The Boundaries of AI and the Irreplaceability of Humans in Deeply AI-Dependent Workflows
As Artificial Intelligence becomes deeply embedded in professional workflows, exploring the irreplaceability of humans within the "human-in-the-loop" framework has become a critical discourse. This paper analyzes the underlying logic of Large Language Models (LLMs), identifying their "memory" as a form of finite and pollution-prone context management, in stark contrast to the native, continuous, and sophisticated multi-layered memory and forgetting mechanisms of humans. The study argues that while engineering optimizations may expand the boundaries of AI context, LLMs remain constrained by their lack of subjectivity, specificity, and a tendency to stall in "dead ends" during complex problem-solving. Ultimately, the paper proposes that "Cognition" and "Resolve" constitute the primary human barriers: cognition is derived from long-term practical experience, while resolve represents the courage to bear responsibility based on that cognition. This capacity to face consequences serves as the final shackle that keeps AI as a tool and preserves human primacy.