u/WorldJobsData

For the past couple of years, the conversation around AI and jobs has mostly been split into two camps. One side says AI is about to replace millions of workers. The other says the technology is overhyped and businesses aren't really using it.

After reading Anthropic's latest labour market research, I think the reality sits somewhere in the middle.

One statistic stood out to me more than anything else.

ICT professionals, which includes software developers, systems analysts and similar technical roles, have a theoretical AI exposure of 94%. In other words, AI is now capable of performing almost all of the tasks that make up those jobs.

But the observed exposure is only 33%.

That is a gap of 61 percentage points between what AI can technically do and what organisations are actually allowing it to do.

I think that gap explains a lot of the confusion surrounding AI and employment.

People see AI writing code, debugging programs, creating documentation and producing working applications. They naturally assume software jobs should already be disappearing at scale.

Instead, many companies are hiring more cautiously rather than replacing entire engineering teams.

Anthropic's research offers several reasons why.

Large organisations move slowly. Security reviews, compliance checks, procurement processes and legal approvals can easily take a year or two before new AI systems become part of everyday work.

Even after deployment, someone still has to review AI-generated code, validate business decisions and accept responsibility when something goes wrong. The more expensive the mistake, the more valuable experienced human judgement becomes.

The contrast becomes even more interesting when you compare this with customer service.

Customer service clerks have a theoretical exposure of 78% and an observed exposure of 70%.

The gap is only eight points.

That means AI isn't just capable of doing customer service work. It is already doing most of it.

Automated chat, email drafting, call routing and first-line support have already become normal in many organisations. Human agents increasingly deal with escalations rather than routine questions.

To me, the biggest lesson isn't that some jobs are safe while others are doomed.

It's that deployment speed matters just as much as technical capability.

A role with high AI capability but a large deployment gap may still have several years for workers to adapt.

A role with a very small gap may already be going through its biggest transition.

The technology isn't arriving all at once.

Different occupations are moving at different speeds because regulation, trust, liability and organisational change all slow adoption in different ways.

That's a much more useful way to think about the future of work than simply asking whether AI can do your job.

Full analysis and interactive tool in comments.

When people imagine automation in Germany, they usually picture robots building cars in factories.

The data suggests something very different is happening.

I analyzed Germany's workforce using Eurostat employment data and found that the occupations with the highest AI exposure are not manufacturing jobs at all. They're office jobs.

General and keyboard clerks scored 9.0/10 on AI exposure, the highest score I've seen across any occupation group in any country analyzed so far. ICT professionals and customer service clerks followed at 8.5/10.

Meanwhile, many manufacturing occupations showed relatively low AI exposure but very high robotics risk. Assemblers scored only 2.5/10 on AI exposure, but 8.5/10 on robotics risk.

That distinction matters because we're increasingly talking about "automation" as if it's one thing. The data suggests there are actually two different transitions happening at once.

One transition is AI replacing or augmenting information-processing tasks. That's primarily affecting administrative, clerical, and knowledge-work occupations.

The second transition is physical automation through robotics. That's affecting production and manufacturing roles.

Germany is especially interesting because it has a workforce-weighted AI exposure score of 5.3/10 and a Risk Velocity score of 9.6/10, the highest in the dataset. Risk Velocity measures how prepared a country is to deploy AI rapidly through digital infrastructure, software adoption, and workforce digital literacy.

The most surprising finding for me was that Germany's greatest automation vulnerability isn't on the factory floor. It's in the administrative systems that support the economy.

AI exposure scores are modelled estimates, not predictions of job loss. They reflect how much of an occupation's daily tasks current AI systems can perform or significantly augment today.

The future of work may arrive first in offices, not factories.

Full analysis and interactive tool in comments.

When people imagine automation in Germany, they usually picture robots building cars in factories.

The data suggests something very different is happening.

I analyzed Germany's workforce using Eurostat employment data and found that the occupations with the highest AI exposure are not manufacturing jobs at all. They're office jobs.

General and keyboard clerks scored 9.0/10 on AI exposure, the highest score I've seen across any occupation group in any country analyzed so far. ICT professionals and customer service clerks followed at 8.5/10.

Meanwhile, many manufacturing occupations showed relatively low AI exposure but very high robotics risk. Assemblers scored only 2.5/10 on AI exposure, but 8.5/10 on robotics risk.

That distinction matters because we're increasingly talking about "automation" as if it's one thing. The data suggests there are actually two different transitions happening at once.

One transition is AI replacing or augmenting information-processing tasks. That's primarily affecting administrative, clerical, and knowledge-work occupations.

The second transition is physical automation through robotics. That's affecting production and manufacturing roles.

Germany is especially interesting because it has a workforce-weighted AI exposure score of 5.3/10 and a Risk Velocity score of 9.6/10, the highest in the dataset. Risk Velocity measures how prepared a country is to deploy AI rapidly through digital infrastructure, software adoption, and workforce digital literacy.

The most surprising finding for me was that Germany's greatest automation vulnerability isn't on the factory floor. It's in the administrative systems that support the economy.

AI exposure scores are modelled estimates, not predictions of job loss. They reflect how much of an occupation's daily tasks current AI systems can perform or significantly augment today.

The future of work may arrive first in offices, not factories.

Full analysis and interactive tool in comments.

Most discussions about automation focus on generative AI replacing office work or humanoid robots replacing physical labour. After analysing South Korea's workforce, I think it's more useful to look at both separately.

I used official International Labour Organization (ILO) employment data covering 28.8 million workers together with an occupation-level AI exposure model.

The highest estimated AI exposure isn't in manufacturing.

It's among clerical support workers.

Around 3.6 million people work in clerical occupations, and they receive an estimated AI exposure score of 8.5/10. These roles involve document processing, scheduling, customer communication and administrative work that today's AI systems are increasingly capable of assisting with.

Where humanoid robots become more interesting is in the occupations that are relatively safe from software AI.

Plant and machine operators score just 3.0/10 for AI exposure, but 7.5/10 for robotics exposure. Skilled agricultural workers score 3.0/10 for AI but 6.5/10 for robotics, while elementary occupations score only 2.0/10 for AI and 5.5/10 for robotics.

That suggests South Korea is facing two parallel automation trends.

The first is software AI transforming knowledge and administrative work.

The second is robotics—and potentially future humanoid robots—expanding into jobs that require physical movement, manipulation and operation in real-world environments.

South Korea is an especially interesting country to watch because it already has the world's highest robot density in manufacturing and is home to companies investing heavily in advanced robotics.

Whether humanoid robots become commercially viable over the next decade or not, the workforce data suggests their impact is likely to be concentrated in a very different set of occupations than generative AI.

The employment figures come from the International Labour Organization. AI and robotics exposure scores are modelled estimates based on occupational tasks and should not be interpreted as official government statistics.

I'd be interested to hear whether people here think general-purpose humanoids will first augment workers in these occupations or eventually replace some of the repetitive physical tasks entirely.

Full analysis and interactive tool in comments.

When people talk about AI replacing jobs, the conversation usually focuses on programmers, designers or other highly skilled professionals.

I wanted to see what the data actually suggests for South Korea.

Using official International Labour Organization (ILO) employment data covering 28.8 million workers, together with an occupation-level AI exposure model, one result stood out immediately.

The occupations with the highest estimated AI exposure aren't software engineers or researchers.

They're clerical support workers.

Around 3.6 million South Koreans work in clerical occupations, and they receive an estimated AI exposure score of 8.5/10. These jobs include document processing, scheduling, administrative support and routine business communication—exactly the kinds of tasks modern AI systems are increasingly capable of assisting with.

Professionals, despite making up the country's largest occupational group at 6.6 million workers, score a lower 6.5/10. Many professional roles still require judgement, regulation, client interaction and specialised expertise that extend beyond today's AI capabilities.

What makes South Korea particularly interesting is that AI is only one side of the automation story.

Plant and machine operators have relatively low AI exposure (3.0/10) but a much higher robotics exposure score of 7.5/10. Since South Korea consistently ranks among the world's most robot-intensive manufacturing economies, factory workers face a different kind of technological transition than office workers.

Overall, the country's workforce has an estimated weighted AI exposure score of 4.85/10, suggesting the biggest short-term changes may occur in administrative knowledge work rather than across every occupation equally.

One thing I found useful was separating AI software exposure from robotics exposure instead of treating automation as a single trend. Looking at both dimensions produces a much clearer picture of how different occupations may evolve over the coming years.

The employment figures come from the ILO, while AI exposure scores are modelled estimates based on occupational tasks rather than official government statistics.

Full analysis and interactive tool in comments.

When people imagine automation in Germany, they usually picture robots building cars in factories.

The data suggests something very different is happening.

I analyzed Germany's workforce using Eurostat employment data and found that the occupations with the highest AI exposure are not manufacturing jobs at all. They're office jobs.

General and keyboard clerks scored 9.0/10 on AI exposure, the highest score I've seen across any occupation group in any country analyzed so far. ICT professionals and customer service clerks followed at 8.5/10.

Meanwhile, many manufacturing occupations showed relatively low AI exposure but very high robotics risk. Assemblers scored only 2.5/10 on AI exposure, but 8.5/10 on robotics risk.

That distinction matters because we're increasingly talking about "automation" as if it's one thing. The data suggests there are actually two different transitions happening at once.

One transition is AI replacing or augmenting information-processing tasks. That's primarily affecting administrative, clerical, and knowledge-work occupations.

The second transition is physical automation through robotics. That's affecting production and manufacturing roles.

Germany is especially interesting because it has a workforce-weighted AI exposure score of 5.3/10 and a Risk Velocity score of 9.6/10, the highest in the dataset. Risk Velocity measures how prepared a country is to deploy AI rapidly through digital infrastructure, software adoption, and workforce digital literacy.

The most surprising finding for me was that Germany's greatest automation vulnerability isn't on the factory floor. It's in the administrative systems that support the economy.

AI exposure scores are modelled estimates, not predictions of job loss. They reflect how much of an occupation's daily tasks current AI systems can perform or significantly augment today.

The future of work may arrive first in offices, not factories.

Full analysis and interactive tool in comments.

Most discussions about AI and jobs assume that displacement happens inside a formal labour market with unemployment benefits, retraining programmes, and institutions that can absorb the shock.

Mexico presents a very different picture.

I was analysing ILO and OECD data covering 59 million Mexican workers and one number stood out immediately: 56.9% of employment is informal. That means more than 33 million people work without formal contracts or access to many of the systems designed to help workers adapt to economic change.

Clerical support workers are the most exposed group, scoring 8.5/10 on estimated AI exposure and covering 3.7 million workers. Plant and machine operators face 7.5/10 robotics risk, affecting another 6.2 million workers.

But the overall workforce average is only 3.83/10, lower than several countries we've analysed. Service and sales workers plus elementary occupations account for over 24 million workers and currently face relatively low direct AI exposure.

What makes Mexico interesting from a future-of-work perspective is that this is effectively a two-speed transition.

Formal-sector bank employees, office workers, and professionals are seeing AI adoption first. Meanwhile, informal workers may experience slower disruption, but with almost no institutional support if income disappears.

Mexico is also benefiting from nearshoring and manufacturing investment. Yet many of the new factories being built are becoming increasingly automated, meaning employment growth per dollar invested may be smaller than in previous decades.

The future of work may not simply be about which jobs AI can automate. It may depend just as much on whether societies have systems capable of helping workers navigate the transition.

The technology may be global, but the consequences will look very different from country to country.

Full analysis and interactive tool in comments.

Most discussions about AI and jobs assume the impact will spread evenly across the economy. Brazil's workforce data suggests something very different.

I was looking at ILO ILOSTAT data covering 102.1 million Brazilian workers and found that AI exposure is concentrated in surprisingly specific areas rather than across the workforce as a whole.

Clerical support workers are the most exposed group, scoring 8.5/10 on estimated AI exposure. That covers about 8.6 million people working in administration, office support, customer service and related roles.

Brazil's growing professional class, which includes software developers, engineers, analysts and healthcare workers, scores 6.5/10. This group has expanded from 8.4 million workers in 2012 to 13.7 million in 2025.

Meanwhile, Brazil's largest occupation group is service and sales workers. They represent 22.7 million people and score only 3.5/10 on AI exposure because many tasks still require physical presence and human interaction.

Another interesting finding is that plant and machine operators face the highest robotics risk at 7.5/10. In other words, physical automation and AI are affecting different parts of the labour market.

Perhaps the biggest factor shaping Brazil's future is that around 35.6% of workers are employed informally. That means automation will arrive very differently compared with Europe or North America. Some informal jobs may prove surprisingly resilient, while informal workers displaced by software have far fewer safety nets.

The future of work does not appear to be arriving evenly.

AI exposure scores are modelled estimates rather than official government statistics, but they provide a useful way to compare how current AI capabilities overlap with occupational tasks.

Full analysis and interactive tool in comments.

Most people assume the first jobs AI will transform are creative roles or Silicon Valley tech jobs. The French labour data suggests something different.

I analysed employment data covering 28.8 million French workers across 42 occupation groups using Eurostat employment figures and OECD wage data. AI exposure scores are modelled estimates of how much of a job's day-to-day tasks current AI systems can plausibly perform or augment.

The highest score in France is 9.0/10 for general and keyboard clerks, covering about 952,000 workers. That matches Germany's highest score and is the highest score in the WorldJobsData dataset so far.

ICT professionals score 8.5/10, covering 893,000 workers. Business and administration professionals score 8.0/10 across 1.8 million workers. Overall, around 7.2 million French workers, roughly one quarter of the workforce, are in occupations scoring 7.0/10 or above.

Interestingly, the safest occupations are not futuristic ones. Personal care workers, cleaners and agricultural labourers score just 1.5-2.0/10. France's ageing population and labour shortages make many care roles relatively resilient.

Another interesting pattern is that manufacturing workers face lower AI exposure but much higher robotics risk. Assemblers score only 2.5/10 on AI exposure, but 8.5/10 on robotics risk. It suggests that software automation and physical automation are two separate transitions happening at the same time.

France's labour protections and strong public sector may slow adoption, but friction slows disruption rather than eliminating it.

The future of work may not arrive through sudden mass unemployment. It may arrive gradually through attrition, fewer new hires, and increasingly AI-assisted jobs.

Full analysis and interactive tool in comments.

I was looking through Japanese labour data recently and found something that surprised me.

Most countries seem to have one automation story. Japan appears to have two.

Office workers and factory workers are facing completely different technologies.

Clerical support workers represent about 14.5 million people, roughly 20.5% of the workforce, and score 8.5/10 on AI exposure. Meanwhile, plant and machine operators represent 13.3 million workers and face a robotics risk score of 7.5/10.

Together, those groups account for almost 40% of Japan's workforce.

The surprising part is that Japan actually has the lowest overall AI exposure score among six OECD economies analysed, at 4.92/10.

Almost 30% of Japanese workers are employed in service and sales roles. Those occupations score just 3.5/10 on AI exposure. Human interaction and Japan's omotenashi culture seem to provide some protection against current AI systems.

The demographic angle makes this even more interesting. With an ageing population and labour shortages across healthcare, logistics and manufacturing, automation in Japan often fills gaps rather than replacing excess labour.

That doesn't mean displacement isn't real. A 55-year-old office worker displaced by AI may not easily transition into nursing or construction. But Japan's labour market resilience score was the highest among countries analysed.

AI exposure scores are modelled estimates, not official statistics or predictions of unemployment.

Curious whether people living in Japan are already seeing these changes in offices, factories or service jobs.

Full analysis and interactive tool in comments.

I was looking through Japanese labour data recently and found something that surprised me.

Most countries seem to have one automation story. Japan appears to have two.

Office workers and factory workers are facing completely different technologies.

Clerical support workers represent about 14.5 million people, roughly 20.5% of the workforce, and score 8.5/10 on AI exposure. Meanwhile, plant and machine operators represent 13.3 million workers and face a robotics risk score of 7.5/10.

Together, those groups account for almost 40% of Japan's workforce.

The surprising part is that Japan actually has the lowest overall AI exposure score among six OECD economies analysed, at 4.92/10.

Almost 30% of Japanese workers are employed in service and sales roles. Those occupations score just 3.5/10 on AI exposure. Human interaction and Japan's omotenashi culture seem to provide some protection against current AI systems.

The demographic angle makes this even more interesting. With an ageing population and labour shortages across healthcare, logistics and manufacturing, automation in Japan often fills gaps rather than replacing excess labour.

That doesn't mean displacement isn't real. A 55-year-old office worker displaced by AI may not easily transition into nursing or construction. But Japan's labour market resilience score was the highest among countries analysed.

AI exposure scores are modelled estimates, not official statistics or predictions of unemployment.

Curious whether people living in Japan are already seeing these changes in offices, factories or service jobs.

Full analysis and interactive tool in comments.

Most AI discussions focus on the US or Europe, but China's workforce scale changes the picture entirely.

I spent some time analyzing China's latest ILO employment data and one number really stood out. China has 362 million workers in the dataset, making it the largest workforce covered.

Clerical support workers score 8.5/10 on AI exposure, covering 33.6 million people. That's roughly the size of Canada's entire workforce.

Professionals score 6.5/10 across 81.8 million workers. Meanwhile, craft and related trades workers total 93.6 million people, the largest occupational group in the entire dataset, but score only 2.5/10 on AI exposure.

Another interesting distinction is that AI and robotics are not affecting the same groups. Plant and machine operators score just 3.0/10 on AI exposure, but 7.5/10 on robotics risk. China's automation story appears to be as much about physical robots as generative AI.

China's weighted average AI exposure comes in at 4.48/10, above Brazil and India but below Germany.

The broader implication is that even if adoption rates resemble those in Western countries, the absolute number of workers affected is much larger simply because of scale.

AI exposure scores are modelled estimates rather than official government statistics. Employment figures come from ILO ILOSTAT 2025 data.

Full analysis and interactive tool in comments.

I was looking at China's 2025 workforce data and one thing surprised me.

The country's largest occupational group isn't professionals or factory workers. It's craft and related trades workers at 93.6 million people.

Despite their size, they score only 2.5/10 on AI exposure.

Meanwhile, clerical support workers score 8.5/10 and cover 33.6 million workers. Professionals score 6.5/10 and account for 81.8 million people.

Another interesting finding is the split between AI and robotics. Plant and machine operators score 3.0/10 on AI exposure but 7.5/10 on robotics risk.

China's weighted average AI exposure is 4.48/10.

What stood out most to me is that scale changes everything. China's clerical workforce alone is larger than the entire workforce of many countries.

The employment data comes from ILO ILOSTAT. AI exposure scores are modelled estimates based on occupation tasks and are not official government statistics.

Curious how others think AI adoption and robotics deployment interact in manufacturing-heavy economies.

Full analysis and interactive tool in comments.

Most discussions about automation assume a single wave driven by AI. Looking at Japan's labour market, I found something that surprised me.

Japan appears to be facing two different automation pressures simultaneously.

Clerical support workers, representing roughly 14.5 million people or 20.5% of the workforce, score 8.5/10 on AI exposure. These jobs involve document processing, administration and routine information tasks that current AI systems are increasingly capable of performing.

At the same time, plant and machine operators, representing around 13.3 million workers, face a different challenge. Their AI exposure is relatively low, but robotics risk reaches 7.5/10 because manufacturing automation and industrial robots are already deeply integrated into Japanese industry.

Together, these groups account for nearly 40% of Japan's workforce.

What surprised me most is that Japan actually has the lowest average AI exposure among six OECD economies I examined, with a score of 4.92/10. Almost 30% of Japanese workers are employed in service and sales occupations, which remain relatively resistant to AI because they rely heavily on physical presence, cultural understanding and interpersonal interaction.

Demographics make the picture even more interesting. Japan's ageing population and persistent labour shortages mean automation often serves a different purpose than in many Western discussions. Instead of replacing excess workers, it increasingly fills gaps created by a shrinking workforce.

Japan also scored highest on workforce recovery resilience among the countries analysed.

AI exposure scores are modelled estimates rather than official statistics and should not be interpreted as forecasts of job losses.

I'm curious whether Japan's experience represents an early preview of how ageing societies will adapt to AI and robotics over the next few decades.

Full analysis and interactive tool in comments.

Most AI employment discussions only look at AI exposure. Japan turned out to be interesting because that approach misses half the picture.

Using ILO occupation classifications and our task-based AI exposure model, I looked at Japan's 70.5 million workers. The AI side behaves almost exactly like every other country we've studied. Clerical support workers sit at the top with an 8.5/10 exposure score, professionals score 6.5/10, and elementary occupations remain low.

But the robotics layer tells a different story.

Plant and machine operators score just 3.0/10 on AI exposure, yet 7.5/10 on robotics risk. Skilled agricultural workers score 3.0 on AI but 6.5 on robotics. Service workers are relatively AI-resistant at 3.5, but robotics exposure rises to 4.5.

What surprised me is that Japan's overall AI exposure average is actually the lowest among six OECD economies analysed, at 4.92/10. Occupational composition matters more than many people assume.

The really interesting part is demographics. Japan has an ageing workforce, labour shortages and one of the highest robot densities in manufacturing. Automation there functions partly as labour replacement and partly as labour supplementation.

Recovery resilience also scores highest among the six countries we examined at 8.0/10, suggesting worker transitions may be absorbed more easily than headline risk numbers imply.

AI exposure scores are modelled estimates rather than official statistics. Robotics scores reflect current deployment potential and industry structure rather than forecasts of job losses.

Curious to hear criticism on methodology and whether people think combining robotics and AI layers is more useful than analysing AI alone.

Full analysis and interactive tool in comments.

I was looking through Japanese labour data recently and found something that surprised me.

Most countries seem to have one automation story. Japan appears to have two.

Office workers and factory workers are facing completely different technologies.

Clerical support workers represent about 14.5 million people, roughly 20.5% of the workforce, and score 8.5/10 on AI exposure. Meanwhile, plant and machine operators represent 13.3 million workers and face a robotics risk score of 7.5/10.

Together, those groups account for almost 40% of Japan's workforce.

The surprising part is that Japan actually has the lowest overall AI exposure score among six OECD economies analysed, at 4.92/10.

Almost 30% of Japanese workers are employed in service and sales roles. Those occupations score just 3.5/10 on AI exposure. Human interaction and Japan's omotenashi culture seem to provide some protection against current AI systems.

The demographic angle makes this even more interesting. With an ageing population and labour shortages across healthcare, logistics and manufacturing, automation in Japan often fills gaps rather than replacing excess labour.

That doesn't mean displacement isn't real. A 55-year-old office worker displaced by AI may not easily transition into nursing or construction. But Japan's labour market resilience score was the highest among countries analysed.

AI exposure scores are modelled estimates, not official statistics or predictions of unemployment.

Curious whether people living in Japan are already seeing these changes in offices, factories or service jobs.

Full analysis and interactive tool in comments.

I recently analysed UK occupation data to see which job categories appear most exposed to current-generation AI systems.

The results are probably not what most people here would predict.

Using ONS workforce data mapped to ISCO-08 occupation groups, I assigned AI exposure scores based on how much of an occupation's core task bundle can already be completed or substantially augmented by current models and automation systems.

The highest score was not software development.

It was clerical support work.

Clerical occupations scored 8.5/10 across roughly 3 million UK workers. This includes administrative assistants, receptionists, customer service representatives, data-entry workers, call-centre staff, and bookkeeping clerks.

The reason becomes obvious when you break occupations into tasks.

Modern LLMs are exceptionally good at:

• Information retrieval
• Structured communication
• Summarisation
• Classification
• Form completion
• Draft generation
• Customer interaction workflows

Those capabilities overlap directly with a large percentage of clerical work.

Professionals scored 6.5/10. That category includes lawyers, engineers, accountants, analysts, architects, and software developers.

What's interesting is that exposure and displacement aren't the same thing.

A lawyer using AI to draft contracts becomes more productive.

A customer-support department replacing a large portion of repetitive ticket handling with AI may reduce headcount entirely.

The underlying capability overlap can be similar while labour-market outcomes are very different.

The lowest-risk categories remain occupations requiring physical adaptation to unpredictable environments. Trades and elementary occupations scored between 2.0 and 2.5.

One takeaway is that AI discussion often focuses on whether models can write code. The labour-market impact may arrive first through administrative and support functions because those workflows are already highly structured and relatively easy to automate.

Curious how others here would score exposure versus actual displacement risk.

Full analysis and interactive tool in comments.