While simulations can be a useful tool for machine learning, relying solely on simulated data can hinder perpetual improvement, making it necessary to eventually incorporate real data to account for real-world limitations and challenges. This is particularly important for developing reliable systems and addressing issues that may not arise in purely simulated environments.