Toward Layered AI Autonomy: A Strategic Framework for Managing Dependency Risks in Transition Economies A Conceptual Framework with Illustrative Evidence from Vietnam
DOI:
https://doi.org/10.65150/EP-jefrr/V2E6/2026-07Keywords:
AI sovereignty, vendor lock-in, transition economies, digital policy, Vietnam, AI governance, platform dependencyAbstract
The rapid proliferation of artificial intelligence (AI) platforms from a small number of advanced-economy technology firms has created a structural dependency problem for developing nations. Existing concepts of AI sovereignty and digital sovereignty, primarily developed in the context of advanced economies, provide insufficient guidance for resource-constrained transition economies that must simultaneously navigate the imperative of AI adoption and the risks of technological lock-in. This paper proposes the Layered AI Autonomy for Transition Economies (LAAT) framework, a three-layer strategic model that enables transition economies to manage AI dependency risks across four dimensions: financial, technical, legal-contractual, and geopolitical. Drawing on switching costs theory (Shapiro & Varian, 1999), market thickness theory (Roth, 2012), platform economics (Parker et al., 2016), and techno-economic paradigm cycles (Perez, 2002), the framework reconceptualizes AI autonomy not as a binary state but as a multidimensional spectrum achievable through strategic layering. We apply the LAAT framework to Vietnam, a rapidly digitalizing lower-middle-income economy with an ambitious national AI strategy, demonstrating how the framework can guide evidence-based AI policy. The analysis reveals that Vietnam exhibits high dependency vulnerability across all four dimensions, with concentrated risk in the legal-contractual and geopolitical dimensions. The June 12, 2026 export control action against Anthropic's Fable 5 and Mythos 5 models serves as a critical empirical case for the framework's applied significance. We conclude with a three-phase policy roadmap translatable to other transition economies at comparable stages of AI adoption.
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