How were these figures calculated?

The map covers 103 occupational groups based on the CBS Occupational Classification ROA-CBS 2014 (BRC 2014). Together these occupations represent approximately 5.8 million jobs — roughly 60% of the Dutch working population (CBS: 9.7 million in Q3 2025).

The missing 40% consists primarily of niche specialisations, undefined occupations and occupational groups that fall outside the CBS BRC 2014 top-level categories. The dataset does not claim to cover the full labour market.

The AI exposure score indicates what proportion of the core tasks of an occupation is technically automatable by current AI technology (LLMs, computer vision, robotics, agentic AI). It is a measure of technical exposure, not of actual displacement.

The vulnerability score indicates to what extent technical AI exposure also translates into real impact in the Dutch context. High exposure does not necessarily lead to high vulnerability when protective factors are present.

Factors taken into account:

• EU AI Act Art. 14: Requires human-in-the-loop for government decisions (lowers vulnerability of public sector roles)
• DBA legislation: Self-employment regulations influence the pace at which roles can be replaced
• Healthcare sector regulation: BIG registration and statutory requirements protect healthcare occupations
• Demand elasticity: When demand grows faster than AI displaces (e.g. ICT), vulnerability decreases
• Social preference: Societal preference for human contact (healthcare, education) slows adoption
• Labour market tightness: Acute shortages (healthcare, construction, installation) make displacement unlikely

The model answers the question: if current trends continue and AI automation increases, how many jobs will there be in 2030?

It combines two forces: momentum (how has the occupation developed in recent years?) and AI disruption (how much downward pressure does automation exert on this occupation?). NL vulnerability determines how much of that AI pressure actually feeds through in the Dutch context.

A damping factor of 0.7 is applied to the momentum. This is standard practice in forecasting (mean reversion): recent trends rarely persist at full force. UWV signals, for example, that ICT labour tightness is easing — confirming that growth is decelerating. The result is presented as a range: lower bound (with damping) to upper bound (without damping).

The AI Exposure Map uses two scoring systems side by side. The primary score is the Janssen Practical Exposure (JPE), developed for this map. Additionally, we show the academic Felten AIOE as a reference.

• AI exposure scores are model estimates, not empirical measurements. They have been cross-validated with academic indices but have not been independently audited.
• The dataset covers approximately 60% of the working population. Niche occupations are not included.
• Historical indices are modelled on the basis of CBS EBB trends, not taken directly from CBS tables.
• The 2030 forecast is a directional analysis, not a labour market forecast. The model does not account for economic shocks, policy changes or breakthrough technologies.
• Not all contraction is AI-related. Agriculture and fisheries are declining due to policy (nitrogen regulations, quotas), not automation.
• Salaries are medians; the spread within occupational groups can be substantial.
• The 2030 forecast is presented as a range (lower bound to upper bound). UWV signals that the growth rate for ICT occupations is slowing; WEF confirms the direction but not the pace. Point estimates would imply false precision.
• Demographic ageing is not included in the forecast model. Age distribution data per occupational group (CBS 85276NED) is available at occupational class level (12 groups) but not at the detail level of our 103 occupational groups. Ageing data is shown as a contextual indicator in the career scan and the insights page. In heavily ageing sectors (e.g. administrative: 24.6% aged 55+), natural attrition may soften the AI transition. This is an important nuance that the model itself does not quantify.

The AI exposure scores and forecasts have been compared with five independent sources:

• Felten, Raj & Seamans (2023) — Occupational Heterogeneity in Exposure to Generative AI. Our top scores (translators, writers, developers) align with their highest AIOE values. Our lowest scores (construction, frontline healthcare) match their lowest.
• Georgieff & Hyee (2021) — Artificial intelligence and employment: New cross-country evidence. OECD Social, Employment and Migration Working Papers, No. 265. Confirms the pattern: knowledge work high, physical work low.
• Frey & Osborne (2017) — The Future of Employment. Nurses scored 0.009 automation probability, consistent with our 2/10.
• WEF Future of Jobs Report (2025) — Confirms that software developers are among the fastest-growing specialist roles. +6 million developers projected globally by 2030. Growth direction consistent with our data.
• UWV Labour Market Forecast (2025) — Signals that labour market tightness for ICT occupations is easing. Less tightness ≠ fewer jobs, but the growth rate is moderating. This is the rationale for the damping factor (0.7) in our forecast model and the range presentation.

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Cite as: Janssen, S.D. (2026). Dutch AI Exposure Map: 103 occupational groups analysed for AI impact. simondjanssen.nl/en/ai-map. Retrieved [date].

Methodological inspiration: Karpathy/jobs (MIT License)

This section describes what this research can and cannot tell you. Transparency about methodology is not a weakness — it is what separates research from marketing.

The Janssen Practical Exposure scores are estimated using large language models (Claude, Anthropic). The prompt asks the model to assess what percentage of an occupation's tasks can be assisted or replaced by current AI capabilities, on a scale of 1-10. Scores were generated in March-April 2026.

They are cross-validated against the Felten AIOE academic index for US occupations (r = 0.87) but no equivalent academic benchmark exists for European occupations. Cross-country proxy validation against Felten AIOE for equivalent ISCO codes shows JPE scores are systematically 1.7 points lower than Felten across all three non-US countries (UK: −1.72, DE: −1.67, FR: −1.72, based on 56, 66, and 48 ISCO matches respectively). This gap is consistent with the known methodological difference: Felten measures theoretical capability while JPE measures practical disruption. The uniformity of the gap across countries suggests consistent scoring, not country-specific calibration error. The scores reflect a single point-in-time assessment and may not be reproducible with different models or prompts.

The 2030 forecast uses country-specific dampening factors (0.6–0.75) and AI pressure tables. These parameters are informed by labour market research but are ultimately judgment calls, not empirically measured values. A formal sensitivity analysis is available (see below).

The US forecast uses a fundamentally different projection method (direct BLS growth percentages, factor 1.0) compared to other countries (index-based, factor 2.5). Results are presented on the same scale but are not directly mathematically comparable. The direction of findings is consistent; the magnitude may differ.

A sensitivity analysis (available in full at /docs/sensitivity-analysis.md) shows:

• Dampening ±0.1 shifts forecasts by 0–2 percentage points for high-exposure occupations. Declining occupations are unaffected because changeFromNow is driven by the full-momentum scenario.
• The ranking of countries (UK and NL steepest decline, DE and FR mildest, US as structural outlier) is robust to parameter changes within tested ranges.
• The NL positive aggregate outlook depends on the large employment share of low-AI-exposure occupations (care, education, trades) whose growth trend carries the aggregate — not on the dampening parameter.
• AI pressure ±1 shifts individual occupation forecasts by 2–3 percentage points for non-US countries and ~1 percentage point for the US.
• The UK's position as “sharpest decline” is sensitive to its uniquely aggressive ai≥9 pressure value (-9). A reduction to -7 would move the Netherlands into that position.

All countries now report estimated gross annual salary. French figures were converted from INSEE net salary data using an estimated 23% social contribution rate. Cross-country salary comparison should account for purchasing power parity, tax regimes, and social benefits, which this dataset does not adjust for.

The datasets cover major occupational groups but not all workers in each country. Estimated coverage: NL ~95%, US ~54%, DE ~64%, UK ~53%, FR ~56%. Gig economy workers, military personnel, and some creative/freelance occupations are underrepresented or absent.

Five different national classification systems are used (CBS BRC 2014, BLS SOC 2018, KldB 2010, UK SOC 2020, PCS 2020), cross-walked via ISCO-08. Occupation definitions do not map perfectly across systems. A French “ingénieur informatique” may cover a broader scope than a UK “programmer.”