Skip to content
AI Work Index

About This Project

562 occupations · 88 roles · No LLM in scoring · MIT licensed · global-first methodology

Structural AI pressure scores with a global baseline and country-specific demand layers. Not a prediction of job losses — a measure of how much current AI capabilities overlap with each job's tasks, adjusted for human bottlenecks and local demand.

Structural Score

Core model. Displacement pressure × (1 − demand resilience). Published as the primary dataset.

Labour Monitor

Quarterly live-market data (Singapore). Vacancy rates, hiring, retrenchment. Cluster-level, not per-occupation.

Offset & Support

Separate support layers. Offset potential, transition pathways, official skills programmes (e.g. SkillsFuture for Singapore), and scenario guidance. Useful context, not a forecast.

This model measures one side of the equation

In the Acemoglu & Restrepo (2019) framework, AI's net impact = displacement - reinstatement. We measure displacement only. Scores likely overstate net risk for occupations where AI creates new work.

State of the science (early 2026)
  • Single exposure scores are poor unemployment predictors — ensembles do better (Frank et al., 2025)
  • No consensus on measurement — "still in the first inning" (Brookings/PIIE, 2026)
  • Entry-level workers face earliest pressure (Stanford DEL, 2025; Anthropic, 2026)

Model Card

Direct / Reproducible

  • Reliability-weighted 4-source exposure ensemble when matched (AIOE + Anthropic + Eloundou + ILO)
  • Theta complementarity scores (O*NET survey data)
  • Net risk formula (fully reproducible)
  • Official demand signals (SOL 2026, Jobs in Demand)

Estimated / Group-Level

  • Market resilience (group-level employment trends + occupation wage structure)
  • Crosswalk quality (national occupations mapped to ISCO-08)
  • Labour monitor (cluster-level, not occupation-level)
  • Observed-usage calibration (Anthropic usage, not universal AI adoption)
  • BLS convergent check (ρ = −0.14, broad directional check)

Synthetic / Illustrative

  • Modern role estimates (weighted SSOC priors + workflow/context adjustment)
  • Transition support (deterministic feasibility estimates + official programme infrastructure)
  • Offset potential (heuristic demand, redesign, and friction layer)
  • Outlook/scenario modelling (rule-based guidance, not prediction)
  • Seniority modifiers (research-grounded, not independently validated)

Still Limited

  • Occupation-level backtesting is still limited; current public validation remains cluster- and family-level, not occupation-level
  • Company-size modifiers (not part of the current structural model)
  • Causal displacement claims are out of scope (current evidence is correlational)
  • Occupation-level employment counts (not publicly released; requested from agencies)
Data Vintage

Wages

2024 MOM data (Singapore)

Demand Signals

SOL 2026 + Jobs in Demand 2025

Labour Market

Q4 2025 full

Model Version

V7 · 221 checks

Inspiration & How We Differ

Inspired by Andrej Karpathy's AI Job Exposure Map (2026) and Josh Kale's extended visualization, which score 342 US occupations using LLM-generated ratings (Gemini Flash, 0–10 scale).

What we do differently:

  • No LLM in scoring — we use deterministic transforms of published research and official data, not live model-generated ratings
  • Multi-country coverage. Singapore and the United States are scored with country-specific demand signals and labour-market data layered on the structural score
  • Three-layer structural score — exposure ensemble, human bottleneck, and market resilience are kept separate rather than hidden inside one opaque score
  • Externally cross-checked — cluster-level directional checks, BLS convergent evidence, and 221 internal structural checks
  • Seniority modifiers — research-grounded experience level adjustments (Stanford DEL, Anthropic 2026)
  • 88 synthetic roles — modern job titles (AI Engineer, Prompt Engineer) scored as weighted occupation blends

Frequently asked questions

What is the AI Work Index?

The AI Work Index scores 562 occupations and 88 modern roles for structural AI displacement pressure. The headline score combines displacement pressure with local demand resilience where a country layer has evidence. The product is global-first, with Singapore as the first fully scored country and the United States as the next country layer. No LLM is used in the scoring pipeline.

How is the AI job risk score calculated?

Headline risk = displacement pressure x (1 - demand resilience), where displacement pressure = exposure ensemble x (1 - human bottleneck). The exposure layer combines available matches from AIOE, Anthropic observed usage, Eloundou GPT exposure, and the ILO occupational exposure index. Human bottleneck comes from Pizzinelli theta, while demand resilience comes from country-specific demand, wage, and labour-market signals.

Is the AI Work Index open source?

Yes. The entire scoring pipeline, data, and website are MIT licensed and available on GitHub at https://github.com/kirso/aiworkindex. Anyone can reproduce the results by running the deterministic scoring script.

Author & Independence

The AI Work Index is built and maintained by Kirill So as an independent project.

Self-funded, no sponsors. No advertisers, no paid placements, and no commercial relationship with any data provider or government agency. The full pipeline is open source and every score is reproducible from public inputs.

Contact: LinkedIn · GitHub issues · Press & citation

Corrections: mistakes are fixed publicly. Every methodology revision and score change is recorded in the changelog with per-release score diffs.

How to cite

So, K. (2026). AI Work Index (V7 release): structural AI displacement pressure for 562 Singapore occupations. https://aiworkindex.com

@misc{aiworkindex,
  author = {So, Kirill},
  title  = {AI Work Index (V7): structural AI displacement pressure for 562 Singapore occupations},
  year   = {2026},
  url    = {https://aiworkindex.com},
  note   = {Data vintage 2026-06-11}
}

License & Credits

MIT License. Adaptable for other countries via ISCO-08 crosswalks.

Made by Kirill So with Claude (Anthropic) & Codex (OpenAI). Data from MOM, BLS, O*NET, Felten et al. (2021), Pizzinelli et al. (2023), Anthropic Economic Index, Eloundou et al. (Science, 2024), ILO, and Stanford DEL.

← Home
Methodology Appendix V7 Release Data