Houmo AI's Yang Dawei: AI Agents Are Ushering in a New Decade

The Agent Wave Arrives as Edge AI Reaches a Historic Inflection Point

As large model technology continues to evolve, AI's next growth engine is gradually shifting from the cloud to the edge. Houmo AI's Yang Dawei recently shared his perspective, stating clearly that AI Agents are opening an entirely new decade-long development cycle. Alongside this trend, core technological capabilities accumulated in the cloud will accelerate their migration to endpoint and edge devices, profoundly reshaping the entire AI industry landscape.

Yang Dawei believes the edge market has entered its breakout "year one," with over 80% of data processing set to be completed at the edge in the future. This assessment is not only relevant to the chip industry's technology roadmap decisions but will also profoundly impact a wide range of application scenarios, from smart terminals to the Industrial Internet of Things.

From Large Models to Agents: A Critical Leap in the AI Paradigm

Over the past two years, the capabilities of large language models have advanced by leaps and bounds, yet their application form has largely remained at the level of "conversational interaction." The emergence of Agents is pushing AI from passive responses toward a new stage of proactive perception, planning, and execution. Agents no longer just answer questions — they can autonomously decompose tasks, invoke tools, and interact with their environment, truly becoming intelligent entities with the capacity for action.

Yang Dawei's assertion that "Agents are opening a new decade" is essentially a precise encapsulation of the critical turning point where AI transitions from "capability demonstration" to "value realization." When Agents need to perceive the physical world in real time, process multimodal data, and make rapid decisions, relying solely on cloud computing power creates multiple bottlenecks around latency, bandwidth, privacy, and cost. This is the fundamental logic behind the rise of edge computing.

Cloud Technology Migration to the Edge: Why Now?

The migration of cloud technology to the edge is not a new concept, but progress was previously slow due to technical bottlenecks such as insufficient edge chip computing power and limited model compression effectiveness. Today, multiple factors are converging to accelerate this process:

First, model lightweight technologies are maturing. Continuous breakthroughs in quantization, distillation, and pruning techniques have enabled models with meaningful reasoning capabilities to run efficiently on edge devices. Compressing models from billions of parameters down to hundreds of millions or even smaller scales has dramatically improved their usability at the edge.

Second, edge AI chip computing power is advancing rapidly. Chip companies represented by Houmo AI are leveraging innovative architectures such as computing-in-memory to significantly improve the energy efficiency of edge chips, enabling them to handle more complex AI inference tasks.

Third, Agent application scenarios inherently demand edge capabilities. Whether in autonomous driving, robotics, smart wearables, or industrial quality inspection, Agents need to complete low-latency perception and decision-making locally. Uploading all data to the cloud and waiting for results cannot meet requirements for real-time performance and privacy.

Fourth, cost is a driving factor. As AI applications scale explosively, the computing costs of cloud-based inference continue to climb. Offloading large volumes of routine inference tasks to the edge is an inevitable choice for reducing overall system costs.

80% of Data Processing at the Edge: How Will the Industry Landscape Be Reshaped?

Yang Dawei's assertion that "over 80% of data processing will be completed at the edge in the future" implies a fundamental shift in the AI industry's center of gravity. This trend will bring several far-reaching impacts:

The chip sector faces a new round of reshuffling. Edge AI chips will become the segment with the greatest growth potential. Unlike cloud GPUs that pursue maximum computing power, edge chips emphasize energy efficiency, low power consumption, and high integration — providing companies with innovative architectures an opportunity to leapfrog the competition. Houmo AI's focus on computing-in-memory technology is precisely aimed at this structural opportunity.

Hardware-software co-design ecosystems are accelerating. Deploying Agents at the edge requires not only chips but also supporting compiler toolchains, inference frameworks, and development platforms. The hardware-software ecosystem formed around edge AI will become a critical competitive moat.

The data privacy and security landscape is changing. When the vast majority of data is processed at the edge without needing to be uploaded to the cloud, data privacy protection gains inherent, architecture-level safeguards. This is of great significance for AI deployment in sensitive sectors such as healthcare, finance, and government services.

Challenges and Outlook

Of course, the large-scale explosion of edge AI still faces challenges. Issues such as the capability ceiling of edge models, fragmented hardware ecosystems, and the complexity of cross-platform deployment all require coordinated efforts across the industry chain. Moreover, cloud-edge-endpoint collaborative architecture design is also crucial — not all tasks are suitable for edge processing, and how to intelligently allocate computing workloads between the cloud and the edge will be a core challenge in future system design.

But the overall direction is clear. As Yang Dawei has assessed, Agents are opening a new decade for the AI industry, and the primary battleground of this decade will gradually extend from the cloud to every edge node. For companies like Houmo AI that are deeply invested in edge AI chips, their window of opportunity is opening. The breakout year for the edge market may be just the beginning of an even grander transformation.