In this presentation, we examine the predictability of artificial intelligence (AI) models for weather prediction. Using idealized settings with Lorenz 1963's model as well as deep-learning architectures trained on the ERA5 data, we show that different time-stepping techniques can have a strong influence on the model performance and weather predictability due to the chaotic nature of weather systems. Specifically, a small-step approach for which the future state is predicted by recursively iterating an AI model over a small-time increment displays strong sensitivity to the type of input channels, the number of data frames, or forecast lead times. In contrast, a big-step approach for which a current state is directly projected to a future state at each corresponding lead time provides much better forecast skill and a longer predictability range. In particular, the big-step approach is very resilient to different input channels, or data frames. In this regard, our results present a different method for implementing global AI models for weather prediction, which can optimize the model performance even with minimum input channels or data frames. Our method based on the big-step approach can also be extended naturally to search for a practical predictability limit in any chaotic system.