Hi everyone, I'm Dear Derbosa, live in Berlin, and I am pleased to be joined by Nvidia CEO Jensen Huang, fresh off the stage here at GTC Berlin. Thank you for coming right to me. I appreciate that.
Thank you. Incredible, incredible journey. We started in, I guess we started in France or London — London to France to Sweden to here. And last year we've invested about $10 billion in companies here, and this week we announced 14 more. So we're in the process of building the AI economy here.
Right. And some of your message has been: start building, stop overthinking, start building. Do you think that message got through?
I hope so. And you know, Chancellor Merz also told everybody to lean in — let's go build this thing. You know, I think the fact of the matter is the technology had to be invented for the last 15 years, where we were kind of in invention mode. But six months ago, we really transitioned to AI being useful mode. And the thing that I wanted to do during the keynote was to demystify what makes AI useful, what are the components of AI, and why they could build it incredibly here.
Right. And you talked a lot about that on stage just moments ago. I want to talk about one of your announcements, DSX pod, an AI factory in a shipping container. Is that a fair sort of representation of it? You know, it's fascinating because it gets at the supply chain, but it also gets at this financing question that's been sort of rattling the AI trade over the last few weeks, especially the neoclouds. So if you can give lenders essentially more certainty about what a data center costs and when it starts earning money when it comes online, do you think that makes it easier to finance?
This is going to make it way easier. Let me tell you why. So, a gigawatt of data center is like $50 billion. And that $50 billion, from the moment that we ship the technology components, it goes through an OEM, those computers land at a data center. From that point to operationalizing could be four to six months. And during that four to six months, you're wiring these computers up, you're getting them all booted. And so what if we were to do some of the manufacturing of that in the supply chain? The advantage is that you might be able to shorten that four to six months to maybe three to four months, or take three to four months out of it. And as a result of that, your time to revenue is going to be accelerated, but also that idle cash, because you had to pay for it, that idle cash would be reduced.
Right, so it's sort of narrowing the cone of uncertainty, right? And that has been the major concern right now. Markets are sort of — they're volatile, we'll say — worried about neoclouds, then optimistic. What do you think the chances of a shakeout, any kind of shakeout, are?
I think this is going to get solved, and the reason for that is because the demand is so good. You know, just last night SpaceX just announced, and I think Elon said that demand is many times the capacity he has, and he has a lot of capacity, and so he's building more because the amount of demand is just incredible. CoreWeave is kind of the same way, NBS is the same way, and Scale is the same way. I think the challenge, of course, is that — and this is kind of what you were trying to get at — from the moment that the computers are shipped to the moment that they start earning could be many months, and the amount of float, because we're talking about a fairly significant investment, the amount of float is quite large. And so we need to help these companies reduce that. And I think reducing the supply chain, making it easier for them to stand up the computing, is going to make a big difference.
Right, so you're doing this on the Nvidia side, but what about some of the physical bottlenecks like power? How much of a timeline can you really take out of the equation if the customer, you know, is waiting years for grid connection?
This is really one of the reasons why we're so resilient to this, because we have so many different customers. You know, we're the only computing company that supports every single AI model, every single CSP, every single computer company across neoclouds in every single country. And so there might be some bottlenecks somewhere, but because the overall demand is so great, we'll be able to get the computing out to the market wherever they need it as soon as possible.
Right. And what is your outlook on the power question, though? Some of those physical bottlenecks — do you think that gets solved in the next few years? You know, there's a lot of efficiencies on the chip side, on what you're doing with the DSX pods. But what is sort of the bigger picture there? And do you think that's the biggest bottleneck?
It is one of the major bottlenecks. The bottleneck keeps shifting, and the reason — it started with wafers, to CoWoS, to HBM memories, to silicon photonics, to even connectors. And as those bottlenecks are, they're going to continue for probably a couple more years. Now that we're ramping up these AI factories around the world, power becomes increasingly so. But you know, oftentimes the power is available, but it's the land, power, and shell — either maybe a construction limitation, or maybe the power generator is being delivered, or connection to the grid, or you know, whatever. You know, a lot of construction challenges because the number of jobs that this has created for construction workers around the world is incredible. And so you know, there's limitations there. And so anyhow, I think the bottlenecks just — these are physical bottlenecks, and they're part of the physical world.
Are you sort of getting at some of the permitting bottlenecks as well, getting these online?
Some countries have permitting issues, but some countries don't. Some states do, but some states don't. And so I think the NVIDIA diversity really makes us quite resilient to this. You know, we'll be able to ship it somewhere, and whoever has a bottleneck, we'll work with them to make sure that as soon as they get the bottleneck resolved, we'll ship computers to them as fast as possible. Meanwhile, we'll ship the computers to somebody who can use it.
Right. And you're standing ready. I want to talk to you a little bit about compute as a commodity. It's starting to trade like one. There's GPU futures on the CME. NBS has moved to spot pricing that can change every 15 minutes. Still early days. What does it mean for Nvidia when computing power gets treated like capacity?
Well, this is one of our advantages, because Nvidia's computing systems are so universally used, and the performance is so good, that's productive for so long. And because we're software programmable, and we keep enhancing the software throughout the entire life of the asset, we keep refreshing it so that it's productive for a very long time. And so people are discovering that what people thought used to be a four-year life cycle really is probably eight to maybe more. And irrespective of the customers that use the compute, they're always companies that can use the compute, and so there's just so many offtakers. So it's productive, it's fungible, and it's durable. Now when that happens, you might be able to underwrite a loan against the asset itself. Today most computers, you know, don't have value, don't have very little residual value, but people are now recognizing Nvidia's compute has a great deal of residual value, and so you might be able to use that to backstop the debt itself.
There are some of the financial instruments and derivatives around the AI space. Does anything about that worry you?
Well, I think that one of the pillars for this growth — because we're talking about infrastructure buildout — the capital market will have to think about how to support it, and now there'll be different capital instruments. But I think the real advantage that we have is because of the durability, because of the productivity and fungibility of our computing system, the risk involved in underwriting Nvidia's asset, in our Nvidia computing, is the lowest of any asset out there. And so I think this is an area that's going to really play into our advantage.
Right. You mentioned the durability. One of the most surprising things of the last few years has actually been the durability, the lifetime of chips, even older generations. There's some really great data from Silicon Data that shows, you know, Blackwell rental prices are still rising. I think you mentioned durability. Did you say seven, eight years a moment ago? Is that what you think the durability is for a Blackwell?
What do you think that is for a Vera Rubin, the next generation?
Well, I think that Vera Rubin is probably going to be the same. It'll be, you know, seven, eight years. And of course the technology is changing, so long as we continue to upgrade the software. This is where Nvidia's CUDA is such a big advantage. We're introducing new algorithms, new software layers all the time. And when we invest in enhancing the install base, you'll be able to continue to use it for a long time.
Let me bring in Gavin Baker. He's waiting in the wings. He was my GTC pregame host.
Apparently, you guys did pretty amazing. Superstars.
We had a lot of fun. We talked about safety. We talked about open-source models. One topic that we didn't get to a huge amount, which I've sort of been talking to you about now, is sort of...
Gavin, how are you, my friend? Good to see you, brother.
You know, this is why we do this live, because we can have surprises here, right? So, Gavin, thank you for joining us. You know, I've been talking to Jensen a little bit about some of the jitters in the markets, which, you know, always come around. I wonder how seriously do you take these ones now? And are we sort of out of it? Comments from Elon Musk, as you mentioned, on the earnings call. We've got Jensen here right now saying that demand is through the roof. Is there still that financing risk that's being overestimated or underestimated?
Well, when I think about financing risk, I read something incredible last night. An open-source model, DeepSeek, said that they are monetizing at $38 per GPU hour. Last time I checked, I haven't heard anybody renting GPUs anywhere near that high. So maybe this explains Blackwell prices up 17 days in a row.
Yeah. How do you think about the intersection of what is clearly extraordinary demand with supply from a capital perspective?
Well, you know, the single problem that nothing can solve is demand. The fact that there's so much demand across the world. I mean, this isn't just from one model. We're talking about — OpenAI has got incredible demand, Anthropic has incredible demand, Meta has got incredible demand, you've got Grok, incredible demand, Gemini just coming out, you just mentioned DeepSeek, Moonshot's models got great demand, GLM's models got great demand. And so you've got all these different models, closed models, open models. The demand is just extraordinary. And we're really actually going into the inflection point of useful AI as we speak. And more people are using it, they're using it to do more useful things. And that's the reason why token demand is so high. I think all of the other stuff is going to get resolved. You know, without demand, nothing is resolvable.
Yeah. With demand, everything will be resolvable.
Demand can be incredibly high and usefulness can be going up, but if the financing is ill-timed — and you've heard people like Dario Amodei, Satya Nadella, others talk about this — that can sort of trump everything else. How much are you thinking about sort of the macro environment?
