Recursive Artificial Intelligence (AI), where systems can design, optimize, or evolve other AI systems, represents a significant turning point in the development of autonomous technologies. As recursive mechanisms become increasingly integrated into machine learning workflows, the potential for rapid innovation also comes with substantial technical and ethical risks. This paper critically examines the development and use of recursive AI systems through real-world examples and theoretical insights. It highlights key challenges, including model collapse, error amplification, alignment drift, recursive deception, and the loss of human interpretability and oversight. By examining explainability tools such as LIME and SHAP, case studies like AlphaGo, and potential paths into cognitive and multi-agent recursion, the work highlights the urgent need for responsible research and regulation. The paper aims to reveal overlooked dangers and spark discussion about the fragility, unpredictability, and governance challenges in recursively self-improving AI systems.