Recent advancements in large language models (LLMs) have sparked a significant debate within the artificial intelligence community. A new academic paper titled 'Approaching Human-Level Forecasting with Language Models' highlights the capabilities of LLMs in forecasting, suggesting they can nearly match or even surpass human forecasters in accuracy. The RAG system, which automatically searches for relevant information and aggregates predictions, along with LLMs' ability to achieve non-vacuous generalization bounds, demonstrates their potential to outperform human predictions when applied to competitive forecasting platforms. This development raises questions about the future role of human forecasters and the potential for LLMs to generalize beyond their training data. Moreover, discussions around whether LLMs can achieve true understanding or are merely simulating it continue among AI researchers. Despite skepticism about their ability to replace human coders and concerns over their hype, the collaborative use of LLMs, noted for their high accuracy, has been shown to improve accuracy in tasks.