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One of the biggest stories that we have been following in the last twenty four hours, though, of course, has been the important keynote address from Jensen Wong at the Consumer Electronics Show. It has been feeding a number of storylines and moving the stock ever since that took place in Las Vegas yesterday. And joining us right now for a very special conversation, We've got good news for you.
It's Bloomberg's Ed Ludlow with the CEO of Nvidia, Jensen Wong, as well as the president's CEO of Siemens, Roland Bush. They're at the cees right now and we want to hand things out to Las Vegas. Ed Ludlow, you have the con.
Thank you, Joe.
Over one hundred and seventy five years, Siemens has been at the forefront, if i'd say, several industrial revolutions. In Vidia is at the forefront of the latest AI industrial revolution, and it's a pleasure to have you both here with us. Gentlemen, Roland, I want to try and understand how real this is. You call it an industrial AI operating system. We're going to talk about how you're joining forces on the software
side and the hardware side. But I think the most useful place to start would be, could you outline a timeline for when Semens goes beyond its own footprint around the world using this operating system to customers actually doing things at scale, because I think when you're on stage, that's the kind of sense I was getting.
You want to accelerate to scale.
Well, I mean this technology is already in place and it's working.
I mean we see there are many examples, and it brought some on stage today where you have customers starting with a digital twin of a product. They want to manufacture a digital train of the manufacturing side and bring that all together and not before that you.
Have it optimized them and build in the real world.
And AI is already working on the shop for they call it machine learning. But the point is with the new models, you can bring that to the next level. That means you go away from giving an advice to somebody with technology, but really act on your behalf. This is when things go more autonomous or adaptive, and you see that already starting.
The big thing is how can we scale it?
Because it requires a lot of skills from our customers, a lot of technology, and it's still not that easy to implement the working on it to.
Make it easy to deploy and easy to use, but you see picking up momentum in each stuff we do, and it's also great examples as a shipbuilding is a short everlenge, but even start ups using our technology like commentalst Fusion.
Systems Jensen, we've discussed the five layer cake often. You know within video it started with the GPUs, but it's now software, the element of simulation. On stage, you talked about integrating the software side, in particular into DA Again, a similar question for you, more of a timeline. What is it you think you'll do first in how you guys work closer together.
The first thing, let me just say very quickly, we're announcing a big partnership between us. We've known each other for a long time, but this is a partnership were announcing is really a big deal.
One.
We're accelerating their EDA software, We're accelerating their simulation software. We're integrating AI technology, physical AI and agentic AI into their team center and their factory automation operating system, and so we're working together across this entire spectrum. When we accelerate the software, then we'll get to use it to design our chips and systems. When we accelerate their simulation software, we'll use it in our AI factories to simulate the
thermal properties of our AI factories. When we integrate our automation and agentic systems into their AI industrial operating system, we can then use it in our factory floors with our partners, for example, fox Con And so we're working across this entire spectrum together and we're going to put the technology to use basically as soon as we can.
What's the net effect for you, Jensen?
Is it improves efficient capital allocation. I know that might sound a bit dry, but actually, right now, that's the answer everyone's searching for. How is this investment in AI and use of the technology actually changed things in the real world for me?
Announced yesterday Vera Rubin.
It takes six different chips to integrate into this incredible system called Vera Rubin. And when you're done, each one of these Vera Rubin GPUs is two hundred and forty thousand wants and it is ten times more energy efficient than a last generation. It is ten times more cost efficient than a last generation but it's still the technology is insanely complicated. One hundred fifteen thousand engineering years came
together to build this system. And so when we accelerate EDA tools, when we accelerate simulation tools, and when we can eventually and I'm hoping very soon design entire vera Ruben systems inside a Semen's digital twin, the chance the ability for us to create much much more complex systems will scale. We'll do it much more efficiently. And so this is really about being able to do the impossible, and being able to do it impossible the impossible right the first time.
And and and and once they then realize that AI creates real world impact, this is where it really deploys the full power.
And also the economic power.
It's not only in the data centers are any eye factories which we see, but also on the edge because once you start influencing with low latency, you bring this technology to the edge. This is a huge potential for our customers to to deploy this technology. And this includes of course in pluts hardware where we come from from the JIPS.
It goes into the controllers.
Some of our controllers run on GPUs and then it goes.
All the way to the industrial PC exactly.
And these are aids. We super charge it and they can now run algorithms train than the cloud.
They can run it on the shop floor and.
Do all that trick what we talked about it in retime optimization and running in a planned and that makes a huge difference.
So it de drives economy. The question that's being searched for is how is this going to manifest in.
The real world.
You know, the emphasis that this is cs I think is physical AI is not the manifestation of the final stage of physical AI, just one giant robot that you guys call a factory In the manufacturing context, is that where you're seeing demand from actual customers, you know that they need a factory that is automated one hundred percent and genuinely autonomous in some degree.
Number one is if you want to build a factory, and very often you're missing out on labor, and we talk about on skilled labor, it's hard to find Number one. Number two is once you build it autonomous and automated, then you have a much much higher yield, but you can generate you use lesser energy. By the way, at the same time, before you optimize it in a way.
So therefore there's a lot of benefits. And if you want to talking about the United States up manufacturing the United States, you need to go as digital and as automated and AI super changed as possible.
A factory is robotic and it is orchestrating robots that are building systems that are also robotic, like for example, self draging cars a robotic system.
And the reason why it's.
So hard to deploy robots today is because it's hard to program these robotic systems. The software expertise that necessary to customization necessary is really intense.
It's just too much.
And so the fact that we could now apply artificial intelligence physical AI technology to these robotic systems make them easier to teach. You show it a few demonstrations, and the AI learned it by itself.
You think, would you guys are, say Jenson, that you've solved for that software limitation.
That software limitation is the chat GPT moment of it is now here. I think over the course of the next couple two three years, we're going to make some really big breakthroughs.
Let's please talk about energy, electricity, power supply, call it what you will in turn. For each of you, how worried are you about it as a bottleneck and what is your experience day stay in running both companies.
In that respect, energy should always be a bottleneck for any industry, and this is a new industry that's growing incredibly fast.
As you know.
AI is both the technology that's going to revolutionize many applications, and we're talking about some of them here, but the AI industry itself, the manufacturing of the artificial intelligence takes energy. It takes energy, it takes AI factories. It's exactly the reason why from Hopper to Blackwell we increased energy efficiency by tenx. From Blackwell to Reubin, we increased energy efficiency
again by tenx. And that translates directly to our customer's revenues because in the case of an AI factory, whatever factory size you have, you're limited by the power, and that power within that power constrained, you want to have the most tokens or most AI per want that you can possibly generate. And so every time we improve energy efficiency, we're effectively improving both the AI capabilities for our customers and their revenues because they're always constrained by power.
The response to your keinot and forgive me rowan one minute. Was you know vera rubin ten x through put really important tokens generated at one tenth. But they'll still say, hey, Jensen, what if the electricity is just not there, it's not available.
Well, there's electricity, they're never enough electricity.
That's actually what is the anxiety level for you both about that issue.
There's always energy, they're never enough energy. And this is every and every industrial revolution will be energy constrained.
And this industrial revolution is also energy constrained.
Now one of the most important things here speaking about the United States, if not for President Trump's pro energy growth agenda, we would have a very hard time growing at all. In order for our new industry to emerge, you need energy, and so I think it's safe to say that we wish we had more energy in United States. You wi should have more energy. I think the world all wish we had more energy, and so we have to invest in all sorts of different forms of energy.
But whatever energy you have, you have to make it as energy efficient as possible. And that's one of the reasons why both of us drive our technology roadmap so hard, because every single new generation of technology is more energy efficient than the last.
That's kind of the nature of it.
You are in the AI factory. I won't say data center is data centers where you store data. That you are in the AI factory supply chain.
You must see it.
So what we see is number one is you see that the energy demands is roughly scaling with the GDP growth. It's the coupling because executive of this effect that we are providing more and more efficient technology, So that means the link. But still it grows along with economic growth. And then on top comes a demand for data centers. They demand high quality of energy at the same time, which in some cases creates bottlenecks. Is it not from power generation? And the talk is it either renewables or
gas turbines. There's a huge bottleneck for gas turbines as we speak. It goes all the way to high voltage transformers, to medium voltage into switch technologies. So therefore you see along the whole supply chain, obviously there's a huge demand we are serving that. There's a reason why this business of ours is growing extremely fast. We can keep up with the demand of our customers. But in some cases you might end up in bottlenecks if you keep on going as fast as possible.
It's a regional dependence.
In some cases, where you have good programs, good policies, you see that it's catching up along with the demand, and others you might end up in a gap. But you see the demand is higher than supplypply chain and we.
Are ramping up standing we're invested, which is investing ultimately the condition we want.
We want a condition where the demand is very strong because there's great demand for new technology.
Specific to you, Jensen, how severe is the memory bottleneck right now?
Well, the memory bottleneck is severe.
But we're fortunate to have worked with all of you know, first of all, and Video is the only company that works with all three of the HBM suppliers, and all three of them are major customers and major suppliers of us, and so we had the good fortune of working with them for a very long period time, and we've got things all planned out.
It's gonna be ak.
You've been asked a lot about China in the last twenty four hours. I'm not going to ask you a slightly different question. What is the attitude of the Chinese government in allowing h two hundred to go into the country. I understand the position on licenses, the administration here in the United States position, but what does China's government say to you about how they want to approach it.
I haven't spoken directly to them, but.
Ultimately the way they'll commune ok through us to us, we'll be through the companies. If the companies are allowed to buy in video products in China, then they'll be strong demand. And we're seeing strong demand, and so I think indirectly they've communicated with their companies.
I've learned a lot about Semens's work in software, and I've always thought the word Semens to be snall us with industrial manufacturing, physical things. It's the same lesson we've been through recently with in video, trying to understand.
Actually the software competencies is.
That an area where we might see you be active in m and a think about what you else you might need that you might not get from the Jensen in video partnership.
So you might because we will increase our software competence our software Tolio actually it's alibit short of thirty billion, which Semens invested in MNA to below our software competence, which brings us to the point that's missing today exactly today we can build the most comprehensive physics based digital.
Twin of whatever you want to build.
Still, there are pieces and operational software software which runs plans where you can look into that I gives new spaces obviously be invested in dogmatics.
It's life science, so it's about molecules. We can imagine doing more there also in simulation. But as we're building.
A data backbound for this life science industry super elevent. We did it for other industries with Team Center. We repeat it now with Luma for life science, which is again shortening cightful times and reducing costs for life science and drugs and medicines. So and I mean the beauty of it is that we live in both worlds. We have to domain know how we know how to run things, how to operate, how to automate plans, buildings, grades, trains, and we have their software competence too, and that makes
us in a unique position. Then and then we need strong partners which have complimentary technology, which is so amazing. If that comes together, you'll see matching things happen well.
In video will have access to new technology on the infant side through GROC. I'm going to do my classic DNSEN. Could you please clarify for me question, which is is this an acquisition or is it a licensing deal spread over time. Because Jonathan and Sunny joined.
We hired four hundred or something like that a little bit less incredible engineers, and we also license their technology. They designed an architecture that's very, very different than what we've done, and it's focused on low latency token generations and video is incredibly good at inference, and we're great at training post training as well as the inference phase and test time scaling of AI. And so we've got
that space covered. I'm excited about some of the work that we might be able to do together to invent a new segment that might be able to address future use. I haven't described it to people, but the time will come.
So you know, Jonathan is kind of behind the TPU and the LPU, and you know, before of this happened, there was Rock V two and V three and you know all of that, and it's leading to me to ask, what would that new platform or segment be.
What is it that you're trying to build on.
With those I haven't told anybody yet, but I'm here, Jens. You can't tell me.
You know, I'm going to look in the camera and tell you that that come to a GtC conference in the future, and I will tell you all the secrets.
I'm going to make a hard pivot because I have to data centers in space I reported just before the holiday. Is that the reason SpaceX wants to go public, Bear with me, Bear with me. The reason that SpaceX wants to raise thirty forty billion dollars whatever that is needs to buy the GPUs. Have you discussed data centers in space with Elon and SpaceX, Jensen.
I can't discuss what I have told everybody. I've discussed with anybody.
You think it's a viable technology platform, sure?
Sure, I mean there's lots of energy in space, right and and the cooling is abundant in space, and so that the challenges of AI factories are different out in space.
It would be AI factories in space. Yeah, forgive the ignorance almost, but are we literally talking about the same architecture for the GPU that goes into an AI factory still just being able to go into kind of a satellite form factor?
Yeah?
Sure, but the way that you would cool it empower it would be very different, and so the system design will be radically different.
The chips will be the same.
You're pretty concerned with data centers or WAI factories here on Earth to seem and see the need for or viability of such a technology.
I don't know whether we explored or potentials on Earth yet, but there's one beauty in this idea. Think about any kind of manufacturing, which want you want to bring to space. Everything but you produce you want to bring down to Earth. It's hard if you do energy, how to bring it down to Earth. It's hard if you produce any kind of hardware how to bring it down. But tokens intelligence,
I mean, you can transfer easily to Earth. So therefore, if I would starting produce something in space, I would start there.
We should talk about what's on Homas driving.
Have you seen Elon Musk's response to your keynote yesterday?
What do you say part one in short ways, Well, we were.
Already doing that smiley emoji on.
X the the other part of it, by.
The way, I would be surprised.
First of all, I think I think the Tesla stack is the most advanced AV stack in the world, and I think the Tesla AV operations is the most advanced in the world. And I'm fairly certain that that they were already using n ai yes, and whether whether their their AI also did reasoning or not, it is somewhat secondary to that first part.
His point was that the first ninety nine is hard enough, but the long tail thereafter is important.
You know, it's very difficult.
The question I got from the audience that was at your keynote was on a dollar per mile basis, what is the fundamental difference on your stack and your software approach to Tesla's vision based approach?
Ours is also vision based.
You know, of course we have we have in addition to to vision, we also have radar and light r but but otherwise otherwise the approach is rather similar.
I think.
I think Elon's approach is about as state of the art as anybody knows of a tunnel of striving robotics, and so it's it's a it's a stack that's hard to criticize.
I wouldn't criticize it.
I would just encourage them to continue to do what they're doing.
They're doing a great.
Job physically for you is and forgive my pronunciation, but er langan manufacturing site. When's that real? It's the same question.
I mean, in contrast to the side you was talking about before this one exists. I mean, we are we are ramping up, we are automating it as we speak, using technology. I showcase some of that, and we are we are really deploying step by step now on that very same side, this technology we was talking about, the iebrain, the digital trim composer, real worlds and really time connectivity to what happens on the shop floor, so you can act and drive basically dynamically what's happened on the shop floor.
So and we will talk about more along two thousand.
And there Semens using technology in the Semens factory, Epion is using Semens technology in our funds find and Rea factory, and so you know, it's a great partnership.
Jensen, you are one of the most important and biggest employers in Silicon Valley. Have been down to Santa Clarency. You are the leader of the technology industry. Right now, the industry and those of us that live in California are reviewing the billionaires tax. The question that a lot of people submitted to me to ask you is, how does that impact that talent pool and the industry and Silicon Valley.
Is it something that's concerned to you or it is not.
I haven't thought about it even once.
We work in Silicon Valley because because that's where the talent pool is, and and we have offices all over the world. Wherever there's talent, we have offices, we have office. Some in Germany we have office, and you know, all over the world. And so so we chose to live in Silicon Valley and and what whatever taxes I guess they would like to.
Apply, sy be it.
Maybe I'm perfectly fine with it.
It didn't. It never crossed my mind once.
I appreciate the answer.
And again, you know it's right now in the technology industry, that's what people are reflecting on.
We've had it really not this person, not this person.
This person's trying to build the future of AI.
And that's what the conversation was about today.
Roland Bush, Siemens CEO, Jensen Wong and Video CEO