4.5 million jobs. 75 occupational groups. One question: how much is generative AI really changing the Austrian labour market? The debate is heated, yet rarely grounded in data. Job Radar Austria turns that question into something you can explore for yourself, drawing on official employment data, a 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 take away from it
Table of Contents
What the Map Shows
Job Radar Austria is an interactive treemap of the Austrian labour market. Each tile represents an ISCO-08 occupational group, and its area reflects the number of employees – the larger the tile, the more people work in that field.
Four Layers, Four Perspectives
The map offers four interchangeable colour layers, each revealing a different dimension of the labour 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.
The application also offers seven navigation views: Map (the interactive treemap), All Occupations (a sortable table of all 75 groups), Major Groups (ISCO families), Industries (ÖNACE sectors with interactive charts), Comparison (a direct AT vs. US comparison), Verification (a 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%) sits in the moderate range (4–5). Occupations with minimal AI impact account for just 7% – mostly roles that depend on physical presence, such as emergency medical services, roofers and midwives.
A high AI impact score does not mean a profession will disappear. Software development, for example, scores 8–10 – yet demand for software rises as every developer becomes more productive. The score measures technical substitutability, not job displacement. Elasticity of demand, regulatory hurdles and societal preferences are not factored 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 distinctive is that the AI exposure scores are not assigned by hand but generated through an LLM pipeline. A prompt defines the scoring rubric, and the model assesses each job description in turn. The whole process is reproducible and publicly available 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 decoupled from output. | Warehouse logistics, public administration, long-haul transport |
| Very high (8–10) | Most core tasks can already be handled by current models. | Software development, financial accounting, translation, performance marketing |
The decisive signal is whether the work is fundamentally digital. If a job can be done entirely from home – writing, programming, analysing, communicating – the AI impact is inherently 7 or above. Professions that demand physical presence, manual dexterity or face-to-face interaction in the real 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 rebuilt from scratch for the Austrian labour market. It is also explicitly an independent, unofficial adaptation, with no institutional ties to AMS, WIFO, Statistics Austria or any other public body.
| 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 from 0 to 10. The values are curated and documented as qualitative assessments against the clearly defined anchor points in Karpathy's rubric (cognitive/digital task content). One caveat: 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 is that the framework is extensible. The same data model also supports additional assessment dimensions – such as humanoid robotics exposure, offshoring risk or climate impact. This calls for clearly defined rules and documented criteria, not an opaque black box.
Data Sources and Reproducibility
Every data point can be traced back to an official source, and all raw data is freely available as Open Government Data. The generation pipeline is open-source, deterministic and safeguarded on every regeneration by 99 hypothesis-driven verification tests, including 13 row-by-row comparisons against the 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: An integer from 0 to 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 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 carry the highest AI impact? Where is upskilling worthwhile, and where are new roles needed? The map gives you 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 fields are changing the most. The education level layer reveals where qualification requirements are rising.
Evidence-based labour market policy
Combine outlook and AI impact: which occupational groups are both shedding employment and facing high AI impact? That is where the need for action is most urgent.
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) make direct comparisons possible.
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. Here are a few directions 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 analyse 75 occupational groups and 4.5 million jobs in concrete terms – with official data, a transparent methodology and open-source code.
It is not the technology that decides which professions will change, but how quickly organisations recognise and shape that change.
The key takeaways:
- Data-driven, not speculative: All employment and income data comes from official sources (Eurostat, Statistics Austria OGD) and is fully reproducible.
- Transformation, not elimination: High AI impact means a change in how work is done, 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: The source code, data pipeline and integrity checks are publicly available on GitHub – ready to fork, extend and build on.