Recently, Carnegie India hosted the Global Technology Summit Innovation Dialogue 2025 in New Delhi, focusing on AI’s societal impact and Global South collaboration, as a prelude to the AI Impact Summit 2026.

Need for AI Adoption in Developing Nations

• Rapid Adoption Pace: A UNDP flagship report highlights that AI is one of the fastest-adopted technologies ever, reaching 1.2 billion users in just three years.
• Skewed Global Reach: The Global South, representing 80% of the world’s population, but only ~15–20% of global compute capacity.
  • Although nearly 70% of users are in developing countries, overall access and intensity of use remain uneven across regions.
• Deep Digital Divide: While two-thirds of people in high-income countries use AI tools, adoption in low-income economies lingers at about 5%, underscoring a stark AI divide.
• Leapfrogging Opportunity for the Global South: AI enables rapid SDG acceleration by bypassing traditional bottlenecks, enhancing productivity and jobs, and transforming healthcare (local-language information), agriculture (data-driven advisories), and education (personalised, scalable learning).

AI Potential and Collective Leverage of the Global South

The Global South stands at a critical juncture where Artificial Intelligence acts as both a transformational force for development and a source of strategic leverage in global governance.

• The Significance of AI for Development: AI is not just a technological advancement; it is a developmental tool that can help the Global South overcome traditional barriers and achieve accelerated progress.
  • Transformational Force: AI has the potential to revolutionize key sectors—including healthcare, governance, finance, education, and security—by enhancing efficiency, speed, and decision-making.
  • Developmental Potential: For developing nations, AI offers unique opportunities to leapfrog legacy infrastructure and development challenges through the implementation of Digital Public Infrastructure (DPI) and local innovation.
  • Geopolitical Relevance: AI has emerged as a strategic asset, directly shaping global power hierarchies and determining techno-economic leadership.
• The Global South’s Collective Influence and Strategic Leverage: The region possesses significant, yet often under-leveraged, assets that must be channeled into the global AI discourse through cooperation and a unified voice.
  • Strategic Bargaining Power: The region’s leverage is derived from its massive, essential inputs for the global AI economy:
    • Vast Data and Scale: The sheer size of the population provides immense, diverse data sets—the ‘fuel’ for AI models—creating a critical strategic bargaining chip.
    • Critical Resources: The region holds crucial reserves of rare minerals, which are essential inputs for the hardware and infrastructure of the AI economy.
  • Accelerated Progress via Shared Learning (South-South Cooperation): The commonality of development challenges creates an ideal foundation for mutual growth and shared learning:
    • Shared Expertise: Experiences in digital adoption and legal infrastructure reforms (e.g., from Africa) offer valuable insights, while nations with established AI talent (e.g., India) provide crucial expertise for shared innovation.
  • Need for a Unified Voice: Initiatives like India’s efforts to convene policymakers, industry, and researchers are crucial for establishing a unified voice and a shared agenda to ensure the Global South’s interests are reflected in global AI norms.

Success Stories of South-South Cooperation in AI

• Democratizing Compute & Infrastructure:
  • Shared AI Compute Backbone (India): India’s initiative to deploy a shared Graphics Processing Units (GPUs) infrastructure with subsidized access for researchers and startups. This model serves as a template for other nations to democratize computation and build technological self-reliance.
  • Regional Compute Hubs: Efforts to co-finance regional compute hubs across Africa, ASEAN, and Latin America, pooling resources to make large-scale AI infrastructure affordable.
• Bridging Language & Cultural Gaps:
  • Digital India Bhashini: A national mission creating and releasing open-source multilingual models for all 22 scheduled Indian languages. 
    • Its models and platform are a Digital Public Good offered to other linguistically diverse nations for building Voice-First interfaces.
  • African Language AI Movements (Masakhane): Grassroots regional cooperation focused on developing datasets and machine translation tools to expand access to low-resource African languages, driving indigenous AI innovation.
• AI for Real-World Impact (Health & Agriculture):
  • AI for Tuberculosis Elimination (India): Integration of AI-driven Computer Automated Detection (CAD) software with portable X-ray machines for mass, rapid screening in remote villages. 
    • This model is critical for resource-constrained public health systems globally.
  • Precision Farming and Water Conservation: AI systems using satellite data to optimize farming, enabling farmers to significantly reduce water usage and precisely schedule irrigation, addressing a critical issue for agrarian economies in the Global South.
  • Life-Saving Case Study: The AI-enabled tool “Sakhi” delivers medically verified information in local languages over WhatsApp, demonstrating a clear path for closing information gaps that contribute to high maternal mortality rates.
• Multilateral Governance & Shared Strategy:
  • BRICS Cooperation: The BRICS AI Study Group and New Development Bank (NDB) invest in pooled AI applications across member states to facilitate shared technological advancement and resource pooling.
  • African Union Strategy: Working to establish a unified Continental AI Strategy to guide development, ensuring cohesive regional policy and amplifying the continent’s collective bargaining power.

Challenges of AI Adoption and Development in the Global South

• Structural and Resource Imbalances: This category details the unequal access to essential infrastructure and the ecological consequences.
  • Compute Inequality and Access: Access to powerful computing resources (compute capacity) is heavily concentrated in the Global North, severely limiting the ability of the Global South to leverage and innovate with frontier AI models. 
    • This disparity is the most significant bottleneck.
  • Cultural Irrelevance of Benchmarks: Global AI benchmarks and models often fail to align with the diverse cultural, linguistic, and socio-economic settings of developing nations, leading to unreliable and non-contextual systems.
  • Environmental and Resource Stress from Data Centres: The massive expansion of AI requires a proliferation of data centres with intensive land, water, and energy demands. 
    • Resource-scarce regions are increasingly targeted as hubs, aggravating water insecurity and ecological degradation.
  • Public Resistance: Widespread local protests (e.g., in Uruguay, Chile, Brazil, and internationally) reflect growing concern over unsustainable infrastructure expansion.
• Human and Ethical Costs: This focuses on the exploitation of human labor and the immediate risks to users and vulnerable populations.
  • Exploitative Labour Practices (Ghost Work):
    • Outsourced Labour: Tasks like data annotation and content moderation are outsourced to low-income countries at minimal wages, demonstrating exploitative labour practices to maximize corporate profits.
    • Psychological Trauma: Workers are routinely exposed to disturbing content (violence, sexual abuse) without adequate mental health safeguards, causing severe psychological distress.
    • Labour Rights Violations: The environment is marked by job insecurity, hazardous conditions, a lack of transparency, and reported instances of child labour.
  • High Real-World Safety Risks: Advances in AI, especially multilingual Large Language Models (LLMs), increase the stakes for reliable outputs. 
    • Incorrect guidance (e.g., in agriculture, healthcare) can have severe real-world consequences like crop failure, necessitating strong, context-specific safeguards.
  • Risk of Bias and Harm: The lack of robust regulation exposes users to risks such as algorithmic bias, data exposure, and harmful recommendations.
• Governance, Power, and Capacity Gaps: This addresses the political structure of AI development and the resulting knowledge and skill deficits.
  • Inequitable Global AI Governance: AI development remains overwhelmingly dominated by a few Big Tech corporations and advanced economies.
    • Marginalisation of Global South Voices: Development priorities of poorer nations are inadequately reflected in global AI norms and standards, leading to digital colonialism where resource extraction (natural and human) mirrors historical exploitative patterns.
  • AI Literacy Gap: A lack of basic knowledge and understanding about AI, particularly among women and youth, hinders effective adoption and democratized access, raising a critical need for expanded AI literacy programs.

India’s Pivotal Role and Strategic Actions for the Global South

India is uniquely positioned to lead the Global South in shaping a responsible, ethical, and inclusive global AI order, leveraging its unique strengths to serve as both a test bed and a bridge-builder for the developing world.

• India’s Unique Strengths and Strategic Role: India’s domestic characteristics and policy outreach offer a viable “middle way” for the Global South, balancing ambition with a moral imperative for social justice.
  • Unique Strengths and Global Test Bed: India’s massive scale, highly diverse multilingual environment, and robust policy outreach make it a unique “test bed for AI adoption” globally. 
    • Large-scale deployment in sectors like healthcare and agriculture can quickly prove scalable solutions for the rest of the developing world.
  • The “Middle Way” for Compute and Democratization: India can serve as a “middle way” by balancing the proprietary models of the US with open-source focus. 
    • Initiatives like the IndiaAI Mission (providing GPU access) and AI4Bharat (open models for 22 languages) embody the principle that “AI is for all,” democratizing resources for its neighbors.
  • Normative Leadership and Bridge-Builder: Hosting key forums like the AI Impact Summit 2026 positions India to provide normative leadership, shaping discourse around ethical, inclusive, and sustainable AI.
    • India acts as a bridge-builder, ensuring that technological ambition is balanced with an imperative to advance human dignity and social justice.
• Strategic Imperatives for Technological Sovereignty: To avoid being victims of skewed power balances, the Global South must focus on indigenous innovation through efficient, open alternatives and strengthening labor protections.
  • Develop Indigenous and Efficient Models (The SLM Strategy): The Global South must focus on developing indigenous models and more computationally efficient alternatives that are aligned with local interests.
    • Small Language Models (SLMs) are the core of this strategy and are significantly cheaper to train and deploy, require less energy, and are perfectly suited for on-device or local, offline functionality.
    • Strategic Benefit: This efficiency reduces dependence on expensive cloud infrastructure and proprietary services, directly addressing the compute inequality and leading to enhanced data privacy and faster, low-latency performance in low-connectivity environments.
  • Strengthen Labor Protections and Transparency: To address the exploitation inherent in the current model, the Global South must strengthen labour laws and enhance transparency across the entire AI supply chain, particularly in data labelling and content moderation.
  • Foster Ideological and Economic Cohesion: Key forums like the AI Impact Summit 2026 must be leveraged to take a firm leadership stance, foster ideological and economic cohesion among like-minded nations, and collectively further the interests of the Global South.
• Member of GPAI (Global Partnership on AI): India is a founding member and led the 2024–25 agenda on AI for sustainable development.

Key Domestic Constraints in India’s AI Leadership Ambitions

• High-End Skills Gap: Despite expanding AI literacy, India faces a shortage of advanced AI researchers and data scientists, slowing the development of indigenous foundational models and high-end global solutions.
• Data Governance and Trust Deficit: While India’s Digital Public Infrastructure (DPI) strengthens its leadership claim, wider Global South adoption depends on creating a globally trusted data governance framework, including strong privacy and security safeguards under the DPDP Act, 2023.

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Way Forward

Ensuring equitable, inclusive, and responsible AI requires a paradigm shift, led by the Global South’s collective action across four key pillars.

• Technological Sovereignty and Indigenous Capability: The path forward hinges on reducing reliance on proprietary models and developing solutions aligned with local contexts.
  • Tailored R&D and Model Development: Invest heavily in creating foundational AI models that understand local linguistic and cultural diversity. 
    • This includes prioritizing the development of indigenous models (e.g., Indic LLMs) and open-source ecosystems that are better aligned with local interests.
  • Prioritizing Efficiency and Alternatives: Focus on innovations that stress computational efficiency and open weights (e.g., DeepSeek). 
    • The development of Small Language Models (SLMs) is crucial, as they offer potential offline functionality and reduce dependence on massive cloud infrastructure, neutralizing compute concerns.
  • Voice-First Interface: Prioritize AI that enables citizens with low digital literacy to interact with services using their native spoken language, ensuring technology is truly accessible.
• Governance, Safeguards, and Regulatory Strengthening: The Global South must actively shape the rules of the AI economy to protect its citizens and resources.
  • Agile, Human-Centric Regulation: Implement robust AI safeguards (similar to social media regulation) to protect users from bias, data exposure, and harmful recommendations. 
    • Adopt a pragmatic, ‘pro-innovation’ stance focusing on ‘Do No Harm’ guidelines and ethical AI standards.
  • Protecting Digital Labor: Enforce robust labour laws and enhance transparency across the entire AI supply chain. 
    • Policy efforts must focus on mapping the AI value chain and implementing regulation for digital labor platforms to protect workers’ rights and improve compensation in data annotation and content moderation.
  • Environmental Safeguards: Enforce environmental safeguards across the AI supply chain to mitigate the resource strain caused by data centres.
  • Fiduciary Duty of AI Developers: The Global South should legally require AI developers to act in the best interest and safety of users, especially in high-risk areas like healthcare and finance.
  • Ethics-by-Design and Accountability: AI systems must be ethically built from the start, with clear accountability, traceability, and human oversight, particularly when used in vulnerable communities.
• Infrastructure and Access Democratization: The solutions must center on democratizing the access to high-end infrastructure and key digital public goods.
  • Democratizing Compute: Implement initiatives (like the IndiaAI Mission) to deploy large-scale, shared AI computing infrastructure (GPUs) with subsidized access for local startups and researchers.
  • Co-finance Regional Hubs: Foster South–South cooperation to co-finance regional computer hubs in Africa, ASEAN, and Latin America, and share digital public goods (like the India Stack) directly with partners.
  • Global Test Bed and Replication: Leverage India’s unique scale and policy as an “unmatched test bed for AI adoption,” ensuring validated solutions in sectors like agriculture, education, and healthcare can be easily replicated globally.
• Leadership and Capacity Building: Sustained change requires unified political action and a focus on expanding human capital.
  • Strategic Leadership and Convening Power: India must continue its convening role (e.g., hosting the AI Impact Summit 2026), bringing together policymakers, industry, and researchers to create unified Global South strategies, foster ideological and economic cohesion, and influence global AI governance frameworks.
  • AI Literacy: Prioritize expanding AI literacy programs for all demographics, especially women and youth. 
    • Programs must go beyond technical skills to teach users how to recognize persuasive design, read provenance cues (signals of AI-generated content), and verify critical advice.
  • Co-Design and Partnerships: International organizations are urged to move beyond consultation to genuine co-design, embedding Global South representation in advisory boards to ensure development priorities are reflected in global norms.
  • Extension of Strategic Autonomy: India’s push for equitable AI should be framed as part of its Strategic Autonomy, ensuring technological choice and preventing a US–China–dominated techno-bipolar order.
  • Preventing Digital Colonialism: India must ensure AI does not enable digital colonialism, where developing countries supply data and minerals but remain dependent on foreign proprietary technologies.

Conclusion

AI is a tool of immense promise, but its benefits must be shared equitably. By leveraging its unique strengths and acting as a pivotal force, India is charting a path for the Global South to move from technological dependency to empowered innovation. 

• This collaborative and inclusive approach ensures that technology ultimately serves humanity, fulfilling the ethos of “Vasudhaiva Kutumbakam” (The world is one family).

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