Jensen Huang – Will Nvidia’s moat persist? (transcript)

We’ve seen the valuations of a bunch of software companies crash because people are expecting AI to commoditize software. And there’s a a potentially naive way of thinking about things which is like look Nvidia sends a GDS2 file to TSMC. TSMC builds the logic dies. It builds the switches. Um then it packages them with the HBM that SK Highex and Micron and Samsung make. Then it sends it to an ODM in Taiwan where they assemble the racks. And so Nvidia is fundamentally making software that other people are manufacturing. And if software gets commoditized, does Nvidia get commoditized? >> Well, in the end, something has to transform electrons to tokens. That transformation um there’s no the transformation of electrons to tokens uh and making those tokens more valuable over time. I I I don’t I think that that that’s hard to hard to um completely commoditize the transformation from electrons to

[00:01:00] tokens is such an such an incredible journey and and making that token. You know, it’s like making a one molecule more valuable than another molecule, making one token more valuable than another. the amount of artistry, engineering, science, invention that goes into making that token valuable. Obviously, we’re we’re watching it happening in real time. And so, so the the the the transformation, the manufacturing, um all of the science that goes in there is far from un deeply understood and it’s far from the journey is far from far from over. And so, so I I doubt that it will happen. Um we’re going to make it more efficient, of course. I mean the whole the whole thing about Nvidia in fact the way that you frame the question is is my mental model of our company the input is electron the output is tokens that is in the middle Nvidia and our job is to to do as much as necessary as

[00:02:02] little as possible to enable that transformation to be done at incredible capabilities and and what I mean by as little as possible whatever I don’t need to I partner with somebody and I make it part of my ecosystem to do. And if you look at Nvidia today, we probably have the largest ecosystem of partners both in supply chain upstream, supply chain downstream. all of the computers, computer companies and all the application developers and all the model makers and all the you know AI is a five five layer cake if you will and and we have ecosystems across the entire five layers and and so we try to do as little as possible but the part that we have to do as it turns out is insanely hard and and um >> I I don’t think that that gets commoditized in fact in fact um >> uh I also don’t think that the the enterprise software ware companies uh the tools makers you know most of the software companies today are tools makers um some of them are not um but

[00:03:02] are some of them are workflow um codification you know systems um but for a lot of companies they’re tool mmakers for example you know Excel is a tool powerpoint’s a tool uh cadence makes tools synopsis makes tools I I actually see the opposite of what people see I think the number of agents are going to grow exponentially. The number of tool users are going to grow exponentially and it’s very likely that the number of instances of all these tools are going to skyrocket. It is very likely the number of instances of synopsis design compiler is going to skyrocket and the number of number of agents that are going to be using the floor planners and all of our layout tools and our design design rule checkers. The number of agents that are today we’re limited by the number of engineers. Tomorrow those engineers are

[00:04:01] going to be supported by a bunch of agents. We’re going to be exploring out the design space like you’ve never seen explore before and want to use the tools that we use today. And so, so I think I think tool use is going to cause cause these software companies to skyrocket. The reason why it hasn’t happened yet is because the agents aren’t good enough at using their tools yet. And so either these companies are going to build the agents themselves or agents are going to get good enough to be able to use those tools. And I think it’s going to be a combination of both. Um I think in your latest filings it was you had almost hundred billion dollars in purchase commitments with people foundaries memory packaging and then uh semi analysis has reported that you will have $250 billion of these kinds of purchase commitments and so one interpretation is Nvidia’s mode is really that you’ve locked up many years of these scarce components that are uh you know somebody else might have an accelerator but can they actually get the memory to build it? Can they actually get the logic to build it? And this is really Nvidia’s big mode for the next few years.

[00:05:00] >> Well, it it’s one it’s one of the things that we can do that is hard for someone else to do. The reason why we could we we’ve made enormous commitments upstream. Um some of it is explicit. These commitments that you mentioned, some of it is implicit. Um, for example, a lot of the investments that are upstream are made by our our supply chain because I said to the CEOs, “Let me tell you how big this industry is going to be and let me explain to you why and let me reason through it with you and let me show you what I see.” And so as a result of that that process of of uh informing inspiring um aligning with CEOs of all different industries upstream they’re willing to make the investments. Now why are they willing to make the investments for me and not someone else and the reason for that is because they know that I have the capacity to buy it buy their supply and sell it through my downstream. the fact

[00:06:00] that Nvidia’s downstream supply chain and our downstream demand is so large, they’re willing to make the investment upstream. And so if you look at GTC um and and uh you know, people are marveled by the scale of GTC and the people that go, it’s a 360° that the entire universe of AI all in one place and they they’re all in one place because they need to see each other. I bring them together so that the the downstream could see the upstream. The upstream could see the downstream and all of them could see all the advances in AI and very importantly they can all meet the AI natives and all the AI startups that are all you know being being built and all the amazing things that are happening so that they could see firsthand all the things that I tell them. And so I spend a lot of my time informing directly or indirectly um our supply chain and our partners and our ecosystem about the opportunity that’s that’s in front of us. You know, most of my keynotes, you know, some some people

[00:07:02] always say, you know, Jensen in most keynotes, it’s like one announcement after another announcement after another announcement after another announcement. our keynotes are there’s always a part of it that’s a little torturous in the sense that it’s almost comes across like an ed like education and and in in fact that’s exactly on my mind. I need to make sure that the entire supply chain upstream and downstream the ecosystem understands what is coming at us, why it’s coming, when it’s coming, how big is it going to be, and be able to reason about it systematically just like I reason about it. and and so so I think the the the the mode as you you describe it we’re able to of course um build for a future uh if our next next several years is a trillion dollars in in scale we have the supply chain to do it without our reach

[00:08:02] the velocity of our business you know just as there’s cash flow there’s supply chain flow there turns uh nobody’s going to build a supply chain for an AR architecture if the architecture the business turns is low. And so our ability to sustain the scale is only because our downstream demand is so great and they see it and they all hear about it. They they see it all coming. And so that’s it allows us to do the things that we’re able to do at the scale we’re able to do. >> I do want to understand more concretely whether the upstream can keep up. Um for many years now you guys have been 2xing revenue year-over-year. You guys have been more than tripling the amount of flops you’re providing to the world year over year >> and 2xing at the scale now is really incredible. >> Exactly. >> So then you look at logic say you’re the biggest customer on TSMC’s N3 node and um you’re one of the biggest on uh AI as a whole this year is going to be 60% of N3. It’s going to be 86% next year

[00:09:01] according to some analysis. How how do you 2x if you’re the majority? Um and how do you do that year-over-year? So are we are we in a regime now where the growth rate in AI compute has to slow because of upstream? Do you see a way to get around these you know you how do we build 2x more fabs year-over-year ultimately? >> Yeah, at some at some level um the the instantaneous demand uh is greater than the supply upstream and downstream uh in the world. And and it could be at any instant any instance we could be limited by the number of plumbers. >> Mhm. >> Which which actually happens. >> The plumbers are invited to next year’s GTC. >> Yeah. You know, by the way, great idea. >> But that’s a good condition. You you want you want you want a market you want an industry where the instantaneous demand is greater than the total supply

[00:10:01] of the industry. Um the opposite is obviously less good. If we’re too far apart, uh if one particular item, one particular component is too far too far away, um obviously obviously the industry swarms it. So for example, notice people aren’t talking very much about co-ass anymore. >> Yeah. >> And the reason for that is because for two years we swarmed a living daylights out of it and we double double double on on several doubles and and now I think we’re in a fairly good shape. And TSMC now knows that co-ass supply has to keep up with the rest of the logic demand and the memory demand and and so so they’re scaling co-ass um and their scaling uh you know future packaging technologies at the same level as a scale logic which is terrific because for a long time co-ass was rather specialty and um uh HBM was rather specialty but they’re not specialties anymore people now realize they’re mainstream computing technology Um and and then and of course uh we’re

[00:11:02] now much more able to influence a larger scope of our supply chain. In the past in the past um you know in the beginning of the AI revolution all the things that I say now I was saying 5 years ago and some people believed in it and invested in it. for example, uh, Sanjay and and the Micron team. I still remember the meeting really well where where I I was clear about exactly what’s going to happen and why it’s going to happen and and the predictions the predictions that that um of today and they they really doubled down on it and we partnered with them and uh across LPDDR across you know HBM memories uh they really invested in it and and it it it obviously has been tremendous for the company. uh some some people came a little bit later and uh but they now they’re all here and so I I think the each one of these generation each one of these bottlenecks gets a great deal of attention um and

[00:12:02] now we’re we’re prefetching the bottlenecks uh years in advance. So for example uh the the the investments that we’ve done uh with uh with Lum and Coherent and um all of the silicon photonix ecosystem uh the last several years we really reshaped the ecosystem and the supply chain silicon photonix. We we u built up an entire supply chain around TSMC. We partnered with them on coupe uh invented a whole bunch of technology. We licensed uh those patents to the supply chain. Keep it nice and open. Um, and so we’re preparing the supply chain through invention of new technologies, new workflows, uh, new test, new testing equipment, double-sided probing, um, investing in companies, helping them scale up their capacity. Um, and so, so you could see that we’re trying to shape the ecosystem so that it’s ready, the supply chain so that it’s ready to support the scale. It seems like some bottlenecks are easier than others. And so scaling up co-ass

[00:13:01] versus scaling up >> I went to the hardest one by the way >> which is >> plumbers. >> Yeah, >> it’s true. >> Yeah. Yeah. I actually went to the hardest one. Yeah. >> Yeah. Plumbers and electricians. And the reason for that is because >> because and this is one of the concerns that I have about of all the doom the doomers um describing the end of end of work and killing of jobs. And you know, one of the things that that that um if we discourage people from being software engineers, we’re going to run out of software engineers. And and uh the same prediction 10 years ago, some of the some of the doomers were were uh uh saying that we’re telling people whatever you do, don’t be a radiologist. And you might hear some of those some of those videos are still on the web. You know, radiology is is going to be the first career to go. Nobody’s the world’s not going to need any more radiologists. Guess what? But we’re short of radiologists. >> Oh, but okay. So, going back to this point about well some things you scale other things like how do you actually

[00:14:00] get how do you actually manufacture 2x the amount of logic a year? Ultimately that’s bottleneck by memory and logic are bottleneck by UV. How do you get to 2x as many UV machines a year? >> Yeah. >> Year over year. >> None of that none of that’s impossible to scale quickly. You just need to you you could do all of that is easy to do within two or three years. You just need a demand signal that it’s not it once you once you can build one you can build 10 and once you can build build 10 you can build a million and so these things are not not hard to replicate. How far down the supply chain do you go where you do you go to ASML and say hey if I look out three years from now for me to for Nvidia to be generating two trillion in a year in revenue we need way more AUV machines and >> some of them I have to directly uh some of them are indirectly and some of them um if I can convince TSMC as ASML will be convinced and so that’s that you know we have to think about the critical critical pinch points and uh but if TSMC is convinced uh you’ll have plenty of EV

[00:15:00] machines in a few years. And so none of that my point is that none of the bottlenecks last longer than a couple 2 three years. None of them. And meanwhile meanwhile we’re uh improving computing efficiency by 10x 20x in the case of Hopper to Blackwell some 30 50x um we’re coming up with new algorithms because CUDA is so flexible. Uh we’re we’re developing all kinds of new techniques so that we drive efficiency. uh in addition to increasing capacity. Yeah. And so so there those those are those are things that that none of that worry me. >> It’s the stuff that’s downstream from us. Um energy policies that prevent energy from from you know you can’t grow you can’t create you can’t create an industry without energy. You can’t create a whole new manufacturing industry without energy. Uh we want to re-industrialize the United States. We want to bring back uh chip manufacturing and computer manufacturing and packaging and we want to build new things like EVs and robots and we want to build AI

[00:16:01] factories and you you can’t build any of these things without energy and those things take a long time but more chip capacity that’s a two threeear problem more coass capacity 2 three year problem >> interesting I I feel like I have guests tell me the exact opposite thing sometimes and I don’t in this case I just don’t have the technical knowledge to adjudicate but >> well the beautiful thing is you’re talking to the expert Yeah, true, true. Um, okay. I want to ask about um your competitors. >> Yeah. >> So, if you look at TPU, >> arguably two out of the top three models in the world, Claude and Gemini, were trained on TPU, what does that mean for Nvidia going forward? >> Um, well, we have we have a very different we built a very different thing. Um, you know, what what Nvidia built is accelerated computing. not a tensor processing unit. And uh accelerated computing is used for all kinds of things. You know, molecular dynamics and quantum chromodnamics and

[00:17:02] it’s used for data processing, data frames, structured data, unstructured data. It’s used for um fluid dynamics, particle physics, you know, and in addition, we use it for AI. And so accelerated computing is is um much more diverse and and although AI is the conversation today is obviously very important and impactful uh computing is much broader than that and what Nvidia has done is reinventing reinvented the way computing is done from general purpose computing to accelerate computing. Our market reach is far greater than any any TPU can any ASA can possibly have. And so if you look at our position, uh we’re the only company that that accelerates applications of all kinds. We have a gigantic ecosystem and so all kinds of frameworks and algorithms all run on Nvidia. And because our computers

[00:18:04] are designed to be operated by other people, anyone who’s an operator could buy our systems. Most of these homebuilt systems you have to be your own operator because it was never designed to be flexible enough for other people to operate. And so as a result of the fact that anybody can operate our systems, we’re in every cloud including Google and Amazon and you know Azure and OCI and right and so whether you want to operate it to rent or operate it if you want to operate to rent you better have large ecosystem of customers in many industries that be the offtakers. if you’re operating it if you if you want to operate it for yourself um we you know we obviously have the ability to help you operate yourself like for example for Elon with XAI and uh because we could we could enable operators uh in any any company in any

[00:19:02] industry you could use it uh to build a supercomput for uh scientific research and drug discovery at Lily and so we can help them operate their own supercomputer and and use it for the entire diversity of drug discovery and biological sciences um that that we accelerate >> and so so there there just you know a whole bunch of applications that we can address that you can’t do so with TPUs because Nvidia’s built CUDA as a fantastic tensor processing unit as well but it does you know it does every every life cycle of data processing and computing and AI and so on so forth and so I our our market opportunity is just a lot larger. Our reach is a lot greater and because we have such a large um we basically support every application in the world now you could build Nvidia systems anywhere and know that there will be customers for it >> and so it’s a very different thing. Uh

[00:20:00] this is going to be sort of a long question but you know you have spectacular revenue um and this revenue is mostly you’re not making 60 billion a quarter from uh pharma and um quantum you’re making it because AI is unprecedented technology that is growing unprecedentedly fast and so then the question is what is best for AI specifically and I’m not in the details but I talked to my AI researcher friends and they say look when I use a TPU it’s this big systolic array that’s perfect for doing matrix multiplies whereas a GPU is very flexible It’s great when you have lots of branching when you have um irregular memory access but with these you know what what is AI just like these very predictable matrix multiplies again and again and again and you don’t have to give up any die area for warp schedulers for you know switches between threads and memory banks and so the TPU is really optimized for the majority the bulk of this growth in revenue and use case for uh compute that is coming online right now um yeah I I wonder how you react to um

[00:21:01] matrix multiplies is an important part of AI but it’s not the only part of AI and if you want to come up with a new attention mechanism or if you want to disagregate in a different way if you want to come up with a whole new type of architecture altogether for example you know a hybrid SSM uh if you want to use a you want to create a model that that um that fuses diffusion and auto reggressive somehow. Uh you you want an architecture that’s just generally programmable and and we run everything you can imagine. And so that’s the advantage. It allows for invention of new algorithms a lot more a lot a lot more easily. >> And so because it’s a programmable system and and the ability to invent new algorithms is really what makes AI advance. So quickly, you know,

[00:22:00] TPUs like anything else is impacted by Moore’s law. And we know that Moore’s law is increasing about 25% per year. And so the only way to really get 10x leaps, 100x leaps, is to fundamentally change the algorithm and how it’s computed every single year. >> And that’s Nvidia’s fundamental advantage. The only reason why we were able to make black well to hopper 50 times, you know, I said it was 35 times and and and when I first announced it was going to black wall is going to be 35 times more energy efficient than hopper. Uh nobody believed it and and uh and then and then Dylan wrote an article. He said he said in fact in fact I sandbagged it’s actually 50 times. And you can’t reasonably do that with just Moore’s law. And so the the way that we solve that problem is new out new models um uh parallelized and disagregated and

[00:23:02] and distributed uh uh across a computing system uh and without the ability to really get down and come up with new kernels with CUDA, it’s really hard to do and and so the combination of the programmability of our of our architecture uh the the fact that Nvidia is an extreme codeesign company where we could even offload some of the computation into the fabric itself, MVLink for example into the network spectrum X um uh and that we could affect change across the processors, the system, the fabric, the libraries, the algorithm. All of that was done simultaneously. Without CUDA to do that, I wouldn’t even know where to start. >> My sponsor Cruso was among the first clouds to offer Nvidia’s Blackwell and Blackwell Ultra platforms, and they just announced their Nvidia Vera Rubin

[00:24:00] deployment scheduled for later this year. But access to state-of-the-art hardware is only part of the story. For example, most inference engines already do KV caching for a single user’s forward passes, but Cruso does it across users and GPUs. So if a thousand agents are running on the same system prompt, Cruso only has to compute the KV cache once for it to become available to every single GPU in the cluster. This is especially important as systems get more agendic and require much longer prefixes in order to use tools and access files. In a recent benchmark, Crusoe was able to deliver up to 10 times faster time to first token and up to five times better throughput than VLM. This is just one among many reasons that you should run your inference workload with Cruso. And if you need GPUs for training, you don’t need to switch clouds. Cruso’s got you covered there, too. Go to cruso.ai/torcashe to learn more. >> So, this gets at a interesting question about um Nvidia’s clientele where if 60% of your revenue is coming from these big five hyperscalers, you know, in a in in a

[00:25:01] different era where different customers, let’s say it’s professors who are running experiments and they are helped a bunch by they need CUDA. um they can’t use another accelerator. They need to just run PyTorch with CUDA and have everything optimized. But if you got these hyperscalers, they have the resources to write their own kernels. In fact, they have to to get that extra last 5% that they need for their specific architecture. Um Anthropic, Google are mostly running their own accelerators or running TPUs um and Tranium, but even OpenAI using GPUs has um has Triton which they’re like we need our own kernels. So they’ve um down to CUDA C++ they’ve instead of using Kublas and Nickel and everything they’ve got their own stack which compiles to other accelerators as well. Um and so if most of your customers can can and do make replacements for CUDA to what extent is CUDA really the thing that is going to make Frontier AI happen on Nvidia? CUDA. CUDA is um is a a rich ecosystem and so

[00:26:04] if you want to build on any computer first, building on CUDA first is incredibly smart and because the ecosystem is so rich uh we support every framework. uh if you want to create custom kernels uh if you need for example we contribute enormously to Triton and so the back end of Triton um huge amounts of NVIDIA technology we’re delighted to help every framework uh become as great as it can be and there’s lots and lots of frameworks there’s Triton there’s VLM there’s SG lang and then there’s more right and now there’s there’s a whole bunch of new reinforcement learning frameworks coming out you know you got Verl you got Nemo RL you got a whole bunch of new and then the the now with with with post- trainining and reinforcement learning that entire area is just exploding right and so if you want to build on on an architecture building on a CUDA makes the most sense because you know that the ecosystem is great you know that if

[00:27:01] something happens it’s more likely in your code and not in the mountain of code underneath you know don’t forget the amount of code that you’re dealing with when you’re building these systems when something doesn’t work was it you or was it the computer, you would like it always to be you and to to be able to trust the computer and and you know, obviously we still have lots and lots of lots and lots of bugs ourselves, but but our system is so well rung out that you could at least build on top of the foundation. So that’s number one is that the richness of the ecosystem, the programmability of it, the capability of it. The second thing is is um if you were a developer and you were building anything at all, the single most important thing you want more than anything is install base. You want the software that you run to run on a whole bunch of other computers. You don’t want to build a software. You’re not building software just for yourself. You’re building software for your fleet or for everybody else’s fleet because you’re a framework builder. And Nvidia’s CUDA ecosystem is ultimately its great

[00:28:01] treasure. We are now I don’t know how many several hundred million GPUs. Every cloud has it goes back to A10, A100, H100, H200, you know, the L series, the P series. I mean, there’s a whole bunch of them and and they’re they’re they’re in all kinds of sizes and shapes. And if you’re a robotics company, you want that CUDA stack to actually run in the CUDA in the robot itself. We’re literally everywhere. And so the install base says that once you develop the software, once you develop the model, it’s going to be useful everywhere. And so the install base is just too incredibly valuable. And then lastly, the fact that we’re in every single cloud makes us genuinely unique because you’re an AI company and you’re an AI developer. You’re not exactly sure which CSP you’re going to partner with and where you would like to run it. And we’d run it everywhere, including on prem for you if you like. And so so I think that that the the

[00:29:02] richness of the ecosystem, the expansiveness of the of the of the install base and the versatility of where where where we are, that combination is is uh makes CUDA invaluable. >> That makes a lot of sense. I guess I I guess the thing I’m curious about is um whether those advantages matter a lot to your main customers. um like there there’s many people who who they might matter for for the kind of person who can actually build their own software stack who are make up most of your revenue um especially if you go to a world where AI is getting especially good at the things which have tight verification loops where you can RL on them and then this question of how do you write a kernel that does attention or MLP the most efficiently across a scale up it’s a very verifiable sort of feedback loop and so oh can everybody can all the hyperscalers write these custom kernel for themselves. Um, and they might still Nvidia has uh still has

[00:30:01] great price performance. So, they might still prefer to use Nvidia. But then the question is does it just become a question of who is offering the best specs, the best um flops and memory and memory bandwidth for a given dollar where historically Nvidia has just had and still has you know the best margins in all of AI across hardware and software 70% plus because of this CUDA mode. And the question is, oh, can you sustain those margins if for most of your customers they can actually afford to build build instead of the CUDA mode. The number of engineers we have assigned to these AI labs is insane. working with them, optimizing their stack. And the reason for that is because because um nobody knows our architecture better than we do. And these architectures are not not as general purpose as a CPU. The reason the reason why a CPU is so, you know, a CPU is kind of like like a Cadillac, you know, it’s it just always, you know, it it’s a nice cruiser. It never goes too

[00:31:01] fast. Everybody drives it pretty well. You know, it’s got cruise control. you know, and everything is easy. But in a lot of ways, Nvidia’s GPUs are accelerators are kind of like F1 racers. And yeah, I I could imagine everybody’s able to drive it at 100 100 miles an hour, but it takes quite a bit of expertise to be able to push it to the limit. And we use we use a ton of AI to create the kernels that we have. And um I’m pretty sure we’re going to still be needed for quite some time. And so our expertise um helps our our our um uh our AI labs partners get another 2x out of their stack easily. Often times it’s not unusual that we you know by the time that we’re done optimizing their stack or optimizing a particular kernel their model sped up by 3x 2x 50%.

[00:32:00] Um, that’s a huge number, especially when you’re talking about the installed base of the fleet that they have of all the hoppers and black walls that they have. When you increase it by a factor of two, that doubles the revenues. That directly translates to revenues. Nvidia’s computing stack is the best performance per TCO in the world, bar none. Nobody can demonstrate to me that any single platform in the world today has better performance TCO ratio. Not one company. And in fact in fact the the uh the benchmarks are out there uh Dylan’s right inference max is sitting out there for everybody to to use and not one TPU won’t come trrenium won’t come. I I encourage them to use inference max and demonstrate their incredible inference cost. It’s really really hard. Uh not nobody wants to show up. Uh ML

[00:33:00] Perf I would I would welcome Trrenium to demonstrate their 40% that they claim all the time. I would I would love to to hear them demonstrate the the uh cost advantage of TPUs. It makes no sense in my mind. it makes absolutely zero sense on first principles. It makes no sense. And so I I think the I think the the the reason why we’re so successful is simply because our TCO is so great. There’s a second you say um 60% of our customers are the top five but most of that business is external. For example, most of AWS is most of Nvidia in AWS is for external customers not internal use. Most of our customers at Azure, obviously all of our customers are external. All of our customers at OCI are external, not internal use. The reason why they they favor us is because our reach is so great. We can bring them all of the great customers in the world. They’re all built on Nvidia. And the reason why all these C companies are

[00:34:00] built on Nvidia is because our reach and our versatility is so great. And so so I think I think the flywheel is is really install base the programmability of our architecture the richness of our ecosystem and the fact that there’s so many AI companies in the world there’s tens of thousands of them now >> and if you were one of those AI startups what architecture would you would you choose you would choose an architecture that’s most abundant where the most abundant in the world >> the one has the largest installed base where the largest installed base and one that has a rich ecosystem. And so that’s the flywheel that that’s the reason why between the combination of one, our perf per dollar is so great um that that uh uh they have the lowest cost tokens. Second, our perf per watt is the highest in the world. And so if if uh uh one of these companies if our partners built a 1 gawatt data center that 1 gawatt data

[00:35:01] center better deliver the maximum amount of revenues that and number of tokens which directly translates to revenues you wanted to generate as many tokens as possible maximize the revenues for that data center. We have the highest tokens per watt architecture in the world. And then lastly if your goal is to rent the infrastructure we have the most customers in the world. M and so that’s the reason why the flywheel works. >> Interesting. I I I guess the question comes down to what is the actual market structure here because even if there’s other companies there could have been a world where there’s tens of thousands of AI companies uh that have roughly equal share of compute but if even through these five hyperscalers really the people on Amazon using the computer anthropic openai um and these big big foundation labs who who can themselves afford and have the ability to make different accelerators work um >> no I I I think your your your assumption is is um premise is wrong. >> Maybe um let me let me let me ask you a

[00:36:00] slightly different question which is >> come back and make me correct your your your um your premise. >> Okay, let me just ask a different question which is okay if everything >> but still make sure that make me come back and okay and fix because it’s just too important to AI it’s too important to the future of science is too important to the future of the industry that that premise >> the premise look let me just finish the question and then we can address it together. Yeah. >> So what do you think if if all these things are true about uh price performance and performance per watt etc are true why why do you think it is the case that say um anthropic for example just announced a couple days ago they have a multi- gigawatt deal with Broadcom and uh Google for TPUs and majority of their compute obviously for Google it’s um TPU majority comput so if I look at these big AI companies it seems like a lot of their there was some point where it was all Nvidia and now it’s not. And so I’m curious how to square

[00:37:00] if these things are true on paper, why are they going with other accelerators? >> Yeah, anthropic is is an is a unique instance um and not a trend. Uh without an anthropic, why would there be any TPU growth at all? It’s 100% anthropic. Without anthropic, why would there be any tranium growth at all? It’s 100% anthropic. And I think that’s fairly wellnown and well understood. It’s not that it’s not that there’s an abundance of ASIC opportunities. There’s only one anthropic, >> but OpenAI deals with AMD. They’re building their own Titan accelerator. >> Yeah. But they’re mostly I we could all acknowledge they’re vastly Nvidia and and we’re going to still do a lot of work together. >> Yeah. And we’re not we’re not I’m not offended by other people using something else and trying things. If they don’t try these other things, how would they know how good ours is, you know? And sometimes you got to be reminded of it and and um we we got to and we have to

[00:38:00] continuously earn earn um uh the position that we’re in. Uh you there always claims and look at the number of AS6 that have been cancelled. Just because you’re going to build an ASIC, you still have to build something better. than Nvidia. And it’s not that easy building something better than Nvidia. It’s not sensible actually, you know. It’s we Nvidia’s got to be missing something. Seriously, you know, and because our our scale, our velocity, we’re the only company in the world that’s cranking it out every single year. Big leaps every >> I guess their logic is that, hey, it doesn’t need to be better. It just needs to be not more than 70% worse because they’re paying you 70% margins. >> No, no, no. Don’t forget uh even an AS6 margin is really quite high. Nvidia’s margin 6 70% let’s say but an ASIC margin is 65. What are you really saving? >> Oh, you mean from Broadcom or something? >> Yeah, sure. >> You got to pay somebody. >> Yeah. >> And so so I think the the ASIC margins are are incredibly good from what I can

[00:39:01] tell and and they believe it. They believe it so too. And so they’re they’re quite proud of their their incredible ASIC margins. And so you ask the question why. A long time ago we just didn’t have the ability to do it. And and this is this is this is and at the time I at the time I didn’t deeply internalize how difficult it would be to build a a foundation AI lab >> like OpenAI and Anthropic. uh and the the fact that they needed huge investments from the supplier themselves. Uh we just weren’t in a position to make the multi-billion dollar investment into anthropic so that they could use our use our compute but Google and and AWS were and they put in huge investments in the beginning so that anthropic um in return use their compute. uh we we just weren’t in a position to do so uh at the time. Nor

[00:40:02] nor did I I would say my mistake is I didn’t deeply internalize that they they really had no other options that that that a VC would never put in 510 billion of investment into an AI lab with the with the hopes of it turning out to be anthropic. And so that was my miss. Uh but even if I understood it, I don’t think we would have been in a position to do that at the time. But um I’m not going to make that same mistake again. And and um uh I’m delighted to invest in OpenAI and and um I’m delighted to to uh help them scale and I believe it’s essential to do so. And then and then when um uh when I was able to uh anth when Anthropic came to us, I’m delighted to be an investor, delighted to help them scale and um uh but we just weren’t at at the time able to do so. >> If I if I could uh rewind everything, uh

[00:41:01] Nvid Nvidia could have been as big back then as we are now, I would have been more than happy to do it. This is this is actually quite interesting which is um for many years Nvidia has been this um the company in AI making money making lots of money and um now you’re investing it it’s been reported that you’ve done up to 30 billion in open AI and 10 billion in um anthropic um but now their valuations have increased and I’m sure they’ll continue to increase um and so if over overall these many years you know you were giving them the compute you saw where yeah was headed and then they were worth like onetenth what they are now a couple years ago or even a year ago in some cases um and you had all this cash there there’s a world where either Nvidia themselves becomes a foundation lab um does the huge investment to make that possible or has made the deals you’ve made now at current valuations much earlier on um and you had the cash to do it so I am curious actually why

[00:42:00] not have done it earlier >> we did it as soon as we could We did it as soon as we could have and and and um if I could have, I would have done it even earlier. Um at the time that Anthropic needed us to do it, we just weren’t in a position to do it. It wasn’t it wasn’t, you know, it wasn’t in our sensibility to do so. How’s that like a cash thing or just >> Yeah, the level of investment, you know, we never invested outside the company at the time and not that much and um and we didn’t realize we needed to, you know, I always I always thought that they could just go raise VCs for God’s sakes like like all companies do. Um but but um uh what they were trying to what they were were trying to do uh couldn’t have been done through VCs. What OpenAI wanted to do couldn’t have been done through VCs. And and I recognize that now. I didn’t know it then, you know, but that’s their genius. That’s why they’re smart,

[00:43:00] >> you know, and so so they realized they realized it then that they had to do something like that. And I’m delighted that they did, you know, and and even though even though um we we caused Anthropic to have to go to somebody else, um I’m still happy that it happened. Anthropic’s existence is great for the world. I’m I’m delighted for it. >> Uh I guess you still are making a ton of money and you’re making way more money um quarter after quarter. >> It’s still okay to have regrets. Um so then the question still arises okay well now that we’re here and you have all this money that you keep making um what should Nvidia be doing with it and there’s one answer which says look there’s this whole middleman ecosystem that has popped up for converting um capex into opex for these labs so that they can rent compute um because the chips are really expensive they make a lot of money over their lifetime through because the models are getting better the value that they generate their tokens is increasing but they’re expensive to set up Nvidia has the money to do the capex. So, and in fact, you

[00:44:00] are you’re it’s been reported you’re back stoping core. We have up to 6.3 billion and have invested 2B. Um, but yeah, why why doesn’t Nvidia become a cloud themselves? Why doesn’t become a hyperscaler themselves and run this computer out? You have all this cash to do it. >> This is a philosophy of the company and and I think is wise. We should do as much as needed as little as possible. And and what that means is the the work that we do with building our our computing platform. If we don’t if we don’t do it, I genuinely believe it doesn’t get done. If we didn’t take the risk that we take, if we didn’t build MVLink the way we built, if we didn’t build the whole stack, if we didn’t create the ecosystem the way we did it, if we didn’t dedicate ourselves to 20 years of CUDA while losing money most of that time, if we didn’t do it, nobody else would have done it. If we didn’t create all the CUDA X libraries so that they’re all domain specific, you know, this is several a decade and a half ago, we pushed into

[00:45:01] domain specific libraries because we realized that if we didn’t create these domain specific libraries, whether it’s for ray tracing or image generation or even the early works of AI, these models, if we didn’t create them for data processing, structure data processing or vector data process, if we didn’t create them, nobody would. And I am completely certain of that. We created a a library for computational lithography called KU litho. If we didn’t create it, nobody would have. And so accelerated computing wouldn’t advance the way it has if we didn’t do what we did. And and so we should do that. We should dedicate our company all of our might wholeheartedly to go do that. However, the world has lots of clouds. If I didn’t do it, somebody show up. And so following the the recipe the philosophy of doing as much as needed but as little as possible as little as possible that philosophy exists in our company today and everything I do I do it with that lens

[00:46:02] in the case of clouds if we didn’t support coreweave to exist these neo clouds these AI clouds wouldn’t exist if we didn’t help cororeweave exist they would not exist If we didn’t support Nscale, they wouldn’t be where they are today. If we didn’t support NBS, they wouldn’t be where they are today. Now, they are they’re doing fantastically. Is that a business model where no, we should do as much as needed as little as possible. And so, we’re trying we invest in our ecosystem because I want our eco ecosystem to thrive. And I want our our I want I want the architecture and I want AI to be able to connect with as many industries as possible, as many countries as possible and make it possible for you know the planet to be built on AI and to be built on the American tech stack. And so so th that vision I think is exactly what we’re pursuing. Now, one of the things that

[00:47:00] that you mentioned, um, there are so many great amazing foundation model companies and we try to invest in all of them. And this is this is another thing that we do. We don’t pick winners and we we like we we we need to support everyone and it’s part of our part of our our our joy of doing so. It’s it’s an imperative to our business, but we also go out of our way not to pick winners. And so when I when I invest in one of them, I invest in all of them. Why do you go out of your arena not to pick winners? >> Because it’s not our job to. Number one. Number two, when Nvidia first started, there were 60 graphics companies, 60 3D graphics companies, uh we are the only one that survived. If you would have taken those 60 companies, 60 graphics companies, and asked yourself which one was going to make it, >> Nvidia would be the top of that list not to make it. You know, this is long before you, but Nvidia’s graphics architecture was precisely wrong. It’s not a little bit wrong. We created an

[00:48:01] architecture that was precisely wrong. And and it was an impossible thing for developers to support. It was never going to make it. We reasoned about it for good re for from good first principles, but we ended up in the wrong solution. and and um uh everybody would have kind everybody would have counted us out and and here we are. And so I’m I’m I’m enough humility to recognize that, you know, don’t don’t pick winners. >> Yeah. >> Um >> either let them all take care of themselves or take care of all of them. >> Um one thing I didn’t understand is you said, “Look, we’re not prioritizing these neoclouds just because there are new clouds and we want to prop them up.” But you also said you listed a bunch of new clouds and you said they wouldn’t exist if it wasn’t for Nvidia. >> Yeah. >> And so how are those two things compatible? >> Um first of all they they need to want to exist and they come to ask us for help. And when they when they um uh when they want to exist and they have they have a business plan and they you know

[00:49:01] they have expertise and you know they have the passion for it. Uh they obviously have to have some capabilities themselves. Uh but if at the end of the day they need some investment in order to get it off the ground, uh we we would be there for them. Um but but the sooner they get their flywheel going, you know, your question was do we want to be in the financing business? The answer is no. >> Yeah. We don’t want to be we want to we because there are people in the financing business and we rather work with all of the people who are in the financing business than to be a financeier ourselves. And so so I think the the uh our goal is to focus on what we do, keep our business model as simple as possible, support our ecosystem. Um when someone like like uh Open AI needs an investment of $30 billion scale um because it’s still before their IPO and and uh u we deeply believe in them. Uh we deeply believe that I deeply believe that that they’re going to be they’re going to be an well they’re an extraordinary company already today.

[00:50:00] They’re going to be incredible company. uh the world needs them to exist. The world wants them to exist. I want them to exist and and uh they have everything on they have the wind at their back. Let’s let’s support them and let them scale. And so so to those those investments will do because we’re they need us to do it. And um uh but we’re we’re not trying to do as much as possible. We’re trying to do as little as possible. >> I spend way too much time copy pasting text back and forth from Google Docs to chatbots. And so I built what’s basically a cursor for writing which operates the way I think an AI co-researcher should operate. I can tag it and it can talk with me through inline comment threads and help me dig deeper and brainstorm. I wrote this entire thing over the weekend with cursor and their new composer 2 model. With a lot of agentic coding tools, I feel like I have no idea what’s going on under the surface. I just have to relinquish control and hope for the best. But cursor let me try a bunch of different ideas while staying on top of the implementation. I did most of my brainstorming in the agents window. And after I got some basic files in place, I used a diff window to track changes. The few times that I needed to make a quick

[00:51:00] tweak by hand, I just used the editor. If you want to try my AI code researcher yourself, I’ve linked the GitHub repo in the description. And if you have a tool that you’ve been wanting to build, you should make it happen. Go to cursor.com/cash to get started. This may be sort of an obvious question, but we’ve lived many years in this situation where there’s a shortage of GPUs and it’s grown now because models are getting better. >> We have a shortage of GPUs. >> Yes. >> Yeah. >> And Nvidia is known for diving up the scarce allocation not just based on highest bidder but rather on hey we want to make sure that these neo neo clouds exist. Let’s give some to core. Let’s give some to Cruso. Well, let’s give some to Lambda. Um, why is it good for Nvidia? First of all, would you agree with this characterization of fracturing the market? >> No. No. Yeah. Your premise is just wrong. >> Yeah. >> Um, we’re we’re sufficiently um mindful about these things. I We’re very mindful

[00:52:00] about these things. First of all, if you don’t place an if you don’t place a PO, all the talking in the world won’t make a difference. And so until we get a PO, what are we going to do? And so the first thing is is we work with we work really hard with everybody to get a forecast done because these things take a long time to build and the data centers take a long time to build and so we align ourselves um with demand and supply and things like that through forecasting. Okay, that’s job job number one. Number two, um, everybody who, you know, we’ve tried to forecast with was with with as many people as possible, but in the fin in the final analysis, you still had to place an order and maybe maybe um, for whatever reason, you didn’t place your order, what can I do? And so at some point, first in first out, but beyond that, if you’re not ready because your data center is not ready or certain components aren’t ready to to enable you to stand up a data center, um we might decide to serve

[00:53:02] another customer first. That’s just maximizing the throughput of our of our our own factory. And so uh we might do some adjustments there. Aside from that, uh the prioritization is is first in first out. >> Yeah. You gota you got to place a PO. If you don’t place a PO, now of course there there’s stories about that, you know, like for example, all of this kind of started from from uh it was a article about Larry and Elon having dinner with me where they where they begged for GPUs. >> That never happened. We had we absolutely had dinner. We absolutely had dinner. Um and it was a it was a wonderful dinner. In no time did they beg for GPUs and so it they just had to place an order and once they place an order we do our best to get the capacity to them. Yeah. We’re not complicated. >> Okay. So it sounds like there’s a cue

[00:54:01] and then um uh based on whether your data center is ready and when you place a purchase order, you get them a certain time. But it still doesn’t sound like highest bidder just gets it. Is there a reason to do it? >> We never do that. >> Okay. >> We never do. >> Why not just do highest bidder? >> Because it’s it’s a bad business practice. You you set your price. You set your price and then and then people decide to buy it or not. And and um uh there there I I understand that that others in the chip industry um uh change their prices when demand is higher. Uh but we just don’t we just don’t that’s just never been a practice of ours. You can count on us, you know. I I prefer to be to be um uh dependable uh to be the foundation of the industry. And I you don’t need to you don’t need to second guess. >> You know, if if you if I quoted you a price um we quoted you a price, that’s it. >> And if demand goes through the roof, so

[00:55:01] be it. >> And on the other end, that’s why you have a productive relationship with TSMC, right? >> Yeah. Yeah. Yeah. Uh Nvidia has been in business, we’ve been doing business with them for uh I guess coming up on 30 years and Nvidia and TSMC don’t have a legal contract. There’s there is always some rough justice and um sometimes I’m right, sometimes I’m wrong. Uh sometimes I got I got a better deal, sometimes I got a worse deal. Uh but overall in the in the whole the relationship is incredible and and I can completely trust them. I completely depend on them and and our our one of the things that we you can count on with Nvidia is that next year this year Ver Rubin is going to be incredible. Next year Ver Rubin Ultra will come. The year after that Fman will come and the year after that I haven’t introduced the name yet. And so so every single year you can count on us. And this is an you you’re going to have to go find another ASIC team in the world. Pick

[00:56:01] your ASIC team where you can say I can bet the farm of I can bet my entire business that you will be here for me every single year. Your cost, your token cost will decrease by an order of magnitude every single year. I can count on it like I can count on the clock. Well, I just said something about TSMC. No other foundry in history can you possibly say that. You can say that about Nvidia today. You can count on us every single year. If you would like to buy a billion dollars worth of AI factory compute, no problem. If you like to buy $100 million, no problem. You’d like to buy $10 million or just one rack, not a problem. Or just one graphics card, okay, no problem. If you would like to place an order for a hundred billion dollar AI factory, no problem. We’re the only company in the world where you can say that today. I can say that about TSMC as well. I want

[00:57:01] to buy one buy 1 billion. No problem. we just got to go through the process of planning for it and you know all the all the things that that mature people do >> you know and so so I I think the the uh this ability for Nvidia to be the foundation of the world’s AI industry this is a this is a position that has taken us decade several dec couple of decades to arrive at enormous commitment enormous dedication and um the stability of our company the consist consistency of our company is really really important. >> Okay. I want to ask about China. >> Yep. >> And I always like to take uh I don’t actually don’t know what I think about whether it’s good to sell chips to China or not, but I like play devil’s advocate get against my guest. So when Dario was on who supports tax controls, I asked him why can’t America and China both have >> country of geniuses in a data center. But since um you’re on the opposite side, I’ll >> ask you in the opposite way. Um and look one way to think about it is Enthropic

[00:58:00] actually announced a couple days ago mythos pre this model mythos are not even releasing publicly because they say it has such cyber offensive capabilities that we don’t think the world is ready until we get we make sure these zero days are patched up but they say it found thousands of high severity vulnerabilities across every major operating system every browser it found one in open BSD which is this operating system that’s been specifically designed to not have zero days and it found one uh for 27 years it’s existed Um, and so if Chinese companies and Chinese labs and the Chinese government had access to the AI chips to train a model like Claude Mythos with these cyber offensive capabilities and run millions of instances of it with more compute, the question is, oh, is that a threat to American companies to American national security? Uh first of all um Mythos was was uh trained on fairly mundane capacity and a fairly mundane amount of it um by an extraordinary company. Uh and

[00:59:00] so the amount of capacity and the type of compute that’s it was trained on is abundantly available in China. And so you just have to first realize that chips exist in China. They manufacture 60% of the world’s mainstream chips, maybe more. It’s a very large industry for them. They have some of the world’s greatest computer scientists. As you know, most of the AI researchers in all of these AI labs, most of them are Chinese. They have 50% of the world’s AI researchers. And so the question is if you’re concerned about them, what is the considering all the assets they already have? They have an abundance of energy. They have plenty of chips. They got most of the AI researchers. If you’re worried about them, what is the best way to create a safe world? Well,

[01:00:03] victimizing them um uh turning them into an enemy. uh likely isn’t the best answer. They are an adversary. We want the United States to win. Um but I think having a having a dialogue and having research dialogue is probably the safest thing to do. This is an area that that is glaringly missing because of our current attitude about China as an adversary. It is essential that our AI researchers and their AI researchers are actually talking. It is essential that we try to both agree on how to what not to use the AI for with respect to finding bugs in software. Of course, that’s what AI is supposed to do. Is it going to find bugs in a lot of software? Of course. There’s lots and lots of bugs. There are lots of bugs in the AI software. And so, um,

[01:01:03] that’s what AI is supposed to do. And I’m delighted that that uh uh AI has reached a level where it could help us be so much more productive. Um one of the things that that um is is uh under underhmphasized is the richness of ecosystem around cyber security, AI, cyber security and AI security and AI privacy and uh AI safety. that whole ecosystem of AI startups that are trying to create this future for us where where you have one AI agent that’s incredible surrounded by thousands of AI agents keeping it safe, keeping it secure. That future surely is going to happen. And the idea that you’re going to have an AI agent running around with nobody watching after it is kind of insane. And so uh we know very well that this

[01:02:00] ecosystem needs to thrive. It turns out this ecosystem needs open source. This ecosystem needs open models. They need open stacks so that all of these AI research and all these great computer scientists can go build AI systems that as are as formidable and can keep um AI safe and uh and and and so one of the things that we need to make sure that we do is we keep the the open- source ecosystem vibrant and um and that can’t be ignored. That can’t be ignored and and a lot of that is coming out of China. Um I we we had to we had to not suffocate that. You know with respect to to China we want to have of course we want United States to have as much computing as possible. Uh we’re limited by energy. Um but you know we got a lot of people working on that and we we got to not make energy a a bottleneck for our our country.

[01:03:00] Um, but what we also want is we want to make sure that all the AI developers in the world are developing on the American tech stack and making the contributions, the advancements of AI, especially when it’s open source, available to the American ecosystem. And it would be extremely foolish to create two ecosystems. the open source ecosystem and it only runs on the Chinese tech tech foreign tech stack and a closed ecosystem and that runs on the American tech stack. I think that that would be that would be a horrible outcome for United States >> since there are a lot of things. Let me just triage the um response. I mean I think the concern going back to the flop difference and the hacking is yes they have compute but there’s some estimates that because they’re at 7 nanometer uh they don’t have UV because of chip making export controls the amount of flops they’re about to actually produce they have like oneten the amount of flops that the US has and so with that

[01:04:02] could they train eventually a model like mythos yes but the question is because we have more flops uh American ABS are able to get to these level capabilities first and because Anthropic got to it first they say okay we’re going to hold on to it for a month while all these American companies we give them access to it they’re going to patch up all their vulnerabilities and now we release it further if they even if they train a model like this the ability to deploy it at scale you know if you had a cyber hacker it’s much more dangerous if they have a million of them versus a thousand of them so that inference compute really matters a lot and in fact the fact that they have so many researchers are so good is the thing that makes it so scary because what is it that makes as engineer researchers more productive is compute. Um if you talk to any lab in America they say the thing that’s bottlenecking them is comput. So and there are quotes from deepseek founder or uh coin leadership or whatever they say like the thing we’re bottlenecked on is compute. Um so then the question is isn’t it better that we get to get American companies because they have more comput get to get get to the level

[01:05:00] of spud or mythos level capabilities first prepare our society for it before China can get to it because they have less compute. We should always be first and we should always have more. But in in order for that outcome for you to to what you described to be true uh you have to take it to the extremes. they have to have no compute and um and if they have some compute the question is how much is needed the amount of comput they have in China is enormous is I mean you’re talking about the country is the second largest computing market in the world if they want to deploy aggregate their compute they got plenty of compute to aggregate >> but is that true I mean there’s people do these estimates and they’re like well smick is actually behind on the process nodes So they’re >> I’m about to tell you, >> okay, >> the amount of energy they have is incredible, isn’t that right? AI is a parallel computing problem, isn’t it? >> Why can’t they just put four, 10 times

[01:06:01] as much chips together? Because energy is free. They have so much energy. They have data centers that are sitting completely empty, fully powered. They’ve, you know, they have ghost cities. They have ghost data centers. They have so much capacity of infrastructure. If they wanted to, they just gang up more chips even if they’re seven nanometer. And their capacity of building chips is one of the largest in the world. The semiconductor industry knows that they monopolize mainstream chips. They overcapacity. They have too much capacity. And so the idea that China won’t be able to have AI chips is completely nonsense. Now, of course, if you ask me, um, uh, would would would United States be be further ahead if if the entire world had no compute at all? But that’s just not an outcome. That’s not a scenario that’s true. They have plenty of compute already. The amount of threshold they need for the for the

[01:07:00] concern you’re worried about, they’ve already reached that threshold and beyond. And so, so I think the you misunderstand that AI is a five layer cake. And at the lowest lay layer is energy. When you have abundant of energy, it makes up for chips. If you have abundance of of chips, it makes up for energy. For example, uh United States is scarce on energy. which is the reason why Nvidia has to keep advancing our architecture and do this extreme code design so that with the few chips that we ship, okay, with the few chips because the amount of energy is so limited, our throughput per watt is off the charts. But if your amount of watts is completely abundant, it’s free. What do you care about performance per watt for you plent So 700 meter 7 nanometer chips are essentially hopper the ability to for hopper um I got to

[01:08:01] tell you today’s models are largely trained on hopper you know hopper generation and so so hopper 7 nmter chips are plenty good the abundance of energy is their advantage >> but then there’s a question of okay well can they actually manufacture enough chips given their >> but they do uh uh What’s what’s the evidence? Huawei just had the largest single year in the history of their company. >> How many chips did they shift? >> A ton. Millions. Millions is way more way more than Anthropic has. >> So there’s a question of how much logic Smick and Chef and there’s a question of how much memory. >> I’m telling you what it is. They have plenty of they have plenty of logic and they plenty of HPM2 memory. >> Right. But as as you know the bottleneck often in training and doing inference on these models is the amount of bandwidth. So if you HBM2 I don’t know the numbers off hand but like versus the newest thing you have you know it can be almost an order of magnitude difference in memory bandwidth which is >> Huawei is a networking company.

[01:09:02] >> but that doesn’t change the fact that you need EUV for the most advanced HBM. >> Not true. Not at all true. You could gang them together just like we gang them together with MVLink72. They’ve already demonstrated silicon photonics connecting all of these compute together into one giant supercomputer that your your premise is just wrong. The fact of the matter is their AI AI development is going just fine. And and the best AI researchers in the world because they are limited in compute they also come up with extremely smart algorithms. Remember I just what I said I said that Moore’s law is advancing about 25% per year. However, through great computer science, we could still improve algorithm performance by 10x. What I’m saying is great computer science is where the lever is. There is no questione

[01:10:01] invention. There’s no question all the incredible attention mechanisms reduce the amount of compute. We have got to acknowledge that most of the advanc advances in AI came out of algorithm advances not just the raw hardware. Now if most advances came from algorithms and computer science and programming tell me that their army of AI researchers is not their fundamental advantage. And we see it. Deepseek is not inconsequential advance. And the day that Deepseek comes out on Huawei first, that is a horrible outcome for our nation. >> Why is that? Cuz I mean, currently you can have a model like Deep Seek that can run on any accelerator if it’s open source. Why Why would that stop being the case in the future? >> Well, suppose it doesn’t. Suppose it optimized for Huawei. Suppose it optimized for their architecture. It would put us at a disadvantage. You you described a situation that I

[01:11:01] conceived I I perceived to be good news that that a company developed software developed an AI model and it runs best on the American tech stack. I saw that as good news. You you set it up as a premise that it was bad news. I’m going to give you the bad news that AI models around the world are developed and they run best on not American hardware. That is bad news for us. >> I guess I just don’t see the evidence that there’s these huge disparities that would prevent you from switching accelerators. There’s American labs, you know, are running their models across all the clouds, across all >> the evidence. You take a model that’s optimized for Nvidia and you try to run on something else, >> but they American labs do that >> and they don’t run better. Nvidia success is perfect evidence. The fact that AI models are created on our stack runs best on our stack. How is that illogical to understand? I >> I’m just looking. Look, Entropics models are run on GPUs. They’re run on

[01:12:00] trainium. They’re run on TPUs. >> A lot of work has to go into it to change. But go to the global south, go to the Middle East, coming out of the box. If all of the AI models run best on somebody else’s tech stack, you’ve got you’ve got to be arguing some ridiculous claim right now that that’s a good thing for United States. >> But I I guess I don’t understand argument. Like if uh if say um Chinese companies get to the next mythos first, they find that all the security runner releasing American software first, but they can do it on Nvidia hardware and they ship it to the global south. They does it on NVIDIA hardware. Like how how is that how is that good? I mean I just Okay, it runs on hardware. >> It’s not good, >> right? >> It’s not good. So let’s not let it happen. >> Why do you think it’s perfectly funible that if you didn’t ship them computer would exactly be replaced by Huawei? They are behind, right? They have they have worse chips than you. >> It’s completely there’s evidence right now. their chip industry is gigantic. >> You can just look at the flop or bandwidth or memory comparisons between the H200 and the Huawei 910C. It’s like half half. >> They use more of it. They use twice as many. >> I guess it seems like your argument is

[01:13:00] they have all this energy that’s ready to go, right? And they need to fill it with chips >> and they’re good at manufacturing. >> And I’m sure eventually they would be able to just out manufacture everybody, but there’s these few critical years. >> What What is the critical year you’re talking about? >> These next few years we’ve got these models that are going to do all the cyber attacks. If the critical years, the next crit critical years is critical, then we have to make sure that all of the world’s AI models are built on American tech stack. These critical years, >> okay, how would that prevent if they’re built on American tech stack, how would that prevent them from if they have more advanced capabilities from launching the mythos equivalent cyber attacks on >> there’s no guarantee either way, >> but if you have it earlier, we can prepare for it. >> Listen, why are you why are you causing one layer of the AI industry to lose an entire market so that you could benefit another layer of the AI industry. There’s five layers and every single layer has to succeed. The the the layer that has to succeed

[01:14:00] most is actually the AI applications. Why are you so fixated on that AI model, that one company? For what reason? Because those models make possible these incredibly offensive capabilities and you need computer energy, the chips, the ecosystem of AI researchers make it possible. >> A few months ago, Jane Street spent about 20,000 GPU hours trading back doors into three different language models. Then they challenged my audience to find the trigger phrases. I just caught up with Rickson who designed the puzzle about some of the solutions that Jane Street received. If you think the the base model was here and the back door model was here, you can kind of linearly interpolate the weights to like adjust the strength of the back door, but you can also extrapolate it to make the back door even stronger. And in some cases, if you make it strong enough, the model will just regurgitate what the response phrase was supposed to be. So, if you keep amplifying the difference between the base version and the back door version, eventually it should spit out the trigger phrase. But this technique only worked on two out of the three models. Even Ricken isn’t sure why

[01:15:01] it didn’t work on the other. Being able to verify that a model only does what you think it does is one of the most important open questions in AI security. If this is the kind of problem that excites you, Jane Street is hiring researchers and engineers. Go to janestreet.com/thorcash to learn more. Okay, stepping back, it has to be the case that China is able to build enough 7 nanometer capacity. And remember, they’re still stuck on 7 nanometer while you will move on to 3 nmter and then 2 nmter or 1.6 nometer with fineman. So while you’re on 1.6 6 nometer they’re still going to be on 7 nmter and they have to produce enough of it to make up for the shortfall and they have so much energy that the more chips you give them the more compute they’d have right like so I just there’s it comes to the question of ultimately they are getting more computers in input to training and in friends >> I I just think you you speak in absolutes um I think that United States ought to be ahead the amount of compute in United States is 100 times more than anywhere else in the world The United States ought to be ahead. Okay, the

[01:16:00] United States is ahead. Nvidia builds the most advanced technologies. We make sure that the US labs are the first to hear about it and the first chance to buy it. And if they don’t have enough money, we even invest in them. The United States ought to be ahead. We want to do everything we can to make sure the United States is ahead. Number one point. Do you agree? And we’re doing everything we can to do that. >> But how is shipping chips to China keeping the US They’re botted. We have Vera Rubin for United States. Now, United States. Am I in United States? Do you consider me part of the United States? >> Yes. >> Nvidia, you consider Nvidia a United States company? Okay. Number one, why is it that we don’t come up with a regulation that’s more balanced so that Nvidia can win around the world instead of giving up the world? Why would you want United States to give up the world?

[01:17:00] The chip industry is part of the American ecosystem. It’s part of American technology leadership. It’s part of the AI ecosystem. It’s part of AI leadership. Why? Why is it that your policy, your philosophy leads to United States giving up a vast part of the world’s market? >> The the claim here is Alfred Dario had this quote where he said it’s like Boeing bragging that we’re selling North Korea nukes but the missile casings are made by Boeing and that’s somehow enabling the US technology stack. Like fundamentally you’re giving them this capability >> comparing AI to anything that you just mentioned is lunacy >> but AI similar to enriched uranium right and then it can have positive uses it can have negative uses we still don’t want to send enriched uranium to other countries >> who’s who’s sending enriched >> the analogy is enriched uranium >> because it’s a lousy it’s a lousy analogy it’s an illogical analogy but if it’s if that computer can run a model that can do zero day exploits against all

[01:18:00] American software How is that not a weapon? >> First of all, we got to the way to solve that problem is to have dialogues with the researchers and dialogues with China and dialogues with other countries to make sure that people don’t use technology in that way. That’s a dialogue that has to happen. Okay. Number number one. Number two, um we also need to make sure that United States is ahead. Everything that Ruben Vera Rubin Blackwell is available in United States in abundance. mounds of it. Obviously, our are our our results would show it. Abundance of tons of it. Tons of it. The amount of computing we have is great. We have amazing AI resources here. It’s great. We have to stay ahead. However, we also have to recognize that AI is not just a model. That AI is a five layer cake. That AI industry matters across every single layer. And we want United States to win at every single layer, including the chip layer. and conceding the entire

[01:19:00] market is not going to allow United States to win the technology race long-term in the chip layer in the computing stack. That is just a fact. I guess then the crux comes down to how does selling them chips now help us win in the long term. Like Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China, extremely good. They didn’t cost some lock in. China will still make their version of EVs and they’re dominating or smartphones dominating. >> When we started the conversation today, you would you would acknowledge and you acknowledged that Nvidia’s position is very different. You use words like moat. The single most important thing to our company is our richness of our ecosystem which is about developers. 50% of the AI developers are in China. We don’t want to we shouldn’t the United States should not give that up. But we have a lot of Nvidia developers in the US and that doesn’t prevent American labs from also being able to use other accelerators in the future in in fact

[01:20:00] right now they’re using other accelerators as well which is fine and great. I don’t I don’t see why that wouldn’t be the case in China as well if you sell them Nvidia chips just the same way that Google can use TPUs and Nvidia. >> We have to keep innovating and you know as you as you probably know our share is growing not decreasing. the premise that even if we competed in China that we’re going to lose that market anyways. I don’t you’re not talking to somebody who woke up a loser. And that loser attitude, that loser premise makes no sense to me. We are not we’re not a car. We are not a car. it. The fact that I can buy a car, this car brand one day and use another car brand another day. Easy. Computing is not like that. There’s a reason why the x86 still exists. There’s a reason why ARM is so sticky. These ecosystems, these ecosystems are hard to replace. It costs an enormous amount of time and energy and most people don’t want to do it. And

[01:21:01] so it’s it’s our job to continue to nurture that ecosystem to keep advancing the technology so that we could compete in the marketplace. Conceding a marketplace based on the premise you described, I simply can’t acknowledge that. It makes no sense because I don’t think the United States is a loser. You our industry is now a loser. And that that losing proposition, that losing mindset makes no sense to me. >> Okay, I’ll move on. I just I just want to make sure >> you don’t have to move on. I’m enjoying it. >> Okay, great. Then then I um I appreciate that. Um >> but I think the maybe the crux and thanks for walking around the circles with me because then I think it helps bring out what the crux here is. >> The crux is you’re going to extremes. Your argument starts from extremes that if we give them any compute at all in this narrow moment, we will lose everything. >> No, I think what my argument is >> those extremes they’re They’re childish.

[01:22:00] Yeah. >> The idea is not that there is some key threshold of compute is that any marginal compute is helpful, right? So if you have more compute, you can train a better model. >> And I just want you to acknowledge that any marginal sales for American technology industry is bene is beneficial. >> I actually don’t I mean if the AI models that run on those chips >> Yeah. >> are capable of cyber offensive capabilities or training models are capable of cyber defense is running more models at those instance. It is not a nuclear weapon, but it is it enables a weapon of a kind. >> The the the logic that you use, you might as well say it to microprocessors and DRAMs. You might as well say it to electricity. >> But in fact, we do have export controls on the technology that is relevant to making the most advanced DRM, right? We have all kinds of export controls on China for all kinds of shipping. >> We we sell a lot of DRM and CPUs into China. And I think it’s right. >> I guess this goes back to the fundamental question of is AI different, right? If you have the kind of technology that can find these zero days in software, is that something where we

[01:23:01] want to minimize China’s ability to get their first place to be ahead? >> We can control that. >> How do we control that if the chips are already there and they’re using that to train that model? >> We have tons of compute. We have tons of AI researchers. We’re racing as fast as we can. >> Again, we have more nuclear weapons than anybody else, but we don’t want to send enriched uranium anywhere. >> We’re not enriched uranium. It’s a chip and it’s a chip that they can make themselves. >> But there’s a reason they’re buying it from you, right? And we have quotes from the founders of Chinese companies that say that we’re bottling that technology >> because our chips are better. On balance, our chips are better. There’s just no question about it. In the absence of our chip, in the absence of our chip, can you acknowledge that Huawei had a record year? Can you acknowledge that a whole bunch of chip companies have gone public? Can you acknowledge that? >> Can you acknowledge that? Can you can also acknowledge that the fact that we used to have a very large share in that market and we no longer have the large share in that market. We can also acknowledge that China is about 40% of the world’s technology industry. That

[01:24:00] market to leave to leave that market concede that market for United States technology industry is a disservice to our country. It is a disservice to our national security. It is a disservice to our to our technology leadership. All for the benefit all for the benefit of one company. It makes no sense to me. I guess I’m confused of it feels like you’re making two different statements. One is that we’re going to win this competition with Huawei because our chips are going to be way better if we’re allowed to compete. And another is that they would be doing the same exact thing without us anyways. Right? How can those two things be the same true at the same time? >> It’s obviously true. In the absence of a better choice, you’ll take the only choice you have. How is that illogical? It’s so logical. >> The reason they want Nvidia chips is they’re better. Better is more compute. More comput means you can train a better model. >> It’s better. It’s better because it’s easier to program. It’s e we have a better ecosystem. Whatever the better is. Whatever the better is. And of course we’re going to send them compute. So what? So what the fact of the matter is we get the benefit. Don’t forget we

[01:25:00] get the benefit of American technology leadership. We get the benefit of developers working on the American tech stack. We get the benefit as those AI models diffuse out into the rest of the world. The American tech stack is therefore the best for it. We can continue to advance and diffuse American technology that I believe is a positive. It’s a very important part of American technology leadership. Now the policy that you’re advocating resulted in the American telecommunication industry being policied out of basically the world to the point where we don’t control our own telecommunications anymore. I don’t see that as smart. It’s a little narrow-minded and it led to un unintended consequences that I’m describing to you right now that you seem you seem to have a very hard time understanding. >> Okay, let let’s just step back. It it seems like the crux here is >> there’s a potential benefit and there’s a potential cost and we’re desri we’re trying to figure out is the benefit worth the cost. I guess I’m trying to get you to acknowledge the potential

[01:26:01] cost that compute is an input to training powerful models. powerful models do have powerful, you know, offensive capabilities like cyber attacks. It is a good thing that American companies got to claim mythos level capabilities first and then now they’re going to hold off on those capabilities so that the American companies and American government can make their software more protected before this level cap announced if China had had more computer had more power comput if we could have had made a mythos level model earlier and deployed it widely that would have been very bad. One of the reasons that hasn’t happened is that we have more compute thanks to companies like Nvidia in America. Um that is a cost of sending to China. And so let’s leave the benefit aside for a second. Do you acknowledge that this is a potential cost? I will also tell you the potential cost is we allow one of the most important layers of the AI stack, the chip layer to concede an entire market, the second largest in second largest market in the

[01:27:00] world so that they could develop scale so that they could develop their own ecosystem so that future AI models are optimized in a very different way than the American tech stack. As AI diffuses out into the rest of the world, their standards, their tech stack will become superior to ours because their models are open. I >> I guess I just believe enough in Nvidia’s kernel engineers and CUDA engineers to think that they could optimize. >> AI is more than kernel optimization as you know, >> of course, but there’s so many things you can do from distilling to a model that’s well fit for your chips. >> We’re going to do our best. >> You have all this software. I just hard to imagine that there’s a long-term lock in to Chinese ecosystem. They have this like slightly better open source model for a while. >> China is the largest contributor to open source software in the world. Fact, right? China is the largest contributor to open models in the world. Fact. Today it’s built on the American tech stack and

[01:28:01] fact. All five layers of the tech stack for AI is important. United States ought to go win all five of them. They’re all important. The one that is the most important of course is the AI application layer. The layer that diffuses into society, the one that uses it most will benefit from this industrial revolution most. But my point is that every a every layer has to succeed. If we if we scare this country into thinking that AI is somehow a nuclear bomb so that everybody hates AI and everybody’s afraid of AI, I don’t know how you’re helping the United States, you’re doing a disservice. If we scare everybody out of doing software engineering jobs because it’s going to kill every software engineering job and we don’t have any software engineers as a result of that, we’re doing a disservice to United

[01:29:00] States. If we scare everybody out of radiology, so nobody wants to be a radiologist because computer vision is completely free and no AI is going to do a worse job than a radiologist. And we we misunderstand the difference between a job and the task the job of a radiologist patient care task to read a scan. If we misunderstand that so profoundly and we scare everybody out of going to radiology school, we’re not going to have enough radiologists and good enough healthcare. And so I I’m making the case that when you make these make a premise that is so extreme, everything goes from zero or infinity. We end up scaring people in a way that’s just not true. Life is not like that. Do I do we want United States to be first? Of course we do. Do we need do we do we need to be uh a leader in every layer of that stack?

[01:30:01] Of course we do. Of course we do. Is today you’re talking about mythos because mythos is important. Sure. That’s fantastic. But in a few years time, I’m making you the prediction that when we want the American tech stack, when we want American technology to be diffused around the world, out to India, out to the Middle East, out out to to Africa, out to Southeast Asia, when our country would like to export because we would like to export our technology, we would like to export our standards. On that day, I want you and I to have that same conversation again. And I will tell you exactly about today’s conversation about how your policy and how what you imagined literally cause the United States to concede the second largest market in the world for no good reason at all. We shouldn’t concede it. If we lose it, we lose it. But why do we concede it? Now, nobody is advocating Nobody is

[01:31:00] advocating an all or nothing. Nobody’s advocating all or nothing, meaning we ship everything to China at all times. Nobody’s advocating that we should always have the best technology here. We should always have the most technology here and the first. But we should also try to compete and win around the world. Both of those things can simultaneously happen. It requires some amount of nuance, some amount of maturity instead of absolutes. The world is just not absolutes. >> Okay. the the argument hinges on they’ve built a they’ve built models that are specified for their architect their the best chips that they make in a few years and those chips get exported around the world that sets a standard um because of EUV um export controls as we said you’re going to move on to 1.6 6 nometer there’s still going to be on 7 nometer even after a few years from now and it might make sense that domestically they would prefer hey we got so much energy we can manufacture sets scale we’ll still keep using 7 nmter but the

[01:32:01] exporting thing their 7 nanometer chips have to be competitive against your 1.6 nmter chips and their models have to be so far optimized for the 7 nometer it’s better to run their models on 7 nanometer than to run their models on your 1.6 6 nometer. >> Can we can we just look at the facts then? Okay. Is Blackwell 50 times more advanced lithography than Hopper? Is it 50 times? Not even close. I just kept saying it over and over again. Moore’s law is dead. Between Hopper and Blackwell from the transistors themselves, call it 75%. It was 3 years apart. 75%. Blackwell is 50 times hopper. My point is architecture matters. Computer science matters. Semiconductor physics matter as well. But computer science matters.

[01:33:00] AI the impact of AI largely comes from the computing stack which is the reason why CUDA is so effective which is the reason why CUDA is so so so beloved. It’s it’s a ecosystem a computing architecture that allows for so much flexibility that if you wanted to change an architecture completely create something like create something like diffusion create something you know that’s disagregated you could do you could do so it’s easy to do and so the fact of the matter is AI is about the stack above as much as it is about the architecture below to the extent that that we have architectures and software stacks that optimized for our stack, for our ecosystem. It is obviously good because we started the conversation today about how Nvidia’s ecosystem is so rich, why people always love programming on CUDA first. They do. They do and so do the researchers in China. But if we are forced to leave China, if we’re

[01:34:01] forced to leave China, it would be it would be well, first of all, it would it’s a policy mistake. obviously has backlash has has backlash. Obviously, it has fired, you know, has has uh uh has turned out badly for for the United States. It enabled it accelerated their chip industry. It forced all of their AI ecosystem to focus on their internal architectures. It’s not too late, but nonetheless, it has already happened. You’re going to see in the future they’re not stuck at 7 nanometer. Obviously they’re good at manufacturing. They will continue to advance from seven and beyond. Now is there 10x difference between 5nanmter and 7 nanometer? The answer is no. Architecture matters. Networking matters. That’s why Nvidia bought Melanox. Networking matters. Energy matters. And so all that stuff matters.

[01:35:01] It’s not it’s not simplistic like the way you’re trying to distill it. >> Uh we can move on from China, but that actually raises an interesting question about um we were discussing earlier these bottlenecks at TSMC and memory and so forth. And so if we’re in this world where you know you’re already the majority of N3 at some point you’ll be N2, you’ll be a majority of that. Do you see that you could go back to N7 this spare capacity at an older process node and say hey the demand for AI is so great and our capacity to expand the leading edge is not meeting it so we’re going to make a hopper or ampier about everything we know about a numeric today and all the other improvements you described do you see that world happening within before 2030 >> it’s not necessary to and the reason for that is because with every every generation the architecture the architecture um is more than just is more than just uh the transistor scale.

[01:36:02] It also you’re doing so much engineering and packaging and stacking and and the numeric and you know the system architecture um when you run out of capacity to easily go back to another node that’s a level of R R&D that that no one no one could afford. You know we we could afford to lean forward. I don’t think we could afford to go back. Now, if the world simply says, if on that day, if on that day, let’s do the thought experiment. On that day, we go, listen, we’re just never going to have more capacity ever again, would I go back and use seven in a heartbeat? >> Yeah, of course I would. >> Um, one question somebody I was talking to had is why Nvidia doesn’t run multiple different chip projects at the same time with totally different architectures. So you could do like a cerebra style >> wafer scale. You could do a dojo style huge package. You could do one without CUDA, you know. Um you have the resources and the engineering talent >> to do all these in parallel. So why put

[01:37:02] all the eggs in one basket given who knows where AI might go and architectures might go. >> Oh, we could. It’s just that that we don’t have a better idea. >> Yeah. Yeah, we we could do all of those things. Um it’s just not better. And we simulate it all. they’re in our simulator provably worse and so we wouldn’t do it. Yeah, we’re we’re doing we’re working on exactly the projects that we want to work on. And and um I if the workload were to change dramatically um and I don’t mean I don’t mean the algorithms, I actually mean the workload. The um and that that depends on the shape of the market. um uh we may decide to add other accelerators like for example recently we added uh Grock um and we’re going to fold Grock into our CUDA ecosystem and and um uh we do we’re we’re doing

[01:38:00] that now because the value of tokens um have gone up so high that that you could have different pricing of tokens. Back in the old days in the, you know, just a couple years ago, tokens are either free or barely, you know, barely expensive, right? And so, but now you can have different customers and those customers want different answers. And so, because the customers make so much money, like for example, our software engineers, if I can give them much more um responsive tokens so that they’re even more productive than they are today, I would pay for it. >> But that market has only recently emerged. And so I think that we now have we now have the ability to have the same model based on the response time have different segments and that’s the reason why we decided to expand the paro frontier and and create a segment of inference that is faster response time even though it’s lower lower throughput at the mo until now higher throughput is

[01:39:02] always better. Um we we think that there there could be a world where there could be very high ASP tokens and and um even though the even though the throughput is lower in the factory the ASPs make up for it. >> Yeah. That’s the reason why we did it. But otherwise from an architecture perspective um I I think Nvidia’s architecture is I would I would rather put if I if I have more money I put more behind the architecture. M I I think this idea of extremely premium tokens and just the disagregation of the inference market is very interesting. >> The segmentation y final question um supposed deep learning if revolution didn’t happen. Um what would Nvidia be doing? Obviously games but given >> accelerated computing >> accelerated computing the same thing we’ve been doing all along. I the the premise of our company is that Moors law Moore’s law is going to more general purpose computing is good for a lot of

[01:40:01] things but for a lot of computation is not ideal and so we combined an architecture called a GPU CUDA to a CPU so that we can accelerate the workload of the CPU and so different different kernels of code or algorithms could be offloaded onto our GPU and as a result you speed up an an application by you you know 100x 200x and where can you use that? Um well obviously engineering and science and physics and you know so on so data processing um uh computer graphics image generation I mean all kinds of things even if AI doesn’t exist today Nvidia will be very very large yeah and so so I think the the reason for that is is fairly fundamental which is which is the ability for general purpose computing to continue to scale has largely run its course and the only the the not the only way but the the way to do that is through domain specific acceleration and one of the domain that

[01:41:00] we started with was computer graphics but many there are many many other domains I mean there’s you know you know all kinds of uh scient particle physics and fluids and you know and and so structured data processing all kinds of different types of of algorithms that benefit from CUDA and so our our mission was uh really to bring accelerated computing to the world and advance the type of applications that general purpose computing can’t do and scale to the level of of uh capability that helps break through certain fields of science. And and so some of the early applications were uh molecular dynamics, uh seismic processing for energy discovery, um uh image processing of course, uh and so all of those kind of fields where where general purpose computing is just simply too inefficient to do so. And so yeah, if if there was no AI, I would be very sad. Um, but because of because of

[01:42:00] of the advances that we made in computing, we democratized deep learning. We made it possible for any researcher, any scientist anywhere, any student to be able to access a PC or, you know, a a GeForce adding card and and uh do amazing science. And um uh that that fundamental promise uh hasn’t changed, not even a little bit. And so if you see GT if you watch GTC, there’s the whole beginning part of it, none of it’s AI. That whole part of it with with uh computational lithography or or uh our quantum chemistry work or you know uh all of that stuff, data processing work, all of that stuff is is uh unrelated to AI and and and it’s still very important. I mean there’s, you know, I I know that that AI is is very interesting and and quite exciting. Um but but um there’s a lot of people doing a lot of very important work that’s not not AI related and tensors is not the

[01:43:01] only way that you compute with >> and um I and we want to help everybody. >> It doesn’t. Thank you so much. >> You’re welcome. I enjoyed it. Me too. Sweet.