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AMD has signed a definitive agreement with open ai to deploy six gigawatts of AMD GPUs. AMD says it will equate to tens of billions of dollars in revenue. Open Ai will get up to one hundred and sixty million am D shares in tranches and set against both operational and financial milestones, the focus is inference. Let's bring in AMD CO Lisa Sou and Open Ai president Greg Brockman. Both of them join us on set here at Bloomberg Tech in San Francisco. Good morning, Good morning, It's great
to see you here. Let's frame the opportunity. Lisa, you know that the market reaction is very clear, But for AMD and the AI industry at large, what do you think this represents.
Well, look, this is a huge milestone for AMD. You know, we are so thrilled with the partnership with the open Ai team, and it's also you know, a huge moment for the AI industry because you know, when you get to the breakdown to it, you need more AI compute. I mean, that's where we are today. Compute is a foundation for all of the intelligence we can get from AI and you know, we are a compute provider. We
have spent years on our roadmap. We've spent years working with open Ai and the team and you know, together now we're embarking on you know, a massive build out of six gigawatts of AI compute, and it's it's a big deal for us, for our shareholders, for our teams, and for really you know, the partnership and the overall AI ecosystem.
Greg I say that the top the focus is inference. I think that's really important to be specific about what you will do with this capacity. So so literally explain that part. And I'm conscious that you know, in the first instance, the first target is one gigawatt and then eventually six gigawatts. But what will you use it for?
Well, I think that the world continues to underestimate the amount of demand for AI compute, right that just we've seen this explosion of demand with things like chat GBT. You know, we're at eight hundred million weekly active users. Now this probably didn't even exist three years go, and
we're in a position where we cannot launch futures. We cannot launch new products simply because of lack of computational power and we see these models continuing to get exponentially better, and I think we're just heading to a world where so much of the economy is going to be lifted
up and driven by progress and AI. And so we're very much heading to a world by default that I think looks like a compute desert, right that there's just not enough compute to go around, and so we're trying to build as much as possible, as quickly as possible. So we're starting with one gigawatt simply because you've got to start somewhere, But honestly, we're building as fast as we possibly can and trying to bring as much computational power to bear for the economy and for the world.
Lisa, this is such a big commitment to Instinct in particular as a customer. Does it make open AI the largest for that particular product.
Well, this is certainly the largest deployment that we have announced by far. I mean, you know, six gigawatts of compute. As Greg said, we're going to start with the first gigawatt in the second half of twenty twenty six on our new next generation four fifty chip. I think the thing to understand is, you know, these types of partnerships actually take you know, years to really get comfortable with the idea that we're going to you know, go all
in together. And this isn't all in partnership in terms of building out you know, the AI compute that open ai needs for everything that they're offering to the world. So, yes, it's a huge deal, and it also says a lot about you know, how much needs to come together for
you know, this entire ecosystem to operate. So you know, we are setting up you know, certainly there's a lot of engineering work, but our teams are working together on hardware, software, We're ensuring the supply chain, all of those elements are set up and ready to deliver on this massive commitment.
Greg, talk us through a little bit about the players that you need to also lean on. This has been years in the making, as you say.
With a m D.
But what other cloud providers were involved? How you thinking about this working with an Oracle or others out there.
Yeah, we really think of this as an industry wide effort, and in general, we think that compute is something that does require the entire supply chain to really wake up and to really to start building much more than people we're planning on. I think this starts from energy to try to get far more power to be built. Things like nuclear I think are going to be very important to come online. The cloud providers are an important part of this as well. So we're going to be deploying
AMD in our own data centers. We'll be deploying them together with cloud providers. You know, we have a deal with Oracle, lots of other cloud providers out there. You can really see that we're very much in the We just want compute as much compute as possible. We think this is important for the economy, we think this is important for the nation, we think this is important for humanity.
And so really we're working with everyone in this whole industry in order to get as much compute power online as quickly as we can.
Lisa, I'm sorry specifics where is this data center going to be? Is it one single site? Is it Oracle that we'll partner with you on this?
Well, actually, what this really is is an announcement of what you know, AMD and open a are going to do together. You know, open a I has a lot of partners in terms of you know, where they deploy I imagine a lot of it will be in cloud service providers. It's really up to you know, open Ai and Greg and Sam and the team. But the way to think about it is, for this amount of compute, it's going to have to be in a lot of different places.
It's a massive amount, multiple locations.
Multiple locations, I would imagine, you know, multiple providers to really get this online as fast as possible.
Greg, there is a lot of focus on where open ai is going to get the money from to fund all of this. Sam Altman's big picture commitment is well documented, right, and the numbers to his mind are in the trillions. But have you specifically thought about debt financing for this relationship with a MD? Have you thought about doing a specific equity raise? You are very committed across multiple projects.
Yeah.
Look, the way that I would the way that I would look at this is that AI revenue is growing faster than I think almost any product in history, and that ultimately, at the end of the day, the reason this compute power is so important and is so worthwhile for everyone to build is because the revenue ultimately will be there. Now as a company that is trying to move as fast as we can, we look at everything right, we look at equity debt, we look at trying to
find creative ways of financing all of this. That's been actually a huge focus of us for the past couple of years as thinking about how can we possibly build the amount of compute that is required in order to really transform this whole economy into an aipowered economy. And so I think you'll see lots of creative ideas, but fundamentally, I think at the end of the day, it is because we believe.
Sorry to jump in an interrupt and carriage, just forgive me on this one. The condition of AMD issuing the stock to open Ai requires you to spend money basically because you have to deliver that gig awad of capacity first. Lisa, I have to ask you if you have assurances that open Ai is good for it.
Well, let me be clear. I mean, this deal is a win for am D, it's a win for open Ai, and it's a win for our shareholders. And that's kind of the way we put this together. I have full confidence in you know, open Ai, Sam, Greg Sarah. I mean, this is a massive opportunity for us right now. Right here, it's about who has the most compute and how fast can we get it online? And we're committing to doing this together. And the fact is as open ai buys chips,
that's great for AMD. Our revenue goes up, our earnings go up. You know, we expect that it will also be very very accretive to our shareholders from day one. And as we do that, you know, we're very happy to have open Ai as a deep partner and we win together. So it's like a virtuous positive cycle in how we build out. You know, this big vision for having all this compute out there, and yet we.
Still question as you were just talking about greg some of the other supply chain elements. You're talking about the need for nuclear for power. What's really interesting is we are you feeling confident enough about the rest of the compute, the supply chain is there? Is this going to be US manufactured? From your perspective, were you looking and also building out internationally with MD.
Yeah, we've been looking at really all options our preference and really the core thing that we try to do is build as much as possible in the US. And you can see the commitments that we've made over the past year, you know, five hundred billion dollars of investment in the US, and that's not stopping. We're continuing to build. I do think that international that there it is also going to be important for the world to have compute.
I think that computer is going to become this like national security strategic resource, and every country is going to need computational power, and so that we are really not limiting our sort of sites in terms of where to build. But we do think it is important that the US leads in this technology, leads in computational power, and we're
expanding the supply chain. But you can see that we've really been working with partners across the globe in order to actually meet the demand that we expect to becoming in upcoming years.
Lisa, the manufacturing of these chips, will you look to Intel at all for it? Do you think of the future?
Well, as you know, the supply chain is something that we work on, you know, very very meticulously. I think we have a very strong supply chain. We're certainly deeply partnered with you know, TSMC across the supply chain. You know, just to that earlier question, we're absolutely prioritizing building in the United States because I think that's super important. This
is the US AI stack. We want to have as much of it in the US as possible, and you know, we continue to really look at, you know, how do we ensure that there will be a strong supply chain, you know, going forward.
Greg Sam posted on x that this deal with a m D is incremental to what's already being done with Nvidia. But as least know so, I spent quite a lot of time looking at them I family and the newer generations of products to come. Is there a very clear specific benefit to using a m D technology for inference relative to the capabilities of Nvidia, or do you just see it broadly as some sort of diversifying factor.
Well, I would look at it this way, that there's a huge fixed cost to getting AI models running on any platform, and so that when we look at what's out there, that actually getting AI training to work is a huge, huge amount of lift. That's something we've really only done the work for in Vidia, but for inference, that that's something that's much more that there's an easier
barrier to entry there. And one thing we found is that I think that the work that Lisa and team have been doing on the M four to fifty series. It's looking like it's going to be a really incredible chip. I think that there's the way that these things work is that there's niches for different balances of memory and computational power, and so as we have a diversity of workloads, we're finding that having a diversity of chips also really accelerates what we're able to do.
Lisa. At the beginning of this conversation, I said, there are both operational and financial milestones to be met, and Greg explained, you've got to start somewhere. So in the first instance, one giga what But would you just sort of draw out the pathway to that first giga what? You know, it seems like you're prepared to move quickly here.
Yeah.
Absolutely, And and maybe ed, if I can just build on something that Greg said, I think he's absolutely right. You know, we're a believer in there's a diversity of workloads and there will be a diversity of workloads across you know, customers, models, use cases, and from that standpoint, you know, we feel really good about how we're positioned.
You know, we we love the work here because you know, frankly, you know, open Ai is the ultimate power user of our chips and and and test us in very good ways. So I think that's that's what gives us confidence that you know, the technology is there. And then to your point about milestones, Yes, I mean this is you know, clearly a case where we are tied to each other. Uh, the first gigawatt of deployment is super important. We're going to start that, you know, second half of next year,
and we're going to build on from there. And it really is not just the technology, but you know, commercial milestones, adoption milestones, and and just how we proliferate the capability going forward. But I'm looking forward to building this as fast as possible. Well, you know, we're already working with a number of cloud service providers who are also very active on our technology, and I think this is a great catalyst to get the industry to build faster.
Tied to each other is such an interesting turn of phrase. And Greg, look, you are seeing more AI users and chip makers and designers becoming more financially tied to each other. Is this going to continue? Is this the step forward for how you see this financing going forward?
Well, I really see the world transitioning to this AI powered economy and The interesting thing is within open AI that we've really seen what it's like when your progress is limited and accelerated as true sides of the coin by computational power, like teams within open EI, that their ability to deliver really is tied to the amount of compute that they get. And I think we're heading to a world where that is how the whole economy will function.
And we're starting to see it right that people having access to better AI tools. If you're a coder, you're able to do far more, or if you have access to better AI models, And we're heading to a world where if you can have ten times as much AI power behind you, you will probably be ten times more productive. And so I think that we're moving to a world where the whole industry is waking up to the fact
that we have just not planned. We have not planned for this moment where this explosion in AI demand is happening. So it's happening all the way from the power to the silicon, and I think this whole industry has to find a way to actually rise to meet the occasion.
Lisa, you have given us a look into the future before about how you see the total addressable market the industry now that the ink is dry with open AI and Greg, are you rethinking either your bigger picture analysis of the market for AI accelerators and GPUs or do you see AMD now having an improved position in that market relative of course to your friends at Nvidia.
Well, again, I think, and I've told you before, I believe that this is a huge market we have saw is just the AI accelerator TAM being you know, over five hundred billion dollars in TAM over the next few years. I think some might say, you know, maybe I was a little conservative in that TAM analysis, but the way to think about it is there's so much need from compute.
I mean, you just heard it from Greg, so you know this is a huge pie and you're going to see the need for you know, more players coming into it. And you know, from my standpoint, this is a big validation of our technology and our capability. You know, as much as we love the work with open AI, we're working with a lot of other customers as well. There's a lot of excitement in the industry around m I four fifty, so we're ready for it.
M d C Lisa s f and AI President Greg Brockman, it's been a joy having you on the show. Thank you both very much.