Qualcomm is positioning robotics as its next major growth frontier, with CEO Cristiano Amon projecting that the segment will begin to scale materially within two years — a notable timeline for a company whose fortunes have long been tied to smartphones.
Speaking to CNBC at the Mobile World Congress in Spain, Amon said robotics is approaching a commercial turning point.
“I think robotics will start to get scale within the next two years,” he said. “I think it’s going to become like a larger opportunity within two years.”
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The comments come as Qualcomm accelerates efforts to diversify revenue beyond handsets, where global unit growth has matured, and pricing pressures remain intense. In January, the company introduced a robotics-focused processor under its Dragonwing brand, extending its playbook from mobile into a category increasingly defined by AI-driven autonomy.
From Snapdragon to Dragonwing
Qualcomm’s smartphone dominance was built on Snapdragon processors that offered device makers a standardized, power-efficient platform integrating computing, graphics, connectivity, and AI acceleration. The Dragonwing robotics processor reflects a similar systems-level strategy: build a chipset adaptable across industrial arms, warehouse bots, service robots, and emerging humanoids.
Robots require complex real-time processing. They must fuse data from cameras, lidar, radar, and tactile sensors; interpret that data through AI models; and translate decisions into mechanical movement through actuators — all while maintaining tight energy budgets. Latency is critical. A delay of milliseconds can mean instability or failure in physical tasks.
Qualcomm’s edge AI capabilities — optimized to run inference locally rather than in the cloud — are central to its pitch. Unlike purely cloud-dependent architectures, on-device AI allows robots to function with lower latency, reduced bandwidth requirements, and greater resilience in disconnected environments.
That capability could become more important as robotics shifts from controlled factory floors to dynamic, human-centered settings such as hospitals, retail spaces, and homes.
The Rise of “Physical AI”
Robotics is gaining renewed momentum largely because of advances in generative and multimodal AI. These systems can interpret vision, language, and spatial cues, allowing robots to adapt to unpredictable environments rather than operate through fixed scripts. Industry executives increasingly refer to this convergence as “physical AI.”
Amon underscored that point, saying robotics has become “a lot more useful” because of these AI advances.
The broader industry agrees that embodied AI represents a long-term growth vector. Jensen Huang, chief executive of Nvidia, has identified robotics as one of Nvidia’s key future expansion areas. Nvidia supplies high-performance GPUs for AI training and simulation environments widely used in robotics development.
Qualcomm’s approach is distinct. While Nvidia dominates the training and simulation layer, Qualcomm is targeting deployment — embedding efficient AI compute directly into the robot. The distinction mirrors broader shifts in AI architecture, where training occurs in massive data centers but inference increasingly migrates to edge devices.
A Market of Vast Projections — and Uncertainties
Forecasts for robotics underscore both promise and uncertainty. McKinsey & Company has projected that the market for general-purpose robots could reach $370 billion by 2040. Analysts at RBC Capital Markets have estimated a total addressable market for humanoid robots as high as $9 trillion by 2050.
Those figures hinge on several variables, such as declining hardware costs, improvements in battery density, advances in mechanical dexterity, and regulatory clarity around autonomous systems. Humanoid robots — including those under development by Tesla and several Chinese firms — remain in early stages, with production economics still unproven.
At this year’s Mobile World Congress, robotics was a visible theme. Chinese smartphone maker Honor previewed its first humanoid robot, signaling that consumer electronics firms are exploring robotics as an extension of their AI and device ecosystems.
The convergence is logical for one significant reason. As smartphones plateau, companies are looking toward new AI-enabled hardware categories. Robots — especially service and companion devices — could eventually integrate connectivity, voice interfaces, and cloud services in ways reminiscent of early smartphone ecosystems.
The Imperatives for Qualcomm
For Qualcomm, robotics represents more than an adjacent market. It is part of a broader strategy to reposition the company as an AI-centric compute provider across verticals: automotive, industrial IoT, edge computing, and now robotics.
The automotive parallel is instructive. Qualcomm successfully expanded into vehicle infotainment and advanced driver-assistance systems, leveraging mobile expertise in connectivity and power efficiency. Robotics could follow a similar trajectory if adoption accelerates.
Yet execution risks remain.
Robotics adoption cycles are typically tied to capital expenditure budgets. Industrial buyers make multi-year procurement decisions, and scaling production requires manufacturing partnerships, supply chain resilience, and long-term software support.
There is also the question of competitive intensity. Nvidia’s ecosystem advantage in AI tooling and simulation could influence robotics OEMs’ hardware choices. At the same time, emerging chipmakers and specialized AI startups are designing custom silicon optimized for robotic workloads.
Qualcomm’s differentiation will likely rest on integration — combining CPU, GPU, neural processing units, and wireless connectivity into a cohesive platform with strong developer support.
A Two-Year Window
Amon’s two-year timeline suggests Qualcomm expects tangible commercial traction rather than distant theoretical growth. That would require robotics manufacturers to move from prototypes to scaled deployments in logistics, manufacturing, healthcare, and potentially consumer applications.
Macro conditions could accelerate or slow that transition. Labor shortages in advanced economies and rising wage costs strengthen the economic case for automation. Conversely, global economic uncertainty can delay capital investment in robotic fleets.
Even if robotics does not immediately rival smartphones in revenue contribution, achieving early scale would validate Qualcomm’s diversification thesis. In a semiconductor industry increasingly shaped by AI demand, positioning at the intersection of compute, connectivity, and physical autonomy could prove strategically decisive.
If AI’s first wave transformed how people interact with screens, the next may redefine how machines move through the physical world. Qualcomm is betting that when that shift occurs at scale, its chips will be embedded at the core.