Feature engineering has long been a cornerstone of tabular machine learning.
Tabular foundation models (TFMs) are pretrained on a wide range of tabular datasets and applied via in-context learning.
Their rise raises a natural question: does manual feature construction still matter as these models become more capable?
To answer this, we perform a controlled study across several versions of two major TFM families, testing a wide range of existing feature engineering techniques on benchmark datasets from TabArena.
We find a consistent pattern: feature engineering gains are concentrated in earlier model generations and become negligible for the strongest models.
These results suggest that stronger TFMs depend less on explicitly engineered input representations.
In a complementary experiment, however, adding in-context information from related datasets still improves performance.
Our findings indicate a shift in the source of performance gains for stronger TFMs: re-representing existing inputs becomes less effective, while providing additional task-relevant context remains beneficial.