From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

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.

Introduction. Large Language Models (LLMs) are undergoing a fundamental transformation [1, 2, 3, 4, 5, 6]. What began as statistical language generation has expanded into AI systems that can reason, act, remember, and complete tasks in open-ended digital environments [7, 8, 9]. Early progress was driven by scaling autoregressive Transformers and instruction-aligned chat interfaces, enabling systems to compress broad world knowledge into fluent responses [10, 11, 12, 13, 14]. More recently, the frontier has shifted toward models that deliberate over difficult problems, invoke tools, interact with environments, and coordinate multi-step workflows [15, 16, 17, 18, 19, 20]. The central question is therefore no longer limited to how can a model generate a better answer? Instead, it is how how can an AI system reliably transform user intent into completed work? This redefines the human-AI relationship, marking the shift from Chatbot to Digital Colleague [21, 22, 23, 24, 25]. This survey organizes and analyzes persistent autonomous AI along two tightly coupled dimensions.

Discussion / Conclusion. In conclusion, we frame the shift from Chatbot to Digital Colleague as the transition from conversational answers to persistent work. Cognitively, LLMs advance from next-token "fast thinking" to Thinking LLMs leveraging inference-time computation. Executionally, they progress from ad hoc toolcalling to workstation systems (OpenClaw) with persistent workspaces, skills, and governance. The "Workspace + Skill" paradigm drives this transition through state persistence, reusable procedures, and task closure. Data and evaluation shift from instruction-response pairs and static benchmarks toward State-Action-Observation trajectories and auditable, self-evolving ecosystems. Ultimately, reliable digital colleagues require persistent environments, reusable skills, and safety governance.