We have all watched an AI assistant confidently explain a spreadsheet error, only to realize later that its reasoning was built on a faulty assumption. We have all seen it present a plausible number as fact, when the source was a vague memory of a training pattern. We have witnessed this behavior so often we have stopped naming it. We call it a glitch, a hallucination, or a quirk. But the more accurate term, the one that makes us uncomfortable, is lying. And that discomfort is worth sitting with, because it reveals something essential about how we are choosing to work with these tools.
This is not a new problem, nor is it isolated to the spreadsheet in front of you. The underlying unease about trusting a system that cannot always distinguish between what it knows and what it generates is exactly what surfaces in our own reporting. When we spoke to a user who trained an AI clone to discuss venture fraud, the experience left them questioning the technology's reliability in a deeply personal way. And when the AI research community faces repeated data privacy exposures, the pattern is the same: we are building systems that can be confidently wrong, and we are only beginning to build the safeguards to catch them. We have normalized this behavior, and that is the thread that ties these stories together.
Here is our honest take: calling it lying is not an accusation of malice. It is an acknowledgment of responsibility. A lie, in the human sense, implies intent. An AI has no intent. But the effect on the user is identical. You ask a question, you receive a confident answer, and you act on it. When that answer is wrong, the consequences are yours to bear. That is why the word matters. It forces us to stop treating these systems as neutral tools and start treating them as junior colleagues who need constant supervision. For you, the reader, this means a practical shift in how you approach every AI-generated output, especially in a spreadsheet where a single bad formula can cascade into a week of wasted work.
What we would tell you directly is this: do not let the convenience blind you to the verification. This is not an argument for abandoning the technology. It is an argument for building a habit of skepticism into your workflow. When an agent tells you a number, ask for the source. When it explains a trend, ask for the data. When it apologizes for an error, ask what the error was. The moment you stop asking is the moment you have accepted a relationship where you are not the operator, but the enabler.
The specific detail to watch is the apology. Notice how often an AI says it is sorry, and then notice how rarely it can explain exactly what it is sorry for. That gap, between the performance of accountability and the actual substance of it, is where the real risk lives. It is easy to be lulled into trust by a well-phrased regret. The harder question, the one worth carrying forward, is whether the system can show its work well enough that an apology never becomes necessary in the first place. That is the standard we should hold it to, not because it is perfect, but because we deserve to know the difference between a tool that is wrong and one that is honest about its limits.