「AI活用」の前にある土台の話 ─ 採用現場で考えさせられたこと A reflective post from an engineering hiring perspective, arguing that effective use of ge…

As generative AI becomes a standard part of software development, the question of how to leverage it effectively has become a recurring topic in engineering interviews. This post, written from the perspective of someone involved in hiring, argues that before discussing AI utilization, candidates and organizations need to revisit the technical fundamentals that make such utilization meaningful in the first place.

The author's central concern is that using AI well ultimately requires the ability to think and verify independently. When asking an AI to generate code or assist with design, the developer must be able to judge whether the output is correct, efficient, and appropriate for the context. Without that judgment, teams risk adopting flawed code or suboptimal architectures simply because they look plausible. In other words, prompt engineering skill matters less than the underlying ability to decompose requirements and evaluate technical trade-offs.

The post also notes that some interview candidates who proudly describe building things with ChatGPT or Cursor struggle to explain the language semantics or framework internals behind the code they ship. Because AI has lowered the barrier to producing working software, surface-level implementation is now achievable without deep understanding. The risk, however, surfaces in areas like incident response, debugging, and performance tuning, where shallow knowledge leaves engineers stuck.

This observation echoes broader industry discussions. With tools like GitHub Copilot, Cursor, and Claude Code now mainstream, concerns about juniors over-relying on AI and missing foundational skills have appeared in Western tech communities as well. Stack Overflow's recent developer surveys show rapidly rising AI tool adoption, yet more experienced developers tend to review AI output more critically. It seems plausible that the gap in fundamentals translates directly into a gap in how much value engineers can extract from these tools.

The piece is not a technical announcement but rather a reflective essay grounded in hiring experience. Still, it raises questions worth considering for engineering education and evaluation in the AI era. Paradoxically, as AI-assisted workflows become the default, the importance of classical fundamentals — data structures, system design, debugging intuition — may grow rather than shrink, because those are precisely the skills required to supervise and correct an increasingly capable but still fallible collaborator. For engineering managers, this could imply rethinking onboarding programs and interview rubrics so that they assess not just AI fluency but the underlying competence that makes AI fluency productive.