More and more people are realizing that AI is undergoing a 'hard fork'. From the 'big model' of wanting to do everything in the past, gradually shifting towards a more focused 'professional model'. @The five reasons mentioned in Openledger HQ's tweet are the multiple-choice questions that the entire industry is doing. From a practical application perspective, general AI is like a universal tool that can do anything, but cannot do anything accurately. And specialized AI is a solution tailored to specific scenarios. Targeted problem-solving Specialized AI models are deeply optimized for specific fields. AI does not need to handle irrelevant data and scenarios, all computing power and parameters are focused on solving a vertical domain problem. The effect of this focus is an exponential increase. Interpretability becomes essential In high-risk scenarios like finance, we cannot rely solely on black boxes to provide answers. Regulatory requirements, compliance requirements, and responsibility determination all require every step of AI's reasoning process to be traceable and validated. Specialized models can design corresponding decision paths for specific scenarios. ⛓️ On chain verification and trust mechanism Blockchain technology enables AI's reasoning process to be permanently recorded and verified. Professional AI combined with on chain authentication can build a complete trust chain. Every inference and every decision node can be traced, which is essential for enterprise scenarios that require auditing and compliance. Addressing the issue of hallucinations The illusion of general AI largely comes from the complexity and diversity of training data. Specialized AI can significantly reduce the illusion rate by limiting the scope and quality of training data. In the vertical domain, data quality is controllable, knowledge boundaries are clear, and models are more likely to provide accurate and reliable answers. Recalculation of cost-effectiveness Although general AI may seem like a single model can solve all problems, the actual deployment cost is extremely high. Specialized AI can achieve better results with smaller model scales, lower inference costs, and more flexible deployment. @Openledger HQ's proposal of these five points is actually a response to the real needs of the industry. This is not just a competition over routes, but also a choice between efficiency and feasibility. The big model represents exploration, while the professional model represents industrialization. And teams like Openledger that lay out on chain infrastructure are likely downstream infrastructure providers of this wave of change.