The patterns behind the data

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.

39 occupations are growing (together +181,000 jobs). 34 occupations are shrinking (together −246,000 jobs). 30 occupations remain stable. The net effect: a decline of approximately 62,000 jobs out of a total of 5.8 million. That is −1.1%. No mass unemployment. But a quiet shift from office work towards healthcare, construction and technology.

AI risk by sector

Individual occupations tell only part of the story. At sector level the patterns become visible. Creative work, media and the business and administrative sector score the highest. Construction, installation and healthcare score 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 ranges. 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, which fits the conservative end of the range. What stands out: gainers are spread across healthcare, technology and ICT. Decliners concentrate in administrative and commercial work. Office work is shrinking. The shop floor and the screen (with the right skills) are growing.

The paradox: high AI score, yet growth

A group of occupations scores 7 or higher on AI exposure. Core tasks are technically automatable. And yet they are growing towards 2030. That seems contradictory, but the explanation differs per occupation.

Software developers become more productive through AI (GitHub Copilot, Claude Code, Cursor), but at the same time every organisation wants to build more software. Demand grows faster than productivity rises. For cybersecurity specialists it is even clearer: threats are increasing exponentially. AI helps with defence, but does not replace the human specialist.

The lesson is relevant for every occupation: if the underlying market demand is structurally growing, AI exposure can be more of a productivity boost than a threat. That is the Solow Paradox in miniature. The technology is there, but the effect on jobs depends on the context.

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 6.0 on AI exposure, university-level occupations 6.1. 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 €50,000 to €75,000 group has the highest average AI exposure (6.6). 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.

Largest absolute impact

Percentages are useful for comparison. But they abstract from what is actually happening. A decline of 24% in an occupational group of 245,000 people means that an estimated 59,000 people need to find different work. That is the equivalent of a city like Middelburg. With families, mortgages and careers that need 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. Administrative employees are at the top by a wide margin. 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. Their role is then not replaced rather than someone being made redundant. That makes the transition "soft."

CBS data shows that the business and administrative sector, where the decline is greatest (−153,000 jobs), 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 AI transition therefore largely overlaps with natural attrition. In ICT it is the opposite: only 17.4% are aged 55 or over. A young profession. But demand is growing there, so ageing is not a factor.

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?

The most interesting quadrant is bottom right: high exposure, low vulnerability. That is where occupations like software developers and IT project managers sit. AI can technically take over much of their work, but demand is so great that replacement does not materialise. Top left is the reverse: low exposure but rising vulnerability. That is where care coordinators and teaching assistants sit: AI is affecting their administrative tasks while their core task (human contact) remains safe for now.

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.

Part-time and AI vulnerability

The Netherlands is Europe's part-time champion. In this dataset 43% work part-time. That raises the question: is there a correlation between part-time work and AI vulnerability?

That correlation exists, but is not linear. Occupations with the highest part-time percentage (nurses 72%, primary school teachers 72%) have low AI scores. The work is physical and relational. Occupations with the lowest part-time percentage (fire brigade 15%, welders 8%, lorry drivers 12%) also score low. That work is likewise physical.

The vulnerable middle group has a part-time percentage of 30 to 45%. Administrative workers (58% part-time, AI 8/10), secretaries (65%, AI 7/10), marketers (45%, AI 8/10). These are occupations where the work largely takes place behind a screen. And that is precisely the domain where AI is advancing fastest.

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