The illusion of competence trap shows how automated code generation deprives new programmers of building real expertise

Today's prevailing bet in the software sector rests on a simple premise that AI models can take over writing and correcting code, freeing developers from dealing with complex technical details. However, field data and recent academic studies have begun to reveal a troubling paradox: the developers who benefit most from these tools are the long-experienced ones whose skills were honed before generative models appeared, while new developers fall into a trap of over-reliance that hinders the formation of genuine programming expertise.

An extensive study conducted by GitBrins, a developer-tools company, analyzing live coding sessions of novice developers, showed that participants treated the smart assistant as a private tutor, but the outcome was entirely opposite. The study recorded that beginners who relied heavily on generative tools skipped the basic planning and problem-solving stages, ending up with an illusion of competence without a real understanding of what was written. In contrast, beginners who limited their use of the models achieved better results because they developed what the study called negative expertise, the ability to disregard inaccurate suggestions and steer the tool to produce what they had planned beforehand.

These findings align with a study from the University of Pennsylvania that involved a thousand students, which found that using generative models without training controls led to a 17 % drop in student performance compared with those who relied on traditional textbooks, despite AI users believing they were superior. However, the same study revealed that turning the model into a Socratic interlocutor that asks questions without providing ready answers boosted training performance by 127 %. A study by Anthropic also confirmed that cognitive effort and confronting difficult programming bugs remain indispensable conditions for building professional competence.

Generative tools shorten execution time but do not shorten the path of cumulative understanding.As David Kramer, co-founder of the Centri platform, warned, the belief that future models will automatically fix all accumulated faulty code is a risky experiment, because models act as extrapolators of training patterns and lack architectural judgment or pedagogical intent, which recalls Jol Spolski’s law of software abstraction: abstractions always leak details, and the only way to deal with them is to understand the underlying mechanisms behind them.

This reality imposes a direct shift on technical team leaders and hiring managers in the growing tech hubs of Riyadh, Dubai, Cairo and Amman. Adopting generative AI tools without clear training plans inside companies threatens to strip emerging staff of solid analytical skills. What regional workplaces need is not to ban the tools or impose them haphazardly, but to restructure the assessment of new developers: measuring their ability to review code, spot model errors and design software architectures, and using AI as a partner for technical dialogue rather than letting it write code unchecked, to ensure the sustainable development of software leaders who possess enough engineering intuition to manage sensitive infrastructures.