The article discusses how recursive training on synthetic data can lead to model collapse due to sample selection bias, particularly in low-resource environments like healthcare and finance. This bias occurs when local data references fail to capture the broader distribution, causing important data to be overlooked. The findings highlight the need for careful data selection strategies to prevent diversity loss in AI models. Collaborative approaches using Wasserstein proxy references can help mitigate these risks without sharing raw data.