AI coding assistants, such as GitHub Copilot and OpenAI's Codex, have become integral in modern development environments. They assist in code generation, error detection, and code refactoring, thereby streamlining the development process. By integrating seamlessly with popular Integrated Development Environments (IDEs), these tools offer real-time suggestions, reducing the time developers spend on routine coding tasks.

However, the adoption of AI-assisted coding is not without challenges. Studies have indicated that the accuracy of code generated by AI assistants varies significantly, with correctness ranging between 31.1% and 65.2%. This variability raises concerns about the reliability of AI-generated code and underscores the necessity for human oversight to ensure code quality and functionality. ​

Moreover, there is a risk of developers becoming overly reliant on AI tools, potentially leading to a decline in their coding proficiency and problem-solving skills. The convenience offered by AI assistants might discourage developers from engaging deeply with the code, resulting in a superficial understanding of the underlying logic and architecture.

To mitigate these issues, it is essential to use AI coding assistants as complementary tools rather than replacements for human expertise. Developers should critically evaluate AI-generated suggestions and maintain an active role in the coding process to preserve and enhance their skills. Organizations are encouraged to establish guidelines that promote the responsible use of AI assistants, ensuring that they augment rather than diminish the capabilities of their development teams.​

Sources-

​blog.codacy.com

8thlight.comforbes.com

​techopedia.com