AI is not here to take over “completely”
With the advent of AI-powered code editors like Cursor and Windsurf, software engineers no longer need to manually write large portions of code. Instead, they can rely on AI-generated suggestions to get their desired results, sometimes with a single prompt and other times through multiple iterations of refinement. These advancements have significantly boosted developer productivity, reducing the time and effort required to build software solutions.
The rise of AI-powered code editors
AI editors have come a long way, offering engineers an unprecedented level of assistance. They not only generate code but also provide recommendations, optimize performance, and even suggest bug fixes. These tools have made software development more accessible and efficient, allowing engineers to focus more on high-level problem-solving rather than repetitive coding tasks.
For experienced developers, these tools act as accelerators, enhancing workflow and enabling rapid prototyping. They assist in reducing the cognitive load associated with syntax and implementation details, allowing engineers to experiment with ideas more fluidly. As a result, projects that once took weeks to complete can now be executed in days or even hours.
The potential pitfall for new engineers
While AI-powered tools are incredibly beneficial, they pose a potential problem for upcoming engineers. New developers entering the field now have access to AI tools that can generate more than half of their required code, often without them writing a single line manually. This convenience may lead to a lack of foundational learning, as many junior developers might never feel the need to grasp the core concepts of programming languages, algorithms, and software architecture.
Without fundamental knowledge, engineers might struggle when AI-generated code fails to work as expected. Debugging issues, optimizing performance, or understanding deeper system-level interactions will remain challenging for those who have not practiced the fundamentals. If the reliance on AI tools replaces the learning process entirely, we may end up with a generation of engineers who can prompt but not truly code.
Why engineers still have to learn how to code?
Despite the efficiency of AI-powered tools, engineers must still learn to code. While AI can generate code and suggest fixes, it is not infallible. Bugs, logical errors, and edge cases often arise — some of which AI may not be able to resolve. When these situations occur, engineers must step in with their understanding of the codebase to identify and fix the problems themselves.
Moreover, AI-generated code is often a starting point rather than a final solution. Engineers need to review, modify, and optimize the generated code to ensure it meets the project requirements. This means that a solid grasp of programming fundamentals is not optional — it is necessary for effective debugging, maintenance, and innovation.
Based on recent developments in the IT industry and insights from developers sharing their experiences online, it is clear that AI-powered tools are transforming software engineering. However, this transformation does not eliminate the need for learning fundamentals. On the contrary, it makes it more crucial than ever for engineers to master the basics.
AI can assist, but it cannot replace the critical thinking, problem-solving skills, and deep understanding of code that define a proficient software engineer. With AI tools like Cursor and Windsurf becoming more prevalent, the best way forward is to embrace their benefits while ensuring that foundational knowledge remains a priority. By doing so, engineers will be better equipped to build, debug, and innovate in an era where AI is a powerful collaborator rather than a complete replacement.
