The Influence of AI on White-Collar Employment in India: An Examination of ‘Bullshit Jobs’

Earlier this year, India published its annual Economic Survey, which includes a thought-provoking chapter titled ‘Labour in the AI Era: Crisis or Catalyst’. This chapter closely examines AI adoption trends and their potential effects. It suggests that “estimates about the magnitude of labor market impacts (by AI) may be well above what might actually materialize.” This finding reflects the current early stages of AI development, where clear predictions about its labor market effects remain elusive.

AI Adoption Trends in India

Recent studies indicate that India leads globally in AI adoption, with 30% of Indian businesses implementing AI technology, surpassing the global average of 26%. According to NASSCOM’s AI Enterprise Adoption Index 2.0, India’s AI adoption index score rose to 2.47 in 2024, compared to 2.45 in 2022. This upward trend signifies an impressive rise in the number of companies reaching the Expert stage of AI adoption. The country’s AI market is projected to grow at a rate of 25-35% CAGR over the next 3-4 years, showing that AI’s integration into Indian enterprises is anticipated to expand.

While the PhonePe Case illustrates AI’s impact on employment—laying off 60% of its customer support staff in recent years due to AI adoption—data on the overall job automation from AI remains limited. A 2024 study by the Indian Institute of Management, Ahmedabad, found that 68% of white-collar workers think AI will partially or fully automate their jobs in the next five years. Furthermore, 40% of them believe their current skills will become obsolete.

The Historical Context of AI and Employment

While the effects of AI on jobs are still being researched, history provides context. During the computer revolution, researchers observed labor polarization characterized by a growth in high-skill jobs and a decline in middle-income routine roles. Initial studies indicated automation primarily affected simple, routine tasks. However, more recent findings by scholars like Frey and Osborne in 2013 challenge this notion, suggesting that AI could also automate non-routine jobs and skilled positions.

Categories of Automation and ‘Bullshit Jobs’

The IIMA Study identified three major areas subjected to AI-driven automation:

• Repetitive Tasks: Occupations such as data entry, language translation, and quality inspection are increasingly replaced by AI.
• Supervisory Roles: Human oversight is being substituted with AI management systems.
• Compliance Roles: Positions like quality control inspectors and IT support are impacted by emerging AI solutions.

Despite the ongoing automation of jobs, AI also generates new employment opportunities. According to the IIMA Study, 63% of business executives believe AI will create specialized roles, including visualization experts and natural language processing (NLP) specialists.

David Graeber’s theory of ‘bullshit jobs’—positions that may appear unnecessary—remains relevant in this context. He identified five categories: Flunkies, Goons, Duct Tapers, Box Tickers, and Taskmasters. These categories represent positions that can be easily duplicated by AI or lack substantial productivity. Recent trends in India indicate a shift towards the automation of roles defined as Box Tickers (data entry and clerical jobs) and Duct Tapers (compliance and support roles), while Taskmasters (supervisory jobs) remain intact.

The study suggests that while jobs at risk of automation largely fall within the ‘bullshit jobs’ category, those requiring human interaction—like Flunkies—are less likely to be automated. Nonetheless, new forms of AI-driven Flunkies will emerge, such as AI tools for note-taking in meetings.

Future Trends and Regulatory Considerations

The emerging trends signal that jobs like data entry clerks will see significant reductions due to automation. However, as AI continues to implement standardized protocols and compliance measures, we may see the rise of new roles aimed at ensuring ethical AI deployment, managing AI biases, and upholding regulations. The rise of AI taskmasters—automated oversight systems—will likely become standard in corporate environments.

Overall, the conversation surrounding AI’s impact on the labor market, particularly in white-collar jobs, illustrates a complex dynamic of job reduction and creation driven by technological advancements. Understanding this phenomenon continues to be vital in shaping future workforce policies and regulations.