THREAT ASSESSMENT: Generative AI Exposure Gap Widening Caste Inequality in India's Graduate Labour Market
![flat color political map, clean cartographic style, muted earth tones, no 3D effects, geographic clarity, professional map illustration, minimal ornamentation, clear typography, restrained color coding, flat 2D map of India, clean vector lines dividing states, subtle gradient washes in muted greens and browns for low-exposure regions versus sharp azure and gold for high-exposure zones, thin annotation lines pointing to eastern and central states with faint callouts labeled 'Agriculture', 'Elementary Occupations', 'Low AI Penetration', overhead directional light casting slight shadows on labels, atmosphere of quiet division and structural imbalance [fal-ai/z-image/turbo]](https://cdn.digitalrain.dev/thelongview/viral-images/0291b805-8e3f-409b-bea1-820fc16ad2a9_viral_1_square.jpg "flat color political map, clean cartographic style, muted earth tones, no 3D effects, geographic clarity, professional map illustration, minimal ornamentation, clear typography, restrained color coding, flat 2D map of India, clean vector lines dividing states, subtle gradient washes in muted greens and browns for low-exposure regions versus sharp azure and gold for high-exposure zones, thin annotation lines pointing to eastern and central states with faint callouts labeled 'Agriculture', 'Elementary Occupations', 'Low AI Penetration', overhead directional light casting slight shadows on labels, atmosphere of quiet division and structural imbalance [fal-ai/z-image/turbo]")
Among India’s graduate cohort, exposure to generative AI aligns closely with historical caste hierarchies. The wage premium tied to digital access now reinforces occupational segregation, not merely reflects it.
Bottom Line Up Front: Generative AI exposure in India’s graduate labor market is unevenly distributed along caste lines, with marginalized groups significantly underexposed, threatening to exacerbate existing wage inequalities.
Threat Identification: A systemic disparity exists in exposure to generative AI between upper-caste and historically disadvantaged Scheduled Castes (SC) and Scheduled Tribes (ST) graduates. This gap persists even within the same geographic districts, indicating structural rather than regional causes.
Probability Assessment: The disparity is already present as of 2025, with SC and ST graduates exhibiting 0.24–0.37 standard deviations lower AI exposure. Given current labor market trajectories and entrenched occupational segregation, this gap is highly likely to persist and intensify over the next decade unless mitigated [1].
Impact Analysis: Exposure to generative AI commands a wage premium of up to 20%, meaning reduced access for SC and ST graduates directly translates into lower earning potential. One in four SC and one in three ST graduates are employed in AI-non-exposed sectors like agriculture and elementary occupations, while underrepresentation in high-exposure fields (e.g., software, finance, management) further limits upward mobility. This threatens to institutionalize AI-driven economic inequality along caste lines [1].
Recommended Actions: (1) Reform higher education and vocational training to ensure equitable access to AI-relevant skills for marginalized castes; (2) Implement labor market monitoring systems to track AI exposure and wage impacts by social group; (3) Develop targeted public-sector employment initiatives to increase SC/ST representation in AI-exposed white-collar occupations; (4) Support affirmative action policies adapted for the digital economy.
Confidence Matrix: Threat Identification – High confidence; Probability Assessment – High confidence; Impact Analysis – High confidence; Recommended Actions – Medium confidence (dependent on policy implementation).
[1] Mishra, K. (2025). The Privilege of Exposure: Caste and Generative AI in India's Graduate Labour Market. arXiv:XXXX.XXXXX.
Published June 12, 2026