At its most basic level, the pattern of AI development is simple: scale keeps winning out. AI has a ravenous need for compute and data, and more of both equals more intelligence. In turn, better products attract more users, generating more data that feeds back into superior technology. This product cycle is part of a broader scaling advantage that becomes increasingly important as internet data is exhausted and access to private user data becomes critical. Given this dynamic, it is unsurprising that the United States and China dominate the AI value chain and will continue to do so. However, this poses a critical question for middle powers: how can smaller nations maintain strategic relevance when the fundamental economics of AI favour those with the largest user bases, deepest data pools, and most extensive compute resources? Singapore's ambitious National AI Strategy 2.0, announced in December 2023 with over $1 billion committed to triple the country's AI practitioners to 15,000 within five years, represents one of the most sophisticated attempts to answer this challenge.
Yet Singapore's experience also reveals the fundamental limits of digital sovereignty in the AI era. Even this exceptionally wealthy and efficient city-state, blessed with geopolitical positioning that most nations cannot replicate, remains forced to rely on foreign infrastructure for cloud computing, semiconductors, and foundational AI technologies controlled by US and Chinese ecosystems.
Singapore's case thus illustrates both the possibilities and constraints facing smaller nations in technological bipolarity. While countries should indeed seek specific niches within the AI value chain based on their national characteristics to maintain relevance and influence in global policy direction, even the most successful middle powers will face technological dependencies on US and Chinese infrastructure. Singapore's experience shows that maximising agency within these constraints rather than pursuing impossible independence offers the most viable path forward, though success requires the rare combination of economic prosperity, institutional efficiency, and strategic positioning that few nations possess.
(Memorial to Admiral Sir Clowdisley Shovell., Sebastiano Ricci; Marco Ricci (1725))
Singapore’s AI policy
Ostensibly, Singapore appears poorly positioned for AI leadership. Its tropical climate and limited available land make compute-intensive operations expensive, with a population of only 5.4 million leading to a lack of scale in terms of data generation. However, rather than attempting impossible technological sovereignty, Singapore recognises the nation's inherent constraints and focuses instead on gaining a strategic advantage over aspects of the AI value chain. In doing so, Singapore's outsized relevance on the global AI stage originates from positioning itself as a research-driven melting pot where applied innovation flourishes, enabling Singapore to lead regional AI development while building critical capabilities in R&D and talent cultivation.
Importantly, Singapore’s early recognition and swift pivot to AI was not a flash in the pan. Rather, it originates from decades of state-led digital planning. From the 1980 National Computerisation Plan through IT2000 and Intelligent Nation 2015, methodical institutional development enabled decisive action when AI emerged as critical technology, launching its National AI Strategy, the first country to do so, in 2019 and committing a further $743 million through NAIS 2.0 in 2023.
Building on efficient regulatory policy, the Singaporean government’s stewardship extends to direct deployment, running over 200 AI implementations across healthcare, transport, and urban planning. These government entities serve as production testbeds that attract international investment while building practical expertise, with public sector demand de-risking enterprise adoption for commercial markets. This has created an exceptionally efficient regulatory environment where AI companies can be established within 24 hours and ethical guidelines like the Personal Data Protection Act are quickly implemented, a result of seamless integration between AI R&D and government deployment.
The results of Singapore's effective government policy have been to increase the levels of invested capital in AI to the point where, according to CSET, government supported R&D spending as a percentage of GDP now exceeds the United States eighteen-fold. However, it has also allowed for Singapore's continued position as a neutral territory where both the technological superpowers can operate. Major American firms like Google, Meta, and Salesforce have established AI research centres alongside Chinese giants including Alibaba and Huawei, creating a unique competitive advantage where Singapore benefits from knowledge transfer and long-term investment from both technological superpowers simultaneously. In doing so, Singapore positions itself at the coordination layer of the AI value chain, becoming an intermediary investment hub where both superpowers conduct research, test applications, and access regional markets.
Ultimately, Singapore's achievements demonstrate how smaller nations can secure outsized influence by leveraging institutional efficiency and financial capital rather than competing simply on raw compute scale. The city-state has transformed existing strengths into a unique value proposition as a staging post for global AI development, occupying the critical applied research and governance layers of the AI value chain. Rather than building costly hyperscale infrastructure or training frontier models, Singapore has become the essential platform where AI technologies transition from research to real-world application, where global talent congregates, and where both superpowers can operate without triggering bilateral tensions. However, success illuminates fundamental constraints: Singapore's influence extends only to the application and policy layers of the AI stack, whilst remaining entirely dependent on American and Chinese infrastructure for cloud compute, semiconductors, and foundational models that ultimately determine technological sovereignty.
(Lawrence Wong | Prime Minister of Singapore)
So how much do the United States and China dominate the AI value chain?
Put simply, the answer is overwhelmingly. Far from democratising innovation, artificial intelligence has reinforced a global digital hierarchy with the United States and China now controlling key layers of the AI value chain, from semiconductor design and cloud infrastructure to training data and deployment platforms. This dominance is not incidental but rather the result of scale, capital, and system-wide integration that few other nations can realistically match. As AI systems grow more complex and capital-intensive, the barriers to entry continue to rise, leaving most countries structurally excluded from full participation.
At the hardware level, semiconductors reveal the depth of this dominance. The United States and its allies control over 90 percent of global semiconductor manufacturing equipment, and American firms such as Nvidia and AMD lead in chip design, with Nvidia alone capturing more than 80 percent of the data-centre GPU market in 2023. Yet the fabrication of these chips takes place primarily in Taiwan , through TSMC, who manufacture the majority of the world’s most advanced semiconductors. While the United States consolidates its advantage through design leadership and control of chip design software, it relies on a network of aligned states for fabrication. China, by contrast, is investing heavily to localise its entire semiconductor supply chain, though it remains several generations behind. For smaller nations, the capital required to participate meaningfully in this layer—often exceeding ten billion dollars for a single fabrication facility—renders entry effectively impossible.
Cloud infrastructure further reinforces this asymmetry. As of 2024, only 32 countries host AI-ready data centres, with American firms such as Amazon, Microsoft, and Google controlling over 65 percent of global cloud services. Collectively, they plan to spend more than 300 billion dollars on AI infrastructure in 2025. In parallel, China is scaling its domestic compute capacity through state-backed firms such as Alibaba Cloud and Huawei, supported by industrial policy that prioritises technological self-reliance.
Data and model development add another layer of structural advantage. American firms benefit from the global dominance of English and expansive user bases, enabling the collection of vast, high-quality datasets that improve model performance over time. Chinese companies, drawing on a domestic population of 1.4 billion, generate unmatched volumes of Chinese-language data through the firms such as Baidu, Tencent, or Alibaba. Even under export controls, firms such as DeepSeek and Moonshot AI have released models approaching international benchmarks. Both superpowers also control the tools and frameworks on which global AI development depends such as TensorFlow, PyTorch, and cloud-based APIs, further embedding their dominance throughout the software layer. For most countries, the absence of linguistic reach, platform ownership, and domestic scale limits any realistic pathway to sovereign model development or deployment.
To be clear, other nations play important roles within specific layers. Taiwan is essential to chip fabrication, Japan leads in robotics, and the European Union has set global standards in AI regulation. Yet without direct control over compute, data, or foundational models, these contributions remain partial. The AI value chain is tightly coupled, and each layer depends on upstream integration controlled by the superpowers. This consolidation reflects more than just temporary leadership. It is a structural outcome shaped by economies of scale, population size, network effects, and sustained capital investment. For every other country, the costs of competing across the full AI stack now exceed the bounds of national feasibility. Even targeted efforts at localisation tend to remain dependent on US or Chinese infrastructure at some point in the chain.
The result is a deeply uneven AI economy. The gap between the two superpowers and the rest of the world is no longer merely one of capability but of possibility. For middle powers, the question is no longer how to catch up, but how to remain relevant within a value chain that has already been consolidated.
So what can ‘middle powers’ do?
Singapore’s experience delivers a crucial lesson for middle powers: achieving full technological sovereignty across the entire AI stack is not only unrealistic for most countries, but also unnecessary. Instead, the best way to maintain agency and influence is to identify strategic opportunities within the AI value chain that match national strengths and circumstances.
The routes taken by the United States and China rely on vast internal markets, tremendous financial resources, and highly integrated ecosystems. Fundamentally, middle powers cannot hope to match these advantages in the foreseeable future. However, not all hope is lost, with Singapore’s example illustrating how it is still possible to shape the global AI landscape by efficiently and effectively focusing on targeted areas. Even without direct control over foundational infrastructure or frontier models, smaller nations can secure roles of real significance.
At the same time, it is essential to recognise that Singapore’s approach is shaped by factors that are not replicable for most countries, including its history, scale, institutional efficiency, and geopolitical position. Rather than attempting to copy Singapore, other middle powers must carefully evaluate their own demographic, economic, and institutional strengths to design national AI strategies that suit their specific contexts. This might involve focusing on specialised R&D hubs, fostering startups, or playing a convening role in regional or international AI collaboration. By identifying and investing in these tailored strategic niches, middle powers can maximise their agency within the AI value chain and avoid being left behind completely by the increasingly dominant US and China.
Ultimately, the core lesson is that middle powers do not need to compete across every layer of the AI stack to remain relevant. Instead, they should invest in complementary capabilities and seek partnerships where their contributions add unique value to the currently dominant ecosystems. Singapore’s pragmatic focus on leveraging what it does best, rather than pursuing unattainable self-sufficiency, provides an important example. For other middle powers, long-term influence in the AI era will depend not on matching the superpowers in every dimension, but on wisely selecting and developing areas of true differentiation and strategic advantage.