The long-term average intelligence is going to be free, and the average intelligence will get smarter. Land, permits, energy, this is the thing that is going to get priced for the next three to five years. Hold up, Nikesh Arora joins us in the studio? Oh yeah, baby. Palo Alto Network CEO, $280 billion company, for this incredible session today. And we discuss Leo Ashenbrenner's situational awareness imploding. Sad face. Airtable being acquired by Bending Spoons for $1.285 billion. Sad face again, they were worth $11 billion before. Anthropic's model breaching three companies. Oh God, when do the security problems end? That and so much more in this incredible conversation.
Absolutely right on trend. Absolutely wrong on portfolio construction. It's almost like it was inevitable. If you bought in in April, May, or June, you've been wiped. We did a hedge fund, but it appears it wasn't hedged. And we lost all our money in a week. I think it's a bit of a gold rush moment. I don't think every consumer app will get rewritten in the next five to 10 years. In the face of insatiable demand, all things are possible. Palantir can do it. They came back from 15% growth four years ago. Why can't you do it, kids? Work harder.
Team, it is so good to be back. And we have the one and only Nikesh joining us. Nikesh, thank you so much for agreeing to join Rory and me and Jason. I am a little apprehensive watching Jason and Rory there in their full glory. So let's see how this plays out. We have Professor O'Driscoll in the corner. But we're going to start on the news of the day. And the news of the day is Airtable. One of the big names from the last decade has been bought by Rory.
The company was doing 485 million ARR, growing 20% year on year. Ultimately, at a $1.285 billion acquisition price. And a price value. It's not the outcome that everyone quite wanted or expected. But it's what we have today. Rory, why don't I hand over to you first? I'm sure you've got some perspective. Well, we're all going to do the Airtable side of the analysis. But it's just worth pointing out the Bending Spoons side. I'm buying stuff at 2.8 while I'm trading in the market at 0 to 10 times revenues. They're going to do this all day, every day.
And I think, as we said a few weeks ago, one of the big advantages is they're in the game with capital and a traded currency to hoover up a whole bunch of this stuff. So on their side of the table, totally get it. Obviously, on the Airtable side, there's a lot of comments on, is this giving up? Is this a reflection of where SaaS is? If we weren't anchoring off the $11 billion, it would be a great price. If you said someone set up a company 10 years ago, grew it to $450 million in revenues, and sold for $2 million plus or minus, they'd be like, that's an amazing outcome. Right? But, of course, we all anchor off the $11 billion 2021 price, and it feels like a lowball.
But I think it's still a great value creation achievement. And we have a talented entrepreneur on the show with us, and we need to start with that. It's a great outcome. Can I ask Nikesh one particular question on it, Harry, if it's okay? I was going to ask you. I have a lot of interesting thoughts on this deal, whether Airtable matters in the age of agents and AI. But Nikesh is also one of the best dealmakers out there. The shocker to me with Airtable wasn't the price, because I think it's low market. The shocker to me is no one else stepped up. No PE firm, no Tomo Bravo, no Vista. It's 20% at $500 million with some AI dust going on it.
Do you think there was even another offer? I just assumed someone would outbid them. And you're asking me that because I'm like. You're the dealmaker par excellence. We just happened to have the king dealmaker on the show. Look, I met Javi a few times, a great guy. He's built a great business. I think, to Rory's point, there's a bit of founder fatigue here. I think he's been through a lot of ups and downs internally and in the market, but he's got a good product. I think the broader question, which Jason hits on, is, what is going on in the SaaS marketplace?
You see dislocation. Is this a pricing dislocation, or is the fundamental change in the long-term growth rate that people expect out of SaaS? If it's a fundamental change in the long-term growth rate people expect out of SaaS, then the multiples are right. Now, that's where the market is grappling with this. I think the people you mentioned, Jason, the PE guys, they might have a full roster of stuff they'd like to sell to Bending Spoons as opposed to they'd like to buy against Bending Spoons. So I think they're caught in the demand and supply problem right now. They have a lot of inventory. It just worries me.
Because you see more deals than we do on the acquirer side, right? We see it on the target side. It's just, when you look at Francisco Partners. Long-term growth businesses. Say that again, sorry. So we stick to long-term, currently unprofitable, long-term growth businesses, but yes. No, I get it. And that might be the answer. I just, with Francisco Partners raised 22 billion to do deals like this, right? Tomo Bravo was like, we're looking for AI-infused B2B companies. We could pick an Airtable, but it did do that. It did infuse AI workflows and others. And I don't know that it's growing, but it did that, right? 20% and 500 million in AI workflows isn't nothing.
I just would have, for all the founders out there looking to be picked up, this one would have seemed to me to be just above the fold. They should have leaned in on this one, not the one growing 8% and shrinking because it was destroyed by AI. And it's cash flow positive, and it has plenty of cash. So all the boxes you check are there as an attractive target. And yet no one outbid them. Well, look, Bending Spoons did buy them. So clearly somebody saw value there. Not everybody saw it. But, Jason, I want to go back to something you said on AI infusion. I'm a little wary about this AI infusion stuff.
And this is what I talk to my team about every day from an operator perspective. I say to them, are we Mercedes? We're trying to sprinkle a little bit of AI in our car and say, I have a little bit of AI. Are we Tesla? Are we making sure that our car will drive the next 10 exits by itself and might have to grab the steering wheel once in a while? And am I building a Waymo? And the question back to you is, did Airtable do a bit of a Mercedes action, a little Tesla action, or a little bit of a Waymo action? Because my biggest fear is a bunch of people out there in the garages getting funded by Harry and Rory. And they're going to build Waymo's future.
And we'll be busy putting lipstick on the pig. It's a tough one, right? Yeah, it is. And I always, because I didn't feel, Jason, that this was the kind of category that PE would sweep up. Because, kind of merging the two sets of comments, one is, yeah, they definitely, you're trying to add some AI pixie dust, but you're fundamentally your core productivity app. And you've been the proselytizer, Jason, that, oh my God, look at all you can do with Lovable. If I wanted to build my own CRM and I wanted it to be personalized, 10 years ago I might have used Airtable because it's way more configurable than Salesforce.
But today, if I'm the nerd that wants to build my own CRM, I might just go to Lovable, Replet, or Claude Code and just bang it out from scratch. So if you want to do that, I'd call you a person with low imagination. There's so many other good things to build. There's a lot of things you want to build as a new CRM. I agree. Because, yeah, merging the two sets of comments. One is, yeah, they definitely, you're trying to add some AI pixie dust, but you're fundamentally your core productivity app. And you've been the proselytizer, Jason, that, oh my God, look at all you can do with Lovable.
If I wanted to build my own CRM and I wanted it to be personalized, well, 10 years ago, I might have used Airtable because it's way more configurable than Salesforce. But today, if I'm the nerd that wants to build my own CRM, I might just go to Lovable, Replet or Claude Code and just bang it out from scratch. So if you want to do that, I'd call you a person with low imagination. There's so many other good things to build. There's a lot of things you want to build as a new CRM. I agree. And I am a person with low imagination, and I come to that. But I agree. But the point is this.
What it means if you own a horizontal productivity app that's mainly individual user, I just think that's one of the tougher categories for PE to get their head around. Ironically, given the Evernote purchase, this is right in the bending spoon sweet spot. If you think about it, this is just like Evernote, it's like the people who have stuck with this product, they're going to stick with it. They're going to apply their formula. Maybe what I'm saying slowly as I process is capitalism works, and it ended up in the arms of the best owner of that product, which is the people who can take it and turn it into a cash flow machine.
I bet you two years from now, it's doing 600, not 900, but I bet you it's 300 million of free cash flow. Yeah, or more, right? Just double the price and your data is locked for two years, right? To your point, I don't think PE has the stomach or the willingness to do that. But I don't think it's what they do real well, right? But yeah, I think... I think their stomachs might be full. I think it's unfair to say that. Yeah, that's exactly right. As I often say to people when they show me a turnaround deal in my business, I say, look, if I wanted a shitty turnaround deal, all I have to do is look at my portfolio. I'll have four of them already.
I don't need a fifth problem, right? I make problems on my own accidentally. I don't need to go actively, proactively say, let me get more of this shit. You're exactly right now. Anyone in PE has done five software restructurings in the last 12 months. They need a sixth like a hole in the head. Nikesh's points... Both those points are obviously great. The one on the Waymo versus the whatever we can come back to. The founder fatigue one's a tough one today because another way to look at Airtable is, man, so early to no code, right? Such a clever product back in the day. There were two products that I wasn't even smart enough to understand why they were cool in the day.
There was Airtable, which turned a database into a spreadsheet, so I understood it, right? And then there was Notion, which turned a database into a document that I didn't even realize. And they were both so clever pre-AI, and they both took off in different ways. But we don't really, Supabase is doing a million Postgres databases a week on its own. We don't need that no-code database today. And as a founder, after all these, what, founded in 2013, it's tough to pick yourself off. And he already picked himself off the floor, right? Already did the layoffs, already got profitable, already rebooted, already went founder mode again. I'm all in.
And you're looking at yourself and you're like, can I do it another 13 years? And it's a tough one today when you've already done the whatever. I'm sorry, Nikesh. What's below the Waymo? Tesla. For the two? Well, you've already checked the box and done it. And you're like, God damn it. I got us to 20% growth. It's tough to not tap out. We're human beings. It's tough to not tap out. One of the things that's interesting here, and I've said this in the context of venture in general, is your common difference. The technology trends moved on from the thing they built.
And one of the weird things about venture with the holding private for longer thing, right, is now the holding period of venture is longer than the technology platform chain cycle. So if you join halfway through, right, this is coming at you. And in the old, yeah, 20 years ago, this would long since have been public. It would be trading as common stock, and it would just get hoovered up on that basis. A lot of these late-stage rounds long since would have been public in another world. I think the biggest fear right now is something that was started 10 years ago. Is it past the point of rebuilding? Yes.
And are you better off building from scratch than trying to tinker with something that was built 10 years ago? And that's where the challenge is. That's interesting. You've done some recent acquisitions. In fact, it's interesting. It used to be my mental model was apps last longer because end users get stuck in place and they keep the shit forever. And the infrastructure market moved quickly. But we've definitely seen some of the apps companies get stranded, right? And whereas the infrastructure companies that have been able to evolve to link in, you're absolutely worth it. But to link into the AI demand have been able to actually go from strength to strength.
I mean, look at Datadog. We were investors in J-Frog privately held. You guys are killing it. But you're coattaching to the AI trend, and the poor little apps companies, there's just nothing to coattach to to give you lift. I think it's a moment in time. I think one of the things which we all know, we don't talk about it too much, is AI still has a lot of false positives. There are too many edge cases that it can't solve. You still need grinders to solve the edge cases. The Waymo doesn't drive on the street without tons and tons of people being paid for labeling and tens of billions of dollars to find every tree and mark it.
So it's the equivalent of the Waymo, mark the tree, that's a tree, idiot. That stuff needs to happen for a lot of enterprise for AI to be effective. So I think until that process, we go through that process, we leverage AI, put all the hoops around it. From a machine learning perspective, there's life for infrastructure businesses. And the choice we have in the next five, six years is can we build all that plumbing, all that guard railing with machine learning and chain the core engine to some version of AI at the right price? We survive. If you don't, then maybe bending spoons it is. Bending spoons it is. Bending spoons it is. Bless those kids. Bless those kids. Okay.
Harry, Lunasama. Final question before we move on. It's just like, does this put a marker in the ground in terms of enterprise value for companies like this? If you're a Notion that raised 10 billion last time, how do you feel looking at this? If you're on monday.com, again, two products with similar motions. It was a shocker to see it, right? Especially the way everybody presented it, right? Enterprise value and all this. But Rory's right. It's market low. It's market low. And we'll find out tomorrow. True. True. I think one of the things that's going to happen, either like the Leo... Most so far. What's the hedge fund guy? Sorry, that we might not even talk about.
We're going to mention that. Leo Asher. Okay, I already forgot about him. Okay, so I think one of two things is going to happen. We're going to forget about Airtable tomorrow because other stuff's going to happen. We are. Or what I think might happen is this is the one where people capitulate, both founders and investors, where they say, look, folks have already had markdowns since 2021, but they're not consistent. This deal, in many ways, was everyone capitulated. Ever the late stage got one X. The founders made 150, a lot less than they thought, but certainly enough to survive, even today in San Francisco with rents up, right?
Everyone said they capitulated to the markets. And I think we're also all in board meetings where we're seeing the opposite. 30% growth at nine figures where we're going all in, guys, right? We're going to mention that. Leo Asher. Okay, I already forgot about him. Okay, so I think one of two things is going to happen. We're going to forget about Airtable tomorrow because other stuff is going to happen. We are. Or, or, or what I think might happen is this is the one where people capitulate, both founders and investors, where they say, look, folks have already had markdowns since 2021, but they're not consistent. This deal in many ways was everyone capitulated.
The late stage got 1X. The founders made 150, a lot less than they thought, but certainly enough to survive. Even today in San Francisco with rents up, right? Everyone said they capitulated to the markets. And I think we're also all in board meetings where we're seeing the opposite. 30% growth at nine figures, where we're going all in, guys, right? We're 20% growth, but we may see a quiet wave of air tabling it. It's time, guys. It's how we did it. It's time. It's time to capit. He's a great founder, but that time has moved on and it's time to capitulate. And that's a question, right? It may create more conversations or it may be forgotten about in three hours.
Not sure which, but it was a jaw-dropper for a brief moment in time, like losing, losing most of your hedge fund during your wedding. But we move on. Well, we'll talk about that. That's a brilliant transition. Leo Ashenbraner, famed wonder kid who wrote the situational awareness piece, which was an incredible memo that then he parlayed into a $225 million vehicle that at one point had $45 billion of assets, really rode the wave, so to speak. He did it with 4X leverage. And in the past week, it came crashing down. And then Ken Griffin and Citadel bought his public book for a reported $16 billion.
Ken has made out like a bandit, reportedly making about $3 billion on the back of it in a very short amount of time. Ken Griffin, how did we think about this? He was the wonder kid of the AI wave. Ken Griffin, I think he was absolutely right on the trend. And remains to date right on the trend. In other words, the data just last week about CapEx absolutely supports his memo. So conceptually right on the trend. And then absolutely wrong on portfolio construction. Ken Griffin, if you accumulate a portfolio of high-volatility stocks with 4X leverage, the math makes it clear your probability of getting wiped out once is just very high. This is as simple as that.
Absolutely right on trend. Absolutely wrong on portfolio construction. It's almost like it was inevitable. I'm really sorry. How do your investors let you get to that place? Because you made them 10X last year and you probably don't question anything. He did amazing. And which of us really, let's ask ourselves honestly, when someone makes you a 10X, do you sit there? Is your first response, yeah, but what can go wrong? I was like, oh, can I put in more money? That's what happened. And my wife said when I was talking about it, she said, I don't want to see any schadenfreude. She was like, there's a lot of schadenfreude, the laugh and the poor guy. I feel sorry for him.
It was a tough call to have to go through that, just to put it out there on a human level. He was clearly wrong on the bet, but that was a brutal week. Will he be okay? It says that he's still managing both the private and the public, but he's got his Anthropic position, and then other people are like, oh, no, lawsuits are coming and it's not going to be okay. Is he going to be okay? I think he'll be fine. I promise you he's not going to be caught at the same place again. So good news is he's learned a lesson and he's going to live. He's going to survive to live. It's your job to mentor some of these younger kids like Leo. So I think maybe you could step in. What?
He's 26 or something like that. He was 25 to 25. Yeah. So I think it's time for you to become the elder statesman in the industry and start mentoring him on leverage, when to lever up to 4X, when not to. In all seriousness, I think someone smarter. I've read it, drooping plants and expanding homes with multiple people living in the same house. Don't bother him. I assume his LPs or his investors knew this was a highly levered fund, right? Am I wrong? Rory, if they know it's 4X levered, then they know there are black swan issues when there are short squeezes and others. And I don't think that his investors should cry if they knew how it was playing.
I mean, my limited experience as an LP in funds with leverage, not quite this much, as you know, it's not free.
I have a feeling his LPs didn't lose any money. If you are up 4 and 40% and you go down from day one billion, you're back to where you started. So I think it's fine. The interesting thing about that, not quite, because actually this is where I think it could get a little hard. It all depends on timing, right? Because the hedge fund things are weird. If you came in early, you made a ton of money, and then you lost two thirds of what you made and you still made money, right? Brutal comedy, if you came in in the last six months, you might have been wiped 80%. Because hedge funds, unlike venture funds, people come in at different times at different bases. True.
So I think the real, and I thought even perhaps Jane Street had put in some money recently, but a bunch of people had put in money. And if you bought in in April, May, or June, you've been wiped.
And then to your comment on those people, I mean, the litigators, I think fundamentally, yes, he will be fine. And Larry Fink, who founded and runs BlackRock, had a blowup early in his career. There are lots of people who had blowups early in their careers. Nikesh has worked for one of the most aggressive risk-taking human beings on the planet, that's SoftBank. He's seen ups and he's seen downs. So you can survive. What? What's Forex? Yeah, Forex is for babies. It's like a level. But genuine comment here. So you can survive, and 10 years later, it's a successful in France. I think the crux of the near-end question will be those investors who came in late, who,
let's be really direct here, will be pissed. You put money in a hedge fund in April and you lose 90 cents on the dollar in July, you're going to read the docs real carefully. If there are any disclosures that weren't made or if you've done something beyond the remit of the fund, you will have liability. All this will happen. In the end, this is America. Everyone will sue everyone and it will all be fine. But there will be some dynamics going on now. Because can you imagine going back to your investment committee and saying, we did a hedge fund, but it appears it wasn't hedged, and we lost all our money in a week?
But if you're the Collisons, on the other hand, you came in on day one, you still made out great. Well, that's a relief. I was worried the Collisons would be short of cash. So that's good to know. Good to know that at least that whole, or if they need some money to buy PayPal. If they need some money to buy PayPal. They need cash in Silicon Valley, they'll be fine. They need money to buy PayPal. So they do. And they need money to buy, what was it? Open router. They're doing such a lot. Actually, we're just talking about, it's been interesting to see them do all this corporate development while still private.
It's just super interesting in terms of, a lot of this stuff would be marginally perhaps easier with a public start. But I had a CEO of OpenRouter on the show on Friday, Rory. So there we go. Cool. I'm excited for this next topic because with Nick Cash, I think we've got the most prescient person. Anthropic's models breach three companies too. This is obviously on the back of the OpenAI and the Hugging Face debacle. Really? Anthropic's breaching three models also? Is this just the most epic beginning of a bull run in security? First and foremost, this is all flex, right? They all want to tell you how good their models are, how powerful they are.
So it's bizarre because normally if you end up breaching somebody's infrastructure, it's not a good thing. But we're all saying, look, look at these models. They're so powerful. It's just super interesting in terms of a lot of this stuff would be marginally, perhaps, easier with a public start. But I had a sea of open router on the show on Friday, Rory. So there we go. Cool. I'm excited for this next topic because, with Nick Cash, I think we've got the most prescient person. Anthropics models breach three companies too. This is obviously on the back of the open air and the hugging face debacle. Really? Anthropics breaching three models also?
Is this just the most epic beginning of a bull run in security? First and foremost, this is all flex, right? They all want to tell you how good their models are, how powerful they are. So it's kind of bizarre because normally, if you end up breaching somebody's infrastructure, it's not a good thing. But we're all saying, look, look at these models. They're so powerful. So fine. Granted, they're all very powerful.
I think the first things our friends at Anthropic and Open Eyes should have done, which I've told them, is point your models at your own sandbox to make sure your sandbox doesn't have any zero-day vulnerabilities and make sure your sandbox is your first capture the flag exercise. But they decided to give a target to say, go out in the wild, persist, take as long as you want, and go capture a flag. So fine. We have these models with these capabilities. I think the challenge we have from a cybersecurity perspective is we're finding vulnerabilities which would take us days, months to find.
The average time to patch a vulnerability, a zero-day vulnerability found in the wild, is 55 days. Just think about that. These things are finding vulnerabilities in split seconds and then turning around and building an attack on the back of that. So I think the fundamental speed at which cyber attacks will happen and need to be defended changes. And this is good for us. It's kind of like the sound of revenue. But I think from a more fundamental perspective, I was thinking this capability is going to show up in six months. I think I said that with you, Hari. And it showed up in four months.
I think in two or three months from now, open source distilled all these capabilities. And we'll find open source models out there, which you can fine-tune if you're an attacker, to actually do this on a task basis. So it's going to change the game. How are your enterprise customers reacting? Because this feels to me like the mother of all of it. I mean, security sells on fear. And this is terrifying. So what are you seeing in the enterprise customer base when this is knowable? Sure. Look, the good news is the flex that Anthropic did with Mythos has every CEO talking about Mythos. I spent eight years trying to get CEOs to talk about cybersecurity.
Couldn't get them to do it. And Dario did it in one fell swoop. So this is good news. He's got everybody all hot and heavy about Mythos and the capabilities of Anthropic and how these models are going to go attack your infrastructure. I've never had so many CEOs call their CEOs and say, are we ready? What's going to happen to us? Well, the answer is you're not. Because what being ready means is that I have no vulnerabilities either in my code, any vendor that I've got deployed in my infrastructure, any open source I'm using. That is fundamentally not true. Now, we found 14,000 vulnerabilities in open source in the last 14 weeks testing open source packets.
So it's a bunch of stuff that's being used out there. So, A, every company has vulnerabilities. They've got to figure out a way to patch them. B, these models will figure out misconfigurations. If you've left the door open, if you've got a device configured wrong, if you've got a piece of software configured wrong, and there's tons of that out there. Not every IT person building infrastructure or configuring infrastructure is a genius. There's misconfigurations. All these things need to go away. So at the base, as a face of it, a lot of organizations are going to have to go fix a bunch of these vulnerabilities and misconfigurations.
On the flip side, even if you fixed most of these, the bad guy has just got to be right once. So he's going to find one. She's going to find one to get into infrastructure. The question is, what is your time to detect and respond in that circumstance? The average time to detect and respond is four days. How are you going to get it down to a minute? So it's not a fear problem. It's a capability problem. It's an infrastructure readiness problem. And now it's come to bear. It's time to pay your taxes. Yeah. I mean, that's the last sentence. I mean, you're right. We can argue fear versus thing.
But what you're basically saying is the security infrastructure that you had a year ago is wholly unfit for purpose in the next year. And you, Mr. Enterprise buyer, are going to be buying a whole load more stuff. Are you going to be the weakest link when these capabilities are everywhere? Yeah. Yeah. You said it so well, Rory. I mean, it just feels so good. I don't mean to be. I think you should be on the podcast talking about how people need to buy more cybersecurity. I'm just going to buy the stocks, but— But now, Nikesh, can we get him some swag? I mean, Jesus, he's like— That's it. We don't have swag. But give Nikesh a hard question. You're the one's over.
I want to ask Nikesh, as my cybersecurity therapist, I had two Fable issues. And they're internal security, but I'd love to get your—and you can make fun of me for this. I've got—I pretend to have a thick skin. I don't, but I love criticism. I already figured I don't have a thick skin in the first 30 seconds of this conversation. Okay, good. So I'm building an app for the Sastra community.
It's called Sastra Connect to help with recruiting. Details don't really matter, but it's the biggest thing I've built myself in this era. And among other things, I've got a Google Doc. It's called Jason's Gems. It's my ideas on how to improve that. It's just ideas. They're just scratch notes. Okay. No one's seen them. That's not ready. Just a side doc I keep. So the other day I went into Claude and I just turned on the Google Drive connector since it's one of the three primary connectors. This is not esoteric. This is not third party. And I never knew it went in, scanned all my docs, found Jason's Gems, found the ideas.
Fable then went and changed my code and my algorithm without telling me. Never got a notice. Never was told. Never was in a change log. Never was anywhere. I only found out later when the agent flashed conflict with Jason's Gems when I was trying to fix something else. I mean, I'm not saying it's terrifying, but how can organizations deal with this fact when an LLM will go out and change your core code, your core corporate OS, without even telling you? What if you have a thousand employees doing this? And this is good. The hack, the part I didn't tell you, is the way it did it is MCP did.
So I had Google Drive to Claude to Fable to MCP, and so I was able to do whatever it wanted, probably thinking it should implement Jason's Gems, but it shouldn't have. And it never asked me and it never told me it did it. Is this scary? Is it not scary? Is this Orwellian? What happened to me? It's the Wild West. It's wonderful. It's the Wild West. And part of the challenge is, the good news is that the bad news, I know what you want to look at. I think the small business entrepreneurs, people playing with their own stuff, are doing this without any regard for security. Same reason here in TikTok. Right. So people are doing that with no regard for security.
They want to experiment with open cloud. They want to connect to all their stuff. They have no idea if all that data is being used for training. They have no idea what credentials are going to get used, what permissions these agents have. And that's happening all over the place. On the enterprise side, there is some cohesion around it. I think the most obvious ones are people saying you can't use this. Now, that only encourages people to use it more. But there is a stream of thought saying, if I don't allow you to use it, I have time to go figure out how you're going to use it. But the challenge you have, Jason, is that I think this is burdened.
And when the Wright brothers built a plane, they didn't invent TSA. That was not the first thought that crossed their mind. TSA came a lot after. So you don't think about security when you start playing with new things and cool technology. And that's what's happening. You're seeing people play with open claw. They have no idea what credentials are going to get used, what permissions these agents have. And that's happening all over the place. On the enterprise side, there is some cohesion around it. I think the most obvious ones are people saying you can't use this. Now, that only encourages people to use it more.
But there is a stream of thought saying, if I don't allow you to use it, I have time to go figure out how you're going to use it. But the challenge you have, Jason, is that I think this is burdened. And when the Wright brothers built a plane, they didn't invent TSA. That was not the first thought that crossed their mind. TSA came a lot after. So you don't think about security when you start playing with new things and cool technology. And that's what's happening. You're seeing people play with open claw. You see people play with agents. You're seeing people play with all this stuff with LLMs. Everything's happening.
LLMs are training on data if you're not careful, because that's the value of giving. And what is it? What is the adage that if the product is free, you're the product? Well, guess what? So we are the product of all these post-training data that has been collected by every model out there on our consumption, which is not regulated, ring-fenced, the enterprise use case. That's why enterprises are paying a lot of money for all the free stuff that consumers are getting. So we are the product. It's learning on all your behavior. Is the security architecture a year from now more the same, better, faster?
Or are there some new things that you just have to do utterly differently to protect? Is it just the same problem, just higher velocity? Or is it, oh, shit, we never even thought about that before? Yes. Both of the above. Both of the above. Both of the above. Look, fundamentally, cybersecurity is kind of a very straightforward thing. If it's a known bad, I'll stop it at the door. Right? You show up with guns blazing. I know who you are, and I've got security at the perimeter. I'll stop you. Yeah. The known bad, I'll stop you at the door. The problem is no cyberattack happens because I stopped a known bad. Every cyberattack happens because... I didn't know.
You didn't know it was bad until it got into your infrastructure. So the question becomes, if you know it's a known bad, you stop it at the perimeter. If it gets through, how quickly can you find it and stop it before it creates harm or damage? So from a cyber perspective, you want to be in the perimeter business. You want to be on as many perimeter endpoints in the world as you can because that becomes a sustaining business. The more perimeters I'm on, the longer my tenure for my business is. So I'm on endpoints. I'm on devices. I'm on servers. I'm on firewalls. I'm protecting the perimeter for multiple infrastructure components in the world. That's good.
That's kind of good. Now, the question is, how quickly I find known bad will change using AI, right? There's a concept of data classification. You have to write static rules. Guess what? An LLM can suss it out much faster from a content perspective. We track every malicious website in the world. Can AI tell me it's a malicious website much faster? Yes, it can. So the ingredients of my perimeter security will change using AI. The act of stopping things in line will still be needed. So when people tell me, oh, OpenAI is going to eat my lunch or Mythos is going to eat my lunch, guess what?
There are no perimeter security scenarios, which means I still need to block the bad guy. They need to be the ingredient in my product. They're not going to take me out of business because people have all kinds of infrastructure on the perimeter. The other part is if you want to suss out all the bad stuff in the infrastructure and find out the bad actor, guess what? Imagine collecting all the enterprise data and running LLMs on it and saying, find me all the abnormalities. Find me behavior that you've never seen before. Now, I'm ingesting 19 petabytes of data a day. Think about it. 19 petabytes of data a day of enterprise data to look in it for anomalous behavior.
I have machine learning techniques. I have static techniques. I have rules that I look at. Guess what? I'm going to throw some LLMs in there just for fun to see what they find. Now, if I can find the unknown bad actor in your infrastructure much faster using LLMs, I can detect it and block it. Right now, we run it one minute. Okay. Machine learning. This is a good thing. The only problem is I only have 1,200 customers who bought and deployed it. I need to get the rest of the world to go buy it and deploy it. So that's the second half of the problem. The third part is there is stuff which is new, which does not have any security guardrails that have been built. Agents.
The world is talking about agents. We can have a whole episode, 90 minutes on, about what are agents, what is really an agent, how do you give agency, and how do you control an agent? People tell me they've agentified stuff, but then I ask them, does it actually have agency? Like, what does that mean? I'm like, Waymo has agency. It can drive you into the wall without human intervention. This is a bad problem. But most people haven't actually given agency to their agents. So they're running glorified workflows, which are deemingly agentifying things.
But when you start giving agency to things, when a piece of code can decide what happens next, we're going to have a whole different conversation on how do you secure those agents, how do you build kill switches, how do you intercept them in line, how do you stop them from doing bad things. Just like Jason's agent, which did a bad thing and took Jason's gems, and now the whole world will find out what Jason's gems are. Totally agree. It's crazy. We said open, we said China. Whether we have comment on it, Moonshot closes 3.5 billion at a 35 billion valuation. And if it's free, you're the product. Moonshot's free.
I think one of the comments on the if it's free, you're the product is totally true, especially on the consumer side. The interesting thing here is I'm not sure how it's true. Put it another way. Unless you're... One of the really interesting things about these open-weight models is the impact they're having and the ability to be a drag on price for the US closed-source frontier model companies. If you're running the inference as well, then I get the model. You get the business model. The model is free, and the inference is how you make your money.
It's not as clear to me long term if it's possible to continue on a sustaining basis, offer open-weight models without monetizing in some way. And we'll see what it is when people like Reflection and Thinking Machine start, when these models start to happen in the US, it will be interesting to see what the business model is of which an open-weight model is a part. It's definitely not, hey, download it, have a go, and you can do whatever you want, wherever you want. Look, open-source has evolved the business model of support. So it'll be just interesting to see what version of if it's free, you're the product emerges for these companies in the medium term.
I'm going to give Harry a soundbite. Good. Average intelligence is going to be free in the long term. And the average intelligence will keep getting better. Nice. Exceptional intelligence will be paid for. Can you give me some tone with that, Nikesh? That was all monitor. I want drama. Come on, you've got to deliver the sound. I was watching somebody speak the other day, and they said, if you whisper loudly into the mic, people lean over and pay more attention. So I'll say it again. I say in the long term, average intelligence is going to be free, and the average intelligence will get smarter. Oh. But do you think we'll rely less on frontier intelligence?
We won't need it? Oh, no. We'll need exceptional intelligence. We need exceptional intelligence to discover the cure for cancer. We need exceptional intelligence to send rockets to the moon. We need exceptional intelligence to build a space data center. Those are exceptional intelligence tasks. They are still going to require exceptionally intelligent people or exceptionally intelligent models. And people will pay for it because the outcome is so spectacular. I don't think you need to pay $6 a million tokens to answer a call saying, how can I help you? I'm so sorry. Your network connection is not working. Agreed. Yes. Customer support will not be using frontier models.
Maybe. But my limited, listen, of course, you're right over the long term, right? In the short term, customer support is requiring more and more tokens to do more and more sophisticated resolution. And guess what? Oh. But do you think we'll rely less on frontier intelligence? We won't need it? Oh, no. We'll need exceptional intelligence. We need exceptional intelligence to discover the cure for cancer. We need exceptional intelligence to send rockets to the moon. We need exceptional intelligence to build a space data center. Those are exceptional intelligence tasks. They are still going to require exceptionally intelligent people or exceptionally intelligent models.
And people will pay for it because the outcome is so spectacular. I don't think you need to pay $6 a million tokens to answer a call saying, how can I help you? I'm so sorry. Your network connection is not working. Agreed. Yes. Customer support will not be using frontier models. Maybe. But my limited, listen, of course, you're right over the long term, right? In the short term, customer support is requiring more and more tokens to do more and more sophisticated resolution. And guess what? Those are the primary candidates that all these open models are going after with open weight fine-tuning, saying, I don't need hallucination. I need...
I think that the part where we're... This is what I mean, the capability and intent gap is. I think there's a lot of work that needs to happen to go from building a frontier model or any model and taking that and making it useful in the enterprise context. The amount of effort that goes. The problem we have is... Sorry to go back to the Waymo example, because it's... I think it's the most obvious one out there. It took... I drove in the first set of Google self-driving car. I don't know what I was thinking. In 2009, when I used to work there, it was a Lexus with a bunch of cameras.
It drove me from San Francisco to San Martin on the highway, and my hands were not on the wheel. And then they told me at 11 p.m. to take the wheel in my hands. I was driving to Cordova, and I did. And I was more relaxed. I was saying, oh, maybe it's just going to figure it out when I make a wrong turn, because it's so smart. It drove me. I was like, no, dude, this does not drive when it turns off. So that was 2009. It's taken 14 years after that to get one with all the edge cases trained from a machine learning perspective for us to rely on that as being the agency that we've given the agency to, that replacement.
So I don't believe we're going to give 100% agency to use cases for some time. And for us to be able to do that, the amount of data collection and context we're going to create is going to be humongous. You have to literally take every edge case in customer support, get it into your AI brain of your organization so that you can start relying on AI instead of the human. So you're getting 80% right now. You're getting 80% of customer support solved. All the edge cases are waiting to be solved with AI. So you're getting 80% of the data. Then there is the, for a given app, how much of the value is purely in the model versus all the other things. And you're right.
For something like customer support, you're probably paying 10% or 15% of the revenue you're getting for intelligence. And the rest of it is all the other shit it takes to make that intelligence actionable in the context of answering tickets. And one of the things we look at is just super interesting on the app level is the tokens as a percentage of total revenue. And it varies from the Salesforces, we were in Intercoms, stuff like that, where it's plus or minus 10, 15%. Obviously, in coding and things like that, it's 70, 80%, which means it's just raw intelligence and a mild harness. And those are just very different.
I think in the next three, four years, we won't be paying for intelligence. We'll be paying for compute through our nose. Speaking of paying for compute through our nose, we often get chastised for being too public-markets-focused or too Anthropic- and OpenAI-focused. Valar Atomics triples to $6 billion, prices Sequoia bets on nuclear for AI. It's a three-year-old small modular reactor company, raised at $2 billion, now Sequoia leading a round at $6 billion. And specifically, there's an NVIDIA partnership to power AI data centers, which caused a lot of excitement for the company. I met somebody who, I was talking to him, and he's in
the business where they take chicken feces and turn that into methane and produce gas. And I thought, oh, it's a cute project he's got running somewhere in the middle of the country. And then he told me that it raised money in billions of dollars. And he's selling the energy to hyperscalers. So anybody who can produce any energy source, it doesn't matter where you are, is right now trading in multiples because land, permits, energy, compute. This is the thing that is going to get priced for the next three to five years. And I think it's almost like the question will become, between Anthropic and OpenAI, who has more access to more compute in the next three to five years?
And that's what people are going to buy. It's very hard to find compute right now. You can also take all the free Chinese models you want. Where are you going to run them? Jason? No, no, for sure. Just to Nikesh's point, I actually used to be a little bit in advanced energy storage in my first startup, and things like chicken manure didn't used to make sense. These models actually used to work. It's literally chicken shit. So did cows. So did cows. I even looked at some of these things, but the margins were so low. The IR was so low, but the business works now. There's such a demand for compute. Everything works, including all types of nuclear, like Valar, right?
Including chicken manure. You laugh, but I remember talking to a manure farmer doing this back in the day, and he's like, well, the best we can commit to is 8% annual return if everything goes well. And it's hard to get VC excited for those returns. But maybe it's 80 today. I never thought you were going to be talking about that on the show. Me neither. You brought it up. But everything and nothing in energy storage worked before AI, right? There's the battery startup, right? What's the one that just raised $12 billion too? Yeah. Yeah. None of these things worked without AI, right? Neither did RAM. None of these products really were that great.
But now they're the greatest products in the... Give me my RAMs. I can't even get my Mac Studio with more than 64 gigabytes. Give me my... Everything's working, right? I'm going to ground us in a little facts here, just on the three, right? Because I think of all... Valar and Base10, the base power, super interesting, but very different. I mean, you're right, the Valar is purely the we-need-more-power-for-compute bet. You're right. And they did plug into an NVIDIA chip and they basically showed that you can get criticality and generate power, but no shit, I think everyone knew that. I mean, just to say, I think the regulatory journey for all these things is still a haul,
just to be clear, in terms of when you can actually plug it in on an ongoing basis, right? And it's interesting. There's a bunch of these private and then a bunch of these public. And I think, is it NuScale, is the one that's doing the existing technology that's well understood. I think light water, not the NuclearX, but it's like, this is the way we built them so far, and it's pretty much the same. And it's the furthest along on the regulatory path. And then all these guys, including Valar and Okla, are doing new different things. And the big question will be, after you get the initial demonstration that it works, what's the regulatory path?
I mean, you have to be wildly supportive because there's no way to get cheap electricity without doing nuclear. So from a public policy perspective, go team. But just from a don't-spend-the-electricity-yet, you've got to plow your way through the bureaucracy. And at some level, you want people to be mildly cautious before you permit these things. Probably less cautious, dare I say it, than we've been for the last 30 years, where I think we've stifled innovation, and possibly a little more cautious than you might be right now to get it right. So there is an approval journey ahead, but it's awesome we're doing it.
The question we're debating is, where's the money going to go? How much are we going to pay for intelligence? And once we pay for intelligence, we talked about compute. I'm pretty sure Harry will want us to talk about all the capex that's going to happen out there. At some point in time, somebody's got to pay for all this compute. doing nuclear. But from a public policy perspective, go team. But just from a don't-spend-the-electricity-yet perspective, you've got to plow your way through the bureaucracy. And at some level, you want people to be mildly cautious before you permit these things.
Probably less cautious, dare I say it, than we've been for the last 30 years, where I think we've stifled innovation, and possibly a little more cautious than you might be right now to get it right. So there is an approval journey ahead, but it's awesome we're doing it. The question we're debating is, where's the money going to go? How much are we going to pay for intelligence? And once we pay for intelligence, we talked about compute. I'm pretty sure Harry will want us to talk about all the capex that's going to happen out there. At some point in time, somebody's got to pay for all this compute. And that money has to come from some version of some people paying for AI.
And that's the only thing that's going to allow the Valors and the chicken manure company in the world to actually be worth something. Totally. Yes. In the end, someone's got to buy a trillion dollars' worth of tokens in corporate America. And yeah. Over Europe? Well, if we buy a trillion, they'll buy half a trillion a decade later. I hate to be cold, but as a former European, I can say that. I can't your opinion. Whatever. It's a cynical dude on Europe you'll ever meet. I know. I'm just a liar. I have two Europeans here. Fucking unbelievable. Unbelievable.
Is this not just another layer of companies, which is dependent on, Rory, to your point, OpenAI and Anthropic continuing to go on their charge and hit their number? We've never had an ecosystem that will be so dislocated if OpenAI and Anthropic do not hit their 2027 numbers. I don't think so. Whether OpenAI or Anthropic hit their 2027 numbers or not is orthogonal to the fact that there is infinite demand for AI at this moment. And that infinite demand needs to be satisfied by compute. Now, whether it's OpenAI that builds the data centers or buys the data centers or pays for them, or somebody else pays for them, there is demand in the market.
Look, if you think about what's going on, I still posit 70% of the compute demand for AI is being consumed by consumers who are getting a free ride. So maybe there will be reallocation. Maybe we're going to have to give more compute to enterprises over time as they become better monetization capabilities. Or you'll find that eventually the promise of consumer monetization is going to start showing up. We all talk about why can't an agent book my airline ticket and make me a restaurant reservation. And these are simple use cases. I don't need to go solve cancer to get that stuff to work. That stuff's going to work.
When that stuff works, there's going to be monetization opportunities on the consumer side. So I believe that, at first principles, there will be tremendous amounts of compute that will be needed to satisfy the AI use cases, both in consumer enterprise. Which player ends up monetizing them becomes a question for the markets to decide. And that's a timing question, no different than Leo's question. That's a question of who builds the capability and the services. Google was not the first search engine. I'm arguing against, and just at one level, obviously, if you zoom out enough, you're right.
But if you zoom back down, and we notice in these discussions, I'm always delivering the... I see where you live, Rory. Yeah. I live... Hey, dude, I'm just... You're running your $280 billion. I'm just trying to turn a 20 million investment into 100 million and call it a day. I'm a small guy. But the genuine comment is mixing infinite demand for intelligence. And let's assume it's just the enterprise now, because I think you are right. The consumer side is super interesting, especially for OpenAI. But let's leave it to the side because we can only do one thing at a time.
I think the question where it does matter is, right now, the assumption is 70%, 80% of that demand gets channeled through Anthropic and OpenAI. In other words, because there are 70% of the Google compute backlog, 70% of the Amazon backlog. So, in the short term, the market is assuming that OpenAI and Anthropic buy the compute, buy all the stuff that's further down the stack, buy the chips, and resell that intelligence on a frontier model basis to US enterprises. And if it doesn't happen that way, there's going to be a pretty big dislocation. Yes. It's perfectly possible that there is a public market dislocation because the players at the table might change.
And that's great. That's called a buying opportunity, because that doesn't take away the infinite demand. It's extremely possible that perhaps this wonderful company called Moonshot, which we talked about three seconds ago, could be the model of choice, and that somebody is going to take that compute, which is not going to be used by Frontier LLMs, and put Moonshot on it and sell it to enterprises at $0.10 on the $0.10 tokens. Yeah. Moonshot is happy. NVIDIA is happy. Enterprise is happy. OpenAI, very, very sad. You're right. That's the dislocation. But the question becomes, which ones of these are the markets going to support?
Is the market going to give you infinite capital to be able to build the compute because they believe you're the anointed winner? Or does the market believe that you're running it differently and it wants you to run differently? So I don't think the demand goes away. I think in all these conversations, one variable goes away when we run into this technology shift, infinite bull market. We take execution out of the picture. Yeah. Doesn't matter. Every chicken manure company and every nuclear reactor company who says the words in PowerPoint is going to get funded by everyone because they assume flawless execution.
And you look around, and then poor Jason is looking at SaaS companies and saying, holy shit, some of them are not executing as well as the others. So eventually, execution matters. And that's where we decide the winners and losers in the market, not the shift of which intelligence is the best. The best example of that would be two years ago, OpenAI was first and Anthropic was second, and now Anthropic is first and OpenAI. And Google was written off. And Google was written off. Yeah. There was a show point that Gemini was non-existent. Google was written off. Now suddenly Google has the compute, the cloud sales, and Gemini. Yeah.
But still not the amazing open frontier model. Still not the coding agent. There's still not the coding agent. Your customer support agent is going to be extremely unhappy because he didn't get a chance to answer it using the best model. Just kidding. Well, to the guest's point, Harry kicked this off by saying, have we ever had an ecosystem so dependent on the success of OpenAI, Anthropic? It is. But maybe to Nikesh's point, so much has changed since we started the show. When we started the show, it actually seemed like everyone would benefit because average intelligence, or whatever term Nikesh would use, would permeate software. And that would be good enough.
That is now the front that we've... this is the revenge of the frontier, right? We may not care in a year what model we like. We need frontier models. We need the best, but we may not care who wins. We may not care who wins this battle. This may all blow over, and all may be about compute. Whoever wins wins. Whoever wins, I'll plug in. Jason, I think the models will get better and better. And the distinction between models may not be enough for you to decide to rip one out. I think the part which we are starting to build, and we will be building for the next three to five years, is context. So think about it for a second.
When I run a simple firewall company, or a simple complicated firewall company, you can stick any model you want. The model doesn't know why my customer's infrastructure is down. It does not know. Because my model doesn't know what product my customer is using. My model does not know what operating system it's using. My model does not know what the configuration of the customer is. My model does not know why this happened the last five times as a customer. All that knowledge, all that learning, is being captured by me in effectively vector DBs and in context learning systems. And that's what my team is doing. I have more people collecting context than I've ever had.
It's kind of like the Waymo thing. I've got people planting, saying this is a tree. This is why it goes down. So as I build that organizational context, then I can stick any model I want on it. And the model distinction will not matter because the context will become as important or perhaps more important. you can stick any model you want. The model doesn't know why my customer's infrastructure is down. It does not know. Because my model doesn't know what product my customer is using. My model does not know what operating system it's using. My model does not know what the configuration of the customer is.
My problem model does not know why this happened the last five times as a customer. All that knowledge, all that learning, is being captured by me in effectively vector DBs and in-context learning systems. And that's what my team is doing. I have more people collecting context than I've ever had. It's like the Waymo thing. I've got people planting, saying this is a tree. This is why it goes down. So as I build that organizational context, then I can stick any model I want on it. And the model distinction will not matter because the context will become as important or perhaps more important.
And you're clearly 100% tracking, Satya, with the Microsoft comments recently on companies. And it makes it into it. Enterprises need to build their own value, build their own context, rather than do it in front of your model. Right? And that's... Well, I think it's, yes, he's saying something different. I understand what he's saying. That's a different comment. Mine is a different comment. I think there's three parts to it. There is the model, which is the raw intelligence. Let's just call it that. There is the context needed to answer your queries or needed to answer your problems. And then there's the context needed to train that ecosystem.
I'm talking about the context needed to train the ecosystem, which means I've got every customer case that ever happened at Palo Alto getting transcribed. So my model knows what is a good answer and what's a bad answer. Right? And what is your model? What core model will you start to build all this context on, do you think? Or have you decided? I remember calling Thomas Kurian at Google when the whole thing just started, this shiny object called LLMs. And I said, hey, do I need to build a cyber model? He's like, dude, over time, what's going to happen is the models are going to get smarter and smarter, and small models will not be as smart as the big models.
And he was right. The small models are more intelligent than the big models. Now, at some point in time, if your average intelligence becomes smart, which is what I said, then the distinction between little more intelligent, less intelligent is less important than knowing the domain and the context. Got it. So I think we're coming to a world where, in the next five years, domain becomes equally important with the model intelligence. And I think Satya is saying something different. Satya is saying you can't parse every problem into multiple models without carrying the context to the model to give it enough context to get the answer. So he's giving an architectural point.
Yeah. Because he's saying, put all the context in a harness which is sitting beside the model, which I provide, and then use whichever model you want and commoditize it. Every model company is saying, no, I'm going to only make my model smarter, the context, because otherwise they get commoditized. So I think that's a bit of a commodity, commoditization battle that's going to happen between model and models plus context. But, Nikesh, just on that, but you also said something not in conflict to it, but really, so you've got all your intelligence in your vector database or whatever it is, all your context, right? And then you can pick and choose your LLM on top of it.
But as you said, the LLMs don't perform the same, even Opus 5 and Fable and in Palo Alto Networks, you have a team that can manage that, right? Those changes. No, we're learning as we go along. So you're learning. What about the average enterprise that doesn't have as strong a team as you? How can you really switch out these LLMs, even if all the context is in your vector database, and have confidence that the results will be the same? Bending spoons. Bending spoons. Bending spoons. None of the above. But I mean, it's a Darwinian moment. Yes, I got it. I got it. Darwinian moment does not suggest that everybody survives. I understand. Now I understand.
In other words, what you're really saying is if we don't figure this out, we will be working for the Italians. So we're going to figure it out. Got it. I love the way I went to private markets to get the private market discussion, and the straightaway discussion is, well, it depends on what OpenAI and Anthropica are willing to pay for it. And it goes back to that. And it's just funny how everything just rotates back to compute and what the big buyers are willing to pay. And you're right, Ari, because I pushed on why we always talk about just the same two companies, but we internalized that no matter what you talk about, you end up back
talking about them because they're, to negotiate, they're the giant sucking sound on demand that's just pulling everyone along all the way up and down the chain. Right? Which is why I think you're correct. And that's why we're all focused on the poster charts of the trend. But I think the trend is bigger than the poster children. Yeah. AI and intelligence is bigger than OpenAI and Anthropica, is what you're saying. Yeah. You are right. And enterprises are going to want to consume it a lot. And if it's, but the structure, the change, if it turns out to be 70, 80% beneficially thought of in OpenAI and Anthropica, there will be a pretty significant dislocation up and down.
Right? I mean, I think you've covered- I think it's a $1 billion company at one point in time. And two years later, I joined Google as a $14 billion company. Good call. You're a good stock picker. We have former Mr. Google, we touched on Microsoft and Google being told to, what was it? You have to dance like two or three years ago, whenever it was. We obviously had all of them coming out saying, CapEx, we're going to keep spending, and baby, it's going up. How did we analyze the results and the reaction from them? Obviously, cloud was an acceleration from both Microsoft and Amazon. Really incredible numbers. How did we analyze this?
Roy, do you want to set context in any way? You often like to set context in a way that- Yeah, I mean, it's not that hard. I mean, you had four people report that would be relevant here. You had Amazon, Google, Microsoft, and then Meta, right? And the big picture is the people who have a business selling cloud inference all had an amazing quarter. I mean, Google Cloud, the smallest, grew 82%. AWS grew 37% at scale.
It's always hard to know at Microsoft because they bundle a bunch in, but they grew 20%, 30%. So the big picture comment is people sold a shit ton of inference, right? And because of that, people said, I'm going to buy a lot more compute because it appears that I can turn compute into money. The CEO of AWS in particular made a very declarative, the ROI, he was amazing. And the market was really happy. In particular, Amazon and Microsoft got marked up pretty significantly. And then, by contrast, Meta also said, I'm going to spend a lot of money, but it wasn't as obvious how they're going to make money. So their stock went down.
Probably the most surprising thing, going right back to the layer thing, is, God, those were really strong numbers. I mean, all these people are selling a shit ton of compute. I mean, these are $400 billion run-rate businesses, plus or minus, in total, and they added 30%, which means 100 billion more a year of revenue across these four companies in compute. It's just the scale of the things you can lose sight of. That was, for me, the big aha. Will it persist? Who the hell knows? We can talk about that again. But the facts on the ground, the new information in Q2, was bullish. That was my take. Well, I think we already hit that. To me, maybe it's perpendicular,
so I don't want to take us off track. But to me, the Palantir, which just happened, was more interesting, right? I mean, growing almost 100%, right, in bookings, up 153%, backlog. I mean, you can sell this AI. Yeah. What would you, Jason? What should we take from that? Like, hey, enterprises need help with it. Palantir is the best. I think what we should do is send it to our portfolio companies and tell them to work harder because there's no excuses. I mean, if Palantir can do it, they came back from 15% growth four years ago. Why can't you do it, kids? Work harder. Work harder. We can talk about that again.
But the facts on the ground, the new information in Q2, was bullish. That was my take. Well, I think we already hit that. To me, maybe it's perpendicular. So I don't want to take us off track. But to me, the Palantir, which just happened, was more interesting, right? Growing almost 100%, right, in bookings, up 153% backlog. You can sell this AI. Yeah. What would you, Jason? What should we take from that? Hey, enterprises need help with it. Palantir is the best. I think what we should do is send it to our portfolio companies and tell them to work harder because there's no excuses. If Palantir can do it, they came back from 15% growth four years ago.
Why can't you do it, kids? Work harder. Work harder. I don't know what the message is. Certainly, certainly, to Nikesh's point, I'd love to hear Nikesh's thoughts. If you can package and capture intelligence, right, the demand is inexhaustible at Palantir, right? And you can also capture somewhat model-agnostic intelligence. At some level, Palantir is a very sophisticated harness on top of massive amounts of data, right? And maybe vectorized databases. Nikesh's point. I might be wrong or oversimplifying it, but they've captured that to a magical element in the age of AI. People need to solve these problems with data. They need answers.
And Palantir gives, I think, less than a thousand customers, right? A thousand forty-nine customers. They're giving eight billion dollars' worth of answers, growing 100%. These thousand customers will pay almost anything to get these questions answered with AI. They'll pay almost anything. We're in a CapEx cycle. There is a trillion dollars of CapEx that has been committed for the next one year across all these people, broadly speaking. And the market is saying, great. I see these large companies, which have the ability to fund this trillion dollars of CapEx. And there are signs that they're getting compensated for some part of the CapEx that's out there.
Now, whether that's because of higher price being commanded, people demanding deployment of AI and deployment of cloud, this is good news. That CapEx dislocation is not happening today. That could happen tomorrow if some of these people committing capital are not able to show up with the capital. But for now, we have one more run at the roulette table. So that's what's happening. We're being shown that this market is going to support CapEx until it can't. I think it's a bit of a gold-rush moment. I think every consumer app will get rewritten in the next five to ten years. Why would I not have my agent talk to my DoorDash app or Uber app?
Why do I have to go to every one of them and click seven times and have it have no context of learning? If you talk about constant learning and agent. So everything is up for grabs. Every consumer app that was ever put from the iPhone has to be redone. Every enterprise app in SaaS you just debated has to come back with, I have an opinion. So the demand, the construction, the work that's needed is humongous. Let's take that for granted, that that's going to happen. This market is proving that. I think until the market can keep funding it and the timing works, I think the biggest problem we have right now is the timing problem.
Will the revenue show up fast enough to keep funding the CapEx cycle, or is there going to be a dislocation in CapEx versus outcomes? Now, the telecom industry is very used to this because they used to spend billions of dollars building 3G, 4G, 5G, and then they'd see the rewards would come later. So they went through a CapEx cycle, and that's pretty established in the market. It looks like we're going through this compressed version where CapEx and revenue have to show up pretty close to each other because the numbers are just way too big to be funded by speculators for long periods of time. So I think that's what we're seeing.
And that's why this brings back the whole open and tropic debate. It doesn't matter if they show up with the money or not. Somebody will show up because there's enough demand. I think the next dislocation could happen in the supply of compute. You can bring all the CapEx to bear, but to your point, the Valors may not get their regulatory set of approvals. Europe may not allow data centers. You may find 30 states with picket fences that say no data centers in my state.
So there's a supply problem that happens on the compute side, which could have a knock-on impact on all our infrastructure buddies in the semiconductor space, saying, Holy shit, it doesn't look like all the stuff they're building is going to go out as fast as we thought it was going to go out. I think that's where we are at the market mechanics level. I don't think there's a demand problem. I don't think there's a jobs problem. I don't think there's an appetite or intent problem in terms of all of us wanting to rewrite this stuff. And I think, to Jason's point, why not? Palantir is at the party. They also are saying, I can package intelligence, make sense of it for you.
You don't have the capability. You don't have the resources. Let me make sure you don't become extinct in this wave of technology. I'm going to go back, in 1997, 98, 99, when we saw the last big, pivotal technology called the internet, a lot of the characteristics were similar, except you just didn't need a trillion dollars a year to keep building the internet. I think the interesting thing, as I play with all the comments you've made, is the odd thing is, if the most likely failure mode is not ultimate demand, and I agree it isn't, but just an enterprise ability to digest at speed.
Then to some extent, and I think Gavin Baker made this point, to some extent, if enterprise can't digest fast enough, then to some extent, if the spend slows down because they can't get it online quick enough, it may be timed perfectly with the enterprise ability to digest. Right? And if... Or the better digesters will win, and the poor digesters will have heartburn. No, that is an interesting point, is that that's all that data that says the companies that are digesting AI quickly are growing faster than companies that are not. So you are...
And I think that's not true in everyone, but my guess is, to your point, if, for example, you're playing in finance and your competitor is using advanced LLMs and you're not for trading or whatever, at some point, you will be bending spooned, to use your point. Yeah. Those may be lewd. They may not have been bending spooned. Yeah. Though the other thing is... Nikesh, in your next analyst call, can you do an ode to us, where when you get a shithard question, you just say bending spoons and just drop the mic?
I think you'll find, Harry, that when you're worth 280 billion dollars or whatever enormous market cap this man has, you're not paid to joke on the earnings call, Harry. You're paid to look down the line and deliver the product, and that's how you keep your job. Rory, if you hadn't figured, I ain't here to bring IQ to the conversation, okay? Yeah. That's a briefcase. Yeah. Do you think more established enterprises can process this rate of change infinitely? Do you think they've changed permanently? Because it's so much...
What I've found with a lot of vendors now is, for example, the last year, they've made one-year commitments, or before it might be three or five or seven, right? And they're like, well, the world's going to change so much. I want to see what agents and what AI products, that's totally rational today. But most enterprises traditionally, you can't rebuild your whole stack every eight to 12 months. It's destructive on the org. But your point is that that's a skill to win today. Do you think that's changed? Do you think we'll revert to the mean, where we can only process change every five years after we get over a hump? What are you seeing?
I think the enterprise's ability to absorb this or digest this or perhaps leverage this to their advantage depends on their ability to create training data as fast as they can. And I think not enough people are focused on training data. This is not a problem Palantir can solve for me. This is not a problem that Fireworks can solve for me. This is a problem I have to solve. I have to parse through freaks. And I'm sorry to go back to the same thing. I get 400,000 customer cases a year. I know when they come in, I don't have enough context. Some human beings solve it.
that's a skill to win today. Do you think that's changed? Do you think we'll revert to the mean where we can only process change every five years after we get over a hump? What are you seeing? I think the enterprise's ability to absorb this or digest this, or perhaps leverage this to their advantage, depends on their ability to create training data as fast as they can. And I think not enough people are focused on training data. This is not a problem Palantir can solve for me. This is not a problem that Fireworks can solve for me. This is a problem I have to solve.
I have to parse through freaks. And I'm sorry to go back to the same thing. I get 400,000 customer cases a year. I know when they come in, I don't have enough context. Some human beings solve it. I don't know how they solve them. I don't know what logic they apply, but they solve them. I need to get into the brains of those people who solve them and abstract, extract all that knowledge, and codify it so that I can write my own playbooks and rules as to how to solve the problem the next time it shows up.
So I've told my team, every new phone call, every new case is a learning opportunity. It's not just to solve it. You have to learn. So you have to go into this learning mode as enterprises. You should never let your VP of finance just decide. You should say, every time the VP of finance reaches a conclusion, you have to surface it to the human called Jason and say, no, dear VP of finance, book it, because we booked every transaction.
So you have to give the organizational knowledge to some learning system that you have to build. And I think that still is going to take three to five years, every enterprise, every use case. And I think that's what we're not paying attention to.
I think the same thing applies to SaaS companies. They all have to go rebuild their stacks, but not just the stack. The stack rebuild is the easy part. Now, can I string along? I'm pretty sure Fireworks will take my money and fine-tune an open-weight model for me if I want, and keep training my use cases to a point. But beyond that, how do I get from 70% accuracy to 99% accuracy? That's the problem. The problem is, I don't know which 30% is inaccurate, so everything is useless.
It's funny, your point on learning. I was literally just trying to make sure I got the quote right. But there's the Darwin quote that said, it's not the strongest of the species that survives, or even the most intelligent, but the one that's quickest to learn. And I think you are right about that. Doing what it takes to digest it quicker will be the key management skill in the next five or 10 years.
I think what Palantir is selling, and you're right, maybe it's not a full set. I think the reason they're doing so well is they're able to say, dude, we know this is the biggest problem, Mr. CEO. I at least have some kind of answer here. Let me help. Give me $2 million. It'll be great. Look, I think, don't necessarily underestimate what Palantir might be doing. There is a capability that AI has already demonstrated, which it can troll large corpus of data, summarize it, look for anomalous behavior, look for trends, capture them, reason around them, and reach conclusions.
Now, the good news is, if you're doing any kind of offensive work, if you're looking for amazing insights, it could troll through petabytes of data and produce 20 amazing insights. And you can go judge them and say, well, 15 of them are okay and five are amazing. But the five that are amazing will change my ROI and give me 200 basic points on my top line and improve my margin by 100 basic points. Hallelujah. You just paid for everything. You don't have to put a learning system into place, nothing. It's just taking enterprise data and doing a lot of that stuff. And I think places like oil discovery, or nation-state analysis, or a whole bunch of stuff where lots of people are required to go through this and write code, doesn't need to happen anymore.
Totally. Agreed. We have final one. We have new CEO at Scale AI as an option. They hit a billion and a half in ARR. We mentioned the importance of data there. Obviously, Scale AI would be one of the biggest providers of data. We have Mailchimp revenue declines for eight straight quarters. Fuck me. That's not a nice headline, is it? Rory sells DroneDeploy to Procore for $900 million. Go, Rory. 13-year journey, amazing outcome. We have Visa cutting 2,600 jobs. Nikesh, you said it's not a jobs problem. Well, CEO of Visa says it's efficiency and shaping the way work gets done. So 2,600 people gone there. Whatnot raising at $20 billion.
Can I ask Rory about DroneDeploy? Because it ties at the beginning of the conversation with deals and Nikesh, right? So that deal, what's interesting, so DroneDeploy was bought by Procore, right? Great classic software founder, founded by 2E to do software for real estate, dominated it, had a great run, right? Growth slowed, right? Most importantly, net new customer count stopped growing. Growth slowed to 17.
So they make a big bet. And I'm not an expert on DroneDeploy, obviously Rory is, but they buy a next-generation platform, right? To use drones to accelerate this construction industry. And structurally, what's interesting, and I find these deals are always really stressful, okay? So Procore market cap is beaten down. It's got to come up with $900 million, a lot of it debt, pay 11, 12x while it's trading at 4. I find in the old days, I'm not saying that happened here, these deals are stressful, man. They are. It's not Palo Alto Networks spending 0.01% of its market cap on some smart kids. This is bet the farm at a much higher revenue multiple. Doesn't have to work, but man, this is the big bet, right? And it wasn't cheap. I mean, you'll say it's cheap because you're on the board, right? But Procore is going to think this is expensive to pay 12x when it's trading at 4x, right? And we started this on deals with Nikesh, and we started this on whether 3x to 4x for Airtable is a lot. Well, Procore is one of the ones trading there too.
So is this deal super stressful? Did you lose hair? Were people shouting and throwing things through the window? So I'm not going to speak for the acquirer because I'm not on that side of the room, but genuinely, one of the least stressful deals I've ever done, because honestly, I would have been happy to continue. This was not a founder tired.
I mean, I think actually some of the interesting lessons, there's about two or three interesting lessons here. First of all, when you have capital discipline and modest fundraisers, you're set up for success, not failure. None of, you know, we always raised below the price we sold at. We didn't raise a ton of money. We were profitable. We just, you know, it was a fine little company growing nicely.
And then the second thing is, I think it's really important, the trend was our friend, not our enemy. I think some of these very basic SaaS companies, you look back and go, there's been a platform shift and you're on the wrong side of it. When you're software that's enabling drones and robots, you're actually on the side of the future. And in fact, one of the lessons I learned having invested 10 years ago is, in the physical world, AI takes a lot longer to happen. I mean, when it happens, it's amazing, but it's clearly, you know, I look back 10 years ago, I thought drones would have exploded five years ago. They're really starting to explode now, as are robots. So it took a long time.
So in fact, we were on the upswing of this. It feels really good. We're happy to hold. And then obviously we got an offer that made us do different. I don't want to comment on specifics of the offer, but I think one of the ahas here is building companies is hard. And, you know, by being disciplined, by putting ourselves in position, the founding team did an amazing job, three founders all together, all still widely actively involved.
So no, it was genuinely not a stressful thing at all. It's like, at the right price, you'll do this deal. At another price, you won't. And for what it's worth, from a distance, I think it's interesting, super interesting for the other side too. I think actually market expansion is what you need to do in some of these spaces. You need to say, and probably Nikesh has done these kinds of big strategic where you just say, hold. And then obviously we got an offer that made us do different. I don't want to comment on specifics that he offered, but I think one of the ahas here is building companies is hard. And
by being disciplined, by putting ourselves in position, the founding team did an amazing job, three founders all together, all still widely actively involved. So no, it was genuinely not a stressful thing at all. It's at the right price, you'll do this deal; at another price, you won't. And for what it's worth from a distance, I think it's interesting, super interesting for the other side too. I think actually market expansion is what you need to do in some of these spaces. You need to say, and probably Nikesh has done these kinds of big strategic where you just say, I need, my thing is this big, I need to add the next thing my customer wants. And I think
at some level, the customer wants not only to be told the accounting of his business project, but also the physical progress of his building project. And that's what things like physical inspection do. Nick Match, what percent of market cap does a deal become a BFD, a big deal, a core strategic, this needs to work? Nikesh, look, every deal needs to work. We're not buying companies because of money to spare, or my shareholders think we should rely on waste. And not, I think the hit rate requirement in us is more than a VC. I think in the last eight years, we've bought north of 40 companies. And I want to say 75% have worked, 25% haven't. Our largest deal
was a $28 billion deal, which probably is currently valued at north of $50 billion. That one's got to work. That was a career-defining move. If you take a company at $28 billion, when your market cap is $200, and you spend 14% of your market cap or 16% of market cap, and buy something, it better work. Now, when you make that work, then you can, the market gives you credit for making deals work. I said that in my earnings call, and they got all freaked out. And then I'm just saying, you have to make the big ones work. If you don't make the big ones work, then you lose the license to run your business. And that big one was CyberArk, right? Yes. Yeah, that was great. Got it.
The rumor has it, agents are going to be important. If agents are important, they're going to need identities. They need to be treated like privileged identities. So that's our thesis. It's simple. Like all the best deals. One of my partners always said, if you can't express it in a sentence, it's probably a bad deal. And if you can, it's probably a good one. Got it. As you've seen with my example, you don't know what these agents are going to do, man. In your case, you're just going to restrict agent behavior, Jason. But they're so good. But man, they're so good. But it's so powerful.
Yeah. Jason, to the point when, actually, it ties back to what Nakesh said. I just was reading some stuff last night that really does a quote to what Nakesh said. Some of them made the point, if you can't express what they're doing, you have to be very clear on who they are as an identity and where they're allowed to go. If you've got this, as I said, this AI employee, and you're not quite sure what they do, you've just got to bound the systems they can access very tightly. So I actually think, I totally get your point, Nakeh, that the ability to… The only danger is if you take it to the extreme, that's called automated workflows. That's
deterministic outcomes. If it's deterministic outcomes, we already had that technology for the last 20 years. So the question is, at what point in time do you let an agent think? Do something. Yes, that's the big debate. But the flip side is, it does a really good job. And listen, we don't have the perfect security profile, to your point, right? But even with what we have, which is probably one agent has about a thousand rules, to your point, right? The rest probably have five, right? Or zero, right? But even with the zero to five, 99% of the time today, right? It's pretty, since January,
since the model's upgraded, pretty darn good in the last couple of months, really good, right? So it's a trade-off, right? It just takes one destructive example to make it all unwind. If you give it access to a bank account, let's see what it does. If you have even your finance allowed to write checks, I might want to have a conversation. Speaking of people being wrong, I'm going to say I was totally wrong on something. Scale.ai, the fact that they've continued that business, I would have taught the acquisition left them a husk. But I think it proves one of those rules that you know, but you forget,
which is when you're in a great market and you have a product that can meet that need, even losing your top people, it's all fine. They were selling data products to an insatiable demand for data. And I give them huge credit. They kept the thing going. Hey, Winsurf sold to Cognition, right? Yeah, exactly. All these stub deals are working. But they sold really quickly. Both sides sold quickly and then value. This is even more impressive because they were kind of, and I even called it a husk a year ago. They were left like a husk, but I was wrong. They built a business out of that. So all credit to them. Grok could be the next one too. What? Grok could be the next one too.
Yeah, you're right. The remaining Grok. You're right. Yeah, they're doing, they're offering hosted inference with their technology. Yeah. No, I mean, in the face of insatiable demand, all things are possible. Grok could be the next one. That's the aha. That's a good quote, Rory. Now, Nikash, do you see why I go home early from dinners? Because I need to be fresh for podcasting. You see, this is hard. This is hard work. You builders building enterprise value in your public companies. This is where the real grind is. He's sitting there going, he's doing this eyes closed, and he'll go back to making his $280 billion market cap company work later.
Great. It's gotta be built one deal at a time, my friend. Totally. This is the enterprise is 1% inspiration, 99% perspiration. No, I do. I do not. That's what I tell my agents every day, guys. Agents don't sweat. Get to work. Stop it. Get to work, boys. Perspiration. Nikash, it's been fantastic, dude. Thank you so much for having it. Thank you, guys. Fun being with you. Yeah, I really appreciate the time. Thank you. Amen. Amen.
Amen. Amen. Amen. Amen. Amen. Amen. Amen. Amen. Thank you. Yeah, that was great. Got it. The rumor has it, agents are going to be important. If agents are important, they're going to need identities. They need to be treated like privileged identities. So that's our thesis. It's sort of simple. Like all the best deals. One of my partners always said, if you can't express it in a sentence, it's probably a bad deal. And if you can, it's probably a good one. Got it. As you've seen with my example, you don't know what these agents are going to do, man. In your case, you're just going to restrict agent behavior, Jason.
But they're so good. But man, they're so good. But it's so powerful. Yeah. Jason, to the point when, actually, it kind of ties back to what Nakesh said. I just was reading some stuff last night that really does a quote to what Nakesh said. Some of them made the point, you know, if you can't express what they're doing, you have to be very clear on who they are as an identity and where they're allowed to go. If you've got this, as I said, this kind of AI employee and you're not quite sure what they do, you've just got to bound the systems they can access very tightly. So I actually think, I totally get your point, Nakeh, that the ability to…
The only danger is if you take it to the extreme, that's called automated workflows. That's deterministic outcomes. If it's deterministic outcomes, we already had that technology for the last 20 years. So the question is, at what point in time do you let an agent think? Do something. Yes, that's the big debate. But the flip side is, it does a really good job. And listen, we don't have the perfect security profile, to your point, right? But even with what we have, which is probably one agent has about a thousand rules, to your point, right? The rest probably have five, right? Or zero, right?
But even with the zero to five, 99% of the time today, right? It's pretty, since January, since the model's upgraded, pretty darn good in the last couple of months, like really good, right? So it's a trade-off, right? It just takes one destructive example to sort of make it all unwind. If you give it access to a bank account, let's see what it does. If you have even your finance allowed to write checks, I might want to have a conversation. Speaking of people being wrong, I'm going to say I was totally wrong on something. Scale.ai, the fact that they've continued that business, I would have taught the acquisition, left them a husk.
But I think it proves one of those rules that you kind of know, but you forget, which is when you're in a great market and you have a product that can meet that need, even losing your top people, it's all fine. They were selling data products to an insatiable demand for data. And I give them huge credit. They kept the thing going. Hey, Winsurf sold to Cognition, right? Yeah, exactly. All these stub deals are working. But they sold really quickly. Both sides sold quickly and then value. This is even more impressive because they were kind of, and I even called it a husk a year ago. They were left like
a husk, but I was wrong. They built a business out of that. So, you know, all credit to them. And, you know, um, Grok could be the next one too. What? Grok could be the next one too. Yeah, you're right. The remaining Grok. You're right. Yeah, they're doing, they're offering hosted inference with their technology. Yeah. No, I mean, in the face of insatiable demand, all things are possible. Grok could be the next one. That's the aha. That's a good quote, Rory. Now, Nikash, do you see why I go home early from dinners? Because I need to be fresh for podcasting. You see, this is hard. This is hard work.
You builders building enterprise value in your public companies. This is where the real grind is. He's sitting there going, he's doing this eyes closed, and he'll go back to making his $280 billion market cap company work later. Great. It's gotta be built one deal at a time, my friend. Totally. This is the enterprise is 1% inspiration, 99% perspiration. No, I do. I do not. That's what I tell my agents every day, guys. Agents don't sweat. Get to work. Stop it. Get to work, boys. Perspiration. Nikash, it's been fantastic, dude. Thank you so much for having it. Thank you, guys. Fun being with you. Yeah, I really appreciate the time.
Thank you. Amen. Amen. Amen. Amen. Amen. Amen. Amen. Amen. Thank you.