India's AI Governance Ambitions Face Reality Check at Global Summit

India's ambitious attempt to position itself as a global AI rulemaker collided with geopolitical realities during last week's New Delhi AI Summit. While Indian officials proposed comprehensive international governance frameworks for artificial intelligence, delegates from the United States and representatives from major American tech companies largely dismissed the initiatives as premature or misaligned with current power dynamics. This muted reception highlights the persistent challenges for nations outside the US-China AI duopoly seeking to influence the trajectory of transformative technologies.

The summit's core tension centered on fundamentally different visions for AI governance. Indian policymakers advocated for binding international agreements addressing ethical AI development, algorithmic transparency, and equitable access – proposals modeled after the country's Digital Public Infrastructure approach. US State Department representatives countered that such frameworks would stifle innovation during a critical development phase, emphasizing voluntary corporate commitments and sector-specific guidelines instead. Microsoft and Google executives privately echoed this stance, arguing that India's proposals lacked technical feasibility assessments.

Behind the diplomatic friction lies India's contradictory position in the AI ecosystem. Despite boasting world-class technical talent pools and ambitious national AI strategies, India remains overwhelmingly dependent on US-developed foundation models and Chinese hardware infrastructure. Less than 15% of AI research papers from Indian institutions make the global top-cited percentile, while domestic AI startups receive just 2.3% of global AI venture funding according to Stanford's 2025 AI Index. This dependency creates inherent limitations when advocating for governance models that would require significant concessions from technological leaders.

Community sentiment analysis reveals divergent perspectives on India's role. Some technologists argue that India's democratic credentials and massive user base lend unique legitimacy to its governance proposals. "Having a major democratic economy articulate comprehensive AI principles counters China's alternative governance models," notes Bharath Reddy, researcher at Bengaluru's Center for AI Policy. However, others contend India must first demonstrate domestic AI success before claiming global leadership. "You can't govern what you don't build," tweeted former Google executive Rajan Anandan, pointing to India's absence from the LLM development frontier.

Counter-arguments highlight legitimate concerns driving India's governance push. Smaller nations fear being relegated to passive consumers in an AI ecosystem controlled by US corporations and Chinese manufacturers. India's proposal for mandatory algorithm audits responds to documented cases of cultural bias in Western-developed models processing South Asian languages. Additionally, its call for "sovereign AI clouds" addresses genuine national security vulnerabilities exposed during recent cross-border data disputes.

The summit's outcome signals a broader trend: emerging economies face structural barriers in influencing foundational technology governance. While India successfully established itself as a digital payments rulemaker through UPI, replicating this in AI requires technological leverage that remains concentrated in Silicon Valley and Shenzhen. Until New Delhi cultivates either indispensable AI infrastructure or unignorable market scale, its governance ambitions will continue encountering polite dismissal from established powers – regardless of the substantive merits of its proposals.

Looking ahead, India appears to be recalibrating its strategy. Rather than pursuing comprehensive global frameworks, diplomatic sources indicate new focus on bilateral agreements and regional coalitions. The recently announced India-France AI partnership focusing on climate modeling and healthcare applications suggests a pragmatic pivot toward building demonstrable use cases rather than abstract governance principles. This incremental approach may ultimately prove more effective than attempting to rewrite global rules from a position of technological dependency.