The 2026 Zhongguancun Forum in Beijing has signaled a definitive shift in China’s artificial intelligence narrative, moving away from a fascination with model parameters and toward the gritty reality of industrial integration. Zhao Jiehui, the founder and CEO of DeepEx (Deepexi), articulated this transition through a provocative metaphor: the choice between 'raising a lobster' and 'training an employee.' In this framework, the 'lobster' represents the general-purpose large language model (LLM) fed on a diet of chaotic internet data—a creature whose logic often clashes with the rigorous requirements of a corporate environment.
Zhao argues that the true benchmark for AI success in 2026 is no longer the raw power of a model, but its ability to inhabit a specific job function. This 'AI Employee' concept moves beyond the idea of a simple digital assistant. Instead, it envisions a future where a single senior human manager oversees a fleet of 15 to 20 AI subordinates, each capable of executing complex, role-specific tasks within a company’s unique logic and knowledge hierarchy. This shift reflects a broader industry realization that 'one-size-fits-all' models often pose operational risks when their internet-derived reasoning deviates from enterprise protocols.
The bottleneck to achieving this vision lies not in the algorithms themselves, but in the 'dirty work' of data governance and ontology modeling. Zhao highlights that the most significant barrier for modern enterprises is converting unstructured proprietary data—ranging from engineering blueprints and technical manuals to complex internal tables—into a coherent, logical knowledge system. This process requires a sophisticated 'ontology' that can map business semantics to data tokens, a task that many general-purpose AI giants are ill-equipped to handle because they lack the deep, sector-specific project experience that specialized firms have accumulated.
This evolution suggests that the competitive moat in the AI sector is shifting. While the initial phase of the AI race was won by companies with the most compute and the largest datasets, the current 'scenario-driven' phase favors those who possess high-quality, synthesized data from industry leaders. Zhao contends that the real value lies in the 'data perspectives' of veteran engineers across different firms, which, when synthesized through model training, create a level of generalization that internet data simply cannot replicate. In this new era, the winner is not the firm with the smartest 'lobster,' but the one with the most reliable digital workforce.