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

22:04Harry Stebbings: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 stock. I had a sea 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 anthropics models breach three companies too this is obviously on the back of the open a 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 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.

22:56But 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 OpenEye 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-as-a-flag exercise. But they decided to give a target to say, go out in the wild, persist, take as long as you want, to go capture a flag. So fine, we have these models which have these capabilities. The challenge we have from a cybersecurity perspective is we're finding vulnerabilities, which would take us days, months to find.

23:31The average time to a patch of 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 the fundamental speed at which cyber attacks will happen and need to be defended changes. And this is good for us. It's kind of like the sound of revenue. But I think from a more fundamental perspective, I was thinking this capability is going to show up in six months. I think I said that with you, Harry. And it showed up in four months.

23:59I think in two or 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.

24:09Harry Stebbings: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 normal? The good news is the flex that Anthropic did with Mythos has every CEO talking about Mythos. I spent eight years trying to get CEOs to talk about cybersecurity, couldn't get them to do it. And Dario did it in one fell swoop. So this is good news. He's got everybody all hot and heavy about Mythos and the capabilities of Anthropic and how these models are going to go attack your infrastructure.

24:46I've never had so many CEOs call their CEOs and say, are we ready? What's going to happen to us? Well, the answer is you're not, because what being ready means is that I have no vulnerabilities, either in my code, any vendor that I've got deployed, my infrastructure, any open source I'm using. That is fundamentally not true. Now, we found 14 ,000 vulnerabilities in open source in the last 14 weeks, testing open source packets. So it's a bunch of stuff that's being used out there. So, A, every company has vulnerabilities. They've got to figure out a way to patch them. B, these models will figure out misconfigurations.

25:15If 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've fixed most of these, the bad guy has 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?

25:44The average time to detect and respond is four days. How are you going to get it down to a minute? So it's not a fear problem. It's a capability problem. It's an infrastructure readiness problem. And now it's come to bear. So it's time to pay your taxes.

25:58Harry Stebbings:Yeah, I mean, that's the last sentence. I mean, you're right. We can argue fear versus thing. But what you're basically saying is the security infrastructure that you had a year ago is wholly unfit for purpose in the next year. and you, Mr. Enterprise Buyer, are going to be buying a whole load more stuff, are you going to be the weakest link when these capabilities are everywhere? Yeah, you said it so well, Rory. I mean, it just feels so good. I don't mean to be... I think you should be on a podcast talking about how people need to buy more cybersecurity. I'm just going to buy the stocks, but...

26:29Harry Stebbings:Now, Nikesh, can we get him some swag? I mean, Jesus, he's like, that's it. Come on, Jason, give Nikesh a hard question. You're the one building. I want to ask him as my cybersecurity therapist, I had two fable issues and their 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. I already figured I don't have a thick skin the first 30 seconds of this conversation. Okay, good. So I'm building an app for the Sastra community. It's called Sastra Connect to help with recruiting. Details don't really matter, but it's the biggest thing I've built, right?

27:02Harry Stebbings: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.

27:32Harry Stebbings:Never was told. Never was in a changelog. 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 just the hack. The part I didn't tell you is the way it did it is it MCP'd in. So I had Google Drive to Claw, 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.

28:08Harry Stebbings:And it never asked me and it never told me it did it. Is this scary? Is it not scary? Is this Orwellian? What happened to me? It's 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 cloud. They want to connect 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.

28:39On the enterprise side, there is some cohesion around it. I think the most obvious ones are people saying, you can't use this. Now, that only encourages people to use it more, but there is this stream of thought saying, if I don't allow you to use it, I have time to go figure out how you're going to use it. The challenge you have, Jason, is, and I think this is burdened. When the Wright brothers built a plane, they didn't invent TSA. That was not the first thought that crossed their mind. TSA came a lot after. You don't think about security when you start playing with new things and cool technology, and that's what's happening.

29:09You're seeing people play with OpenClaw. You see people play with agents. You're seeing people play with all this stuff with LLMs. Everything's happening. LLMs are training on data if you're not careful. And 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-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. It's learning on all your behavior.

29:38Harry Stebbings: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? Yes. Board of the above. Okay, board of the above. 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 you are, and I've got security at the perimeter, I'll stop you. The known bad, I'll stop you at the door.

30:11The problem is no cyber attack happens because I stopped a known bad. Every cyber attack happens because - 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 from a cyber perspective, you want to be in the perimeter business. You want to be on as many perimeter endpoints in the world as you can because that becomes a sustaining business. The more perimeters I'm on, the longer my tenure for my business is.

30:42So I'm on endpoints, I'm on devices, I'm on servers, I'm on firewalls, I'm predicting 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 bads will change using AI. There's a concept of data classification. You have to write static rules. Guess what? Nell-Lim can suss it out much faster from a content perspective. We track every malicious website in the world. Can AI tell me it's a malicious website much faster? Yes, it can. So the ingredients of my perimeter security will change using AI. The act of stopping things in line will still be needed.

31:17So when people tell me, oh, OpenAI is going to eat my lunch or Mithal 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 infrastructure and find out the bad actor, guess what? imagine collecting all the enterprise data and running an LLM right on it and say, find me all the abnormalities, find me behavior that you've never seen before.

31:47Now, I'm ingesting 19 petabytes of data a day. Think about it. 19 petabytes of data a day of enterprise data to look in it for anomalous behavior. I have machine learning techniques. I have static techniques. I have rules that I look at 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 1 ,200 customers who bought and deployed it. I need to get the rest of the world to go buy it and deploy it.

32:19So 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. so you can have a whole episode, 90 minutes on about what are agents, what is really an agent, how do you give agency, and how do you control an agent. It's funny. People tell me they've agentified stuff, but then I ask them, does it actually have agency? Like, what does that mean? I'm like, Waymo has agency. It can drive you into the wall without human intervention. This is a bad problem. But most people haven't actually given agency to their agents.

32:51So they're running glorified workflows, which are deemingly agentifying things. But when you start giving agency to things, when a piece of code can decide what happens next, we're going to have a whole different conversation on how do you secure those agents, how do you build kill switches, how do you intercept them in line, how do you stop them from doing bad things. Just like Jason's agent, which did a bad thing and took Jason's gems, and now the whole world will find out what Jason's gems are.

33:16Harry Stebbings:Totally agree. It's crazy. We said open, we said China. Whether we have comment on it, Moonshot closes$3.5 billion at a$35 billion valuation. 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 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.

33:55Harry Stebbings:The model is free and the inference is how you make your money. It's not as clear to me long term if it's possible to continue on a sustaining basis, offer open weight models without monetizing in some way. And we'll see what it is when people like Reflection and Thinking Machine start. When these models start to happen in the US, it will be interesting to see what the business model is of which an open weight model is a part. 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.

34:33I'm going to have Harry a soundbite. Good. Average intelligence is going to be free in the long term. And the average intelligence will keep getting better. Nice.

34:42Harry Stebbings:Exceptional intelligence will be paid for. Can you give me some tone with that, Nikesh? That was all monitor. I want like drama. Come on. You've got to deliver the sound. I was watching somebody speak the other day, and they said if you whisper loudly into the mic, people lean over and pay more attention. So I'll say it again. I say in the long term, average intelligence is going to be free, and the average intelligence will get smarter. But do you think we'll rely less on frontier intelligence? We won't need it? Oh, no. We'll need exceptional intelligence. We need exceptional intelligence to discover the cure for cancer.

35:13We need exceptional intelligence to send rockets to the moon. We need exceptional intelligence to build a space data center. Those are exceptional intelligence tasks. They are still going to require exceptionally intelligent people or exceptionally intelligent models. And people will pay for it because the outcome is so spectacular. I don't think you need to pay$6 a million tokens to answer a call saying, how can I help you? I'm so sorry, your network connection is not working. Agreed.

35:38Harry Stebbings: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. 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 and intent 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.

36:08The amount of effort that goes. The problem we have is like, sorry to go back to the Waymo example because it's kind of, I think it's the most obvious one out there. I drove in the first set of Google self-driving car. I don't know what I was thinking. In 2009, when I used to work there, it was a Lexus with a bunch of cameras. It drove me from San Francisco to San Martin on the highway, and my hands were not on the wheel. And then it told me at 11 p.m. to take the wheel in my hands. I was driving to Cordoba, and I did. And I sort of was more relaxed about saying, oh, maybe it's just going to figure it out when I make a wrong turn because it's so smart.

36:39It drove me. I was like, no, dude, this does not drive when it turns off. So that was 2009. It's taken 14 years after that to get one with all the edge cases trained from a machine learning perspective for us to rely on that as being the, 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.

37:16So you're getting 80 % right now. You're getting 80 % of customer support solved. All the edge cases are waiting to be solved with AI. Yeah.

37:23Harry Stebbings:Then there is the, for a given app, how much of the value is purely in the model versus all the other things. And you're right. For something like customer support, you're probably paying 10 % or 15 % of the revenue you're getting for intelligence. and the rest of it is all the other shit it takes to make that intelligence actionable in the context of answering tickets. And one of the things we look at is just super interesting on the app level is the tokens as a percentage of total revenue. And it varies from the sales forces, we were in intercoms, stuff like that, where it's some plus or minus 10, 15%.

37:54Harry Stebbings:Obviously, in coding and things like that, it's 70, 80%, which means it's just raw intelligence and a mild harness. And those are just very different. I think in the next three, four years, we won't be paying for intelligence. will be paying for compute through our nose. I mean, speaking of paying for compute through our nose, we often get chastised for being too public markets focused or too Anthropocon OpenAI focused. Valar Atomics triples to$6 billion prices Sequoia bets on nuclear for AI. It's a three-year-old small modular reactor company, raised at$2 billion, now Sequoia leading around at$6 billion.

38:29Harry Stebbings: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, I was talking 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, it's a cute project he's got running somewhere in the middle of the country. And then he told me that he raised money in billions of dollars and he's selling the energy to hyperscalers. So anybody who can produce any energy source, it doesn't matter where you are, is right now trading in multiple because land, permits, energy, compute.

39:01this 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 you can take you can also take all the free chinese models you want

39:18Harry Stebbings:where are you going to run them jason 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 models actually used to work it's literally chicken shit so did cows i even looked at some of these things, but the margins were so low. The IR was so low, but the business work now AI is, 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 8 % annual return if everything goes well.

39:51Harry Stebbings:And it's just, you know, it's hard to get 20 VC excited for those returns. I never saw that he was going to be talking about that on this show. Me neither. You brought it up, but everything. And nothing in energy storage worked before AI, right? There's the battery startup, right? What's the one that just raised it 12 billion to? 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, give me my RAMs. I can't even get my Mac Studio with more than 64 gigabytes. Give me my, everything's working, right?

40:21Harry Stebbings:I'm going to ground us in a little facts here, just on the three, right? Just because I I think they're all, I mean, Valor and base power, super interesting, but very different. I mean, you're right. The Valor is purely the 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.

40:49Harry Stebbings: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 Lightwater, 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. And then all these guys, including Valor and Okla, are doing new different things. And the big question will be after you get the initial demonstration that it works, what's the regulatory path? You have to be wildly supportive because there's no way to get cheap electricity without doing nuclear.

41:23Harry Stebbings:So from a public policy perspective, go team. But just from a don't spend the electricity yet, you've got to plow your way 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 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 we're doing it. 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?

41:55And once we pay for intelligence, we talked about compute. I'm pretty sure Harry will want us to talk about all the capex that's going to happen out there. At some point in time, somebody's got to pay for all this compute. And that money has to come from some version of some people paying for AI. And that's the only thing that's going to allow the Valors and the chicken manure company in the world to actually be worth something.

42:16Harry Stebbings:Yes. In the end, someone's got to buy a trillion dollars worth of 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 as a former European, I can say that. You know, a current European, whatever. Nick, he's the most cynical dude 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, 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.

42:56I 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 give me reallocation. Maybe we're going to have to give more compute to enterprises over time as they become better monetization capabilities.

43:35Or you'll find that eventually the promise of consumer monetization is going to start showing up. We all talk about why can't an agent book my airline ticket and make me a restaurant reservation. And these are simple use cases. I don't need to go solve cancer to get that stuff to work. That stuff's going to work. When that stuff works, there's going to be monetization opportunities on the consumer side. So I believe that at first principles, there will be tremendous amounts of compute that will be needed to satisfy the AI use cases both in consumer and enterprise. Which player ends up monetizing them becomes a question for the markets to decide.

44:08And 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.

44:16Harry Stebbings:Argueing 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 - I see where you live, Rory. Yeah, hey, dude, you run in your$280 billion 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 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.

44:45Harry Stebbings:I think where it does matter is right now, the assumption is 70, 80 percent of that demand gets channeled through entropic and open AI. In other words, because there are 70 percent of the Google compute backlogs, the 70 percent of the Amazon backlog. In the short term, the market is assuming that open AI and shopping, 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 U.S. enterprises. And if it doesn't happen that way, there's going to be a pretty big dislocation. Yes. It's perfectly possible that there is a public market dislocation because the players at the tables might change.

45:25And that's great. That's called a buying opportunity because that doesn't take you with the infinite demand. It's extremely possible that perhaps this wonderful company called Moonshot, which we talked about three seconds ago, could be the model of choice and that somebody is going to take that compute, which is not going to be used by Frontier LMS, and put Moonshot on it and sell it to enterprises at$0.10 a dollar for tokens. Yeah.

45:46Harry Stebbings:Moonshot is happy. NVIDIA is happy. Enterprise is happy. OpenAI, very, very sad. You're right. That's the dislocation. 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.

46:19We 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. 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 of them.

46:46Harry Stebbings:I mean, the best example of that would be two years ago, OpenAI was first and Anthropic was second, and now Anthropic is first in OpenAI. And Google was written off. And Google was written off. Gemini was non-existent. Google was written off. Now suddenly, Google has the compute, the cloud sales, and Gemini. But still not the amazing Open Frontier model. Still not the coding agent. All your customer support agent is going to be extremely unhappy because he didn't get a chance to answer it using the best model. Just kidding. Well, to Nadege's point, Harry kicked this off by saying, you know, Will, have we ever had an ecosystem so dependent, right, on the success of open anthropic.

47:21Harry Stebbings: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 is now the front that we've, this is the revenge of the frontier, right? We may not care in a year what model we like. We need frontier models. We need the best, but we may not care who wins. We may not care who wins this battle. We may, this may I'll blow over and it all may be about compute and we may not, whoever wins wins, whoever wins, I'll plug in.

47:51Jason, I think the models will get better and better. And the distinction between models may not be enough for you to decide to rip one out. Because I think the part which we will build, we are starting to bid 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 or a simple complicated firewall company, you can stick any model you want. The model doesn't know why my customer's infrastructure is down. It does not know because my model doesn't know what product my customer is using. My model does not know what operating system it's using.

48:21My model does not know what the configuration of the customer is. My model does not know why this happened the last five times as a customer. All that knowledge, all that learning is being captured by me in effectively vector DBs and in context learning systems. And that's what my team is doing. I have more people collecting context than I've ever had. It's kind of like the Waymo thing. I got people plant, I think 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.

48:55Harry Stebbings:And you're clearly 100 % tracking, Satya, with the kind of Microsoft comments recently on, you know, age companies. And it makes into it. Enterprises need to build their own value, build their own context rather than do it in front of your model, right? And that's... I think it's, yes, he's saying something different. I understand what he's saying. That's a different comment. Mine is a different comment. I think there's three parts to it. There'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.

49:29I'm talking about the context needed to train the ecosystem, which means I've got every customer case that ever happened at Palo Alto getting transcribed. so my model knows what is a good answer and what is a bad answer.

49:39Harry Stebbings: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 Corrine at Google when the whole thing just started, you know, this shiny object called LLMs. And I said, hey, do I need to build a cyber model? He's like, dude, over time, what's going to happen is the models are going to get smarter and smarter and small models will not be as smart as the big models. And he was right. The small models are more intelligent than the big models. Now, at some point in time, if your average intelligence becomes smart, which is what I said, then the distinction between little more intelligent, less intelligent, is less important than knowing the domain and the context.

50:15Got it. So I think we're coming to a world where in the next five years, domain becomes equally important with the model intelligence. And I think Satya is saying something different. Satya is saying you can't parse every problem into multiple models without carrying the context to the model to give it enough context to get the answer. So he's giving an architectural point 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 context, because otherwise I get commoditized.

50:45So I think that's a bit of a commoditization battle that's going to happen between model and models plus context.

50:50Harry Stebbings:But Nikesh, just on that, you also said something not in conflict to it, but really, so you've got all your intelligence in your vector database or whatever it is, all your context, right? And then you can pick and choose your LLM on top of it. But as you said, the LLMs don't perform the same. You know, even Opus 5 and Fable and at Palo Alto Networks, you have a team that can manage that, right? Those changes. No, we're learning as we go along. So you're learning. What about the average enterprise that doesn't have as strong a team as you? How can you really switch out these LLMs, even if all the contacts in your vector database and have confidence that the results will be the same?

51:23Bending spoons. It's a Darwinian moment. Darwinian moment do not suggest that everybody survives.

51:30Harry Stebbings:In other words, what you're really saying is, if we don't figure this out, we will be working for the Italians. So we're going to figure it out. Got it. I love the way I went to private markets to get the private market discussion. And the straightaway discussion is, well, it depends on what OpenAI and Anthropik are willing to pay for it. And it goes back to that. And it's just funny how everything just rotates back to compute and what the big buyers are willing to pay. And you're right, Eric, because, you know, I pushed on why we always talk about just the same two companies. but we internalized that no matter what you talk about, you end up back talking about them because they're, to negotiate, they're the giant sucking sound on demand that's just pulling everyone along all the way up and down the chain, which is why I think you're correct.

52:14Harry Stebbings:If that demand signal turns out to be attenuated or dips or even is true in the long term, but blinks for a year or two, it'll be a weird time in tech. And that's why we're all focused on the poster charts of the trend. But I think the trend is bigger than the poster children. Yeah, AI and intelligence. is bigger than OpenAI and Anthropic is what you're saying. Yes. You are right. And enterprises are going to want to consume it a lot. But if it turns out to be 70, 80 % beneficially other than OpenAI and Anthropic, there will be a pretty significant dislocation up and down. Right? I mean, I think - Yeah, it was a$200 billion company at one point in time.

52:49And two years later, I joined Google as a$14 billion company. 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 that you think - Yeah, I mean, it's not that hard. I mean, you had four people report that would be relevant here.

53:26Harry Stebbings: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 the CEO of AWS in particular made a very declarative, the ROI, he was amazing. And the market was really happy.

54:02Harry Stebbings:In particular, Amazon and Microsoft got marked up pretty significantly. And then by contrast, Meta also said, I'm going to spend a lot of money, but it wasn't as obvious how they're going to make money. So their stock went down. Probably the most surprising thing, going right back to the layer thing is, God, those were really strong numbers. I mean, all these people are selling a shit ton of compute. 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.

54:35Harry Stebbings: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, 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 can sell this AI. Jason, what should we take from that? Like, hey, enterprises need help with it. Palantir is the best. I think what we should do is send it to our portfolio companies and tell them to work harder because there's no excuses.

55:12Harry Stebbings: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 to Nikesh's point, I'd love to hear Nikesh's thoughts. If you can package and capture intelligence, right, the demand is inexhaustible at Palantir, right? And you can also capture somewhat model agnostic intelligence. But the demand here for intelligence, for compute at the app, I mean, at some level, Palantir is a very sophisticated harness on top of massive amounts of data, right? And maybe vectorized databases, to Nikesh's point.

55:47Harry Stebbings:I might be wrong or oversimplifying it, but they've captured that to a magical element in the age of AI. People need to solve these problems with data. They need answers. And Palantir gives, I think, less than 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 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.

56:17I see these large companies, which have the ability to fund this trillion dollars of CapEx. And there are signs that they're getting compensated for some part of the CapEx that's out there. Now, whether that's because of higher price being commanded, people demanding deployment of AI and deployment of cloud, this is good news. You know, that CapEx dislocation is not happening today. That could happen tomorrow if some of these people 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.

56:46We'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. 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 from the iPhone has to be redone. Every enterprise app in SaaS you just debated has to come back with, I have an opinion.

57:17So 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 the timing works, I think the biggest only problem we have right now is the timing problem. Would the revenues show up fast enough to keep funding the CapEx cycle, or is there going to be a dislocation in CapEx versus outcomes? Now, the telecom industry is very used to this because they used to spend billions of dollars building 3G, 4G, 5G, and then they'd see the rewards would come later. So they went through a CapEx cycle, and that's pretty established in the market.

57:50It 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 this brings 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 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, developers may not get their regulatory set of approvals.

58:18Europe may not allow data centers. You may find 30 states with picket fences which 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 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.

58:48And I think to Jason's point, why not? Palantir is at the party. They also are saying, I can package intelligence, make sense for you. You don't have the capability. You don't have the resources. Let me make sure you don't become extinct in this wave of technology. I'm going to go back, you know, like in 1997, 98, 99, when we saw the last big pivotal technology called the Internet, a lot of the characteristics were similar, except you just didn't need a trillion dollars a year to keep building the Internet.

59:13Harry Stebbings:I think 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 it isn't, but just an enterprise ability to digest at speed. Then to some extent, and I think Gavin Baker made this point, to some extent, if enterprise can't digest fast enough, then to some extent, if the spend slows down because they can't get it online quick enough, it may be timed perfectly with the enterprise ability to digest. Or the better digesters will win and the poorer digesters will have heartburn. That is an interesting point.

59:45Harry Stebbings:That's all that data that says the companies that are digesting AI quickly are growing faster than the companies are not. And I think that's not true in everyone. But my guess is to your point, if, for example, you're playing in finance and your competitor is using advanced LLMs and you're not for trading or whatever, at some point you will be bending spooned, to use your point. Yeah, those may be lewd. They may not have been a bending spoon. Nikesh, in your next analyst call, can you do like an ode to us where when you get a shit hard question, you just say bending spoons? I think you'll find, Harry, that when you're worth$280 billion or whatever enormous market cap this man has, you're not paid to joke on the earnings call, Harry.

1:00:27Harry Stebbings:You're paid to look down the line and deliver the product. And that's how you keep your job. Rory, if you hadn't figured, I ain't here to bring IQ to the conversation, okay? Yeah. Nickash, 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.

1:00:56Harry Stebbings:But most enterprises traditionally, 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? Do you think we'll revert to the mean where we can only process change every five years after we get over a hump? What are you seeing? I think the enterprise's ability to absorb this or digest this or perhaps leverage this to their advantage depends on their ability to create training data as fast as they can. And I think not enough people are focused on training data. This is not a problem Palantir can solve for me.

1:01:31This is not a problem that Fireworks can solve for me. this is a problem I have to solve. I have to parse through freaks. And I'm sorry to go back to the same thing. I get 400 ,000 customer cases a year. I know when they come in, I don't have enough context. Some human beings solve it. I don't know how they solve them. I don't know what logic they apply, but they solve them. I need to 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 as to how to solve the problem the next time it shows up.

1:02:00So 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 booked every transaction. So you have to give the organizational knowledge to some learning system that you have to build. And I think that still is going to take three to five years.

1:02:30every 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. Can I string along? I'm pretty sure Fireworks will take my money and fine-tune an open weight model for me if I want and keep training my use cases to a point. But beyond that, how do I get from 70 % accuracy to 99 % accuracy? That's the problem. The problem is I don't know which 30 % is inaccurate, so everything's useless.

1:02:58Harry Stebbings: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, Mr. CEO. I at least have some kind of answer here. Let me help. Give me$10 million.

1:03:25Harry Stebbings:It'll be great. That's not to underestimate what Palantir might be doing. There is a capability that AI has already demonstrated where you can trawl large corpuses 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 trawl through petabytes of data and produce 20 amazing insights, and you can go judge them and say, well, 15 of them are okay and five are amazing, but the five that are amazing will change my ROI and give me 200 basic points on my top line and improve my margin by 100 basic points.

1:04:04Hallelujah. You just paid for everything. You don't have to put a learning system into place. You have nothing. It's just taking enterprise data and doing a lot of that stuff. And I think places like oil discovery or nation state analysis or a whole bunch of stuff where lots of people are required to go 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 ARR. We mentioned the importance of data there. Obviously, ScaleAR would be one of the biggest providers of data. We have MailChimp revenue declines for eight straight quarters. Fuck me. That's not a nice headline, is it? Rory sells drone deployed 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?

1:05:00Harry Stebbings:Can I ask Rory about Drone Deploy? Because it ties at the beginning of the conversation with deals and the cash, right? So that deal, 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.

1:05:30Harry Stebbings: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. These deals are stressful, man. man. 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. It 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?

1:06:04Harry Stebbings:But Procore is going to think this is expensive to pay 12X when it's trading at 4X, right? And we started this on deals with Nikesh and we started this on whether 3X to 4X for Airtable is a lot. Well, Procore is one of the ones basically trading there too. So is this deal like super stressful? Did you lose hair? Were people shouting and throwing things through the window? I'm not going to speak for the acquirer 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 founder tired.

1:06:33Harry Stebbings:I mean, I think actually some of the interesting lessons, there's about two or three interesting lessons here. First of all, when you have capital discipline and modest fundraisers, you're set up for success, not failure. We always raise below the price we sold at. We didn't raise a ton of money. We were profitable. It was a fine little company growing nicely. And then the second thing is, I think it's really important, the trend was our friend, not our enemy. I think some of these very basic SaaS companies, you look back and go, there's been a platform shift and you're on the wrong side of it.

1:07:01Harry Stebbings:When you're software that's enabling drones and robots, you're actually on the side of the future. And in fact, one of the lessons I learned having invested 10 years ago is in the physical world, AI takes a lot longer to happen. I mean, when it happens, it's amazing. But it's clearly, you know, I look back 10 years ago, I thought drones would have exploded five years ago. They're really starting to explode now as are robots. So it took a long time. So in fact, we were on the upswing of this feels really good. We're happy to hold. And then obviously, we got an offer that made us do different. I don't want to comment on specifics of the offer.

1:07:33Harry Stebbings: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 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 probably Nikesh has done these kind of big strategic where you just say, my thing is this big.

1:08:03Harry Stebbings: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. 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 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.

1:08:40And I want to say 75 % have worked, 25 % haven't. Our largest deal was a$28 billion deal, which probably is currently valued at north of$50 billion. That one's got to work. That 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 the market gives you credit for making deals work. I said that in my earnings call, and they got all freaked out. And then I'm just saying, you have to make the big ones work. If you don't make the big ones work, then you lose the license to run your business.

1:09:15Harry Stebbings:And that big one was CyberArk, right? Yes. Got it. 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. It's sort of simple. Like all the best deals. One of my partners always said, if you can't express it in a sentence, it's probably a bad deal. And if you can, it's probably a good one. Got it. As you've seen with my example, you don't know what these agents are going to do, man. In your case, you're just going to restrict agent behavior, Jason. But man, they're so good, but it's so powerful.

1:09:42Harry Stebbings:And on that point, Jason, to the point when actually it kind of ties back to what Nikesh said. I just was reading some stuff last night that really does a quarter of what Nikesh has said. Some of them 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. The only danger is if you take it to the extreme, that's called automated workflows.

1:10:12That'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.

1:10:22Harry Stebbings:The flip side is it does a really good job. And listen, we don't have the perfect security profile to your point, right? But even with what we have, which is probably one agent has about a thousand rules to your point, right? The rest probably have five, right? Or zero, right? But even with the zero to five, 99 % of the time today, since January, since the model's upgraded, pretty darn good in the last couple of months, like really good, right? So it's a trade-off, right? It just takes one destructive example to make it all unwind. If you give it access to a bank account, let's see what it does.

1:10:51If your VP of finance is allowed to write checks, I might want to have a conversation.

1:10:55Harry Stebbings: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 products to an insatiable demand for data. And I give them huge credit. They kept the thing going. Hey, Winsurf sold to Cognition, right? Exactly. But they sold really quickly.

1:11:26Harry Stebbings: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. Grok could be the next one, too. 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. That's a good quote, Rory. Now, Nikash, do you see why I go home early from dinners?

1:11:55Harry Stebbings: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. 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. Rory, it's got to be built one deal at a time, my friend. The enterprise is 1 % inspiration, 99 % perspiration. Don't forget. No, I do. Dude, I do not. That's what I tell my agents every day, guys. Agents don't sweat. Get to work. Stop it. Get to work, boys. Inspiration. Nikash, it's been fantastic. Thank you, guys.

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