Nikesh Arora: Mythos is Real, Analytical SaaS is Dead, and Google can be a $10T company

The big daddy of the cybersecurity space. Palo Alto Networks is an outperformer in the space. CEO Nikesh Aurora. Despite come as news to you, but humans have been riding back court for a very long time.

I spent 10 years at Google and in a Google search was democratizing information. If you think that analogy and think about what AI is doing, AI is democratizing intelligence. Why needs a way to keep track?

Yeah. It's not the court. You've been the CEO of Palo Alto Networks for eight years. Coming up at eight years this week, years.

“And I think when you started, it was $17 billion mark”

a capital fiber member correctly.

And this morning, I checked, it's $238 billion.

Which, if you listen to what we said yesterday, now that you passed a hundred, you're more likely to actually ten X. So the first ten X was actually much much harder. So you're on your way to a trillion dollars.

From your mouth to God's ears. Why, I think you are. Okay, so let's just double click into what you see because you are sort of in a really interesting position to see all of it.

You see the birth of AI. Maybe you've seen the rise and fall of sass. All the models talk to you. You were one of the rise again, right? The rise again.

You were one of the first in the few that got access to mythos. So just, let me just push the button. Go and a cash start. Well, first of all, thank you for having me here. I think AI is exciting.

I think it's exciting to see all the stuff that's gone down

the last, possibly 24 months. I think Sarah just said it. They were right and anticipating the huge amount of compute that was going to be needed. So all that stuff's going on.

But you can see that this notion, which we talked about briefly last time, that AI is really democratizing intelligence. What that means is, I have 250 people are marketing. They produce varied forms of output.

Now you can get 90% of the output to be consistent across those 250 people. I have 5,000 people who talk to customers. There's my failure mode is when 5,000 people do different things where people say,

I want to talk to Joe because he knows how to solve a problem and Jim doesn't. So now you can get 5,000 people to act almost consistently in their interactions with people on the other side.

“But I think it's going to have a phenomenal impact”

to how we run businesses, how we operate. It's going to change the entire landscape. Now in that context, you've touched upon Mizzos and our David has been very involved with this. Mizzos has shown us that all the bad code

that humans have written all the last 50 years can be assessed by AI and shown the vulnerabilities can be shown. We tested for six weeks and in six weeks, we found what have taken us 5 to 7 years. Wow.

It's said that one more time. In six weeks, we found vulnerabilities which have normally taken us 5 to 7 years to find. So Mizzos, these are vulnerabilities where? Sorry.

These are vulnerabilities in your own code base. It's in your own code base. Oh, wow. So Mizzos was not oversold. It was legit.

The capabilities of AI in being able to assess vulnerabilities in code are real, not just that. If you put it on ultra mode, which is persistent thinking, so it keeps trying until it gets an answer, you can actually desi chain vulnerabilities

“are you finding a new attack path into your capabilities?”

Now, we pride ourselves as a top percentile of companies that test our code because we're in cybersecurity business. If you take that and compound that across all the companies that exist in the world that write their own code

or the 10 million developers write code,

this thing is going to find stuff which wouldn't have taken us as 10 years to find. How much did it cost? Did you track the token costs was it $100 million, $10 million? No, it was in the low millions.

But again, the cost is set. The cost goes going to come down already. Open the eyes, go to model, which is cheaper, and more consistent. And trapeze come out with another model.

Why you buy the hype? It's not hype. It's true. That's the way. It's the capabilities.

You know that. The capabilities are true. Yes. I mean, you saw IBM announced a project for $5 billion to fix open source.

That's the biggest problem. What would have happened if Claude didn't have the restraint and they put it out in the public? Do you think it would have been a real attack vector and caused chaos in the corporation?

I think we're three months away. If not already there, from this being available in the world. Okay, open source. Yeah, just three months. Yeah.

Because I mean, we've been saying that it's roughly six months away before mythos, level capabilities. Yes. Available in Chinese models, you know, open models, whatever. But you're saying three months.

Well, look, there's what is 4.8 is already out, 5.5 is already out. They have similar capabilities. And look, you don't need to crack the hardest code to crack.

Just take an old industrial system,

“which is running an old decode on the edge.”

You can find that vulnerability reasonably easily. So we're in a race right now between the cyber defenders, finding these vulnerabilities and patching them before the cyber attackers do the same thing. Yes.

And how do you feel like we're doing in that race? So not as well as we should be doing, which is great for our business, but that's a different story. So like, every company has to go look at their code base and figure out where the vulnerabilities are and fix them.

So if you talk to CIOs today, their biggest problem is, all the vendors are showing up saying, please patch my piece of boxes that hardware that you have, please patch my code that you have because I found vulnerabilities fixing. While the CIOs are busy finding their own vulnerabilities

to fix their own vulnerabilities. And then this huge thing called open source, which nobody knows quite how to solve. So is it fair to say that it has model capabilities go up, systemic business risk of large enterprises also goes up?

On the cyber side, yes.

There are antidotes being built by people like us and others,

where we're going to provide some capability where you don't have to patch everything. But like, Sarah said something very interesting around harnesses, memory and context. Right?

The part we don't talk about here is organizations don't have memory and context and everything they do every day.

“That's why in this story a lot more data enterprise wide”

to learn what good looks like and what bad looks like. The same problem is in cyber security. We need to collect 10 times a data in the enterprise on a cyber perspective to be able to understand how to defend ourselves against the AI attackers.

Do you think that the traditional companies, like the SaaS businesses that have existed in this world, what is their place? As all this knowledge becomes more persistent and stored, what happens to SaaS?

Well, you see, SaaS is different pieces, right? If you're an analytical SaaS company, it's over. It's over.

What is an analytical SaaS company?

Somebody that says, I'm going to collect a lot of data for you and analyze it for you. I don't need you to analyze it for me. I can run models against data and analyze them myself. So if you think about, there's a lot of,

every SaaS company has a marketplace. You can buy Salesforce marketplace. What do they say? You have Salesforce data. I'm a marketplace app.

Take me and I'll help you analyze the data. I don't need you. You don't need that. I can just go run an LM against the data. So the entire incrementality that has been sold

as incremental software modules to all of us doesn't need to be sold to us because I'd much rather have LMs running against it. Interesting. You bring this up.

We had an instance with a SaaS product with 20 seeds. Nobody was logging in and using it, but the data was there. Yes. So we created like three accounts.

We've got rid of 17 connected it to Slack, connected it to Cloud. And now everybody can interface it. They're a natural language. And we've reduced our bill by 90%. But not just that.

What are you going to do next? Jason's like, you're going to take data from different products. But them in one place. They're analytics against that. I want my data for my sales reps

by productivity data, my inventory data from SAP. I want to all one place. So I can run analytics against it and say, who's selling a lot? Where do I have less inventory?

Let's build inventory in the region where my sales people are extremely productive. To run that query, you'd have to have talk to three different SaaS products. Tomorrow, you can throw all the data in one place.

So that's kind of category one. Analytical SaaS data. Catalytic category one analytics dead. Yes. In the medium term, you've got all these bounces today

into more of these are marginally irrelevant. Infrastructure software undervalued. What is infrastructure software? Stuff that gives you databases. You collect data into it.

Stuff that allows infrastructure to work. But there is, say, database software. This is like Databricks, snowflake. Like Databricks, snowflake, MongoDB, Oracle, or all these things. You need more storage infrastructure.

We are going to need 10 times a data store and enterprise than we have today, 10 times. So anything that helps you collect infrastructure data manage it, you need. I think the category in the middle is called,

it's called a system of work or system, you know, a record people call them. Those are deeply embedded in the way business is work. I have 6,000 salespeople. They know how this works.

What's going to happen is step one. We will take away UI and let agents with the work. UI enterprise software and consumer software UI

“is the worst thing we did as technologist.”

You had a couple of examples of this. You told me this story. I don't know if you want to repeat it of this one company. They tried to hold you hostage on a license. Yeah, that was analytical sauce.

So that's over. And you just pointed AI at it and you just-- Yeah, so we just got rid of them. That's a different issue. But I mean, think about it today.

We spend our lives having product managers design UI. So all humans can interact with data behind the UI.

and I say, I just tell an agent, look, figure out from my sales call, figure out the key points, and go post it into whatever sales tracking system I have with this Oracle or Salesforce, right? An agent conceptually should be able to do it.

It should be spending trillion dollars building these agentic backends. We need these agents to be able to do it. If that happens, UI goes away. If UI goes away, I can rewire my system of work.

Right? I have sales guys show up and says, I had the sales call. Do all the paperwork and all the shit that needs to happen to the back of the company, and just-- I'm done.

If I can change the way work happens, which is where you will get true efficiency, where five people become one in a company, all these SaaS software that does system of work needs to be re-engineered for the next five years.

And it's also happening passively, which is really interesting. It's looking at email. It's automatically taking the Zoom transcript and summary. So the sales system of record is now, like, you don't even need to input it.

It's like, I already have the Zoom call notes. I have the deck-- the deck was made. The sales deck was made by AI. It's just-- we're all going to be looking at a chat window

and just saying, here's what I want.

Get audit trail, it becomes a lot better, because humans are not touching your data. It's always been managed by agents.

“So I think the whole system of work system”

of record gets re-invented in the next five years. Yeah, there's no data entry. That's an interesting point. It's not about national security for a second. I just want to maybe zoom out.

So the one side of mythos, as you said, is like the value that it has to you and to enterprises-- the red team version of mythos where foreign state actors can essentially create economic havoc inside of a country.

As these models escalate in their capability, what do you think should happen when these models are ready? The set truth is, in a year, there's a few thousand breaches or attacks that happen. They happen for pretty rudimentary reasons.

It's not because somebody cracked the heart to crack thing. It happens because 89% of the attacks happen because credentials get stolen. But is your name in password? In password is password.

Yeah, I'm sure it is. Did you have dollar sign? Dollar sign pass. That's fantastic. Well, don't see it.

You know, you're head of everybody else. So 89% of breaches happen because of simple things. I don't think we need more models to go crack the stuff.

Now, these models can attack critical infrastructure

and things we try and protect from a national security perspective. Yes, we need defenses there. I'm not worried about the national security part being protected because they're very on it. They're the right people.

They spend 10% of their budgets and ID on security. I'm worried about the small offices across the country where they're using some piece of packet software and you're running a dentist office or doctor's office. And remember when the change health care got breached,

every physician's office shut down. Shut down in advance of where? Because the ransomware changed health care. That's what's the clearing system. That's when you don't help that to actually

have you billions of dollars of credits to the physicians to be able to run their businesses at that point in time. That's what one should worry about. It's less about the big nuts.

We'll get credits. Less about cracking some PG&E power generation facility. It's more economic chaos. Yes. And so what do we do?

I don't think there's a sort of a silver bullet. I think this will take time.

“I think this will basically take a while”

until every system gets upgraded when you fix it over time. I just think they increase the terminal value of the industry, right? Do you think that there's a world in which these models become so good that you could see yourself advocating for more nationalism around how they're controlled

and how they're managed and where we point them? Or do you think there should be maybe a set of these models

that never see the light of day that only the NSA

and other folks get have access to? Or guys like you? I have a slightly differentiated reabout models and how they live in the world versus what we heard earlier from an open AI perspective.

I think I still believe models are going to become a utility layer. You will be able to buy intelligence on the fly. Or we can say, I don't need 180 IQ person to go do the task, give me 120 IQ. And I need 250 IQ to do the task.

I'll pay $10 for this half a 1 cent. So I don't know, there's a 1 size fit. It's all giving the most up-to-date model to answer my customer calls and say, sorry sir, I have no idea how to solve it for a problem.

So I think models are good differentiated from utility and perspective.

“So we'll get already what's happening in the market, right?”

The profit pools, I don't know, applications dotted models. More, I'm going to say I talked about codex running away. She didn't say, open AI is running away. She says codex is running away. Just say, just the way I'm sure Dario says,

for odd code is running away. So you're seeing that they're attacking profit pools. They're attacking profit pools

The profit pools are in applications

that companies can use.

“The profit pools are not in model usage by companies”

because most companies have no idea how to use models. Look at these companies in a way, open AI in a traffic as the new Microsoft office coming in and doing all applications, all productivity software for organizations.

No, I see there's going to be application companies that you're going to arbitrage between models and solve your business problem. So you still think they won't go to the application library because this is a big debate.

Should you engage with open AI and train their systems to then take your business from you? And anthropic keeps releasing their legal model, their accounting model. And it does feel like, in order for them

to hit their revenue numbers, they might need to do what Microsoft did, which is released the office product on top of the operating system. If I'm a company, I don't want to write every piece of software myself.

I want my HR system software, which is agentic enabled, NEI enabled to be delivered by some application company. Could be a new AI application company. I want my sales management system built

by the new agentic AI sales force of the world, whether it says for somebody else. So I want applications. Now what Sarah said is the profit pools are in the application layer.

“That's why they want to be the application layer.”

So I think we're still waiting for that layer of companies to be invented or created where applications will set. Because 50,000 companies need the same application. Why would I build it myself? It's highly inefficient.

It's silly for me to use OpenAI directly, and rewrite my entire sales system because I'm smart. I'm not. I want somebody to do it from. So I think that layer of companies is still not fully formed.

We're still going to be waiting for it. We want to control plane, a harness, and then-- That's right. They will build a harnesses in the memory into those application layers.

Now the question is how big is the application layer? Is it one application? Is it one enterprise application that does everything? Or is it specializing that it? You did it.

And you kicked out the software vendor. You did it because they were being abusive and pricing. So that-- We simply use a different vendor. What's that?

We swapped out for a different vendor. We just took more control. Love it. So it really is a pricing issue. And that's where the SaaS apocalypse in some ways

makes sense. They're not having pricing power. Because you could say, I'll just put 10 developers on this,

and I'll save $10 million.

Yes.

“I think the part back to some outset about the regulation”

or whether you want to regulate these higher-powered models, the questions that some point in time, when these newer models, which are even more powerful, get built, they will come at a different price point. And they might have to go to a certain vetting process

to understand what their capabilities are. But I think we're in a global race. I don't think holding back our models for three to six months is going to help us any. Somebody else is going to put them out in open source.

I was shocked to hear when I started to see you on one of these model companies and says, the entire waste of their most recent model can fit into USB stick. Now, say that again, the entire waste--

entire model waste of their newest model fits on a USB stick. That's the IP. That's incredible. Because all the data can be distilled in under 24 to 48 hours and model comes out.

Be curious. So that's the IP. So are you telling me that we get hold on to that for six months? Wait, we have a debate about how difficult it is to make a frontier model.

Some companies are starting to think about making frontier models using their data advantage to build their own. Have you thought about that, Palo Alto? Because it does seem like you have proprietary knowledge on how security works.

Could you build your own large language model or a VSML, a small language model that would give you some advantage? Here's the part. Nobody talks about?

Yeah. Is the false positive rates on the models? What is the false positive rate on 4.8 and 5.5? No idea. You guys want to talk about it?

You should. The false positive rate on myths was 30%. Oh, wow. Do you want more thought? It found something, but it hadn't.

Yes.

So the problem is, it's great for attack.

It's horrible for defense. Because if it finds 30% of the time, we'd find something that I found a problem and he said, let's plug the hole. Wait, there wasn't a hole there in the first place. No missile inbound.

Right. So now the same problem applies in enterprise. If you use the model without the right harnesses, the right training, you could be running into 10, 20% false positive rates. Let's use the model to pay, I don't know, insurance claims.

Yeah. Oh, great. 10% and 20% false positive. I just lost money. The stochophantic nature of these is ridiculous.

So the problem is not who wants to do a model. The problem is, how do you take that model with 20% of 10% false positive and make it 0.01% false positive? In my business, I want 0%. Without losing the false negative.

So without losing the negative, the false negative. Yes, but it's like saying, hey, let's take the new self-driving car.

And you can just sit in the car and it's going to drive you. I'm not putting my kids in that car with the 10% false positive rate. Are you? So there's a lot going to happen. It's supposed to model, which needs to happen to make this thing useful and effective

in the business context.

Let me slightly pivot for a second.

You were, for very long time, the chief business officer at Google, you were the president of soft bank. Now you're the CEO of PolytechnoWorks. So let's play armchair CEO. I'm chair CEO.

Armchair CEO. I'm still bristling from David Freedberg trying to create a distinction between founders CEOs and non-founders CEOs. Just saying. Just saying David.

It falls positive. Say it. It falls negative, too. Give us what you would keep what you would change and what you like about the following companies.

This is going to get a recording, put it out there. It's just more just getting your thoughts. You're one of the stars. You don't get people. I don't like that.

You're one of the slow business people. We're asking you if you should don't. He's ready. He's ready. He's ready.

Yeah, sure. Okay. Why you keep what you change what you like, what you don't like. Uber. You know what they want.

What if it dude? I can't talk about it. I'm the board of Uber. I'm not going to talk about it. I'm not going to talk about it.

I'm not going to talk about it. I'm not going to talk about it. I'm not going to talk about it. I'm not going to talk about it. I'm going to talk about it.

Don't talk about it. He's the CEO. He's a great guy. Okay. Waymo.

Show again. Be fired. Waymo. What do I look like? What do I look like?

The cars work. It's amazing. They should have more. In many more cities in the world. Faster.

I just, I would say that a tigeter. I think I was Google writ large. I think Google's underrated.

It's going to be the first to enter into a company in our lifetime.

“I think they have all the assets that are needed to make the successful.”

I think people underestimate. You can be a model company. You still need to have a sales force that convinces customers to go out there and embrace these bottles and buy them. If you think about it, three high-press scalars have the biggest number of sales people out

there. So they should... Part of why they're a little bit undervalued is just to go on their nature as hard to understand. I don't know.

You guys are smart of that stuff. I'm just a hard time CEO. I didn't say that. Let's just be quick. I was providing a thesis on recovery out of the Seth Pockel and so on.

Okay. Just to be fair, there's a way to segment that basket. Okay. And you're not in that basket. I think you're making a distinction about how people who are found or CEOs have the right

to take more risk and are a lot to take more risk. I was saying that. And I think you provide a unique counterpoint to that. And there's not a lot of false bonds that I've been involved with. The same was true of Jeff Weiner.

And I think there's a few other really great CEOs, but they are like Neo in the Matrix type anomalies. And I think you're one of those people. And there's a very rare kind of personality profile of someone that's willing to take risk and take ownership of something that wasn't there's in the first place and they make

it there. And it's a extraordinarily unique trait. Far more unique, actually, than being a scalable founder. It's being credible, safe. Yeah, good safe.

Pratimal, quick. That was incredible. That was incredible. That was really sick of it. The chat you've been taking.

Secondly, I think that's the best. Let's go back to our future. I'm liking this. He has been having more of it. They do self-aster.

Open AI. They should self-aster, right? They should self-aster. I mean, you said it.

“Didn't you just say it when you said it was here that anthropic seems to have improved the ARR much faster than open AI?”

You mentioned this statistic. They went all in on enterprise and I think including specifically. I think that's the conversation right now is, it's a race to take over the profit pools. If you are going to need tens and tens of billions of dollars every year to get what is that?

One gigawatt is 10 billion.

What are the costability? What are the costability? So what are the most exciting profit pools? So we got coding. That's been the breakout application over the past year.

It's massive. You've got infrastructure like you said. The new databases. I think cyber security is clearly one of them because it's very exciting. Patching cycle is so much more dynamic.

There's a slight difference. Yes. As you can see, these models are trying to be the enables of better cyber security, which is good. Because all of us need to use them to test and you're probably going to see. I mean, if you saw.

And Tropic is already made their cyber-capable model available generally so that everyone can use it. And OpenI has got one and sure Google has one too. To understand, this is a place where C-cells or chief security officers want to use it to test the code.

So it's really not a profit pool. I think we haven't seen the onslaught against the application software companies yet. I think there's 10, then 10s of billions of dollars in application software, which is waiting to get reinvented.

“As we talked about, I think eventually you'll see these people say.”

What if I took this $40, $50,000 a time down? Yeah. I can build a whole brand new backbone. We're using it. And it'd be so differentiated that it will cause customers to move.

We are seeing it as a playbook in the accelerators.

This is a $1,000 seat per year. $500 a month seat. Sash software. We can do it for less. We're going to charge them based on consumption.

We're going to take 80, 90 percent of the cost out.

As much as you're about to do with 80, 90. Two fastest places to make revenue. In enterprise, our replacement tabs. If you replace something, I already have a budget. It's easy.

I take something bad or replace something better. I get money. So, replacement tabs are beautiful. If you can replace an industry, replace the profit pool is great. The second place is consumer revenue.

It's a lot easier to get five bucks over it per user on a consumer side. Yeah. I mean, look at it. I think we collectively probably pay more on subscriptions per month than we ever did historically. And you saw your cable bill was high.

Yeah. Do you think that you're going to end up building more or less hardware in the future if you had to guess? Hardware, even today, is the cheapest way to manage low latency high throughput bits. You still need a data set? Yeah.

What's the data set? You're managing high throughput low latency bits.

“That's why if you look financial services is the most reluctant industry to go to the cloud.”

Because you increase latency. If you increase latency, you reduce profit. So, if you look at every of your largest financial services company that has gone in our J.B. Morgan Morgan market style industry. These guys, they're really hardware. Try to get them to run their business on the cloud.

They can't. Because they will have higher latency. They will lose money. Right. So, hardware is still be made.

I remember when I used to buy silver. They can. I heard Dell was done. Nobody wanted hardware.

I think Dell might be back to like 3,400 billion on market cap.

So, hardware is still going to be done. We're going to need it. It's the fastest way to move. Our hardware development cycles changing because of AI. Like are you seeing a lot of like generative design,

stuff moving in silica that historically was manual and long cycle? Yeah.

“But the long pull of the tent is ever designed, right?”

Yeah. It's a production. Today, you can't get a box produced because every. Every piece of hardware componentry is back ordered. Right.

Everything's expensive. And every factory in the world is back ordered because we're trying to build all these GPUs based, you know, chip cards for every year. You think the US is equipped to fill that supply chain need? Can we do that here?

10 years. 10 years. With a firm top down commitment. Well, I mean, the good news is that. I think the hardware industry is seeing a balance of a lifetime.

And generally when you see a balance of lifetime,

you can go commit 10, 20, 50, 100 billion dollars.

And when I see CEO on television commit to go 100 billion dollars, plan to go build more memory. So, that's good. That means they have the money to go put the money in the ground, literally to go build these things for the future.

“So, I think that gets us more certain that the.”

I think the tax incentive has a big. That's a lot to do with that. The accelerated depreciation on the the capex. You've got a hundred percent right off in the first year, right? Under the under the year.

Just the final question as we wrap up. You over the last eight years. You've grown organically very aggressively, but you've also been pretty acquisitive. You'll, you know, you'll take shots and they've generally worked.

So, you have a ton of permission in the market. When you hear what Bill Hackman said about how there's this kind of. Overbeat and companies, there's a few that get celebrated. That's a right pool for you to pick from. But some of that would require you to go,

maybe a little horizontally far afield, some would say. How do you maintain the discipline? Or do you see yourself at some point considering things that are. Not nearly so much right down the middle of of cyber. So, I think what until about an year and a half ago.

Used to buy product companies and throw them into our go to market engine. We could rewire their backends so they can work better with our go to market engine. So, for me, if I'm selling 10 million dollars to a customer next time I go to a later. If I can sell them 20, it's the most efficient way for me to advertise my go to market spend. Right?

So, that was the model. We played that. We ran that playbook to 0 to 150 billion. Then we go to a point and we say, oh, we see an inflection arriving in identity. It's going to be important from an agentic perspective.

It's a pretty perspective. See, about a 25 billion dollar company, which we closed three months ago. Now, it's actually a very different opportunity as presented itself. And the different opportunity sort of goes like this. If you can be the best at leveraging AI to run the most efficient enterprise business in the world,

your operating margin can be far in excess of the industry. And if you can crack that code, gross in the 90s, not in the 40s. Yeah. If you can crack that code, then it doesn't matter what you buy. Yeah.

If I think the problem right now is the execution problem. Most sub-scale companies cannot afford to go optimize their company and run it better.

then the street will say, fine, if you take something at a 20% margin and make it eight.

Your first lemonade was really tough.

“They were pretty skeptical and then you kind of shoved it in their face.”

They're pretty skeptical and they found a guy who didn't know cyber security didn't know enterprise show up.

Who were to Google and they're, you know, the track record of people leaving Google and being successful out of Google is still buried.

So basically you're saying the man who's open.

“I think we need the next six to 12 months to figure out how this AI settles down and how can we use that effectively in enterprises.”

I think if you think about it, you know, the people keep hoping that less people will be needed to run companies. I actually have a count of you.

“I think we'll have more people at Palo Alto on the technology side than we've ever had before.”

Because AI is causing everything to ask for a transformation. So I have more technical people today than I would have had if AI didn't exist. Ladies and gentlemen, see you Palo Alto Networks in the Kesheora. Thank you guys. Thank you, sir.