The article explores the current limitations of Large Language Models (LLMs) like ChatGPT and challenges the optimistic predictions about achieving Artificial General Intelligence (AGI) by 2025. It emphasizes that despite impressive capabilities in language processing and generation, LLMs face fundamental constraints that prevent them from achieving true human-like intelligence. Key limitations include their inability to understand causality, lack of genuine reasoning capabilities, and tendency to generate plausible-sounding but potentially false information. The article highlights that LLMs are essentially pattern recognition systems trained on existing text data, without true comprehension or consciousness. Experts cited in the article argue that the path to AGI requires more than just scaling up current LLM technology, suggesting that entirely new approaches and breakthroughs in understanding intelligence itself may be necessary. The piece also addresses concerns about overhyped AI capabilities and the importance of maintaining realistic expectations about AI development. While acknowledging the significant advances in AI technology, particularly in language processing, the article concludes that we are still far from achieving AGI, and predictions of its imminent arrival by 2025 are likely premature and oversimplified.