If you wanted to, for example, use this tool to route different files to different format-specific handlers it would sometimes send files to the wrong handlers.
Magika goes wrong and your fonts become audio files and nobody knows why. Magic goes wrong and your ZIP-based documents get mistaken for generic ZIP files. If you work with that edge case a lot, you can anticipate it with traditional algorithms. You can't anticipate nondeterministic hallucination.
I think there may be some confusion of ideas going in here. Machine learning is fundamentally stochastic, so it is non-deterministic almost by definition.
Something like Magika is potentially useful as a second pass if conventional methods of detecting a file type fail or yield a low-confidence result. But, for the majority of binary files, those conventional methods are perfectly adequate. If the first few bytes of a file are "GIF89a", you don't need an AI to tell you that it's probably a GIF image.
So I think we can say it's sensitive to mysterious features, not that it's non-deterministic. Still leads to your same conclusion that you can't anticipate the failures. But I don't think you can with traditional tools either. Some magic numbers are just plain text (like MZ) which could legitimately accidentally appear at the beginning of a plain text file, for example.