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the business model of google is going to evolve multiple times in the future so i'm not sure we need to go find ourselves the best ad sales executive we need to find ourselves a good executive who can roll the punches and adapt
The closed models are can be helpful because both OpenAI and Anthropic, and most likely Google as well, have very powerful models now, which can go, if
you can actually put it in a perimeter defense product like ours or Cloudflare has one or other firewalls have one. You put the patch there. It's a virtual patch, so any attacker trying to access that vulnerability gets stopped at the perimeter because we know what they're looking for.
most likely the answer is somewhere in the middle. I'm going to have to get bare metal, yes. Most likely I'm going to get from either a hyper scalar or a cousin of a hyper scalar, or somebody's going to be stitching, you know, sleeves of compute from around the world to meet my needs.
I'm a buyer. I'm a taker of terms, not a dictator of terms. It's very hard to go and dictate terms anywhere because yeah, sure, you can make any plans you want, but those plans are you know, you talk to Jensen, he can't give you chips till 2028. You talk to you know, when you get land power and shares and shells. So, it's very hard to go say yes, I can go build my own data centers, but it's kind of an empty threat.
six, eight years ago when I came to Palo Alto, I shut down all data centers. I said, 'We cannot run a global data center sort of footprint at the scale, speed, and reliability as I could probably rent it from hyperscalers.'
we'd like to embrace AI as quickly as many of the startups. And we've gone through all the understanding, all the analysis, open source versus cloud models, and we preference open source open weight because it allows us to modify the weights and train them.
you talk to Jensen, he can't give you chips till 2028.
you talk to Jensen, he can't give you chips till 2028
We found out about this a few days ago. We scanned everything in our infrastructure to make sure that there were no issues. We made sure that our products had patches available for our customers who deploy them. So there's two ways to solve this problem. One, you can go in and patch the vulnerability. You can go in, find the code, patch what has been given to you by PHP, and make it work. The other is you can actually put it in a perimeter defense product like ours or Cloudflare has one or other
But most likely the answer is somewhere in the middle. I'm going to have to get bare metal, yes. Most likely I'm going to get from either a hyperscaler or a cousin of a hyperscaler, or somebody's going to be stitching, you know, sleeves of compute from around the world to meet my needs in the hope that I'm going to migrate to something more resilient and more robust over time.
Eight years ago, when I came to Palo Alto, I shut down all data centers. I said, we cannot run a global data center sort of footprint at the scale, speed, and reliability as I could probably rent it from hyperscalers. And life was great. And suddenly what's happened is, in the current sort of incarnation of where we are, compute is highly dear. It's very hard to find compute in the world, period. So I'm a buyer. I'm a taker of terms, not a dictator of terms.
The challenge we have is, it's very hard to find a global, resilient, highly available inference provider outside of the hyperscale community right now.
And we preference open source, open weight, because it allows us to modify the weights and train them. And something which most people don't talk about is it allows us to interrogate the chain of thought. Because over time, what's going to happen is all of those enterprises are going to need explainability, auditability,
if you want to take all that traffic, run that through OpenAI using tokens for every employee in your company, sounds like the sound of revenue. Like, OpenAI would probably generate all their revenue in 1 year from one company.
if you want to take all that traffic run that through open air using tokens for every employee in your company sounds like the sound of revenue like open i would probably generate all the revenue in one ear from one company right so that's not a practical example of how model do so
The biggest contrast for us is we like to buy a product which we can integrate and sell to customers. We have a go-to-market engine. I think if I'm going to buy a company at eight to ten times revenue, I'm just overpaying for customers and sales. I have all the customers already. Why would I pay eight to ten times revenue to buy a customer I already have it on a different product? So I'd rather buy the product, use my go-to-market capabilities, go sell them to the customer base, unless I can tak
The biggest contrast for us is we like to buy a product which we can integrate and sell to customers. We have a go-to-market engine. We like to keep it the way it is. I think if I'm going to buy a company at eight to ten times revenue, I'm just overpaying for customers and sales. I have all the customers already. Why would I pay eight to ten times revenue to buy a customer I already have it on a different product? So I'd rather buy the product, use my go-to-market capabilities, go sell them to t
So there you have, we have a product that watches all these apps and make sure that what you're using, what you're uploading is not being sent to dangerous apps. Or if you don't want your employees to send it, it will block you. The other one, which is kind of interesting is that I think almost every company is experimenting with deploying LLMs internally because they all want their favorite proprietary chat interface. And there, you need to be careful because you can, what you used to do with S
The biggest contrast for us is we like to buy a product which we can integrate and sell to customers. We have a go-to-market engine. We like to keep it the way it is. I think if I'm going to buy a company at eight to ten times revenue, I'm just overpaying for customers and sales. I have all the customers already. Why would I pay eight to ten times revenue to buy a customer I already have it on a different product? So I'd rather buy the product, use my go-to-market capabilities, go sell them to t
When I joined Palo Alto, we were in two. We're in 24 right now, in the top right.
If you come to Palo Alto, we'll take your equity away first. We'll say, I'm going to give you back one and a half times your equity just saves me for three years.
we've done that 19 times. We had 7 out of 10 we've gotten right.
So why don't we buy the guy who's worth a billion dollars? We'll be number one. We'll be leading the market. We have brute force that go to market. We'll go use that. And we're probably going to slow them down a little bit because they're a larger company. So we'll compensate for slowdown with go to market that we bring to them and we'll let them lose.
If you can go into a sector where there's no demand problem, you can look at it and say, what did everybody get wrong? Well, everybody sort of lived in their swim lane. So we were in our swim lane. We did one thing. There are five swim lanes in the cybersecurity. In six years, we looked forward and said, where is the world going to? It's going to the cloud. There's a bunch of AI. That was our sort of plastics moment, cloud and AI. So we said, let's not go reinvent the past. So one of the things
And it's a subsector of technology with the most amount of fragmentation. Look around, you know, Benioff, I think it's going to be able to build a platform for Salesforce. You have ServiceNow, you have Workday. There's no cybersecurity platform. You sit there and say, this is a phenomenal opportunity. One. Two, it's a company that's fully public, so I don't have to deal with voting controls and founders who have to deal with, which have different motivations. It's an evergreen sector. The more w
it's a $180 billion industry on an animal basis. The largest market share is 1.5%, which is us.
SPEAKER_43: when he was, he came to see Larry, Sergey, and Eric, and said, I'd like to do a search deal with you. SPEAKER_75: I have Yahoo Japan. I tried to explain to him there's something called, you can't have two search engines, both powered by Google and Japan. SPEAKER_78: To his credit, he says, you can, as long as the advertising systems are different. So if you look, in Japan today, Yahoo Japan is powered by Google and Google's powered by Google, but the advertising systems are different
I learned that with Larry and Sergei at Google, that they were obsessed about product on a constant basis.
And what is interesting is when I joined, Google was 24% of global revenue. When I moved to the US, it was 49%. You mean Europe? Yes. And that's one of the few tech companies in the world whose European revenue was higher than the US revenue for a brief period of time.
Look, Google has one of the best flywheels there is in the consumer space, right? So we were blessed that we were working with a product that nobody had ever seen. Everybody wanted to use. It's funny, like, every one of us worries about customer support. You didn't need it. It was an amazing, simple product, easy to use, free. And our job was to go monetize advertising.
we have 62 000 customers with firewalls who have been analyzing their data for the last 17 years and in the last four years more so we have customers with you know 14 million plus endpoints on various different technologies so we're collecting first party data and delivering ai outcomes that's on the precision ai side and i think that's hard to beat if you haven't been doing it if you haven't been collecting data many of our competitors haven't i'd say there's probably four out of 3 000 who have
we're collecting 75 terabytes in our own in our own instance at pala alto we connect four petabytes a day across our customers
i think you know we're going to talk about ai but we did a big analyst here on friday and we try to distinguish uh or try to figure out if this term will hold i call it precision ai versus generative ai so if you're if you're in your tesla you don't want it to hallucinate oh i thought that was a good turn that's kind of like life i'm on somebody's lawn i'm on zucks lawn that's right
four five years ago when i joined palo alto we were an 18 billion dollar company we were in one swim lane today like you said we're now a 70 plus billion dollar company we play in three swim lanes and our big underpinning and our big pivot five years ago was to focus on collecting good data across the enterprise and applying ai so we at any point in time we have about a thousand plus machine learning models that run underlying our ai product to solve the problem
what we've done is we basically said we're going to watch every bit floating through your infrastructure and we'll tell you if it's good or bad so we collect 75 terabytes of data a day at palo alto we analyze that we take those 80 000 alerts make them 200 events if they're real so we basically flipped it to an ai problem
our plastics moment was cloud
we used to spend 12 of our revenue on r d i said that's not enough that's that's the amount of money that you spend if you're sort of you're milking your last cash cow
we did that by effectively building three cyber security platforms which are sort of in the early stages and i think our best one is still ahead of us because we were able to build an ai based cyber security platform which suddenly with all the conversation around generative ai and ai has suddenly become center stage for us
we've convinced our customers are going to be evergreen
we turned the whole company around into what i call a cyber security innovation engine
we have 19 products that are in an enterprise there's a single magic quadrant where you have to be you know people buy you if you're good we're the third company in tech ever after ibm or microsoft we have been in so many magic coordinates ever
we now play in three out of the four biggest swim lanes in cyber security
we bought 17 companies all in cloud security and ai in the last five years
we're in the security business customers have to believe we solved their problem
look we did inherit i did inherit 5 000 people and um some of them have moved on because we've been transforming the company we have hired possibly north of 14 000 people in the last five years
having spent the time at google and google was chomping at the bit about ai even in 2014 when i left
it was definitely unique even for a western culture for somebody to interview you know tens of people for one job and then eventually you had to wait with bated breath for about a week because all these packages went to larry and larry would read through them
in five years i opened 26 physical locations for google we hired 4 000 people and we went from i think 800 million in revenue to 4 billion
the business model of google is going to evolve multiple times in the future so i'm not sure we need to go find ourselves the best ad sales executive we need to find ourselves a good executive who can roll the punches and adapt
they had a very unique idea around advertising and connecting marketers with customers
i think his very simplistic pitch he just basically said listen the world is getting more and more connected there's more and more information around us it's going to be hard to parse through it without any sort of something helping you out and the thing that's going to help you out is google
all the 12 vice presidents were internally promoted