13- Labour in the AI Era: Crisis or Catalyst?

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Introduction

The rise of Artificial Intelligence (AI) has sparked both excitement and fear across global labour markets. While AI has the potential to enhance productivity and economic growth, it also raises concerns about job displacement, wage stagnation, and the concentration of economic power in the hands of a few.

India, with its large and youthful workforce, faces unique challenges and opportunities in adapting to AI-driven automation. The country’s services-led economy, coupled with a significant share of jobs in low-value-added services, makes it particularly vulnerable to labour displacement. However, with the right institutional frameworks, skill development programs, and social infrastructure, AI can become a catalyst for equitable economic transformation rather than a crisis.

This chapter explores:

The impact of AI on labour markets
Historical lessons from previous technological revolutions
The role of social infrastructure in managing AI transitions
Challenges in large-scale AI adoption
Opportunities for AI-driven economic growth
Policy recommendations to ensure AI benefits all sections of society

1. The Impact of AI on Labour Markets

1.1 Job Displacement and Economic Inequality

AI is expected to outperform humans in several decision-making processes, including healthcare, financial services, and criminal justice.
The International Labour Organization (ILO) estimates that 75 million jobs globally are at risk of AI-driven automation.
Goldman Sachs predicts that nearly 300 million full-time jobs worldwide could be impacted by AI.
A study by the Bank for International Settlements (BIS) suggests that 45% of high-income jobs in the US are at risk from AI automation.

1.2 The Concentration of AI Power

AI research and development is currently dominated by a handful of large corporations, including Google, Microsoft, OpenAI, and Meta.
Anton Korinek and Joseph Stiglitz (2021) argue that AI could exacerbate global inequality, benefiting capital-rich developed nations while leaving labour-rich developing economies behind.

1.3 The Indian Context

India’s labour market is heavily reliant on low-skilled jobs, making it particularly vulnerable to AI-driven disruptions.
An IIM Ahmedabad survey (2024) found that 68% of white-collar workers in India believe their jobs will be automated within the next five years.
NASSCOM estimates that the Indian AI market will grow at a compound annual growth rate (CAGR) of 25-35% by 2027, highlighting both risks and opportunities.

2. Lessons from Previous Technological Revolutions

2.1 Historical Patterns of Labour Disruption

The Industrial Revolution (1760-1840) led to massive job displacement in agriculture but ultimately created new manufacturing jobs.
The rise of the automobile industry in the early 20th century eliminated jobs in horse-drawn transport but generated millions of new jobs in car manufacturing, road construction, and maintenance.
Automation in the textile, steel, and automotive industries (1950s-1980s) initially reduced employment in those sectors but ultimately increased productivity and wages.

2.2 Labour Market Adaptation Over Time

Bessen (2018) found that automation does not always lead to job losses; in many cases, it results in strong employment growth over time.
Albanesi et al. (2024) suggest that AI-driven automation could lead to an increase in high-skilled jobs by 3.1% to 6.6% over a decade.

2.3 Implications for AI

AI will not eliminate all jobs, but it will change the nature of work, shifting demand towards higher cognitive skills, creativity, and problem-solving.
Policy intervention is essential to ensure a smooth transition for displaced workers.

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3. The Role of Social Infrastructure in Managing AI Transitions

3.1 Building Enabling Institutions

Enabling institutions focus on equipping workers with new skills to transition into AI-augmented jobs.
National Skill Development Corporation (NSDC) has already trained 12 million youth, but AI-specific skill programs need to be expanded.
National Apprenticeship Promotion Scheme (NAPS) can be leveraged to integrate AI-based training modules.

3.2 Strengthening Insuring Institutions

Insuring institutions provide financial and social safety nets for displaced workers.
Universal Basic Income (UBI) and unemployment insurance could be considered as long-term social protection measures.

3.3 Establishing Stewarding Institutions

Stewarding institutions oversee AI regulation and ethical implementation.
The National AI Strategy (NAIS) should focus on fair AI adoption, preventing biases, and ensuring transparency in AI decision-making.

4. Challenges in Large-Scale AI Adoption

4.1 Reliability and Ethical Concerns

AI models suffer from “hallucinations” (generating incorrect or misleading outputs).
AI-driven hiring tools have been found to exhibit biases against gender and minority groups.

4.2 Infrastructure and Resource Constraints

AI computing power demands are skyrocketing, with training costs for models like GPT-4 exceeding USD 78 million.
Data center energy consumption is projected to equal India’s total electricity demand by 2030.

4.3 Skills Mismatch and Workforce Preparedness

Only 22% of India’s workforce has formal digital skills, limiting their ability to transition into AI-powered roles.
Higher education curricula need urgent revisions to incorporate AI, automation, and data science skills.

5. Opportunities for AI-Driven Economic Growth

5.1 AI-Augmented Jobs

AI can enhance customer support, financial services, and medical diagnostics.
A study by NBER (2023) found that AI-assisted workers in customer support increased productivity by 14%.

5.2 AI in Scientific Research and Innovation

AI is accelerating drug discovery, space exploration, and climate modeling.
Google’s AI-powered DeepMind recently solved protein-folding problems, revolutionizing biomedical research.

5.3 India’s AI Advantage

India has a large talent pool of STEM (Science, Technology, Engineering, Mathematics) graduates.
Government AI initiatives like the “AI for All” mission can help bridge the AI skill gap.

6. Policy Recommendations for an AI-Resilient Workforce

Expand AI-Focused Skill Development Programs
- Integrate AI and machine learning training into higher education and vocational courses.
Introduce Regulatory Frameworks for Ethical AI
- Develop clear guidelines to prevent AI biases and ensure transparency.
Strengthen Labour Market Safety Nets
- Introduce retraining programs for displaced workers.
- Consider Unemployment Insurance for AI-affected sectors.
Promote AI Research and Innovation
- Increase public and private funding for AI startups and research institutions.
Encourage AI-Driven Entrepreneurship
- Create AI-focused startup incubators to help new businesses leverage AI for job creation.

Conclusion: AI as a Catalyst, Not a Crisis

While AI presents significant risks to job security, it also offers immense opportunities for productivity, efficiency, and innovation. The key to maximizing AI’s benefits lies in: