All right, a few more sessions and then we'll get to close this amazing conference of two days. So, Mark is the CTO of AMD and I have to admit that I have a major regret that I didn't invest in AMD 6 months ago. You guys were 200 bucks. Here we are at 500 or close to. You're the underdog in CPUs against Intel. You're the underdog against Nvidia for GPUs. But I cannot call you an underdog when you're nearly a trillion in market value. So Mark, what is it that the market has seen the last 6 months that I personally missed?
Well, first of all, Kareem, thanks for the chat here today. Looking forward to it. But I'll go back before six months because when you think about the AMD story, we really had to reinvent what we were doing. It started with getting a leadership CPU. We got that out in 2017. So we've actually been getting very steady market share gain in CPU and in fact we're probably on a path to have server market share leadership in the next few months. So very exciting path. And with GPU, we've always been in GPUs and the key was first getting those GPUs back to be fully competitive. We did that. We won the top number one supercomputer in 2022, 2024 we beat that and had yet again a number one supercomputer, and then we flipped into AI and that's when we started. We went from 0 to 5 billion revenue in one year. That was in 2023 and we're on an annual cadence of new GPUs. But what happened in the last 6 months is agentic AI. So now those leadership CPUs are needed more than ever because when you're running the agentic applications, it's not just the GPU, you're actually using more and more CPU and that's how we're getting fundamental productivity gains. It's truly game-changing.
And then your core strength has always been CPU. So in a way the market is absolutely right, and we'll talk about how things are going to evolve over time. But what people don't know about Mark, which is quite cool, actually, is that he led the engineering and R&D through the PC world. If you remember, IBM back then, he was there. The internet, mobile, he worked with Steve Jobs on both the iPad and the iPhone. We've gone through cloud and here we are in AI. So Mark, you've kind of seen every massive technology transformation that's ever happened. What's so special about this one? Why is it going so fast?
Yeah, it's a great question and I am very fortunate to have hit each of those inflection points. I mean, think about PC brought information to all of us that used to have to dig through exhaustive research, but the PC plus the internet brought all that information to us, and mobile with the phone brought it to where it was with us everywhere, as an extension of our body. And so all those were huge changes. But what's so different here is now with AI, with the type of models that we have now that have a reasoning capability that's outstanding, and with the agentic AI where you can really put the pieces together to get work done. We're going from productivity gains that we had prior to the last 6 months of 10% kind of gains to now dramatic improvements in productivity. And just in our own chip design at AMD, we're shaving months off time to market. We're taking new features that we needed that would take many, many months and we're getting it down to weeks and days.
That's incredible. And I think being normally a chip manufacturer, you're probably seeing the trend internally in your own business to kind of figure out what's happening outside. And if I step back for a second, this has been a massive theme across the last two days. Agentic AI brings a lot more complexity, more metrics, more logs, more information, faster thinking. And if you think about two things, first of all, CPUs, you need a lot more processing power. So one quote that I read is in today's agentic world, it takes four times more CPUs to do what these agents are doing. And from a GPU perspective, which is kind of like the matrix thinking that crushes everything in a very simple way, we need a lot more efficiency there. So how are you seeing the stack evolve over time, and if you're in an enterprise company, how should you be thinking about your enterprise?
Well, what you talk about is certainly we're all seeing that if you're transforming your business into being an AI-first, AI-driven business, and when you think about complexity, it's actually what's giving you the efficiency I just mentioned a moment ago. Because how are you getting massive efficiency gains? You're actually orchestrating many, many parallel workflows that used to be run serially. One team would get a piece of work done, they pass it over to the next team. It might be in a technology aspect, it might be on a manufacturing floor, it might be in accounting. These are all serial processes. Well, with agentic, we all hear about agentic agents. They're great. I have an agentic agent that speeds up my everyday. It's my right hand. But it's agentic workflows that are adding that complexity because now you're running many, many agents at one time. You're creating sub-agents that represent skill classes, groups that use...
Agents, got to orchestrate.
A gazillion million different functions.
Exactly. And the context window is larger. So think about the role of CPUs. Used to be, hey, I have a CPU as a head node, a controller for my GPU. So that's what everyone says: AI is GPUs. Well, now you have a bigger and bigger context window. We're all asking it, but the prompts are much more complex. We have to orchestrate that and that's done in the CPU. So you have this whole orchestration and reasoning level to do that reasoning. It's much more CPU to orchestrate, and then you've got to get the work done. And where is the work done? That's generally been CPU-based workloads. We have x86 CPUs, everything runs on x86 CPU. So all of that is changing from a ratio of one CPU to eight GPUs, 1 to 4, now it's literally becoming one CPU to one GPU. And also, so that's one way is just get more CPUs in. The other way is can we get GPUs to be more efficient? And you've got now a lot of different companies. Someone's going to have to win at some point, but trying to drive more efficiencies between GPUs. This is getting quite technical, but I had to learn all this stuff. It is GPUs, but honestly, Kareem, it's the whole system. Like we've changed at AMD. We used to think about and we used to be organized with a CPU division, a GPU division, and we have all those teams have to work together. Gone is the day of creating one little piece. You have to do holistic design. You have to design for the system all the way up through the application stack, and that's how we get the efficiencies. So it's feeding the beast. The beast is that huge CPU, GPU, your compute engines. So you need networking to be able to scale them together efficiently. You need to be able to analyze from the software. You have to look at where are all the bottlenecks and you have to eliminate those bottlenecks. And then you just have to really truly optimize one against the other, not one at the expense of any one component at the expense of the other components.
And Mark, I mean we all talk about it, but obviously more compute is more energy, more energy is more money, more cost. All CFOs have the usage of token on their mind to make sure that cost doesn't get out of control. So far we haven't seen yet. We've seen a correlation of more compute, more energy. But do we ever see a point where the output becomes more efficient and we need less energy so it becomes a more affordable approach?
Well, two things. One, there's going to be efficiency of getting the job done like I said earlier. So really thinking about truly the inflection we're in, where you're optimizing for agentic workflows, you're running much more efficiently because you've got these racks of computers that used to run at a fraction of capacity. With agentic workflows and the parallelization you can do, you can actually run at a much higher efficiency. So one, you're utilizing the compute infrastructure you have in a much better way. And then what we're all doing, we're power limited across the world. We're energy gated in terms of building out. So that's a huge impetus to all of us in industry to collaborate, to work together. We do it in every aspect of our design, driving more energy efficiency into the chip. You're seeing new types of chips that are running inference much more efficiently, running computing more efficiently. So it's economically and frankly from just a pure ability to meet the demand, we have to drive more.
When will we get there, do you think?
Constant process. I mean you literally every generation you're going to see significant efficiency improvements.
Okay. Something else is super interesting. Obviously people think AMD is just about selling chips, but you're not. You're also selling solutions. You're selling applications and even to the point where now you're partnering up to build already built up data centers that already have the chips, the cooling. I mean that's a completely different business model. It's a different way of operating. Tell us a little bit about how you're adding to your strategy and how you're opening as a business. I mean your go-to market must be completely different now.
