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Artificial Intelligence (AI) Breakthrough

[29th September 2025] The Hindu Op-ed: An Engel’s pause in an AI-shaped world

op-ed snap | Science Tech | Mains Paper 3: Awareness in various sc and tech fields

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[UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in the healthcare?

Linkage: This question reflects the exact dilemma discussed in the Engels’ pause analogy—AI promises higher productivity (e.g., clinical diagnosis, efficiency) but without governance, the welfare gains (privacy, equitable access, trust) may lag, creating social costs.

Mentor’s Comment

The rise of Artificial Intelligence (AI) is hailed as the new Industrial Revolution, but as Geoffrey Hinton warns, it could also deepen inequality by making a few rich while leaving the majority poorer. This paradox, reminiscent of Friedrich Engels’ 19th-century observation, raises a pressing question for policymakers: Are we entering a modern “Engels’ pause” where productivity soars but living standards stagnate? For UPSC aspirants, this debate is central to GS 1 (industrial revolution parallels), GS 2 (governance), GS 3 (technology, economy), and GS 4 (ethics of equity in innovation).

Introduction

The concept of an Engels’ pause, coined by economist Robert Allen, describes a historical paradox in 19th-century Britain: industrial output grew rapidly, yet wages stagnated, food prices soared, and inequality widened. The benefits of industrialization reached the majority only after decades, with reforms and institutional adjustments.

Today, AI as a general-purpose technology (GPT)—akin to steam power, electricity, or the internet—brings unprecedented productivity potential but also risks replicating this paradox. With Nobel Laureate Geoffrey Hinton warning of AI enriching a few at the expense of many, and evidence of uneven benefits emerging globally, the Engels’ pause metaphor becomes a crucial analytical lens.

Why in the News?

Artificial Intelligence is reshaping global economies, but early signs suggest a disconnect between productivity gains and broad-based prosperity. A recent Stanford study showed younger workers are more vulnerable to AI displacement, while an Indian IT giant laid off 12,000 employees in its AI pivot. Meanwhile, a MIT study revealed that 95% of AI pilots are failing to deliver visible gains due to weak complementary capabilities. In the Philippines, call centres recorded 30–50% productivity jumps with AI copilots, yet wages stagnated and workloads intensified. PwC forecasts AI could add $15.7 trillion to global GDP by 2030, but gains are concentrated in a few countries and firms. These developments highlight the possibility of an AI-induced Engels’ pause, making it a critical debate for global governance.

Are We Facing a Modern Engels’ Pause?

  1. Historical Parallels: Like 19th-century Britain, current AI-driven growth risks benefiting capital over labour, delaying welfare gains for the majority.
  2. Vulnerable Workers: Stanford research shows younger workers are most exposed to AI disruptions.
  3. Sectoral Displacement: IT, healthcare, education, and even government (e.g., Albania’s AI Minister) are witnessing job/task reconfigurations.

What Are the Markers of an AI Engels’ Pause?

  1. Stagnant Wages despite Productivity Gains: Philippines call centres show higher efficiency but little improvement in wages.
  2. Rising Costs of Complements: Cloud computing, retraining, coding bootcamps, and cybersecurity raise the “price of staying relevant”.
  3. Unequal Distribution of Gains: PwC’s $15.7 trillion AI GDP addition is concentrated in the U.S., China, and a few tech firms. IMF (2024) warns 40% of global jobs are AI-exposed, with advanced economies at greater risk of skilled substitution.
  4. Intensified Inequality: Research on India shows stronger IPR regimes widened wage inequality during tech races.

How Can Governance Break the Pause?

  1. Skilling and Transition Models: Singapore’s SkillsFuture programme and MBZUAI (world’s first AI university) highlight proactive reskilling.
  2. Redistribution Tools: Robot taxes and Universal Basic Income (UBI) pilots in the UK and EU aim to channel AI rents toward social welfare.
  3. AI Infrastructure as Public Good: Compute and data should be democratized; initiatives like K2Think.ai (UAE) and Apertus (Switzerland) are steps in building open, public AI models.

Why This Time Might Be Different

  1. Stronger Welfare Systems: Unlike 19th-century Britain, today’s democracies have safety nets and global institutions.
  2. Rapid Diffusion of Technology: Smartphones reached billions within a decade; AI could follow a similar trajectory.
  3. Potential Social Benefits: AI could lower costs in healthcare, education, and energy if deployed equitably.

Conclusion

The Engels’ pause analogy underscores a profound warning: productivity gains do not automatically translate into welfare improvements. AI governance, skilling programmes, redistribution mechanisms, and public-good infrastructure will determine whether AI becomes a human welfare revolution rather than just a productivity revolution. Political will, not just technological breakthroughs, will decide if this pause is short-lived or prolonged.

Value Addition

Scholarly References and Thinkers

  1. Robert C. Allen (2009): Coined Engels’ Pause in economic history; wages stagnated despite industrial productivity growth in 19th-century Britain.
  2. Nicholas Crafts (2021): Noted that GPTs like AI need institutional reforms and complementary innovations before welfare spreads.
  3. Bojan Jovanovic & Rousseau (2005): Documented “technology shocks” in U.S. economy → initial dislocation before long-term growth.
  4. Geoffrey Hinton (2024, FT Interview): Warned AI may “make a few rich and the rest poorer.”
  5. Agrawal, Gans & Goldfarb (2018): Defined AI as lowering the cost of prediction.

Key Reports and Data Points

  1. PwC Report (2018): AI could add $15.7 trillion to global GDP by 2030; 70% of gains concentrated in U.S. and China.
  2. IMF Report (2024): 40% of global jobs are AI-exposed; higher risk of high-skilled substitution in advanced economies.
  3. MIT Study (2023): Found that 95% of AI pilot projects failed to show visible gains due to lack of complementary capabilities.
  4. Stanford Study (2023): “Canaries in the Coal Mine” → younger workers are most vulnerable to AI disruption.
  5. OECD AI Principles (2019): Global governance framework emphasising fairness, transparency, accountability.

International Best Practices / Programs

  1. Singapore – SkillsFuture (2015): Provides continuous education credits for workers to reskill; considered a global model.
  2. UAE – Mohamed bin Zayed University of AI (MBZUAI, 2019): World’s first dedicated AI university.
  3. European Union – AI Act (2021 Draft): Risk-based framework regulating AI applications.
  4. United Kingdom – UBI Experiments: Pilots to test redistribution of tech-driven wealth.
  5. Albania – First AI Minister (2024): Institutional adoption of AI governance in public administration.

Indian Context and Initiatives

  1. NITI Aayog’s National Strategy on AI (2018): “AI for All” approach—priority areas: healthcare, education, agriculture, mobility.
  2. Digital India Programme: Expanding digital infrastructure to enable AI adoption.
  3. National Programme on AI (2019): Envisioned as a Center of Excellence ecosystem for skilling, research, and governance.
  4. NASSCOM FutureSkills Prime: Public–private initiative to reskill 2 million professionals in emerging tech, including AI.
  5. IndiaAI Portal (2023): Central knowledge hub for AI use cases and policy discussions.

Key Concepts for Thematic Depth

  1. General-Purpose Technology (GPT): Technologies with cross-sectoral transformative impact (steam, electricity, internet, AI).
  2. Complementary Innovations: Need for institutional reforms, new tasks, and human capital for GPT diffusion.
  3. Job Polarisation: Middle-skill jobs displaced → low-skill and high-skill jobs expand; seen in OECD labour markets.
  4. Robot Tax (Bill Gates’ Proposal): Idea of taxing automation to fund welfare.
  5. Universal Basic Income (UBI): Redistribution mechanism to tackle inequality in tech-driven economies.

Comparative Historical Perspective

  1. Industrial Revolution (19th c. Britain): Productivity rose but welfare stagnated → Engels’ Pause.
  2. Gilded Age (U.S.): Huge inequality, labour unrest; later corrected via welfare state reforms.
  3. Digital Revolution (1990s): Internet adoption uneven; productivity surge lagged behind wages initially.

Ethical and Governance Dimensions

  1. Equity and Justice (GS4): AI could worsen inequality unless governed inclusively.
  2. Privacy: Particularly sensitive in healthcare (HIPAA in U.S.; India’s Digital Personal Data Protection Act, 2023).
  3. Transparency: AI “black box” models challenge accountability.
  4. Democratic Deficit: AI development is corporate-heavy; needs citizen-centric governance.

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