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Omdia: Distributed intelligence and system-level orchestration will become key to the large-scale adoption of Agents.
AI Large-Scale Adoption Faces Architectural Bottlenecks, Distributed Intelligence Architecture Becomes Industry Core Direction Omdia pointed out in its latest analysis that today's average user owns multiple smart devices, switching frequently between phones, PCs, wearables, smart home devices, and in-car systems every day, yet the AI experience remains highly fragmented. Most of these devices still operate in silos, with context and memory frequently lost across devices, forcing users to take on the role of "system integrator."
Current AI assistants face three major structural limitations. First, a passive architecture—systems can only respond to commands rather than anticipate needs. Second, device silos—AI capabilities are confined within a single ecosystem. Third, technical burden—ordinary users find it difficult to master complex prompt design. Omdia emphasizes that "the long-term impact of AI depends on ease of use, trust, and everyday practicality—not just AI capabilities."
The reality of multiple devices calls for a rethinking of AI system design: AI should not be confined to a single device but should serve as a pervasive, accessible intelligence layer that optimizes the overall user experience. As the most personal, always-on, and computationally powerful terminal, the smartphone is evolving from an independent device into the core anchor of the personal AI ecosystem. Omdia proposes that this transformation requires three key shifts: agent-centric cross-device collaboration, AI agents as a unified interaction layer, and a transition from passive response to proactive contextual intelligence services.
In addition, relying solely on cloud-based centralized models faces severe cost and scalability constraints when providing continuous services to billions of users. Omdia's estimated data shows that with 100 million active users, each making 50 AI requests per day, and a typical cost of approximately $0.003 per request under a pure cloud architecture, annual cloud expenditure would reach $5.5 billion. At a scale of 1 billion users, this expenditure would exceed $50 billion. However, by achieving an 80% local processing rate through distributed architecture, cloud operational costs can be reduced from $5.5 billion to about $1.2 billion, while also improving response speed, data privacy security, and system reliability.
Leading Enterprises Forward-Looking Computing Architecture Layout, Smartphones Evolve into Key Anchors for Personal AI Facing dual challenges of architecture and cost, many companies have already made forward-looking arrangements. Omdia points out that ecosystem participants represented by Qualcomm are building end-to-end intelligent computing capabilities spanning terminal devices, edge nodes, and the cloud, comprehensively optimizing connectivity, AI intelligence, energy efficiency, and cross-device collaboration efficiency to construct a user-centric smart architecture.
Omdia believes that the large-scale deployment of agents is a structural change comparable to the leap from feature phones to smartphones, with its core in architectural innovation. As the ideal personal device anchor for building distributed personal AI systems, smartphones require the industry to coordinate intelligent deployment across devices, edges, and the cloud. It should drive smartphones from independent terminals into collaborative, user-centric core anchors, prioritize the development of cross-device collaboration capabilities, integrate edge systems and cloud services, ensure workloads run efficiently in optimal environments, and lay a solid foundational architecture for AI adoption.
Core of Personal Digital Life Shifts to Agents, Terminal Landscape Moves Toward Multi-Device Collaboration In a recent interview with Fortune magazine, Qualcomm CEO Cristiano Amon shared a highly forward-looking personal AI vision, strongly aligning with Omdia's industry judgment. In the interview, Amon explicitly defined 2026 as the first year of agents and revealed that major AI vendors worldwide are currently deploying personal AI devices, with some special form-factor terminals yet to be released. The future shipment volume of smart hardware is expected to reach hundreds of millions or even billions, and Qualcomm is collaborating with almost all leading companies.
Amon also made bold predictions about the future device landscape: "In the current agent interaction mode, the core of personal digital life is no longer the phone, but the agent." He believes that future smart hardware will see a multi-device coexistence pattern rather than monopoly by a single category, and stated that he "is very optimistic about smart glasses." Amon expects that by 2027 to 2028, a large number of daily task processing will shift to wearable devices. At the same time, Amon pointed out that smartphones will also undergo profound changes. Smartphones will not be eliminated, but the industry's competitive focus will shift from phone operating systems and app stores to the agent services chosen by users.
In the interview, Amon further elaborated on Qualcomm's unique positioning in this transformation. He noted that Qualcomm has a technology layout covering multiple scenarios, not only possessing world-leading cellular communication, Wi-Fi, Bluetooth, and positioning technologies, but also full-category computing R&D capabilities, with self-developed CPU, GPU, NPU, ISP and other core components. It can leverage existing technology roadmaps for large-scale expansion, adapting to the needs and standards of various industries.
This full-stack capability from connectivity to computing, from chips to systems, along with long-term multi-business layout, enables Qualcomm to play a key role in distributed intelligent architecture. By providing high-performance, low-power computing solutions across the cloud, edge servers, and a wide range of terminals, Qualcomm is driving the large-scale deployment of agent AI, allowing AI to benefit a broader user base and unleash the true value of technology.
Conclusion From industry analysis to enterprise practice, distributed intelligence and cross-device collaboration have become the core direction for the large-scale development of AI agents. Solving the cost dilemma, breaking device silos, and building a user-centric intelligent architecture are key to achieving large-scale deployment and expansion of the AI industry. As technology continues to mature and industry consensus gradually solidifies, AI agents will progressively overcome cost and device barriers, enter more people's daily lives, and propel the entire AI industry into a more inclusive and efficient new phase.
(Source: Ifnar)