Mesh Aurora: Insights on Cybersecurity and AI Challenges

Mesh Aurora consistently drives innovation and success in the cybersecurity industry through a focus on great products and effective execution.

### The Evolution of Cybersecurity: Insights from Industry Leader Mesh Aurora
* 🔐 Mesh Aurora, a prominent figure in the cybersecurity space, has transformed his brand into a **verb**, showcasing his **influence in the industry**.
* 💡 Joining Google in 2004 and leaving in 2014, he played a key role in **increasing the company's revenue from $3 billion to an astounding $66 billion** as the SVP and chief business officer.
* 📈 After his tenure at Google, he became Vice Chairman and President at SoftBank, before taking the helm as Chairman and CEO of Palo Alto Networks, where he **grew the company's market cap from $20 billion to $110 billion** in just six and a half years.
* 🛠 Aurora emphasizes the importance of building great products at the center of successful businesses, noting that "**if you don't have a great product, you're not going to build a great business for the long term**."
* 🤝 He highlights the necessity of teamwork and collaboration, stating that it is **impossible for one person to execute a vision at scale**, thus requiring a **group of talented individuals to drive success**.
* 🌐 The discussion also covers the evolution of cyber threats, noting that **nation-states have become increasingly involved in hacking**, with sophisticated operations that now leverage **economic incentives for cybercrime**.
* ⚠ Aurora warns about the risks associated with AI, both in terms of **enhancing cyber attacks** and the potential for employees to **inadvertently share sensitive data** with AI applications.
* ✈ As he prepares to fly to Geneva, Aurora leaves the audience with a **deeper understanding of the complexities of cybersecurity** and the **importance of innovation in addressing modern threats**.

our next speaker is actually fortunate enough to have seen his brand name turn into a verb. One of the probably most prolific executives in this current generation is Mesh Aurora, the Big Daddy of the cybersecurity space. These guys are really at the forefront of the industry. There are very few people who consistently, time and time again, find a way to just persevere, be relevant. NES is one of those people. This is a man who has tremendous insight into technology. He helped turn Google into the dominant player in search. This is the most innovative industry in the world. We're constantly paranoid from an innovation perspective. I've got to be on my toes because once we figured out how they did it last time, they're trying a new way to do it next time. This is the country where your dreams come true, and if you go around the world and you ask young people where do they want to go, they still want to come to America. I think this is one of the most successful democracies in the world. This is where

capitalism thrives, all right, ladies and gentlemen. Niora guy, appreciate you, thanks for coming. David, David, how are you, what's up. BR, um, let me just do this intro properly. Look at all the phones go up, wow. You joined Google in 2004, although there was a nice prolific buildup to that career, but you joined in 2004, you left in 2014, um, you started in ad sales, and you left as the SVP and chief business officer of Google. Revenue went from 3 billion, I checked this actually just to make sure 'cause I have, it's staggering, to 66 billion when you left. And then you, because we're going to talk about that, and then you got seduced to go work with Masa Yoshian at SoftBank, where you're Vice Chairman and President. Yeah, that must have been interesting. Jason has a look at Jason, he's looking ATP, so many good questions, um, there we go. But then you left, yes, and look, I've known you for a long time, we've been

very good friends for a long time. I was surprised because I got, you know, you called and you're like, Hey, I'm going to be SE chairman and CEO of Palo Alto Networks. And I had known what it was, but I didn't really understand. Uh, and then meanwhile in the last, what's it been, seven years, six and a half, six and a half years. Um, market cap is up by 5x. You took a 20 billion company, it's 110 billion as it stands. I think you've tripled revenue. Um, so this is clearly no longer luck. So now you're, you're in the skill camp. Oh good. Um, I'm in founder mode. No, founder mode is cocaine. I was wondering when that, I was going, I was wondering founder mode on you. No, he has to fly to Europe. You cannot bring founder mode. No waffles on the plane. No waffles on the, you can get founder mode in Europe though. Heard start. Let, let's just start and just, um, actually let's just start there. Okay. Um

you've seen a lot of different executives. You've played a lot of different roles. You've seen founders. You've advised a lot of founders. Tell us what it takes to be successful, and you can use these labels or not—founder mode, manager mode, whatever it is—but what does it take to figure things out consistently? Look, um, I think you already put that out there, didn't you? Didn't you say that, uh, if you think about building great businesses, at the center of great businesses, great products. If you don't have a great product, you're not going to build a great business for the long term. And this is something I know Serge is here. I learned that with Larry and Sergey at Google, that they were obsessed about product on a constant basis. So when I came to my job, I said the first thing I'm to focus on is build a great product. But I think it's slightly different in consumer and enterprise. In consumer, you build a great product, you find the flywheel, you try and figure

out how the fly continues to work an Enterprise. Eventually, you take a great product, you got to figure out how to get it out to all the amazing customers out there. So, I think it requires a tremendous amount of focus, a tremendous amount of sort of detail inspection, but I think at the same time, you got to find a way of taking lots of amazing people, getting them on the same train, and getting them to execute at scale. It's impossible for one human being to do that at scale, so you have to have a lot of people doing it amazingly well. That's the trick. Tell us about that first story or that first version of that story inside of Google because you were there for a long time and a lot of good things happened. What was that like? What did you learn? 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, and it's funny, like every one of us worries about

customer support. He didn't need it. It was an amazing simple product, easy to use, free, and our job was to go monetize advertising. So, part of that was how do you scale that around the world in every country where there is a single product with a single use case, where you have to see how you can attract lots of advertisers, and that requires building a system. How you get thousands of people around the world to build a system and execute. So, you build a system, you build a programmatic system, you look at stuff, you inspect, and you have really amazing people who gotten to do their best that they can. And when you're doing that and the thing is growing so fast, what is the what has to happen for you to go from running Europe? I think is how you started to being the head of business there. What does that take? Well, you know, it was such an amazing Juggernaut that you had to figure out how to differentiate. And what is interesting 'cause when I joined Google was 24% of global Revenue when I moved to

the US. It was 49% and in Europe, yes, in this one of the few tech companies in the world whose European revenue was higher than the US revenue for a brief period of time. So, I think somebody noticed, and what happens? You get the call, and you're like, We need you to move to America. Yeah, I was on a trip to Russia trying to open an office there, which had to be shut down at some point in time for a bit. And I got a call from Eric Schmidt, and he said, Your boss is retiring. We'd like you to come here and do what he does. That was it. And so, why? What then motivates you to leave a job like that? Because you're kind of then at the top of the pinnacle. You see everything. You're meeting everybody, I guess. I wanted more. I wanted to do more, get involved sort of the overall business, wanted to do some product work as well. I didn't have product jobs at Google. I've seen as a

sales guy at P. All I do is product for the first six years of my life. Yeah, so do be able to go out and do that differently, but I had to take a brief Soldier on to my Japanese trip. Lots of good sushi and lots of it was a great vacation. Let's talk about it. Oh, come on, it wasn't a vacation. Well, great surger, but I mean it. This was the largest Venture fund ever created, $100 billion, and you have this Mercurial brilliant individual, Masi Yoshian, and he starts placing bets in a way that we've never seen. What was the genius in that, and what was the Achilles heel? Look, um, Masa is one of those people whose risk appetite grew as he grew older, and you mention that because that is a very unique thing. It usually goes, think about, I have two young kids, and every time, you know, I'm constantly trying to drisk them, saying, Hey, be careful when you cross the road, be careful when you do this. You get married.

people tell you to be careful, buy a house, go settle down, so we're constantly risking our lives as we get older on a constant basis. All of us do it; we don't realize we do it, right? Mas is the opposite. The older he gets, like, come on, let's go all in. He's like, you guys, right? He wants to go all in, so he's like, N, I have a great idea. We'll put a billion, we'll borrow 19 billion. How does that work again? All you got is a billion? Yeah, I have one great idea: a billion and 19 billion. That's what he did. That's how he built SoftBank Japan. I think he was the richest man in the world for 88 days in the last internet boom. Then he was left with a billion dollars. Unbelievable. From scratch again. So yes, un, so there were a series of incredible bets. Yes, maybe walk us through some of those bets because there was Nvidia, there was Arm. So all those happened after I left, but that's, and then you had Arm happen after.

you left that was where we kind of you know unpack it sorry unpack that uh unpack this uh well Masa likes a trillion. He likes the number one trillion. Yes, so okay, it's a good number. It's better than a billion. Beats a billion. It beats, it is greater than a billion. It's greater than a billion last day. Check. Yes um and when I met him the first time after we'd done a deal at Google. He was uh you know we met when he was uh he came to see Larry, Sergey, and Eric and said I'd like to do a search deal with you. I have Yahoo Japan. I tried to explain to him there's something called like you can't have two search engines both powered by Google in Japan and to say 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 hence it's non-competitive. So he got that done and then he's uh says to me showed me this plan he was going

to buy a lot of companies in Telecom and get to $100 billion in EBITDA, which at 10 times EBITDA would be a trillion dollar. Okay, so then that kind of fizzled out. He lost interest after a while, and then his next idea was to raise, I think it was 100, 200, 300, and 400 billion dollars, yes, for the Vision Fund. So that'll make a trillion on a second. Let's, we're doing that. Yeah, that's a trillion dollars. One, two, three, four. Yeah, yeah, you know, it's interesting. And then, like, he had the whole portfolio of companies, and there's a bunch of Japanese analysts who sit in the office, MBS from the University of Tokyo, and eventually they took all the business we had and forecast their free cash flow at the end where the DCF was a trillion. So he was good at setting goals. So he thought ARM was going to be a trillion dollar company. Got it. Um, we were, let me ask, do you think the was the

mistake not the mistake is. Is it about being financially oriented as opposed to product or impact-oriented? Is there an orientation thing there where if money is the goal, it becomes a lot harder to achieve versus, well, I look, money is a way to keep track, it's not the goal. That was the way he kept track, I get it, but you know he was not financially oriented as much as he went by his gut. He, you know, it's like many Founders, when he believed in it, he was all in, he totally believed in it, and sometimes to a fault. And you saw that one thing I did learn from which is very fascinating is, you know, like a person who's like used to getting things, wanting to get things right. I'd make an investment with him and then be one investment say, That's not going. Let me go and talk to the company, help them, help them fix it. We can get them up and run. He calls me S one day, This Nik, you're spending too much time with the mistake. He said, If you go spend that time with a

company that's growing at three times. They can grow at six times. We'll make our money up six times in that company instead of you trying to fix that from half back to one. So, it's kind of interesting, you know, that's an incredible lesson actually cut sunken cost. I mean, there's a million ways to say it, but you have to let your winners ride. You got to focus on the winners, go all in on the winners, go all on the... It's hard to do. It's hard to say, Oh my God, I made a mistake. You go and say, I can fix it. I'm good. I'm going to Sal. Yeah, that's an e-problem. That's a Hubert's problem. I don't want to have a mistake on my record or a good person wants to help the founder, you know, realize their vision. And the cutthroat nature of this with the power law is such that six Xing something that was at 3x is much, much more likely than getting a zero to a one. Do you apply that?

principle. It's an operator, and if so, like how at P Alto networks look uh in the last 6 and a half years. I've got 19 companies right. We can show the you can the slides. We got slid. We got some slides that just here's your stock good job thank you. It's not bad. Go back. Let's see if you go back. It's go back a second back y. What's the market cap now? 110 billion. 110 billion. Yeah, that's where they. And when you started, it was at 20. 20. 20. What you're seeing here is um the um economic principle of uh founder mode. Okay, and the re the revenue and the uh operating income yum yum. Which so how do you how do you look at something like this when you were first approached for the job? How do you underwrite the job? Like what are you looking at? And you said you you. I mean you spent the last six and a half years in product. Did you see a product that just it was was it

missing something. That's the thing I want to hear about it. Like, did you go all in on one or two brok? That was always the Steve Jobs model: pick the winner and go all in on it, get rid of all the other stuff. Does that principle apply here or no? This is a slightly different principle. Look, yeah, it's a $180 billion industry on an annual basis. The largest market share was one and a half%, which is us, and it's a sub-sector of technology. The most amount of fragmentation. You look around, you know, bennyhoff, I think, is going to be here, builds a platform for Salesforce. You have ServiceNow, you have Workday. There is 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 I have to deal with, which have different motivations. It's an evergreen sector. The more we get connected, the more people want to hack.

the more you're going to connect it, the more data is there for people to take away, so you're not going to have a demand problem. Yeah, sadly, 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? So, 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 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 I also learned during my time at Google and Masa is like a lot of people get hung up in trying to make the stuff work, assuming everything around you is going to stay the same. So, say now, We're just going to focus that. Assume that 50% of the world is in the public cloud. What's security going to

look like then assume latency is low. You can process in the cloud. Data storage is cheap. What's going to change? So we built for that. We bought 19 companies. We went and looked at how everybody does M&A and who failed. Oh, what did you learn from that? Well, we learned that people, things trade at a price for a reason. So very often, people say, you know what, ah, number one is a billion dollars, number four is $200 million. I can take $200 million and clean it up and fix it. It's at 200 for a reason, and the billions of billion of reason, they'll still be around, yes. So why don't we guy the buy buy buy the guy who's got 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. So we're the

only company where when we acquired companies there a funny story is I got a guy who says oh great we're buying a company in Cloud security. I'm the senior vice president of blockchain cloud and AI. I'm like great welcome to your new boss. It's like what do you mean. I said that's the guy going to work for like we just bought his company. I said yeah he kicked your ass with low resources out there in the market. You're going to learn something from him. There you go Welcome to New York boss well you know there is an analogy you uh share a passion for basketball as well. I see you all the time at the Warriors game and I I I don't think this is a jump to say watching that team play and how they manage talent and play as a team definitely informed how you play the game. Yeah that not lately but yes in the past but look we've done that 19 times. We had seven out of 10. We've gotten right and we still possibly have the

most number of Founders who still work for palal. Yeah, actually, can you explain that? So, when you buy a company, isn't the typical motivation wait till I cliff, it's a year, and then most people just Vose, they're gone. No, no. So, here's how it works. If you come to palal, we'll take your Equity away first. We like, I'm going to give you back one and a half times of equity if you stay with me for 3 years. Oh, wow. To the founder, yes. Okay, wait a second. So, the founder owns, let's say it's a 100 million, a billion-dollar company. They own 20%. They got 200 million. You say, hey, stay again, I'll give you 300 million, right? Got to work for me for three years. Pretty good. Because when you buy a company, you're buying a half-half a product and a full vision. Ah, right. I lose the vision part of it, I get half a product, right? That's how much of the success is predicated on the engine at Palo Alto Networks to drive sales to

sell into the Enterprise. How much do you come in, and then the founder feels like there's an interference model now that's like, How are you getting in my way? And so, how do you manage that balance? So, what happens is like, Look, the customers in security want the best product. That's why everybody lived in their swim lanes. We said, We got to be in multiple swim lanes. To be in multiple swim lanes with multiple people saying, I've got great products. You know, in Enterprise, there's this bizarre thing called Magic Quadrants, which Gartner has, and your badge of honor is you're on the top right, which is the leader quadrant in Gartner. When I joined P in two, we're in 24 right now in the top right. So, now when you go to customers saying, Hey, I got some great products. You're going to sell me some good and some bad. Like, You know, you pick. There's all 24 are in the top right, and they all work together better. So, for that, we need the founders.

building the product and staying there. Yeah, they do feel a little, sometimes they feel like they're being directed, but there's also another rule. I had a wonderful conversation with the founder, which was my first acquisition, and I think we didn't set the bit right, so we had to fix it in future deals. SP, the founder, reasonable amount of money, probably $850 million to the company. He had about $150 million he was going to get, 2, 200. Then he comes into my office and says, Cas, I had a problem. Um, I think we should do it this way. You're telling us to do it this way because this way is the way it's going to work for us. He's like, Yeah, but when I came here, you know, with my company, I said, 'Oh, wait a minute.' I said, 'Have you ever sold a house?' It's like, 'Yeah.' I said, 'Who decides what and you, you get to stay in it. Who decides what color the wall is going to be painted?' The new owner. The new owner, of course. I said, 'Thank

you so he stayed there. He really liked us, and he stayed for three years. He made two and a half times that money he got. But from then on, we changed the game. When we acquire a company, we sit the founder down, say, Okay, the lawyers will do their thing. You're going to sit in my head of product and design a product strategy we both agree on. Yeah, so we don't buy a company until we have a joint agreed product strategy with the founder. Now, in Cisco, Oracle, Salesforce, they've all kind of had this M&A playbook that they claim is part of their engine of success. How differentiated is it for you? What do you, what's kind of the biggest contrast for your playbook versus those Eng? The biggest contrast for us is we like to buy 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 8 to 10 times revenue, I'm just overpaying for customers and sales. I

have all the customers already. Why would I pay 8 to 10 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 take the two companies, merge them, and I can make it worth 16 times. If you, you, you as an investor can buy both our companies, enjoy yourself. Why would I have to pay a premium to buy it at 8 times revenue? So, we're very clear we don't want to buy customer bases. We want to buy products which we can integrate and sell into our customer base. Let's talk a little bit about the threats that are out there in the modern world, how they're evolving with artificial intelligence. Obviously, it can be used on both sides of the um of this competition to see who can protect information and then who can steal it. Who are the actors? What's the motivation today and how are they coordinating? Because it feels like there is now this new um Allegiance between American

hackers, very young anonymous, working with social engineering, working with some brute force tools out of China, Russia, and other places. Um, who's orchestrating these very large um, you know, hacks that occurred at the casinos recently. And then we can get into how should our government, if at all, be thinking about stopping these and partnering with corporate America um, and to neutralize these threats because some of them are involving the governments of these countries. Yeah, so I think, look, if you trace the history of cyber hacking, we had these big hacks which used to take 30 or 50 days to figure out. People were doing them as a hobby. You'd think of your notion of a hacker as some kid sitting in their parents' basement who didn't get out of there on his little, you know, PC trying to hack this and trying to get all the data out of there. And then suddenly people discovered, wait, I can get better than this, right? Because it was usually to prove it's a badge of honor. Oh, I hacked into this database or

I hacked in there. I got in there. You guys aren't strong enough. Now, as the world got more connected, what happens was people says, Wait, why am I wasting my time hacking one user, one company at a time? Let me go after a piece of the supply chain. If I hack The Exchange Server, everybody uses an exchange server, it's fair game. If I hack an agent or not an antivirus, I can get everybody's computer. If I hack a large email provider, I can have access to every dissident email. Which is when nation-states got involved. Nation-states said, Wait a minute, if I want data, why bother hacking one person? Let me go hack the back end and get in. Let me Gmail, yes, more effective. So when that began to happen, nation-states started getting wise. That's interesting, if I can do that, I can destabilize nations, I can get data about other people that I want. So that became a bit of a nation-state activity. Now, cybersecurity offenses is way

easier than defense. The defense you got to right 100% of the time. Offense is going to find one door. So, put that aside. Then what happened on top of that is that nation-states started cultivating these entrepreneurs in the hacking world, saying, Listen, that's how you keep your skills up to date. If you go after stuff, we'll look the other way while we're going and doing it because if we need you, we have found a way. And then we discovered this notion, Wait, there's tremendous economic value now in hacking. So, we can run somewhere people want to says you, and there's like a magic number. They ask for $30 million or less because that's director's liability insurance. Wait, sorry, sorry, sorry. When you get hacked for ransom, $30 million is what companies can give you where it's covered by insurance. Oh, it's covered by insurance, purely working backwards from that policy, huh? So, there's about $2 billion that's been paid in the last 12 months.

on ransomware. Wow, wow. That if you think here's here's the anatomy of a hack, right? Somebody says, I found a solar wind server. It's hackable. So, they, some set of guys, go quickly and plant themselves at 1,800 solar wind servers which are exposed to the internet. Then, there's a separate industry sub-segment. They sell it to saying, Listen, I'm only in the seating business. You can go run Ransom as a Service, ransomware as a service negotiations with customers. Wow, that's their go-to-market, yes. Say, They go to market amplification to system integration. Yes, yeah. And then, there's a third set of people who are payment clearing people say, I'll collect the money. I know how to process $30 million bit. Oh my, so sophisticated. This is so sophisticated. And where are they geographically? Like, where is it? All over everywhere, everywhere, everywhere. Where extradition treaties are light and, uh, are there specific foreign nationals that it moved to?

jurisdictions to do this is this like there's there's a lot of people in the world out there who do this and they're hard to find and remember think about the enforcement like where are you going to go go to your local police station you're freaking me out somebody takes a million doll away from your bank account who you going to local police guy says actually sir this looks like somebody in Greece yeah do you know our our our like panic attack yeah let me let me shift the conversation I want to talk about AI for a second cuz you mentioned it as well um but I want to first ask it to you more as just a smart Observer of the market you're in the market you've invested in a bunch of companies as well um what's the state of AI take it however you want whever yes I mean look what's interesting is I think a lot of people are chasing llms and I think there's a very well established expectation out there these llms will get smarter and smarter inflence will

come latency will go down, uh, cost to deploy, cost to train will all come down. So, the good news is we've seemed to have established a nicely competitive space out there between all these people that you can expect some sort of economic rationality to Trail, and a lot of people are sort of investing a lot of dollars to get it there. And thanks to Mark Zuckerberg throwing out open-source models, he keeps them honest and fair everywhere around there, so we'll all get access to these models. But for the most part, as you get into the application of these models, I think the world changes in consumer enterprise. In consumer, you got to figure out how these models are going to translate into consumer services and make them better. And you can see that the question is, do we get a whole new Google that's formed or a whole new Facebook that's created, or do the existing players move fast enough to embody, sort of, to embed AI in there? And our hooks, those services have to us, are so strong that we don't

shift yeah our usage yeah so let's spark that for a second. We'll go back there in a minute. On the Enterprise side, it's not useful unless you can train on my data, and you'll disc. 90% of companies has bad data. 90% of companies, like how do you solve this from I don't know. I don't know how I fix the last firewall that broke down if I don't have that data. And if I don't have 10 good instances, how do I make it work in the 11th instance? So, we're all busy refactoring our data, figuring out how to collect good data on the Enterprise side, which is going to happen. It's all the easy stuff that Sebastian will tell Clar, I've got it figured out. I'm going to answer questions. Those are easy questions. How much balance do I owe you? When do I owe you? Can I pay you tomorrow? No, you have to pay me today. That even in iBot can answer that question, right? But it's very

hard to say my firewall broke down. I don't know what happened. How do I fix it? CU. I need a lot of data, so I think on the Enterprise side, a lot of companies have to do a lot of work to get their data sorted, and that's in process. What we've done is we stimulated all of us to go out and get that figured out. On the consumer side, it's going to be very interesting. I think we can all imagine a future which says, Hey, my favorite phone or favorite Hardware device or favorite interface, go book me a ticket to Geneva, which I'm going after this, and book me a restaurant and a hotel room. Now you just in your brain said, Wait, wait, wait, wait. I just did booking.com. I did OpenTable, and I did hotels.com. Now we're going to see this happen. Who's going to control the user interface, and whose agent is going to talk to who, right? Try telling any of the existing app guys that, Listen, suppressor UI, I'm just going to send you an API.

call send it back to me. I'll control the data about the consumer. Yeah, let's see how far that lasts. I mean, that's yeah might. You're just handing over your business to them. Yeah, well then what's going to happen? One or two things happen that always happens, right? These people become the Legacy players, and you'll have new companies that are formed which are agent-based only. It's like, you know, yeah, half your fortune is better than none. Yeah, so if I start a company tomorrow, I would say, listen, I only have an agent that does Airline bookings. Just pay me 20%. I'm good. I don't need a brand. What happens then? So I think there's going to be, I think there's going to be much more upheaval in the consumer space than any one of us realizes. I think 5 million apps will be redesigned in the next 10 years. They'll all become agents, right? The ones that want to survive will do that first, but it's very hard. Yeah, your margins, my

opportunity, I guess, is the way we say it in the industry. Yes, yes, but it's very hard. Can you try going to any of these large branded apps that sit on everybody's phone and say, Listen, shut it off. Yeah, become a service provider of data. They would just talk to Siri, just talk to whatever it is, Yeah, and we'll get it done for you. AI and attacks and sophistication of the attacks that occur. How often does human factors, phishing, tricking people come into play these days, and how much of that is going to be exacerbated by AI, deep fakes, etc.? Oh, the attacks are most simple, most simple. Explain, you know, We had a whole B Des company, and they do this as a thing. They said, Listen, we're going to penetrate your defenses. It's not possible. It's just impossible. We're going to figure it out. The guy goes in the morning, 8:00 at the parking lot, drops a bunch of USB sticks with little tape on it, Taks my home videos. Oh

my god wow he drops about 25 of them. Six of them log in with the USB stick in the computer in the office they're in. Oh my God oh my God so great oh my so I don't know if you need to go like get a battery RAM and break your door do this is It's human behavior human behavior it's like and they possibly said something more colorful than my home videos on that but I'm just going to keep it PG here right so you can decide at what point in time your curiosity with a z that got 100% yeah so it's like these are not heart attacks like you know there's we had a we sent an email out there we saying National Pet Day please take a picture of your fluffy pet at home and upload it to with his website and the person who does it will give $10,000 to the SPCA. Oh my God you seen the beautiful fuzzy pictures uploaded to this hacking site where we had all your details all the IP addresses everything yes everything no no you had to actually

add to your username, oh godword, and there's things like, is your pet so wonderful that he used their name as a password? Yeah, let's, what's your pet's name? Love it, let's uh, love it, let's F so great. This is not hard. You don't need like, you know, use cyber security sensors to block this stuff. Uh, okay, wait, flip it around for a second. There's something going on. I don't know if you read, you probably did. Uh, there's a, there's a, an explosion of deep fake porn in South Korea going on right now. That's not my area of specialization. No, you guys, you guys might know more about that. Heard from a friend. I don't have time for that kind of stuff. No, no, I met more. He read on Twitter and jam search for and there wasn't any. No, but so there's all this fake content that's going to emerge. There's going to be all, is it fake? Somebody's fake? Is somebody's reality? Yeah, I know, but my point is more

different. How do you, if you're asked by a customer, tell me if that's real or not? How do you figure out tomorrow if something is real? Well, look, there's a huge conversation going on that there needs to be some form of regulation that insists that watermarking needs to happen. If you generate a video using any AI tool, it has to say created by AI. If it doesn't, it's very hard to tell the difference. Yeah, and as I was saying to somebody else the other day, it's like most likely if it seems too perfect, it was probably created by AI. How is that different? Let me just ask philosophically, how is that different than airbrushing in Photoshop, than making the person look completely different? We don't have any of those disclosures today. I always feel like there's a spectrum that I guess scale would be the issue, right? And fidelity, I don't know, sorry, scale and fidelity. Like, you know, the number of people who can do what you're saying is .1% of the population or 1%. Now it's 100%.

I think if it's with the intent to deceive, I see ah yeah anyway. And then what about on the other side in terms of Defense? Have you started to make AI, you know, uh Shields that yes yes. So look, the two biggest risks today in AI are is that I think about 20 to 30% of most companies have employees, the younger side, who are using AI apps to try and get their job done faster and easier. Write me a marketing blog, try and figure something out, you know, write me a script for this, or take this data, analyze it for me. The risk there is that you're sending proprietary data up into a model, oh yeah, for company, you know, here's a napkin drawing of a chip design I just made for inferencing. Turn this into real CAD drawing for me. They could do it, but except that that llm was brought down from hugging face and it's going back to North Korea, yep. So there's that risk that your employees are being targeted with AI apps in your company.

who are uploading proprietary data in a happy way, very nicely for you. Same guy who picked up the USB stick. So there you have, we have a product that watches all these apps and makes 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, 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 SQL injection, you can do with prompt injection. You can bombard models, you can bias them, you can do a whole bunch of stuff. So we have, you know, what we call a firewall that will protect you. I want to be sensitive to time. I know you have to fly to Geneva. Thank you very much for coming, ladies and gentlemen. The C, thank you for [Applause].

Nikesh Arora | All-In Summit 2024

Chamath Palihapitiya, Jason Calacanis, David Sacks & David Friedberg cover all things economic, tech, political, social & poker.

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All-In Podcast

Mesh Aurora's Impact on the Cybersecurity Industry

Mesh Aurora, a prominent executive in cybersecurity, has played a key role in shaping the industry's landscape.

He joined Google in 2004, driving its revenue from $3 billion to $66 billion by 2014, before moving to SoftBank and then becoming the CEO of Palo Alto Networks.

Under his leadership, Palo Alto Networks' market cap has increased from $20 billion to $110 billion, demonstrating his consistent success in building great products and leading teams to execute at scale.

The product was simple, free, and easy to use, with the focus on monetizing through advertising.

The rapid growth required building a programmatic system and attracting talent worldwide.

The speaker's success in Europe led to a call from Eric Schmidt to move to the US and take on a larger role, while Masa Yoshian's unique risk-taking approach at SoftBank led to significant investments and a series of successful bets.

Ambitious Goals in Telecom and Tech Investments

The speaker discusses a person's ambitious plan to acquire telecom companies and reach $100 billion in EBITDA, eventually aiming for a trillion-dollar valuation.

The individual also raised significant funds for the Vision Fund, with a similar trillion-dollar goal.

The speaker reflects on the importance of focusing on winners in investments and the unique opportunity presented by the fragmented cybersecurity sector.

Strategic Growth and Talent Management in Cybersecurity

The speaker emphasizes the importance of connecting data and identifying demand in the cybersecurity sector.

They shifted focus to the cloud and AI, acquiring 19 companies to build a diverse portfolio.

The unique approach to talent management involves offering founders equity incentives to stay, fostering collaboration and driving the company's success.

The Unique Approach to Acquisitions and Cybersecurity Threats

The founder's experience with the company's acquisition led to a shift in the approach, focusing on joint product strategy with founders.

The company's M&A playbook differs by prioritizing product acquisition for integration and customer sales, rather than buying customer bases.

The evolution of cyber hacking has shifted from individual efforts to coordinated attacks by nation-states, leading to a more complex and impactful threat landscape.

Hacking has become economically valuable, with nation-states cultivating hackers and offering protection in exchange for their skills.

Ransomware has generated around $2 billion in payments over the past year, with a common ransom amount of $30 million, often covered by insurance.

The AI market is competitive, with a focus on improving models and translating them into consumer and enterprise services, while companies struggle with bad data and the need to refactor for effective AI training.

The Future of Data Management and Consumer Services

The speaker's firewall has broken down, highlighting the challenges of data management for companies.

They predict a shift in the consumer space, with apps redesigning to become agent-based services for tasks like booking travel and restaurants.

The speaker also emphasizes the role of human behavior in security breaches and the increasing sophistication of attacks, exacerbated by AI and deep fakes.

The speaker discusses the easy use of personal information for passwords and the growing issue of deep fake porn in South Korea.

They emphasize the need for regulation and watermarking to distinguish AI-generated content.

The speaker also highlights the risks of employees using AI apps with proprietary data and the importance of implementing safeguards to protect against potential threats.