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AI is changing technology work and increasing demand for some AI-related skills, but current evidence does not show that it is creating more tech jobs than it eliminates. Exposure to AI, new job postings, productivity gains and net employment are different measures. To judge what is happening, it helps to separate them.
What does it mean for AI to affect tech jobs?
AI can affect employment through three routes: it can automate tasks people do now, create new tasks or occupations, and change productivity. The net employment result depends on how those forces combine—not simply on how many tasks AI could perform. That is the distinction emphasized in the OECD’s 2026 synthesis, Skills in the AI age.
- Exposure estimates whether an occupation includes tasks that generative AI could affect. It does not establish that an employer will adopt AI or eliminate a position.
- Task automation means that AI performs some work previously done by a person. A job may still require human judgment, oversight or other tasks.
- Vacancies show what employers advertise for, not how many people they ultimately hire or how many jobs exist after hiring and separations.
- Productivity measures output or efficiency in a given context. A productivity gain can support growth and hiring, or let an employer produce the same output with fewer workers.
- Net employment is the overall change in jobs after creation, displacement, hiring and job losses are counted together.
The OECD’s concise formulation is that “AI is transforming jobs, but not necessarily destroying them.” Transformation, however, is not proof of net job creation.
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The International Labour Organization’s 2025 update estimates that one in four workers globally is in an occupation with some degree of generative-AI exposure. The ILO says most jobs are more likely to be transformed than made redundant because they continue to require human input. The one-in-four figure is an exposure estimate—not a prediction that one in four jobs will disappear.
The updated methodology covers nearly 30,000 tasks. Its mean automation score is 0.29, compared with 0.30 in the 2023 assessment; the standard deviation is 0.14, compared with 0.30. These are measures within the ILO’s exposure methodology, not counts or percentages of jobs lost. They should not be read as a direct forecast of how many employers will automate work.
Are employers creating AI-related tech jobs?
There are signs of demand for relevant skills, but they describe vacancies rather than net employment. An IMF staff analysis published in 2026 examined job postings across six countries. AI-related skills made up nearly one-third of new IT skills in that analysis. Generative AI had the largest absolute increase in skill demand in 2024 among the four advanced economies in its sample.
The examples in the analysis include software engineering, data engineering, Python, SQL and data pipelines. A posting that asks for one of these skills is a signal about employer demand; it does not show that the role is newly created, that the posting is filled, or that total technology employment increased. Nor does it establish that every worker who learns a listed skill will find a job.
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Adoption is growing, too. OECD data in the 2026 synthesis put the share of firms using AI in OECD countries at around 7% in 2021 and 20% in 2025. That increase indicates wider uptake, not a matching increase in headcount. The OECD also identifies skills shortages as one barrier to diffusion, so adoption can vary between firms and sectors.
What does the evidence say about productivity and displacement?
The ILO’s 2025 evidence review reports productivity gains of 20% to 60% in controlled randomized controlled trials and 15% to 30% in field experiments it reviewed. Those figures describe the studies’ settings, not a general productivity boost for every technology worker or company. Experimental results can depend on the task, workers, tools and workplace conditions.
The same review finds substitution in some writing and translation work, as well as evidence suggesting that demand for novice workers may decline in some settings. These findings matter for technology careers because an entry-level role can include routine tasks that are easier to automate or assist with. They do not establish that all junior tech positions are disappearing; outcomes depend on what else the role involves and how employers reorganize work.
The ILO cautions that generative AI is changing work processes, task composition and organizational design, while its effects on employment and the wider economy remain unresolved. Many reviewed experiments concern simpler tasks, and the models and workplace settings studied are limited. Exposure scores alone cannot show whether a firm will adopt a system or whether adoption will replace labor.
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Why can’t current tech job cuts be attributed to AI alone?
Technology employment responds to more than automation. The OECD’s 2026 Employment Outlook describes weaker labor-market conditions since 2023, low hiring in the United States, and cuts in high-tech and information sectors following pandemic-era overhiring. It also notes challenges for young entrants. Those conditions make it difficult to attribute a particular hiring slowdown or layoff to AI without evidence about the employer and its decisions.
A company can reduce hiring because demand has cooled, budgets have tightened or it is correcting earlier overexpansion. AI may also change the tasks a team needs, but an employment trend by itself does not identify which cause mattered. The available sources do not establish AI’s net effect on total technology employment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which tech skills are worth building?
No skill list guarantees a job, and no whole occupation can be labeled safe or doomed from exposure alone. A practical approach is to combine technical competence with skills that help people use, evaluate and apply technology in real settings.
- Build foundations: strengthen literacy, numeracy and ICT capabilities, which the OECD identifies as important foundations for work in the AI age.
- Develop AI literacy: learn what AI tools can and cannot do, how to assess their outputs, and how to use them responsibly in a workflow.
- Pair technical skills with problem-solving: software engineering, data engineering, Python, SQL and data pipelines appear among the skill examples in the IMF posting analysis. Treat them as areas to explore, not a guaranteed career recipe.
- Practice complementary skills: critical thinking, creativity, collaboration and continuing learning are among the capabilities highlighted by the OECD. They help workers frame problems, check results and work with others as tasks change.
- Evaluate the local market: check actual postings and hiring patterns in your country, industry and experience level. A vacancy signal in one market does not prove the same demand elsewhere.
When considering a role, look at its task mix rather than its title: how much work is routine, how much calls for judgment or interaction, and how does the balance differ for novices and experienced staff? Also distinguish an employer asking for AI-related skills from an employer adding positions. Those questions give a more useful picture than a binary prediction about whether an occupation will survive.
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