Reddit: CS student asks if backend skills matter in AI era

A Computer Science student based in Pakistan opened a Reddit thread in r/MachineLearning seeking advice on whether they were focusing on the wrong skill set in an era where artificial intelligence is advancing rapidly. The student is about to enter their fourth semester and outlined three explicit long-term goals: maintaining a high GPA to qualify for a fully funded Master's degree abroad, eventually securing a position at a top-tier technology company such as one in the FAANG category or similar, and becoming a genuinely skilled software engineer rather than someone who can merely assemble working projects.

The student described themselves as someone who enjoys programming and derives satisfaction from logic, problem-solving, debugging, and understanding how systems work at a fundamental level. With this foundation, they had sketched out a concrete learning plan for the remainder of the year—August through December—that centered on traditional software engineering skills: Java as a primary language, Spring Boot as a web framework, backend development methodology, competitive programming and data structures via LeetCode and DSA study, SQL for database work, foundational system design, and building portfolio projects to showcase on GitHub.

This plan was challenged by the student's brother, also a Computer Science student, who holds a different worldview. The brother is deeply engaged with AI, automation, and AI agents—what he calls "vibe coding." He made a direct argument to his sibling: that investing substantial time in learning to code at depth is diminishing in value because artificial intelligence has progressed to the point where it can generate entire applications. The post cuts off before the student articulates their own response or seeks further clarification, leaving the core tension unresolved for the reader.

The post captures a generational inflection point in software engineering education. The student's stated goals—high GPA, admission to a top Master's program, and employment at FAANG companies—are grounded in traditional computer science foundations: algorithmic thinking (LeetCode, DSA), backend infrastructure (Spring Boot, SQL), and systems knowledge. These remain the basis of technical interviews and the actual operation of the systems these companies maintain.

However, the brother's position reflects a genuine shift in the market: large language models and code-generation tools have made certain routine coding tasks faster and lower-stakes. This has created uncertainty among students about whether traditional depth still matters. The post does not resolve this tension—it cuts off mid-articulation—but the fact that a CS student felt compelled to ask the question online, and that a peer with credible domain knowledge offered a strong counterargument, suggests the uncertainty is real enough to merit serious consideration.

What the body does not address is whether "becoming less valuable" means "worthless" or simply "no longer sufficient on its own." Hiring at top companies has historically valued both breadth and depth, and the actual systems those companies operate still require engineers who understand databases, performance, scaling, and failure modes—areas where AI tool output still requires expert judgment.

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