Arm Holdings plc

Good day and thank you for standing by. Welcome to the third quarter fiscal year 2026 webcast and conference call. At this time, all participants are in a listen only mode. After the speaker's presentation, there'll be a question and answer session. To ask a question during the session, you will need to press star one and one on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star one and one again. Please be advised that today's conference is being recorded. I would now like to hand the conference over to your first speaker today, Jeff Cavale, Head of Investor Relations. Please go ahead.

Thank you very much, and welcome to our third quarter fiscal 26 earnings call. On the call are Rene Haas, Arms Chief Executive Officer, and Jason Child, Arms Chief Financial Officer. During the call, Arm will discuss forecasts, targets, and other forward-looking information about the company and its financial results. While these statements represent our best current judgment about future results, Our business is subject to many risks and uncertainties that could cause actual results to differ materially. In addition to any risks that we highlight during this call, important risk factors that may affect our future results and performance are described in a registration statement on Form 20F filed with the SEC. ARM assumes no obligation to update any forward-looking statements. We will refer to non-GAAP financial measures during this discussion. Reconciliations of certain of these non-GAAP financial measures to the most directly comparable GAAP measures can be found in our shareholder letter, as can a discussion of certain projected non-GAAP financial measures that we are not able to reconcile without unreasonable effort and supplemental financial information. Our earnings materials are available at investors.arm.com. And with that, I'll turn the call to Rene.

Thank you, Jeff, and welcome, everyone. Arm delivered a record third quarter. Revenue grew 26% year-on-year to $1.24 billion, our fourth consecutive billion-dollar quarter. Royalties increased 27% to a record $737 million, driven by record units with strength across AI and general-purpose data center. Our data center royalty revenue has grown more than 100% year-on-year, and we expect in a few years our data center business to be our largest business, larger than mobile. License revenue was $505 million, up 25% year-on-year. as more leading companies signed high-value licenses for next-generation technologies. That performance lifted our non-GAAP EPS to 43 cents, even as we continue to increase R&D investment. Our performance this quarter reinforces the strength of the Arm platform and our continued commitment to investing in innovation across a broad spectrum of compute technologies. The fundamentals of the Arm business have never been stronger. AI is changing how compute is built. and where it runs across cloud infrastructure, edge devices, and physical systems. The industry requires platforms that deliver high performance, energy efficiency, and flexibility across a broad range of power envelopes and use cases. Only ARM's compute platform can address these demands, supporting AI workloads ranging from milliwatts to gigawatts. To align with how our customers deploy AI, we've organized ourselves around three business units, Edge AI, Physical AI, and Cloud AI. Edge AI comprises the smartphone and IoT businesses. Physical AI includes automotive and robotics, and cloud AI encompasses data center and networking. A key driver of our royalty momentum is compute subsystems, or CSS. We launched CSS nearly two and a half years ago, and demand continues to exceed expectations. This quarter, we signed two additional CSS licenses for Edge AI tablets and smartphones, bringing us to 21 CSS licenses across 12 companies. Five customers are now shipping CSS-based chips, including two shipping a second-generation platform. And the top four Android smartphone vendors are shipping CSS-powered devices. CSS helps customers get to market faster by lowering integration risk and complexity. As demand scales, it increases the value that ARM delivers per chip, creating a significant tailwind to royalties. In cloud AI, the shift towards inference is reshaping data center design. And increasingly, that inference is agent-based. These workloads are persistent, always on, and power constrained. This is a fundamental change in how AI systems operate. This is because agent-based AI requires coordination across many agents running continuously, and that the CPU can only do coordination. As this model scales, customers need CPU chips with higher core counts, and better power efficiency to operate continuously within tight power and cost constraints. This trend directly benefits ARM. ARM-based CPU chips deliver industry-leading performance per watt, enabling customers to scale core counts and run always-on AI workloads. We are now seeing this trend play out in the market where Neoverse CPUs have surpassed 1 billion cores deployed, and ARM's share amongst the top hyperscalers is expected to reach 50%. Leading hyperscalers are launching new products with increased core counts to address this opportunity. AWS launched its fifth-generation Graviton processor with 192 cores, doubling the core count from Graviton4 and delivering 25% higher performance and up to 33% lower latency versus Graviton4. NVIDIA's next-generation Vera CPU features 88 ARM-based cores up from 72 cores in the gray CPU generation. Microsoft introduced Cobalt 200, built on the higher-performance ARM Neoverse CSS v3 with 132 cores, up from 128 cores in Cobalt 100, which was based on the prior Neoverse N2 platform. And Google previewed its second ARM-based server processor with Axion-powered N4A instances delivering up to 2x better price performance and 80% better performance per watt than the comparable x86 offerings. Google has now migrated over 30,000 applications to the ARM instruction set. We are also seeing more integrated platform designs to improve system efficiency, often translating to more AI output or more tokens per watt within the same power envelope. AWS integrates Graviton with ARM-based Nitro DPUs and training accelerators, and NVIDIA pairs GPUs with ARM-based Gray CPUs and ARM-based Bluefield DPUs, which has transitioned to Vera, delivering a 6x increase in DPU compute capability over the prior generation. Together, these trends make it clear that as AI inference becomes more agent-based, the importance of CPUs is only increasing. And as a result, ARM's role at the center of the modern data center architecture continues to grow rapidly. Outside the data center, AI is now moving to everyday devices. The edge and physical AI markets are opening up new growth opportunities. These systems operate in real time under strict power, safety, and reliability constraints, where efficient and predictability general-purpose compute is essential. ARM strengths, power efficiency, predictable latency, and always-on operation are best suited to on-device agents that continually monitor inputs, prioritize tests, and invoke models when needed to preserve battery life. Our common software foundation across devices, vehicles, and robotics lets customers scale deployments without rebuilding software stacks. We now see that momentum in customer innovation. Rivian announced its third-generation autonomy computer based on the ARM-based Rivian autonomy processor, the first production vehicle based on a custom ARM chip, and the first to deploy ARMv9 in production car. Tesla's upcoming Optimus humanoid robot is also powered by a custom ARM-based AI processor, and platforms from leading silicon providers like NVIDIA's Jetson Thor and Qualcomm's Dragoon platforms are scaling ARM-based solutions across robotics and autonomous systems. To close, AI is moving to every environment and every power envelope. ARM provides the foundation for that shift, a platform that spans milliwatts to gigawatts and a developer ecosystem over 22 million developers, more than 80% of the global total. We are now seeing the results of strategies we put in place years ago, focusing on the data center, power efficiency, and compute subsystems. As a result, as more and more applications move to agentic AI, ARM will be the compute platform connecting cloud, edge, and physical AI use cases. And with that, I'll now hand it over to Jason.