Palo Alto Networks’ Nikesh Arora: AI, Security and the New World Order

Palo Alto Networks’s CEO Nikesh Arora dispels DeepSeek hype by detailing all of the guardrails enterprises need to have in place to give AI agents “arms and legs.” No matter the model, deploying applications for precision-use cases means superimposing better controls. Arora emphasizes that the real challenge isn’t just blocking threats but matching the accelerated pace of AI-powered attacks, requiring a fundamental shift from prevention-focused to real-time detection and response systems. CISOs are risk managers, but legacy companies competing with more risk-tolerant startups need to move quickly and embrace change. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Cortex XSIAM: Security operations and incident remediation platform from Palo Alto Networks

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Like I have a principle that I always joke even in our own hands and say, I have never met a person who comes to work to screw up. I wake up in the morning, let's go sunshine, it's time to go to work. Let's me see how badly I can do today.

• 0:14Speaker 00

  Everybody walks in with the right attitude. It's something that happens at work that we create that causes the unintended outcomes. It's not the person who walks in. If you found the right person with the right domain knowledge, right intelligence, the right attitude, then the rest is upon us.

• 0:47Pat Grady?

  Today on Training Data, we have a very special episode with Nikesh Arora, the CEO of Palo Alto Networks. Since joining Palo Alto in 2018, Nikesh has built it into the largest and most valuable cybersecurity company in the world with 70,000 customers and more than $120 billion of market value. Prior to Palo Alto, Nikesh spent a decade at Google as the chief business officer as the company grew from $3 billion to about $65 billion in revenue. Nikesh is an extraordinary CEO with an inquisitive mind and a wonderful sense of what is happening in the world of AI, thanks to being in the center of it with Palo Alto and all of their customers.

• 1:30Pat Grady?

  Please join us for a wide-ranging conversation about AI, its impact on security, and what excellent leadership looks like. We hope you enjoy.

• 1:40Pat Grady?

  Nikesh, thank you for joining us on Training Data. So, we emailed you and asked you if you'd join us on the show and your response was, and I quote, "As long as we can talk about DeepSeek and the New World Order." Let's start there. Tell us more.

• 1:54Speaker 00

  Well, you know, we all have our interpretation of AI and there's a bunch of us trying to figure out and rationalize this in some sort of mental framework. So like everybody else, I've had my own. And I think from my perspective, what we've seen in the last 12 months has been phenomenal. And we have people trying to build effectively a brain, right, of some sort, a brain with immense capacity to remember everything, to process everything and do pattern recognition, which is kind of like my interpretation of an NLM. Now, that brain, because it's being trained in data that's out there, is susceptible to reaching the wrong conclusions depending on the data it's using to train itself. So this is not a secret. And you hear of that in various contexts of

• 2:38Speaker 00

  hallucination or not having the right answer because I've never seen it before. And that's fine. You can call it the early brain. But at some point in time, these things are going to become very smart, possibly as smart as you, Pat.

• 2:49Pat Grady?

  You know, it takes several more years to hit Sonya stage.

• 2:52Speaker 00

  Exactly. So at that point in time, I think we all had to start getting a little worried. So the question is, how much money does it take to build this brain? One. And two, how can all of us use it effectively? I think we can all use it effectively today in certain use cases, as we've seen out there, whether it's creative use cases or search use cases, or data aggregation use cases, or data regurgitation use cases. At some point in time, you're going to take this brain and give your arms and legs and let it do stuff. And that's where things start getting dangerous. And we've seen examples where people gave these brains the right to do stuff too soon, too early, where they started giving you free cars or refunding airline tickets, which is not a good idea, because that is their version of hallucinating and giving stuff away.

• 3:36Speaker 00

  But, you know, on the other hand, people are sitting in cars where these brains have arms and legs and are driving us around without a driver. So there are examples where there are precision use cases, which are narrow and task specific, where we are letting these things get access to. So I'm sorry for the long preamble, but the whole notion of the New World Order was You know, and I won't, I don't have an opinion on whether it costs six million dollars or more. My opinion is that if somebody built a brain cheaply and made it available cheaply, it just expands the opportunity for a lot of these startups, a lot of people to try and deploy that brain to do various tasks. And that to me is is a major shift in what has been sort of the mainstream of this industry, where we all thought you spend a lot of money to build amazing models.

• 4:20Speaker 00

  And it looks like there could be task specific models which could be built a lot cheaper.

• 4:24Pat Grady?

  And you mentioned some of the hallucinations and the attempts to jailbreak these models or prompt inject these models. There's a report that came out a few days ago about DeepSeek R1 that said fifty out of fifty prompt injections worked. So basically a hundred percent success rate on attacking the model. Is that a DeepSeek thing? Is that an open source thing? Do you have a perspective on what the implications of that might be? Maybe it's not as simple as six million bucks gets you the same thing you get out of OpenAI.

• 4:52Speaker 00

  Well, the question is which one do you want, right? And like at the, at the end of the day, every model is putting a bunch of guardrails around it. These models are all raw, if they're in the raw, they have, they're in the raw. They've, have you remember the early versions of ChatGPT and Gemini? I think it was even called Vertex, what they call that. It's called something else before Gemini. And those things had the opportunity for us to prompt inject as well. So there were versions of these models which had to have guardrails built around them. And those things, that's what it costs money to do is build guardrails. And the guardrails initially were skin deep. As you know, we've heard of these phenomenal stories in the early days where people were able to jailbreak them and get around them and get models to start and doing crazy stuff.

• 5:33Speaker 00

  So I think we will see more and more guardrails, more and more simpler attempts being blocked. I think there are still sophisticated things that can be done to these models. Even the more, in your mind, more expensive models have loopholes or have side doors which can be used to attack them to some degree. And we've seen that happen in the past. So yes, perhaps DeepSeek is not as guardrails as it is. And perhaps it was built cheaply. But in the end of the day, whichever model it is, when you deploy it for a precision use case and give it arms and legs, it doesn't matter. What guardrails the model comes with, you will have to superimpose better guardrails and controls around it. This is where people like us come in, where we say, it doesn't matter what you got.

• 6:14Speaker 00

  I'm still going to put a Palo Alto firewall and put a straight jack around this and make it only respond to task specific stuff. I.e. if the model is designed to improve your manufacturing process, you can't talk to it about, you know, rewriting Shakespeare.

• 6:26Pat Grady?

  Let's talk about that for a minute. What's in scope and what's out of scope for Palo Alto Networks as it relates to securing AI?

• 6:34Speaker 00

  Well, from our perspective, look, we're seeing two interesting use cases. One, we're seeing a lot of people who are employees, who are kids, who are users using AI in some way, shape or form to augment their day job. And you can call it augment for now, and maybe it'll creep up and do more and more of your day job. But it's being used as human augmentation for now, right? Because we're not giving you control. I'm not telling an AI agent, go write me a paper for my class. Or I'm not telling an AI agent to write me a blog and possibly one of these days they will. But for now, it's been used for human augmentation. And the general fear in the enterprise is my employees are taking proprietary data and putting it in some model.

• 7:12Speaker 00

  And it'll be used for training and over time, you know, it'll get out of copyright, it'll get stolen, or it'll be, you know, become part of the general knowledge base. So that proprietary data. So we have a use case where we can intercept data, which has been used by employees, or AI that's been used by employees, and provide visibility to enterprises and provide control so they can stop employees from going and using AI models or AI apps without any control. So that's kind of one use case. It's kind of interesting. You see a lot of companies who want their employees to use AI, but they want them to be able to do it in a controlled fashion. Other more interesting use cases, I haven't found a company which is not experimenting with some sort of AI project.

• 7:54Speaker 00

  Whether it's as simple as a customer service chatbot, which seems to be the most popular example, or some sort of workflow automation capability, which is another example. To the extreme where people are using it to perhaps slowly edge giving it control over certain, you know, control systems, which may not be mission critical, but they're experimenting there. In all these scenarios, the biggest fear is the model runs amok. The model gives the wrong answer, or the model takes control, or somebody hijacks the model.

• 8:26Speaker 00

  All those are scenarios which customers are very bad, which is kind of like understandable. In that scenario, we have a product which effectively we formally call it the AI firewall. The AI firewall which inspects anything going in, anything going out of the model. It'll make sure the model doesn't have backdoors, nobody can access it. The data is not being sent out of the model somewhere else. You can run it on prem, you can run it in your, you know, protected cloud instance. So those are kind of the two use cases we're seeing. The behavior of the model is the responsibility of the people generating the model.

• 8:57Speaker 02

  Uh huh.

• 8:58Speaker 00

  Our job is to make sure that a model doesn't get hijacked, doesn't get intercepted, doesn't get taken over or manipulated so that, you know, people lose control of their quote unquote AI brain.

• 9:11Speaker 02

  Can you say a little bit more about, you know, what are the real threats from AI versus the perceived or the hypothetical risks? Like I remember back when, when self-driving cars were still, you know, a little bit of a pipe dream. And everyone was saying, we're going to have these adversarial, you know, images, QR codes in the rows. They're going to make the cars, you know, become weapons and, you know, things are going to go crazy. And that ended up being like very academic theoretical risk. It feels like there's some of that happening in LLM lands. Like what are, what do you think are the made up academic risks? And what are the very real risks you think are gonna, you know, where AI is actually going to really help the bad guys? And we have to protect ourselves.

• 9:46Speaker 00

  Well, look, there is the, these two scenarios, right? A scenario where the bad guys is just going to use the LLM to attack us faster, right? Because.

• 9:55Speaker 02

  Are you, has that happened already?

• 9:57Speaker 00

  It is already happening. If there's a critical security incident or vulnerability in a product, you can go to certain jailbreak models or open source models out there, which will give you a recommendation on how to exploit the CVE. Because 3000 models are hung face. You can pick a model which hasn't been given guardrails or given any morals effectively in the context of a brain and saying, hey, here's a CVE. What are the five steps you take to protect it? And. You know, or the five steps that bad guys could use to attack it. So it says, oh, by the way, watch out for these five things bad guys could do. So there are models out there that can actually give you a recipe to figure out how to exploit a C E. Or you can actually tell it, I tried to attack a customer with option A. I tried option B, none of them worked because it give this return response. And it says, hey, how about you try option C?

• 10:42Speaker 00

  So there's a whole bunch of ways that these models can be used very, because they're very helpful right now. Yeah. So they don't try and solve your problem. And there's a risk that, like, actually, not just a risk, it's actually true right now. And what that does is it reduces your, your mean time to attack and exfiltrate data or mean time to breach. Which means the only way to solve that problem is to be as nimble, as effective and as quick as the bad guys are, which sort of like, you know, it's a, it's a, as I always say, it's kind of a disbalance problem. They have to be right once, we have to be right a hundred percent of the time, which means they might need this sliver of data to attack you. We need the entire corpus of enterprise logs, enterprise data from every I T system to be able to understand. Where there may, there may be an almost activity which is being driven by A I.

• 11:26Pat Grady?

  So is that mean right now A I is at the margin more helpful for the bad guys than it is for the good guys?

• 11:31Speaker 00

  Well, it depends. No, it can, we can always sell our book and tell you if you're deployed or X I M product, we can be as equally effective and equally helpful and tort the bad guys. But yes, you know, they're not fully deployed, not everybody has it. So yes, there's a possibility that it just has made the ability to attack much faster for the bad guys. Right? And that's kind of a real threat. It's not a perceived threat. And I think if you, if you play the movie forward and say, and let's abstract ourselves from this today, and this is version one or version two of A I. You know, in five years from now, everything will be happening in a real time basis. Everything, every bad actor or bad L L M agent would be able to attack an enterprise infrastructure which is not fully secured. And there'll be agents running around the infrastructure, trying to make sure that every loophole, every door, every window is locked and constantly monitored.

• 12:15Speaker 00

  So you can imagine the battle of the agents on either side. Uh, I don't think it's infeasible, it's possible, but to get there, there's going to be serious upheaval required of the enterprise data that exists in the company, which by the way, is not. Unlike the fact that to get effective AI for organizations, we're gonna have to have a lot of good data to automate or manage or, you know, run businesses. So I think that's kind of where we're gonna end up. In terms of the other part, Sonya, you asked about the perceived versus real threat. Look, the, the, think about it this way. You know, let's assume, and we all, I think, I don't know, I haven't heard you guys talk about this, but I'm guessing you agree that at some point in time, these models get smarter and smarter, and they'll be more and more capable. So let's assume that's gonna happen.

• 13:00Speaker 00

  They get very capable. This person is equivalent of a PhD researcher from, you know, pick your favorite university and, and do drug discovery. Now, you've trained it, you give it all the data that exists in enterprise, all proprietary, it's all the drug data for Alzheimer's, Parkinson's, you pick your favorite, you know, research project that you wanna do, and you ask the model, or this brain, to give you an antidote to this medication. It could be amazing for society. Now, the question is, in the wrong hands, this trained brain could also be asked to make a virus, to create that situation, right? Create a bioweapon. It's possible. This, this brain has no guardrails. You've trained all the data, it has all the knowledge that you need to have. Then the question is, can I make

• 13:42Speaker 00

  sure that this brain cannot be taken over by the wrong people? That it falls in bad hands?

• 13:48Pat Grady?

  So just for fun, if you were supreme ruler of the universe, and you had a magic wand, and you could determine exactly what regulation was gonna apply to this hypothetical, what sort of regulation would you craft?

• 14:03Speaker 00

  You know, Pat, this is an interesting debate, and, and I had a debate about this with a very, a very smart, uh, person who's involved in some of the regulatory aspects of this. Look, at the end of this, there will be two versions, I think. One version is critical systems, where before giving AI control of critical systems, you'll have to go through a serious certification, discovery process with some part of the US government, right? You cannot give the control systems to AI for shipping routes and running cargo containers which can crack and burn, or control the entire electrical grid of the United States. You can't give it to an AI model because you need to have controls in place and need to be able to have a, you know, conversation around what the fallbacks are

• 14:48Speaker 00

  and all the controls. So I think there'll be a set of classified activities which you'll need some degree of consultation, some degree of certification, validation. It's kind of like- You know, FDA does drug approval. So there'll be some version of, you know, AI approval, which can have critical irreversible impact if you give control to AI. And that'll have to be some sort of certification mechanism. And I think where it is not as fatal, where it's not as critical, perhaps, you'll have some degree of self-responsibility, you know? You make a bad car, people have a problem with it, you're responsible.

• 15:23Speaker 00

  Not every car goes through inspection process. But there is tremendous amount of accountability to the car companies that don't have seat belts, that don't comply with regulations, that they're responsible for the bad outcomes. It's this way, if you deploy AI in a bad way in your company and give it arms and legs and control, then you're responsible. There'll be some degree of self, because it is impossible for any government to be authority to create an inspection system of this amount of computer data, which can get it right every time. So there will have to be self policing and self accountability in there, just the way it exists in today in many industries.

• 15:54Speaker 02

  Nikesh, do you think AI labs get nationalized in this, you know, your version of supreme ruler of the universe?

• 16:02Speaker 00

  AI labs get nationalized? I don't think so. I think the problem is if, you know, given that we're living in hypothetical, if there's, it is true that a new model can be produced at a lot lower cost, right, which is in the single digit millions or tens of millions? And AI lab could be anywhere. It'd be impossible to find, discover and, you know, control. So what's stopping somebody from, and part of like these challenges, these really, the concepts are very dangerous on a global basis today, which we live in effectively a world with no borders, even though I know that we have a whole different conversation on borders. But essentially, what's stopping somebody from deploying $50 million

• 16:45Speaker 00

  in a server cluster in a country which has lax regulation vis a vis this stuff? And me building it there or somebody building it there? So I don't think that the idea that, yes, of course, if it's a $500 billion AI cluster that needs, is needed to build the world's super brain in AGI, yeah, you can find a way of, you know, maintaining some degree of oversight perhaps on it. But if the answer is this 20 million bucks and I can build a world-class model, which is really smart, then I think all bets are off.

• 17:16Pat Grady?

  Let's say I am not necessarily a CISO, maybe a CEO. Let's say I'm a corporate executive of some sort. And I see the potential for AI. So I'm excited about trying to use AI, but I'm very scared. I'm very scared because I think that when my people use AI, they're just increasing the attack surface and making us more vulnerable. And I'm also scared because I think there are bad guys out there who are going to weaponize AI against us and sneak in in ways that they might not have been able to sneak in before. What would your advice be? You know, top three things that you advise this person to do? What can people do to get the benefits of AI without exposing themselves to unnecessary risk?

• 17:53Speaker 00

  I think that that would be ill-informed fear in my mind.

• 17:57Pat Grady?

  Okay.

• 17:59Speaker 00

  I think there are perfectly fine use cases, which I'm sure you can innovate and Sonya can, and a lot of people can, where you can run a model in a constrained on-prem or dedicated cloud cluster, which cannot be intercepted, cannot be manipulated. In the end, that model is only useful if you put your own data into it. And if all you do is have the model generate responses, which you're not letting it give it any control, you can run experiments. You can look at what the model produces and compare that to other things. You can do A/B testing and say, wow, the model says this and my best researcher says this. And you can run experiments, understand the power of AI without giving it any control. So I think that's why the fear is a bit mislaid because it's not doing anything. It's just trying to give you the outcome

• 18:43Speaker 00

  and you can see if it's faster, better. And both are possible or one is possible, right? Some things happen faster, some things happen even better. So I think running A/B testing, being able to test it is easily possible in today's world without having any fear. I think it's even possible for letting employees experiment with it in a way that it is not manageable. I think where it starts to get more interesting, not dangerous perhaps, is when you start letting AI act on your behalf, right, in whichever capacity. And that's where I think any, any person, not just CEOs, anybody would have to go through a rigorous amount of testing to see how it reacts in various circumstances. Because depending on what you're giving control to, it could have a significant impact to whatever product, service, business that you're running. But that's where I think it becomes more interesting.

• 19:27Speaker 00

  But I think for now, running experiments, running models, which cannot be hijacked or manipulated, models that won't run amok, it's all possible today. I think, you know, it would be irresponsible for companies to not experiment.

• 19:43Pat Grady?

  Well put.

• 19:44Speaker 00

  Because I don't think that this thing's going away.

• 19:48Pat Grady?

  Yeah, you may not know exactly how to get to the future, but you know, if you do nothing, you're going to get left behind.

• 19:53Speaker 00

  But I learned about chat G P T on a flight to India. I was going there to go speak at my alma mater and I read about this thing. I was sitting at Dubai airport, not doing anything for two hours. I kept playing with it and I rewrote my entire speech. I went and said, you're about to witness the biggest technological revolution. Now, I just say it before Jensen said, this is the iPhone moment, but more important, he's got a bigger mouth and he's a supreme commander of A I. So we'll let him, we'll attribute that quote to him, but that's okay. And I felt it was a seminal moment and I came back and I said, you know what? First things first, I have no idea about this thing. So I called a bunch of my teams, like, what do you guys know? What do we know? Nothing, you know, like, you know, a bunch of the important uninformed, but we're important. So we were, but we had an opinion.

• 20:35Speaker 00

  So the first thing I did is I put them all into a, like a training room. I invited everyone from Thomas Kurian's team to, you know, Nat Garman now, his team or a bunch of startups, like just brain dump on us. And we did that for two days a month. We get people to brain dump. We had a bunch of our people go come with ideas. We had 70 ideas, people want to execute, cut them down to seven. We started playing with it. And we ran everything, every possible problem that you could run with the vertex A I or with the first model of chat G P T or the first model of Claude. We tried everything. You ran everything through. We had models running with A I, we had models running with semantic search. You were training with all kinds of data and we learned. We learned what is useful. We learned where it's not useful.

• 21:20Speaker 00

  Now it's doing some things by itself, which we had to go jetty rig. But, you know, we are partially informed, which is better than being totally ignorant.

• 21:27Pat Grady?

  What was the biggest surprise from those learnings?

• 21:29Speaker 00

  The biggest surprise? Well.

• 21:33Speaker 00

  You know, the early version of this thing was pattern recognition, was data summarization, was, I'll call it infinite memory, right? Once you train it on some data, it's never going to forget it. Now, there are use cases where I have fifty people solving the same problem. And depending on who answers your phone, they're going to solve it differently. In this case, I improved the general level of awareness and knowledge for my entire team, playing, get it to tell you the answer and then work from there. So it did sort of lift the average intelligence of the average capability of the teams. And I think as it gets better and better, it's going to shorten the time to answers. And I mean, at the end, I want to expose all of this stuff to our customers, right? So they can go solve this problem themselves. So my problem is if you don't start learning, when every startup is learning,

• 22:17Speaker 00

  eventually the startups do your business, right? You've seen that in every technological revolution that we've run into, whether it's the cloud, mobility, the internet. We saw it every time. And every time, there was these large, now we can call them legacy, but large businesses with dominant market share, with every asset at their behest, which they could have deployed. And nobody should have seen the light of day who was competing with them with the new technology. But for some bizarre reason, every time you turn around, there was a Travis, there was a, you know, Chad Hurley at YouTube, and there was a Larry Page, and there wasn't Mark Zuckerberg, and there wasn't Neil O'Mus. So the challenge is that if we don't go embrace this as early as we can and learn, while everybody else is learning, we run the risk that we're late. And then we go, and you know, law of unintended consequences.

• 23:03Speaker 02

  Maybe on that note, one thing I'd love to understand is it seems like the biggest platform companies in security are kind of formed around these platform shifts, like, you know, the firewall, identity as a perimeter, you know, maybe the cloud and CSPM. Like, do you think AI is a new platform shift opportunity from a security point of view? And do you think a new security platform company, which could be you guys, emerges? Or is this very much, you know, is similar kind of set of tools is going to, is going to serve the AI first world?

• 23:34Speaker 00

  I think AI has the opportunity to turn security on its head.

• 23:44Speaker 00

  And the reason I say that is that the security is a needle in a haystack problem, right? Because you don't worry about it until you have to worry about it. So you suddenly wake up and get really, really smart very, very quickly because something's happened in your infrastructure. And it's just impossible to go from zero to thousand like overnight because somebody calls, oh, shit, there's somebody in our infrastructure, they've exfiltrated, it's unmounted, or they are in the midst of exfiltrating data. And traditional security has been, I'd say, ninety-five percent at the border around prevention and five percent are detection and mediation. Right? You buy a firewall, it inspects everything that's coming in, you block a bunch of stuff, you buy

• 24:27Speaker 00

  some sort of, you know, remote access endpoint agent, you buy an endpoint XDR capability. And that all works because there's a lot of prevention that happens in that process. But the problem in breaches is it's not what you prevent, it's what you let in.

• 24:41Speaker 00

  And there's things like zero day attacks, which have never been seen before. So you haven't seen it very often, you can't prevent it. And the only way you figure out all that stuff is you ingest a lot of data, you look at it and look for anomalous behavior, right? You can't rely on security signatures. So if you're going to look at anomalous behavior, you need to be able to ingest all data. You gotta look at pattern recognition. You gotta say, does this happen like this every time? And say, well, I don't know, but it looks like something's different happening. So I think this whole notion of doing pattern recognition, ingesting a lot of data, analyzing it on the fly, and looking for things is easily possible with a call to machine learning, call it AI, call it whatever you want to call it. But I think that's the only way we can do this at real time speed.

• 25:22Speaker 02

  What about what it means to be a security team, a CISO, a security practitioner in this new world? And, you know, we get twenty pitches a week right now for like the AI powered SOC analyst or the AI SOC. What is your vision for, you know?

• 25:35Speaker 00

  You already have one, so you send them our way, it's like, you know, try it.

• 25:40Speaker 02

  Let's hear about how that evolves and what the end state is for, for kind of humans and security.

• 25:45Speaker 00

  Like insecurity at a very

• 25:49Speaker 00

  First principle level, we sell two things, right? We sell a sensor with sensors at the edge of your perimeter, whatever the perimeter is, whether it's your laptop, whether it's your application, is it your customer accessing your bank account? That's the perimeter, right? The perimeter is the edge of your technological footprint. That's a perimeter. So we all sell sensors that sit at the perimeter and inspect. It's like having like a digital security guard at the perimeter. We all sell parameters and we protect parameters, we inspect the parameters, we block the parameters, right? And then we, and then what happens is that somebody did it in a bad app, somebody did it in the back door, somebody did it in the side door by mistake, then people enter through there. So we, that's we sell sensors, we protect parameters, and then we analyze data in the backend to look for any vulnerabilities that you might have been created by the infrastructure that you have.

• 26:35Speaker 00

  That vulnerability could be exploitable in the future, so we look for potential exploits and that's kind of what we do. Which means, and the reason I tell you stories that, which means

• 26:44Speaker 00

  if I want to sell an AI-powered anything, I need to be at points of data collection in your enterprise. Because AI requires data. So again, this is the old adage, right? I am in the best place to collect all this data and analyze it. And of course, that doesn't mean anything because in history, people who were in the best place got knocked off their knees and somebody else came and built something better because they were lazy sitting on their haunches. Now, the only thing is, we don't want to be lazy. We don't want to sit on our haunches. We're out there hustling as fast as we can, not as nimble as possibly in, you know, a startup, but we're nimble enough as a company. We've done twenty seven products, which are in the magic quadrant of the right, so we're not shy. But I think every security company, every security startup is going to walk to every customer saying, I can build this for you.

• 27:29Speaker 00

  The customer's like, great, how do we start? It says, well, let me go deploy a bunch of sensors around your perimeter so I can collect the data. Holy shit, I already got a bunch of you guys in the industry, you've got sensors out there. Then what do you want me to do? Then give me all your data. I'm like, wait a minute, you want all my data? Who are you again?

• 27:44Speaker 00

  So I think that's kind of, that's kind of the risk you run into is this is a large data problem and large data problems are harder to solve as a startup. Not to say it's not being done. There are people out there raising $500,000, but not every we can do that in security.

• 27:59Speaker 02

  Fair enough. Do you think security teams are comfortable giving arms and legs, I think, so to speak, like agentic?

• 28:06Speaker 00

  Oh, no, I think they're petrified.

• 28:08Speaker 02

  Do you think that flips at some point and when?

• 28:11Speaker 00

  Well, I think most of the security teams aren't asked, right? I mean, Waymo wouldn't exist if they had to talk to a security guy. Or Tesla, if as deep as possible, it would not exist as a security guy. So are you crazy? You're giving the car control to your car? All kinds of bad things could happen, right?

• 28:27Speaker 02

  Yeah.

• 28:27Speaker 00

  So from a security perspective, these are all bad things happen. Look, I mean, security leaders are, for the most part, risk managers, right? Right. They're, they're risk managers. They're trying to understand what the business need is and how do I deliver the business need with the least amount of risk possible. The safest room in the world is one with no windows and no doors, but it's not very useful. So you gotta let doors and windows be created, which means you're managing risk. So security people are risk managers. They sit down with the business, understand what potential risk does it cause? They'll give you some ideas as to how to make sure that you protect against that risk and they'll set up a whole bunch of safeguards. It's a, you know, gate one, gate two, gate three. If it doesn't stop, get stopped here, get stopped here, get stopped

• 29:08Speaker 00

  here. And then off to the races. So I think security people will allow the arms and legs have to, because that's kind of the crying need of the hour. I think the question will be what kind of security tools do we have in place to create those protections that customers can comfortably go ahead and use these capabilities? But that's been true with every technology.

• 29:27Pat Grady?

  Our, our partner, Jim Getz, and for any listener who's not familiar, Jim, Jim has been involved with Palo Alto since formation. And Jim, Jim has a creative mind. One of the, one of the things that Jim mentioned was after you came in about seven years ago as CEO of the innovation engine and Palo Alto really started to pick up. And I think we see that today also with how quickly you guys have pounced on AI. I guess the question, maybe two questions. Question one.

• 29:54Pat Grady?

  If you had to rate yourself, if you had to rate Palo Alto on agility, nimbleness, ability to respond to market conditions, I know you're a tough grader, so you can't give yourself an A+, how would you grade yourself? And then question number two, you do have all the advantages of scale and data and distribution and being at those points where you need to collect the information to do whatever detection remediation you need to do, but it's hard to get a big organization to move fast enough to respond to the market. So question one, how would you grade yourself? Question two, how do you drive agility at this scale? Like, just practically speaking, what do you do to make that happen?

• 30:30Speaker 00

  You know, let's go first, uh, first, I'd give us a seven or seven and a half on a scale of 10 in terms of agility, because we have about 15,000 people, probably five, six thousand people on our product side. So there's a lot of stuff, a lot of complexity, a lot of legacy stuff that has to be brought along, a lot of stuff that has to be ticked and died to make sure that these things work. And, you know, I think part of the challenge, you know, is that you have a small base of 70,000 customers, right? Any tweak you make, which impacts 70,000 customers, brings their infrastructure down, you lose your license to operate. So it's not like we can innovate, throw shit at the wall and see what sticks and go with that and ignore the other stuff.

• 31:12Speaker 00

  So we have a serious responsibility in making sure stuff that we build, that we put in line, has to keep performing and not bring down anyone's infrastructure, because the best security is in line. We have to be able to watch what's going on. Inline security has the property that if it doesn't behave, it can impact your infrastructure. So we have a very high responsibility from an availability perspective and not disrupting our customers, that we have to apply a higher precision standard as it relates to inline securities. From that perspective, I think seven and a half is not a bad place to be. I see probably we were at three or four, seven years ago as an industry. I don't want to say Palo Alto, I think as an industry with three or four, and I said the industry's moved its agility. If I look at some of the newer players, they're moving faster, they're not sitting back anymore because they see the playbook for the future is not where you let other people sort of

• 31:57Speaker 00

  come by in the new swim lane and say, nice to see you, you know, congratulations, great job. Now it's like, holy shit, how did they get there? We're gonna go chase them down. So I think the industry dynamics have changed. In terms of how do you drive agility, you, as you know, possibly, you know, from talking to Jim and from talking to us, that we have no... Sort of qualms about going and finding people who are doing it amazingly well and embracing them and saying, you got this figured out, let's go do it together, we'll run fast. Right? Sometimes companies get trapped in this idea that I have so many resources, I can take them down. They don't understand, there's a team of fifty, hundred motivated people funded by people like you who are out there running at sort of, you know, light speed, who built

• 32:40Speaker 00

  an amazing product, which are gonna, then if they get traction, then they're your competition on every day in the market. And they get better and better and stronger. So the question is, when's the right time to say, oh shit, let's embrace them, they've got less resources, but still managed to kick our ass, let's go make them part of our team. We've done that nineteen times, as you've seen. So we're not, we're not shy about embracing innovation if it doesn't come for them. Having said that, I was looking at it the other day, I think more than half of our products are made in Palo Alto, right? Not acquired. So it's not like, you know, we only have one strategy, we do both, because in some cases, building on our platform is a lot easier from a go-to-market and deployment perspective than buying something and spending time integrating it. So we've gone to the point of scale that it's more important for us to innovate on our platform than just go out then willy-nilly, try and look at the fastest

• 33:25Speaker 00

  innovator and try and stick them onto our tech platform. So I think from that perspective, it's that constant balance, what do you buy, what do you build, how do you embrace somebody else doing it better? And then you gotta be nimble and say, you know what, I am gonna get some stuff wrong. The question is, when you get punched in the face, how quickly you recover, right? Don't let them count to ten. So it's kind of like how you maintain agility. And then, I mean, the only other thing is, I call it relentless inspection. Relentless inspection of your go-to-market capabilities, of your deal, relentless inspection.

• 33:55Pat Grady?

  What form does that take? Like, what's the sort of thing you might do in one of those relentless inspection conversations that somebody else might not do?

• 34:02Speaker 00

  Well, I'll give you an example, you know. For the longest times, I kept seeing us doing really well on certain things, and our teams would create all these incentive programs to drive more behavior, to get people to sell. And I have my sales leaders tell me that everybody has an account plan. I said, these things look like very interesting things I should take a look at one. So one fine day, this is possibly an year and a half ago, we're having a tough quarter, I said, great, here's what we're gonna do. Or to start from customer number one and keep going. And you show me your account plan. So does that mean, send them five slides, they fill those five slides and show up on a Zoom call. Just have to show up and explain those account plans to me. And I'll fast forward, I've probably been through seven hundred and fifty of them so far in the company.

• 34:47Speaker 00

  I did about fifteen yesterday. And it's, it's like theater now, there are five hundred people dialed in from across the company. Wow. Y'all get to watch.

• 34:57Speaker 00

  Get your salesperson, I also can dial in, watch an account review and process. Because for me, it's, it's basically them learning how to do it. And we go through it and say, who's the person, who's the buyer? Does they understand the product? What did you pitch? How do you sell it? Did you sell it? Did you talk about this? Did you not talk about this? Why you talk about this? And by the way, the best thing for our teams is if you feel that this is, doesn't look as robust as we'd like it to. Me, you know, B. Jenkins, our president, many of our product leaders, we will get involved. We're not there to, we're not just randomly inspecting, we're actually assisting. And like, you'll be surprised, if people spin up their laptops are open, like people are pinging people on LinkedIn, texting people saying, hey, do you know this person, this company? I don't think our plan is robust enough, we don't know the right people, let's go.

• 35:40Speaker 00

  So that's kind of like grassroots. You know, I, I, I have a, I have a little

• 35:49Speaker 00

  whiteboard in my office where I write down things which I want my team to remember. And the second thing written on it, it says, sales is a math problem.

• 35:57Speaker 00

  So, which people, people find hard to understand. Like, look, if you have the best product in the market and you are able to win and generate billions of dollars of DCV a year, then the question is, why are you losing?

• 36:12Speaker 00

  It's not the product, because there are people buying the product, it's working. It's not that nobody's willing to buy it. It's not that lots of people are willing to buy it. What happened in that process where you were selling that? You didn't win, somebody else did. Let's go inspect it.

• 36:27Pat Grady?

  Yeah.

• 36:28Speaker 00

  And sometimes you'll find some product things that you need to fix. Many times you'll just find execution errors.

• 36:33Pat Grady?

  Let's keep going on this thread of driving performance out of people because one of the other things that Jim said was that you have sort of an exceptional ability to recruit and retain really exceptional people. And that you you sort of drive followership in a way that's unique, like you're pretty hard on people and you demand a lot from them.

• 36:50Speaker 00

  Yeah, I went to I went to speak yesterday for for a person who I worked with at Google. Her name is Lexi Reese and she actually ran for Senate. She has a startup now and she introduced me by saying, I didn't quite enjoy my time when I had to work for you, but I'm a better person. I learned a lot. So I'm like, well, I'll take it whichever you give it to me. But anyway, sorry.

• 37:08Pat Grady?

  Well, and Jim said you balance that with a very nice human approach where people know that you care about them and you'll go to bat for them, you know, in the right situation. So I'm just curious what you've now been an executive in a variety of contexts and you've been successful every time. You know, Google is a consumer business, Palo Alto is an enterprise business. You know, you kind of grew up around marketing and sales. You become more of a product person. And so you've got this diversified set of experiences. From that.

• 37:35Speaker 00

  I'm an enterprise person.

• 37:37Pat Grady?

  You're a full-fledged enterprise person from now on, sure. I guess what leadership principles or what leadership techniques are sort of context independent that worked at each step in your journey? And are there other things that are kind of Palo Alto specific? But I'm mostly curious, like, what are the sort of core principles of your approach to leading people?

• 37:58Speaker 00

  Not a lot is Palo Alto specific, right? Because at the end of the day, my senior executives are not writing product documents. Right? They're analyzing strategy, analyzing go-to-market, they're understanding, of course, do I have experts that's out of security? Of course, we couldn't survive without that, you know? So here's Zook, our founder, Lee Kair, I told you, product officer, some of the other product leaders that we have. They're very smart guys. They understand what our products exist in this industry. They act as sounding board. Sometimes I'll challenge them. I'll find some, I'll push back, some people will accept. So there's there's no

• 38:31Speaker 00

  getting this done right without the right domain knowledge. So you have to have domain knowledge. But I think outside of that fact that there's domain knowledge, you still have to have the right people, like, as is possibly understood that I don't suffer food. Because I can fix a lot of things I can't fix without.

• 38:48Speaker 00

  And if somebody doesn't get it, life for sure. And you have to make sure you surround yourself with smart people. But if you find them, keep them. Because the next question becomes, you know, what is the attitude these people bring to the table? As long as they are willing to learn and humble and they understand they're part of a team, all systems go. Like, I have a principle that I always joke even on All Hands and say, I've never met a person who comes to work to screw up. I wake up in the morning, let's go sunshine, it's time to go to work. Let's me see how badly I can do today. Everybody walks in with the right attitude.

• 39:23Speaker 02

  Yeah.

• 39:25Speaker 00

  It's something that happens at work that we create that causes the unintended outcomes. It's not the person who walks in. If you found the right person with the right domain knowledge, the right intelligence, the right attitude, then the rest is upon us. And then the question is, how does management create the environment that people can thrive? And it's not just about, you know, happy-go-lucky environment. Like, I always say, there are three jobs that I, we have as leaders. One is we have to identify the North Star. People have to know which mountain we're gonna climb. You get the best climbers, they need to find out you haven't told them which one. They're all on eight different mountains around you. You're totally sure what happened, they're all in different places. So my job is to make sure I identify the mountain, I fight, I argue, I debate. You know, I cajole whatever needs to happen to make sure we have a plan or record where we're going.

• 40:10Speaker 00

  But ample degree of input, but at the end, somebody's got to make a decision. Then the next thing is, is achievable. Can we write a plan to make it happen? Because the last thing you want is people come and say, I get it. He wanted to build this, but he gave me, you know, one pickaxe and one shovel and two people. Right? And he wanted me to go dig a platinum mine. Takes a lot more than that. So the next question is, is there a feasible plan to get it done? And are you resourcing it right? Right? And very often, companies, hey, look at our industry, right? A lot of people had the right ideas. When I came to Palo Alto, it's funny, I joke sometimes, I didn't do anything different. We had a cloud security acquisition we'd done. We had an XDR acquisition we had done. We had the idea of building a SIM, we just hadn't resourced it. We hadn't written the plan for it.

• 40:53Speaker 00

  We kind of knew what we wanted to do, but we haven't sat down, debated, argued, said, what does the future look like? And we hadn't, we hadn't written the plan. We hadn't resourced it. We have one tenth the resources we needed. So you, you don't have a plan, you have an idea. Ideas are not good enough. You gotta have a plan and a North Star. You had a resourcing that you can sort of execute it to plan. The third job is management is to keep communicating it and weeding out things that block the execution of the plan. Whatever it is, whether it's a a person is not doing it, whether it's a resource that's not available, whether it's a contract that's not working. You know, it's blocking and tackling and sort of making way for your team so they can go and execute behind you. So if you follow those principles and find the right people around you and, you know, don't suffer fools and

• 41:39Speaker 00

  have a good time while doing it. Uh, sometimes some people do get more scrutiny than the others, but it's good for their, for, for, for their career and good for their character. It's amazing.

• 41:49Pat Grady?

  Let me ask one more question on sort of management leadership and then maybe we can go back to some AI topics. So, um, you mentioned the nineteen companies that you guys have acquired in the last six, seven years. Maybe two questions on that. One,

• 42:03Pat Grady?

  at the moment when you're pulling the trigger,

• 42:06Pat Grady?

  what goes through your mind? Like, how do you, when it's time to make the go, no-go decision? How you decide that an acquisition is, is actually an acquisition you want to make? And then maybe second question, one of the things our partner Jim mentioned was that pretty much all the founders who've joined forces with Palo Alto Networks have stuck around.

• 42:23Speaker 00

  The majority of them.

• 42:23Pat Grady?

  So, yeah. So what do you do post-acquisition to actually keep them around? And maybe it's the same thing that you were just talking about with leadership generally.

• 42:31Speaker 00

  I learned more on that. Like, um, I think before we get to decide it's an acquisition we want to make, we spend enough time to understand, you know, is it even worth engaging with the company for a few hours or a few days? Right? And we have some principles. I don't like buying number two or three. It's a lot of founders, a lot of companies say, you know what, well, you know what? The first one's a billion, the third one's two hundred million. Let's just take the third one. We got enough resources. We'll spit and shine. It's going to make it brand new. Maybe worth a billion dollars.

• 43:00Speaker 00

  Well, there's a reason it trades at two hundred million, not a billion, first of all, which means they possibly have some gaps which the customers have identified and you haven't. Two, you didn't actually take out the biggest player in the market. It's still gonna be four steps ahead of you. So now all you've done is taken a nimble startup into number three. It possibly made it slower despite all your love and attention to it. He's like, say, okay, well, let me like, bring me along, bud. So now you've suddenly slowed down number three. You've enhanced the opportunity for number one and two. So you sit there and say, okay. And a lot of times you joke about it, saying, I wish this this competitor we bought by somebody in competition because it's slowed down. So, so we make sure we're only looking at one and two at best. And sometimes they're neck to neck. Sometimes they're chosen to a different parts and we did that in the browser space. And we're very happy with the acquisition of talent. I think things string really well.

• 43:44Speaker 00

  We're that in DSPM with Dig. Actually, in our industry, what has happened since Pat and Sonya is that at first people looked at me and said, what the hell? This guy's buying these companies. I don't know what his plans are. And now we actually have a slide we keep track of. Once we buy something in a category, that category becomes hot.

• 43:59Pat Grady?

  Hmm.

• 44:00Speaker 00

  So people think we know something. They've done a good job about that. But like, I think the principle is you got to make sure you're buying the right sort of right player in the market. Then you got to make sure that you can convince the founders that they believe a better together story than they go it alone story, right? There's no sell and dump because it's not gonna happen. We're not gonna take the asset because when you're buying, you're basically buying a North Star, an execution plan and a team that executes. But usually they're a third or forty percent down the journey. It takes more to seven years to build a great product. Usually these things are three years out, three or four years out. They haven't fully matured into a full product that's gonna win in the market. So you need them around and use a team around.

• 44:41Speaker 00

  And so once we figure out this is the right company, the right attitude, you know, we can actually make it work. And there are now, given our scale, there are some technical considerations. Do we have to rewrite the stack, which takes longer? Is this a complementary area that can just run on our stack easily? Is this something you've never done before? In which case it doesn't matter, it stacks on. So a lot of those considerations from an integration and time to market perspective. But let's assume all those hurdles have been surmounted and we're actually engaged with the company. I have a rule. I walk in and tell my team and I, I, I live it. I say, treat them on day one as they're part of your team.

• 45:18Speaker 00

  Right? Because if they're gonna work with you, they're gonna remember every interaction.

• 45:23Speaker 00

  Very often, I find many companies, the most well-intentioned companies start treating it as acquirer and acquired. Earlier, I come from a country which was acquired or ruled, and I don't like this idea. There's no like, one rules the other. It's like, you're part of the team, and the day the deal gets signed, we'll all be part of the same page, we'll all be trying to drive the same stock price and the same business forward. So for that six weeks that you're in that discussion phase, how is it important that you're the acquirer and you're the acquired? So let's assume we do that. We like a company, then I send off the finance and accounting guys and legal guys to do diligence, and I tell this founder and their team, saying, "Your job now in the next six weeks is to build a joint product plan and a joint org chart.

• 46:08Speaker 00

  At the end of six weeks, if you don't like the product plan, or I don't like it, and you don't like the org chart and I don't like it, there's no deal." This is learned behavior. The first two, three times, we didn't do that, and we discovered we spent the next six months arguing about what the product should be and who should be the boss. And this doesn't work. This is a bad idea. So I'm like, "Hey, buddy, you want the money? You can have the money. It's my house. I'm gonna paint it yellow. You don't like the color? Tell me now. You can get somebody else to paint it pink, no problem." So that has an amazing cleansing property, because you're making a decision with all the facts in front of you, saying, "If I get part of Palo Alto, this is going to be the product strategy. It could be yours, it doesn't have to be mine. I'm not smart enough."

• 46:51Speaker 00

  So, but we have a joint product strategy, it's a no start, and we have a joint plan of execution. And then, very often, Pat, this goes back to Jim's comment, most often, the founders we have bought companies from become the senior vice presidents of our company running their business. Our people work for them, which I think is unique in the market. Very often, you'll find there was an acquirer SVP who ran crypto or blockchain, or pick your favorite. I'm using non-security terms to keep it, protect the innocent. But you say, "Oh, since I'm responsible for this, these people are gonna work for me." I'm like, "Wait a minute. You had all the resources, you lost to them. We're not gonna have them go work for you. Maybe you can learn a few things." So we did that a bunch of times, and in some cases, our teams worked for them really well.

• 47:35Speaker 00

  In some cases, our teams left, which is fine. So I think those are some of the things that allow us to make these amazing founders can work here and actually drive more value for us collectively.

• 47:45Speaker 02

  Nikesh, I want to ask you about some of the chess that's now happening on the AI stage. Because I think you've played the chess game so flawlessly in the security market. And you have so clearly emerged the winner. The AI space, by contrast, feels just white-hot competitive hunger games right now. I'm curious your view.

• 48:03Speaker 00

  I think a lot clearer than that. I think it's just, it's just, it's just, it's just not clear to the naked eye. But I think it's a lot clearer, but going.

• 48:10Speaker 02

  Say more.

• 48:11Speaker 00

  Tell us more, yeah.

• 48:12Speaker 02

  Yeah, tell us more.

• 48:13Speaker 00

  Look, if you think about the state and maturity of AI, you know, there are two extremes. We'll call them the very precise, the AlphaGo-type situations, which, you know, Demis and Google built together, which are, I'd say, fine-tuned models, which are designed for drug discovery or the biopharma field. And there you see that they did a really good job. They focused on the trained, right data. They hopefully tweaked the models in such a way that that can actually become a useful thing for society. So you have that, which is highly tuned AI models, very task-specific or category-specific. And then you have the generic ones. And the generic ones are, you know, the rage today

• 48:55Speaker 00

  between the clods and the misstraws and the geminis and the open AIs of the world. And those are large, they're all-encompassing, all-knowledgeable. But you saw this movie before, right? You saw this movie in search. And as a Google, you know, then you had vertical search. Because the large Google search could not do as good a job of local search. So you had local search. They couldn't do a little job of product search. So you had product search in Amazon. So how can you be amazing at everything in this space when you couldn't do it in the last few technological evolutions? So I think over time, we're going to have to figure out what the distinction between, you know, general-purpose, large-scale, I-know-everything, I can do everything model versus models that are fine-tuned for tasks.

• 49:39Speaker 00

  And I don't believe that all the perfect information in the world exists in open domain, that you can go out and build it without, you know, specialization. Which means you are going to need specialized proprietary data to build these models. And I don't know how you share data between GlaxoSmithKline and Novartis and Pfizer and say, I can build the best drug discovery model in the world because I have perfect information, right? So that's a question that remains to be seen. So I think over time, you'll see a bifurcation from an enterprise use case. And in our business, in the enterprise side, you need precision. I can't afford to be wrong. You know, a wrong turn by a Tesla is going to kill somebody. A wrong, you know, block by Palo Alto is going to bring somebody's infrastructure down, or a wrong permission is going to let a bad actor in. So I don't have that tolerance that consumer models can have because they have low consequence.

• 50:25Speaker 00

  So I think high consequence, high consequence applications require a lot better models, a lot more training and more precise domain data. I think that's going to become a sort of thing of its own. I think everything we're seeing today is general purpose models and eventually they'll be like, and I don't know the answer, was that general purpose models become task specific evolved models? Or there's a new category of task specific evolved models, which are built more in the sort of genre of the AlphaGo version. Now, on the, on the general models, I think the people who can deploy them against existing consumer properties are sitting pretty.

• 51:00Speaker 00

  Right? Because it creates more retention, more continued monetization of space. So whether Google can deploy a whole bunch of AI against its three plus billion users across multiple properties, or if Mark Zuckerberg can do it vis a vis Facebook and see billion users across Instagram, you know, WhatsApp and, and Facebook, that's, that's cool. I think Sam's done a great job in building a consumer direct business on the subscription side, which he continues to drive very well. And that's become sort of, sort of his, his moat now because no other model has built a subscription based consumer model. So I think you're seeing the general purpose models being built by existing large consumer properties. You're seeing a new consumer property emerge vis a vis, you know, OpenAI.

• 51:43Speaker 00

  I think the enterprise use case is still early because we haven't seen the mission critical applications be developed because of the lack of, you know, great training data. So, anyway, that's what I think, but.

• 51:56Pat Grady?

  Let me ask you, one of the, one of the things that's not on your LinkedIn profile is prior to, prior to SoftBank, prior to Google, prior to T-Mobile, if I had my facts straight, you were an award winning equity research analyst covering telecom. And I, I believe that one of your claims to fame was calling the internet bubble and the bursting of the internet bubble.

• 52:17Speaker 00

  I still have that note, a cell note that I wrote in November 99.

• 52:20Pat Grady?

  Not bad. So are we in an AI bubble?

• 52:24Speaker 00

  Lightning doesn't strike twice. How would I know?

• 52:29Speaker 00

  Look, again, for the number of times I've heard it's different this time, you know, we could all be very rich. So, but there are some things which are different, right? If you look at where the AI inflation has happened in the equity markets, it's still in the plumbing. And the plumbing is real, right? It's not like people are driving the plumbing up without substance because you're selling four times or 10 times more chips than you sold two years ago. So there's real revenues that underpin that. Now, clearly, people are projecting that into a trajectory, which I don't understand. And, you know, every day you see a new development. You tell me, is Target the future or is DeepSeek the future? And I don't mean with all its negative connotations. I mean, as a concept, are we going to have cheaper models being built

• 53:14Speaker 00

  for large scale application with limited specialization? Or are we going to have a supermodel in the context of AI, which is going to be expensive, but be able to do everything amazingly? You tell me the answer and I'll tell you mine.

• 53:30Pat Grady?

  Sunny, should we head into lightning round or do you have more? Let's do it. Great, lightning round, okay. You just bought a cricket team. Why?

• 53:36Speaker 00

  You know, there's a bunch of us who are together, not just me. There's 10 of us, including your partner Jim Getz.

• 53:45Speaker 00

  We're all failed cricketers, we're all sports aspirants. So there's a part of that, that's, there's part of the passion that says, wow, I can be associated with the sport at the highest level without having the talent. It's kind of interesting. That's one reason for it. Now, you couldn't have a bunch of us buy it who are business savvy and say, well, is there a business model here or not? If you look at it, the only thing that's left in streaming that is linear is sport.

• 54:12Speaker 00

  No longer news, television, movies, nothing is linear. The only thing that's linear is sport. You want to watch it when it's happening. Pretty much when it's done, you know the score and you lose the interest to watch that, that event, right? The post, post event viewership is a lot lower in sport than live viewership. Every other is the other way around.

• 54:31Speaker 00

  Every other streaming content is the other way around. The post-launch viewership is higher than launch viewership. Whether it's movies or television, or any podcast, or any video streaming that do, hopefully this one, right? So it's the only linear sport out there. Uh, it's being bid up. Cricket is the second most watched sport in the world. IPL is the biggest franchise. Hundreds is the next best thing. It's in the country which is the home of cricket. And then have we followed the same philosophy that I told you about my startups? You're gonna buy something, buy the best. So we bought Lords, which is the home of cricket. It's gonna be fun.

• 55:06Pat Grady?

  Love it. What did cricket teach you about life or leadership?

• 55:09Speaker 00

  It's a team sport. It doesn't matter how good you are.

• 55:14Speaker 00

  If the other ten people suck,

• 55:17Speaker 00

  it doesn't matter. It teaches you that, right? You can have a bad day and you can still win because you participated with the rest of ten people. So it teaches you about life. It teaches you about business.

• 55:29Pat Grady?

  You're wearing a Pebble Beach pullover. I hear you won a Pro-Am recently. What's your handicap?

• 55:33Speaker 00

  My handicap's nine, NCGA. And it's a combination of the best pros that I could find, luck, and a few misplaced good shots.

• 55:46Pat Grady?

  Nvidia. Hundred and eighteen bucks a share, $2.9 trillion market cap. Thirty-nine times earnings. Earnings are growing about 150% year over year. Long or short?

• 55:57Speaker 00

  I don't understand it.

• 56:00Pat Grady?

  If you were Jensen, would you be making the same moves?

• 56:03Speaker 00

  Uh, Jensen has played a very long game. I think he's built a phenomenal franchise. I think what he's done is like no less than what Elon has done for electric cars. I think, you know, he took something that she built for gaming, thought about it, understood the large need for compute, and put all his energy and thought behind it. He's been the longest-serving CEO in the world, right? So you can't take it away from him. You just can't even trivialize it. We have to talk about him with tons of respect. What he's done is amazing. He has a vision. And he's taking it beyond just the chips, because he's slowly building an ecosystem, saying, my chips work with a lot of other things which together. So I don't think from a long-term perspective, you can argue that AI is not gonna be relevant. I don't think you can argue from a long-term perspective that we will be constantly doing some form of development, which requires more and more compute.

• 56:48Speaker 00

  Like, in the history of mankind, compute and bandwidth and memory have never shrunk.

• 56:55Pat Grady?

  Yeah.

• 56:56Speaker 00

  So it's not about to start now. I think he's sitting up phenomenal asset. You know, is it a three trillion or asset, three trillion dollar asset today? I don't know. There'll be a three trillion dollars add dollar asset in ten years, possibly more.

• 57:09Speaker 00

  What CEO do you admire most? You know, I have a collection of CEOs. I admire traits that CEOs exhibit. It's very hard to have one idol in life because one idol and has the property that they could disappoint. But if you admire certain things, certain people do, you know, you learn a lot from that aspect of it. And if you have the same circumstance, you might do the same thing. You have a different circumstance, you might do a different thing. I mean, I admire Elon's creativity and what he's done for the world. I wouldn't want to work for him, but I admire what he's done. It's amazing. Like, you know, I always joke with my team. I'm saying, would you go on a rocket to Mars built by the guys around you? People are, I don't think so. But imagine he's got a bunch of people who've built a rocket and people go up on that thing. That's amazing. We could sit in the car, which has got no driver in it. And so people have done it.

• 57:54Speaker 00

  So it's like what Satya did to Microsoft. Like he took somebody nobody believed he could turn this around at three trillion dollar company. And he did. It's amazing, right? So like a Tim Cook, you know, Steve Jobs is a hard act to follow. And Tim's done a phenomenal job in taking that amazing company and maintaining it down the middle and constantly elevating. What can Mark Zuckerberg recently, right? He's taken that thing around and turned it around. Now, you know, the fact that there are certain things that they've done, which I respect is amazing. That doesn't mean anything about the rest of their lives and I don't need to worry about it. Which CEO is executing the best in AI right now? For all the conversation around Sam, I think what he's done is amazing. Right?

• 58:36Speaker 00

  I mean, before ChatGPT came about, we weren't talking about AI. Right? And before ChatGPT came out, you know, you think Google didn't know about AI? I knew about AI when I was at Google. Do you think Google didn't have a self-driving car then? They did. No, do you think Satya didn't know what AI means? He did. But look at what's happening right now. You can't, you can't, you can't run into a CEO who wants to use the words AI. So Sam has created the next, sort of the impetus for the next technological revolution. That's the way Steve Jobs did it with the iPhone. And the fact that was a straight face, you can go out there and get people to commit to spending half a trillion dollars in building infrastructure. And every, I think the mag seven, everyone of the CEOs is spending way more money in

• 59:19Speaker 00

  building computer and data centers because nobody wants to be left behind. I just sound a great job executing AI now. You know, history is is hard and business is hard and we don't know. That means that you'll be the winner in the future, but damn, has he done a great job in getting us to where we are. Yes. I think that's it. Thanks, Nikesh.