Log-optimal e-variables at fixed horizons, also called numéraires, have been conjectured to ``almost'' form an e-process for exponential-family null hypotheses. A related open question is whether an e-process can attain the optimal e-power at every horizon up to a bounded loss. Given a final horizon, we study the minimax regret of e-processes relative to the numéraires at all times up to it. For the Gaussian location null and the composite alternative of Gaussian distributions with a smaller variance and an unknown location, we show that the minimax regret in the worst case over the location grows like the iterated logarithm of the final horizon. A single e-process attains this rate at every horizon. This growth answers the question negatively. It also refutes the conjecture if ``almost'' means a regret bounded uniformly over horizons. For this Gaussian location problem, we thus exhibit the frontier precisely: the minimax regret is bounded if and only if the alternatives have at least the variance of the null.

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