We discuss an interview with Jure Lescovec, co-founder of kumu.ai and a computer science professor at Stanford, regarding the application of foundation models to structured enterprise data. Lescovec explains that traditional **machine learning** methods for this type of data are manual, expensive, and time-consuming, contrasting them with new relational foundation models that leverage a **graph-based approach** to eliminate the need for manual **feature engineering** and **model training**. The technology, which is a next-generation form of **graph neural networks**, is designed to provide rapid, accurate predictions for tasks like churn prediction, forecasting, and recommendation systems by connecting directly to databases and representing them as graphs for **attention mechanism** processing. The discussion emphasizes that the goal is not to displace data scientists but to enhance their productivity by providing a powerful tool capable of achieving **superhuman accuracy** with proper fine-tuning, as demonstrated through successful use cases at companies like DoorDash and Reddit.