The patterns behind the data
I analysed 103 occupational groups on AI impact. 6.8 million jobs. What I found was not always what I expected. On this page I share the patterns that only become visible when you put the data side by side.
Three observations from the data: HBO and university graduates score higher on AI exposure than vocational (MBO) graduates. Middle incomes (€50–75K) are more exposed than the lowest and highest. And occupations with a high AI score actually grew faster over the past ten years, although that advantage has largely disappeared since 2022. Reality is more nuanced than the dominant narrative suggests.
The job balance: growth vs decline
The central question: are jobs being added or lost on balance? The answer is more nuanced than you might expect. The net shift is limited. But beneath that surface a substantial redistribution is taking place.
Towards 2030, 24 occupations grow by 3% or more, 13 shrink by more than 3% and 66 remain stable. Add up every plus and minus per occupation and 96,277 jobs are added while 139,328 disappear. The net effect: a decline of approximately 43,051 jobs out of a total of 6.8 million. That is -0.6%. No mass job loss. But a substantial shift, with most growth per sector in healthcare and social work, academic professions, construction and installation.
Net per sector: growth and decline within the same sector cancel out, which is why these totals are smaller than the per-occupation sums above.
+68K
-111K
-43K
Healthcare and social work grows fastest in absolute numbers (+49K jobs). Sales and commercial shrinks the most (-35K). In relative terms Sales and commercial shrinks the most (-4.8%). Of the 17 sectors, 7 grow on balance and 10 shrink.
Five occupations, 45% of the decline
The expected decline is not spread evenly. Five occupational groups together account for 45% of all expected job decline: 62,310 of 139,328 jobs. That is the decline from all causes combined: lower demand, AI and robotics. The AI share on its own looks different. There, Software developers (−14,700 jobs), Marketing professionals (−13,000 jobs), Administrative staff (−8,600 jobs) come out on top.
This means transition policy does not need to be generic. These are specific roles, in specific sectors, each with its own cause and therefore its own reskilling pathway.
Under the surface: 236,000 jobs in motion
The net figure (−43,051 jobs) suggests a modest shift. But it masks what is really happening. The gross movement is 235,605 jobs, 3.4% of the working population in this dataset.
+96K
all pluses per occupation
-139K
all minuses per occupation
236K
3.4% of total
The net figure is what economists report. The gross figure is what people experience. Around 236,000 jobs shift from one occupation to another. Even when the balance is close to zero, the transition is large.
Three groups by exposure
Not all occupations are hit equally hard. Based on AI exposure and NL vulnerability, the occupations fall into three groups. This is a classification, not a timeline. The forecast only uses the AI score: across all occupations, the AI share costs an estimated 134,986 jobs towards 2030. Below is how that plays out per group.
These are the occupations where the technology reaches furthest and protection is smallest. Microsoft Copilot takes over administrative work, AI chatbots customer service, GitHub Copilot writes code. The AI share of the forecast costs this group an estimated 36,750 jobs towards 2030. Whether the total shrinks also depends on demand: the forecasts range from −8% to 0%.
Managers, marketers, policy officers, recruiters. AI exposure is high, but regulation, social preference or labour market tightness offer more protection. Because this group is large, the AI share adds up to a lot of jobs here: an estimated 65,436 towards 2030. The forecasts range from −13% to +5%.
Healthcare, construction, installation, education, security. Physical work, relational work, regulated work. Here AI strengthens the professional rather than replacing them. Of these 69 occupations, 23 grow and 8 shrink towards 2030. Where decline occurs, it comes mainly from demand or robotics, not from AI.
AI risk by sector
Individual occupations tell only part of the story. At sector level the patterns become visible. Business and administration and creative and media score the highest, construction and installation, agriculture and fisheries, hospitality and facilities the lowest. That is no coincidence. AI excels at language, analysis and communication. Bricklaying, nursing or electrical installation fall outside what current AI models can handle. That line runs through the entire dataset: the more the work revolves around information processing, the higher the exposure.
Top 10 gainers and decliners
Forecasts are deliberately presented as a band. Exact percentages suggest a certainty that does not exist. The direction is more reliable than the number itself. UWV signals, for example, that the ICT shortage is easing: a reason to read the whole band rather than the midpoint. For small occupational groups (below ~30,000 workers) the Labour Force Survey sample can swing considerably from year to year; there the direction says more than the number.
Strongest gainers
Strongest decliners
* The CBS figure for this occupation jumped within two years. The forecast is therefore extra uncertain.
Who is hit hardest?
The common view is that AI mainly threatens low-skilled work. Robots in the factory, self-checkout tills in the supermarket. But the data shows something different. HBO-level occupations score an average of 6.1 on AI exposure, university-level occupations 5.8. Vocational level-1 scores 3.6. That difference is substantial. The explanation: AI is exceptionally good at precisely what knowledge workers do. Producing text, analysing data, writing reports, streamlining communication. A nurse, an electrician or a carpenter does work that largely falls outside the reach of current AI models.
With salary the same pattern emerges. The €50–75K group has the highest average AI exposure (6.7). Not the lowest incomes. Not the medical specialists or directors at the top. It is the accountant with ten years of experience, the marketing manager, the policy officer at the municipality. Occupations where the daily work largely overlaps with what ChatGPT, Copilot and specialised AI tools can already do.
Education vs AI exposure
HBO and university graduates score clearly higher than vocational graduates. Knowledge work is more exposed than physical work.
Salary vs AI exposure
The €50–75K group is the most exposed. Not the lowest wages, but the knowledge worker on an above-median salary.
Largest absolute impact
Percentages are useful for comparison. But they abstract from what is actually happening. A decline of 8% in an occupational group of 288,000 people means an estimated 23,040 jobs disappear. Behind each of those jobs is someone with a family, a mortgage or a career that needs to be rebuilt.
Below are the occupations where absolute job loss is greatest. These are not necessarily the occupations with the highest percentage decline, but the occupations where the societal impact weighs heaviest. Shop sales assistants are at the top, followed by administrative staff. If there is anywhere where the conversation about guidance and retraining is urgent, it is there.
Ageing: soft or hard landing?
Not every job that disappears has to cost someone their position. If an occupational group is heavily aged, employees will retire in the coming years. A role can then disappear when someone leaves, without anyone being made redundant. That makes the transition "soft."
CBS data shows that the business and administrative sector, which is expected to lose 29,060 jobs towards 2030, is also heavily aged: 24.6% are aged 55 or over. That is 457,000 people who will retire in the next 10 years. The decline therefore largely overlaps with natural attrition. In ICT it is the opposite: only 17.4% are aged 55 or over. A young profession. Those affected there can rely less on natural attrition.
Source: CBS Statline table 85276NED, employed workforce 2024. "Soft landing" = high ageing + high AI score (transition via retirement). "Hard landing" = low ageing + high AI score (retraining needed).
The administrative sector is estimated to lose 29,060 jobs towards 2030. But 457,000 employees in that sector are aged 55 or over. Natural attrition is far greater than the expected decline. That makes the transition manageable, provided we start now by not filling departing roles rather than waiting until automation is forced. In creative and language work (23.6% aged 55+, AI 7.2) the same applies. In ICT (17.4% aged 55+) it does not.
Exposure × Vulnerability
This chart places the two dimensions of this analysis side by side. Horizontal: the technical AI exposure. What can AI theoretically take over? Vertical: the NL vulnerability. What will actually happen in the Dutch context, given our regulation, labour market tightness and societal preferences?
What stands out most: the bubbles lie almost on a diagonal. Vulnerability tracks exposure almost one to one (r = 0.96). That is why, since forecast v3, vulnerability no longer counts in the forecast and is shown here as context. In the top right (42 occupations) both are high, with Marketing professionals, Administrative staff, Software developers as the largest groups. In the bottom right (21 occupations) exposure is higher than vulnerability, for example for Warehouse workers, Taxi and delivery drivers, Medical specialists. Top left is empty: no occupation combines low exposure with high vulnerability.
The size of each bubble represents the number of jobs. The larger, the more people affected. Click a bubble for the full analysis of that occupation.
High exposure and high vulnerability. 42 occupations, including admin workers, copywriters, customer service.
High exposure, but the NL context offers more protection. 21 occupations.
Low exposure, high vulnerability. Empty in this dataset.
Low on both axes. 40 occupations: construction, installation, physical care.
What the data reveals (and what it doesn’t)
Correlations tell you something different from what you might expect. Occupations with high AI exposure actually grew faster over the past decade (r = 0.5, CBS 2015-2025). 6 of the ten fastest growers score AI 7 or above. So far, high exposure has gone hand in hand with growth rather than decline. Note: this is the past. Since 2022 that growth advantage has largely disappeared.
The r = 0.87 between JPE and Felten AIOE confirms that our LLM-estimated scores align with the academic reference framework. The deviations sit exactly where you’d expect: teachers, nurses and judges. There Felten says “AI can do it” but JPE says “it isn’t happening.” That is the core distinction of this analysis.
Part-time and AI exposure
The Netherlands is Europe’s part-time champion. In this dataset 48% work part-time (weighted by jobs). That raises the question: is there a link between part-time work and AI exposure?
Hardly (r = −0.16). Occupations with the highest part-time share (Care workers 89%, Waiters and hospitality staff 86%) have low AI scores. The work is physical and relational. At the other end sit mostly managers: of the eight occupations with the least part-time work, 5 are management roles (ICT managers 8%, Production managers 8%).
On average, occupations with 60% or more part-time work score 4.2 on AI exposure, occupations with 15% or less 5.8, and the group in between 5.5. The working-time pattern says little about how strongly AI affects the work.
23 occupations. Avg. AI: 4.2
6 occupations. Avg. AI: 5.8
48% part-time, weighted by jobs.
Much part-time work is in care, hospitality and cleaning, with low AI exposure. Little part-time work is found mainly among managers, who score higher. Occupations scoring AI 8 or above are spread across the spectrum: from 21% to 71% part-time.
Most part-time
Least part-time
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What now? These are not predictions. They are directions. The question is not whether the percentages are exactly right. The question is: do you recognise the pattern? And what will you do with it? The career scan provides a personal report with forecast, reskilling advice and concrete tools. The full methodology and dataset are openly available under CC BY 4.0.