Computex Taipei 2026 demonstrated that robotics is no longer a side story at the world’s largest computing show. For the first time in its 45-year history, Computex dedicated an entire exhibition zone to Robotics and Physical AI. Major semiconductor vendors arrived not just with products but with a strategic position on who should own the market for the processors used in robotics.
NVIDIA came to Computex with the boldest claim: that it intends to own the software layer that every robot in the world is developed on and runs on. Jensen Huang unveiled a complete platform spanning AI models for humanoid robots, a world simulation environment that lets developers train robots faster and at far lower cost, and a reference robot design that any manufacturer can build upon. Partners such as Stanford and ETH Zurich are already committed to the platform.
NVIDIA is releasing models and development tools openly to entice the robotics ecosystem onto its platform. It is taking the same playbook that built its dominance in datacenter AI and applying it to robotics. NVIDIA’s solutions are higher cost and have higher power consumption than its competitors.
NVIDIA’s benchmark claims are strong, but the real test is unstructured real-world performance over sustained operating periods, not controlled evaluations. The reference design approach smartly avoids NVIDIA competing with potential hardware partners. Watch whether robot manufacturers outside the Unitree partnership accept a platform built on a competitor’s silicon roadmap. That tension is where the ecosystem story either succeeds or stalls.
Qualcomm made a sharper, more immediate argument. Building a robot today means stitching together components from dozens of vendors, and every seam in that system is a source of cost, delay, and failure. Qualcomm’s answer is a single fully integrated platform, the Dragonwing IQ10, that collapses that complexity into one deployment-ready system. Cristiano Amon drove the point home by bringing a full-sized humanoid robot on stage and demonstrating it live. The commercial target is clear: the growing tier of robot makers and industrial operators who want to move from prototype to production without building their own technology stack. Early partners include NEURA Robotics, Advantech, and NEXCOM, with broader availability by September 2026.
Qualcomm’s position is built on a decade of designing chips for cars, where real-time reliability is non-negotiable. That heritage is a genuine differentiator in industrial Robotics. Pricing and actual partner shipments will be the proof points to watch.
Intel took the longest-term angle of the three. The company formally launched ‘Intel Robotics’ as a dedicated business and introduced an open-source framework designed to close the gap between robots that work in the lab and robots that work reliably on the factory floor. With more than 130 commercial design partnerships already in place, Intel has a broader installed base than its keynote visibility might suggest.
The clearest demonstration came from Sensory AI’s Ella, a robot barista operating in live retail environments, running multiple AI tasks simultaneously on a single Intel Panther Lake SoC. The one SoC has replaced what would have required multiple processors and a more complex system.
Intel’s open-platform strategy is a smart way to compete without going head-to-head with NVIDIA’s brand authority or Qualcomm’s automotive credibility. The risk is that open ecosystems take time to build, and Intel needs its developer community to grow faster than the incumbent platforms consolidate
Intel is also building on a long history of providing processors for edge infrastructure used for industrial automation and robotics, including the coordination of robots in a factory. Intel also has a history with its RealSense camera sensors, demonstrating drones that could fly through a forest, for example, dodging trees, and providing drone show coordination solutions. Intel is not new to robotics or to working with robotics companies, and it will be able to leverage decades of experience.
NXP used the closing keynote of Computex to make the most pointed argument of the show. CEO Rafael Sotomayor’s talk, ‘Bringing AI into the Real World,’ unveiled the Neural Axis architecture — a three-layer, biologically inspired framework spanning reasoning, coordination, and reflexive intelligence. His thesis: the defining challenge of physical AI is not how smart a machine is, but whether it can react in milliseconds without round-tripping to the cloud. Intelligence, he argued, cannot be centrally scaled; it has to be distributed so that no single point of failure can stop the machine.
NXP demonstrated the architecture across drones, software-defined vehicles, and humanoid robots, wrapped it in a trust framework built on containment, protection, verification, and adaptation, and tied it to its eIQ developer toolkit and its $307 million acquisition of edge-AI NPU maker Kinara. The framing casts NXP as the owner of the robotic nervous system—the reflexes and safety layer beneath whichever “brain” handles high-level reasoning.
NXP’s wide portfolio of processors, microcontroller units (MCUs), neural processing units (NPUs), connectivity technologies and analog components is highly complementary to the main processor. NXP can own the deterministic, safety-critical layer where decisions happen in real time. Its decades of heritage in automotive and industrial silicon are hard to replicate. It can apply its experience in reliable solutions and functional safety to the robotics space. NXP is also partnering with Nvidia and supporting its software stack.
Computex’s new AI Robotics Zone drew Taiwan’s full supply chain of components, motors, and system builders. AI-related industries are forecast to account for around 70% of Taiwan’s exports over the next six months.
Beyond the headline platforms, Computex surfaced a sharper debate about what robotics requires to succeed at scale. NXP, as covered above, pressed the case that responsiveness — not raw intelligence — is the real constraint on physical AI. ABB, the industrial automation giant, showed that its NVIDIA partnership is enabling simulation accuracy close enough to real-world conditions that training times and deployment risks are falling significantly. ASUS entered the consumer market with service robots for healthcare and senior care, backed by an orchestration platform designed to work across brands and devices.
Robotics companies will have choices across processor vendors, processor architectures, closed versus open development platforms and software solutions, and various performance, power consumption, and cost specifications for CPUs and accelerators. The robotics market is not new, but the training and inference on new AI models – physical AI – is new, and the semiconductor vendors that can best support these new models with low power consumption and low cost will be best positioned to hit the sweet spot of unit volume and ASPs. There is also a lot of opportunity for adjacent companies such as NXP, IP vendors such as MIPS, and all the other semiconductors that provide other processors, connectivity, sensors, and power-related components.
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Phil Solis is Research Director within IDC’s enterprise infrastructure global research domain. He focuses on client computing and connectivity as part of the Semiconductors and Enabling Technologies subdomain. Phil’s coverage spans semiconductors in PCs, media tablets, smartphones, and wireless and mobile connectivity technologies.
Navkendar Singh is a Associate Vice President with IDC India, based in Gurgaon. His research domains encompass deep-dive research and insights in and around mobile devices, smart homes, PCs, tablets, wearables, and the printing market in India, Bangladesh, and Sri Lanka. He is also involved in building IDC's successful channel research programs for these domains at city and state levels. Navkendar also leads research related to analyzing the role of devices, emerging business engagement models, the impact of emerging technologies on devices, and emerging personas related to Future of Work.
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