The Core Divergence
The key insight is that while America focuses on achieving artificial general intelligence (AGI) as quickly as possible, viewing it almost like a nuclear arms race where the first-mover advantage could be decisive, China is pursuing what might be called a “practical application” strategy.
America’s Approach: The AGI Race
The US strategy centres on breakthrough models and theoretical capabilities. American leaders and tech executives frame AI development in terms of reaching superhuman intelligence, with some believing AGI could arrive within just a couple of years. This approach is heavily influenced by the belief that whoever achieves AGI first will gain a “100-year dynasty” of technological dominance.
The massive financial commitment reflects this thinking: America plans to spend over $1 trillion by 2030 on AI infrastructure, primarily focused on advancing large language models and achieving the theoretical breakthrough.
China’s Alternative Strategy
China’s approach, as articulated by Xi Jinping and other leaders, treats AI more like electricity than nuclear weapons, emphasizing practical deployment across industries rather than pursuing superhuman intelligence. Their “AI+” campaign mirrors the successful “Internet+” strategy that helped China dominate e-commerce and digital payments.
This strategy has two main components:
- Undermining AI monopolies by creating open-source alternatives (like DeepSeek’s free model weights) that shift value from model-makers to application developers
- Building application ecosystems that could create defensive moats through user data and deployment scale, similar to how TikTok gained global dominance
Strategic Implications
The article suggests that China’s approach may be driven partly by necessity, acknowledging gaps in AI talent and advanced semiconductors, but it could prove strategically sound. By the time AGI arrives, China aims to have established superior application infrastructure and user bases.
An engaging economic projection from the IMF suggests that AI could boost America’s economy by 5.6% over ten years, compared to 3.5% for China, mainly due to China’s smaller services sector limiting productivity gains from AI deployment.
However, the practical implications are already visible: Apple reportedly struggles to access local AI partnerships in China, potentially making American tech products less competitive in Chinese markets—nd perhaps globally over time.
This represents a classic strategic choice between breakthrough innovation versus methodical application and deployment, each with distinct advantages and risks.
Analyzing the US-China AI Divide and Singapore’s Strategic Position
The divergent AI approaches between the US and China create both opportunities and challenges for Singapore, which has positioned itself as a pragmatic bridge between these superpowers.
The Core Strategic Differences
United States: The AGI Sprint
- Pursuing artificial general intelligence as the ultimate prize
- Massive capital deployment ($1+ trillion by 2030) focused on breakthrough models
- “Winner-takes-all” mentality driven by the belief that the first-mover advantage in AGI creates generational dominance
- Export controls on semiconductors to maintain technological supremacy
- Abstract, research-heavy approach prioritizing theoretical capabilities
China: The Practical Deployment Marathon
- Focus on real-world applications and integration across industries
- “AI+” strategy emphasizing adoption in existing operations
- Open-source model sharing to democratize access and shift value to applications
- Building user ecosystems and data moats through widespread deployment
- Resource-constrained approach that turns limitations into strategic advantages
Singapore’s Unique Position and Strategy
Singapore has carved out a distinctive “third way” that leverages its geographic and strategic position:
Singapore’s AI Strategy 2.0 (“AI for the Public Good”) Singapore’s refreshed National AI Strategy promotes “AI for the Public Good, for Singapore and the World,” backed by over SGD 1 billion in investment over five years to boost AI computing resources, the talent pool, and industry development.
Key Strategic Elements:
- Neutral Hub Positioning: Singapore leads as a global IP licensing hub for AI and SaaS, driving innovation and cross-border growth worldwide.
- Balanced Partnerships: Singapore maintains AI cooperation with both the US and China, with nearly 6,000 US companies operating in Singapore and bilateral trade supporting nearly 250,000 jobs across the United States. Additionally, Singapore has digital cooperation programs with China.
- Refuge for Cross-Border Innovation: Singapore has become a haven for investors and startups fleeing the tensions between the US and China, with numerous Chinese AI firms relocating to Singapore to access global markets and secure venture capital.
Impact on Singapore
Opportunities:
- Geographic Arbitrage: Singapore benefits from US-China tensions by attracting firms from both sides seeking a neutral ground for global operations
- Investment Magnet: Temasek focuses on early AI adopters in the US, with the US remaining cautious on China, positioning Singapore as an investment intermediary. Singaporean state investor Temasek focuses on early adopters of AI in the US, but is cautious about China.
- Talent Hub: The divergent approaches create demand for Singapore’s skilled workforce that can work with both Western and Chinese AI ecosystems
- Regulatory Leadership: Singapore’s sector-specific rather than blanket AI regulation approach offers a middle path between heavy regulation and laissez-faire policies
Challenges:
- Technology Access Risks: US export controls could limit Singapore’s access to cutting-edge semiconductors and AI technologies
- Forced Choice Scenarios: As tensions escalate, Singapore may face pressure to choose sides, with experts noting it “will have to somehow de-conflict cooperation with the United States with the digital and cyber cooperation programs it also has with the PRC,
- Scale Limitations: Unlike the US and CUSNA, Singapore lacks the domestic market size to independently develop major AI ecosystems
- Dependency Risks: Heavy reliance on both superpowers makes Singapore vulnerable to their strategic decisions
Strategic Implications
Singapore’s approach appears to synthesize elements from both superpowers while maintaining independence. Like China, it emphasizes practical applications and public sector deployment. Like the US, it maintains strong partnerships with American tech giants and research institutions.
The success of this strategy depends on Singapore’s ability to remain valuable to both sides while the US-China competition intensifies. Its role as an AI IP licensing hub and neutral meeting ground for global tech firms positions it well. Still, the sustainability of this balancing act will be tested as the technology becomes more strategically critical.
For Singapore, the US-China AI divergence represents both its most excellent opportunity to cement its role as a global tech hub and its most significant risk if forced to choose sides in an increasingly polarised technological landscape.
Navigating the US-China AI Divide: A Researcher’s Perspective from NTU Singapore
An in-depth analysis of divergent artificial intelligence strategies through the lens of a Singaporean academic caught between two superpowers
Introduction: Standing at the Crossroads
As I walk through the corridors of NTU’s College of Computing and Data Science, past laboratories humming with servers processing neural networks and offices where researchers debate the future of artificial intelligence, I’m acutely aware that Singapore—and institutions like mine—sit at the epicenter of perhaps the most consequential technological rivalry of our time. The United States and China aren’t just competing for AI supremacy; they’re pursuing fundamentally different visions of what artificial intelligence should become and how it should transform society.
From my vantage point as a researcher at one of Asia’s leading technological universities, these competing paradigms aren’t abstract geopolitical concepts—they’re daily realities that shape funding decisions, research partnerships, talent recruitment, and the very questions we’re allowed to explore. This analysis examines the profound philosophical and strategic differences between American and Chinese approaches to AI development and explores how institutions like NTU navigate an increasingly polarised technological landscape.
The Philosophical Divide: AGI versus Application
America’s Promethean Quest
The American approach to artificial intelligence is fundamentally Promethean—a quest to steal fire from the gods by creating machines that not only match but exceed human cognitive capabilities. This vision, championed by companies such as OpenAI, Anthropic, and Google DeepMind, views artificial general intelligence (AGI) as the ultimate prize, comparable in strategic importance to the Manhattan Project.
The underlying philosophy is rooted in Silicon Valley’s technological utopianism and American exceptionalism. Sam Altman’s prediction that AGI could arrive within years, followed by superintelligent systems that recursively improve themselves, reflects a belief that breakthrough innovation creates winner-take-all scenarios. The first nation to achieve “take-off”—the moment when AI systems begin improving themselves autonomously—could theoretically dominate all subsequent technological development.
This approach manifests in massive capital deployment: America’s planned $ 1 trillion-plus investment in AI infrastructure by 2030 represents an unprecedented bet on transformative breakthrough technology. The focus remains heavily concentrated on large language models (LLMs), with companies racing to scale up parameters, training data, and computational resources in pursuit of emergent capabilities that might suddenly approximate human-level reasoning.
From a research perspective, this creates an environment where theoretical advances and capability demonstrations receive disproportionate attention and funding. The American AI research ecosystem rewards papers that push the boundaries of what’s possible, even if practical applications remain distant or unclear.
China’s Pragmatic Integration
China’s approach represents a fundamentally different philosophical framework—one that treats AI as a powerful tool for societal and economic transformation rather than an end in itself. Xi Jinping’s characterization of AI as being “more like electricity than nuclear weapons” captures this utilitarian perspective perfectly.
The Chinese strategy, formalised through the “AI+” campaign and articulated in authoritative Communist Party publications such as Qiushi, emphasises the practical deployment and integration across existing industries. Rather than pursuing superhuman intelligence, China focuses on what they term “general-purpose AI”—systems that can be widely applied to solve concrete problems in manufacturing, governance, and consumer services.
This approach reflects China’s historical success in adopting and adapting technology. Just as the “Internet+” campaign helped China leapfrog Western e-commerce and digital payment systems, the AI+ strategy aims to create superior application ecosystems that could eventually provide defensive moats against Western technological dominance.
The Chinese conception of AGI (tongyong rengong zhineng) differs semantically and conceptually from its American counterpart. While American researchers envision AGI as approaching or exceeding human cognitive abilities across all domains, Chinese researchers typically frame it as “general-purpose” systems with broad applicability. This more modest, yet arguably more achievable goal, aligns with immediate economic and social needs.
Strategic Architectures: Capital vs. Ecosystem
The American Model: Vertical Integration and Capital Intensity
America’s AI strategy reflects its venture capital-driven innovation ecosystem and winner-take-all market dynamics. The approach emphasizes vertical integration within major technology companies, with firms such as Google, Microsoft, and Meta developing proprietary AI capabilities that provide competitive advantages in their core business areas.
This model creates powerful synergies—Google’s AI research enhances its search algorithms, Microsoft’s investments in OpenAI strengthen its productivity suite, and Meta’s AI developments improve its social media platforms. However, it also creates dependencies on continued access to cutting-edge hardware and massive computational resources.
The semiconductor export controls implemented by the United States government reveal both the strengths and vulnerabilities of this approach. While these restrictions aim to maintain American technological leadership by limiting China’s access to advanced graphics processing units (GPUs), they also underscore the current AI paradigm’s reliance on specialized hardware and extensive computational infrastructure.
For researchers, this creates a peculiar dynamic where access to the most advanced tools becomes a function of geopolitical alignment rather than scientific merit. At NTU, we’ve observed how export controls can complicate international research collaborations, even with allied nations like Singapore.
The Chinese Model: Horizontal Deployment and Ecosystem Building
China’s strategy emphasizes horizontal deployment across industries and sectors, creating what researchers refer to as “application ecosystems” that generate value through widespread adoption rather than technological superiority. This approach leverages China’s manufacturing base, large domestic market, and government coordination capabilities.
The strategy has two key components: First, undermining Western AI monopolies by developing open-source alternatives that shift value from model creators to application developers. DeepSeek’s decision to release model weights freely exemplifies this approach—democratizing access to competitive AI capabilities, China reduces the strategic advantage that American companies derive from proprietary models.
Second, building a comprehensive application infrastructure that can provide defensive moats, even if Western AI capabilities remain superior. The goal is to create such extensive deployment and user engagement that Western competitors struggle to displace Chinese applications, similar to how TikTok achieved global dominance despite American social media platforms having technological head starts.
This approach also includes exploring alternative technical pathways that bypass American advantages in traditional neural network architectures. Research initiatives in Shanghai and Beijing are investigating brain-inspired computing, multimodal systems that interact with physical environments, and novel algorithmic approaches that might circumvent the computational bottlenecks that favour American semiconductor dominance.
Research Implications: Navigating Institutional Complexities
The NTU Experience
As a researcher at NTU, I’ve witnessed firsthand how these competing paradigms create both opportunities and challenges for institutions positioned between the two superpowers. Singapore’s strategic location and neutral stance have made NTU an attractive partner for both American and Chinese entities, but this positioning requires careful navigation of increasingly complex geopolitical dynamics.
NTU’s research partnerships reflect this balancing act. The university maintains significant collaborations with American institutions and companies. —Imperial College London partnerships focus on AI applications, while corporate laboratories, including those with companies such as Singtel, HP, and Rolls-Royce, emphasize the practical deployment of AI technologies. Simultaneously, data suggest that China’s AI research collaboration with Singapore has been steadily rising over the past decade, positioning NTU researchers at the intersection of both paradigms.
The practical implications become apparent in daily research decisions. When designing experiments, we must consider not just scientific merit but also potential export control implications, funding source restrictions, and the political sensitivities surrounding different types of AI research. Projects involving dual-use technologies require additional clearances, and international collaborations must navigate increasingly complex compliance requirements.
Funding and Resource Allocation
The divergent approaches create different funding landscapes that influence research priorities and methodologies. American funding, whether through government agencies such as the NSF and DARPA or private companies, tends to reward breakthrough-oriented research with high-risk, high-reward profiles. The emphasis on achieving AGI creates incentives for researchers to pursue ambitious theoretical advances, even if practical applications remain unclear.
Chinese funding, filtered through Singapore’s partnerships and collaborations, emphasizes practical applications and measurable deployment outcomes. Research proposals must demonstrate clear pathways to implementation, with success metrics often tied to adoption rates, economic impact, or social benefit rather than purely scientific novelty.
At NTU, this creates interesting dynamics where the same research might be framed differently depending on the funding source. A computer vision project might emphasize theoretical advances in neural architecture when pitched to American collaborators, while highlighting manufacturing applications and deployment scalability when engaging with Chinese partners or Singapore government initiatives.
Talent and Career Implications
Strategic competition also influences talent flows and career trajectories in ways that directly impact researchers like me. American universities and companies offer substantial resources and prestige for AI researchers, but increasingly require consideration of national security implications. Chinese institutions offer various opportunities, often with a focus on the rapid deployment and scaling of research outcomes.
Singapore’s position as a neutral hub creates unique opportunities for researchers who can navigate both ecosystems. However, it also requires careful attention to intellectual property, export controls, and potential conflicts of interest as the strategic competition intensifies.
The recent movement of Chinese AI firms to Singapore, seeking access to global markets and venture capital while avoiding direct US-China tensions, has created new opportunities for collaboration but also increased scrutiny from both superpowers regarding technology transfer and strategic influence.
Technical Trajectories: Divergent Innovation Paths
Computational Paradigms
The technical differences between American and Chinese AI development reflect their strategic priorities and resource constraints. American research continues to push the boundaries of transformer architectures, scaling laws, and large-language model capabilities. The focus remains on achieving increasingly sophisticated reasoning capabilities through parameter scaling, advanced training techniques, and novel architectural innovations.
Chinese research, while still engaging with transformer-based approaches, is increasingly exploring alternative computational paradigms that may circumvent the advantages of traditional neural networks in the United States. Research initiatives in brain-inspired computing, neuromorphic architectures, and quantum-classical hybrid systems represent attempts to find technological pathways that don’t depend on the massive GPU farm that characterizes American AI development.
At NTU, we observe these different technical emphases in collaborative research proposals and joint projects. American collaborators typically focus on pushing the boundaries of model capabilities, while Chinese partners emphasise efficient deployment, edge computing optimisation, and seamless integration with existing industrial systems.
Data Strategies
The approaches to data also reflect more profound strategic differences. American AI development relies heavily on large-scale internet data scraping and the generation of synthetic data to train increasingly large models. This approach assumes that scale and diversity of training data will eventually produce emergent capabilities that approximate human intelligence.
Chinese development emphasizes surat-ed, application-specific datasets that support targeted deployment in specific sectors. Rather than training general-purpose models that might eventually achieve artificial general intelligence (AGI), Chinese researchers focus on creating specialised systems optimised for specific use cases, such as manufacturing optimisation, innovative city management, or consumer service applications.
These different data strategies have practical implications for researchers. Projects aligned with American paradigms require access to massive and diverse datasets, as well as computational resources, for large-scale training and analysis. A Chinese-aligned project emphasizes quality, relevance to application, and deployment efficiency over raw scale.
Geopolitical Implications: Export Controls and Research Freedom
The Semiconductor Bottleneck
The semiconductor export controls implemented by the United States represent perhaps the most significant challenge to China’s AI development strategy, but they also reveal vulnerabilities in the American approach. By restricting China’s access to advanced graphics processing units (GPUs), the US aims to maintain its technological advantage in the high-computational-power paradigm that characterizes current AI development.
However, these controls also incentivize Chinese innovation in alternative approaches that don’t depend on American semiconductor technology. The development of more efficient algorithms, novel architectures, and application-specific integrated circuits (ASICs) represents China’s attempt to circumvent American technological chokepoints.
For researchers at institutions like NTU, export controls create practical complications in international collaboration. Equipment purchases, research partnerships, and even academic exchanges must now consider export control implications that were previously irrelevant to academic research.
The DeepSeek breakthrough in January 2025 demonstrated both the effectiveness and limitations of export controls. While Chinese researchers achieved competitive results with limited access to cutting-edge hardware, they did so by developing more efficient approaches that potentially reduced the computational advantages that American companies assumed were sustainable.
Alliance Dynamics and Institutional Positioning
Strategic competition has created pressure for countries and institutions to choose sides; however, Singapore’s experience suggests that neutrality remains possible, although it is increasingly challenging to maintain. NTU’s ability to collaborate with both American and Chinese entities reflects Singapore’s broader strategy of maintaining beneficial relationships with both superpowers while preserving institutional autonomy.
However, this balancing act requires constant attention to potential conflicts and sensitivities. Research topics that were previously purely academic, such as computer vision, natural language processing, and robotics, now carry geopolitical implications that must be carefully managed.
The establishment of new research partnerships must consider not just scientific merit but also strategic implications for all parties involved. American partners may require assurances about technology transfer and Chinese access, while Chinese collaborators may seek guarantees about intellectual property protection and research independence.
Economic Models: Value Creation and Capture
The American Venture Capital Model
America’s AI development operates within a venture capital ecosystem that emphasizes rapid scaling, market capture, and winner-take-all outcomes. This model creates powerful incentives for breakthrough innovation but also generates extreme concentration of resources and capabilities within a small number of companies.
The venture capital model works well for developing transformative technologies that can create new markets or disrupt existing ones. However, it may be less effective for the gradual and systematic deployment that characterizes China’s approach to AI integration.
For researchers, the venture capital model creates opportunities for commercializing research outcomes, but also pressures them to focus on technologies with clear market applications and scalable business models. Academic research increasingly must demonstrate commercial potential to attract funding and support.
The Chinese State-Coordinated Model
China’s approach involves more direct state coordination and support for AI development, with less emphasis on individual company dominance and a greater focus on ecosystem-wide advancement. This model enables systematic deployment across industries and sectors, but may sacrifice some of the innovative dynamism that characterizes the American approach.
The state-coordinated model can be particularly effective for infrastructure development and systematic deployment of established technologies. China’s success in areas like high-speed rail, mobile payments, and e-commerce demonstrates the effectiveness of this approach for technologies that benefit from network effects and coordinated implementation.
However, this model may be less effective for generating the breakthrough innovations that drive paradigm shifts. The emphasis on practical deployment and risk reduction may discourage the high-risk, high-reward research that produces transformative technological advances.
Future Trajectories: Convergence or Divergence?
Scenarios for AI Development
The divergent approaches of the United States and China create several possible scenarios for future AI development, each with different implications for researchers and institutions positioned between them.
Scenario 1: American Breakthrough. If American researchers achieve AGI or significant advances toward superintelligence, the transformative capabilities could provide decisive strategic advantages. This scenario would validate the high-risk, high-reward approach and potentially create winner-take-all dynamics in the AI technology sector.
However, even successful American AGI development would face challenges in deployment and integration, areas where China’s ecosystem approach might provide advantages. The most sophisticated AI capabilities remain valueless without effective implementation and user adoption.
Scenario 2: Chinese Integration Success. Alternatively, China’s ecosystem approach might prove more effective for creating sustainable competitive advantages. Even if American AI capabilities remain superior, Chinese applications and deployment infrastructure could capture most of the economic value generated by AI technologies.
This scenario would mirror China’s success in mobile internet, where American companies developed many underlying technologies. Still, Chinese companies captured a significant portion of the economic value through superior application development and user engagement.
Scenario 3: Technological Bifurcation A third possibility involves the development of parallel AI ecosystems with limited interoperability. American and Chinese AI systems may develop along different technical trajectories, resulting in separate technological standards and application environments.
This scenario would create challenges for countries and institutions seeking to maintain connections with both ecosystems, potentially forcing difficult choices about technological alignment and strategic partnerships.
Scenario 4: Synthesis and Convergence. Finally, competitive pressure may drive both approaches toward synthesis, combining American innovation capabilities with Chinese deployment efficiency. Countries like Singapore may play a crucial role in facilitating this convergence by serving as neutral grounds for collaboration and technology transfer.
Implications for Research Strategy
These different scenarios require different research strategies and institutional positioning. Researchers must strike a balance between pursuing scientific excellence and being aware of geopolitical implications and strategic consequences.
The optimal approach likely involves maintaining flexibility and avoiding excessive dependence on any single paradigm or partnership. Diversified collaborations, technology-agnostic research methods, and emphasis on fundamental scientific principles rather than specific technological implementations provide resilience against geopolitical volatility.
Conclusion: Navigating Complexity with Principled Pragmatism
As a researcher at NTU Singapore, I’ve learned that navigating the US-China AI divide requires constant attention to both scientific merit and strategic implications. The competition between American breakthrough-oriented research and Chinese deployment-focused development creates opportunities for institutions positioned between them, but also requires careful management of relationships, resources, and research priorities.
The divergent approaches reflect more profound differences in technological philosophy, economic systems, and strategic priorities. America’s pursuit of AGI represents a characteristically American belief in transformative innovation and winner-take-all competition. China’s emphasis on practical deployment and ecosystem development reflects a more pragmatic approach focused on sustainable competitive advantages through systematic implementation.
Neither approach is inherently superior; both offer valuable insights and capabilities that can contribute to the development of beneficial AI. The optimal outcome likely involves the synthesis of both paradigms, combining American innovation capabilities with Chinese deployment efficiency and practical focus.
For researchers, institutions, and countries positioned between these superpowers, the key is maintaining principled engagement with both approaches while preserving independence and scientific integrity. This requires careful attention to geopolitical sensitivities without allowing strategic competition to compromise scientific excellence or international collaboration.
The future of AI development will likely be shaped by this competition, but the ultimate beneficiaries should be humanity as a whole. Institutions like NTU have opportunities to facilitate productive engagement between competing paradigms while advancing research that serves broader human interests rather than narrow strategic advantages.
The challenge is maintaining this balanced approach as strategic competition intensifies and pressure for alignment increases. Success will require institutional wisdom, diplomatic skill, and unwavering commitment to scientific principles that transcend geopolitical boundaries.
As I continue my research at the intersection of these competing visions, I remain optimistic that principled pragmatism can navigate the complexities of technological competition while promoting the beneficial development of artificial intelligence for the benefit of all humanity. The future belongs not to any single approach or nation, but to those who synthesise the best of all paradigms in service of human flourishing.
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