AI and the Labour Market 2025: Who is Really at Risk – and Who Isn't

A recent Anthropic study provides real-time data instead of forecasts for the first time. The results contradict everything we thought we knew up until now.

No more forecasts – real data

For years, economists, management consultants and technologists have been predicting which professions will disappear due to artificial intelligence. The predictions were mostly dramatic – and just as often wrong. The panic about outsourcing in the early 2000s cost hardly any jobs as forecast. The wave of automation in industry proceeded more slowly and selectively than expected.

The fundamental problem with these forecasts has always been the same: they were based on what machines could theoretically achieve – not on what they actually do on a daily basis. A language model can theoretically formulate legal documents, produce financial reports, and debug code. But how often does this actually happen? To what extent? In which professions?

A study by researchers at Anthropic now answers precisely this question: Maxim Masenkov and Peter Macroory have developed a new metric – the so-called Observed Task Coverage (OTC). And the results are as astonishing as they are revealing.

The Method: Theory Meets Reality

The researchers combined three data sources:

O*NET – the US state occupational classifier. This database breaks down every occupation into precise micro-tasks with time allocations.
Theoretical AI performance evaluation – assessments of how much AI can accelerate tasks.
Real-world usage data of the Claude model – what tasks people actually delegate to AI.

The crucial difference: instead of assumptions, real behaviour was analysed.

The Food Processor Paradox

To illustrate the core problem of earlier studies, the researchers use a vivid analogy:

A food processor can theoretically cook a five-course meal – in practice, it's mostly used for chopping onions.

The same applies to AI. The central question isn't what it can do – but what is actually being used. The answer: we're still in the "onion-chopping" phase.

The figures: The gap between potential and reality

An example from practice:

Theoretical AI potential (IT professions): 94 % of tasks
Actual usage: only about 3 %

This enormous gap arises from three central barriers:

Legal liability (e.g. medicine, law)
Outdated IT infrastructure in companies
Human control and decision-making processes

The limitation is not technological – but institutional.

Who is truly at risk? The ranking

Rang	Field of work	Observation Cover	Main task
1	Programmer	74,5 %	Code schreiben und pflegen
2	Customer support	70 %	Communication
3	Data entry	67 %	Data processing
4	Medical Documentation	~55 %	Create reports
5	Marketing analysis	~50 %	Reporting
6	Sales	~45 %	Offers & Follow-ups
7	Financial analysis	~44 %	Forecasts
8	Software testing	~40 %	Test cases
9	IT Security	~38 %	Threat analysis

At the lower end: artisanal and manual professions – often with 0 % AI coverage.

The Paradox of Education

A surprising result:

17 % from the at-risk group have a master's degree
Only 4.5 % of this group are barely affected by AI

The higher the qualification, the higher the AI exposure, often.

Tasks that are purely cognitive – writing, analysing, structuring – are particularly well-suited for automation.

Where are the mass layoffs?

Die Antwort: Sie passieren indirekt. Unternehmen entlassen keine erfahrenen Mitarbeiter. Stattdessen: The answer: They happen indirectly. Companies do not lay off experienced employees. Instead:

Senior staff become more productive
Fewer junior positions are being created

Data shows:

New hires (aged 22–25) in AI roles: −14% % since 2022

The door to the job market is quietly closing – not visibly.

Long-term effects

Statistical models show:

+10 % AI coverage → −0.6 % employment growth

Over years, this adds up to significant effects – particularly in highly automated professions.

Strategies: What to do?

For experienced professionals

Using AI as a productivity booster
Automate workflows
Take on strategic tasks

For those starting their careers

Focus on skills that AI cannot replace
Communication, judgment, responsibility

For non-academics

Craftsmanship and physical labour remain stable
High future security in practical professions

For career changers

Choosing professions with social interaction
Mastering AI as a tool

Conclusion

The Anthropic study doesn't show an extreme scenario – but rather a nuanced reality. AI won't suddenly replace millions of jobs. Instead, it will change them: