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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TOFP

The Bare Hand

“Your hands know things your brain can't explain”

High Risk

Ersetzungswahrscheinlichkeit

55%–73%

Prognostizierter Jahresbereich

2030–2037

Risikostufe

High Risk

Tacit + Objective + Flexible + Product: Clear standards, fixable errors, results-only, but needs physical experience

Superkraft

Physical dexterity and embodied knowledge gained through years of practice

Schwäche

Robotics is catching up — and robots don't get tired

Vier Barriere-Dimensionen

Lernbarkeit — Implizit (T)

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 — Flexibel (F)

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

Work is objectively evaluated, errors are fixable, and only the output matters — AI can match or exceed human quality in measurable, forgiving domains.

Natürliche Verteidigung

Your knowledge is tacit — learned through years of hands-on physical experience that AI cannot yet acquire through data alone. This buys significant time, especially pre-AGI.

Typische Berufe

Installation, Maintenance & Repair (49), Transportation & Material Moving (53) — warehouse, Food Preparation & Serving (35) — chain, Building & Grounds Cleaning/Maintenance (37)

Beispielberufe-Risiko

Laborers and Freight, Stock, and Material Movers
Unstructured physical environments resist full automation — warehouse robots still need human oversight
2
Heavy and Tractor-Trailer Truck Drivers
Long-haul autonomous vehicles emerging but full deployment still requires regulatory and safety validation
2
Stockers and Order Fillers
Picking and placing in varied retail environments — robotics advancing but not yet cost-effective everywhere
2
Janitors and Cleaners
Varied surfaces, obstacles, and standards make full robotic cleaning commercially limited
2
Maintenance and Repair Workers, General
Diagnostic and hands-on repair in unpredictable physical situations — judgment and dexterity required
2
Landscaping and Groundskeeping Workers
Outdoor, seasonally varied physical work — robotic mowers exist but full automation remains limited
2

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