Ai Career Threat Index

Jott212112stars

Open dataset: 300 US professions scored on AI displacement risk — four published sub-scores per role incl. exposure to autonomous agents. Quarterly updates, SOC crosswalk, interactive explorer, MCP se

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1# Ai Career Threat Index

README.mdMCP server

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Status: Local

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RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

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Host a copy with our keys and logs.

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Transport: stdio
License: MIT
Last checked: 2026-10-27

Stars: 12
Tools: —
Uptime: —

Overview

About Ai Career Threat Index

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Ai Career Threat Index?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Ai Career Threat Index need an API key?

No required environment variable is listed on this record.

What transport does Ai Career Threat Index use?

Ai Career Threat Index runs locally over stdio.

When was Ai Career Threat Index last checked?

Last checked 2026-10-27. Open dataset: 300 US professions scored on AI displacement risk — four published sub-scores per role incl. exposure to autonomous agents. Quarterly updates, SOC crosswalk, interactive explorer, MCP server. MIT.

From the publisher

Documentation

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)

Score distribution

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.

Biggest movers

Agentic exposure by category

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);

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)

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.

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

Ai Career Threat Index

Jott212112stars

Data Science Ml

Install Run on PlugMCP

GitHub Website

Run on PlugMCP

1# Ai Career Threat Index

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-27

Stars: 12
Tools: —
Uptime: —

Overview

About Ai Career Threat Index

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Ai Career Threat Index?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Ai Career Threat Index need an API key?

No required environment variable is listed on this record.

What transport does Ai Career Threat Index use?

Ai Career Threat Index runs locally over stdio.

When was Ai Career Threat Index last checked?

From the publisher

Documentation

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)

Score distribution

What this is

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

Biggest movers

Agentic exposure by category

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);

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)

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

Methodology

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)

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

About

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

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