Speaker 1: that never panned out. But I think for a lot

138
00:05:57,640 --> 00:05:59,960
Speaker 1: of us, at least the layman out there, you can

139
00:06:00,040 --> 00:06:02,520
Speaker 1: actually see the tangible qualities of what they at least

140
00:06:02,520 --> 00:06:05,800
Speaker 1: were trying to build. AI just seems a lot more Ephemerald.

141
00:06:06,360 --> 00:06:08,440
Speaker 3: Well, I mean, there's a couple of things. First of all,

142
00:06:08,760 --> 00:06:11,839
Speaker 3: let's take about the Let's talk about the enterprise adoption.

143
00:06:12,040 --> 00:06:14,360
Speaker 3: Right where can great value can be created in these

144
00:06:14,480 --> 00:06:18,560
Speaker 3: enterprise companies and s Guys running these companies realize they've

145
00:06:18,600 --> 00:06:21,520
Speaker 3: done the pilots, they've experimented, they've done these things, and

146
00:06:21,560 --> 00:06:24,000
Speaker 3: they're going to chase productivity in the short term, but

147
00:06:24,000 --> 00:06:26,600
Speaker 3: they're going to drive innovation in the long term. And

148
00:06:26,720 --> 00:06:30,160
Speaker 3: that's the innovation and the enterprise. There are still limitations

149
00:06:30,160 --> 00:06:33,960
Speaker 3: in the technology I mean, for example, accuracy and transparency

150
00:06:33,960 --> 00:06:37,880
Speaker 3: and validity. If you're doing financial services, or you're doing

151
00:06:38,600 --> 00:06:41,240
Speaker 3: national security, or you're doing healthcare and you're worrying about

152
00:06:41,279 --> 00:06:43,640
Speaker 3: people that are going to live or die, you need

153
00:06:43,680 --> 00:06:46,160
Speaker 3: those issues to be addressed, and they will be addressed.

154
00:06:46,160 --> 00:06:47,840
Speaker 3: And there are a lot of startups trying to address

155
00:06:47,880 --> 00:06:50,800
Speaker 3: a lot of those and those areas of inadequacy that

156
00:06:50,880 --> 00:06:54,520
Speaker 3: exist today. So therefore you see things being applied to

157
00:06:54,560 --> 00:06:57,359
Speaker 3: what I called a simpler use case like marketing, like

158
00:06:57,400 --> 00:07:00,960
Speaker 3: customer service, et cetera, et cetera. You believe that now

159
00:07:01,240 --> 00:07:03,800
Speaker 3: these cycles, as I go back to the Internet, they're

160
00:07:03,880 --> 00:07:07,040
Speaker 3: like ten to fifteen year cycles, you know, right, So

161
00:07:07,160 --> 00:07:09,440
Speaker 3: I mean we're in the year what three maybe depend

162
00:07:09,560 --> 00:07:11,480
Speaker 3: upon how you count this stuff. So I mean we

163
00:07:11,560 --> 00:07:14,920
Speaker 3: have a long way to go. And from an investor,

164
00:07:15,080 --> 00:07:17,560
Speaker 3: I mean I'm basically a VC today. I'm not involved

165
00:07:17,560 --> 00:07:20,400
Speaker 3: in large companies. I'm an advisor, but I'm not really

166
00:07:20,400 --> 00:07:23,960
Speaker 3: involved day to day. Fundamentally, from a VC perspective, we're

167
00:07:23,960 --> 00:07:27,200
Speaker 3: making bets in those industries where the technology you can

168
00:07:27,280 --> 00:07:30,040
Speaker 3: have a massive impact, for example, the delivery of healthcare

169
00:07:30,440 --> 00:07:33,640
Speaker 3: or therapeutics and those sorts of things, massive impact, to

170
00:07:33,640 --> 00:07:37,840
Speaker 3: improve the system, to drive productivity, to improve outcomes that

171
00:07:37,880 --> 00:07:40,640
Speaker 3: could be significant. Financial services. There's a lot of things

172
00:07:40,640 --> 00:07:43,600
Speaker 3: that can be done in financial services again to improve

173
00:07:44,360 --> 00:07:47,560
Speaker 3: the value to society and value to the companies themselves.

174
00:07:47,800 --> 00:07:49,960
Speaker 1: All right, Sam, have to leave it there, great insights,

175
00:07:50,000 --> 00:07:51,520
Speaker 1: as always, have a wonderful day.

176
00:07:51,600 --> 00:07:51,800
Speaker 2: Sam.

177
00:07:51,800 --> 00:07:54,600
Speaker 1: Paul Masano is the chairman of the Center for Global Enterprises.

178
00:07:54,640 --> 00:07:57,520
Speaker 1: He said, a vc funder in the AI space, and

179
00:07:57,560 --> 00:08:00,840
Speaker 1: of course a long time IBM employee EU rose to

180
00:08:00,880 --> 00:08:02,080
Speaker 1: the top of the mountain there