[CCID Consulting: Large Models with Over 100 Billion Parameters Only Achieve 5% ROI] According to a report by Jinse Finance on November 6th, a study published in CCID Consulting's all-English special issue, *Foresight*, shows that the "bigger is better" development paradigm of the generative AI industry is facing challenges from commercial realities. In structured business scenarios, the performance of large models reaches a plateau at 40-60 billion parameters, with a ROI of only 5% for models with over 100 billion parameters, while domain-specific models with 7-30 billion parameters can achieve 35%. Inference is the main cost drain, accounting for 70%-90% of the total cost throughout the model's lifecycle; giant monolithic models also bring compliance and governance risks. The report proposes a new paradigm of "precise intelligence," relying on four components: domain-fine-tuned models, RAG retrieval enhancement, tool proxies, and human-in-the-loop verification. This allows model complexity to match business tasks, achieving optimal business results with minimal resources. The industry's competitive logic is shifting from competing on the scale of model parameters to competing on the effectiveness of intelligent architecture implementation.