【0602直播】COMPUTEX 2026 Matt Murphy 馬特·墨菲 Chairman and CEO, Marvell總裁暨執行長/畫面由COMPUTEX TAIPEI 2026提供

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. 3 nanometer, 2 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 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 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. 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 in to 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, the team briefed me 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. 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. 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 were focusing but we acquired Avera to establish our custom silicon business and then Aquantia to bolster our connectivity portfolio. In 2021 we followed all that up by acquiring Inphi for $10 billion. It was our largest acquisition to date and we got world-class data center connectivity technology into the company through that and we acquired Innovium 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 XCON 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 7 nanometer completely. We made a full no 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, Aqua, Inphi 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 has 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 dollars 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 and a half times over the last 5 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. 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. This is a very different company than we used to be and the thesis has largely played out 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 was a phenomenal, phenomenal result. 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 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 multi-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. And then you have Marvell. We're different. We're unique today. The vast majority of our revenue actually comes from connectivity. So we built this company around data movement and today the vast majority of our revenue comes actually from connectivity. 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, NVLink 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. So with that, let me please welcome to the stage Jensen Wong.

What's up Jensen? How you doing?

Are you out of breath? You okay? I know.

There you go. Yeah. Congrats on a great kickoff yesterday, GTC. You guys are off to the races this week.

Look, maybe you heard some of what I just said. So, we're talking about connectivity today.

Whoa, that would be exciting. Let's do it together. Let's do it together. But it really all starts with what's happening today in AI infrastructure kind of more broadly. So, how do you see that from just the big picture standpoint? We're at this extraordinary moment. Customer demands through the roof. How do you see connectivity playing into this and the interconnect that's required?

Yeah, we're seeing it.

We got a little work to do, but we're on our way. We're on our way. Thank you, Jensen. Well, let's talk about scale. I mean, we used to talk about tens of GPUs and CPUs 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 do you think about connectivity playing that role and what kinds of technologies do you think are important there?

Yeah.

Yeah. No, 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 huge for us. We're honored to have it.

It's done well since you invested. So, yeah.

Just follow him. Give Matt all my money and just watch him make money. That's what I'm doing every day. But I think these things you talked about which we brought to fruition, NVLink Fusion working together on optics. I mean, I think the era of agents and kind of your new platform now, I think it's ideally suited. I mean, NVLink Fusion, we had this idea years ago, right? But I think it was a little ahead of its time. And now when I wanted to see if you agree when you think about your platform and then some of the custom networking and compute needs that our customers have and the ability and the need to interoperate and work together. It seems like the time is now between Marvell and Nvidia to really go enable our customers to have that flexibility that they're looking for and really use the era of agents to scale our platforms together.

You know, we're happy to support you and support the customer. And so I think that between the two of you you have the benefit of a general purpose very high efficiency you know a system that is very well built starting with you know of course Vera Rubin but anything that you want to extend to specialize you can do so as well which is the reason why your customers and mine do this and it's wonderful to see Marvell expand into all of these different clouds. Yeah, great. Thanks. Hey, one last one for you. Just leave some business for me. You know, look, we're your best salespeople right now.

Final question for you. A lot of my talk is about some of the transition, especially as you go to inside the rack from copper to optical. It's obviously not going to be a one zero. It's going to take some 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?

Yeah, 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. So ladies and gentlemen, these beautiful slides.

You take it from here. All right, Jensen Wong. Good to see you, brother. All right. Take care.

Thank you, Jensen.

All right. Outstanding. Outstanding. Super fun to have Jensen here as always. All right. So, we've been talking a lot about connectivity. Jensen and I just covered this. So, let's like dive in now, right? Let's go one level deeper. So AI infrastructure spans every distance. It spans from hundreds or even a thousand kilometers between data centers to just millimeters inside the package. Every one of those distances requires a different solution. It's a different technology, different engineering team. It's a different completely different set of experts and in many cases it's a different supply chain. So these are not variations of the same problem. What you have here is fundamentally different engineering challenges and that's what we're going to walk through next. All right. So let's start with the longest distance. Jensen referred to this. This is scale across connecting data centers together. Now every major cloud provider has hundreds of data centers around the world and all of those data centers need to communicate with each other. This is fundamentally a long distance connectivity problem. We're talking about links that can span hundreds or even a thousand kilometers. This requires very specific, very complex technology called coherent modulation. And at the heart of it is a specialized digital signal processor or DSP that's designed to push enormous amounts of data across fiber optic cables over very long distances with extremely high reliability. There's only a few companies in the world that build these coherent DSPs, and we're one of them. Marvell has been a leader in this technology for many generations. We build optical modules that contain all the electronics needed to drive and modulate the laser and transmit data over long distances. So, I've got a little show and tell here in my pocket. Not holding up a chip this time. I'm holding up an optical module. This is one of our coherent optical modules. This is an incredibly complex piece of engineering. At Marvell, we build the entire module. This is ours. It includes the advanced node CMOS DSP. It's among the most complex chips, just the DSP alone that we design at Marvell, but it also incorporates inside our fourth generation silicon photonics technology. That's inside here. And we've been developing that technology in production for a decade on silicon photonics. It also includes our own broadband analog components that we designed which is designed in silicon germanium. So Marvell pioneered this technology starting with 100 gigabits per second a decade ago then moving to 400 gig and now shipping 800 gig in volume and later this year we'll be sampling the world's first 1.6 terabit 2 nanometer coherent optical solution. And that couldn't come at a better time. Demand for bandwidth has never been greater. All right, now let's go inside the data center. So these data centers can be very large spanning hundreds of meters and they contain racks and racks of compute servers. Now each rack typically has a switch at the top with servers connected into that switch. Those rack level switches connect to the spine and then the core switches. This creates the network fabric that ties the entire data center together. And all of that is connected through fiber optic cables. Now once again optical modules drive data transmission over those fiber optic cables. But this time the modulation scheme is different. Instead of coherent technology we use a more power optimized modulation technology which is called PAM 4. So the two key semiconductor solutions for this part of the market are the PAM 4 chipset inside the module and then the cloud switching infrastructure that ties the data center together. Marvell builds both. Starting with the PAM 4 chipset, we build the industry's leading PAM 4 DSP solution and also the high-speed analog components that go around them including trans impedance amplifiers or TIAs and laser drivers. These are also in silicon germanium by the way. And we've led the industry through every major transition of PAM technology starting at 50 gig, 100 gig, 200, 400, and 800. Then last year, we began ramping Marvell's 1.6T 3 nanometer PAM 4 solutions, leading the industry's transition to 1.6T connectivity. Now, for Ethernet switching, Marvell has a similarly complete portfolio of products from 12.8 terabits to 51.2 terabits. And today we announced our new 100T Ethernet switch specifically designed for AI data centers with the industry's lowest power. Special announcement for Computex. We waited. So you put it all together. We provide a complete solution for connectivity inside the data center. Now let's move inside the rack. The goal here is to connect the largest possible number of processors together in a full any to any configuration. In other words, every processor can communicate directly with every other processor. And Jensen talked about this. The first company to bring this architecture to market was Nvidia with NVL 72 named for the 72 GPUs connected together inside a single rack. And this required a completely different approach to connectivity. It was a different class of switch and the ability to drive very high-speed signals over copper back planes inside the rack. So today this is not the domain of optics. This is the domain of copper and the core differentiator here is the electrical SerDes technology rather than the optical. Now Marvell also has leading electrical SerDes at 200 gigabits per second today and we've demonstrated already over the last couple of years 400 gigabits per second for the future. So we're building this SerDes technology into our customers' custom silicon and their XPUs and also into our own scale-up switches. All right, now let's go all the way inside the package. We're not talking about meters anymore. We're talking about millimeters. And you might not actually think about this as a connectivity challenge, but today most advanced chips have multiple chiplets inside the package. So when you have 2.5D or 3D packaging, it's fundamentally a connectivity technology actually. And it allows these chiplets to sit very close together inside a package and communicate through ultra high-speed short-reach die-to-die interfaces. Now, Marvell has leading die-to-die SerDes and leading capability in advanced packaging, allowing our customers to build some of the most complex, unique, multi-die chips in the industry. So, as you can see, connectivity for AI data centers requires a very broad portfolio of technologies. Each distance requires a very different solution. And Marvell has the industry's most complete portfolio from millimeters to kilometers. Every hop, every distance. And it turns out having all of those capabilities under one roof is unusual. It's unique. When we go and compete, normally there's a different set of companies that we compete against in each one of these categories across these different distances. But this is what makes us unique. We're the one-stop shop. We're the leader across the entire connectivity stack. And that brings us to the next major challenge facing the industry.

So, what you probably notice as I described these different solutions in the last couple of slides is there's different solutions for different distances and that some of those connections today are optical and some of those connections today are electrical. And it's actually defined by distance. And so the connections on the left side of this chart are optical today. That means they use fiber optic cables to transmit light with complex electronics on either side of the cable to drive and modulate the laser that's transmitting that light. The connections on the right side of this are electrical. So they use copper cables or just copper traces that are printed on the circuit board or even microscopic copper routing inside the package. So the common theme here is copper. And in the middle you see the wall, the copper wall. And the wall is defined by the longest distance you can transmit a signal over copper. So before you have to move to an optical connection. So this is an important distinction because copper is simple and it's low cost and as Jensen said you want to use it for as long as you can. It's very practical. But optics is more complicated. It requires lasers, photonics, complex electronics. So it's a bigger lift but it's going to be needed. And the copper wall, what I'm here to tell you today is it's about to move. It's going to move again and it's going to take over the rack itself. So, this is creating an explosion in demand for the optical industry. Incredibly complex engineering challenges are coming along the way. So, why is this happening? It's not just somebody's preference to go do this. This is physics. The distance a signal can travel over a copper cable is inversely proportional to the bandwidth. So every time you double the bandwidth you have to cut the distance in half. Today the highest speed production systems in the world run at 200 gigabits per second per lane just to give you an example. So at that bandwidth the cable length is limited to roughly 2.5 meters. Now by comparison systems running at 100 gig could use about 5 meter cables and the height of the rack is about 2 meters. So once you account for all the routing inside the rack, 2.5 meters is right at the limit. So when we move to 400 gig, we can no longer fully connect the rack with copper. So the wall is moving and it's moving now. And going forward, even the connections within the rack will become optical and the whole industry knows this is coming. So we've been preparing for this moment, not just Marvell, but the industry. And you see this in Taiwan, by the way, and the supply chain and the ramp up that's happening. The ramifications for this are actually enormous because each time the wall moves one step to the right, the number of connections that you have goes up by at least an order of magnitude. So, it's creating this explosion in demand as I mentioned and the optical supply chain needs to scale up massively and be ready. But we've seen this movie before, okay? I mean 20 years ago and I remember this when state-of-the-art was 10 gigabits per second inside the data center. It was 10 gig and we used copper cables all across the data center. Optics back then was reserved for just very, very long distances. It was essentially like a telecom technology. But when the wall moved, the optics industry actually rose to the challenge. And today all the hyperscaled data centers in the world, they're all optically connected. And as we saw in that transition, it did require new solutions. You couldn't use the same power-hungry kind of telecom approach, which is where PAM 4 came in. It was optimized for power, density, and reach and requirements specifically tuned to inside the data center. And Marvell was one of the key innovators there. So, we're about to see the same wave of innovation needed as optics moves inside the rack. And that's with a technology called co-packaged optics or CPO. You hear a lot about this now. I'm going to tell you more. CPO is a technology where we bring the optical connections all the way to the package itself right next to the compute either the custom compute or the switching silicon and the fundamental challenge we're solving with CPO is density and power. Now remember the number of connections inside the rack is like 10x the number of connections between the racks. So if we just try to use the same optical technology used across the racks in the data center, you wouldn't have enough power. You wouldn't have enough physical space. You cannot fit all these standard optical modules and cables as they are today. It just doesn't work. It's not possible. So, the industry has been inventing this co-packaged optics concept which brings the optical fiber right to the package and it tightly couples the electronics that drive the signal over the fiber directly with the custom compute or switching silicon. So this is a massive change and it's hard because you're combining some of the most advanced technologies in the chip industry. Leading edge CMOS, silicon photonics, advanced packaging, optical interconnect, all manufactured in a small tightly integrated system. So the complexity is very high, but it's the only way to continue scaling bandwidth and overcome this limitation that I talked about with copper while reducing power at the same time. So this is where the industry is headed and this is one of the reasons that Marvell has invested for more than a decade in silicon photonics, optical DSPs, all the analog components around it, and all the advanced packaging you need to pull this off. It needs to all come together actually in CPO. So this isn't some futuristic thing, guys. It's happening now and in fact I brought a couple of Marvell examples with me today. So let's do a quick show and tell.

Okay. So, over here you have a traditional Ethernet switch. This is our 100T Teralynx switch that we announced today. And you guys are the first to see it. Actually, everybody here in the room. You can see the switch in the middle of the board. Copper traces inside the PCB carry the signal to the front panel, which is here. And this is where all the optical modules plug in. Now, let's move over here. This is a CPO based switch right here. Now, notice that there's still the switch silicon in the middle. That's right in the center of the die of the package. In this case, this is our 51.2T switch. And all around the edges are 16 3.2T optical engines. So, 16 times 3.2 you get 51.2. So, the fiber is directly attached now to these engines. It's not to the front panel. So, we've completely eliminated the copper traces on the PCB. Light comes directly out of the package. This is a very, very complex piece of engineering and it was very cool to be able to show this off today. So, co-packaged optics is here and the industry is scaling up to meet the challenge and as we've seen time and time again, every time we reach a physical barrier, we break through it with technology and innovation. In this case, by replacing copper with fiber, because unlike electrons traveling over copper wires, the distance that photons can carry a signal through glass is largely unrelated to the bandwidth. So, as AI infrastructure demands even higher transmission speeds, and needs to scale to larger and more complex systems, spanning millions of processors woven together now, not thousands or hundreds, optical connectivity will increasingly become the de facto solution. So the real question becomes what does it take to deliver optics across the full AI infrastructure stack? What's it going to take? Well, it starts with recognizing there is no single technology for the entire data center. It's not how this works. There's no one-size-fits-all solution. There's no shortcuts. There's no easy way to the end here. There's not a single architecture, modulation scheme, frequency band, or unique technology that's going to do it all. There's no free lunch. That's why we are pursuing a bunch of different unique optical paths across every distance to get here. Each one of these technologies is optimized for a different design point. Each one enables a critical part of the infrastructure and addressing different requirements for density, bandwidth, power, and integration all across the stack. So if optical interconnect is the underlying technology for which next generation AI infrastructure is built, then Marvell is building the broadest portfolio with the deepest bench in the industry. But no company can deliver this transformation alone. And as Jensen talked about earlier, right, it takes an ecosystem to get here.

So, like I said, technology innovation is great. It's part of the challenge, but not all of it. But demonstrating this at scale is really what matters. And at this point, if you're just operating on a PowerPoint or a demo press release, it's not going to get you there. Customers need solutions now that are ready. They're reliable. They need to be manufacturable and be ready to deploy at scale. So Marvell and our ecosystem partners have been doing this for a long time. We've already shipped hundreds of millions of DSPs. We've accumulated through our volumes tens of billions of device hours of data in the field. This experience matters because these products have to work not just in the lab but in the world's largest data centers at very high volume and very reliably for years. So that requires investing ahead in the manufacturing ecosystem. You've got to build the capacity and the supply chain infrastructure before the market arrives. This is why the ecosystem matters so much and it matters a lot here in Taiwan by the way. Now, one of our most important partners at Marvell in this journey has been Advanced Semiconductor Engineering or ASE. Now, ASE is one of the world's leading semiconductor manufacturing companies. They have more than 100,000 employees with operations in Asia and actually all around the globe with a decades-long track record of helping enable pretty much every major technology transition we've gone through in the semiconductor industry. Now leading ASE through this period of transformation is someone that I know quite well. He spent more than 25 years helping shape both the company and the industry. Today I'm thrilled to have my next guest speaker come up which is ASE CEO Dr. Tien Wu. Tien, please join me on the stage. Thank you.

Hey, Tien, how are you?

Great to see you. It's an honor to have you on stage with us.

Look, we've been working together a long time and, you know, when I became the CEO, you know, we had a set of ambitions. We talked to a lot of our suppliers. I've known you even before I was the Marvel CEO, when I was an executive back at Maxim and we worked together there. But part of what I want to maybe explain to the audience too is that sometimes people don't realize that as a key supplier into this ecosystem, you have to make bets, right? You got to make bets on the companies you work with. You got to make bets on who you think is going to be successful. And we really appreciate that ASE bet on Marvell very early, very early. And we've seen great success actually based on that. But I'm just curious if you could share your perspective maybe on where Marvell was, what your thought process was, and then where are we today in our journey together. So, it would be great to hear from you, Tien. Thank you.

Yes. We also appreciate that over the last 10 years we have gone through a lot of strategic discussion, right? You make commitment to us, we make investment for you, and over time we're going to produce more of your parts. I think that's the really short story for how that decision was come.

Maybe one more for you. You know, the ecosystem here in Taiwan is so unique and like you said it takes like a decade of investment before you really can see the return. And there's just such power that's happening here. How do you describe it to people here and also there's a lot of people around the world watching and then what makes it possible here? Why is it unique? And then what also makes it difficult to replicate this in the rest of the world? But at the same time there's globalization. So how do we think about those dynamics? I think that'd be an interesting one.

Right. Great. Well, thank you so much. I appreciate the partnership so much. We're off to the races. Tien, thank you, Tien Wu.

Okay. So, like we said, the future of AI data centers is all optically connected infrastructure. And you heard him say it, right? This is going to drive a tidal wave of growth innovation that's needed in scale and in manufacturing. But what does that inevitable future actually look like? I mean, if you just take a step back for a minute and you actually don't think about right now, think about 10 years in the future and it's a world where a lot of the copper connections are gone and just think about a world where data transmission now at some point is all optical. This is a world where distance doesn't matter actually and that's a profound change. Servers, racks, and overall data center architectures today have all been designed around the constraints of distance. And software workloads actually have also been optimized around those same constraints. But what if distance no longer matters? How might the architecture itself change? And what new capabilities become possible when the infrastructure is no longer constrained by distance? So let's start with the scale-up network in the rack. As we discussed earlier, this is where we can connect the largest possible number of processors together in a full any to any configuration. Now, in the past, the size of this domain was limited by the length of the copper connection. But with optics, distance doesn't matter. So now we can change the size of the scale-up domain from 72 or 144 XPUs or GPUs to a thousand or more all optically interconnected. The implications for workloads are enormous. Today AI workloads must be broken down into smaller subproblems that fit within the scale-up cluster because communicating outside the cluster today is slower, much lower bandwidth. But optically interconnected systems can manage workloads on an order of magnitude larger. And it does not stop there. By the way, what happens when the optical connectivity comes inside the server itself? Modern AI servers are composed of a certain number of CPUs, XPUs, memory, and network interfaces. And the reason they're all in the same system is because of distance. The CPUs and XPUs need to access memory at very, very high bandwidth, which means they need to sit right next to each other on the board with copper traces serving as the connections between them. But in a future where these connections are all optical, distance actually doesn't matter. You can imagine a completely disaggregated architecture. XPUs in one system, memory in another, generic CPUs in another, which unlocks another possibility. In today's systems, the ratio of CPU and XPU or GPU, it's fixed. So these ratios have to be defined at the time the system is built and deployed. But no two workloads require exactly the same ratio. Jensen talked about this actually, which means at any given time some portion of the compute or memory could be underutilized for a given workload that costs money. But once we decompose the system into separate pools of compute and memory and they're all optically interconnected, then we can compose dedicated systems on the fly that are optimized for whatever the workload is. So imagine future data centers, a globally optically interconnected data infrastructure. These rigid boundaries we have today in the systems we have, they begin to disappear. Compute can now be pooled. Memory can be pooled and infrastructure can be composed dynamically at scale. For the first time, architects can begin designing AI systems around the needs of the model, not around the limits of the interconnect. So, this is where AI infrastructure is headed. It's a data center without distance where compute, memory, networking, and photonics operate as one unified system where millions of resources across the data center can work together as if they were one machine. An architecture defined by the needs of the workload, not the limits of the connectivity. We believe this is the next era of computing infrastructure and Marvell is helping build the connectivity foundation that will make all this possible. Thank you very much for your time today.