1 00:00:02,520 --> 00:00:08,800 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Alvin Krishna joins us, 2 00:00:08,840 --> 00:00:11,400 Speaker 1: it is a joy to have you on our network, 3 00:00:11,800 --> 00:00:13,560 Speaker 1: Ovin and look, speed is the name of the game. 4 00:00:13,680 --> 00:00:16,840 Speaker 1: This deale has completed swiftly. And indeed Confluent is all 5 00:00:16,880 --> 00:00:20,639 Speaker 1: about speed and data analysis. How is that important in 6 00:00:20,640 --> 00:00:21,400 Speaker 1: this age of AI? 7 00:00:22,239 --> 00:00:24,400 Speaker 2: Yeah, so, Karlin, it's great to be here with you 8 00:00:24,480 --> 00:00:27,960 Speaker 2: and on Bloomberg. So just look at what Confluence does. 9 00:00:28,360 --> 00:00:32,479 Speaker 2: Moving data in real time so that it gets available 10 00:00:32,720 --> 00:00:36,600 Speaker 2: both for the enterprise for analytics, but more importantly for 11 00:00:36,720 --> 00:00:39,680 Speaker 2: AI agents and doing that in a way that is 12 00:00:39,920 --> 00:00:43,519 Speaker 2: the most capable product in the world. Is why it 13 00:00:43,560 --> 00:00:45,920 Speaker 2: is so exciting to get it done. And your point 14 00:00:45,960 --> 00:00:50,760 Speaker 2: on speed, I think the regulatory environment is definitely friendlier 15 00:00:51,159 --> 00:00:54,880 Speaker 2: where we got this done in just under four months, 16 00:00:55,000 --> 00:00:57,600 Speaker 2: whereas it used to take a lot longer a few 17 00:00:57,720 --> 00:00:58,280 Speaker 2: years back. 18 00:00:58,720 --> 00:01:02,720 Speaker 1: If regulatory environment is friendlier, should we be doing more 19 00:01:02,760 --> 00:01:04,520 Speaker 1: of it? Should there be more m and a particularly 20 00:01:04,520 --> 00:01:07,840 Speaker 1: with some beaten up overall valuations of software companies At 21 00:01:07,840 --> 00:01:08,480 Speaker 1: the moment. 22 00:01:08,840 --> 00:01:12,279 Speaker 3: I'll just say, what's the space, Oh, what's this space? 23 00:01:12,319 --> 00:01:13,720 Speaker 1: Okay, but where would you want to add on in 24 00:01:13,760 --> 00:01:15,440 Speaker 1: this moment? I mean what would make sense to be 25 00:01:15,480 --> 00:01:16,520 Speaker 1: adding to your portfolio. 26 00:01:17,200 --> 00:01:20,600 Speaker 2: So we are very focused hybrid cloud and AI and 27 00:01:20,640 --> 00:01:24,039 Speaker 2: the intersection. So if you look at Confluent, some of 28 00:01:24,080 --> 00:01:26,360 Speaker 2: the data is in cloud, some of the data as 29 00:01:26,400 --> 00:01:29,080 Speaker 2: in SaaS property, some of the data is on premise. 30 00:01:29,319 --> 00:01:31,759 Speaker 2: An AI agent needs to get hold of it wherever. 31 00:01:32,120 --> 00:01:35,199 Speaker 2: So that's the hybrid piece combined with the AI piece. 32 00:01:36,000 --> 00:01:39,320 Speaker 2: Our speet spot is going to be hybrid cloud, AI 33 00:01:39,720 --> 00:01:43,200 Speaker 2: automation are the areas where we are very very focused 34 00:01:43,200 --> 00:01:46,520 Speaker 2: on M and A activities as well as organic development. 35 00:01:46,880 --> 00:01:48,680 Speaker 2: If you look at what we have done around are 36 00:01:49,000 --> 00:01:53,560 Speaker 2: what's the next product products, what we've done around mainframe modernization. 37 00:01:53,680 --> 00:01:55,800 Speaker 2: These are all things that we have built organically, but 38 00:01:55,880 --> 00:02:01,200 Speaker 2: then we supplement it with targeted ACQUA positions where the 39 00:02:01,280 --> 00:02:04,280 Speaker 2: multiple makes sense, where it fits our strategy, and where 40 00:02:04,320 --> 00:02:08,040 Speaker 2: we can normally increase the growth rate of the target property, 41 00:02:08,160 --> 00:02:10,920 Speaker 2: which we certainly hope to do in the Confluent case. 42 00:02:11,440 --> 00:02:13,679 Speaker 1: Let's talk about what's at X a little bit. Let's 43 00:02:13,680 --> 00:02:16,359 Speaker 1: well more at partnerships, because of course that's what was 44 00:02:16,400 --> 00:02:18,560 Speaker 1: announced A little bit more of a deeper partnership within 45 00:02:18,639 --> 00:02:22,120 Speaker 1: video yesterday helped your stop. We're seeing expanding collaboration. Again, 46 00:02:22,160 --> 00:02:25,839 Speaker 1: this is about faster data analysis, but cheaper, more more effective. 47 00:02:26,400 --> 00:02:28,200 Speaker 1: How does that help you seal more deals? 48 00:02:28,960 --> 00:02:31,359 Speaker 2: So in that one, actually the work we're doing together 49 00:02:31,400 --> 00:02:35,400 Speaker 2: with Nvidia was a five times speed up, so five times, 50 00:02:35,480 --> 00:02:39,000 Speaker 2: not five percent, not a little amount, but five times. 51 00:02:39,160 --> 00:02:42,920 Speaker 2: So there we began to leverage the Nvidia GPUs together 52 00:02:43,000 --> 00:02:47,320 Speaker 2: with some of their CUDF or software, combining you with 53 00:02:47,360 --> 00:02:50,560 Speaker 2: our WHATSONEX to our data. And the example we used 54 00:02:50,840 --> 00:02:54,440 Speaker 2: was our client Nesle, where together we managed to get 55 00:02:54,440 --> 00:02:57,400 Speaker 2: that speed up across their massive amounts of data. And 56 00:02:57,440 --> 00:03:02,120 Speaker 2: that really is important case combining some of the technologies 57 00:03:02,160 --> 00:03:04,560 Speaker 2: we work on also an open source with the Presta 58 00:03:04,880 --> 00:03:09,600 Speaker 2: Presto data engine Nvidia and the example at Nesle. But 59 00:03:09,639 --> 00:03:11,800 Speaker 2: then we're very excited we're going to do more work 60 00:03:11,800 --> 00:03:14,040 Speaker 2: on that and then take it into the market and 61 00:03:14,080 --> 00:03:15,639 Speaker 2: take it out to hundreds of clients. 62 00:03:16,080 --> 00:03:17,960 Speaker 3: From there, you're offering these tools. 63 00:03:18,040 --> 00:03:21,760 Speaker 1: We're also helping companies like Nesle just embed AI make 64 00:03:21,800 --> 00:03:24,240 Speaker 1: sure they're using in the most effective manner possible. When 65 00:03:24,280 --> 00:03:26,440 Speaker 1: your consultants go in, how much does a Nesle want 66 00:03:26,440 --> 00:03:30,840 Speaker 1: to use your offerings? But those of anthropic of open AI, 67 00:03:31,080 --> 00:03:32,639 Speaker 1: of others. How much do you see that as a 68 00:03:32,680 --> 00:03:34,560 Speaker 1: competitive force or a competitive threat. 69 00:03:35,160 --> 00:03:37,560 Speaker 2: Look, our goal has always been that we want to 70 00:03:37,600 --> 00:03:41,560 Speaker 2: help our clients integrate the best capabilities from where they come. 71 00:03:42,000 --> 00:03:45,080 Speaker 2: And the word integration comes in here because to some 72 00:03:45,240 --> 00:03:48,520 Speaker 2: extent be a model agnostic. We believe that our clients 73 00:03:48,520 --> 00:03:51,680 Speaker 2: are going to use the frontier models from all three 74 00:03:51,760 --> 00:03:53,960 Speaker 2: or four of the main providers, They're going to use 75 00:03:54,000 --> 00:03:56,920 Speaker 2: open source models, and they're also going to use models 76 00:03:56,920 --> 00:03:59,360 Speaker 2: from us, though we tend to make much smaller models 77 00:03:59,800 --> 00:04:02,880 Speaker 2: helping them really get value. And I really believe twenty 78 00:04:02,920 --> 00:04:05,480 Speaker 2: twenty six is the year when enterprises are going to 79 00:04:05,480 --> 00:04:08,880 Speaker 2: be focused and obsessive on ROI or what they're doing 80 00:04:08,960 --> 00:04:11,480 Speaker 2: on AI. And so if you take Nestley, how do 81 00:04:11,520 --> 00:04:13,440 Speaker 2: you take cost out of procurement? How do you take 82 00:04:13,440 --> 00:04:17,479 Speaker 2: costs out of HR, the finance function, procurement, all of 83 00:04:17,520 --> 00:04:20,960 Speaker 2: these things? And that's where our consultants and our technologies 84 00:04:21,000 --> 00:04:24,240 Speaker 2: come together to help them do that, using both technologies 85 00:04:24,240 --> 00:04:26,800 Speaker 2: from IBM, but also in the case of Nestlayer, from 86 00:04:26,800 --> 00:04:29,440 Speaker 2: many other of the partners and the software vendors that 87 00:04:29,480 --> 00:04:29,920 Speaker 2: they use. 88 00:04:30,200 --> 00:04:34,080 Speaker 1: So do you think the investor base and just more 89 00:04:34,120 --> 00:04:37,479 Speaker 1: broadly your customers as well, see that AI is in a. 90 00:04:37,520 --> 00:04:38,480 Speaker 3: Double edged sword forew. 91 00:04:38,520 --> 00:04:40,800 Speaker 1: But actually that competitive threat that sunk the shares so 92 00:04:40,880 --> 00:04:44,440 Speaker 1: much on the twenty third was a misunderstanding of what 93 00:04:44,480 --> 00:04:45,719 Speaker 1: anthrobert could do with COBYL. 94 00:04:46,360 --> 00:04:47,960 Speaker 3: I'm going to be much more straightforward. 95 00:04:49,000 --> 00:04:52,559 Speaker 2: I believe that the investors and parts of the media 96 00:04:52,720 --> 00:04:56,920 Speaker 2: misunderstood what that blog said and did. I actually am 97 00:04:56,960 --> 00:04:59,520 Speaker 2: convinced AI is the tailwind for us. It's not a 98 00:04:59,520 --> 00:05:02,640 Speaker 2: mixed half headwind half tail win. AI is a tailwind 99 00:05:02,640 --> 00:05:06,279 Speaker 2: for us in terms of how our technology will get adopted. 100 00:05:05,839 --> 00:05:07,560 Speaker 3: In terms of what we do with our clients. 101 00:05:08,000 --> 00:05:10,520 Speaker 2: And by the way, we had put our tools to 102 00:05:10,600 --> 00:05:13,800 Speaker 2: convert and modernize scoball two and a half years ago, 103 00:05:14,000 --> 00:05:16,520 Speaker 2: so there was no new news in that blog. But 104 00:05:16,720 --> 00:05:17,960 Speaker 2: they certainly can get a lot of. 105 00:05:17,880 --> 00:05:21,320 Speaker 1: Attention you talk, though, what also gets attention is being 106 00:05:21,320 --> 00:05:23,840 Speaker 1: able to become more productive take costs out of the 107 00:05:23,880 --> 00:05:26,120 Speaker 1: business using AI. A lot of people see that as 108 00:05:26,400 --> 00:05:29,760 Speaker 1: the costs of people. We're seeing Block remove forty percent 109 00:05:30,120 --> 00:05:32,880 Speaker 1: of their employee base. You hear about tire headline after 110 00:05:32,920 --> 00:05:35,559 Speaker 1: headline is that what's going to happen to the labor force? 111 00:05:35,600 --> 00:05:37,440 Speaker 3: Haven't so I'll take ourselves. 112 00:05:37,520 --> 00:05:39,640 Speaker 2: We have been very public that we have taken four 113 00:05:39,680 --> 00:05:42,479 Speaker 2: and a half billion dollars out of the enterprise with 114 00:05:42,560 --> 00:05:46,520 Speaker 2: the mixture of AI and automation. That's a hard number. 115 00:05:47,040 --> 00:05:50,320 Speaker 2: Now over three billion of that got reinvested into the 116 00:05:50,320 --> 00:05:53,719 Speaker 2: business in more R and D, in more sales, in 117 00:05:53,800 --> 00:05:56,800 Speaker 2: more marketing, in more delivery, in more what we would 118 00:05:56,800 --> 00:05:59,720 Speaker 2: call client engineering, where clients love the fact that our 119 00:05:59,720 --> 00:06:02,240 Speaker 2: engine can work with them to help them solve problems. 120 00:06:02,640 --> 00:06:06,200 Speaker 2: So there is reshaping of the workforce, but there isn't 121 00:06:06,240 --> 00:06:09,400 Speaker 2: a net decrease. And to put our money where our 122 00:06:09,440 --> 00:06:12,400 Speaker 2: mouth is, we will do two to three times as 123 00:06:12,480 --> 00:06:15,640 Speaker 2: much entry level meaning college hires this year as we 124 00:06:15,680 --> 00:06:16,400 Speaker 2: did last year. 125 00:06:17,080 --> 00:06:17,359 Speaker 3: Wow. 126 00:06:17,400 --> 00:06:20,039 Speaker 1: So you're still seeing that demand for that level of 127 00:06:20,240 --> 00:06:23,760 Speaker 1: technical prowess coming straight out of University of in But 128 00:06:23,960 --> 00:06:25,800 Speaker 1: I mean you have said in the past, look you 129 00:06:25,880 --> 00:06:29,000 Speaker 1: see job displacement, your term and phrase of five to 130 00:06:29,040 --> 00:06:31,120 Speaker 1: ten percent. Is that done now at IBM? 131 00:06:31,200 --> 00:06:31,600 Speaker 3: Do you think? 132 00:06:32,160 --> 00:06:35,320 Speaker 2: I think that we are probably halfway down that total 133 00:06:35,400 --> 00:06:38,120 Speaker 2: journey and I still stick to my total numbers in 134 00:06:38,160 --> 00:06:41,279 Speaker 2: that range, and I think we're halfway through that journey. 135 00:06:41,600 --> 00:06:44,960 Speaker 2: But our total employment has remained roughly stable, So to 136 00:06:45,000 --> 00:06:47,280 Speaker 2: the point that I make about how there will be 137 00:06:47,279 --> 00:06:49,800 Speaker 2: more opportunities, but you've got to be willing to obscale 138 00:06:49,800 --> 00:06:51,600 Speaker 2: yourself and recrail yourself. 139 00:06:52,080 --> 00:06:55,719 Speaker 1: What's not stable is the geopolitical environment of in How 140 00:06:55,839 --> 00:06:58,400 Speaker 1: is that affecting you? How is that affecting the consulting 141 00:06:58,440 --> 00:06:59,600 Speaker 1: part of the business in particular. 142 00:07:00,520 --> 00:07:03,320 Speaker 2: Look, let's acknowledge there's a lot of pain and disruption 143 00:07:03,440 --> 00:07:05,440 Speaker 2: that's going on in the Middle East right now. I 144 00:07:05,480 --> 00:07:09,720 Speaker 2: think we should feel a lot of sympathy for our people. 145 00:07:09,800 --> 00:07:11,640 Speaker 3: We have thousands of employees in that region. 146 00:07:12,280 --> 00:07:16,680 Speaker 2: The vast majority I think are reasonably stable and are 147 00:07:16,680 --> 00:07:19,840 Speaker 2: getting their work done. There are about twenty percent of 148 00:07:19,840 --> 00:07:22,760 Speaker 2: the people in the Middle East who are disrupted, who 149 00:07:22,760 --> 00:07:25,400 Speaker 2: are not able to get to their clients, who are 150 00:07:25,400 --> 00:07:27,920 Speaker 2: not able to get the work done. 151 00:07:28,280 --> 00:07:31,320 Speaker 3: I think within a quarter is going to. 152 00:07:31,240 --> 00:07:33,760 Speaker 2: Be very, very minor, because most of this work is 153 00:07:33,840 --> 00:07:36,880 Speaker 2: much more long cycle than short cycle, as in days 154 00:07:36,920 --> 00:07:40,600 Speaker 2: or weeks. If this goes on for many more months, 155 00:07:40,840 --> 00:07:43,400 Speaker 2: then I do think that we will take a slight headwind. 156 00:07:43,760 --> 00:07:46,520 Speaker 2: But in the end of the day, our consulting business 157 00:07:46,560 --> 00:07:49,160 Speaker 2: in the Middle East is only a few single digit 158 00:07:49,240 --> 00:07:52,920 Speaker 2: percentages of our total business, So it won't impact IBM's 159 00:07:52,960 --> 00:07:55,080 Speaker 2: top line and bottom line, but it is going to 160 00:07:55,080 --> 00:07:56,920 Speaker 2: be impactful to some of our people and some of 161 00:07:56,920 --> 00:07:58,120 Speaker 2: our clients who are. 162 00:07:57,960 --> 00:07:58,600 Speaker 3: In the region. 163 00:07:58,920 --> 00:08:01,840 Speaker 1: Thanks for that new once and I'm interested just on 164 00:08:01,840 --> 00:08:05,240 Speaker 1: the nuance of total sales growth in consulting. Will that 165 00:08:05,520 --> 00:08:07,440 Speaker 1: come back to growth do you think in the near term? 166 00:08:08,320 --> 00:08:12,800 Speaker 2: I think when I was talking in January and also 167 00:08:12,880 --> 00:08:15,480 Speaker 2: in October, I felt that the second half of this 168 00:08:15,560 --> 00:08:18,560 Speaker 2: year will be a lot better on consulting growth. Then 169 00:08:18,640 --> 00:08:21,240 Speaker 2: right now we kind of said that we could see 170 00:08:21,280 --> 00:08:24,360 Speaker 2: it inflection coming, where the first half of last year 171 00:08:24,400 --> 00:08:27,280 Speaker 2: was negative, the second half of the year turned kind 172 00:08:27,320 --> 00:08:31,280 Speaker 2: of flatish. I think that you'll see continued improvement in 173 00:08:31,280 --> 00:08:34,600 Speaker 2: that inflection to maybe slight growth in the first part 174 00:08:34,600 --> 00:08:37,040 Speaker 2: of the year, but true growth, I think is still 175 00:08:37,080 --> 00:08:38,880 Speaker 2: out in the second half of the year. 176 00:08:39,400 --> 00:08:42,360 Speaker 1: True growth, true excitement about quantum always seems to be 177 00:08:42,400 --> 00:08:44,720 Speaker 1: in the second half of the decade or the millennium. 178 00:08:44,760 --> 00:08:47,600 Speaker 1: But you really are putting your money where your mouth 179 00:08:47,640 --> 00:08:50,560 Speaker 1: is when it comes to quantum computing becoming you know, 180 00:08:50,720 --> 00:08:53,040 Speaker 1: really tangibly useful by the end of the decade. You 181 00:08:53,080 --> 00:08:55,800 Speaker 1: want to have a fault tolerant quantum supercomputer by twenty 182 00:08:55,840 --> 00:08:58,120 Speaker 1: twenty nine. How on track are we there? How on 183 00:08:58,200 --> 00:09:00,520 Speaker 1: track are we to integrate in quantum within the day Center. 184 00:09:01,720 --> 00:09:04,520 Speaker 2: So in all three of your questions, Caroline, I think 185 00:09:04,600 --> 00:09:07,360 Speaker 2: we are completely on track with what p had said. 186 00:09:07,720 --> 00:09:11,680 Speaker 2: We said that we're going to have better cubits, better 187 00:09:11,760 --> 00:09:15,800 Speaker 2: quantum processes, and our processor at the end of last 188 00:09:15,880 --> 00:09:19,760 Speaker 2: year is now being used. Whether it's for medical work 189 00:09:20,280 --> 00:09:25,400 Speaker 2: at Cleveland Clinic, whether it's for bond pricing at HSBC. 190 00:09:25,760 --> 00:09:28,880 Speaker 2: Those are real use cases that are coming out. We 191 00:09:28,920 --> 00:09:31,480 Speaker 2: expect to see error correction between this year and next 192 00:09:31,559 --> 00:09:33,760 Speaker 2: year demonstrated and out. 193 00:09:33,520 --> 00:09:35,880 Speaker 3: There, so that'll be another big plus. 194 00:09:36,480 --> 00:09:39,760 Speaker 2: We're investing very heavily in how we bring together our 195 00:09:39,760 --> 00:09:42,600 Speaker 2: normal computers and quantum computers. We put out a roadmap 196 00:09:43,000 --> 00:09:46,440 Speaker 2: for quantum centric supercomputing that has gotten a lot of 197 00:09:46,480 --> 00:09:50,520 Speaker 2: attention or partnerships with academia, with institutions. 198 00:09:51,600 --> 00:09:52,800 Speaker 3: I'm so excited. 199 00:09:52,400 --> 00:09:56,000 Speaker 2: About what we're doing in Illinois, but also at RPI 200 00:09:56,280 --> 00:10:00,320 Speaker 2: with MIT, and in other places we have done work 201 00:10:00,360 --> 00:10:03,240 Speaker 2: with the National Labs. This is all tells us how 202 00:10:03,320 --> 00:10:05,480 Speaker 2: tangible it is, and you can feel it. People are 203 00:10:05,520 --> 00:10:08,000 Speaker 2: not realizing this is not science fiction. This is not 204 00:10:08,160 --> 00:10:10,480 Speaker 2: engineering to get through the next two or three years. 205 00:10:10,640 --> 00:10:13,320 Speaker 1: I think I can feel your energy today. Congratulations on 206 00:10:13,320 --> 00:10:16,400 Speaker 1: the completion the Confluent deal and we are staying tuned 207 00:10:16,440 --> 00:10:18,600 Speaker 1: from any more m and a come back on when 208 00:10:18,600 --> 00:10:21,520 Speaker 1: it happens IBM, CEO OF and Christna is great speaking 209 00:10:21,559 --> 00:10:22,080 Speaker 1: with you.