Overfitting, the bane of machine learning models, has traditionally been addressed with techniques like Bayesian neural networks. But what if the very nature of modern AI training, particularly with Large Language Models (LLMs), makes these complex solutions unnecessary? New research suggests that in the data-rich world of LLMs, where models are trained on massive datasets for only a single epoch (meaning each data point is seen just once), overfitting might not be the problem we thought it was. Think of it like this: if an LLM is only exposed to each piece of information once, it doesn't have the chance to memorize the data and become overly specialized, which is the essence of overfitting. This research argues that standard maximum likelihood training, a simpler method, effectively optimizes the model for real-world data (the 'test loss') in single-epoch training, achieving the same goal as more complex Bayesian methods. This is because the massive datasets used to train LLMs act as a representative sample of the real-world data distribution, allowing the model to learn general patterns without getting bogged down in specific examples. So, in the era of massive datasets and single-epoch training, the role of Bayesian methods and other overfitting mitigation techniques may be diminishing. This shift could lead to more efficient training processes for increasingly complex models, focusing on powerful optimizers that maximize learning from each unique data point. While Bayesian methods remain crucial in scientific research where understanding uncertainty is paramount, the future of AI training might be simpler than we expected, driven by ever-larger datasets and streamlined learning strategies.