1 00:00:02,520 --> 00:00:09,039 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Let's stick into earnings 2 00:00:09,039 --> 00:00:10,559 Speaker 1: a little bit more now, but this time is Palo 3 00:00:10,640 --> 00:00:14,400 Speaker 1: Alto Networks, a cybersecurity firm reporting sixteen percent increase in revenue. 4 00:00:14,600 --> 00:00:16,599 Speaker 1: That was yesterday, but it also announced a three point 5 00:00:16,640 --> 00:00:19,279 Speaker 1: four billion dollar plan to acquire a Chronosphere. It's a 6 00:00:19,320 --> 00:00:23,640 Speaker 1: boost to its AI enabled cybersecurity offerings. Paral Alto Network 7 00:00:23,720 --> 00:00:25,919 Speaker 1: CEO Nikesha Urora joins us for more now. And look, 8 00:00:25,960 --> 00:00:29,080 Speaker 1: your stock is lower because many are saying it's an 9 00:00:29,240 --> 00:00:32,800 Speaker 1: M and a well digestion risk that maybe is being 10 00:00:32,840 --> 00:00:35,320 Speaker 1: faced here. You put twenty five billion dollar offer into 11 00:00:35,360 --> 00:00:38,239 Speaker 1: cyber Arc now another M and a piece. Why does 12 00:00:38,280 --> 00:00:38,839 Speaker 1: it work for you? 13 00:00:40,280 --> 00:00:42,559 Speaker 2: Well, Carol, I'm nice to see you. 14 00:00:42,880 --> 00:00:45,400 Speaker 3: This is our twenty eighth acquisition in about seven and 15 00:00:45,400 --> 00:00:49,160 Speaker 3: a half years, and we've demonstrated to the market that 16 00:00:49,200 --> 00:00:53,400 Speaker 3: we have been able to establish our business and adjacent 17 00:00:53,479 --> 00:00:56,680 Speaker 3: markets for the last seven years by being attention to 18 00:00:56,680 --> 00:00:58,960 Speaker 3: the market, looking at where the puck is going, understanding 19 00:00:58,960 --> 00:01:02,040 Speaker 3: what is important for our customers. And I think Coronasphare, 20 00:01:02,360 --> 00:01:05,280 Speaker 3: which is our latest acquisition, fits right. 21 00:01:05,400 --> 00:01:07,520 Speaker 2: Banks make in the middle where the market's going. 22 00:01:07,600 --> 00:01:09,119 Speaker 3: If you look at what you guys were talking about 23 00:01:09,120 --> 00:01:10,400 Speaker 3: before this, You're talking about. 24 00:01:10,200 --> 00:01:11,000 Speaker 2: In Vidia AMD. 25 00:01:11,160 --> 00:01:14,240 Speaker 3: Eventually, all this compute power is going to result in 26 00:01:14,280 --> 00:01:17,840 Speaker 3: people building faster and more and more relevant applications the 27 00:01:17,959 --> 00:01:21,319 Speaker 3: end consumer or enterprises. All those applications have to be 28 00:01:21,400 --> 00:01:25,280 Speaker 3: observed and make sure they don't have problems or go down. 29 00:01:25,360 --> 00:01:26,840 Speaker 2: And that's what Coronos Fair does. 30 00:01:26,920 --> 00:01:30,240 Speaker 3: It observes applications and infrastructure, make sure you have nine 31 00:01:30,319 --> 00:01:32,760 Speaker 3: nine point nine percent uptime, and then we are going 32 00:01:32,800 --> 00:01:35,120 Speaker 3: to combine that with our agentic capabilities to make sure 33 00:01:35,120 --> 00:01:37,120 Speaker 3: agents will go fix that if they go down. So 34 00:01:37,120 --> 00:01:40,520 Speaker 3: I think it's a phenomenal opportunity going forward. That's fine, 35 00:01:40,600 --> 00:01:43,240 Speaker 3: the stock will recover. I think investors are beginning to 36 00:01:43,319 --> 00:01:46,199 Speaker 3: understand the story, all right. 37 00:01:46,520 --> 00:01:49,600 Speaker 1: I mean, obviously has an outperformed price target two fifty, 38 00:01:49,880 --> 00:01:51,440 Speaker 1: well above where you are, and they're saying, look, you're 39 00:01:51,440 --> 00:01:54,680 Speaker 1: a top TIS software vendor seeing AI tailwinds as it 40 00:01:54,680 --> 00:01:58,200 Speaker 1: grows ahead of industry pairs. But how does observability take 41 00:01:58,240 --> 00:02:00,600 Speaker 1: you into a whole new ecosystem. We are now fighting 42 00:02:00,720 --> 00:02:03,840 Speaker 1: data job dina trace, Why is that the right total 43 00:02:03,840 --> 00:02:06,120 Speaker 1: addressable market for you? As well as security? 44 00:02:07,240 --> 00:02:08,640 Speaker 2: Look observability is a space. 45 00:02:08,680 --> 00:02:12,000 Speaker 3: As I said, as we get more and more AI deployed, 46 00:02:12,080 --> 00:02:14,080 Speaker 3: we're going to need more and more real time capability, 47 00:02:14,080 --> 00:02:17,320 Speaker 3: real time capability and actions, real time capability and applications. 48 00:02:17,480 --> 00:02:20,920 Speaker 3: Real time capabilities required ninety nine point nine nine percent availability, 49 00:02:20,960 --> 00:02:22,880 Speaker 3: which means you have to make sure your infrastructure is 50 00:02:22,919 --> 00:02:24,959 Speaker 3: always up and running. If it has a problem, you 51 00:02:24,960 --> 00:02:26,720 Speaker 3: should be able to fix it right away, sort of 52 00:02:26,760 --> 00:02:29,239 Speaker 3: similar to security. We also have to watch security on 53 00:02:29,280 --> 00:02:31,359 Speaker 3: a constant basis, make sure something happens, we fix it 54 00:02:31,440 --> 00:02:33,960 Speaker 3: right away. So actually, over the last ten to fifteen years, 55 00:02:34,000 --> 00:02:35,920 Speaker 3: if we look, there are companies who tried to play 56 00:02:35,919 --> 00:02:37,079 Speaker 3: in both spaces, and. 57 00:02:37,040 --> 00:02:39,480 Speaker 2: Typically that ended up really well in one side not 58 00:02:39,600 --> 00:02:42,160 Speaker 2: the other. We've not made a foray into. 59 00:02:42,040 --> 00:02:44,640 Speaker 3: Observability because we never thought we could build it organically. 60 00:02:44,760 --> 00:02:47,160 Speaker 3: We don't have the skill set. But we went out 61 00:02:47,200 --> 00:02:50,080 Speaker 3: looking for data pipelining. We found Cronospare. Curnis Fair has 62 00:02:50,080 --> 00:02:53,040 Speaker 3: some of the best engineers in the space. Observably suffers 63 00:02:53,040 --> 00:02:56,160 Speaker 3: from two problems. One is too expensive and two it 64 00:02:56,200 --> 00:02:59,680 Speaker 3: doesn't scale well well. Curnis Fair solve that problem. It 65 00:02:59,760 --> 00:03:01,520 Speaker 3: is two and a half times cheaper than anybody else 66 00:03:01,560 --> 00:03:04,160 Speaker 3: on the market. And two it gets scale to giggle 67 00:03:04,200 --> 00:03:05,680 Speaker 3: what size in terms of what. 68 00:03:05,680 --> 00:03:06,959 Speaker 2: Is needed from an AI perspective. 69 00:03:06,960 --> 00:03:09,320 Speaker 3: So we think the time is right, the asset is right, 70 00:03:09,400 --> 00:03:10,520 Speaker 3: and the opportunity is right. 71 00:03:12,280 --> 00:03:15,360 Speaker 4: Taken in aggregate, that is an astonishing amount of M 72 00:03:15,400 --> 00:03:18,799 Speaker 4: and A. Is there any reason why you can't just 73 00:03:18,919 --> 00:03:21,520 Speaker 4: keep going and keep using M and A as the 74 00:03:21,560 --> 00:03:24,840 Speaker 4: tool to position the platform where you want it in. 75 00:03:24,800 --> 00:03:28,560 Speaker 3: Akesh look, in the last seven and a half years, 76 00:03:28,560 --> 00:03:32,120 Speaker 3: i'd say thirty percent of our opportunity has been created 77 00:03:32,160 --> 00:03:35,280 Speaker 3: by strategic and timely M and A. Seventy percent of 78 00:03:35,280 --> 00:03:38,240 Speaker 3: our opportunity has been on organic innovation as a company. 79 00:03:38,520 --> 00:03:40,920 Speaker 2: Now, that's worked out really well for us. 80 00:03:41,440 --> 00:03:45,720 Speaker 3: Even now we've announced two big deals, cyber Arc and Chronosware. Collectively, 81 00:03:45,760 --> 00:03:48,040 Speaker 3: we're going to spend slightly hundred and thirty billion dollars. 82 00:03:48,120 --> 00:03:50,920 Speaker 3: But that gave us the confidence to increase our targets 83 00:03:50,920 --> 00:03:53,960 Speaker 3: for ARR in five thirty five billion dollars. If I 84 00:03:53,960 --> 00:03:56,360 Speaker 3: can go spend thirty billion dollars and buy five billion 85 00:03:56,400 --> 00:03:57,920 Speaker 3: dollars of AR five years, so now I do that 86 00:03:57,960 --> 00:03:58,400 Speaker 3: every day. 87 00:03:59,080 --> 00:04:00,880 Speaker 1: Let's go back to the fund mentals where you are 88 00:04:00,920 --> 00:04:05,560 Speaker 1: already RPO, in particular remaining performance obligation growth. It was 89 00:04:05,600 --> 00:04:07,880 Speaker 1: strong in the court to just announce. But when you're 90 00:04:07,880 --> 00:04:10,760 Speaker 1: pushing out to fiscal twenty twenty six, our own intelligence, 91 00:04:10,800 --> 00:04:12,720 Speaker 1: ANEL is just a little bit worried about the slowdown 92 00:04:12,760 --> 00:04:13,280 Speaker 1: in growth. 93 00:04:13,560 --> 00:04:14,480 Speaker 2: Are you worried about it? 94 00:04:15,840 --> 00:04:17,200 Speaker 3: You know, when I started seven and a half, he's 95 00:04:17,200 --> 00:04:19,920 Speaker 3: go a two billion dollar company. RPR nows fifteen and 96 00:04:19,960 --> 00:04:22,279 Speaker 3: a half billion dollars, set a target of twenty billion 97 00:04:22,320 --> 00:04:25,240 Speaker 3: dollars in ARR. Those are big numbers. So I think 98 00:04:25,279 --> 00:04:27,679 Speaker 3: part of what people fail to understand is absolute numbers 99 00:04:27,680 --> 00:04:30,400 Speaker 3: get bigger and bigger on the margin growth rates change. 100 00:04:30,400 --> 00:04:33,080 Speaker 3: But I think from a pero capability perspective, if we 101 00:04:33,120 --> 00:04:36,000 Speaker 3: start doing twenty billion dollars in ARR, we'll be generating 102 00:04:36,000 --> 00:04:37,479 Speaker 3: ten or fifteen billion dollars a free. 103 00:04:37,279 --> 00:04:37,880 Speaker 2: Cast flow year. 104 00:04:38,040 --> 00:04:40,040 Speaker 3: That's a far cry from where we started at a 105 00:04:40,080 --> 00:04:43,080 Speaker 3: few hundred dollars seven years ago. So I think you 106 00:04:43,160 --> 00:04:45,880 Speaker 3: have to look at it from a slightly multi year perspective. 107 00:04:45,880 --> 00:04:47,719 Speaker 2: From that perspective, we think we're well positioned. 108 00:04:47,720 --> 00:04:50,000 Speaker 3: We think this is going to be the largest cybersecurity 109 00:04:50,120 --> 00:04:52,400 Speaker 3: company in the world, and we have aspirations to take 110 00:04:52,440 --> 00:04:53,839 Speaker 3: it and double the triple for where we. 111 00:04:53,839 --> 00:04:56,200 Speaker 2: Are right now. 112 00:04:56,400 --> 00:05:01,560 Speaker 4: Any given technology company might use a dozen several dozen 113 00:05:01,640 --> 00:05:04,760 Speaker 4: different tools across cyber and AI, right and your pitch 114 00:05:04,880 --> 00:05:08,080 Speaker 4: is you just put everything in one place, you know, 115 00:05:08,680 --> 00:05:12,320 Speaker 4: offer a suite of things in one place. Is that 116 00:05:12,560 --> 00:05:17,080 Speaker 4: strategy working to convince software companies in particular that it's 117 00:05:17,120 --> 00:05:20,159 Speaker 4: better it's come to paramount to networks then do business 118 00:05:20,160 --> 00:05:21,600 Speaker 4: with a handful of different players. 119 00:05:23,360 --> 00:05:25,840 Speaker 3: Yes, of course it is working. We call that platformization 120 00:05:25,920 --> 00:05:28,320 Speaker 3: at Power. So we continue to at fifty to seventy 121 00:05:28,320 --> 00:05:32,039 Speaker 3: five customers every quarter and sometimes more in about the 122 00:05:32,080 --> 00:05:34,360 Speaker 3: fourth quarter. But I think more importantly look at the 123 00:05:34,400 --> 00:05:38,640 Speaker 3: evolution of technology. Almost every industry, vertical and technology has 124 00:05:38,680 --> 00:05:41,920 Speaker 3: started that way. From the application perspective, people had fifteen 125 00:05:41,960 --> 00:05:45,320 Speaker 3: or twenty different vendors that you did the entire solutioning 126 00:05:45,360 --> 00:05:49,240 Speaker 3: for CRM. Today we see single vendors platforms in CRM, 127 00:05:49,520 --> 00:05:51,760 Speaker 3: same thing in HR. When I used to work in 128 00:05:51,800 --> 00:05:54,400 Speaker 3: programming twenty five years ago, we used to have multiple 129 00:05:54,400 --> 00:05:57,240 Speaker 3: applications that solved the problem. Today there's one platform, same 130 00:05:57,240 --> 00:05:58,200 Speaker 3: thing in ITSM. 131 00:05:58,520 --> 00:05:58,720 Speaker 2: I do. 132 00:05:58,800 --> 00:06:01,120 Speaker 3: The same thing is coming to cyber security. Cybersecurity is 133 00:06:01,120 --> 00:06:03,880 Speaker 3: a twenty year old industry. It usually it takes thirty 134 00:06:03,920 --> 00:06:06,640 Speaker 3: thirty five years to build that platform capability in industries 135 00:06:06,839 --> 00:06:08,920 Speaker 3: and make them become ubiculous. So I think we're the 136 00:06:09,000 --> 00:06:12,760 Speaker 3: right place right time. We are seeing that move towards platformization. 137 00:06:12,839 --> 00:06:14,960 Speaker 3: If you look at what happened most recently a few 138 00:06:15,000 --> 00:06:17,680 Speaker 3: weeks ago, there was an attack using an AILLLM. 139 00:06:18,320 --> 00:06:20,600 Speaker 2: That means the I was used. 140 00:06:20,360 --> 00:06:23,120 Speaker 3: By bad actors to go and attack customers, and they 141 00:06:23,120 --> 00:06:25,240 Speaker 3: were able to do it in real time. You have 142 00:06:25,320 --> 00:06:28,160 Speaker 3: to have real time capability on your side to defend yourself. 143 00:06:28,440 --> 00:06:31,560 Speaker 3: The only way deliver real time capability in your side 144 00:06:31,800 --> 00:06:34,160 Speaker 3: is to not have a mess of forty or fifty products. 145 00:06:34,200 --> 00:06:36,640 Speaker 3: The idea is to have them all be consolidated, running 146 00:06:36,640 --> 00:06:39,560 Speaker 3: on a singular data layer, and building agents that go 147 00:06:39,600 --> 00:06:41,600 Speaker 3: and defend you, just the way bad actors are using 148 00:06:41,640 --> 00:06:42,520 Speaker 3: agents can attack you. 149 00:06:44,000 --> 00:06:47,840 Speaker 4: In case, we just very briefly dropped into negative territory 150 00:06:47,880 --> 00:06:49,960 Speaker 4: on the Nasdaq one hundred, having been up at one 151 00:06:50,000 --> 00:06:52,719 Speaker 4: point two point four percent, And the story this morning 152 00:06:52,720 --> 00:06:56,120 Speaker 4: when I woke up was that in Vidia's forecast, it's 153 00:06:56,240 --> 00:06:59,640 Speaker 4: print with soothing fears that we have about an AI 154 00:06:59,760 --> 00:07:02,560 Speaker 4: mark hit bubble. This might be the last chance I 155 00:07:02,560 --> 00:07:04,520 Speaker 4: get for a while, So I'm going to do it. 156 00:07:04,640 --> 00:07:07,800 Speaker 4: Give me the nikeshur nikesh Aurora. 157 00:07:08,040 --> 00:07:09,800 Speaker 2: Cool. Are we or are we not? 158 00:07:10,040 --> 00:07:11,080 Speaker 4: In an AI bubble? 159 00:07:12,280 --> 00:07:15,920 Speaker 2: Look, we are in an exuberant phase of AI. 160 00:07:16,280 --> 00:07:19,920 Speaker 3: I think it is the fastest technology evolution we've seen 161 00:07:19,960 --> 00:07:22,280 Speaker 3: in our lifetimes, and I don't think it's about to stop. 162 00:07:22,520 --> 00:07:25,360 Speaker 3: You can see huge bets being made by almost everybody. 163 00:07:25,800 --> 00:07:28,520 Speaker 3: I think there's a lot of promise you are seeing 164 00:07:28,560 --> 00:07:31,760 Speaker 3: the consumer case. I think we underestimate how much the 165 00:07:31,920 --> 00:07:35,160 Speaker 3: entire consumer landscape is changing, and it's going to change. 166 00:07:34,880 --> 00:07:36,119 Speaker 2: In the next twenty four months. 167 00:07:36,640 --> 00:07:38,800 Speaker 3: The idea of having applications where we have to go 168 00:07:38,840 --> 00:07:40,960 Speaker 3: to all the work ourselves is going to become our gane. 169 00:07:41,200 --> 00:07:43,520 Speaker 3: You know, you'll have agents that will go book your Uber, 170 00:07:43,600 --> 00:07:45,960 Speaker 3: We'll go get your food from door dash, we'll book 171 00:07:46,000 --> 00:07:46,760 Speaker 3: your airline ticket. 172 00:07:46,880 --> 00:07:49,200 Speaker 2: We can only imagine a future like that. We all 173 00:07:49,240 --> 00:07:50,440 Speaker 2: want a future like that. 174 00:07:50,440 --> 00:07:53,560 Speaker 3: That's going to require significant amounts of compute at the end. 175 00:07:53,680 --> 00:07:57,120 Speaker 3: So I think, from an infrastructure perspective in history, if 176 00:07:57,160 --> 00:08:00,280 Speaker 3: you've never had a situation where we built infrastructure get 177 00:08:00,280 --> 00:08:02,280 Speaker 3: consumed if we want it more so, I don't think 178 00:08:02,280 --> 00:08:05,640 Speaker 3: the infrastructure problem is a real problem. Whether the demand 179 00:08:05,680 --> 00:08:07,800 Speaker 3: comes right away or it comes an ear or two later. 180 00:08:08,000 --> 00:08:10,640 Speaker 3: I think that time will tell. But from a demand 181 00:08:10,680 --> 00:08:13,720 Speaker 3: from a technology fit, we're there. From an AI perspective, 182 00:08:13,840 --> 00:08:17,120 Speaker 3: enterprises are slightly lagging that consumer adoption, but I think 183 00:08:17,160 --> 00:08:20,040 Speaker 3: they're actually working hard to get there. You know, every 184 00:08:20,120 --> 00:08:23,960 Speaker 3: time we do some AI experimentation internally, we come back 185 00:08:24,000 --> 00:08:26,160 Speaker 3: and say, Wow, that is cool. I wish we could 186 00:08:26,160 --> 00:08:28,880 Speaker 3: deploy that faster across the entire enterprise. So I think 187 00:08:28,920 --> 00:08:31,280 Speaker 3: the demand's going to come. The timing is to you guys. 188 00:08:31,280 --> 00:08:32,840 Speaker 3: You guys follow that on a daily basis. 189 00:08:32,880 --> 00:08:33,240 Speaker 2: We don't. 190 00:08:33,320 --> 00:08:35,040 Speaker 3: We put our heads down and see where am I 191 00:08:35,120 --> 00:08:36,480 Speaker 3: going to be three to five years from now? How 192 00:08:36,520 --> 00:08:38,840 Speaker 3: do I position power out a network in that context? 193 00:08:38,880 --> 00:08:40,360 Speaker 3: And are we going to plot our way there? And 194 00:08:40,400 --> 00:08:43,559 Speaker 3: if we think that AI opportunity is going to create 195 00:08:43,720 --> 00:08:48,000 Speaker 3: explosive opportunity for almost every technology subsector and even cybersecurity. 196 00:08:49,200 --> 00:08:52,160 Speaker 4: Palo Alto Network CEO Nikesha Rora, it's great to have 197 00:08:52,240 --> 00:08:53,360 Speaker 4: you back on Bloomberg Tech