Guardrails in AI growth to protect developing nations

Why in the News?

The United Nations General Assembly established a Global Dialogue on AI and an Independent International Scientific Panel on AI, marking the first attempt to create a global scientific body dedicated to this technology. This development has exposed a core tension: AI governance is simultaneously moving toward global coordination and fragmenting into competing national regulatory frameworks. The asymmetry between AI-capable and AI-dependent nations determines who controls both the risks and the benefits of this transition.

What is the current global AI governance landscape and why is it structurally insufficient?

1. Parallel and voluntary structures: Most existing frameworks have voluntary participation, varying legal force, and focus on specific aspects, safety, ethics, or standards, with no common binding floor.
2. EU AI Act 2024: The most comprehensive binding framework to date. It prioritises safe, transparent, non-discriminatory, and environmentally friendly AI. Its extraterritorial reach is limited to EU-market participants.
3. UN Global Dialogue on AI: UNGA invited every country to participate. An Independent Scientific Panel makes periodic assessments to inform the Dialogue. It lacks enforcement authority.
4. Annual global AI summits: The most recent edition was held in New Delhi in February 2025. Outcomes remain consultative and have not produced enforceable international agreements.
5. Regulatory fragmentation: Each country developing its own framework forces companies to satisfy differing requirements across geographies, creating pressure to favour permissive jurisdictions.
6. Innovation slowdown risk: Companies may roll out services only in regulatory-friendly markets, deepening access inequality for developing nations.

What makes global AI governance necessary?

1. Cross-border technology: AI systems operate across jurisdictions and affect multiple countries simultaneously.
2. Regulatory fragmentation: Different national regulations increase compliance costs and slow innovation.
3. Unequal regulatory capacity: Many developing countries lack the expertise and institutions needed to regulate AI effectively.
4. Global public impact: AI influences economic growth, governance, healthcare, education, and security.
5. Need for common standards: Shared principles can improve safety, interoperability, and trust.

How does regulatory fragmentation produce asymmetric harm for developing nations?

1. Infrastructure concentration: A few countries already possess the computing, talent, and financial resources to support the entire AI ecosystem, before global rules are set.
2. Regulatory capacity deficit: Many countries in Asia and Africa lack institutions to frame robust domestic AI regulations or protect their national interests in international negotiations.
3. Data sovereignty trap: Insisting that all AI development remain within national boundaries accelerates power concentration rather than distributing it.
4. Digital colonisation risk: Developing countries become consumers of AI systems designed elsewhere, with no input into their values, benchmarks, or constraints.
5. Denial of transformative benefits: AI is a technology of the order of the steam engine. Excluding developing nations from its benefits is a disservice to humanity, not merely to affected countries.
6. Minimum regulatory floor: A globally agreed set of minimum standards is the only mechanism that ensures developing countries benefit from AI advances without surrendering domestic policy space.

Does global AI regulation resolve the equity problem or does it risk replicating the nuclear non-proliferation trap?

The equity problem refers to the structural exclusion of predominantly the Global South from the economic benefits, decision-making processes, and capacity building surrounding artificial intelligence.

1. Non-proliferation analogy: Global AI regulation could restrict unrestricted AI development to only certain countries or companies, creating a permanent hierarchy between technology producers and users.
2. Nuclear regime parallel: This outcome embeds existing power differentials into binding international law, replicating a governance structure that legitimises asymmetry rather than correcting it.
3. Biological and chemical weapons treaties: Existing international agreements already control dangerous dual-use technologies. Proposals may extend this logic to AI models and to the infrastructure required to build them.
4. Logic of restriction: The case for restricting AI capable of enabling next-generation biological or chemical weapons is logically defensible. The risk is who draws the boundary and in whose interest.
5. Political capture risk: “Responsible AI” defined by incumbent powers locks in first-mover advantage and treats developing nations as permanent recipients rather than co-producers of governance norms.

What do international governance models demonstrate about the feasibility of a globally agreed AI floor?

1. EU AI Act: binding regulatory precedent: Demonstrates that comprehensive, legally enforceable AI governance is achievable at supranational scale. Sets de facto global standards through market leverage.
2. UN Global Dialogue: universalist participation model: Universal country invitation distinguishes it from club-based governance. Participatory architecture is its most relevant design feature for developing nations.
3. Google AI Commons: private open-access precedent: Demonstrates that large AI actors can adopt open-access norms voluntarily. Lacks enforceable accountability.
4. Trusted AI Commons: India-hosted hybrid model: A one-stop repository of tools, benchmarks, datasets, and protocols for testing AI deployment, with liberal licensing. Significant as a Global South-led governance mechanism.
5. Limits of existing models: None produces a binding universal minimum floor. The EU Act covers only its market; the UN Dialogue lacks enforcement; Commons models are voluntary. The gap between architecture and enforceable standards remains open.

What is the Trusted AI Commons and does it constitute an adequate institutional response to the governance deficit?

1. Definition: A repository of tools, benchmarks, datasets, and protocols needed to develop and deploy AI systems safely and responsibly. Functions as a one-stop shop for AI testing and deployment support.
2. Institutional origin: Main outcome of the New Delhi AI Impact Summit, February 2026. Hosted and managed by India through India’s AI Mission.
3. Licensing design: Open, accessible, with liberal licensing. Aggregates tools already developed worldwide, including by IIT Madras, rather than commissioning new ones.
4. Practical function (example): A country testing an AI system for agriculture can use the Commons to locate available tools, benchmarks, datasets, and protocols in one place, without needing domestic AI infrastructure to find or validate them.
5. Adequacy gap: Addresses the access and deployment deficit. Does not create a binding minimum floor. Does not build regulatory capacity in developing nations. Necessary but insufficient.
6. India’s strategic significance: Hosting the Commons positions India as a norm-setter rather than a norm-follower, consistent with its broader foreign policy of strategic autonomy: the ability to act independently of major power blocs in international affairs. 

The Trusted AI Commons

1. It is an open, federated, and voluntary global platform designed to serve as a consolidated repository for AI safety benchmarks, evaluation tools, standards, and deployment frameworks.
2. The initiative was integrated into the New Delhi Declaration on AI Impact.

Core Objectives & Utility: The platform is designed to act as a “one-stop shop” for developers, researchers, and regulators to access non-proprietary resources.

1. Open Accessibility: Provides tools under liberal, open-source licensing to prevent safety mechanisms from being locked behind big-tech barriers.
2. Standardised Evaluation: Hosts cross-jurisdictional benchmarks to test AI behavior against bias, misalignment, and operational errors before deployment.
3. Global Interoperability: Fosters cross-border collaboration by mapping technical safety frameworks across different international standards.

Hosting and Management

1. Initial Leadership: The Trusted AI Commons is initially hosted and managed by India under the auspices of the Ministry of Electronics and Information Technology (MeitY) and the IndiaAI Mission.
2. Collaborative Network: Rather than building every mechanism from scratch, it aggregates tools from leading global research bodies, such as the Centre for Responsible AI (IIT Madras), the UK AI Security Institute, and Mozilla

Conclusion

Fragmented national AI regulation concentrates power in AI-capable nations and denies developing countries both protection and access. A globally agreed minimum regulatory floor is the necessary condition for equity but if framed through non-proliferation logic, it encodes existing power hierarchies into international law. The Trusted AI Commons addresses the access deficit but does not substitute for binding global governance. The central unresolved precondition is universal participation in the design of global AI rules, not merely in their implementation.