Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: One of the biggest stories that we have been following
Speaker 2: in the last twenty four hours, though, of course, has
Speaker 2: been the important keynote address from Jensen Wong at the
Speaker 2: Consumer Electronics Show. It has been feeding a number of
Speaker 2: storylines and moving the stock ever since that took place
Speaker 2: in Las Vegas yesterday. And joining us right now for
Speaker 2: a very special conversation, We've got good news for you.
Speaker 2: It's Bloomberg's Ed Ludlow with the CEO of Nvidia, Jensen Wong,
Speaker 2: as well as the president's CEO of Siemens, Roland Bush.
Speaker 2: They're at the cees right now and we want to
Speaker 2: hand things out to Las Vegas. Ed Ludlow, you have
Speaker 2: the con.
Speaker 3: Thank you, Joe.
Speaker 4: Over one hundred and seventy five years, Siemens has been
Speaker 4: at the forefront, if i'd say, several industrial revolutions. In
Speaker 4: Vidia is at the forefront of the latest AI industrial revolution,
Speaker 4: and it's a pleasure to have you both here with us. Gentlemen, Roland,
Speaker 4: I want to try and understand how real this is.
Speaker 4: You call it an industrial AI operating system. We're going
Speaker 4: to talk about how you're joining forces on the software
Speaker 4: side and the hardware side. But I think the most
Speaker 4: useful place to start would be, could you outline a
Speaker 4: timeline for when Semens goes beyond its own footprint around
Speaker 4: the world using this operating system to customers actually doing
Speaker 4: things at scale, because I think when you're on stage,
Speaker 4: that's the kind of sense I was getting.
Speaker 3: You want to accelerate to scale.
Speaker 5: Well, I mean this technology is already in place and
Speaker 5: it's working.
Speaker 6: I mean we see there are many examples, and it
Speaker 6: brought some on stage today where you have customers starting
Speaker 6: with a digital twin of a product. They want to
Speaker 6: manufacture a digital train of the manufacturing side and bring
Speaker 6: that all together and not before that you.
Speaker 5: Have it optimized them and build in the real world.
Speaker 6: And AI is already working on the shop for they
Speaker 6: call it machine learning. But the point is with the
Speaker 6: new models, you can bring that to the next level.
Speaker 6: That means you go away from giving an advice to
Speaker 6: somebody with technology, but really act on your behalf. This
Speaker 6: is when things go more autonomous or adaptive, and you
Speaker 6: see that already starting.
Speaker 5: The big thing is how can we scale it?
Speaker 6: Because it requires a lot of skills from our customers,
Speaker 6: a lot of technology, and it's still not that easy
Speaker 6: to implement the working on it to.
Speaker 7: Make it easy to deploy and easy to use, but
Speaker 7: you see picking up momentum in each stuff we do,
Speaker 7: and it's also great examples as a shipbuilding is a
Speaker 7: short everlenge, but even start ups using our technology like
Speaker 7: commentalst Fusion.
Speaker 4: Systems Jensen, we've discussed the five layer cake often. You
Speaker 4: know within video it started with the GPUs, but it's
Speaker 4: now software, the element of simulation. On stage, you talked
Speaker 4: about integrating the software side, in particular into DA Again,
Speaker 4: a similar question for you, more of a timeline. What
Speaker 4: is it you think you'll do first in how you
Speaker 4: guys work closer together.
Speaker 1: The first thing, let me just say very quickly, we're
Speaker 1: announcing a big partnership between us. We've known each other
Speaker 1: for a long time, but this is a partnership were
Speaker 1: announcing is really a big deal.
Speaker 5: One.
Speaker 1: We're accelerating their EDA software, We're accelerating their simulation software.
Speaker 1: We're integrating AI technology, physical AI and agentic AI into
Speaker 1: their team center and their factory automation operating system, and
Speaker 1: so we're working together across this entire spectrum. When we
Speaker 1: accelerate the software, then we'll get to use it to
Speaker 1: design our chips and systems. When we accelerate their simulation software,
Speaker 1: we'll use it in our AI factories to simulate the
Speaker 1: thermal properties of our AI factories. When we integrate our
Speaker 1: automation and agentic systems into their AI industrial operating system,
Speaker 1: we can then use it in our factory floors with
Speaker 1: our partners, for example, fox Con And so we're working
Speaker 1: across this entire spectrum together and we're going to put
Speaker 1: the technology to use basically as soon as we can.
Speaker 3: What's the net effect for you, Jensen?
Speaker 4: Is it improves efficient capital allocation. I know that might
Speaker 4: sound a bit dry, but actually, right now, that's the
Speaker 4: answer everyone's searching for. How is this investment in AI
Speaker 4: and use of the technology actually changed things in the
Speaker 4: real world for me?
Speaker 5: Announced yesterday Vera Rubin.
Speaker 1: It takes six different chips to integrate into this incredible
Speaker 1: system called Vera Rubin. And when you're done, each one
Speaker 1: of these Vera Rubin GPUs is two hundred and forty
Speaker 1: thousand wants and it is ten times more energy efficient
Speaker 1: than a last generation. It is ten times more cost
Speaker 1: efficient than a last generation but it's still the technology
Speaker 1: is insanely complicated. One hundred fifteen thousand engineering years came
Speaker 1: together to build this system. And so when we accelerate
Speaker 1: EDA tools, when we accelerate simulation tools, and when we
Speaker 1: can eventually and I'm hoping very soon design entire vera
Speaker 1: Ruben systems inside a Semen's digital twin, the chance the
Speaker 1: ability for us to create much much more complex systems
Speaker 1: will scale. We'll do it much more efficiently. And so
Speaker 1: this is really about being able to do the impossible,
Speaker 1: and being able to do it impossible the impossible right
Speaker 1: the first time.
Speaker 6: And and and and once they then realize that AI
Speaker 6: creates real world impact, this is where it really deploys
Speaker 6: the full power.
Speaker 5: And also the economic power.
Speaker 6: It's not only in the data centers are any eye
Speaker 6: factories which we see, but also on the edge because
Speaker 6: once you start influencing with low latency, you bring this
Speaker 6: technology to the edge. This is a huge potential for
Speaker 6: our customers to to deploy this technology. And this includes
Speaker 6: of course in pluts hardware where we come from from
Speaker 6: the JIPS.
Speaker 5: It goes into the controllers.
Speaker 6: Some of our controllers run on GPUs and then it goes.
Speaker 3: All the way to the industrial PC exactly.
Speaker 6: And these are aids. We super charge it and they
Speaker 6: can now run algorithms train than the cloud.
Speaker 5: They can run it on the shop floor and.
Speaker 6: Do all that trick what we talked about it in
Speaker 6: retime optimization and running in a planned and that makes
Speaker 6: a huge difference.
Speaker 4: So it de drives economy. The question that's being searched
Speaker 4: for is how is this going to manifest in.
Speaker 3: The real world.
Speaker 4: You know, the emphasis that this is cs I think
Speaker 4: is physical AI is not the manifestation of the final
Speaker 4: stage of physical AI, just one giant robot that you
Speaker 4: guys call a factory In the manufacturing context, is that
Speaker 4: where you're seeing demand from actual customers, you know that
Speaker 4: they need a factory that is automated one hundred percent
Speaker 4: and genuinely autonomous in some degree.
Speaker 6: Number one is if you want to build a factory,
Speaker 6: and very often you're missing out on labor, and we
Speaker 6: talk about on skilled labor, it's hard to find Number one.
Speaker 6: Number two is once you build it autonomous and automated,
Speaker 6: then you have a much much higher yield, but you
Speaker 6: can generate you use lesser energy. By the way, at
Speaker 6: the same time, before you optimize it in a way.
Speaker 6: So therefore there's a lot of benefits. And if you
Speaker 6: want to talking about the United States up manufacturing the
Speaker 6: United States, you need to go as digital and as
Speaker 6: automated and AI super changed as possible.
Speaker 8: A factory is robotic and it is orchestrating robots that
Speaker 8: are building systems that are also robotic, like for example,
Speaker 8: self draging cars a robotic system.
Speaker 5: And the reason why it's.
Speaker 1: So hard to deploy robots today is because it's hard
Speaker 1: to program these robotic systems. The software expertise that necessary
Speaker 1: to customization necessary is really intense.
Speaker 5: It's just too much.
Speaker 1: And so the fact that we could now apply artificial
Speaker 1: intelligence physical AI technology to these robotic systems make them
Speaker 1: easier to teach. You show it a few demonstrations, and
Speaker 1: the AI learned it by itself.
Speaker 4: You think, would you guys are, say Jenson, that you've
Speaker 4: solved for that software limitation.
Speaker 1: That software limitation is the chat GPT moment of it
Speaker 1: is now here. I think over the course of the
Speaker 1: next couple two three years, we're going to make some
Speaker 1: really big breakthroughs.
Speaker 4: Let's please talk about energy, electricity, power supply, call it
Speaker 4: what you will in turn. For each of you, how
Speaker 4: worried are you about it as a bottleneck and what
Speaker 4: is your experience day stay in running both companies.
Speaker 1: In that respect, energy should always be a bottleneck for
Speaker 1: any industry, and this is a new industry that's growing
Speaker 1: incredibly fast.
Speaker 5: As you know.
Speaker 1: AI is both the technology that's going to revolutionize many applications,
Speaker 1: and we're talking about some of them here, but the
Speaker 1: AI industry itself, the manufacturing of the artificial intelligence takes energy.
Speaker 1: It takes energy, it takes AI factories. It's exactly the
Speaker 1: reason why from Hopper to Blackwell we increased energy efficiency
Speaker 1: by tenx. From Blackwell to Reubin, we increased energy efficiency
Speaker 1: again by tenx. And that translates directly to our customer's
Speaker 1: revenues because in the case of an AI factory, whatever
Speaker 1: factory size you have, you're limited by the power, and
Speaker 1: that power within that power constrained, you want to have
Speaker 1: the most tokens or most AI per want that you
Speaker 1: can possibly generate. And so every time we improve energy efficiency,
Speaker 1: we're effectively improving both the AI capabilities for our customers
Speaker 1: and their revenues because they're always constrained by power.
Speaker 4: The response to your keinot and forgive me rowan one minute.
Speaker 4: Was you know vera rubin ten x through put really
Speaker 4: important tokens generated at one tenth. But they'll still say, hey, Jensen,
Speaker 4: what if the electricity is just not there, it's not available.
Speaker 5: Well, there's electricity, they're never enough electricity.
Speaker 4: That's actually what is the anxiety level for you both
Speaker 4: about that issue.
Speaker 1: There's always energy, they're never enough energy. And this is
Speaker 1: every and every industrial revolution will be energy constrained.
Speaker 5: And this industrial revolution is also energy constrained.
Speaker 1: Now one of the most important things here speaking about
Speaker 1: the United States, if not for President Trump's pro energy
Speaker 1: growth agenda, we would have a very hard time growing
Speaker 1: at all. In order for our new industry to emerge,
Speaker 1: you need energy, and so I think it's safe to
Speaker 1: say that we wish we had more energy in United States.
Speaker 1: You wi should have more energy. I think the world
Speaker 1: all wish we had more energy, and so we have
Speaker 1: to invest in all sorts of different forms of energy.
Speaker 1: But whatever energy you have, you have to make it
Speaker 1: as energy efficient as possible. And that's one of the
Speaker 1: reasons why both of us drive our technology roadmap so hard,
Speaker 1: because every single new generation of technology is more energy
Speaker 1: efficient than the last.
Speaker 5: That's kind of the nature of it.
Speaker 4: You are in the AI factory. I won't say data
Speaker 4: center is data centers where you store data. That you
Speaker 4: are in the AI factory supply chain.
Speaker 3: You must see it.
Speaker 6: So what we see is number one is you see
Speaker 6: that the energy demands is roughly scaling with the GDP growth.
Speaker 6: It's the coupling because executive of this effect that we
Speaker 6: are providing more and more efficient technology, So that means
Speaker 6: the link. But still it grows along with economic growth.
Speaker 6: And then on top comes a demand for data centers.
Speaker 6: They demand high quality of energy at the same time,
Speaker 6: which in some cases creates bottlenecks. Is it not from
Speaker 6: power generation? And the talk is it either renewables or
Speaker 6: gas turbines. There's a huge bottleneck for gas turbines as
Speaker 6: we speak. It goes all the way to high voltage transformers,
Speaker 6: to medium voltage into switch technologies. So therefore you see
Speaker 6: along the whole supply chain, obviously there's a huge demand
Speaker 6: we are serving that. There's a reason why this business
Speaker 6: of ours is growing extremely fast. We can keep up
Speaker 6: with the demand of our customers. But in some cases
Speaker 6: you might end up in bottlenecks if you keep on
Speaker 6: going as fast as possible.
Speaker 5: It's a regional dependence.
Speaker 6: In some cases, where you have good programs, good policies,
Speaker 6: you see that it's catching up along with the demand,
Speaker 6: and others you might end up in a gap. But
Speaker 6: you see the demand is higher than supplypply chain and we.
Speaker 5: Are ramping up standing we're invested, which is investing ultimately
Speaker 5: the condition we want.
Speaker 1: We want a condition where the demand is very strong
Speaker 1: because there's great demand for new technology.
Speaker 4: Specific to you, Jensen, how severe is the memory bottleneck
Speaker 4: right now?
Speaker 5: Well, the memory bottleneck is severe.
Speaker 1: But we're fortunate to have worked with all of you know,
Speaker 1: first of all, and Video is the only company that
Speaker 1: works with all three of the HBM suppliers, and all
Speaker 1: three of them are major customers and major suppliers of us,
Speaker 1: and so we had the good fortune of working with
Speaker 1: them for a very long period time, and we've got
Speaker 1: things all planned out.
Speaker 5: It's gonna be ak.
Speaker 4: You've been asked a lot about China in the last
Speaker 4: twenty four hours. I'm not going to ask you a
Speaker 4: slightly different question. What is the attitude of the Chinese
Speaker 4: government in allowing h two hundred to go into the country.
Speaker 4: I understand the position on licenses, the administration here in
Speaker 4: the United States position, but what does China's government say
Speaker 4: to you about how they want to approach it.
Speaker 5: I haven't spoken directly to them, but.
Speaker 1: Ultimately the way they'll commune ok through us to us,
Speaker 1: we'll be through the companies. If the companies are allowed
Speaker 1: to buy in video products in China, then they'll be
Speaker 1: strong demand. And we're seeing strong demand, and so I
Speaker 1: think indirectly they've communicated with their companies.
Speaker 4: I've learned a lot about Semens's work in software, and
Speaker 4: I've always thought the word Semens to be snall us
Speaker 4: with industrial manufacturing, physical things. It's the same lesson we've
Speaker 4: been through recently with in video, trying to understand.
Speaker 3: Actually the software competencies is.
Speaker 4: That an area where we might see you be active
Speaker 4: in m and a think about what you else you
Speaker 4: might need that you might not get from the Jensen
Speaker 4: in video partnership.
Speaker 6: So you might because we will increase our software competence
Speaker 6: our software Tolio actually it's alibit short of thirty billion,
Speaker 6: which Semens invested in MNA to below our software competence,
Speaker 6: which brings us to the point that's missing today exactly
Speaker 6: today we can build the most comprehensive physics based digital.
Speaker 5: Twin of whatever you want to build.
Speaker 6: Still, there are pieces and operational software software which runs
Speaker 6: plans where you can look into that I gives new
Speaker 6: spaces obviously be invested in dogmatics.
Speaker 5: It's life science, so it's about molecules. We can imagine
Speaker 5: doing more there also in simulation. But as we're building.
Speaker 6: A data backbound for this life science industry super elevent.
Speaker 6: We did it for other industries with Team Center. We
Speaker 6: repeat it now with Luma for life science, which is
Speaker 6: again shortening cightful times and reducing costs for life science
Speaker 6: and drugs and medicines. So and I mean the beauty
Speaker 6: of it is that we live in both worlds. We
Speaker 6: have to domain know how we know how to run things,
Speaker 6: how to operate, how to automate plans, buildings, grades, trains,
Speaker 6: and we have their software competence too, and that makes
Speaker 6: us in a unique position. Then and then we need
Speaker 6: strong partners which have complimentary technology, which is so amazing.
Speaker 6: If that comes together, you'll see matching things happen well.
Speaker 4: In video will have access to new technology on the
Speaker 4: infant side through GROC. I'm going to do my classic DNSEN.
Speaker 4: Could you please clarify for me question, which is is
Speaker 4: this an acquisition or is it a licensing deal spread
Speaker 4: over time. Because Jonathan and Sunny joined.
Speaker 1: We hired four hundred or something like that a little
Speaker 1: bit less incredible engineers, and we also license their technology.
Speaker 1: They designed an architecture that's very, very different than what
Speaker 1: we've done, and it's focused on low latency token generations
Speaker 1: and video is incredibly good at inference, and we're great
Speaker 1: at training post training as well as the inference phase
Speaker 1: and test time scaling of AI. And so we've got
Speaker 1: that space covered. I'm excited about some of the work
Speaker 1: that we might be able to do together to invent
Speaker 1: a new segment that might be able to address future use.
Speaker 1: I haven't described it to people, but the time will come.
Speaker 4: So you know, Jonathan is kind of behind the TPU
Speaker 4: and the LPU, and you know, before of this happened,
Speaker 4: there was Rock V two and V three and you
Speaker 4: know all of that, and it's leading to me to ask,
Speaker 4: what would that new platform or segment be.
Speaker 3: What is it that you're trying to build on.
Speaker 5: With those I haven't told anybody yet, but I'm here, Jens.
Speaker 5: You can't tell me.
Speaker 1: You know, I'm going to look in the camera and
Speaker 1: tell you that that come to a GtC conference in
Speaker 1: the future, and I will tell you all the secrets.
Speaker 4: I'm going to make a hard pivot because I have
Speaker 4: to data centers in space I reported just before the holiday.
Speaker 4: Is that the reason SpaceX wants to go public, Bear
Speaker 4: with me, Bear with me. The reason that SpaceX wants
Speaker 4: to raise thirty forty billion dollars whatever that is needs
Speaker 4: to buy the GPUs. Have you discussed data centers in
Speaker 4: space with Elon and SpaceX, Jensen.
Speaker 1: I can't discuss what I have told everybody. I've discussed
Speaker 1: with anybody.
Speaker 4: You think it's a viable technology platform, sure?
Speaker 1: Sure, I mean there's lots of energy in space, right
Speaker 1: and and the cooling is abundant in space, and so
Speaker 1: that the challenges of AI factories are different out in space.
Speaker 4: It would be AI factories in space. Yeah, forgive the
Speaker 4: ignorance almost, but are we literally talking about the same
Speaker 4: architecture for the GPU that goes into an AI factory
Speaker 4: still just being able to go into kind of a
Speaker 4: satellite form factor?
Speaker 5: Yeah?
Speaker 1: Sure, but the way that you would cool it empower
Speaker 1: it would be very different, and so the system design
Speaker 1: will be radically different.
Speaker 5: The chips will be the same.
Speaker 4: You're pretty concerned with data centers or WAI factories here
Speaker 4: on Earth to seem and see the need for or
Speaker 4: viability of such a technology.
Speaker 6: I don't know whether we explored or potentials on Earth yet,
Speaker 6: but there's one beauty in this idea. Think about any
Speaker 6: kind of manufacturing, which want you want to bring to space.
Speaker 6: Everything but you produce you want to bring down to Earth.
Speaker 6: It's hard if you do energy, how to bring it
Speaker 6: down to Earth. It's hard if you produce any kind
Speaker 6: of hardware how to bring it down. But tokens intelligence,
Speaker 6: I mean, you can transfer easily to Earth. So therefore,
Speaker 6: if I would starting produce something in space, I would
Speaker 6: start there.
Speaker 3: We should talk about what's on Homas driving.
Speaker 4: Have you seen Elon Musk's response to your keynote yesterday?
Speaker 3: What do you say part one in short ways, Well,
Speaker 3: we were.
Speaker 4: Already doing that smiley emoji on.
Speaker 3: X the the other part of it, by.
Speaker 5: The way, I would be surprised.
Speaker 1: First of all, I think I think the Tesla stack
Speaker 1: is the most advanced AV stack in the world, and
Speaker 1: I think the Tesla AV operations is the most advanced
Speaker 1: in the world. And I'm fairly certain that that they
Speaker 1: were already using n ai yes, and whether whether their
Speaker 1: their AI also did reasoning or not, it is somewhat
Speaker 1: secondary to that first part.
Speaker 4: His point was that the first ninety nine is hard enough,
Speaker 4: but the long tail thereafter is important.
Speaker 3: You know, it's very difficult.
Speaker 4: The question I got from the audience that was at
Speaker 4: your keynote was on a dollar per mile basis, what
Speaker 4: is the fundamental difference on your stack and your software
Speaker 4: approach to Tesla's vision based approach?
Speaker 5: Ours is also vision based.
Speaker 1: You know, of course we have we have in addition
Speaker 1: to to vision, we also have radar and light r
Speaker 1: but but otherwise otherwise the approach is rather similar.
Speaker 5: I think.
Speaker 1: I think Elon's approach is about as state of the
Speaker 1: art as anybody knows of a tunnel of striving robotics,
Speaker 1: and so it's it's a it's a stack that's hard
Speaker 1: to criticize.
Speaker 5: I wouldn't criticize it.
Speaker 1: I would just encourage them to continue to do what
Speaker 1: they're doing.
Speaker 5: They're doing a great.
Speaker 4: Job physically for you is and forgive my pronunciation, but
Speaker 4: er langan manufacturing site. When's that real? It's the same question.
Speaker 6: I mean, in contrast to the side you was talking
Speaker 6: about before this one exists. I mean, we are we
Speaker 6: are ramping up, we are automating it as we speak,
Speaker 6: using technology. I showcase some of that, and we are
Speaker 6: we are really deploying step by step now on that
Speaker 6: very same side, this technology we was talking about, the iebrain,
Speaker 6: the digital trim composer, real worlds and really time connectivity
Speaker 6: to what happens on the shop floor, so you can
Speaker 6: act and drive basically dynamically what's happened on the shop floor.
Speaker 6: So and we will talk about more along two thousand.
Speaker 1: And there Semens using technology in the Semens factory, Epion
Speaker 1: is using Semens technology in our funds find and Rea factory,
Speaker 1: and so you know, it's a great partnership.
Speaker 4: Jensen, you are one of the most important and biggest
Speaker 4: employers in Silicon Valley. Have been down to Santa Clarency.
Speaker 4: You are the leader of the technology industry. Right now,
Speaker 4: the industry and those of us that live in California
Speaker 4: are reviewing the billionaires tax. The question that a lot
Speaker 4: of people submitted to me to ask you is, how
Speaker 4: does that impact that talent pool and the industry and
Speaker 4: Silicon Valley.
Speaker 3: Is it something that's concerned to you or it is not.
Speaker 5: I haven't thought about it even once.
Speaker 1: We work in Silicon Valley because because that's where the
Speaker 1: talent pool is, and and we have offices all over
Speaker 1: the world. Wherever there's talent, we have offices, we have office.
Speaker 1: Some in Germany we have office, and you know, all
Speaker 1: over the world. And so so we chose to live
Speaker 1: in Silicon Valley and and what whatever taxes I guess
Speaker 1: they would like to.
Speaker 5: Apply, sy be it.
Speaker 1: Maybe I'm perfectly fine with it.
Speaker 5: It didn't. It never crossed my mind once.
Speaker 3: I appreciate the answer.
Speaker 4: And again, you know it's right now in the technology industry,
Speaker 4: that's what people are reflecting on.
Speaker 3: We've had it really not this person, not this person.
Speaker 5: This person's trying to build the future of AI.
Speaker 3: And that's what the conversation was about today.
Speaker 4: Roland Bush, Siemens CEO, Jensen Wong and Video CEO