From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI
Authors: name: Yongheng Zhang, name: Ziang Liu, name: Jiaxuan Zhu, name: Shuai Wang, name: Xiangqi Chen, name: Haojing Huang, name: Jiayi Kuang, name: Siyu Chen, name: Ao Shen, name: Hao Wu, name: Qiufeng Wang, name: Qian-Wen Zhang, name: Junnan Dong, name: Wenhao Jiang, name: Ying Shen, name: Hai-Tao Zheng, name: Yinghui Li, name: Di Yin, name: Xing Sun, name: Philip S. Yu
Organization: Tencent

Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shift from Chatbot to Digital Colleague: from conversational answers to persistent work. We organize this transition along two tightly coupled dimensions. First, at the cognitive core level, LLMs are advancing from Chatbot-era "fast thinking" systems driven by next-token prediction toward Thinking LLMs that leverage inference-time computation, Chain-of-Thought reasoning, reflection, process supervision, and reinforcement learning to support more deliberate and reliable cognition. Second, at the tool-augmented task execution level, LLMs are progressing from tool-calling Agents that invoke external resources in an ad hoc manner toward OpenClaw-style workstation systems (OpenClaw) equipped with persistent Workspaces, skills, verification loops, and governance. The "Workspace + Skill" paradigm makes episodic tool use colleague-like via state persistence, reusable procedures, task closure, and experience reuse. We examine data construction shifts from instruction-response pairs to State-Action-Observation trajectories and evaluation from static benchmarks to sandboxed, auditable, self-evolving AI ecosystems.