Why the UPI Model Fails for AI: A Critical Examination of India's Policy Approach
Within minutes of almost any policy discussion concerning digital technologies and artificial intelligence in India, one reference inevitably emerges: the Unified Payments Interface, commonly known as UPI. This state-backed, no-fee payment system has become more than just a success story—it has transformed into a default analogy, a governing metaphor, and increasingly, a policy instinct. The argument frequently presented suggests that if a state-supported payment framework could unlock massive scale and foster low-cost innovation, why not replicate that exact model for artificial intelligence? However, this line of reasoning is fundamentally flawed and risks stalling genuine progress in the AI domain.
The Flawed Analogy Between AI and Payment Systems
The proposed comparison typically unfolds along familiar lines. Artificial intelligence development demands significant computational infrastructure, most notably high-end graphics processing units (GPUs), which are currently dominated by companies like Nvidia. These resources are both scarce and largely unaffordable for many Indian startups and research laboratories. Consequently, capital is framed as the primary constraint. The suggested solution involves state intervention: the government would procure GPUs at scale, create a shared computational pool, and rent access at nominal costs. The underlying assumption is that removing capital as a barrier will naturally spur innovation, mirroring the UPI success narrative.
But artificial intelligence is not a payment system and cannot be governed through a compulsory licensing regime. AI represents a complex, evolving global supply chain that has been decades in the making. Unlike UPI, which emerged through a relatively linear process of state-led design followed by private sector adoption, AI has evolved through intersecting layers of code, data, labor, frameworks, and computational power—each advancing at different velocities. Capital alone did not determine outcomes, nor did scale or state backing exclusively drive progress.
The Global Reality of AI Development
Until recently, a prevailing belief suggested that training competitive AI models required access to tens of thousands of GPUs, effectively restricting serious development to a handful of capital-rich corporations. This perception influenced global policy thinking, including the sweeping semiconductor export controls imposed by the United States on China. These controls were premised on the notion that AI could be placed under a form of compulsory licensing through chip access. That assumption proved to be misplaced.
While sanctions increased costs and altered pathways, they did not confer durable control. Chinese firms responded by reconfiguring the supply chain—they sought sovereignty not merely over models, but over data, frameworks, and silicon itself. Frameworks like PyTorch remained central, even as efforts accelerated to reduce dependence on Nvidia hardware through adapters and alternative chips such as Huawei's Ascend series. The supply chain adapted because it was neither shallow nor centralized.
Structural Differences Between AI and UPI
This global dynamic holds crucial implications for India's AI policy debate. It demonstrates that even the most powerful state, acting in concert with allies, cannot place AI under a compulsory regime. The reason is structural: AI's supply chain is both deep and broad. Computational power represents just one input among many. Optimizing or subsidizing only that variable does not produce breakthroughs; it merely distorts incentives.
UPI, by contrast, succeeded precisely because the problem was narrow. Payments could be standardized, interoperability could be mandated, and participation could be licensed. The institutional architecture—spanning regulators, operating entities, and the banking system—made this centralized approach feasible.
The Risk of Creating Protected National Champions
India's policy history reveals a recurring tendency to identify national champions, channel subsidies toward them, and align regulatory and administrative resources to ensure their success. Over time, this creates a symbiotic relationship where boundaries between state and firm blur, even if formal separation remains intact. Such an approach may be defensible in sectors characterized by stable technologies and scale-driven economics. In artificial intelligence, it is far more problematic.
AI models succeed not because they are politically favored, but because they are genuinely useful. When utility surges and commercial success follows, protected national champions face an uncomfortable reality: they may be preferred domestically, but struggle to compete internationally—the arena where AI competitiveness is ultimately tested. Historically, the response has been predictable. Firms either petition the state for mandates, restrictions, or bans to sustain relevance, or they pivot away from frontier innovation into adjacent niches. The result is familiar: national champions become local heroes but globally irrelevant.
A More Nuanced Path Forward for India
None of this constitutes an argument for complete state withdrawal. Public investment in research, skills development, data infrastructure, and access remains essential. However, AI sovereignty cannot be manufactured through administrative tools or central allocations alone. It emerges from active participation in global supply chains, not insulation; from the diffusion of capability, not the concentration of control.
Computational availability represents the most visible input, but it is also the easiest to misprice, misallocate, and overemphasize. When access is centrally provisioned, incentives shift from problem selection to resource consumption, privileging entities adept at navigating bureaucratic committees over those with genuine technical expertise. In a field where progress often originates from small teams pursuing unfashionable ideas with limited resources, such an approach biases the entire system against the kind of exploratory work that truly moves artificial intelligence forward.
Artificial intelligence is fundamentally not UPI. Designing policy as though it were would represent an expensive misreading of both technologies. A more effective approach requires recognizing AI's unique characteristics as a global, decentralized, and rapidly evolving ecosystem that thrives on diversity, competition, and open participation rather than centralized control.
