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fierce global technological competition. However, the challenge is not merely
technological investment but also building a national strategy capable of balancing the
promotion of innovation with ensuring safety, ethics, and social trust in AI. International
experience shows that without an appropriate governance framework, AI development
can lead to risks concerning privacy, algorithmic bias, and labor market instability (OECD,
2019).
In this context, Singapore has emerged as a pioneer in systematically and long-term
integrating AI into its national digital economy development strategy. The launch of NAIS
2.0 in 2023 marks a significant shift from approaching AI as a technological field to
positioning AI as a core national capability, serving public interest and enhancing global
competitiveness (Smart Nation Singapore, 2023). This strategy simultaneously emphasizes
three pillars: technological capability, talent development, and responsible AI governance,
reflecting a balanced approach between innovation and risk management.
Although many studies in Vietnam have addressed digital transformation and the
digital economy, most focus on a general level or analyze individual policies, without
clarifying the relationship between AI, governance institutions, and the digital economic
structure in the latest development context. Therefore, analyzing the Singaporean model
is significant from both theoretical and practical perspectives. This study aims to
systematize the constituent elements of Singapore's AI strategy, clarify the policy logic
behind this nation's success, and thereby propose feasible policy implications for Vietnam
in shaping a sustainable digital economy development roadmap in the AI era.
2. Theoretical framework and research methodology
To construct a robust analytical framework, this study combines modern economic
growth theory with contemporary technology governance frameworks. This approach
allows for simultaneously explaining two dimensions: (i) AI as an endogenous driver of
growth and digital economic restructuring; and (ii) AI governance as an institutional
condition ensuring sustainable and trustworthy growth.
2.1. Endogenous growth theory and the role of AI in the digital economy
According to endogenous growth theory, notably Romer (1990), knowledge and
technological innovation are formed through investment in R&D and human capital,
thereby increasing total factor productivity (TFP) and fostering long-term growth. In the
context of the digital economy, AI can be considered a General Purpose Technology (GPT)
with widespread applicability and cross-sectoral spillover effects (Bresnahan &
Trajtenberg, 1995). AI not only automates complex cognitive tasks but also generates new
knowledge through machine learning, thereby endogenously enhancing TFP (Brynjolfsson
& McAfee, 2014). However, the impact of AI depends on data capabilities, digital
infrastructure, and an institutional environment that supports innovation (OECD, 2019).
The development of AI is linked to the shift towards a data-driven economic model,
where data becomes a strategic production factor. However, data only creates value
when transformed through algorithms and computing infrastructure. AI acts as a
"transformation engine" that converts data into added value through production
optimization, market forecasting, and service personalization (Brynjolfsson & McAfee,
2014; OECD, 2019). Therefore, the digital economy in the AI era is not merely about
digitizing information but about restructuring the entire data-driven decision-making
ecosystem.
2.2. Technology governance framework and digital trust
Alongside its growth potential, AI also poses risks related to algorithmic bias, privacy,

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