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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ESRP

The Pressure Alchemist

“When failure isn't an option, they call a human”

Medium Risk

Ersetzungswahrscheinlichkeit

36%–54%

Prognostizierter Jahresbereich

2032–2041

Risikostufe

Medium Risk

Explicit + Subjective + Rigid + Product: AI can learn, but quality is subjective + errors have serious consequences

Superkraft

Making irreversible creative decisions under pressure — stakes too high for AI

Schwäche

AI is getting better at high-stakes reasoning every quarter

Vier Barriere-Dimensionen

Lernbarkeit — Explizit (E)

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

Bewertungsobjektivität — Subjektiv (S)

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 only the output matters — AI can learn the theory and produce work anonymously.

Natürliche Verteidigung

Quality is subjective AND errors are irreversible — AI must clear both the creative judgment bar AND the safety bar. This dual gate significantly slows adoption.

Typische Berufe

Architecture & Engineering (17), Life, Physical & Social Science (19), Business & Financial Operations (13) — investment management

Beispielberufe-Risiko

Electrical Engineers
System design and safety sign-off require licensed professional judgment
6
Architects, Except Landscape and Naval
AI generates designs but code compliance, liability, and client negotiation keep humans central
6
Mechanical Engineers
Physical prototyping, tolerances, and manufacturing constraints require hands-on expertise
4
Civil Engineers
Infrastructure design with public safety accountability — PE licensure is a hard moat
1
Industrial Engineers
Process optimization in physical environments requires operational context AI cannot observe
1
Medical Scientists, Except Epidemiologists
Experimental design and hypothesis generation require deep domain creativity
1

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