A product team ships an assistant that drafts customer replies. On Monday, a member of the leadership team asks two questions in the same breath: do we have to tell people they are talking to an AI, and can we keep the chat transcripts to train next year's model?
A team has built a model that predicts which job applicants will succeed in a role. It performs well on the test set. The team wants to put it into the hiring workflow, and the discussion in the room goes like this: The engineer says the model is better than the current interviews at predicting performance, and that the data is already there. The hiring manager says candidates deserve a human who looks at their whole record. The lawyer asks who is accountable if the model sys
Ask a language model for the birthday of a moderately obscure person. You will often get a specific date, stated plainly, with no hedging. Ask again in a fresh session and you may get a different date. Neither answer is marked as uncertain. This is the failure mode people call hallucination. It is not a crash, not an error message, and not obviously different in tone from a correct answer. That is what makes it dangerous in professional use: it fails silently and fluently.
At 23:40 on a Sunday, a ninety-second video appears on three platforms at once. Your chief executive is on camera, in a room you recognize, saying something you know they never said. The audio is plausible. The lip movement is close. The account that posted it has existed for six days.
You have a client you have worked with for eight months. You have explained the background four times, re-uploaded the same three documents twice, and last week the assistant gave you advice that contradicted a decision you both made in June, because it had no idea June had happened.
