Reforms 3.0 — Towards the Bharat Rate of Growth

UPSC Syllabus: Gs Paper 3- Indian economy and Infrastructure

Introduction

For nearly 45 years after Independence, India remained stuck at the 3% “Hindu rate of growth” until the 1991 economic reforms transformed its economic trajectory. Today, Artificial Intelligence (AI) presents a similar opportunity to drive the next phase of structural reforms. With its strong digital public infrastructure and growing technological capabilities, India has the potential to use AI to accelerate innovation, productivity and achieve a sustained “Bharat rate of growth” of 8% and beyond.

Why AI is India’s Next Transformational Reform

1. Lessons from the 1991 Reforms: The balance-of-payments crisis forced India to liberalise its economy. The reforms sharply increased GDP growth and showed that major reforms can produce long-term economic gains.
2. AI as the Next Growth Multiplier: AI offers the same transformational potential that economic liberalisation provided in 1991. Investing in AI is becoming a necessity rather than a choice.
3. India Has Proven Its Ability to Leapfrog: India has repeatedly skipped older stages of development by adopting digital technologies at a large scale. This experience provides confidence for the next AI-led transformation.
4. Aadhaar Built a Strong Digital Foundation: Aadhaar enrolled 1.38 billion people, creating the world’s largest biometric identity system and enabling nationwide digital services.
5. UPI Created a Global Digital Payment Platform: UPI processes around 250 billion annual transactions worth $3.4 trillion and handles nearly 50% of the world’s real-time digital payments, showing India’s ability to build digital systems at global scale.
6. Jio Expanded Affordable Digital Access: Reliance Jio added 100 million subscribers within five months of its 2016 launch and made mobile data nearly free. This rapidly expanded internet access across the country.
7. Digital Success Can Be Extended to AI: The achievements of Aadhaar, UPI and Jio show that India can also build an AI ecosystem capable of serving people at a national scale.

Why India Needs to Invest in AI

1. Low Spending on Research and Development: India spends only 0.65% of GDP on R&D, far below China (2.4%), the United States (3.5%), South Korea (4.9%) and Israel (5.4%). This limits scientific research and technological progress.
2. AI Investment Is Financially Affordable: Providing AI tokens to the top 100 universities and national R&D institutions along with 5,000 high schools would cost about $2 billion annually, or only 0.06% of GDP.
3. Current Subsidies Are Much Larger: India spends nearly $49 billion every year on subsidies for calories, chemicals and carbon. The proposed AI investment is only a small share of this expenditure.
4. Higher Returns with Limited Public Spending: The AI investment would be about one-fourteenth of the food subsidy, one-tenth of the fertiliser subsidy, and lower than the compensation paid for LPG under-recoveries in one quarter. Such spending can generate long-term economic and knowledge gains.
5. Investment Needs New Priorities, Not More Money: India does not need additional financial resources for AI. It needs to change spending priorities and reallocate existing resources towards future productivity.
6. AI Supports Scientists and Students: Wider access to AI can strengthen research, innovation and learning by making AI a regular knowledge partner for researchers and students.
7. AI Can Produce Long-Term Economic Benefits: Unlike many welfare expenditures, investment in AI has the potential to generate continuous and compounding returns through higher productivity, innovation and economic growth.

Challenges and Strategic Requirements

1. Dependence on Foreign AI Infrastructure: India mainly consumes AI through foreign Application Programming Interfaces (APIs) instead of hosting large language models within the country. This limits technological sovereignty.
2. Need for Sovereign AI Infrastructure: Although Sarvam has shown that frontier AI models can be trained in India, the country still needs the capability to host and operate such models at a national scale.
3. Balancing Sovereignty with Open Innovation: India must reduce dependence on a single AI ecosystem while benefiting from open-source models and domestic AI capabilities.
4. Operational Complexity of AI Infrastructure: Hosting large language models requires expertise in high availability, low latency, efficient computing, data residency, prompt injection defence and audit trails. Building this capability is a major technical requirement.
5. Vendor Lock-in Risks: NVIDIA controls more than 80% of AI training hardware, creating financial and strategic dependence on a single company.
6. High Cost of AI Compute Infrastructure: Building national AI infrastructure only with NVIDIA hardware is financially difficult. India needs a more affordable computing ecosystem.
7. Need for Cost-Effective and Resilient Compute Capacity: India requires a diversified AI hardware ecosystem that supports lower costs, reliable operations and long-term technological resilience

Way Forward

1. Launch a National AI Token Policy: India should announce a National AI Token Policy and implement it over the next 24 months to expand AI access in a phased manner.
2. Provide Free AI Tokens for Research and Education: The first step should provide free AI tokens to the top 20 IITs, the IISc, the top 100 universities and national R&D institutions, and 5,000 high schools so that AI becomes a regular tool for learning and research.
3. Build Public-Private Partnerships: India should partner with AWS, Google and Microsoft to create a multi-vendor sovereign compute framework. In return, it can offer data centre land, power support and data sovereignty assurances for free inference capacity.
4. Use Smarter Financing Models: AI access can be financed without reducing existing welfare benefits by freezing subsidy growth for one year. If needed, paid enterprise services can also cross-subsidise free access for schools and research institutions.
5. Create the Right Market Conditions: India should follow the approach used for affordable mobile data. Instead of directly funding AI services, it should create supportive regulations so that competition reduces AI costs, just as data prices fell from about $3 per GB to $0.10 per GB.
6. Expand AI Access in Phases: After the initial rollout, India should open an API sandbox for 500 startups, extend AI access to 100 universities, and launch an AI literacy programme in 500 high schools across 10 States.
7. Develop National AI Benchmarks: The programme should publish India’s first sovereign Indic AI model benchmarksto measure progress and strengthen domestic AI capability.
8. Scale AI Across Priority Sectors: Fine-tuned AI models should be deployed in healthcare, agriculture, judiciary and education. The programme should then expand to 5,000 high schools and support all 22 Indian languages.
9. Strengthen India’s Global AI Position: As AI adoption expands, India can become one of the top five countries in AI token consumption, build 10,000+ AI-native startups, and make India-trained AI models competitive on international benchmarks.

Conclusion

Artificial Intelligence can become India’s next major structural reform after the 1991 economic liberalisation. By strengthening sovereign AI infrastructure, expanding affordable AI access, promoting open models and diversifying compute hardware, India can boost research, innovation and productivity. With decisive leadership and timely implementation, AI can drive the Bharat rate of growth and establish India as a global AI leader.

Question for practice:

Examine how Artificial Intelligence (AI) can serve as India’s next major structural reform and accelerate the transition towards the “Bharat rate of growth.”

Source: The Hindu