Why Sovereign AI Demands More Than Just Data Centers and Chips
Experts debate regulation, competitiveness, and democratic risk in public-sector AI deployment.
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On Day 2 of the India AI Impact Summit 2026, currently taking place in New Delhi, a session titled “AI and the State: Policy and Practice in Government” brought together experts to explore a growing dilemma in digital governance. They discussed the implications of governments acting as both regulators of artificial intelligence and significant users of the technology.
Gaia Marcus, Director, Ada Lovelace Institute, who has spent six years studying public-sector AI at the Ada Lovelace Institute, described government deployment of AI as a form of “dogfooding” its own regulatory system. When the state uses AI tools in welfare, policing, or immigration, it effectively stress-tests the guardrails it has put in place. Unlike consumers in private markets, citizens often cannot opt out of state services. That higher standard, Marcus argued, exposes regulatory gaps—whether in liability, governance, or oversight of foundation models—that may be manageable in the private sector but are amplified in public administration.
The tension sharpens further in countries that host frontier AI companies. Jaan Tallinn, the Estonian computer programmer and investor, noted that governments nurturing domestic AI champions face competing imperatives. On one hand, they seek competitiveness and economic growth; on the other, they are tasked with imposing export controls or safety constraints that may run counter to corporate interests. Decisions around semiconductor access—where state security concerns collide with pressure from industry leaders such as Nvidia—illustrate the friction. “They can’t seem to be trying to do both,” Tallinn suggested, capturing the balancing act between regulation and national advantage.
For data scientist Rumman Chowdhury, who has worked with governments on AI implementation, the problem is less about ambition and more about infrastructure. Sovereign AI initiatives, particularly in countries investing heavily in data centers and homegrown models, often overlook spending on privacy, security, and responsible use.
Generative AI systems, she emphasized, require new forms of evaluation distinct from traditional machine learning. “We’ve just scratched the surface,” she said, calling for public investment in test-and-evaluation methodologies that could set norms for both state and industry deployments.
From a civil-society perspective, Stephanie Ifayemi of the Partnership on AI argued that the tension depends on whether governments can demonstrate best practices. Deploying AI agents in public services demands robust monitoring systems, real-time failure detection, and clear chains of accountability. Without strong documentation and traceability, the promise of trustworthy AI in government remains aspirational.
The democratic stakes, however, may be highest. Alondra Nelson warned that in an era of low public trust, algorithmic failures in welfare allocation or public benefits could erode social cohesion. Citizens, she argued, should expect more transparency from the state than from corporations—including a right to understand how AI systems act on their behalf.
As hype cycles accelerate, Marcus added, even calls for cautious evaluation are sometimes dismissed as obstructionist. Yet the panel’s consensus was clear: if governments cannot get AI governance right within their own systems, the broader regulatory project risks faltering.
