From $18B to $300B: How Nikesh Arora Rebuilt Palo Alto Networks

Nikesh Arora.

Nikesh Arora.

Nikesh Arora.

Nikesh Arora.

This is the beginning. This is not a moment. You've seen, most recently, all the AI labs are flexing, showing how cool their models are, and how they can basically become an attacker and attack infrastructure rapidly because of all the vulnerabilities they can find.

It's a lot easier to attack as an AI lab; it's much harder to defend. If you found a vulnerability, a zero-day vulnerability, the average time to fix it was 55 days. The average time Mythos will find it and try and attack you is in minutes.

For Black Hat, we launched a capability which allows us to deliver patches in 4 hours and deploy them to every one of our customers, which is huge. From 55 days to 4 hours. None of the software written in the last 20 years came without a painting.

So the entire software industry will get rewritten in the next 10 years.

What do you think the biggest question is that people are not asking?

Nikesh Arora, welcome to Sourcery.

Thank you for having me.

Thank you for having us here at Palo Alto Networks.

Well, you know, you guys have done a really good job in making this place look beautiful, so please feel free to come back anytime.

So I think a good place to start, you guys are having a bit of a momentright now. Cybersecurity is very hot. AI is making it really top of mind. So I have to ask you, what is your hottest takeright now?

The hottest takeright now is, this is the beginning. This is not a moment. I think, uh, you've seen, most recently, all the AI labs are flexing, showing how cool their models are, and how they can, like, basically become an attacker and attack infrastructure rapidly because of all the vulnerabilities they can find.

Well, guess what? That's going to become par for the course. Well, who's going to protect them? And it's a lot easier to attack. As an AI lab, it's much harder to defend. So I think all the cybersecurity companies are on a tear for that reason, because the market understands that to get all the infrastructure in the world up to snuff in terms of its capabilities to be able to protect against this deluge of AI attacks that are going to be upon us, you need all the cybersecurity companies.

So how are you keeping track of everything that's going on? Do you.

Really hard.

Read?

I'm watching your podcast. Listening to. Look, what's fascinating in the last 2 years, you've seen we've been through so many iterations of AI. We started with ChatGPT. OpenAI was going to go run away with it. Anthropic came from nowhere.

People had written Google off, and they were not going to be able to compete. And like, 2 years hence, we're sitting here watching all the announcements from all the cloud companies, which are sort of growing gangbusters because people want to use more compute, want to use more AI.

And now we've gone from LLMs to agents. Agents are going to help us do a whole bunch of stuff. We've gone from agents to open-weighted models, open source, closed source. So there's so many variables because the market continues to evolve on a daily basis.

In cybersecurity, you've got to go make sense of these trends and see which ones of these trends are likely to catch upon catch on. So we've got to go build the security infrastructure and harnesses around it. So it's kind of a bit of a dancing on your toes and constantly being nimble trying to figure out where this thing is going to land.

Uh, it's interesting. I think some things are beginning to emerge. I think a lot still needs to be figured out. But I think one thing is clear: the appetite for AI is huge. And I don't think that trend is going to reverse itself.

So if you believe the demand is infinite, then a lot of things have to fall into place for this to be successful. And one of them, clearly, is cybersecurity.

With more anomalies popping up and rogue agents, which who knew that would happen, how are you handling that? Like, how do you stay on track of that kind of thing?

I think it's, uh, I told you, it's part of the flex. It's part of demonstrating the capabilities of the technology. I think for all practical purposes, the biggest use case you've seen is coding. Everybody's using AI to code.

I think that's kind of mainstream because the capabilities of AI models have, sort of, it's fair to say, have surpassed humans in certain cases from a coding perspective. You still need humans to watch what's done, sort of ensure that, you know, it's theright solve, test it, run it through clear processes.

But clearly, you can see that the use case has been established, the productivity case has been established, and there's a huge amount of consumption in that space. Outside of that, I think people are still feeling their way. How agents are going to work.

How do you give agents what we call true agency? How do you let them decide? I think there's still a whole bunch of experiments going on over there, and it'll take time before people get really comfortable unleashing agents into the enterprise.

There's a little bit of controversy with what happened with the OpenAI thing and then.

Just a little?

Just a little bit. And then also how the response was after that. The same thing happened with Anthropic, and then now those leaders are asking for a slowdown.

So what part do you believe is controversial in that? Just out of curiosity.

Well, I guess the main question is now they're asking for a slowdown to potentially cover their tracks later on.

Are they asking for a slowdown?

Yeah.

Are they asking for permission?

They're.

To be able to go release these?

I don't know. You tell me.

I don't know. Look, I think it's clear from all the recent developments that these models are getting really powerful. And the edge case intelligence is really strong. Like, you know, it can solve some unique things. You saw some math problems being solved 2 days ago.

You've seen that it can find cyber vulnerabilities. It can daisy-chain vulnerabilities, attack infrastructure. So it's clear these models are going to be extremely powerful. They already are. And I think it's important that before, you know, who we have to understand liability.

You have to understand who's responsible at the end of the day. It's very easy to ascribe responsibility and liability to human beings. If you do something wrong, it's your fault. If I do something wrong, it's my fault. If I use a model and the model does something wrong, whose fault is it?

Is it the model's fault? Is it my fault for using the model? So I think all these things are going to become very thorny issues. And I think a lot of the AI labs want to get ahead of it, make sure there is some governance framework around it to ensure that they can keep developing the technology at the pace at which they'd like to.

So I think they, in a way, they're probably doing theright thing. It doesn't seem like it, but I think they are doing theright thing in trying to get some governance around it so they don't get hauled back. As you saw, they did get hauled back when they tried to, you know, launch Mythos or Fable 5.

They got hauled back because the model was not appropriately guardrailed. So I think we're going to go through a bunch of these growing pains.

Mythos was a big moment.

It still is.

It still is. So how are you handling the Mythos?

You know, for 8 years, I spent my career at Palo Alto trying to convince CEOs they need to pay attention to cybersecurity. Tried everything. Called them. Tried to talk to them. And they usually send you off to their technology team.

Go talk to those guys. You know what Mythos did? Every CEO wants to talk about Mythos, which is great. So the first time, Mythos has every CEO, you know, sitting at the edge of their seat saying, "Am I vulnerable?

Is something going to happen because of what Mythos is? Do we have Mythos? We're so important. Why don't we have Mythos? Why can't we get Mythos so we can test our own infrastructure?" So I think Mythos has created a bit of a moment for cybersecurity.

And what's fascinating to watch is that moment has, I think, for now, sort of advantaged incumbents. Advantaged the big players in cybersecurity where the customers are going back to them and saying, "Listen, you're my cybersecurity partner of choice.

What should I do? What am I supposed to do? How do I get my hands on Mythos? What have you done? How have you done your testing?" So I think it is a moment because it has got everybody's attention.

But I also think it's a moment because it is going to change the way cybersecurity is done in the future.

Like what?

People would buy cybersecurity products, and it was okay. You know, if you found a vulnerability, a zero-day vulnerability, the average time to fix it was 55 days in the industry. The average time Mythos will find it and try and attack you is in minutes.

So now you've got to go get ahead of it, test all your software, tell us, test all your open source, understand the vulnerabilities, and patch them before. And 55 days is too long. So this morning, actually, as part of Black Hat, we launched a capability which allows us to deliver patches in 4 hours and deploy them to every one of our customers, which is huge.

From 55 days to 4 hours. So we do have the benefits of AI from a defensive perspective, which you're beginning to see. So I think what is happening is now it's become apparent to the market that the time from discovery of vulnerability to an attack is going to compress tremendously, which means you have a lot less time to go fix it or find a bad actor in your infrastructure.

When time gets compressed, it requires our customers to modernize their infrastructure. It requires our customers to start using AI in the deployment in terms of the defensive capabilities that they must have. That's good news for the cybersecurity industry.

Great news. It's like.

I think so.

There's not many I mean, there's a lot of fear-mongering that goes around with AI, so it's nice to have positive, optimistic stuff.

Look, every technology eventually needs theright, let's say, tentpoles for it to succeed. You need to make sure that things are done in a certain way so that customers feel comfortable deploying the technology. It's like, I don't know, take a Waymo ever?

Sometimes.

You're comfortable in it?

Yeah.

You feel safe?

Sometimes. Unless it goes down those hills. Have you done that?

In San Francisco?

Yeah.

No, I haven't.

You haven't taken one in San Francisco?

I have taken a Waymo. Of course I have.

Okay.

I just haven't gone downhill on the rolling hills of San Francisco yet.

Highly recommend you both do that.

Got it. Got it. Well, the reason I ask you the question is, a lot had to get doneright for you and I to feel comfortable walking into a Waymo and having it drive us. We effectively gave agency to AI to act and not be threatened by it or not feel unsafe around it.

That process needs to happen in every useful use case that is going to be deployed using AI. And that's a journey. It's not going to happen overnight, but I think the ingredients are in place for that to happen.

And it's cybersecurity is one of those things that needs to get gottenright for people to be comfortable that no bad actor is going to take over my Waymo and drive me faster down the rolling hills or, God forbid, take me away.

The main topic was really on cybersecurity. You were part of that. You signed it as well. So why did you make that decision?

Look, at the end of the day, if you want the diffusion of technology in a way that everybody can use it in every way, shape, or form, you want to make sure that there is no constraint to innovation.

And having open source, open weight, having closed weight, all these things are important parts of the puzzle to make sure that people can deploy them in different circumstances. So I don't think it's necessarily bad to hold back the development in open source or open weight for that matter.

Open source will allow people to make these things available globally at theright price point for various people to be able to use. Open weight will allow a significant amount of fine-tuning to make sure that can you adapt a model to your specific use case in a way that is more effective and efficient for the task at hand.

So all these are important parts of the puzzle to make sure we get innovationright. So that's the reason we signed it. I think the there's an over-indexing on the model part of it. I think to make AI useful, the models are important.

But I think it's also important to get all the context collected and all the training dataright. I think billions of dollars were spent to train my favorite example of Waymo. I think billions of dollars will be spent over the next few years training a whole bunch of use cases in enterprise or consumer to get that partright.

Why do you like Waymo so much?

Uh, it's not about liking Waymo so much. I think it's the most obvious, relatable example of AI getting agency where a human does not get involved and AI is allowed to make decisions which could mean life or death for human beings.

And, like, that's itright there. And if I tell you, you know, are you comfortable letting OpenAI make a life-or-death decision for you, what is your answer?

I mean, I don't want to be in that situation.

See? But you did put yourself in a Waymo. So there's my example.

Yeah.

My example is if you spend enough amount of money, enough guardrails, enough training, you can get comfortable in a scenario where AI can be used

instead of a human who has agency. So I think that's what I mean by saying, look, would you let AI prescribe medication to you? And take it without.

Depends.

Take it without asking for a second opinion?

No.

Would you allow your doctor to do that?

Yeah.

You would. Now, do you believe it's possible for AI to get trained as well as your doctor and perhaps better?

Yes.

Good. So you would.

Potentially.

Right. But you won't take the models in their current raw form and let it happen. You would still wait for a whole bunch of contextual training data that needs to be deployed, a whole bunch of edge cases to be understood, even more context about you to be understood, but then you would.

And I think that's what needs to happen. We're over-indexing on the model. I think models are great, but being able to take that model, package that with all that context, all that knowledge, all that training, and be able to deliver a solution to you is what the next big sort of revolution needs to be.

And that needs to happen in thousands of different use cases.

You're clearly a big beneficiary of this, and it's amazing. It's you joined the company at $18 billion valuation.

Market cap, yes.

Market cap. And now it's at around $300 billion, which is crazy.

Who's counting?

I don't know. Market cap definitely.

Keeping our paying attention.

Uh-huh. It's crazy.

Probably two days it matters. The day you get stock, the day you sell stock. Every other day, it's a vanity number.

But.

But it's nice.

To get to that point, like, one of the main things that I took away when we were walking around the office and we were meeting different people is, one, you're super aggressive. Two, we were talking with Hamza, and he said, "You're just as good as an operator as you are an investor."

Super aggressive. Explain that to me.

You're aggressive. You go after things.

You watch sport?

Yeah.

When you see somebody bowing through the defense and trying to make a basketball who, like, make a shot, are they called super aggressive or are they called people trying to get it done?

I guess they try to they just do it.

Good. So I prefer that characterization as opposed to super aggressive. I'm going to get it done.

Mm-hmm.

Yes. That doesn't require to be super aggressive. But your question was different. You were talking about Hamza.

Yeah.

And?

So talk through that. You have an amazing career as both an investor and an operator. How does that come together in this role here so well?

If you look at what the markets reward, if you look at what how business gets rewarded, business gets rewarded effectively in metrics like market cap, perhaps. What is the market cap? The market cap is the sum total of the expectations of the world about the strategy, execution, and the potential for your business,right?

NVIDIA trades at $5 trillion because people believe it has tons of demand that's going to happen. Jensen is a great executor, and they're doing a whole bunch of stuffright. So you can actually work your way back from market expectations or the market expects that makes for a successful business.

Now, the market is pretty straightforward in its expectations at some level. It says, "If you have a durable business that grows at a robust rate, which you can run profitable and generate tons of cash flow, we like you."

Now, that's great, which means you have to run a good business, must have good cash flow, and must grow well. But the market is sort of smarter than that. It says, "Well, not just that. I want to see the durability of it."

What does durability mean? Can you grow at a rapid pace for a long period of time? Like, if you're a CEO trying to deliver in that environment, which means every time you think that you've done it, the market expects you to grow again.

Like, you get from $1 to $1.15, the market says, "Great. Tell me tomorrow. Can you grow 15% or 100%?" So the market is expecting you to grow at a certain rate. The numbers keep getting bigger. And the question is, how do you keep making sure your business continues to grow in that regard?

And that's kind of what the art and science of leading a business is, to make sure I don't spend all my time worrying about what happens next quarter. I worry about what happens two years from now because I can see for the next two years how my business is going to progress, what we're going to be able to sell, what do we need to go fix, how do we rally to make things work.

And that's great. But after two years, your visibility begins to thin. It's like, "Oh my God, what if the market shifts? What if competition gets stronger? What if different products come to the market?" My job is to say, "Well, if all this went well, what would our business need to look like two years from now?

And what need would the growth levers need to be for us to deliver on that business?" That's kind of the paranoia I live with. And in that paranoia, you see if you bought 40-plus companies in the last eight years because sometimes you're looking for interesting products, sometimes you're trying to fill gaps, sometimes you're anticipating the market and saying, "How can we get ahead?"

So all of that goes into that little thing. You shake it together and say, "That's what the market expects from an investment perspective." But what does that mean for our strategy? And what does that mean from how we execute the business?

And sometimes, you know, we disagree with the market. Sometimes they say, "You know what? Market, don't worry about it." We went and bought a $28 billion company called CyberArk. The market didn't like it for a certain period of time.

And they turned around. We showed them results in a short period of time. They're like, "Holy shit, we love it."

Why is M&A so kind of consequential for cybersecurity companies? Because this is a common theme. They're very acquisitive.

It is the most innovative industry in the world because the bad guys are trying to figure out how to attack you in a different way every time. The moment we suss out how they did it, they move on to find you the next time.

So we're constantly trying to chase them, saying, "Holy shit, they figured out another way to attack us. Let's go figure that out." By the time we get to there, they move on. So you're constantly chasing and anticipating the bad guys who are constantly looking for a new way into your infrastructure, which makes them extremely innovative.

And they're all over the world, nation-states, people in their basements, people with, you know, Nintendo in front of their hand or a laptop. They're all trying to figure out, either for trophy reasons or for economic reasons, how do I break into something?

So it requires us all to be very innovative. Every new technology that comes to the market requires a different kind of sort of harnesses, different kind of tools, different kind of capabilities in our products. And if you don't pay attention to every new technology, the customers start buying something else.

So it's almost like we have to stay on our toes on a constant basis to anticipate technologies and to anticipate bad actors. Makes us the most innovative companies, most innovative sector. In that environment, it's impossible that all the innovation is going to come from us,right?

Because there's always somebody else who's got a different angle. There's always somebody else who's tried something that's going to work better than what I thought about. So you just have to you have to live in this industry with humility to understand that you may not always have all the answers.

The question is, however, are you smart enough to anticipate who has the answers, make them part of your team, charm them, be part of Palo Alto? And when you do that, can you then deploy that as quickly as you can to your customers?

So I think in the last eight years, it's fair to say we've struck theright balance between what we build internally, what we can go rapidly and build internally and layer on top of our platforms, and what is unique out of the market where if we partner/acquire somebody, how can we bring them into the fullest part of Palo Alto and deploy the capability to our customer as quickly as we can?

The north star always is, how do I deliver that capability to my customer as quickly as I can? And sometimes it's buy, sometimes it's build because it's too complicated to buy and integrate. Sometimes we look at it and say, you know, it's not worth it.

Let somebody else serve that part of the market. We can find a way to integrate them with them.

So once you do make an acquisition, what is the playbook for getting them to your customers? How do you onboard them, one, on the product basis, but then also on the team?

Well, you know, we've done acquisitions of all shapes and sizes. Some of them have been easier because they're clear product acquisitions in categories we don't play in. So that becomes a lot easier because then we all we do is we say, "Listen, you're going to be part of Palo Alto.

We might slow you down, but we're going to throw more resources at you so we can make you get more scale and more speed." And that's kind of worked well. We bought a browser company. We didn't have to we had to integrate it into part of our product, but we were able to let them lose.

We bought a bunch of AI security capability. Again, something we didn't do. We had to put more resources in there, put it together, and let them lose. Now, what letting them lose means is we actually make them part of our go-to-market engine.

Our customer, our teams out in the field have tons of relationships. They are used to talking to their customers about unique things that we do. In that context, we sort of plug these capabilities into our go-to-market pipeline. We make sure our customers are aware of the capabilities.

That allows our customers to go out there and use these capabilities as fast as we can give it to them.

What do you think the biggest mistakes are that people make during the acquisition?

I think the biggest mistake that people make during acquisitions is underestimate the intelligence of the people who built the business that you acquired. Because sometimes you take on the imperialistic attitude, "I bought you, hence you must work for me."

Our attitude is, "You kicked our ass. Come tell us what we did wrong. Come run this for us because you did well without our money, our resources, and our scale. So you must have figured something out." So we spend our time trying to understand what they figured out, find a way that we can make that part of our culture, make that absorb that capability, and we let them run it.

And that sometimes really makes it hard for my teams because they suddenly have a new boss in a category where they thought we were acquiring something. But it's just, like I said, you have to approach this with humility because there are people out there who are smarter, faster, better resources, more resourceful than you in certain categories.

And if you can embrace them in theright way, it allows us to build a durable business.

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How do you remain curious in this, like, exploration process of finding new potential acquisitions, new companies to bring in?

Paranoia.

Paranoia?

Fear of failure. Remember in the fear-mongering business?

Mm-hmm.

I also live with the fear of failure. It's this constant idea that imagine that something happens out there and there's no security solution. And today, we're delused with people like you doing these podcasts with these cool CEOs who are building all kinds of new tech.

And, like, I can barely understand half of them. "Oh, we're going to do open-rate models versus closed-rate models. Well, how do I figure out what an open-rate model is?" And you sit down and say, "Well, now you got an open-rate model.

What are the consequences from a security perspective? Does it change the game? What do you do with agents?" "Oh, shit. We got agents now. How do we deal with security with agents? How do I find one in the first place?"

"Well, they're going to be all over the place." So we start thinking, "Why don't we bring a bunch of people together and say, 'Dude, these things called agents, where are they going to be?'" Well, they're going to be everywhere.

They're going to be in SaaS software. They're going to be in our infrastructure. They're going to be on-prem. How do we collect them together? I don't know. We got to figure this out. So we then start looking at the market and say, "Is anybody working on this problem?

How are we thinking about it?" And that's where we find out, "Oh, these guys are thinking about it the same way we are. They started thinking about it one year ago. That's cool. What do we do? Should we build it because they're thinking about it the same way, or should we acquire them?"

We talk to them, saying, "Do you want to come?" It's like, "Nope. We're going to be so big, we don't need you." They can move on. So there's a whole sort of discovery process of what is the technology, what are the implications of security, how do we solve the security problem, how do we think about it, where does it fit in our portfolio, is it going to be big?

If so, should we build it? Can we build it? Are we late? And should we also, at the same time, look at what's happening in the market, which we do, and then decide, "Well, it's probably better to build it because it's going to be more complicated to buy that integrated, or is it just better to buy something because it's a lot easier for us to go run with it because they're ahead?"

Hamza mentioned when we were over there that you do see things many years before. Like, you're very good at predicting things, like the SaaS apocalypse.

Well, actually, I didn't predict the SaaS apocalypse. I think the market.

I thought you did.

No, I predicted the end of the SaaS apocalypse as opposed to the beginning of it. I think the market got ahead of itself. I think, you know, when AI came out, I think people started trying to predict what's going to happen to the AI.

And there was this notion that AI is going to eat software. And the market indiscriminately decided that every software company was destined to zero. And you saw the entire sort of SaaS market went down 50%. You sat there and said, "This makes no sense."

AI can be great at finding vulnerabilities. AI can be great at 80% use cases. We live in the 0.1% use case. We're looking for the needle in the haystack. AI is not good at looking for every needle in the haystack.

We write machine learning code. We look for a whole bunch of attack techniques. We find the 0.1% use case just the way somebody goes and tells the car where to turn, and that's a tree. And that's the edge case.

AI doesn't understand every edge case. It does the mainstream cases. So we declared the SaaS apocalypse for cybersecurity was over. We did not believe that we were going to get impacted. And you can see now that was six months ago, and we seem to be having a moment.

What do you think, though, about the broader macro market? Do you think that some of these companies will actually not recover?

What do you mean?

Some of these companies are down, like, 90% and still haven't.

Look, that's a fair question. I think every company is different. Every company is different in terms of what the capability they bring to the market, where the market believes that either that capability is going to be par for the course in AI.

So I think there's a lot of questions in terms of what the native capability of AI models is going to be. And if that native capability is exposed to me, you, as a consumer, or as a professional, do I need to buy the package software that existed that solved that problem before?

And is it better or worse? I think some of those categories, the market has already declared those companies dead. In certain categories, the market is wondering, "You built software 15 years ago. There's a new game in town called AI.

The shape of software is going to change." The market is making a judgment call on whether you, your team, and your product will survive this transition to AI. So I think all that is happening. So each company is different.

I think on a fundamental level, I believe we spent our lives building software. It had no opinion. So we bought software, and it did kind of deterministic tasks. In the future, software will come with an opinion,right? Your AI doctor will actually have an opinion.

But somebody has to train it. Somebody has to give it context, and it has to learn. We can build intelligence into our products. If you build intelligence into our products, they will come with an opinion. None of the software written in the last 20 years came with an opinion.

So the entire software industry will get rewritten in the next 10 years. And the market is making judgment calls, which ones of these categories will survive, which ones of these companies will survive. Possibly getting a bunch of them wrong.

We'll see. Time will tell.

What do you think the biggest question is that people are not askingright now?

That's a hard question to answer. Like, I don't know what question. I don't know if it's a question per se. I think the market is, in a way, confused. And you can see that every day. That, you know, some things go up rapidly for a week, and suddenly they go down rapidly for a week because the market changed its mind.

Some of the long-term trends are obvious. It's obvious that this technology is big enough that it is going to have a long, far-reaching impact in our lifetimes, the next tens of years. I think this is the early days.

It's also clear that this technology is extremely compute-consumptive than any other technology in the past. We need to build lots more capacity around the world. And you can see that in the prices of in the elements of what goes into building compute.

You can see that in energy prices. You can see that in nuclear. You can see that in generators. You can see that in, you know, states saying, "I don't want more data centers. I'm up to my eyeballs in data centers."

So you're seeing all of that. But it's clear that there's going to be huge demand going forward. I think it's also clear that every one of us believes that our personal AI should be able to do a lot more, and it's going to need to get better,right?

I'm sure you are an avid user of some version of Claude, Gemini, or OpenAI. Are you? ChatGPT?

I use, like, all of them.

All of them,right?

Literally Grok, OpenAI, Claude.

When you use them, have you ever caught yourself thinking, "I wish it could do a bit more than what it just did"?

Every day.

Right?

I get very frustrated with them.

Right.

Yeah.

So you're telling me this thing's not as good as it needs to be.

No, it's definitely not.

Which means somebody's got to have to train a lot more, get a lot more compute, get it trained, get better,right?

Yeah.

So that means there's immense capacity, immense demand for capacity in the next few years. We're going to have a lot more capacity demands on AI because we've got to train the models better, make this intelligence get smarter. A lot of us in enterprise are frustrated because it doesn't understand edge cases.

They're saying, "Why can't it be smart enough to understand? It was so smart here. Why not here?" So we're all waiting for it to get more capability and more capacity. So I just think this is an unstoppable trend that's ahead of us.

I think we're underestimating demand across all dimensions of this, whether it's compute or intelligence or what these models are capable of, which means there's going to be tons and tons of development. I think every piece of software is going to get rewritten.

Every consumer application you use, a lot of the applications we use on our phones, the way we're used to using them, there's a lot of UI involved, there's a lot of manual work involved. If AI is going to be so good at being an agent and using an MCP server, it's going to fix all that stuff,right?

So there's lots and lots of stuff that's going to happen that needs to get done. So there's tremendous demand. The market is just going through its digestion phase of figuring out what gyrations are going to cause which thing to be overvalued or undervalued.

Compute is a huge topic. We were just interviewing Fall yesterday. Do you know Fall? Generative media AI company. They do both compute. They also have APIs and all the other kinds of layers. But they started as compute. And then they noticed, with all these video models, the voice, the 3D, like, audio, all this kind of stuff that's going on, those ones are really just starting.

Yes.

And there's tremendous demand. And those ones are going to need more compute than anybody else. And so we were talking through all the different layers of that and, like, all the Neo clouds that are coming out and how even with hyperscalers, I was talking to I forget who it was, but it was at the RAIS summit.

I think it was Andrew Feldman from Cerebras.

Cerebras.

And he was talking about this. We were talking to CJ at MongoDB, and he was saying hyperscalers are turning away their top customers because they don't have capacity.

Yes.

So how do you think about that as a CEO in this era with everything changing around and your customers and your company and all this kind of stuff?

Look, obviously, everything you said is true. There is a constraint in computeright now because there's tremendous amounts of demand. Everybody needs more compute. Your video model friends need more compute to be able to build better videos and edit them.

Your ChatGPTs need more compute to be smarter, to be able to satisfy and not have you frustrated. Your enterprise models need more compute because we need to put more intelligence into our enterprise capabilities so we can write, rewrite software with opinions.

So that's the point. The way you think about it is you're in for a very long build phase in the industry. I think in the next 10 years, it's highly possible that 10 to 20% of our operating spend moves more towards technology than it already has.

So there's a huge amount of spend that's going to happen in technology. It'll happen in people. We're going to need more AI-ready people. So I don't buy the jobs argument. I think we have so many things to do.

There's not enough people to do it. Either it's retraining or hiring more people that go to understand this AI stuff. I think in that process of technological upheaval, people are going to want more robust security infrastructure. So from that perspective, we know the demand is there.

We just have to make sure we get both products, new products that serve that demand. And also, we have to make sure that our existing products don't fall short in customers' expectations of what AI must be embedded into those products as well.

How do you think about this with your workforce? Are you checking on people if they're AI-native? I mean, we've interviewed some CEOs that are really strict about this.

It's very hard to check on people who are AI-native.

You're not deploying little tests?

I think there are two or three things you can do. One, what we're doing is, you know, when there's no expert, then people learn from each other. So twice a week, I run this meeting called AIAIO. It's like the old McDonald had a farm.

It's just not EA. It's like AIAIO, OK? We get the top 24 technical people on a call every two days for two hours in the morning. And they walk through what they're working on, how they're thinking about it, why they're doing certain things.

So it suddenly, you know, gives more strength to the other 23 people to understand, "Oh my God, this person is a smart engineer. Here's how he's thinking about it." They get a chance to ask questions. They get to learn from that person.

And vice versa. We do that every twice a week so that people start understanding what's important and how do we get it. At a more micro level, it's happening in teams. You know, we will take a team and say, "OK, go out and take a third of your workforce and make sure you're hiring through hackathons because they're learning themselves."

I think those are the true AI-native people. We infuse those AI-native people into teams and say, "Keep hiring until you get to make sure that these people are more people in that team than people who've been there before."

So once you start overwhelming these teams with more AI-native people, you start watching that you start seeing a change in behavior. Features start getting out faster. Some of the people who've been there for a longer term, who haven't played with AI, start playing with it more.

So you really have to create sort of a transformation. And if enough people start being part of the transformation, I think some people who don't get it will self-select out. So that's the approach we have. It's unlike the approach of some other people who are saying, "Oh, I don't need a third of my people because they're not going to get it."

That's not the way to do it.

When did you start hiring from hackathons?

About, I'd say, nine months ago.

Really?

Yeah.

How did you come up with that idea?

How am I going to know that if you know how to use OpenClaw, well, how am I going to know you understand what an agent is? If you're not playing with it already, you're not sitting going back home from work saying, "I can't wait to get my hands on the new development that came out yesterday," or use Azure Foundry, or use Anthropic.

If you're not going home and figuring stuff out yourself, that's a problem. If you're not curious and not learning, where am I going to find these people?

Damn. That's pretty creative. So before speaking with you, I spoke with Carl, who's on your board. And I asked him, like, I asked many people who's around this office if they have any questions for you, anything I should ask.

For some reason, no one in the office would answer the question besides Lee. And I'll ask that afterwards. But with Carl, he mentioned he was on the hiring board. He was on the team when they hired you, and that you were a bit of a controversial hire.

Most likely, yes. I would be a controversial hire, for sure. I've never done cybersecurity in my life. This is a cybersecurity company. I've never been a public company CEO. This is a public company CEO job. And I've never sold enterprise.

I was a consumer guy. Other than that, they got everythingright. So yes, it must have been controversial. I was in the room.

What was your learning process and getting up to speed like?

Imposter syndrome all the way.

Do you still have that?

Possibly, sometimes, yeah. Because I, like, didn't grow up in this stuff. That's why I'm blessed to have people like Lee around. You know, when I started, I had Lee and Nirzook around me. And I'd sit in meetings, learn a few things, keep looking at Lee, what he thought about what I said, ask him after everybody left.

And then I'd call him on my way home, and I'd call Neer on the way in and say, "Hey, Neer, what do you think?" So I spent all this time trying to get it out of people in terms of what they thought.

And over time, I started understanding pattern recognition in terms of what works, what doesn't work in cybersecurity, what's theright thing to do. You know, we did our first acquisition because we wanted to do something in a space we had no capability in.

So you learn over time. I began to learn. I still rely a lot on him and other technical people in the company.

His question was, "Why do you like LinkedIn so much?"

You know, there is a conspiracy. There is a conspiracy in my I think the other people didn't ask you to ask me questions because they all fed it to him.

Really?

The conspiracy is what happens is, in my moment of paranoia, I'll go troll X and LinkedIn to see what's going on in the market. I'll see a post from some of our competitors. I'll see a post from somebody about something.

And it's probably between the hours of 4:30 and 6:30, people will get a copy of that LinkedIn post and say, "What are we going to do about this?" Or, "What do you think about this?" And that's why it's like, "Holy shit.

He's on LinkedIn again."

Has that ever led to making, like, pretty big business decisions?

Well, yes. Our general counsel I hired off of LinkedIn. I had a dinner last night with somebody who I found on LinkedIn because I think he'd be great for our company. So damn, it's a great place to find people.

You get to go look at everything they say, if they're intelligent or not. They're not interviewing when they're posting on LinkedIn,right?

No.

So if I can read what people have written over the last four years on LinkedIn, I can tell you who they are without having to ask them. I bring them in the room for half an hour. They'll, you know, if you can't fool me for half an hour, then you shouldn't come work here anyway.

This is, like, probably one of the hottest takes. I don't know anybody who likes LinkedIn.

I think it's the way to think about it. I started at Google in 2004. And when you work at Google, you build this sort of thing where you don't meet someone before you Google them. You're like, "Obviously, I can find out a lot more information about this person, this product, this capability because I work at Google."

Not that you can't because there's no special Google version for Google people. But, you know.

There's not?

This becomes, of course, not. It becomes part of your sort of the way you do things with Google people. And then you get in the mode like, "Well, if there's any information I can have, I should have it."

So by definition, I will go find out whatever I can. Now, LinkedIn and X are wonderful places to find out what's happening, both from a business perspective and from a technology perspective. So my team doesn't like that because then I keep sending them only things like, "I have my days.

Plan. I don't have time to answer your questions."

What does a typical day look like for you?

Like, podcast in the afternoon, breakfast in the morning, golf in the evening. Just kidding. No. Enterprise jobs are interesting. They're, I'd say, 1% inspiration, 99% perspiration. So in some way, shape, or form, either you're fixing a product, you're adapting strategy, you're trying to hire people in places, or you're trying to meet customers.

But look, I joke that every job in the company which requires some accountability is already taken. Like, I have a CFO. He's responsible for finance. I have a marketing person. It's like I kind of don't have a job,right?

My job is to orchestrate these people and the strategy. So my job is to set the north star, define the strategy. My job is to make sure I resource the north star. If I want to go win in this, I just need to understand how many people it takes, how many hours does it take, what other things do I need, and then give it to theright people.

Then my job is to remove obstacles from their way and course correct them if they're falling short. That's the job,right? It's course correcting, getting people lined up behind you, making sure that you have theright people in theright place.

You know, when I came to Palo Alto, for the first three months, they all sat and looked at me. "Who is this guy? He's strange. He has different ideas. What's he about?" Then I realized we didn't speak the same language, didn't understand me.

So I sat back on a weekend, I wrote down something called my belief document, which basically describes why I do certain things a certain way, what I believe. And then I walked up on a Monday morning and said, "Allright, guys.

Here's my belief document. This is why I act the way I do. Now we can debate this. Once we debate it, I'm happy to change certain parts of it if you don't like it and if I agree. But once we decide this is the way we act, then we're going to act like this."

For example, I get really tough on hiring senior people. I want to spend time. I want to understand who they are. I want to have references. I want to meet that person. And people always say, "Why are you slowing me down?

I need to get going. I need to hire this person." I'm like, "Listen, you hired the wrong person. They have 500 people that work for them. It means 500 people in my organization is headed in the wrong direction.

It's not climbing the mountain I want them to climb, not doing the way we all want to do it. So it's an important hire. I'd rather have nobody and do it myself until I find theright person." If you explain it like this, I'm like, "Oh, I get it."

Otherwise, I was just stopping and saying, "You're not hiring that person." I was like, "Oh my God, he's frustrating me. He's not letting me hire people." I'm like, "No, I'm not letting you hire people for a reason." So sometimes you realize as leaders, we don't communicate the why.

We spend we just tell people a lot. "Go do this." If you explain the why, you know, people actually do a much better job. I always have this thing like, people don't come to work to screw up. It's like, "Good morning.

I'm going to go to work, and I'm going to do the worst possible job I can." That's not how people they all come to say, "I'm going to do the best." And at the end of the day, it says, "Holy shit.

I didn't do the best," or, "Somehow, my boss wasn't happy because he seemed or she seemed like they weren't happy with what I did." Well, maybe you aren't communicating.

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Founders scale faster on Deel. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit deel.com/sourcery. That's D-E-E-L.com/sourcery. I have two questions. One, what were the changes that you made, if you made any, and two, what are the key traits you look for in leaders?

I did make changes over time because, you know, we've morphed our business from, you said, it was an $18 billion business to a much bigger business that required us to get into new product categories, sell new things. So you definitely like, you need people.

I have this framework which basically says, "Ever built a house? Have you yet?"

My mom, I grew up, my mom built houses.

Yeah.

Lots of them.

You build a house. You hire an architect,right? What's their job? They sort of build this beautiful aesthetic. Here's what it needs to look like. Here's what's going to be beautiful. Then they do a bunch of drawings. Then you get a builder.

He or she starts building the house. And then when they're gone, there's a person who comes and maintains it,right? You know, makes sure stuff's working. You'd never let a maintenance person be the builder, would you? That'd be a bad idea.

No.

You would never let a builder architect your house because they may not have the design aesthetic. You probably don't want an architect building your house either because they don't have the capability. Yet at work, we expect our people to be all three.

Say, "I have a new idea." Great. Why don't you architect the idea? Then you build the idea and make sure when it's working, you run the idea. And all of us have a different mix of capability amongst us.

We're part architects, part builders, part maintenance people, and everyone's different. So for almost every leadership job, I need part architect, part builder. And if I get someone who's too architecture-oriented, then I need to make sure they're coupled with the best builders in the world.

So I think building teams is a combination of finding that fit amongst people. And that's my job as a leader, is to build a team around me that is part architect, part builder, and know who's capable of what, and then make sure they have people with them who can build.

So it's not like, you know, there's a consistent set of traits across people. It's always a combination of people in terms of what they can do. Yes, but there's a basic level of smarts they must have. Because if you're not smart, if you're not creative, there's a risk you will miss the inflection, you will miss the and then you have to have a little bit of what do you call it?

Super aggressive? A little bit of.

A little aggressive.

A little bit of.

The just do it.

Go get it attitude. How's that?

Agency.

There you go. I give you.

We all have a little bit of agent in us.

I have to give the agency. If I don't give agency to people, then they feel constrained. So yes, we all have to have a little bit of agency from people. My board gives me agency. I give my team agency.

But you only give agency with theright guardrails and training,right?

Yeah.

Otherwise, your agent is going to do shit you don't want it to do.

So where did that develop for you? Like, how do you get this? Some people call it a chip on their shoulder. Where did that come for you?

The chips are in high demand.

Yeah.

Just kidding. No time to have the hanging on your shoulder. Sell them. They get for a fair price. No, it's not a matter of chip on your shoulder. I think it's learned behavior,right? Over time, you understand businesses. You invest in businesses.

You operate businesses. You figure out what patterns make it successful, what patterns make it not so successful. So you kind of pattern recognition over time. You figure out and you try things. You know, I'm sure all of us have tried things that haven't worked as well as they should have.

And I'm sure we've tried things that have worked out spectacularly.

I want to ask you this because you have such a unique perspective of all the points in your career. What was the day-to-day like at Google and at SoftBank? Because you explained out really well what it's like here and how it's changed over time too.

Google's a great place. Still is. Was a great place when I was there. Because remember, Google had this interesting business,right, where we all kind of spent time on search. And Google had built the best or had built the best commercial model around search in terms of how to monetize it, in terms of search advertising.

So for the most part, Google was a scale problem. How do you keep scaling the business in such a way that the business keeps working, nothing goes down, nothing fails? There's constant innovation in the pipeline, which keeps attracting more and more consumers to consume these internet kind of services, whether it was search, whether it was YouTube over time.

And then how do you build a monetization harness around it, whether it's search ads or

video ads, et cetera. I believe that most companies take on the form of their leader. You know, Larry Page had this firm belief that great products win. And Google is product-obsessed. If you think about it, there are so many products even today that don't make money,right?

Gmail doesn't pay for itself. You and I get Gmail for free.

Google Maps doesn't make as much money. But it was a product. You know, Google Chrome doesn't make money. There's so many products that Google built over the years, which are great products, which they didn't think about a monetization model because that was the philosophy.

Go build a great product. If it's great, we'll figure out a way to monetize it, or else it's going to contribute to the brand. The good news is it kind of worked. And as I said, it was a bit of a scalability thing.

Now, at Masa, it's the same thing. We take on the form of leader. He's the oldest man I know with the risk appetite of a teenager. As he gets older, his risk appetite becomes bigger. He keeps things very simple, and he's very focused on winning.

I remember he told me once in a I'd made an investment with him in a company, and he saw me a bit disturbed, and he saw me grinding away talking to the CEO multiple times a week. He said, "What are you doing?"

So I'm trying to talk to the CEO because, you know, when we invested six months ago, we thought this was going to happen. And I think we need to coach him because he's down 50% from where we thought he was going to be.

We can coach him. We can get him back on course correction. In six months, he should be back where we started, and then he can go from there. He looked at me and says, "If you put in that much effort on the company that's doubling, they might quadruple.

You might make more money on the one that quadruples than you fixing the one that's broken." That's an insight. Like, that was an insight from him to me. It's like, as operators, our tendency is to try and fix everything because we don't want things to break.

As an investor, he said, "Double down on your winners. They're going to be way more interesting for you than the ones that are going to not make money."

Concentration and power law.

Something like that. Yes. See, if I knew all those words then.

So I know we're well into the conversation, but is there anything that we haven't covered that you want to talk about?

You can talk about anything you want. We can talk about

AI. We can talk about spending. We can talk about whatever you want.

So we had a special guest in here earlier. Can you explain who your newest intern is?

Oh, my son was running around here. He loves coming hanging out here. He's hanging out here, and he was very intrigued by all the cameras being put and all the stuff being arranged because some famous podcaster was going to be here.

So he came over, and he sort of was part of the arrangement. And today, he wanted to see the fruits of his labor. So he came by to see what a podcast looks like. I think he's become the newest intern in our comms department.

I mean, he'd be great. I mean, you should have him run some strategies over there. I don't know. Does he have any content ideas for you?

He's always full of ideas on how I should do things differently, but they'll be better. So.

I was curious. So as we think about the next 12 months, I know you think a little like you think two years out or so. Do you think, one, do you think that will compress? Has that compressed over time?

You're going to think two to five years out. I think at the pace at which we are, things will happen much faster than they used to. So you just have to believe that what will happen, what you thought was going to take five, is going to take two.

What you thought was going to take ten, is going to take five. So you just have to change your horizon in terms of what you want to think. I think if you go back and think about when we went through a technological sea change last like this, it was in the late '90s with the internet.

There were crazy valuations on certain companies because people thought that these things were going to grow infinitely. And we're seeing sort of a bit of a phenomenon at this point in time, similarly with the markets perhaps getting ahead of itself or not, where it believes there's infinite capacity, infinite demand for AI, which I think there is.

I think it'll be interesting to watch. Some players will move around because the market moved so fast in terms of capability. At the present, the market is pricing and perfect execution for every company. Every idea that you see, the market wants to reward it because it thinks that their returns are outsized and the gains are going to be so huge that it doesn't matter.

Even if you fumble your way to some amount of success, it's going to be a lot better than where you are today. I think two years from now, that would become less apparent. I think the market would have figured out its fair shares of failures and successes.

And the market will get more discerning, which is what typically happens at that point in time in the cycle, which usually takes five years. But then we talked about compressing timelines. I think it doesn't take away from the immense appetite for AI.

It doesn't take away from the amount of reimagination and redevelopment that's going to be needed from a software perspective. But yeah, I think the market could go through some stumbles and bumbles over the course of the next two to five years.

One of my favorite questions that we ask every interview is a partner question. So one of my partners is Brex. They're the performance credit card. They're super intelligent finance.

I was an investor in Brex when it had just started.

Before the acquisition?

I was an investor at a billion-dollar valuation when they were there. My daughter used to work there. My son-in-law used to work there.

No way.

I know Henrique really well. Yes.

Oh, amazing.

Pedro. Yes.

So this is great. This is my favorite question. So because they're all about performance, I like to ask people, you've had an outstanding career. You've learned from Larry. You've learned from Masa. You learned from yourself. You learned from Lee.

You learned from everybody in this office every day. But I'm curious if there's anybody throughout that arc that has really inspired you and kept you motivated.

Yeah, I struggle to find one role model in life because every role model has certain parts of their life that you don't want to emulate. But there are certain parts you do. And I think you don't have to go sort of spend time with them incessantly, but you obviously get a chance to spend time.

But look, take Elon. What is not there to get inspired by him? He built electric cars, which when people didn't think electric cars existed, he put a rocket up in space. He's got things landing in Mars and the Moon.

And things he's done since NASA was funded for years and didn't do as well as he's doing them. So when he puts what he's got Starlink. He's got, you know, satellites up there where we're sticking them on our cars, boats, and planes to make sure that we have connectivity.

And so there's tons of stuff that he's done, which is so radical, which none of us had ever thought. And I think the principle he's sort of explained to us there is if you take a really hard problem nobody's working on, if you get itright, you win.

And you win big. And if you look around you, a lot of entrepreneurs are busy trying to solve small problems because this is the problem they can see. They can see that far to solve the problem. Elon cannot see when he comes up with a problem he's trying to solve.

He can't see that far, but he thinks if he tries to put his mind to it, that problem gets solved. Like, you know, we're talking about space companies, space manufacturing, space like mining, space data centers. Shit. No, don't know how to where he started,right?

But they're out there thinking about it. Buns of people thinking about it. So I think that's inspirational. I think you look at Masa, you know, he's got a crazy appetite for risk. You know, he's taken that business, which used to be SoftBank, was a software bank.

He used to sell software, package software when he started his company. And he's pivoted it 20 times since then. He's been the richest man in the world, and he's become poorer. He was the richest man for 80-some days.

And I think back being worth nothing, he's gone back and built himself. So these people are inspirational for different reasons, for their creativity, their innovation, their big thinking, for their relentlessness, for their persistence. People like Larry, you know, there's so many people who can inspire you in different aspects of life.

So you just have to find the person to inspire you for the particular thing you're looking at. I was somewhere last week. I went to a conference, and Steph was on stage speaking, and my son was there listening to him.

My son is obsessed with basketball. And Steph talked about this next-play mentality. It's like, you can't win if you can't get it at the last play that you missed. You got to focus on the next play. That's kind of that's an interesting lesson, whether you're in business or you're in sport.

So you can find different people to inspire you.

As you get more and more successful, how do you continue to find people that challenge you and don't just become yes-men?

Oh, I'm constantly feeling like I'm an underachiever every time I look around me. There's like young people running large hedge funds who've done so well in investing until the time they go back and reinvent themselves. There are people who put stuff on Mars.

And compared to their achievements, I'm just running a regular cybersecurity company trying to make sure that, you know, we protect the world.

So what comes next?

Next comes tomorrow. And tomorrow's going to be a wonderful day. It's going to be beautiful. We're going to wake up really excited about the day. We'll work hard, and we go home really excited to hang out with the family.

And then it'll be day after. We'll do the same thing.

Pretty good answer.

You don't have to. The problem is if you set too many expectations on yourself, you're bound to feel disappointed. But if you don't set that high an expectation, if you set an expectation of doing your best, then good things will happen.

Were you always this calm?

This is my calm in terms of it's kind of the karmic calm. The karmic calm is that you have to try to do your best. You have to put in heart and soul into it. You have to want to do it.

But do you see like it's like kind of contradicting because like.

I understand.

You're so paranoid, but you're also like, it'll be OK.

Life is a series of contradictions,right? It's the yin and the yang. Like, if I sort of torque myself up every time and I start feeling freaked out if I fail, something doesn't work out, I'll be a mental mess, and I won't be able to make anything happen.

So I have to work on a principle of do your best, and then things will take their course. And if it works against you, wake up in the morning, shake it off, and do your best again. This is the next-play mentality.

If you get hung up on what happened yesterday, you won't be good. You can't be somebody else because if you be somebody else, then you'll lose all the other things that are you about you. So yes, you can be all the things I said from a go-get-it-done, be paranoid because you want to win.

At the same time, you have to have some degree of inner calm to be able to deal with the moves, the pressures, everything else to jog.

Good. No AI psychosis over here, I guess.

There's no AI psychosis.

Amazing. Well, I think that's a great positive, optimistic way and place to end it. Nikesh, thank you so much for taking the time, letting us rearrange your office.

Thank you for coming all the way here.

Of course.

We appreciate that. Thank you.

My pleasure. Thank you. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, Sourcery.vc, where we deliver a once-a-week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sourcery today.

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