Artificial Intelligence as India's Next Digital Public Infrastructure
Syllabus Mapping: GS-2/3: E-Governance, Digital Governance, Science & Technology—Developments and their Applications, Artificial Intelligence and Inclusive Development.
India’s Digital Public Infrastructure (DPI), built around Aadhaar, UPI, DigiLocker and ONDC, has transformed public service delivery through open, interoperable and population-scale digital platforms. As AI becomes a foundational technology, the next frontier lies in developing Artificial Intelligence as Public Infrastructure (AI-DPI) that democratises access to compute, data and AI models while ensuring inclusion, trust and innovation.
Why is there a Need to Shift from Traditional DPI to AI-based DPI?
- From Digital Access to Intelligent Services: Traditional DPI enables digital transactions, while AI-DPI enables intelligent, predictive and personalised public service delivery.
- Rising Demand for Population-Scale Intelligence: The growing volume of digital transactions and public data requires AI-driven systems for real-time analysis and evidence-based policymaking. Eg: AI-assisted fraud detection in digital payments and welfare schemes.
- Democratising Access to Intelligence: Just as DPI democratised identity and digital payments, AI infrastructure can democratise access to advanced computing, models and intelligent services for citizens, startups and public institutions. Eg: IndiaAI envisions AI infrastructure as a shared national resource.
- Enhancing State Capacity: AI can augment governance through automation, decision support and predictive analytics, improving efficiency and responsiveness.
- Building Digital Sovereignty: Developing indigenous AI infrastructure reduces dependence on foreign compute, cloud platforms and proprietary foundation models.
- Strengthening Economic Competitiveness: AI is emerging as a general-purpose technology capable of driving productivity, innovation and global competitiveness across sectors.
- Next-Generation DPI: As digital governance matures, embedding AI into DPI can transform static digital platforms into adaptive, interoperable and citizen-centric public infrastructure.
Challenges in Building AI as India’s Next Digital Public Infrastructure
- AI Colonialism: Excessive dependence on foreign compute infrastructure, proprietary foundation models and hyperscale cloud providers may constrain India’s technological autonomy, policy flexibility and long-term AI competitiveness.
- Extractive AI Economy: India risks remaining a supplier of raw data, AI talent and annotation services while importing high-value AI models and applications, limiting domestic value creation.
- Compute Monopoly: High GPU costs and the concentration of advanced computing infrastructure among a few global firms can restrict AI innovation, competition and equitable access.
- Closed AI Ecosystems: Dependence on proprietary AI models and APIs may create vendor lock-in, reduce interoperability and constrain the development of an open and competitive domestic AI ecosystem.
- Regulatory Uncertainty: The absence of a comprehensive AI governance framework creates uncertainty regarding liability, accountability and safety standards.
- Digital Divide & AI Inclusion: Unequal access to digital infrastructure, AI literacy and multilingual AI tools may exclude rural communities, MSMEs and vulnerable groups from AI benefits.
- Energy & Environmental Costs: Large AI models require energy-intensive data centres, increasing electricity demand, cooling requirements and carbon emissions.
- Human Capital Deficit: Shortage of advanced AI researchers, chip designers and AI engineers constrains India’s ability to build globally competitive AI ecosystems.
Towards Building AI as India’s Next Digital Public Infrastructure
- AI Stack: Develop an India AI Stack comprising compute infrastructure, trusted datasets, foundation models, interoperable AI gateways and public applications to create an open, scalable and population-scale AI ecosystem
- Compute as Infrastructure: Treat AI compute as a strategic public utility by expanding affordable GPU infrastructure and ensuring equitable access through public investment and competitive markets. Eg: IndiaAI Mission aims to build a national AI compute ecosystem.
- Open Digital Public Goods: Promote government-funded open-weight foundation models and open standards to accelerate innovation, interoperability and reduce dependence on proprietary AI ecosystems.
- National AI Gateway: Develop a UPI-like interoperable AI gateway that enables secure, standardised and seamless access to multiple AI models for governments, businesses and citizens.
- AI Data Commons: Create anonymised, consent-based and interoperable public datasets under robust privacy safeguards to support responsible AI innovation and public service delivery.
- Indic AI Ecosystem: Build multilingual foundation models leveraging India’s linguistic diversity to ensure inclusive AI adoption across governance, education, healthcare and justice. Eg: BHASHINI provides a strong foundation for Indian language AI.
- AI Credits: Provide subsidised AI compute credits for startups, MSMEs, universities and research institutions to democratise innovation and lower entry barriers.
- Responsible AI Governance: Establish a comprehensive AI governance framework with clear standards for transparency, accountability, safety and human oversight to build public trust. Eg: IndiaAI Safety Institute.
- AI Talent & Research Ecosystem: Strengthen AI research, semiconductor design, interdisciplinary education and public-private partnerships to build indigenous AI capabilities. Eg: Dedicated Centres of Excellence for AI.
By treating AI as a public digital utility rather than a proprietary technology, India can harness its transformative potential to strengthen governance, accelerate innovation and realise the vision of Viksit Bharat@2047.
PRELIMS BOOSTERS
1 . Chandrayaan-5 (LUPEX – Lunar Polar Exploration Mission)
- ISRO and the Japan Aerospace Exploration Agency (JAXA) reviewed preparations for Chandrayaan-5 (LUPEX), India’s first joint lunar mission, targeted for launch in 2028.
- The mission will explore the Moon’s south polar region to study water ice and subsurface volatiles.
- ISRO is developing the lander, while JAXA is developing the rover. The mission is proposed to be launched aboard Japan’s H3 Launch Vehicle.
- The rover will carry seven scientific instruments to study lunar water ice, subsurface volatiles, lunar soil (regolith), geology and topography.
- The mission is designed for a long-duration surface operation (~100 days), significantly longer than Chandrayaan-3.
- Payload participation under discussion with NASA and the European Space Agency (ESA) highlighting potential for global collaboration in lunar exploration.
- The mission builds upon the success of Chandrayaan-3 (2023), which made India the first country to achieve a soft landing near the Moon’s south polar region.
- Chandrayaan Programme:
- Chandrayaan-1 (2008): First Indian lunar mission; confirmed the presence of water/hydroxyl molecules on the Moon.
- Chandrayaan-2 (2019): Orbiter remains operational and continues scientific observations.
- Chandrayaan-3 (2023): Successful soft landing and rover operations near the lunar south pole.
- Chandrayaan-4: Chandrayaan-4 is India’s proposed lunar sample-return mission.
- The lunar south pole is of immense scientific interest because it contains permanently shadowed craters (PSRs) that may preserve water ice, an important resource for future human habitation and deep-space missions.
2. Cloudbursts in India
- According to the India Meteorological Department (IMD), a cloudburst is an event where 100 mm or more rainfall occurs within one hour over a localized area of about 20–30 sq. km, often triggering flash floods and landslides.
- Cloudbursts are most common in the Himalayan region (Jammu & Kashmir, Ladakh, Himachal Pradesh, Uttarakhand, Sikkim) and parts of the Western Ghats, where orographic lifting forces moisture-laden winds to rise rapidly.
- Formation involves strong convection, rapid condensation into cumulonimbus clouds, intense updrafts, and sudden collapse of water droplets, resulting in extremely intense rainfall over a small area.
- Seasonal Pattern: Most occur between June & August under the southwest monsoon.
- Cloudbursts are highly localized and short-lived, making them difficult to forecast using conventional weather models. Mountainous terrain also creates radar blind spots and limits the coverage of Automatic Weather Stations (AWSs).
- Cloudburst ≠ Heavy Rainfall: Every heavy rainfall event is not a cloudburst. The IMD definition is based on rainfall intensity and spatial extent, not merely the damage caused.
3. NavIC (Navigation with Indian Constellation)
- The Government informed Parliament that NavIC is currently operating in a multi-constellation mode, as the existing constellation does not have the minimum number of operational satellites required for standalone Positioning, Navigation and Timing (PNT) services.
- ISRO is developing NVS-03 and additional satellites to restore full independent capability.
- NavIC (Navigation with Indian Constellation), earlier known as the Indian Regional Navigation Satellite System (IRNSS), is India’s indigenous regional satellite navigation system, developed by ISRO.
- It is designed to provide accurate Positioning, Navigation and Timing (PNT) services over India and up to 1,500 km beyond its borders, with wider coverage extending into the Indian Ocean Region.
- NavIC constellation was originally planned with 7 satellites:
- 3 Geostationary (GEO)
- 4 Geosynchronous (GSO) satellites placed in inclined geosynchronous orbits.
- It provides two services:
- Standard Positioning Service (SPS) – Open for civilian users.
- Restricted Service (RS) – Encrypted service for authorised users such as defence and strategic agencies.
- It operates primarily in the L5 and S-band, while the second-generation NVS satellites also support the L1 band, improving compatibility with smartphones and commercial GNSS receivers.
- NVS-01 (2023) is the first second-generation NavIC satellite, carrying an indigenous Rubidium Atomic Clock developed by ISR
- Global Navigation Satellite Systems (GNSS)
- GPS – United States
- GLONASS – Russia
- Galileo – European Union
- BeiDou (BDS) – China
- QZSS – Japan (Regional augmentation system)
