Hydrological models combining physics with AI are becoming popular for predicting river flow, but are often unnecessarily complex. We tested these models across US river basins and found three key results: simpler designs perform equally well, extensive input data adds little value, and time-varying parameters do not represent actual physical processes. These results challenge assumptions that complexity improves predictions or understanding, arguing instead for simpler hybrid model development.