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

Absolutely right, on-trend, absolutely, are going to put for your construction. It's almost like it was inevitable. If you bought in April, May, or June, you've been wiped. We did a headstrong, but it appears it wasn't hedged. And we lost all our money in a week. 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 table might change. Yeah. Mooshaw is happy. Videos happy. Enterprise is happy. Open AI is very, very sad. You're right. That's the dislocation. God, those were really strong numbers. I mean, all these people are selling a shit ton of compute. The Palantir can do it. They came back from 15% growth four years ago. Why can't you do it, kids? Work harder. 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. In the face of insatiable demand, all things are possible. This is 20 VC with me, Harry Stabbing's. It's my favorite show the week. Roryo Driscoll, Jason Lampkin, and what, what, what, what, hold up? Nikasha Roryo joins us in the studio. Oh, yeah, baby. Palo Alto Network's $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, air table being acquired by bending spoons for $1.285 billion. Sad face again, they were worth $11 billion before and through its model breaching three companies. Oh, God, when does the security problems end? That and so much more in this incredible conversation with three of my favorite people. But before we dive into the show today, you have the idea, but with most AI tools, you hit a wall. The setup, the config, the gap between what you pitched and what you actually ship. 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Leading companies like Gamma, Asana, DoorDash, and Crypto.com already use and love fin to deliver better customer experiences. So for a limited time, you can get $500 a month in fin 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 Nikash joining us. Nikash, 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 city plays out for-- We have Professor O'Driskel and the hall. But we're going to start on the news of the day. And the news of the day is air table. One of the big names from the last decade has been bought by Rory, European-based, Bending Spoons. The company was doing $485 million there, or 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 didn't I hand over to you first? I'm sure you've got some perspective. We're all going to do the air table side of the analysis. Just worth pointing out the Bending Spoon side. I'm buying stuff at 2.8 while I'm trading in the market at north of 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 trade and currency to who for up a whole bunch of this stuff. So on their side of the table totally get it. Obviously, on the air table side, 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 to 450 million in revenues and sold for 2 million plus or minus, that'd be like, that's an amazing outcome. 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. You know, can I ask Nikesh one particular question on it, Harry, if it's okay? Because I have a lot of interesting thoughts on this deal, whether air table matters in the age of agents in AI. But I mean, Nikesh is also one of the best deal makers out there. The shocker to me with air table wasn't the price because I think it's low market. The shocker to me is no one else stepped up. No P firm, no Tom O'Brawvo, no Vista. It's 20% at 50 million with some AI dust going on it. Do you think there was even another offer? That's the, I just assume someone would outbid them. And you're asking me that because I'm like, You're the deal maker, you're a deal maker, par excellence. (laughing) We just happen to have the king deal maker on the show. Yeah, look, I, you know, I met how a few times a great guy, he's done build a great business. I think there's a bit of founder for DeGear. He's been through a lot of ups and downs in terms of sort of internally 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 the fundamental change in the long-term growth rate that people expect out of SaaS? If it's the fundamental change in the long-term growth rate, people expect out of SaaS, then the multiples are right. And that's I think where the market is grappling with this. I think the people you mentioned, Jason, the Pee 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 they think they, you might be caught in the sort of 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 choir side, right? I mean, we see it on the target side. It's just, so we stick a long-term, unprofitable, currently unprofitable long-term growth businesses. But yes, I see what you mean. I mean, Francisco Partners raised $22 billion to do deals sort of like this, right? Tom O'Bravaux was like, we're looking for AI infused B2B companies. We could pick an air table, 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% 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 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-lapositive. 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's knowing out of them. Well, I like bending spoons, jet-buy. So clearly, somebody saw value there. Not a receded. But Jason, I want to go back to something and say, "An AI infusion." I'm a little worried about this AI infusion stuff. And this is what I talk to my team 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 I'm building a way more. And the question back to you is, did air table do a bit of a Mercedes action, little Tesla action or a little bit of a Waymo action? Because my biggest fear is a bunch of people out there in the garage is getting funded by Harry and Rory. And they're going to build Waymo's the future. And we'll be busy putting lipstick on the big. It's the tough one, right? Because I didn't feel Jason that this was the kind of category that PE would sweep up. Because 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 a core productivity app. And you've been a Pulsar Tivh. Jason that, you know, oh my god, look at all you can do it lovable. If I wanted to build my own CRM and I wanted to be personalized, but 10 years ago, I might have used air table 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 web platform or Claude Coden and just bang it out from scratch. So you do want to do that. I'd call you personal, low imagination. There's so many other cool things to build. Yeah, I have a person with low imagination. I can't do that. But I agree, but the point is this, what it means if you own a horizontal productivity app that's many individual use, I just think that's one of the tougher categories for PE to get the head on. I wanted to give them 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. Yeah, or more, right? Just double the price and your data is locked for two years, right? You know, to your point, I don't think PE has the stomach or the willing has to do that. But I don't think it's what they do well. I think their stomachs might be full. I think it's unfair to say that. As I often say to people when they show me a turnaround deal in my business, right? I said, 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 theft problem. 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 restructuring in the last 12 months. They need a six like a hole in the head. Necaches points. I mean, both of these points are obviously great that the one on the Waymo versus the whatever we can come back to the founder fatigue ones a tough one today because another way to look at air table is man so early to no code, right? Such a clever product back in the day. Like, there are two products that I wasn't even smart enough to understand why they were cool in the day. There was air table, which turned a database into a spreadsheet. So I understood it, right? And then there was notion which touched turned a database into a document that I didn't even know. And they're both so clever pre AI and they both took off in different ways. But we don't really, you know, super basis 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 they 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, Nick, I see what's what's below the waymo. Tesla. Well, you've already kind of 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. 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 jaceness. Like basically the technology trends moved on from the thing they built. One of the weird things about venture with their holding private so longer thing is now the holding period of venture is longer than the technology platform chain cycle. So if you join halfway true, right? This has come an at you and you know, and 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. 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. 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 shed 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 strength to strength. I mean, look at data dog, you know, we were investors in DreamFarge privately held. You guys are killing it. 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 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 way more 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 the sort of the waymo mark the tree that's the tree yet. That stuff needs to happen for a lot of enterprise for AI to be effective. So I think that we go through that process. We sort of leverage app or all the hoops around it from machine learning perspective. There's life for infrastructure businesses. The choice we have 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 that maybe bending spoons it is bending spoons it is. That's not bending spoons it is. That's okay. Now final question before we move on is 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. It was a shocker to see it right especially the way everybody presented it right enterprise value and all this but we're always right it's market it's market low. We'll find out tomorrow. Jew. 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. We're going to be very nice to you. We're going to be ready for God about him. Okay so I think one or two things going to happen. We're going to forget about air table tomorrow because other stuff's going to happen. 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 marked down since 2021 but but they're not consistent. This deal many ways was everyone capitulated ever 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 table in it. It's time guys it's like how we did it it's time it's time to he's a great founder but that time has moved on and it's time to capitulate and that's a question right there 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 it during your wedding but we move on. Well I mean what we'll talk about that that's a brilliant transition Leo Ashian Branna famed one the 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 wrote the wave so to speak he did it with 4x leverage and in the past week it kind of came crashing down and then can't reference it out bought his public book for a reported $16 billion can 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 absolutely right on the trend and we means 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 and portfolio construction if you accumulate a portfolio of high volatility stocks with 4x leverage the math makes it clear you're probably getting wiped out once is just very high. This is simple as that absolutely right on trend absolutely wrong and portfolio construction it's almost like it was inevitable. I'm really sorry how do you invest as let you get to that place because you made them to an x last year and you probably don't question anything and he did amazing you know and 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 or it's like oh can I put him on money that's what happened and my wife said when I was talking about she said I don't want to see any shadden for that she was like you know there's a lot of shadden for the laugh on the pro guy for sorry for him it was a tough call to have to go through that just to put it out down a human level I mean he was clearly wrong on the bat but that was a brutal week will he be okay it says that he's still managing both the private down in public but he's got his hands for opposite position and then other people are like oh no no lawsuits are coming and it's not gonna be okay is he gonna be okay I think there'll be five I promise he's not gonna be caught at the same place again good news he's he's learned a lesson and he's gonna live he's gonna survive to sort of live it through 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 it was 25 25 yeah so I think you've got it's time for you to become the elder statesman in the industry and start mentoring them on leverage when to leave her up to 4x when not to Harry's busy with the drooping plans and yes expanding homes with multiple people living the same house don't don't bother him I assume his LPs or his investors knew this was a highly levered fund right am I wrong worry if 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 was playing my limited experience as an LP in funds with leverage not quite this much as you you know it's not free right I have a feeling he's all the health is didn't lose any money. If you are up 40% and you go down from 45% to 10 billion, you're back to where you started. So I think it's fine. -The interesting thing about that, not quite, 'cause actually this is where I think it could get a little hard. It all depends on timing, right? 'Cause 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 comedy, if you came in in the last six months, you might have been wiped 80%. 'Cause hedge funds are like, unlike venture funds, you'll come in at different times at different bases. So I think the real, and I think, I thought even perhaps James needed 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 think fundamentally, yes, he will be fine. And there's a lot, Larry Fink, who founded and runs BlackRock, had a blow up early in his career. There's lots of people who had blow up early in his career. Nick Keshe has worked for one of the most aggressive risk-taking human beings on the planet at Softbank. He's seen ups and he's seen down. So you can survive. - What's the law? (laughing) - You're for it's for babies. You're like, "Oh, love it." But genuine comment here. So you can survive in 10 years later, by Alexis Eiffel and 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 up 90 cents on the dollar in July. You're gonna 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. In the end, there's America. I don't see where everyone in the law will 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." Yeah. But if you're the Colossons on the other hand, you came in on day one. You still made out great. - Well, that's a relief. I was worried the Colossons would be sure of cash. So that's good to know. - Yeah. - At least that whole worry. Thank God. - I love to have money in 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 an 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 start. - I had a CEO of Open Reader on the show on Friday, Rory. So there we go. - I'm excited for this next topic. Because with Nick Ashley, I think we've got the most press in person. Anthropics models breach three companies too. This is obviously on the back of the Open A and the Hugging Faster Bachel. Really? Anthropic breaching three models also. Is this just like the most epic beginning of like a ball run in security? - First and foremost is off 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 is 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 are friends that Anthropic and Open A should have done, which after all of them, point your models at your own sandbox. To make sure sandbox doesn't have any zero-div vulnerabilities and make sure your sandbox is your first capture of flag exercise. But they decided to give a target to say, go to the wild, persist, take as long as you want and go capture flag. So fine, we have these models which have these capabilities. The challenge we have from a cybersecurity perspective is refiling vulnerabilities which would take us days months to find. The average time to patch of vulnerability, zero-div 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 going to like kind of 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 or three months, from now open source of distilled all these capabilities will find open source models out there which you can find to unifender attacker to actually do this sort of a toss 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, I mean, security sells on fear. And this is terrifying. So what are you seeing in the enterprise customer base when this is no? The good news is the flex that anthropic did with Mithos has every CEO talking about Mithos. I spent eight years trying to get CEO's talk about cyber security. Couldn't get him to do it. And Dario did it in one fell swoop. So this is good. Yes. Everybody like all hot and heavy about Mithos 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 C.I. 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've gone 14,000 vulnerabilities in open source in the last 14 weeks testing open source packets. So it's much a start that's been used out there. So every company has vulnerabilities. They got to figure out a way to passion. B, these models will figure out misconfigurations. If you've left the door open, if you've got a device can figure it wrong. If you've got a piece of software can figure it 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 just got to be right once. So he'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 you 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. See it's time to pay your taxes. Yeah, I mean, that's the last sense. I mean, you're right. We can argue fear versus the same. 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, you said it's so well, Rory. I mean, I just feel so good. I don't mean to be. I think you should be on a podcast talking about how people need to buy more cybersecurity. I'm just going to buy the stocks, but fucking out of the cash. Can we get him some swag? I mean, Jesus, he's like, he's a, he come on Jason. Give, give, give me cash a hog question. You're the one building. I want to ask him is my cybersecurity therapist. I had two fable issues and they're internal security. But I love to get you and you can make fun of me for this. Like 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 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 at Google Doc. It's called Jason's Gems. It's my ideas on how to improve it. 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'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. This is just the hack that part I didn't tell you is the way it did it is at MCP did. So I had I had Google Drive to claw to fable to MCP. And so I was able to do with whatever it wanted probably thinking it should implement Jason's gems, but it shouldn't have an it never asked me and it never told me it did it. Is this scary? Is it not scary? Is this or well? And what happened to me? 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 an either guard for security people are doing that with no regard for security. The only experiment of the open cloud they want to connect to all their stuff. They have no idea of 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 them or but there is a scream with our saying, and if I don't allow you to use it, I kept 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 the right brothers built a plane, they didn't invent TSA. That was not the first time that crossed their mind. All right. 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 people play with agents. You're seeing people play with all this stuff with LLM's. Everything's happening. LLM's are training on data if you're not careful. And what is the what is the adage that if the product is free you're the product. We are the product of all these both training data that has been collected by every model out there on our consumption, which is not regulated ring fence 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 is learning on all your behavior. 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? I was an old shit. We never even thought about that before. Yes. What are you above? Okay. What are you above? It's fundamentally. The 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 you are and I'm got security at the perimeter. I'll stop you. 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 you didn't know. You didn't know it was bad until it got into your infrastructure. 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, 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 perimeter I'm on, the longer my tenure for my businesses. So I'm on endpoints, I'm on devices, I'm on servers, I'm on firewalls, I'm predicting the perimeter for multiple infrastructure, sort of components in the world. That's good. That's kind of good. Now, the question is, how quickly I find known bads will change using AI. There's a concept of data classification. It has to write static rules. Now, let them can sus 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 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, opening the eyes, I'm going to eat my lunch or mid-sauce 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 sus out all the bad stuff in the infrastructure and find out the bad actor, guess what? Imagine collecting all the enterprise data and running an LLM briot on it and say, find me all the abnormal, these find me behavior that you've never seen before. Now, I'm ingesting 19 petabytes of data a day. Think about it. I'm going to put the 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 and block it. Right now, we run it one minute. We've machine learning. This is a good thing. The only problem is I only have 1200 customers who've bought and deployed it. I need to get the rest of the world to go buy 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 an 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. It's funny. People tell me they've identified stuff, but then I ask them, does it actually have agency? Like, what does that mean? I'm like, a way more 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 deemed agentifying things. But when you start giving agency to things, when pieces of code can decide what happens next, we're going to have a whole different conversation now 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. It's like, yes, it's agent, which is a bad thing and took Jason's gems and now the whole world will find out what Jason's gems are. Totally great. It's crazy. We said open, we said China, but whether we have comment on it, moonshot closes three and a half billion and a 35 billion valuation. If it's free, you're the product. Moonshot's free. I think one of the comment on the if it's free or the product 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 close off frontier model companies. It's not as, and if you run in 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. We'll see what it is when people like reflection and thinking machines 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. 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 or the product emerges for these companies in the medium term. I'm going to hire you sound by good. Origin 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, Nick? That was a woman. I want like drama. Come on. You 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. Do you think we'll rely less on frontier intelligence? We won't eat. 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 intelligence 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 million in tokens to answer calls saying, how can I help you? I'm so sorry, your network connection is not working. Great. Yes. Customer support will not be using frontier models. Maybe, but 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. There's the primary candidates that all these open models are going after with open wait fine tuning saying, I don't need hallucination. I need this is what I mean. I think the capability in 10 gapers, 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, a amount of effort that goes. The problem we have is like, sorry to go back to the waymo example because I think it's the most obvious one out there. I drove in the first set of Google self driving car. I know what I was thinking in 2009 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 out of the wheel. And then I told me at 11, be able to take the wheel and my hands as driving a quarter wall and I did. And I said, it was more relaxed about saying, well, maybe it's just going to figure it out when I make a wrong turn because it's so smart. Drone 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 machine learning perspective for us to rely on that as being the, 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. 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 80% right now. You're getting 80% of customer support sold. All the edge cases are waiting to be sold with the AI. 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 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 that tokens as a percentage of total revenue and it varies from, you know, the sales forces, the, we're in inner calm stuff like that where it's, you know, some plus or minus 10, 15%. Obviously, encoding 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 for the next three, four years, we won't be paying for intelligence. We'll be paying for compute through our nose. I mean, it's being paying for compute through our nose. We often get chastised for being too public markets focused or too anthropocon open AI focused. Valar, TomEx, triples to $6 billion prices to core bets on nuclear for AI. It's a three year old small modular reactor company, raised it to billion now, so core leading around it six. Specifically, there's an Nvidia partnership to power AI data centers, which caused a lot of excitement for the company. Harry, I met somebody who's got, you know, as I've talked to them and he's in the business where they take chicken feces and turn that into methane and produce gas. And I thought it's like, oh, he's a cute project. He's got running somewhere in the middle of the country and then he told me that there is money, billions of dollars, and he's selling the energy to hyperscalers. So anybody who can produce any energy source, it doesn't matter what you are, is right now trading in multiple because land permits energy compute. And I think it's almost like the question will become between an topic 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 a computer right now. And you can take all the free Chinese models you want where you're going to run them. Jason. To Nick Esch is 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 models actually used to work. It's literally chicken shit. So do cows. I even looked at some of these things, but the margins were so low, the IR was so low, but the business work now AI is a, there's such a demand for compute. Everything works, including all types of nuclear like valor, right? You can check them in or you laugh, but like I'm ever 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 8% annual return of everything goes well. And it's just, you know, it's hard to get 20 VC excited for those returns. I mean, never saw he was going to be talking about that in the show. Me neither. You brought it up. But, uh, but, uh, - I know, thank you. - And nothing an energy storage worked before AI, right? There's the battery startup, right? What's the one that just raised a 12 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 them, it gave 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 gonna ground us in a little facts here, just on the three, I just, 'cause I think it will, I mean, Valo and baseball, super interesting, but very different. I mean, you know what, the Valo is purely, they we need more power for compute, bet, you 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 whole, 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 it's a new scale, it's the one that's doing the existing technology that's well understood. I think Lightwater, not the nuclear act, 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. And then all these guys, including Valo and Oclah, 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, 'cause there's no way to get cheaper electricity without doing nuclear, but, so from a public policy perspective, go team, but just from a, don't spend the electricity yet, you gotta 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 there, 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 I'm just, it's awesome with doing it. I think the question works, we're sort of debating is where is the money gonna go? How much are we gonna pay for intelligence? I want to pay for intelligence, and we talked about compute. I'm pretty sure how do we, how do you want us to talk about all the capex that's gonna happen out there? At some point in time, somebody's gonna 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 gonna be, that's gonna allow the values in the chicken manure world to actually be worth something. Yes, in the end, someone's gotta buy a trillion dollars where it tokens in corporate America. Not corporate Europe? Well, if we buy a trillion, they'll buy half a trillion a decade later. I hate to be cold, but that's a former European, I can say that, you know, I can't even be, whatever. Neckache is the most cynical deed on Europe, you'll ever meet, and he's Irish. Yes, we have two Europeans here. Unbelievable. Is this not just another layer of companies, which is dependent on Rory to your point, open AI and anthropic, continuing to go on that charge and hit that number. We've never had an ecosystem that will be so dislocated if open AI and anthropic do not hit that 2027 numbers. I don't think so. I know whether open AI or anthropic hit there 2027 numbers or not, is orthogonal to the fact that there is infinite demand for AI at this moment. And that demand needs to be satisfied by compute. Now, whether it's open AI 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 come to the allocation. 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. You'll talk about why Canon agent booked my airline, take it and buy, 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 to 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 on 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 is not the first engine. It's arguing against. And 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 right guy. I'm really, really, really, really, really, really, really, yeah, hey, David, you're running your 280 billion out of come. I'm just trying to turn 20 million investment into 100 million on call of the day. I'm a small guy. But do you anyone comment is infinite demand for intelligence? And let's assume it's just an enterprise now. Because I think you are right. The consumer side is super interesting, especially for open AI. 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 and traffic in open AI. In other words, because there's 70% of the Google compute backlogged, the 70% of the Amazon backlog. In the short term, the market is assuming that open AI and traffic 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, it's going to take-- it's going to be a pretty big dislocation. Yes. It's perfectly possible that there is a public market dislocation because the players of 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 LMS, and put Moonshot on it. And seller enterprises at 10 cents a dollar tokens. Yeah. Moonshot is happy. Videos happy. Enterprise is happy. Open AI very, very sad. You're right. But the question becomes, who 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 is 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. It doesn't matter. Every chicken manure company and every nuclear reactor company who saves 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 his ass company saying, holy shit, some of them not executed, not executing as well as the others. So eventually execution matters. And that's to decide the winners and losers in the market, not the shift of which intelligence is the best. I mean, the best example of that would be two years ago, OpenAI was first and then Thropic was second. And now on Thropic is first and OpenAI is second. Google was written off. And it was went off. It was a job point. The Gemini was non-existent. Now suddenly Google has to compute the cloud sales and Gemini. But still not the amazing open frontier model. Still not the coding agent. There's still not the coding agent. It's 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, will have we ever had an ecosystem so dependent on the success of OpenAI and Thropic? I mean, it is. But maybe to Necash'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 Necash would use would permeate software. And that would be good enough. Now this is the revenge of the frontier. We may not care any 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. 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. Now I run a simple firewall company. I'm 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 problem model does not know why this happened the last five times the 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 way more thing. I got people planting, I think this is a tree. This is why it goes down. So as I built that organizational in 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. And you're clearly 100% tracking sat here with the kind of Microsoft comments recently on age companies. And it makes them to enterprises need to build their own value, build their own context, rather than do it from here model. And that's what I think it's, yes, he's saying something different. I understand what he's saying. That's a different comment, minus different comment. I think there's three parts to it. There is, there is a 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 by auto getting transcribed. So my model knows what is a good answer was a bad answer. 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 Kruehn at Google when the whole thing just started into this shiny object called a lens. And I said, hey, do I need to build a cyber model? He's like, dude, overtime 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. 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 Santiago is saying something different. Santiago is saying you can't parse every problem into multiple models without carrying the context to the model to give it an context to get the answer. So he's giving you 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 than 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. But in a cache, just on that, you also said something not in conflict to it, but really, so you've got all your intelligence and 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 Pal Elton 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 even if all the context in your vector database and have confidence that the results will be the same? Bending spoons. It's a Darwinian moment. No, we're in a moment. Do not suggest that everybody survives. In other words, what you're really saying is if we don't figure this out, we will be working for the Italian. 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 open AI and anthropic warnings 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 write it because I pushed on why we almost talked 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 negate, but 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 it's true on the long term, but blanks for a year or two, it'll be a weird time and tech. And that's why we're all focused on the poster charts with the trend. But I think the trend is bigger than the poster children. Yeah, AI and intelligence is bigger than open AI and anthropics, what you're saying. Yes. You are right. And enterprises are going to want to consume it a lot of nibbles. But if it turns out to be 70, 80% beneficially thought of an open AI and a traffic, there will be a pretty significant dislocation up and down. Right. I mean, I think you're going to want to build our company at one point in time. And two years later, I joined Google as a 14 billion dollar 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 that you have to dance two or three years ago, whenever it was. We obviously had all of them coming out saying, "CappEx, 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 set context in a way that you can do it. Yeah, I mean, it's not that hard. I mean, you had four people report that will 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, Google 82%, AWS, Google 37% at scale. It's always hard to know what Microsoft, because they bum a bunch in, but they grew 20, 30%. So the big picture comment is people saw the shit ton of inference. And because of that, people said, "I'm going to buy a lot more compute because it pairs 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 my contrast meta also said, "I'm going to spend a lot of money, but it won the zombies having going to make money." So let's talk one 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. 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 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. Jason? Well, I think we already hit that, to me, maybe it's perpendicular to the sudden, I don't want to take off the track. But to me, the Palantir, which just happened was more interesting. I mean, growing almost 100% and bookings up 153% backlog, you can sell this AI. What should we take from that? Hey, Amtipraises need help with it? Palantir is the best. I think what we should do is send it to our portfolio companies and tell them the work harder because there's no excuses. I mean, a Palantir can do it. Work harder. Work harder. I mean, I don't know what the message is. I mean, certainly, certainly to Nick Keshe's point. I'd love to hear Nick Keshe's thoughts. If you can package and capture intelligence, right? The demand is is inexhaustible at Palantir, right? And you can also capture somewhat model agnostic intelligence. But the demand here 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, didn't get to this point. I might be wrong or oversimplified 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. We're in a capex cycle. There's a trillion dollars of capex that is being committed for the next one year across all these people broadly speaking. And the market is saying, great, I see these large, large companies, it has 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 now. Well, that's because of higher price being commanded. People from 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. It's some of these people are committing to capital or not able to show up with the capital, but for now, we have one more run at the real at table. So that's what's happening. We're being told that this market is going to support capex until it can't. You know, why would I not have my agent talk to my door dash 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 to learning if you talk about constate learning and agents. So everything is up for grabs every consumer app that was ever put from the iPhone has to be redone every enterprise app and so 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 the timing problem with the revenue show 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. 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 it's bringing back the whole open and anthropic debate. It doesn't matter if they show up with the money or not. Somebody will show up because they're not 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 values may not get their regulatory sort of rules. Europe may not allow data centers. You may find 30 states with you know, big advances would say no data centers in my state. So there's a supply problem that happens in compute site which could have a knock on impact in all our infrastructure bodies and semiconductor space saying, holy shit, doesn't look like all the stuff they're building 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 all sort of saying, I can package and tell us makes sense for if it for you. Your has a capability to have 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, 1998, 1999, 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 here to keep building the internet. And the interesting thing is 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 greet you, it isn't. But just an enterprise ability to digest at speed. Then to some extent, and I think I'm making a main point is 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. Are the better digesters will win and the poorer digesters will have heartburn? That is an interesting point is that that's that all that data that says the companies that are digesting AI quickly are going faster than the companies 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 training or whatever, at some point you will be bending spoon to use your point. Those may be leoed, but they're not even bending spoon. Nick Asher, in your nice analyst school, can you do like an ode to us? Well, when you get a shit hard question, you just just say bending spoons. I think you'll find Harry that when you were a 200 and a billion dollars or whatever, a enormous market cap this man has, you're not paid to joke on the earnings call. How are you paid to look down the line and deliver the product? That's how you keep your job. Roy, if you hadn't figured I ain't had to bring I key to the conversation. Okay? You just already speak. Nick Asher, 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 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 enterprise nutritionally, you know, 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? 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 see? 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 started 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 being 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 the brains of those people who solve them and abstract 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 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 transactions. You have to you have to give the organizational knowledge to some learning system that you have to build. I think that still is going to take three to five years, very 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 easy part. Now can I string along? I'm pretty sure fireworks will take my money and find you in 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 we start to present then accurate. Everything's useless. If funny, your point on learning, I was literally just try 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 are 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 the quicker will be the key man management skill in the next five or 10 years. And then what Palantir is selling, the way he's not doing so well is to be able to say, dude, we know this is the biggest problem is to see you. I at least have some kind of answer here. Let me help. Give me $10 million. It'll be great. That's an underestimate what Palantir might be doing. There is a capability that AI has already demonstrated where it can troll large corpses of data, summarize it, look for anomalous behavior, look for trends, capture them, reason around them, and reach conclusions. That's 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 bad advice data and produce 20 amazing insights and you couldn't 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 base points on my top line. And it improved my margins and my 100 base points. Hallelujah. You know, it just paid for everything. That's you don't have to put a learning system into place or nothing. It's just taking enterprise data and doing a lot of that stuff. And I think places like, you know, oil discovery or nation's data analysis or all bunch of stuff. Well, lots of people are required to go to this and write good. Doesn't need to happen anymore. We have final one. We have new CEO at scale AI as an option. They hit up billion and a half an hour. We mentioned the importance of data that obviously scale AI would be one of the biggest providers of data. We have Mailchim revenue declines for eight straight quarters. Fuck me. That's, that's not a nice headline is that Rory sells drone deployed to Pro Cool. Go Rory. 13 year journey. Amazing outcome. We have visa cutting 2,600 jobs. Neckash, you said it's not a jobs problem. Well, CEO visa says it's efficiency and shaping the way work gets done. So 2,600 people gone there. What not raising at 20 billion dollars? Can I ask Rory about drone deploy because it ties to the beginning of the conversation with deals in the cash, right? So that deal, the what's interesting. So so drone deploy was bought by Pro Core, right? Great classic software founder founded by two e to do software for real estate dominated it. It had a great run, right? Growth slowed. Most importantly, net new customer counts 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 an 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 Pro Core market cap is beaten down. It's got to come up with 900 million. A lot of it debt pay 11 12 X while it's trading at four. I find in the old days, I'm not saying that happened here. These deals are stressful, man. It's not it's not palo Alto network 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 Pro Core is going to think this is expensive to pay 12 X when it's trading at 4 X, right? And we started this on deals with the cash and we started this on whether 3 X to 4 X for air tables a lot. Well, Pro Core is one of the ones basically trading there too. So is this deal like super stressful? Did did you lose hair or people shouting and throwing things through the window? So I'm not going to speak for the acquire because I'm not in 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 found 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 fund raises, you're set up for success, not failure, you know, we always raised below the price we sold that. We didn't raise a ton of money. We were profitable. We're just, you know, it was a fine little company growing nicely. And the second thing is, and it 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 actually on the side of the future. And in fact, one of the lessons I learned of 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 really started to explode now as our 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 enough of that made us do different. I know what a common and specific city offer. But I think one of the hards 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 wildly 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 you know, probably in the cash has done these kind of big strategic way. 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. I think that what percent of market cap does a deal become a BFD, a big fucking deal, a core strategic this needs to work. 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 a lot of line wasting. 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% of work, 25% haven't. Our largest deal was a 28 billion dollar deal, which probably is currently valued at $50 billion dollars. That's since 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 he's spent 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 get the market gives you credit for making it 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 cyber rock, right? Yes. Got it. Rumoraz that 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 how it is. It's sort of simple. Like all the best deals. One of my partners always said, if you can 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 these agents are going to do. In your case, you're just going to restrict agent behavior, Jason. But man, they're so good. But it's so powerful. And on that point, Jason, to the point when actually kind of ties back when I said I just was reading some stuff last night that really does a quarter on the cash. I said, well, someone made the point. If you can't specify what they're doing, and 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 and employee and you're not quite sure what they do. You just got to bound the systems they can access very tightly. So I actually think I totally get your point together. The only danger that you take into the extreme, that's called automated workflows. That's deterministic outcomes. If it's deterministic outcomes, you 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. But the flip side is it does a really good job. And listen, 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. And the last couple months, like really good, right? So it's a trade off. >> 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. You know, even your finance allows you to write checks. I might want to have a conversation. >> Speaking of people big long, 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, which 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 again, huge credit. They kept the thing going. >> Hey, we didn't serve sold to cognition, right? >> Exactly. >> But they sold really quickly, both sides sold quickly and then valued. 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, >> Rock could be the next one too. >> Yeah, you're right. The remaining rock, 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. >> That's a good quote, Rory. >> Not only cashed, you see why I go home early from dinners, because I need to be fresh for podcasting. You see, this is hard work. You build this building enterprise value in your public companies. This is where the real grind is. >> He's sitting there going, he's doing this ice clothes, and he'll go back to making his $2.0 and he'll build a not a market cap company, work-life. >> Rory, he's got to be built one deal of time, my friend. He enterprises 1% inspiration, 99% perspiration. >> Don't forget. >> No, I do not. What I tell my agents every day, guys. >> Agents don't swear. >> Get to work. Stop it. Get to work, boys. >> Expression. >> Nick Ash, 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 to set up the config, the gap between what you pictured and what you actually ship. You describe it. Apps, websites, AI agents, real working products. Built in minutes using nothing but plain language. That's base44.com. Founders and operators spend way too much time every week, jumping between meetings, investicles, brainstorms, customer conversations, and then trying to piece everything back together afterwards. Plaud instantly captures conversations, voice notes, meetings, random ideas with one press, and then turns them into clean summaries, action items, mine maps, and searchable notes that you can actually come back to later. The Plaud Note Pro is literally as small and thin as a credit card. 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