India’s AI Middle Path Offers Lessons for Australia and New Zealand

2026-03-24 · Source: Tech Policy Press · Field: Government & Public Sector — Public Policy & Governance, Digital Government & E-Government, Regulatory & Compliance · Depth: Intermediate, medium

Summary

India's "AI middle path" offers a strategic framework for countries like Australia and New Zealand to develop sovereign AI capabilities without attempting to outspend major global powers. This approach focuses on treating key AI capabilities as public infrastructure, grounding governance in local data rules, and centering affected communities' voices. The strategy aims for sectoral sovereignty, identifying critical areas where domestic or regional control over data and models is non-negotiable, particularly in public services like immigration and biosecurity. It also advocates for treating AI as a public good, similar to India's Aadhaar and UPI systems, by establishing core capabilities like curated datasets and secure environments as utilities. Finally, it emphasizes governance rooted in data sovereignty and inclusion, particularly for Indigenous communities, to prevent data colonialism and ensure local contexts are reflected in AI systems.

Key takeaway

For Directors of AI/ML in government or critical infrastructure, your teams should evaluate current dependencies on foreign AI systems, especially in sensitive public services. Prioritize investing in domestic or regionally controlled AI infrastructure and datasets for critical sectors to mitigate data sovereignty and accountability risks. By treating core AI components as public goods and ensuring local community inclusion, you can build national advantage and attract responsible innovation that aligns with local standards.

Key insights

Middle powers can achieve AI sovereignty by treating key capabilities as public infrastructure and prioritizing local data governance.

Principles

• Prioritize sectoral AI sovereignty in critical domains.
• Treat core AI capabilities as public goods.
• Ground AI governance in data sovereignty and inclusion.

Method

Identify critical sectors for domestic AI control, establish core AI components as public utilities, and implement governance frameworks that ensure local data sovereignty and community co-design.

In practice

• Identify critical sectors like immigration for domestic AI control.
• Fund shared AI components as digital public goods.
• Integrate Indigenous data sovereignty into AI ethics frameworks.

Topics

• AI Geopolitics
• AI Governance
• Data Sovereignty
• Digital Public Infrastructure
• Indigenous Data Sovereignty

Best for: Executive, Policy Maker, AI Ethicist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Policy Press.