According to CoinMeta, a research report in CCID Consulting's all-English publication “foresight” indicates that the development paradigm of “the bigger, the better” in the generative AI industry is facing challenges from commercial reality. In structured business scenarios, large models reach their plateau at around 40-60 billion parameters, and the return on investment for models with over 100 billion parameters is only 5%, whereas models in the 7-30 billion parameter range can achieve a return of 35%. Inference constitutes a major cost burden, accounting for 70%-90% of the total lifecycle costs of these models. Additionally, such large, monolithic models also pose risks related to compliance and governance. The report proposes a new paradigm of “precision intelligence,” which relies on four key components: domain-specific fine-tuning of models, RAG retrieval enhancements, tool proxies, and human-in-the-loop validation. This approach aims to match the complexity of models to business tasks, enabling optimal business outcomes with minimal resources. As a result, the competitive focus in the industry is shifting from competing based on the scale of model parameters to focusing on the practical effectiveness of intelligent architectures.