Ai Career Threat Index

AI Career Threat Index

> How exposed is your job to AI? 300 professions scored on an open rubric — > four published sub-scores per role, including exposure to autonomous agents. > Updated quarterly. MIT licensed.

[](LICENSE)

[](https://github.com/Jott2121/ai-career-threat-index/actions/workflows/validate.yml) [](https://huggingface.co/datasets/Jott2121/ai-career-threat-index) [](https://doi.org/10.5281/zenodo.21227026)

▶ Explore all 300 roles interactively · [Winners & Losers this quarter](reports/2026-q3-winners-losers.md) · [Methodology](pipeline/RUBRIC.md) · [Ask it through your AI assistant (MCP)](mcp/)

What this is

The AI Career Threat Index scores 300 US professions on AI displacement risk, 0–100. Unlike a single black-box number, every score decomposes into four published sub-scores with written rationales:

| Sub-score | Question it answers | |---|---| | taskAutomation | What share of the role's core work could AI do end-to-end today, at ≥90% reliability? | | toolMaturity | How mature and deployed are the AI tools targeting those tasks? | | adoption | What share of employers actually use AI for those tasks in production? | | agenticExposure | What share of the role could autonomous multi-step agents own — not copilots, agents? |

combined by an open formula (task automation sets the ceiling; maturity and adoption determine how much is realized; agentic exposure adds forward pressure):

score = taskAutomation × (0.45 + 0.30·toolMaturity/100 + 0.25·adoption/100) + 0.10·agenticExposure

The agenticExposure factor is, to our knowledge, the first published per-occupation scoring of exposure to autonomous agents specifically — the thing that changed between 2024's copilots and 2026's agent deployments.

Honesty note: scores are structured editorial estimates made against a [published rubric](pipeline/RUBRIC.md) with named anchors and calibration examples — informed by O*NET task lists, BLS OES data, and public adoption research. They are not measurements, and we don't claim otherwise. Every input is in this repo; if you disagree with a sub-score, [open an issue](../../issues) and argue with the rationale — that's the point of publishing them.

Get the data

git clone https://github.com/Jott2121/ai-career-threat-index.git
# or grab the files directly:
curl -LO https://raw.githubusercontent.com/Jott2121/ai-career-threat-index/main/data/ai-career-threat-index.json
curl -LO https://raw.githubusercontent.com/Jott2121/ai-career-threat-index/main/data/ai-career-threat-index.csv
curl -LO https://raw.githubusercontent.com/Jott2121/ai-career-threat-index/main/data/soc-crosswalk.csv

| File | What's in it | |---|---| | [data/ai-career-threat-index.json](data/ai-career-threat-index.json) | Full dataset: 300 roles, sub-scores, rationales, tasks, defense skills, quarterly history | | [data/ai-career-threat-index.csv](data/ai-career-threat-index.csv) | Flat table, one row per role — Excel/pandas/R ready | | [data/soc-crosswalk.csv](data/soc-crosswalk.csv) | Every BLS SOC 2018 occupation (867 codes) mapped to its nearest scored role | | [data/changelog.md](data/changelog.md) | Version history and notable movements |

The crosswalk means any US occupation — even ones we don't score directly — resolves to a scored neighbor with a stated match quality.

Use it in code

Python

import requests
data = requests.get("https://raw.githubusercontent.com/Jott2121/ai-career-threat-index/main/data/ai-career-threat-index.json").json()

# Roles most exposed to autonomous agents specifically
hot = sorted(data["roles"], key=lambda r: -r["subscores"]["agenticExposure"])[:10]
for r in hot:
    print(f'{r["title"]:35} agentic={r["subscores"]["agenticExposure"]} overall={r["score"]}')

JavaScript

const url = "https://raw.githubusercontent.com/Jott2121/ai-career-threat-index/main/data/ai-career-threat-index.json";
const data = await fetch(url).then(r => r.json());
const rising = data.roles.filter(r => r.score - (r.historicalScores["Q2 2026"] ?? r.score) >= 3);

R

roles  JSON + CSV + crosswalk + quarterly report
python3 pipeline/generate_svgs.py  # README charts
pytest tests/                      # 30+ integrity checks (CI runs these on every push)

Historical quarters (Q1 2025 – Q2 2026) were published under methodology v1 (50/30/20 weighted composite, no agentic factor) and are retained as published. Quarterly moves larger than ±8 points require a stated restatement reason — see the [quarterly report](reports/2026-q3-winners-losers.md).

Cite it

The MeritForge Team (2026). AI Career Threat Index v2026.3.
https://github.com/Jott2121/ai-career-threat-index (DOI: 10.5281/zenodo.21227026)

See [CITATION.cff](CITATION.cff) for BibTeX and more formats. When citing in editorial content, a link to this repo or the interactive explorer is appreciated.

Contributing

Disagree with a score? The sub-scores and rationales are published precisely so you can attack them: [open an issue](../../issues) naming the role, the factor, and the evidence. Better adoption data for an industry is especially welcome. Accepted corrections land in the next quarterly review with attribution.

About

Maintained by Jeff Otterson / The MeritForge Team. The interactive tool also lives at meritforgeai.com.

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⭐ If this dataset is useful, star the repo — stars are how researchers and journalists find it.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Jott2121
• Source: Jott2121/ai-career-threat-index
• License: MIT
• Homepage: https://jott2121.github.io/ai-career-threat-index/

Install and usage instructions live in the source repository linked above.