International AI Safety Report 2026

Labour market impacts

Key information

• General-purpose AI systems can automate or help with tasks that are relevant to many jobs worldwide, but predicting labour market impacts is difficult. Around 60% of jobs in advanced economies and 40% in emerging economies are exposed to general-purpose AI, but the impacts of this will depend on how AI capabilities develop, how quickly workers and firms adopt AI, and how institutions respond.

• Current evidence shows rapid but uneven AI adoption with mixed employment effects. Adoption and productivity gains vary widely across countries, sectors, occupations, and tasks. Early evidence from online freelance markets suggests AI has reduced demand for easily substitutable work like writing and translation, but increased demand for complementary skills like machine learning programming and chatbot development.

• Economists disagree on the magnitude of future impacts. Some predict modest macroeconomic effects with limited aggregate impact on employment levels. Others argue that, if AI surpasses human performance across nearly all tasks, it will significantly reduce wage levels and employment rates. Disagreements stem in part from differing assumptions about whether AI will eventually perform nearly all tasks more cost-effectively than humans and whether new kinds of work will be created.

• Since the publication of the previous Report (January 2025), new research from the US and Denmark found no relationship between an occupation’s AI exposure or AI adoption and overall employment. However, multiple other studies found declining employment for early-career workers in the most AI-exposed occupations since late 2022, while employment for older workers in these same occupations remained stable or grew.

• A key challenge for policymakers is enabling productivity benefits without causing significant negative impacts for workers impacted by automation or changing skill demands. This is particularly difficult because labour market risks and productivity gains often stem from the same AI applications. Since evidence of impacts is likely to emerge gradually over time, the appropriate timing of any potential policy responses is also difficult to determine.

Experts expect the diffusion of increasingly-advanced general-purpose AI to transform many occupations by accelerating job turnover and reshaping labour demand. However, the magnitude and timing of these effects remain uncertain. General-purpose AI systems can perform tasks relevant to a significant share of jobs worldwide. One study estimates that around 60% of jobs in advanced economies and 40% in emerging economies are highly exposed to general-purpose AI, in the sense that tasks performed in these roles could be affected because AI systems can technically perform or complement them. AI’s labour market impacts will depend on how capabilities develop, how quickly systems are adopted, and whether AI systems substitute for humans performing existing tasks, augment workers’ productivity, or create entirely new tasks for humans to perform. Institutions will also shape these outcomes through their responses: for example, by setting incentives and policies that steer AI development toward task creation and labour augmentation rather than automation (or vice versa).

AI adoption has been rapid, but uneven

To date, adoption of general-purpose AI has been rapid in some places but highly uneven across countries, sectors, and occupations. In the US, general-purpose AI has diffused faster than earlier technologies such as the internet (Figure 2.14). Globally, adoption rates range from over 50% in the United Arab Emirates and Singapore to under 10% in many lower-income economies (Figure 2.15). Even within individual countries, variation can be large. In the US, for example, reported usage across sectors varies from 18% in Information to 1.4% in Construction and Agriculture. Evidence on usage patterns suggests that current systems mainly benefit high-income workers in cognitive jobs, offering fewer gains to lower earners.

Productivity impacts differ across tasks and jobs

Productivity impacts from general-purpose AI also vary significantly across jobs and tasks. A recent review of task-level productivity studies found that productivity gains usually range from 20–60% in controlled studies, and 15–30% in most experiments within real-world work settings, though there are outliers on both the high and low end. Productivity boosts from AI usage can have varied effects on outcomes like wages and employment. For example, when productivity gains enable workers to produce more output, this can increase employment and/or wages if demand for that output grows at equivalent or greater scale. However, when productivity gains allow firms to maintain the same output with fewer workers, they may choose to reduce employment or wages if demand does not expand. While automation can initially reduce labour demand in affected tasks, the resulting productivity gains may later stimulate economic growth and increase demand for human labour in non-automated activities, creating new employment opportunities.

Early employment effects are mixed but suggest concentrated impacts on certain jobs and on junior workers

Early evidence on AI’s employment effects is mixed. Two national-level studies from Denmark and the United States find no discernible relationship between AI exposure or adoption and changes in overall employment. Despite minimal aggregate effects, other research has found concentrated impacts on specific jobs. For example, one study found that four months after ChatGPT was released, writing jobs on one online labour platform declined by 2%, and writers’ monthly earnings fell by 5.2%. Recent research also found that demand for freelance work using substitutable skills such as writing and translation decreased sharply after the release of ChatGPT, but demand for machine learning programming increased by 24%. Some studies also suggest that AI adoption is disproportionately affecting junior workers. New data finds that employment in AI-exposed jobs in the US has declined for younger workers but either held steady or has risen for older workers since the release of ChatGPT. In the UK, one study found that firms with high AI exposure have slowed new hiring, particularly for junior positions.

Future scenarios and uncertainties

AI could lead to periods of labour market adjustment in which skill demands change rapidly

While current AI systems require human oversight for complex tasks, there is concern about the labour market impacts of potential future systems that could cost-effectively automate a wider range of work with greater reliability and autonomy. Forecasting how such systems would affect employment is challenging. In the past, new automation technologies have led to varied effects on workers, resulting in adjustment periods as workers shifted from displaced forms of work to new jobs with growing labour demand. Historically, these periods of adjustment have caused significant hardship for displaced workers, but were also followed by strong gains in real wages for many workers in the longer term. This historical precedent suggests that even if AI capabilities advance significantly, there may still be plentiful employment opportunities, but that a core policy challenge will be ensuring that workers can adapt to fast-changing skill demands as AI diffuses throughout the economy.

The impacts of general-purpose AI may differ from those of previous automation technologies

Other economists argue that if general-purpose AI surpasses human performance across nearly all tasks, it could ultimately reduce wage levels and employment rates significantly. Some evidence suggests that automation produces better labour market outcomes when it is accompanied by the creation of new labour-intensive tasks. Whether AI development will generate new labour-intensive tasks at scale remains uncertain. As computational resources expand and AI systems become more cost-efficient, competitive pressures to automate human workers could intensify.

Key factors shaping future impacts

The magnitude of labour market impacts will depend on several key factors. First, how broadly capable AI systems ultimately become: many disagreements among economists stem from differing assumptions about whether general-purpose AI will eventually perform nearly all economically valuable tasks more cost-effectively than humans. Second, how quickly capabilities improve: if AI agents gained the capacity to act with greater autonomy across domains within only a few years – reliably managing longer, more complex sequences of tasks in pursuit of higher-level goals – this would likely accelerate labour market disruption. Third, the pace of adoption: even if capabilities advance rapidly, diffusion may be slowed by institutional and organisational frictions, system integration requirements, and cost barriers. If systems remain narrow, capabilities improve gradually, and adoption is slow, effects will likely be more muted and both workers and policymakers will have more time to adapt.

Implications for inequality

General-purpose AI could widen income and wealth inequality within and between countries. AI adoption may shift earnings from labour to capital owners, such as shareholders of firms that develop or use AI. Globally, high-income countries with skilled workforces and strong digital infrastructure are likely to capture AI’s benefits faster than low-income economies. One study estimates that AI’s impact on economic growth in advanced economies could be more than twice that in low-income countries. AI could also reduce incentives to offshore labour-intensive services by making domestic automation more cost-effective, potentially limiting traditional development paths.

Updates

Since the publication of the previous Report (January 2025), new research has provided greater clarity on the relationship between changes in employment and both AI exposure and AI adoption. As discussed above, new national-level studies from Denmark and the US found that AI adoption and exposure had no effect on aggregate employment. However, other studies find declining demand for younger workers in AI-exposed occupations, and according to one UK study, new hiring slowed significantly at firms highly exposed to AI after the release of ChatGPT, particularly for junior positions. Additionally, recent research confirms that impacts of automation generally vary significantly depending on which tasks within a job are automated: automating relatively expert tasks tends to lower the skill requirements for a given job, expanding employment opportunities in that job but reducing wages. On the other hand, if relatively novice tasks within a job are automated, that tends to raise the job’s skill requirements, increasing wages but reducing total employment. Adoption has also accelerated since the previous Report: the share of US workers using general-purpose AI rose from 30% in December 2024 to 46% by mid-2025.

Evidence gaps

There is limited data on AI adoption and its links to employment outcomes. Most studies rely on proxy measures for AI usage, such as ‘AI exposure’, because occupation-level adoption data remains scarce (particularly outside the US). It is difficult to gather usage data and connect it to employment, wages, or hiring trends, making it harder to track how AI diffusion affects different populations of workers or to make empirically-grounded forecasts. Furthermore, while research on labour market risks is often concerned with automation and displacement effects, less work has been done to determine what new jobs AI adoption is creating or how career paths may change as a result of AI. Finally, evidence on effective worker protections is limited: though retraining is often proposed as a policy solution, studies of its effectiveness show mixed results.

Mitigations

Technical measures proposed to mitigate labour market risks include pacing AI deployment to allow time for workforce adaptation and prioritising AI development that complements workers by creating new labour-intensive tasks alongside task automation. However, it is often difficult to predict in advance whether a given AI system will displace workers, complement them, or create new opportunities – outcomes will depend on how systems are deployed and how labour markets respond.

Evaluations and monitoring may also help workers and policymakers prepare for and respond to labour market impacts. Benchmarks that test AI systems’ capabilities on real-world work tasks may not reliably predict the employment or wage effects of those systems after deployment. However, they can provide some indication of which tasks, occupations, and sectors are most likely to be affected. Collecting post-deployment data on how AI adoption affects employment and wages can also improve visibility into actual impacts and improve forecasts of future effects.

Challenges for policymakers

For policymakers, a central challenge will be supporting workers through AI-related labour market disruptions without stalling productivity growth across the economy. This requires balancing the productivity gains from AI adoption against the costs of involuntary job displacement that may occur for some workers. Given uncertainty about the pace and scale of AI’s labour market impacts, researchers have emphasised the need for mitigations to be adaptable, while still providing sufficient regulatory certainty for business investment and worker training decisions. As general-purpose AI systems become more capable and widely deployed, policymakers can monitor AI adoption rates, employment and wage changes across occupations, and shifts in employer skill demands to help them anticipate impacts and adjust policy responses.