QUALCOMM Incorporated (QCOM) Earnings Call Transcript & Summary

Good afternoon, everyone, and welcome to Qualcomm's 2026 Investor Day. It's great to be here in New York, and it's great to see so many familiar faces. Now a lot of you have been asking me recently why I joined Qualcomm. And well, I think it's pretty clear. I think we have a really compelling investment case. And today is an opportunity to really share with you why we're so excited about what lies ahead for Qualcomm. We've got a lot to share with you today. Before we jump into things, I just want to say a big thanks to everyone involved from Qualcomm and making this day possible. It's a huge amount of work. I really had no idea how much man hours goes into put an event like this on. And just wanted to say thanks to everyone. It's really amazing. And I also wanted to say a big thanks to all the executives from Qualcomm who are here today. You've traveled a long way, and we really appreciate your support. And I also want to thank the modular executives here today. We have Chris and Tim, and we'll hear from them a little bit later. Now an investor day wouldn't be the same without a disclaimer. And so I direct your attention to this slide, which contains important information regarding our use of non-GAAP financial measures and forward-looking statements as well as the use of Qualcomm throughout the presentation. So we have a packed agenda today. Cristiano will start the day with a strategic overview. Lots of things happening at Qualcomm, which he will go through. And then we will pivot to data center. And I know you've all been waiting to hear from us on data center. Tony Pialis will -- who's new to Qualcomm and runs our data center division -- will talk you through why we're so excited about the differentiation we can bring to the data center and why we see big architecture changes happening with agents. Nakul will then address how Qualcomm is moving up the value chain with full stack solutions across automotive, but more recently, industrial AI and robotics, where we see a really big opportunity for Qualcomm over the next 3 to 5 years, particularly in physical AI. And then Cristiano will come back to talk about how agents drive new edge opportunities for Qualcomm, including 6G. We're also going to share some important updates on the software side. I'm sure you saw the announcement today with the acquisition of Modular. Akash will be our last speaker of the day, he will be bringing everything together and giving some important updates to our financial outlook. After Akash, we will have a short Q&A, and then we encourage you all to join us for drinks behind the curtain in the demo room. And you'll also see some of the new products and experiences we want to share with you today. And just one more thing. I was actually going to put my [ kill ] on today. It's a big time in the U.S. with the World Cup. And tonight, Scotland will be taking on the mighty Brazil. And I think Scotland only need 1 point to qualify here. So after 6, you may not see me, I'll be going to join the [ Tartan ] army. So with that, please join me in welcoming Qualcomm's President and Chief Executive Officer, Cristiano Amon.

Thank you so much, Brett. Appreciate it. Thank you. Thank you, everyone, for joining us. I really appreciate it. This exciting day for Qualcomm. Actually, as a matter of fact, June 24, this -- I became CEO of Qualcomm June 30. So it's been -- actually, I picked this week on purpose. This is exactly 5 years since I became CEO of this company. Well, thank you. This incredible company that I kind of joined as an engineer about 30 years ago. And what I'm going to tell you today and to tell the reason this is special, it's going a little bit about the transformation. 2021, we put a strategy together. We acted an Investor Day in 2021 in that year, and we said that we're going to do. And I think we are at a point now that we put everything in place to execute on the strategy, and it's time to start the new chapter of Qualcomm. And that's what my intent is to tell you about that today. This is really exciting days. We have a lot of things back when we did the best that we could to pack a lot of big pieces of information in a short period of time. Brett walked you through the schedule, we have Q&A at the end. Many of you will be tempted to run to some memory guy, earnings call starting at 4:30, but I would encourage you to reconsider and stay for the Q&A. It's going to be an awesome Q&A. So with that, let me just get to the -- with the presentation and before we start to each one of these chapters. So this is where we started. And I think it's been a company that has been probably the most focused semiconductor company in the mobile market. And I know -- I think the mobile market, to some extent, I think, out of favor when everyone in Earth had a smartphone, but it's going to become very interesting. But the reality is we were the most focused semiconductor company on this 1 market. And we put together a strategy and start executing that strategy, that's what I told you. And the next -- the following 5 years, this is what we built. We built a diversified edge leader across multiple end markets. We built an automotive business. We built we call an IoT, and we're going to start -- I know there's a lot of things within this IoT segment for us. So we started to break down for you, what we call personal AI and compute, which -- how wearables and virtual reality of materiality evolve into personal AI devices, as well industrial, networking and robotics. And that is the company that we built, really focus on the edge to create a diversified edge leader across multiple industries. And we are now in this transition to what we're going to do in the next 5 years, and this is the next chapter of Qualcomm. And that, there's basically 3 pillars to it. There's actually 3 dimensions to the Qualcomm future. The first one, and I believe many of you came here today to see what we're going to do in the data center. We're building a data center platform. It's a comprehensive portfolio of solutions. I think you should think about it -- what we have done in the past few years like a submarine strategy. We've just been executing, executing, collecting assets. And when we get to this point, we feel that we have a comprehensive portfolio to enter the next phase of the data center. And you hear about it as inference scale as we see the segregation. A lot of people ask me this question. And before you ask in the Q&A, I'm just going to even answer. A lot of people ask, "Oh, this crowded market, is it too late?" Never too late for Qualcomm, I think, because this is a market that moves very, very fast. So if you have technology leadership, there's always room for you. And I think that's how we think it. People cannot like the company about many different things, but I never had anybody that said Qualcomm does not have a solid, I think, technology capability. And I think that's what we put into work. The second part is now that we have been building this platform of devices at the edge, we're moving to a full stack player. So as agentic start to transform the devices on the edge, we're going to build on those assets. And it is one of the broadest portfolio of semiconductors, and we'll be kind of a full stack player in physical AI, compute, everywhere. And that's what you're going to see in the presentation today. And then it gets to the part number -- actually, I wanted to -- before I go to Part #3, I wanted to break that down. There's actually one more thing before the full stack execution in automotive, industrial network and robotics. It's what's going to happen with our mobile business. And I actually have a portion of the presentation today to tell you how you should be thinking about the future mobile edge devices that consumers are going to utilize, and that's part of the second dimension of Qualcomm. And that gets to the number three. And then number three, is how we're going to go from silicon to platform solutions, fully integrated platforms across hardware, software developer ecosystem also making Qualcomm a developer-first company. So that is the 3 dimensions of this new chapter of Qualcomm and what we've been going to be doing in the next 5 years, and we're going to show you some of the early wins that we have on this. And I want to summarize to you as I get off the stage, what is the Qualcomm advantage. We're always very proud of our technology and IP, but we're also a company that can build very broad relationships. That's one of the assets of the company. We have built strategic relationships across industries. We were not a player in many of the new industries that we enter. And if you look at today, Qualcomm built very strong relationships with all of the leaders of the industries, and we built an ecosystem around it. The other part of it, and that's why when people ask about if it's late to enter the data center, you think about scale and execution. Our engineering capabilities, our operations and supply chain is one of the leading scale and execution machines in the semiconductor industry. When I say about the technology advantage of Qualcomm it's really very broad. It's really a broad portfolio. And we take pride in providing leadership solutions. We have been, in many areas, a creator of standards. And that with everything connectivity was just wireless, now it's wireless and wireline, including connectivity in the data center. And that is the portfolio that we have been building. It's every form of compute. When data centers talk about disaggregated computing, this is the reality of mobile from a very long time ago. So we have every disaggregated compute -- and actually, we do this for different industries, including what we do on safety and industrial-grade processors. We're building a comprehensive software and developer ecosystem, and you're going to hear more about that today. Sensing becomes even more important when you think about physical AI multimedia, advanced packaging memory. And we have a very strong patent portfolio, which is a result of about $100 billion cumulative in R&D investment. Our customer reach -- and you should be thinking about those concentric circles. We started building an ecosystem partnerships in mobile, and you see as we go into every other industry has now expanded. And that is a unique asset of our company. Our customers believe in Qualcomm, and that is true, especially when we enter new markets, and that's what we're going to be doing right now in the data center. And the last part is the execution. I will spend a little bit on this. We're actually a proud member of the TSMC fabless ecosystem. We work with a large number of foundries and manufacturing partners, and we have incredible scale. I'm just going to give you a few highlights. We consume over 1 million leading node wafers, this is just annual scale. We do over 75 chips, tape-outs per year. Of that 75, over 30 is an advanced transistor. We ship 40 billion components, total of 2.5 million wafers. That's a lot of scale. And we also have some things that are very unique. Some other semiconductor companies actually tell us, Cristiano, you're crazy. Sometimes we tape out a chip. Before we get the chip back, as soon as TSMC finishes the mask, we go to production, and we go to production at scale. That's the maturity of our manufacturing capabilities, and we ramp completely new nodes to 100,000 wafers. That's the nature of the phone business within about 2 quarters. There's a very big ecosystem. And I think that is an asset that we've also been bringing to the table. And the unique thing about Qualcomm -- and I said about this in [ Computex ] -- is we are now present across the entire compute continuum, from sub-2 milliwatts to about 200 kilowatts. And if this gets transformed with AI and we apply our technology and we build on a developer-friendly unique software platform -- I'm sure you're going to hear more about that -- that creates an incredible opportunity for the company not only for the business that we have built, but what we're going to build in the next 5 years. So that's our strategy. This is what we're doing. And now what I'd like to do is start unpacking that to you. I think I want to bring to stage Tony, who joined us from [ Alphawave ] and now runs our data center business. And I'm sure you're going to be amazed about what we're doing and what Tony is going to share with you. Tony, please come on stage.

Hey, everyone. I'm Tony Pialis, General Manager of Data Center for Qualcomm. First of all, where is Brett Simpson? Brett, I thought I told you. I never want to follow Cristiano. I mean, in terms of being a visionary in the industry and a legend, I'm so lucky to be part of his team. So folks, one of the most common questions I've been receiving this morning as I've been meeting with many in the press is why would I want to leave my role as Founder and CEO of a semiconductor company to join Qualcomm to run their data center business? For me, look, I've always been good at math. The equation for that has been really simple. It's all about accelerating value creation. That's what we're here to do. That's Cristiano's vision. That's why I came, and that's what I'm here to explain to you today, how are we going to accelerate value for all of our shareholders and customers. So let's get to that. Agentic AI changes the economics of compute. What does that mean? It means token counts are skyrocketing as we introduce agents. It means CPU attach rates are soaring through the roof. You can't find CPUs anywhere. They've already been bought up. So traditional infrastructure will not scale to the needs of agentic AI. So the industry needs a paradigm shift in order to deliver this. I will explain this paradigm shift and how we will lead it. But first, let me introduce to you, Dragonfly. This is our data center infrastructure to lead the industry into the next stage of AI. Let's hit it. [Presentation]

Well, Cristiano, the submarine is surfaced and here we are today in New York. All right. So team, agentic AI requires a new compute infrastructure. This chart illustrates why. The industry has blown through Gen AI and reasoning, and here we are in the cusp of deploying agents en masse. But what does that mean? It means a single agent queries generating 50x to 100x inference requests. We have over 1 million tokens being generated by a single query. Traditional compute infrastructure cannot support the scale. A paradigm shift is needed. So first, what you see up on the screen, which by the way, I love the screen, I need to get one in my basement. What you see up on screen is a traditional GPU-based compute that's been deployed across data centers worldwide. It's been built to support both training and inference. It runs hundreds of kilowatts today. Other competitor solutions will be running north of 500 kilowatts. How can we deploy this and support 100x more inference calls than we do today? We can't. A new solution is needed to lead the industry. And so what we and our submarine have been doing is building a world-leading disaggregated compute infrastructure. Cristiano used the analogy, well, we already did this in mobile. That is what's needed to lead in data center, bespoke solutions that deliver hardware acceleration for each and every function needed to deploy agentic compute. Various forms of CPUs performing specific functions, unique XPUs, some targeted to attention for prefill, some targeted for [ KV ] cashing during [ deco ]. All of this [ blaze ] together using both copper and interconnect that deliver world-leading connectivity, creating 1 compute network that will transform the industry. Now as I've also been asked earlier today, how is Qualcomm positioned to win? Aren't you late? Let me give you some background. I've been formally in the company for the last 6 months, but I've worked with the company for years as a supplier and partner to them. What I will tell you is this is an engineering-first company. They blazed the trail and led the world in wireless communications. Fast forward, they lead in mobile compute. Now we are leading in both automotive and PC. When the company turns its attention to solve a new problem, we revolutionalize the solution and push our way to the forefront. And folks, I am here to tell you today, that is what we will do and are doing in data center. We are pushing our way to the forefront, and our customers are pulling us the rest of the way in. So how are we going to deploy our agentic infrastructure? 4 simple steps. The first one starts right now. We have our connectivity portfolio that comes from [ Alphawave ]. That is in high finally. I've been talking about getting this into production for the last 2 years. I am proud to say this year, we are qualified in our first leading major hyperscaler, generating meaningful revenue for the company over the next 4 quarters. Follow that by custom silicon. Lots of speculation, and I'll have a lot more to say, so you can hold your questions. We are winning in this space, and we'll be delivering meaningful revenue at the end of this year, calendar year, starting fiscal quarter 1, 2027. Then in 2027, we are launching our third generation of AI accelerator. What makes the third generation unique is it's the industry's first near compute AI accelerator that will transform inference. A lot more to come on that. And we're not done yet, folks. You layer on top of that, in the middle of 2028, we will be launching the industry's first Oryon server-class compute solution. We will launch a fleet of a agentic, general purpose and head node compute that will complete the Qualcomm infrastructure. Now let's get to it. So this is the best worst kept secret or the worst best kept secret, however you want to view it, that's limiting the industry. I've been in the industry for 30 years. Compute has increased by more than 60,000x in that span. Kudos to the amazing Qualcomm engineers for accomplishing this. But guess what, where transformer sizes are growing 240x over a span of 2 years these days, look, compute memory is only doubling in that same time span. So what does this mean? It means there's no point packing more compute unless we solve the memory bottleneck. So before I show you how we do that, what you see here is how a modern day XPU works. You see that GPU off to the side. It is pulling and pushing data to a HBM stack. You have thousands and tens of thousands of wires constantly carrying data, generating tremendous amount of heat, burning significant amounts of power, moving data back and forth in order to support the scaling transformer sizes. This solution cannot keep up with the growth in AI models. So an innovation is needed, and this is what the submarine has been doing. This is what I was most pleasantly surprised to find when I joined Qualcomm. We have broken through the memory bottleneck. How have we done it? We have rearchitected compute for XPUs. We have separated the AI accelerator from the XPU. And what you see, we now put our XPU right under a DRAM stack. What does this mean? Very, very important. We offer all of the performance advantage of SRAM, but with the density and the memory capacity that HBM stacks offer. And so what does this mean to the industry? It means that congestion that you saw with HBM is gone. What happens now? A great analogy I use with reporters is imagine working in the same building that you live in. And so you only travel up and down. And what does that mean for the highways and the roads that connect the suburbs to the city? Guess what? The roads are clear. So the value this brings to the industry is lower power consumption, less heat. And that expensive road of silicon interposer that HBM solutions use are no longer needed. We can deploy multiple HPC stack within a single compute device using standard packaging. That is a tremendous value that we deliver to the industry in terms of performance per cost advantage. Now how do we quantify these benefits? Look off to the side. These are ultra low latency workloads, things like coding workloads. HPC offers 200x capacity per watt, better solution than SRAM. There are many that are announced seeing SRAM-based solutions today, look what we can deliver with HPC. Now you look at the other end of the spectrum for high-throughput workload. These are the workloads that GPUs and HBM have dominated for years. With HPC, we deliver 6x the bandwidth per watt versus competitor HBM-based solution. And so while the rest of the industry is now trying to deploy 2 unique solutions to solve these bookmarked endpoints -- and those in the middle, well, guess what, they're kind of stuck in the lurches. With HPC, we offer a single solution that can seamlessly span this entire sphere of workload and deliver multiple fold performance per watt and performance per dollar benefit. That is a direct TCO advantage that we offer to the industry. So I go back to those asking questions. Are you late to the game? With this kind of performance advantage, the industry is demanding and pulling us in. So who better to introduce HPC to the industry than one of our lead partners, Microsoft? So it is my greatest pleasure and my first but not only surprise to you today to introduce Satya Nadella, who will talk to you about how Microsoft will be deploying HPC within Azure data centers. Let's hit it.

Hello, everyone. is great to be back at Qualcomm's Investor Day. At Microsoft, we have had the opportunity to partner closely with Qualcomm across multiple waves of computing, from the PC to mobile, and now, AI. And across all of them, we have shared a deep commitment to innovation at the systems level, bringing together the silicon and software to deliver meaningful advances for our customers. This includes our continued collaboration to reinvent the PC for the AI era, which will only become more critical as we deliver unmetered intelligence at the edge with Windows. And we are not stopping there. In fact, with Project Solara, we are collaborating on a new platform purpose-built for agent-first devices. And it's been fantastic to see the reception since we announced it together earlier this month. And now we are excited about your innovation in the data center, especially around high-bandwidth compute, and we look forward to building on that together. HBC implements an innovative architecture with high memory bandwidth and integrated compute that unlocks significant improvements in cost and performance for the next generation of AI infrastructure, and there's so much more to come. We look forward to our continued partnership as we build next-generation of computing together. Thank you very much.

Folks, if I had a microphone rather than a lapel device, I would drop it right there. So that's our first surprise. Stay tuned, many more on its way. All right. So how are we deploying HBC? Well, we're deploying it to target a $680 billion addressable market for us. HBC earns us the right to win a significant portion of that market over the next few years. Look on the left side of the chart. To quantify Satya's comment in terms of performance benefit deploying HBC, we deliver, depending on the workload, anywhere from a 4x to 8x advantage. That directly translates to TCO advantage that we deploy to our customers. I am super excited for our launch of our first HBC product in the middle of 2027. That's it, folks. Take those pictures. You're going to be seeing this to last for years to come. Our AI250 product will introduce the first near-memory compute that employs HBC and will be a complete game-changer to the industry. We're following that in 2028 with AI300, which launches our second generation of HBC. It will integrate [ UAL and East ] on the latest scale-up network fabrics. And for scale-out, we will be deploying both copper and optical networks to connect AI clusters. Now look, as amazing as this hardware stack is, it really is just a foundation for running software. Software is where the magic is, and it takes a lot for me, a hardware designer, to acknowledge that. We will be deploying a full software solution stack that includes the most sophisticated orchestrators that will manage and route the traffic across a disaggregated compute cluster, all the way down through frameworks. And most importantly, open frameworks that will allow model developers to both develop and deploy it [ scare ] their models. We offer all the kernels and compilers that are necessary in order to optimize the latest models onto our specific hardware. While others in the industry build moats trying to protect their hardware deployments, myself and we at Qualcomm, we believe in building bridges to unite the industry. And so my second surprise, which I know came out through an announcement, but it is still my honor to announce that today, we published an announcement for Qualcomm to acquire Modular. Modular is a world leader in developing AI software solutions. And who better to introduce how we will jointly transform the AI industry with our open solution to disaggregated hybrid compute than Tim Davis, Co-Founder and President of Modular. Tim, will you come up on stage?

Thanks, Tony. And you can obviously tell between Cristiano and Tony. That's why we're so excited to be joining Qualcomm. But hey, everyone. I'm Tim, one of the co-founders of Modular, and I've been building AI infrastructure for almost a decade. First, at Google Brain for 6 years, building core AI data center and edge infrastructure for mobile devices and TPUs. And then at Modular as Co-Founder and President. Modular has assembled one of the best teams in the industry that have helped found, build and contribute to most of the core AI infrastructure in use today. Now you may have read this morning that we are beyond excited that Modular is joining Qualcomm to supercharge our AI infrastructure and distribute it to the world. But you might be wondering, what is Modular? Well, I'm here to tell you, Modular is building AI's unified compute layer, a software layer that enables AI models to run on any hardware and is heterogeneous by design. For developers and enterprises, that means building once, deploying anywhere, lowering the cost of running AI at scale and accelerating innovation into their production data center workloads. Modular is the portable alternative to NVIDIA's software stack, designed from day 1 for every AI accelerator. Let's walk through the stack. Mojo gives developers the high-performance, low-level programming model they need without locking them into 1 platform. MAX gives them the model in serving layer that they need without relying on [ Triton ] or TRT LLM. And Modular Cloud gives enterprises the distributed serving infrastructure they need without being tied to a single silicon vendor. Together, this is a full AI compute platform for the heterogeneous data center. And we are up to 50% faster when executing AI inference workloads on third-party hardware. And we have the numbers to prove it. After 4 years of R&D, Modular is rapidly coming to market with industry-leading performance across many of the world's most foundational AI models. Now importantly, our platform turns heterogeneous data center systems into multi-silicon AI token factories. In this world, enterprises, partners and developers can use the best silicon for each workload without being locked into a single hardware stack. Because Modular is heterogeneous by design, the industry can achieve lower TCO, higher performance and greater portability across the world's compute infrastructure. We are incredibly excited that Qualcomm will help us scale our technology to data center customers everywhere, enabling broad hardware independence for the world. Chris Lattner, my co-founder at Modular, will share more about our incredible future with Qualcomm later today. Back to you, Tony.

Thank you, Tim. Look, rather than preparing for this event, I've been fielding calls all morning from hyperscalers and customers asking, "Hey, how can we begin incorporating Modular's technology." So I'm super excited about what we will be doing together. So now let's talk about CPU technology. Qualcomm has a long lineage in leading in CPUs. They pioneered mobile compute with Snapdragon. We are winning now in both PC and automotive. The company's focus is now transitioning over to data center. And so today, there's been a lot of speculation about this, but I am excited to introduce Qualcomm's [ C1000 ]. It is a data center fleet of processors. These processors will run the industry fastest cores, running greater than 5 gigahertz. This is more than 30% faster than any of the competition. Coupled to that, we offer more than 250 cores to run the highest throughput workload. Combine that with Alphawave's leading [ PCI-Express ] technology delivering greater than 2 terabytes of IO bandwidth. Then add on Qualcomm's memory leadership delivering the highest performance, lowest cost memory solutions, employing LPDDR. We have server-class [ RAS ] security embedded directly in the hardware. And finally, our CPU is also AI native. That amazing HBC technology that I walked you through for our AI inference engines couples directly as an HBC attach to accelerate AI workloads natively onto the [ C1000 ]. Now how are we deploying it? Through 3 various product lines for the [ C1000 ]. The first is our agentic CPU. Leveraging our HBC attach, we deliver industry-best performance. We then deploy it through general-purpose CPUs running virtualized container workloads. And finally, our AI head node CPUs running and orchestrating all the traffic across disaggregated, heterogeneous compute data centers. All of this targeting a $200 billion market, and that number is growing every day with each and every analyst report that gets published. So my next surprise for you, I'd love to introduce to you Mark Zuckerberg, Founder and CEO of Meta, as he introduces how Meta plans to deploy the [ C1000 ] into its next generation of data centers. Hit it.

Hey, everyone. Great to be here at Investor Day with you. Meta and Qualcomm have been partners for a long time, and we're doing some great work together. We first started on the Quest headsets, and then we brought Llama to Snapdragon so people could run AI right on their phones. And today, Snapdragon is powering our AI glasses, too. Now we're bringing that partnership into our data centers. With our latest model, Muse Spark, we're delivering AI to billions of people every day across our apps. The data centers, the energy, the compute to run billions of model inferences, that's what makes it all possible. Our goal is to deliver personal super intelligence to everyone in the world. And as our teams work hard to build state-of-the-art models, we need to innovate with how we get the power we need, scale it and make it accessible to everyone. So that's why our work with Qualcomm is so critical. They've spent decades figuring out how to get the most performance out of every watt. They're really good at it, and now is the right time to expand this partnership. So today, I'm excited to share that we've entered a multigenerational collaboration for Qualcomm to supply CPUs for our data centers and help power our next-generation server fleet. This will help put personal super intelligence into billions of people's hands. There's a lot more to come, and I'm looking forward to building together for a long time. So thank you to Cristiano and all of the teams at Qualcomm for all the work you do here.

Satya and Mark already in my presentation, and folks, I'm not done yet. So let's move on to the third product line, custom silicon. There's been rampant speculation in terms of what we're doing here. First off, I want to establish, we are in custom silicon to target the highest tier of customer where we can deliver the most value add by bringing our incredible IP portfolio to play. So our wins to date are based on both Alphawave legacy wins that are scaling into production. And most interestingly, in the first 6 months here, I am extremely excited to announce we have won 2 major hyperscaler deals that will contribute meaningful revenue to Qualcomm starting at the end of this year. And so folks, how do we win in custom silicon? We work with our customers. We take their specs, and we help them build their chips, whether it's in the front-end RTL design or whether we help them convert their designs into chiplet-based solutions, delivering the most advanced computer networking solutions in the world. And then using our manufacturing scale and know-how, we optimize their yield and enable them to deploy their bespoke solutions en masse to their data centers. I've been in this space for 30 years. The way you differentiate and win in custom silicon is through your IP portfolio. We have the world's best custom silicon IP portfolio. We have our own compute that we can optimize for our customers. We have HBC, a complete game changer in the AI industry. Add on to that, Alphawave's leading electrical and optical [ SerDes ] that kicks the butt of its competitors. We've been working in the silicon photonics and optics space for more than 5 years. We bring that to play in order to bring connectivity directly into compute. And you couple that with Qualcomm's leading manufacturing and supply chain. This is how we've been winning in the first 6 months. I have not had to push my way into hyperscale customers. They've been pulling us in. And when they pull us in, it gives me a chance to expand and bring the rest of my solutions to play. The final product line I will walk you through is our connectivity. This is the third bottleneck in the industry. The first was memory. We solved that with HBC. The second was the software stack. Modular, Tim and Chris will help us solve that. The third is connectivity, and this is near and dear to me. This is where hyperscalers, as they deploy clusters of compute, as AI compute doubles, so does connectivity every 2 years. And then you have this transition from copper cables to optical solutions with new low latency scale-up and scale-out fabrics. This is a race to the forefront, and we have the technology pieces needed to win. We have everything you need to scale from the millimeter of connectivity all the way through to tens of kilometers, from our leading die-to-die technology to our co-packaged optics interfaces that bring the world's fastest lower power connectivity right next to our compute. Add on to that, leading PAM4 electrical and optical [ SerDes ] to drive scale up and scale out networks. Today, in production at 224 gig, soon, we'll be in production with 448 gig. And finally, [ Coherent Light ] today, it drives optical connectivity across campuses. But when PAM4 runs out of steam, [ Coherent Light ] will be connecting compute clusters within the data center. So with all these pieces, I'm excited to announce our connectivity portfolio. We are already in production with our first generation of 800 gig electrical and optical DSPs, including our first generation of [ Coherent Light ]. By the end of this year, we will be in production with our second generation of electrical and optical solutions, deploying 224 gig solutions. These products are anchored with the lead hyperscaler win already. And then looking forward to 2028, we will be bringing in our third generation of connectivity solutions. This will be based on 448 connectivity. And we'll also deploy our next generation of [ Coherent Light ] solutions. So folks, stepping back now. We have developed a transformational infrastructure that is already winning in the industry. Four product lines, each of them already anchored with multiple customer wins and a pipeline that will blow your heads in terms of accumulated value. Incredible metrics. Look at that, up to 8x better tokens per watt per second than traditional GPUs, greater than 200x memory capacity compared to SRAM solutions, 6x memory bandwidth per watt. And for our CPUs, greater performance than 2x than our competition. All of this is direct TCO advantage. So why are we entering now? Because we have the performance that the industry needs. So folks, final takeaways I want you to remember. Tokens per watt replaces flops. The race has changed. Embedded solutions, embedded providers, they're playing the old game. There's a new game in town, and it's all about delivering agentic first rack scale platforms that delivers the world's best TCO. That is what we've been building in our submarine. Add on to that, 4 product lines that we've already anchored with hyperscaler wins. And now with today's announcement, we have the world's best software stack that will build bridges across all the hardware of the industry. And finally -- and I will leave the numbers to our man, Akash. I am very, very proud to announce within the first 6 months on the job, we will deliver multiple billions of revenue starting fiscal '27. And for those of you that aren't aware, that means starting this calendar year. And so I am excited now to introduce my colleague, Nakul. But before he comes on stage, I have one last person I want you to hear from. It's Tareq Amin, the CEO of Humain. He is a visionary in the industry, and he has been our first data center customer. Let's hear what Tareq has to say. Let's hit it, and thank you.

Congratulations to Cristiano and the entire Qualcomm team on this bold milestone. AI is no longer a technology trend. It is becoming the operating system for every industry, every economy, and every society. I believe the next decade belongs to inference, billions of agents, trillions of interaction, continuous intelligence operating across devices, enterprises and government. Through our collaboration, Humain and Qualcomm are deploying the next-generation AI infrastructure by combining Qualcomm breakthrough in semiconductor innovation with Humain full-stack AI capability from infrastructure, cloud platform, foundation model and [ Salmon ] AI system. Success will not be measured by peak performance alone. It will be measured by performance per watt, performance per dollar, performance per outcome. This is where Qualcomm brings something extraordinary, a really fundamentally different approach to AI compute that challenge really conventional assumptions about power consumption. Years from now, we'll look back on this moment as the beginning of a new era for AI.

Good afternoon, ladies and gentlemen, and a very big round of applause for Tony, first of all. [ Nakul Duggal ] here. I run Qualcomm's automotive industrial and robotics businesses, and someone who's been with the company for over 30 years. There has not been a more exciting time to be at Qualcomm. Each of these 3 businesses are quite different. They're quite unique, and they need different strategies. But over the years, to diversify Qualcomm, we've had to build new muscles that strengthens over time. We see the next several years belonging to physical AI and massive transformative change that physically as men will drive in industrial and enterprises and especially robotics becoming a key catalyst. You've seen what we've done with auto. I'm going to give you a sense over the next 30 minutes, how we are preparing for physical AI. Physical AI is the next great computing wave. It doesn't run in the cloud. It runs on the edge. It's going to run in factories and warehouses, in retail, in hospitality, in hospitals. And robots are going to be a very important part of physical AI. Automotive is the first example, and you've seen what we have done with the automotive business. Industrial and embedded, I gave you an update on this space in November of '24. And we've been preparing -- and I'll give you a sense as to how this business is progressing. And robotics is a space that we entered only in the last 9 months or so, and we have expanded very quickly into multiple environments. We are finding strategies to move up the stack. One thing that is very unique about Qualcomm. While these are 3 unique businesses, there is 1 single common theme, 1 IP road map, 1 product foundation, 1 physical AI platform. If you look at the way physical AI is now moving into our lives, you've seen human facing AI. You've started to now see machine facing AI and ultimately, [ embedded ] AI. These 3 layers are highly interwoven, and they compound over time. And you will start to see how this plays out. As I walk through automotive, industrial and robotics, hopefully, this will all become clear. But they're all underpinned by the same technology layers that create vast automation capability. Humans facing AI has changed our interaction layer, first with chatbots and digital assistants, but now with body cameras, with XR glasses. And you are changing the physical space interaction layer between humans and the devices, the products that they own. Instrumented AI, machine AI is about putting AI that is embedded into sensors, embedded into cameras. And it mostly comes down to that sensor, that endpoint being situationally aware. The real economic unlock is, however, physical AI and embedded AI. And what we see here is the ability for devices to perceive, to reason and to actuate with the goal to be able to complete a physical task, and that evolution is just starting. We find ourselves at this inflection point. And as this matures, we see a massive edge content update cycle hit us. This content shift across the edge is expected to be pretty significant across automotive, across industrial and across robotics. In automotive, over the next 7 years, you will see 500 million vehicles produced that will have AI cockpits that will have anything from L2 to L4 autonomy. This was not the case if you look at what was getting deployed, what was getting shipped over the last 5 years. 50 billion IoT endpoints by 2035. And over 1 million robots will get deployed globally. What is today a $300 billion addressable market is going to become over $1 trillion within the next decade. Right now, while we see automotive and industrial as very large TAMs, we expect this to invert, where robotics will actually become very large. This is the market that we are going to lead. Our ambition is simple. We need to own the solution, the silicon, the software and the stack for physical AI. We will build full-stack platforms where there's white space. Our operating mantra -- and you've seen this, we will win in automotive. We will disrupt industrial, and we will define robotics. Let's get started. We introduced our first generation of automotive products, especially compute products 10 years ago. Today, we are 1 of the largest automotive compute and advanced connectivity players globally. Five generations of compute silicon delivered in 10 years. Today, from first silicon to start of production of the vehicle, we have brought that time line down to 15 months. This is as fast as consumer product life cycles. We now have over 500 million Snapdragon cars on the road, 90 million cockpits powered, and we only entered the cockpit business in 2016. We have launched [ 415 ] new car models since 2021, which is 2 new models every week for the last 5 years. The Snapdragon original chassis is the underpinning of vehicle compute and connectivity globally. That is not a modern vehicle that is built without the Snapdragon digital chassis. We will exit FY '26 at $6 billion in annualized revenue. And this is after delivering 23 consecutive quarters of double-digit year-over-year growth. We have built a $65 billion design win pipeline. Our content value from Gen 3 to Gen 5 has uplifted 8x. We are engaged with over 70 automakers and over 100 Tier 1s and Tier 2s globally. This is what true diversification looks like. We are a systems company, and we have shaped the automotive industry, its platform architecture across hardware, across software, across compute, across AI, across opportunities like ADAS. And we will do this consistently over multiple generations. We are, ladies and gentlemen, on track to become the largest automotive semiconductor supplier across all pure-play automotive. If you look to the right, we have built leadership in cockpit because all of the value, all of the IP, all of the differentiation that Qualcomm brings, all of the breadth of access that we have to so many ecosystems. As we start to see AI coming to our lives, AI is transcending the traditional domain architecture of what is a cockpit and what is ADAS. So we designed for this. Gen 5 was designed keeping in mind that we can't really be traditional in the way of thinking. We're designed for a mixed criticality fabric. That means the customer can run cockpit applications, ADAS applications as they feel. They can run them separately, and they can run them together. As we build more powerful chips, that necessitates customers to be able to figure out how do these architectures change. And this is creating tremendous optionality for customers to be able to figure out how do they design the next generation vehicles. With AI now, you can process any sensor input across any specific vehicle domain. You don't have to tie the physical hardware to a specific domain. We are running 30 billion parameter models on the cockpit today already, commercial. Concurrently, we can run L2-L4 stacks. And then as you see on the left, what we used to call the software-defined vehicle, SDV, last year or the year before, this has now become an AI defined vehicle because we can now run agents directly on top of SDV that get access to vehicle context. We now have use cases where a car drives to a parking lot, sees a QR code, scans it, pays for it, and that's an agent. This is how quickly the AI-defined vehicle is going. ADAS was a new space to us about 3 years ago. We didn't really have any customers to talk about either in the SoC space or in the stack space. We are now at 25 OEMs. The open platform strategy that we have built with ADAS has allowed us to be able to provide tremendous optionality to customers because this is a complicated business. It is about safety, it's about cost sensitivity, about which part of the world you're deploying in. We have a dozen different stack partners that we have engaged with, and we are building our own stack as well. Now Snapdragon provides you the best performance per watt per dollar capability across the industry. And that is why customers are moving to our platforms. In parallel, we have built the Snapdragon [ Ride Pilot ] stack ourselves, we debuted this last year with BMW, and we are now validated in 60 countries. Stellantis is the latest OEM who has picked not only our Ride Pilot stack, but the entire Snapdragon digital chassis, which we will deploy starting SOP '28. As automotive is evolving, we see a tremendous amount of new growth opportunities. Robotaxis are things that you might have questioned how -- will the robotaxis be real. They're starting to happen. We do expect that by the end of this decade, we will start to see these scale. And our strategy with robotaxi is actually very straightforward. Tony talked about HBC. We will actually build accelerators that will connect our SoCs and HBC Gen 2 to provide that same tech to our automotive customers, and we are starting to plan to go to them in the '28 time frame. We're also seeing this very interesting transition around token generators inside the car. As automakers are putting in so much of compute and so much of memory to be able to run models locally, we are receiving requests to see an offline mode. Could those be part of a federated use case for token acceleration. We will use the same exact HBC capability for token acceleration in the car. The other area that we are seeing a lot of interest is in AI/ML use cases for the car, for the powertrain for the drivetrain for battery management. But there are so many domains in the vehicle that need local machine learning processing. We acquired a company called EdgeImpulse a year ago, and we are actually running their ML ops locally in the vehicle. That allows us to use the Snapdragon NPU for local AI compute. Same exact chassis, no big difference, just add more local processing capability. And finally, we are starting to see a buildup in satellite connectivity need. We've had a tremendous telematics and connectivity portfolio over the years. Customers are now looking to add satellite to that as well. So here is why we keep winning. We operate at the full system architecture of a car. We support a global footprint. We are multigenerational in our silicon road map. We go across every tier. We go across every domain. We are compatible across generations. And we allow customers to plan a decade against our road map. We are building a stack ourselves because we know that this technology is going to be standardized across every vehicle. We have built the deepest and widest software and AI stack in the industry. We partnered with Google. We've integrated Gemini. We partner with every global digital ecosystem, and we bring in OEM preferred ecosystems. We've built years of safety expertise. We're a smartphone company, but we have made this transition, we have diversified. We now build safety as part of every single chip, every single piece of software. Even our stack, our tools are all safety grade. And we've built tremendous supply chain complexity and resilience because we have become 1 of 1 in the automotive space. We understand supply complexity, capacity complexity, reasonable geopolitical complexity, and we've been able to scale this business up very well. So a big thank you to the Qualcomm team who's been driving this for years. Automotive is a playbook for diversification for the company. And we've gained leadership, we have bring tremendous scale, and we've built a multigeneration strategy. And as AI is upon us, we find ourselves very well prepared for the transition. Now we've learned a lot about how to diversify through our automotive experience. And I'll share with you what we have done in the last 18 months in the industrial and embedded space. Before we get started, if you look to the slide, the OP or the operational plane in any industry, in any enterprise that has traditionally never had to do any processing at the edge. It was always about capturing information and sending that information in the cloud. It was mostly deterministic. It was mostly static. And the concept was send data to the cloud, and processing happens in the IT layer. Now as you start to see AI come into the picture, it's really the same concept. You have data at the edge, you have enough information at the edge to be able to process to get to a specific outcome, whether it's to detect an anomaly, whether it's to extract specific analytics. The operational technology plane is where these endpoints are getting deployed, and that OP plane is getting rearchitected. Even brownfield settings are now being rearchitected because you can add an intelligent AI aggregator. This architecture creates a once-in-a-generation opportunity for the entire OP plane to become more intelligent, more data, smarter models, better insights and more intelligent endpoints. So we started to build our road map for this specific space. If you look at what we have built with [ Dragon Bank ] over the last 18 months, a variety of different solutions that are very vertical focused. So we have dived very deep into what our vertical customers need, and we have built solutions based upon that. We have a silicon road map that addresses connectivity, camera, commercial processors, industrial processors. These power everything from AI boxes, connected industrial gateways, edge appliances, industrial PCs, payment terminals, smart home appliances and drones, body cameras. And we picked 3 vertical categories that we focus on: industrial, commercial and mobility. And these are further segmented across 12 verticals. So we are building for every device class, every connectivity standard and every stack layer. Let me give you an example of what we have done in the vision space. So we believe that vision is the major unlock in industrial. We've had tremendous expertise in the company from our smartphone heritage, from our automotive heritage, and we are building industrial machine vision. We are building robotics, and we are building surveillance as 3 additional layers of capability. Now vision AI is a major unlock because the physical world is best processed through the lens of a camera frame. We understand lighting conditions and how to improve them, we can reconstruct scenes in 3D. We can semantically annotate these scenes. We can feed them to a BLA so that it can tell us what the artifacts of interest are. We can even predict what the next scene needs to look like in a situational awareness scenario. And so we've built an entire video AI stack, from camera chips, edge AI boxes, on-appliance to a full video AI service. And we are deploying this across every vertical, retail, small and medium businesses, smart cities, venues, any use case, all verticals. We believe video intelligence is a major edge play. The other area that we had to spend a lot of time on was to figure out how do we become much more developer-centric, much more developer-first, as Cristiano mentioned in his remarks. And these were 2 problems. One was, how do you simplify access to the product? And how do you exert the journey from prototyping to commercialization? As Tim mentioned in his remarks, Modular is going to be a significant game changer for Qualcomm because we will now be able to write and serve models faster. We will also be able to build application-specific acceleration in the libraries that Modular is going to bring to be able to make our entire AI stack that much stronger. Over the last 18 months, we made 3 additional acquisitions to be able to be very developer-centric. One was Arduino. Arduino brought us 33 million developers, massive global footprint, completely open source, allowed us to be able to get access to pretty much every vertical out there. We acquired EdgeImpulse. That allowed us to be able to get model training and tuning and containerized development of models at the edge. And we acquired Foundries, which allowed us to be able to manage industrial-grade [ Linux ]. So developers now have the ability to prototype rapidly on Arduino and [ Dragonwing ], and we scale these projects across standardized system on modules or chip-on-board capabilities across all of these various use cases that I mentioned. Last October, we launched Arduino UNO Q, which was the first Dragonwing product on the Arduino ecosystem with tremendous success really across every vertical, every type of developer and [ prosumer ] market. We are able to launch VENTUNO Q in August this summer, 40 [ tops ] of AI, octacore, 12 cameras, safety island built-in. Built-in AI models, part of this overall ecosystem that we talked about. It will focus on industrial, consumer and embedded. And, of course, robotics. This runs full upstream Linux. We have not been a company that has been on that path. We are now a full upstream Linux company, and these will our out the box. So all of the goodness of the Qualcomm platform available in full upstream Linux. We are also working on agentic development. So you can essentially take these development boards and code directly with Claude, with [ Codex ], with [ Cursor ]. You can start by coding, buy these on Amazon. Qualcomm is changing, and we are becoming massively developer-centric. So while we were pulling together this product portfolio, this developer center -- centricity, this video AI, we were also, in parallel, building out focus on verticals. We have 12 different verticals across 3 major industry types, and I'll give you a sense as to what we are doing in 3 of them: retail, energy and utilities and oil and gas. But we are building blueprints so that they are repeatable. So we understand what the OP blueprint is that we need to be able to replicate. And we are leveraging the entire Dragonwing portfolio for this. We've even built a solutions engineering team for Dragonwing. Let me step through a few of these quickly. In retail, the store is evolving. It is continuously sensing. It is making decisions by itself. It has to act because stores are now hybrid. Some stores in the evening are almost not manned. They need to have more autonomy. There are AI cameras that are getting added for loss prevention, for shelf intelligence, electronic shelf labels, RFID for dynamic pricing. AMRs and robots for automatic restocking, for removing products that have expired, for cleanups. And we are partnering with key partners in retail like [ Vision Group ] to be able to drive this expansion. Similarly, in energy and utilities, Qualcomm addresses every step of the value chain, generation, transmission, distribution, the entire grid. We go after sensors, industrial gateways, fixed cameras, meters. In our hometown in San Diego, we worked with [ Sanio Gas and Electric ] to help mitigate wildfires with autonomous drone inspection to execute automatic power shutoff. Schneider has been a great partner of ours. We've built with them, capability to be able to deploy industrial gateways as part of their substation concentrators. In oil and gas, we have built solutions for upstream, midstream and downstream capabilities. We are proud to have worked very closely with Aramco, with Schlumberger to bring connectivity and edge compute to very high-risk environments in a vital industry, from autonomous drones, to well drilling operations, to monitoring the safety of workers. A tremendous amount of complexity in these in these industries. And the results are showing. Our indirect revenue is up 77% from '24 to '26. We have tens of thousands of unique customers, and we are working to grow many thousands more. Over 200 hardware and tech solutions, more than 35 leading distributors, 45 global GSIs. Our partners span all verticals, and we address every step of the value chain. The channel has become the multiplier of what we are building. To summarize, AI is and will continue to reactivate the operational plane. And that disruption is going to create this upgrade cycle across billions really of endpoints, which is a massive market opportunity for us. And in the last 18 months, we have rebuilt our entire product portfolio, our developer platform and a vertical go-to-market. We have purpose-built silicon for software and AI. We have a clear prototype to commercialization path across 38,000 customers, and this is repeatable full-stack blueprint across industries. This business has really helped us in understanding very deeply as we take on new challenges like robotics as to how to figure out how to diversify the company. And really, all of these build up on each other, all these learnings compound. The automotive and industrial business has created a lot of focus to on-ramp the teams to be able to go after robotics. We understand how to [ lose ] safety. We understand how to get into vertical-specific markets. And robotics requires 4 key building blocks: computing at the edge, connecting the edge, enabling high-performance AI hardware and orchestrating intelligence systems. And all of this builds on our collective experience, knowledge and investments from various automotive and industrial initiatives. Now robotics is where embodied AI gets physical. So the objective is to perform human tasks, which would include mobility or motion, perception and reasoning and actuating or manipulating in the physical space that is around you. So these are systems that -- they have a sense, they have to think, they have to act. This is, in our mind, at least a $1 trillion opportunity over the next decade. And it requires a very broad set of technologies, products and experiences that no real general purpose chipmaker has today. Before we get into what we are building, I want to maybe describe to you a little bit as to what will robotics do. And then really, you can think of this as a time continuum. So embodied AI implementations will encounter many tasks that will have varying degrees of complexity across the mobility, the actuation and the intelligence domains. Starting with inspection, where mobility is the underlying skill. Tasks are going to include reporting status, visually documenting, measuring and surveying the real world. Next is transportation and the movement of goods, of tools of packages and even people. After that comes the interaction with the physical world, core skills to start and then finer, more precise, more dexterous skills, pick-and-play, sorting, assembly, insertions. Some can be single shot, some can be a longer horizon. This builds up to multi-agent fleets, teams of robots that are working in partnership, in coordination with each other across multiple use cases. And then finally, robotics comes to the home with consumer interaction, which will require tremendously high levels of safety, of testing, of trials. And each of these steps adds more and more unique value. We have silicon shipping in every single tier today, and let me share with you what we are doing in this space. So as we have done in pretty much every market that we get into, we always look at our full stack approach. We want to be able to make sure that we capture value across the stack. We want to be able to make sure that we can drive the pace of the inflection point if a market is ready to mature. We are focused on 6 layers: the compute, the next-generation operating system, the simulation, the data pyramid, the data flywheel, the models and the hardware reference design. And as we have learned from our automotive experience, we capture disproportionate content value. And we've built tremendous systems expertise whenever we take a full stack approach. A robot is not 1 computer, it's 3 computers. They work in concert, and they have an in tandem hierarchy. System 2 is the reasoning brain. It's the cerebrum. It's the heavy mixed critical AI workloads that require [indiscernible]. System 1 is the action layer. It's the layer that plans the motion. System 0 is executing the motion. It is your reflex system. is the millisecond control. It's the highly decentralized part, what you call the nervous system. And the main takeaway is this is a heterogeneous compute problem statement, something that we understand a few things about. And this requires you to have this optimal balance of distributed thinking, planning and real-time reflexes that require the appropriate compute engine. We have solved similar challenges in the history of the company, most recently with flex in automotive, which is, in our mind, another initiation of physical AI. And to my knowledge, we are the only company that is architecting across all 3 domains. I'm going to get a little bit technical. The hierarchical compute architecture comprises of the thinking brain with central compute, of several limbs and joints with their own local compute and split second motor control and intelligent sensing for end effectors. We are building this entire system from brain to fingertip. The Dragonwing IQ10 is our center compute, SOC. It's purpose-built robotic silicon, and it is already commercial. The perception IP allows us to visualize the world around us across multiple context modalities. The motion control IP for trajectory and balance, the actuation and control IP at the servo motor control level, wireless and wired IP for time-sensitive networking and obviously, all this on sensing. We've taken this chassis mindset that we adopt from our learnings in automotive, where we start off with a specific area that we are good at. And over time, we expand to get to a system level focus. This platform-level thinking allows us to think about the embodiment, which is a unique differentiator that Qualcomm has. One maybe last complicated slide, second last. Let me orient you to this slide. If you look to the robot to the left, and I use this as an example that Cristiano was -- Cristiano and I were discussing this. Think about the concept of a robot picking a jug of water and pouring it into a cup. As it is doing that, the weight and the shape -- is a paper cup, the weight in the shape of the cup is going to change. And that requires a robot's hand to sense and to adjust its grip pressure in real time. This is decentralized. This is exactly what you will feel as a human. That is the complexity of a robot. When we talk about bringing a robot into the home, just think about what level of complexity you're talking about introducing technology to. And that is why this is a longer gestation cycle time period. T systems are active at the same time. The brain controller system too is highly perceptive. It is aware of the [ cinematic ] shape of the environment. It knows its degrees of freedom. It knows the range of motion. It's responsible for balance control. It can identify the cup. It can identify the water jug. The body controller, system 1, it controls the limbs, it controls the movement of the hand to go to the jar, knows where the cup is and actually takes that motion on. It coordinates that movement. System 0 is actually able to sense the grip pressure, the tactile feedback, the temperature, the moisture, the weight, which allows it to act flexibly. Across these different systems, we have multiple real-time local loops that run within a system, and we have slower looks at running across systems. And we are building embodiments across all these 3 different systems. We are also building a full software and application stack complete with [ raw ] support and STKs for manipulation to write to various types of central and effectors. We will ship sample applications, pick-in-place, robotic arms, office scout applications, AMR for navigation, follow me applications. And this is open to every developer ecosystem, including the Arduino ecosystem that we just enabled. The other aspect of robotics development is the simulation data and training model flywheel. We are building this environment in-house. We are building, as you can see on the left, a simulation platform where before a robot ever touches a real world, you have to be able to train it in the virtual world. It has to be aware of the physics, the sensors and the rendering as to how that will take place in the real world. This saves you tremendously in terms of the physical involvement of trial and error. Then we have the data pyramid. That's the fuel for these systems. We combine real-world data that Qualcomm has access to, synthetic data that we generate ourselves and a lot of open source data. And then on the right, we train the foundation model. This is a single model that can take multimodal input like vision, like depth, like touch, natural language and it generalizes across use cases. We train these models with simulators, with behavioral cloning and teleoperations, and the reinforcement learning. So the workflow is end-to-end. We build the hardware, we generate the data, we develop the models, we deploy them into the customer environment. We announced our IQ10 reference design at [ Confetex ] in June, and this is purpose-built silicon, which is shipping today. We also have IQ9 and IQ8 for simple embodiments. And we're already designed into the [ Neura ] robots, which you can see outside in the demo area, with whom we offer a complete reference design that powers the [ Neura Mira ] cognitive robot arm as well as the [ 41 human audit ]. These robots are trained in the new gym with a robotics foundation model, and they run the [ new verse ] application platform. This is a full stack, silicon, solutions with a key customer, a key partner in less than 6 months. Today, we are powering every type of embodiment, and several are shipping already. We have over 100 engagements spanning the entire robotic stack with companies like [ Neura ] and [ Fiber ], [ Kuka ]. We are working with several drone OEMs, many AI sensor and embodiment partners. With physical AI upon us, IQ10, the robotic [ tepro ] design, the end-to-end solution stack ensures that customers and partners is with us. We are taking the same approach that has allowed us to scale very quickly in other businesses. Robotics is already a reality at Qualcomm, and we are very excited to be powering this next generation of physical AI, where we believe we are very well positioned. To conclude, a few takeaways. Automotive, I hope you are all believers, is now a track record. We've had 23 consecutive year-over-year double-digit quarters of growth. We don't expect to let you down any time soon. $65 billion in design win pipeline we have delivered, and we are still accelerating. And we are on track to becoming the largest automotive semi player globally. We are now a category leader in every domain we enter, and that's not easy to do. Industrial and embedded IoT is now scaling. 18 months in, we have built a product portfolio with Dragonwing. We have built developer muscle with 3 acquisitions, 4 in Modular. And a full vertical stack that goes from silicon to solutions. And robotics is happening now. It's already shipping Dragonwing IQ10, IQ9 and IQ8 are all in production. Partners are integrating them into every environment, from humanoids to quadripeds, from cognitive arms to AMRs and drones. 3 industries, 1 IP foundation, 1 physical AI platform. Thank you very much. And before I turn it over to Cristiano, I would like to play a video from one of our partners, David from [ Neura ]. Thank you.

Hello, everyone. My name is David Reger, and I'm Founder and CEO of NEURA Robotics. What we do is we are building robots and enable them to have cognitive abilities to see, hear, feel and think and react fully autonomously, all kind of physical tasks, like humans do. The benefit of working with Qualcomm together, is giving a robot more than just a brain. What I mean by that is, today, we're seeing physical AI is mainly seen as the vision [ liquids oxomodel ], but it's actually much more. It's a little bit similar to swimming. You can't learn swimming by just your brain and by vision. So it means you cannot just watch a video and think you are a swimmer. How to learn to swim is simply going into the water, trying it out of yourself and then actually training your memory effect of your muscles, then training also your reflexes and nervous system, how to breathe, how to move your body to actually stay above the water. The task always require more than actually just vision. They need a feel of touch. They need to hear. And combine that all to build the foundational model, which can actually do all the physical tasks on this planet. And we did build the physical AI platform we call [ DVRs ]. This is a deployment platform where everyone in the world can actually train and contribute to make this 1 brain actually smarter. What you can expect for all the partnership with NEURA and Qualcomm is basically setting a new standard in physical AI, enabling every robot on the planet and every human on the planet to actually train the robots and enable them for all kind of physical tasks on our platform, [ NEURAverse ].

All right. So I got to the last part of the presentation, I think, before Akash will come in to walk you through the financials. And I think -- before I start what I'm going to tell you next, I think this is what unique about Qualcomm. I know we have a limited amount of time, but there's a lot of new vectors of technology that -- and hopefully, you'll be able to see that. It's not only about 1 solution in the data center. But also, when we think about we're doing automotive, we're thinking about in industrial, which is a whole different industry in this field of robotics. You need to have the breadth of semiconductors and technology that we have. And I think that's an opportunity with Qualcomm. With that, I'm going to talk to you about the future of mobile edge devices. I'm going to try to unpack a couple of different trends that are going to happen in -- as we think about the role of agents. I said it in the keynote of [ Computex ]. The event of agents and orchestrator was a very significant milestone that actually provide clarity how those devices are going to evolve. And the industry tends to think in binary terms. Is there all of a sudden everything they can stop and you're going to go do the other thing. No, but they're going to coexist. But devices are going to have different type of use cases, and that's what I'm going to try to unpack in this presentation. The first one I want to talk to you is, we all have been used with the mobile, the smartphone at the center of your digital life. And everything is around that smartphone. The OS, the app store, it becomes the control point of the OS. And the apps understand the human intentions and everything is around the smartphones, even other devices is just an extension of the smartphone. That's not the case anymore. Actually, every AI company, every foundational model company now talk to us, the devices are the end points for agents. That's where the humans are. And the agent is at the center. It's not about the phone at the center anymore. The agent for the agentic experience, once you understand human intentions, the agent is at the center, devices are just endpoints of the agent. And the purpose of my presentation right now is to tell you how the device is going to change because everything that has happened. Let's start about -- start talking about the user experience. So those devices have been built for the human as the user. So the workflow is based on the human going to an app and doing things at the human speed. But now the device with the orchestrator and the agents are also going to do other things on behalf of the human. So it's going to operate the device, and we're starting to see that right now. If you ask me where is the epicenter of the start of new agentic use cases exactly happening in China right now? And you started to see the agents go to your device and operate the device for you, and it goes to the web for the agentic experience. That tells you that the device now has 2 use cases. It was interesting. I said this before, I'm going to repeat it. When people want a computer to run OpenClaw and they have the computer running OpenClaw. Once you start having that experience with you, not about just the amount of software developers they use -- they exist in the world, but the 6 billion people that have smartphones. When they started to use agentic experience as part of your experience and interaction with the device, you're not going to carry the computer. It's all going to happen the same device. And the device is going to have 2 users because there's 2 different workflows. There's you and there's agents. So that's 1 big change. The other big change is perception in sensing. And that's what is actually changing the device in itself and enabling different endpoints like personalized devices. So if we have now the computer that interact with us the way we interact with other than the context that we are inserted, especially as we think about we communicate with audio, we speak, we listen, we see. And then we are integrated into our surrounding, the context become important. So you now have a lot of sensing data that needs to be a part of the processing of those devices, and that is enabling also a different class of device. That's why I told you about what happened to wearables and what happened to augmented reality, mixed reality and virtual reality transition is very important because -- the reason we've been very, very focused on glasses is because glass is as close to our sensors, close to our eyes, to our mouth to our ear. And those devices, wearables as extension of the smartphone and the smartphone and the center for the agentic experience is the actually an end point of an agent. And the stuff that you actually wear becomes very interesting. And especially when you think about glasses -- just as a side comment on Microsoft build such announced Project Solara, a bench with a camera. It's a new class of devices. We have 40 different designs today with some of the largest AI and model companies in the world thinking about those new form factors. There's 1 form factor we know is going to get scale is glasses. And the use case is see what I see and hear what I hear. And I'm going to come back to the use case because there's also going to talk about another change that's going to happen on devices at the edge. But now what I would like you to do is new companies are looking at this big change. The devices now are endpoints, the barriers to entry of OSs and app store for new experience is no longer the same. You're going to see a lot of excitement. I want to start by showing a video from Amazon.

We're in the middle of a fundamental shift in computing right now. And what's most exciting about this next wave of AI is what it unlocks for people: for their creativity, their curiosity, the things only humans can do. Now as technology continues to fade into the background, the customer must stay at the center. That shift creates new requirements not only for the devices already in people's homes, but for entirely new devices built for AI-first experiences. At Amazon, we're building for this future with Alexa, creating experience that works seamlessly for a customer when they're in the home as well as when they're on the go. Qualcomm is one of our critical partners in expanding both the capabilities and reach of these experiences, so AI can seamlessly move with people throughout their daily lives. This relationship is about building what comes next together.

So there's a lot of exciting things coming. And by the way, I'm actually so grateful. I have a relationship with [ Panel ], spends more than decades. He's an incredible individual visionary, and I'm actually very grateful for the partnership. So the other thing is we talk about the orchestrator. And you heard about today, those agents, they generate demand for a lot of tokens. The reason a lot of the hyperscalers just see a wall in front of them of compute demand is because the economics of AI are fundamentally changing. Agents and orchestrators, that's why I said the OpenClaw was an incredible milestone. They're redefining the architecture and economics of AI, not only creating an entry point for Qualcomm into the data center, but actually, creating a fundamental change in the architecture of compute that touches all the devices on the edge. And this order of magnitude increase. If you look at how we started with conversational to now agents, you see the order of magnitude increase. The projection is 40x the increase in annual token demand between 2026 and 2030. Now I'm going to show an example for you next. And I'll tell you what you're going to see. We've been doing a number of those things. And you can do, you can try yourself. You can try different prompts. And you're going to see it changes. Sometimes you get 10%, 20%, 30%, 40%, 50%. I just pick a very simple example for you to see today. We've been showing this. So what you're going to see right now is we've got 2 computers. Those computers have orchestrators. Those orchestrators, you give them a prompt, a complex prompt. You ask them to do some research, design a web page, put the results. One, we're going to be using is just Claude. We use [ OPPO's ] 4.6, 100% of the cloud. You see the thing working. The other one use smart routing. You use some models are locally installed into the machine, and some others in the cloud. And you get those things to work. And what you see at the very end that you see what hybrid AI really means because you will get to exactly the same outcome that you want. But now when you think about things like mix of experts, when you think about different kind of models, you can actually see how the architecture of AI is evolving. The reason you saw some foundational model companies saying, "I am going to give up to do video creation." It's very obvious because you can use your compute capacity to monetize tokens of higher value. And that's what we're starting to see. Actually, when Microsoft also said the build that they are now create an unmetered intelligence and people really understand what AI PC is, this is happening everywhere. And the beauty of the devices like phone has actually happened on the same device with the different users. And you can see lot of useless debates I have seen over the years about is this edge or this is cloud. It is actually the wrong conversation to say, I have something that need to do on the cloud. Can I do it on the edge and vice versa. That's the wrong approach. Things are going to be done in the cloud is going to be done in the cloud. The growth of the cloud is incredible, but the edge now also become a computing that is going to generate tokens and I think how the industry is naturally going to evolve. And the message here to you is like what's happened in the data center, inference is actually becoming disaggregated and distributed everywhere. This is a new form of compute. For example, when you look of the architecture, you're going to have the cloud data center, which has hundreds of megawatts. This diagram, I'll bring it back to you. You understand I'm going to overlay 2 diagrams that are going to make it very interesting. But you look at the data center with hundreds of megawatts, 2 gigawatts, you're going to have regional data center with tens of megawatts in on-prem. There's a lot. If you look at results of some of the server companies like that, there's a lot of movement for on-prem right now. It's not against the cloud. They're both growing. And you also see, as you have more of a hybrid AI, you're going to see more computing happening on PCs and devices at the edge. So inference become distributed. Just don't take my word for it. I was -- we have an incredible partnership with Google, and I want you for you to hear from Google about that.

Hello, everyone. I'm [ Rick Osterloh ], Senior Vice President of Platforms and Devices at Google. And I'm incredibly excited to talk about our long-standing partnership with [ towards ] agentic workflows. We're moving beyond simple responses to offer true digital agents. These are proactive multistep systems that seamlessly anticipate user needs across your entire ecosystem of devices. To bring this to life, our teams are focused on bringing a shared full stack vision. We're combining Google's advanced Gemini models and Android system-level intelligence with Snapdragon silicon. Together, we're innovating side by side to scale on device AI and Gemini intelligence to the most advanced mobile devices. With AI, we're redefining next-generation automotive experiences and pushing into new frontiers with wearables like XR glasses and intelligent eyewear. And this innovation is also at the heart of our brand-new Google Books effort to revolutionize the laptop experience. True agentic workloads require what we call distributed intelligence. By efficiently balancing processing between the cloud and on the device edge, we can deliver seamless experiences that are private, instantaneous and personalized. Our deep engineering collaboration with Qualcomm ensures that the broader ecosystem and our OEM partners can scale these innovations rapidly. Gemini Intelligence will elevate the Android ecosystem, making it smarter, more proactive and more helpful than ever before. Cristiano, congratulations on Investor Day. Thank you for our outstanding partnership, and I'm really excited about our road ahead.

So big thank you. Thank you, Rick, for the partnership and the confidence what we're going to do together. So -- and I have to pick up the pace now and get to my last part of this presentation, which is about 6G. So we have been designing 6G for this AI era. And I'm going to now show the role of 6G in this conversation we just have, and I'm going to go fast. So I just talked about new classes of devices, like glasses. So the goal of the connectivity of 6G is to transform all of us in walking cameras in this world. I need a very fast, high definition video, the opposite what we did with 5G, which is enable streaming a high-definition video. We're going to do that for uplink and across the sell side, so everyone has the ability to stream high-definition video, foresee what I see. That's very important context for agentic experiences. That's what we're going to connectivity. I'll go to the details. There's a lot of improvement on the connectivity side. But that's only 1/3 of the story. The other part of the story is the computing part. When the computing is going to be required because the 6G infrastructure is no longer a dedicated equipment for communications. The network of 6G, actually, you need to be thinking about -- that was a network that was designed for voice. It now transported bits and is going to also generate tokens. And the reason that's important because the premise of how 6G is going to deal with radio frequencies, going to look at radio franchise physical AI. And you're going to need compute. Look at the architecture is kind of the same of the distributor. You're going to have a big data center. You're going to have a regional data center that's the core, the network. You have to have an edge data center, and you're going to have the cell site and the devices at the edge. And that's why we're also building our data center solution to be scalable because when you think about 6G becomes 1 sovereign AI workloads. And some operators will actually be selling token generation machine capacity like a CSP for AI companies for this distributed compute. And as you have all of this compute, it brings the next part of 6G, which is sensing. Because every single RF is going to be treated as a radar. And you use models trained on that RF performance in the RF radio characteristics, you're going to be able to sense everything. Drone detection is on the top of mind. It becomes critical infrastructure, everything that moves and fly and becomes an important context for for models, and that's part of the perception. So with that, I'm going to summarize it, what's happening with devices at the edge right now. They're evolving for agentic experiences. They're going to have more than 1 user. And we're building the next architecture of those devices. You need now a CPU for the orchestrator. The orchestrator is going to operate your device for you. I think everybody now understands the CPU matters, and it's important. You need to have a different architecture on inference because you have to have very high performance, low power inference even when you are not using the device as a human. And that even that high bandwidth computer we're doing on the data center, we're building a version of that as a coprocessor for phones. There's a completely change on the perception and sensor as well as a new modem, and it's just not about just the phone. It's different classes of devices. And that is also bringing other people to this space. So I'm going to finish this part of the presentation asking [ Hark ], a new company, also founded by Brett, which is going to show what they're doing.

I found it [ Hark ] because today's AI is just not good enough and nobody is taking real advantage of it. It constantly forget things about you, and it runs on devices built a long time ago. So at [ Hark ], we're an AI lab building the world's most advanced personal intelligence. I think the next AI platform is something that truly knows you, that can see, hear and act in the world with you. And you don't get there by bolting AI onto existing devices. You do it all together. The models, the hardware and the interface is 1 product. That's what we're building at [ Hark ]. And intelligence that thinks like you and sometimes ahead of you. We're grateful to have excellent partners like Qualcomm, who are helping us realize some incredible ideas. We're excited to release the [ Hark ] platform this summer and then the next generation of consumer devices after.

The obvious question that everybody is asking, what is the timing? It's hard to predict the timing right now. And I think actually, the mobile market is dealing with the uncertainty of the memory situation. But it's very interesting. We are incredibly encouraged with the design activity, the number of new entrants. Actually, when I talk about China, I am in a situation right now, I don't know what the mobile customers are anymore because they're OEMs, but every single AI foundational model company building agents, also our customers. The surface area is tremendous. The 6 billion phones, 2 billion personal AI, 2 billion PCs and 500 million cars. And when you think about this new form factor, I would just give you an idea how you should think about the glasses. We're just at the beginning of this, just at the beginning. 600 million glasses are shipped annually. There's less than 1% market penetration for smart glasses. But all you need to do, you can actually -- we're building a very small reference design that you can build as to any glasses. And you can see, you can have ability to access an agent for audio, multimode or premium display. And you can see the [ smart E-band ]. So that is a great opportunity, and that's what's also going to happen on the mobile devices business of Qualcomm. So hope you saw, there's a lot of interesting trends in technology. And as I get to the end of my presentation -- and I think you're all eager to see what Akash has in store for you. But I am just going to go talk about this #3 pillar that we've been talking in this presentation about the next chapter of Qualcomm. From silicon to platform solutions into really building a fully integrated platform for harder software, but changing also the company to a developer-first company mindset. And you saw we're doing this with all the new business we built on the edge. And we're going to be doing this from everything we have on the compute continuum. That's why this acquisition that we made of Modular is so significant for Qualcomm because this also builds on the pillars of the Qualcomm advantage. Not only the technology, but the focus on deep customer partnerships and creation of ecosystem and the scale. We have proved that we can partner across the industry. And it's never the role of 1 company to innovate, though that creates an incredible opportunity. Before I bring Chris up here -- and he's going to talk to you for a few minutes. I am going to say -- and I may be a little bit aggressive saying this, but I'm going to say, I'm willing to bet that not everyone here will understand what we're trying to do. I think some of you will understand. And this is not a negative comment in any shape or form. It just takes a while to understand how we've been thinking about this. I've been talking -- we've been starting this journey with Modular for more than a year, and it took a lot, I think, for me to convince Chris and team, and he will share his story. But I think we may have an Android moment here. And maybe I'm going to be as bold to say maybe there's even a Linux moment. I don't know. But we -- I think we have something good. And I think we have momentum as AI goes everywhere as compute becomes distributed as you have every single end point become an endpoint for agents during inference and you have an industry that wants an open ecosystem, maybe that's what Qualcomm can do, supporting everyone. With that, I would like to bring to the stage, a legend, Chris Lattner, I think the Founder and CEO of Modular, which should tell you about what we're going to be doing. Chris, please come on to the stage.

All right. Thank you, Cristiano. By way of introduction, I spent my early career building today's software platforms. This includes the compiler technology that runs every phone no matter from whose vendor is in your pocket, the data centers that span all the hyperscalers, but the SWIFT programming language at Apple. Also built the software stack that powers Google's amazing TPU AI scale platform. Now I joined -- I decided to join Qualcomm because I found the team recognizes something. They recognize the opportunity of AI today. They have the ambition to do something big, but also they've made all of the investments already that put them in a perfect position to do something about it. Today feels familiar to me. It feels a lot like back at Apple when it was about to take off. Back then, there were a lot of doubters. People did not really understand what was going on, but we had all done the formative work already. And so all we had to do is get people to see it through the products and their lives. So let me walk you through what I see today. So it turns out that compute has fundamentally changed. You've heard about that a lot today. It's no longer about a single chip. Compute today is a large-scale data center distributed systems problem. We all need to program diverse AI accelerators from multiple different vendors. When you get the best performance, the best TCO. And we need usability because doing all this is harder than is ever been before. Now the world is still struggling to get individual systems to compete with the industry leader. But that's where Modular comes in. At Modular, we spent the last 4.5 years building a novel platform that actually scales, all with the goal from the beginning of unifying the industry and opening a new chapter for accelerated compute. Now we built this platform to scale across the full spectrum, starting from the data center, but then going all the way down to the edge. And so this is why I'm so excited that Modular is joining Qualcomm. We are bringing together the perfect combination of scalable hardware and scalable software. This is joined with a shared ambition from both teams to make a world that is better for everyone with AI. And I got to tell you a little bit about where we're going. So now let us remember that nobody wants -- everybody wants tokens, but they also want amazing economics. Now most people don't really actually want to know how it works. The systems, the software components, all the different pieces going to this are amazing. And for nerds like me, I love it. It's great. I think I'm a fellow company here. But a lot of people just want a solution. And so together, we're lifting the Qualcomm AI silicon business. Silicon no longer. Now is about solutions, full it just work solutions, and an AI solution business is far more valuable. Now this end-to-end solution approach starts in the data center, of course, that's our core focus, but it won't end there. This is the first software platform that was designed from the beginning to unify edge and data center, utilizing diverse accelerated compute in all the crazy form factors that are pervasive in our lives. Now we've been using a lot of operating systems over the course of the last decades. But this platform will grow into a full operating system built natively distributed, natively accelerated and agentically native by design. And we are not just building this for us. We're building an open developer platform, enabling AI developers, researchers and app developers to innovate like never before. Together as an industry, we understand that we need to scale incredible amounts of compute. We know that this will require many different organizations to come together to make it possible. The result of doing this all is an incredible opportunity for everyone, all of our partners, millions of developers and of course, all the consumers that will benefit from AI products and our lives. Now throughout my career, I've had the privilege to drive open standards. I built several very large-scale open source communities. I have been part of multiple waves of compute. And I feel that today, Qualcomm is really quite well positioned to be the best in the industry to lift the entire world. And as such, we're committed to this being an open platform. Qualcomm is obviously an incredible hardware company, but today, we're going further. We're now a full stack vertical AI solution company. I couldn't be more excited to build together. So thank you.

I hope you share our enthusiasm about this change in the company. This opportunity change in the industry, what we're going to do together. And we're super happy to have Chris, Tim and the rest of Modular's part of the Qualcomm family. So I was about to end here, but I thought maybe there's 1 more thing. Hopefully, you're entertained. But we have 1 more thing, and that's just building on this. We're also very happy to announce a very strategic partnership that we're making with Hugging Face. And I'll tell you about this partnership. Qualcomm and Hugging Face started because they share on exactly the same vision we presented to you today. For the data center, Dragonfly, Hugging Face will be very focused on demand creation from Qualcomm Dragonfly silicon. Both their inference and storage services will map to all the Qualcomm Dragonfly products. There will be agentic model onboarding, combining the Hugging Face. There's 16 million developers. And what we're doing with Modular across the whole family of Qualcomm chipsets from Snapdragon to Dragonwing to Dragonfly all models are going to be onboarded on Qualcomm technology platforms using an agent that's going to handle the setup, the optimization deployment with 0 manual integration work. And then just what you heard from Chris, build on an end-to-end agentic AI with distributed intelligence. And that distributed AI framework when agents can operate seamless across the entire compute continuum, leveraging Qualcomm technologies model and also cooperating with everyone. And I want to hear from [ Clement ].

Hi, everyone. I'm [ Clem ], Co-Founder and CEO of Hugging Face. If you haven't noticed recently, something big is happening in AI. More and more, the world is running on open source and local models, and for good reasons. They're way more affordable in the big LLM APIs, more customizable by companies. And because they run on your own device, they're private by design. Your data stays yours. Today, over 16 million AI builders create this future in the open on Hugging Face. And that's why I couldn't be more excited to announce a new collaboration with Qualcomm. Together, we're going to make open models easy to run everywhere from a device in your hand to a full rack in the data center. Snapdragon, Dragonwing and Qualcomm's Dragonfly Cloud, all powered by the open source community. You'll be able to take any model they go small, deploy it, optimize on any Qualcomm platform with agents running on device and orchestrating across the cloud. We'll also offer Hugging Face subscriptions to many developers using Qualcomm platforms. Local, private, affordable for everyone. That's the future of AI we want, and we can't wait to build it together. Thank you very much.

So that's it. I think I got to the end of the presentation. Hopefully, we gave you an opportunity to understand, I think, the -- what Qualcomm is going to do over the next 5 years. And I'm going to summarize it to you. Data center will add a meaningful new vector of growth. And I think what you saw is -- when we originally talked about this, we talk about we're building a data center portfolio. We expect revenue to be in fiscal '28. Then we get more traction, we move it to fiscal '27. We got more traction. We get to fiscal '26. And you heard from Tony, we're just starting. Automotive industrial robotics will extend Qualcomm in the next frontier of physical AI. We build the platform, we have the market scale, and we're executing on all the technology trends. Agentic AI at scale will drive an upgrade cycle on across edge devices. Those are going to be machines going to generate tokens. And they are going to be interacting not only with the users, they're going to be interacting with agents, and that's going to happen across the entire industry. The token economics will make distributed inference inevitable. We're excited about the growth in the cloud. That's what created opportunity for us to enter in this aggregate. And that will continue. We're just at the beginning of that. But everything will become an AI computer, and I think that is going to fundamentally change and create a massive opportunity for us across the compute to continue. 6G will be foundational infrastructure for the age of AI. And if you haven't forgot, we have that asset, too. And we're going to be expanding beyond silicon to full stack software platform with the most industry friendly, I think, platform. And at the end of the day, I think we have seen in our industry, open horizontal systems will win. And I think that's kind of our bet. So with that, thank you so much for listening to our presentation. And now I think the main attraction of today, our CFO, Akash.

All right. Good afternoon in New York. It's incredible to be here. Lots of familiar faces. Great to see all of our investor friends here in the room as well. It looks like all of you decided to stay here rather than go to an earnings call. That's a great decision. That's a great decision. We're going to make it worth your while. This is the climax of the show. So we have closed the doors now. You're stuck here. You'll have to listen to the rest of what I have to say. Just kidding aside, we -- you heard Cristiano, Tony, Nakul talk through all the great stuff we are doing across our businesses. And so my job is now to try to wrap it up in a financial framework, and so let's just get to it. Through my presentation today, I'll try to address these key areas. Revenue and EPS are going to grow much faster than what we had told you before. We're going to see diversification. And growing into data center, the mix of businesses will change radically versus our previous estimate. Our operating scale is going to be a key differentiator for us going forward, and you heard a little bit about that in the various presentations. And then capital return, capital allocation remains consistent with what we have told you before. But before I go through all of this, let me just quickly address how Qualcomm has changed over the years. We obviously started off as by inventing 3G. We led 4G, we led 5G. But today, we have changed. We are more of a computing company, then we are a connectivity company. We're still the best in world in connectivity, mind you, but we are a computing company today. So for a lot of investors who have known Qualcomm for a long time ago, it's important to make the switch that we are a computing leader that also happens to be best-in-class in connectivity. The second change in Qualcomm, the second transformation started when Cristiano became CEO 5 years ago. We went from being a smartphone company to all edge devices: auto, personal AI, networking, industrial, PC, all of these devices, we are a leader in now. The third transformation starts now. We're going to go from being a devices company to being a cloud and device company. And as we change, as we transform, it completely redefines what's in front of the company and how you should think about the company going forward. Let's start with some financial look back. This is the last 5 years, what has happened to Qualcomm overall. We've doubled revenue during this period, $44 billion. During this period as well, we tripled EPS. And this performance and track record validates our growth strategy. It also sets the platform for what we're going to do going forward. Within this period, what did QCT do? QCT far exceeded the performance of overall Qualcomm, much more than 2x growth in revenue. We also had double-digit CAGRs in each of our revenue streams. I'll highlight auto, 44% CAGR over this period. Within handsets, within Android handsets, we grew at a CAGR of 12%. This is a market that's perceived as mature. But during this period, we grew because content increased and the mix across tiers got stronger. We expect this trend to continue. If you think about what happened to earnings during the same period, we grew 4x, 2x faster than revenue. And this is because of operating leverage and really investing and growing and diversifying across the businesses. This slide outlines the businesses we are in today. We're in licensing, Android handsets, automotive, IoT, and now in data center. Across these businesses, we addressed $1.7 trillion of TAM. And as we diversify more as we launch new products, a very large portion of this becomes addressable to us. Over the next few slides, I will focus on 3 key areas in terms of financials. I'll talk about data center. I'll talk about automotive, and I'll talk about IoT. But let's first start with the numbers. I'm sure you're waiting for the revenue forecast. Last time when we were here 18 months ago, we set a target of $22 billion in non-handset revenue. This is auto. And I remember a lot of investors said, "Wow, that's a very aggressive target. Are you going to be able to meet the target?" 18 months later, we are here again, and we are very happy to say that we are revising the target. Our fiscal '29 revenue target is now $40 billion. Just to repeat. Let me just repeat this. Same year. Last time, 18 months ago, we said $22 billion. Now we're saying $40 billion. This is nearly 2x increase in the target for revenues in fiscal '29. What this also means is 4-year CAGR from '25 to '29 is 40%. Very strong growth as a result of the diversification efforts. We have an incredible opportunity in front of us. Okay. So now I'm going to start with data center and talk through the data center financial forecast. As Tony outlined, incredibly exciting product portfolio. It's built on the basis of technology leadership, and this is by far our largest growth opportunity. In terms of revenue ramp timing, we have revenue today in fiscal '26 from the connectivity products we acquired through [ Alphawave ]. As we get to fiscal '27, that's only 3 months away, we will start ramping custom silicon revenue in first quarter of fiscal '27. In second half of fiscal '27, we'll ramp AI accelerator revenue. And then second half of fiscal '28, we will ramp CPU revenue. All these revenue streams will layer on top of each other, similar to what we did in auto. We started with connectivity cockpit ADAS, same thing in data center, and that's how our revenue will scale. Across all of these markets, there's a $1 trillion TAM opportunity for us. So we're incredibly excited. As we launch these new products, we'll be able to access a very large portion of that TAM. So this is our financial forecast for the data center business. Let's start with fiscal '27. We're targeting $5 billion of revenue in fiscal '27. And let me highlight a few key things. We will have hyperscaler customers that are at global scale that will drive at least $1 billion of revenue within the year. This is not a concentrated revenue stream. We have diversification within the customer base. We also expect custom silicon gross margin to be slightly below our overall Qualcomm gross margin, but it will be accretive at the operating margin level. So this is a very attractive financial business for us. In terms of AI accelerator and CPU, we'll be investing ahead of revenue ramp in fiscal '27. Let's talk about fiscal '29 now. We are targeting $15 billion of revenue in data center in fiscal '29. And this revenue that what gives us the confidence in being able to achieve this revenue is the diversity of products, diversity of customers and the fact that we are talking about multi-generations across our customer base. So this is the forecast that we're putting out for fiscal '29. But let's talk about the opportunity beyond that. As we discussed earlier, is a $1 trillion market cap -- $1 trillion TAM opportunity for us. And we have an incredible product portfolio. Across all this, we are targeting greater than 5% share in 5 to 7 years. So this is not a question of what will we do in fiscal '27, which is very strong. It's not just a question of what we'll do in fiscal '29, but the long-term opportunity for us is incredible. So I'll go to automotive now. Snapdragon has become the platform of choice for the automotive industry. We are very proud of what the auto team has built. And as Nakul discussed, that becomes the platform with which we jump into industrial and then into robotics. Content increase has been a key part of the story. And I often get the question on, hey, auto market is mature. When will you stop growing, you're growing much faster than everyone else in the industry. The reality is that the part of the industry that we are in, the amount of content growth is tremendous. Between our fourth -- third generation product, fourth generation product and fifth generation product, there's an 8x increase in content. This translates into financial growth and really a very long runway for us. We just launched our fifth generation product. As you think about the overall TAM for cars, in that flat market, our SAM is going to grow -- easily grow double digits, and we are going to grow much faster than that. The drivers of those growth includes digital cockpit capabilities, increased sensors, ADAS going to L2++. And then finally, generative AI capabilities, and we are leading in each one of these areas. Let's go to the automotive design win pipeline. As Nakul mentioned, $65 billion. 2 years ago, we were here, we were talking about $45 billion design win pipeline. Now we are at $65 billion. As a reminder, what design win pipeline conveys is really the cumulative revenue expected over the designs that we have won. But what's incredibly impressive about the design win pipeline is the diversification. Diversification of products, you have cockpit connectivity and ADAS. We have diversification of customers, and we have diversification within the regions. And this is a very important attribute of our pipeline. We are really winning globally across all OEMs. We're looking forward to continue to grow this pipeline going forward, okay? So now I'll talk about the revenue forecast. This is the last forecast we gave 18 months ago. And we said we'll be at $8 billion in '29 and we'll be greater than $9 billion in '31. And at that point, we had pulled in our revenue ramp by 2 years. So what we're going to do now is we're going to pull it in again by 2 more years. We will hit $10 billion of revenue in fiscal '29. And as Nakul mentioned, we'll be the largest automotive silicon supplier shortly. And we are not done yet. If you think about the growth vectors that remain, we are incredibly excited about them, robotaxis, autonomy going to L4, token accelerators, a separate accelerator that will use HBM for and then AI HBC and AI workflows. So tremendous amount of vector still remaining. We'll continue to grow this business for a very long period of time. I'll go to IoT next. Cristiano outlined this, we're thinking about IoT in 2 buckets going forward. You have personal AI and compute and industrial networking and robotics. For personal AI and compute, agentic AI is driving an inflection point in these devices. We used to talk about glasses. We don't talk about glasses anymore. It's not just glasses. It's a bunch of different devices that come with it. For PCs, is -- of course, we are in Windows PCs, but now we're extending to Chromebooks in addition to tablets. For industrial networking and robotics, digital transformation is creating an incredible opportunity for us. So I'll talk through both of these areas in some more detail, but here's the financial forecast. We're going to be over $14 billion across these markets, which is a CAGR of 20% from where we were in 2025. We have strong growth drivers in place in each one of these areas. Let's start with personal AI. Personal AI is a set of devices that can see what you can see, hear what you can hear, and you can have an agentic AI conversation with it. We have the broadest technology portfolio that allows us to win here. If you think about things that are required, small form factor, low power, connectivity, great camera, sensor fusion, object tracking. We have all these technologies in-house, we integrate it into our products, and it has been adopted by all kinds of customers. You have the traditional OEMs, you have the hyperscalers and you have new entrants, and all of them are using our chips. We have included a modest forecast in our financials for this market. If the vision that Cristiano outline plays out and this market turns out to be much bigger, we have tremendous financial upside opportunity that is not captured into our forecast. Let's talk about PC next. Actually, this week, 2 years ago was when we got into the PC market. It's been a very short period of time. And here's our report card since then. We've launched products across all tiers, flagship to entry. We are the performance leader in every single tier. We have multiple generations of products now, so we can launch current generation products and previous generation products at the same time. But when we got into the market, one of the questions was, will you be able to build a channel. Here are the metrics on the channel. We now have not just design wins at OEMs, but very broad acceptance of applications that have been ported over to our platform, printers, peripherals, consumer channels with retailers and enterprise channel. We've built a very, very strong platform over the last 2 years, and we are ready to scale the volume as a result of it. Agentic AI is also driving an inflection point, and it changes the way the device is going to be used, and we're great very well positioned to take advantage of that. Rick mentioned about Google Book. We're the lead partner for Google Book. We'll be launching devices with various OEMs over the next several months. And these are devices that will bring Snapdragon along with Gemini together to deliver the best agentic AI experiences. So while there is more work to do for us in PC, we are tremendously proud of the progress that we have made, and we are set for takeoff. Finally, industrial networking and robotics. This is another market, as Nakul outlined, AI is accelerating the transformation. 18 months ago, when we were here, we were talking about how micro controllers will move to microprocessors and AI. Today, I know a lot of our peers come in and talk about that same transition. This positions us extremely well for what's going to happen going forward. Like PC, one of the criticisms that we heard is, are you going to have the channel that is required to drive the volume here? So we've built that channel. Now we have hardware and technology partners, we have distributors, we have system integrators and 38,000 unique customers. And we are ready to ramp the volume based on the products we have. We are targeting $8 billion in '29 for this business as well. Okay. So let me summarize what we just said on the financials and add a few more metrics to it. Not $22 billion anymore, $40 billion in revenue outside handsets with $15 billion for data center. We are forecasting Android handset revenues to grow modestly at 5% going forward. This assumes that the current memory environment doesn't materially change. And it also does not assume any uplift from the agentic AI conversation we just have. Both of those things would be upside to our handset forecast. Licensing. We continue to be very happy with the way that business is stable, and it will continue to scale with 4G and 5G units globally. And then finally, operating margins. No change to the targets we've said before. We expect QCT as we include data center into it to be at 30% in the long term, and we expect QTL to continue to be at 70%. So what does this due to the mix of businesses across QCT? When we get to '27, handsets will be less than half of our revenue. This is a conversation we have with investors all the time, but you are a handset company. Here's how the mix is going to change. We'll be less than 50% in '27. In '29, with the forecast we just showed you, handset will be 1/3 of our revenues. We will be truly diversified across handsets, data center and industrial IoT and automotive. Our capital allocation strategy really is unchanged. We are continuing to proceed down the path that we have discussed with you in the past. Our priority is investing in the business, maintaining the leadership on technology, and continuing to accelerate diversification. For M&A, we've done 35 acquisitions over the last 5 years, and each 1 of them was contributing to our growth strategy. We've picked the strategy, and each acquisition comes in and says, how do you help me accelerate something that I'm planning to do anyways. And there's Alphawave, Modular are great examples of what we have done. In terms of capital returns, over the last 5 years, we've returned $40 billion to the shareholders. Over the last 10 years, we have retired 30% of our shares. As we go forward, the strategy remains unchanged. We're going to keep increasing dividends in mid- to low single digits, and we'll return most of our free cash flow to shareholders. Also through this, we'll maintain a strong balance sheet. We'll retain financial and strategic flexibility, which is so important in our industry. Let's talk about OpEx for a second. What has happened over the last 5 years is as we have diversified, we have grown revenue at -- but we have grown OpEx only at 6%. This is in spite of funding all the diversification efforts. And what that did is OpEx as a percent of revenue came down from 31% to 23%. As we go forward, we expect that to decline further to 19% to 20%. So very happy with the way we are managing the need to invest and focus on diversification while realizing operating leverage financially. So we've talked about '27. We've talked about '29. So the question is, are we well positioned to continue to grow beyond that. So before I wrap up, I want to talk about all these growth drivers that we have beyond '29. Data center, clearly a massive growth vector for us as we look forward. Robotics, as that becomes one of the largest markets in the long term, we are positioned to win there as well. Industrial, we'll see an upgrade cycle that will happen over a very long period of time, positioned to win there as well. ADAS, both from ADAS and autonomy perspective, we have an opportunity in silicon and stack, and we're executing on all those opportunities as well. Personal AI, as Cristiano outlined, tremendous opportunity. If this becomes a scenario where everyone in the world has a couple of devices that are personal AI devices, in addition to phone and PC, we are going to have a very significant growth vector beyond '29. And then finally, 6G, as we outlined before. So very excited. Our growth curve is not done in '29. We have a long-term growth opportunity, and we are focused on executing to it. Finally, I want to wrap this up with some key takeaways. We have a clear line of sight to diversified revenue base where handset will be 1/3 of our revenues in '29. We expect non-handsets to be $40 billion within QCT by '29. Our EPS target is of greater than $18 in that same time frame. And then finally, when you think about long term, we have an opportunity with the things we outlined to scale our revenue to $100 billion. Thank you very much for coming. Thank you for listening to us. We're very excited about what's in front of us. Thank you very much, and I'd like to invite Cristiano, Nakul and Tony back on stage for Q&A.

Thank you so much for staying with us. I'm glad most of you stayed.

Before we start, Akash, given that financial performance, can I buy some more shares?

Go for it. Ask him before you do.

All right. What's going to go first?

Please go ahead.

Chris Rolland, Susquehanna. I think data center is probably the most interesting here, the $15 billion. And then it sounded like more than 50 over time. If you could talk about the linearity of this given your product releases and then also customer deployments, I think that would be great. And your overall ability, you think, to address this with your partners as well, given supply constraints, et cetera?

So I think the best way to answer the linearity question is you have a number for fiscal '27 of $5 billion, fiscal '29 of $15 billion, right? And as we said, our product launch is the way pans out is '29 will have the benefit of CPU accelerator and custom silicon and connectivity. So all 4 product launches. And then '28, well, CPU comes in at the end of the year. So you're going to see this ramp that happens between $5 billion to $10 billion, but it aligns with the product launches time line across the period.

Maybe just add a few things to try to answer your question on the supply chain. So as we outlined, we have visibility right now of $5 billion in fiscal '27. For debt revenue, we have secured capacity as well as secure memory. So even our customer commitments right now on the high-bandwidth compute HBC technology, we have secure memory as well. We're not a small company. I think we have a capacity allocation. We consume a lot of leading node wafers. I also thank our suppliers are betting on Qualcomm and want Qualcomm to succeed. And I think that is reflected in the capacity commitments we have for the projected revenue we made of fiscal '27 of $5 billion.

The one thing I'll just add is '29, as Akash said, is when all 4 product lines truly launch, and that's just the beginning of the launch. So '30, '31 is when we start delivering multiple generations of these products, and that trajectory is going to change from what you'll see over the next 3 years.

[indiscernible] the microphone. I think there's a couple of hands.

[ Jan Kavinder ], [ Arete Research ]. I was wondering if you can zoom into the CPU commentary that you laid out for us? I mean, some of those statistics -- performance statistics versus your peers were quite compelling, the 5 gigahertz. I was just wondering if you can maybe just hold my hand a bit and just tell us how you get to this, I mean, versus your competition, given how significant the TAM expansion has been over the last couple of months?

Look, that's a great question. As I mentioned during my talk, this is a company founded by engineers. Everything we do is about technical innovation. The [ Oryon ] CPU core is transformational. As you've mentioned, 5 gigahertz is remarkable. And is not even a custom hand-built design. It's built using automated tools, and it's updated and refreshed each and every year. So look, it's foundational in architecture, okay? You cannot just ditch this type of performance in, it has to be built from the ground up. So even though is based on mobile compute, the server class compute has been built from the ground up to lead in terms of performance. So I've been asked, why are you launching in '28? Because even in '28, we will have the industry's best performance in compute and I/O capability. And then when you think of bolting on the HBC attach to integrate native AI inference workloads on top of this industry-leading compute, folks, this is game over across the industry.

Maybe I'm just going to add a few things. So -- and I'll kind of remind you a little bit of our journey, right? So we have been building, I think, our own CPUs. I think the first thing we did, we build a CPU to have an Apple compete, I think, as we enter the PC space to create an Apple competes as following the Apple M Series. Then we build a CPU for mobile devices. We build a CPU safety grade for automotive. So this is the next-generation CPU that we design. One commentary, I think, to what you said. Right now, the demand for CPU is massive. So everybody has CPU chips. I've seen this in the pandemic. But if you look of what happened in the other markets with design CPU, our metrics have been very, very good from a performance, from power. The feedback that we got on our CPU that we -- Tony outlined. I want to tell a little bit where we receive very specific requirements from all the hyperscalers about the CPU that they want to see in '28 went to start shipping it. And the feedback is this is good to be true. When can I get the silicon? So you should be thinking about us having the capability, understand where the puck is going for agentic and building a CPU for that. Right now, everybody is shipping. It almost like it doesn't matter the demand is high, but we expect by that time frame, if you have like true competition, Qualcomm is going to fare very, very well in this area.

Josh Buchalter from TD Cowen. I was hoping you can maybe speak to your software maturity as we think about you merging into the data center ecosystem. I think we appreciate Qualcomm's rich heritage in silicon design and manufacturing, but it's a new venture for you guys and one where others have been inhibited by their software platforms. So could you speak to that and sort of what modular bring specifically as we think about merging that into your road map?

Very good. Thanks, Josh, for the question. So let me break this conversation to pieces. I think what we're doing right now and what we're going to do soon, right? So first, we've been very focused on inference, and that is the focus right now, the disaggregated inference. It doesn't mean that's the only thing we're going to be doing, but we've been very focused on inference for the disaggregated, I think accelerator into the data center. We also bring another interesting capabilities, especially what you heard when you think about open source model because when models have to run on the edge, they have to run on Qualcomm. We have been embracing. I think a lot of the industry standards, we've been supporting executors -- we've been supporting, Triton, for example, and we have been working over the years, there was the purpose. I told you that we have been preparing for this, building assets. There was a purpose on AI 100. The purpose of AI 100 is basically to start understanding how we need to mature software stack to the point now, we have new models in 24 hours that way is running on our accelerators. So that's what we haven't been building. We've been building the capabilities, supporting all of the different open ecosystems. But now there's something else we're going to be doing because the reality is, some of those platforms right now are old. I think the incumbent platform has been designed about 20 years ago. And I think a company like Modular has a very modern platform, which has been properly designed for the disaggregated heterogeneous compute and is open. And I think that's how we're going to change the conversation a bit, not only creating something that delivers higher performance and is easier for developers to use, not only for us, but for the rest of the industry and actually make sure that happens on the data center as well on the edge. So those are the 2 vectors. We'll continue to check the box and do what everybody is doing, I think, for inference and -- but we want to do something much better.

Lou Miscioscia here with Daiwa Capital. So to continue on the modular situation, it definitely seems very interesting. Maybe you could just talk about the obstacles. Where do you think it's going to be deployed, hyperscalers, neo clouds, enterprise? Because obviously, once software does get defined, obviously, NVIDIA has made a lot of progress in this area. It's hard to overcome, as you've seen with windows and other things, but there has been success stories like [ VMware ] throughout the year. So it seems like you could have an opportunity. So more details would be great.

Well, so I'll definitely -- I'll tell you what we see right now, and I'll talk a little bit about the vision. But first, I'll answer to the question is, if for inference, NVIDIA was the only option, NVIDIA will be the only thing shipping right now, which is actually not the case. And I also believe that as you think about what the industry really wants and what a lot of the industry has been developing, you see the progress of Google with TPUs, as an example. You saw the acquisition that NVIDIA made of Groq. So you have kind of different architectures. And I think the moat of inference is actually not as strong as it has been for training. But it also create an opportunity because you now have clusters of compute with a bunch of different hardware and you want a solution that exactly could actually abstract that problem for developers. Now I'm going to tell you what I see. What I see is the modular team, I think [ Chris ] and team, which are here, and we can't wait to get them as part of Qualcomm. They develop something that is modern and actually abstract this for developers and get a lot of performance of the hardware. And just don't take my word for it, if you actually look at what they have done, they have achieved a lot of performance working within NVIDIA hardware, AMD hardware, with CPUs. And it's truly an open platform that can actually run across the entire different types of environments and compute and also scale for the edge. That's how we start working with them. So you're always going to have this conversation, which is somebody is going to say, I'm just going to stay with [ CUDA ], which is tied to NVIDIA hardware. I'm just going to stay there. And I am just going to get one ecosystem or I'm actually going to see the benefits of having heterogeneous compute, a disaggregated compute and see what's going to happen on the edge, which will happen regardless. Like I'm telling you, I'm starting to see -- you see, I am getting some of the major AI companies, meeting with us. You saw some of those videos today saying, I need to move all of those workloads to the edge. I have a better use for some of my tokens in the cloud. That's going to happen, and that's going to bring different type of hardware. With that, I'm going to tell you what the vision is. At the end of the day, we have a lot of customers that make things and those customers that make things, they are going to start adopting a lot of AI in inference compute. You saw that across physical AI. So I think the customer reaction is they're actually looking forward for a platform that scales, scales across the edge is open, it's easy to use, and we expect there's a lot of different customers in the cloud. They are dealing with the effect that they have 3 or 4 or 5 different software stacks that they have to play with it. That's the bet. And as I said, we're going to be actively driving it. We're going to continue, what you heard from Chris. It's going to be open. It's going to be available to everyone, and we're going to see what happens.

It's Chris Caso from Wolfe Research. For the fiscal '27 data center guidance, I think it would be helpful if you could clarify exactly what's in that guidance. And I guess from the product launches you've discussed, it sounds like it's the accelerator plus maybe some of the connectivity you got from Alphawave? And then with regards to the customers, you've already announced Domaine as an accelerated customer. Microsoft was today, and you talked about 2 customers. So are those the 2 that are included in that?

So from a product perspective, I'd say the largest part of the revenue base will be custom silicon. As I mentioned, there are 2 customers who are global hyperscalers who will each be greater than $1 billion. So by far, that will be the largest part of the revenue. There will be a portion of AI accelerator coming in towards the end of the year. And then connectivity products that came from Alphawave acquisition will be also a portion of it. So it's really those 3 product lines with AI accelerator are really coming at the end of the year. From a customer perspective, as I said, for custom silicon, the 2 large customers will drive the custom silicon revenue. And then we have a very large customer base in connectivity that came through the acquisition. And we'll have human as a significant portion of it as well. So that's the base.

Jim Schneider from Goldman Sachs. Maybe just to follow up on the prior question. Can you maybe talk a little bit about how you expect the customer diversity change from fiscal '27 onward? You expect -- I think you talked about 3 customers you sort of indicated, maintaining like the largest kind of lion's share of the custom silicon. Do you have orders for all of that $5 billion already to be covered with that revenue? And then maybe talk about how much more diverse do you expect that revenue base to get in '29? And can you actually hit the '29 targets based on the customers you have now?

So let me address it in 2 parts. I think first is we have a high confidence in our forecast, and so I'll leave it at that. The way you should think about the '29 forecast is we're engaged across a variety of customers today. We are engaged across a variety of products with them, and then we are talking about multi-generation. So it's a combination of those factors that gives us confidence in the fiscal '29 number.

And the one thing I will add is, remember, in data center, the discussions are moving from megawatts to gigawatts. And when you deploy full infrastructure, as I outlined today, a few gigawatts can get you to the '29 numbers.

Constellation Research. I would be amiss not asking the Brazilian against who Brazil will be in the world.

I don't know. This is a tough one. I think I'm kind of encouraged that Brazil starting playing bad. Because when they start playing bad, usually, it brings down, I think it brings more humility in the team. They start playing together and they start to improve in the second half. So I'm going to take that as probably a consolation for what the pro forma as seen in the beginning of the season.

Perfect. To the serious question, AI tells me you're building between 250 or 500 chipsets. I know you don't make that number complex, but I estimate with all your plans for 2029, that number might easily double. How do you plan to handle the complexity because every chipset business venture, not all the ventures are happy from a human skill, capacity, risking, supply chain perspective?

Okay. Look, I'm going to give the -- I think, the answer that we always give to ourselves, right? So we're actually called quality communications. Maybe I need to -- it was interesting. I think during their 40-year anniversary of Qualcomm, we had an event and our founder, Dr. Evan Jacobs, he came and to speak and he said to me, Cristian, I make a mistake. I think I made a mistake. I should have done Qualcomm with one M because then it could be communications or compute interchangeably. But I think the story here is we actually have a quality reputation. You saw -- when you talk about our customers, we get award all the time from mobile customers about the lowest defect density, we ramp brand-new chip IP in a very fast period of time, we get over and over messages from Apple has been probably 1 of the best quality suppliers. And you saw what happened in automotive. So I think we look at that skill that we have developed is something that is very important, what we saw, and this is a little bit complicated this is a little bit complicated. I'm going to try to simplify the complexity. There's often a discussion about leading our design and process technology. There's often the discussion about who's best in yield and all of this. And one of the things we earn, for example, when we do Snapdragon for mobile phones, that has to ramp very, very fast. You have to design the product to a very narrow spec. I could never afford to do, for example, what Intel us and say, I have this distribution, and I'm going to sell this as a high clock speed as the i9. This is going to be the i7. And I'm just going to bin it. I have to develop the same exact part because you never hear Samsung saying, this is a fast Galaxy or not. And we saw what happened with this incredible demand on a data center. We hear anecdotal data from a lot of the customers about parts they're going to have rework and failure rates. We actually look as a vector of differentiation for Qualcomm which is our ability to reliability. And we're not small. We shipped 40 billion components, I think, every year. As I said, we do a large number of tape-outs of leading node chipsets, do all of them in parallel. You saw record dates on very complex automotive industry, which is very strict from all the gates we have to go to. We're breaking new records from tape-out to cars. So we're going to bring all of that to the data center. And this is already happening. I wanted to use this opportunity -- sorry for the long answer, but I think sometimes the word is actually a lot more simpler than it looked like. Alphawave had customers. They have been licensing IP and they have engaged them with customers. What we saw as we close off the wave. That conversation accelerated because just Qualcomm add more muscle, we just had more capacity. We have a bigger supply chain, and we're willing to actually make commitments that people will bet a large volume on it. And I think that's kind of what happened. That's when the accelerated a lot of the custom ASIC engagement, engagements they had Alphawave IP on it. And I think that's what I expect to happen. I think size really matters. As I said, all of the $5 billion we outlined, and we forecasted right now, we have wafers, and we have memory committed to those.

We have time for one final question.

It's Joe Cardoso from JPMorgan. Maybe more of a question for Tony on the connectivity side. Nice to see the road map here across copper and optical solutions. However, curious in one of your earlier slides, you also mentioned CPO. How are you thinking about that opportunity on the connectivity side? How is Qualcomm looking to participate in maybe time line around the road map there?

Thanks for that question. So at Alphawave, we had started working on silicon photonics and co-packaged optics about 5 years ago. And so what the plan is right now is to initially deploy first generation of silicon photonics in our AI 300 series, all right? So that will immediately empower optical scale-out, drive down power consumption dramatically because you're no longer going from copper to optics with you're going straight to photons. And look, beyond that, AI fabric will be moving optical. So we start with scale out in and around 2028 and then scale up beyond that.

That's it. All right. I think that -- thank you so much.

Thank you. Thank you for being here with us. Really appreciate it.