Jaeden raises concerns about the future of synthetic data and its implications for training AI models. Andrew argues against relying on synthetic data, warning that errors can compound and lead to flawed models. He emphasizes the abundance of non-synthetic data available for training, while also acknowledging the potential need for synthetic data in niche cases, such as identifying rare financial crimes. The discussion hints at the existence of untapped data sources that could be valuable for AI development.
