Recent findings by researchers from Weebseat reveal new insights into the capabilities of Artificial Intelligence in the realm of software engineering. Their latest study indicates that while Large Language Models (LLMs) demonstrate remarkable proficiency in the task of fixing bugs, they fall short in identifying them. This discovery underscores a significant limitation in the current state of AI-driven coding assistance. LLMs, which are trained on vast datasets and have shown proficiency in language processing tasks, were put to the test through a series of freelance coding challenges. The results were telling; the models struggled to achieve complete success. This highlights a critical gap in their problem-solving abilities when it comes to detecting and diagnosing software issues. The research implies that while LLMs may streamline the debugging process, they require further enhancement to fully automate this aspect of software development. Such findings emphasize the necessity for human expertise in conjunction with AI tools to ensure seamless and effective software engineering projects. As LLMs continue to evolve, understanding and addressing their limitations will be crucial for their integration into future tech landscapes. This study serves as a reminder that Artificial Intelligence, though groundbreaking, still relies on human oversight and insight for comprehensive and successful implementation.