Editorial-16/09/2026
AI cooperation: On Artificial Intelligence at a crossroads
Artificial intelligence stands at a decisive juncture: its trajectory will be shaped less by technology alone and more by the quality of international cooperation that governs its development, deployment, and distribution. While AI promises transformative gains in health, education, agriculture, and governance, fragmented regulatory philosophies, geopolitical rivalry, and asymmetries in compute and data risk entrenching a new digital divide. The recent 18th BRICS Summit in New Delhi and parallel UN-led initiatives underscore both the urgency and the opportunity to choose a cooperative, inclusive path over a zero-sum, geopoliticised one.
The crossroads: promise versus peril
AI is a general-purpose technology with cross-border externalities. Models trained in one jurisdiction can affect labour markets, information ecosystems, and even security dynamics globally. Yet governance has fragmented: the EU enforces a risk-based, mandatory-compliance regime; the US leans toward innovation-first, sectoral rules; and many Asia-Pacific states favour voluntary, industry-led frameworks. This regulatory patchwork complicates compliance for developers, slows diffusion of benefits, and leaves critical safety gaps unaddressed.
At the same time, the concentration of advanced AI capabilities in a few countries and corporations creates structural barriers to cooperation. Compute, data, and talent are unevenly distributed; private investment dwarfs public regulatory capacity; and great-power competition incentivises “AI sovereignty” over shared standards. Without deliberate course correction, the world risks a future where AI amplifies existing inequalities and becomes another arena for strategic rivalry rather than a tool for shared prosperity.
Why cooperation is indispensable
Cooperation is not an idealistic add-on; it is a functional necessity for safe and equitable AI. Three imperatives stand out:
The crossroads: promise versus peril
AI is a general-purpose technology with cross-border externalities. Models trained in one jurisdiction can affect labour markets, information ecosystems, and even security dynamics globally. Yet governance has fragmented: the EU enforces a risk-based, mandatory-compliance regime; the US leans toward innovation-first, sectoral rules; and many Asia-Pacific states favour voluntary, industry-led frameworks. This regulatory patchwork complicates compliance for developers, slows diffusion of benefits, and leaves critical safety gaps unaddressed.
At the same time, the concentration of advanced AI capabilities in a few countries and corporations creates structural barriers to cooperation. Compute, data, and talent are unevenly distributed; private investment dwarfs public regulatory capacity; and great-power competition incentivises “AI sovereignty” over shared standards. Without deliberate course correction, the world risks a future where AI amplifies existing inequalities and becomes another arena for strategic rivalry rather than a tool for shared prosperity.
Why cooperation is indispensable
Cooperation is not an idealistic add-on; it is a functional necessity for safe and equitable AI. Three imperatives stand out:
- Safety and risk management: High-risk applications—autonomous systems, biotech, critical infrastructure—require shared safety benchmarks, incident reporting, and red-team protocols that no single country can credibly enforce alone. Proposals such as model registries, pre-deployment testing, and international safety certifications gain force only with multilateral buy-in.
- Interoperability and innovation: Divergent rules raise compliance costs, fragment markets, and stifle cross-border research. Common, technology-neutral definitions of AI systems and aligned risk categories can enable interoperable regulation without harmonising every detail.
- Equity and the Global South: Without coordinated capacity-building, technology transfer, and access to compute and data, many countries will be rule-takers rather than rule-makers. Inclusive governance is essential to ensure AI serves development goals—from precision agriculture to public-health surveillance—rather than deepening dependency.
The BRICS alternative: a developmental, non-geopolitical frame
The BRICS framework offers a distinct narrative: AI as a public-good enabler, governed through cooperation rather than containment. At the New Delhi Summit, leaders called for wider access to AI resources and stronger international collaboration to keep technologies safe, secure, inclusive, and reliable. The New Delhi Declaration 2026 charts a technology roadmap spanning AI, cybersecurity, digital public infrastructure (DPI), quantum, and advanced manufacturing.
Two elements are particularly significant for UPSC-oriented analysis:
The BRICS framework offers a distinct narrative: AI as a public-good enabler, governed through cooperation rather than containment. At the New Delhi Summit, leaders called for wider access to AI resources and stronger international collaboration to keep technologies safe, secure, inclusive, and reliable. The New Delhi Declaration 2026 charts a technology roadmap spanning AI, cybersecurity, digital public infrastructure (DPI), quantum, and advanced manufacturing.
Two elements are particularly significant for UPSC-oriented analysis:
- Open-source and community-building: China’s proposal for a “BRICS AI open-source community,” supporting large language model cooperation, specialised seminars, and training, seeks to counter unilateral dominance and foster an open ecosystem. For India, this aligns with its DPI playbook (Aadhaar, UPI, ONDC) and the vision of “AI for All” that leverages open standards and public digital rails.
- Development-first governance: The Declaration emphasises energy efficiency, trustworthy AI, and leveraging AI for economic growth and social good, explicitly foregrounding the Global South’s needs. Prime Minister Narendra Modi’s caution against the “weaponisation” of technology and resources complements this by warning against securitisation that could choke off developmental applications.
This approach does not reject safety or rights; it reframes priorities to ensure that governance does not become a barrier to entry for emerging economies. In a world where some frameworks risk becoming de facto trade barriers, BRICS’ emphasis on access, capacity-building, and shared infrastructure offers a pragmatic counterweight.
Global governance architecture: progress and limits
Parallel efforts at the UN and other fora show both momentum and constraints. The ITU’s Annual AI Governance Report 2025 stresses moving from principles to operational tools—transparency mechanisms, capacity-building, and environmental sustainability—while noting divergent regional models. The UN’s Global Dialogue on AI Governance has opened space for the Global South and civil society but has struggled to confront the power structures driving AI harms. New UN mechanisms aim to strengthen cooperation, yet they face chronic challenges: funding gaps, US–China rivalry, and weak enforceability.
Scholars warn against searching for a single “silver bullet” institution for AI governance. Instead, they recommend focusing international cooperation on issues with significant cross-border externalities—such as malicious use and systemic safety risks—while building capacity in underrepresented regions to ensure meaningful participation. This “problem-driven” approach suits a technology as heterogeneous as AI, where one-size-fits-all rules may be both impractical and undesirable.onlinelibrary.
India’s strategic opportunity
For India, the current moment is strategically fertile. Domestically, the IndiaAI mission and related initiatives seek to democratise access to compute, datasets, and models while promoting responsible innovation. Internationally, India can leverage its G20 legacy, BRICS leadership, and growing digital diplomacy to advance three pillars:
Global governance architecture: progress and limits
Parallel efforts at the UN and other fora show both momentum and constraints. The ITU’s Annual AI Governance Report 2025 stresses moving from principles to operational tools—transparency mechanisms, capacity-building, and environmental sustainability—while noting divergent regional models. The UN’s Global Dialogue on AI Governance has opened space for the Global South and civil society but has struggled to confront the power structures driving AI harms. New UN mechanisms aim to strengthen cooperation, yet they face chronic challenges: funding gaps, US–China rivalry, and weak enforceability.
Scholars warn against searching for a single “silver bullet” institution for AI governance. Instead, they recommend focusing international cooperation on issues with significant cross-border externalities—such as malicious use and systemic safety risks—while building capacity in underrepresented regions to ensure meaningful participation. This “problem-driven” approach suits a technology as heterogeneous as AI, where one-size-fits-all rules may be both impractical and undesirable.onlinelibrary.
India’s strategic opportunity
For India, the current moment is strategically fertile. Domestically, the IndiaAI mission and related initiatives seek to democratise access to compute, datasets, and models while promoting responsible innovation. Internationally, India can leverage its G20 legacy, BRICS leadership, and growing digital diplomacy to advance three pillars:
- Standards with development sensitivity: Advocate for global standards that recognise different risk profiles and capacity levels, ensuring that compliance does not exclude smaller firms and emerging markets.
- Digital public infrastructure for AI: Extend the DPI model to AI—open datasets, model hubs, evaluation sandboxes, and skill-building platforms—packaged as global public goods through BRICS and UN channels.
- Safety without securitisation: Support robust safety protocols (model registration, incident reporting, red-teaming) while resisting narratives that overly securitise civilian AI applications, which could constrain developmental use-cases in health, education, and agriculture.
By anchoring its approach in inclusivity, openness, and developmental outcomes, India can help steer global AI governance away from bloc politics toward a more plural, functional architecture.cio.economictimes.
The road ahead: choices that will define the AI era
The path chosen now will determine whether AI becomes a force for broad-based progress or a new axis of inequality and instability. Key decision points include: embracing partnership over unilateral control; investing in shared safety infrastructure and open ecosystems; and ensuring that governance frameworks are agile, interoperable, and attuned to the Global South’s realities.
The BRICS New Delhi Declaration and related initiatives signal that an alternative is possible—one that treats AI not merely as a strategic asset but as a developmental lever requiring collective stewardship. The task for policymakers, researchers, and citizens is to convert this vision into concrete mechanisms: joint R&D, shared testing regimes, skills exchanges, and norms that balance innovation with responsibility.
At this crossroads, cooperation is not optional; it is the condition for realising AI’s promise while containing its perils. The choices made in the next few years will echo for decades, shaping not just economies and industries, but the very texture of democratic life and global equity.
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The road ahead: choices that will define the AI era
The path chosen now will determine whether AI becomes a force for broad-based progress or a new axis of inequality and instability. Key decision points include: embracing partnership over unilateral control; investing in shared safety infrastructure and open ecosystems; and ensuring that governance frameworks are agile, interoperable, and attuned to the Global South’s realities.
The BRICS New Delhi Declaration and related initiatives signal that an alternative is possible—one that treats AI not merely as a strategic asset but as a developmental lever requiring collective stewardship. The task for policymakers, researchers, and citizens is to convert this vision into concrete mechanisms: joint R&D, shared testing regimes, skills exchanges, and norms that balance innovation with responsibility.
At this crossroads, cooperation is not optional; it is the condition for realising AI’s promise while containing its perils. The choices made in the next few years will echo for decades, shaping not just economies and industries, but the very texture of democratic life and global equity.
