[1:51]Please welcome Marvell, Chairman and CEO, Matt Murphy.
[2:09]It's great to be here to kick off day one at Computex and it's great to be back here in Taiwan. You know, the first time I came here was nearly 30 years ago. It was my first business trip to Asia. And I remember back then visiting some of the key technology companies here at the time. Many of them were still young, small companies, emerging companies. And today, those same companies have become the most important technology leaders in the world. Now, I've had the opportunity to come back many times and see Taiwan continue to grow in importance as one of the world's leading technology centers. And today, so much of the future of AI infrastructure is being built right here. I have a question for all of you. What defines the performance of AI infrastructure? Now, maybe you're thinking about the processor, the GPU, the XPU, or maybe it's the process node used to build it. Three nanometer, two nanometer, or soon A14, A16. Those are great metrics. They tell you a lot about the speed, the efficiency, and the density of the compute. And AI workloads are certainly compute intensive. But that's not the whole story. Now you might say, well, what about memory? AI workloads are incredibly memory intensive as well. More memory, higher bandwidth, all of that matters, it's all critical. No doubt. But that's still not the defining characteristic of the system. Because one processor, no matter how fast it is, no matter how much memory it has attached to it, is simply not enough for today's AI workloads. You need tens of thousands and eventually millions of processors working together as a single massive compute engine. That's why computing at this scale is fundamentally a connectivity challenge. And increasingly, it is the architecture and characteristics of connectivity that defines the performance of the system. Now, look, we've seen incredible breakthroughs in accelerated computing, and we've seen the emergence of high bandwidth memory to meet the AI challenge. But I'm here to tell you, the next major wave of innovation and scale will come from the underlying connectivity of these systems. And as those and as those connections move from copper to optical, they will unlock new architectural possibilities. So today I'm going to explain why connectivity is becoming one of the defining characteristics and challenges of the AI era. And why this technology transition matters to optics. Now, this isn't something far out in the future. It's happening right now, this year, next year, we're in the ramp. And at Marvell, we've been preparing for this moment for nearly a decade. We built the company very deliberately around the infrastructure required to move data at massive scale. And to understand why we made that bet, let's go back in time, 10 years ago when I joined Marvell as a CEO. So prior to Marvell, I spent 22 years at one company, Maxim Integrated products, which was a leading analog semiconductor company. And one of the unique things about working at an analog company is that your products go into virtually every piece of end equipment, every electronic system, every on market in the planet. So over those two decades, I had a front row seat to just about every major technology trend. First personal computing, then notebooks, digital still cameras, smartphones, eventually data center. And I watched wave after wave of technology reshape the whole industry. So I joined Marvell, and I didn't start off actually thinking about, well, what products do we have. I reflected on where the industry was headed.
[6:09]And it seemed clear to me, even at that time, back in 2016, that the next major growth cycle for semiconductors in the world really was going to be driven by the data platform companies. Back then, it was still the same ones as today, companies like Google, Amazon, Microsoft, Meta. And more specifically, the semiconductor technologies that were required for those markets, to move data, store data, process data, and secure data, do it at massive scale. That was the vision we had.
[6:42]But when I looked at the products we had at that time, very few of these were actually exposed to that trend. It was kind of a problem. Less than 10% of our revenue 10 years ago was coming from data center. That's it, a couple hundred million bucks. But more than 60% of our revenue back then was coming from consumer. And so it was exciting time. We were in virtual reality headsets, we were in gaming consoles, streaming devices, wearables. In fact, our claim to fame back then, was Marvell was designed into the first Wi-Fi connected Barbie Dreamhouse. That was our big design win. It was real. In fact, the first week I was at Marvell, they team briefed me on on what a great design win this was. So that's where we were. So we had a vision. There was a pretty big gap though between the reality that we were facing and where we saw the industry heading. But we had conviction. We had conviction, so we decided to bet the whole future of Marvell on it. So to do that, we needed a clear vision, and our vision at that time was pretty simple, and by the way, this is still the same vision that we have today, 10 years later. Which is build a best-in-class pure play company focused on semiconductor solutions for data infrastructure. Now, at that time, data infrastructure was not a recognized market category. It was the term that we used to describe the infrastructure that was going to be required to move the world's data. Store the world's data, process the world's data, and secure it. But like I said, we were not in that business yet, and frankly, we didn't even have a lot to work with as we went after it. We had some. So my team and I came to a conclusion, which is that we would need to build these capabilities internally, and others we would need to build through strategic M&A. And we had to get focused, because when you're transforming, it's not just deciding about what you're going to do. It's equally important to decide what you are not going to do. So with that strategy in place, we got to work. We began systematically building Marvell around that vision, and it wasn't just one move. There was a series of deliberate choices. We looked for the premium assets in the markets that mattered the most, the best companies, best technologies, the best teams with the strongest market positions. Now, we first started by divesting businesses that weren't aligned with our strategy. You can see some of those there. Well then very quickly we acquired Cavium to strengthen our compute and networking capabilities, that was back in 2018. 2019, we divested our Wi-Fi business, again, we we're focusing. But we acquired Avera to establish our custom silicon business, and then Aquan to bolster our connectivity portfolio. In 2021, we followed all that up by acquiring Infi for $10 billion. It was our largest acquisition to date, and we got world-class data center connectivity technology into the company through that. Then we acquired a Novium the same year, adding high-end data center switching capability to the portfolio. So, then we took a break, we took a few years to digest, and focused on unifying and building out our whole technology platform to address the data infrastructure opportunity. But over the last 12 months, we fired up the M&A engine again. We divested our automotive Ethernet business, again, power of focus and acquired Celestial AI for its photonic fabric technology, and Econ for scale-up switching. So if you add it all up, over the last decade, we've invested roughly $22.5 billion through acquisitions. We spent $18 billion organically, inside of Marvell to develop the platform. And then we divested approximately $4.5 billion worth of assets. So all in, we've invested roughly $36 billion investing in this platform. Now let me show you the result of some of these investments. First of all, we have built an incredible technology platform. And it all starts with the advanced process node. It's one of the most important decisions we made actually was to become a process node leader. Now, Marvell, Cavium, and some of the companies we acquired had all been fast followers, meaning you're like a node or two behind on everything you do, and that's largely a result of just not having enough scale. That's usually why people do that. But as we integrated these businesses, we made the decision that if we're going to compete in data infrastructure, we had to be at the absolute leading edge, no choice. Now, here's a little known fact. Marvell skipped seven nanometer completely. We made a full node jump at that time, from 14 and 16 nanometer, all the way to five. I mean, nobody does this. Nobody takes that kind of a risk or a bet, but we did and it worked. It worked really well, flawlessly, actually. Our engineering team did an outstanding job executing this transformation. So in early 2020, we released our first world-class IP platform, complete with die-to-die interfaces, custom SRAM, high-speed serdes, and more. Now, serdes is a good example of how we built this platform. It combined Marvell's own core engineering strength with exceptional talent from Avera, Aquan, Infi and others. Now, today, that is a 1,500 person organization at Marvell. Second to none in terms of engineering scale and capability. So to support the process data portion of our mission, we built a best-in-class custom compute platform, working in deep partnerships with the world's leading hyperscalers. And that business is doing been doing very well for us. In store data, we built a whole portfolio of storage controllers, CXL-based memory poolers and near-memory compute. But here's where we really went all in. And that was in data movement. And this is where our high-speed connectivity portfolio. And when you look at Marvell's data center business today, the vast majority of our revenue actually comes from connectivity. From high-speed optical interconnect inside the data center to long reach optics, between data centers, to high-speed switching infrastructure. So today we are the undisputed connectivity leader. And when you step back and look at what we built and where the market ultimately went, I think the results speak for themselves. So back in 2016, Marvell was a $2.3 billion company. As we embarked on the transformation, actually in the first five years, we doubled the company. $4.5 billion in revenue. Over the next five years, our growth accelerated. And according to consensus estimates, on Wall Street, for the current year we're in, we're set to grow about 2.5 times over the last five years, to 11.4 billion. But in the recent couple of years, if you actually drill down, Marvell has been growing like 40% a year. So the growth rate is actually accelerating in the last few years. So at this point, Marvell is off to the races, okay? Um, and based on the outlook that we shared in our earnings call last week, consensus estimates have come up, and they expect us now to deliver 16.4 billion in revenue next year. So as I said earlier, when we started this journey, data center represented less than 10% of our revenue. And we bet the farm on it. Last quarter it was over 75% of our revenue and growing very rapidly. So this is a very different company than we used to be, and the thesis is largely played out, but, but we're still in the early innings of this infrastructure buildout. The next phase is all in front of us, it'll have a different set of requirements, and that brings us back to connectivity. So for the past several years, as AI has created new demands on the infrastructure, we've seen the industry solve one major bottleneck after another. And first it was compute. I mean the industry needed dramatically more compute to enable modern AI. And Nvidia did an incredible job leading that revolution and along the way became the world's first $5 trillion market cap company. Congratulations to Jensen and his whole team that's here. It's just a phenomenal, phenomenal result.
[15:18]Next came the memory bottleneck. Larger models required enormous amounts of memory and bandwidth. And the memory companies are scaling aggressively now to meet that demand. And just recently, we've seen three new $1 trillion market cap companies emerge in that market. But the bottleneck is shifting again. Now, it's connectivity that will define the limits of the infrastructure, just like with compute and memory. The industry will rally to meet this challenge. Now, this isn't just me saying this. This is what we're hearing from our largest customers. The world's largest hyperscalers are now reimagining their entire network architectures. They recognize that scaling AI infrastructure is now, first and foremost, a connectivity challenge. As reasoning models, mixture of experts architectures, agentic AI, it all continues to evolve. More data has to move across the infrastructure, demanding higher bandwidth and lower latency. And as workloads no longer fit within one data center, guess what? They need to build larger data centers, or full campuses full of data centers, and all the high-speed connectivity between them. Thus, the connectivity becomes a critical enabler of scaling compute. And increasingly, our customers recognize that optics is the way forward. And they're looking to leaders like Marvell to help them build larger, faster networks and at scale. So when you look across the semiconductor industry, at the leading companies supporting this infrastructure buildout, it becomes clear each of us is focused on a different part of the infrastructure. And that shows up in the revenue mix. Some of the companies are compute first, means the vast majority of their revenue is tied to compute, with some of it tied to connectivity, but most of it's compute. And it's obviously a critical part of the stack, and that's why we have several, you know, trillion dollar plus companies in this group. Then you have the companies focused on memory, and again, all trillion dollar market cap companies at this point. It's unbelievable. Now then you have Marvell. We're we're different, we're unique. Today, the vast majority of our revenue actually comes from connectivity.
[17:42]Now, this spans a broad range of technologies. And even the portion of our revenue that's from compute, which you can see, is fundamentally because customers embed our connectivity in their compute engines. So this gives us a unique position and perspective on these technology transitions that are happening. And it creates a very different relationship that we can have with the rest of the ecosystem. We partner deeply with the compute companies. We partner deeply with the memory companies. These are very strategic relationships, and in many ways, we are the Switzerland of the industry and we work with everybody. Now, one of the best examples of the role that Marvell plays in this ecosystem is the recently announced strategic partnership and expansion with Nvidia. And as part of this announcement that we made a few months back, Nvidia invested $2 billion into Marvell. And we're expanding our partnership now across multiple dimensions, including optics, photonics, NV Link fusion. And I'm thrilled to announce that Jensen himself is here today. He's going to join me on stage, we're going to spend a few minutes chatting about the partnership, and we're going to see where AI infrastructure goes from here. And so with that, let me please welcome to the stage, Jensen Wong. What's up, Jensen? How you doing? Boy, that's a huge stage. I had to run a long ways. You out of breath, you okay? I know. Let's fire up. Good to see you. Good to see you. How you doing?
[19:21]Good to see you. Congrats on a great kickoff yesterday, GTC, you guys are off to the races this week. Thank you. Thank you. Um, look, maybe you heard some what I just said. So we're talking about connectivity today. The next trillion dollar company, ladies and gentlemen. Whoa!
[19:39]That would be exciting. Let's do it together. Yeah, let's do it together. Um, but it really all starts with what's happening today in AI infrastructure, kind of more broadly. So how do you see that like just from the big picture standpoint, we're at this extraordinary moment, customer demands through the roof. How do you see connectivity playing into this, and in the interconnect that's required. Yeah, that's really great. You know, yesterday, um, I I said that useful AI has arrived. It's the reason why your demand is going through the roof, it's the reason why my demand's going through the roof. And, and this new computing pattern that makes it possible, it's called agents. And these agents has a particular computing platform, computing pattern that is disaggregated and distributed. When you take a computing problem and you disaggregated into a lot of parts and you distribute it across the entire data center, what's necessary is connectivity. That's the reason why Matt's doing so well. That's the reason why Marvell's so essential. We've distributed and disaggregated computing so that it runs across these enormous clusters, so that we could get or aggregating the total compute, the total memory, the total bandwidth that we have. And that what makes it possible is connectivity. Yeah, we're we're I mean we're we're seeing it and then. That's why they're going to be the next trillion dollar company. We got a little work to do, but we're we're on our way. We're on our way. Thank you, Jensen. Well, let's talk about let's talk about scale. I mean, we used to talk about tens of GPUs and CPUs and and XPUs connected. Now thousands, now maybe millions at some point. So as you scale the compute and you scale the connectivity, I think we talked about things like agents, but how do you think about that, you know, across data centers, within data centers? How you think about connectivity at large playing that role and what kinds of technologies you think are important there? Well, at the foundation of it, the agent computing pattern requires um an orchestration system that allows the large language models, the computing to be able to think and reason and come up with plans, but it also has to use tools and you know, browse the internet, access memory, access long-term memory, deal with short-term working memory. All of that requires a lot of connectivity.
[22:01]But it's also it's also the case, and if you look at the way we introduced Vera Rubin. Hopper was designed for training. Grace Blackwell introduced NV link 72, our first scale up fabric, and it introduced the idea of extremely fast inference for MOE models that are very large, mixture of expert models that are extremely large. And so Grace Blackwell was for inference. Vera Rubin is to run agents. Which is the reason why the Vera Rubin system includes, of course, the Vera Rubin thinking AI, but it also includes Vera CPUs for orchestration. It includes Vera CX for storage acceleration, for managing long-term memory. And the way that I think about these systems, you know, sometimes, uh, maybe the CSP wants to design their own custom chip. And between us, we also partner together on MV Link Fusion. Which makes it possible for you to use the same system architecture, and with Vera Rubin inside, some of your semi custom chips, a lot of your interconnect, silicon photonics and optics and technologies such.
[23:14]And we can create essentially a disaggregated, distributed, and heterogeneous data center. And so that's that's the big idea. And yet they're they're system architectures identical, their networking technology can leverage a lot of Nvidia stack. The CPU could be Vera, and yet it can leverage a lot of your stack. So NV link Fusion is about taking Nvidia's technology and our platforms, Marvell's technology and plan, and we fuse it. That's why it's called Fusion. Yeah, I know, I think, you know, I think about the partnership, and we've been working together a long time. I think memorializing it with the investment, which we really appreciate, I think it's been it's been huge for us. We're honored to have it. I I you know, who doesn't love making money. It's nice to give. It's done well since you invested. So I think you're, you know. I love getting rich. Just follow him. Jensen invests. You know, all of your best sales people. We're your best sales people right now. Okay. Working together. Final question for you. Um, a lot of my talk is about some of the transition, especially as you go to inside the rack from copper to optical. So it's obviously not going to be a 1-0, it's going to take time, you know, there's time and there's different use cases, but how do you see that playing out right now, the transition from copper to optics, and maybe how we can work together there, too.
[26:21]Well, we should use copper as much as we can for as long as we can, but copper has its limits. Copper has its limits with bandwidth and also with distance. And so, so ultimately, um, the the right strategy is to scale up with copper as much, as long as you can. After that, you scale up further with optics, and you scale out with optics, and you scale across with optics. And so you use optics wherever you must, you use copper wherever you can. And so, I think that that that intersection is going to continue for a long time. Here's here's the the bottom line is in the next 5-10 years, we're going to use a ton of copper, and we're going to use tons and tons of optics. And so these these data centers are part of infrastructure now. And the reason why I say that AI is now useful, useful AI has arrived is because now AI is profitable, and tokens are profitable. When token production is profitable, everybody wants to make more tokens, which is the reason why, you know, Marvell's demand is so high. is our demand is so high because everybody wants to produce more tokens because it's used all over the place by agents. Absolutely. Well, I think you touched on a bunch of things I'm going to cover later. If you want to do the rest of my presentation, you can, if you want. Yeah, so ladies and gentlemen, just sit right there. I'll be. It's beautiful slides, you know. You take it from here. All right, Jensen Wong, good to see you, brother. Thank you. Okay, you guys. Thank you. Thank you, Jensen. Buy Marvell. I know.
