Chinese tech workers are starting to train their AI doubles–and pushing back

Chinese tech workers are being ordered to train AI agents designed to automate their own roles, sparking internal resistance among early adopters. A GitHub project called Colleague Skill enables companies to extract worker skills and personality traits into replicable AI systems, raising questions about job displacement and worker agency in AI-driven labor markets.
Modelwire context
Analyst takeThe resistance angle is the buried lede here. Workers being compelled to document their own replaceability is a different category of labor conflict than the usual 'AI might take jobs someday' framing — it's coerced knowledge transfer happening right now, inside specific companies, with a named tool.
Import AI 453 (covered April 13) introduced MirrorCode alongside a broader discussion of gradual disempowerment frameworks, and Colleague Skill fits squarely into that same conceptual territory: systems designed to mirror individual workers rather than augment them. The WIRED piece from April 17 on AI in newsrooms showed a parallel dynamic in Western knowledge work, where editorial labor is being repackaged as a productivity input. What's distinct about the China story is the institutional mandate — this isn't workers voluntarily adopting tools, it's companies directing the extraction. InsightFinder's April 16 funding round for AI agent observability also becomes relevant context: if these worker-replica agents fail or behave unexpectedly, there's currently no clear accountability layer sitting between the company and the output.
Watch whether Colleague Skill or a comparable tool surfaces in job postings or HR vendor pitches outside China within the next two quarters — if it does, the resistance patterns documented here will likely repeat in other labor markets with less worker documentation of the pushback.
Coverage we drew on
- Import AI 453: Breaking AI agents; MirrorCode; and ten views on gradual disempowerment · Import AI (Jack Clark)
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsColleague Skill · GitHub · China
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