Southeast Asia AI "bargains": The reshaping of technological power and regional dependency traps behind investments by US tech giants

Southeast Asian AI "Bargains": The Reshaping of Technological Sovereignty and Regional Dependency Traps Behind Investment by US Tech Giants

In the Southeast Asian region, the investment by US tech giants (such as OpenAI, Meta, Google) in the field of artificial intelligence is accelerating. However, this is not just a competition at the application layer; it is a profound contest over technological sovereignty, data governance, and control over the industrial chain. As experts point out, this cooperative model is recreating existing patterns of technological dependency, raising serious concerns about regional technological self-reliance.

The "Double-Edged Sword" of Cooperation: Embedding Data and Infrastructure

Cooperation between US AI companies and Southeast Asian telecom giants and digital service providers offers a mechanism of dual value exchange. On one hand, these collaborations allow US enterprises to access key resources: massive user bases, valuable behavioral data, and mature local infrastructure and payment interfaces. Companies like Singtel in Singapore, Telkomsel in Indonesia, and Bharti Airtel in India, through these partnerships, are upgrading their positioning from mere connectivity service providers to AI distribution platforms, embedding AI capabilities into mobile and broadband ecosystems.

On the other hand, this integration embeds AI models directly into existing data flows and regulatory frameworks. Research shows that this model drives customer acquisition costs close to zero while allowing US companies to use local data to train and iterate their models (such as Gemini, Llama, ChatGPT), achieving localization of model training without fully mastering core algorithms and data ownership.

Structural Challenges to Technological Sovereignty and Governance

Although the implementation of AI applications in Southeast Asia is still in its early stages, and the public remains optimistic about the economic empowerment potential of AI, the complexity from an application perspective far exceeds surface optimism. The real risk lies in the fact that the deployment of the AI application stack is no longer just a cloud interface issue; it is closely intertwined with regional labor division, modes of production, trade systems, and geopolitical realities.

At the model development level, data obtained through these collaborations is repackaged and commercialized by US tech companies, with ultimate control remaining in foreign entities. More critically, at the legal and jurisdictional level, cooperation terms are subject to US laws (such as the US Cloud Act), rather than the physical location of storage or application development. This means that even if the region strives to develop "sovereign large language models" (such as Malaysia's ILMU or Cambodia's Khmer model), their core drivers still depend on proprietary US algorithms and foreign legal systems.

Infrastructure Layer: From Computing Power to Strategic Resources

The operation of the AI ecosystem relies on underlying physical infrastructure, involving multiple links from rare earth mining and GPU manufacturing to energy grids and satellite connectivity networks.## Infrastructure Layer: From Computing Power to Strategic Resources

The operation of the AI ecosystem relies on the underlying physical infrastructure, involving multiple links such as rare earth mining, GPU manufacturing, energy grids, and satellite communication networks. Control over these links is often held by a few entities with capital and political influence. The rapid deployment of AI places higher demands on regional energy and critical minerals, further highlighting the tension between technological development and regional economic security.

Conclusion: Choosing the Path for Reconstructing the Asian AI Ecosystem

The development of AI in Southeast Asia is facing a structural choice: whether to continue accepting this "high-cost AI transaction" in exchange for short-term economic transformation and the superficial spread of AI, or to adopt a more forward-looking strategy dedicated to building a truly decentralized AI governance and technology ownership framework. To achieve genuine technological self-sufficiency, regional policymakers need to shift from being mere recipients of technology transfers to rethinking the mechanisms for the allocation of AI value, data localization strategies, and power balancing in cross-border cooperation.

Long-term Trend Outlook: The center of future growth in Asia is accelerating, but how to ensure that AI technology does not become a tool to exacerbate the digital divide and technological colonialism will determine whether Southeast Asia can evolve from a passive "application market" to an active "innovation source" in the next round of global technological competition.