3.1.3 New Superpowers and Alliances

Omar Al-Mansoori works for MGX, the UAE's strategic investment vehicle, from an office in Abu Dhabi overlooking construction sites where data centers are rising from the desert.

In 2024, almost no one talked about the UAE as an AI superpower. By 2026, it ranks among the top three globally—behind the United States, ahead of China in some analyses. The rise was neither accidental nor inevitable. It was engineered.

The UAE recognized earlier than most that AI power in the twenty-first century is not primarily about research labs or software talent. It is about infrastructure—the massive computational capacity needed to train and run frontier models—and infrastructure requires two things: capital and energy. The UAE has both in abundance.

Omar oversees "Stargate UAE," a one-gigawatt supercomputing complex in Abu Dhabi that began coming online in phases in early 2026, one of the largest AI-focused data centers in the world. Beyond that, the UAE and the United States have agreed to develop the largest AI-focused campus outside the United States: a 26-square-kilometer site in Abu Dhabi designed to house five gigawatts of data center capacity when complete, with an initial 200-megawatt cluster already operational. For context, five gigawatts is roughly equivalent to the total power generation of some small countries.

The UAE is not attempting to out-innovate Silicon Valley or out-research Beijing. Instead, it is positioning itself as a global AI utility provider—a jurisdiction capable of hosting, powering, and scaling the hyperscale compute infrastructure that the next generation of AI models requires. Tech companies, governments, and research institutions are lining up to rent capacity, drawn by the combination of massive computational power, stable energy, favorable regulatory treatment, and geopolitical neutrality that few locations can offer. Omar's job is to position the UAE at the center of as many of these networks as possible, because in the AI era, being indispensable beats being dominant.

The Middle Power Moment

The U.S.-China rivalry dominates headlines, but it obscures a more complex reality: a growing number of middle powers are gaining disproportionate influence in the AI ecosystem by controlling critical nodes that larger competitors depend on.

The Gulf states exemplify this dynamic most dramatically. Saudi Arabia, like the UAE, combines sovereign wealth, abundant energy, and geopolitical flexibility to position itself as an infrastructure power. Both countries are investing tens of billions in data center capacity and AI compute at a pace that few nations can match, and both have secured partnerships with leading American technology firms that trade technology access and strategic alignment for capital and hosting capacity. The result is an AI superpower status built not on algorithmic research but on the unglamorous yet indispensable foundations of power, land, and money.

India's AI position rests on a different combination of strengths. The country has crossed 34,000 GPUs in national compute capacity and hosts one of the world's largest AI talent pools, with over six million people employed in the tech and AI ecosystem. Its population scale generates data volumes that few countries can match—a structural advantage as AI systems grow increasingly dependent on vast, diverse training datasets. India's government has reinforced these natural assets with its national AI Mission, coordinating public research institutions, private companies, and international partners to build domestic capability while selectively engaging with foreign collaboration.

Israel's influence is narrower but exceptionally deep. With over 9,000 startups, including approximately 2,300 AI-driven ventures, it has built a concentration of sophisticated AI research and application that is remarkable for a country of its size. A dedicated AI Directorate within the Prime Minister's Office now coordinates national strategy. Israel's most significant leverage, however, lies in defense and cybersecurity: Israeli firms supply critical AI-enabled technologies to militaries and intelligence agencies across the democratic world, giving the country influence over some of the most sensitive and consequential AI applications in existence.

Singapore has chosen a different path, positioning itself as a trusted governance hub and neutral intermediary where companies and governments from different geopolitical camps can collaborate, sign agreements, and establish shared standards. Its regulatory frameworks, legal protections, and financial infrastructure make it a natural home for regional AI operations. Singapore's explicit neutrality is itself a strategic asset: actors who cannot easily cooperate directly often find common ground there, making the city-state an essential node in an otherwise fragmented global network.

South Korea and Japan are embedding AI into manufacturing, robotics, and semiconductor production at scale, deepening capabilities in the industrial applications that will define much of AI's near-term economic impact. Taiwan remains indispensable as the world's primary hub for advanced chip fabrication—a concentration of specialized capability with no near-term substitute. Canada has built strong academic institutions and AI talent pipelines whose graduates populate labs around the world, giving it soft influence over the intellectual direction of the field even as its commercial AI sector remains smaller than those of the leading powers.

None of these countries will surpass the United States or China in overall AI capability. But they do not need to. Each is gaining influence by specializing—controlling specific nodes in the global AI network that larger actors depend upon. And because AI systems require contributions from multiple layers—chips, data centers, energy, models, applications, governance—middle powers that excel in even one layer acquire leverage across the entire system.

The New Alliances

The fragmented AI landscape is accelerating alliance formation as countries recognize they cannot compete alone but can achieve collective capabilities through cooperation. The resulting partnerships span bilateral deals, regional security arrangements, and broad multilateral governance frameworks, each reflecting a different theory of how AI power should be organized.

The United States and Saudi Arabia formalized their partnership in November 2025, signing a Strategic Artificial Intelligence Partnership covering AI infrastructure, research, and deployment. For the United States, the deal secures influence in the Gulf and access to Saudi capital and energy. For Saudi Arabia, it provides technological access, security guarantees, and alignment with the leading AI superpower. The arrangement illustrates a pattern that recurs across AI diplomacy: the exchange of infrastructure and capital for technology access and strategic alignment, in which neither side need share identical values to find common interest.

India's AI Impact Summit, hosted in New Delhi in February 2026, demonstrated how middle powers are constructing their own cooperative networks independent of either superpower. The summit brought together countries to develop partnerships around collaborative AI development and global governance, with substantive agreements rather than aspirational communiqués. India and Israel deepened cooperation in AI, climate resilience, and digital governance. India and the UAE expanded an already close relationship across defense, space, energy, and technology. The summit signaled that middle powers working together can create alternatives to bilateral dependence on Washington or Beijing by pooling complementary strengths.

The Partnership for Global Inclusivity on AI takes a different form, assembling the U.S. Department of State alongside Amazon, Anthropic, Google, IBM, Meta, Microsoft, Nvidia, and OpenAI in a public-private coalition. Its stated mission is to promote AI access and governance principles globally, particularly in developing countries that might otherwise lack the resources to shape international norms. It simultaneously serves as a vehicle for extending American influence through corporate partnerships, ensuring that U.S. technology companies help define how AI is deployed across partner nations.

Security alliances have become AI alliances as well. The Quadrilateral Security Dialogue—comprising the United States, Japan, Australia, and India—and AUKUS, the trilateral partnership between Australia, the United Kingdom, and the United States, coordinate on AI supply chains, compute infrastructure, and cyber threat intelligence. Both groupings have identified AI as critical to future military capability, with emphasis on cybersecurity, quantum computing, hypersonics, electronic warfare, and undersea operations. The 2025 period saw a marked increase in joint military exercises focused precisely on these AI-dependent capabilities, reflecting how deeply defense cooperation has become entangled with AI development.

At the governance level, the G7 formalized an AI pact in mid-2025 emphasizing transparency, ethics, and regulatory coordination among democratic states. The agreement establishes shared standards intended as a counterweight to China's governance model, grounding AI oversight in accountability, contestability, and the protection of individual rights. The European Union pursues a parallel but geographically broader strategy, engaging on AI with partners across North America, Asia, Oceania, Latin America, and Africa, while participating actively in the G7, G20, OECD, the Global Partnership on AI, and the United Nations. The EU's approach leverages the Brussels Effect—the tendency of EU regulatory standards to become de facto global norms because firms operating in the EU's large market must comply—to extend governance influence even where it cannot compete in raw computational terms.

Strategic Interdependence

These alliances collectively reflect a fundamental shift in how nations think about AI power: from national self-sufficiency toward strategic interdependence. The logic is straightforward. No single country can dominate all layers of the AI stack simultaneously—chips, energy, data centers, models, talent, applications, and governance each require different capabilities and resources. Rather than attempting autarky, countries specialize in their areas of genuine advantage and form partnerships that pool complementary strengths.

Country / Region Primary AI Specialization
United States Frontier model development, advanced chip design
China Industrial-scale deployment, state-integrated AI systems
UAE / Gulf States Compute infrastructure, sovereign capital
India AI talent, data at population scale
Israel Defense and cybersecurity AI
Europe Governance frameworks, regulatory standards
Singapore Neutral collaboration hub
Taiwan Advanced chip fabrication
South Korea / Japan AI-integrated manufacturing and robotics

This division of labor creates collective capabilities that no individual actor could assemble alone. Each partner contributes something the others need, and the combination produces systems with more layers of depth and resilience than any single country could sustain.

The emerging scale of these collaborations is captured in Goldman Sachs' prediction that 2026 will be defined by "mega alliances"—headline partnerships of unprecedented scope involving governments, technology companies, energy providers, and investment funds operating together across national boundaries. The UAE-U.S. data center partnership is already a working example: it weaves together sovereign governments, energy companies, major technology firms, and sovereign wealth funds in an arrangement that blurs conventional lines between public and private, domestic and international, commercial and strategic. India's AI Mission coordinates similar cross-sector complexity at the national level. AUKUS integrates military, intelligence, and commercial AI development across three nations simultaneously.

These mega alliances are arguably necessary. The capital required for frontier AI—data centers costing tens of billions, chip fabrication plants costing hundreds of billions, energy infrastructure requiring decades to build—exceeds what most individual actors can manage. Pooling resources and distributing risk enables projects that would otherwise be unreachable. But this concentration of capability in a small number of alliances also concentrates power, and access to these networks is becoming a prerequisite for meaningful participation in the AI future. Countries and organizations outside the major alliance networks risk being structurally excluded from the computational, financial, and knowledge resources that frontier AI requires.

The model is resilient in some respects. If one partner becomes unreliable, the alliance can adapt; if geopolitical tensions disrupt one relationship, others can partially compensate. The networked structure distributes risk in ways that purely bilateral dependencies cannot. But it is also fragile: alliances require sustained trust, aligned interests, and ongoing coordination—all of which can fracture under pressure from conflicts, sanctions, leadership changes, or economic shocks. The more complex the network, the harder it becomes to maintain coherence when stresses accumulate across multiple relationships simultaneously.

The Risks of Fragmentation

The proliferation of alliances creates risks alongside opportunities. The global AI ecosystem is fragmenting into competing blocs with diverging standards, duplicated infrastructure investment, and incompatible governance models.

The U.S.-led alliance promotes a vision of AI that is market-driven, lightly regulated, and aligned with liberal democratic values. China's model emphasizes state control, industrial policy, and integration with authoritarian governance. India balances sovereignty with strategic openness. The UAE prioritizes infrastructure neutrality and pragmatic partnership over ideological alignment. These visions are not easily reconciled, and as alliances harden they create barriers—technical, regulatory, and political—that make interoperability progressively more difficult to achieve.

The trajectory points toward a world in which American and Chinese AI systems operate in growing isolation from each other, European regulations prevent the deployment of models developed under different standards, and data cannot move across borders because of conflicting sovereignty laws. This outcome would impose serious costs: duplicated infrastructure investment, fragmented markets, and elevated risk of conflict where incompatible systems encounter each other in contested domains such as finance, communications networks, or military operations.

The optimistic counterargument holds that competition among governance models can spur innovation, allow institutional experimentation, and give smaller countries meaningful choices about alignment rather than leaving them subject to a single global standard. A more pessimistic reading sees fragmentation producing a digital Cold War that stifles innovation through duplication and raises the probability of escalation when incompatible systems interact in high-stakes environments. Which future materializes depends substantially on whether alliances remain flexible and open to overlapping membership—as the UAE's hub strategy exemplifies—or become rigid blocs demanding loyalty over pragmatism. Countries such as the UAE, Singapore, and India are currently betting they can maintain productive relationships with multiple competing blocs simultaneously. Whether that hedge remains viable as competition between the United States and China intensifies is one of the defining strategic questions of the coming decade.

Key Takeaways

The AI geopolitical landscape of 2026 is multipolar, networked, and rapidly evolving. The United States and China remain dominant, but their dominance is incomplete and increasingly contested. A new class of middle powers—the UAE, Saudi Arabia, India, Israel, Singapore, Taiwan, and others—is gaining influence not by matching the superpowers directly but by controlling critical nodes in the global AI supply chain, from compute infrastructure and chip fabrication to talent pipelines, data scale, defense applications, and governance frameworks.

Alliances are forming rapidly around complementary capabilities and shared interests, ranging from bilateral partnerships like the U.S.-Saudi AI agreement to complex multilateral arrangements like AUKUS, the Quad, and the EU's global engagement strategy. These partnerships pool resources, distribute risk, and enable investments at a scale that no single actor could sustain alone, giving rise to a "mega alliances" era in which the boundaries between public and private, domestic and international, commercial and strategic have become difficult to draw.

Strategic interdependence has replaced self-sufficiency as the dominant organizing logic of AI geopolitics. Countries specialize, form partnerships, and hedge across multiple alliances simultaneously—a strategy that builds resilience against disruption while also introducing fragility wherever geopolitical pressure mounts across interconnected relationships at once.

The central risk of the current trajectory is fragmentation: a global AI ecosystem divided into incompatible blocs with diverging standards, duplicated infrastructure, and diminishing cooperation on challenges that no single actor can address alone. Whether the emerging order stabilizes into durable, open partnerships or hardens into a digital Cold War will depend on choices being made now—in Abu Dhabi's data centers, in New Delhi's summit halls, in Brussels' regulatory chambers, and in Washington and Beijing. Those choices will shape not only the future of artificial intelligence but the structure of global power for decades to come.

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Last updated: 2026-02-25