Content
- India’s IT dream is at a crossroads
- The case for energy efficiency
India’s IT dream is at a crossroads
Why in News ?
- Mass Layoffs: TCS announced 20,000 layoffs in one quarter (3.2% workforce) — its sharpest ever; other IT majors (Infosys, Wipro, Tech Mahindra) following suit.
- Sectoral Churn: ~50,000 IT jobs likely to be lost by FY2025–26 due to automation and shrinking client budgets.
- Structural Transition: AI-driven automation, tightening U.S. immigration norms, and declining cost arbitrage threaten India’s traditional IT model.
- Economic Significance: IT contributes 7% of India’s GDP, employs ~6 million, and earns $280 billion+ in annual revenue (NASSCOM 2025) — making this transition crucial for India’s growth model.
Relevance
- GS-3 (Economy, Employment, Science & Tech):
Transformation of India’s IT-led growth model under AI disruption.
Job displacement vs productivity paradox; future of digital economy.
Role of reskilling, innovation, and tech-driven entrepreneurship.
- GS-2 (Governance & Policy):
Need for curriculum reform, AI-literacy missions, and labour protection.
Centre–industry partnerships for skilling and innovation ecosystems.
- GS-1 (Society):
Middle-class employment insecurity and social adaptation to automation.
Human–machine coexistence and future of work ethics.
Practice Question :
- “India’s IT-led growth model is facing an existential test in the age of AI.” Examine the structural challenges posed by automation and suggest policy measures for a resilient digital economy.(250 Words)
India’s IT Sector: A Pillar of Economic Transformation
- Historical Role: Since the 1990s liberalization, IT became the engine of India’s global integration and middle-class rise.
- Employment Share: Directly employs only ~1% of workforce, but generates ~7% of GDP and >50% of India’s services exports.
- Major Hubs: Bengaluru, Hyderabad, Pune, Chennai, NCR.
- Top Firms: TCS, Infosys, Wipro, HCLTech, Tech Mahindra — collectively employ ~3 million professionals.
The Structural Shift – Why the Crisis Now?
Automation & Artificial Intelligence
- AI-driven automation is replacing repetitive coding, coordination, and maintenance work.
- Rise of Agentic AI systems (autonomous, reasoning models) — cutting workforce needs for mid-level roles.
- Productivity gains: Developer productivity up 30–50% with generative AI tools (McKinsey, 2025).
- Impact: Decline in demand for routine IT services (Java, .NET, SAP ECC); surge in demand for cloud, cybersecurity, and AI integration skills.
Global Economic Tightening
- U.S. & EU clients (80% of India’s export market) slashing IT budgets amid economic uncertainty.
- Preference shifting from “scale and cost” → “specialization and speed.”
Visa & Policy Barriers
- U.S. H-1B visa fee hikes and protectionist policies → localization of workforce in client countries.
- Reduced on-site deployment weakens India’s cost advantage.
Skill Mismatch
- Many mid-career professionals have obsolete skillsets; lack AI/Cloud expertise.
- Freshers face entry barriers, as firms prefer lean, specialized teams.
Quantitative Snapshot (2025)
| Parameter | Data / Trend |
| GDP Contribution | ~7% |
| Total Employment | ~6 million |
| Exports | ~$280 billion |
| Forecast Job Loss (FY25–26) | ~50,000 |
| AI Upskilling (TCS) | 5.5 lakh trained (basic); 1 lakh (advanced) |
| Global Comparison | U.S. layoffs: Amazon (14,000), Meta (8,000) |
The New Paradigm: From Outsourcing to Innovation
Old Model:
- Labour-intensive, low-cost coding, maintenance, client-site dependency.
Emerging Model:
- Cloud-native, cybersecurity, AI-driven digital transformation, product-based ecosystems.
- Shift from “manpower” → “mindpower.”
- Clients demand end-to-end AI integration and domain expertise, not volume hiring.
Policy & Institutional Response
Skill Reorientation
- Mandatory AI-literacy programs in engineering colleges.
- Integration of machine learning, ethics in AI, and design thinking in curricula.
- Public-private reskilling partnerships — TCS model to be scaled nationally.
Social Protection
- Proposal for 6–9 months severance pay for laid-off IT employees.
- Need for career transition support, retraining subsidies, and mental health resources.
Startup & Innovation Ecosystem
- Promote deep-tech, AI, and product-based startups through tax incentives and VC support.
- Encourage domestic innovation hubs and AI research clusters.
Global Policy Engagement
- Ensure data sovereignty, cross-border talent mobility, and AI regulation clarity in trade agreements.
Implications for India
Economic
- Short-term job losses; long-term productivity gains.
- Transition from IT-enabled services → knowledge-based economy.
Social
- Middle-class insecurity due to white-collar layoffs.
- Urgent need for continuous learning and social safety nets in formal sector.
Strategic
- Opportunity for India to become AI talent hub of the Global South.
- Must align Digital India, Skill India, and AI Mission policies.
Way Forward
- National AI Upskilling Mission – integrate AI into ITIs, engineering colleges, and reskilling platforms.
- Incentivize firms for employee retraining (tax rebates).
- Promote AI-based product innovation, not service dependency.
- Strengthen domestic R&D through industry–academia linkages.
- Establish social safety nets for formal white-collar workers.
Conclusion
India’s IT journey is not ending — it’s evolving. The assembly-line model of coding is obsolete; the next phase demands AI fluency, innovation, and adaptability.
To sustain its global digital leadership, India must shift from outsourcing scale to innovation depth, invest in future skills, and ensure that human potential grows alongside artificial intelligence.
The case for energy efficiency
Why in News?
Despite India achieving a major milestone — non-fossil fuel capacity crossing 50% of total installed capacity (June 2025) — the Grid Emission Factor (GEF) has paradoxically risen from 0.703 tCO₂/MWh (2020–21) to 0.727 tCO₂/MWh (2023–24) (Central Electricity Authority). This indicates that India’s electricity is getting dirtier even as renewable capacity expands.
Relevance
- GS-3 (Environment, Energy, Economy):
Paradox of higher emissions amid renewable expansion.
Data-backed insights on GEF, energy efficiency, and BEE impact.
Systemic need for “efficiency + flexibility” in decarbonisation.
- GS-2 (Governance):
Implementation of National Electricity Plan, Perform, Achieve and Trade (PAT) scheme.
Coordination among MoP, CEA, BEE, ISA.
Public–private partnership models for energy-efficient transition.
- GS-1 (Geography & Society):
Spatial pattern of coal vs renewable generation regions.
Demand dynamics and urban energy behaviour patterns.
Practice Question :
- Despite India’s renewable capacity crossing 50%, its grid is getting dirtier. Analyze the paradox with reference to the capacity–generation mismatch and the role of efficiency.(250 Words)
Overview
Understanding Key Terms
- Grid Emission Factor (GEF): Carbon emitted per unit of electricity generated (tCO₂/MWh).
- Installed Capacity vs Generation: Capacity refers to potential; generation is actual output. Renewables have low capacity utilisation.
- Energy Efficiency (First Fuel): Reducing demand before generation — most cost-effective decarbonisation tool.
The Paradox Explained – Why the Grid Is Getting Dirtier
a. Capacity–Generation Mismatch
- Renewables ≈ 50% of capacity, but only 22% of actual generation (2023–24).
- Capacity Utilisation:
- Solar/Wind: 15–25%
- Coal/Nuclear: 65–90%
- Growing demand met by coal, raising GEF.
b. Temporal (Time) Mismatch
- Solar generation peaks at noon, but demand peaks after sunset.
- Fossil fuel plants act as “shock absorbers”, bridging evening gaps — increasing emissions.
c. Economic and Structural Constraints
- Coal dependence remains high due to reliability and cheap dispatch.
- Slow grid flexibility — storage, transmission, and demand response lag behind renewable expansion.
Data & Global Comparison
| Parameter | 2020–21 | 2023–24 | Target 2031–32 |
| Grid Emission Factor (tCO₂/MWh) | 0.703 | 0.727 | 0.430 |
| Non-fossil Installed Capacity (%) | 40 | 50 | 60–65 |
| Renewable Share in Generation (%) | 18 | 22 | 40 (Goal) |
- India: 0.727 tCO₂/MWh (coal-heavy grid)
- France/Norway/Sweden: 0.1–0.2 tCO₂/MWh (hydro & nuclear dominance)
The Role of Energy Efficiency
- Energy saved (FY2017–18 to 2022–23): 200 MTOE
- CO₂ reduction: 1.29 GT
- Economic savings: ₹7.6 lakh crore (BEE data)
- Efficiency flattens peak demand, enabling better renewable integration.
Round-the-Clock (RTC) Renewable Energy
- RTC clean power now costs < ₹5/kWh, cheaper than new coal plants.
- Yet scaling is slow due to land, grid, and financing barriers.
Policy Roadmap – What Needs to Be Done
Demand-side (Efficiency & Flexibility):
- Accelerate BEE appliance standards (shift market to 4–5-star).
- Promote efficient motors, pumps, and industrial retrofits in MSMEs.
- Encourage dynamic tariffs to shift demand to renewable-rich hours.
Supply-side (Integration & Innovation):
- Expand battery storage and virtual power plants (distributed storage).
- Promote green cooling and scrappage incentives for inefficient systems.
- Strengthen transmission & grid balancing mechanisms.
Long-term Outlook
- National Electricity Plan (CEA):
- GEF projected to fall to 0.548 (2026–27) and 0.430 (2031–32).
- Requires flexible system design, not just capacity growth.
- India has already cut emission intensity by 33% (2005–2019) — next leap hinges on efficiency + flexibility.
Core Message:
India’s green energy story will succeed not by adding gigawatts, but by making every watt cleaner, smarter, and more efficiently used. Efficiency is the first fuel; flexibility is the real transition.
