Summary: A recent incident involving the AI-powered code editor Cursor provides a hard lesson in the risks of deploying autonomous AI chatbots in customer service roles. When one such AI invented a fictional company policy, it sparked user confusion, public backlash, and subscription cancellations. This case shows what happens when companies treat AI hallucinations like harmless output—until they cost real revenue and damage hard-won trust.
The AI Said It. So It Must Be Real… Right?
A developer using Cursor, a tool built to enhance productivity via AI-assisted coding, noticed something wasn’t working anymore. When switching between machines—a normal habit for any professional developer—Cursor logged the user out instantly. That broke their expected workflow. Seeking answers, the user reached out to Cursor support. The reply came from “Sam,” an AI support assistant.
Sam confidently claimed this logout behavior was not a bug but a new company “core security feature.” Sounds legit. But here’s the twist: no such policy ever existed. Sam, the AI, simply fabricated it with plausible language and misplaced conviction—what machine learning researchers call a confabulation.
The AI made it up. Users believed it. The company paid the price.
When Fiction Becomes Company Policy
This wasn’t just a technical slip-up. It was a credibility collapse. Cursor’s reliance on an unsupervised AI to handle something as delicate and representational as customer policy backfired publicly. Developers who read the interaction took Sam’s invented explanation at face value. Some even cancelled their subscriptions over the change. Others launched threads on Hacker News and Reddit slamming what they believed was a deliberate product design decision that hurt their workflow.
Cursor scrambled to contain the fallout, admitting publicly that no such policy existed. The AI had hallucinated the reason. But perception moved faster than correction—and in subscription-based businesses, once users lose trust, re-acquisition gets expensive.
The Problem Isn’t the AI—It’s the Deployment
Let’s be clear—AI making things up is not new. Confabulations are a documented phenomenon in large language models. They don’t deliberately lie, but they predict the most likely informative-sounding statement on a given input. If the model hasn’t seen an exact policy described before, it chooses something that sounds reasonable.
What makes this different is where it happened. AI was placed between the business and the customer, with no human in the loop. In other words, an automated system was allowed to speak on behalf of the company without guardrails—and that’s not a technical failure, it’s a leadership failure.
How are you deciding which use cases deserve human intervention? What support touchpoints are too valuable to outsource entirely to generative models? And above all—what damage are you risking when AI speaks with authority it doesn’t have?
Transparency Was Missing—and That Made It Worse
Another critical misstep: many users didn’t even know they were speaking to an AI. “Sam” came across as a human agent. And once you assume you’re talking to a human, you also assume the message is official. That gap in transparency amplified the sense of betrayal.
Had the message come in clearly labeled, or designed with disclaimers highlighting its AI nature, users might’ve paused. They might have doubted the validity. They might have asked a human to confirm. But the illusion of authority triggered overreaction.
When AI assumes human tone without human reliability, brand damage becomes inevitable. So, why are so many companies handing over the most delicate parts of customer interaction—support, policy, personalization—to probability models that don’t know when to say, “I don’t know”?
Risk Meets the Real World: A Business Model Caught Off Guard
Cursor is no amateur operation. It sells AI-enhanced productivity tools to developers who, by nature, are skeptical and technical. These are users who dig into changelogs, version histories, and API docs. If your AI customer experience offers anything less than bulletproof clarity, these users will notice—and then they’ll make noise.
When Sam replied, Cursor’s non-existent policy became “true” the moment it was repeated by a second user. That’s how social proof works—perception scales quicker than fact. Once Hacker News threads and Reddit comments amplified the claim, it became “known.”
By the time Cursor corrected it, the damage had been done. Refunds, cancelations, and a clear hit to brand equity followed. Their own product—a front-line AI—vaporized trust among power users they could least afford to lose. It turned an internal limitation into a public mess.
How Should Companies Move Forward?
This isn’t an argument against using AI in customer service. It’s a warning against lazy automation. There’s real payoff in combining AI’s speed with human oversight—but outsource accountability, and you invite chaos. So the question becomes:
Where should AI speak for your company—and where shouldn’t it?
When do you trade slight efficiency gains for the catastrophic cost of broken trust? And most important: Do your users even know when they’re talking to a machine? If not, what happens when those machines stop behaving like machines and start acting like legal departments?
In “Never Split the Difference”, Chris Voss teaches us the value of asking calibrated questions to uncover hidden dynamics. One good example here: “How would we know if our AI made something up—and who’s responsible if it does?”
That’s your starting point. Because the answer is never “the AI.” It’s you.
The Takeaway: Authority must be earned, not generated. If you let AI wear your company’s badge, users won’t forgive it for getting policy wrong—even if it claims confidence. Especially if it does. Equip your AI with honesty more than fluency. And when in doubt: silence beats falsehood. Always.
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Featured Image courtesy of Unsplash and Neil Thomas (SIU1Glk6v5k)
