Today’s technology industry has reached a point that goes far beyond the mere adaptation of new tools. There is a huge fascination in the market with the speed at which people with relatively little experience can deliver complex code, using generative artificial intelligence.
However, this phenomenon creates a dangerous illusion of instant perfection. The enthralment of widespread automation, combined with the drastic reduction in recruitment for entry-level positions, is akin to taking out a high-interest mortgage on the future productivity of the organisation.
This raises the crucial question for business continuity of who will take responsibility for strategic systems architecture in a decade’s time if the space for young talent to learn the craft is taken away today.
Generative artificial intelligence excels as an advanced assistant, but it is a mistake to treat it as a substitute for a real expert. These tools allow lower-skilled profiles to solve repetitive problems efficiently, impressing decision-makers at first glance and having a positive impact on short-term metrics.
However, generating a syntactically correct result is not the same as understanding it in depth. A programmer who relies solely on the prompts of the algorithm gradually loses his or her ability to make a critical assessment. It is then difficult to verify whether the proposed solution is optimal, safe and scalable in the long term.
After all, the real value of software engineering lies not in knowing the syntax alone, but in being able to look at a system holistically and solve complex business problems.
Market data from 2022 onwards mercilessly expose a worrying trend. The number of vacancies for junior positions is falling dramatically. Companies, in a natural reflex to optimise costs, are choosing to delegate the simplest tasks to language models.
However, this closes a key testing ground. Technical mastery cannot simply be transferred to the human mind with a suitably formulated question.
Proficiency is forged through hundreds of hours spent painstakingly analysing bugs, testing hypotheses and seeking answers to the fundamental question of why a piece of architecture is not working as intended. By eliminating this unimpressive early career stage, organisations are unwittingly dismantling the natural incubator in which future designers of advanced systems mature.
In the face of these changes, the knowledge of experienced professionals becomes more valuable than ever. It is the senior experts who have the necessary business context to decide which processes are worth automating and how to integrate the fragments generated into a stable, secure ecosystem.
However, the phenomenon of invisible work intensification arises here. When less experienced employees generate code en masse with the help of artificial intelligence, a gigantic bottleneck is created at the review stage. Instead of spending the freed up time on high-level innovation and mentoring, the best specialists are drowning in the processes of reviewing thousands of lines of code, trying to catch machine hallucinations and logic gaps.
Working extended hours does not translate into higher productivity in this case, and the growing technological debt is beginning to overwhelm the most competent individuals in the company.
The new definition of leadership in the age of artificial intelligence requires an understanding that digital transformation is not only about the smooth implementation of modern programming assistants. It is first and foremost a rigorous strategy for managing quality and generational knowledge within the company.
Automation that is not accompanied by a talent reproduction plan leads the organisation down a dead end. It becomes necessary to consciously maintain mentoring programmes and space for the development of budding engineers, even if in the short term this seems financially a considerable burden.
It is worth recalling at this point Seneca’s philosophical maxim that for a ship that knows no port of destination, no wind is auspicious. Similarly, in the technology business, artificial intelligence is only a powerful driving wind, not the ultimate goal.
Market success will not be measured by the sheer volume of software generated or the hours saved. In the long term, the organisations that will win will be those that do not get carried away with speed, but maintain control over the quality of the products delivered, basing their structures on teams capable of critical and independent thinking.
