The race to dominate artificial intelligence has become the defining geopolitical contest of the decade. With market projections estimating the global AI sector could surpass $1.8 trillion by 2030, Washington, Beijing, and New Delhi are pursuing strikingly different pathways to secure technological supremacy. Each nation brings a distinct philosophy to the table: the United States bets on deregulation and private-sector dynamism, China channels state capital at unprecedented scale, and India experiments with a public infrastructure model that could reshape how developing economies access AI. Understanding these divergent strategies reveals not just a competition over technology, but a deeper contest over the governance norms and institutional architecture that will define the AI era.
Governance Philosophies: Three Models for Regulating AI
The most consequential differences among these three AI powers lie not in their investment figures but in their regulatory philosophies. Beijing operates through a security-first statutory framework, layering obligations across its Personal Information Protection Law, Data Security Law, and sector-specific rules covering algorithmic recommendation, deepfakes, and generative AI. This approach imposes mandatory risk assessments, government filings, and content watermarking requirements, granting the state extensive oversight over how AI systems function within its borders. China’s AI regulatory architecture reflects a broader governance logic: innovation is encouraged, but never at the expense of political control or social stability.
Washington, by contrast, has pivoted sharply toward deregulation under the current administration. The 2025 AI Action Plan released by the White House rests on three pillars — accelerating innovation, building domestic infrastructure, and leading international AI diplomacy. A defining moment in this shift came with the rescission of the AI Diffusion Rule in May 2025, a framework that had attempted to manage exports of advanced AI chips and models through a tiered, risk-based system. Officials deemed the rule too bureaucratic and potentially damaging to alliances, opting instead for assertive but streamlined export controls paired with incentives for global adoption of American AI standards.
New Delhi has charted a middle course, articulating a governance vision often described as “enable first, regulate later.” India’s recently published AI Governance Guidelines outline seven core principles, including trust, accountability, fairness, and safety, while leaning on the Digital Personal Data Protection Act of 2023 and softer normative frameworks rather than prescriptive legislation. This stance reflects a calculated bet that premature regulation would stifle an ecosystem still in its formative stages.
Capital and Compute: The Investment Arms Race
The financial scale of AI competition is staggering, and the mechanisms through which each country deploys capital reveal fundamentally different theories of industrial policy. China launched its National AI Industry Investment Fund in January 2025 with $8.2 billion in initial capital, complemented by a much broader $138 billion National Venture Capital Guidance Fund that channels resources into startups across AI, robotics, and adjacent sectors. Private technology giants including Alibaba and ByteDance inject additional billions into research and development, while local governments sweeten the ecosystem through subsidies and relaxed regulations in designated AI pilot zones. A distinctive feature of Beijing’s approach is its compute voucher program, which subsidises access to processing power for companies building large language models, alongside strategic infrastructure investments like the National Integrated Computing Network.
The United States relies more heavily on private capital and targeted federal programs. The CHIPS and Science Act authorized approximately $280 billion in new funding to boost domestic semiconductor research and manufacturing — a recognition that AI supremacy ultimately depends on control over the hardware supply chain. The current administration’s emphasis on reducing regulatory friction aims to encourage private firms to scale data centers, expand energy capacity, and invest in next-generation chip fabrication without bureaucratic delays.
India’s investment profile is more modest but strategically targeted. The IndiaAI Mission, sanctioned in 2024, commits over INR 10,300 crore across five years to construct shared, subsidised compute infrastructure. The government has deployed access to 38,000 GPUs and 1,050 TPUs at heavily discounted rates through a compute-as-a-service model, dramatically lowering barriers for startups and smaller enterprises. Parallel efforts through the India Semiconductor Mission aim to build long-term self-reliance in chip fabrication, addressing a critical vulnerability in the national AI supply chain.
The Global South as a Strategic Frontier
One of the most underappreciated dimensions of the AI rivalry is the competition for influence across the developing world. Beijing has been the most explicit in courting Global South nations. Through its Digital Silk Road initiative, China exports bundled packages of cloud services, data center infrastructure, and surveillance-capable “safe city” systems, often accompanied by financing and technical standards. Critics argue these arrangements risk creating vendor lock-ins and expanding state surveillance capabilities, but for recipient nations, the appeal of turnkey infrastructure at competitive prices remains powerful.
China’s ambitions extend to institutional architecture as well. At the 2025 World Artificial Intelligence Conference in Shanghai, Beijing proposed establishing a World Artificial Intelligence Cooperation Organization, intended to serve as a global coordinating body for AI standards. The proposed Shanghai-headquartered organization, along with China’s Global AI Governance Action Plan, explicitly targets developing nations, promising joint innovation and technology transfer in a bid to establish Beijing as the default partner for countries building AI capabilities from scratch.
India’s counter-proposition is arguably more transformative for the Global South. New Delhi is extending its pioneering Digital Public Infrastructure model — the open, interoperable stack that underpins systems like Aadhaar and UPI — into the AI domain. Rather than exporting proprietary platforms, India envisions a layered public infrastructure approach where shared computing resources, open datasets, and interoperable platforms enable diverse public and private actors to build context-specific AI applications. Given that multiple developing nations have already adopted DPI principles for governance and service delivery, a DPI-inspired framework for AI could offer a genuinely open-source alternative to Beijing’s full-stack model.
The United States, meanwhile, positions itself as a provider of comprehensive AI solutions — combining hardware, software, security architectures, and technical standards — with a focus on allied and partner nations rather than the broader developing world.
Bottlenecks Standing Between India and AI Leadership
Despite ranking third globally on Stanford HAI’s AI Vibrancy Index — a remarkable ascent from seventh place just one year earlier — India confronts structural barriers that could limit its trajectory. The most pressing constraint involves compute infrastructure. While the government’s GPU deployment program represents a significant step forward, the country still lacks sufficient energy-efficient data centers backed by high-performance computing facilities and sustainable power systems capable of training and deploying frontier AI models at scale.
Data availability presents another systemic challenge. Initiatives such as AIKosh, the Open Government Data Platform, and the National Data and Analytics Platform have improved access to public datasets, but high-quality, domain-specific data remains fragmented and unevenly distributed. Interoperability issues between platforms and cautious institutional data-sharing practices further constrain the ability of researchers and enterprises to develop high-impact applications.
Perhaps most critically, talent gaps threaten to undermine India’s AI ambitions. While the country produces an enormous volume of IT graduates, it suffers from a persistent shortage of advanced AI researchers capable of contributing to frontier research. Brain drain compounds this problem, as top talent frequently migrates to better-resourced ecosystems in the United States and elsewhere. Addressing this deficit will require coordinated investments in academic-industry collaboration, domain-specific research programs, and meaningful incentives for skilled professionals to remain within or return to India’s AI ecosystem.
Competing Visions for the AI-Powered Future
The three-way competition among Washington, Beijing, and New Delhi is ultimately a contest over whose vision of AI development becomes the global default. The American model prioritises speed and market dominance, treating regulatory restraint as a competitive advantage and betting that private enterprise will deliver both innovation and security. China’s approach relies on massive state mobilisation, integrating AI into a broader strategy for technological self-sufficiency and geopolitical influence. India offers something distinct: an inclusive, infrastructure-centric model that prioritises broad access and may prove especially resonant across the developing world.
Each approach carries risks. American deregulation could produce powerful but poorly governed AI systems. China’s state-led model may sacrifice innovation at the margins while deepening global concerns about surveillance and data sovereignty. India’s resource constraints could prevent its ambitious public infrastructure vision from reaching critical mass before competitors lock in global standards and supply chains.
The outcome of this contest will depend not just on investment volumes or research output, but on which governance frameworks, institutional partnerships, and infrastructure models prove most adaptable to the diverse needs of a world increasingly shaped by artificial intelligence. For nations still formulating their own AI strategies, the choices made in Washington, Beijing, and New Delhi will define the menu of options available for decades to come.
Original analysis inspired by Debajyoti Chakravarty from Observer Research Foundation. Additional research and verification conducted through multiple sources.
