Our results illustrate that ML models for lifetime prediction can be biased to learn the intentionally induced protocol-to-protocol variations in a dataset. In some use scenarios, such as prognosis predictions and cycling protocol optimization, this is advantageous. However, in other scenarios, such as production quality control and chemistry/cell design optimization, one wants to detect the intrinsic variability between the cells rather than how cycling conditions determine the lifetime. In such use cases, battery cells are cycled identically, making the prediction model task harder, as we show here.