Nikesh Arora: Mythos is Real, Analytical SaaS is Dead, and Google can be a $10T company

(0:00) Palo Alto Networks CEO Nikesh Arora joins the Besties! (0:47) Claude Mythos found years of vulnerabilities in Palo Alto's code in weeks (5:15) Are cyber defenders losing the race against AI attackers? (6:50) Analytical SaaS is dead, so what survives the AI wave? (14:06) If models become a utility, where will the money be made? (20:35) Armchair CEO: Nikesh rates Waymo, Google, and OpenAI (28:22) Palo Alto's M&A playbook and the path to $1 trillion Thanks to our partners for making this possible! EY - AI ambition isn't enough. EY.ai Value Blueprints move organizations beyond pilots embedding measurable business value by design. https://www.ey.com/en_us/services/ai/value-blueprints? WT.mc_id=3501320&AA.tsrc=sponsorship https://www.ey.com/en_us/services/ai/value-blueprints? WT.mc_id=3501320&AA.tsrc=sponsorship NYSE - Thank you to our partner, the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE. https://www.nyse.com Plaud - Never miss a moment. Plaud, our official wearable AI note-taking partner at All-In Liquidity Summit, captured every insight. https://www.plaud.ai Follow Nikesh: https://x.com/nikesharora Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  It's one of the biggest winners right now. The big daddy of the cybersecurity space. Palo Alto Networks is an outperformer in this space.

• 0:07Speaker 00

  CEO Nikesh Arora.

• 0:09Speaker 01

  This might come as news to you, but humans have been riding bad court for a very long time. I spent 10 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.

• 0:29Chamath Palihapitiya?

  You've been the CEO of Palo Alto Networks for eight years?

• 0:33Speaker 01

  Coming up on eight years this week.

• 0:34Chamath Palihapitiya?

  Eight years. And I think when you started, it was $17 billion market cap, if I remember correctly.

• 0:39Speaker 01

  Thereabouts.

• 0:39Chamath Palihapitiya?

  And this morning I checked, it's 238 billion, which if you listen to what we said yesterday, now that you passed 100, you're more likely to actually 10x. So the first 10x was actually much, much harder. So you're on your way to a trillion dollars.

• 0:51Speaker 01

  From your mouth to God's ears.

• 0:53Chamath Palihapitiya?

  Well, I think you are. Okay, so let's just double click into what you see because you are sort of in a really interesting position to see all of it. You see the birth of AI, maybe you've seen the rise and fall of SaaS. All the models talk to you.

• 1:09Speaker 01

  The rise again, right?

• 1:11Chamath Palihapitiya?

  The rise again. You were one of the first and the few that got access to Mythos. So just, let me just push the button. Go Nikesh, start.

• 1:21Speaker 01

  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 24 months.

• 1:33Speaker 01

  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.

• 1:53Speaker 01

  What that means is, I have 250 people in marketing. They produce varied forms of output. Now you can get 90% of the output to be consistent across those 250 people. I have 5,000 people who talk to customers. There's my my failure mode is when 5,000 people do different things where people say, I want to talk to Joe because he knows how to solve the problem and Jim doesn't. So now you can get 5,000 people to act almost consistently in their interactions with people on the other side. So I think it's going to have a phenomenal impact to how we run businesses, how we operate. It's going to change the entire landscape. Now, in that context, you touched upon Mythos and I know David's been very involved in this.

• 2:36Speaker 01

  Mythos has shown us that all the bad code that humans have written over the last 50 years can be assessed by AI and shown, uh, the vulnerabilities can be shown. We tested for six weeks and in six weeks we found what have taken us five to seven years. Wow.

• 2:53Jason Calacanis?

  Say that one more time.

• 2:54Speaker 01

  In six weeks, we found vulnerabilities which would have normally taken us five to seven years to find.

• 2:59Jason Calacanis?

  So Mythos is not... But these are vulnerabilities where?

• 3:02Speaker 01

  Sorry?

• 3:02Jason Calacanis?

  These are vulnerabilities in your own code base?

• 3:04Speaker 01

  In our own code.

• 3:05Jason Calacanis?

  Or in your customer, oh, in your own code? Oh, wow. So Mythos was not oversold. It was legit.

• 3:11Speaker 01

  The capabilities of AI in being able to assess vulnerabilities in code are real. Not just that, if you put it on ultra mode, which is persistent thinking, so it keeps trying until it gets an answer, you can actually daisy chain vulnerabilities, i.e. finding a new attack path into your code, into your 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 million developers write code, this thing is going to find stuff which would have taken us 10 years to find.

• 3:50Jason Calacanis?

  How much did it cost? Like, did you track the token cost? Was it $100 million, $10 million?

• 3:56Speaker 01

  No, it was in the low millions. But again, the cost, as Sarah said, the cost curve is going to come down. Already OpenAI has got a model which is cheaper, more consistent. You know, Anthropic's come out with another model.

• 4:06Jason Calacanis?

  So you buy, you buy the hype?

• 4:08Speaker 01

  It's our hype, it's true.

• 4:10Jason Calacanis?

  It's that's the point.

• 4:11David Sacks?

  The capabilities are real.

• 4:12Speaker 01

  You know that, the capabilities are true.

• 4:14David Sacks?

  Yes.

• 4:15Speaker 01

  I mean, you saw IBM announced a project for $5 billion to fix open source, that's the biggest problem.

• 4:19Jason Calacanis?

  What would have happened if Claude didn't have the restraint and they put it out in the public? Do you think it would have been like a real attack vector and caused chaos in corporations?

• 4:29Speaker 01

  I think we're three months away, if not already there, from this being available in the wild.

• 4:38Jason Calacanis?

  Okay, open source.

• 4:39Speaker 01

  Yeah.

• 4:40David Sacks?

  Just three months.

• 4:41Speaker 01

  Yeah.

• 4:41David Sacks?

  Yeah, because I mean, we've been saying that it's roughly six months away before Mythos level capabilities are

• 4:47Speaker 01

  Yes.

• 4:47David Sacks?

  available

• 4:48Speaker 01

  Yes.

• 4:48David Sacks?

  in Chinese models, you know, open models, whatever. But you're saying it could be three months.

• 4:53Speaker 01

  Well, look, there's, what is 4.8 is already out, 5.5 is already out, they have similar capabilities. And look, you don't need to crack the hardest code to crack. You just need to find a few vulnerabilities in code that are out there. Just take in, take an old industrial system which is running OT code on the edge. You can find that reasonably easily.

• 5:14David Sacks?

  So we're in a race right now between the cyber defenders finding these vulnerabilities and patching them before the cyber attackers do the same thing.

• 5:25Speaker 01

  Yes.

• 5:25David Sacks?

  And how do you feel like we're doing in that race?

• 5:29Speaker 01

  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, 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.

• 6:01Jason Calacanis?

  So is it, is it fair to say that as model capabilities go up, systemic business risk of large enterprises also goes up?

• 6:10Speaker 01

  On the cyber side, yes. There are antidose 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 where you need to store a lot more data enterprise-wide to learn what good looks like and what bad looks like.

• 6:37Jason Calacanis?

  Right.

• 6:37Speaker 01

  The same problem is in cybersecurity. We need to collect 10 times the data in the enterprise from a cyber perspective to be able to understand how to defend ourselves against the AI attackers.

• 6:49Jason Calacanis?

  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?

• 7:03Speaker 01

  Well, you see, SaaS is, as Bill said, SaaS is different pieces, right? If you're an analytical SaaS company, it's over.

• 7:11Jason Calacanis?

  It's over. What is an analytical SaaS company?

• 7:13Speaker 01

  Somebody that says, "I'm going to collect a lot of data for you and analyze it for you. I don't need you to analyze it for me. I can run models against data and analyze them myself." So if you think about, there's a lot of, every SaaS company has a marketplace. You can buy Salesforce Marketplace. What do they say? You have Salesforce data, I'm a marketplace app, take me and I'll help you analyze the data. I don't need you.

• 7:34Jason Calacanis?

  You don't need that.

• 7:34Speaker 01

  I could just go run an L M 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 L M's run against that data.

• 7:46Jason Calacanis?

  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.

• 7:55Speaker 01

  Yes.

• 7:55Jason Calacanis?

  So we created like three accounts.

• 7:58Speaker 01

  Yes.

• 7:58Jason Calacanis?

  Got rid of 17, connected it to Slack, connected it to Claude.

• 8:02Speaker 01

  Yes.

• 8:02Jason Calacanis?

  And now everybody can interface it through a natural language and we've reduced our bill by 90%.

• 8:08Speaker 01

  Well, not just that, what are you going to do next? Jason is like, you're going to take data from different products, put them in one place, run analytics against that. I want my data for my sales reps, my productivity data. My, you know, inventory data from SAP, I want it all in one place so I can run analytics against it and say, who's selling a lot, where do I have less inventory, let's build inventory in a region where my salespeople are extremely productive. To run that query, you'd have to have, talk to three different SaaS products. Tomorrow, you can pull the data in one place. So, so that's sort of category one, analytical SaaS is dead.

• 8:41Chamath Palihapitiya?

  Okay, category one analytics dead.

• 8:45Speaker 01

  Yes, in the medium term, you've got all these bounces today and tomorrow, that's, these are marginally irrelevant. Infrastructure software, undervalued.

• 8:53Chamath Palihapitiya?

  Okay, what is infrastructure software?

• 8:55Speaker 01

  Stuff that gives you databases, you collect data into it, stuff that allows infrastructure to work, whether it's a, you know,

• 9:01Chamath Palihapitiya?

  This is like State of Brick, Snowflake like that?

• 9:03Speaker 01

  State of Brick, Snowflake, MongoDB,

• 9:05Chamath Palihapitiya?

  Oracle.

• 9:06Speaker 01

  Oracle, all these things, you need.

• 9:08Chamath Palihapitiya?

  Or storage infrastructure, or data.

• 9:09Speaker 01

  We are gonna need 10 times the data stored in enterprise than we have today.

• 9:13Chamath Palihapitiya?

  Right.

• 9:13Speaker 01

  Next three years, 10 times.

• 9:15Chamath Palihapitiya?

  Okay.

• 9:15Speaker 01

  So, anything that helps you collect infrastructure data, manage it, you need. I think the category in the middle is called, let's call it system of work, or system, you know, a 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 gonna happen is step one, we will take away UI and let agents do the work. UI, enterprise software and consumer software UI is the worst thing we did as technologists.

• 9:45Chamath Palihapitiya?

  You had a couple of examples of this, you told me this story, I don't know if you want to repeat it, of this one company, they tried to hold you hostage on a license.

• 9:51Speaker 01

  Yes, that was analytical SaaS, so that's over.

• 9:54Chamath Palihapitiya?

  And you just pointed AI at it and you just.

• 9:55Speaker 01

  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.

• 10:05Chamath Palihapitiya?

  Yeah.

• 10:06Speaker 01

  If all like, if you believe agents are gonna work, and I say, I just tell an agent, look, figure it out from my sales call, figure out the key points and go post it into, you know, whatever sales tracking system I have with this Oracle or Salesforce, right? An agent conceptually should be able to do it, shit, we're spending trillion dollars building these agentic backends, we need these agents to be able to do it. If that happens, UI goes away. If UI goes away, I can rewire my system of work.

• 10:34Speaker 01

  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 the back 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 years.

• 10:55Jason Calacanis?

  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 going to be looking at a chat window and just saying, here's what I want.

• 11:20Speaker 01

  Your audit trail becomes a lot better because humans are not touching your data. It's always been managed by agents. So I think the whole system of work, system of record gets reinvented in the next five years.

• 11:30Jason Calacanis?

  Yeah, there's no data entry. That's an interesting point, yeah.

• 11:32Chamath Palihapitiya?

  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.

• 11:53Speaker 01

  Yes.

• 11:54Chamath Palihapitiya?

  As these models escalate in their capability, what do you think should happen when these models are ready?

• 12:00Speaker 01

  Yeah, 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 a hard to crack thing. It happens because 89% of attacks happen because credentials get stolen.

• 12:15Jason Calacanis?

  Username and password.

• 12:16Speaker 01

  That's it.

• 12:17Jason Calacanis?

  My password is password.

• 12:19Speaker 01

  Yeah, I'm sure it is. Do you have dollar signs?

• 12:21Jason Calacanis?

  Dollar sign password.

• 12:22Speaker 01

  Fantastic, well done. See, you're already ahead of everybody else. So 89% 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 packet software and you're running a dentist's office or a doctor's office. Remember when Change Healthcare got breached?

• 12:59Speaker 01

  Every physician's office shut down.

• 13:01Chamath Palihapitiya?

  Shut down and it's ransomware.

• 13:02Speaker 01

  Because of ransomware to change healthcare. That's what's a clearing system. That's when UnitedHealth actually have 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.

• 13:17Chamath Palihapitiya?

  It's less about cracking some PG&E power generation facility. It's more economic chaos.

• 13:23Speaker 01

  Yes.

• 13:24Chamath Palihapitiya?

  And so what do we do?

• 13:28Speaker 01

  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 increases the terminal value of the industry, right?

• 13:43Chamath Palihapitiya?

  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?

• 14:05Speaker 01

  I have a slightly differentiated view about models and how they will evolve versus what we heard earlier from an OpenAI perspective. I 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 a 180 IQ person to go do this task. Give me 120 IQ and I need a 250 IQ to do this task. I'll pay $10 for this. For this, I'll pay $1. So I don't know. There's a one size fits all, give you the most up-to-date model to answer my customer call saying, sorry, sir, I have no idea how to solve your problem. So I think models will get differentiated from a utilitarian perspective.

• 14:47Speaker 01

  So if you look at already what's happening in the market, right? The profit pools are in applications, not in models. More, Sarah talked about codex. Running away. She didn't say OpenAI is running away. She says Codex is running away. Just say, that's the way I'm sure Dario says Claude Code is running away. So you're seeing that.

• 15:03Jason Calacanis?

  They're attacking profit pools.

• 15:05Speaker 01

  They're attacking profit pools because that's where the money is going to come from. The profit pools are in applications that companies can use. The profit pools are not in model usage by companies because most companies have no idea how to use the models.

• 15:15Jason Calacanis?

  So you can look at these companies, in a way, OpenAI and Anthropic, as the new Microsoft Office, coming in and doing all applications, all productivity software for organizations.

• 15:27Speaker 01

  No, I see there's going to be application companies that are going to arbitrage between models and solve your business problem.

• 15:33Jason Calacanis?

  So you still think they won't go to the application layer because this is a big debate. Should you engage with OpenAI and train their systems to then take your business from you? And Anthropic keeps releasing their legal model, their accounting model. And it does feel like in order for them to hit their revenue numbers, they might need to do what Microsoft did, which is release the Office product on top of the operating system.

• 15:57Speaker 01

  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 sales force 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

• 16:41Speaker 01

  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 for it.

• 16:50Jason Calacanis?

  You want a control plane, a harness, and then...

• 16:52Speaker 01

  That's right. They will build the harnesses and the memory into those application layers. Now, the question is, how big is the application layer? Is it one application? Is it one, you know, enterprise application that does everything? Or is it specialized application?

• 17:05Jason Calacanis?

  And you kicked out this software vendor. You did it because they were being abusive in pricing.

• 17:10Speaker 01

  We still use a different vendor.

• 17:12Jason Calacanis?

  What's that?

• 17:12Speaker 01

  We swapped out for a different vendor. We just took more control.

• 17:14Jason Calacanis?

  Love it. So it really is a pricing issue. And that's why the SaaSpocalypse 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 million.

• 17:27Speaker 01

  Yes. I think the part back to what Chamath said about the regulation or whether you want to regulate these higher powered models. The question is at some point in time, when these newer models, which are even more powerful, get built, they will come at a different price point. And they might have to go 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 of one of these model companies says, the entire weights of their most recent model can fit on a USB stick.

• 18:02Jason Calacanis?

  Say that again. The entire weights?

• 18:04Speaker 01

  Entire model weights of their newest model fits on a USB stick. That's the IP.

• 18:12Jason Calacanis?

  That's incredible.

• 18:12Speaker 01

  Because all the data can be distilled in under 24 to 48 hours and model comes out.

• 18:18Jason Calacanis?

  I'm curious.

• 18:18Speaker 01

  So that's the IP. So are you telling me that, you know, we can hold on to that for six months?

• 18:25Jason Calacanis?

  Right. 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?

• 18:52Speaker 01

  Here's the part nobody talks about is the false positive rates on the models. What is the false positive rate on 4.8 and 5.5?

• 19:03Chamath Palihapitiya?

  No idea.

• 19:04Speaker 01

  You guys don't talk about it, you should. The false positive rate on MITOS was thirty percent.

• 19:09Chamath Palihapitiya?

  Oh, wow.

• 19:10Speaker 01

  Right?

• 19:11Chamath Palihapitiya?

  So we thought it found something, but it hadn't.

• 19:13Speaker 01

  Yes. So the problem is, it's great for attack, it's horrible for defense.

• 19:20Speaker 01

  Because it finds thirty times, thirty percent of the time it finds something, because I found a problem, and you say, let's plug the hole. Wait, there wasn't a hole there in the first place.

• 19:26Chamath Palihapitiya?

  No missile inbound.

• 19:27Speaker 01

  Right.

• 19:27Chamath Palihapitiya?

  Yeah.

• 19:28Speaker 01

  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.

• 19:41Chamath Palihapitiya?

  Yeah.

• 19:42Speaker 01

  Oh, great. Ten percent, twenty percent false positive. I just lost money.

• 19:45Chamath Palihapitiya?

  The sycophantic nature of these is ridiculous, yeah.

• 19:48Speaker 01

  So, so the problem is not, who wants the US model? The problem is, how do you take that model with twenty percent or ten percent false positive and make it point zero one percent false positive? In my business, I want zero percent.

• 20:00David Friedberg?

  Without losing the false negative.

• 20:02Speaker 01

  Sorry?

• 20:02David Friedberg?

  Without losing the negative, the false negative.

• 20:04Speaker 01

  But it's like saying, hey, let's take the new self-driving car. Mercedes is going to use Opus four point eight, and you can just sit in the car and it's going to drive you. I'm not putting my kids in that car with a ten percent false positive rate. Are you? So there's a lot of work that happens post the model, which needs to happen to make this thing useful and effective in the business context.

• 20:24Chamath Palihapitiya?

  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 Apollo Network. So let's play armchair CEO.

• 20:36Speaker 01

  Armchair CEO.

• 20:37Chamath Palihapitiya?

  Armchair CEO.

• 20:37Speaker 01

  I'm still, I'm still bristling from David Friedberg, trying to create a distinction between founder CEOs and non-founder CEOs. Just saying. Just saying, David.

• 20:45David Friedberg?

  By the way, false positives.

• 20:46Speaker 01

  Sorry?

• 20:47David Friedberg?

  False negatives, too.

• 20:49Chamath Palihapitiya?

  Give us what you would keep, what you would change, and what you like about the following companies.

• 20:54Speaker 01

  This is going to get recorded and put out there?

• 20:55Chamath Palihapitiya?

  We're just getting your thoughts. You're one of the smartest business people.

• 20:58Speaker 01

  I don't get to live the glory of the all-in podcast.

• 21:01Chamath Palihapitiya?

  We're asking you a question. Don't.

• 21:03Speaker 01

  Hazing people and roasting people.

• 21:05Chamath Palihapitiya?

  Are you ready?

• 21:05Speaker 01

  Yeah, sure.

• 21:06Chamath Palihapitiya?

  Okay.

• 21:08David Friedberg?

  What you keep, what you change, what you like, what you don't like. Uber.

• 21:12Speaker 01

  I'm on the board of it dude, I can't talk about my company.

• 21:15David Friedberg?

  Oh, you're on the board of Uber?

• 21:15Speaker 01

  I'm the board of Uber, I'm not gonna talk about Uber.

• 21:17David Friedberg?

  I didn't know that, sorry.

• 21:18Speaker 01

  Dr. Dara, he's the CEO, he's a great guy.

• 21:21David Friedberg?

  Okay. Waymo.

• 21:23Speaker 01

  Try again, be fired.

• 21:25David Friedberg?

  Waymo.

• 21:26Speaker 01

  What 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 Teeger, I think she knows.

• 21:36David Friedberg?

  Google writ large.

• 21:38Speaker 01

  I think Google's underrated. I think it's gonna be the first ten trillion dollar company in our lifetime. I think they have all the assets that are that are needed to make this successful. I think people underestimate. You can be a model company, you still need to have a sales force that convinces customers to go out there and embrace these models and buy them. And if you think about it, three hyperscalers have the biggest number of salespeople out there, so they should be...

• 21:58David Friedberg?

  Part of why they're a little bit undervalued is just the conglomerate nature is hard to understand?

• 22:03Speaker 01

  I don't know, you guys are smart at that stuff. I'm just a hired hand CEO.

• 22:07David Friedberg?

  I didn't say that! Reebok said that, let's just be clear.

• 22:10Speaker 01

  I know, I know.

• 22:11David Friedberg?

  I was, I was providing a thesis on recovery out of the SARS apocalypse, okay?

• 22:15Speaker 01

  Okay, God.

• 22:16David Friedberg?

  Just to be clear.

• 22:17Speaker 01

  Reebok and I used to work together at Google.

• 22:18David Friedberg?

  There's a way to segment that basket. And you're not in that basket.

• 22:21Speaker 01

  Either you're making a distinction about how people who are founders, CEOs, have have the right to take more risk and are allowed to take more risk.

• 22:30David Friedberg?

  I was saying that. And I think, and I, and I think you, you provide a unique counterpoint to that. And, and there's not a lot of...

• 22:37Speaker 01

  And the false positive in your model.

• 22:38David Friedberg?

  And by the way, I 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 an extraordinarily unique trait. Far more unique, actually, than being a scalable founder.

• 23:03Jason Calacanis?

  It's an incredible save.

• 23:04Speaker 01

  You're forgiven.

• 23:05Jason Calacanis?

  Yeah, good save, credible save.

• 23:06David Friedberg?

  Let's go back to Armchair CEO.

• 23:07Jason Calacanis?

  Wow, that was...

• 23:08David Friedberg?

  I mean, he's more sycophantic than ChatGPT. He's like, actually, I think we're the best. Let's go, let's go back to Armchair CEO.

• 23:16Speaker 01

  I'm liking this, you guys have been maddening more often, yes. They do sell faster.

• 23:22David Friedberg?

  OpenAI.

• 23:22Speaker 01

  They should sell faster, right?

• 23:24Jason Calacanis?

  They should sell faster.

• 23:26Speaker 01

  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?

• 23:33Jason Calacanis?

  I mean, that's just the statistics.

• 23:35David Sacks?

  They kind of went all in on enterprise and coding specifically.

• 23:38Speaker 01

  I think that's like the conversation right now is it's a race to take over the profit pools. If you are gonna need tens and tens of billions of dollars every year to get, what is that? One gigawatt is 10 billion of revenue?

• 23:51Jason Calacanis?

  1 gigawatt, 10 billion a year of revenue.

• 23:53Speaker 01

  What is it cost to build you?

• 23:54David Sacks?

  So what are the most exciting?

• 23:55Speaker 01

  It costs 50. So this is a great deal.

• 23:57David Sacks?

  So what are the most exciting profit pools then? So we got coding. That's been the breakout application over the past year. It's massive. You've got infrastructure, like you said, the new databases. I think cybersecurity is clearly one of them because of threats and patching cycles are so much more dynamic.

• 24:14Speaker 01

  There's a slight difference in, yes, 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 gonna see, I mean, if you saw, Anthropic has already made their cyber-capable model available generally so that everyone can use it. And OpenAI has got one. I'm sure Google has one too, but they understand this is a place where CISOs or chief security officers want to use it to test the code. So this is another profit pool. I think we haven't seen the onslaught against the application software companies yet. I mean, there's tens and tens of billions of dollars in application software, which is waiting to get reinvented, as we talked about. I think eventually you'll see these people saying, what if I took this 40, 50, 100 billion dollar TAM down?

• 24:59Speaker 01

  I can build a whole brand new backbone with a GPT- AI and it'd be so differentiated that it'll cause customers to move.

• 25:06Jason Calacanis?

  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 $1,000 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 80, 90% of the cost out as to what your math is doing with 80, 90.

• 25:27Speaker 01

  The two fastest places to make revenue? In enterprise, a replacement TAM. 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 five bucks per user on a consumer side.

• 25:50David Friedberg?

  Netflix.

• 25:51Speaker 01

  So that's where, I mean, look at it. I think we collectively probably pay more on subscriptions per month than we ever did historically.

• 25:58David Friedberg?

  Yeah.

• 25:58Speaker 01

  And you thought your cable bill was high.

• 26:00David Friedberg?

  Yeah. Do you think that you're going to end up building more or less hardware in the future if you had to guess?

• 26:05Speaker 01

  Hardware, even today, is the cheapest way to manage low latency, high throughput bits. You still need a data center.

• 26:17David Friedberg?

  Yeah.

• 26:17Speaker 01

  What's a data center? It's just managing high throughput, low latency bits.

• 26:21David Friedberg?

  Yeah.

• 26:22Speaker 01

  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 companies, whether it's Goldman or JP Morgan, Morgan Stanley.

• 26:36David Friedberg?

  Main Street.

• 26:37Speaker 01

  Or these guys.

• 26:37David Friedberg?

  Yeah.

• 26:38Speaker 01

  They're doing hardware.

• 26:39David Friedberg?

  Yeah.

• 26:39Speaker 01

  Try to get them to run their business in the cloud. They can't because they will have higher latency, they will lose money.

• 26:44David Friedberg?

  Right.

• 26:44Speaker 01

  So hardware is still be made. I mean, I remember when I used to buy Silver Lake and I had heard Dell was done. Nobody wanted hardware. I think Dell might be back to like a three, four hundred billion dollar market cap. So hardware is still going to be around. We're going to need it. It's the fastest way to move it.

• 27:01David Friedberg?

  Are hardware development cycles changing because of AI? Like, are you seeing a lot of like generative design stuff moving in silicon that historically was manual and long cycle?

• 27:12Speaker 01

  Yeah, but the long pole in the tent is never designed, right?

• 27:14David Friedberg?

  Yeah.

• 27:15Speaker 01

  Long pole in the tent is production. Today, you can't get a box produced because every, every piece of hardware componentry is backordered.

• 27:25David Friedberg?

  Right.

• 27:25Speaker 01

  Everything's expensive and every factory in the world is backordered because we're trying to build all these GPUs based, you know, chip cards for every data center in the world, so.

• 27:34David Friedberg?

  Do you think the US is equipped to fill that supply chain need? Can we do that here?

• 27:38Speaker 01

  In ten years.

• 27:40David Friedberg?

  Ten years. With a, with a, with a firm top-down commitment?

• 27:44Speaker 01

  Well, I mean, the good news is that. I think the hardware industry is seeing a bonanza of a lifetime. And generally, when you see a bonanza of a lifetime, you can go commit 10, 20, 50, 100 billion. And I've seen a CEO on television committing to go 100 billion dollar plan to go build more memory. So that's good. That means they have the money to go put the money in the ground, literally, to go build these things for the future. So I think that gets us more certain that the.

• 28:11David Friedberg?

  I think the tax incentive has a big, has a lot to do with that. The accelerated depreciation on the capex. You get 100% write-off in the first year, right?

• 28:20Chamath Palihapitiya?

  Just a, 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, you know, you'll take shots and they've generally worked. So you have a ton of permission in the market. When you hear what Bill Hackman said about how there's this kind of over-beaten companies, there's a few that get celebrated, that's a ripe pool for you to pick from. But some of that would require you to go maybe a little horizontally far afield, some would say. How do you maintain the discipline or do you see yourself at some point considering things that are not nearly so much right down the middle of cyber?

• 28:58Speaker 01

  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 backend so they can work better with our go-to-market engine. So for me, if I'm selling 10 million dollars to a customer, next time I go too late, if I can sell them 20, it's the most efficient way for me to amortize my go-to-market spend, right? So that was the model. We played that, we ran that playbook to north of 150 billion. Then we got to a point where it says, oh, we see an inflection arriving in identity. It's going to be important from an agentic perspective, security perspective. So we bought a 25 billion dollar company, which we closed three months ago.

• 29:35Speaker 01

  Now, it's actually a very different opportunity has presented itself. And the different opportunity sort of goes like this. If you can be the best at leveraging AI to run the most efficient enterprise business in the world, your operating margin can be far in excess of the industry. And if you can, if you can crack that code.

• 29:53Chamath Palihapitiya?

  Gross and net, you're saying. Gross in the 90s, net in the 40s.

• 29:56Speaker 01

  Yeah. If you can crack that code, then it doesn't matter what you buy.

• 30:00Chamath Palihapitiya?

  Yeah.

• 30:00Speaker 01

  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, fine, if you take something at a 20% margin, make it a.

• 30:17Chamath Palihapitiya?

  Your first M and A was really tough, no? Like, they were pretty skeptical and then you kind of shoved it in their face.

• 30:22Speaker 01

  They're pretty skeptical and 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.

• 30:33Chamath Palihapitiya?

  So basically, you're saying the menu's open.

• 30:35Speaker 01

  I think we need the next six to 12 months to figure out how this AI settles down and how can we use that effectively in enterprises. I think if you think about it, uh, you know, the, the, the people keep hoping that less people we need to run companies. I actually have a counter view. I think we're going to have more people at Palo Alto on the technology side than we've ever had before, because I think AI is causing everything to ask for a transformation. So I have more technical people today than I would have had if AI didn't exist.

• 31:04Chamath Palihapitiya?

  Ladies and gentlemen, CEO of Palo Alto Networks, Nikesh Shohra.

• 31:07Speaker 01

  Thank you, guys.

• 31:11Jason Calacanis?

  Thank you, sir.