Your parents told you to get a degree, get a white-collar job, and you'd be fine. They were right — for forty years. They are not right anymore.

We know this because we measure it. Every month, we pull employment figures, wage data, and job vacancy counts from 12 government statistical agencies across 39 countries. We compute a composite risk score for every occupation we can find reliable data on. We have 2,860 of them now.

Here is what the numbers say, as of this month: 41% of all tracked occupations are rated HIGH or CRITICAL risk. Six months ago it was 34%.

The acceleration is not in factory work. It is not in trucking, or retail, or food service. It is in the soft middle — the professional services layer that absorbed every previous generation of displaced workers.

Who is actually getting hurt

Forget the AI hype cycle for a moment. Forget the demos and the product launches. Look at what is actually happening in the employment data.

In Luxembourg, our highest-risk economy, the average risk score across all occupations is 63.6. Four occupations are CRITICAL. Luxembourg is dominated by financial services and EU institutions — exactly the sectors where document analysis, compliance checking, and workflow automation hit hardest.

In Bulgaria (63.0), Estonia (63.1), Latvia (63.4), and Slovakia (64.9), the pattern is different but the outcome is the same. These are economies where professional services were outsourced from Western Europe on the basis of cost. AI doesn't care about your cost advantage. It cares about whether your tasks are routine and cognitive. If the answer is yes, you are competing with software that costs $20/month.

The United States looks deceptively safe. Average risk score: 43.1. But this is an artefact of granularity — we track 1,103 US occupations, including hundreds of manual and service roles that AI cannot touch. Filter for white-collar professional roles and the average jumps above 55.

The Nordics are interesting. Denmark (49.5), Sweden (52.4), Iceland (52.0). Notably lower than the EU median despite similar economic composition. What's different? Union density, institutional retraining infrastructure, and labour protections that slow the transmission of displacement into permanent job loss. The data suggests that institutions matter — not because they prevent displacement, but because they buy time.

Ghost demand

The most important signal in our data is not employment or wages. It is in job vacancies.

We track vacancies across 18 countries using Adzuna, Eurostat, and four national employment agencies. What we see is something we call ghost demand: vacancy counts that remain stable while the nature of the roles changes completely.

A company posts a listing for "Data Analyst." The role now requires AI tool proficiency and is expected to produce the output of three people. The vacancy exists. The hiring count is maintained. The aggregate statistics look healthy.

But the density of work per role has increased, the number of humans needed for a given output has decreased, and the wage premium for the role is compressing as the skill barrier drops.

None of this shows up in vacancy counts. All of it shows up in our wage change and employment change data.

This is why a single metric is useless. You need employment, wages, vacancies, and AI exposure scores measured simultaneously to see what is actually happening. We built the model specifically because no single government report tells the whole story.

The part nobody wants to hear

The professional services economy — consulting, analysis, administration, mid-tier management, routine legal, routine finance — was not just a sector. It was the absorber. It was where displaced workers went.

A factory worker in 1985 could retrain as an office administrator. A coal miner in 2005 could retrain as a data entry clerk.

A displaced office administrator in 2026 cannot retrain as — what, exactly? The roles above require institutional trust and relationship capital that take decades to build. The roles below pay less. The roles sideways are being automated by the same technology.

The soft middle — the band of occupations between manual labour and senior leadership — is not experiencing a transition. It is experiencing a compression. The band is getting thinner. The people in it are not disappearing from the economy. They are competing with each other for fewer seats, at lower wages, while the tools that replaced them get better every quarter.

Our data shows this clearly: in the occupations rated HIGH risk, median wages are declining year-over-year in 67% of cases. In CRITICAL occupations, it is 89%.

What this means for you

If you are reading this and your job involves primarily:

  • Processing information that follows known patterns
  • Producing documents from templates
  • Analysing data that is structured and repeatable
  • Coordinating workflows between other people

...then your occupation is very likely in our HIGH or CRITICAL tier. You should search for it specifically. The score will tell you more than any article can.

If your work involves physical presence, unpredictable human interaction, creative judgment in novel situations, or institutional trust that cannot be replicated — you are likely in our LOW or STABLE tier.

The dividing line is not education level. It is not salary. It is not prestige. It is whether a language model can do 80% of your daily tasks by reading your job description.

We track 2,860 occupations across 39 countries using data from BLS, Eurostat, OECD, ONS, ABS, Statistics Canada, e-Stat, Adzuna, and four national employment agencies. Scores are updated monthly. This is quantitative analysis, not career advice.