Livestream: Qualcomm Investor Day 2026

Thank you everyone for joining us. This is an exciting day for Qualcomm. As a matter of fact, I became CEO of Qualcomm on June 30th, and I picked this week on purpose — it's exactly five years since I became CEO of this incredible company that I joined as an engineer about 30 years ago. What I'm going to tell you today is about the transformation. In 2021, we put a strategy together at our Investor Day and said we were going to do certain things. I think we are at a point now where everything is in place to execute on that strategy, and it's time to start the new chapter of Qualcomm. We have a lot to pack into a short period of time. We have Q&A at the end, so I'd encourage you to stay — it's going to be an awesome Q&A. This is where we started. We were probably the most focused semiconductor company in the mobile market. Over the next five years, we built a diversified edge leader across multiple end markets: automotive, IoT broken down into personal AI and compute — how wearables and VR/AR evolve into sub-personal AI devices — as well as industrial, networking, and robotics. Now we're transitioning to the next chapter of Qualcomm, and there are three dimensions to it. First, we're building a data center platform with a comprehensive portfolio of solutions. Think of the past few years as a submarine strategy — we've been executing, collecting assets, and now we have a comprehensive portfolio to enter the next phase of the data center as inference scales and disaggregation happens. A lot of people ask if it's too late in this crowded market. Never too late for Qualcomm — this market moves very fast, and if you have technology leadership, there's always room. People may like or dislike the company for many things, but I've never heard anyone say Qualcomm doesn't have solid technology capability. The second dimension is that now that we've built this platform of devices at the edge, we're moving to a full stack player. As agentic AI transforms devices on the edge, we'll build on those assets with one of the broadest semiconductor portfolios to become a full stack player in physical AI compute everywhere. Before that, our mobile business is also evolving — I'll share how to think about future mobile edge devices that consumers will utilize. The third dimension is going from silicon to platform solutions — fully integrated platforms across hardware, software, and developer ecosystem, making Qualcomm a developer-first company. Let me summarize the Qualcomm advantage. We're always proud of our technology and IP, and we build very broad strategic relationships across industries. We've built strong relationships with all the leaders of the industries we've entered and built ecosystems around them. In terms of scale and execution, we're one of the leading machines in the semiconductor industry. Our technology advantage is truly broad — we've been creators of standards, from wireless to wireline including connectivity in the data center. When data centers talk about disaggregated computing, this has been the reality of mobile for a very long time. We have every form of disaggregated compute, including safety and industrial-grade processors. We're building a comprehensive software and developer ecosystem. Sensing becomes even more important for physical AI, along with multimedia, advanced packaging, and memory. We have a very strong patent portfolio from about $100 billion cumulative in R&D investment. Our customer reach has expanded in concentric circles from mobile into every other industry — our customers believe in Qualcomm, especially when we enter new markets. And our execution: we're a proud member of the TSMC fab ecosystem. We consume over one million leading-node wafers annually, do over 75 chip tape-outs per year with over 30 in advanced transistors, ship 40 billion components totaling 2.5 million wafers. We sometimes tape out a chip before we get it back — as soon as TSMC finishes the mask, we go to production at scale. We ramp completely new nodes to 100k wafers within about two quarters. We're now present across the entire compute continuum from sub-two milliwatts to about 200 kilowatts. If this gets transformed with AI and we apply our technology on a developer-friendly software platform, that creates an incredible opportunity for the company. Now let me bring to the stage Tony Pialis, who joined us from AlphaWave and now runs our data center business.

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's unique about Qualcomm. I know we have a limited amount of time, but there's a lot of new vectors of technology that hopefully you'll be able to see. It's not only about one solution in the data center but also when we think about automotive, when we think about 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 different trends that are going to happen as we think about the role of agents. As I said in the keynote at Computex, the advent of agents and orchestrators was a very significant milestone that actually provides clarity on how those devices are going to evolve. The industry tends to think in binary terms—is all of a sudden everything going to stop and 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 about is we all have been used to 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. The OS and the apps understand the human intentions and everything is around the smartphone. Even other devices are just an extension of the smartphone. That's not the case anymore. Actually every AI company, every foundational model company now talks to us that 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. For the agentic experience, once you understand human intentions, the agent is at the center. Devices are just end points of the agent. And the purpose of my presentation right now is to tell you how the devices are going to change because of everything that has happened. Let's 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 human speed. But now the device with the orchestrator and the agents are also going to do other things on behalf of the human. It's going to operate the device and we're starting to see that right now. If you ask me where's the epicenter of this start of new agentic use cases, it's actually happening in China right now. And you start to see the agents go to your device and operate the device for you, and it goes to the web. Once you have the agentic experience, it tells you that the device now has two use cases. It was interesting—I said this before, I'm going to repeat it—when people bought a computer to run OpenClaw and they have the computer running OpenClaw, once you start having that experience with you, it's not about just the amount of software developers they exist in the world, but the six billion people that have smartphones when they start to use agentic experience as part of your experience and interaction with the device, you're not going to carry a computer. It's all going to happen in the same device and the device is going to have two users because there's two different workflows. There's you and there's agents. So that's one big change. The other big change is perception and sensing, and that's what is actually changing the device itself and enabling different endpoints like personal AI devices.

If we have now the computer that interacts with us the way we interact with each other, then the context that we are inserted into, especially as we communicate with audio—we speak, we listen, we see—and then we are integrated into our surroundings, that context becomes important. So you now have a lot of sensing data that needs to be 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. That transition is very important because the reason we've been very, very focused on glasses is because glasses are close to our senses, close to our eyes, to our mouth, to our ear, and those devices—wearables—were extensions of the smartphone. When the smartphone was in the center, for the agentic experience they are actually an endpoint 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, Satya announced Project Sora, a badge 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 one form factor we know is going to get scale, and that's glasses. And the use case is see what I see and hear what I hear. And I'm going to come back to that use case because it'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 see is that new companies are looking at this big change. The devices now are endpoints, the barriers to entry of OS and app stores for new experiences are no longer the same. You're going to see a lot of excitement. I want to start by showing a video from Amazon.

So there's a lot of exciting things coming. And by the way, I'm actually so grateful. I have a relationship with Panos for more than a decade. 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 with 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. When we've been doing a number of those things, and you can try yourself, you can try the different prompts and you're going to see it changes—sometimes you get 10%, 20%, 30%, 40%, 50%. I just picked 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 got two 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, it's just Claude. We're using Opus 4.6, 100% in the cloud. You see the thing working. The other one uses smart routing. You use some models that are locally installed into the machine and some models in the cloud, and you get those things to work. And what you'll see at the very end is 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 mixture 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 they are going to give up on 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 at Build that they now create an unmetered intelligence, and people really understand what AI PC is—this is happening everywhere. And the beauty of the devices like phones is actually happening on the same device with two different users. And you can see a lot of useless debates I have seen over the years about is this edge or is this cloud—it is actually the wrong conversation. To say I have something that I 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, it's going to be done in the cloud, the growth of the cloud is incredible. But the edge now also becomes a computing platform that is going to generate tokens. And I think how the industry is naturally going to evolve—the message here to you is what's happened in the data center, inference is actually becoming disaggregated and distributed everywhere. It's just a new form of compute. For example, when you look at 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 so you understand—I'm going to overlay two diagrams that are going to make it very interesting. But you look at the data center with hundreds of megawatts to gigawatts. You're going to have regional data centers with tens of megawatts, and on-prem—there's a lot. If you look at results of some of the server companies like Dell, 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 hybrid AI, you're going to see more computing happen in PCs and devices at the edge. So inference becomes distributed. Just don't take my word for it—we have an incredible partnership with Google, and I want you to hear from Google about that.

So big thank you. Thank you, Rick, for the partnership and the confidence in what we're going to do together. So I have to pick up the pace now and I 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 had. 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 into walking cameras in this world. I need a very fast, high-definition video. The opposite of what we did with 5G, which was enable streaming and high-definition video. We're going to do that for uplink and across the cell site. So everyone has the ability to stream high-definition video for see-what-I-see. That's very important context for agentic experiences. That's why we're going to the connectivity. I'll go to the details. There's a lot of improvement on the connectivity side. But that's only one third of the story.

The other part of the story is the computing part. And the computing is going to be required because the 6G infrastructure is no longer dedicated equipment for communications. The network of 6G actually—you need to be thinking about it—that was a network that was designed for voice. It now transports bits and is going to also generate tokens. And the reason that's important is because the premise of how 6G is going to deal with radio frequency is going to look at radio frequency physical AI, and you're going to need compute. Look at the architecture—it's kind of the same as the distributed compute. You're going to have a big data center, you're going to have a regional data center—that's the core of the network. You have to have an edge data center, and you're going to have the cell site and then 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, it becomes sovereign AI workloads. Some operators will actually be selling token generation machine capacity, like a CSP for AI companies, for this distributed compute. And once 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 and the RF radio characteristics. You're going to be able to sense everything. Drone detection is on top of mind. It becomes critical infrastructure. Everything that moves and flies becomes important context 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 one user, and we're building the next architecture of those devices. You need now a CPU for the orchestrators. 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 for inference because you have to have very high performance, low power inference, even when you are not using the device as a human. And even that high-bandwidth compute that we're doing on the data center—we're building a version of that as a co-processor for phones. There's a complete change on the perception and sensor as well as a new modem. And it's just not about the phone—there are different classes of devices. And that is also bringing other people to the 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 you what they're doing.

The obvious question that everybody's 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 tariff 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 where I don't know what the mobile customers are anymore, because there's the OEMs but every single AI foundational model company building agents is also our customer. The surface area is tremendous. There's six billion phones, two billion personal AI devices, two billion PCs, and 500 million cars. And when you think about this new form factor—I'll 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—we're building a very small reference design that you can build into any glasses—and you can have the ability to access an agent for audio multimodal or premium display, and you can see the smart earbud. So that is a great opportunity and that's what's also going to happen on the mobile devices business of Qualcomm. So I hope you saw there's a lot of interesting trends in technology.

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 third pillar that we've been talking about in this presentation—about the next chapter of Qualcomm from silicon to platform solutions, and to really building a fully integrated platform for hardware, software, but also changing the company to a developer-first company mindset. And you saw we're doing this with all the new businesses we built on the edge and we're going to be doing this for everything we have on the compute continuum. That's why this acquisition that we made of Modular is so significant for Qualcomm, because it 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 one company to innovate alone. 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 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 endpoint becoming an endpoint for agents doing 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 this stage a legend, Chris Lattner, the founder and CEO of Modular, who will tell you about what we're going to be doing. Chris, please come on stage.

Thank you. I hope you share our enthusiasm about this change in the company, this opportunity changing the industry, what we're going to do together. And we're super happy to have Chris, Tim, and the rest of Modular as part of the Qualcomm family. So I was about to end here, but I thought maybe there's one more thing. Hopefully you are entertained. But we have one 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 a very unique collaboration because they share 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 an agentic model onboarding, combining Hugging Face'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 is going to handle the setup, the optimization, deployment with zero manual integration work. And then just what you heard from Chris, built on end-to-end agentic AI with distributed intelligence, that distributed AI framework where agents can operate seamlessly across the entire compute continuum, leveraging Qualcomm technology as a model and also cooperating with everyone. And I want you to hear from Clement.

So that's it. I think I got to the end of the presentation. Hopefully we gave you an opportunity to understand what Qualcomm is going to do within the next five years. And I'm going to summarize it for 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 talked about building a data center portfolio. We expected revenue to be in fiscal 28. Then we get more traction, we move it to fiscal 27. We get more traction, get to fiscal 26. And you heard from Tony—we're just starting. Automotive, industrial, robotics will extend Qualcomm into the next frontier of physical AI. We built 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 across edge devices. Those are going to be machines that 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 creates the opportunity for us to enter the disaggregated compute space, 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 continuum.

6G will be foundational infrastructure for the age of AI. And if you haven't forgotten, we have that asset too. And we're going to be expanding beyond silicon to a full-stack software platform with the most industry-friendly 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 the day, our CFO, Akash.

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

Maybe I'll add a few things and remind you of our journey. We've been building our own CPUs. The first thing we did was build a CPU to compete with Apple, following the Apple M series. Then we built a CPU for mobile devices, a safety-grade CPU for automotive. This is the next generation CPU that we design. The demand for CPUs is massive right now. Everybody has CPU chips—I've seen this in the pandemic. But if you look at what happened in other markets with our designed CPUs, our metrics have been very good from a performance and power perspective. The hyperscalers gave us very specific requirements for what they want in fiscal 28, and the feedback is 'this is too good—when can I get the silicon?' You should be thinking about us having the capability to understand where the puck is going for agentic AI and building a CPU for that. Right now everybody's shipping and the demand is high, but we expect by that time frame, with true competition, Qualcomm is going to fare very well in this area.

Very good, thanks Josh. Let me break this into pieces. We've been very focused on inference—specifically disaggregated inference. That's the focus right now. We also bring an interesting capability when you think about open source models, because when models have to run on the edge, they have to run on Qualcomm. We've been embracing industry standards, supporting x86, supporting Triton, and building assets over the years. There was a purpose to AI 100—to start understanding how we need to mature a software stack to the point where new models run on our accelerator within 24 hours. Now there's something else we're going to do, because some of those platforms are old. The incumbent platform was designed about 20 years ago. A company like Modular has a very modern platform designed for disaggregated heterogeneous compute that is open. That's how we're going to change the conversation—not only creating something that delivers higher performance and is easier for developers, but making sure that happens in the data center and on the edge. Those are the two vectors. We'll continue to do what everybody is doing for inference, but we want to do something much better.

I'll tell what we see right now and talk about the vision. First, if for inference Nvidia was the only option, Nvidia would be the only thing shipping right now—which is actually not the case. I believe that as you think about what the industry really wants, you see Google's progress with TPUs, you saw Nvidia's acquisition of Mellanox. You have different architectures, and the moat of inference is actually not as strong as it has been for training. It creates an opportunity because you now have clusters of compute with different hardware, and you want a solution that can abstract that problem for developers. What I see is the Modular team—Chris and team, who we can't wait to get as part of Qualcomm—have developed something modern that abstracts this for developers and gets a lot of performance out of the hardware. They've achieved significant performance working with Nvidia hardware, AMD hardware, and CPUs. It's truly an open platform that can run across different environments and scale for the edge. There's always going to be a conversation about staying within CUDA tied to Nvidia hardware versus seeing the benefits of heterogeneous, disaggregated compute and what's going to happen on the edge. That's going to happen regardless—I'm starting to see major AI companies meeting with us, saying they need to move workloads to the edge. Our customers that make things are going to start adopting AI inference compute. They're looking forward to a platform that scales across the edge, is open, and is easy to use. We're going to be actively driving this. It's going to be open and available to everyone.

I don't know—it's a tough one. I'm actually kind of encouraged that Brazil started playing badly, because when they start playing badly it usually brings more humility in the team. They start playing together and improve in the second half. I'm going to take that as a consolation based on what I see at the beginning of the season.

Look, I'll give the answer we always give ourselves. We're actually called Qualcomm Communications. During the 40-year anniversary, our founder Dr. Irvin Jacobs came to speak and said, 'Christian, I made a mistake. I should have done Qualcomm with one M, because then it could be communications or compute interchangeably.' But the story here is we have a quality reputation. We get awards from mobile customers about the lowest defect density. We ramp brand-new chip and IP in a very fast period of time. Apple has called us one of their best quality suppliers, and you saw what happened in automotive. There's often discussion about leading-edge design and process technology. One of the things we learned from Snapdragon for mobile phones—which has to ramp very fast—is you have to design the product to a very narrow spec. I could never afford to do what Intel does and bin parts as i9 or i7. I have to develop the same exact part. We saw the incredible demand in data center and heard anecdotal data from customers about rework and failure rates. We look at this as a vector of differentiation for Qualcomm—our ability to deliver reliability. We ship 40 billion components every year. We do a large number of tape-outs of leading-node chipsets, all in parallel. We set record dates in the strict automotive industry. We're going to bring all of that to the data center, and this is already happening. Alpha Wave had customers and had been licensing IP. As soon as we closed Alpha Wave, that conversation accelerated because we added more muscle—more capacity, a bigger supply chain, and we're building to make commitments for large volumes. That's why they got to accelerate a lot of the custom ASIC engagements. Size really matters. All of the $5 billion we outlined—we have wafers and memory committed to those.