One particularly stark 'issue' I've noticed with floating point values in AI (which posits would seem to help with) is the difference between 0 and 1. In binary classification problems we generally assign 1 to one subset of our data, and 0 to its complement - there is not necessarily an inherent asymmetry here and the problem will often not meaningfully change if you switch labels 0<->1.

But... if we take for example float16, the smallest model prediction you can represent greater than 0 is 2^{−24} = 5.96*10E−8, but the largest you can represent _less_ than 1 is (binary) 0.111... = 1-2^{-10} = 1-9.77*10E-4. So values around 1 are about 4 orders of magnitude more quantized than those around 0. I don't know if that's necessarily a 'problem' but I have noticed this fact when looking at model predictions.