The Exchanges

Q Do you think we fundamentally underestimate the Chinese capabilities?

A A hundred percent. I, I think, and it is one of the most obvious and frequent, uh, errors in judgment. Is that you underestimate the other side. I think, ah, you, you have to, to look carefully at what they're doing, and their investment in infrastructure has been extraordinary. Ah, the rate at which they generate engineering talent is exceptional. The government's ability to have a policy and implement it, you know, they're, you know, that's not a democracy. They weren't designed to have checks and balances there, right? Um, the, uh, funding that flowed into the development of, of AI technology, that their venture capitalists were backed up by their government, that, uh, they have national champion companies, that they've developed, uh, a belt and suspender strategy to sort of make much of the third world dependent. Uh, on them and their technologies. I, I, I think they absolutely should not be underestimated. They have a lot of people, and we see a tiny fraction of it. I, I think they have produced industrial policy. That has moved their nation forward.

AI assessment note: “A hundred percent. I, I think, and it is one of the most obvious”

Q terms of the different models there. Again, sorry to cite it, but it's, it's kind of handy having just done it. Jonathan said that you would definitely have OpenAI and Anthropic build out their own ships, because then they would have control of their own destiny. Do you think OpenAI and Anthropic build their own ships so they don't have self-reliance on NVIDIA in the way that they do today?

A I think that there is a long history of software companies failing to build chips. The list is, is very large. I think, uh, whether, uh, OpenAI can do it, uh, whether they can do it through partnership with other vendors, with Broadcom, with smaller, more innovative companies is an open question. Um, but I, I think that, uh, you know, companies at the size of Microsoft have been unable to deliver, uh, chips, right? I think, uh, uh, there are plenty of examples as you look across the Fang, uh, group where chips were tried. I mean, probably the most successful is Google and they're 10 years in, right? Maybe longer. You know, soft, modern software does not fit well In a chip making framework. I mean, weekly sprints don't work well on two year long projects. Um, you know, move fast, break things often is not the way you think in the chip world. The way you think in the chip world is measure twice before you cut once because your bugs cost you six months and tens of millions of dollars. And so it's a very different mentality. And where there's been success, it has frequently been acquired. Apple got into the chip business through buying PA Semi. Amazon got into the chip business through acquiring Annapurna. Um, uh, Google acquired the talent from a collection of companies, uh, and then set it in a BU that was a side and under somebody who, who had sort of enormous respect in the org…

AI assessment note: “there is a long history of software companies failing to build chips.”

Q I love it. I get it from a consumer perspective and from an expectations perspective. If we move the needle on compute algorithms and data, what does that mean for the experience of AI?

A It gets faster and cheaper. Faster and cheaper. Faster and cheaper is the first answer. The second is, is when things become faster and cheaper, new applications emerge. So it, it's used everywhere, right? When, uh, when computers became faster and cheaper, suddenly they were in cars. And then you were in your pocket. And then they were in your dishwasher and in your TV. And, right, that's what happens. I mean, we, 30 years ago, you're like, I need a computer in my TV. Are you kidding me? I need one in my pocket. Now you've got powerful computers in your pocket. You've got them in your TV. You've got in your kids' toys. You've got in the car. That's what happens. Diffusion of innovation accelerates. When you make things faster and cheaper.

AI assessment note: “It gets faster and cheaper. Faster and cheaper. Faster and cheaper is the first answer.”

Q I love it. I get it from a consumer perspective and from an expectations perspective. If we move the needle on compute algorithms and data, what does that mean for the experience of AI?

A It gets faster and cheaper. Faster and cheaper. Faster and cheaper is the first answer. The second is, is when things become faster and cheaper, new applications emerge. So it, it's used everywhere, right? When, uh, when computers became faster and cheaper, suddenly they were in cars. And then you were in your pocket. And then they were in your dishwasher and in your TV. And, right, that's what happens. I mean, we, 30 years ago, you're like, I need a computer in my TV. Are you kidding me? I need one in my pocket. Now you've got powerful computers in your pocket. You've got them in your TV. You've got in your kids' toys. You've got in the car. That's what happens. Diffusion of innovation accelerates. When you make things faster and cheaper.

AI assessment note: “when things become faster and cheaper, new applications emerge. So it, it's used everywhere”

Q Do you worry about, like, AI as a brand? You know, you see Meta lay off a huge amount of people today, and it's, it's challenging to see at four a.m. emails from Zuck and, you know, jobs being lost.

A I, I do worry about it. Those are, those are people. And they have families. I think there are sort of two views, Harry. I, I, I think to date, most of the layoffs were AI washed. They were, because we did boneheaded hiring, During COVID. It is actually because a great deal of productivity gains has been, have occurred over the years that we're just now harvesting. The ability to gather information from across the organization to synthesize it and put it in one place is now changing what it means to be middle management, right? The role of information gatherers and presenters is being eliminated. The ability for us to automate roles None of this is AI yet, right? That is really 90%, 95% of what the, in my view, what the, the terminations have been about. It's easy to, to, to put them under the umbrella of AI. Now, AI is starting just now to have meaningful enterprise impact, but if, if you are an engineering organization that can't see how to take advantage of vastly more productive engineers, I don't think you're long for this world. I mean, the list of things I want our engineers to do is 50 times as much as we have engineers, right? If we, if we get, as we get more productive, we do more things, we're gonna hire more engineers. We're not gonna hire less engineers.

AI assessment note: “most of the layoffs were AI washed.”

Q You, you've compared past cycles before in this conversation. Sarah Fry said about, um, kind of cloud providers can be, ah, similar into some perspective to what we're seeing today in terms of frontier models, and she said that last night. To what extent do you think you see the commoditization there, and they essentially become utilities versus differentiated providers with meaningful modes?

A I think it has been Nvidia's strategy to try and create competitors. For the traditional hyperscalers. I, I think that has been a strategy of theirs. I think they have funded and backstopped and over allocated to the neoclouds. I think, um, I, I think they're, they have created a dependence, which is probably not healthy, but I, I think the truth is, is that what, what AWS and Azure offer is extremely useful for most enterprises. They offer credibility and legitimacy. They offer security. They offer layers of different software for different parts of your organization. If you'd like to enter in the AWS world, you can enter with Bedrock. You can use tools like SageMaker. You have a collection of different ways to, to enter, and you can store your data there. You, you have your S three instances. I mean, you can have an entire offering, and I, I think that is Really valuable to a segment of the, of the, of the market. I think there might be other segments of the market that are like, give me cheap compute. I don't care about anything else. And in that case, your strength as a, as a hyperscaler becomes your weakness. You have the security. You have the other layers of software, and you have some of the costs that are associated with that. And if people don't want that, if you don't care about leather seats, right, and there are leather seats in the truck, there's extra cost in the…

AI assessment note: “what AWS and Azure offer is extremely useful for most enterprises”

Q anticipate, what are the bottlenecks today in your mind? You know, again, we had Jonathan at Grok on and he was like, actually, you know, I had someone come and demand five times the supply that I have in total, and I only, that was from one customer, um, supply is mine. How do you think about the bottlenecks that we have to reach the insatiable demand that you mentioned?

A I think if you go back to planning, if you've got customers demanding five X that your capacity, I mean, you probably didn't get your planning right. Right. Uh, you probably should have, you should have planned better. I think there are bottlenecks, uh, at every level that are meaning real and meaningful. I think, uh, I think that the first one is expertise. I, I think, uh, we have, uh, fundamental limitations in AI expertise. We're not making enough AI practitioners. We're not making enough data scientists who understand, uh, data pipelines. We are not, we're not, we're just, our universities aren't minting enough. And our, our challenges in the US with immigration, uh, don't help that. Um, we, uh, have historically sucked the best and the brightest first on J ones to come to our schools and H ones to stay. If that is not our policy, we need to make them, right? If the government decides that that is not the way they want to build a workforce, instead they want to build it, uh, out of people who live here, we need to do a better job of training those people. We need to do a better job of teaching them in K through 12. We need to do a better job of educating them in our universities in order to, to, to make the number of, uh, of, uh, of engineers we need to, to meet this demand. That's a bottleneck. And it's why the, the best and the brightest are, are getting such extraordinar…

AI assessment note: “I think that the first one is expertise.”

Q What's the other bottleneck? You said expertise is one. What's another?

A TSMC can't build fabs fast enough. I think the truth is, is that these are both for TSMC and Samsung. These fabs are the most amazing manufacturing plants on the planet. And, you know, these are 30,000,000,050 billion dollar factories. Um, and the, their ability to build them quickly enough is very much limited. Um, I, I think that in turn limits and keeps the supply, uh, below where it would like to be of chips, not just our chips or Nvidia, but everybody's chips below where it might otherwise be. Keeps the cost up. Um, I think, uh, uh, right now there's a shortage of, of data center capacity. Um, and I I think, uh, there's a huge amount of investment that has gone into that. Um, there's a lot of words, but where are these giga gigawatt facilities that everybody's been talking about? Everybody's committing to them. Where are they? Well, they're not up yet.

AI assessment note: “TSMC can't build fabs fast enough.”

Q know, when I spoke to, you know, Jonathan at Grock before, he said there were a huge amount of data centers being built that were not actually really equipped properly, and that we've seen this massive supply of data centers that are really kind of done by tourists, so to speak, and that is a massive problem, and that the provisioning of these data centers isn't there. Do you agree?

A I think the following. I, I think, ah, a data center is a, is a construction project to begin. It's a access to power, and then it's a construction project, and it's got a design engineering component. I think there's been a, a huge push for new construction data centers, and I, I think, ah, we, we will see. We, we don't know if they're gonna be good enough. I, I think many of them will be fine. I think the guys who were there early were some of the Bitcoin mining companies, like Tara Wolf, the guys at Crusoe, and, and others, ah, guys in Europe, ah, ah, They were early in building buildings near low cost power in order to run compute that used a lot of power. And they are some of the leaders now in some of the largest projects. Now, those are certainly not tourists. Those are extremely sophisticated data center builders. Now, sure, there's some tourists, but there are a lot of, of very, very knowledgeable data center builders building huge facilities right now. I mean, gigawatt scale facilities, both domestically and internationally.

AI assessment note: “Now, sure, there's some tourists, but there are a lot of, of very, very knowledgeable”

Q And the utility value of synthetic is the same as human?

A I think this, I think when you teach a pilot to fly in a simulator, right, there is a lot of potential data that isn't very useful in teaching her to fly, right? They spend a lot of time going straight doing nothing as a pilot. Now, takeoff and landings are where you want to spend your time, and that's why when we put them in simulators, that's what we have them doing. And in simulators, we can create data where engines blow, where there are a whole set of problems where learning can take place. That's simulated data. And in the same way, as we think about creating data, uh, whether it's for self-driving, whether it's for other forms of AI, what we want is the data that's hard to gather. Right? Otherwise, we just have a bunch of data of people driving straight on a freeway. Not, not difficult. We've been able to do that for a decade. What we want is an unprotected left turn in the snow. It's snowing. It's hard to see. You've got an unprotected left turn. That's a difficult thing. And you want that thousands of different ways, millions of different ways. That's where the synthetic data comes along. Is to use it to fill in The empty parts where it's really expensive or painful to get that type of data. Think of the pilot, right? You want them spending a huge amount of time on things that are rare in their training. Same with a surgeon. A huge amount of time on things that are rar…

AI assessment note: “That's where the synthetic data comes along. Is to use it to fill in”

Q What have you learned in the G-forty-two relationship build process that, that, that makes you dial a good partner in a way that you worked?

A We, we've deployed, uh, tens of exaflops of compute vastly more than, than anybody else that, that, that isn't AMD or NVIDIA, right? I mean, a huge amount of compute. Um, we, Uh, our software has been hardened on some of the largest AI clusters in the world. Uh, we've gone through the growing pains of increasing manufacturing, two X and five X and two X again through unbelievable growth in manufacturing. We've worked with our supply chain partners to, uh, to be sure that they're ready for this extraordinary growth. We've, I mean, I, I think when you, uh, work with a strategic partner, um, Of this size, uh, your organization comes out different on the other side, and there are things you've learned, and there are mistakes you've made, and, and, you know, you, I hadn't done a big relationship in the Middle East. There was a huge amount to learn, and, um, you know, I think you come out a much better company. And much better prepared to do, uh, business with a hyperscaler, to do business with another massive partner, to do business with, uh, another sovereign, but it, it's, it takes real work. And your team has to learn.

AI assessment note: “much better prepared to do, uh, business with a hyperscaler”

Q biggest firms in the world are now going, oh shit, we need Legal, our clients are saying we need, sorry, we need AI, our clients are saying we need AI, Harvey or Lagora. I'm not gonna get into which one, but like, there's two options, boom. Do you think all industries will follow the tipping, or do you think most will follow the slow agreement that it's the new normal?

A I, I think, uh, what's happening is the leaders are tipping. I, I think even Jensen told a story that, that he was battling with his own internal lawyers around the, the use of, I think it was cursor, and finally he just decreed. We're gonna, we're gonna do it. And I, I, I think I got that right, but somebody will correct me for sure if I got it wrong. But, um, I, I think at some point leaders weigh the productivity gains against the unseen boogeyman of Brisk. And the problem with unseen boogeyman is sometimes they're actually real. Right? Not often, but sometimes. And that, that's the problem. What does he call him in, in John Wick? Baba Yaga? John Wick is the guy you send to kill Baba Yaga.

AI assessment note: “I think, uh, what's happening is the leaders are tipping.”

Q I mean this in the nicest way. Do you not have to say nice things about it then?

A Like if someone's giving me a question, no, I, I went there to do business as a Jewish guy, uh, before we, uh, before we had any business done, right? I, I, what I found surprised me and we, we don't do much in Saudi and I think they're making great strides and we don't do anything in Qatar right now. I think they're making great strides. So I, I don't think it's just It, it may well sort of be colored by the fact that I, I spend time in, in Abu Dhabi, and I spend time in Dubai, and I spend time in Riyadh, and I spend time in Doha. Um, and, uh, sure, it's colored by, by those things.

AI assessment note: “no, I, I went there to do business as a Jewish guy”

Q Can I ask you, when you look at the inference market today, how has it developed in a way that you did not expect?

A I think it's really hard for the mind to wrap itself around geometric growth or exponential growth, right? I think there is nothing confusing about the greater growth of inference. The greater growth of inference is the number of people who use it, Times the frequency of use. Times the amount of compute needed per use. Right? It is three different variables multiplied by each other. The problem is they're all growing fast. And that produces some mind-numbing effects. More people are using AI. Once they start using AI, they use it more frequently. And what they want to do with it is bigger and more complicated, so it uses more compute. And so you, you have three variables, the size of the markets, the product of the three, all growing fast. And, uh, we knew that going in, we see that and it still takes your breath away.

AI assessment note: “we knew that going in, we see that and it still takes your breath away.”

Q Were you very impressed with Deep Seek? And what impressed you most?

A I think it was a result of focused engineering. And that impressed me. It was designed to be better. And, uh, they, they weren't confused about being Sort of model intellectuals, or they weren't confused about whether it was important to break new ground, or they were interested in being better. And from an invention standpoint, that's a little boring. But from an engineering standpoint, that was sweet effort. They really built a model that was just plain better at many, many things. And that's cool. I, I like good engineering projects. Now, that they chose to announce it right around Trump's inauguration and the politics of it, that, that, that's all a separate matter, and, and we can talk about that later, but, um, Is distillation wrong?

AI assessment note: “I think it was a result of focused engineering. And that impressed me.”

Q Were you very impressed with Deep Seek? And what impressed you most?

A I think it was a result of focused engineering. And that impressed me. It was designed to be better. And, uh, they, they weren't confused about being Sort of model intellectuals, or they weren't confused about whether it was important to break new ground, or they were interested in being better. And from an invention standpoint, that's a little boring. But from an engineering standpoint, that was sweet effort. They really built a model that was just plain better at many, many things. And that's cool. I, I like good engineering projects. Now, that they chose to announce it right around Trump's inauguration and the politics of it, that, that, that's all a separate matter, and, and we can talk about that later, but, um, Is distillation wrong?

AI assessment note: “I think it was a result of focused engineering. And that impressed me.”

Q Do you think they should sell to external customers?

A I think you are already seeing them step outside of their own data centers for this exact reason. Right? What it says in your friend's construction is our ability to sell hardware is constrained by our ability to build data centers. Now, one can imagine a world where you don't want that constraint. You would like to be able to sell hardware to anybody's data center. And so I, I think these arguments are extremely complicated, um, rarely unfold in, in a simple form. Um, but it is true that when Google or when Cerebris puts our equipment in our own data center, right, we have a, a significant advantage Over a NeoCloud, because NeoClouds are buying hardware with gross margins of 70, 80% for NVIDIA. So the hardware in those data centers, and then they have to make their margin. That's not what Google's doing. That's not what we're doing.

AI assessment note: “I think you are already seeing them step outside of their own data centers”

Q Are export controls being implemented properly? Do you think that is a good idea? You know, everyone was going with Deepsea, wow, how did this happen? They must have stolen chips. How could this be? What do you think about export control?

A It turns out that they probably did use chips in Singapore. Um, I think the following, I, I think managing Software and managing hardware compliance are, are extremely different things. Because their, their vector of diffusion is different. There's different weights. Right? If you sell a, a server that weighs five or 600 pounds, arrives on a pallet, you can go visit it. Right? You, you want to deploy it in Kazakhstan. You can put a data center, and you can have somebody from the embassy visit it. Take photos of it once a month. It's not going anywhere. Right? You, you can keep track of who uses it and provide logs, and that's much, much harder with software. And open source is a, a whole nother level. Right? And so, um, that's the first observation. The second is that We had, I got to know the, the, the leadership in commerce in the previous administration. I didn't always agree with their policies, but it is a world of unintended consequences. You, ah, sought to limit Chinese access to EDA tools to delay the growth of a Chinese chip market. And so U.S. venture capitalists backed tons of Chinese companies in Shenzhen to build EDA tools, right? I mean, right? Right. This is a unbelievably slippery, dynamic, challenging problem, and I don't know if it's a tractable problem to delay another nation's progress on a technical trajectory Is an enormously challenging thing. And, ah, I,…

AI assessment note: “This is a unbelievably slippery, dynamic, challenging problem”

Q Why is it? What will we replace it, and what does that look like?

A I don't know. I don't know whether they're going to be state-based models. I don't know whether they're going to be other types of models, but what I know for sure is that innovation doesn't stop, and that, ah, that the transformer, ah, has some weaknesses that, that people are desperate to overcome. Um, there's a quadratic effect in the attention head. Um, the, there's all sorts of things that, that could be improved. But it's pretty darn good now. It's the best we have, and that's what you run with. You run with the best you have, and the minute it's not the best you have, you drop it in favor of the best you have. And I, I think that's, that's what we're seeing. We're seeing, I mean, the number of, of innovative companies designing models is large. And what DeepSeek showed us is you, you don't need 5000 people and, you know, billions of dollars of gear. You can do it with 200 smart people. And more gear than Deep Seek said they had, but less gear than others had.

AI assessment note: “I don't know whether they're going to be state-based models.”

Q um, security, permissioning, legal, everything in between. They get even more frickin' nervous when it's open source. Like, they shit the bed. How do you think about that? I see more and more companies, especially in the valley, Really push the boundaries on with Frontier and then try and get as close as possible with open source given the cost advantages. Is that the future? And what does that mean?

A Look, I, I think we as a, as an ecosystem have made real progress in sort of the, the legal, uh, gunk around open source, but it, the result has been a complexity that hurts your head. And if you, if you ever want to, to dive down a rat hole that has sort of no bottom, begin a discussion with lawyers about open source software. And there is no end to the depth and the boredom which you will suffer as you head down this hole. This is made doubly worse by some of the best open source models were made by Chinese companies. And, uh, and they are exceptionally good models. Kimi Ketu, Deep Seek. When the, the GLM, these are extraordinarily good models. They're not quite as good as the closed source models, but they're exceptionally good models. And I, I think that is a case of, uh, people trying to decide, uh, whether it makes sense to, to, to, to save money. Uh, they have been, you know, easy for us to adopt, to, to demonstrate extraordinary speed on. Um, it, it's a hard problem. I mean, I don't envy the, the, the, the legal team and, and the security groups that are thinking about these things, but the truth is the tidal wave is so big and the demand is so high that, that they often just get, get washed over.

AI assessment note: “the truth is the tidal wave is so big and the demand is so high”

Q Where are people investing today in AI across the stack? You can choose any part where you're like, what? Why is so much cash going to that part? I'm not saying that company.

A I think part of the, the, the dynamic in your industry is, is Sometimes money needs, needs to find a home, right? Some guys have raised really, really big funds, and they got to find a home for their money. Um, and some people don't like to be left out, and they're willing to, to make investments for, Maybe for some status purposes or other reasons that, that don't seem to make sense. I don't know. I haven't thought about it. I mean, I, I think there's some underappreciated places of investment. I'd say in the chip world, the, the sub milliwatt, really tiny, tiny little chips that live next to sensors that do, uh, inference. These are tiny little things that will, uh, only send back useful data. Is a, an extremely interesting market, and they will sell enormous volume. Now, I, it's not a part of the market I, I love to play in. I like to build bigger things and sell them to the data center, but I think that part is extremely interesting. I think they'll be fundamental for robotics. I think, um, Uh, that's an area where, uh, I, I think it's, it's extremely underappreciated.

AI assessment note: “I think there's some underappreciated places of investment. I'd say in the chip world”

Q What was your craziest prediction in terms of how AI reshapes the future in five years? So for example, Jonathan said, hey, I think AI will create massive labor shortages. It will create so many jobs for so many people that we will have massive labor shortages.

A In five years. Absolutely wrong. Economic dislocation. Uh, isn't resolved in very short periods of time. That might be true in 15 years. But, uh, I, I think, uh, in the three to five year time frame, uh, I, I, I certainly don't believe that will be the case. Um, I, I think, uh, AI will, uh, the adoption of AI or the diffusion of AI into the economy, it will nibble its way in. Right? I mean, let's ask this question. Uh, AlphaFold solved one of the hardest problems in, uh, in chemistry. A problem that had been open for years. Name a drug that's resulted from it. Not one. Now, I believe there will be, but AlphaFold's what, four years old now? Three years old, right? This was a massive breakthrough for which the, the, the inventors were given Nobel Prizes. Where's the drug? Now, right? Show me the, the, ah, the medical benefit. It will get there. It will be important. Continuations of the model will have fundamental impact, but sort of where are the X-ray crystallographers who are displaced because of it, right? That was what X-ray crystallographers were doing only physically. Um, they're, they're not out of work. They're, in fact, there's more demand for them. Um, so I, I think, uh, uh, I think what it means to I think it will have really interesting, uh, effects on the way we educate children, and that's an interest of mine. Um, you know, we, we've sort of been educating children…

AI assessment note: “I think it will have really interesting effects on the way we educate children”

Q What was your craziest prediction in terms of how AI reshapes the future in five years? So for example, Jonathan said, hey, I think AI will create massive labor shortages. It will create so many jobs for so many people that we will have massive labor shortages.

A In five years. Absolutely wrong. Economic dislocation. Uh, isn't resolved in very short periods of time. That might be true in 15 years. But, uh, I, I think, uh, in the three to five year time frame, uh, I, I, I certainly don't believe that will be the case. Um, I, I think, uh, AI will, uh, the adoption of AI or the diffusion of AI into the economy, it will nibble its way in. Right? I mean, let's ask this question. Uh, AlphaFold solved one of the hardest problems in, uh, in chemistry. A problem that had been open for years. Name a drug that's resulted from it. Not one. Now, I believe there will be, but AlphaFold's what, four years old now? Three years old, right? This was a massive breakthrough for which the, the, the inventors were given Nobel Prizes. Where's the drug? Now, right? Show me the, the, ah, the medical benefit. It will get there. It will be important. Continuations of the model will have fundamental impact, but sort of where are the X-ray crystallographers who are displaced because of it, right? That was what X-ray crystallographers were doing only physically. Um, they're, they're not out of work. They're, in fact, there's more demand for them. Um, so I, I think, uh, uh, I think what it means to I think it will have really interesting, uh, effects on the way we educate children, and that's an interest of mine. Um, you know, we, we've sort of been educating children…

AI assessment note: “the adoption of AI or the diffusion of AI into the economy, it will nibble”

Q You said that, you know, computing hardware, that's where the value is. How does that value distribution shake out? You know, we've obviously got the 800 pound gorilla that is NVIDIA. How do you think about how the distribution of value shakes out in hardware and in compute over the next five years?

A Historically, um, what one of the One of the barriers to entry was sort of the capital intensity intensity of a product, of a project. And in, in the world of building chips, there's both scarce resources and expertise, and it's very expensive. Um, and historically, it, it hasn't fit very comfortably in a software company. And the things that software, modern software companies value are Not entirely conducive to chip making. And so, when I look down the road, I mean, I, I think, uh, who has endured in, uh, in much of infrastructure tech, uh, People who build systems. Cisco, Juniper. Endured. Um, uh, chip makers have endured. There's a reason that Apple and Nvidia are among the most valuable companies on Earth. There is, what they do is hard. And I, I think it's, that's why it's worth challenging. Right? That's, if it weren't hard, if it wasn't enormous and difficult, You know, why spend time being the underdog and challenging it?

AI assessment note: “There's a reason that Apple and Nvidia are among the most valuable companies”

Q Can I ask, when we think about kind of that speed, I am sure you said that kind of you're one of one with wafer and kind of the architecture associated. What does that mean in terms of cost? With such efficiency, is it inherently more expensive? And what does that look like from a cost profile?

A This isn't our, our, our first dance. We, we've been building computers for, for a long time. And when you make a choice like wafer scale, you, you have to weigh the tradeoffs. We use less power. We use less power because one of the most power hungry things on a, on a chip are the IOs, right? Are moving data off chip. And so if you are moving data off chip frequently, you're using more power than if you can keep it in the silicon domain on chip. So we knew we would use less power. We knew if you went to wafer scale that you had to solve some problems that people said were impossible to solve, like yield. So we had to invent techniques that allowed us to yield wafer. In fact, we invented techniques that allow us to yield as well or better than others who are building much smaller jobs.

AI assessment note: “And when you make a choice like wafer scale, you, you have to weigh the tradeoffs.”

Q I mean this in the nicest way. Do you not have to say nice things about it then?

A Like if someone's giving me a question, no, I, I went there to do business as a Jewish guy, uh, before we, uh, before we had any business done, right? I, I, what I found surprised me and we, we don't do much in Saudi and I think they're making great strides and we don't do anything in Qatar right now. I think they're making great strides. So I, I don't think it's just It, it may well sort of be colored by the fact that I, I spend time in, in Abu Dhabi, and I spend time in Dubai, and I spend time in Riyadh, and I spend time in Doha. Um, and, uh, sure, it's colored by, by those things.

AI assessment note: “Like if someone's giving me a question, no”

Q To what extent is it just completely unshakable at this point for them? Like where the scale and the size of money is like, you know, Jonathan Ross from Grok said on the show, they will unwaveringly get to 10 trillion dollars within a five-year timeline.

A I hope he's long on them then. Um, I, I, I don't pick public market stocks. I, I, I think I don't like public picking public market stocks. I think in the public market, you can lose money on good companies. You can make money on shitty companies. And that for me, doesn't sit well. Uh, as an entrepreneur, as a David in the battle with Goliath, I, I want to make money when, when we build a great company. Period. But, um, can they continue to grow? I think we are seeing some things that big companies do as they begin to worry about growth. I think use your balance sheet more and your technology less, right? This is something that historically large companies have done as they feared for their, their, their technical prowess.

AI assessment note: “can they continue to grow? I think we are seeing some things that big companies do”

Q I'm sorry, what does all-to-all connected mean?

A In many of the layers in a neural network, every element is connected to every other one, and that's Not the way actually the learning happens. Some are more valuable and some are not valuable at all, right? And imagine you're gonna read 50 books, you wanna learn something, you can read all 50 books, or you could read three books that are really important, or you could read summaries of the three books that are the most important. The problem is we don't know which they are at the beginning. And there's a process that you could learn. There's things called dropout and all these other techniques to, to use sparsity to help, help solve these problems. We are early in the evolution of AI. Plays right into this point that we'll get better at these algorithms. You know, transformers aren't the end of the world, right? We'll get better. Better will mean faster, more accurate, and more efficient. And I, I think that's what's exciting about an ever-changing industry. I mean, that's not, that's why I'm not in all these other industries that don't change quickly. Same nine years ago as they are today.

AI assessment note: “every element is connected to every other one”

Q Jason, I'm happy to dive in. Can I ask you, does CoreWeave open up a wave of excitement, a wave of investor excitement from here? Does it open up a wave of net new M&A, IPOs? How does that, going public, change sentiment, change investor appetites, change the ecosystem?

A I think, uh, you know, Core Weaves was, uh, was, uh, it wasn't an easy path to, to IPO. They are an unusual business. They're a creative business. Many of their innovations are in the financing structure. Um, they were among the first to use the type of debt that they're using to recognize that you could borrow against GPUs and use their access to, to leverage advantage. I, I think, uh, Uh, I, I think for them, for their team, what an enormous success to get out the door. I mean, this is hard, and the number of people who tell you you can't do it, the number of parasites that jump on to try and nibble a little bit, you know, you, you tell somebody, I want a great, I want a great slide deck, and they say, 40,000 dollars, you say, it's for an IPO roadshow, they say, 125, and, and there's just this, we're Nibbling away at you, trying to tell you, you've got to do it their way, or you've got to be sure that their clients get the big part of, of the, the first day bump. And I think as entrepreneurs, we got to be focused on, on the people who are behind us, the teams that we built, who've invested careers behind us, and our investors who've been with us long periods of time, and we got to be focused on them. And I, I think to, to a person, the entrepreneurs inside of core, we were extremely happy at the outcome. And yesterday's performance was phenomenal. The short sellers got squash…

AI assessment note: “I think for them, for their team, what an enormous success to get out”

Q To what extent is it just completely unshakable at this point for them? Like where the scale and the size of money is like, you know, Jonathan Ross from Grok said on the show, they will unwaveringly get to 10 trillion dollars within a five-year timeline.

A I hope he's long on them then. Um, I, I, I don't pick public market stocks. I, I, I think I don't like public picking public market stocks. I think in the public market, you can lose money on good companies. You can make money on shitty companies. And that for me, doesn't sit well. Uh, as an entrepreneur, as a David in the battle with Goliath, I, I want to make money when, when we build a great company. Period. But, um, can they continue to grow? I think we are seeing some things that big companies do as they begin to worry about growth. I think use your balance sheet more and your technology less, right? This is something that historically large companies have done as they feared for their, their, their technical prowess.

AI assessment note: “can they continue to grow? I think we are seeing some things that big companies do”