From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI
For AI researchers and practitioners, this provides a conceptual framework for building and evaluating next-generation autonomous AI systems, though it is primarily a position paper without empirical validation.
The paper conceptualizes the transition of LLMs from conversational chatbots to persistent autonomous AI systems (Digital Colleagues), organizing this shift along cognitive and tool-augmented dimensions. It proposes the 'Workspace + Skill' paradigm for persistent task execution and outlines data and evaluation shifts.
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.