u/Iapetus_Raven

I’ve been researching how AI shapes/reshapes labor markets across countries as a side quest to my PhD.

One thing that has been obvious to me was that most tech careers will not experience AI disruption equally wrt geography.

So I built a global map estimating where developers are most exposed to AI-driven automation pressure.

The model combines:

Frey & Osborne automation probabilities

Oxford Insights Government AI Readiness Index

World Bank employment data

UNDP education/employment indicators

Tools used: Python (Pandas, NumPy)

Ollama (LLM-assisted occupation classification)

Matplotlib / GeoPandas for visualization

QGIS for map refinement/layout

Custom scoring pipeline

This is not a prediction engine for“heya, which jobs are gonna disappear next". It is an estimation that is supposed to tell where:

AI adoption is likely to accelerate automation pressure

labor markets are more structurally exposed

reskilling capacity differs

workflow automation may emerge faster

Some interesting variations I noticed were due to geography, labor structure, legislative protection and adoption speed by the ecosystem in that country.

The methodology is fully deterministic/reproducible using open institutional datasets.

Curious where people think the map gets things right/wrong, or differs from the ground truth, especially for developers outside the US.