According to @AIatMeta, the SAM 3 model has achieved double the performance compared to baseline models by leveraging a meticulously curated dataset containing 4 million unique phrases and 52 million corresponding object masks. Kate, a researcher on the SAM 3 team, highlighted that this leap in accuracy and efficiency was driven by their advanced data engine, which enabled scalable data collection and annotation at unprecedented quality and scale. This development underlines the critical importance of large, diverse datasets for next-generation AI models, particularly in segmentation and computer vision applications. The business opportunity lies in developing robust data engines and high-quality annotated datasets, which are now proven to be key differentiators for AI model performance, as evidenced by SAM 3's results (Source: @AIatMeta, Nov 20, 2025).