You keep using that word ‘skilled’
Entry-level software development will change in the face of AI, but it won’t go away. As LLMs increasingly handle routine coding tasks, the traditional responsibilities of entry-level developers—such as writing boilerplate code—are diminishing. Instead their roles will evolve into AI supervisors; they’ll test outputs, manage data labeling, and integrate code into broader systems. This necessitates a deeper understanding of software architecture, business logic, and user needs. Doing this effectively requires a certain level of experience and, barring that, mentorship.
The dynamic between junior and senior engineers is shifting. Seniors need to mentor junior developers in AI tool usage and code evaluation. Collaborative practices such as AI-assisted pair programming will also offer learning opportunities. Teams are increasingly co-creating with AI; this requires clear communication and shared responsibilities across experience levels. Such mentorship is essential to prevent more junior engineers from depending too heavily on AI, which results in shallow learning and a downward spiral of productivity loss.
Across all skill levels, companies are scrambling to upskill developers in AI and machine learning. A late-2023 survey in the United States and United Kingdom showed 56% of organizations listed prowess in AI/ML as their top hiring priority for the coming year. Gartner predicts that by 2027, roughly 80% of developers will need at least a fundamental AI skill set. How will this play out? Well, junior devs might start their careers using AI tools to handle routine coding tasks (learning from the suggestions as they go) while quickly taking on more complex tasks. Meanwhile, senior engineers become coaches, reviewers, and orchestrators, ensuring AI-generated code fits business requirements and meets security standards.
