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Kate Taylor’s experiment with AI versions of two editors showed that a chatbot can imitate recognizable writing habits without becoming a reliable stand-in for the person. Built with Gemini and selected material about her coworkers, the bots sometimes helped with small tasks—but they also produced errors, recycled old writing, and made the people they imitated uncomfortable.
What Taylor built
In her September 22, 2026, WIRED feature, Taylor describes creating “Brian Bot” and “Sophie Bot” with Gemini, including its Gem feature. She used publicly available material alongside selected conversations and archives to give each chatbot examples of a coworker’s language and work.
These were text-based imitations, not autonomous employees or replacements. Taylor says she limited what she supplied because of privacy and company-policy concerns. That boundary mattered: material that might make an imitation more convincing could also be sensitive or unauthorized to upload.
Why the imitations felt weird
Brian Bot sounded recognizable, then veered off
Brian Bot picked up some mannerisms, but Taylor found its workplace language canned and its emphasis on improv inaccurate-sounding. She added source material and adjusted the instructions; the result seemed more normal to her, but remained limited. More input improved the impression in some respects without making the bot a dependable proxy for Brian’s judgment.
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Sophie Bot could mimic style—and still get the work wrong
Sophie Bot reproduced informal writing habits and a nickname. Some posts it generated were convincing to people who knew Sophie. But it also repeated itself, and a request related to an interview sometimes brought back material from Taylor’s earlier articles instead. The contrast is important: a familiar voice can make output seem authentic even when its substance is misplaced.
Did the bots help?
Taylor says the bots occasionally suggested interviewees or helped with small tasks. Those informal benefits did not outweigh the editors’ dislike of being imitated, and Taylor questioned whether the experiment improved her working relationships. The story is an account of one writer’s experience, not a controlled test showing that AI coworkers improve productivity.
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The feature also cites a Boston Consulting Group finding that people detected 18 percent fewer errors in work presented as coming from an AI employee than in work presented as coming from an AI tool. Taylor’s article does not identify the underlying paper or its year, so the figure should be treated as a statistic reported there—not as a fully specified or independently verified result.
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The discomfort was not just about whether a chatbot could sound like a colleague. It was also about how people respond when software is framed as a person. Sarah Franklin, CEO of Lattice, told WIRED: “When you have an entity that feels and emulates a human interaction, it will similarly manipulate and know your emotional response.” The quote reflects her view as reported in the feature, rather than a measured finding about these two bots.
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Julie Bedard, a BCG partner, described the broader uncertainty this way: “We’re all toddlers in this AI world.” She also said, “Everybody is very busy trying to figure all of this out, and the pace of change is fast.” Those comments underscore why a human-sounding interface does not resolve questions of appropriate use, accountability, or error checking.
For anyone considering a similar experiment at work, the practical questions come before the prompt-writing:
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- Authorization and data handling: Is this use approved by the employer, and are the source conversations or archives permitted in the AI service?
- Task fit: Is the goal narrow—such as drafting in a general tone—or does it depend on the person’s current knowledge and judgment?
- Fidelity and errors: Can the output be checked against reliable source material, especially when it sounds confident or familiar?
- Human accountability: Who reviews the result and owns any decision made using it?
- Consent and relationships: Would the colleague be comfortable with the imitation and the way it is presented to others?
Taylor’s editor Brian Barrett summed up his reaction to the proposal in the feature: “Genuinely my nightmare.” That response, alongside the limited task help, is part of the experiment’s outcome—not a side effect that can be separated from whether the tool worked.
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Taylor’s report illustrates that selected personal material can produce both recognizable mimicry and conspicuous mistakes, and that increased familiarity in the output does not guarantee accuracy or acceptance. It does not compare AI products, establish how common coworker clones are, or demonstrate that the practice is safe or productivity-enhancing across workplaces.
WIRED also published a companion video listing on September 25, 2026. Its description says no sensitive material was fed into Gemini and that the bots were not involved in making the video. Read Taylor’s WIRED feature.
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