India Job Market Visualizer Inspired by karpathy.ai/jobs

An unofficial research tool visualizing 115 representative occupations across 13 sectors of the Indian economy, covering roughly 61 crore (614 million) workers — India's full estimated workforce per the Ministry of Statistics' Periodic Labour Force Survey (PLFS). Each rectangle's area is proportional to estimated employment. Color shows the selected metric — toggle between projected growth outlook, median pay, typical education, and AI exposure. Click any tile to look up that occupation. Like the original project this is modeled on, this is not an official report or a rigorous economic publication — it's a research/exploration tool.

Why estimates, not official stats: Unlike the US, India has no single BLS-style database with per-occupation pay, education and growth projections. This tool combines real aggregate data — PLFS 2023–24/2025 sector employment shares, NASSCOM IT workforce figures, UDISE+ teacher counts, NITI Aayog gig-economy projections, NMC/INC registered doctor & nurse counts, government employment data — with researcher estimates (salary surveys, sector reports, informed judgment) to fill in occupation-level detail. Treat absolute numbers as directional, not precise.

LLM-scored AI Exposure: Each occupation's "Digital AI Exposure" score (0–10) was assigned using the same rubric popularized by the original US project — how much AI could reshape the occupation, weighing whether the work is fundamentally digital versus physical/in-person. One India-specific caveat worth flagging: a high score signals technical exposure to automation, not that automation will actually happen soon. In India, cheap labor, informality, and slower enterprise AI adoption mean many technically-exposed jobs will change more slowly than in higher-wage economies.

View the AI Exposure scoring rubric
Rate the occupation's overall AI Exposure on a scale from 0 to 10. AI Exposure measures: how much will AI reshape this occupation? Consider both direct effects (AI automating tasks currently done by humans) and indirect effects (AI making each worker so productive that fewer are needed). A key signal is whether the job's work product is fundamentally digital. If the job can be done entirely from a computer — writing, coding, analyzing, communicating — then AI exposure is inherently high (7+), because AI capabilities in digital domains are advancing rapidly. Conversely, jobs requiring physical presence, manual skill, or real-time human interaction in the physical world have a natural barrier to AI exposure — a barrier that is often reinforced in India by low labor costs, which reduce the economic incentive to automate. Anchors: - 0-1: Minimal. Almost entirely physical/manual work in unpredictable environments (farm laborer, construction laborer, domestic worker). - 2-3: Low. Mostly physical/interpersonal; AI helps only peripheral tasks (electrician, security guard, driver, police constable). - 4-5: Moderate. Mix of physical and knowledge work (nurse, general physician, school teacher). - 6-7: High. Predominantly knowledge work with some human-presence requirement (accountant, insurance agent, journalist, HR manager). - 8-9: Very high. Almost entirely computer-based digital work (software developer, graphic designer, data analyst, BPO agent). - 10: Maximum. Routine, fully-digital information processing (data entry operator).

Caveat: These are rough estimates, not rigorous predictions. A high exposure score does not mean the job will disappear — software developers score 9/10, but demand for software could keep growing as each developer gets more productive. Scores don't account for demand elasticity, regulatory barriers, unionization, or social preference for human workers. Many high-exposure jobs will be reshaped, not replaced. Sources for aggregate figures include the MoSPI/PLFS, NASSCOM, UDISE+, and NITI Aayog.

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