GLOBAL EXPOSURE
1 in 4
workers are in occupations with some GenAI exposure
The ILO's 2025 global index estimates that one in four workers worldwide is in an occupation with some degree of exposure to generative AI.
Source ↗Research hub · Updated 2026
AI is changing work.
What does the evidence actually show?
AI is affecting tasks, productivity, hiring and the structure of careers. But “AI will replace jobs” is too simple to explain what is happening.
This page separates what current research has found from what remains uncertain — and connects the evidence to what workers can actually do next.
The central distinction
AI exposure is not the same thing as job loss.
The ILO's global occupational research estimates that one in four workers is in an occupation with some degree of GenAI exposure. But because occupations contain many different tasks, exposure does not automatically mean the occupation disappears.
Its 2026 review of empirical evidence finds that productivity gains are real but uneven and that large-scale displacement has remained limited so far. At the same time, it identifies important risks around younger workers, inequality and changes in job quality and work organization. Read the ILO evidence review ↗
The numbers
A labor market already showing signs of change.
These numbers come from different studies and populations. They should not be combined into a single estimate of “jobs lost to AI.”
YOUNG WORKERS
19%
employment gap in highly exposed U.S. occupations
Stanford researchers report that employment among U.S. workers aged 22–25 in highly AI-exposed occupations was about 19% below the counterfactual path of similarly aged workers in less-exposed occupations by June 2026.
Source ↗ADOPTION
88%
of surveyed organizations reported AI use
Stanford's 2026 AI Index reports that organizational AI adoption reached 88% in its survey data, although adoption levels and uses differ substantially across organizations.
Source ↗PRODUCTIVITY
14–15%
more issues resolved per hour in one customer-support study
Stanford's AI Index summarizes research finding productivity gains in structured, measurable work, including a 14–15% increase in issues resolved per hour among customer-support agents using a conversational AI assistant.
Source ↗Five channels
AI can affect employment in more than one way.
Automating tasks
AI can perform parts of a job that previously required human time. The important unit of analysis is often the task, not the job title.
Augmenting workers
AI can help people complete existing tasks faster or with less effort without eliminating the occupation itself.
Changing hiring
Companies may change which experience levels, skills and roles they hire as the mix of human and AI work changes.
Creating new work
New products, services, workflows and technical infrastructure can create tasks and occupations that did not previously exist.
Reorganizing work
Even when headcount does not immediately change, AI can alter team structures, responsibilities, supervision and performance expectations.
Look at the task, not just the title
One occupation can contain tasks with completely different AI exposure.
This is why occupational-level headlines can be misleading. A worker may use AI to automate one part of their day while becoming more valuable in another part.
Task
Potential AI role
Human contribution
Task
Writing a first draft
AI role
High potential assistance
Human contribution
Define objectives, provide context, evaluate accuracy and decide what should actually be communicated.
Task
Summarizing information
AI role
High potential assistance
Human contribution
Verify important facts, identify what matters and apply domain-specific judgment.
Task
Routine data transformation
AI role
High potential automation
Human contribution
Define requirements, validate outputs and handle exceptions.
Task
Customer support
AI role
Assistance + partial automation
Human contribution
Handle complex cases, relationships, escalation and situations requiring judgment.
Task
Software development
AI role
Assistance + task automation
Human contribution
Architecture, requirements, testing, security, debugging and responsibility for the system.
Task
Strategic decision-making
AI role
Decision support
Human contribution
Set objectives, evaluate tradeoffs, understand organizational context and take responsibility.
One emerging warning signal
The career ladder may be changing before the whole labor market does.
Stanford Digital Economy Lab researchers using high-frequency payroll data report no evidence of widespread economy-wide job displacement. But they find a different pattern among young workers: employment for ages 22–25 in highly AI-exposed occupations was about 19% below the path of similarly aged workers in less-exposed occupations by June 2026.
Their analysis suggests the adjustment is primarily occurring through reduced hiring of younger workers rather than increased separations. That matters because many careers traditionally depend on junior roles as an entry point into professional work.
This is evidence from U.S. payroll data and should not be treated as a global estimate.
Read the Stanford study ↗Global evidence
The effect is not necessarily the same for junior and senior workers.
A September 2026 Stanford study analyzes 1.25 billion job postings and 154 million employment records across 41 countries. The researchers find that firms adopting AI reduced the junior share of their workforce relative to comparable firms, with the change driven primarily by growth in senior employment rather than falling junior employment. They also find suggestive evidence of modest overall employment growth.
This points to a potentially important question for the future: if AI handles more of the routine work traditionally assigned to beginners, how will workers acquire the experience needed to become senior?
Read the 41-country research ↗Not one global story
AI's impact depends on where and how people work.
A 2026 ILO working paper examining 135 countries finds that exposure is higher in advanced economies, while developing economies may have lower aggregate automation exposure but substantial potential for task augmentation.
The researchers highlight a digital-infrastructure problem: workers whose jobs could benefit from AI may not have the connectivity needed to capture those gains. They also caution that global occupational exposure measures can overestimate potential effects in developing economies because the same job title can contain very different tasks in different countries.
Read the ILO global analysis ↗Separate signal from noise
Five things we should stop assuming about AI and jobs.
AI exposure means your job will disappear.
Exposure measures how much of an occupation's task content could potentially be affected. It does not tell us whether an employer will automate those tasks or whether employment will fall.
AI only affects software engineers.
GenAI exposure exists across many occupations, particularly work involving language, information processing and other digital tasks.
Every AI-related layoff proves AI replaced those workers.
A layoff can result from many factors including demand, restructuring, financial conditions, strategy, mergers, over-hiring and automation. AI may be one factor without being the sole cause.
If AI makes workers more productive, fewer workers automatically become necessary.
Higher productivity can reduce labor demand for a particular task, but it can also reduce prices, increase output, create new tasks or shift demand toward other workers.
Learning AI automatically protects your career.
Tool knowledge can help, but durable career value also depends on domain knowledge, judgment, communication, problem solving and the ability to work effectively within real systems.
Community conversation
The research and the lived experience are asking similar questions.
Discussions in communities such as r/Layoffs increasingly focus not only on whether AI is replacing jobs, but on what happens to career progression, entry-level hiring and the ability to build a stable life after a layoff.
These discussions are valuable for understanding people's concerns, but they are not substitutes for labor-market data. We use them as a source of questions and lived experience, then compare those questions with research.
Explore r/Layoffs ↗“Are companies actually replacing workers with AI, or are they using AI as a reason to restructure?”
“If AI makes a developer twice as productive, does that mean a company needs half as many developers?”
“Why are entry-level jobs disappearing if AI is supposed to create productivity?”
“Should people learn AI tools or focus on skills that AI cannot easily perform?”
“What happens to the career ladder if junior workers no longer perform routine tasks?”
What this means for workers
Don't ask only what AI can do. Ask what you can do with AI.
The practical question is how to build a career around changing tasks and demand. That can mean learning technology, but it can also mean developing domain expertise, moving closer to customers, taking responsibility for systems, entering a growing physical industry or combining several existing skills.
Entry-level workers
Early-career workers can be particularly exposed when organizations use AI to reduce the amount of routine work that previously served as a training ground.
Explore →Experienced workers
Experienced workers may benefit when AI amplifies domain knowledge, but their responsibilities can also change as organizations redesign workflows.
Explore →Managers
Managers may face a changing span of control, new expectations around AI adoption and more responsibility for redesigning teams and processes.
Explore →Developing economies
The effects can differ substantially by country. Digital infrastructure and local task composition influence whether workers experience automation pressure or productivity gains.
Explore →Continue
Turn research into action.
Careers
Explore career paths across technology, healthcare, energy, trades, business and more.
Skills
Identify transferable skills and capabilities worth building.
Opportunities
Explore ways changing technology can create new work and businesses.
Industrial Revolution
Compare today's transition with previous technological transformations.
FAQ
AI and jobs FAQ
Is AI actually replacing jobs?+
AI is already changing tasks and some hiring decisions, but the evidence does not support the idea that all AI-exposed jobs are simply disappearing. The ILO's 2026 evidence review finds large-scale displacement remains limited so far, while other research finds more concentrated effects among younger workers in highly exposed occupations.
How many jobs are exposed to generative AI?+
The ILO's 2025 global index estimates that one in four workers worldwide is in an occupation with some degree of GenAI exposure. Exposure does not mean that the worker will lose their job; it measures potential effects on occupational tasks.
What is the difference between AI exposure and automation risk?+
AI exposure describes how much an occupation's tasks could potentially be affected by AI. Automation risk goes further by considering whether those tasks could actually be performed without human involvement. A highly exposed occupation can therefore experience substantial augmentation rather than job elimination.
Why are young workers being affected by AI?+
Recent Stanford research finds a widening employment gap for U.S. workers aged 22–25 in highly AI-exposed occupations. The researchers report that the adjustment appears to operate primarily through reduced hiring of young workers rather than increased separations.
Does AI increase productivity?+
Evidence from controlled and workplace studies shows productivity gains in some types of work, particularly structured tasks with clear feedback. Results vary by occupation and task, and some research finds limited or negative effects where deeper reasoning or learning is important.
Will AI create new jobs?+
AI can create new tasks, products and occupations, but the scale and distribution of those opportunities are uncertain. Historical experience shows that productivity improvements can change the composition of employment rather than produce one simple outcome.
Should I learn AI to protect my career?+
AI literacy can be useful, but it should be considered alongside domain knowledge, communication, problem solving, judgment and other transferable capabilities. The OECD reports increasing demand for workers who can use, analyse and interpret data as AI adoption expands.
Why doesn't every AI-related layoff mean AI caused the layoff?+
Companies can reduce headcount for many reasons. AI adoption can be one factor, but restructuring, changes in demand, financial conditions, mergers, strategy and previous over-hiring can also contribute. Individual announcements need to be examined rather than assuming AI was the sole cause.
Research sources
Primary research behind this page
- ILO — The impact of GenAI on jobs, productivity and work organization ↗
- ILO — Generative AI and jobs: A 2025 update ↗
- ILO — Disruption without dividend? GenAI across 135 countries ↗
- Stanford HAI — 2026 AI Index: Economy ↗
- Stanford Digital Economy Lab — Canaries in the Coal Mine? ↗
- Stanford Digital Economy Lab — AI and Labor Demand Across 41 Countries ↗
- OECD — AI and skills ↗
Understanding the change is only the first step.
Next, look at the skills that can transfer across changing occupations and the opportunities emerging around new technology.