Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and machine learning communities suggest pseudo-inputs, or inducing points, one strategy to obtain an approximation easing that computational burden. However, we show how placement points their multitude can be thwarted by ...