100%
1/1 resolved calls right
1 scored · avg 88
◆ 4 sources
11
companies · 78 data points
I understated the $100 billion because we're already, the first six months of this year, we did $10 billion in physical AI.
Until we fully convert all general purpose computing to accelerated computing AI, until we do that, I think the chances are extremely low.
the token generation rate is going up exponentially. Yeah. And the customer use is going up exponentially. I think they're at 800 million weekly active users or something like that. ... That's right. Exactly. And so the first thing is, because the token generation rate is going up so incredibly, two exponentials on top of each other, we have to, unless we increase the performance at incredible rates, the cost of token generation will keep growing because Moore's Law is dead, right?
We just really evolved from a GPU company to an AI factory company.
What happened: By Jul 2026, corpus confirms Nvidia making ~00B revenue, highest margins, described as the most unbelievably accretive well-run company. AI factory thesis fully validated by inference explosion and agentic compute demand.
look at obviously what Elon has done to the automotive industry, turned it into essentially a computing industry, a technology industry.
We're the only company in the world that does that.
We're the world's largest physical AI company. We did $10 billion in the first six months. We did $10 billion in the last year. And that was our first $10 billion year.
I understated the $100 billion because we're already, the first six months of this year, we did $10 billion in physical AI.
the first six months of this year, we did $10 billion in physical AI.
we've now shipped one and a half, 1.67 billion units.
we invented a new computing model. This is probably the most pervasive computing model in the world today. It's the only computing architecture that's in every cloud. It's in every computer company. It's used in every field of science. It's used by gamers. It's used by creators and builders. And it's used by scientists. It powers intelligence.
the way to think about China is really good at high volume production. And this is just a matter of time.
They're going to get there by 2030. By 2030. Yeah. And 2030 is just around the corner.
my self-driving car, I don't want you to make me an omelet. I just want you to drive the car. Right. That is superintelligent.
we're the world's best 3Ds company
the NVIDIA culture and the NVIDIA employee base, incredibly happy. They like the fact that the company is consistent, that we're stable, that our core values are consistent with taking care of the families and creating the conditions by which they can do their life's work.
jensen said to me on the pod he's like nobody comes close microsoft doesn't come close you know google doesn't come close in terms of standing it up in that time frame
anybody can operate our systems, we're in every cloud, including Google and Amazon and, you know, Azure and OCI.
we're the only company that, that accelerates applications of all kinds. We have a gigantic ecosystem.
Our market reach is far greater than any, any, any TPU can, any ASIC can possibly have.
none of the bottlenecks last longer than a couple, two, three years. None of them. And meanwhile, we're improving computing efficiency by 10x, 20x, in the case of Hopper to Blackwell, some 30, 50x.
That is in the middle NVIDIA. And our job is to do as much as necessary, as little as possible, to enable that transformation to be done at incredible capabilities.
We've made enormous commitments upstream. Some of it is explicit, these commitments that you mentioned. Some of it is implicit.
It's one of the things that we can do that is hard for someone else to do.
I don't think that that gets commoditized.
I think that that's hard to completely commoditize.
If we didn't support NScale, they wouldn't be where they are today.
And even though we caused Anthropic to have to go to somebody else, I'm still happy that it happened.
I'm delighted to invest in OpenAI and I'm delighted to help them scale. And I believe it's essential to do so. And then when I was able to, when Anthropic came to us, I'm delighted to be an investor, delighted to help them scale.
I didn't deeply internalize how difficult it would be to build a foundation AI lab like OpenAI and Anthropic.
Anthropic's existence is great for the world.
Without Anthropik, why would there be any TPU growth at all? It's 100% Anthropik.
I would love to hear them demonstrate the cost advantage of TPUs. It makes no sense in my mind. It makes absolutely zero sense. On first principles, it makes no sense.
the relationship is incredible and, and I can completely trust them. I can completely depend on them.
Some of them I have to directly, some of them I indirectly, and some of them, if I can convince TSMC, ASML will be convinced. And so that's, that, you know, we have to think about the critical, critical pinch points. And, but if TSMC is convinced, you'll have plenty of EUV machines in a few years. And so none of that, my point is that none of the bottlenecks last longer than a couple, two, three years. None of them.
I think China is, is a formidable. And the reason for that is because their micro electronics, their motors, their rare earth or magnets, which is foundational to robotics, they are the world's best. And so in a lot of ways, our robotics industry relies deeply on their ecosystem and their supply chain. And and, and they're, you know, obviously moving very quickly. We're going to, you know, our robotics industry will have to rely a lot on it. The world's robotics industry will have to rely on a l
NVIDIA gave up a 95% market share in the second largest market in the world. And we're at 0%. I know. That's right. President Trump wants us to get back in there. And the first thing is to get licensed for the companies that we're going to be able to sell to. We've got many companies who have requested for licenses. We've applied for licenses for them, and we've got approved licenses from Secretary Lutnik. Now we've informed the Chinese companies, and many of them have given us purchase orders.
In the case of AWS, I think they just announced, I think it was yesterday, that they're going to buy a million chips in the next couple of years. I mean, that's a lot of chips from AWS and that's on top of all the chips they've already bought.
I love to use Gemini.
Anthropic is incredible. They're going to be enormous.
we now have Anthropic coming to NVIDIA.
I love to use Cloud.
Claude and GPT and ChatGPT have reached a level that is really very good.
When you look around this audience, you will see that Anthropic and OpenAI is represented here. But in fact, 99% of everything that is here is all AI and it's not Anthropic and OpenAI. And the reason for that is because AI is very diverse, I would say that the second most popular model as a category is open models. Number one is open source, open ways, open source. OpenAI is number one, open source is number two, very distant third is Anthropic.
I would say that the desire to warn people about the capability of the technology is also really terrific. We just have to make sure that we understand that the world has a spectrum and that warning is good, scaring is less good. Because this technology is too important to us. I think that it is fine to predict the future, but we need to be a little bit more circumspect. We need to have a little bit more humility that in fact we can't completely predict the future and the ability and to say thin
the technology is incredible. We are a large consumer of Anthropic technology. Really admire their focus on security, really admires their focus on safety. The culture by which they went about it, the technology excellence by which they went about it, really fantastic.
There's several areas that we're involved in in healthcare. One is AI physics, or AI biology. Using AI to understand, represent, predict biology, behavior, biological behavior. And so that's one. That's very important in drug discovery. There's second, which is AI agents. And that's where the assistance in helping diagnosis and things like that. Open evidence is a really good example. Hippocratic is a really good example. Love working with those companies. I really think that this is an area whe
We have CUDA in satellites around the world. They're doing imaging, image processing, AI imaging. And that kind of stuff ought to be done in space instead of sending all the data back here and do imaging down here. We ought to just do imaging out in space. And so there's a lot of things that we ought to do in space. And in the meantime, we're going to explore what is the architecture of data centers look like in space.
NVIDIA is not making chips. Number one, making chips does not help you solve the AI infrastructure problem anymore. It's too complicated.
we're gaining share for several reasons. One, our velocity has gone. We help people realize it's not about building the chip. It's about building the system. And that system is really hard to build.
I believe that buying from NVIDIA still is one of the most economic things they could do.
we're the only architecture that could be in every cloud. And that gives us some fundamental advantages. We're the only architecture you can take from a cloud and put into on-prem, in the car, in any region space. ... about 40% of our business.
In the case of AWS, I think they just announced, I think it was yesterday, that they're going to buy a million chips in the next couple of years.
we're gaining market share. If you look at where we are today, we're gaining share.
You know, I think helium could be a problem, but it's also the case that the supply chain probably has a lot of buffer in it. These kinds of things tend to have a lot of buffer.
With respect to Taiwan, we have to do three things. One, we have to make sure that we re-industrialize the United States as fast as we can. And whether it's the chip manufacturing plants, the computer manufacturing plants, or the AI factories. How are we doing on that? We're doing excellent. By gaining the strategic support, by gaining the friendship of the supply chain of Taiwan, by gaining their friendship, by gaining their support, we were able to build Arizona in Texas and California at incr
I was also asked, you know, given what's happening in the Middle East, is that an area where we believe that we can expand artificial intelligence, too? I believe that there's a reason we went to war. And I believe at the end of the war, Middle East will be more stable than before. And so if we were there, if we're considering it before, we should absolutely be considering it after. And so I'm 100% in on that.
At the current moment, as we speak, NVIDIA gave up a 95% market share in the second largest market in the world. And we're at 0%. I know. That's right. President Trump wants us to get back in there. And the first thing is to get licensed for the companies that we're going to be able to sell to. We've got many companies who have requested for licenses. We've applied for licenses for them. And we've got approved licenses from Secretary Lutnik. Now we've informed the Chinese companies, and many of
We're trying to. Let me give you the thought experiment. Let's say you have a software engineer or AI researcher and you pay them $500,000 a year. We do that all the time. Okay. This is happening all of the time. That $500,000 engineer at the end of the year, I'm going to ask them, how much did you spend in tokens? And that person said, $5,000, I will go ape something else. Yes. Right. If that $500,000 engineer did not consume at least $250,000 worth of tokens, I am going to be deeply alarmed.
We have 43,000 employees, you know, I would say 38,000 are engineers.
when we went from generative to reasoning, the amount of computation we needed was about a hundred times. When we went from reasoning to agentic, the computation is probably another hundred times. Now we're looking at in just two years, computation went up by effect 10,000x. Meanwhile, people pay for information, but people mostly pay for work. Talking to a chat bot and getting an answer is super great. Helping me do some research, unbelievable. But getting work done, I'll pay for. And so that's
When you look around this audience, you will see that Anthropic and OpenAI is represented here. But in fact, 99% of everything that is here is all AI and it's not Anthropic and OpenAI. And the reason for that is because AI is very diverse, I would say that the second most popular model as a category is open models. Number one is open source, open ways, open source. OpenAI is number one, open source is number two, very distant third is Anthropic. And that tells you something about the scale of al
We are a large consumer of Anthropic technology.
I think in the case of digital biology, I think we are literally near the ChatGPT moment of digital biology. We're about to understand how to represent genes, proteins, cells. We all know how to understand chemicals. And so the ability for us to represent and understand the dynamics of the building blocks of biology, that's a couple of two, three, five years from now. In five years' time, I completely believe that the healthcare industry or digital biology is going to inflect. And so these are a
Physical AI as a large category, it's technology industry's first opportunity to address a $50 trillion industry that has largely been void of technology until now. And so we need to invent all of the technology necessary to do that. I felt that that was a 10-year journey. We started 10 years ago. We're seeing it inflecting now. It is a multi-billion dollar business for us. It's close to $10 billion a year now. And so it's a big business and it's growing exponentially. And so that's number one.
In a final analysis, that's the job of the CEO. Yeah. And our job is to define the strategy, define the vision, define the strategy. We're informed, of course, by amazing computer scientists, amazing technologists, great people all over the company, but we have to shape that future. Well, part of it has to do with, is this something that's insanely hard to do? If it's not hard to do, we should back away from it. And the reason for that, is if it's easy to do, obviously. Lots of competitors. A lo
The big takeaway, the big idea is that you should not equate the price of the factory and the price of the tokens, the cost of the tokens. It is very likely that the $50 billion factory. And in fact, I can prove it that the $50 billion factory will generate for you the lowest cost tokens. And the reason for that is because we produce these tokens at extraordinary efficiency, 10 times, you know, the difference between 50 billion. Now it turns out 20 billion is just land power and shell, right? Ri
We think that there's three computers in the problem at the largest scale when you take a step back. There's one computer that's really about training the AI model, developing, creating the AI. Another computer for evaluating it, depending on the type of problem you're having. Like, for example, you look around, there's all kinds of robots and cars and things like that. You have to evaluate these robots inside a virtual gym that represents the physical world. So it has to be software that obeys
Part of that 33% or 50%, a lot of it's going to be storage processors. It's called Bluefield. Some of it will be, a lot of it I'm hoping, will be Grok processors. And some of it will be CPUs. And so all of this is going to be running basically the computer of the modern industry called Agents.
My sense is, and so we added, we used to be a one rack company. We now add a four more racks. Right. So NVIDIA's TAM, if you will, increased from whatever it was to probably something, call it 33%, 50% higher.
we just really evolved from a GPU company to an AI factory company.
In November, 2025, we entered into an agreement subject to certain closing conditions to invest up to $10 billion in Anthropic. There is no assurance that we will enter into definitive agreements with respect to the open AI opportunity or other potential investments, or that any investment will be completed on expected terms, if at all.
Until we fully convert all general purpose computing to accelerated computing AI, until we do that, I think the chances are extremely low.
Google is a very special case. I mean, we just have to, you know, show respect where respect is really deserved. I mean, TPU is on TPU 7. Yes. Right? And so, and it's a challenge for them as well, right? And so, the work that they do is incredibly hard.
Our partnership with Intel is recognizing that general purpose computing needs to be fused with accelerated computing to create opportunities for them.
Another thing, what else did he say? Yeah. He said token generation is doubling every few months.
Eddie, last night, Eddie Wu at Alibaba said, between now and the end of the year, and, excuse me, now and the end of the decade, they're going to increase their data center power by 10x.
without AI these days, it's just simply not possible to build what we build.
the token generation rate is going up exponentially. Yeah. And the customer use is going up exponentially. I think they're at 800 million weekly active users or something like that. ... That's right. Exactly. And so the first thing is, because the token generation rate is going up so incredibly, two exponentials on top of each other, we have to, unless we increase the performance at incredible rates, the cost of token generation will keep growing because Moore's Law is dead, right?
this is likely going to be the next multi-trillion-dollar hyperscale company. And who doesn't want to be an investor in that?
10 gigawatts is like $400 billion, right? Something like that. And that $400 billion will have to be largely funded by their offtake, right? Their revenues. Right. Which is growing exponentially. It has to be funded by their capital, the money they've raised through equity, and whatever debt they can raise. Those are the three vehicles. And the equity that they could raise and the debt that they could raise has something to do with the confidence of the revenues that they could sustain. For sure
Data processing represents the vast majority of the world's CPUs today. It still completely runs on CPUs. You know, if you go to Databricks, it's mostly CPUs. If you go to Snowflake, it's mostly CPUs. SQL processing at Oracle, mostly CPUs. Everybody's using CPUs to do SQL, structured data. In the future, that's all going to move to AI data.