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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EORH

The License Wall

“AI has the knowledge, but not the license on the wall”

Medium Risk

Ersetzungswahrscheinlichkeit

38%–55%

Prognostizierter Jahresbereich

2032–2040

Risikostufe

Medium Risk

Explicit + Objective + Rigid + Human: Transparent knowledge + regulated + clients trust you — AI only lacks entry

Superkraft

Regulatory barriers and institutional trust that protect your position

Schwäche

Regulations change — and AI lobbying is a thing

Vier Barriere-Dimensionen

Lernbarkeit — Explizit (E)

Kann KI das Wissen und die Fähigkeiten erwerben und erlernen, die Ihr Job erfordert?

Bewertungsobjektivität — Objektiv (O)

Gibt es bei Ihrer Arbeit eine „richtige Antwort"? Lässt sich die Qualität objektiv messen?

Fehlertoleranz — Rigide (R)

Wenn die KI einen Fehler macht, sind die Folgen tragbar? Kann man ihrem Output vertrauen?

Personenabhängigkeit — Mensch (H)

Liegt Ihr Wert in „was Sie produzieren" oder in „wer Sie sind"?

Warum exponiert

Knowledge is explicit and quality is objectively measurable — the two dimensions AI masters fastest. Once trust barriers erode, replacement accelerates.

Natürliche Verteidigung

Errors are irreversible AND clients trust you personally — a double barrier. Regulatory licensing, professional liability, and personal relationships create a multi-layered defense.

Typische Berufe

Business & Financial Operations (13) — licensed roles, Legal (23) — notary/compliance

Beispielberufe-Risiko

Market Research Analysts and Marketing Specialists
Data synthesis is AI-native — analysts who cannot interpret qualitative signals are exposed
31
Human Resources Specialists
Resume screening and onboarding workflows heavily automated — human judgment at edge cases
21
Business Operations Specialists
Process optimization roles increasingly augmented by AI tools — value shifts to implementation
14
Management Analysts
Structured problem-solving and report writing — AI handles research; humans handle client trust
13
Project Management Specialists
Coordination and status tracking automated by PM tools — value is in stakeholder management
11
Accountants and Auditors
Compliance and professional liability create barriers — AI augments rather than replaces CPAs
7

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