Content

1. AI Diffusion, Not Chip Dominance: India’s Real AI Advantage
2. End the exploitation

 AI Diffusion, Not Chip Dominance: India’s Real AI Advantage

 Why is it in News?

• A widely cited op-ed by Samir Saran, President of the Observer Research Foundation (ORF), argues that India’s real AI opportunity lies beyond chips and data centres, at a moment when:
  • India is scaling IndiaAI Mission (₹10,300+ crore),
  • Global AI geopolitics is fragmenting into Compute-rich vs Compute-poor blocs,
  • Policymakers are debating sovereignty, regulation, and economic capture in AI.

Relevance

GS III – Science & Technology / Economy

• AI as a General Purpose Technology (GPT).
• Industrial policy vs innovation ecosystems.
• Digital Public Infrastructure as growth multiplier.

GS II – Governance

• Role of state in technology diffusion.
• Regulatory capacity in emerging technologies.

Practice Questions

• “Artificial Intelligence as a General Purpose Technology rewards diffusion more than invention.”Examine this statement in the context of India’s AI strategy. (250 words)

Core Thesis of the Article

• Compute (chips, data centres) is necessary but not sufficient.
• India’s comparative advantage lies in AI diffusion, applications, and governance, not in winning the chip arms race against the US or China.

Three Distinct AI Phases Identified 

1. Compute Era

• Dominated by:
  • Advanced semiconductors (≤5 nm chips),
  • Hyperscale data centres,
  • Capital-intensive infrastructure.
• Reality Check for India:
  • Global AI compute market dominated by US firms (NVIDIA, AMD, hyperscalers).
  • Cutting-edge fabs require $10–20 billion per plant and long gestation.
  • India currently imports >85% of high-end chips.

Inference: Competing head-on here offers low returns, high dependency risks.

2. Diffusion Era (India’s Sweet Spot)

• Focus shifts from who builds models to who deploys them at scale.
• Involves:
  • AI adoption across health, agriculture, logistics, MSMEs, governance.
  • Integration with existing digital public infrastructure (DPI).

India’s Structural Advantages:

• Population-scale platforms:
  • Aadhaar (1.3+ bn),
  • UPI (≈12 bn transactions/month in 2024–25),
  • CoWIN, DigiLocker, ABHA.
• Cost-efficient innovation:
  • Lowest marginal cost of digital delivery globally.
• Talent pool:
  • ~1.5 million engineering graduates annually.
• Precedent:
  • India led global diffusion of digital payments without owning core hardware.

3. Value Creation Era

• Economic value accrues not to model builders alone, but to:
  • Domain-specific AI solutions,
  • Workflow integration,
  • Localised, trusted AI systems.
• Example logic:
  • LLM ≠ value,
  • LLM + sectoral data + regulation-aware deployment = value.

Key Warnings in the Article

• Mistaking LLMs for the entire AI stack:
  • Models are commodities over time.
  • Differentiation lies in use-cases, data pipelines, and institutional embedding.
• Over-centralised AI policy:
  • Risk of stifling innovation if regulation precedes diffusion.
• Copy-paste Western regulation:
  • EU-style heavy ex-ante AI regulation may be misaligned with India’s developmental needs.

Policy Prescriptions Suggested (Implicit & Explicit)

1. Strategic Compute, not Maximal Compute

• Secure baseline sovereign compute for:
  • Research,
  • Critical public services,
  • Strategic sectors.
• Avoid prestige-driven chip nationalism.

2. State as Market-Maker

• Government to:
  • Anchor demand via public procurement,
  • Enable sandboxes for AI deployment in welfare, justice, climate, urban governance.
• Historical parallel:
  • UPI succeeded because state created rails, private sector built innovation.

3. Regulate Outcomes, not Innovation

• Focus on:
  • Accountability,
  • Bias,
  • Safety in high-risk use cases.
• Avoid regulating models in abstraction.

Critical Evaluation

Strengths

• Realistic assessment of India’s constraints.
• Shifts debate from hardware fetishism to developmental outcomes.
• Anchored in India’s proven DPI success model.

Limitations

• Underplays long-term strategic risks of compute dependency.
• Requires high-quality state capacity to avoid diffusion without accountability.

Conclusion

• India’s AI race is not about owning the fastest chips, but about deploying intelligence at population scale.
• If the 20th century rewarded those who controlled factories, the 21st will reward those who control platforms, workflows, and trust.
• The article reframes AI from a geopolitical arms race into a governance and development challenge—where India holds asymmetric advantage.

End the exploitation

 Why is it in News?

• The Supreme Court of India, in a 19 December 2025 judgment, termed child trafficking a “deeply disturbing reality” in India.
• The Court upheld convictions under the Immoral Traffic (Prevention) Act (ITPA) in a Bengaluru case involving sexual exploitation of a minor by organised trafficking cartels.
• The ruling comes amid persistently low conviction rates, despite multiple anti-trafficking laws and institutions.

Relevance

GS II – Polity & Governance

• Protection of vulnerable sections.
• Role of judiciary in rights enforcement.
• Police and criminal justice reforms.

GS I – Society

• Child rights, social evils, organised crime.
• Impact of poverty, migration, and urbanisation.

Practice Questions

• Despite a robust legal framework, conviction rates in child trafficking cases remain abysmally low in India. Analyse the structural and institutional reasons for this gap. (250 words)

Key Judicial Observations (Doctrinal & Practical)

• Nature of Crime:
  • Child trafficking is a grave violation of dignity, bodily integrity, and Article 21 protections.
  • Operates through multi-layered organised networks: recruitment → transport → harbouring → exploitation.
• Victim-Centric Jurisprudence:
  • A trafficked child is not an accomplice.
  • Testimony of a minor victim must be treated as that of an “injured witness”.
• Evidentiary Standards:
  • Courts must show “sensitivity and latitude”.
  • Minor inconsistencies in testimony cannot be grounds for disbelief, given trauma and age.
• Bench: Justices Manoj Misra and Joymalya Bagchi.

Scale of the Problem: Data Snapshot

• Human Trafficking Cases (2018–2022): 10,659 cases
  (as informed by the Ministry of Home Affairs to Parliament)
• Conviction Rate:~4.8%
  • Indicates a severe enforcement and prosecution gap, not absence of law.
• Forms of Child Exploitation:
  • Sexual exploitation (dominant in urban networks),
  • Forced child labour,
  • Domestic servitude,
  • Begging rackets,
  • Online grooming and trafficking via digital platforms.

Legal & Institutional Framework (India)

• Substantive Laws:
  • Immoral Traffic (Prevention) Act, 1956.
  • Juvenile Justice (Care and Protection of Children) Act, 2015.
  • Bonded Labour System (Abolition) Act, 1976.
  • Child Labour (Prohibition and Regulation) Amendment Act, 2016.
• Constitutional Mandate:
  • Article 23: Prohibition of trafficking.
  • Article 39(e) & (f): Protection of children from abuse and exploitation.
• Institutional Gaps:
  • Anti-Human Trafficking Units (AHTUs) exist on paper in many districts but suffer from:
    • Understaffing,
    • Poor training,
    • Weak coordination with NGOs and prosecutors.

Why Laws Are Not Enough ?

• Low Conviction Rates:
  • Weak investigation,
  • Hostile witnesses,
  • Poor victim protection during trial.
• Rehabilitation Deficit:
  • Rescue often ends with one-time compensation.
  • Inadequate focus on:
    • Long-term psychological care,
    • Education continuity,
    • Skill development.
• Federal & Inter-State Complexity:
  • Trafficking networks operate across states; policing remains largely state-bound.
• Digital Transformation of Crime:
  • Use of social media, messaging apps, and encrypted platforms for:
    • Grooming,
    • Recruitment,
    • Sale and movement of victims.

Prevention Lens: What the Editorial Emphasises

• Education as Prevention:
  • Enforce Right to Education Act promise of schooling up to 14 years.
  • School retention is among the strongest safeguards against trafficking.
• Community & Civil Society Role:
  • Early warning systems,
  • Community vigilance,
  • NGO–police collaboration.
• Need for Comprehensive Anti-Trafficking Law:
  • A standalone, modern Anti-Trafficking Act with:
    • Victim-centric procedures,
    • Inter-state investigation powers,
    • Time-bound trials.

Critical Takeaway

• India’s child trafficking challenge is no longer a legal vacuum problem, but a governance, enforcement, and rehabilitation failure.
• The Supreme Court has clarified the judicial approach; the remaining burden lies with:
  • Executive capacity,
  • Police professionalism,
  • Prosecutorial sensitivity,
  • Civil society participation.

Conclusion:

• Child trafficking will not end with harsher laws alone—it demands a coordinated ecosystem of prevention, sensitive justice, and long-term rehabilitation, with the child placed firmly at the centre of the response.