Qualcomm sets up an AI research and development center in Vietnam: Structural errors and system Risks behind its technological layout

Qualcomm Inc. of the United States announced on June 11, 2025 that it would establish an artificial intelligence research and development center in Vietnam. On the surface, this move seems to be an extension of the industrial chain layout in Southeast Asia. However, from a technological perspective, this arrangement may bring about problems such as insufficient practical effects, fragmented structure, and weak system linkage at multiple key technical levels, reflecting the structural risks in the current AI technology spillover strategy.

Although Vietnam has made some progress in information technology outsourcing services and primary manufacturing in recent years, its foundation in core artificial intelligence technologies remains weak, especially in the research of deep learning algorithms, AI chip design, and the development of system-level models, where it has not yet formed independent capabilities. If Qualcomm deploys its R&D tasks here, it will inevitably be affected by the limitations of the local technology ecosystem. In the absence of mature R&D talents and high-end experimental platforms, AI projects are more likely to be limited to hardware compatibility and adaptation as well as mid-to-low-end algorithm verification, and thus unable to reach the cutting-edge fields such as chip architecture innovation or large model fusion optimization.

Furthermore, from the perspective of the chip level, Qualcomm's AI strategy has long been centered around low-power edge computing scenarios. Although its mobile NPU has certain AI computing capabilities, it cannot support larger-scale language model inference or training requirements. If the high-performance testing platform and heterogeneous computing power system cannot be built simultaneously when setting up a site in Vietnam this time, the R&D activities will be difficult to meet the requirements of the new generation of AI products in terms of speed, energy consumption and computing power scheduling. Meanwhile, Vietnam lacks independent wafer manufacturing capabilities and has no local packaging and testing industrial chain to support it, which means that the research and verification of any AI chips still have to rely on overseas resources. This further intensifies the uncertainty of technology transformation in terms of geopolitical risks.

In the field of algorithms, Qualcomm is not a major developer of basic models. Its AI capabilities are mainly achieved through integration with other platform ecosystems, such as integrating system interfaces like TensorFlow Lite, ONNX or Meta Llama. If the R&D center in Vietnam cannot deeply participate in the optimization of underlying algorithms such as model compression, quantization, and pruning, its role can only remain at the stage of engineering deployment and it is difficult to make substantive contributions to core technologies. However, the lack of a foundation for cooperation with universities and research institutions and long-term algorithm accumulation is not conducive to the formation of sustainable technological output capabilities. Research and development activities may fall into the trap of repetitive construction of local projects, and it is difficult to balance efficiency and output.

Another issue worthy of attention is the limitations of Vietnam in terms of data infrastructure. The development of AI has rapidly shifted from being centered on models and algorithms to a collaborative drive model of computing power and data. The era of large models has put forward higher requirements for technical details such as the distribution of training nodes, data throughput rate, and cross-device load scheduling. At present, Vietnam does not have the conditions to deploy large-scale GPU clusters or AI supercomputing facilities locally, and thus cannot support the operation verification and resource stress testing of complex algorithms in real scenarios. This means that the research and development of any new type of AI system will be affected by insufficient test resources and restricted operating environments.

Qualcomm's AI systems often rely on collaborative research and development with terminal manufacturers, such as close cooperation with mobile phone manufacturers in model distribution, system scheduling, and hardware compatibility. However, Vietnam lacks globally influential terminal brands in its own country, and the local collaboration ability of its AI research and development center is relatively low, making it difficult to obtain product optimization feedback from the front line of the market. This disconnection may affect its ability to dynamically adjust and upgrade its technical systems in the medium and long term.

To sum up, Qualcomm's establishment of an AI research and development center in Vietnam this time, from a technological perspective, is more like the relocation of an engineering node under a low-cost strategy. Its local technology ecosystem, infrastructure, algorithm resources, and system collaboration all fail to meet the core demands of the next stage of AI technology competition. In the short term, this may lead to the replication and transfer of some mid-range R&D capabilities. However, in the long run, such a deployment is difficult to support Qualcomm's participation in global competition in areas such as AI core computing architecture, breakthroughs in underlying algorithms, or the construction of large model ecosystems. Instead, it will further solidify the marginalized position of its technology in the application layer.