尼凯什·阿罗拉：从谷歌到 Palo Alto Networks

David Friedberg (00:00:00): Our next speaker is actually fortunate enough to have had seen his brand name turn into a verb. Jason Calacanis (00:00:06): One of the probably most prolific executives in this current generation is Nikesh Arora. The big daddy of the cybersecurity space, these guys are really Chamath Palihapitiya (00:00:15): 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. Nikesh is one of those people. This is a man who has tremendous insight into technology. Jason Calacanis (00:00:27): He helped turn Google into Chamath Palihapitiya (00:00:29): the dominant player in search. Nikesh Arora (00:00:30): This is the most innovative industry in the world. We're constantly paranoid from an innovation perspective. I've gotta be in my toes because once we figure 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 wanna 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. Chamath Palihapitiya (00:00:53): Alright. Ladies and gentlemen, Nick Echora. Jason Calacanis (00:00:59): My guy, appreciate you. Thanks for coming. Nikesh Arora (00:01:03): David? David? How are you? Chamath Palihapitiya (00:01:06): What's up, broski? 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 build up to that career. But you joined in 2004. You left in 2014. You started in ad sales, and you left as the SVP and chief business officer of Google. Revenue went from 3,000,000,000. I checked this actually just to make sure because I have it's staggering, to 66,000,000,000 when you left. And then you because we're gonna talk about that. And then you got seduced to go work with Masayoshi Son at SoftBank, where you're vice chairman and president. Yep. That must have been interesting. Jason has look at Jason. David Friedberg (00:01:51): Yummy. He's flicking his So Nikesh Arora (00:01:54): many questions. There we go. Chamath Palihapitiya (00:01:57): 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 chairman and CEO of Palo Alto Networks. And I had known what it was, but I didn't really understand. And then meanwhile, in the last what's it been? 7 years? Nikesh Arora (00:02:17): 6 and a half. Chamath Palihapitiya (00:02:17): 6 and a half years. Market caps up by 5 x. You took a $20,000,000,000 company. It's 110,000,000,000 as it stands. I think you've tripled revenue. So this is clearly no longer luck. So now you're you're in the skill camp. Oh, good. Nikesh Arora (00:02:34): I'm in founder mode? Chamath Palihapitiya (00:02:36): No. Founder mode is cocaine. Nikesh Arora (00:02:37): I was wondering when that Chamath Palihapitiya (00:02:37): was the start. Wait. I was Nikesh Arora (00:02:39): gonna I was wondering when Is there a founder mode on you? No. We have Chamath Palihapitiya (00:02:43): to fly to Europe. You cannot bring founder mode. Nikesh Arora (00:02:44): There are no waffles on the plane. No waffles on the plane. Jason Calacanis (00:02:48): You can get founder mode in Europe though. Chamath Palihapitiya (00:02:50): I guess I've heard Let let let's just start and just, actually, let's just start there. Nikesh Arora (00:02:54): Okay. Okay? Okay. Chamath Palihapitiya (00:02:56): 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 what what takes what what does it take 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? Nikesh Arora (00:03:14): Look. I think you already put that out there, didn't you? Didn't you say that, if you think about building great businesses, at the center of great business is great products. If you don't have a great product, you're not gonna build a great business for the long term. And this is something, and I know Sergei's here, I learned that with Larry and Sergei 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 gonna 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 flywheel continues to work. In 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, tremendous amount of sort of detailed inspection. But I think at the same time, you've 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. Chamath Palihapitiya (00:04:15): Tell us about that first that first story or that 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? Nikesh Arora (00:04:25): Look, Google has one of the best flywheels there is in the consumer space. Right? So we were blessed that we're 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. So part of that is how do you scale that around the world in every country where there's 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 know, you build a system. You build a programmatic system. You look at stuff. You inspect, and you hire really amazing people who got into their best that they can. Chamath Palihapitiya (00:05:07): 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? Nikesh Arora (00:05:18): Well, you know, it was such an amazing juggernaut that you had to figure out how to differentiate. And what is interesting is when I joined, Google was 24% of global revenue. When I moved to the US, it was 49%. Chamath Palihapitiya (00:05:32): In Europe? Nikesh Arora (00:05:33): Yes. And it is 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. Chamath Palihapitiya (00:05:43): And what what happens? You get the call and you're like, we need you to move to America? Nikesh Arora (00:05:47): Yeah. I was, 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. Does it? Chamath Palihapitiya (00:06:04): And so why what then motivates you to leave a job like that? Because you're you're kind of then at the top of the pinnacle. You see everything. You're meeting everybody. Nikesh Arora (00:06:14): I guess, I wanted more. I wanted to do more get involved, sort of, the overall business. I wanna do some product work as well. I didn't have product jobs at Google. I was seen as a sales guy. I've and Palo Alto, all I do is product for the 1st 6 years of my life. Yeah. So to be able to go out and do that differently, but I had to take a brief sojourn to my Japanese trip. Lots of good sushi and lots of Jason Calacanis (00:06:35): It was a great vacation. Let's talk about it. Nikesh Arora (00:06:37): Oh, come on. It wasn't a vacation. Wow. Jason Calacanis (00:06:39): A great surge. But I mean, this was the largest venture fund ever created, a $100,000,000,000 and you have this material, brilliant individual, Masayoshi san, 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? Nikesh Arora (00:07:01): Look. Masa is one of those people whose risk appetite grew as he grew older. Jason Calacanis (00:07:10): And you mentioned that because that is a very unique thing. It Nikesh Arora (00:07:12): usually goes up a way. I have 2 young kids, and every time and I'm constantly trying to derisk them. It's like, hey, be careful when you cross the road. Be careful when you do this. You get married, people tell you, be careful. Buy a house. Go settle down. So we're constantly derisking our lives as we get older on a constant basis. All of us do it. We don't realize we do it. Right? Mars is the opposite. The older he gets, like, come on, let's go all in. He's like you guys. He wants to go all in. So it's like, Ditesh, I have a great idea. We'll put a 1,000,000,000 we bought on 19,000,000,000 dollars. Well, how does that work again? All he got is a 1,000,000,000. I have one great idea a 1,000,000,000 in 19,000,000,000. 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 $1,000,000,000. Unbelievable. And he built from scratch again. So yes. So there were Jason Calacanis (00:08:02): a series of incredible bets. Yes. Maybe walk us through some of those bets, because there was Nvidia. There was ARM. Nikesh Arora (00:08:09): So all this happened after I left, but that's Right. I can David Friedberg (00:08:11): And then you had ARM happen after you left? Nikesh Arora (00:08:13): That was where we kind of, you know Unpack it. Sorry? Jason Calacanis (00:08:17): Unpack that. Unpack this. Nikesh Arora (00:08:24): Well, Masa likes a trillion. Jason Calacanis (00:08:27): He likes the number 1 trillion. Yes. Nikesh Arora (00:08:30): Okay. It's a good number. It's better than 1,000,000,000. Beats a billion. Jason Calacanis (00:08:34): It beats a it is greater than a 1,000,000,000. Nikesh Arora (00:08:35): Yeah. It's greater than a 1,000,000,000. Last I checked, yes. And when I met him the first time after we'd done a deal at Google, he was, you know, we met when he was, he came to see Larry, Sergey and Eric and said, I'd like to do a search deal with you at Yahoo! Japan. I tried to explain to him there's something called, you can't have 2 search engines both powered by Google in Japan. And 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 is powered by Google. But the advertising systems are different, hence it's non competitive. So he got that done. And then he says to me, show me this plan. He was going to buy a lot of companies in telecom and get to $100,000,000,000 in EBITDA, which at 10 times EBITDA would be $1,000,000,000,000. 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 1, 2, 3, 4. A 100, 200, 300, and 400. $1,000,000,000. Yes. For the Vision Fund, so that'd make a trillion. Chamath Palihapitiya (00:09:36): Hold on a second. Let's we're doing that. Yeah. That's a $1,000,000,000,000. 1, Nikesh Arora (00:09:40): 2, 3, 4. Yeah. Jason Calacanis (00:09:41): You know, it's interesting. Then, like, Nikesh Arora (00:09:42): he had the sole portfolio of companies, and there's a bunch of Japanese analysts who sit in the office, MBAs from University of Tokyo. And eventually, they took all the business that we had and forecast their free cash flow at the end, where the DCF was a trillion. Jason Calacanis (00:09:56): So he was good at setting goals. Nikesh Arora (00:10:02): So he thought Arm was gonna be a $1,000,000,000,000 company. Got it. Jason Calacanis (00:10:06): We were Sorry. Let me ask it, Jorge. David Friedberg (00:10:08): 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 Nikesh Arora (00:10:22): Well, look, money is a way to keep track. Yeah. It's not the goal. That was the way he kept track. I get it. But, you know, he was not financially united 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 he saw that. One thing I did learn from it, 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 there would be 1 investment, Shit. That's not going let me go and talk to the company, help them, help them fix it. We can get them up and running. He calls me and said, I won't do this. Nikesh, you're spending too much time with the mistake. He said, if you go spend that time with a company that's growing at 3 times, they can grow at 6 times. We'll make our money up 6 times with that company instead of you trying to fix that from half back to 1. So it's kinda interesting. You know? Chamath Palihapitiya (00:11:12): That's an incredible lesson, actually. Nikesh Arora (00:11:13): It's an honest thing. David Friedberg (00:11:14): It's all investing. Jason Calacanis (00:11:14): Cutting base Nikesh Arora (00:11:15): All investing. Jason Calacanis (00:11:16): Sunken cost. David Friedberg (00:11:16): I Jason Calacanis (00:11:17): mean, there's a million ways to say it. Nikesh Arora (00:11:18): Yeah. Jason Calacanis (00:11:18): But you have to let your winners ride, and you gotta focus on the winners. But it's David Friedberg (00:11:22): Go all in on the winners. Jason Calacanis (00:11:23): Go all in on the winners. Nikesh Arora (00:11:23): 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 gonna salvage it. David Friedberg (00:11:30): Yeah. That's an ego problem. That's a hubris problem. I don't wanna have a mistake on my record. Jason Calacanis (00:11:33): Before a good person Yeah. Wants to help the founder, you know, realize their vision, and the cutthroat nature of this with the power law is such that 6 x ing something that was at 3 x is much much more likely than getting a 0 to a 1. David Friedberg (00:11:52): Do you do you apply that principle as an operator? And if so, like, how at Palo Alto Networks? Nikesh Arora (00:11:57): Look, in the last six and a half years, I've got 19 companies. Chamath Palihapitiya (00:12:02): Right. Nikesh Arora (00:12:02): We can Chamath Palihapitiya (00:12:03): show that you can Oh, yeah. Here's the slide. David Friedberg (00:12:04): So we got some great slides. Chamath Palihapitiya (00:12:05): We got some slides that Nikesh Arora (00:12:06): just Wow. David Friedberg (00:12:07): Here's your stock, good job. Nikesh Arora (00:12:09): Thank you. It's not bad. Alright. Go back. Let's see. Revenues are right now. Chamath Palihapitiya (00:12:14): If you go back, it's Go back a second. It's really bad. It's really bad. It's Nikesh Arora (00:12:16): bad. Yep. What's the market Chamath Palihapitiya (00:12:18): cap now? 110,000,000,000. David Friedberg (00:12:19): 110,000,000,000. Yeah. Jason Calacanis (00:12:20): That's what they And Nikesh Arora (00:12:21): when you started, it was at Chamath Palihapitiya (00:12:23): 20. 20. David Friedberg (00:12:23): 20. Jason Calacanis (00:12:24): What you're seeing here is, the, economic principle of, founder mode. David Friedberg (00:12:32): And the rev the revenue and the, operating income. Nikesh Arora (00:12:36): Yum yum. David Friedberg (00:12:36): Which Chamath Palihapitiya (00:12:37): 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 I mean, you've spent the last six and a half years in product. Did you see a product that just was it missing something? That's the thing David Friedberg (00:12:51): I wanna hear about it. Like, did you go all in on water tubing? It's broken. That was always the Steve Jobs model, was pick the winner and go all in on it. Get rid of all the other stuff. Does that principle apply here? Or is No. This is Nikesh Arora (00:13:00): like a different principle. Look Yeah. It's a $180,000,000,000 industry on an annual basis. The largest market share is 1 and a half percent, which is us. David Friedberg (00:13:09): And it's Nikesh Arora (00:13:09): a sub sector of technology with the most amount of fragmentation. Look around, Benioff, I think is 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. 1. 2, 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 a 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. Jason Calacanis (00:13:42): So if Nikesh Arora (00:13:42): you can go into a sector where there's no demand problem, you can look at it and say, what did everybody get wrong? Say, well, everybody sort of lived in their swim lane. So we were in our swim lane. We did one thing. There are 5 swim lanes in cybersecurity. In 6 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, you know, my time at Google and NASA is, like, a lot of people get hung up in trying to make the stuff work, assuming everything around you is gonna stay the same. So saying that we're just gonna focus on assume that 50% of the world is in the public cloud, what's security gonna 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 worked 19 companies. We went and looked at how everybody does M and A and who failed. Oh, what did you learn from that? Well, we learned that things trade at a price for a reason. So very often people say, you know what? Number 1 is a $1,000,000,000. Number 4 is $200. I can take $200 and clean it up and fix it. It's at 200 for a reason. The 1,000,000,000 and 1,000,000,000 of reason. They'll still be around. Yes. So why don't we buy the guy who's got who's worth $1,000,000,000? We'll be number 1. We'll be leading the market. We have brute force and 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 I have a funny story. I got a guy who says, oh, great. Nikesh Arora (00:15:17): 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. He's like, what do you mean? I said, that's the guy you're gonna work for. He said, we just bought his company. I said, yeah. He kicked your ass with low resources out there in the market. You're gonna learn something from him. David Friedberg (00:15:36): There you go. Nikesh Arora (00:15:36): Welcome to the new boss. Jason Calacanis (00:15:38): Well, you know, there is an analogy. You, 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? Nikesh Arora (00:15:58): That should not lately, but yes, in the past. Yeah. Okay. But look, we've done that 19 times. We had 7 out of 10 we've gotten right. And we still possibly have the most number of founders who still work for Palo Alto. Chamath Palihapitiya (00:16:11): 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 vamoose. They're gone. Nikesh Arora (00:16:18): No. No. So here's how it works. If you come to Palo Alto, we'll take your equity away first. We'll see. I'm gonna give you back one and a half times their equity if you stay with me for 3 years. Chamath Palihapitiya (00:16:28): Oh, wow. To the founder? Jason Calacanis (00:16:29): Yes. Okay. Wait a second. So the founder owns, let's say it's a $100,000,000 a $1,000,000,000 company, they own 20%, they got 200,000,000. You say, hey, stay again, I'll give you 300,000,000. Right. Gotta work for me for 3 years. Pretty good. Because when you buy Nikesh Arora (00:16:42): a company, you're buying a half half a product and a full vision. Right. I lose the vision part of it. I get half a product. Right. That's But David Friedberg (00:16:53): 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 how do you manage that balance here? Nikesh Arora (00:17:08): So what happens is, look, the customers in security want the best product. That's why everybody lives in their swim lanes. We said, we gotta be in multiple swim lanes. To be in multiple swim lanes is 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 leaders quadrant in Gartner. When I joined Palo Alto, we were in 2. We're in 24 right now, in the top right. So now when you go to customers saying, hey, I got some great products. I don't know. You're gonna sell me some good and some bad. Like, you know, you pick. There's all 24 that 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 a founder, which was my first acquisition, and I think we didn't set the bid right, so we had to fix it in future deals. He's paid the founder a reasonable amount of money, probably $850,000,000 to the company. He had about $150,000,000. He's gonna get 200. Then he comes into my office and says, Nikesh, I had a problem. I think we should do it this way. You're telling us to do it this way. I'll say, because this way is the way it's gonna work for us. It'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? He'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? Chamath Palihapitiya (00:18:32): The new owner. He said, the new owner, of course. Nikesh Arora (00:18:34): I said, thank you. So he stayed there. He really liked us, and he stayed for 3 years. He made 2 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 and say, okay. The lawyers will do their thing. You're going to sit with my head of product and design a product strategy we both agree on. Yeah. So we will buy a company until we have a joint agreed product strategy with the founder. David Friedberg (00:18:58): Now in Cisco, Oracle, Salesforce, they've all kind of had this M and A playbook that they claim is part of their engine of success. How differentiated is it for you? What's kind of the biggest contrast for your playbook versus those engines? Nikesh Arora (00:19:14): 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. 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 owe to customers already. Why would I pay 8 to 10 times revenue to buy a customer I already have it under 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 2 companies, merge them, and I can make it worth 16 times. You as an investor can buy both our companies and 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 wanna buy customer bases. We wanna buy products which we can integrate and sell into our customers. Jason Calacanis (00:20:02): 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, of this competition to see who can protect information and 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, allegiance between American hackers, very young, anonymous, working with to do the social engineering, working with some brute force tools out of China, Russia, other places. Who who's orchestrating these very large, 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, and and to neutralize these threats? Because some of them are involving the governments of these countries. Nikesh Arora (00:21:01): Yeah. So, look, if you trace the history of cyber hacking, you know, you 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 was some kid sitting in their parents' basement who didn't get out of there on his little 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 and it was usually to prove. It's a badge of wonder. Oh, I hacked into this database or I hacked in there. I got in there. You guys aren't strong enough. And as the world got more connected, what happened was people said, wait, why am I wasting my time hacking 1 user, 1 company at a time? Let me go after a piece of supply chain. If I hack the Exchange server, everybody who uses Exchange server is fair game. If I hack a, you know, agent or not, an antivirus, I can get in everybody's computer. If I hack, you know, a large email provider, I can have access to every dissident's email, which is when nation states got involved. Nation states said, wait a minute. If I don't want data, why bother hacking 1 person? Let me go hack the back end and get in. Chamath Palihapitiya (00:21:59): Let me hack Gmail. Nikesh Arora (00:22:00): Yes. More effective. Right. So when that began to happen, nation states started getting more. I think 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, offense is way easier than defense. And defense, you got to be 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, we're saying, listen, that's how you keep your skills up to date. If you go after stuff, we'll look the other way while you're going and doing it, because if we need you, we would have found a way. And then we discovered this notion, wait, there's tremendous economic value now in hacking, so we became ransomware. People want to say and then there's, like, a magic number. They ask for $30,000,000 or less because that's director's liability insurance. Chamath Palihapitiya (00:22:54): Wait. Sorry. Sorry. Sorry. When when you get when you get hacked for ransomware, 30,000,000 is what? Nikesh Arora (00:22:58): Is what companies can give you where it's covered by insurance. Chamath Palihapitiya (00:23:02): Well, it's covered by insurance. They all purely working backwards from that policy. Nikesh Arora (00:23:07): So So there's about $2,000,000,000 that's been paid in the last 12 months on ransomware. Wow. Wow. If you think here's the here's the anatomy of a hack. Right? Somebody says, I found a SolarWind server that's hackable. So they some set of guys go quickly and plant themselves at 1800 SolarWind servers, which are exposed to the internet. Then there's a separate industry subsegment. They sell it to say, listen, I'm only in the seeding business. You can go run ransomware as a service negotiation with customers. Wow. Jason Calacanis (00:23:35): That's their go to market. Nikesh Arora (00:23:36): Yes. It's their go to market amplification to systems integration. Yes. Yeah. And then there's a third set of people who are payment clearing people. I'll collect the money. I I know how to process Jason Calacanis (00:23:45): I'm gonna Nikesh Arora (00:23:45): $30,000,000 bill. Oh my gosh. David Friedberg (00:23:46): Alright. So sophisticated. Chamath Palihapitiya (00:23:47): This is so sophisticated. David Friedberg (00:23:48): And where are they geographically? Like, where is it all over? Nikesh Arora (00:23:51): Everywhere. Everywhere. Everywhere where expedition treaties are light. Mhmm. David Friedberg (00:23:55): And, Are there specific foreign nationals that move to jurisdictions to do this? Is this like Nikesh Arora (00:24:02): 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, who are you gonna go? Go to your local police station. David Friedberg (00:24:11): You're freaking you're freaking me out. Nikesh Arora (00:24:14): Somebody takes a $1,000,000 away from your bank account. Who are you gonna look up? Police says, actually, sir, this looks like somebody in Greece. Yeah. Chamath Palihapitiya (00:24:20): Do you know our our our Like, panic attack? Yeah. Let me, let me shift the conversation. I wanna talk about AI for a second because you mentioned it as well. But I wanna 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. What's the state of AI? Take it however you want. Nikesh Arora (00:24:41): Yes. I mean, look, what's interesting is I think a lot of people are chasing LLMs. I think there's a very well established expectation out there. These LLMs will get smarter and smarter. Inference will come. Latency will go down. 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 prevail 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 gonna 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 into us are so strong that we don't shift Yeah. Our usage. Yeah. So let's spark that for a second, and 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 discover 90% of companies have bad data. 90% of companies. Like how did you solve this from I don't know. I don't know how I fixed 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 gonna happen. It's all the easy stuff. Sebastian will say, I'm calling, I've got it figured out. I'm gonna answer questions. Nikesh Arora (00:26:22): 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. Even an AI bot 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? Because I need a lot of data. So I think on the enterprise side, a lot of companies would have to do a lot of work to get their data sorted, and that's in process. What we've done is 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 you're gonna see this happen. Who's gonna control the user interface and whose agent is gonna talk to who? Right? Try telling any of the existing app guys that, listen, suppress your UI, I'm just gonna 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. Jason Calacanis (00:27:26): I mean, that's you might you're just handing over your business to them. Nikesh Arora (00:27:30): Yeah. Well, then what's gonna happen? 1 of 2 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 totally. 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 the brand. Mhmm. What happens then? So I think there's gonna be I think there's gonna be much more upheaval in the consumer space than anyone else realizes. I think 5,000,000 apps will be redesigned in the next 10 years. They'll all become agents. Chamath Palihapitiya (00:28:04): Right. The ones that wanna survive will do that first. Nikesh Arora (00:28:07): But it's very hard. Jason Calacanis (00:28:09): Yeah. Your margin's my opportunity, I guess, is the way we Nikesh Arora (00:28:12): say it Jason Calacanis (00:28:12): in the industry. Nikesh Arora (00:28:13): Yes. Yes. But I I it's very hard. Can you try going to any Chamath Palihapitiya (00:28:15): 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 they would Jason Calacanis (00:28:23): Just talk to Siri. Just talk to whatever. Nikesh Arora (00:28:25): Whatever it is. Jason Calacanis (00:28:26): Yeah. And we'll get it done for you. AI and attacks and sophistication, of the attacks that occur, how how often does human factors, phishing, tricking people, come into play these days? And and how much of that is going to be exacerbated by AI deepfakes, etcetera? Oh, the attacks are most simple. Nikesh Arora (00:28:49): Most simple. Explain. You know, we had a whole pen test company, and they do this thing. They say, listen, we're gonna penetrate your defenses. It's not imp not possible. It's just impossible. We're gonna figure it out. The guy goes in the morning, 8 o'clock at the parking lot, drops a bunch of USB sticks with little tape when it says my home videos. Oh my god. Wow. He drops about 25 of them. 6 of them log in with the USB stick and computer in the office. There is. Oh my God. Jason Calacanis (00:29:21): Oh my God. Chamath Palihapitiya (00:29:22): Oh my God. So Jason Calacanis (00:29:24): great. Oh my God. Nikesh Arora (00:29:25): So I don't know if you need to go, like, get a, you know, battery ram or break your door down. This is you know Chamath Palihapitiya (00:29:31): It's human behavior. Human behavior. Nikesh Arora (00:29:33): It's like, and they possibly said something more colorful than my home videos on that, but I'm just gonna keep it PG here. Yeah. Right? Chamath Palihapitiya (00:29:39): So you Nikesh Arora (00:29:40): can decide at what point in time you're curious about the Jason Calacanis (00:29:41): I knew it with a z. That got a 100%. Nikesh Arora (00:29:46): Yeah. So it's like, these are not heart attacks. Like, you know, there is we had a we sent an email out there saying, National Pet Day. Please take a picture of your fluffy pet at home and upload it to this website, and the person who does it will give $10,000 to the SPCA. Oh my goodness. It's seen the beautiful, fuzzy pictures uploaded to this hacking site where we had all your details. David Friedberg (00:30:09): All the IP addresses, everything. Nikesh Arora (00:30:11): Yes. Everything. Yeah. Oh, no. No. You have to actually add to your username. Oh, god. And say Password. And there's the things like, is your pet so wonderful that you use their name as a password? Chamath Palihapitiya (00:30:18): Yeah. Let's What's your pet's name? Love it. Let's, love it. Let's fix it. This is Jason Calacanis (00:30:25): not right. Nikesh Arora (00:30:25): It's not hard. You don't need, like, you know, to use cyber security sensors to block this stuff. But we Okay. Chamath Palihapitiya (00:30:31): Wait. Flip it around for a second. There's something going on. I don't know if you read you probably did. There's a there's a an explosion of deep fake porn in South Korea going on right now. Nikesh Arora (00:30:42): That's not my area of specialization. Chamath Palihapitiya (00:30:43): No. No. No. You you Nikesh Arora (00:30:45): should you guys you guys might know more about that. I heard Chamath Palihapitiya (00:30:48): from a friend. I don't have time for Nikesh Arora (00:30:49): that kind of stuff. No. Chamath Palihapitiya (00:30:50): No. No. No. No. I met more. David Friedberg (00:30:51): You read on Twitter. And, Shamak's search for Chamath Palihapitiya (00:30:53): So there's all this Jason Calacanis (00:30:54): And there wasn't any. Chamath Palihapitiya (00:30:55): No. But so there's all this fake content that's going to emerge. There's gonna be all Nikesh Arora (00:30:58): this Is it fake? Somebody's fake is somebody's reality. Yeah. Chamath Palihapitiya (00:31:00): No. But my 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 Nikesh Arora (00:31:09): 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 creative by AI. If it doesn't, it's very hard to tell the difference. Mhmm. Yeah. And as I was saying to somebody else the other day, it's like, most likely, if it seems too perfect, it will probably be created by AI. David Friedberg (00:31:29): How is that different let me just ask philosophical. How is that different than airbrushing in Photoshop than making the person look completely different? We don't we don't have any of those disclosures today. I always feel like there's a spectrum Nikesh Arora (00:31:40): that I'm in. David Friedberg (00:31:40): Yeah. I Jason Calacanis (00:31:41): guess scale would be the issue. Right? And fidelity? I don't Nikesh Arora (00:31:43): know. Sorry? Jason Calacanis (00:31:44): Scale and fidelity. Like, you know, the number of people who can do what you're saying is 0.1% of the population or 1%. Now it's a 100. Nikesh Arora (00:31:52): I think if if it's with the intent to deceive. David Friedberg (00:31:54): I see. Yeah. Anyway. Jason Calacanis (00:31:57): And then what about on the other side? In terms of defense, have you started to make AI, you know, shields that Yes. Nikesh Arora (00:32:04): Yes. So like, the 2 biggest risks today in AI is that I think about 20% to 30% of most companies have employees on 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 a company. You know, here's a here's a napkin drawing of a chip design I just made for inferencing. Turn this into a 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 to the 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 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, we'll 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 an AI firewall that'll protect you. Chamath Palihapitiya (00:33:26): I wanna be sensitive to time because I know you have to fly to Geneva. Thank you very much for coming. Ladies and gentlemen, Nick Chatura. Thank you.