Same methodology. Five labor markets. Different outcomes.
I scored over 500 occupations across five countries using the same JPE methodology. What I found: AI exposure is remarkably consistent. How countries absorb it is not.
The core finding: A bookkeeper scores 8/10 in all five countries. The technology does not care about borders. But the speed at which that score translates into actual job change varies dramatically. Germany has Kurzarbeit and Betriebsräte. The Netherlands has collective agreements and a 48% part-time rate. The US has at-will employment and minimal buffers. The UK sits between the US and EU models — flexible employment with moderate protections. France has the 35-hour week, CDI/CDD contracts and CNIL oversight. Same score. Different trajectories.
The numbers side by side
| Metric | 🇳🇱 Netherlands | 🇺🇸 United States | 🇩🇪 Germany | 🇬🇧 United Kingdom | 🇫🇷 France |
|---|---|---|---|---|---|
| Occupations scored | 103 | 105 | 98 | 106 | 86 |
| Jobs covered | 6.8M | 97.8M | 32.8M | 18.2M | 19.8M |
| Weighted avg. JPE | 5.1 | 4.8 | 4.8 | 5.2 | 4.8 |
| Score ≥ 7 (high exposure) | 34 | 25 | 27 | 30 | 22 |
| Score ≤ 3 (low exposure) | 26 | 28 | 26 | 25 | 24 |
| Classification | CBS BRC 2014 | SOC 2018 (BLS National Employment Matrix) | KldB 2010 (concordance per occupation) | SOC 2020 (unit groups) | FAP 2021 (DARES families) |
| Employment source | CBS Statline | BLS Employment Projections 2025 (incl. self-employed) | BA Beschäftigungsstatistik + QuBe 2025 | ONS Annual Population Survey 2025 | DARES 2024 (Enquête Emploi, incl. self-employed) |
| Salary source | CBS / CAO | BLS OEWS May 2025 | BA Entgeltatlas | ONS ASHE 2024 | DARES net median (net, not gross) |
| Forecast 2030 | JPE forecast v3: ROA + AI + robotics† | JPE forecast v3: BLS projections + AI + robotics§ | JPE forecast v3: QuBe + AI + robotics¶ | JPE forecast v3: Working Futures + AI + robotics‡ | JPE forecast v3: Métiers 2030 + AI + robotics‖ |
Same score, different speed
AI exposure is a property of the work, not the country. An accountant processes the same type of structured data in Rotterdam, Houston, Munich, London and Paris. The JPE score captures that. What differs is how fast it translates into actual change on the ground.
In the US, an employer can restructure a team next quarter. In Germany, that same decision passes through the Betriebsrat, gets mitigated by Kurzarbeit, and takes two years. Neither approach is inherently better. The American model is faster. The German model is more predictable. The Dutch model sits in between — collective agreements provide structure, but the labor market is more flexible than Germany and less volatile than the US. The UK post-Brexit model resembles the US in speed but retains more worker protections. Notice periods and unfair dismissal law slow things down compared to at-will employment, but far less than German co-determination.
Why Germany absorbs slower
German labour market institutions slow down how fast a change in tasks becomes a change in headcount.We describe the mechanisms here; the German forecast does not put a separate number on them. The main buffers:
Companies reduce hours instead of headcount. The government pays the difference. This keeps people employed through transitions that would cause layoffs in the US.
Co-determination is mandatory from 5 employees. Any significant technology change must be negotiated. This slows AI adoption in a way that has no equivalent in the Anglo-Saxon world.
Dismissal protection is substantially stronger than NL or US. Role changes happen gradually, through attrition and retraining, not sudden restructuring.
Germany has 1.3 million apprentices. The Ausbildung system creates deep occupational identity and high switching costs. A Mechatroniker does not easily become a data analyst.
The net effect: an occupation with AI score 8 will change faster in the US, but more predictably in Germany. The Netherlands sits in between. Dutch CAO agreements provide some structure, but the labor market is considerably more flexible than Germany's.
Forecasts: v3 for the Netherlands, the US, the UK, Germany and France
This section used to compare the forecast parameters per country (dampening factors and an AI pressure table). An audit in September 2026 showed that the forecasts for the US, the UK, Germany and France were driven mostly by those parameters and by estimated or synthetic history, not by observed trends. We withdrew them and rebuilt each country on official statistics. The US, the UK, Germany and France are done; each map links to its own methodology. The AI exposure scores (JPE) are not affected and remain comparable across all five countries.
On 27 September 2026 the UK was rebuilt on official data: job counts, part-time and female share from the ONS Annual Population Survey (2021-2025), salaries from ASHE 2024 and occupations linked to SOC 2020 unit groups. The 2030 forecast uses the same engine as the Netherlands, with the DfE Working Futures baseline as the base instead of ROA, plus the AI and robotics layers. Working Futures differentiates growth only at the level of 26 occupational sub-major groups, so differences between occupations within one group come from the AI and robotics layers. The forecast per occupation is on the UK map.
Structural differences that matter
| Factor | 🇳🇱 | 🇺🇸 | 🇩🇪 | 🇬🇧 | 🇫🇷 |
|---|---|---|---|---|---|
| Dismissal protection | Moderate (UWV permit) | Weak (at-will) | Strong (Betriebsrat + law) | Moderate (unfair dismissal after 2yr) | Strong (CDI protection, Code du Travail) |
| Co-determination | CAO collective agreements | Minimal | Betriebsrat (right from 5 staff, ~40% of firms) | Moderate (union recognition) | CSE (right from 11 staff) + CGT/CFDT |
| Transition safety net | UWV + retraining | Limited | Kurzarbeit (cyclical) + Qualifizierungsgeld (structural) | Universal Credit + retraining | France Travail + CPF training credits |
| Part-time rate | 43-48% (highest in EU) | ~17% | ~28% | ~25% | ~18% |
| AI regulation | EU AI Act | Light (AI Act 2026) | EU AI Act + KI-Strategie | Pro-innovation (AI Safety Institute) | EU AI Act + CNIL data oversight |
| Labor shortage sectors | Healthcare, IT, construction | Varies | Pflege, IT, Handwerk | NHS, IT, social care | Healthcare, IT, hospitality |
| Economy structure | Services-heavy | Services + tech | Industrial + Mittelstand | Services + finance (City) | Services + public sector + luxury |
Germany's industrial base means a higher share of occupations in production, trades and engineering — sectors that are inherently less exposed to AI. The Netherlands is more services-heavy, which pushes the overall exposure profile up. France combines a large public sector (with near-permanent civil service contracts) and strong CDI protections, making it structurally similar to Germany in absorption speed but for different reasons. The UK and the US have less friction when change does come, but the UK's NHS acts as a massive anchor.
The developer paradox
Software developers score 9/10 in every country. In the Netherlands they also show some of the strongest employment growth: +96% (2015–2025, CBS).In the Netherlands that growth stalled between 2022 and 2025 before picking up again in 2026: JPE forecast v3 expects -15% to +5% by 2030. In the UK, programmers and software developers grew +20% between 2021 and 2025 (ONS Annual Population Survey); JPE forecast v3 expects -10% to +10% by 2030.
This is not a bug in the model. For years, demand for software grew faster than AI made the people who write it more productive. The Dutch forecast shows this is not guaranteed: with a modest ROA base (+4% by 2030), the AI layer tips the central estimate to -3%. The UK shows the same pattern: the Working Futures base is +5% by 2030, and with the AI layer the central estimate is -1%.
High AI exposure combined with rising demand can mean growth, not replacement. The score measures exposure, not destiny.
What this means for you
If you work in the Netherlands, your occupation will change at roughly the average pace of AI adoption. Strong collective agreements provide some buffer, but the services-heavy economy means broad exposure.
If you work in the US, changes will come faster. At-will employment means faster restructuring. But also faster creation of new roles. The American labor market is volatile in both directions.
If you work in Germany, Kurzarbeit and Betriebsräte give you more time to adapt. Use it. The transition will be slower but it will happen. The dual vocational system is an asset — but only if Ausbildung programs integrate AI skills. That is happening, but not fast enough.
If you work in the UK, you have a flexible labour market with a strong services economy — particularly finance (City of London) and the NHS. The pro-innovation AI approach means fewer regulatory buffers than the EU but more than the US. The NHS workforce is a unique factor: healthcare demand is rising while budgets are tight, making AI adoption both an opportunity and a necessity.
If you work in France, strong CDI protections and a large public sector slow restructuring down, and France Travail and the CPF training account fund retraining. Even before generative AI, the national projection Les métiers en 2030 expected fewer jobs in administration, banking and accounting; AI adds to that pressure.
Across all five: the occupations that thrive are the ones where people use AI as a tool, not the ones where people compete with it.
Methodology note: All five maps use the same Janssen Practical Exposure (JPE) methodology, making scores directly comparable across countries. The Netherlands, the US, the UK, Germany and France have a 2030 forecast (JPE forecast v3). Full methodology: NL · US · DE · UK · FR. All data CC BY 4.0.
Important limitations: JPE scores are LLM-estimated, not reproducible science. The AI layer of forecast v3 is an informed judgment, not empirically measured. Employment data precision varies: NL uses observed CBS data; the UK uses ONS Annual Population Survey counts, which carry survey sampling error for small occupations; French salaries are DARES net medians, not gross. See the full limitations and known constraints section for details on coverage, classification crosswalks, salary comparability, and what this research does not cover.