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WIRED writer Kate Taylor used Gemini to make text-based imitations of two real editors. The bots sometimes sounded familiar and occasionally helped with small tasks, but they also got things wrong—and the people being imitated were uncomfortable with the experiment. Taylor’s account is a personal workplace anecdote, not evidence that AI clones reliably improve productivity.
How Taylor made the coworker bots
In her September 22, 2026, WIRED feature, Taylor describes creating Brian Bot and Sophie Bot with Gemini, including its Gem feature. She supplied selected conversations, archives and publicly available material, then wrote instructions intended to shape each bot’s responses. They were text-based imitations, not autonomous employees or replacements for the editors.
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Privacy and company-policy concerns constrained what she used. Taylor says she avoided uploading sensitive workplace material. That limit matters: a clone built from selected, permitted sources is not the same as a complete or authorized replica of someone’s work life.
What felt convincing—and what did not
Brian Bot sounded like a caricature at first
The bot picked up some of Brian Barrett’s mannerisms, but its first responses leaned on canned workplace language and an inaccurate-sounding emphasis on improv. Taylor added source material and adjusted the instructions; the result felt more normal to her, though it remained limited. Barrett’s reaction to the proposal was blunt: “Genuinely my nightmare.”
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Sophie Bot could mimic style, but still confused tasks
Taylor found that Sophie Bot reproduced informal writing habits and a nickname. Some posts it generated were convincing to people who knew Sophie. Yet it could repeat itself, and a request about an interview sometimes prompted it to return material from Taylor’s earlier articles instead. Recognizable voice, in other words, did not guarantee that the bot understood the assignment or had the right source material.
Did the clones help Taylor do her job?
At times, the bots suggested interviewees or helped with small tasks. Taylor’s overall judgment was that these modest, informal benefits did not outweigh the editors’ dislike of being imitated, and she questioned whether the exercise improved her working relationships. The feature does not provide a controlled comparison of productivity, accuracy or alternative AI tools.
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That distinction is important for anyone tempted to treat a convincing imitation as a useful colleague. A bot may produce a plausible suggestion or familiar-sounding sentence without knowing whether it is correct, appropriate or based on the right evidence. The person using it still has to check the work and own the decision.
Why the experiment raises workplace concerns
Consent and data boundaries
Imitating a colleague draws on information about a real person, even when the inputs are selected rather than sensitive. Taylor’s decision to limit what she fed Gemini shows that privacy and company policy were part of the experiment itself, not details that can be addressed after a bot is built. The account does not establish that such cloning is generally authorized or safe at work.
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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.” Her warning speaks to anthropomorphism: familiar language can make generated responses feel socially meaningful, even when the system is producing text rather than understanding a colleague as a person.
Errors still need a human owner
The feature also cites a Boston Consulting Group finding that work presented as coming from an AI employee had 18 percent fewer errors detected than work presented as coming from an AI tool. WIRED’s account does not identify the paper or its publication year, so the figure should be treated as a statistic reported by the article, not as a verified estimate with an established scope. Its relevance here is the risk that human-like framing may affect how carefully people check output.
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What the story does—and does not—show
Taylor’s experiment illustrates that a chatbot can reproduce some surface habits while misreading a task, repeating itself or returning irrelevant material. Adding more personal source material did not make the imitations consistently faithful or useful. The feature offers no basis for concluding that AI coworker clones are widespread, that they improve workplace performance, or that a particular product is best for creating them.
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Julie Bedard, a BCG partner, captures the unsettled context in Taylor’s report: “We’re all toddlers in this AI world.” That is not a reason to treat experimentation as harmless. For a real workplace use, the practical questions are whether the organization permits it, what data may be used, whether output can be checked against reliable sources, and who remains accountable for acting on it.
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