The growing gap between rapid advances in artificial intelligence (AI) and inadequate verification mechanisms has created several challenges for the technology. In this context, global AI governance has become a central topic of discussion in the tech world. However, the discourse around AI focuses primarily on how the technology will be regulated. The central problem of global AI governance is no longer whether AI should be regulated, but whether states possess the power to regulate the actors and infrastructures on which they increasingly depend.  

The international system has begun responding swiftly to evolving AI dynamics. Notably, the UN Global Digital Compact has established a framework for digital cooperation. The General Assembly has created the Independent International Scientific Panel on Artificial Intelligence, along with a Dialogue on AI Governance. In parallel, the European Union (EU) has moved towards an enforceable risk-based regulation through the AI Act. Likewise, the Council of Europe has also launched the first internationally binding treaty on AI. These initiatives are a step in the right direction. However, they lack a coherent global regime or direction, which impacts their effectiveness.  

As the latest initiatives show, the U.S. relies on targeted export controls on advanced chips, voluntary frameworks, and a pro-innovation approach that preserves U.S. dominance. China, on the other hand, favors a state-driven approach, built around controls on generative AI. The EU, by contrast, prioritizes enforceable rules for high-risk and general-purpose AI, along with strong fundamental rights protection. The emerging AI order is therefore not one model, but several operating simultaneously. This fragmentation is not accidental. It reflects competing power struggles among national interests. In such an environment, the search for a global AI governance appears misguided.  

Future AI governance is likely to mirror a hybrid model of international finance or trade regimes. Against this backdrop, private companies remain incredibly powerful, given their monopoly over data, models, computing power, and expertise, which are concentrated in a handful of private companies. This trend creates a governance paradox. Governments aim to regulate the private sector while remaining excessively dependent on it, creating a capacity challenge.  

These trends point to three prominent developments for the future. Regulatory fragmentation is likely to intensify as rival technological blocs establish incompatible standards. Ultimately, technologically backward states opt accordingly. Second is procedural multilateralism, where states agree on universal principles, but actual power remains concentrated among a few powerful entities. The third option is that states retain their distinct regulatory systems but agree on some conditions. These scenarios and trends are not mutually exclusive and can coexist, with different approaches emerging across sectors. As a result, the landscape is expected to remain complex. Whichever the scenario, the technological ecosystems are likely to become tools of influence vis-à-vis cloud access, advanced semiconductors, data centers, export policies, global supply chains, digital infrastructure, and divergent AI standards.  

For the Global South, the ongoing trend poses multifaceted challenges. The foremost is structural dependence. While these states may draft their own AI policies, they will remain dependent on foreign technology. Likewise, consensus among states remains elusive. This becomes increasingly complex in a future environment where AI governance may become more infrastructural. This might involve the development of measurable controls. The key challenge for the Global South is not mere exclusion; it is participation without agenda-setting influence. In addition, AI innovation moves much faster than regulation, creating a governance lag and adding to the challenges. The main priority is to define a meaningful role for itself in this environment. Its main priority should be to integrate into regional frameworks, shared datasets, and coordinated diplomatic moves.  

The international organizations have a limited but important part in global governance. They can expand their role by laying the ground for common scientific assessments, incident-sharing mechanisms, and capacity building. The approach should complement existing frameworks while acknowledging divergent national systems. Fostering coordination where political consensus allows remains central for relevant international organizations.  

AI Governance in the contemporary era is complex. As the ongoing developments suggest, the future of AI governance will not be manifested as a single regulator or a universal AI rulebook. Instead, it will emerge as a highly contested polycentric order, characterized by an environment where laws, norms, technical standards, and UN/international organization-led coordination overlap. In this context, the U.S., China, and the EU will continue to assert their dominance and institutionalize their edge into rules and norms. In parallel, countries in the Global South are likely to resist and consolidate their own influence. The pivotal challenge in such an environment will not be whether AI is governed. Instead, it will be whose security interests and priorities prevail in the technological age. Hence, the tussle will be to convert technological capability into institutional influence. 

Shaza Arif is a Senior Research Associate at the Centre for Aerospace and Security Studies (CASS), Islamabad, casss.thinkers@casstt.com. The views in this article are the author’s own and do not necessarily represent those of NIDS.

The Politics of Global AI Governance was originally published on Global Security Review.