The Dark Side of AI-Assisted Code Generation: A Case Study of Cursor

This article critically assesses the effectiveness of AI-assisted code generation tools. Using a code modification suggestion showcased on the Cursor editor's homepage as a case study, the author demonstrates how AI-generated code can not only fail to improve productivity but can introduce errors and inefficiencies, such as useless length validation and questionable string sanitization. The author argues that a good AI tool should identify and avoid these issues, providing programmers with the context needed to make informed decisions rather than simply offering a potentially flawed solution. Current AI code generation tools, as exemplified, fall short of this goal, resulting in a net negative impact on productivity.