Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Welcome back to Bloomberg Tech Lisa Sue Helios with m
Speaker 2: I four to fifty five x AMD's first RAX scale
Speaker 2: system solution. But inside it AMD's first in the world's
Speaker 2: first two nanimeter.
Speaker 3: Chip of that type.
Speaker 2: A lot was made of it when you actually just
Speaker 2: stood on stage and held it in your hand for
Speaker 2: the first time.
Speaker 3: Why is it significant?
Speaker 4: Well, first of.
Speaker 1: All, Ed, it's great to be here with you at CS.
Speaker 1: I think CS is always a great way to kick
Speaker 1: off the year because you get so much perspective. So
Speaker 1: it was fun giving the keynote last night. Look, Helios is.
Speaker 4: A massive system, you can see it in the.
Speaker 1: Background here, and m I four fifty five is just
Speaker 1: an incredibly powerful chip. And probably the context I would
Speaker 1: give Ed is, you know, one of the things that
Speaker 1: we're so clear about is that the demand for AI
Speaker 1: compute is just continuing to increase. And you know, we
Speaker 1: have seen that over the last five years. When you
Speaker 1: think about just how much you know, new capabilities have
Speaker 1: come on board, we've now seen a real inflection in
Speaker 1: the number of people who are using AI. So if
Speaker 1: you think today they're probably more than a billion active
Speaker 1: users using AI and we expect that to scale to
Speaker 1: over five billion users over the next five years. So
Speaker 1: for all of that, you need compute and lots and
Speaker 1: lots of compute. And from that standpoint, you know, m
Speaker 1: I four fifty five is a significant leap forward in
Speaker 1: terms of technology capability, made up of two and three
Speaker 1: nanometer trips, three hundred and twenty billion transistors, just a
Speaker 1: lot of performance.
Speaker 2: And a lot of the timeline for it to be
Speaker 2: deployed in the real world, and then who will be
Speaker 2: the principal first user of it.
Speaker 1: You'll see it in the second half of twenty six
Speaker 1: and it will ramp from there. And you know, we
Speaker 1: have very strong partnerships open AI. Greg Brockman was on
Speaker 1: stage with us last night talking about all.
Speaker 4: The use cases that they see.
Speaker 1: We've announced a partnership with the Oracle, many others as well.
Speaker 2: So given that it's two h it's in full production now,
Speaker 2: it's getting ready to are.
Speaker 4: We are absolutely getting ready to ship it.
Speaker 2: That's at one end of the sort of scale and spectrum.
Speaker 2: At the other you have m I four forty x,
Speaker 2: which is for small data centers. I'm trying to simplify it,
Speaker 2: but it's basically an enterprise product. What was it that
Speaker 2: you were trying to solve for with that?
Speaker 1: Yeah, I think what we're trying to solve for is,
Speaker 1: you know, the world is a very heterogeneous world.
Speaker 4: You have all kinds of use.
Speaker 1: Cases for AI from you know, sort of the very
Speaker 1: biggest cloud data centers that are doing you know, large
Speaker 1: scale training and inference to enterprise applications as well as supercomputers,
Speaker 1: and so we actually have a family of chips.
Speaker 4: At the highest end, is there.
Speaker 1: M I four fifty five for the cloud environment, But
Speaker 1: we announced last night a m I four forty which
Speaker 1: is actually using the same basic building blocks, but is
Speaker 1: now really focused on enterprise applications so that you can
Speaker 1: go into you know, let's call it current data centers
Speaker 1: with the new technology.
Speaker 4: So we're excited about that as well.
Speaker 1: You know, there is enterprises are starting to increase their
Speaker 1: adoption of AI. In some cases they want their own
Speaker 1: control of their data centers in terms of on prem environments.
Speaker 3: What are they doing with it though?
Speaker 2: I mean, you know, we've been so fixated on frontier
Speaker 2: models with hundreds of billions of parameters and the scale
Speaker 2: of infrastructure needed for that with MI I F forty,
Speaker 2: we're talking about something slightly different. I think it's just
Speaker 2: really interesting if you could explain what the demand is
Speaker 2: from those enterprises, what they want with the technology.
Speaker 1: Well, I think you see many enterprises now using AI
Speaker 1: all throughout their business processes, whether you're talking about things
Speaker 1: in their workflow. Even AMD we're using AI through every
Speaker 1: part of our development process. We're seeing a lot of
Speaker 1: applications in financial services and healthcare, and these are areas,
Speaker 1: especially in financial services, that people actually don't want everything
Speaker 1: necessarily in the cloud. They'd like to be able to
Speaker 1: have their own on prem deployment or private cloud deployments.
Speaker 1: And in this case, you don't want to have to
Speaker 1: build a brand new data center for every new generation
Speaker 1: of chip. M four point forty allows us to use
Speaker 1: some of those existing data centers in upgrade with the
Speaker 1: new capabilities.
Speaker 3: Welcome.
Speaker 2: If you're watching us on Bloomberg Television or you're listening
Speaker 2: on Bloomberg Radio. We're live in Las Vegas and we're
Speaker 2: with AMD CEO Lisa Sue, and we're talking about the
Speaker 2: latest generation of accelerators. What makes this generation of AMD
Speaker 2: accelerators the better option, particularly for on prem and at
Speaker 2: the edge over what Nvidia is offering right now.
Speaker 1: Well, the best way to think about it, ED is
Speaker 1: we're in this place where AI is at a inflection point.
Speaker 1: We're seeing AI now in every part of compute. We
Speaker 1: see it in the largest models, you know, when you're
Speaker 1: thinking about things like track, GPT and Gemini and Rock.
Speaker 1: You know, we're also seeing you know, many use cases
Speaker 1: in UH, you know, new capabilities like you know, video production, entertainment, healthcare,
Speaker 1: where you're doing drug discovery, all of these various things.
Speaker 1: You know, our claim to fame is really you know,
Speaker 1: outstanding performance at you know, very advantage total cost of ownership.
Speaker 1: And the other thing that you know we believe very
Speaker 1: strongly in is an open ecosystem and deep partnerships, you know,
Speaker 1: with our UH you know, with our overall.
Speaker 4: Ecosystem coming together.
Speaker 1: So when you put those things in perspective, I think
Speaker 1: we have a great set of applications that will take
Speaker 1: advantage of these newest generationships.
Speaker 2: You mentioned that Greg Brockman, who's the Opening Eye president,
Speaker 2: was on stage with you last night, and one of
Speaker 2: the basic points that he made was there are tools
Speaker 2: and functions they would love to release and put out
Speaker 2: into the world, but they are compute constrained. I often
Speaker 2: ask you to quantify demand, but is there a way
Speaker 2: to quantify the severity in the lack of compute, you know,
Speaker 2: the deficit that's out there right now.
Speaker 1: Well, let me just give you some numbers to kind
Speaker 1: of ground what we think the demand environment is looking like.
Speaker 1: So if you think, you know, today we have about
Speaker 1: a billion active users and we're ramping that to you know,
Speaker 1: five billion over the next five years, and we have
Speaker 1: about let's call it, one hundred zet of flops of.
Speaker 4: Compute you know, all around the world.
Speaker 1: And that's just a generic number that that aggregates all
Speaker 1: of that. You know, we think we have to increase
Speaker 1: compute by another one hundred times as you go over
Speaker 1: the next you know, four or five years. And I
Speaker 1: introduced a term last night, the YadA flop. You know,
Speaker 1: people are like, what is a YadA flop? A YadA
Speaker 1: flop is actually ten to the twenty fourth in terms
Speaker 1: of flop.
Speaker 4: So that's a one followed by twenty four zeros.
Speaker 1: And to give you, you know, just a view of
Speaker 1: just how much things have really increased. I mean, that's
Speaker 1: another one hundred times more compute than we have today.
Speaker 1: So that gives you an idea. Now you think, what
Speaker 1: are you going to use all that compute for? I
Speaker 1: mean the truth is, the models that we have today
Speaker 1: are great. I mean they do amazing things. You know,
Speaker 1: we talked about a number of use cases. Uh you know,
Speaker 1: perhaps you know one that's you know, very hits very
Speaker 1: close to home is is writing software. Like you know,
Speaker 1: people are using uh, the AI tools right now to
Speaker 1: significantly enhance the productivity of software developers.
Speaker 4: But it's good, but it can get so much better.
Speaker 4: And I mean, I think that's the key point.
Speaker 2: You know.
Speaker 1: We we like to say that AI is really going
Speaker 1: to be everywhere, and it's really for everyone, and it's
Speaker 1: for each one of us to make our businesses more productive,
Speaker 1: you know, each one of us more productive, you know,
Speaker 1: going forward. And so we're still in the very early
Speaker 1: innings of really unlocking the power of AI.
Speaker 2: So where we stand is we Okay, there's a compute
Speaker 2: deficit and software has kind of hit the limits of
Speaker 2: what current generation.
Speaker 3: Compute can offer.
Speaker 2: Help us understand the bottlenecks and barriers that to deploying
Speaker 2: that compute a lot at the moment about memory chips,
Speaker 2: what else, energy, electricity, what's crossing your desk, Lisa that
Speaker 2: gives you pause and say this is a problem right now.
Speaker 1: Well, our job as a technology industry is to push
Speaker 1: the bleeding edge.
Speaker 4: I mean, that is our job.
Speaker 1: And so you know, when we think about like the
Speaker 1: four fifty five deploying you know two nanimeter and three
Speaker 1: and nanimeter chips, having the latest generation memory high bendw
Speaker 1: with memory that is out there, and really deploying these
Speaker 1: big systems, the important thing is that the entire ecosystem
Speaker 1: come together and we plan together for this next big
Speaker 1: inflection in compute.
Speaker 4: And that's exactly what we're doing right now.
Speaker 1: I think we're working very closely with the entire supply
Speaker 1: chain to ensure that we have the the resources to
Speaker 1: expand this compute environment. And yes, you know some of
Speaker 1: the things that you mentioned are let's call it constrained, but.
Speaker 3: Which is most severely so, you know, I don't.
Speaker 4: Think that's any one thing.
Speaker 1: I think we're all looking at, you know, how do
Speaker 1: we build faster? You know, certainly power is one of
Speaker 1: those areas where you know, you see throughout the world,
Speaker 1: you know, power is being brought online as fast as possible,
Speaker 1: certainly from a silicon standpoint. You know, we're ramping our
Speaker 1: production capabilities with our partners. From a memory standpoint, our
Speaker 1: partners ramping as well. So it's not any one thing,
Speaker 1: I think, it's all of these things have to go
Speaker 1: sort of in tandem, and that's why partnership is just
Speaker 1: so important in this business.
Speaker 2: We started this conversation talking about helios first, RAC scale
Speaker 2: architecture and infrastructure from AMD, could you talk about the
Speaker 2: future and how much of the content you want to
Speaker 2: own in a server?
Speaker 3: You know, we start this story with the.
Speaker 2: GPU, Frank k If you look at what Nvidia is doing,
Speaker 2: they want to increasingly own all of what's inside the box.
Speaker 2: Is that something that AMD's focused on too.
Speaker 1: You know, what's most important for us is to ensure
Speaker 1: that we have turnkey solutions that are very very easy
Speaker 1: for our customers to deploy, because when you think about,
Speaker 1: you know, how do you use all of this AI
Speaker 1: compute most effectively?
Speaker 4: You want it to go into the data.
Speaker 1: Center and really be up and running on day one,
Speaker 1: and for that you have to optimize a full system.
Speaker 1: But from that standpoint, you know, we are very focused
Speaker 1: on an open ecosystem. So yes, we designed the CPUs
Speaker 1: and the GPUs and some of the networking elements, but
Speaker 1: we also work you know, really with the broad ecosystem
Speaker 1: of partners with industry standards. It's all about ensuring that
Speaker 1: we get the best of all worlds when we put
Speaker 1: our solutions together.
Speaker 2: Looking ahead to m I five hundred twenty twenty seven,
Speaker 2: that has one thousand times the performance of the I
Speaker 2: three hundred generation. So your last generation of real well
Speaker 2: deployed gear. Something's coming that's a thousand times better. How
Speaker 2: did you make it a thousand times better?
Speaker 1: It is just incredible engineering at every level. So m
Speaker 1: I four fifty five is ten times better than the
Speaker 1: trip that we just launched six months ago them I
Speaker 1: three fifty five and m I five hundred is another
Speaker 1: ten x you know. On top of that, we are
Speaker 1: using the most advanced technology out there. We have a
Speaker 1: very you know, very clear focus on you know, hardware, software,
Speaker 1: system code design, and it is you know, clearly the
Speaker 1: pushing the bleeding edge of capabilities.
Speaker 2: What is the status of a m d's ability to
Speaker 2: sell products into China right now?
Speaker 4: So you know, China is an important market for us.
Speaker 1: You know, we actually sell a broad range of tips
Speaker 1: into China, including our you know, our PCs as well
Speaker 1: as you know other embedded chips.
Speaker 3: In the data sets context of course, sorry, in the data.
Speaker 1: Center context, we are you know, certainly we see China
Speaker 1: as an important market. We were We did get some
Speaker 1: licenses from the US government you know, late last year
Speaker 1: as it relates to some of our previous generation are
Speaker 1: m I three oh eight you know chips, and we
Speaker 1: are in the process of applying for new licenses with
Speaker 1: ourm I three twenty five chips that were recently allowed
Speaker 1: to apply for licenses. We haven't gotten those licenses yet,
Speaker 1: but we continue to view China as an important market.
Speaker 2: For the reason I ask about it is in part
Speaker 2: because a lot of the work that's being done in
Speaker 2: open source models and bridging the gap between open and
Speaker 2: closed it is being done in China to some extent.
Speaker 2: There's been a lot of discussion about the demands being
Speaker 2: there in China, But could you reflect a little bit
Speaker 2: on that demand, but also what the Chinese government's attitude
Speaker 2: is to you taking a later gen neration of tech
Speaker 2: to the country.
Speaker 1: Well, I do think the demand for you know, AI
Speaker 1: in general and in China is high for all the
Speaker 1: reasons that we talked about. I think we are in
Speaker 1: a demand environment where more compute is beneficial across the world.
Speaker 1: We think, you know, China is an important market for US,
Speaker 1: and it's very active in having our solutions deployed, so
Speaker 1: you know, we continue to view it as something that's important.
Speaker 4: We're working with the US government as.
Speaker 1: Well as our Chinese customers, you know, to find good
Speaker 1: solutions there.
Speaker 2: And there are signs from both governments that the licensed
Speaker 2: process is moving. Commerce is kind of notorious for things
Speaker 2: sitting on a desk for quite a long time.
Speaker 1: I think we are optimistic that, you know, we'll have
Speaker 1: an opportunity to get some of those licenses.
Speaker 2: Granted you're watching Bloomberg Television, you're listening to Bloomberg Radio,
Speaker 2: this is Bloomberg Tech, and we live in Las Vegas
Speaker 2: with the A M D CEO, Lisa Sue. Last question,
Speaker 2: really in the data center context, is the markets and
Speaker 2: investors want data and signs that you're taking markets share.
Speaker 2: What would the metrics be that you'd point to, either
Speaker 2: that already exist or over the coming twelve months that
Speaker 2: would evidence that well.
Speaker 1: I think M I four fifty five is a clear
Speaker 1: inflection point in both our technology capability as well as
Speaker 1: the deep partnerships that we have across the industry. So
Speaker 1: we're excited about, you know, what we see in front
Speaker 1: of us. And you know, we've talked about you know,
Speaker 1: tens of billions of dollars in an AI revenue as
Speaker 1: we get into twenty twenty seven, and I think these
Speaker 1: are important metrics, you know, for us as a company
Speaker 1: when we think about the AI potential.
Speaker 2: For all the focus on data centers, some forget that
Speaker 2: a m D is leader in PC in many respects.
Speaker 2: The forecasters have very different opinions of what will happen
Speaker 2: this year. Some see, you know, shrinking market, some see
Speaker 2: modest growth driven literally by just AIPC. You've been able
Speaker 2: to tape market share and grow irrespective of what the
Speaker 2: broader conditions are, but it haven't been great. How have
Speaker 2: you done that and do you expect that to continue
Speaker 2: to be the case.
Speaker 1: Well, the PC market is a very good market for us.
Speaker 1: You know, we grew a ton in the PC market
Speaker 1: in twenty twenty five, and that really came from the
Speaker 1: strength of our product portfolio.
Speaker 4: We bet early in aipcs, so.
Speaker 1: It was a clear area where we believe that the
Speaker 1: technology would generate demand. We also went through a refresh
Speaker 1: cycle with Windows eleven, and as we go into twenty
Speaker 1: twenty six, I think we'll want to see how a
Speaker 1: few quarters play out. I think the general demand for
Speaker 1: computing is certainly there. There are some supplies chain constraints
Speaker 1: that you know, we're working through and we want to
Speaker 1: you know, watch going forward. But you know, our case
Speaker 1: is one where we are still underrepresented in parts of
Speaker 1: the market.
Speaker 4: You know, we are very very strong in gaming, we're
Speaker 4: very strong in consumer.
Speaker 1: I think we're under represented in enterprise laptops, and we view.
Speaker 4: This as a growth area for US.
Speaker 3: Is AI PC change that.
Speaker 1: Aipcs absolutely help in terms of, you know, just the
Speaker 1: upgrade cycle coming in. We're excited about some of our
Speaker 1: work with AI development systems as well. We announced a
Speaker 1: new AI development system last night that you know, we
Speaker 1: think will be also very attractive.
Speaker 2: Those constraints you were talking about in the PC context
Speaker 2: are specifically dram or it's broader than that.
Speaker 4: It's more around the memory side.
Speaker 1: So when you think about memory overall, I think we
Speaker 1: have so much demand coming from let's call it AI
Speaker 1: Data center compute that we want to see how it
Speaker 1: impacts you know, sort of the rest of the memory
Speaker 1: market out there.
Speaker 3: One of the other areas that you discussed with Greg.
Speaker 2: Brockman on stage from Open AI was sort of the
Speaker 2: net or broad economic impact of AI, not just for
Speaker 2: the companies. I think you were talking more about economy again,
Speaker 2: very difficulty, so that how does one measure progress in
Speaker 2: whether AI has or has not had a direct positive
Speaker 2: economic impact around the world in any given year.
Speaker 1: You know, It's true, it's hard to deconvolve all of
Speaker 1: the things that are happening. But I think from a
Speaker 1: sense of you know, what we see in the business,
Speaker 1: and you know, many people want to see direct return
Speaker 1: on investment for a particular a set of investments, what
Speaker 1: I would say is that we know that AI.
Speaker 4: Is making a difference in the productivity of companies.
Speaker 1: We know that I can see that, you know, within
Speaker 1: a MD in terms of as we deploy AI, you know,
Speaker 1: we're able to get products to market faster, We're able
Speaker 1: to you know, significantly improve some of our business processes.
Speaker 1: So you know, as we go forward over the next
Speaker 1: several years, I think you're going to see that much
Speaker 1: broader enterprises. Every CEO that I talked to is talking
Speaker 1: about AI. It is front and center in terms of
Speaker 1: how to build a better company, how to build the
Speaker 1: better portfolio, and so you know, I think what you
Speaker 1: know Greg was talking about is when you aggregate all
Speaker 1: of that, AI has to impact you know the world
Speaker 1: at a GDP level, and we'll see that over the
Speaker 1: next few years.
Speaker 2: You're watching Bloomberg Television, you're listening to Bloomberg Radio. This
Speaker 2: is Bloomberg Tech and we're live in Las Vegas. We're
Speaker 2: speaking to A m d's CEO, Lisa Sue. You are
Speaker 2: an investor in Generative baronyx's also technology partner, and they
Speaker 2: have unveiled a humanoid robot here at Las Vegas cees.
Speaker 2: In fact, if the magic of television can happen, and
Speaker 2: we cut to the why.
Speaker 3: Do we have to see it in the background? Right?
Speaker 2: You know, this is the first tangible sign I feel
Speaker 2: we've seen from AM and D on how you intend
Speaker 2: to play in physical AI. Yes, explain your strategy. It
Speaker 2: is the next big market, right, Yes.
Speaker 1: And I wouldn't say it's the first time, but it's
Speaker 1: probably one of the areas where we don't highlight as
Speaker 1: much because there's so much focused on data center and
Speaker 1: cloud and the opportunities there are you know, very much.
Speaker 4: In front of us.
Speaker 1: But when we look at physical AI, you know, starting
Speaker 1: from all of the work we've done in FPGAs and
Speaker 1: embedded real time you know capability, we have been in
Speaker 1: this space for a long time. You know, we already
Speaker 1: power a lot of robotic applications you know out there.
Speaker 1: But I think as we go into the humanoid capability,
Speaker 1: and you know, we're excited about our partnership with you know,
Speaker 1: g Bionics and the work with on June one, I
Speaker 1: think that takes us to another level in terms of
Speaker 1: capability and intelligence and what we're trying to do so.
Speaker 2: Is the business model to be all things the brain
Speaker 2: inside of the humanoid robot and inference side that, the
Speaker 2: underlying software being traded on a trained on a m
Speaker 2: D accelerators. Just I don't what's to go to the market,
Speaker 2: I guess is what I'm asking you should.
Speaker 1: Expect that our partnerships extend all through all of those levels,
Speaker 1: so we have the components that can power the humanoid robots,
Speaker 1: you know, sort of real time local capability, which is
Speaker 1: a very very important and then we also have the
Speaker 1: technology behind that in terms of, you know, how to
Speaker 1: train and inference on these humanoids.
Speaker 3: When last we met in person, it was in Washington,
Speaker 3: d C.
Speaker 2: And the President had just outlined a broad strategy for
Speaker 2: America in AI and it really centered around infrastructure deregulation
Speaker 2: allowing those building the infrastructure to move faster. That was
Speaker 2: kind of in the second half of last year. In
Speaker 2: the months that have followed, have you seen any signs
Speaker 2: that it worked and anything that you could point to
Speaker 2: that says, yeah, people are able to build faster maybe
Speaker 2: to address some of the compute deficits we discussed.
Speaker 1: Well, I can say for sure, you know, the President's
Speaker 1: AI action plan. You know, when we met, and I
Speaker 1: think this was back in July when it came out,
Speaker 1: I was very optimistic about having a really forward leaning
Speaker 1: strategy from you know, sort of the whole view of
Speaker 1: what does it take for the US to lead an AI.
Speaker 1: And I think we've made a ton of progress along
Speaker 1: the way. And you know, I had Michael Kratzios joined
Speaker 1: us last night on stage as well to talk about
Speaker 1: the Genesis Mission, which is you know, another you know,
Speaker 1: sort of public private partnership approach to really advanced science
Speaker 1: in the United States, and when you look at you know,
Speaker 1: all of these things, you know, building faster, ensuring that
Speaker 1: we have you know, the right export controls so that
Speaker 1: we were able to have the US stack adopted.
Speaker 3: Across the controls.
Speaker 4: Currently, we are certainly working.
Speaker 1: Very closely with uh, you know, the the various parties
Speaker 1: in the US government to ensure that we have the
Speaker 1: right balance there. And we also have you know, this
Speaker 1: notion of how do we invest more here and ensure
Speaker 1: that in the United States that we are you know,
Speaker 1: running as fast as possible to bring you AI capacity
Speaker 1: you know online, to help us in you know, science
Speaker 1: and you know sort of the broader you know, economic benefits.
Speaker 2: Lisa, what what happens in twenty twenty six, what happens
Speaker 2: in the world of AI, and what do you think
Speaker 2: will define this year in terms of the progress that
Speaker 2: your industry hopes to make.
Speaker 1: Well, I started our keynote last night with the sentence
Speaker 1: that you know, you ain't seen nothing yet.
Speaker 4: That's really how I feel.
Speaker 1: I Mean, we're sitting here in January and it's just
Speaker 1: amazing how much progress is made, you know, every week
Speaker 1: and every month when we see how these models are developing,
Speaker 1: when we see how the use cases are developing, and
Speaker 1: then when we see the tangible results on businesses and outcomes,
Speaker 1: I believe that, you know, we saw a good amount
Speaker 1: of that, you know, come to Fruition in twenty twenty five.
Speaker 1: We're going to see much more of that in twenty
Speaker 1: twenty six, So that everyone should understand that, you know,
Speaker 1: AI is not just you know, hype out there. It's
Speaker 1: not just you know, sort of things that people are
Speaker 1: talking about in the investment community. It's things that people
Speaker 1: are using every day, real time and feeling like, Hey,
Speaker 1: my life is better because I have this technology.
Speaker 4: And I think we're going to see that in twenty
Speaker 4: twenty six.
Speaker 2: Lisa suit AMD CEO AMD with it's in the world's
Speaker 2: first two animeter chip going into Helos, its first rack
Speaker 2: scale system solution