What is the AI Kill Threshold?

The AI Kill Threshold is a probability (0-100%) indicating how likely your occupation will be replaced by AI. It is calculated using the Swiss Cheese Barrier Model across four independent dimensions: Learnability, Evaluation Objectivity, Risk Tolerance, and Human Presence.

How is the replacement probability calculated?

Using a Weighted Power Mean with r=-2 across four dimensions. Learnability has weight 0.30, Evaluation Objectivity 0.25, Risk Tolerance 0.20, Human Presence 0.25. An additional E+O interaction bonus applies when both knowledge is explicit and evaluation is objective.

What data sources does AIR use?

AIR aggregates from BLS OES 2023 (798 occupations employment data), O*NET (task structure labels), Eloundou et al. 2023 (LLM exposure measurement across 19,265 tasks), OpenAI GDPval (AI vs human expert blind evaluation), and the World Economic Forum Future of Jobs Report 2025.

How does AIR differ from MBTI?

MBTI measures personality. AIR measures occupational AI-replacement risk. Both use four binary dimensions producing a 4-letter code, but AIR's dimensions are grounded in automation economics literature and the output is backed by employment data from 798 real occupations.

What is the current industry-wide AI replacement rate?

As of 2026, the employment-weighted industry-wide AI replacement rate is 24.7%, calculated as Task Exposure (34.8%, Eloundou et al. 2023) times AI Win Rate vs human experts (71.3%, OpenAI GDPval).

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ESFP

The Taste Maker

“AI generates a thousand options — you know which one is right”

High Risk

代替確率

55%–72%

予測年範囲

2029–2035

リスク等級

High Risk

Explicit + Subjective + Flexible + Product: AI can learn, errors are tolerable, only results matter, but quality is subjective

超能力

Aesthetic judgment and cultural intuition machines can't learn from data

弱点

AI is already generating content that passes the taste test

4つのバリア次元

学習可能性 — 顕在型 (E)

AIはあなたの仕事に必要な知識やスキルを習得・学習できますか？

評価客観性 — 主観型 (S)

あなたの仕事に「正解」はありますか？品質を客観的に測定できますか？

許容度 — 柔軟型 (F)

AIがミスをした場合、その結果を許容できますか？その出力を信頼できますか？

人格依存性 — 成果型 (P)

あなたの価値は「何を作るか」にありますか、それとも「あなたが誰であるか」にありますか？

なぜ脆弱なのか

Knowledge is learnable, errors are tolerable, and only output matters — AI can iterate endlessly at near-zero cost in these three dimensions.

自然な防御

Quality is subjectively judged — "good" depends on taste, context, and cultural nuance. AI struggles with the moving target of subjective standards.

典型的な職業

Arts, Design, Entertainment, Sports & Media (27) — design/planning, Computer & Mathematical (15)

代表的な職業リスク

Software Developers
AI coding assistants accelerating productivity — and also changing what developers need to know
39
Software Quality Assurance Analysts and Testers
Test generation and regression automated — exploratory and edge-case testing still requires humans
22
Computer User Support Specialists
Tier-1 troubleshooting increasingly automated by AI; complex issues still need human diagnosis
21
Data Scientists
Automated ML pipelines lower the bar — data scientists shift toward problem framing and interpretation
20
Computer Systems Analysts
Requirements translation and integration work — business context requires humans in the loop
18
Network and Computer Systems Administrators
Infrastructure monitoring increasingly automated — complex configurations and security still need humans
16

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