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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EORP

The Final Stamp

“AI knows the rules — you enforce the consequences”

High Risk

Ersetzungswahrscheinlichkeit

58%–75%

Prognostizierter Jahresbereich

2030–2036

Risikostufe

High Risk

Explicit + Objective + Rigid + Product: AI can learn and do it, but errors are too costly / regulated

Superkraft

Accountability and regulatory authority that AI cannot legally hold

Schwäche

AI already knows every regulation better than you do

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 — Produkt (P)

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

Warum exponiert

Knowledge is digitized and quality is objectively measured — AI learns these quickly. Output-only evaluation means AI can substitute anonymously.

Natürliche Verteidigung

Errors carry real consequences — organizations are cautious about deploying AI where mistakes are irreversible. Regulatory barriers and liability concerns slow replacement.

Typische Berufe

Production (51) — quality inspection, Healthcare Practitioners & Technical (29) — pharmacy/lab roles

Beispielberufe-Risiko

First-Line Supervisors of Production Workers
On-floor decision-making and worker management require physical presence
2
Inspectors, Testers, Sorters, Samplers, and Weighers
Sensory judgment and standards compliance — machine vision helps but cannot fully replace
2
Packaging and Filling Machine Operators
Machine operation and troubleshooting on physical production lines
2
Welders, Cutters, Solderers, and Brazers
Precision manual skill with physical safety responsibility — hard to fully automate
2
Machinists
CNC programming and custom part production require technical judgment and adaptation
2
Bakers
Industrial baking is mechanized; artisan quality relies on sensory skill and creativity
2

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