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 | 106 | 98 | 106 | 86 |
| Jobs covered | 5.8M | 90.7M | 29.1M | 17.7M | 15.9M |
| Weighted avg. JPE | 4.9 | 4.8 | 5 | 4.9 | 4.8 |
| Score ≥ 7 (high exposure) | 34 | 25 | 27 | 30 | 22 |
| Score ≤ 3 (low exposure) | 26 | 28 | 26 | 25 | 24 |
| Classification | CBS BRC 2014 | BLS SOC 2018 | KldB 2010 | SOC 2020 | PCS 2020 |
| Employment source | CBS Statline | BLS OEWS 2024 | Destatis 2024 | ONS LFS 2024 | INSEE 2024 |
| Salary source | CBS / CAO | BLS OEWS | Entgeltatlas BA | ONS ASHE 2024 | INSEE DADS |
| Forecast dampening | 0.7 | 0.7 | 0.6 | 0.68 | 0.6 |
| AI pressure (score 9+) | −8 | −5* | −6 | −8 | −6 |
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
The model uses a dampening factor of 0.6 for Germany versus 0.7 for NL and the US. Lower dampening means trends continue with less momentum. Why?
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.
AI pressure: calibrated per market
The AI pressure table — the downward force applied to high-scoring occupations — is different per country. Not because AI is stronger in one place, but because the transmission mechanism is different.
| AI score | 🇳🇱 NL | 🇺🇸 US | 🇩🇪 DE | 🇬🇧 UK | 🇫🇷 FR | Rationale |
|---|---|---|---|---|---|---|
| ≥ 9 | −8 | −5 | −6 | −8 | −6 | Near-complete task automation. NL and UK have highest pressure; US uses different projection method. |
| 7–8 | −5 | −3 | −4 | −5 | −4 | Core tasks exposed. Strong pressure, moderated by country buffers. |
| 5–6 | −2 | −1 | −2 | −2 | −2 | Partial exposure. AI assists but does not replace. |
| 3–4 | 0 | 0 | 0 | 0 | 0 | Limited exposure. Neutral effect on employment. |
| 0–2 | +1 | +1 | +1 | +1 | +1 | AI creates demand (e.g. more construction via AI-designed buildings). |
Important: the US numbers look lower because the US model uses a direct projection method (factor 1.0) rather than index-based projection (factor 2.5). When converted to the same scale, effective impacts are comparable. The direction is the same across all five countries.
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 — in all four
Software developers score 9/10 in every country. They also show the strongest employment growth in every country. NL: +80% (2015–2025). US: +17% (BLS 2023–2033). DE: +35% (IAB 2015–2025). UK: +12% (ONS 2019–2024).
This is not a bug in the model. Demand for software grows faster than AI replaces the people who write it. GitHub Copilot makes a developer 2x more productive. But the world needs 3x more software. The net effect is growth, not decline.
The same pattern holds for data scientists (score 9, growing in all four), IT consultants (score 8, growing), and healthcare managers (score 6, strong growth due to demographics). High AI exposure combined with rising demand equals 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.
Across all four: 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 forecast model uses country-specific parameters to account for structural labor market differences. Full methodology: NL · US · DE · UK. All data CC BY 4.0.
Important limitations: JPE scores are LLM-estimated, not reproducible science. Dampening factors and AI pressure values are informed judgments, not empirically measured. Employment data precision varies: NL uses observed CBS data; UK, DE, and FR figures are estimates with synthetic history. The US uses a different projection formula than other countries. See the full limitations and known constraints section for details on coverage, classification crosswalks, salary comparability, and what this research does not cover.