I'm a computer science undergraduate and often feel anxious whenever a major AI breakthrough is announced. I've been trying to build a strong foundation by learning programming and computer science fundamentals instead of depending on code-generation tools. I use AI occasionally to explain concepts, but I still write my own programs and projects manually.
I'm studying C to better understand how computers execute instructions, and I'm also learning web technologies because I enjoy building for the web and want practical job skills. However, recent claims about increasingly capable AI models have made me wonder whether this effort is still worthwhile. If AI can eventually generate much of the code, should I continue focusing on fundamentals and manual projects? I'd appreciate advice on how a student can learn effectively and prepare for a software career while using AI responsibly.
4 Answers
You’re not wasting your time by learning. Writing programs yourself, compiling them, reading errors, and debugging gives you an understanding that a generated answer can skip. That experience matters when a production system fails, when requirements are unclear, or when generated code is subtly wrong. Using AI as a reference for a concept after you’ve tried to solve the problem yourself is a reasonable workflow.
Think of AI as a powerful instrument, not a substitute for understanding the subject. You don’t want to be the person who can produce output with a tool but cannot explain, debug, or evaluate it. Keep building projects manually so you develop real intuition, then use AI selectively to accelerate research, clarify ideas, and handle routine work.
Even if AI writes more code, engineers still need to review it. Generated code can be incorrect, insecure, difficult to maintain, or incompatible with the rest of a system. Someone has to understand the requirements, test the result, notice hidden problems, and make sound architectural decisions. Your programming knowledge and judgment are what let you do that responsibly.
Don’t let product announcements decide how you value your education. A lot of AI claims are marketing, and the useful improvements are best judged through independent testing and real-world results rather than executive interviews. Keep learning the fundamentals and evaluate tools based on what they actually help you accomplish.
That makes sense. I’m especially concerned that AI tools may eventually provide less explanation, so I’ll focus more on building the foundation first and treat them as supplemental tools.