AI is more likely to change many young workers’ tasks than to make their jobs disappear outright—but the effects will vary by occupation, employer, and labor market. For people starting work, the practical priority is not necessarily to become AI specialists; it is to build adaptable digital, cognitive, communication, and job-specific skills while learning how AI changes the work around them.
Will AI take jobs away from young people?
Some jobs and tasks may be automated, and displacement is a real possibility. But an occupation’s exposure to generative AI is not a forecast that its workers will be laid off. Exposure means that some tasks in the occupation could be affected. Automation means a task can be carried out by a system; transformation means the task mix or the way a job is done changes; displacement means workers lose jobs. Those are related, but they are not interchangeable outcomes.
As an Amazon Associate I earn from qualifying purchases.
The International Labour Organization’s 2025 analysis estimates that one in four workers worldwide is in an occupation with some degree of generative AI exposure. Its conclusion is that most jobs are more likely to be transformed than made redundant, in part because human input remains necessary. That is a broad assessment, not a guarantee for every occupation or worker.
Free tools Windows power users keep installed
One-click scans. No signup required.
The ILO’s revised 2025 index uses task-level data, expert input, and AI predictions to place occupations into four exposure gradients. The highest gradient covers 3.3% of global employment—far less than the broader group with any exposure. A role with some potentially affected tasks should not be treated as equivalent to one in the highest exposure category.
#1 Best Overall
Which jobs and workers are more exposed?
Exposure depends on what people do in a job, not only on its title. Routine, text-heavy, and digitized tasks may be more susceptible to generative AI than work requiring varied judgment and direct human interaction. Even within one occupation, some duties may change while others remain dependent on a person.
Exposure also differs between countries. In the ILO’s 2025 estimates, occupations with some generative AI exposure account for 11% of employment in low-income countries and 34% in high-income countries. Those figures describe exposure, not expected job losses; they also show why a single global prediction cannot describe every young person’s prospects.
There is no universal list of “safe jobs” established by these global findings. A useful way to assess a particular role is to consider:
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Task mix: How much of the work is routine, digital, or text-based, and how much calls for varied judgment or interaction?
- Exposure gradient: Is the occupation in a broad exposure group, or in the much smaller highest-gradient category?
- Complementarity: Could AI help a worker do more or better work, and would that translate into improved autonomy, conditions, or pay?
- Local conditions: What are the employment prospects in the relevant country, region, and occupation, and how do existing inequalities affect access to work?
What skills will matter when someone starts work?
Preparation does not have to mean learning to code or training as an AI developer. OECD analysis finds that most workers exposed to AI will not need specialized AI skills, even though their tasks and skill requirements may change. The needed mix can include digital capability, cognitive skills, communication, judgment, and knowledge of the occupation itself.
In highly AI-exposed occupations, the OECD found an eight-percentage-point increase in the share of vacancies requesting at least one emotional, cognitive, or digital skill. Its analysis also identifies management and business skills among those in demand. This points to a combination: understand the tools relevant to a field, but also develop the human and domain capabilities needed to check outputs, make decisions, work with others, and take responsibility for results.
Training can help, but the evidence should be interpreted carefully. The ILO’s 2024 review of youth employment programs found that training and entrepreneurship programs can improve labor outcomes, with results differing by country income group. Combined programs that include soft skills and certification tended to perform better in that review. This is broader youth employment evidence, not a direct test of a particular AI course or curriculum.
Could AI create opportunities as well as risks?
AI can support workers as well as automate parts of their work. In a late-2024 OECD survey of more than 5,000 small and medium-sized enterprises across Austria, Canada, Germany, Ireland, Japan, Korea, and the United Kingdom, some firms reported that generative AI helped compensate for skill gaps or improved employee performance. The same survey also found firms reporting greater need for highly skilled workers.
Among AI-using SMEs that reported skill gaps, 39% said generative AI helped compensate for those gaps. Among the subset also reporting improved employee performance, that share was 46%. Across SMEs in the survey, 19.7% reported increased need for highly skilled workers, while 9.4% reported a decrease.
Best Value
These are employer reports from seven countries, not proof that AI caused the changes or that the same pattern will hold in every firm. They illustrate why the effect can cut both ways: a tool may help existing staff handle some work while increasing the need for people with other capabilities. Whether productivity gains improve workers’ pay, conditions, or autonomy is a separate question.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How does the wider youth job market affect the outlook?
AI is entering a labor market that was already unequal for young people. The ILO’s 2024 youth report counted 64.9 million unemployed young people worldwide in 2023 and put global youth unemployment at 13%, the lowest rate in 15 years. At the same time, 20% of young people were not in employment, education, or training (NEET); two in three young NEETs globally were women.
These are measures of the broader youth labor market, not outcomes caused by AI. The ILO describes recovery in some measures alongside persistent job insecurity, sharply uneven regional conditions, and particular disadvantages for young women in parts of the recovery. A global average cannot tell an individual what will happen in their town or chosen field.
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Young people’s concerns also include both risk and possibility. The OECD’s education trends report describes anxiety that AI could eliminate jobs as well as optimism that it might make work less boring or better aligned with personal life. In the report’s underlying analysis, major employment effects had not yet been clearly evidenced. That uncertainty is a reason to avoid treating either reassurance or alarm as a settled forecast.
What can a young worker do now?
- Learn the task mix of a target occupation. Look beyond job titles to the activities involved, especially which are routine or digitized and which require judgment, interaction, or specialized knowledge.
- Build transferable foundations. Strengthen digital, cognitive, communication, and problem-solving abilities alongside occupation-specific knowledge; specialist AI development skills are not a universal prerequisite.
- Practice evaluating AI-assisted work. Where relevant tools are available, learn how to check their outputs and identify when a human decision or context is needed.
- Seek applied learning and recognized credentials. Work experience, training, or programs combining practical skills with certification may support employment, though results and access vary by country and program.
- Reassess as the work changes. Employer adoption and job requirements vary, so use local vacancies, workplace guidance, and field-specific training to update your plan rather than relying on a universal “future-proof” job list.
What remains uncertain?
The available global evidence does not establish which specific occupations will be created or eliminated for the next generation, or on what timeline. Exposure indexes describe current occupational task profiles and potential susceptibility; they are not forecasts of net employment. The cited evidence also cannot determine how a particular person’s prospects will change in a specific occupation, city, or national policy setting. Those questions require local labor-market information and will depend on how employers adopt AI and how the benefits and costs are distributed.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
