AI could change parts of your work without eliminating your job. The International Labour Organization (ILO) estimates that one in four workers worldwide is in an occupation with some degree of generative AI exposure, but it says job transformation is more likely overall than job redundancy. Exposure measures potential effects on work tasks; it is not a prediction that one in four workers will lose their jobs.
That distinction is central to Gene Marks’s opinion article, “AI may come for your job. It’s not the end of the world — it’s the start of a new one.” His argument is that technological change can displace some work while reshaping existing jobs and creating new roles. It is a case for adaptation, not a quantified forecast of how many jobs AI will eliminate.
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What the ILO’s AI exposure estimate does—and does not—say
In a May 2025 brief, the ILO estimated that one in four workers worldwide is in an occupation with some degree of generative AI exposure. That is an occupation-level measure of potential task impact, not a count of workers expected to be replaced. The ILO says most jobs are more likely to be transformed than made redundant because occupations usually combine tasks that can be affected by AI with tasks that still require human input. [c002]
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The scale depends on which exposure category is being discussed. In the ILO’s refined index, 3.3% of global employment falls into the highest exposure category. That is a narrower group than the one-in-four estimate, and neither figure says what will happen to a particular person’s job. [c003]
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The index draws on task-level data, worker input, expert validation and AI-assisted scoring. It is designed to assess potential exposure, not to predict each employer’s adoption decisions, the pace of change or the future demand for a specific role. [c002]
Why exposure varies from one job to another
AI exposure is better understood through the tasks inside a job than through a job title alone. The ILO identifies clerical work as the most exposed broad occupational group, while exposure has also expanded in some highly digitized professional and technical work. A job may include tasks that AI can affect alongside responsibilities requiring judgment, communication or other human input; an occupation-level estimate does not tell an individual which tasks their employer will automate or augment. [c003]
Exposure also differs by gender and country income level, according to the ILO. The global averages therefore cannot be assumed to describe every occupation, worker or country. Local conditions—including the tasks involved and how an employer chooses to deploy AI—matter. [c003]
What adaptation can look like at work
Marks’s commentary treats technological change as a reason to respond rather than assume that work will remain unchanged. The ILO evidence gives that argument useful context, while not proving any particular prediction about future jobs: exposure may mean that some tasks change, but the index does not specify what an employer will do or whether a new role will follow.
For workers, a practical starting point is to identify which parts of a role involve repeatable information processing and which depend on responsibilities that require human contribution. That is not a guarantee against disruption; it is a way to have a more specific conversation about how a job may change than relying on its title alone.
- Map the tasks. Separate the recurring activities in a role rather than treating the whole occupation as one indivisible unit.
- Ask how AI will be used. Find out whether a proposed system is intended to assist people with tasks, automate them, or change how work is assigned and assessed.
- Look at the work around the tool. Consider what review, judgment, communication or accountability remains part of the process, and who is responsible for it.
- Raise working conditions as well as productivity. Ask how the change affects workload, monitoring, privacy, security and responsibility for mistakes. Marks acknowledges that technology can create workplace privacy and security concerns.
Why the way AI is introduced matters
AI’s effects depend not only on what a system can do, but also on how organizations decide to use it. The ILO calls for social dialogue involving governments, employers and workers to help manage the transition and improve working conditions as well as productivity. For workers, that makes participation in decisions and clear communication about the intended use of AI material questions—not side issues to consider after a tool is deployed. [c002]
The evidence supports neither a blanket promise that AI will create enough new work to offset every loss nor a claim that exposure means inevitable job loss. It describes the potential for work to change at different rates and in different ways. Marks’s argument is an optimistic interpretation of technological change; the ILO figures help put the scale and limits of current exposure estimates in perspective.
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