1 00:00:02,520 --> 00:00:07,400 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:08,039 --> 00:00:11,600 Speaker 2: Oppenheim and she and coverage on IBM with an outperform 3 00:00:11,640 --> 00:00:15,600 Speaker 2: writing expecting double digit revenue growth in its software portfolio 4 00:00:15,680 --> 00:00:18,200 Speaker 2: that stocks up about a quarter of one percent. Let's 5 00:00:18,200 --> 00:00:20,480 Speaker 2: stick with IBM. Let's head to the World Government Summit 6 00:00:20,600 --> 00:00:23,119 Speaker 2: in Dubai, where bloombags Geamunta BASESSI. She is with a 7 00:00:23,239 --> 00:00:24,479 Speaker 2: special guest, Jamana, I have. 8 00:00:24,480 --> 00:00:25,000 Speaker 1: A CU. 9 00:00:27,960 --> 00:00:30,440 Speaker 3: Guys, thanks for that. Yes, so joining me right now 10 00:00:30,560 --> 00:00:33,280 Speaker 3: is the IBM CEO, Arvin Krishner. Great to have you 11 00:00:33,320 --> 00:00:35,600 Speaker 3: with us here the World Government Summits in the UAE 12 00:00:35,720 --> 00:00:39,240 Speaker 3: of all places. But look, I just want to start 13 00:00:39,280 --> 00:00:42,839 Speaker 3: off with a question that I think is dominating the 14 00:00:42,840 --> 00:00:46,560 Speaker 3: discussions these days, and that is visa VI Artificial intelligence. 15 00:00:46,680 --> 00:00:49,839 Speaker 3: You can't be a US tech company these days if 16 00:00:49,880 --> 00:00:52,920 Speaker 3: you're not seen to have a growing presence in AI. 17 00:00:53,880 --> 00:00:57,040 Speaker 3: How does innovation keep up with demand for a company 18 00:00:57,080 --> 00:00:57,520 Speaker 3: like yours? 19 00:00:57,880 --> 00:00:59,360 Speaker 1: Look, I mean innovation is our life. 20 00:00:59,400 --> 00:01:01,920 Speaker 4: No star there, And I do think that AI is 21 00:01:01,920 --> 00:01:05,360 Speaker 4: a transformative technology. So no company can be in tech 22 00:01:05,400 --> 00:01:07,720 Speaker 4: without having to have to play a role in AI. 23 00:01:08,319 --> 00:01:09,960 Speaker 4: If I look at the role that we want to play, 24 00:01:09,959 --> 00:01:12,000 Speaker 4: we are focused on the B to B market, So 25 00:01:12,040 --> 00:01:14,000 Speaker 4: we do not want to do B two C. So 26 00:01:14,040 --> 00:01:16,360 Speaker 4: B to B means you've got to take advantage of 27 00:01:16,400 --> 00:01:17,959 Speaker 4: an enterprise's private data. 28 00:01:18,680 --> 00:01:19,920 Speaker 1: Here's a shocking statistic. 29 00:01:20,240 --> 00:01:23,640 Speaker 4: Only one percent of enterprise data has found its way 30 00:01:23,640 --> 00:01:25,600 Speaker 4: into any form of AI model so far. 31 00:01:26,280 --> 00:01:28,679 Speaker 1: So that is the unlock that we want to do. 32 00:01:29,040 --> 00:01:31,960 Speaker 4: And as we bring that private data into models, whether 33 00:01:32,000 --> 00:01:35,280 Speaker 4: it's used to refine models or to construct unique use cases, 34 00:01:35,600 --> 00:01:38,040 Speaker 4: it unlocks a huge amount of value for the enterprise. 35 00:01:38,200 --> 00:01:41,800 Speaker 3: Yeah, how has the arrival of deep seek disrupted the 36 00:01:41,920 --> 00:01:44,920 Speaker 3: landscape for generative AI because it's a model that was 37 00:01:45,240 --> 00:01:49,160 Speaker 3: produced with a much lower cost but also equal utility 38 00:01:49,360 --> 00:01:51,760 Speaker 3: as some of the larger language models. Is this a 39 00:01:51,840 --> 00:01:52,720 Speaker 3: major game changer? 40 00:01:52,760 --> 00:01:53,160 Speaker 2: Do you think? 41 00:01:53,280 --> 00:01:53,680 Speaker 1: Actually? 42 00:01:53,880 --> 00:01:56,040 Speaker 4: You see we smile because I think it's a validation. 43 00:01:56,480 --> 00:01:58,600 Speaker 4: We've been on this point for a long time that 44 00:01:58,720 --> 00:02:00,760 Speaker 4: you do not have to spend so much money to 45 00:02:00,800 --> 00:02:03,280 Speaker 4: get these models if you're willing to make fit for 46 00:02:03,320 --> 00:02:05,920 Speaker 4: purpose models. We do believe that the cost should be 47 00:02:05,920 --> 00:02:08,120 Speaker 4: in the millions and not in the hundreds of millions. 48 00:02:08,639 --> 00:02:11,519 Speaker 4: And so how do you begin to distill models down 49 00:02:11,560 --> 00:02:14,200 Speaker 4: to smaller sizes, get them unique for a purpose and 50 00:02:14,280 --> 00:02:17,480 Speaker 4: then run them at two to three percent, so thirty 51 00:02:17,520 --> 00:02:21,240 Speaker 4: times cheaper than the big models, but as accurate and 52 00:02:21,280 --> 00:02:23,440 Speaker 4: as good for a domain specific task. 53 00:02:23,720 --> 00:02:25,480 Speaker 3: Do you think that therefore there could be a day 54 00:02:25,520 --> 00:02:28,160 Speaker 3: of reckoning for some of these big tech companies spending 55 00:02:28,600 --> 00:02:31,560 Speaker 3: billions and tens of billions, even one hundred billion dollars 56 00:02:31,600 --> 00:02:33,200 Speaker 3: on kafak spending funds these year. 57 00:02:33,280 --> 00:02:35,680 Speaker 1: That's probably beyond my ability to computer. Is it a 58 00:02:35,760 --> 00:02:36,320 Speaker 1: day erecting? 59 00:02:36,360 --> 00:02:38,440 Speaker 4: But I can tell you that we're going to find 60 00:02:38,480 --> 00:02:40,720 Speaker 4: that the usage is going to explode as the cost 61 00:02:40,760 --> 00:02:43,840 Speaker 4: comes down, So maybe there's enough quantity increase that all 62 00:02:43,840 --> 00:02:45,120 Speaker 4: of it maps out in the right way. 63 00:02:45,200 --> 00:02:48,040 Speaker 3: Yeah, You've always been a big supporter of open source models, 64 00:02:48,080 --> 00:02:49,800 Speaker 3: and I just wonder in the case of deep Seek, 65 00:02:49,880 --> 00:02:55,200 Speaker 3: whether that actually served as a reason for the disruption 66 00:02:55,360 --> 00:02:57,919 Speaker 3: to take place. Would we not have seen a deep 67 00:02:57,960 --> 00:03:00,359 Speaker 3: seek come to the market had there not been open 68 00:03:00,360 --> 00:03:01,160 Speaker 3: source models? 69 00:03:01,320 --> 00:03:04,360 Speaker 4: No, I don't believe so, because there are enough large 70 00:03:04,360 --> 00:03:06,720 Speaker 4: models that people are building, not in the hundreds, but 71 00:03:06,760 --> 00:03:07,800 Speaker 4: definitely in the tens. 72 00:03:08,040 --> 00:03:08,680 Speaker 1: And it's hard to. 73 00:03:08,639 --> 00:03:11,160 Speaker 4: Imagine that there's an ecosystem which doesn't have a large 74 00:03:11,200 --> 00:03:14,760 Speaker 4: model to start from. Look ideas tend to spread. This 75 00:03:14,800 --> 00:03:16,600 Speaker 4: has been true for two thousand years. People have written 76 00:03:16,600 --> 00:03:19,119 Speaker 4: about this. Once there's an idea, it gets penned down. 77 00:03:19,120 --> 00:03:22,200 Speaker 4: The idea spreads. Once an idea spread, smart people, everywey 78 00:03:22,240 --> 00:03:22,840 Speaker 4: can copy it. 79 00:03:23,040 --> 00:03:24,440 Speaker 1: Yeah, let me ask. 80 00:03:24,320 --> 00:03:26,520 Speaker 3: You a question about regulation. I started off the interview 81 00:03:26,760 --> 00:03:29,600 Speaker 3: asking you whether innovation can keep up with demands. Do 82 00:03:29,600 --> 00:03:33,200 Speaker 3: you think regulation is actually keeping up with the innovation 83 00:03:33,240 --> 00:03:34,960 Speaker 3: that we're seeing in artificial intelligence. 84 00:03:35,480 --> 00:03:37,040 Speaker 1: I'll actually go the other way around. 85 00:03:37,280 --> 00:03:42,120 Speaker 4: Too much regulation early stifles innovation and then doesn't allow 86 00:03:42,240 --> 00:03:45,200 Speaker 4: those companies and those nations where the regulation. 87 00:03:44,920 --> 00:03:48,119 Speaker 1: Is heavy to succeed. We can always look at the example. 88 00:03:48,200 --> 00:03:50,200 Speaker 4: I think that the EU is very good at lots 89 00:03:50,200 --> 00:03:54,120 Speaker 4: of things, but sometimes the over regulation is an inhibitor 90 00:03:54,320 --> 00:03:57,880 Speaker 4: on innovation, and I think we should balance that. I 91 00:03:57,880 --> 00:04:00,840 Speaker 4: think that that balance is incredibly critical. And I talk 92 00:04:00,920 --> 00:04:03,320 Speaker 4: about precision regulation as opposed to. 93 00:04:03,280 --> 00:04:04,640 Speaker 1: Sort of blunt force regulation. 94 00:04:05,120 --> 00:04:08,400 Speaker 4: So be precise and take a risk based approach where 95 00:04:08,520 --> 00:04:11,640 Speaker 4: only the most risky activities are regulated. So ANYI that 96 00:04:11,680 --> 00:04:15,040 Speaker 4: becomes the use cases. So yeah, be more careful around 97 00:04:15,200 --> 00:04:19,080 Speaker 4: life or death activities, but allow innovation in customer service, 98 00:04:19,640 --> 00:04:23,440 Speaker 4: in productivity for your programmers in terms of what we're 99 00:04:23,480 --> 00:04:26,960 Speaker 4: going to do around improving the customer experience, but maybe 100 00:04:27,040 --> 00:04:29,000 Speaker 4: be more careful in life or death activities. 101 00:04:29,160 --> 00:04:32,320 Speaker 3: Yeah, let me just ask you another question which is 102 00:04:32,400 --> 00:04:37,520 Speaker 3: also very important in the context of tech innovation, and 103 00:04:37,560 --> 00:04:40,200 Speaker 3: that is the rise of countum computing. I thought it 104 00:04:40,240 --> 00:04:42,560 Speaker 3: was really interesting that in your investor dage just a 105 00:04:42,600 --> 00:04:46,240 Speaker 3: week ago, you said, well, you updated the quantum computing roadmap, 106 00:04:46,279 --> 00:04:49,000 Speaker 3: and you said that you're aim at demonstrating the first 107 00:04:49,040 --> 00:04:52,000 Speaker 3: contum computer by twenty twenty eight. That's only three years away. 108 00:04:52,040 --> 00:04:55,240 Speaker 3: People are a bit skeptical that that can be achieved in. 109 00:04:55,160 --> 00:04:58,240 Speaker 4: That timeline, tolerant with era correction part of computer. At 110 00:04:58,240 --> 00:05:01,040 Speaker 4: twenty eight, we actually have third quantum computers on the 111 00:05:01,080 --> 00:05:04,480 Speaker 4: cloud today, actual quantum computers over one hundred cubits, so 112 00:05:04,600 --> 00:05:08,240 Speaker 4: not toys, really serious quantum computers. But we believe we're 113 00:05:08,279 --> 00:05:10,160 Speaker 4: going to be on the roadmap to do that by 114 00:05:10,240 --> 00:05:12,560 Speaker 4: twenty twenty eight, and that is a commitment that we 115 00:05:12,600 --> 00:05:15,039 Speaker 4: did make. But I also believe that quantum computers are 116 00:05:15,080 --> 00:05:17,040 Speaker 4: going to unlock a lot of value. We think about 117 00:05:17,040 --> 00:05:19,680 Speaker 4: half a trillion dollars worth of value for our customers 118 00:05:20,000 --> 00:05:24,200 Speaker 4: by the end of this decade. That's exciting materials, climate change, 119 00:05:24,240 --> 00:05:29,560 Speaker 4: carbon sequestration, better fertilizers, better batteries, exciting areas. 120 00:05:29,680 --> 00:05:32,039 Speaker 3: Yeah, do you feel that because the world is so 121 00:05:32,120 --> 00:05:33,960 Speaker 3: focused on one thing at a time and this time, 122 00:05:34,080 --> 00:05:37,559 Speaker 3: you know it's generative AI, we're perhaps not focusing enough 123 00:05:37,760 --> 00:05:41,440 Speaker 3: on how quantum computing is actually going to disrupt our 124 00:05:41,480 --> 00:05:42,080 Speaker 3: day to day lives. 125 00:05:42,320 --> 00:05:44,520 Speaker 4: What do we actually look with that we can do 126 00:05:44,560 --> 00:05:48,440 Speaker 4: our work and when we get there, it'll be a moment, 127 00:05:48,640 --> 00:05:51,080 Speaker 4: and that that moment is going to be important. 128 00:05:51,520 --> 00:05:52,960 Speaker 1: Our customers are pretty focused on it. 129 00:05:53,000 --> 00:05:55,440 Speaker 4: We have two hundred and eighty people different companies and 130 00:05:55,520 --> 00:05:59,200 Speaker 4: organizations who work with us on algorithms. The state of Chica, 131 00:05:59,560 --> 00:06:02,839 Speaker 4: Illinois in the United States just announce the National Quantum 132 00:06:02,880 --> 00:06:08,159 Speaker 4: Algorithmic Center, where multiple universities, startups, large companies, national labs, 133 00:06:08,240 --> 00:06:09,039 Speaker 4: all player role. 134 00:06:09,560 --> 00:06:13,320 Speaker 1: They'll be running on our quantum computer. They probably get others. 135 00:06:13,000 --> 00:06:16,400 Speaker 4: As well if they exist and do that. I think 136 00:06:16,440 --> 00:06:18,640 Speaker 4: that's the excitement that is there. But the fact that 137 00:06:18,640 --> 00:06:22,280 Speaker 4: two hundred and eighty institutions all around the world are 138 00:06:22,360 --> 00:06:25,559 Speaker 4: busy learning how to use them tells me they'll be ready. 139 00:06:25,680 --> 00:06:28,560 Speaker 3: Yeah, Well, we're here in the UAE, and I know 140 00:06:28,600 --> 00:06:31,279 Speaker 3: that you were in Saudi Arabia recently as well. What 141 00:06:31,560 --> 00:06:34,520 Speaker 3: opportunities are you seeing in this part of the world 142 00:06:34,640 --> 00:06:36,839 Speaker 3: for AI growth and growth in your business. 143 00:06:37,120 --> 00:06:39,400 Speaker 4: We are very bullish about our business in both both 144 00:06:39,480 --> 00:06:42,520 Speaker 4: these nations, both in Saudi Arabia and in the UE. 145 00:06:42,720 --> 00:06:46,440 Speaker 4: I think the appetite for digital innovation, both for government 146 00:06:46,520 --> 00:06:50,160 Speaker 4: services and for the private sector is incredible. I think 147 00:06:50,400 --> 00:06:52,560 Speaker 4: both nations have woken up that tech can be a 148 00:06:52,560 --> 00:06:53,839 Speaker 4: big part of their economy. 149 00:06:54,040 --> 00:06:55,880 Speaker 1: If you look globally four to five percent. 150 00:06:56,240 --> 00:06:58,480 Speaker 4: I think both these nations wanted to be ten percent, 151 00:06:58,760 --> 00:07:01,680 Speaker 4: and they're investing appropriate And you can look at the 152 00:07:01,720 --> 00:07:05,080 Speaker 4: services that are there in both countries that are enabled 153 00:07:05,080 --> 00:07:08,760 Speaker 4: by technology. That appetite for AI is massive. You look 154 00:07:08,760 --> 00:07:11,760 Speaker 4: at the investment that is done in the in the OE, 155 00:07:11,920 --> 00:07:17,000 Speaker 4: around nbc UI, around some of the Falcon models. You 156 00:07:17,040 --> 00:07:19,960 Speaker 4: look good too in the in Saudi Arabia and you 157 00:07:20,000 --> 00:07:22,560 Speaker 4: have the universities and you have the Alarm model. 158 00:07:23,040 --> 00:07:24,119 Speaker 1: I think that speaks for itself. 159 00:07:24,200 --> 00:07:26,320 Speaker 3: Yeah, well, I'm sure we'll be seeing a lot more 160 00:07:26,320 --> 00:07:28,360 Speaker 3: of you in the region. Then, Arvin Krishna, really good 161 00:07:28,360 --> 00:07:29,000 Speaker 3: to chat to you.