Despite the exponential growth of data, building high-quality, tailored datasets essential for training AI models remains a challenge, demanding significant cost and effort. While first-generation AI copilot tools represented an initial step towards addressing these issues, they have demonstrated limitations in accuracy, transparency, and cost-efficiency, leading to what is now recognized as 'copilot fatigue' among users. This report addresses these data paradoxes and the shortcomings of existin
