Seems like if you're working with neural networks there's not a simple map from an efficient (in terms of program size, working memory, and speed) optimizer which maximizes X to an equivalent optimizer which maximizes -X. If we consider that an efficient optimizer does something like tree search, then it would be easy to flip the sign of the node-evaluating "prune" module. But the "babble" module is likely to select promising actions based on a big bag of heuristics which aren't easily flipped. Moreover, flipping a heuristic which upweights a small subset of outputs which lead to X doesn't lead to a new heuristic which upweights a small subset of outputs which lead to -X. Generalizing, this means that if you have access to maximizers for X, Y, Z, you can easily construct a maximizer for e.g. 0.3X+0.6Y+0.1Z but it would be non-trivial to construct a maximizer for 0.2X-0.5Y-0.3Z. This might mean that a certain class of mesa-optimizers (those which arise spontaneously as a result of training an AI to predict the behaviour of other optimizers) are likely to lie within a fairly narrow range of utility functions.