In this article, we study heavy-tailed alternatives to Gaussian-process surrogates, namely Student-$t$ processes, $α$-stable processes and Gaussian processes with heavy-tailed noise. These are Gaussian scale mixtures, and the position of the mixing variable decides whether the tails can be learned from a single realization. For process-level mixtures with a free amplitude, the profile likelihood of the mixing law is data-free and is maximized by the Gaussian law, the plug-in predictive is a data-free transformation of the Gaussian-process predictive, and no estimator can be consistent for the mixing law over the model along one path. For observation-level mixtures with symmetric $α$-stable noise, the tail index is identifiable. Further, for one-dimensional inputs under fixed-domain asymptotics with $α_0<2$ and compact hyperparameter sets, a cross-fitted plug-in estimator and the Laplace marginal-likelihood estimate computed at the global posterior mode attain the efficiency bound of the model with a known regression function, also for Matérn-$5/2$ sample paths. We also characterize small-sample failures of the Laplace approximation as folds of a posterior-mode branch and give a diagnostic. Finally, in a repeated Bayesian optimization study, observation-level tails reduced regret under gross outliers, as did Student-$t$ noise, and no degradation was detected under Gaussian noise.

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