AI and Work: A More Important Question Than How Many Jobs Will Disappear

Whenever artificial intelligence is discussed, the debate usually returns to the same question: “Will it take our jobs?” This concern is not unfounded. According to the World Economic Forum’s Future of Jobs Report 2025, approximately 92 million jobs could disappear worldwide by 2030. The same report projects that 170 million new jobs will emerge during this period.

Placed side by side, these figures can look like a “net gain.” But that calculation assumes someone losing a job can automatically move into a new one. In real life, turning a fifty-year-old accounting employee into a data analyst, or a call center worker into a software testing specialist, is far from straightforward. This is where the real issue begins.

A profession disappearing entirely is relatively rare. More often, its repetitive tasks are transferred to machines. In translation, for example, software can now produce an initial draft in seconds. Checking a legal document’s accuracy, understanding context, and accepting responsibility remain human tasks. As someone who has worked with documents and texts for years, I see this transformation closely. The nature of the work changes, rather than simply its volume.

With appropriate training, this change can be a major opportunity. For a worker caught unprepared, it brings a risk of exclusion.

If productivity rises, who receives the gains?

The International Labour Organization’s 2026 agenda highlights considering artificial intelligence together with the concept of “decent work.” At its heart lies a simple question: if the same work can be completed faster, how will the resulting gains be shared? Will they appear only in company profits, or also in working hours, wages, and access to training?

If technology is used to force workers into a more intensive, closely monitored pace, progress remains on paper. Software that summarizes call center conversations can ease a worker’s burden. If that same software begins scoring every pause as “inefficiency,” pressure increases. The difference is determined by the management using the technology.

Large companies can provide AI training for employees. How will someone working in a small business, a city in Anatolia, or insecure employment obtain the same opportunity? If digital transformation policy focuses only on specialists in major cities, existing inequalities will deepen. Public education centers, vocational schools, university continuing education departments, and employers can close this gap through short, practical, free programs. Such programs are particularly important in cities with large young populations, including Diyarbakır.

When software is used for recruitment, performance assessment, or shift planning, workers should have a right to know the reasons behind decisions. They should be able to find a human contact to challenge an incorrect assessment. “That is what the system calculated” is not a fair explanation.

In the coming years, the AI debate needs to shift from “How many jobs will disappear?” to “Who will share the costs and gains of transformation, and how?” The answer to the second question will matter far more than the technology itself.