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.

▶ Explore all 300 roles interactively · Winners & Losers this quarter · Methodology · Ask it through your AI assistant (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 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 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	Full dataset: 300 roles, sub-scores, rationales, tasks, defense skills, quarterly history
data/ai-career-threat-index.csv	Flat table, one row per role — Excel/pandas/R ready
data/soc-crosswalk.csv	Every BLS SOC 2018 occupation (867 codes) mapped to its nearest scored role
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 <- read.csv("https://raw.githubusercontent.com/Jott2121/ai-career-threat-index/main/data/ai-career-threat-index.csv")
cor(roles$score, (roles$salary_low_usd + roles$salary_high_usd) / 2)

More in examples/.

Ask it through your AI assistant

The repo ships a zero-dependency MCP server — threat-index-mcp on npm — so Claude (or any MCP client) can query the dataset directly:

claude mcp add threat-index -- npx -y threat-index-mcp

Then ask: "Compare paralegal, bookkeeper, and welder — which is most exposed to autonomous agents, and what should each build to defend?"

Schema (v2026.3)

Each role record:

Field	Type	Description
slug / title / category	string	Identity; 15 categories
socCode	string	BLS SOC 2018 detailed occupation code
tier	1 | 2	1 = head role (full editorial depth + industry modifiers)
score	int 0–100	Composite displacement risk (higher = more exposed)
riskLevel	enum	Low 0–35 · Moderate 36–50 · High 51–75 · Very High 76–100
subscores	object	The four published factors, each 0–100
rationales	object	One-sentence published justification per factor
agenticRisk	enum	Band on agenticExposure alone
tasksAtRisk / tasksGrowing	string[]	Concrete task lists
salary, salaryRange, salarySource, salaryTrend	—	BLS-OES-anchored range with named anchor
defenseSkills	object[]	Three highest-leverage skills to build now
insight	string	Headline finding
historicalScores	object	Quarterly history — new roles start at their first published quarter, never backfilled
industryModifiers	object	Tier-1 only: per-sector adjustments
restatement	string?	Present when a quarterly move exceeded ±8 — with the reason

CSV columns are a flat projection; v2026.3 columns were appended after the v2026.2 set, so existing consumers keep working. Scores within 5 points of a band boundary may retain their prior band across quarterly reviews (stability rule).

Methodology

Methodology v2 (Q3 2026 onward) is fully specified in pipeline/RUBRIC.md: factor definitions, scoring anchors, calibration examples, the formula, band hysteresis, salary anchoring, and sources. The dataset regenerates deterministically from per-role source files:

python3 pipeline/score.py          # role sources -> 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.

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 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 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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