📍 WHERE INDIA SITS — WEF FOUR FUTURES FRAMEWORK (JAN 2026)
The World Economic Forum's Four Futures for Jobs in the New Economy: AI and Talent in 2030 (January 2026) maps four scenarios along two axes: pace of AI advancement and workforce readiness. This is the index author's analytical positioning of India within that framework — not an explicit WEF country assessment.
⚠ SCENARIO 4 — INDIA'S DEFAULT RISK
Stalled Progress: Steady AI advancement meets a workforce lacking critical skills. Gains concentrate in AI-ready geographies. Displacement hits routine roles. The value of skilled trades increases — but most displaced workers can't reach them. India's 90% informal workforce, 50–55% AI talent gap, and 41% of CEOs citing insufficient AI investment all point toward this trajectory without deliberate intervention.
🚀 SCENARIO 3 — WHAT THE OPPORTUNITY HORIZON POINTS TO
Co-Pilot Economy: Gradual AI progress + widespread readiness → augmentation over mass automation. Human–AI teams reshape value chains. 40%+ of skills change by 2030 through reskilling, not displacement. Countries that invested early in training and digital infrastructure absorb the transition. Every Opportunity Horizon in this index maps a pathway from Scenario 4 risk to Scenario 3 outcomes.
Source: WEF Four Futures for Jobs in the New Economy: AI and Talent in 2030, January 2026 · Scenario positioning is the index author's analytical judgment based on India's labour market data
STEP 0 — WAGE DATA METHODOLOGY
⚠ Important: Wages are median gross monthly estimates, not exact official figures
All wage figures shown are indicative median gross monthly earnings for a typical worker in that occupation in India as of FY2024–25. They represent the central tendency across skill levels and geographies — actual earnings vary significantly by state, employer type, experience, and sector tier.
PLFSPrimary Source — PLFS Wage Data (Q2 FY26)
Median usual weekly earnings from PLFS Q2 FY26 (MoSPI QB-27), converted to monthly (×4.33). Covers regular salaried and self-employed workers across 14 sectors. Used as the baseline for all informal and semi-formal occupations: agricultural labourers, construction workers, domestic workers, street vendors, transport drivers, etc.
7th CPCGovernment Pay — 7th Pay Commission + State Pay Scales
Government occupations (IAS/IPS, police, municipal workers, defence, judiciary staff, tax officers) use 7th Central Pay Commission (2016, as revised) Grade Pay structures. State-cadre figures use revised state pay scales where available (Tamil Nadu 6th State Pay Commission for TN-specific entries). Figures represent mid-scale basic + DA as of FY25.
NASSCOMIT/ITeS Salaries — NASSCOM Compensation Benchmarks
IT sector wages (Software Developers, Data Scientists, BPO/KPO, Cloud Engineers, AI/ML Engineers, GCC Managers, Technical Writers) sourced from NASSCOM Strategic Review 2025 median compensation tables and corroborated with Naukri.com and LinkedIn Salary Insights FY25. BPO/KPO reflects tier-2 city median; GCC Managers reflect Chennai/Bangalore median senior manager CTC.
IRDAI / SEBIFinancial Services — Regulatory + Industry Surveys
Insurance agents (IRDAI Annual Report FY25 renewal income estimates), stockbrokers/dealers (NSE member firm compensation surveys), actuaries (Institute of Actuaries India salary survey FY25), wealth managers (AMFI MFD income disclosure data), and CAs (ICAI placement survey FY25). Bank clerk wages use IBA bipartite settlement scale effective FY25.
NMC / MCIHealthcare — NMC / NHWA + Government Facility Pay
Doctor and specialist wages based on NMC/government facility pay scales and National Health Workforce Account (NHWA) 2018 FY25 projected earnings. Private sector premiums estimated using Medscape India Survey 2024. ASHA/Anganwadi incentive structures from NHM state-wise payment guidelines FY25. Nursing wages use INC-recommended pay scales.
AISHEEducation — AISHE + UGC / State Pay
Higher education faculty wages based on UGC 7th Pay revision scales (Band I: ₹57,700 basic). School teachers use state government teacher pay scales (average across major states). Private coaching tutors use FIITJEE/Byju's/Allen disclosed salary ranges from placement data. ITI vocational trainers use DGT contractual rates.
ASI / MoLEManufacturing — ASI Wage Data + Minimum Wage Schedules
Factory-based wages (garment workers, auto assembly, electronics, food processing, pharma) derived from ASI FY24 wage per worker data by industry division, supplemented by Ministry of Labour & Employment scheduled minimum wage rates for skilled/unskilled workers in scheduled employment categories. Garment worker wages cross-checked with Garment Workers' Rights India Survey 2024.
NoteInformal Sector Approximation
For occupations with 90%+ informal employment (domestic workers, street vendors, barbers, laundry workers, repair technicians), wages represent median take-home including tips and irregular income, not formal CTC. Estimates cross-validated against PLFS earnings distribution by industry and ILO India informal wage floor estimates. All figures are gross monthly — no deductions for PF, ESIC, or income tax applied.
STEP 1 — EMPLOYMENT BASELINE
Base562M Total Workforce (PLFS Q2 FY26)
The Periodic Labour Force Survey Q2 FY26 (MoSPI QB-27) establishes the employment denominator. Each occupation's e field represents millions employed, derived from sector-wise distribution tables. Tamil Nadu figures use state-level estimates from ASI FY24 and TN State Economic Survey 2025–26 (State Planning Commission, GoTN).
Sector14 Sector Classification
Occupations are grouped into 14 sectors matching MoSPI/NIC-2008 classification: Agriculture, Manufacturing, Construction, Retail & Trade, Transport, Healthcare, Education, Financial Services, IT & Technology, Government, Professional Services, Hospitality, Media & Arts, and Personal Services.
STEP 2 — AI EXPOSURE SCORING (1–10)
CoreAnthropic Observed Exposure Framework
Primary scoring methodology adapted from Massenkoff & McCrory (2026) — "Labor Market Impacts of AI: A New Measure and Early Evidence." This framework combines theoretical LLM capability (task-level analysis of what AI can do) with observed real-world usage (actual Claude usage data across job categories). This closes the gap between "what AI could do" and "what it is actually doing."
Calibr.India-Specific Calibration
Raw US-based exposure scores are adjusted downward for: (a) 90% informal employment rate — informal workers face slower AI adoption than formal equivalents; (b) wage-cost differential — low-wage occupations face slower ROI on AI substitution; (c) infrastructure readiness — rural and semi-urban deployment lags metro by 5–8 years; (d) regulatory pace — Indian sector-specific AI adoption is slower than US comparisons.
AnchorEcon Survey Ch.14 Anchoring
The Economic Survey 2025–26 Chapter 14 projects AI will impact 38M organised workers by 2030. This serves as the calibration anchor: occupations scoring ≥7/10 represent the organised formal workforce most likely within that 38M risk pool. The score distribution is tuned so that high-risk occupations in IT, Finance, and Professional Services aggregate to approximately this figure.
NASSCOMIT Sector Calibration
NASSCOM SR 2025 "Beyond Disruption" provides sector-specific AI adoption rates for IT–BPM. BPO/KPO (8/10), Software Developers (8/10), and Data Entry (9/10) scores are anchored to NASSCOM's finding that 82% of CXOs are increasing AI spend and that BPM faces ~20% near-term role disruption.
STEP 3 — DISRUPTION CLOCK (TIMELINE ESTIMATION)
LogicScore → Year Mapping
Disruption year is derived from AI score using a calibrated monotonic mapping: Score 9–10 → 2026–2028 (already disrupting); Score 7–8 → 2028–2032; Score 5–6 → 2032–2037; Score 3–4 → 2037–2044; Score 1–2 → beyond 2044 or resistant. Years are probabilistic midpoints, not forecasts. The 2030 anchor from Econ Survey Ch.14 is used to validate mid-range scores.
PhasesThree-Phase Disruption Model
Each occupation shows three phases: Augmentation (AI assists human workers, productivity rises), Disruption (AI begins substituting roles at scale), and Transformation (new role definition emerges). Phase timing derived from occupation-specific task composition and sector adoption curves.
YouthYouth Hiring Slowdown
Anthropic (2026) documents a 14% drop in job-finding rate for workers aged 22–25 in highly exposed roles post-2024. This is applied to India's 12M annual labour market entrants, explaining why Disruption Clock timelines for entry-level IT/BPO roles are set earlier than physical-skill occupations.
STEP 4 — OPPORTUNITY HORIZON
LogicHistorical Analogy Framework
Each occupation's Opportunity Horizon opens with a historical analogy of a previous automation wave that did not eliminate but transformed employment — drawn from agriculture (Green Revolution), manufacturing (CNC automation), and services (banking computerisation). The bridge problem frames the specific human skill that will remain valuable post-AI. Opportunity data draws on ILO India 2024 sector growth projections and Econ Survey Ch.12 employment outlook data.
STEP 5 — GDP CONTRIBUTION VS AI EXPOSURE
GVASector GVA Mapping (FY2024–25)
GVA % figures sourced from MoSPI Provisional Estimates FY2024–25 (current prices) for 4 exact categories: Agriculture (17.94%), Manufacturing (13.89%), Construction (8.75%), and IT (10.9% per Econ Survey 2025–26). The remaining 10 sectors are sub-component estimates derived by decomposing MoSPI's 3 broad service categories using RBI Handbook of Statistics, NASSCOM SR 2025, NHWA 2023, and AISHE 2022–23.
AILive Sector AI Score
The AI Exposure Score shown in the GDP chart is not hardcoded — it is a live employment-weighted average computed from the filtered occupation dataset, identical to the Summary card calculation: Σ(ai × employment) / Σ(employment). This ensures consistency between all views.
STEP 6 — TAMIL NADU SUB-INDEX
TNState-Level Calibration
36 of 103 occupations are flagged tn:true with TN-specific employment figures (the tne field). Sources: ASI FY24 (TN #1 in factory share at 15.4%), TN State Economic Survey 2025–26 (State Planning Commission, GoTN), NASSCOM data on Chennai/Coimbatore GCC expansion, and AISHE for the 17 NIRF top-100 institutions in TN.
POLICY PLAYBOOK — HOW EACH TAB IS COMPUTED
The Policy Playbook footer panel contains three lenses — Protect, Invest, and Reskill — each with 5 tabs. All computations are live: they update when you change the Region, Sector, or AI Score filters. Results always show the top 3 sectors or education tiers matching the criterion.
🛡️ PROTECT — Where is worker displacement risk highest?
Protect identifies which sectors and worker groups face the greatest near-term AI displacement risk and therefore require the most urgent policy intervention — social protection, transition support, or income floor mechanisms.
At-Risk
Total at-risk workers — sectors ranked by absolute employment in occupations scoring ai≥7. Shows where the largest number of workers face displacement. Metric: Σ employment where ai≥7, per sector.
Declining
Declining sectors — ranked by share of workforce in occupations tagged "Declining" outlook. Captures structural contraction beyond AI alone. Metric: Σ employment where ol=Declining, per sector.
Wage Risk
Wage vulnerability — sectors where at-risk workers earn the lowest wages. Low-wage + high-AI-exposure is the most acute vulnerability. Metric: Employment-weighted average wage of ai≥7 workers, ranked ascending.
Informality
Informality proxy — sectors with high AI exposure but low formal education requirements. Informal workers face AI disruption without access to formal reskilling pathways. Metric: Average ai score weighted by employment in no-formal/secondary-educated occupations.
Urgency
Disruption urgency — sectors with the soonest disruption years across their occupations. Identifies where the policy window is shortest. Metric: Minimum disruption year (dy) across all occupations in the sector.
🚀 INVEST — Where should government and capital direct investment?
Invest identifies sectors offering the best return on policy capital — skilling infrastructure, job creation schemes, and economic development investment. Five lenses let policymakers choose their priority: growth momentum, transformation potential, safety, AI-human synergy, or scale.
Growing
Growth momentum — sectors where the highest % of workers are in occupations tagged "Growing" outlook. Best sectors for job creation schemes and talent pipeline investment. Metric: Σ(employment where ol=Growing) / Σ(total employment), per sector.
Opportunity
Transformation opportunity — sectors with the highest average AI exposure score. High AI scores mean these sectors will change most — creating new roles requiring investment in reskilling and infrastructure. Metric: Employment-weighted average ai score per sector.
Safe Bets
Low-risk growth — Growing sectors with the lowest average AI disruption risk. Best of both worlds: expansion without displacement threat. Safest place to direct workforce investment. Metric: Sectors with ol=Growing filtered, ranked by ascending average ai score.
Transform
AI–human synergy plays — sectors that are both growing AND high AI-exposure (avg ai≥6). These sectors need human-AI collaboration investment: workers who can work alongside AI tools to produce outsized output. Metric: Growing sectors with employment-weighted ai score ≥6.
Volume
Scale impact — sectors with the largest absolute number of workers in growing roles. Where investment reaches the most people. Metric: Σ employment where ol=Growing, per sector, ranked descending.
🎓 RESKILL — Which education tiers need the most urgent upskilling?
Reskill segments India's workforce by education level — No formal, Secondary, ITI/Vocational, Graduate, and Postgraduate/Professional — and identifies which tiers face the greatest AI displacement burden relative to their reskilling capacity. This informs ITI reforms, PMKVY targeting, and university curriculum changes.
At-Risk %
Exposure rate by education tier — which education group has the highest share of its workers in at-risk (ai≥7) occupations. Identifies where AI disruption is most concentrated by qualification level. Metric: Σ(employment in ai≥7 roles) / Σ(total employment), per education tier.
Volume
Absolute headcount — education tiers with the most workers in at-risk occupations. Tells policymakers where reskilling programmes need the greatest throughput capacity. Metric: Σ employment where ai≥7, per education tier, ranked descending.
Wage Gap
Income vulnerability — education tiers where at-risk workers earn the least. Low education + low wage + high AI exposure = most vulnerable cohort with least ability to self-fund retraining. Metric: Employment-weighted average wage of ai≥7 workers, ranked ascending by tier.
Readiness
Transition readiness — education tiers with the most workers already in "Growing" outlook occupations. Indicates which tiers are best positioned to absorb displaced workers from at-risk roles. Metric: Σ employment where ol=Growing, per education tier, ranked descending.
Youth
Youth and entry-level exposure — education tiers with the most workers in low-wage (≤₹25K/mo) AND high-risk (ai≥7) roles. Captures the cohort of new labour market entrants facing the worst outlook. Based on Anthropic (2026) finding of 14% drop in job-finding rate for 22–25 year-olds in exposed roles. Metric: Σ employment where wage≤₹25K AND ai≥7, per education tier.
💡 IDEA BOOK — HOW EACH LENS IS COMPUTED
The Idea Book footer panel is designed for entrepreneurs, investors, and innovation policymakers. It surfaces startup and venture opportunities directly from the displacement and transformation data — live-filtered to whatever sector or score range is currently selected. It has two modes: Data Lens (algorithmically generated from occupation data) and Graveyard Lens (curated dead YC startups mapped to Indian opportunities).
📊 DATA LENS — Three venture theses from live occupation data
The Data Lens generates three startup opportunity categories algorithmically from the filtered occupation dataset. Each card shows the occupation, disruption timeline, surviving human skill, and a generated venture brief. All results update live with the current filter.
🔴 Displacement Plays — Build the AI tool or the reskilling platform
Criterion: Occupations with AI score ≥8 AND disruption year ≤2028. These roles are in active displacement right now. Sorted by employment size, top 3 shown.
Venture thesis: Two plays exist simultaneously — (1) build the AI tool that automates this work (supply-side), or (2) build the reskilling platform that transitions displaced workers into the surviving skill. Both are urgent — the window is 2025–2028.
Filter: ai ≥ 8 AND disruption year ≤ 2028 → sort by employment → top 3
🟡 Augmentation Plays — Build the copilot that 10× the human
Criterion: Occupations with AI score 5–7 AND "Growing" outlook. These roles expand with AI augmentation — demand is rising AND they're not being replaced.
Venture thesis: Build a workflow copilot that lets one skilled worker do the work of ten. AI handles the routine layer; the human keeps judgment, relationships, and accountability. Strongest PMF in India because these roles are already growing in headcount.
Filter: ai ≥ 5 AND ai ≤ 7 AND outlook = Growing → sort by employment → top 3
🟢 Counter-Position Plays — Build for workers AI cannot touch
Criterion: Occupations with AI score ≤3 AND employment ≥1M workers. Large, deeply AI-resistant workforces being ignored by the tech industry.
Venture thesis: Counter-position against AI-first startups by going where AI cannot go. These 100M+ workers are underserved — their surviving skill creates a wedge for a platform, marketplace, or tool tailored to their needs.
Filter: ai ≤ 3 AND employment ≥ 1M → sort by employment → top 3
💀 GRAVEYARD LENS — 12 dead YC startups mapped to Indian opportunities
Each of 12 failed YC-backed startups is mapped to an Indian market opportunity that now exists — because the infrastructure barriers that killed them in the US (no UPI, no Aadhaar, no rural connectivity, no government health scheme) have been resolved in India.
Filtering logic: Cards shown match the current occupation/sector filter. If the linked occupation or sector is in the active view, those cards appear first. If no match, all 12 cards show. Each card has a direct link to the Disruption Clock for the related occupation.
Tutorspree → Education
AI-verified tutor marketplace. ₹58,000Cr coaching market.
LendUp → Financial Services
Account Aggregator + GST data = MSME credit gap ₹20-25L Cr.
Gigster → IT & Technology
Platform turning 5M Indian devs into AI orchestrators.
FutureAdvisor → Financial Services
AI-assisted, human-confirmed guidance for 89M demat accounts.
FarmLogs → Agriculture
Jio 4G + PM-KISAN → WhatsApp AI crop advisory, 22 languages.
Make School → Education
NSDC outcome-linked skilling, government as payer.
Call9 → Healthcare
AB-PMJAY 50Cr beneficiaries + ASHA last-mile distribution.
Webvan → Retail & Trade
AI kirana dark store network for Tier-2/3 q-commerce.
VOIQ → Financial Services
Sarvam Indic voice AI for 3.2M bank staff transition roles.
Compound → Financial Services
ESOP liquidity + NRI diaspora $125B remittance wealth layer.
Plasticity → Media & Arts
Indic language NLP — 22 official languages underserved by LLMs.
Breaker → Media & Arts
Regional language podcast creator monetisation & distribution.
LIMITATIONS
📊AI scores are estimates, not predictions. They reflect theoretical exposure calibrated to Indian conditions — actual disruption will depend on adoption pace, regulation, and infrastructure readiness.
📋Employment figures carry MoSPI RSE uncertainty. All occupation headcounts are derived from PLFS Q2 FY26 sector distributions — individual occupation-level precision varies.
⏱Disruption years carry ±3–5 year uncertainty bands. They represent probabilistic midpoints, not forecasts. Faster AI adoption or policy changes could shift timelines in either direction.
🏛Tamil Nadu figures are state-level estimates. Derived from ASI FY24 and TN State Economic Survey 2025–26 — not a direct PLFS state sample.
📉GVA sub-sector data is partially estimated. 4 of 14 sector GVA figures are exact MoSPI data; the remaining 10 are sub-component estimates derived from RBI, NASSCOM, NHWA, and AISHE sources.