By John Ohiogwehei

Every week, someone asks whether it is still worth getting into tech, but that is the wrong question. The bigger story is not simply whether AI will replace entry-level jobs. It is that the apprenticeship model that produced experienced professionals is disappearing faster than the technology industry is replacing it.

For decades, the career ladder into technology followed a familiar path. Graduates began with routine work. Routine work became experience, experience developed judgment, and judgment eventually became expertise. Over the past two years, however, conversations about AI have focused mainly on skills. Which programming language should people learn? Which AI tools should they master? How should universities update their curricula? These are important questions, but they are not the most important ones.

The real disruption is not simply that AI is changing the skills people need. It is changing how people become professionals.

For years, routine work was treated as low-value work, but its true value was misunderstood. Routine work did more than produce output; it helped develop experienced professionals. Writing documentation, debugging simple issues, analysing data, building small features, and preparing reports may have been repetitive tasks, but they taught people how products worked, how teams made decisions, and how businesses created value. They were the lower rungs of the career ladder.

That apprenticeship model is now disappearing in plain sight. Recent industry research suggests that many enterprises expect AI to reduce entry-level hiring as it becomes embedded in everyday work. At the same time, more of the routine work that once introduced graduates to professional practice is being automated.

The World Economic Forum’s Future of Jobs Report 2025 also finds that employers increasingly value analytical thinking, problem-solving, leadership, resilience, and sound judgment. These are capabilities that have traditionally been developed through workplace experience rather than classroom instruction.

The goal should not be to preserve repetitive work. It should be to replace the learning that repetitive work quietly provided. Teaching more AI tools will not solve the problem if we do not also redesign how people develop professional judgment.

This contradiction is becoming harder to ignore. Graduates struggle to find opportunities because they lack experience, while employers struggle to find people with enough judgment to contribute meaningfully. The Linux Foundation’s 2025 State of Tech Talent Report reaches a similar conclusion: organisations continue to struggle to hire experienced technical talent even as demand for digital skills grows.

That tension is becoming increasingly visible across Africa. The debate sparked by recent comments from Tosin Eniolorunda was not really about one company. It exposed a broader tension across the technology ecosystem. Employers continue to say they struggle to hire experienced talent, while graduates argue that entry-level opportunities increasingly demand experience they have never been given the chance to build.

The disagreement itself is revealing. Whether the problem is described as talent quality, employer expectations, or workforce development, the debate points to the same structural question: how does a technology ecosystem consistently develop experienced professionals?

Across the continent, governments, universities, bootcamps, and private organisations continue to invest heavily in expanding the technology talent pipeline. Companies such as Andela have long argued that Africa possesses a deep and growing pool of technical talent. However, a larger talent pipeline will not solve the problem if the pathway from graduate to experienced professional is breaking down. Producing more graduates is not the same as producing more expertise.

This is where many discussions about AI miss the point. If we believe the challenge is primarily a shortage of technical skills, the solution appears obvious: teach more AI tools, update curricula, and encourage continuous learning. These efforts are necessary, but they do not address the deeper problem if the pathway from beginner to experienced professional is disappearing.

Every institution responsible for developing technology talent, from universities and bootcamps to employers, was built around a career model in which repetition gradually produced judgment. That assumption no longer holds.

None of this is an argument against AI. Technology has always changed work. Every major technological shift has altered the skills people need and the way industries operate. What makes this moment different is that AI is changing not only the work itself, but also the pathway through which expertise is developed.

The industry has spent the past two years asking how people should adapt to AI. The more pressing question is how the industry intends to replace the apprenticeship model it is rapidly leaving behind.

For years, progress has been measured by the number of people entering technology. The harder challenge is ensuring that those people have a clear path to becoming experienced professionals. Producing more graduates is not the same as producing more expertise. Until the industry builds new pathways that consistently develop experienced professionals, it will continue mistaking a talent-development problem for a hiring problem.