The release of OpenAI's GPT-4.5 has sparked discussions about the current state of large language models (LLMs) and the effectiveness of scaling pre-training. Analysts suggest that the performance of GPT-4.5, which is reported to be 10 times larger than its predecessor, GPT-4, has not met expectations, indicating a potential plateau in the scaling of pre-training models. Critics argue that the benchmarks used to evaluate the model may not adequately reflect its capabilities, particularly in critical thinking and ideation tasks. OpenAI's Chief Research Officer, Mark Chen, noted that the company is exploring new paradigms for model training, including reasoning-based approaches. Despite the challenges, some experts believe that advancements in scaling inference and algorithmic improvements could pave the way for future developments in AI. Overall, the sentiment among industry observers is that while the scaling of pre-training may be reaching its limits, there remains potential for innovation in other areas of AI development.