Southeast Asia Needs AI Exit Options, Not AI Independence

Southeast Asian governments are under growing pressure to formulate national artificial intelligence (AI) strategies, attract data centre investment, develop local models and prevent their economies from falling behind. Amid this competition, technological self-sufficiency can appear attractive. But for most states, reproducing the entire AI value chain is neither realistic nor necessary.

Most Southeast Asian countries will not independently produce frontier semiconductors, operate hyperscale clouds, train the world’s largest models and develop every supporting software tool. Attempting to reproduce the full stack — which extends from computer hardware and data centres at one end to model development, deployment and integration at the other — would consume enormous resources while potentially diverting attention from the capabilities that governments genuinely need.

The more credible goal is AI resilience.

A resilient system remains affordable and useful when prices change. It performs effectively in local languages and institutions. It continues operating during infrastructure disruptions. Most importantly, it can be transferred or replaced when a provider changes its terms, withdraws access, or no longer meets public needs.

To turn this objective into policy, governments should focus on three practical pillars of resilience.

Governments and firms should retain a credible ability to leave any model, cloud or technology ecosystem on which an essential service depends. Stack pluralism is not simply competition policy; it is geopolitical risk management.

Dependency mapping must cover the entire AI stack. An agency may use several models while relying on one cloud provider, chip architecture, cybersecurity system or set of proprietary development tools. Regulators should monitor practices that make alternatives less viable, including exclusivity agreements, tying model access to cloud credits and bundling services in ways that make switching prohibitively expensive.

Great power competition may give Southeast Asian users access to cheaper models, greater cloud capacity, and new investment. It may also expose them to price increases, service disruptions, political restrictions, and vendor lock-in.

Public procurement must also look beyond model accuracy and the low introductory cost of a pilot. Agencies should assess the total cost of ownership, including data-egress fees (charges for transferring data out of a system), integration costs and price-escalation mechanisms. Contracts should preserve access to data, prompts, evaluation results, technical documentation and usable export formats.

Exit clauses, however, are insufficient unless portability is tested. Agencies should know how much time, expertise and money would be required to reproduce an essential function with another provider.

Resilience is not only about changing vendors; it also depends on whether systems work in local contexts and can continue operating when remote services fail.

AI access is only part of the challenge; AI systems must also work in local languages and contexts. Widely used benchmarks reveal little about how an AI system will perform in a rural classroom, municipal office or hospital operating in a Southeast Asian language. Systems that work reliably only for English-speaking urban elites risk reinforcing existing social and economic divisions.

Governments and research institutes should support datasets and benchmarks for languages and dialects with limited data for training and testing AI. These resources should include examples of users switching between languages in the same sentence or conversation. Local capability building does not require every country to train its own foundation model. The essential public capability is the ability to assess, fine-tune, audit and compare systems rather than simply accept vendors’ claims.

Continuity of service deserves equal attention. AI may appear to be software, but its reliability depends on electricity, cooling, data centres, fibre networks and subsea cables. Critical applications in healthcare, finance, transport and emergency response should not rely on a single model endpoint or cloud region.

For especially sensitive services, smaller, locally deployable models could provide a backup when foreign AI services or network connections become unavailable. These models need not match frontier performance. Their purpose is to keep essential functions running.

Growing data centre and cloud investment can expand domestic computing capacity but host countries do not necessarily exert control over such infrastructure, nor does storing data domestically necessarily shield host countries from foreign legal demands. For example, under the US CLOUD Act, providers subject to US jurisdiction may be required to disclose data under their control even when the data is stored abroad. Governments should examine ultimate beneficial ownership, operational authority, applicable foreign laws, data jurisdiction and consumers’ ability to migrate to another provider.

Foreign investment will remain necessary for much of Southeast Asia’s digital development. Governments should nevertheless prevent individual facilities or operators from becoming single points of dependence. They should also ensure that data centre investment generates sufficient local benefits to justify its demands on land, electricity and water.

ASEAN is unlikely to establish a fully harmonised AI regulatory regime in the near term. Practical cooperation, however, does not need to wait. Member states could jointly develop regional language datasets, evaluation benchmarks, model-testing protocols, incident-reporting systems, procurement templates and cloud-resilience exercises. Pooling these capabilities would be particularly valuable for smaller states that cannot produce them independently.

Looking Ahead

ASEAN does not need a single regional model or technology stack. It needs shared tools that allow its members to evaluate external technologies, adapt them to local contexts and compare competing providers. Common procurement and portability standards could also strengthen smaller markets’ bargaining positions when dealing with global technology firms.

None of these measures will make Southeast Asia technologically independent. Complete independence is neither economically realistic nor necessarily desirable. The challenge is not to avoid dependence, but to manage it judiciously.

Great power competition may give Southeast Asian users access to cheaper models, greater cloud capacity, and new investment. It may also expose them to price increases, service disruptions, political restrictions and vendor lock-in.

The region’s policy objective should therefore be to make foreign dependence diversified, transparent, and reversible. The real test of AI resilience is not whether a model carries a national label, but whether governments and firms can retain meaningful choices — and can leave, when circumstances change.

2026/270

---

This is an adapted version of Trends in Southeast Asia 2026/24, published on 14 September 2026. The paper and its references can be accessed at this link.