Singapore aims to ensure that the next wave of Artificial Intelligence is not developed solely elsewhere. To this end, the government has announced an investment of more than S$1 billion (approximately US$786 million) to fund public AI research over the next five years, between 2025 and 2030. This move represents a clear step toward building native capabilities in an increasingly competitive global market.
The announcement was made during Singapore AI Research Week 2026, at a gala dinner where Minister of Digital and Information Development, Josephine Teo, explained that the funds will be channeled through the National AI Research and Development Plan (NAIRD). According to the ministry, this initiative aligns with the National AI Strategy (NAIS) 2.0, the updated national AI strategy that Singapore launched to strengthen its position on the international technology stage.
What does “sovereignty” in AI mean for a small but highly connected country?
The concept of “sovereignty” applied to AI is not limited to establishing data centers or acquiring accelerators. In practice, it involves controlling critical competencies: scientific capabilities, access to computing, talent, models tailored or adapted to the local context, and security and reliability safeguards aligned with national priorities.
Singapore is competing with advantages—tech ecosystem, regulatory stability, university and business networks—but also faces structural limitations. The Minister herself highlighted a particularly sensitive factor: AI is resource-intensive, requiring significant energy and water consumption. Singapore already has one of the highest densities of data center capacity in the region, necessitating careful management of any expansion.
Therefore, the new plan is not just a “check” for more infrastructure but an investment to make AI more efficient, safer, and more useful for specific sectors of the economy.
Three pillars of the plan: basic science, real-world application, and talent
NAIRD is structured around three main areas.
1) Fundamental Research: Efficiency, Security, and New Approaches
A significant portion of the budget will be dedicated to fundamental research, focused on long-term questions: how to train models with less data and energy, how to improve their reliability, and how to protect them against malicious uses.
Highlighted areas include:
- Resource-efficient AI: seeking improvements “across the technical stack,” from chip architectures to model design and applications.
- Responsible AI: with particular attention to risk mitigation and resilience against exploitation or abuse.
- Emerging methodologies: including more flexible, multimodal systems or those with greater autonomous operation.
- General-purpose AI: with potential to address complex and cross-cutting tasks, from biomedical research to large-scale information analysis.
The government also plans to establish new “centers of excellence” hosted within public institutions, with teams combining established researchers and emerging talent, and with an emphasis on international collaboration.
2) Applied Research: From Paper to Deployment
The second pillar aims to bridge the gap between laboratory research and production. Singapore wants to turn part of its research into deployable capabilities for industry and services, focusing on strategic areas such as manufacturing and trade, health, urban solutions and sustainability, and science.
The goal is to strengthen what is often the most challenging part: not just inventing a model, but integrating it into systems, ensuring performance and safety, and scaling it operationally. In other words: building AI engineering muscle, not just academic research.
3) Talent: a complete pipeline from early stages to research elite
The third pillar is the most reiterated in public discourse on AI: without people, sovereignty is impossible. Singapore intends to expand and deepen its pool of specialists through initiatives spanning from pre-university levels to PhD and faculty levels.
In parallel, the country maintains a strategic goal already outlined in NAIS 2.0: multiplying its AI professional base and reaching 15,000 AI practitioners, combining local training with international talent attraction. Programs like the AI Visiting Professorship aim to bring top-level researchers into projects aligned with the national agenda.
A geopolitical and economic context driving strategic positioning
Singapore’s move comes at a time when AI has become a key focus in global technological competition. Chips availability, computing capacity, and protection of know-how are now policy variables.
For a country without abundant natural resources but with regional leadership ambitions, the pursuit of “sovereignty” also has a pragmatic aspect: ensuring that companies, government, and universities do not become entirely dependent on external capabilities in an environment where supply chain disruptions, costs, and international tensions could impact access to technology.
Models and languages: sovereignty also involves language
Beyond hardware, sovereignty in AI is measured by the capacity to develop models useful for the local context. Initiatives linked to the AI Singapore ecosystem have worked on models tailored to Southeast Asian languages, reinforcing the idea that “general-purpose” AI should adapt to diverse cultural and linguistic contexts.
This dimension—the development of regionally comprehensive, practical models—aligns with the narrative of the new plan: building lasting capabilities that do not rely solely on third-party agendas.
Frequently Asked Questions
What is “AI sovereignty,” and how is it measured in practice?
It is typically measured by a country’s ability to develop and operate AI independently: local talent, access to computing, independent research, tailored models, security, and control over critical sectors.
How does the new NAIRD plan differ from the national NAIS 2.0 strategy?
NAIS 2.0 provides the strategic framework for AI adoption and positioning; NAIRD is a specific public R&D plan that funds fundamental research, applied work, and talent development for 2025-2030.
Why does Singapore focus on “resource-efficient AI”?
Because training and inference of models consume significant energy and water, and Singapore operates under space and capacity constraints, with a high regional concentration of data centers. Efficiency becomes a competitive and sustainability advantage.
Which sectors are most likely to benefit from this public AI investment?
Targeted sectors include manufacturing and trade, health, urban solutions and sustainability, and science, emphasizing transforming research into operational systems and deployable use cases.