I've been working for about a month and a half at my cousin's company, which builds chatbots, CRM systems, landing pages, e-commerce integrations, and other web services for clients such as retailers, real estate agencies, clinics, and restaurants. I handle technical work and support, but so far I haven't personally written any code. We've been using an AI coding agent: I describe what we need, and it generates the implementation. The most difficult task I've handled directly was securing an e-commerce site with Cloudflare. I'm currently in my seventh semester of computer science and studying Java, so I'm wondering how important it is to become a strong programmer today. Are most companies moving toward AI-generated code, or do software professionals still need to write and deeply understand code themselves?
4 Answers
AI can replace a lot of typing, especially for familiar web work, but it doesn’t replace software engineering. Someone still has to define the architecture, understand the business rules, make design decisions, test edge cases, and catch incorrect assumptions. A junior who can’t evaluate the output will often end up shipping garbage without realizing it. Learn programming fundamentals so you can tell whether the agent’s solution is sound, then use the tool to move faster.
Keep learning to program, especially since you’re studying computer science. Writing code yourself is still the best way to build intuition about debugging, data structures, APIs, state, performance, and failure modes. You don’t necessarily need to hand-type every routine feature forever, but you should be capable of implementing and troubleshooting important parts without an agent. Practice manually on personal projects, use AI afterward for comparison or assistance, and treat generated code as something you own and must verify—not as an unquestionable answer.
It depends heavily on the project. Landing pages, routine integrations, and basic APIs are repetitive, so an experienced developer may delegate much of that work to an agent. Embedded systems, memory management, concurrency, security-sensitive code, legacy platforms, and unusual business logic are much less predictable. AI can produce code that runs while still leaking memory, mishandling threads, or failing under real-world conditions. The harder and more central the component, the more carefully a human needs to design and review it.
People absolutely still write code by hand. Some developers use AI for boilerplate and small snippets, while others barely use it. It varies by company, team, technology, budget, security requirements, and individual preference. Even developers who rarely type code manually generally need to understand every change they approve and be able to explain it during testing or peer review. AI is more like a productivity multiplier than a substitute for competence: if your understanding is near zero, multiplying it doesn’t create a reliable engineer.
The calculator comparison only goes so far. A calculator is deterministic and clearly limited, whereas an AI system can confidently produce different answers, including incorrect ones. That makes programming knowledge even more important for checking its work.
A mistake in an isolated page may affect only that page, but an error in a shared backend component can break the entire product. That’s why the risk level matters when deciding what to delegate and how thoroughly to review it.