Arvind Krishna & Nikesh Arora: Quantum Threat, Open vs Frontier AI, AI Cyber Warfare & SaaS Collapse

I 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 days. about cryptography. and some of the risks to cryptography. I think it 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 Arvin, maybe. we could start and talk a little bit. about where things stand today from your. view. >> Sure. So, um. a little bit of what David just touched. on is uh DARPA's been running a. competition called QBI or Quantum. Benchmarking Initiative. Um 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 uh. 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 5 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. >> Are 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.

>> Yeah. Um, 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,

and 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?". >> Yes. So, today you got toy quantum. computers. I'll call I'll call them toys. because they can do certain problems, but they're 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. uh, 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 will surprise the hell out. of everybody. >> Does this reference kind of this notion. of a logical qubit being kind of an. error corrected operating qubit? Is that. >> It's a Is Is that a Is that a a term. >> you've adopted qubits and. that everyone kind of has the same. milestone, the same metric? >> I 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 uh that's a. term that I 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. >> Nikesh, 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 honey pots? Where do you point that if you were a. bad actor? >> If you think from the perspective of. everybody here has. pretty much. crypto keys running across the 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 us are going to go out and say, "Oh, Let's go burn $5 of IT. infrastructure and let's go build it. again in 1 year because now we need. quantum-safe infrastructure." So, you.

should be built 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 in. the market where you can start, you. know, wrapping. uh. post-quantum ready through keys around. traffic as a stopgap. So, you can do. that, but the time to if he says it's. 2029-30, we're already late. >> We're 3 years away. >> We're 3 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. >> There was a pretty senior AI researcher. that said we don't need to pay. >> Hm? >> 2029? >> 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, you know, and 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 OpenAI math dump this. week. is an early indication that this might. actually be a good prediction that he. made. >> Since you geeked 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. Right. So, if you take that as a thesis, quantum computers do not operate in. arithmetic, right, which is what all of. classical computers, including GPUs, do. Could you have an AI algorithm discover. a new quantum algorithm? Absolutely. Could it run it effectively and. efficiently on today's hardware? 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. or by an order of a thousand or ten. thousand. So, could you prove that it's. a workable algorithm? Yes. Could you get. that benefit? Unlikely. >> 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, arithmetic path. For example, if you had.

a better predictor of the distribution. of prime numbers, you know, 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. The question is how long. will it take because that's always the. question. Yeah. So, I think it it is. going to be possible, but 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. the That's I think where the debate is. going to lie, but it won't prove out.

until it's done. >> Let 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, do to start? >> Sure. 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 to. 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 and can. be validate the things that these things. can do, whether it's from an efficiency. perspective, a revenue perspective, or. product development perspective." That's. kind of I'll call it phase one. But, in phase one, what we've discovered. is there's different horses for. different courses, right? What people don't understand, you know, there was a moment when everybody. thought the cybersecurity 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. with the cost of $20 a year. Right? Hundreds of megabytes of traffic per. endpoint at the cost of $20 to $30 a. year because machine learning is running. on your endpoint, your compute, and we. run it on a 20, 30, 40-MB. payload. Now, if you want to take all that. traffic, run that through OpenAI using.

tokens for every employee in your. company, sounds like the sound of revenue. Like, OpenAI would probably generate all their. revenue in 1 year from one company. >> Right. >> So, that's not a practical example of. how models are used. 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 via So, the. point is. what we're doing is we're trying to. figure out what task 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 Ferrari 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. to stop the breach." Because they know. the business impact of that breach is 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. LLM and say, "Go think about it, reason. the hell out of it and 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. LLMs in unique cases where the business. value. that is going to get disrupted justifies. it. You can do drug discovery if you. want. I think for most practical use. cases in enterprises we're going to end. up training some version of an open. source model and I think when it comes. to even in peacetime for us, for.

example, running a defender model, we're. most more than likely going to take an. open weight model, use our own. proprietary data, train it, manage the. weights because that's what gives us the. auditability, the explainability, and. the the open system where we can. actually explain how that happened. >> I completely agree, but I'm going to. take a slightly different lens to get to. the same exact end conclusions. Let's. take economics. 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 Kimikay3, something 800. billion parameters, and if you run your. own GPUs with a little bit of. optimization, but I'm talking like a. week of engineering work, you 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 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 is 6 to. 12 months at the most. And so, all those. three drive me to conclude. I don't know whether it'll be 75%. to 90%, but that's the range I would put. of all token usage will go to open.

weight models for the reasons I. mentioned added to the reasons that. Nikesh 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? >> Okay. 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 the most expensive the most. expensive answer unless you put a. control on it. >> Right. >> Because it it's easy. >> Yeah. >> And so, that is going to do take it.

there. So, I would say if. experimentation phase you allow that to. prove it works and now I got to put it. 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. >> Nikesh, so were brought in as one of the. small circle of people for mythos. kind of preview it, be it their partner. I'm sure you're now doing that with. OpenAI as well. When you're sitting with.

these guys, is part of you thinking they're looking. at my business and they're like, "Wow, this is a great business." I [laughter]. do. Do you think that they're. Like And and and what is the What is the. actual technical. boundaries that you expect to be. respected? And the reason I ask this is. we've talked to a couple of other CEOs. I think Marc Benioff does the best. example of this where, you know, we've. talked about ZVR and 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 the ZVR 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 trapdoors. So, I'm just wondering like when you're in. the room, what are you thinking? >> When you're in the room, you come out and say this These guys are. not going to get into my business. >> They're not going to. >> They're not going to be able to get into. my business. >> Okay. >> Because. if you think about what when mythos came. out, right? The. We We've been paying billions of dollars.

in coding agents. Well, guess what? We're fundamentally every Not. everybody's got ZVR in coding. There's. tons of people out there using cloud. code who 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 model. good looks like, it also can figure out. what bad code looks like. So, the first. thing that mythos did, it figured out. what bad code looks like. And then if. you let it loose against your code, it'll tell you what is bad. What was.

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 this room, I think, have been. through this journey in the last 12. months is that. everybody here has gone, tested them, whether with Methos or with some cousin. of Methos or some open source malware. they discovered. Every one of them has to be has to then.

be tested, understood, you know, figured out. And I would guess in this. room, if I show of hands, people say. that number is roughly 60 to 80% have. been been have been real and 20% have. been. hallucinations. So, from that. perspective, the models are great to be. able to tell you. what the vulnerabilities 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. >> I 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, but. >> I think to the extent that. you can be better together, you can collaborate. Like Arvind runs a. 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 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. >> Is a genetically written code. error-free? >> No. >> No. Okay, explain that. >> So, look, but first on your war. question. >> Yeah. >> 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 Nick is referring to. Can we find. vulnerabilities, can we patch them, can. we put boundaries around them that makes. it 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 we get into this weird loop in. computer science. Okay, do I. >> fully properly verifiable? >> It'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's doesn't become what you were. talking about in number theory 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. If the. input can be anything then I think it's. really hard. >> Right. >> Does that make sense? >> Yeah. >> 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. in 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 the 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 going to. kind of have a view change around this. because of that point. >> I think it's abundantly clear that. nobody's stopping. Everybody seems to be working at a. frenetic pace to try and make sure that.

they get a better, smarter, faster. model. Um it's. kind of interesting. You can take the. charitable side and say there's no. technology in the world that was built. where people were building security and. power. I don't think TSA existed when they. built planes. It's just like Wright brothers say, "Great, we have planes. Let's figure out. how we're going to. >> Take your shoes off. >> Let's frisk everybody before they get on. the plane." That was not what they were. thinking about. >> [laughter]. >> Keep explosives. >> keeping 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 a. narrative 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 uh. you said it and Arvin 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. >> Yeah. Cuz you wrote the software. >> Yeah. >> Yeah, and this whole argument that you. didn't write the software, that the. software was wrote itself. >> because the engineer is going to 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 with the. company. >> you're a commercial enterprise, >> Exactly. >> you're taking economic value in return. for giving me something, you're required.

to abide by the responsibility 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 they don't take. responsibility. >> Well, but David, it's actually. impossible to control it, unless we. think the entire world is going to sing. Kumbaya and say let's slow down, which. Archi said is unlikely. So, if that's. the case, fine. You put a slow down. here, maybe. Well, okay, does it mean Japan slows. down? Does that mean China slows down?

Does that mean India slows down? No. chance. They'll say, "Great, you're. slowing down. 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 the. real dangers. You get afraid of all of. them and you impose regulations first. against such a vibrant tech economy. there, right? >> Yeah. >> Well, I mean, neither of you actually. answered my question about conversations. you've had which uh. cuz 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's a big number. Big number. >> Have 50 states ever agreed on anything. in this country? >> No. Well, a few things about six. >> be amazing. >> [laughter]. >> Okay, get everyone together. Well, whenever anyone agrees on something, you. know it's not true. But, I think um. >> It'd be like a miracle. >> And and it's that it's it's the fact. that both of those statements are being. being said by someone. So, where do you. think this resolves? Like, how do we. resolve this path forward? >> He's good at this. >> Try to answer the. >> say a direct question. So, look, amongst. us, we have all and all the 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 tends to change 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 I'm going to be 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're. 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. all 50 will ban. Is it possible 30 will. ban? Yes. The remaining 20 will rub. their hands in glee at that point.

>> If you look geopolitically for a second, it seems like we're trending to a place. of haves and have-nots. So, I can. everybody in this room is a have. You. know, you're a Western. market company. You have the benefit of being able to. work with guys like you. But there's a certain price of entry. that may not make sense for the random. pick your company anywhere else around. the world. What happens then when these models are. so capable and effectively free? What is. the actual manifestation? I'm sure. you've had to think about this in terms. of how you want to respond. So, how do.

you how do you think about that problem? This. separation of haves and have-nots. >> Look, I think Arvind said that you have. to make technology so cheap that it can. be easily accessible by everyone. And I think we have the opportunity of. making that happen. I don't think that it's going to be so. expensive that if you can run an. economic enterprise, it should be. possible in the envelope to be able to. find a way to protect yourself. [clears throat]. So, I don't think we're going to end up. in a have have-not scenario. I think the. bigger challenge which. is more pertinent to people in the room. is I fear that we will see fast advances.

in. compute development in two large. countries in the world. We may not see. that as. evenly distributed across the rest of. the world. And compute is very dear. right now. It's very hard to get compute. to run anything at scale. at the current. moment. And the challenge is going to be. that the further way you are from the. epicenter, the less likely you will have. access to this computer. It's not. because of economic reasons, it's purely. because of supply constraints. I think.

that's the bigger challenge, right? You. could be a company in Europe and you. have the right intentions and. motivations to go and. sort of innovate, optimize, and run with. it. And you may just suddenly discover. that because you're far away from the. epicenter, you don't get access as much. as companies in the Western world and in. the US or perhaps in China going to get. >> I think look, I I to look at history. because it gives us some information on. how these things roll out. I think this. is the classic labor versus capital. trade-off that is happening. It happened in the 1700s, it happened.

again in the 1800s, it happened again in. the 1900s. People who spend more on. capital than worried about labor tended. to win every previous one of the seven. or eight cycles like this. So, this is. what is going to happen this time. What Nikesh pointed out is the two. nations here are also quite willing to. be capital intensive. >> Yeah. >> It's not as simple as this, and they're. willing at the end of the day to concede. that labor is going to be less. important. That's the way it goes it's going to. play out. Nations that get very hung up.

on social and left-leaning philosophies. that labor has to be protected at all. costs. Not that there has to be. opportunity for labor, but every. existing job has to be protected, or the. ones are going to lose. Because I think. the ones there, to Nikesh's point, it's. cheap enough. Somebody in one country. can use the capability, but what's going to be in their head is, "Hey, but does it mean there's a job. loss or a job displacement?" Job. displacement you should take because it. may result in a better job. I think. that's the social philosophy that's.

going to determine winners and losers. here. >> I have a final question before I give it. to you for the final question, but the. 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 I think like, you know, markets swing in pendulums. You know, there was a world, like even. in the '60s, '70s, and '80s where there. was the conglomerate, and it was a 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 that you just back there about. vertical integration of IBM at its peak. I just wanted you to and then Nick asked. what your thoughts on vertical. integration over here. >> So, what Jim 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 at 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 got to decide. >> Yeah, of course. >> I'm not going to make our own metal. You. buy metal. >> And right. >> 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. where do you want to be. and where, if it's a commodity, then you. should not be. >> Yeah. >> 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. >> Jensen is at the dirt. >> And he's at the dirt. >> hugging face. He wants to He understands. that there's going to be 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. >> Exactly. >> part of the stack, you you they destroy. the profit pool on that stack, you move. the profit to the other part of the. stack. And you can see that happening. Very 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? >> I think you're already seeing that. And. I think we're we're 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 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 start seeing.

it doesn't make sense that you have a. effectively end-to-end stack. It'll be. It's like a CPA you talked about earlier. when we were chatting. So, it's a cost. per action. The action will require you. to go and traverse the entire logic from. prompt till outcome. And then memory. will require and 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 going to. see that. Look Look at what's happening. in the personal agent space, right? If. you start training a personal agent with. all your context, all your APIs, all. your connectivity, now you built a moat.

Cuz now I can't take that every day and. move it to a 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 to the entire process of. software, reasoning, memory, context. >> To your logical conclusion, everybody. will have their own then. Yes, because. it's just the right thing to do. >> And then brute force distribution wins. again in the enterprise space, right? You're seeing that in the consumer space. brute force distribution is going to. compete. In the next 3 months every frontier lab. is going want a full-on full court press.

on the Muse concept, right? They tried. that 6 to 9 months ago. It didn't work. They Now, with Facebook showing us how. it can work, I think pretty much. everybody has it. There will be APIs. It. will be the largest battle of consumer. control. Every app that exists in the. iPhone is has to become headless. >> 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-sells, and so on. And you've obviously both done it. exceptionally well uh 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? >> Let'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 8 years that. it's silly that our customers have to.

buy 40 different solutions and stitch. them together. We barely understand our. You think you understand our. 10 other vendors with that? 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. And if you. want parallels, when I started [snorts]. my. first job was at Fidelity Investments. many years ago.

We had 23 systems which collectively. would be called CRM today. We had master tables. We had tables that. concatenated with them. We had 23. different applications that did trading. and things. Today, you go buy it off the. shelf and you cringe at the cost of. Salesforce or the cost of Microsoft. Dynamics, Oracle, whatever you choose to. do. So, in 30 years, what's happened is. that's consolidated to a singular. platform. The customers don't stitch the. solutions anymore. Because what happens is these software. solutions become consistent. They all. become like each other. They all. probably do the same thing and there's.

no differentiation. So, I think same. thing's going to happen to cyber. That's. what we try to do. We've tried to find. things that are best in their category, stitch and make them work so our. customers don't have to do the. stitching. So, I think that's kind of. the answer on cyber. For some reason, you know, cyber is enjoying much better. valuations lately because of the the AI. mythos event, which is fine, which will. rationalize 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 5 to 8 years with where we are from. an AI perspective and the notion that. product market fit is getting disrupted. from any of these companies. We all know. with if AI was applied well, how our current software should behave. in an ideal scenario. I know what I want. from how to package the software that I. buy. I know if 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 huge. disruption in the SaaS space over the. next few years. >> Arvind, are you looking at anything. thinking, "Hmm, juicy multiples.". >> So, So, over the year, it was very attractive at. the beginning, not so attractive a month. ago. We'll wait and see how it goes over. the next few. But actually, I want to build on. Aneesh'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 five to 10 main. platforms? That implies massive. consolidation, because that's where the. spend goes. That's what I believe is.

going to play out. So, there's going to. be consolidation, because if you want to. keep your cost of distribution low, then. they can distribute all of it. And if the value of that software is. falling, then you cannot afford. independent uh distribution for each of. those. I believe that's going to play. out, whether it takes 6 months or 6. years. It's somewhere inside that. equation. Uh I do think also software. should be distinguished. There is. software where there is a lot of. interaction value, which as we've seen. clearly can be replaced with a lot of. AI. Both examples you used were things.

which were interaction-rich. >> Right. >> But if in the end you go down to I need. you to be reliable at 10 to the minus. 15, >> Right. >> which is a database lookup for somebody. doing retail banking or closing the. books, I think that's going to be hard to. replace that easily. >> Right. >> with. uh AI-generated software. So, I think. there is going to be a distinction on. how easy it is going to be to do that. >> digital twin for years before you cut. over. >> Correct. >> Yeah. >> And then is there's no cost advantage to.

do that. >> Yeah. Guys, thank you very much. Arvind Krishna, Nikesh Arora, thank you. >> Thank you. >> Thanks, everybody, all of you. >> [applause]. >> I'M GOING ALL IN. >> [music]. [music]. [music]. >> I'm going all in.