20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence

Nikesh Arora is the Chairman and CEO of Palo Alto Networks, the global cybersecurity leader. Since taking over in 2018, he has transformed the company from an $18 billion market cap business into one worth more than $225BN with more than 21,000 employees globally. Previously, Nikesh was President and COO of SoftBank, where he worked alongside Masayoshi Son and helped shape the firm's technology investment strategy. AGENDA: 00:00 Why AI Token Prices Will Fall 90% — And Why That's Bullish for AI 07:40 The Frontier Model Problem: Breadth vs Depth in AI 11:30 Most Enterprises Are Using AI Completely Wrong 13:10 Why AI Could Cut Marketing, HR & Finance Teams in Half 16:00 AI Applications Will Have Opinions — SaaS Never Did 20:00 OpenAI, Anthropic & The Most Important Valuation Question in Tech 24:00 The Real Business Model of AI: Transaction Revenue Beats Advertising 25:10 Why Token Prices Must Collapse 28:20 Where Value Actually Accrues in AI: Models, Memory or Apps? 29:00 Why Memory Becomes the Biggest Moat in AI 32:00 Why Every Enterprise Should Be Scared Right Now 33:15 Should Governments Regulate Frontier AI Models? 37:10 Why Brian Armstrong's AI-First Playbook Doesn't Work Everywhere 40:00 The Biggest AI Mistake CEOs Are Making Today 42:00 How Nikesh Creates Darwinian Competition Inside Palo Alto 43:00 Do AI Companies Really Need Forward-Deployed Engineers? 45:00 Why Enterprise AI Products Still Aren't Ready 52:00 Systems of Record vs Systems of Intelligence: The Future of Software 54:00 Why AI Applications Will Replace Traditional SaaS Workflows 58:00 What Nikesh Learned From Google That Still Matters Today 1:04:00 From $200 and Two Suitcases to Running a $225B Company 1:10:00 Happiness, Gratitude and Why Tomorrow Matters More Than Ten Years From Now

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

• 0:00Speaker 00

  I think the long term token pricing should be one tenth of what it is today. Mitos ended up, I think ends up being an accelerant to cybersecurity. In technology, you miss one trick, you can survive. You miss two tricks, you're partly impaled. You miss three tricks, you could be obsolete. I came to the United States with two suitcases, $200, and I was willing to do anything, anything at all, to make sure that I made a life for myself because there was no way to go back. When I came to the United States, I was a security guard. I took notes with the disabled. I flipped burgers at Burger King. I had $200. I had to find a way of paying my tuition.

• 0:30Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. In the hot seat today, we have Nikesh Arora. Nikesh is the CEO of Palo Alto Networks. They have a market cap of 225 billion. Nikesh is one of the most respected operators in technology. Before, he was CMO at Google, of all things, and I questioned him on marketing in this show. And then he's like, well, I was CMO of Google. God, there are some moments when I think, Harry, you should stop talking. He's an old friend, but this was a very authentic and honest discussion in a way that I don't think you could have had without that friendship. It was in person in London. Nikesh here is one of the best that I've ever seen him. But before we dive into the show today, you have the idea, but often with AI tools, you hit a wall.

• 1:14Harry Stebbings?

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• 2:00Harry Stebbings?

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• 2:44Harry Stebbings?

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• 3:28Harry Stebbings?

  operational traces, and coding data sets that stress models under conditions where failures matter: state changes, workflow branching, brittle tool calls, and the coding errors that break RL agents but never appear in benchmark reports. In reality, a model may demonstrate correct reasoning in your evaluation setup, yet still select the wrong parameter or mishandle a code update in a realistic interface. Turing makes that failure visible and gives teams the signal they need to fix it. For labs advancing agentic systems, Turing provides the structure required to understand why these failures occur. To find out how, visit turing.com/20vc. That's T U R I N G dot com forward slash two zero V C.

• 4:12Harry Stebbings?

  You have now arrived at your destination.

• 4:15Harry Stebbings?

  Nikesh, last time we did a show, I was 22 hours into a 24-hour fast. And I listen back now, and I just think, my word, you had the audacity to be with one of the OGs of this business and be hangry. Like, I was, I was hangry with you and short-tempered.

• 4:35Speaker 00

  Well, the good news is, I told you earlier in the chatting, I have total memory loss. I have no recollection of what I said and what we talked about, so I have to go back to listen to it. Just happy to be here.

• 4:43Harry Stebbings?

  It was a great show. We got on blissfully well. It was wonderful. I don't know what that was about.

• 4:47Speaker 00

  I think we should stop doing podcasts remotely.

• 4:49Harry Stebbings?

  God, I agree.

• 4:50Speaker 00

  I think you should force people to come in here.

• 4:52Harry Stebbings?

  So I actually do. And the nice thing about size is, they do. But A, the only challenge is, they actually sometimes come quite jet lagged because they come like before the day. And that's a little bit tough.

• 5:04Speaker 00

  Maybe you should do it 11pm.

• 5:05Harry Stebbings?

  Maybe.

• 5:06Speaker 00

  Yeah. People from California, it's perfectly nice time for them at 10pm or 9pm. Maybe you need to.

• 5:11Harry Stebbings?

  And maybe I need to adjust. I'm the selfish one. You know, Mark Andreessen said something to me. Mark Andreessen said, I really actually embraced it. Haven't even got to the first question, but fuck it. He said, you need to embrace, how is it all your fault? If you embrace everything in life with, how is it my fault? Actually, a lot of the world changes.

• 5:30Speaker 00

  Actually, let's spin that a bit. How do I make it better? Change the outlook is, how do I make it better? What can I do to make this better? That's how I run my day and even run my life, my company. How can I make it incrementally better today? And how can I make it radically better in three years?

• 5:46Harry Stebbings?

  Have you ever had something that you couldn't make better?

• 5:48Speaker 00

  Not for the lack of trying. So all you can do is try. If it works out, it's great. And if you try a lot, you succeed more often than you think.

• 5:55Harry Stebbings?

  We were talking downstairs about your personal brand. And speaking of making it better.

• 6:01Speaker 00

  I don't think about it like you do, because this is what you do for a living. I think about running my business.

• 6:05Harry Stebbings?

  Okay, so I think this is fundamentally wrong.

• 6:09Speaker 00

  I'm here to learn. Teach me, Harry.

• 6:12Speaker 00

  I'm here to learn. Teach me.

• 6:13Harry Stebbings?

  The audacity of podcast.

• 6:15Speaker 00

  That's all right.

• 6:16Harry Stebbings?

  No, but I actually think your personal brand is your business.

• 6:19Speaker 00

  I think if you build a great product, a great company, and people like your product, eventually your brand survives all of it. I think you can have a great brand, shitty product, shitty execution, and your brand goes to hell in a handbasket. So I flip around.

• 6:32Harry Stebbings?

  Do you think that is still the case today, though? Like, I think brand can be such an accelerant today, especially in a world where it's just so noisy.

• 6:39Speaker 00

  See, I spent many years at Google, as you know. I spent, I actually was Chief Marketing Officer for five years. Five years. And if you look historically in technology, there are companies which have died, who had great brands. Remember Sun Microsystems? It's a darling, you know, thirty years ago. It doesn't exist. Why? Because their brand went dead or the product went to hell. What about Yahoo? Remember that company? It's a great brand. Amazing. It's way before Google. I think it's probably a fraction, probably even a two decimal point number versus what Google is. So I think product helps make brands.

• 7:10Harry Stebbings?

  But I think those were founded and broke into the public diaspora when there was much less noise. And so I think if you were to put that today, you need to have a brand first to get into.

• 7:21Speaker 00

  Look, let me speak against my own thesis. There's a spectrum, okay? On one end of the spectrum is when you have real differentiated product, in which case the product helps build the brand. Google search is now you Google something, right? On the other hand, it's a commodity. It's water. I don't know why this is called Evian, right? But this is a commodity. Here only brand matters. So depends where you are on the spectrum. If all you are a commoditized product, yes, brand matters a lot. If you are a differentiated product, then you build a brand in the back of the differentiated product. And you can decide where you want to be in that spectrum.

• 7:49Harry Stebbings?

  Well, speaking of that brand, I saw your tweet and I was like, this is masterful. But you tweeted, "The frontier model problem is a breadth versus depth problem."

• 7:58Speaker 00

  Yes.

• 7:58Harry Stebbings?

  Can you explain that to me?

• 8:00Speaker 00

  Yeah, look, I've been listening to some of your podcasts and I've been paying attention to what happens to market because I want to understand where all this settles down. Not just because, you know, I want to understand it, but it also impacts how I build my own business. So you've got these phenomenal frontier models and they keep sort of leapfrogging each other. Every few days, there's a net new model that's delivered by OpenAI or by Google or by our friends at Anthropic. And the question becomes, okay, fine, these models are moving at this pace. What do I need to build? What do I need to do with these models? And as we came to that Mythos moment, when everybody was busy chasing Mythos and that was kind of important, you realize even the best model has a high false positive rate. But for some reason, in the consumer space, we don't seem to care. You know, I was talking to my sister this morning. So I just went to ChatGPT and asked these, all these questions.

• 8:43Speaker 00

  It was very helpful. So I guess like what happens is the consumers are way more tolerant of false positives because just it's kind of always the person in the middle, right? There's always somebody who's understanding what the model says and making. Their own judgment, whether they believe the model or not, and somehow people get rid of some false positives. Somehow people don't care about the false positives. Sometimes people believe the false positives. So the consumer is highly tolerant on this notion of false positives. And doesn't seem to distinguish, and it just seems to get better and better. I literally had Jim and I produce an investment memorandum for something I was looking at. I looked at it, it looked pretty accurate, give or take, I tweak a few things, but it seems passable. So on the consumer side, it's a breadth issue, right? It wrote an investment memorandum for me, which is cool. I would have had to hire a banker and a bunch of investment analysts have taken me days and I did it in four minutes.

• 9:28Speaker 00

  So the breadth is there, which means it's my go to place. And as you know, in consumer, if you become the go to brand, talking about brands, it's hugely beneficial, right? Whether it's YouTube, that's the only place to go look for streaming video. Or Google, that's the only place to go do a search. It becomes hugely multiplicative from a distribution perspective. So our frontier model friends are chasing the consumer brand and the false positive doesn't matter. On the enterprise side, false positives matter a lot. They matter because if you imagine a future where an agent's going to make independent decisions and act on it, you have zero tolerance for false positives. Now, take Waymo. In my view, Waymo is the biggest agentic product that is out there because guess what? You've replaced a human being called a driver, right? All decisions are made by AI machine learning.

• 10:13Speaker 00

  It decides when to turn, when to stop, what to do. But think about the amount of edge case training it took to replace that human agent with effectively an AI driven agent. I don't know, tens of billions of dollars. So that's what it takes to take one use case and train the hell out of it. And if you think about what happened there, they could have used equivalent of an AI model, but then they built so much context and intelligence and edge case training and proprietary data to make that happen. That data is not available on the internet. You can't stick the next model of Anthropic into your Mercedes and say, okay, drive me home. It's not going to be able to do it. And that's, that's the depth issue, right? Because you need the depth of the context and understanding and the intelligence in, around the model to make it useful for the truly agentic use case.

• 10:57Speaker 00

  So I just think there's this constant tension. The frontier models want the consumer attention because that drives post-training for models. Drives the consumer brand of the model. On the other hand, the real enterprise revenue is going to come from use cases that are required a lot more context. The one sort of standout use case we all know is coding. Coding is a universal activity. Everybody does it. So everybody's data is helpful in training the model. And that becomes sort of a large enterprise application. Hopefully, there's a few more out there. But I think that's the tension that I wrote about.

• 11:27Harry Stebbings?

  When you think about the workflows and the way that enterprises are engaging with AI, specifically models, what extent do you think we will be locked in a frontier model dominant world versus a the majority of enterprise workflows can be done with open source? And we will be more cost efficient moving towards them most of the time?

• 11:46Speaker 00

  I think more than half the enterprises are still not getting it right on the use of AI perspective. I think we're still busy trying to incorporate AI into our current business practices. So how do I take what I do today? Use a little bit of AI, get marginally more efficient because I don't want to do this the old way. I think the opportunity is to rethink your workflow fundamentally with AI. That's where the true benefit is going to come. I think the winners in the long term are people who actually rethink their companies with AI, not people who adapt their current workflows marginally with AI.

• 12:17Harry Stebbings?

  How can you do that if you're an enterprise? So we have thousands of CEOs of big enterprises that listen. That sounds great. What do I do? Do I do a brainstorming session?

• 12:27Speaker 00

  No, I think like there's going to be perhaps two or three different categories. One category is let's take the workflows of today. We have workflows around ERP. We have workflows around sales, team management. We have workflows around human resource management. There are existing workflows which have been SaaSified. As in, some software company decided, we all have common processes. Let me build a container where these common processes can be marginally customized by enterprise. They can code their workflow into my SaaS application and we're off to the races. Now, that workflow required a lot of human judgment and human interaction. The software is not intelligent. It's been coded, right? You define input, you define output. I do the input, I know what the output I'm going to get. The idea is imagine workflows where in the hiring process, most of your workflows are containers.

• 13:12Speaker 00

  Imagine where AI actually is helping you make judgments. If I put every CV into AI and say, these are twenty people you should interview. This look to the CV, you should ask this person these following questions. Sends a note to Harry saying interviewing this person asked the following ten questions because your three other colleagues only asked the following five. We needed no false positives, the human being. So AI could be hugely helpful in informing and making the process more intelligent. But that requires us to give up human control and let AI do eighty percent of the thinking for us. And that's not how we're doing it right now. All we're doing is let's take this invoice, let's scan it, abstract the data, put it into AI and say, look at that, it's happening twenty percent faster.

• 13:46Harry Stebbings?

  Do you think we are willing to give up that human control? You see cases like matters today where I think seventeen hundred people have signed a petition that they are unwilling to let it track their mouse and keystroke behavior. We are seeing resistance to giving the data.

• 14:01Speaker 00

  I'll talk to him on points. I think two different points. I think mass collection of data to inform AI is a little dangerous because people see the outcome of what that's going to happen, right? And I've heard of companies where people are using cameras to track people folding laundry and ironing clothes because they want to be able to train physically in the future to do those things. So that part aside, I think on the enterprise side, we can actually run a business much more effectively and efficiently. If we decide where we are willing to relinquish control to AI. You have a perfect example, marketing, right? Anything that is required to train a marketing model is already out in the public domain. By definition, marketing is public domain, right? If it didn't market it, it's not in the public domain. So I have the best training data in marketing. I don't need to train an AI model with more marketing content.

• 14:47Speaker 00

  I may need to train it for tone of voice and what my brand is. And I'm pretty sure an AI model, if I throw my marketing collateral into it, will tell me this is not consistent with your brand. If I look at all the things you've been talking about the last ten years, I think some people have done that. They've actually analyzed earnings scripts of companies and said, the last twenty years, what has company X done? And when does the CEO start getting away from a topic because perhaps it's not going so well? So this is really smart. You can do that stuff. So it's the best marketing, training database in the world, the frontier models. Why do I need four hundred, six hundred people in marketing? Because my biggest problem in marketing is I have six hundred people, but I'm not sure they all fully understand how to consistently deliver my tone of voice. My value proposition and how not to break my brand by having different collateral in public domain.

• 15:30Harry Stebbings?

  You have six hundred people in marketing.

• 15:31Speaker 00

  Give or take, I'm twenty one thousand people. Well, it's not going to be six hundred.

• 15:35Harry Stebbings?

  No, what is it going to be?

• 15:37Speaker 00

  I don't know. My rule of thumb is that in the next three years, we'll probably have half the people in G and A type activities in companies. Things like marketing, things like finance, things like H R, because there's a lot of process management there. A lot of process management can be made more intelligent using some version of an adapted future A I application, for lack of a better word. So SaaS applications will give way to A I applications. The difference being SaaS applications have no opinion, A I applications will have opinions. And that's a fundamental rethink we need from a workflow perspective.

• 16:08Harry Stebbings?

  Can you just help me out? A I applications will have opinions. What does that mean in terms of how you use them in terms of the output that they have?

• 16:15Speaker 00

  Everything, right? Like the scholar, whether you want to call it an A I assistant, A I marketing assistant, A I H R assistant is going to say, I looked at your copy, it sucks. It does not good enough. It's not consistent with tone of voice. Here's what I would recommend. This has an opinion. That will make my average employee much smarter than they were today, then I don't need so many of them because they're doing most of the work for you.

• 16:32Harry Stebbings?

  What would you say to the people that say you're wrong? We won't see that halving of those functions and actually, marketing teams will create more copy, more content, be in more places.

• 16:43Speaker 00

  I think the places where people could be wrong is how many technical resources we need in the future. I think we need more, not less. I think there's this fallacy, people believe we're going to have less people working because A I is going to take over our jobs. I don't believe that. I think what's going to happen is you can't imagine the number of people on my team who want more technical resources, more A I savvy resources, because they want to do exactly these things. It's like, oh, I've got an amazing project to transform marketing. I've got an amazing project to transform H R. What do you need? Oh, I need more people who understand how to prompt frontier models, build harnesses, bring proprietary data into play, bring modes. I need more compute, more storage, because I want to learn everything. So I think we're going to need more technical resources. I think we're going to need more sales resources. Because if your product's really good, you need more people to go out there and cover the universe, because not enough people know about it.

• 17:29Speaker 00

  I'm in Europe. I met twenty customers last week. I still see half of them don't know all the stuff we have. I'm like. Dude, we've been around for twenty years. Why is my team not out there pounding the pavement, telling them everything we do? And the problem is not enough time in a day because I'm too busy dealing with some arcane piece of software at work, which I have to go feed. Well, if I had that software be really intelligent, it'd tell me what to do.

• 17:48Harry Stebbings?

  In terms of wanting more technical resources, tokens, I would put in the more technical resources camp. How do you think about effective token allocation today? You're seeing very different camps from your Matters and Ubers and Microsofts who put in budgets to your free for all, be creative. How do you approach it?

• 18:06Speaker 00

  I know this whole world of token maxing has kind of gone topsy turvy with the whole conversations around how many tokens people are using. The challenge right now is ninety percent of the enterprise employees are not AI savvy. They're not. They have to learn. I can't send them to university. There's no course you can take at any school anywhere. They have to be able to learn of their own. I think we're back to a Darwinian moment where everybody has to figure out who's really good. Now, you've seen people like Brian Armstrong and Jack Dorsey go out and say, I'm going to decimate my organization. I'm going to start building from scratch. And they've gone to some version of thirty, forty percent less people because they've figured out there is no redemption. I can't train these people. I'm going to just find the people who are going to come in and help me do this stuff. That's one model. The other model is sort of gradual. We've been hiring people only through hackathons now, right?

• 18:50Speaker 00

  And we see natural attrition of two percent, give or take a month. And we just replace them with people who actually are AI savvy people who are from hackathons. Give me twelve months, I'll have sort of transformed twenty, twenty-five percent of my team. Give me three years, I'll have hopefully enough AI savvy people working in the bottom. So there are two different ways to get there. I think part of what you're seeing in the token maxing world is people are learning, people are experimenting. The risk is your smartest employee, who knows how to use AI really well, could be using twenty times the tokens that an average employee uses. And if you get into this whack a mole moment saying, oh, my God, I'm going to stop people spending too many tokens, it actually will hurt the best AI savvy people more than it will hurt this average employee.

• 19:28Harry Stebbings?

  And by the way, I think the best talent will want to go where they will be best equipped with the most expansive frontier models, the biggest budgets. I think that will almost be like an employee benefit.

• 19:38Speaker 00

  Yes, possibly, but I think part of the challenge is, you know, right now, everybody's experimenting in everything and you have to figure out what is. Is it that I need to build as an enterprise and what can I get off the shelf? If I can get an AI based thinking application that does marketing for me, I don't need to build it. It's a generic problem everybody needs to solve. I can tweak it, I can customize it, just the way I did with SaaS applications. But I don't need to build mine from scratch. So I've made sure that everything my team is building is proprietary to us. This is where do we have unique distinguished knowledge that we bring to bear, which nobody else can do on the outside. Let's put that, let's package it, let's use it. Where it's going to be a generic AI application 12 months, 24 months from now, let's just wait.

• 20:17Harry Stebbings?

  So you have a free for all on tokens, so to allow your teams to do the best.

• 20:21Speaker 00

  We have a used judiciously model for tokens. It's not a free for all, free for all sounds like you can go token max the hell out of it. We have to use it judiciously and we keep track of it to see what people are doing. And if we find somebody who's using it well, we won't constrain them. If you find somebody who's gone a little over the top, we'll find a way to.

• 20:38Harry Stebbings?

  Benioff said the other day that he spends 300 million on Anthropic a year for his devs. And that works out to be about 3.8% of developer salary spend average. If it stays there, the valuations of Anthropic and OpenAI are grossly overvalued. And if it moves to 20%, they're actually very undervalued. And if it becomes what Brandon McCaw said, which is we'll spend as much on tokens as we do on salaries. They're grossly undervalued, grossly undervalued. I want to talk about where you think percent of developer salary spend on tokens will be in three years.

• 21:11Speaker 00

  That's still a narrow lens for me. If I abstract, if I step back today, there's not enough compute for what the world is demanding. Unequivocally not enough compute. Can't buy compute, compute is costing two to three X of Forex more than it used to cost two years ago. There's not enough compute. That scarcity of compute and that excess cost required to build and deliver compute, which allows us to go make AI useful, is causing the constraint and forcing pricing. And interestingly, more than half of the compute is going to feed the consumer, which is a fundamentally loss making entity right now. I don't think any of the frontier models make any money in trying to get you and me to use ChatGPT or Claude or Gemini every day. It's free, that's a lot of compute. Imagine there's.

• 21:54Speaker 00

  Billions of people around the world using for all kinds of queries every day. That's sucking away half the compute, which is making the return. Guess where the pressure goes? The pressure goes on the other half of compute, which is being used for coding in enterprise applications. So now you're saying enterprise applications coding have to pay until we build transaction models or advertising models on the consumer side because they're not ready. Now, you could say, well, that happened in search too, right? Google search was around, that happened YouTube. People used a lot of YouTube, a lot of compute, but it wasn't paying for itself. The problem is the computer requirements and the cost is now 10x of that when we were in that era versus today. That's forcing token prices to go up. I think the long-term token pricing should be one-tenth of what it is today. But that happens, you will see that people will consume more.

• 22:37Speaker 00

  You can decide if 3.8 or 15.8, it's not, not sure we can tell the answer right now. Because pricing will move very drastically in the next three to five years.

• 22:44Harry Stebbings?

  Sorry, so I understand you say you think we'll see dramatic reductions in token prices?

• 22:48Speaker 00

  I think so. I think in the next three to five years, we will see reduction in token pricing. I think at some point in time, the consumer use of AI will get constrained by these frontier AI companies because they have enough post-training data, more than they need. And each user is inherently unprofitable in their activities they do on frontier AI models.

• 23:06Harry Stebbings?

  Do you not think they just build advertising engines like OpenAI is doing now to pay for that business?

• 23:11Speaker 00

  You know, that's an interesting question. It has to come from somewhere. When I started Google in 2004, we were 2% of the global advertising revenue. And global advertising revenue, the estimate would be in $500 or $600 billion. I think online is about 70% by last count of total advertising revenue. And I don't think the overall number has changed by more than 3% a year or 5% a year. So I don't think the total advertising pie is going to increase. You've already taken away 60, 70% of the advertising pie in the online world. Unless you tell me it's going to explosion atop where more people are going to spend more money in marketing, that money that you're hoping to fund consumer AI from advertising will have to come from current advertising revenues. So I don't think that changes the equation drastically to make the consumer profitable.

• 23:53Speaker 00

  I do think there's an opportunity that AI ends up taking more transaction revenue, which has not been under the purview of AI.

• 24:00Harry Stebbings?

  Can you explain that to me?

• 24:01Speaker 00

  Well, think about the marketing chain, right? We do advertising, advertising is inherently efficient. What's the best conversion rate you get in online advertising, you think?

• 24:08Harry Stebbings?

  One, one and a half percent.

• 24:10Speaker 00

  That's fine. The thing, the best of breed is seven to 10 percent. The average probably one and a half to two percent, which means eighty five to ninety percent of your marketing is wasted. So now, if you get really smart, you have memory, you have context, and you get smarter in targeting Harry when he's trying to buy something out there in the world, then your conversion rate goes up. If you look at the entire value from the time you decide to buy something till the end, you get the product. And you take the case of consumer goods. Today, I want to say the cost of consumer goods is probably in the five to eight percent of total list price. The ninety two percent is distribution and marketing. It's highly inefficient. So you could imagine a world where AI makes marketing really efficient, and you get more dollars coming from traditional marketing into the online world because it's coming in the form of transaction.

• 24:56Harry Stebbings?

  If tokens get cheaper.

• 24:58Speaker 00

  Yes.

• 24:58Harry Stebbings?

  Why are we not seeing frontier models get cheaper? We all thought that this would be cheaper, this would be cheaper.

• 25:03Speaker 00

  Well, right now, they're figuring out that, you know, all your frontier model companies are value-maxing, not token-maxing. They're raising money at a trillion dollars. At some point in time, they realize, oh, my God, where's the next hundred billion dollars of compute going to come from? The financial markets are not going to bear the cost of another hundred billion dollars at a trillion dollars or trillion and a half, because I'm going to need it again the following year. So you say, well, I need to build a sustainable business model that starts showing some degree of gross margin profitability. The only lever they have is to take the fastest growing thing that they have in their portfolio from an economic perspective and charge us more for it. That's where you get the price of tokens from. I think the price of tokens is high. Now, you can imagine, if the price of tokens is high, every technologist is trying to figure out, how do I make my compute more efficient, right? In the future.

• 25:46Speaker 00

  So I'm sure we'll see a whole bunch of advances in the world where memory and computers are going to start getting used more efficiently from a modeling perspective. I still believe I don't need Fable five or Mitos five to do ninety percent of what people do with AI today. Why is the last model not good enough, certain tasks?

• 26:01Harry Stebbings?

  I think unwaveringly is. I think the model overhang in terms of capabilities and especially consumer and most enterprise demand. But the models from two years ago were good enough for most of the queries that we asked.

• 26:10Speaker 00

  That's right. The problem is they were inefficient from a compute perspective.

• 26:13Harry Stebbings?

  Totally.

• 26:13Speaker 00

  So you're seeing the efficiency come in. The problem is the cost of R and D is now being spent in terms of what the tokens have to pay for. So I think token prices come down. I think the amount of compute that we need is going to be huge the next ten years. I think the frontier AI models are in a position to capture a significant amount of the future economic value of the use of AI.

• 26:33Harry Stebbings?

  Everyone is fundamentally looking at the stack and this is me being very open as a venture investor. That's why the show has been successful. And I'm just saying what every venture investor feels. We're all looking at it going, oh, Jesus, I have no idea where value is accruing. And you've got, essentially you've got infra, which is kind of top. And then you've got models and apps to be totally.

• 26:51Speaker 00

  Yes, look, infra is making money. Infra is more expensive than it's ever been. That's who you're seeing trillion dollar market caps in the infra space because of the scarcity of compute and the need for speed.

• 27:00Harry Stebbings?

  Do you think we're in an infrastructure bubble like people think or suggest?

• 27:03Speaker 00

  I have a question, which I don't know the answer to, and you can tell me. I spend more time with people here, I have to go do my day job. Is at what point in time does physics kick in and we just can't produce the compute as fast as we want to? Like infrastructure people are gearing up for large amounts of capacity, large amounts of demand on the infrastructure side. And you come to a point where it says, you know what? It's only so many data centers we can build it. There's only so much energy we have.

• 27:25Harry Stebbings?

  Well, I mean now, I mean shortages of copper. Dude, the Panthalassa, which is building data centers at sea, we've got Elon building them in space.

• 27:33Speaker 00

  Yes. So I think there may be a digestion period at some point in time when we think the demand for compute is there. But the capacity to execute is now limited by physics. And the infrastructure players have built up too much capacity for this demand. I don't know when that rationalizes. Maybe it rationalizes and causes us to go think about a different sort of timeframe for putting all this compute out. That doesn't take away from the need of compute. We'll still want as much compute as we can deliver, as fast as we can deliver. I think some of the model companies have outstripped anybody else's ability to build frontier AI models of that capacity at that speed in the trainings. I think you are seeing perhaps a settling down of who's going to be the frontier model player in the future. The question becomes in the economics, what value accrues to the model? What value accrues to the application layer, as you said it?

• 28:17Speaker 00

  And I think the application layer is probably is a simplistic term. Because for the first time, you have memory in applications. Applications understand context. They understand context as specifically as to what you want, what I want. You're seeing that in the consumer space. That has not yet come to the enterprise space, funnily enough. I think that shows up in the enterprise space, which means the demand for compute and memory goes up on the enterprise side. Like how many, I haven't, I don't, I haven't used all the coding models myself, but over time, these coding models. Have to get really smart about understanding individual context of enterprises and humans. That's how they'll be more effective and more efficient. For that, we're going to still need more compute and more memory. So I think that'll start defining where the value accrues. I think the value gets shared between frontier models and the context that gets created in enterprise play.

• 29:01Speaker 00

  I think the frontier models are fully understanding that this is where the gap is. I suspect the frontier AI models as a crystal ball, they will spend a lot more time the next year or two building memory around consumption.

• 29:13Harry Stebbings?

  Building memory around consumption, what do you mean, like expanding context windows?

• 29:16Speaker 00

  More than that, if you look at the consumer interaction that you have with your favorite frontier model, right? It's starting to remember, oh, you asked about this yesterday, you asked about that. Should I take the question you just asked me in the context of everything I know about you? Or should I just limit the answer to as if I don't know anything about you? Now, that having context of what I said to you over the last thirty days, or the last sixty, ninety days, requires you to store a lot of information. It requires a lot of personalized interaction that needs to have. Now, if you want to maintain your moat with Harry and Nikesh in the future, the more context you have about me, the easier it becomes for you to give me the answers in the future. And as you start building context on a user basis, you create stickiness and that becomes your moat.

• 29:57Harry Stebbings?

  To what extent does Mythos cannibalize a business for you? To what extent does it make a business for you?

• 30:04Speaker 00

  Mythos ended up, I think, ends up being an accelerant to cybersecurity. I think what happened when you saw Mythos came out, it demonstrated that all the training we've been giving these models on how great code is written, the models are able to turn around and say, well, I also know how to find bad code. So what happens is you point the gun the different way and the model says, oh, my God, look at all this code that you have. There are so many flaws in it. As we talked about, the challenge is, like every model, it also suffers from false positives. If you're an offensive actor and I point the model against, let's just say, twenty VC enterprises and I go look at everything you've done, somebody's left a web socket open, somebody's done some mess up in IP addressing, et cetera. So it finds the flaws outside in. That allows the bad actor to go, forget about hacking, they daisy chain vulnerabilities and get into your infrastructure.

• 30:46Speaker 00

  It's not good enough from a defensive perspective because I can't use the model and say, go take every vulnerability you found and build a patch. Go patch my system and protect me. So guess what? It's gonna patch thirty percent things you're not wrong. Who knows what that's gonna do to blow up your infrastructure? So when Mythos came, we looked at it, we treated with respect, we ran it against our code. We discovered it finds bad stuff much faster than humans can. We found in six weeks what it had taken us five to six years. So we got it. We ran around, we patch it, but Claude Code helps build the patch. But you still have to run it through human evals, through testing, through production testing, to sandboxing, to see, does this patch break anything in the infrastructure? Only then, after six weeks, we were able to go patch everything. So what does this mean? This means that every enterprise better fix their stuff faster. Because if I point the next generation of models against your infrastructure,

• 31:31Speaker 00

  it's gonna find security flaws, security vulnerabilities, misconfigurations, things that you've not been paying attention to. So it creates a bit of an urgency on the part of the customers to improve their cybersecurity posture, which I think generally is a good thing for cybersecurity companies.

• 31:44Harry Stebbings?

  This is not fundamentally bad. Like when we just summarize what you just said, it allows you to weaponize bad actors to find holes, but isn't good enough to provide the solutions.

• 31:54Speaker 00

  Well, look, the solutions are there. The challenge is getting sometimes getting the attention and focus of the customer saying, listen, I gotta go fix my stuff because it's important. What this has done is it's lit a fire under the security practitioners around the world, saying, this thing is not good. This is gonna weaponize the bad actors. I better make sure my defenses are in place. And remember, the way cyber defense is done is it's fundamentally just cybersecurity is two fundamental things, right? One thing is, if it's bad and I'm at the gate, I'll stop it. Which means you have to have somebody at the gate. Now, we have one hundred and fifty million sensors in the world where we stand at the gate protecting our customers. If I can find a way of infusing AI at the gate and taking all these vulnerabilities and finding a way to protect you, I'm good. I don't have to chain the gatekeeper because there's no Claude endpoint agent that exists out there.

• 32:37Speaker 00

  There's no OpenAI endpoint agent that exists out there that I can replace Palo Alto or the other people in the space with. The problem is not at the gate. What happens is, despite all the perimeter defense you put in, things come in, things leak in, people make mistakes, people passwords get breached, there are vulnerabilities people get in through. Then the question becomes, all right, oh shit, the bad actors in my infrastructure, how quickly can I find them and get rid of it? That becomes an AI task. That becomes the same conversation we're having so far is, I need context, I need intelligence, I need to know what this means. So creating that context, that intelligence within the enterprise of what this intrusion means and how to protect against it becomes a challenge. This is the AI cybersecurity challenge. Something we, you know, again, this is not trying to here to pitch my book, but we spent five years trying to build that capability inside enterprises, so.

• 33:22Speaker 00

  In the net net, it ends up being accelerant. Does that mean I have everything I need? Not everything. Does that mean I need to get AI models to start helping me? Yes, so we're going to infuse more AI into our defense infrastructure.

• 33:35Harry Stebbings?

  Do you think it is good or bad to have government intervention when you have models as powerful as we have them?

• 33:40Speaker 00

  I think we're going through a discovery process. I think this notion of guardrails has not been built robustly enough. These models seem to be easy to get past. Remember the early days when you used to read about somebody had this conversation with a model and found a way to, you know, albeit the guardrails because it asked questions differently. The model was able to get sort of jailbroken. It became a hobby amongst people and they had all kinds of conversation with models. It's the same challenge. How do you make sure that you can put enough guardrails around AI model that you've built to make sure it's only used for the purpose that you had? That's the challenge that there is. I think the guardrail needs to get better. To the extent the government feels the guardrails aren't robust enough, it's trying to tell us that's a national security issue. Need to go fix the guardrails.

• 34:23Speaker 00

  But I think it's a simple matter of trying to fix or treat the guardrails as a real problem and solve it.

• 34:28Harry Stebbings?

  Is it possible?

• 34:29Speaker 00

  I'm hoping it is.

• 34:30Harry Stebbings?

  I hope it is too. If you were to start, and this was, I spoke to one of the world's best cyber investors, who will remain nameless because he asked some spicy questions too.

• 34:36Speaker 00

  Oh, gay discussions for me?

• 34:38Harry Stebbings?

  Yeah, he asked questions for you. He asked some interesting ones, but he asked, if you were to start Palo Alto Networks or a cyber company again today, starting today in the age of AI, what would you do differently that you're not doing now?

• 34:51Speaker 00

  The paranoia I have, if I look at self-driving, there are broadly two or three approaches out there, right? One was my car is not going to have a human in it. It's called Waymo. And I'm going to keep pounding it. I'm going to keep pounding it, training it until it learns by itself to drive and no human shall be holding the steering wheel ever when my customers are in it. You see, it's out there. Many cities have Waymos. I saw one down the street from here. So that's one way of doing product development. And what you do is every edge case gets discovered. You build training around it. Every experience is a learning experience. You build training around, you keep training it, you keep training it, you keep training, get to a point of total autonomy. The other version is I'm gonna start taking segments of the driving and start automating that segment where I get really comfortable. So my car drives 50% of the time, the other

• 35:34Speaker 00

  50% of the human gets in on the edge cases. That's my Tesla. Tesla used to drive just the highway for me and now it's getting better at other streets, it's slowly getting better. But still, I'm holding the steering wheel very often and it'll tell me you're not paying attention. Look at this, look at the camera, otherwise you can't drive. And I know FSD gets better, but that's another way to get there, okay? I'm gonna keep training, I'll fix this, I'm gonna start working the edge cases as my business continues to evolve. The third one is I'll infuse some degree of self-driving into my car because I've come from the traditional model of having amazing cars, great V8 engines, beautiful sleek cars, and I'm not into the technology aspect. You see them out there on the street. They have all three versions out there. My fear is, do we all need to stop and start thinking

• 36:16Speaker 00

  the way more way as enterprises? Or is there room for the Tesla approach to self-driving in our businesses? Right? Because we have an existing set of customers to satisfy. We're not gonna take kindly for me saying, you know, guess what? I changed my product, it's right 80% of the time. And I'm gonna take those three years to train the product to be right 100% of the time. So my fear is, am I pivoting fast enough in my product strategy that over time, my products become more self-driving than they are today? Or do I need to go faster? And can I get there by automating or AI-enabling certain parts of my product where I can apply the models that are capable to do certain aspects of cybersecurity today and keep doing the others through machine learning and managing S-cases, edge cases through machine learning? Or is it time for me to pivot? My view right now

• 37:00Speaker 00

  is you have to have the Tesla approach if you're an enterprise that is building AI-infused capability. But you can't have the approach of traditional car manufacturers which are trying to stick a little bit of AI instead of AI washing their cars and saying, I'll get there eventually.

• 37:13Harry Stebbings?

  Would you like to do the Brian Armstrong, Jack Dorsey? I often think a good question is what would you like to do, but you're held back from doing?

• 37:21Speaker 00

  Different courses for different horses, right? I don't think in our business, we can go implode the organization. Because I don't think the underlying application software is there. I don't think all the things that we've talked about that we need to get done are ready from an AI software perspective. I don't want to build a lot of software that is proprietary to me for things that should be available for everyone. I don't want to build an AI marketing stack. I don't want to build an AI HR stack. I don't want to build an AI ERP stack. I'm hoping that somebody goes and does that much more effectively and efficiently for the world at large. Perhaps it's the next iteration of Salesforce, the next iteration of SAP, or the next iteration of Workday that is going to help me do that. Because I do believe it needs to become more intelligent. We talked about it needs to be more AI-enabled or AI-controlled. I do want to build things that are particular to me.

• 38:05Speaker 00

  There I have all the information, all the intelligence, the context and the memory from an organizational perspective. I think the right way to get there is to hold people accountable. And I run a meeting twice a week now called AI, AI, oh. It's kind of funny. It's like old McDonald had a farm. AI, AI, oh, because everybody in my company wants to do AI. So if we use it as a converging function, as a function to do brainstorming across my team. How do we think about this? Why are we building this?

• 38:29Harry Stebbings?

  What happens in that meeting?

• 38:30Speaker 00

  Everybody comes and shares how they're adapting to the new world of AI. What are they doing from a product development perspective? How are they thinking about it? How are they including agents in their products? How are they going to go build the backend infrastructure? How do we think about how are they using tokens? How do we think about the capability of resources? Remember, for me to transform a 21,000 people organization, I have to get the hearts and minds of the leaders to make sure we're all swimming in the same direction or pulling in the same direction. So this is my way of ensuring the top 15 or 20 technical leaders in my company are pulling in the same direction. What if the direction be? And as you know, if there's no expert, then a group of smart people do better than an expert. I don't think there's an expert for future enterprise design yet. In terms of here's the blueprint, like you said, as if you see, you're saying, holy shit, where's the value going to accrue?

• 39:15Speaker 00

  Is it going to be in models? Is it going to be in the application layer? Trust me, we have to have a point of view on that stuff and what's going to emerge in the future before I can start really transforming my company. Like, how should I build my products?

• 39:26Harry Stebbings?

  Are your leaders AI-pilled? I interview CROs as well, some of the best CROs, some of the best CPOs. And in all honesty, the bigger the company, the less AI-pilled, AI-maxed they are. To the extent my one the other day, a public company doing 5 billion plus in revenue. CRO goes, you know, we don't have that AI talent internally, but we'll bring it in.

• 39:48Speaker 00

  Yeah, like I discovered this in two thousand and four when I was in Europe and Google Europe, I used to go around and meet CEOs. And there was this fad where CEOs would hire this twenty four year old Sherpa. They were called the web Sherpas or internet Sherpas, right? It's like chief internet officer. Remember those companies had chief internet officers at one point in time?

• 40:06Harry Stebbings?

  I was eight, so no.

• 40:07Speaker 00

  Wait, you don't remember? Well, they used, we've seen this movie before. And sometimes it's fine to have seen this movie before. And the task of the chief internet officer was to make sure the organization was ready for the internet because I'm too busy in my traditional business and I don't know who Amazon is, I don't know who Google is, so this wonderful twenty four year old who understands this stuff is gonna help be my savior. And then CEOs would wash their hands of all the internet because they have this wonderful team of people who would be frustrated because they can't get anything done because nobody's giving them attention. The risk of that happening is true with AI as well. I'm so busy doing what I did yesterday, I have no time to think about tomorrow. So meeting my chief AI officer was probably a researcher at some amazing university before and has low execution skills. Until I can get my leadership to understand and agree

• 40:49Speaker 00

  the extent of the AI challenge and the AI opportunity, we're not gonna make progress.

• 40:55Harry Stebbings?

  What specifically are you not focusing on today because of the burdens of today's problem?

• 41:00Speaker 00

  Like everything, when you go talk to a product manager in any large company, I'm pretty sure they have a product roadmap that exists in their heart and their hands. It's just six months or twelve months long. I gotta go fix all these things. I'm like, what's interesting is in the six to twelve month thing, there's nothing called agents in there. How come like the world is talking about agentifying everything and your product roadmap doesn't have that? I'll get to it once I get this done because it's what customers want right now. I'm like, no, that doesn't work. How do I get you to do more agentic work? How many people are you gonna free up doing development the way you're doing it today so I can use that excess resource to go make new things happen? So those are all important conversations. I can have them one at a time across fourteen or twenty people, or I can have them twice a week with them and people demonstrate how they are. And what's fascinating, remember, you have to make sure your leaders are ambitious.

• 41:43Speaker 00

  You have to make sure they're competitive. You have to make sure they wanna win. You have to make sure that they have a learning mindset. When they watch their peers around them. Do cool shit, they want to show up with cool shit the next time. So for me, it's getting fourteen people together and saying, hey, Harry, tell me today, what have you done for AI in the last three days since I last talked to you in your organization? And whatever motivates you, whether the fear of Nikesh asking you three days again, what you did, or your sort of inherent learning ambition, or it's your team pushing you, you will show up with something. Then you'll see what the other guys are doing. I say, oh, my God, I'm doing a lot, or I'm not doing enough. So it creates a little bit of Darwinian competition amongst them. It creates this urge to go embrace this new technology. And I think hopefully I get fourteen people fully motivated and then they go to that with the next set of people.

• 42:26Speaker 00

  Because I need to transform from the top down, not from the bottom up on this topic. There's a bottom up experimentation, of course, right? People using tokens to see who's really good at, that allows me to find the best talent. So it's got to find a way of transforming twenty thousand people over the next three years in that direction.

• 42:41Harry Stebbings?

  Bottoms up, top down, you got to get into these organizations. That sounds naughty.

• 42:47Speaker 00

  I wish I was a VC, I could set up a podcast and talk about stuff and.

• 42:51Harry Stebbings?

  Clearly, I need to get out more when that's.

• 42:55Harry Stebbings?

  I just sit in this dark room all day, Nikesh, I'm sorry. The question is, how do you get in effectively and how do you get implementation and adoption done well? I've had guests on the show say before, you cannot do enterprise adoption without FDEs today. And then I've had Mattan come on the show, a friend from Factory, and say, if you need FDEs, you have a shit product, Bolt. And I love Mattan, and I think it was a great clip. So grateful for the virality that that came with that.

• 43:21Speaker 00

  Okay, is that that one viral?

• 43:22Harry Stebbings?

  That would have very well. Yeah, Shyam from Palantir then obviously chimed in and.

• 43:25Speaker 00

  Of course.

• 43:26Harry Stebbings?

  My job is to create a discussion. What is true and what is right? Do you have to have FDEs to sell into enterprise?

• 43:32Speaker 00

  What is true is we've only been chasing the enterprise dream for AI for the last twelve months at best. If you think about everything that happens on a weekly basis, we see new things come which we don't quite fully understand and grasp, right? We're all busy trying to get our arms around LLMs and how they're going to be great for chatbots to talk to our customers as an enterprise. And suddenly agents showed up. It's like, oh, my God, I got to figure out agents. I'm going to start working internally. Agents can do a lot of stuff. I'm pretty sure you could still have a. Agent fest and have everybody tell you what an agent is, you still walk out and say, I'm not quite sure that his agent or her agent is the same as what the last guy said. So because AI is moving so fast, I don't think the products are fully there yet. Like the enterprise products that the application layer don't exist in their entirety

• 44:14Speaker 00

  because we haven't tested against the enterprise ask. So FTE is a short form for saying, my product's not fully there because it's evolving as the technology evolves. I'm going to send some people across who are going to sit in your office and build my product while I adapt it to your needs. That's what it is, right? That's what we saw from Palantir, that's what we're seeing from all these companies. So what you're seeing is I'm going to send my product engineers or developers to your enterprise, they're going to build my product, if you do it right. Again, FTE is a different version. Some people are just trying to get you to consume AI, which is actually not an FTE, it's just a technical sales consultant who's trying to help adoption. On the other hand, an FTE truly is somebody who actually brings a quote back from a customer site and goes back to your product, I say, listen, I built this at the customer's site

• 44:59Speaker 00

  because they had this need, we should incorporate this into our product because everybody's going to need it. That's an FTE, in my mind. I think FTEs are needed for the short term because, remember, all the enterprise AI startups are hungry for revenue. For some reason, we've created this notion that, don't worry, just keep selling it, there's a huge sort of pent up demand around AI applications, sell it before the product's fully ready, that's what we're seeing. Do you think that's right? I think that's the case. I think as we think things evolve, the next 12 to 24 months, people will switch from one set of products to another because something will emerge as a better product. Look at the coding conversations, right? How many coding companies have you heard of in the last 24 months? I think we had Windsurf, we had Devon, we had, which is now Cognition. Windsurf got sold. Those are the early guys in coding.

• 45:43Speaker 00

  They don't exist in their then form. Now you've got Codex and Claude and Anti-Graffiti, Factory doing SDLC, you've got Cognition doing SDLC. So you can see, as the market evolves, people who have concentrated on different parts of the value chain around coding are getting formed, the products are getting more and more formed over time. Who knows in two, three years who's going to be the leader in that space? Because a product's not fully ready when you start.

• 46:06Harry Stebbings?

  Do you want to make a bet on here, Will?

• 46:07Speaker 00

  No, you do that.

• 46:10Speaker 00

  I just need a good one that my teams can use. I don't need to make a bet.

• 46:13Harry Stebbings?

  How do you choose who you decide to help? You, you know, I spoke to your daughter Ayesha before and she said one thing that, no, she had lots of things that many people don't know about you. But she said, what is you help a lot of people, a lot of founders and you ping them?

• 46:27Speaker 00

  Yes.

• 46:27Harry Stebbings?

  How do you choose who you ping and who you help? Mattan obviously being one of them.

• 46:32Speaker 00

  Well, my current paranoia, as I told you, is this market is moving so fast that based on what you read, that open clock comes out. Suddenly there's this thing that people are going to have agents. There was a moment, if you heard, if you saw, there was agentic browsers, remember? You don't hear about them much, but there was a moment when everybody was going to have an agentic browser. Your browser was going to be your computer and that's going to be sort of do all the agentic tasks. When I hear about these things, I'm trying to assess which one's going to work. If it works, how does it impact my product portfolio? What do I need to build in anticipation of this technology becoming mainstream? And that window from the idea to execution is shortening in the air, what as you can see, right? Like the way these companies are coming out and getting formed in 12 months and 24 months and getting a hundred million ARR is probably the fastest ever.

• 47:15Speaker 00

  Which means if that's what my enterprise customers are using, I have to figure out how to secure that stuff. Well, my team doesn't fully understand all this stuff. So I'm busy listening to podcasts, listening to people, watching people tweet, watching people on LinkedIn and saying, this is an interesting technology, helping the founder. So my first step is to ping somebody who's doing something interesting, which I don't fully comprehend. They seem to be getting to a degree of success, which gives me a feeling this could be something relevant. Perhaps not this company, but the construct that they're working on, the concept they're working on.

• 47:43Harry Stebbings?

  I'm an investor in a world of investing, in a world of uncertainty, you go later where there's more certainty.

• 47:48Speaker 00

  Yes.

• 47:49Harry Stebbings?

  We spoke about this downstairs. You know, I have that luxury in terms of a flexible mandate to do that. You have that luxury too, in terms of the benefits of scale and acquisition budget. Can you not just sit on the sidelines and wait for the right things to percolate and then buy them at a billion?

• 48:06Speaker 00

  Yes and no. Yes, we can wait. That doesn't mean I don't need to learn. If I'm not paying attention to eight different players in the space, which I'm sure you do too. If I'm not paying attention to eight of them, trying to see who succeeded, why, what did they do wrong? What are the guys who got it right do? It's very hard for me to assess what made it work. Was it a fundamental, it was a bad idea? The technology is bad. Agents are not good. Agents are not going to work. It could be that, or this company didn't implement right, the agents are still a phenomenon. Somebody else is going to execute right. I need to understand the underlying technology for sure for, to make sure that my team's thinking about it. Do we adopt it, adapt it, and secure it in the future? Right? We bought an agentic AI company, Gateway, six months ago.

• 48:47Speaker 00

  It didn't cost a lot of money, but I figured out saying, listen, if everybody's going to agenty the enterprise, how are we going to know how many agents you have running around the enterprise? How do I keep track of them? How are we going to govern them? How are we going to secure against them? I said, the only way to do that logically is to find a way to aggregate agent traffic somewhere. If it goes through a certain gateway or firewall or some router, I can watch all the traffic and I can stop an agent from acting. That's the only way it works. So I said, the first thing you need to be able to do agentic security is to have some sort of a gateway. So we bought Gateway product. Now I got it at the right price. If I wait, look at what's happening now. Suddenly people are waking up to the idea we need some sort of a router or gateway that all traffic needs to go through because of optimization reasons, because of routing reasons, because of token maxing reasons.

• 49:27Harry Stebbings?

  Would you have paid double?

• 49:28Speaker 00

  Maybe. It wasn't a big price, but I could have paid double. That's not the point. The point is, things I buy, either they're going to help me 10x or 100x, or they're going to fail spectacularly. It doesn't matter if I paid one or two X at that point in time. Of course, I shouldn't be paying two X and having it fail spectacular all the time. But the one, two X doesn't make a difference. The one is to 10, one is to 100 is what you do. That's what we'd like to do as well, not from an economic return perspective, from a business value perspective in our, in our business.

• 49:52Harry Stebbings?

  Are you more involved in corp dev today than you've ever been?

• 49:55Speaker 00

  No, I've been always more involved in corp dev. This is not a problem.

• 49:59Harry Stebbings?

  Is that normal?

• 50:00Speaker 00

  I think I say I'm more involved in trying to learn what's happening out there from a technology perspective than I've ever been because the stuff is moving so fast. And if I don't have a point of view and if I don't encourage my teams to pay attention to it and we talk about it, I think there's a risk we miss a trick. And if you miss a trick, you remember in life, in technology, you miss one trick, you can survive. You miss two tricks, you're partly impaled. You miss three tricks, you could be obsolete.

• 50:24Harry Stebbings?

  A lot of SaaS providers are feeling obsolete today. Their share price is telling them they're obsolete. Do you think that the majority of SaaS vendors have been oversold? Or do you think that is an accurate reflection of where markets are moving?

• 50:38Speaker 00

  I think what the market is telling us is that the system of work or the systems of record will see a reimagination of workflows. As you and I talked, that going from software that doesn't have an opinion to software that has an opinion and expresses an opinion and also does a lot of work for the human, so the human doesn't have to do repetitive tasks. I don't think those AI applications have been created. I think the SaaS versions exist, we all use them. I think at some point in time, we'll see AI applications that do a lot of the tasks and workflow gets reimagined. I do think a lot of SaaS has built a lot of analytical capabilities to sit on top of the systems of work and system of record. I think it's a lot easier to abstract that data into some large data lake and have L L Ms analyze that data for you and give you the answers.

• 51:20Speaker 00

  So I think the analytic world is getting reshaped already, where you can see people like Snowflake or Glean or Databricks. All these people boast enterprise data lakes where you can bring the data and run L L Ms against it and get you much more synthesized analytics and outcomes than you ever had before. I think the third question, which we started off with, is like people are not sure how many people are going to work in these enterprises in the future. So if you take the confusion of how many seats are going to survive in the SaaS world, take the confusion around analytics are going to get done differently and system of work gets reimagined, so.

• 51:51Harry Stebbings?

  What do you mean analytics done differently? And so I'm again, I'm disclaimer, I'm a podcaster first.

• 51:57Speaker 00

  Oh, you get it. Yeah, you're a successful investor managing lots of people's money. That's true.

• 52:01Harry Stebbings?

  But disclaimer, podcaster.

• 52:02Speaker 00

  Got it.

• 52:03Harry Stebbings?

  I understand the seats question. I understand the workflow. Can you just help me understand the analytics?

• 52:07Speaker 00

  Well, if you look at most SaaS software, right? In the past many years, once you're fully deployed at a company, then somebody says, listen, I've got all this cool data about all your employees in my H R system and I can help you get more. More insight in HR system. If you take Salesforce, they have Salesforce Marketplace with three hundred apps you can use, which are analytical apps that feed off your own system of record go to market data and helps you analyze that data.

• 52:31Harry Stebbings?

  You know Neil Mate?

• 52:32Speaker 00

  Yes, of course.

• 52:33Harry Stebbings?

  Yeah, I love Neil.

• 52:34Speaker 00

  I do too.

• 52:35Harry Stebbings?

  He's one of the most phenomenal people. He always says, the one question is like, are the companies best days ahead or behind it? And that's a very helpful one.

• 52:42Speaker 00

  Very question. Good question, yes.

• 52:44Harry Stebbings?

  Are Salesforce's best days ahead or behind it?

• 52:46Speaker 00

  I don't know. That depends on how they execute from here on.

• 52:49Harry Stebbings?

  If I were to paint a bear case for you, what would that be?

• 52:53Speaker 00

  The bear case is we don't get this transition right of the world going to a AI first future. Because look, these false positives will keep reducing over time. Agents will become a real thing. Agents will do a lot of work for humans, which humans have been doing manually in the past. All that needs to get embodied in your product. If I can't make that transition happen with my team in the next three years, yes, there's a bear case because somebody else will build a better mousetrap.

• 53:16Harry Stebbings?

  And the bull case is you understand it better than any other security provider and you become the default.

• 53:22Speaker 00

  Bull case is that we get that transition right. There's already a trend in our favor underlying that, where people are realizing they can't have forty to sixty cybersecurity companies that they have to manage themselves. So we've been driving this trend of platformization already for the last twenty four months or thirty six months. We already see the fruits of that, where people are saying, you know what, I don't want forty people solving my problem. Let me put Palo Alto, it solves the problem that twenty different companies do together on one platform. The good news is because we are all coming to our senses and saying, yes, we need a lot more enterprise context, enterprise data. It has to be stitched, it has to be seamless. That's what we deliver with our current proposition. I just need to bolt on the right, not bolt on, embrace the right AI capabilities and that stuff.

• 54:01Harry Stebbings?

  Does the platformization remove the ability for venture scale returns? Remember, I need like ten billion dollar companies. This is the big thing that I think most founders still don't kind of fully comprehend. It sounds awful, a billion dollars doesn't do it anymore. It needs to be ten, it needs to be twenty, thirty. With the platformization, I can get the billion dollar asset to you, hopefully. Please buy any of my companies for a billion in cash. I'll give you the catalog. You can take them.

• 54:26Harry Stebbings?

  You know, I didn't pay ten.

• 54:27Speaker 00

  I'm not going to fight against innovation. I think there will be venture scale returns in cybersecurity because remember, we're the most innovative industry in the world. The bad guys are always looking for a new way in. They're not saying, oh, I exploited that two years ago, let's try it again. Maybe somebody hasn't deployed a patch against that. Sure, we fixed that one. You gotta go find a new way to attack.

• 54:44Harry Stebbings?

  I'm far too tired to come up with an innovative way to hack into it. Let's just try it. Do Westlife again.

• 54:50Speaker 00

  Exactly right. So it's highly innovative. There are new attack vectors. People are going out there, trying to chase them. I'm not going to build everything myself. People will build great stuff. And sometimes people will great stuff and build a platform around it. And that's fine. Remember, we come from a different management point. When I started Palo Alto, we were less than 2% market share in the entire revenue of cybersecurity. We're closing in on eight or nine percent right now. There's still a lot of room between eight and nine percent or 20 or 30 or 40. That means there's still 60% of market cap out there to go enjoy in different companies. And that's not all going to be existing players, including us. There is room to build companies which have tens of billions of dollars of market cap in the next 10 to 25 years.

• 55:27Harry Stebbings?

  Fucking enormous market, eh?

• 55:29Speaker 00

  Beautiful.

• 55:30Harry Stebbings?

  You're like, wow.

• 55:31Speaker 00

  Well, think about it, the S and P, what percent of the S and P is tech now? Compare that from 20 years ago.

• 55:35Harry Stebbings?

  Year-to-date gains, like 86%.

• 55:37Speaker 00

  Forget the gains, what's the total, of the total S and P market cap, what percent is tech? And what is that 20 years ago? What will that be 20 years from now? If it was less, it'll be more.

• 55:45Harry Stebbings?

  You know, I, I, 100%, I am borderline in that.

• 55:48Speaker 00

  So the entire tech space, like, you know, what do you call marketing tech in the future? Or marketing spend or tech spend? What do you call HR tech in the future? HR spend or what do you call the spend on all the tokens which replace repetitive human tasks? All becomes tech spend.

• 56:01Harry Stebbings?

  You said a word, bad guys, is China in many people's eyes. And we see a huge amount of incredible open source models.

• 56:08Speaker 00

  Yes.

• 56:09Harry Stebbings?

  Which are being used extensively today at a much cheaper cost. Do you think the proliferation of Chinese open source models is something to be concerned by? Or is it an inevitable feature of a burgeoning ecosystem?

• 56:21Speaker 00

  So for a second, let's play the thought experiment. Take the word China out for a second, answer the question.

• 56:27Harry Stebbings?

  Do I think open source models? No, I don't think open source models are a dangerous.

• 56:32Speaker 00

  Right. So does it matter where they come from?

• 56:34Harry Stebbings?

  Yes.

• 56:35Speaker 00

  Okay, so you're not worried about open source models, you're worried about Chinese open source models.

• 56:39Harry Stebbings?

  One hundred percent. I'm not saying.

• 56:40Speaker 00

  Remember, there's a large tech company which also had open source models for a while.

• 56:43Harry Stebbings?

  Sure.

• 56:44Speaker 00

  So it's interesting to watch open source models. The question becomes in the future, do we end up with, you know, horses for courses? Do we end up with models that are very task specific and very helpful in certain tasks? And do we always need to use this mega frontier AI model for everything? And you already see that with eleven labs and, you know, the voice models that are out there, which are specific to a task. And they probably do that task better than what the frontier AI model does. So over time, if you believe the world bifurcates into many task specific models, which are going to be useful for that task. That task specific model could be better training across a depth in a vertical space. That's going to happen with physical AI, for example. I don't think physical AI will be as easy as having a generic frontier model. Because there's no consumer use case for physical AI, right? It's a depth use case only.

• 57:26Speaker 00

  Question is, is your physical AI model that helps you fly planes going to be the same physical AI model that helps you drive cars? Most likely not. Will it be the same physical AI model that does robotic manufacturing? Probably not. So you're going to see depth in these models. You're going to see a world of bifurcated models. There's some sort of orchestration layer, as we've talked about, and you're busy finding orchestration layer companies. That can allow you to pick the best model for the right task, that those orchestration layers have to get smarter and smarter. They have to understand the context, they have to understand the memory, they have to... So the question is, do I store my memory and context in the orchestration layer? Or do I store that in the frontier model?

• 58:00Harry Stebbings?

  Which one do you think it will be? I know I'm the investor, I should know, but I don't.

• 58:03Speaker 00

  No, I think that the challenge is, right now, the frontier models know this problem. And they're aggressively moving to incorporate memory and context into their models, because they understand that's the mode. And the challenge is, you have to pay for it twice. If you say, no, I don't want to use your memory and context. The model may not be usable if you use an orchestration layer. The orchestration layer today is not as well funded as these models. Risk is, you end up in architecture where the model has a lot of context. And you cannot be model agnostic, you actually be model captive to get maximum efficacy and value for what you want to get done. So you have a choice. You have to go all in on the model. Or you can't call them model. You can't do with one what you can do with the other. And if you want to do with the other, you have to redesign an entire application that is deeply embedded with the capabilities of the second one.

• 58:46Speaker 00

  So in the world of bifurcation and horses or courses, I think open source is a good thing because it allows you to play the cost curve. You don't need the smartest model to do the smartest thing. Open source is good. Whether it comes from a certain country or not, the question becomes, what backdoors are you worried about that these open sources models have? What are you worried about that? And that's true for any nation state, right? If there's a nation state sponsored open source model, what are the backdoors? Can I get in? Does the model wake up one morning and it's got a sleeper agent and it starts sending all the data somewhere else? Those are questions. Those can be secured. That's why you come to Palo Alto, to help you secure the models.

• 59:19Harry Stebbings?

  Always got to secure your backdoors.

• 59:23Speaker 00

  I don't know. What kind of night did he have? So he's not a hangry night. This is a different kind of night that I'm dealing with.

• 59:29Harry Stebbings?

  It's Monday morning.

• 59:30Speaker 00

  All right, all right.

• 59:31Harry Stebbings?

  Just terrible.

• 59:31Speaker 00

  Back to work. What is the best time for me to show up here?

• 59:37Speaker 00

  I'm in your time zone. I'm well rested. I'm not jet lagged.

• 59:42Harry Stebbings?

  The quote from last week. The cash only secure your backdoor science.

• 59:46Speaker 00

  The only two things you seem to have caught on to where it seems that your mind is going in the wrong direction.

• 59:51Harry Stebbings?

  No, I've got, I've got two questions and then we'll do a quick fire. One is, with the incredible success you've had, you've made a lot of money. It's just a question I have is like, what does no one know about having money that they should know? Like one thing for me is I've become much more impatient. We have very different, you're far more successful than me. I've become way more impatient. No one told me I'd become impatient. I'm used to a good quality of everything. And now when it's not that, I'm very pissed. I don't like that in myself, but no one told me it would happen.

• 1:00:17Speaker 00

  What are you going to do to fix it?

• 1:00:18Harry Stebbings?

  Therapy.

• 1:00:20Speaker 00

  This is the common Western solution. Yes. As somebody else tell you, you come back saying, God, I must feel better now because I told somebody that I was going to this.

• 1:00:28Harry Stebbings?

  My therapist told me I should put myself first more often.

• 1:00:30Speaker 00

  And you came back to being impatient because that's how you put yourself first, more important. Believe in yourself, right? Is that what you're supposed to do?

• 1:00:36Harry Stebbings?

  Yeah, yeah.

• 1:00:36Speaker 00

  Optimize for yourself.

• 1:00:37Harry Stebbings?

  Put myself first.

• 1:00:38Speaker 00

  Be confident in yourself.

• 1:00:39Harry Stebbings?

  And it was your dad's fault.

• 1:00:40Speaker 00

  Oh my God. Is that what your therapist told you?

• 1:00:42Harry Stebbings?

  Yeah, yeah, yeah.

• 1:00:42Speaker 00

  This must be British.

• 1:00:43Harry Stebbings?

  Yeah. But no one told me that. I kind of wish they had done. What does no one tell you about having money that they should do?

• 1:00:49Speaker 00

  I think it's not about money as much as it's about it's about success. Remember, we all follow Maslow's hierarchy. I came to the United States with two suitcases, two hundred dollars, and I was willing to do anything, anything at all, within reason, there's the right side of the law, to make sure that I made a life for myself because there was no way to go back. I was gonna use a different word, but I'm not gonna use it because you go crazy again. So, there's no going back, right? It was a one-way ticket, which I did not have, I had no recourse. So, I was willing to do whatever it took. I took notes, became a security guard, I tried to pump gas for a weekend.

• 1:01:23Harry Stebbings?

  You became a security guard?

• 1:01:24Speaker 00

  Yeah, when I came to the United States, I was a security guard. I took notes with the disabled, I flipped burgers at Burger King. I had two hundred dollars, I had to find a way of paying my tuition.

• 1:01:32Harry Stebbings?

  Was one quite transformative to your mindset? Did you hate them? Did you love them? Like, what's that like?

• 1:01:37Speaker 00

  It had to be done. It's karma. This is, when you come from Eastern philosophy, it's karma, right? It's destiny. This is what you need to do to break your destiny. So, you do that. So, you don't worry about what you have to do. Now, at that point in time, there was no-

• 1:01:48Harry Stebbings?

  Did you always know you were gonna be successful?

• 1:01:49Speaker 00

  I don't know. Who knows? Nobody knows they're gonna be successful. You just come in and do your best and hope for the best and see what happens. So, that's very Eastern, Eastern philosophy, right? You believe in karma, destiny. How do you manage billions of people in the world? You make sure they believe in destiny. They believe in destiny and say, oh, this must be what was my destiny in the end. I tried my best, this is where I ended up. That's better than your therapist, just like keeps you centered, saying, okay, I tried my best, I gave it everything I had, but perhaps this is what God intended for me. You find that hard to believe.

• 1:02:17Harry Stebbings?

  If you were my therapist, I don't think I could afford you, though. That's the problem.

• 1:02:20Speaker 00

  Well, see, this is free, you're getting to, getting free.

• 1:02:22Harry Stebbings?

  This is why I host the podcast.

• 1:02:23Speaker 00

  I'm not sure, I fully embrace all of that, but that was where I started. So, if you start from that perspective, over time, you climb up Maslow's hierarchy. It was about food and shelter, and that became about ambition, and it becomes about actualization, conceptually, in Maslow's hierarchy. And you get to a certain amount of money, then you decide there are some things I don't have to do anymore. I don't have to be a security guard. I don't have to flip burgers. But that very quickly goes up further, saying, I don't have to tolerate certain things that I tolerated in my life because I don't need it in my life because I don't have to adapt to the circumstance because I can walk away.

• 1:02:53Harry Stebbings?

  Do you ever get worried that the willingness to walk away makes you softer?

• 1:02:57Speaker 00

  The willingness to walk away makes you softer. No, actually, it's the other way around. The willingness to walk away makes sure you optimize the outcome. When you negotiate, if you're fully vested in the outcome, you fold at some point in time saying, well, I can't let Harry Stebbings walk away because Harry walks away, I have no deal. But if I say, you know what, Harry, it's going to be these terms or no terms. I'm willing to walk away. Then it depends, the battle of wits, right? So it's Harry wanted more, do I wanted more? Does it make you softer?

• 1:03:22Harry Stebbings?

  I don't want to make it political, but isn't that Donald Trump's, like, The Art of the Deal? Like, leverage.

• 1:03:26Speaker 00

  I don't know, I've not read the book, so.

• 1:03:28Harry Stebbings?

  It's quite a good book, actually.

• 1:03:29Speaker 00

  Is it good? Like, at the end of the day, I don't think being willing to walk away makes you softer. I think being willing to walk away makes sure that you understand the pros and cons of what you're dealing with. It makes you understand whether you should spend your time over there or not, which makes you understand whether you can get an outcome that is useful for you as well as the other person. No, we have a lot of choices in life, once you have the amount of wealth you have.

• 1:03:52Harry Stebbings?

  Final one, and then we'll do a quick fire. I care a lot about kids, actually. I love kids and I want to be a really good father when I am one. You're CEO of a public company that is incredible. You've had an insane career and I've had the pleasure of meeting, you know, one of your children. She's amazing.

• 1:04:07Speaker 00

  She is.

• 1:04:08Harry Stebbings?

  What advice do you have for me on lessons on how to be a great dad, but also not lose an inch on work? I'm not willing to sacrifice much on the work side.

• 1:04:17Speaker 00

  You know, this is the hardest problem in the world. I think there's what, 20, 30 billion people who have been born since the beginning of civilization? Yet there is no AI that can train us on what we need to specifically do to create the outcome we'd like to create. It's way too many variables, right? There are all kinds of people in the world and I'm sure their parents, some of the parents are amazing, some of the parents are not as amazing. So I think part of it is you can do your best from your perspective. And I think kids absorb a lot by watching you, your work ethic. They watch your values, they see how you interact with them. Because my daughter probably has a better sense of who I am as a person than anything I can tell her because she spends time around me. She sees me interacting in every sort of micro situation, what makes me impatient, what makes me patient, what makes me do certain things.

• 1:05:00Speaker 00

  At the end of the day, your child believes that you have the best intention for them. I think that goes a long way.

• 1:05:06Harry Stebbings?

  I totally agree. It's, I had a guest on the show and they said, watch National Geographic if you want to be a good parent. And I said, what? They said, look at the elephants. The children follow. And so if you want your child to be nice to waiters, be nice to waiters. If you want them to work hard, work hard.

• 1:05:22Speaker 00

  Yes. But that's true in organizations too, by the way. Organizations take on the form of the leader. I'm pretty sure if you close your eyes and you rattled off, you know, five or six attributes of a company and said, it's a company as a founder and say, how do you compare the company's cultural values vis a vis the founder? And you'd find a remarkable resonance between the two things. Companies act because remember, the organization is trying to please the founder because they figured out that's the way to achieve success. If my CEO is impatient, if my CEO is exacting, if my CEO is ambitious, my CEO suffers and the fools get stuff done, then that must be what they want to reward. So you suddenly find if you, this again depends on if you have the right values or not. You told me a story about a guy who had different values and then shut the company down. But if you

• 1:06:05Speaker 00

  have the right values, people will watch your behavior and want to emulate your behavior.

• 1:06:09Harry Stebbings?

  I want to do a quick focus, otherwise I'll take up all of your time. What is a belief that is held by most top investors and founders in Silicon Valley today that you think is wrong?

• 1:06:19Speaker 00

  My concern would be at this point in time, given the base at which technology is evolving, given the uncertainty in terms of what's going to work, what's not going to work. I'm worried that it might be too much euphoria and a bit of FOMO going around in terms of, oh my God, if I don't invest in something that's interesting in the right founder, I'll be left out. Because people have seen this happen. Look at what's happening at Anthropic, right? You've missed the first round, the second round, the third round, the fourth round, the fifth round, and you look like a guy who's got money. Now, you had 20 years to invest in SpaceX. You had three to invest in Anthropic. That pace is fundamentally different. I'm sure as many people are happy that SpaceX finally went public, as many people are probably sitting there saying, well, being saying, damn, I should have done the Anthropic round two years ago when they showed up on my doorstep. So I think there's a lot of FOMO coupled with euphoria on the other side. And I think the risk is that

• 1:07:04Speaker 00

  we think every company that's going to show up now is going to be the next Anthropic. So we better get into it.

• 1:07:08Harry Stebbings?

  My next one to you is, any moment, any board meeting, what was the biggest, oh shit, in a board meeting?

• 1:07:16Speaker 00

  I got a very interesting insight from one of my board members. Do you know, we're prolific buyers of companies because I'm constantly paranoid that we haven't built it. Somebody else is going to build it, so we better go acquire it and find the team to go get it done. And there's one particular acquisition, it took a lot of effort to get the founders to table, get them to agree, grind through diligence, figure out whether it's going to work or not. It's a substantive amount of money, relatively speaking, hundreds of millions of dollars, close to almost a billion dollars. And I called one of my board members and I said, hey, what do you think about this? See, you're calling me, you don't call me all the time about all the acquisitions you do. So this must, this one must be different. And I said, no, it's not different. I'm just thinking hard about it. It's taken a lot of effort. He says, go for a long walk, ignore all the effort you put in.

• 1:07:59Speaker 00

  He said, because sometimes what happens is you confuse effort with wanting to get the outcome. They spend a lot of time and effort trying to get it, then you feel like when you get it, you better take it because you put all the effort in. And he says, you haven't spent a dollar yet, you just put in three months of effort. But remember, once you put the dollar, then it becomes yours, it's your job to make it successful. So you still have one more chance to decide if you want it or not. I go for a very long walk and say, if this walked in the door right now and there was zero effort involved, all I had to do was write the check, would I take it or not?

• 1:08:29Harry Stebbings?

  Forget the sunk cost.

• 1:08:30Speaker 00

  Yes.

• 1:08:31Harry Stebbings?

  Have the same in the investing business, you spend so long.

• 1:08:34Speaker 00

  Yes, you spend a lot of time, you're like, oh my God, I'm the one getting the term sheet and nobody else has it. I nailed it, I've beaten out eight VCs to it. The question is, that not how many VCs you both beat to get the deal? The question is, if this deal, if this deal stands its own merits, and would you invest in it if there was no competition?

• 1:08:49Harry Stebbings?

  What's the best advice you've ever been given?

• 1:08:52Speaker 00

  The best advice that the early old man gave me on a flight once was, you know, life is simple. If you wake up in the morning, you're really excited about going to do what you do for a living, you're blessed. And if you're done after a long day and you're really excited to go home to your family, you're blessed. Did you like that?

• 1:09:08Harry Stebbings?

  I do, I do. And I actually tweeted last night, I hated school when I was a kid. Sunday nights was the worst.

• 1:09:14Speaker 00

  Yes.

• 1:09:14Harry Stebbings?

  And like, my Sunday night last night was like, thinking about our show. And the comment was, what a great Sunday night, what a great Monday morning.

• 1:09:21Speaker 00

  I was sure where you were gonna go with that.

• 1:09:23Harry Stebbings?

  No, no, like, how lucky am I? Seriously, it's amazing. Final one, what are you most excited for? When you look forward next five to 10 years, what are you most excited for? Is it becoming a grandparent, maybe? I've just seen my mother become a grandmother, it's amazing, she's amazing. Is it the health benefits? Yeah, I'm so excited that AI might be able to solve multiple sclerosis, which my mother has. That'd be incredible.

• 1:09:48Speaker 00

  You never know what tomorrow's gonna bring you. The only way I've been able to do everything I do is not to get too hung up on what's gonna happen to me in a year from now or five years from now, because that's too far. I think you wake up in the morning, you have an amazing day and everything's working around you. Your kids are happy, your family's happy, you enjoy what you do, you have good friends. Like, was it a different place the other day or this week? And they asked me, like, you're not a 996 CEO, what do you do? And I said, look, I try to make sure that I can find something to enjoy every day. Because I have enough things to worry about. I could really get myself in the wrong headspace by worrying about a lot of things. I run cybersecurity for crying out loud, you know? I live off the fact that somebody's gonna hack somebody at some point in time. My phone rings saying, can you help us? I say, why didn't you spend the money before, but can I help you? He says, I can help you. So I think it's a, it's a state of mind thing.

• 1:10:32Speaker 00

  Can you get your state of mind to be optimistic, positive, and one of gratitude and happiness every day? If you can, it's gonna be great. But guess what, if health benefits come, you know, I can start being Benjamin Button, be amazing. If my kids are continuing to be happy and successful, be amazing. My mother, you know, lives for 150 years and she's happy, it'd be amazing. There are so many amazing things that happen and perhaps may not happen. So let's just focus on tomorrow.

• 1:10:57Harry Stebbings?

  Nikesh, I so appreciate you being willing to come back for a second. I mean, after the first, when I suggested, I was like, there's no chance he's doing it.

• 1:11:08Speaker 00

  Good thing I have to go back and listen to the first one now.

• 1:11:10Harry Stebbings?

  But thank you so much, you've been incredible.

• 1:11:12Speaker 00

  Thanks for having me.

• 1:11:15Harry Stebbings?

  But before we leave you today, you have the idea, but often with AI tools, you hit a wall. Well, Base44 is where that friction disappears, turning how you talk into how you build. Full-stack web and mobile apps, sites, autonomous superagents, all built in minutes, not weekends spent on damn configuration. Base44 ships it all out of the box: the backend, the database, the authentication, and the hosting. It handles the heavy lifting, so you can just stay in the flow. It doesn't just replace the busy work, it multiplies you. It makes you so much more capable, an effective version of yourself. In this market, being fast is the baseline. But to win, you've got to be first. And Base44 is that edge. It's the move that lets you skip the troubleshooting and get straight to the breakthrough.

• 1:11:59Harry Stebbings?

  Launch your next big thing at base44.com. That's base44.com. After Base44 helps you launch, Corgi helps you cover what comes next. My word, what an arresting first line. Get your arse covered with Corgi Insurance. And I'll tell you why. If you're running a business right now, you already know this pain all too well. Getting insurance, it's really slow, it's confusing, and my word, it's full of paperwork. Well, that's exactly why Corgi is here to change the game. Corgi is the first and only insurance carrier designed specifically for tech companies, allowing you to get covered in minutes instead of days. Corgi provides essential coverages for all growth stages, such as DNO, eEndo liability,

• 1:12:42Harry Stebbings?

  cyber, commercial, general liability, and more. Get your arse covered, I love the way we say "arse", with Corgi Insurance alongside thousands of other startups at corgi.com/20vc today. That's corgi.com/20vc. You won't regret it. While Corgi handles the coverage, Turing handles the talent. Frontier Labs keep facing the same limitation. Models perform well on benchmarks, but they fall short once they enter real coding tasks, real tools, and real workflows. That disconnect between synthetic evaluation and actual system behavior is now a core block off for agentic models. That's why Nvidia, Anthropic, Salesforce Gemini, and other leading lab partners partner with Turing.

• 1:13:24Harry Stebbings?

  Turing is the research accelerator focused on post-training reliability. They build realistic RL environments, next-generation data quality systems built from real-world operational traces, and coding data sets that stress models under conditions where failures matter: state changes, workflow branching, brittle tool calls, and the coding errors that break RL agents but never appear in benchmark reports. In reality, a model may demonstrate correct reasoning in your evaluation setup, yet still select the wrong parameter or mishandle a code update in a realistic interface. Turing makes that failure visible and gives teams the signal they need to fix it. For labs advancing agentic systems, Turing provides the structure required to understand why these failures occur.

• 1:14:08Harry Stebbings?

  To find out how, visit turing.com/20vc. That's T-U-R-I-N-G dot com forward slash two zero V C.