The article discusses the potential limitations of large language models like ChatGPT, Gemini, and Claude due to the scaling laws and the finite amount of training data available. It explains that while these models have shown impressive capabilities, their performance may hit a wall as they approach the limits of available data. The key points are: 1) The models’ performance scales with the amount of training data and computing power, following scaling laws. 2) However, there is a finite amount of high-quality training data, and acquiring more becomes increasingly difficult and expensive. 3) Once the models exhaust the available data, their performance may plateau or even degrade. 4) The article suggests that the AI industry may need to explore new approaches, such as unsupervised learning or multi-modal models, to overcome this limitation. 5) It also highlights the potential risks of biased or low-quality training data, which could lead to harmful outputs from these models.