TabPFN and TabICL, tabular foundation models pretrained on synthetic data, outperformed tuned XGBoost on all 14 datasets from the Grinsztajn benchmark without requiring training on new tables. These models use in-context learning to make predictions in a single forward pass, potentially eliminating the need for hyperparameter tuning that traditionally consumes significant computational resources.