News Global AI is not UPI: Why going by the UPI model risks stalling progress on artificial intelligenceAI is not UPI: Why going by the UPI model risks stalling progress on artificial intelligence
Analysis challenges applying India's UPI success model to AI development, arguing that state-led compute provisioning and centralized control could stall innovation. Unlike UPI's standardized payment system, AI evolved through complex global supply chains involving multiple components. The piece cites failed US semiconductor controls on China as evidence that centralized approaches don't work, as Chinese firms adapted by reconfiguring supply chains. The authors warn against India's 'national champions' approach, suggesting AI sovereignty requires global participation rather than administrative control.
*this image is generated using AI for illustrative purposes only.
A growing debate in India's technology policy circles centers on whether the country should apply its successful UPI model to artificial intelligence development. However, this reasoning presents fundamental flaws that could potentially stall rather than accelerate AI progress.
The Flawed UPI-AI Analogy
The proposed analogy typically follows familiar arguments about AI development requiring significant compute infrastructure, particularly high-end graphics processing units dominated by Nvidia. With these resources being scarce and largely unaffordable for Indian startups and research labs, proponents argue for state intervention through large-scale GPU procurement, shared compute pools, and subsidized access.
| UPI Model Elements | AI Development Reality |
|---|---|
| Linear state-led design | Complex, evolving global supply chains |
| Standardized payments | Intersecting layers of code, data, frameworks |
| Compulsory licensing regime | Cannot be governed through licensing |
| Narrow, defined problem | Broad, multi-faceted challenges |
However, AI fundamentally differs from payment systems. Unlike UPI's linear progression from state design to private adoption, AI evolved through intersecting layers of code, data, labor, frameworks, and compute power, each progressing at different speeds.
Lessons from US-China Semiconductor Controls
Recent global developments demonstrate the limitations of centralized control approaches. The US imposed sweeping semiconductor export controls on China, premised on the belief that AI could be placed under compulsory licensing through chip access restrictions.
This assumption proved misplaced. While sanctions raised costs and altered pathways, they failed to confer durable control. Chinese firms responded by reconfiguring supply chains, seeking sovereignty over:
- AI models and algorithms
- Data collection and processing
- Development frameworks
- Silicon chip production 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 National Champions Problem
India's policy record shows a recurring pattern of identifying 'national champions,' channeling subsidies toward them, and aligning regulatory resources for their success. This approach creates symbiotic relationships where boundaries between state and firm blur.
| Traditional Sectors | AI Development |
|---|---|
| Stable technologies | Rapidly evolving landscape |
| Scale-driven economics | Innovation-driven success |
| Protected domestic markets | Global competition essential |
| Administrative tools effective | Technical merit determines success |
In AI, this approach proves problematic because models succeed based on utility rather than favoritism. When protected national champions struggle to compete internationally, the typical response involves petitioning for mandates, restrictions, or bans to sustain relevance.
The Compute Provisioning Risk
Centrally provisioned compute access creates problematic incentive shifts. When resources are allocated through administrative processes, the focus moves from problem selection to resource consumption, favoring entities skilled at navigating committees over those with technical expertise.
This bias works against the exploratory work that drives AI progress, which often comes from small teams pursuing unconventional ideas with limited resources.
A Different Path Forward
The analysis does not advocate for complete state withdrawal. Public investment remains essential in:
- Research infrastructure
- Skills development
- Data accessibility
- Educational programs
However, AI sovereignty cannot be manufactured through administrative tools or central allocations. It emerges from participation in global supply chains rather than insulation, and from capability diffusion rather than control concentration.
The authors conclude that designing AI policy as though it were UPI would represent an expensive misreading of both systems, potentially hindering rather than advancing India's artificial intelligence ambitions.
18
