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Vivi Mengjie Xiao, an AI product manager in China and a RedNote content creator with more than 45,000 followers, spent about four hours a day reading posts on X, subscribing to newsletters, and translating English sources into Chinese to stay current on AI industry news. The repetitive nature of the work led her to test whether AI agents could automate the process—and what other tasks might be automated as well.
Xiao began experimenting with OpenClaw, an AI agent platform popular in China. Users jokingly refer to each agent as a “lobster,” describing deployment as “raising lobsters.” At first, she created one agent intended to manage everything, including scheduling, to-do lists, progress tracking, and even personal finances. The setup proved chaotic: the agent jumped between tasks and did not help her focus.
She then reorganized the system into six agents with clearer roles. On the work side, she set up an administrative assistant, a researcher, and an executive assistant that mimicked her boss’s communication style to help her practice and refine presentations. For personal life, she added a life coach, a content assistant, and a financial assistant.
When the agents were connected, Xiao found that overall productivity exceeded expectations. The life coach agent could review conversations from the other five agents, helping her maintain a more thorough daily diary; she said about 70% of diary content is now automated.
Today, around 60–70% of Xiao’s daily operations are handled by AI agents, including information gathering, research, and content distribution. She publishes a daily podcast, tracks real-time finances, runs a knowledge management system, and creates content for RedNote and X while remaining in full-time employment.
However, the workday has not shortened. Xiao said she shifted from routine tasks toward more creative and strategic work, and that her bedtime moved from midnight to 2 a.m. as there is always another task to do or another agent to configure.
Xiao describes a broader “paradox”: as efficiency increases, people do not necessarily work less—they often try to do more. She argues that AI is redefining the meaning of work, comparing the industrial revolution’s standardization of manual labor, the information era’s standardization of knowledge work, and now AI’s standardization of execution—the “how” of completing work.
She suggests competitive advantage may shift toward three factors: judgment and aesthetics, AI coordination skills, and emotional intelligence.
Xiao also imagines a future of “one-person studios,” where individuals can produce output comparable to a team by using AI. For businesses, she frames the question as whether they need 10 junior employees or one highly capable person supported by 10 AI agents.
In her view, the change is not about replacing humans, but about freeing them to focus on what she says AI cannot replace: creativity, connection, purpose, and judgment.
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