20VC: Airtable Sold for $1.285BN | Leo Achenbrenner's Situational Awareness Blows Up | Moonshot AI Raises $3.5B at $35B | Anthropic Model Breaches Three Companies' Security | Big Tech Earnings: Why Palantir Beat The Rest

Special Guest: Nikesh Arora, CEO @ Palo Alto Networks. AGENDA: 04:50 Airtable Sold to Bending Spoons for $1.285B 17:00 Leo Aschenbrenner's Situational Awareness Blows Up as Citadel Buys His $16BN Book 22:30 Anthropic's AI Models Breach Three Companies as Cyber Threat Accelerates 33:35 Moonshot AI Raises $3.5B at $35B as Chinese Models Crush AI Prices 38:05 Valar Atomics Triples to $6B as Sequoia Bets on Nuclear Power for AI 42:50 OpenAI and Anthropic Could Trigger a Massive Public-Market Dislocation 52:55 Big Tech and Palantir Earnings Ignite the Next Phase of the AI Gold Rush 1:04:30 Procore Buys DroneDeploy for $900M in a High-Stakes 12x Revenue Bet 1:10:55 Scale AI Hits $1.5B ARR After the Meta Deal Left It for Dead

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

• 0:00Speaker 00

  Absolutely right, on trend, absolutely wrong in 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 run, but it appears it wasn't hedged. And we lost all our money in a week.

• 0:14Speaker 01

  In the long term, average intelligence is going to be free and the average intelligence will get smarter. Land, permits, energy, compute. This is the thing that is going to get priced for the next three to five years. Yes, it's perfectly possible that there is a public market dislocation because the players at the tables might change.

• 0:34Speaker 00

  Yeah, Moonshot is happy, Nvidia is happy, Enterprise is happy, OpenAI very, very sad. You're right, that's, that's the dislocation. God, those were really strong numbers. I mean, all these people are selling a shit ton of compute.

• 0:47Speaker 02

  The Palantir can do it. They came back from 15% growth four years ago. Why can't you do it, kids? Work harder.

• 0:53Speaker 01

  I think it's a bit of a gold rush moment. I think every consumer app will get rewritten in the next five to 10 years.

• 0:58Speaker 00

  In the face of insatiable demand, all things are possible.

• 1:01Harry Stebbings?

  This is 20VC with me, Harry Stebbings. It's my favorite show of the week. Rory O'Driscoll, Jason Lemkin and whoop, whoop, whoop, hold up. Nikesh Arora joins us in the studio. Oh yeah, baby. Palo Alto Networks, $280 billion company CEO joins us in the studio for this incredible session today. And we discuss Leo Aschenbrenner'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 does this security problems end? That and so much more in this incredible conversation with three of my favorite people.

• 1:42Harry Stebbings?

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• 2:22Harry Stebbings?

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• 3:03Harry Stebbings?

  Honestly, it feels less like a recorder and more like an AI powered like brain memory system. And the crazy part is the hardware itself. The Ploud Note Pro is literally as small and thin as a credit card. So it's just always with you when something important comes up. There are already more than 2 million founders, operators, investors, consultants and professionals using Ploud to stay organized. So think more clearly and stop losing great ideas and important details. Go to ploud.ai/20vc and use the code 20vc for 10% off. That's P L A U D dot A I slash 20vc and use the code 20vc for 10% off. While Ploud captures the conversation, Finn helps continue it.

• 3:46Harry Stebbings?

  As AI agents become more common in customer experience, teams often end up juggling multiple siloed tools for every job. Well, Finn was built to change that. It's a single unified agent that works across your entire customer experience from service to sales to success and beyond. Finn is the agent making perfect customer experiences possible for thousands of customers. It's powered by custom models, trained on years of real customer interactions. So it understands the nuance and complexity of customer service better than any other agent. That means faster resolutions, more consistent support and just better experiences for every customer. It's also designed to be fully self manageable, so you can easily improve and adapt it as your business evolves. No third parties required.

• 4:30Harry Stebbings?

  Leading companies like Gamma, Asana, DoorDash and Crypto.com already use and love Finn to deliver better customer experiences. So for a limited time, you can get $500 a month in Finn credits for your first three months. Learn more at fin.ai/20vc. You have now arrived at your destination. 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.

• 4:59Speaker 01

  I am a little apprehensive watching Jason and Rory there in their full glory. So let's see how this sort of plays out for.

• 5:07Harry Stebbings?

  We have Professor O'Driscoll in the hall. 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, European based Bending Spoons. The company was doing $485 million in ARR, growing 20% year on year, ultimately at a $1.285 billion acquisition price. 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.

• 5:40Speaker 00

  Well, you know, we're all going to do the Airtable side of the analysis, but just worth pointing out the Bending Spoon side. I'm buying stuff at 2.8 while I'm trading in the market at naught 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. You know, obviously on the Airtable side, you know, there are 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?

• 6:24Speaker 00

  But of course, we all anchor off the 11 billion 2021 price and it feels like a low ball. But I think it's, it's still a great value creation achievement. And, you know, we have a talented entrepreneur on the show with us and we need to start with that. It's a great outcome.

• 6:37Speaker 02

  You know, can I ask Nikesh one particular question on it, Harry, if it's okay? Cause I have a lot of interesting thoughts on this deal, whether Airtable matters in the age of agents and AI. But I mean, Nikesh is also one of the best deal makers 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? That's the, I just assumed someone would outbid them.

• 7:07Speaker 01

  And you're asking me that because I'm like.

• 7:09Speaker 02

  You're the deal, you're a dealmaker par excellence. We just happen to have the king dealmaker on the show.

• 7:16Speaker 01

  Yeah, look, I, you know, I, I met Howie a few times, a great guy, he's done, built a great business. I think there's a bit of founder fatigue here. He's been through a lot of sort of ups and downs in terms of sort of internally and in the market. But I think the broader question which Jason hits on right is, you know, what is going on in the SaaS marketplace? Is this a pricing dislocation or is a 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. You know, and that's, I think, 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.

• 7:59Speaker 01

  So I think that you might be caught in the sort of demand and supply problem right now. They, you know, they have a lot of inventory.

• 8:04Speaker 02

  It just worries me because you see more deals than we do on the acquirer side, right? I mean, we see it on the, on the target side. It's just.

• 8:11Speaker 01

  So we stick to long-term, unprofitable, currently unprofitable, long-term growth businesses. But yes, I see what you mean.

• 8:17Speaker 02

  I mean, when Francisco Partners raised 22 billion to do deals sort of like this, right? You know, Tomo Bravo is like, we're looking for AI infused B2B companies. You know, we could pick an Airtable, but it, 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% at 500 million in AI workflows isn't nothing. I would 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. Like the, they should have leaned in on this one, not the one at 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, right, are sort of there as an attractive target and yet no one, no one outbid them.

• 9:00Speaker 01

  Well, look, Bending Spoons did buy them, so clearly somebody saw value there. Not everybody's seen 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 every day from an operator perspective. I say to them, you know, 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 I were 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? Cause 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 of the future and we'll be busy putting lipstick on the pig.

• 9:43Speaker 02

  It's a tough, it's a tough one, right?

• 9:45Speaker 00

  Cause I didn't feel, Jason, that this was the kind of category that PE would sweep up. Cause you're kind of merging the two sets of comments. One is, yeah, you're trying to add some AI pixie dust, but you're fundamentally your core productivity app. And you've been a proselytizer, Jason, that, you know, oh my God, look at all you can do at lovable. If I wanted to build my own CRM and I wanted to be personalized, well, 10 years ago, I might've 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, Replod or Claude Code and just bang it out from scratch. So.

• 10:19Speaker 01

  If you want to do that, I'd call you a person with no, low imagination. There's so many other Google things to build.

• 10:25Speaker 00

  Yeah, I totally agree, totally agree. And I, yeah, and I have a person with low imagination and I come to that. But I agree, but the, 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, you know, just like Evernote, it's like the people who have stuck with this product are 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.

• 11:11Speaker 02

  Yeah, or more, right? Just double the price and your data is locked for, for two years, right?

• 11:15Speaker 00

  You know, 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.

• 11:21Speaker 01

  I think their stomachs might be full. I think it's unfair to say.

• 11:24Speaker 00

  Yeah, that's exactly right. As I often say to people when they show me a turnaround deal in my business, where I said, look, if I wanted a shitty turnaround deal, all I have to do is look in 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. Anyone in PE has done five software restructurings in the last 12 months. They need a six like a hole in the head.

• 11:52Speaker 02

  Nikesh's points, I mean, both his 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. Like, 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, you know, Superbase is doing a million Postgres databases a week on its own. We don't need that no-code database today. And as a founder, you know, after all these, what, founded in 2013, it's tough to pick yourself off. And

• 12:36Speaker 02

  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? It's a tough one today when you've already done the, whatever, I'm sorry, Nikesh, the, what's below the Waymo?

• 12:52Speaker 01

  Tesla.

• 12:53Speaker 02

  Well, you've already kind of checked the box and done it. And you're like, god damn it, I got us to 20% growth. I just, it's tough to not tap out. We're human beings. It's tough to not tap out.

• 13:04Speaker 00

  I mean, one of the things that's interesting here, and I've said this in the context of venture in general, is your common judgment, like, basically, the technology trends moved on from the thing they built. 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 you know, 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 like on that basis. It's like, a lot of these late-stage rounds, long since would have been public in another world.

• 13:43Speaker 01

  I think the biggest fear right now is something that was started 10 years ago. Is it past the point of rebuilding? 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.

• 13:56Speaker 00

  It's interesting. It used to be my mental model was apps last longer because end users are pretty, get kind of 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. Whereas the infrastructure companies that have been able to evolve to link into the AI demand have been able to actually kind of go from strand to strand. I mean, look at Datadog, you know, we were investors in D-Fog privately held, you guys are killing it. But you're co-attaching to the AI trend and the poor little apps companies, there's just nothing to co-attach to, to give you lift.

• 14:29Speaker 01

  I think that's, 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 dive on the street without tons and tons of people being paid for labeling and tens of billions of dollars to find every tree and market. So it's the equivalent of sort of the Waymo mark the tree, that's the tree idiot. That stuff needs to happen for a lot of enterprise for AI to be effective. So I think, sort of, we go through that process, we sort of leverage AI, put all the hoops around it. From machine learning perspective, there's life for infrastructure businesses. The choice we have in the next five, six years is can we build all that plumbing, all that god railing with machine

• 15:12Speaker 01

  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.

• 15:19Speaker 00

  Bending spoons it is. No, bending spoons it is.

• 15:22Harry Stebbings?

  Those kids. Okay. Now, 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 a Monday.com, again, two products with similar motions.

• 15:39Speaker 02

  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, it's market low.

• 15:48Speaker 01

  We'll find out tomorrow.

• 15:50Speaker 02

  True. I think one of the things is going to happen, either like the Leo, what's the hedge fund guy? Sorry that we might not even talk about him.

• 15:58Speaker 02

  Okay, he 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.

• 16:03Speaker 00

  Here we are.

• 16:03Speaker 02

  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, but they're not consistent. This deal in many ways was everyone capitulated. The late-stage got 1X, the founders made 150, 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-tableing it. It's time, guys. It's like

• 16:44Speaker 02

  how he did it. It's time. It's time to, he's a great founder, but like, the 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.

• 17:04Harry Stebbings?

  Well, I mean, we'll, we'll, 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 kind of 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. How did we think about this? He was the wonder kid of the AI wave.

• 17:43Speaker 00

  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. If you accumulate a portfolio of high volatility stocks with 4X leverage, the math makes it clear your probability getting wiped out once is just very high. It's as simple as that. Absolutely right on trend, absolutely wrong on portfolio construction. It's almost like it was inevitable.

• 18:15Harry Stebbings?

  I'm really sorry. How do your investors let you get to that place?

• 18:18Speaker 00

  Because you made them 10X last year and you probably don't question anything. He did amazing, you know? And which of us really, let's ask ourselves honestly, when someone makes you a 10X, is your first response, yeah, but what can go wrong? I was like, ooh, can I put in more money? That's what happened. And my wife said when I was talking about, she said, I don't want to see any shadow fodder. She was like, you know, there's a lot of shadow fodder laughing that 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. I mean, he was clearly wrong on the bet, but that was a brutal week.

• 18:50Harry Stebbings?

  Will he be okay? It says that he's still managing both a private and a 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?

• 19:01Speaker 01

  I think he'll be fine. I promise you, he's not going to be caught at the same place again. Good news is he's learned a lesson and he's going to live, he's going to survive to sort of live it through.

• 19:10Speaker 02

  I mean, Harry, it's your job to mentor some of these younger kids like, like Leo. So I think maybe you, you could step in. What, he's 26 or something like that?

• 19:17Harry Stebbings?

  He was 25, dude.

• 19:18Speaker 02

  25, yeah. So I think you've got, it's time for you to, to become the elder statesman in the industry and start mentoring them on leverage, when to lever up to 4X, when not to.

• 19:27Speaker 01

  Harry's busy with drooping plants and, and, you know, expanding homes with multiple people living in the same house. Don't, don't bother him.

• 19:35Speaker 02

  I assume his LPs or his investors knew this was a highly levered fund, right? Am I wrong, Rory? I mean, if they know it's 4X levered, then they know there is black swan issues when there's short squeezes and others. And I don't think that his investors should cry if they knew how, how it was playing. My limited experience as an LP in funds with leverage, not quite this much, is you, you know, it's not free, right?

• 19:59Speaker 01

  I have a feeling his LPs didn't lose any money. If you are up 440% and you go down from 45 to 10 billion, you're back to where you started. So I think he's fine.

• 20:07Speaker 00

  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. Bural comment, if you came in in the last six months, you might have been wiped 80%. Because hedge funds are like, unlike venture funds, people come in at different times at different bases. So I think the real, and I think, 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. I mean, I think fundamentally, yes, he will be fine. And there's a lot, Larry Fink, who, you know, founded and runs BlackRock had a blow up early in his career. There's

• 20:50Speaker 00

  lots of people who had blowups early in his career. 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.

• 21:02Speaker 00

  Yeah, Forex for babies. But genuine comment here. So you can survive and 10 years later, the wild success from 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. And if there's 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. I mean, in the end, it'll all, this is America, everyone will sue everyone and it'll all be fine. But there will be some dynamics going on now. Because if you, can you imagine going back to your investment committee and saying, yeah, we did a quarter, we did a hedge fund,

• 21:44Speaker 00

  but it appears it wasn't hedged. And we lost all our money in a week. Yeah. But if you're the Collessians, on the other hand, you came in on day one, you still made out great.

• 21:53Harry Stebbings?

  Well, that's a relief. I was worried the Collessians would be short of cash. So that's good to, good to know that at least that whole Rory, thank God.

• 21:59Speaker 00

  Right up there with Nikesh and Silicon Valley, they'll be fine.

• 22:02Speaker 01

  They need money to buy PayPal. So.

• 22:04Speaker 00

  They do. And they need money to buy, what was it, Open Router? I mean, they're doing such a lot. I mean, actually, we're just talking about, it's been interesting to see them do all this corporate development while still private. Just super interesting in terms of, you know, a lot of this stuff would be marginally perhaps easier with a public startup.

• 22:20Harry Stebbings?

  I had a CEO of Open Router on the show on Friday, Rory. So there we go. I'm excited for this next topic because with Nikesh, I think we've got the most prescient person. Anthropic's models breached three companies too. This is obviously on the back of the OpenAI and the hugging face debacle. Really? Anthropic breaching three models also? Is this just like the most epic beginning of like a bull run in security?

• 22:45Speaker 01

  First and foremost, this is all flex. 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 OpenAI 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 of 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 which have these capabilities. The challenge we have from a cybersecurity perspective is

• 23:26Speaker 01

  profiling 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, you know, 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, Harry. And it showed up in four months. I think in two, three months from now, open source have distilled all these capabilities. We'll find open source models out there, which you can find to you if you're an attacker, to actually do this on a task basis. So it's going to change the game.

• 24:11Speaker 00

  How are your enterprise customers reacting? Because this feels to me like the mother of all of, I mean, security sells on fear. And this is terrifying. So what are you seeing in the enterprise customer base when this is normal?

• 24:25Speaker 01

  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 goodness. He's got everybody like 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 CEO 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, we found 14,000 vulnerabilities in open source in the last 14 weeks, testing open source packets. So it's a bunch of stuff

• 25:08Speaker 01

  that's been used out there. So A, every company has vulnerabilities. They 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 configured infrastructure is genius. There's misconfigurations. All these things need to go away. So at the 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 to get into your 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

• 25:51Speaker 01

  problem. It's an infrastructure readiness problem. And now it's come to bear. So it's time to pay your taxes.

• 25:58Speaker 00

  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?

• 26:18Speaker 01

  Yeah. You said it so well, Rory.

• 26:20Speaker 00

  I mean, I'm, it just feels so good. I don't mean to be.

• 26:23Speaker 01

  I think you should be on a podcast talking about how people need to buy more cybersecurity.

• 26:27Speaker 00

  I'm just going to buy the stocks, but.

• 26:29Harry Stebbings?

  Talking about Nikesh, can we get him some swag? I mean, Jesus, he's like, come on, Jason. Give Nikesh a hard question. You're the one building.

• 26:38Speaker 02

  I want to ask him, 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. Like, I pretend to have a thick skin. I don't, but I love criticism.

• 26:49Speaker 01

  I already figured I don't have a thick skin in the first 30 seconds of this conversation.

• 26:52Speaker 02

  Okay, good. So I'm building an app for the Saster community. It's called Saster Connect to help with recruiting. That, details don't really matter, but it's the biggest thing I've built, right, myself in this era. And I'm, 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. It'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

• 27:35Speaker 02

  anywhere. I only found out later when the agent flashed conflict with Jason's gems when I was trying to fix something else. 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 goo, this is just the hack, the part I didn't tell you is, the way it did it is it MCP'd in. So I had, I had Google Drive to Claude, to Fable, to MCP. And so it was able to do with whatever it wanted, probably thinking it should implement Jason's gems, but it shouldn't have. And it never asked me and it, it never told me it did it. Is this scary? Is it not scary? Is this Orwellian? What, what happened to me?

• 28:16Speaker 01

  It's wonderful. It's the Wild West. And the good news is that the small business entrepreneurs, people playing with their own stuff are doing this without any regard for security. People are doing that with no regard for security. They want to experiment with open claw. 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, the most obvious ones are people saying, you can't use this. Now, that only encourages people to use it more. But there is this extreme 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. The challenge you have, Jason, is, and I think this is pertinent, when the Wright brothers built a plane, they didn't

• 28:59Speaker 01

  invent TSA. That was not the first thought that crossed their mind. TSA came a lot after. 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 the 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. And what is, what is the adage that if the product is free, you're the product? 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 or 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.

• 29:38Speaker 00

  Question on this, like, is the security architecture a year from now, is it more the same, better, faster? Or is there some new, new things that you just have to do utterly differently to protect? I mean, is it just same problem, just higher velocity? Or is it, oh shit, we never even thought about that before?

• 29:55Speaker 01

  Yes.

• 29:56Speaker 00

  Both of the above. Okay. Both of the above.

• 29:58Speaker 01

  Fundamentally, cybersecurity is kind of a very straightforward thing. If it's a known bad, I'll stop it at the door. You show up with guns blazing, I know who, who you are, and I've got security at the perimeter, I'll stop you.

• 30:09Speaker 00

  Yeah.

• 30:09Speaker 01

  The known bad, I'll stop you at the door. The problem is no cyber attack happens because I stopped a known bad. Every cyber attack happens because.

• 30:16Speaker 00

  Didn't know.

• 30:16Speaker 01

  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 businesses. So I'm on endpoints, I'm on, you know, devices, I'm on servers, I'm on firewalls, I'm protecting the perimeter for multiple infrastructure set of 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? There's a concept of data classification. You had to write static rules. Guess what? LLM can suss it out.

• 31:01Speaker 01

  Much faster from a content perspective. You know, we we track every malicious website in the world. Can AI tell me it's a malicious website much faster? Yes, it can. So the sort of 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 MIT is going to eat my lunch. Guess what? There are no perimeter security scenario, 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 LLM right on it and say, find me all the abnormalities.

• 31:44Speaker 01

  Find me behavior that you've never seen before. Now I'm ingesting nineteen petabytes of data a day. Think about it. Nineteen 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 it. 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 with machine learning. This is a good thing. The only problem is I only have twelve hundred 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.

• 32:29Speaker 01

  We can have a whole episode, ninety minutes on about what are agents? What is really an agent? How do you give agency? And how do you control an agent? It's funny. People tell me they have identified 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 deceptively 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 around. 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.

• 33:13Speaker 01

  And now the whole world will find out what Jason's gems are.

• 33:16Speaker 00

  Totally agree.

• 33:16Harry Stebbings?

  It's crazy. We said open, we said China. Whether we have comment on it, Moonshot closes three and a half billion at a thirty five billion valuation. If it's free, you're the product. Moonshot's free.

• 33:28Speaker 00

  I think one of the comment on the if it's free, you're the product is is totally true, especially in the consumer side. The interesting thing here is I'm not sure how it's true. Put it another way. 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. It's not as and in the apps, if you're running the inference as well, then I 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 we'll see if what it is when when people like reflection and thinking machines start, when these models start to happen in the US,

• 34:12Speaker 00

  it will be interesting to see what the business model is of which an open weight model is a part. Definitely not, hey, download it, have a go and you can do whatever you want, wherever you want. I mean, look, open source has evolved the business model of support. So it'll be just interesting to see, you know, what version of if it's free, you're the product emerges for these companies in the medium term.

• 34:33Speaker 01

  I'm going to have Harry a soundbite.

• 34:35Speaker 00

  Good.

• 34:36Speaker 01

  Average intelligence is going to be free in the long term and the average intelligence will keep getting better.

• 34:41Speaker 00

  Nice.

• 34:42Speaker 01

  Exceptional intelligence will be paid for.

• 34:44Harry Stebbings?

  Can you give me some tone with that, Nikesh? That was all monitor. I want like drama. Come on, you got to deliver the sound.

• 34:50Speaker 01

  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.

• 35:04Speaker 02

  But do you think we'll rely less on frontier intelligence? We won't eat, we won't need it?

• 35:07Speaker 01

  Oh, no, we'll need exceptional intelligence. We need exceptional intelligence to discover the 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 six dollars a million tokens to answer a call saying, how can I help you? I'm so sorry, your network connection is not working.

• 35:37Speaker 00

  Agreed. Yes, customer support will not be using frontier models.

• 35:41Speaker 02

  Maybe, but but I my limited, listen, of course, you're right over the long term. In the short term, customer support is requiring more and more tokens to do more and more sophisticated resolution.

• 35:50Speaker 01

  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, this is what I mean, the capability in ten gappers. 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 useful the enterprise context. The amount of effort that goes, the problem we have is like, sorry to go back to the Waymo example because like it's kind of, I think it's the most obvious one out there. I drove in the first sort of Google self-driving car. I don't know what I was thinking in two thousand and nine when I used to work there. It was 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 I told me at eleven PM to take the wheel in my hands. I was driving a quarter wall and I did.

• 36:33Speaker 01

  And I sort of was more relaxed towards saying, oh, maybe it's just going to figure it out when I make a wrong turn because it's so smart. Drove me, I was like, no, dude, this does not drive when it turns off. So that was two thousand and nine. It's taken fourteen years after that to get one with all the edge cases trained from machine learning perspective for us to rely on that as being the that we've given the agency that to that replacement. So I don't believe we're going to give hundred percent agency to use cases for some time. And for us to be able to do that, the amount of data collection and and context we're going to create is going to be humongous. Basically, you have to literally take every edge case in customer support, get into your AI brain of your organization so that you can start relying on AI instead of the human. So you're getting eighty percent right now.

• 37:18Speaker 01

  You're getting eighty percent of customer support solved. All the edge cases are waiting to be solved with AI.

• 37:23Speaker 00

  Yeah. Then there is the for a given app, how much of the value is purely in the model versus all the other thing? And you're right, for something like customer support, you know, you're probably paying ten or fifteen percent 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, you know, the sales forces, the we were in intercom, stuff like that, where it's, you know, sub plus or minus ten, fifteen percent. Obviously, in coding and things like that, it's seventy, eighty percent, which means it's just raw intelligence and a mild harness. And those are just very different.

• 38:03Speaker 01

  I think in the next three, four years, we won't be paying for intelligence. We'll be paying for compute through our nose.

• 38:08Harry Stebbings?

  I mean, it's being paying for compute through our nose. We often get chastised for being too public markets focused or too anthropic and open AI focused. Valor Atomics triples to six billion dollar prices. Sequoia bets on nuclear for AI. It's a three-year-old small modular reactor company, raised at two billion, now Sequoia leading around at six. Specifically, there's an Nvidia partnership to power AI data centers, which caused a lot of excitement for the company.

• 38:34Speaker 01

  Harry, I met somebody who's got, you know, I was talking to them and he's in the business where they take chicken feces and turn that into methane and and produce gas. And I thought it's like, oh, it's a cute project he's got running somewhere in the middle of the country. And then he told me that he has money, 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 multiple 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 open AI, 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. And if you can take, you can also take all the free Chinese models you want, where are you going to run them?

• 39:19Speaker 00

  Jason?

• 39:20Speaker 02

  To Nikesh's point, actually, I 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 these models actually used to work.

• 39:28Speaker 01

  It's literally chicken shit.

• 39:29Speaker 02

  So did cows. I even looked at some of these things, but the margin, the margins were so low, the IR was so low, but the business worked. Now AI is a, there's such a demand for compute. Everything works, including all types of nuclear like Valor, right? Including chicken manure. You laugh, but like, I remember talking to a manure farmer doing this back in the day. And he's like, well, the best, the best we can commit to is eight percent annual return if everything goes well. And it's just, you know, it's hard to get twenty VC excited for those returns.

• 39:55Speaker 01

  I never thought he was going to be talking about that on this show.

• 39:57Speaker 02

  Me neither. You brought it up, but but everything.

• 40:00Speaker 01

  I'm telling you.

• 40:01Speaker 02

  And nothing, nothing in energy storage worked before AI, right? There's the battery startup, right? What's the one that just raised a twelve billion too? Yeah, none of these things worked without AI, right? Neither did RAM. Like, none of these products really were that great. But now they're the greatest products in the, but give me my Rams. I can't even get my Mac Studio with more than sixty four gigabytes. Give me my, everything's working, right?

• 40:21Speaker 00

  I I'm going to ground us in a little facts here, just on the three, right? Just because I think of all, I mean, Valor and base power, super interesting, but very different. I mean, you're right, the valor is purely there. We need more power for compute bet. You're right. And, you know, 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, 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. It's interesting. There's a bunch of these private and then a bunch of these public. And I think is it New Scale is the one that's doing the existing technology that's well understood? I think Light Water, not the nuclear, 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 in the regulatory path.

• 41:06Speaker 00

  And then all these guys, including Valor and Ockler, 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? You have to be wildly supportive because there's no way to get cheap electricity without doing nuclear. But so from a public policy perspective, go team. But just from a, don't spend the electricity yet. You got to plow your way to 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 thirty 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 I'm just, it's awesome. We're doing it.

• 41:49Speaker 01

  I think the question we're sort of 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 that money has to come from some version of some people paying for AI. And that's the only thing that's going to be, that's going to allow the valours in the chicken manure world to actually be worth something.

• 42:16Speaker 00

  Yes, in the end, someone's got to buy a trillion dollars worth of tokens in corporate America.

• 42:20Speaker 01

  Not corporate Europe.

• 42:21Speaker 00

  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, you know, I'm a European, whatever.

• 42:30Harry Stebbings?

  Nikesh, he's the most cynical dude on Europe you'll ever meet, and he's Irish.

• 42:35Speaker 01

  Yes, we have two Europeans here.

• 42:37Harry Stebbings?

  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.

• 42:56Speaker 01

  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 they'll go to 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.

• 43:40Speaker 01

  We all talk about why can 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, you know, at first principles, there will be tremendous amounts of compute that will be needed to satisfy the AI use cases, both on consumer and 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. You know, Google was not the first search engine.

• 44:15Speaker 00

  Arguing against, and 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 the real guy.

• 44:24Speaker 01

  I see where you live, Rory.

• 44:25Speaker 00

  Yeah, hey, David, you're running your 280 billion dollar company. I'm just trying to turn 20 million investment into 100 million and call it a day. I'm a small guy. But the genuine comment is 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 a side because we can only do one thing at a time. I think 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's 70% of the Google compute backlog, the 70% of the Amazon backlog. 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,

• 45:08Speaker 00

  and resell that intelligence on a frontier model basis to US enterprises. And if it doesn't happen that way, it's going to take, there's going to be a pretty big dislocation.

• 45:18Speaker 01

  Yes, it's perfectly possible that there is a public market dislocation because the players at the tables might change. And that's great. That's called a buying opportunity because that doesn't take you for 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's 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 10 cents a dollar for tokens.

• 45:45Speaker 00

  Yeah, Moonshot is happy, Vidya is happy, enterprise is happy, OpenAI very, very sad. You're right, that's the dislocation.

• 45:54Speaker 01

  But the question becomes, you know, which ones of these are the markets going to support? Right? Is the market going to give you infinite capital to be able to build a 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 sort of technology shift, infinite bull market. We take execution out of the picture. 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 not executing as well as the others.

• 46:38Speaker 01

  So eventually, execution matters. And that's going to decide the winners and losers in the market, not the shift of which intelligence is the best.

• 46:46Speaker 00

  I mean, the best example of that would be two years ago, OpenAI was first and Anthropic was second, and now Anthropic is first and OpenAI is second.

• 46:53Speaker 01

  And Google was written off.

• 46:55Speaker 00

  And it was written off.

• 46:56Speaker 01

  There was a, your point, Gemini was non-existent. Google was written off. Now suddenly, Google has the compute, the cloud sales, and Gemini.

• 47:03Speaker 00

  But still not the amazing open frontier model. Still not the coding agent. There's still not the coding agent.

• 47:08Speaker 01

  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.

• 47:14Speaker 02

  Well, to Nikesh's point, Harry kicked us off by saying, you know, well, have we ever had an ecosystem so dependent, right, on the success of OpenAI and Anthropic? I mean, it is, but maybe to Nikesh's point, you know, so much has changed since we started the show, right? 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 has, now, 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. We may, this may all blow over, and it all may be about compute. And we may not, whoever wins, wins. Whoever wins, I'll plug in.

• 47:51Speaker 01

  Jason, I think the models will get better and better. And the distinction between models will not be enough for you to decide to grip one out. Because I think the part which we will be built, 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, like, when I run a simple firewall company, 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.

• 48:36Speaker 01

  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 built that organizational sort of 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.

• 48:55Speaker 00

  And you're clearly 100% tracking Satya with the kind of Microsoft comments recently on, you know, age companies. And it makes into enterprises need to build their own value, build their own context, rather than do it frontier model, right? And that's.

• 49:09Speaker 01

  I think it's, yes, he's saying something different. I understand what he's saying. That's a different comment. Mine is different comment. I think there's three parts to it. There is, there's the model, which is the raw intelligence. 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, what's a bad answer.

• 49:39Speaker 00

  And what is your model? What core model will you start to build all this context on, do you think? Or have you decided?

• 49:44Speaker 01

  I remember calling Thomas Kurian at Google when the whole thing just started, you know, the 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, you know? 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.

• 50:15Speaker 00

  Got it.

• 50:16Speaker 01

  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 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 with the context because otherwise it gets commoditized. So I think that's a bit of a commoditization battle that's going to happen between model and models plus context.

• 50:50Speaker 02

  But Nikesh, just on that, you also said something, not in conflict to it, but related. 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. You know, even Opus Five and Fable and at Palo Alto Networks, you have a team that can manage that, right? Those changes.

• 51:11Speaker 01

  We're learning as we go along, so.

• 51:13Speaker 02

  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 in your vector database, and have confidence that the results will be the same?

• 51:23Speaker 01

  Bending spoons. It's a Darwinian moment. Darwinian moment, do not suggest that everybody survives.

• 51:30Speaker 00

  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.

• 51:37Harry Stebbings?

  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 Anthropic 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.

• 51:55Speaker 00

  And you're right, Eric, because, you know, I pushed on why we're always talking 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 Nikesh's point, they're the giant sucking sound on demand that's just pulling everyone along all the way up and down the chain. Which is why I think you're correct. If that demand signal turns out to be attenuated or dips or even is true on the long term but blinks for a year or two, it'll be a weird time in tech.

• 52:23Speaker 01

  And that's why we're all focused on the poster child of the trend. But I think the trend is bigger than the poster children.

• 52:28Harry Stebbings?

  AI and intelligence is bigger than OpenAI and Anthropic is what you're saying.

• 52:32Speaker 01

  Yes.

• 52:32Speaker 00

  You are right, and enterprises are going to want to consume it a lot. But if it turns out to be 70, 80% beneficiaries other than OpenAI and Anthropic, there will be a pretty significant dislocation up and down, right? I mean, I think.

• 52:46Speaker 01

  Yahoo was a $200 billion company at one point in time, and two years later, I joined Google as a $14 billion company.

• 52:52Speaker 00

  Good call. You're a good stock picker.

• 52:54Harry Stebbings?

  We have former Mr. Google. We touched on Microsoft and Google being told to, what was it, that you have to dance 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 maybe 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.

• 53:21Speaker 00

  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. 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. 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. And, you know, the CEO of AWS in particular made a very declarative, the ROI, he was amazing. And the market was really happy, all, you know, in particular, Amazon and Microsoft got marked up pretty significantly.

• 54:06Speaker 00

  And then by contrast, Meta also said, I'm going to spend a lot of money, but it wasn't obvious how they're going to make money, so their stock went down. Probably the most surprising thing, going right back to the Laio thing, is, God, those were really strong numbers. I mean, all these people are selling a shit ton of compute. And 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, 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.

• 54:44Harry Stebbings?

  Jason?

• 54:45Speaker 02

  Well, look, I think we already hit that. To me, just maybe it's perpendicular, so I don't want to take off track. But to me, the Palantir, which just happened, was more interesting, right? I mean, growing almost 100%, right? And bookings up 153% backlog? I mean, you know, you can sell this AI.

• 55:01Harry Stebbings?

  What was your, Jason, what should we take from that? Like, hey, enterprises need help with it, Palantir is the best.

• 55:06Speaker 02

  I think what we should do is, 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. I mean, I don't know what the message is. I mean, 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 it, and you can also capture somewhat model agnostic intelligence. But the demand here for, for intelligence, for compute at the app, I mean, you know, at some level, Palantir is a very sophisticated harness on top of massive amounts of data, right? And maybe vectorized databases, Nikesh's point.

• 55:47Speaker 02

  I might be wrong or oversimplifying it, and 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 1,000 customers, right? 1,049 customers, they're giving $8 billion worth of answers, growing 100%. These 1,000 customers will pay almost anything to get these questions answered with AI. They'll pay almost anything.

• 56:09Speaker 01

  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, large companies that 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. You know, that CapEx dislocation is not happening today. Now, it can happen tomorrow if some of these people who are committing to capital are not able to show up with the capital. But for now, you know, we have one more run at the roulette table. So that's what's happening. We're being told that this market is going to support CapEx until it can't. I think it's a bit of a gold rush moment.

• 56:53Speaker 01

  I think every consumer app will get rewritten in the next five to 10 years. You know, 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 or learning if you talk about constant learning and agents? So everything is up for grabs. Every consumer app that was ever put on 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. Until the market can keep funding it and, you know, the timing works. I think the biggest, only problem we have right now is a timing problem. Would the revenue show up fast enough to keep funding the CapEx cycle?

• 57:35Speaker 01

  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. 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. That's kind of what we're seeing. And that's why I just bring back the whole OpenAI and topic 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 is in the supply of compute. You can bring all the CapEx to bear. And to your point, the valuers may not get their regulatory sort of approvals. Europe may not allow data centers.

• 58:20Speaker 01

  You may find 30 states with, you know, picket fences that say, no data centers in my state. So there's a supply problem that happens in compute side, which could have a knock on impact on all our infrastructure buddies in the semiconductor space saying, holy shit, 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 kind of where we are at the, 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 a 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 have the capability, you have the resources. Let me make sure you don't become extinct in this wave of technology. I'm going to go back, you know, like in 1997, 98, 99,

• 59:04Speaker 01

  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.

• 59:13Speaker 00

  And the interesting thing, as I play all the comments you've made, is the odd thing is, if the most likely failure mode is not ultimate demand, and I agree with you it isn't, but just 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 this, 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.

• 59:39Speaker 01

  Or the better digesters will win and the poorer digesters will have heartburn.

• 59:43Speaker 00

  That is an interesting point is that that's that, all that data that says the companies that are digesting AI quickly are growing faster than companies are not. So you are, and I think that's not true in everywhere. 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, you will be bending spoons, to use your point, yeah.

• 1:00:06Speaker 01

  Those may be lewd, they may not have been bending spoons.

• 1:00:09Harry Stebbings?

  Nikesh, in your next analyst call, can you do like an ode to us? Where when you get a shit hard question, you just, just say bending spoons? Just drop the mic.

• 1:00:19Speaker 00

  I think you'll find, Harry, that when you work $280 billion 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.

• 1:00:31Harry Stebbings?

  Rory, if you hadn't figured, I ain't here to bring IQ to the conversation, okay?

• 1:00:37Speaker 02

  Nikesh, do you think more established enterprises can process this rate of change infinitely? Do you think they've changed permanently? 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 that's, and they're like, well, the world's going to change so much. I want to see what agents and what AI product. That's totally rational today. But most enterprises traditionally, you know, you can't rebuild your whole stack every eight to 12 months. It's, 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 do you, what are you seeing?

• 1:01:13Speaker 01

  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 applied, but they solved them. I need to find, 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

• 1:01:58Speaker 01

  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, just the way. 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, dear VP of Finance, book it because we book every transaction. So you have to, you have to give organizational knowledge to some learning system that you have to build. And I think that still is going to take three to five years for every enterprise, every use case. And I think that's kind of 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, and I'm pretty sure fireworks will take my money

• 1:02:44Speaker 01

  and fine tune an open weight model for me if I want and keep changing 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's useless.

• 1:02:58Speaker 00

  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, the reason they're doing so well is they're able to say, dude, we know this is the biggest problem as the CEO. I at least have some kind of answer here. Let me help. Give me 10 million bucks. It'll be great.

• 1:03:26Speaker 01

  That's an underestimate what Palantir might be doing. There is a capability that AI has already demonstrated where 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, any kind of, it's kind of like 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 basis points on my top line and improve my margin by 100 basis points. Hallelujah, you know, you just paid for everything. That's, you don't have to put a learning system into play except for nothing. It's just taking enterprise data and doing a lot of that stuff.

• 1:04:11Speaker 01

  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 to this and write code, doesn't need to happen anymore.

• 1:04:21Harry Stebbings?

  We have final one. We have new CEO at Scale AI as an option. They hit a billion and a half in AR. 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, that's not a nice headline, is it? Rory sells drone deploy to Procore. 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. What not raising at $20 billion?

• 1:05:00Speaker 02

  Can I ask Rory about drone deploy? Because it ties to the beginning of the conversation with deals and Nikesh, right? So that deal, the what's interesting, so drone deploy was bought by Procore, right? Great classic software founder founded by Tui to do software for real estate, dominated it, had a great run, right? Growth slowed, most importantly, net new customer count sort of stopped growing. Growth slowed to like 17. So they make a big bet and I'm not an expert on drone deploy, 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 four. I find in the old days, I'm not saying that happened here.

• 1:05:45Speaker 02

  These deals are stressful, man. It's not, 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 air tables a lot. Well, Procore is one of the ones basically trading there too. So was this deal like super stressful? Did, did you lose hair? Were people shouting and throwing things through the window?

• 1:06:22Speaker 00

  I'm not going to speak for the acquirer because I'm not in that side of the room. But genuine, 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 you know, modest fundraisers, you're set up for success, not failure. 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 very 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 your software that's enabling drones and robots, you're actually on the side of the future.

• 1:07:06Speaker 00

  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 feels really good. We're happy to hold. And then obviously we got an offer that made us do different. I know what a comment and specifics of the offer, 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 wily, 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.

• 1:07:49Speaker 00

  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 you know, probably Nikesh has done these kind 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.

• 1:08:17Harry Stebbings?

  Nikesh, what percent of market cap does a deal become a BFD, a big fucking deal? A core strategic, this needs to work?

• 1:08:25Speaker 01

  Look, every deal needs to work. We're not buying companies because of money to spare or my shareholders think we should, you know, we should rely on waste. And 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 50, north of $50 billion. That's, that one's got to work. That one's got 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 your market cap and buy something, it better work. Now, when you make that work, then you can, if the market gives you credit for making deals work. I said that in my earnings call and they got all freaked out.

• 1:09:08Speaker 01

  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.

• 1:09:15Speaker 00

  And that big one was CyberArk, right?

• 1:09:17Speaker 01

  Yes.

• 1:09:17Speaker 00

  Got it.

• 1:09:18Speaker 01

  Rumor has it agents are going to be important. If agents are important, they're going to need identities and they need to be treated like privileged identities. So that's our thesis, sort of simple.

• 1:09:25Speaker 00

  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.

• 1:09:33Speaker 02

  As you've seen with my examples, you don't know what these agents are going to do, man.

• 1:09:36Speaker 01

  In your case, you're just going to restrict agent behavior, Jason.

• 1:09:40Speaker 02

  But man, they're so good, but it's so powerful.

• 1:09:43Speaker 00

  Point, Jason, to the point when, actually it kind of ties back when Nikesh said, I just was reading some stuff last night that really does accord with what Nikesh has said. It's what someone made the point, if you can't specify 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, Nikesh, that.

• 1:10:08Speaker 01

  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? That's the big debate.

• 1:10:22Speaker 02

  But the flip side is it does a really good job. And listen, we have, we have a, 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 1,000 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, 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?

• 1:10:45Speaker 01

  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 your VP of Finance is allowed to write checks, I might want to have a conversation.

• 1:10:55Speaker 00

  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, you know? They were selling data, data products to an insatiable demand for data. And I give them huge credit. They kept the thing going.

• 1:11:22Speaker 01

  Hey, windsurf sold to Cognition, right?

• 1:11:24Speaker 00

  Exactly. 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.

• 1:11:38Speaker 02

  Grok could be the next one too.

• 1:11:39Speaker 00

  Yeah, you're right. The remaining Grok, you're right. Because 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.

• 1:11:50Harry Stebbings?

  That's a good quote Rory. Now Nikesh, do you see why I go home early from dinners? Because I need to be fresh for podcasting. You see, this is hard work. You builders, building enterprise value in your public companies. This is where the real grind is.

• 1:12:05Speaker 00

  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.

• 1:12:11Speaker 01

  Rory, it's got to be built one deal at a time, our friend. The enterprise is 1% inspiration, 99% perspiration.

• 1:12:19Speaker 00

  No, I do, I do not.

• 1:12:20Speaker 02

  It's what I tell my agents every day, guys. Agents don't sweat.

• 1:12:24Harry Stebbings?

  Get to work. Stop it. Get to work, boys. First inspiration. Nikash, it's been fantastic. Thank you, guys. Yeah, I really appreciate the time. But before we leave you today, you have the idea, but with most AI tools, you hit a wall. The setup, the config, the gap between what you pictured and what you actually ship. Well, Base44 is where that wall disappears. You describe it? Yeah, Base44 builds it. Apps, websites, AI agents, real working products, built in minutes, using nothing but plain language. And it's all batteries included. The backend, the database, the authentication, the hosting, the heavy lifting is handled. So you just really stay in the flow. This doesn't just take the busy work off your plate, but it gives you an advantage and pushes you past what you thought you could build alone.

• 1:13:07Harry Stebbings?

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