AI Tools: Slowing Down Developers Instead of Speeding Them Up – Here's Why (medium.com)

A developer's recent exploration of major AI coding tools like Cursor, Antigravity, Claude Code, and GitHub Copilot revealed a startling truth: rather than enhancing productivity, these AI tools often slow down the coding process. One striking example highlighted how an AI-generated authentication module led to significant debugging challenges, resulting in a time-consuming ordeal that far exceeded the effort required to write the needed code manually. This experience uncovers a critical flaw in the AI hype cycle, where overly optimistic expectations may not match reality. The significance of this finding for the AI and machine learning landscape is profound. It raises concerns about the reliability of AI in complex development environments, emphasizing that while AI can generate code quickly, it may introduce errors that are difficult to trace and fix. As developers rush to adopt these technologies, the implications could lead to wasted time and increased frustration. Consequently, this raises important questions about the effectiveness of AI tools in real-world applications, suggesting a need for improved accuracy and usability in future AI development to genuinely elevate productivity rather than hinder it.