Artificial intelligence is no longer a neutral technology story. It has become a core arena of geoeconomic competition, where rules, standards, and compute capacity shape power as decisively as markets or military strength. In the emerging global order, AI governance is not just about ethics—it is about who sets the terms of growth, security, and technological access.
Just as oil defined the 20th century, algorithms, chips, and data are defining the 21st.
From Innovation to Strategic Infrastructure
AI has moved from laboratories into national infrastructure. It now underpins finance, defence planning, logistics, healthcare, and governance. As a result, AI governance has become inseparable from industrial policy and national security.
Three inputs determine AI power: data, talent, and compute. Of these, compute—advanced semiconductors, cloud infrastructure, and energy—has emerged as the most potent chokepoint. This has turned export controls and technology access into tools of geoeconomic statecraft.
The US–China Chip War: Governing Through Denial
The clearest manifestation of AI geoeconomics is the escalating US–China contest over advanced chips and compute access. Export controls on high-end semiconductors, restrictions on cloud access, and outbound investment screening are not simply security measures; they are attempts to shape the future distribution of AI capabilities.
This contest accelerates fragmentation. China is pushed toward state-led substitution and alternative AI ecosystems, while US-led systems tighten standards and controls. Governance, increasingly, is exercised through who is allowed to compute.
The Global South and the Risk of Data Colonialism
For much of the Global South, the AI debate raises a deeper concern: data colonialism. Developing economies generate massive volumes of data, yet the value is captured elsewhere—by those who own platforms, models, and compute.
If AI governance evolves into exclusive clubs defined by chip access and regulatory capacity, developing countries risk becoming permanent data suppliers rather than AI producers. This mirrors earlier patterns of commodity dependence, now replayed in digital form.
Inclusive governance must therefore address access to compute, capacity-building, and equitable data arrangements—not just safety principles.
India’s Strategic Moment
India sits at a critical junction. While it lacks dominance in advanced semiconductor fabrication, it possesses scale in data, depth in software talent, and credibility in democratic governance.
India’s opportunity lies in crafting pragmatic, innovation-friendly AI rules aligned with development priorities—healthcare, agriculture, education, and public services—while resisting both regulatory paralysis and technological anarchy. This positions India as a bridge power in AI governance.
The Over-Regulation Trap: When Safety Becomes a Barrier
A crucial but underappreciated risk in AI governance is over-regulation as a form of geoeconomic self-harm. Excessively rigid frameworks—high compliance costs, unclear liability regimes, and pre-emptive bans—can entrench large incumbents while suffocating start-ups, researchers, and late-comers.
From a geoeconomic perspective, this matters because regulation itself shapes market structure. Heavy-handed rules tend to favour firms with scale, capital, and legal capacity—mostly located in advanced economies. Developing countries risk importing regulatory models they lack the institutional capacity to implement, effectively outsourcing innovation to foreign platforms.
There is also a strategic risk. If democratic systems over-regulate while authoritarian systems move faster, governance asymmetry could translate into capability asymmetry, undermining both competitiveness and security.
AI governance must therefore be adaptive, tiered, and outcome-focused, rather than precautionary to the point of paralysis. The objective is risk management, not risk elimination—an impossible standard for a general-purpose technology.
Geoeconomics and AI governance are now inseparable. The contest is not only over who builds the best models, but over who controls compute, standards, and access—and who avoids regulating themselves out of relevance.
For India and other middle powers, the goal is not technological dominance, but strategic autonomy: the ability to innovate, govern responsibly, and remain connected across competing AI ecosystems.
In the age of algorithms, power will belong to those who balance security with scale, ethics with efficiency, and governance with growth.
