A recent survey reveals that 96% of engineers don't fully trust AI-generated code, yet only 48% consistently check it before committing. This discrepancy highlights a troubling trend: engineers are relying on AI outputs without verifying their quality. Many engineers find that AI often produces code that appears correct but is unreliable. About 61% express concern over this issue, indicating a critical need for better scrutiny of AI outputs. Engineers are increasingly integrating AI into their daily work. The survey shows that 72% of those who have tried AI use it every day, and currently, 42% of the code is AI-generated or assisted. Expectations are high for this to rise to 65% by 2027. Internal tools, prototypes, and non-critical workflows are the primary areas where AI is applied. Writing documentation and explaining existing code rank as the top use cases for AI among engineers. Despite the growing use of AI, a significant portion of engineers feels the effort required to obtain reliable code through AI is high. Many believe that the right frameworks and constraints can improve output quality. There's a consensus that skills in code review and validation are essential in this AI-driven landscape. Tools like GitHub Copilot and ChatGPT are leading the way, but the overall impact on code quality, maintainability, and release frequency still has room for improvement.