Speaker 1I know that you guys have been using the T word a lot the last day or two in your conversations, the tokens, but maybe we could kind of pivot a little bit away from AI. I guess it's a little bit rooted in AI, but there's been this big conversation set in the last couple of days about cryptography and some of the risks to cryptography. I think it took root after the OpenAI math drop, but there's more conversations now about this big quantum initiative and how that might kind of take us into an era where we need to kind of start to think about adopting quantum cryptography. But Arvind, maybe we could start and talk a little bit about where things stand today from your view.
Speaker 2Sure. So a little bit of what David just touched on is DARPA has been running a competition called QBI or Quantum Benchmarking Initiative. A couple of hundred companies joined it a couple of years ago. I think it's down to maybe 13 are left. So that tells you kind of what's maybe real. I'm calling it maybe, not quite real. Some of it is real. And so their goal is who can build a quantum computer that works. Let's sort of put the technology side out of it. And the debates are, is it 2028, 2029, 2033? And sort of if you pick the middle point, let's call it 2030, well, it will break today's cryptography. That's not a supposition. That's just a reality. And if that's going to happen, then what's the cryptography that you got to use to protect yourself against that? Unfortunately for some of you, what's even worse, if you have an encrypted hard disk today that gets stolen, somebody could keep it for five years and break it at that point. The good news is post-quantum cryptography is understood. It's well known. The code is written, but people have been too lazy to get to it. But I think the wake up moment is now.
Speaker 3Are the solutions that we understand quantum proof or quantum resistant? Because it's sort of based on our basic understanding today of what quantum probably looks like.
Speaker 2Yeah. No, it is based on understanding at a theoretical level of what quantum could do. So they are quantum proof in the same level of rigor that today's cryptography cannot be broken by today's classical computers. That's actually not a theorem proof level answer. It's more based on complexity theory in computer science, which is grounded at the end on some assumptions, not on. So if you look at where we are today
Speaker 3and that 2030 midpoint, can you just walk us through the evolutionary moments that you need to see where we all have to start paying attention and say, okay, how do we move?
Speaker 2Yes. So today you got toy quantum computers. I'll call them toys because they can do certain problems, but they are rife with errors. They have no way to scale fully yet. So question one will be, who's the first one who can demonstrate not a simulation, not on the side, but some level of error correction or mitigation on a running quantum computer? Step one. And so I'll predict you'll see somebody make claims by next year. Then you should get third parties to evaluate, are those claims real? You'll find one or two will make progress by 2027. Error correction, not at scale, but error correction. Yeah. Question two will be, can you run these things at some level of scale? Not yet complete scale. I call it gate computations for the lack of a better word. Today, most people are somewhere around 10,000, which if you think of a normal computer is not very much when your iPhone does a couple of billion per minute. Yeah. So gate computations, is somebody scaling from 10,000? Are they adding a zero each year? That's the second milestone I would put. You put those two steps together, and now if you put a zero per year, that means you could be at 10 million by 2029. That's remarkable. That can do some things that'll surprise the hell out of everybody.
Speaker 1Does this reference this notion of a logical qubit being an error corrected operating qubit? Is that a term you've adopted, logical qubits, and that everyone has the same milestone, the same metric?
Speaker 2I don't love the term, but I think it's a fair term to just explain to people to say, "Hey, if I say I'm building a computer that's got 10,000 qubits, and they say, "Yeah, but they're all noisy." You're right. So if I begin to error correct, it in some sense decreases the effective capacity of the machine. So a logical then says, "Okay, what's the effective capacity of this machine?" Is I think a reasonable way to look at it. So that's a term that while I don't love, because it's not like I've taken these four things and it's like a DRAM cell and that's it. It's actually the whole machine is error corrected, but it's got some divide. So 10,000 physical, maybe 200 logical.
Speaker 3Nikesh, you have red teams that think about what is at the frontier and pushing it. Let's say that you start to see these ooms, these orders of magnitude start to compound. If you had to guess, where would your red team say are the obvious honeypots? Where do you point that if you were a
Speaker 4bad actor? If you think from the perspective of everybody here has pretty much crypto keys running across their entire IT infrastructure. It's not one thing. Everything is running with crypto keys, which are encrypted. Every vendor in our space has keys at each end. So the first thing that's going to happen is whatever's not quantum proof is going to be breakable. So our view is you can't wait for 2029 or 2030, because it's not like one day all of a us are going to go out and say, oh shit, let's go burn $5 trillion of IT infrastructure and let's go build it again in one year, because now we need quantum safe infrastructure. So you should be replacing your IT stack today with quantum safe or quantum proof infrastructure right now. Now there are technologies, there are capabilities which are already out of the market, where you can start wrapping post-quantum ready keys around traffic as a stop gap. So you can do that. But the time, if he says it's 2029, 30, we're already late. We're three years away. Well, three years away. And I'm pretty sure everybody in this room has got a few hundred million dollars of IT spend that is earmarked for this year. And I'm guessing 50% of your spend is on infrastructure that is not quantum safe.
Speaker 1There was a pretty senior AI researcher that said we don't need to wait. 29? 29? Well, I mean, his view was actually models, AI models today can get us there before quantum computers get us there. Do you have a view on this? And I guess it's rooted in this idea that on the frontiers of number theory, you can see AI start to develop models and proofs that are not apparent to humans today that allow us to rethink how we can effectively do code breaking. And perhaps the open AI math dump this week is an early indication that this might actually be a good prediction that he made.
Speaker 2Since you're giving a little bit by going down that direction, you'll excuse me if I just geek a little bit. It's not going to be possible. At the end of the day, this goes back to stuff that people like Richard Feynman did. You cannot simulate something on a different architecture and expect a more efficient result. So if you take that as a thesis, quantum computers do not operate in arithmetic, which is what all of classical computers, including GPUs, do. Could you have a an AI algorithm discover a new quantum algorithm? Absolutely. Could it run it effectively and efficiently on today's hardware? It's not verified by yourself. Yes, it's not possible. So you need quantum hardware to run these things to get the positive advantage that the quantum algorithm has. Otherwise, it'll be slower by an order of 1,000 or 10,000. So could you prove that it's a workable algorithm? Yes. Could you get that benefit?
Speaker 1Unlikely. I think the argument is using, and I know we're geeking for a minute, but just using classical computing, you can actually have a different path, an arithmetic path. For example, if you had a better predictor of the distribution of prime numbers, like at the frontiers of number theory that we've not really resolved, we can start to have classical models that can start to break cryptography. It is possible.
Speaker 2The question is, how long will it take? Because that's always the question. So I think it is going to be possible. If it takes 10 years to break one key, most people are going to rest in peace. That's not a practical answer. That's, I think, where the debate is going to lie. But it won't prove out until it's done.
Speaker 3Let me move the conversation to something that's happening currently. I would love to get both of your takes on the following concept. There's open source. There's closed source. There's frontier. There's pacing the frontier. There's now effectively very reasonable alternatives that are open weight at a minimum. Your philosophy around the entire spectrum, and just give us a sense of how we should think about models and capabilities over the next couple of years. Nikesh, you want to start?
Speaker 4Sure. Look, I think earlier in the day, somebody said, don't restrict your teams in terms of capability in the experimentation phase. Don't start trying to cost optimize. Don't start trying to stitch your own in that phase because the teams who work for us, 20,000 people, have just discovered AI in the last 12 to 18 months. And it's not fully diffused across the organization. So if you make it harder for them to adopt if you make it harder for them to understand it's going to take longer for us to be ready so right now we're saying go use the best model you can find the easiest way to use it so you can understand can we validate the things that these things can do whether it's from an efficiency perspective a revenue perspective or a product development perspective that's kind of i'd call it phase one but in phase one what we've discovered is there's different courses for different courses right what people don't understand you know there was a moment whenever we thought the cyber security was going to get consumed by ai models and you know our lives are over because mythos is going to solve every problem well guess what we inspect all traffic at an endpoint at the cost of 20 dollars a year right hundreds of megabytes of traffic per endpoint at the cost of 20 to 25 dollars a year because machine learning is running on your endpoint your compute and we run it on a 20 30 40 megabyte payload now you want to take all that traffic run that through open ai using tokens for every employee in your company sounds like the sound of revenue like open i would probably generate all the revenue in one year from one company right so that's not a practical example of how model do so what you do is you take a small language model you compress it you make it task specific you keep keep training it until it gets to 99.9 accuracy and say instead of using it using machine learning algorithms we're going to use generative ai small language models and they're going to do that at the end so my point is what we're doing is we're trying to figure out what tasks requires what level of intelligence and what is the cost of that intelligence for that particular task and you'll suddenly discover you don't need a ferrari for every task and by the way the customers don't want to pay for a friday for every task i'll give you the flip side flip side is let's assume you're undergoing a breach you're being attacked you know you're being attacked at that point in time every customer in the room says don't worry use whichever model you need to stop the breach because they know the business impact of their business model because they know the business impact of their breach in the billions of dollars at that point in time i can run agent swarms i can do all kinds of stuff that you want from a defensive perspective so there i may want to use the most up-to-date frontier lm and say go think about it reason the hell out of it figure out how to stop this breach how to make this happen so what we're discovering is you're going to end up using frontier lms in unique cases where the business value that is going to get disrupted justifies you can do drug discovery i think for most practical use cases in enterprises we're going to end up training some of these companies and then we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and we're going to be able to use them for the next phase of their business model and i think when it comes to even in peace
Speaker 2time for us for example running a defender model we're most more than likely going to take an open rate model use our own proprietary data train it manage so today 20 for a million tokens for probably generation minus one 50 if you want to go to the most extreme uh we could take uh one of the chinese open weight models take a kimmy k3 something 800 billion parameters and if you're on your own gpus with a little bit of optimization but i'm talking like a week of engineering work we could drop the cost to about a dollar to two dollars for a million tokens which enterprise in the world will not accept a 10 times or in this case a 40 times cheaper answer for effectively what can do 95 of the work so i think economics is important the second is going to be at the end of the day um you just talked previously about chain of thought not just uh the end tokens well effectively then you're giving away your ip if that chain of thought can be captured and logged and audited by somebody else so so brand and ip is going to drive people to say i need to run the models on my own well you can't run a frontier model on your own because none of them will give it to you so then you're back to open weight and the third is geopolitics the best open weight models today are chinese by the way i think there's very little danger if you're putting them in a sandbox and running them by yourself sure if you use them from a cloud that's different but i think that over the next year or two i'm very doubtful that there will not be great models that come up in the west that are also open weight and that are as capable as the chinese the gap in these things are 6 to 12 months at the most and so all those three will be open weight models for the reasons i mentioned added to the reasons that negation mentioned and the token consumption that's left over is that because you actually need it or is that because it's a default behavior and people are too lazy to change
Speaker 3okay so i'm going to talk about developer behavior and since i was one i'm making fun of myself in a younger age developers are lazy they're going to use
Speaker 2the most common language and they're going to use the most common language and they're going to use the most common language and they're going to use the most expensive language and they're going to use the most common language and they're going to use the most common language and they're going to use the most common language and they're going to use the most expensive answer unless you put a control on it right because it's easy yeah and so that is going to take it there so i would say if experimentation phase you allow that to prove it works and now i got to put on a cheaper answer it's probably fine if it gets me there a lot faster but doing it for production usage is where i am skeptical that it's of value except for a tiny number of use cases in case you were brought in as one
Speaker 3of this small circle of people for mythos kind of preview it be their partner i'm sure you're now doing that with open ai as well with open ai as well with open ai as well when you're sitting with these guys is part when you're sitting with these guys is part when you're sitting with these guys is part of you thinking they're looking at my of you thinking they're looking at my of you thinking they're looking at my business and they're like wow this is a business and they're like wow this is a business and they're like wow this is a great business i do great business i do great business i do do you think that they're like and and you think that they're like and and what is the what is the actual what is the what is the actual what is the what is the actual technical technical technical boundaries that you expect to be boundaries that you expect to be boundaries that you expect to be respected and the reason i ask this is respected and the reason i ask this is respected and the reason i ask this is we've talked to a couple of other ceos i we've talked to a couple of other ceos i we've talked to a couple of other ceos i think mark benioff does the best example think mark benioff does the best example think mark benioff does the best example of this where of this where of this where you know we've talked about zdr and you know we've talked about zdr and you know we've talked about zdr and he's been incredibly aggressive about he's been incredibly aggressive about making sure there's zero data retention and doing that on behalf of his customers. What's so interesting about these EDR and all of this stuff is like, none of the models say that they won't generate synthetic data. It's literally not in there. You guys should look. There's all these outs, there's all these trap doors. So I'm just wondering like, when you're in the room, what are you thinking?
Speaker 4When you're in the room, you come out and say, these guys are not going to get into my business. They're not going to be able to get into my business. Because if you think about when Mythos came out, right? We've been paying billions of dollars in coding agents. Well, guess what? Not everybody's got ZD out in coding. There's tons of people out there using cloud code. We have no issue about that data training the models. So we are training these models with end users doing coding. These models are getting better and better doing coding. Well, guess what? If you teach a model what good code looks like, it also can figure out what bad code looks like. So that's what I'm trying to do. So the first thing that Mythos did, it figured out what bad code looks like. And if you let it loose against your code, it'll tell you what is bad. Now what's fascinating is when it did that, even then it was fine. It was hallucinating 30% of the time, right? But the problem is the reason it's perfect for attackers is they just need one vulnerability. They don't need 10 vulnerabilities. If you're a defender, you need to find every vulnerability and patch it. So all the people in Zoom I think have been through this journey in the last 12 months is that everybody here has gone testing. They've tested them, whether with Mythos or with some cousin of Mythos or some open source models and discovered every vulnerability has to then be tested, understood, figured out. And I would guess in this room, if I show hands, people say that numbers roughly 60 to 80% have been real and 20% have been hallucinations. So from that perspective, the models are great to be able to tell you what the vulnerability is. But I don't even think anybody in this room, if you ask them to raise their hand, will tell you that they're not going to even use Cloud Code to patch the vulnerability without having to test it again.
Speaker 3I have a very random question, but it makes me think. So you're engaged to defend companies, but it now looks basically like warfare, like both of you guys, you know, it's like warfare. And so is there something that you can learn from how war has been fought or how war is being fought? And do you do that to actually inform how the technology should behave? I don't know if this question makes sense.
Speaker 4I think to the extent that you can be better together, you can collaborate, like Arvind runs the program, we're now saying, we're all finding vulnerabilities. Why don't we pool our vulnerabilities and find the defense ones, as opposed to every company in the room trying to go test all the open source vulnerabilities they have, figure out how to patch them, go patch them individually. So then you take some trusted parties, give us your vulnerabilities, we'll patch them, we'll distribute the patches so you can all be safe at the same time. It's kind of like how cybersecurity works today. If you find an attack technique, you can use that attack technique in a different part of the world. We can take that attack technique, write machine learning code, ingest that into our product and make sure it gets available to every customer.
Speaker 3Is agentically written code error-free?
Speaker 4No. No. Okay.
Speaker 3Explain that. So
Speaker 2look, but first on your war question. So what Nikesh talked about is you can have powerful alliances where the alliance effectively can become more powerful than the attacker. That's technique one. Technique two in warfare, what has been the most effective is asymmetry. And so often, an attacker is cheaper than the defender, so the defender runs out of money and at some point then concedes. Or can you make defense equally cheap, in which case you can basically have symmetric warfare, and then you have a much better chance of success. And the third, which has been the way all wars have been won, is when you get technological superiority on one side. So go back to World War II, go back to Korea, go back to Iraq, go back to anyone. If you get technological superiority on one side, you tend to win. And so our goal is, can we get technological superiority? And that's what Nikesh was referring to. Can we find vulnerabilities? Can we patch them? Can we put boundaries around them? That makes it but really expensive for the other side to now try to find a way through. So that's what I would answer there. Look, at the end, will agentic code be superior to human written code? Absolutely, we can find that. We use agents and coding agents now for probably half the code that is written inside our place and it is superior to what was being written before. But the reason it's superior is the agents can also do evals and basically make sure that everything they can find is actually fixed before it ever goes out. All that said, we're only finding all the flaws that we know how to find. And so you get into this weird loop in computer science.
Speaker 3Okay, do I-- - It's not really properly verifiable. -
Speaker 2It's not fully verifiable because that is a task that is going to be almost computationally infeasible right now. Now, I'm not going to hold out hope that it doesn't become. What you were talking about in number 30 and all that, this is a task in which maybe we'll find a way to actually get to be able to verify the correctness of these things, at least in where the inputs are limited. The input can be anything, then I think it's really hard. Does that make sense? -
Speaker 1Yeah. - Let me ask the implications of the points that you just made. It seems obvious to me that there isn't a world where China and the United States or the West or whomever, a bunch of international countries all hold hands and say, let's all stop AI development. That being the case, it becomes a necessity that there's an arms race, that there's a defensive or offensive superiority requisite, that there's a compute capacity requisite, that there are all of these needs that arise to protect industry, to protect our economy, to protect our jobs. Does that argument win the day with everyone that's trying to stop AI and the political conversations and the social stuff about AI needs to stop, and we need to stop doing this? Does this point about the inevitability of one side having it, requiring the other side to defend it, change the conversation? I'd love for you guys to share any anecdotes you've had with senior party officials who might not be in power today, but might be coming into power and whether they're gonna kind of have a view change around this because of that point. -
Speaker 4I think it's abundantly clear that nobody's stopping. Everybody seems to be, to be working at a frenetic pace to try and make sure that they get a better, smarter, faster model. It's kind of interesting. You can take the charitable side and say, there was no technology in the world that was built where people were building security in power. I don't think TSA existed when they built planes. It was like Wright brothers say, great, we have planes. Let's figure out how to- - Take your shoes off. - Let's risk everybody before they get on the plane. That was not what they were thinking about. - Keep the explosives out of the shoes. - Yes, that's right. That's not what they were thinking about. So unfortunately we've been shown the dark side of these models and we've over-rotated on the dark side of the model, where now the entire conversation is about the dark side and the perils of AI and not on the benefits of AI. I think there's an additive problem. That notwithstanding, I don't think anybody's slowing down. I think the most likely outcome, which I think hopefully is the right outcome, is that in the world, you said it and Arvind said it earlier, if you build something, you're accountable for it. If you build something and unleash it, you can't unleash and say, oops, I don't know how to control it. That argument doesn't work. -
Speaker 1Yeah, 'cause you wrote the software. - Yeah. - And this whole argument that you didn't write the software, that the software wrote itself. -
Speaker 3Because the engineer is gonna say, well, agent 74565 from my harness wrote it. And it's like, no, you wrote it. - Well, but the company still wrote it. So there's product liability. -
Speaker 4But you're a commercial enterprise. - Exactly. - So you're taking economic value in return for giving me something. You're required to abide by the responsibility that you signed up for. So I think that's, in my mind, it's as simple as that. And there's enough regulation in the world, perhaps too much, to hold people accountable for stuff that they put out in the open market, where they don't take responsibility. -
Speaker 2But it's actually impossible to control it unless we think the entire world is gonna sing Kumbaya and say, let's slow down, which as you said, is unlikely. So if that's the case, fine. Put it slow down here, maybe. Well, okay, does it mean Japan slows down? Does it mean China slows down? Does it mean India slows down? No chance. They'll say, great, you're slowing down. It gives me a chance to catch up and get ahead. So a digital technology is impossible to actually slow down. And the European example tells us. So there you get afraid of all the possibilities. Yeah, I'm not saying they're real dangers. You get afraid of all of them and you impose regulations first. I guess it's such a vibrant tech economy there, right? -
Speaker 1Yeah, well, I mean, neither of you actually, answered my question about conversations you've had, which, 'cause I'll tell you a conversation I had last week in DC. I had drinks with a member of Congress, pretty senior ranking guy. And the conversation had two parts to it. One is, I think one of the CEOs of the biggest companies in the world is completely freaked out about cyber and he's losing his mind and he doesn't know what to do. And I just had dinner with him and here's all the stuff he said. And then I also think that by Q4, we might see a 50 state ban on data centers, which isn't likely, but he says, 50 is a big number, big number. -
Speaker 4Have 50 states ever agreed on anything in this country? -
Speaker 1No. - Well, a few things, about 60. - So that would be amazing. - Yeah, get everyone together. By the way, when everyone agrees on something, you know it's not true. But I think, and it's the fact that both of those statements are being said by someone. So where do you think this resolves? Like, how do we resolve this path forward? - He's good at this. -
Speaker 2Try to answer your direct question. So look, amongst us, we have all, and other members of your group have, we all had different conversations with different members of this administration, the previous administration. The Senate runs longer than just one admin. Congress has changed a lot faster. This question has been in there since 2022. The Senate had closed hearings in '22. They had open hearings in '23. Congress had them. The White House had them in both admins. And they go round and round this question. The regulation question on AI and data centers is driven, sorry and cynical, is driven by populism, not by any reality. If I can get more votes, should I look like I'm trying to do those things? Politics is politics. I'm not going to say that's not real. It is real. That said, enough people also care deeply about the long-term economic advantage. And so that's the balancing act that is happening. I do think we are on the correct balance right now, but I'll acknowledge an election can change where we fall on that balancing act. On data centers, I think there's no chance that we're going to be able to do anything about it. I think we're going to be able to do it. I think that's the social philosophy that's going to determine win or lose.
Speaker 3I have a final question before I give it to you for the final question. But my final question to both of you, you mentioned this here, which was great. Can you tell the story about, I'm a huge fan of vertical integration, huge fan. I think like, you know, markets swing in pendulums. And, you know, there was a world like even in the 60s, 70s and 80s, where there was the conglomerate, and it was an incredibly valuable enterprise. And it was in a time of turbulence, it was a safe harbor for capital, because the conglomerate was the mechanism by which you organized. And then when you had peace and prosperity, then people disaggregated. And I think we're moving back to the world of the conglomerate. And you told me just back there about the vertical integration of IBM at its peak, I just wanted you to and then Nick, actually, your thoughts on vertical integration.
Speaker 2So what Chamath and I have often talked about is the IBM mainframe, which by the way, there's a few people in the room who do use it quite well. And that scale, the IBM mainframe has lasted 65 years. Why? It was vertically integrated, we're a little less right now, in the sense we don't go all the way to dirt. In the 60s, we went all the way to dirt, meaning we found sand, we made our own wafers, we built our own fabs, we built our own chips, every single thing out to the metal that surrounded the machine. What that avoided was any IP leakage, but it allowed us to keep the profit of the complete stack in the company. Today, the only piece we don't do is the actual fab. Still, the design of the chips, the design of the engineering, the resilience, the bulk of the software is inside. And that vertical integration, you've got to decide, yeah, of course, I'm not going to make our own metal, you buy metal. And fabs is because of scale. But I think that vertical integration in areas which are of incredible important advantage, which goes back to the infrastructure point, the two of you were making before, is incredibly important to understand what's going on in the infrastructure and where do you want to be and where, if it's a commodity, then you should not be.
Speaker 4Yeah. But you can see that the modern day version of that is NVIDIA. Yeah. If you look at what Jensen is doing, he's a modern day version of the vertically integrated stack. And he went and bought-
Speaker 3Jensen is at the dirt and he's at the application- He's got a hugging face.
Speaker 4He understands that there's going to be a balance between frontier models and other models. He's trying to fund everybody in the ecosystem to ensure that they don't stray from the vertically integrated stack that he's producing. And the good news is if you control the vertical stack, you can decide which part of the stack to commoditize. So you can decide to move profit between layers. And if you see that there are other people attacking a certain part of your stack, you destroy the profit pool in that stack, you move the profit to the other part of the stack. And you can see that happening.
Speaker 3Very powerful market leverage. So to put you on the spot. So if you look at the application layer today, what's the obvious vertical integration strategy that OpenAI or Anthropic should be taking? And then what's the defensive mechanism for the companies that are maybe on the receiving end of that?
Speaker 4I think you're already seeing that. And I think we trivialize this conversation. We start saying open models, open weight. It's literally, it's an open stack versus a closed stack. Because I don't think, I think at some point in time, this notion of running something like an open router become impossible. Because models are getting more and more, each model is requiring more memory, more context, more understanding. You can't separate context and memory from the reasoning. Now, if you want to separate reasoning from the context and memory, then open router is only responsible for moving the reasoning tokens back and forth. So I think over time, you'll start seeing it doesn't make sense that you have a effectively end-to-end stack. It'll be like a CPA you talked about earlier when you were chatting. So it's a cost per action. The action will require you to go and traverse the entire logic from prompt to outcome. And then memory will require or make it easier. And there'll be too high a friction for you to move your task from one stack to the other stack, because it has all your context and memory. And you're just going to see that. Look at what's happening in the personal agent space, right? If you start to move your tasks from one stack to the other stack, you start training a personal agent with all your context, all your APIs, all your connectivity. Now you've built a mode. Because now I can't take that every day and move it to the next stack and the next stack. So I think you will find in the application layer, they're going to have a vertical stack as well, which will go through the entire process of software, reasoning, memory, context.
Speaker 3To your logical conclusion, everybody will have their own then. Yes. Because it's just the right thing to do.
Speaker 4And then brute force distribution wins again in the enterprise space, right? You're seeing that in the consumer space. Yes. And then brute force distribution is going to compete. In the next three months, every frontier lab is going to have a full-on, full-court press on the Muse concept, right? Yeah. They tried that six to nine months ago. It didn't work. Now, with Facebook showing us how it can work, I think pretty much everybody has it. There will be APIs. There will be the largest battle of consumer control. Every app that exists in the iPhone has to become
Speaker 1headless. Maybe we can wrap by going from vertical integration to horizontal expansion. It's been a common business strategy in enterprise technology companies to expand horizontally through M&A, drive synergies, cross-sales, and so on. And you've obviously both done it exceptionally well and executed it well. How has your view been affected in the last year, this year, let's say, in terms of valuations and multiples, cost of capital?
Speaker 4Let's talk about M&A in the context of cyber. Then we can talk about M&A in the context outside of cyber. In the context of cyber… I have always maintained over the last eight years that it's silly that our customers have to buy 40 different solutions and stitch them together. We barely understand our sh*t. You think you understand our sh*t and 10 other vendors' sh*t? You can stitch it. God bless your soul, right? So we're working as fast as we can to take disparate parts of our industry and find the way to stitch them together to have the data work together. I think that's a journey. In terms of parallels, when I started, my first job was at Fidelity Investments many years ago. We have 23 systems which collectively would be called CRM today. We have master tables. We have tables that are concatenated with them. We have 20 different applications that do 20 different things. Today, you go bite off the shelf and you cringe at the cost of Salesforce or the cost of Microsoft Dynamics or Oracle, whatever you choose to do. So in 30 years, what's happened is that's consolidated into a singular platform. The customers don't stitch the solutions anymore. Because what happens is these solutions are consistent. These software solutions become consistent. They all become like each other. They all roughly do the same thing, and there's no differentiation. So I think the same thing is going to happen in cyber. That's what we've tried to do. We've tried to find things that are best in their category, stitch them, make them work, so our customers don't have to do the stitching. That's kind of the answer on cyber. For some reason, cyber is enjoying much better valuations lately because of the AI mythos event, which is fine, which is rationalized over time. Because all these guys are going to go spend unlimited amounts of money on cyber. Outside of cyber, I think it goes back to the conversation, Chamath and we were having earlier about, it's very hard to understand the terminal value of software. It's not clear what software is going to survive the next five to eight years with where we are from an AI perspective and the notion that the product market fit is getting disrupted for many of these companies. We all know if AI was applied well, how our current software should behave in an ideal scenario. I know what I want from half the packet of software that I buy. I know if I could have my magic wand and make it do things, I know exactly how it should work in an AI first world, which means somebody's going to do it. So I think there's going to be a huge disruption in the SaaS space over the next few years.
Speaker 3Arvind, are you looking at anything
Speaker 2thinking, "Juicy, multiples." So over the year, it was very attractive at the beginning, not so attractive a month, a month ago. We'll wait and see how it goes over the next few. But actually, I want to build on Nikesh's thought. This is not just unique to cyber. The average enterprise uses anywhere from 500 to 5,000 distinct software vendors. In a world when you have to protect against it all, it's not just the cyber threat, that's a massive attack surface. So you have to ask yourself, could I replace 490 of them with minor AI capabilities? And do I need to go back and leverage 5 to 3 million? Yeah, I think it's a good question. I think it's a good question. And I think it's a good question. And I think it's a good question.
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