[Answered] Examine the concept of ‘deep-tech democracy’ in India. Critically analyze how shared compute and open data can ensure inclusive Artificial Intelligence (AI) for all citizens.

Introduction

AI’s global concentration—where 90% of advanced compute lies in a few nations (OECD 2023)—risks widening digital inequality. India’s emerging “deep-tech democracy” seeks to democratise compute, data, and talent, enabling inclusive, citizen-centric AI.

Understanding ‘Deep-Tech Democracy’ in India

1. ‘Deep-tech democracy’ refers to India’s model of state-led, public-good-oriented technological development that treats AI as a shared societal resource rather than proprietary capital.
2. Through the IndiaAI Mission (2024), India aims to decentralise access to computation, datasets, and skilling so that innovation is not confined to elite institutions or global corporations.
3. It is anchored in the Samaj–Sarkar–Bazaar framework, integrating society, government, and markets to ensure ethical, accountable, and inclusive technological progress.

Role of Shared Compute in Democratizing AI

1. Reducing the Compute Divide: India’s deployment of 38,000+ GPUs under the national AI compute grid provides affordable high-performance compute to start-ups, students, and researchers. This contrasts sharply with global monopolies where a few firms—OpenAI, Google, Amazon—control frontier compute, restricting innovation in the Global South.
2. Enabling Grassroots Innovation: The compute grid allows: AI-based crop advisory models for small farmers. Local-language applications for governance and citizen services. Affordable R&D for deep-tech start-ups such as in healthcare diagnostics, climate modelling, and precision agriculture. This mirrors the success of DPI systems such as UPI, where shared infrastructure led to innovation at scale.
3. AI as a Public Utility: By socialising compute costs, India reduces entry barriers. Start-ups no longer require millions of dollars for GPU access, promoting equitable participation rather than algorithmic dependency on global tech giants.

Critical Perspective

While transformative, challenges remain:

1. Public compute infrastructure must avoid bureaucratic bottlenecks
2. Ensuring fair access across states and institutions is essential
3. Power shortages and cloud dependence could create operational vulnerabilities

Open Data as the Second Pillar of Inclusion

1. AI Kosh and Local Contextual Datasets: Over 360 curated datasets across agriculture, health, climate, and governance are being made available through AI Kosh. This tackles a major gap identified by UNESCO’s 2023 AI Readiness Report—the Global South’s dependence on Western datasets that fail to represent local realities.
2. Linguistic Inclusion through Bhashini: Digital India Bhashini, backed by Project Vaani’s 150,000 hours of speech data, enables AI systems in 22 Indian languages—critical in a country where only 11% are English proficient.
3. Governance Use Cases: Open datasets enable AI applications in: Precision agriculture (e.g., crop disease prediction), public health surveillance (e.g., TB and maternal health analytics), urban mobility and disaster forecasting. This strengthens evidence-driven policymaking, fulfilling NITI Aayog’s vision of “AI for All” (2018).

Critical Concerns

1. Ensuring privacy-by-design is essential to avoid data misuse
2. Need strong data anonymisation standards under DPDP Act
3. Avoiding dataset centralisation that could marginalise smaller states

Conclusion

As Amartya Sen argues in Development as Freedom, true progress expands people’s capabilities. India’s deep-tech democracy advances this ideal, ensuring AI becomes an empowering public good rather than an exclusionary privilege.