What does Software Engineering mean when machine writes the code (www.shayon.dev)

A recent reflection on the state of software engineering highlights the transformative impact of AI tools that can autonomously write code, echoing the concerns raised during the 1968 NATO conference about the "software crisis." As developers leverage AI coding assistants, productivity has surged—features taking weeks can now be completed in days. However, there are growing concerns about the complexity of the systems being built, as the ease of generating code may lead to a potential disconnect between developers and the codebase, challenging the fundamental understanding necessary for effective debugging and maintenance. The article draws parallels to Jevons Paradox, suggesting that as AI tools make coding more efficient, developers might generate more complex codebases, exacerbating the difficulty of understanding systems they did not fully create. This dynamic poses a particular challenge for junior engineers, who may miss out on critical learning experiences that foster intuition and troubleshooting skills. The author advocates for a balanced approach to AI usage, encouraging practices like using AI for conceptual guidance and code review instead of direct code production. This method aims to preserve deep learning and comprehension, preparing engineers to navigate the increasingly intricate landscape of software systems.