Palo Alto Networks CEO：AI 六周找出五年漏洞

Jason Calacanis (00:00:00): One of the biggest winners right now. Nikesh Arora (00:00:01): The big Jason Calacanis (00:00:02): daddy of the cybersecurity space. Palo Alto Networks is an outperformer in the space. CEO Nikesh Arora. Nikesh Arora (00:00:09): This might come as news to you, but humans have been writing bad code for a very long time. I spent ten years at Google, and, you know, Google search was democratizing information. If you take that analogy and think about what AI is doing, AI is democratizing intelligence. Money is a way to keep track. Yeah. It's not the goal. Chamath Palihapitiya (00:00:29): You've been the CEO of Palo Alto Networks for eight years? Nikesh Arora (00:00:33): Coming up in eight years this week. Chamath Palihapitiya (00:00:34): Eight years. And I think when you started, it was $17,000,000,000 market cap, if I remember correctly. And this morning, I checked, it's 238,000,000,000, which if you listen to what we said yesterday, now that you passed a 100, you're more likely to actually 10 x. The So first 10x was actually much, much harder. So, you're on your way to a trillion dollars. Nikesh Arora (00:00:51): From your mouth to God's ears. Chamath Palihapitiya (00:00:53): Why? I think Nikesh Arora (00:00:53): you are. Okay. Chamath Palihapitiya (00:00:55): So, let's just double click into what you see, because you are sort of in a really interesting position to see all of it. You see the birth of AI, maybe you've seen the rise and fall of SaaS, all the models talk to you. You were one of Nikesh Arora (00:01:10): The rise again, right? Chamath Palihapitiya (00:01:11): The rise again. You were one of the first and the few that got access to Mythos. So, let me just push the button. Go, Nikesh, start. Nikesh Arora (00:01:21): Well, first of all, thank you for having me here. I think AI is exciting. I think it's exciting to see all the stuff that's gone down in the last possibly twenty four months. I think Sarah just said it, they were right in anticipating the huge amount of compute that was going to be needed. So all that stuff's going on. But you can see that there's this notion which we talked about briefly last time that AI is really democratizing intelligence. What that means is I have two fifty people in marketing that produce varied forms of output. Now I can get 90% of the output to be consistent across those two fifty people. I have 5,000 people who talk to customers. My failure mode is when 5,000 people do different things where people say, I want to talk to Joe because he knows how to solve the problem, and Jim doesn't. So now you can get 5,000 people to act almost consistently in their interactions with people on the other side. So I think it's going to have a phenomenal impact to how we run businesses, how we operate. It's going to change the entire landscape. Now, in that context, you've touched upon MITSOS and I know David has been very involved with this. MITSOS has shown us that all the bad code that humans have written over the last fifty years can be assessed by AI and shown the vulnerabilities can be shown. We tested for six weeks and in six weeks we found what have taken us five to seven years. Wow. Jason Calacanis (00:02:53): Say that one more time. Nikesh Arora (00:02:54): In six weeks, we found vulnerabilities which would have normally taken us five to seven years to find. Jason Calacanis (00:02:59): So, So, Chamath Palihapitiya (00:03:01): are vulnerabilities where? Nikesh Arora (00:03:02): Sorry? Chamath Palihapitiya (00:03:02): These are vulnerabilities in your own code base or Oh, in your own Jason Calacanis (00:03:07): wow. So, Methos was not oversold. It was legit. Nikesh Arora (00:03:11): The capabilities of AI in being able to assess vulnerabilities in code are real. Not just that, if you put it on ultra mode, which is persistent thinking, so it keeps trying until it gets an answer, you can actually daisy chain vulnerabilities, I. E. Finding a new attack path into vulnerabilities. Now, we pride ourselves as a top percentile of companies that test our code because we're in cybersecurity business. If you take that and compound that across all the companies that exist in the world that write their own code, or the 10,000,000 developers write code, this thing is going to find stuff, which would have taken us ten years to find. Jason Calacanis (00:03:50): How much did it cost? Like, did you track the token cost? Was it a $100,000,000, $10,000,000? No. Nikesh Arora (00:03:56): No. It was in the low millions. But again, the cost, as Sarah said, the cost curve is gonna come down already. OpenAI has got a model which is cheaper and more consistent. You know, Anthropie has come out with another model. Chamath Palihapitiya (00:04:07): You the hype. Nikesh Arora (00:04:08): It's not hype. It's true. Jason Calacanis (00:04:10): That's David Sacks (00:04:10): the The capabilities are real. Nikesh Arora (00:04:12): You know that. Yes. The capabilities are true. Yes. I mean, you saw IBM announced a project for $5,000,000,000 to fix open source. That's the biggest problem. Jason Calacanis (00:04:19): What would have happened if Claude didn't have the restraint and they put it out in the public? Do you think it would have been like a real attack vector and caused chaos Nikesh Arora (00:04:29): I in think we're three months away, if not already there, from this being available in Jason Calacanis (00:04:37): the wild. Okay. Open source. Yeah. David Sacks (00:04:40): Just three months. Nikesh Arora (00:04:41): Yeah. Yeah. Because I mean, we've been David Sacks (00:04:42): saying that it's roughly six months away before mythos level capabilities are Yes. Available in Chinese models, open But models, you're saying it can be three months. Nikesh Arora (00:04:54): Well, there's what is 4.8 is already out, 5.5 is already out. They have similar capabilities. And look, you don't need to crack the hardest code to crack. You just need to find a few vulnerabilities in code that are out there. Just take an old industrial system, which is running OT code on the edge. You can find that vulnerability reasonably easily. David Sacks (00:05:14): So we're in a race right now between the cyber defenders finding these vulnerabilities and patching them before the cyber attackers do Nikesh Arora (00:05:24): the David Sacks (00:05:24): Yes. Same And how do you feel like we're doing in that race? Nikesh Arora (00:05:29): So not as well as we should be doing, which is great for our business, but that's a different story. So like every company has to go look at their code base and figure out where the vulnerabilities are and fix them. So if you talk to CIOs today, their biggest problem is all the vendors are showing up saying, please patch my piece of boxes, the hardware that you have, please patch my code that you have because I found vulnerabilities, fix it. While the CIOs are busy finding their own vulnerabilities to fix their own vulnerabilities. And then this huge thing called open source, which nobody knows quite how to solve. Chamath Palihapitiya (00:06:01): So, fair to say that as model capabilities go up, systemic business risk of large enterprises also goes up? On the cyber side, yes. Nikesh Arora (00:06:12): There are antidotes being built by people like us and others where we're going to provide some capability where you don't have to patch everything. But look, Cyrus did something very interesting around harnesses memory and context. Right? The part we don't talk about here is organizations don't have memory and context of everything they do every day. That's why you need to store a lot more data enterprise wide to learn what good looks like and what bad looks like. The same problem is in cybersecurity. We need collect 10 times the data in the enterprise from a cyber perspective to be able to understand how to defend ourselves against these AI attackers. Chamath Palihapitiya (00:06:49): Do you think that the traditional companies like the SaaS businesses that have existed in this world, what is their place? As all this knowledge becomes more persistent and stored, what happens to SaaS? Well, you see, is, Nikesh Arora (00:07:05): as Bill said, SaaS is different pieces, right? Okay. If you're an analytical SaaS company, it's over. Chamath Palihapitiya (00:07:11): It's over. What is an analytical SaaS company? Nikesh Arora (00:07:13): Somebody that says, I'm going to collect a lot of data for you and analyze it for you. I don't need you to analyze it for me. I can run models against data and analyze them myself. So, you think about, there's a lot of every SaaS company has You can buy Salesforce marketplace. What did they say? You have Salesforce data. I'm a marketplace app. Take me and I'll help you analyze the data. I don't need Chamath Palihapitiya (00:07:34): to. You don't need that. Nikesh Arora (00:07:35): I can just go run an LM against the data. So the entire incrementality that has been sold as incremental software modules to all of us, doesn't need to be sold to us because I'd much rather have a lens run against them. Jason Calacanis (00:07:46): Interesting, you bring this up. We had an instance with a SaaS product with 20 seats. Nobody was logging in and using it, but the data was there. Nikesh Arora (00:07:55): Yes. Jason Calacanis (00:07:55): So we created like three accounts, got rid of 17, connected it to Slack, connected it to Claude. Nikesh Arora (00:08:02): Yes. Jason Calacanis (00:08:02): And now everybody can interface it through a natural language and we've reduced our bill by 90%. Nikesh Arora (00:08:08): Well, not just that. What are you gonna do Jason? You're going to take data from different products, put them in one place, run I the analytics against want my data for my sales reps, my productivity data, my inventory data from SAP. I want it all one place so I can run analytics against it and say, who's selling a lot? Where do I have less inventory? Let's build inventory in a region where my salespeople are extremely productive. To run that query, you'd have to have talked to three different SaaS products. Tomorrow, you can put all the data in one place. So that's sort of category one, analytical SaaS is dead. Chamath Palihapitiya (00:08:41): Okay, category one analytics dead. Nikesh Arora (00:08:45): Yes. The medium term, you get all these bounces today and tomorrow, these are marginally irrelevant. Infrastructure software undervalued. Okay. What is infrastructure software? Stuff that gives you databases. You collect data into it, stuff that allows infrastructure to work, whether it's a database Databricks, Snowflake like that? Databricks, Snowflake, MongoDB, Oracle. All these things you need. Core storage infrastructure, Chamath Palihapitiya (00:09:09): core data. Nikesh Arora (00:09:10): We are gonna need 10 times the data stored enterprise than we have today. David Friedberg (00:09:13): Right. Nikesh Arora (00:09:13): The next three years, 10 times. Okay. So anything that helps you collect infrastructure data, manage it, you need. I think the category in the middle is called, let's call it system of work or system of record people call them. Those are deeply embedded in the way businesses work. I have 6,000 salespeople. They know how this works. What's going happen is step one, we will take away UI and let agents do the work. Enterprise software and consumer software UI is the worst thing we did as technologists. You had a Chamath Palihapitiya (00:09:46): couple of examples of this. You told me this story. I don't know if you want to repeat it of this one company. They tried to hold you hostage on a license. Nikesh Arora (00:09:52): Yes, that was analytical SaaS. So that's over. And you just Chamath Palihapitiya (00:09:54): pointed AI at it and you just Nikesh Arora (00:09:55): Yes, we just got rid of them. That's a different issue. But I mean, think about it today. We spend our lives having product managers design UI so all humans can interact with data behind the UI. Yeah. If you believe agents are going to work and I say, I just tell an agent, look, figure out from my sales call, figure out the key points and go post it into whatever sales tracking system I have with this Oracle or Salesforce, right? An agent conceptually should be able to do it. Should we're spending trillion dollars building these agentic back ends? We need these agents to be able to do it. If that happens, UI goes away. If UI goes away, I can rewire my system of work. Right? I have sales guy shows up and says, I had the sales call, do all the paperwork and all the shit that needs to happen in back end of the company and just I'm done. If I can change the way work happens, which is where you will get true efficiency, where five people become one in a company, all these SaaS software that does system of work needs to be re engineered for the next five Jason Calacanis (00:10:55): And it's also happening passively, which is really interesting. It's looking at email, it's automatically taking the zoom transcript and summary. So the sales system of record is now like, you don't even need to input it. It's like, I already have the zoom call notes. I have the deck. The deck was made. The sales deck was made by AI. It's just, we're all gonna be looking at a chat window and just saying, here's what I want. Nikesh Arora (00:11:20): Your audit trail becomes a lot better because humans are not touching your data. It's always being managed by agents. So I think the whole system of work, system of record gets reinvented in next five years. Jason Calacanis (00:11:30): Yeah, there's no data entry. That's an interesting point. Yeah. Chamath Palihapitiya (00:11:32): Let's talk about national security for a second. I just want to maybe zoom out. So the one side of mythos, as you said, is like the value that it has to you and to enterprises. The red team version of mythos is where foreign state actors, you know, can essentially create economic havoc inside of a country. Nikesh Arora (00:11:53): Yes. Chamath Palihapitiya (00:11:54): As these models escalate in their capability, what do you think should happen when these models are ready? Nikesh Arora (00:11:59): You know, the sad truth is, in a year, there's a few thousand breaches or attacks that happen. They happen for pretty rudimentary reasons. It's not because somebody cracked the hard to crack thing. It happens because 89% of the attacks happen because credentials get stolen. Username and password. That's it. Chamath Palihapitiya (00:12:18): Have my password as password. Nikesh Arora (00:12:19): Yeah, I'm sure it is. Do you have DollarSign? DollarSign password. Fantastic. Well done. See, you're already ahead of everybody else. So, nine hours of breaches happen because of simple things. I don't think we need more models to go crack the stuff. Now we will need These models can attack critical infrastructure and things we try and protect from a national security perspective. Yes, we need defenses there. I'm not worried about the national security part being protected because they're very on it. They are the right people. They spend 10% of their budgets on IT on security. I'm worried about the small offices across the country where they're using some piece of package software and you're running a dentist office or doctor's office. I remember when Change Healthcare got breached, every physician's office shut down. Chamath Palihapitiya (00:13:01): Shut down and Nikesh Arora (00:13:01): it's ransomware. Because of ransomware to Change Healthcare. That's what the clearing system. That's when United Health Act actually have given billions of dollars of credits to the physicians to be able to run their businesses at that point in time. That's what one should worry about. It's less about the big nuts will get cracked. Chamath Palihapitiya (00:13:17): It's less about cracking some PG and E power generation facility. It's more economic chaos. Yes. And so what do we do? Nikesh Arora (00:13:27): I don't think there's a sort of a silver bullet. I think this will take time. I think this will basically take a while until every system gets upgraded, renewed, fixed over time. I just think it'd increase the terminal value of the industry. Chamath Palihapitiya (00:13:42): Right. Do you think that there's a world in which these models become so good that you could see yourself advocating for more nationalism around how they're controlled and how they're managed and how they're where we point them? Or do you think there should be a maybe a set of these models that never see the light of day that only the NSA and other folks could have access to? Or guys like you? Nikesh Arora (00:14:05): I have a slightly differentiated view about models and how they will evolve versus what we heard earlier from an OpenAI perspective. Think, I still believe models are going to become a utility layer. You will be able to buy intelligence on the fly, or you can say, I don't need 180 IQ person to go do this task. Give me 120 IQ and I need two fifty IQ to do this task. I'll pay $10 for this and for this I'll pay 1¢. So I don't know if there's a one size fits all, give you the most up to date model to answer my customer calls saying, Sorry, sir. I have no idea how to solve your problem. So I think models will get differentiated from a utilitarian perspective. So if you look at already what's happening in the market, right? The profit pools are in applications, not in models. Sarah talked about Codex running away. She didn't say OpenAI is running away. Says Codex is running away. Just say, just the way I'm sure Dario says Cloud Code is running away. So you're seeing that They're attacking profit pools. They're attacking profit pools because that's where the money is going come from. The profit pools are an application that companies can use. The profit pools are not in model usage by companies because most companies have no idea how to use So the Jason Calacanis (00:15:15): look at these companies in a way, OpenAI Anthropic, as the new Microsoft Office coming in and doing all applications, all productivity software for organizations. Nikesh Arora (00:15:27): No, I see there's going to be application companies which are going to arbitrage between models and solve your business problem. Jason Calacanis (00:15:33): So you still think they won't go to the application layer because this is a big debate. Should you engage with OpenAI and train their systems to then take your business from you? Anthropic keeps releasing model, their accounting model. And it does feel like in order for them to hit their revenue numbers, they might need to do what Microsoft did, which is release the office product on top of the operating system. Nikesh Arora (00:15:57): See, if I'm a company, I don't want to write every piece of software myself. I want my HR system software, which is agentic enabled and AI enabled to be delivered by some application company. It could be a new AI application company. I want my sales management system built by the new agentic AI Salesforce of the world, whether it's Salesforce or somebody else. So I want applications. Now, what Sarah said is the profit pools are in the application layer. That's why they want to be the application layer. So I think we're still waiting for that layer of companies to be invented or created where applications will sit because 50,000 companies need the same application. Why would I build it myself? It's highly inefficient. It's silly for me to use OpenAI directly and rewrite my entire sales system because I'm smart. Right? I'm not, I want somebody to do it for me. So, I think that layer of companies is still not fully formed. We're still going to be waiting Chamath Palihapitiya (00:16:50): You for want a control plane, a harness, and then Nikesh Arora (00:16:53): That's right. They will build the harnesses and the memory into those application layers. Now, the question is how big is the application layer? Is it one application? Is one it enterprise application that does everything or is it specialized You Jason Calacanis (00:17:05): did it and you kicked out this software vendor. You did it because they were being abusive in pricing. Nikesh Arora (00:17:10): We still use this different vendor. What's that? We swapped out for a different vendor. We just took more control. Jason Calacanis (00:17:14): Love it. So it really is a pricing issue. And that's why the SaaS pocalypse in some ways makes sense. They're not having pricing power because you could say, I'll just put 10 developers on this and I'll save $10,000,000 Nikesh Arora (00:17:27): Yes. I think the part back to what Shamat said about the regulation or whether you want to regulate these higher powered models, the question is at some point in time, when these newer models, which are even more powerful get built, they will come at a different price point and they might have to go to a certain vetting process to understand what their capabilities are. But I think we're in a global race. I don't think holding back our models for three to six months is going to help us any. Somebody else is going to put them out in open source. I was shocked to hear when I was talking to CEO, one of these model companies says the entire weights of their most recent model can fit on USB stick. Chamath Palihapitiya (00:18:02): Oh, say that again. The entire weights? Nikesh Arora (00:18:04): Entire model weights of their newest model fits on a USB stick. That's the IP. That's incredible. Because all the data can be distilled in under twenty four to forty eight hours and model comes out. I'm curious. So that's the IP. So are you telling me that, you know, we can hold on to that for six months? Right. Jason Calacanis (00:18:26): We have a debate about how difficult it is to make a frontier model. Some companies are starting to think about making frontier models using their data advantage to build their own. Have you thought about that at Palo Alto? Because it does seem like you have proprietary knowledge on how security works. Could you build your own large language model or a VSML, a small language model that would give you some advantage? Nikesh Arora (00:18:52): Here's the part nobody talks about is the false positive rates on the models. What is the false positive rate on four point eight and five point five? No idea. You guys don't talk about it, you should. The false positive rate on mythos was 30%. Oh, wow. Right? Jason Calacanis (00:19:11): So it thought it found something, but it hadn't. Nikesh Arora (00:19:14): Yes. So the problem is, it's great for attack, it's horrible for defense. It finds 30% of the time it finds something, I found a problem and you say, let's plug the hole. Wait, there wasn't a hole there in the first place. Jason Calacanis (00:19:26): No missile inbound. Right. Nikesh Arora (00:19:27): Yeah. So now the same problem applies in enterprise. If you use a model without the right harnesses, the right training, you could be running into ten, twenty percent false positive rates. Let's use the model to pay, I don't know, insurance claims. Yeah. Oh, great. 10%, 20% false positive. I just lost money. Jason Calacanis (00:19:45): The sycophantic nature of these is ridiculous. Nikesh Arora (00:19:48): So the problem is not who wants the newest model. The problem is how do you take that model with 20% or 10% false positive and make it 0.01% false positive. In my business, I want 0%. David Friedberg (00:20:00): Without losing the false negative. Nikesh Arora (00:20:02): Sorry? David Friedberg (00:20:02): Without losing the negative, the Nikesh Arora (00:20:03): false Yes. But it's like saying, hey, let's take the new self driving car. Mercedes is going to use Opus 4.8 and you can just sit in the car and it's going drive you. I'm not putting my kids in that car with a 10% false positive rate. Are you? So there's a lot of that happens post the model, which needs to happen to make this thing useful and effective in the business context. Chamath Palihapitiya (00:20:24): Let me slightly pivot for a second. You were for a very long time, the chief business officer at Google. You were the President of SoftBank. Now you're the CEO of Palo Alto Networks. So let's play armchair CEO. Nikesh Arora (00:20:36): Armchair Chamath Palihapitiya (00:20:36): CEO. Armchair CEO. Nikesh Arora (00:20:38): I'm still bristling from David Friedberg trying to create a distinction between founder CEOs and non founder Just saying. Just saying, David. David Friedberg (00:20:45): By the way, false positives. Sorry? False negatives too. Chamath Palihapitiya (00:20:49): Give us what you would keep, what you would change, and what you like about the following companies. Nikesh Arora (00:20:54): This is gonna get recorded and put out there to Chamath Palihapitiya (00:20:55): say It's just we're just getting your thoughts. You're one of the smartest business people. Nikesh Arora (00:20:59): I don't like that to You're one the smartest business people. In podcast. Chamath Palihapitiya (00:21:01): I'm asking you a question. Don't Nikesh Arora (00:21:03): I think people and grossing people. Are you ready? Yeah. Sure. Chamath Palihapitiya (00:21:06): Okay. What you keep, what you change, what you like, what you don't like, Uber. Nikesh Arora (00:21:12): In a world I'm of beta bored of it, dude. I can't talk about my car. Chamath Palihapitiya (00:21:15): Are you on David Friedberg (00:21:15): the board of Uber? Nikesh Arora (00:21:15): I'm the board of Uber. I'm not gonna talk about Uber. Chamath Palihapitiya (00:21:18): Didn't know that. Sorry. Okay. Nikesh Arora (00:21:19): Doctor. Dara, he's the CEO. He's a great guy. Waymo. Trying to get me fired. Waymo. What do I like about Waymo? The cars work. It's amazing. They should have more in many more cities around the world faster. I would say that to Tegira, I think she Google Chamath Palihapitiya (00:21:36): writ large. Nikesh Arora (00:21:38): I think Google's underrated. I think it's going be the first $10,000,000,000,000 company in our lifetime. I think they have all the assets that are needed to make this successful. I think people underestimate. You can be a model company, you still need to have a Salesforce that convinces customers to go out there and embrace these models and buy them. If you think about it, three hyperscalers have the biggest number of salespeople out there. They should be Chamath Palihapitiya (00:21:59): Part of why they're a little bit undervalued is just the conglomerate nature is hard to understand. Nikesh Arora (00:22:03): I don't know. You guys are smart of that stuff. I'm just a hired hand CEO. Chamath Palihapitiya (00:22:07): I didn't say that. Rebrik said that. Let's just be clear. Nikesh Arora (00:22:10): I know. I know. David Friedberg (00:22:11): I was I was providing a thesis on recovery out of the SaaS pocalypse. Nikesh Arora (00:22:15): Okay? Okay. Okay. Got it. Just to be clear I David Friedberg (00:22:17): should work together. A there's a way to segment that basket. And you're not in that basket. Nikesh Arora (00:22:21): Either you're making a distinction about how people who are founders, CEOs have the right to take more risk and are allowed to take more risk. David Friedberg (00:22:31): I was saying that. Nikesh Arora (00:22:32): And I David Friedberg (00:22:32): think and I and I think you you provide a unique counterpoint to that. And and there's not a lot Nikesh Arora (00:22:37): of false positive in David Friedberg (00:22:38): your model. Think the same was true of Jeff Weiner, and I think that there's a few other really great CEOs, but they are like neo in the matrix type anomalies. And I think you're one of those people. And there's a very rare kind of personality profile of someone that's willing to take risk and take ownership of something that wasn't theirs in the first place and they make it theirs. And it's a it's a extraordinarily unique trait. Far more unique actually than being a scalable founder. Jason Calacanis (00:23:03): It's an incredible save. Nikesh Arora (00:23:04): You're forgiven. Chamath Palihapitiya (00:23:05): Yeah. Good save. Let's go back to Armchair CEO. Wow. That was incredible. Jason Calacanis (00:23:09): He's more sycophantic than Chad GPT. He's like, actually, actually the best. Chamath Palihapitiya (00:23:14): Let's go back to Armchair CEO. Nikesh Arora (00:23:16): I'm liking this. He has been backing me more often. They do sell faster. OpenAI. They should sell faster. Right? Chamath Palihapitiya (00:23:24): They should sell faster. Nikesh Arora (00:23:26): I mean, you said it. Didn't you just say it when Sarah was here that Anthropic seems to have improved their ARR much faster than OpenAI? I mean, that's just the statistic. David Sacks (00:23:35): They kind of went all in on enterprise and coding specifically. Nikesh Arora (00:23:38): I think that's like the conversation right now is it's a race to take over the profit pools. You are going to need tens and tens of billions of dollars every year to get where that one gigawatt is 10,000,000,000 revenue. One gig, 10,000,000,000 What does it cost to build you? Are the most exciting? Costs 50. So, this is a Exactly. Great David Sacks (00:23:57): So, are the most exciting profit pools then? So, we got coding. That's been the breakout application over the It's past massive. You've got infrastructure, like you said, the new databases. I think cybersecurity is clearly one of them because of threats and patching cycles so much more dynamic. Nikesh Arora (00:24:14): There's a slight difference in it. Yeah. So, as you can see, these models are trying to be the enablers of better cybersecurity, which is good because all of us need to use them to test. And you're probably going to see, I mean, you saw Anthropic has already made their cyber capable model available generally so that everyone can use it. And OpenEye has got one, I'm sure Google has one too, because they understand this is a place where CSOs or chief security officers want to use it to test the code. So, is another profit pool. I think we haven't seen the onslaught against the application software companies yet. I mean, there's tens and tens of billions of dollars in application software, which is waiting to get reinvented as we talked about. I think eventually you'll see these people saying, what if I took this $40.50, dollars 100,000,000,000 TAM down, I can build a whole brand new backbone Chamath Palihapitiya (00:25:01): rejecting the Nikesh Arora (00:25:02): AI and it'd be so differentiated that it'll cause customers to move. Jason Calacanis (00:25:06): We are seeing it as a playbook in the accelerators now. The year zero and year one companies, people are coming to us with the pitch. This is a thousand dollar a seat per year, 500 a month seat SaaS software. We can do it for less. We're gonna charge them based on consumption. We're gonna take 90% of the cost out as to what your mom is doing with eighty, ninety. Nikesh Arora (00:25:27): Two fastest places to make revenue. Yeah. In enterprise are replacement TAMs. If you replace something, I already have a budget. It's easy. I take something bad or replace it with something better. I get money. So replacement TAMs are beautiful. If you can replace an industry, replace the profit pool is great. The second place is consumer revenue. It's a lot easier to get $5 per user on a consumer side. Netflix. So that's where, I mean, at it. I think we collectively probably pay more on subscriptions per month than we ever did historically. And you thought your cable bill was high. Chamath Palihapitiya (00:26:00): Yeah. Do you think that you're going to end up building more or less hardware in the future if you had to guess? Nikesh Arora (00:26:05): Hardware even today is the cheapest way to manage low latency, high throughput bits. You still need a data center. What's the data center? It's just managing high throughput, low latency bits. That's why if you look financial services is the most reluctant industry to go to the cloud because you increase latency. If you increase latency, you reduce profit. So, if you look at every of your largest financial services company, whether it's Goldman or JP Morgan, Morgan Stanley Chamath Palihapitiya (00:26:36): or Eight Street, Nikesh Arora (00:26:37): or Palo Alto. These guys, they're doing hardware. Try to get them to run their business in the cloud. They can't because they will have higher latency. They will lose money. Right. So hardware is still be made. I mean, I remember when I used to buy Silver Lake and I heard Dell was done. Nobody wanted hardware. I think Dell might be back to like a $3,400,000,000,000 market cap. So hardware is still going be around. We're to need it. It's the fastest David Friedberg (00:27:01): way to move. Are hardware development cycles changing because of AI? Like, are you seeing a lot of like generative design stuff moving in silica that historically was manual and long cycle? Nikesh Arora (00:27:12): Yeah. But the long pole in the tent is never designed, right? Yeah. Long pole in the tent is production. Today, you can't get a box produced because every piece of hardware componentry is back ordered. Everything's expensive and every factory in the world is back ordered because we're trying to build all these GPUs based chip cards for every data center of the world. David Friedberg (00:27:34): Do you think The US is equipped to fill that supply chain need? Can we do that here? Do think it's ten years. Chamath Palihapitiya (00:27:40): Ten years. David Friedberg (00:27:41): With a firm top down commitment? Nikesh Arora (00:27:44): Well, mean, the good news is that I think the hardware industry is seeing a bonanza of a lifetime. And generally when you see a bonanza of a lifetime, you can go commit $10.20, 5,100,000,000,000. And I've seen a CEO on television commit to go a $100,000,000,000 plan to go build more memory. So that's good. That means they have the money to go put the money in the ground literally to go build these things for the future. So I think that gets us more certain that the David Friedberg (00:28:11): fact I think the tax incentive has a big, has a lot to do with that. The accelerated depreciation on the CapEx. Get a 100% write off in the first year, right? Nikesh Arora (00:28:20): Under the Chamath Palihapitiya (00:28:21): Just a final question as we wrap up. You, over the last eight years, you've grown organically very aggressively, but you've also been pretty acquisitive. You'll take shots and they've generally worked. So you have a ton of permission in the market. When you hear what Bill Hackman said about how there's this kind of over beaten companies, there's a few that get celebrated, that's the right pool for you to pick from. But some of that would require you to go maybe a little horizontally far afield, some would say. How do you maintain the discipline or do you see yourself at some point considering things that are not nearly so much right down the middle of cyber? Nikesh Arora (00:28:58): So I tell you what, until about a year and a half ago, we used to buy product companies and throw them into our go to market engine. We could rewire their back end so they could work better with our go to market engine. So for me, if I'm selling $10,000,000 to a customer, next time I go two years later, if I can sell them 20, it's the most efficient way for me to amortize my go to market spend, right? So that was the model. We played that. We ran that playbook to north of 150,000,000,000. Then we go to a point where it says, Oh, we see an inflection arriving in identity. It's going to be important from an agentic perspective, security perspective. So you bought a $25,000,000,000 company, which we closed three months ago. Now it's actually a very different opportunity has presented itself. And the different opportunity sort of goes like this. If you can be the best at leveraging AI to run the most efficient enterprise business in the world, your operating margin can be far in excess of the industry. And if you can crack that code- Chamath Palihapitiya (00:29:54): Gross and net, you're saying. Gross in the nineties, net in the forties. Nikesh Arora (00:29:57): Yeah. If you can crack that code, then it doesn't matter what you buy. So I think the problem right now is the execution problem. Most subscale companies cannot afford to go optimize their company and run it better. So if we can run our company much better than everybody else and have a higher operating margin, then the street will say, If you take something at a 20% margin, make it a Chamath Palihapitiya (00:30:17): Your first M and A was really tough. No? Like, they were pretty skeptical and then you kind of shoved it in their face. Nikesh Arora (00:30:22): They're pretty skeptical when they found a guy who didn't know cybersecurity, didn't know enterprise, show up, who worked at Google and they're you know, the track record of people leaving Google and being successful out of Google is still varied. Chamath Palihapitiya (00:30:33): So basically you're saying the menu is open. Nikesh Arora (00:30:35): I think we need the next six to twelve months to figure out how this AI settles down and how can we use that effectively in enterprises. I think if you think about it, the people keep hoping that less people we need to run companies, I actually have a counter view. I think we're going have more people at Palo Alto on the technology side than we've ever had before, because AI is causing everything to ask for a transformation. So I have more technical people today than I would have had if AI didn't exist. Chamath Palihapitiya (00:31:04): Ladies and gentlemen, CEO of Palo Alto Networks, Nikesh Shorra. Thank Nikesh Arora (00:31:07): you, guys. Jason Calacanis (00:31:11): Thank you, sir.