One of the biggest winners right now.

[music] The big daddy of the

cybersecurity space. Palo Alto Networks

is a now performer in the space.

CEO Nikesh Arora

>> This might [music] come as news to you,

but humans have been writing bad code

for a very long time.

>> I spent 10 years [music] at Google and

you know, Google search was

democratizing information. If you take

that analogy and think about what AI is

doing, AI is democratizing [music]

intelligence.

>> Money is a way to keep track.

>> Yeah.

>> It's not the goal.

>> You've been a CEO of Palo Alto Networks

for 8 years?

>> Coming up on 8 years this week.

>> And I think when you started it was $17

billion market cap if I remember

correctly.

>> There about.

>> And this morning I checked it's $238

billion. Which if you listen to what we

said yesterday, now that you passed 100,

you're more likely to actually 10X. So

the first 10X was actually much much

harder. So you're on your way to a

trillion dollars.

>> From your mouth to God's ears.

>> Well, I 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 see

you've seen the rise and fall of SAS.

All the models talk to you. You were one

of

>> The rise again, right?

>> The rise again.

Uh you were one of the first and the few

that got access to Mythos. So just Let

me just push the button. Go Nikesh,

start.

>> [laughter]

>> Well, uh 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 in the last

possibly 24 months.

Um

I think Sarah just said it. They were

right in anticipating the huge amount of

compute that was going to be needed. So

all that stuff's going on. But

you can see

that

you know, this this notion which we

talked about briefly last time that AI

is really democratizing intelligence.

What that means is I have 250 people in

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 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 the 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. So 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 touched upon

Mythos, and you know, Dave has been very

involved with this.

Mythos has shown us that all the bad

code that humans have written over the

last 50 years

can be assessed by AI and shown uh the

vulnerabilities can be shown. We tested

for 6 weeks, and in 6 weeks we found

what would have taken us 5 to 7 years.

>> Wow. Say that one more time.

>> In 6 weeks we found vulnerabilities

which would have normally taken us 5 to

7 years to find.

>> So Mythos, but these are vulnerabilities

where?

>> Sorry?

>> These are vulnerabilities in your own

code base or in your customer or in your

own code?

>> Oh, wow.

>> So Mythos 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 daisy chain vulnerabilities,

i.e., finding a new attack path into

your company into your vulnerabilities.

Now, we pride ourselves as a top

percentile of companies that test our

code because we're in cyber security

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

would have taken us 10 years to find.

>> How much did it cost? Like did you track

the token cost? Was it a hundred million

dollars, ten million dollars?

>> No, it was in the low millions. But

again, you know, the cost as Sarah said,

the cost curve is going to come down.

Already OpenAI has got a model which is

cheaper and more consistent. You know,

Anthropic's come out with another model

>> you buy the hype.

>> It's not hype, it's true.

>> It's That's the point.

>> the capabilities are

>> The You know that.

>> The capabilities are true.

>> Yes.

>> I mean, you saw IBM announce a project

for five billion dollars 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 like a real attack

vector and caused chaos in corporations?

>> we're three months away, if not already

there, from this being available in

the wild.

>> Okay, open source.

>> Yeah, just three months.

>> Yeah.

>> Yeah, cuz I mean, we've been saying that

it's roughly six months away before

mythos-level capabilities are available

in Chinese models, you know, open

models, whatever. But you're saying it

could be 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

need to find a few vulnerabilities in

code that are out there. Just take an

Take an old industrial system which is

running, you know, OT code on the edge.

You can find that vulnerability

reasonably easily.

>> So, 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.

>> [laughter]

>> 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 fix it.

While the CISOs 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 is it fair to say that it as

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

look, cyber has done 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 of everything they do every day.

That's why you need to store a lot more

data

enterprise-wide to learn what good looks

like and what bad looks like.

>> Right. The same problem is in

cybersecurity.

>> We need to collect We need to collect 10

times the data in the enterprise from 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 as Bill said, SaaS is

different pieces, right?

>> Okay.

>> 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 LLMs running

against that.

>> Interesting you bring this up. We had an

instance with a SaaS product with 20

seats.

Nobody was logging in and using it, but

the data was there.

>> Yes. So, we created like three accounts,

got rid of 17, connected it to Slack,

connected it to Claude, and now

everybody can interface it through

natural language and we've reduced our

bill by 90%.

>> Well, not just that, what are you going

to do next? They

Jason said, you're going to take data

from different products, put them in one

place, run the analytics against that. I

want my data for my sales reps, my

productivity data, my you know,

inventory data from SAP. I want it all

in 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 a region where

my sales people are extremely

productive." To run that query, you'd

have to have talked to three different

SaaS products. Tomorrow, you can put all

the data in one place. So, so that's

sort of category one.

>> Analytics SaaS today. The analytics

>> Okay,

category one analytics dead.

>> Yes. In the medium term, you get all

these bounces today and tomorrow that's

these are marginally irrelevant.

Infrastructure software undervalued.

>> Okay, what is infrastructure software?

>> Stuff that gives you databases. You

collect data into it. Stuff that allows

infrastructure to work, whether it's a,

you know, database software.

>> Databricks, Snowflake, like that.

>> Databricks, Snowflake, MongoDB, Oracle,

Oracle, all these things you need

core storage infrastructure, core data.

You're going to need 10 times the data

stored in an enterprise than we have

today. Right, 3 years. 10 times.

So, anything that helps you collect

infrastructure data, manage it, you

need.

I think the category in the middle is

called, let's call it system of work

or system, you know, of record, people

call them.

Those are deeply embedded in the way

businesses work. I have 6,000 sales

people, they know how this works. What's

going to happen is step one, we will

take away UI and let agents do the work.

UI

enterprise software and consumer

software UI is the worst thing we did as

technologists.

>> 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.

>> Yes, that was analytical SAS, so that's

over.

>> just pointed AI at it and you just

>> Yes, 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.

>> Yeah.

>> If all like if you believe agents are

going to work, 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

you know, whatever sales tracking system

I have, whether it's Oracle or

Salesforce, right? An agent conceptually

should be able to do it. we're

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 guy should have to

say, I had the sales call, do all the

paperwork and all the that needs to

happen in the back of the company, and

just

I'm done.

>> Right.

>> 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 SAS 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 we're all going to be looking at a

chat window and just saying, here's what

I want.

>> Your audit trail becomes a lot better

because humans are not touching your

data. It's always being managed by

agents, so I think the whole system of

work, system of record, gets reinvented

in the next five years.

>> Yeah, there's no data entry. That's an

interesting point. Yeah. Let's talk

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

your and to enterprises.

The red team version of Mythos is where

foreign state actors or you know, can

essentially create economic havoc inside

of a country.

>> Yes.

>> As these models escalate in their

capability, what do you think should

happen when these models are ready?

>> You know, the sad truth is, you know,

here there's a few thousand

breaches or attacks that happen.

They happen for pretty rudimentary

reasons. It's not because somebody

cracked a hard to crack thing. It

happens because 89% of attacks happen

because credentials get stolen.

>> Or your username and password.

>> That's it.

>> I bet my password is password.

>> Yeah, I'm sure it is. Did you have

dollar sign?

>> Dollar sign password.

>> Fantastic. Well done, see? You're

already ahead of everybody else. So,

89% of breaches happen because of simple

things. So, I don't think we need more

models to go crack this stuff. Now, we

will need 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 on IT on security. I'm

worried about the small offices across

the country where they're using some

piece of packaged software and you're

running a dentist's office or doctor's

office. Remember when then Change

Healthcare got breached?

>> Every physician's office shut down.

>> Shut down and it's ransomware.

>> Because of ransomware to Change

Healthcare. That's was the clearing

system. That's when

United Health had to actually have give

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 will get cracked.

>> about cracking some PG&E power

generation facility. It's more economic

chaos. Yes.

And so, what what 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,

renewed, fixed over time. I just think

it 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 how they're

where we point them? Or do you think

there should be a 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 view

about models and how they will evolve

versus what we heard earlier from an

open AI perspective.

I think

I still believe models are going to

become a utility layer.

You'll be able to buy intelligence on

the [snorts] fly.

Or you can say, "I don't need a 180 IQ

person to go do this task. Give me a 120

IQ and I need a 250 IQ to do this task.

I'll pay $10 for this or for this I'll

pay 1 cent." So, I don't know there's a

one-size-fits-all

give you the most up-to-date model to

answer my customer call saying, "Sorry,

sir. I have no idea how to solve your

problem."

So,

I think models will get differentiated

from utilitarian perspective.

Um so, if you look at already what's

happening in the market, right? The

profit pools are in applications, not in

models. More Sarah talked about Codex

running away. She didn't say

Open AI is running away. She just Codex

is running away. Just say that's the way

I'm sure Dario says Claude code is

running away. So, you're seeing that

>> They're attacking profit pools.

>> They're attacking profit pools because

that's where the money's going to come

from. 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 the models.

>> at these companies in a way Open AI and

Anthropic 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 are going to

arbitrage between models and solve your

business problem.

>> So, you still think they won't go to the

application layer? Because this is a big

debate. Should you engage with OpenAI

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

release the Office product on top of the

operating system.

>> See, 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 and AI enabled to be

delivered by some application company.

It'll 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's sales force or

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 sit. Because

50,000 uh 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

uh I'm smart. Right? I'm not. I want

somebody to do it for me. So, I think

that layer of companies is still not

fully formed.

>> Or so, we're going to be waiting for it.

>> control plane, a harness, and then

>> That's right. They will build the

harnesses and the memory into those

application layers. Now, the question is

how big is the application layer? Is it

one application? Is it Is one, you know,

enterprise application that does

everything? Or is it specialized

application?

>> and you kicked out this software vendor,

you did it because they were being

abusive in pricing. So, that

>> 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 and that's why the SAS

apocalypse in some ways makes sense.

They're not having pricing power because

you could say, "Well, I'll just put 10

developers on this and I'll save $10

million." Yes.

>> I think the part back to what Chamath

said about the regulation or whether you

want to regulate these higher-powered

models, the question is at 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 through 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 3 to 6 months is going to help us

any. Somebody else is going to put them

out in open source.

I I was

I was shocked to hear when I was talking

to the CEO of one of these model

companies. He says, "The entire weights

of their most recent model can fit on a

USB stick."

>> Say that again. The entire weights

>> model weights of their newest model fits

on a USB stick. That's the IP.

>> Yeah.

>> That's incredible because all the data

can be distilled in under 24 to 48 hours

and model comes out.

>> I'm curious

>> That's the IP. So, are you telling me

that

you know we can hold on to that for 6

months?

>> Right.

We we have a debate about um how

difficult it is to make a frontier

model. Some companies are starting to

think about making frontier models using

their data advantage to to build their

own.

Have you thought about that at

Palo Alto because it [clears throat]

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 in the

future?

>> nobody talks about

is the false positive rates on the

models.

What is the false positive rate on 4.8

and 5.5?

>> No idea.

>> You guys don't talk about it. You

should. The false positive rate on MSO

was 30%.

>> Oh, wow.

>> Right?

>> So, it thought it found something, but

it hadn't.

>> Yes. So, the problem is

it's great for attack, it's horrible for

defense.

Cuz you find 30 times 30% of the time

you find something that says, "I found a

problem." And you say, "Let's plug the

hole." Wait, there wasn't a hole there

in the first place.

>> No missile inbound.

>> Right.

>> Yeah.

>> So, now the same problem applies in

enterprise. If you use a If you use a

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% 20% false positive. I

just lost money.

>> The sycophantic nature of these is

ridiculous, yeah.

>> So, so the problem is not who wants the

newest model. The problem is how do you

take that model with 20% or 10% false

positive and make it 0.01% false

positive. In my business, I want 0%.

>> Without losing the false negative.

>> Sorry?

>> Without losing the negative, the false

negative.

>> Yes, but it's like saying, "Hey, let's

take the new self-driving car. Mercedes

is going to use Opus 4.8 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 a 10% false positive rate.

Are you?

So, there's a lot of work that happens

post a 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 a very long time the chief

business officer at Google.

You were the president of SoftBank.

Now you're the CEO of Palo Alto

Networks. So, let's play armchair CEO.

>> Armchair CEO.

>> Armchair CEO.

>> I'm still I'm still bristling from David

Feiberg trying to create a distinction

between founder CEOs and non-founder

CEOs. Just saying.

>> [laughter]

>> Just saying, David.

>> By the way, false positives.

>> Sorry?

>> And false negatives, too.

Give us what you would keep, what you

would change, and what you like about

the following companies.

>> This is going to get recorded and put

out there to say that really something I

don't know.

>> thoughts. You're one of the smartest

business people

>> don't like get to live with the glory of

these all-in podcast sessions.

>> Don't

>> Okay, ready?

>> people and roasting people.

>> ready?

>> Yeah, sure.

>> Okay.

>> [laughter]

>> What you keep, what you change, what you

like, what you don't like. Uber.

In a world of a

>> it, dude. I can't talk about my

>> on the board of Uber?

>> I'm on the board of Uber. I'm not going

to talk about Uber.

>> I didn't know that. Sorry. Okay.

>> Dr. Dara, he's the CEO. He's a great

guy.

>> Okay. [laughter]

Uh Waymo.

>> You're trying to get me fired.

>> Waymo.

>> What do I like about Waymo? The cars

work. It's amazing.

They should have more.

In many more cities around the world,

faster. I I I would say that at the rate

that I'm going to be fired.

>> Google red large.

>> I think Google's underrated.

I think it's going to be the first

trillion-dollar company in our lifetime.

I think they have all the assets that

are

that are needed to make this 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 models

and buy them. And if you think about it,

three hyper scalers have the biggest

number of sales people out there. So,

they should

>> of why they're a little bit undervalued

is just the conglomerate nature is hard

to understand?

>> I don't know. You guys are smarter than

I am. I'm just a hired hand CEO.

>> I didn't say that. Reeboks said that.

Let's just be clear.

>> I know. I know.

>> I was I was providing a thesis on

recovery out of the SAS pack. Let's just

say.

>> Okay. Okay. Got it.

>> Just to be clear,

there's there's a way to segment that

basket. Okay? And you're not in that

basket.

>> I thought you were making a distinction

about how people who are founders CEOs

have

uh have the right to take more risk

and are allowed to take more risk.

>> saying that.

And I think and I and I think you you

provide a unique counterpoint to that.

And and there's not a lot of people like

you. I think the same would be true of

Jeff Weiner. And I think that there's a

few other

uh 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 theirs in the

first place and they make it theirs. And

uh it's a it's a extraordinarily unique

trait. Far more unique actually than

being a scalable founder.

>> That's an incredible save.

>> You're forgiven.

>> Yeah, good save. Incredible save.

>> back to Armchair CEO.

>> Wow, that was incredible.

>> He's more sycophantic than ChatGPT.

[laughter] He's like, "Actually, I'm

actually I'm actually the best."

>> Let's go Let's go back to Armchair CEO.

>> I'm liking this. He has Open AI more

often. Yes. [laughter]

>> They do sell faster.

>> Open AI.

>> They should sell faster, right?

>> They should sell faster.

>> I mean, I I you said it. Didn't you just

say it when you were Sarah was here that

>> And Anthropic seems to have improved

their ARR much faster than Open AI.

>> I mean, that's just the statistics.

>> They kind of went all in on enterprise,

and including specifically.

>> I think I think that's like

the 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? 1 gigawatt is 10 billion

of revenue.

>> to build you? What are the most

What are the most exciting profit pools

then?

>> So, what are the most exciting

>> It costs 50. So, this is not a great

deal.

>> So, what are the most exciting profit

pools then? So, we

>> You've 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

cybersecurity is clearly one of them

because of the threats and patching

cycles so much more dynamic.

>> There's There's a slight difference in

in Yes, as you can see, these models are

trying to be the the enablers of better

cybersecurity, which is good because all

of us need to use them to test. And

you're probably going to see I mean, you

you saw Anthropic is already

uh made their cyber capable model

available generally, so that everyone

can use it. And Open AI has got one. I'm

sure Google has one, too. But they

understand this is a place where CISOs

or chief security officers want to use

it to test the code. So, this is another

profit pool. Uh I think we haven't seen

the onslaught against the application

software companies yet. I mean, there's

tens and tens 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

saying, "What if I took this 40, 50, 100

million dollar time down, I can build a

whole brand new backbone with a

generative AI, and it'd be so

differentiated that it will cause

customers to move."

>> We are seeing it as a playbook in the

accelerators

now. The year zero and year one

companies, people are coming to us with

the pitch, "This is a thousand dollar a

seat per year,

five hundred dollars a month seat SaaS

software. We can do it for less. We're

going to charge them based on

consumption. We're going to take 80, 90%

of the cost out as

>> What are the two fastest places to make

revenue?

>> The two fastest places to make revenue?

>> Yeah.

>> In enterprise a replacement apps. If you

replace something, I already have a

budget, it's easy. I take something bad,

I replace it something better,

I get money. So, replacement apps are

beautiful. If you can replace an

industry, replace the profit pool, it's

great. The second place is consumer

revenue. It's a lot easier to get five

bucks from a per user on a consumer

side.

>> Netflix.

>> So, that's where I mean, look at it. I

think we collectively probably pay more

on subscriptions per month than we ever

did historically, and you thought 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 uh

manage low latency, high throughput

bits.

You still need a data center.

>> Yeah.

>> What's a data center doing is just

managing high throughput, low latency

bits.

>> Yeah.

>> 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,

whether it's Goldman or JP Morgan,

Morgan Stanley, or

these guys, they're using hardware.

>> Try to get them to run their business in

the cloud, they can't because they will

have higher latency, they will lose

money.

>> Right.

>> So, hardware is still being made. I

mean, I remember when I used to advise

Silver Lake, and I had have Dell was

done. Nobody wanted hardware. I think

Dell's might be back to like a 3 400

billion dollar market cap. So, hardware

is still going to be around. We're going

to need it. It's the fastest uh way to

move it.

>> Are 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 pole in the tent is

their design, right? The long pole in

the tent is production. Today, you can't

get a box produced because every

every

piece of hardware componentry is back

ordered. 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 data center in the

world. So,

>> Do you think the US is equipped to fill

that supply chain need?

Can we do that here? Or do you think

we're just done?

>> 10 years.

>> With a with a with a firm top-down

commitment.

>> Well, I mean, the good news is that

I think the hardware industry

is seeing a bonanza of a lifetime.

And generally, when you see a bonanza of

a lifetime, you can go commit 10, 20,

50, 100 billion dollars. I mean, I've

seen a CEO on television committing a

100 billion dollar 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 fact

>> I think the tax incentive has a big has

a lot to do with that. The accelerated

depreciation on the the CapEx. You get a

100% write-off in the first year, right?

Under the under the year.

>> Just a Just a final question as we wrap

up. You, over the last 8 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 Ackman said

about how there's this kind of

over beaten companies, there's a few

that get celebrated, that's a ripe 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'll tell you what. Um

until about a year and a half ago, we

used to buy product companies and throw

them into our go-to-market engine.

We could rewire their back end so they

can work better with our go-to-market

engine. So, for me, if I'm selling $10

million to a customer, next time I go to

her later if I can sell them 20, it's

the most efficient way for me to

amortize my go-to-market spend, right?

So, that was the model. We played that

we ran that playbook to north of 150

billion. Then we got to a point where it

says, "Oh, we see an inflection arriving

in identity. It's going to be important

from an agentic perspective, security

perspective." So, we bought a $25

billion company, which we closed 3

months ago.

Um

now it's actually a very different

opportunity has 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 if you can crack that

code

>> Gross and net, you're saying? Gross in

the 90s, net in the 40s.

>> Yeah, if you can crack that code, then

it doesn't matter what you buy.

>> Yeah.

>> I think the problem right now is the

execution problem. Most subscale

companies cannot afford to go optimize

their company and run it better.

So, if we can run our company much

better than everybody else and have a

higher operating margin, then the street

will say, "Fine." If you take something

at a 20% margin, make it a

>> Your first M&A was really tough, no?

Like they were pretty skeptical and then

you kind of shoved it in their face.

>> pretty skeptical when they found a guy

who didn't know cybersecurity, didn't

know enterprise show up, who worked at

Google, and their you know, the track

record of people leaving Google and

being successful out of Google is still

>> Yeah.

>> varied.

>> So, basically you're saying the menu's

open. And

>> I I think we need the next 6 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, uh you know, the

the the people keep hoping that less

people we need to run companies. I

actually have a counter view. I think

we're going to have more people at Palo

Alto on the technology side than we've

ever had before because I think 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, CEO Palo Alto

Networks, Nikesh Arora.

>> Thank you, guys.

>> [applause]

[music]

>> Thank you, sir.