This paper delves into the critical role of data annotation in the development of artificial intelligence (AI), highlighting the necessity and challenges of high-quality annotations in AI training. It examines the escalating demands for data annotation as AI models grow in complexity, and how these demands create bottlenecks in the AI training process. To address these challenges, the paper discusses the growing development of AI-powered annotation tools that aim to streamline and enhance the annotation process. It then traces the evolution of synthetic data in AI, from early data augmentation techniques to the advent of AI-generated datasets, exploring the increasing syntheticness in AI training data. Alongside these advancements, the paper addresses the ethical dilemmas and challenges posed by synthetic data, including concerns over bias, privacy, and misuse. Finally, the paper looks toward the future of AI and synthetic data, offering insights into the potential trajectory of data generation and the ongoing need for responsible management to ensure fairness, transparency, and accountability in AI systems.