“We’ve Seen a Lot of Crazy Stuff”: AI Trainers on the Unseen Flaws of Chatbots

Before an AI’s bizarre response makes headlines, it has likely been seen, flagged, and debated by the internal teams of human trainers. “Honestly, those of us who’ve been working on the model weren’t really that surprised,” said one rater after a public AI failure. “We’ve seen a lot of crazy stuff that probably doesn’t go out to the public.” Their perspective reveals the vast, unseen landscape of AI’s flaws.
These workers are the gatekeepers who stand between the raw, unfiltered output of an AI model and the public. They see the model at its worst: when it confidently invents facts, fails at basic reasoning, or generates deeply disturbing content. Their job is to catch these errors before they reach the user, but they warn that the system is far from perfect.
The pressure for speed is a major reason why these flaws persist. When a major public blunder occurs, there is an immediate, panicked focus on “quality” within the company. But according to workers, this heightened scrutiny is often short-lived. The relentless push to develop and release new versions of the model means that the focus quickly shifts back to speed, and the underlying quality issues remain.
This cycle of failure, panic, and a return to the status quo has left many trainers cynical. They know that the polished product the public interacts with is just a carefully curated slice of the AI’s true capabilities and limitations. They see the “crazy stuff” every day and know that it’s only a matter of time before the next embarrassing—or dangerous—error makes it past the gates.