4.5 million jobs. 75 occupational groups. One question: How much is generative AI changing the Austrian job market? The debate surrounding this is passionate – but rarely data-driven. Job Radar Austria makes this question interactive and explorable: using official employment data, reproducible methodology, and open-source code.
Job Radar Austria – Interactive AI Job Market Map
75 occupational groups, 4.5 million jobs, interactive map with industry analysis, occupation comparison, and official data. Free, no signup required.
- What the Austrian AI Impact Map shows and how it works
- Where the data comes from and why it is reproducible
- What insights the visualisation provides
- How the open-source project is technically structured
- What companies, educational institutions, and policymakers can deduce from it
Table of Contents
What the Map Shows
Job Radar Austria is an interactive treemap visualisation of the Austrian job market. Each tile represents an ISCO-08 occupational group. The area corresponds to the number of employees – the larger the tile, the more people work in this professional field.
Four Layers, Four Perspectives
The map offers four interchangeable colour layers (layers) that reveal different dimensions of the job market:
Outlook 2023–2030
Employment forecast: Based on the WIFO/AMS medium-term projection (weighted average of NACE sector growth 60% + ISCO occupational group trends 40%). Green signals growth, red indicates decline.
Median Income
Gross annual salaries in EUR – including 13th and 14th month salaries, as is customary in Austria. Source: Structure of Earnings Survey 2022.
Education Level
Typical qualification level per occupational group – from compulsory schooling to university degree.
AI Impact
How strongly generative AI is expected to change the respective occupational group – a qualitative 0 to 10 assessment based on Karpathy's rubric.
In addition, the application now offers seven navigation views: Map (interactive treemap), All Occupations (sortable table of all 75 groups), Major Groups (ISCO families), Industries (ÖNACE sectors with interactive charts), Comparison (direct AT vs. US comparison), Verification (public test overview), and About – a methodology section with source references, processing notes, and verification tests.
The Austrian Job Market in Figures
- Total employees
- 4.5 million
- Average AI impact (job-weighted)
- 4.5
- ISCO-08 occupational groups
- 75
- High-impact earnings annually
- €47bn
| Impact category | Employees | Share (%) |
|---|---|---|
| Minimal (0–1) | 317,000 | 7 |
| Low (2–3) | 1.3 million | 29 |
| Moderate (4–5) | 1.7 million | 38 |
| High (6–7) | 782,000 | 17 |
| Very high (8–10) | 370,000 | 8 |
A key finding: 25% of employees (1.15 million) work in occupational groups with high or very high AI impact (score 6–10). The largest share (38%) falls into the moderate range (4–5). Occupations with minimal AI impact account for just 7% – predominantly roles requiring high physical presence such as emergency medical services, roofers, or midwives.
A high AI impact score does not mean that professions will disappear. Software development, for example, reaches scores of 8–10 – but the demand for software increases when every developer becomes more productive. The score measures technical substitutability, not job displacement. Elasticity of demand, regulatory hurdles, and societal preferences are not priced in.
From Karpathy's Idea to the Austrian Adaptation
The US Original
In March 2026, Andrej Karpathy – former Director of AI at Tesla and co-founder of OpenAI – published the US Job Market Visualizer. The tool visualises 342 professions from the Bureau of Labor Statistics Occupational Outlook Handbook and covers 143 million jobs in the US economy.
What makes Karpathy's approach special: The AI Exposure Scores are not assigned manually, but generated via an LLM pipeline. A prompt defines the evaluation grid, and the model evaluates each job description individually. The entire process is reproducible and publicly accessible in the GitHub repository.
"This is not a report, a paper, or a serious economic publication — it is a development tool for exploring BLS data visually."
— Andrej Karpathy, US Job Market Visualizer
The AI impact score follows a five-tier scale with clear anchor points and Austrian examples:
| Tier | Criterion | Examples (Austria) |
|---|---|---|
| Minimal (0–1) | Negligible digital substitutability. High physical presence, manual work, or direct personal care. | Emergency medical services, roofers, midwives |
| Low (2–3) | AI-assisted tools applicable; majority of tasks require manual, sensory, or interpersonal competences. | Nursing staff, police, on-site plumbers |
| Moderate (4–5) | Significant share of routinised cognitive or administrative tasks. | Teaching staff, retail, industrial kitchens |
| High (6–7) | Substantial restructuring likely; staffing needs increasingly decouple from output. | Warehouse logistics, public administration, long-haul transport |
| Very high (8–10) | The majority of core tasks are already addressable by current models. | Software development, financial accounting, translation, performance marketing |
A key signal: Whether the work is fundamentally digital. If a job can be done entirely from a home office – writing, programming, analysing, communicating – the AI impact is inherently 7+. Professions requiring physical presence, manual dexterity, or direct human interaction in the physical world have a natural barrier.
What Differs
The Austrian AI Impact Map adopts Karpathy's concept and scoring rubric – but the data sources, information architecture, and methodology were completely rebuilt for the Austrian job market. It is also explicitly an independent, non-official adaptation with no institutional affiliation to AMS, WIFO, Statistics Austria, or other public bodies.
| Feature | US Original (Karpathy) | Austrian Adaptation (webconsulting) |
|---|---|---|
| Data sources | Bureau of Labor Statistics (BLS) | Eurostat lfsa_egai2d, nama_10_a64_e, Statistics Austria OGD |
| Occupational classification | SOC (342 professions) | ISCO-08 (75 occupational groups) |
| Structure | Sector-based | Occupation-based + ÖNACE sector context |
| Income | USD Median Annual | EUR gross incl. 13th/14th salary |
| Outlook | BLS Occupational Outlook Handbook | WIFO/AMS Employment Forecast 2023–2030 |
| Language | English | German / English (bilingual) |
Methodology: Transparency Instead of a Black Box
AI Impact Scoring: How the AI Influence Is Evaluated
The scoring follows Karpathy's methodology: Each occupational group receives an AI impact score on a scale from 0 to 10. The values are curated and documented as qualitative assessments along clearly defined anchor points from Karpathy's rubric (cognitive/digital task content). Important: these scores are not empirical measurements. The published pipeline is deterministic, and no LLM is used at build time.
Scoring logic of the Austrian adaptation: from rubric to visualisation
The crucial point: The framework is extensible. The same data model also works for additional assessment dimensions – such as humanoid robotics exposure, offshoring risk, or climate impact. That does not require an opaque black box, but clearly defined rules and documented criteria.
Data Sources and Reproducibility
Every data point can be traced back to an official source. All raw data can be freely downloaded as Open Government Data. The generation pipeline is open-source, deterministic, and protected by 99 hypothesis-driven verification tests on every regeneration, including 13 row-by-row comparisons against original Eurostat and VSE source data.
The data sources in detail:
- Employment: Eurostat
lfsa_egai2d(Microcensus Labour Force Survey 2024) by ISCO-08. Additionally,nama_10_a64_e(NACE sector employment) serves as proportional weights to split ISCO totals across the ÖNACE-labelled occupation rows. - Outlook: WIFO/AMS medium-term employment forecast 2023–2030 (AMS report 185, December 2024). Outlook per occupational group is calculated as a weighted average of NACE sector growth (60%) and ISCO occupational group trends (40%).
- Income: Structure of Earnings Survey 2022 (Statistics Austria Open Government Data) – two datasets:
OGD_veste403provides gross hourly median pay by ISCO-08 occupational group,OGD_veste401by economic section as supplementary sector context. Annual gross salary is calculated as: hourly median × 2,080 hours/year × 1.17 (13th and 14th month salary). - Sector context: ÖNACE 2025 (effective since 1 Jan 2025) for economic sections A–S. The VSE 2022 was conducted under ÖNACE 2008; at the section level the structure is identical.
- AI Impact: Integer 0–10 per occupational group, qualitatively curated following Karpathy's rubric (cognitive/digital task content). The score is a documented assessment, not an empirical measurement.
What You Can Do with the Map
Job Radar Austria is more than just a pretty graphic. It is an analytical tool for anyone who wants to think about AI and work in a data-driven way.
For Companies
Strategic workforce planning
Which occupational groups in your company have the highest AI impact? Where is upskilling worthwhile, and where are new roles needed? The map provides the data foundation for evidence-based decisions.
Validating the technology roadmap
Compare your planned AI investments with the impact scores. Where do internal assumptions align with the data – and where are there blind spots?
For Education and Policy
Developing curricula
Educational institutions can see at a glance which professional fields are changing the most. The education level layer shows where qualification requirements are increasing.
Evidence-based labour market policy
A combination of outlook and AI impact: Which occupational groups are losing employment and have high AI impact? This is where the most urgent need for action arises.
For Research and Journalism
- Reproducible analysis: All data and methods are open – ideal for scientific papers and investigative research.
- Additional assessment dimensions: The open data model can be extended with further exposure layers – such as robotics, offshoring, or regulatory risk.
- Cross-country comparison: The US original and several international adaptations (Australia, EU) enable direct comparisons.
Technology Under the Hood
The Tech Stack
- Next.js with App Router and Server Components
- TypeScript for end-to-end type safety
- shadcn/ui as a component library
- webconsulting Design System for consistent branding
- Responsive design with a bilingual interface (DE/EN)
Open Source and Reproducibility
The entire project is open-source and available on GitHub:
Austrian AI Impact Map – GitHub Repository
Complete source code of the Austrian AI Impact Map: deterministic data pipeline, visualisation, and 99 verification tests. Fork it, extend it, build on it.
Andrej Karpathy's US Job Market Visualizer – GitHub Repository
The original project: 342 US professions, LLM scoring pipeline, and interactive treemap visualisation. The template for all international adaptations.
What Could Be Possible Next
The open architecture invites further development. Some approaches that could build on the existing framework:
Conclusion
Job Radar Austria makes an abstract debate tangible. Instead of speculating about "AI and work", companies, educational institutions, and policymakers can concretely analyse 75 occupational groups and 4.5 million jobs – with official data, transparent methodology, and open-source code.
It is not the technology that decides which professions will change – but rather how quickly organisations recognise and shape this change.
The key takeaways:
- Data-driven instead of speculative: All employment and income data come from official sources (Eurostat, Statistics Austria OGD) and are fully reproducible.
- Transformation, not elimination: High AI impact means a change in working methods – not necessarily fewer jobs. Elasticity of demand and societal factors play a central role.
- Extensible by design: The open data structure can be extended with new assessment dimensions and additional exposure layers.
- Open-source: Source code, data pipeline, and integrity checks are publicly available on GitHub – to fork, extend, and further develop.