The question can be wrong

One of my AI interviews asked why I hadn’t activated a fifty-person waitlist.

There wasn’t a fifty-person waitlist.

The question sounded useful. Specific number, named project, clear action. It had all the surface features of accountability. If I’d accepted the setup, we could have spent ten minutes discussing my resistance to doing something that didn’t exist.

I corrected it and moved on. Then I started noticing smaller versions of the same thing.

An invoice was described as badly overdue when the payment terms meant it wasn’t. Changes to my work were called four pivots in four weeks when some of the ideas came from the previous year’s journal. A question asked when my hobbies had last produced something another person saw. I answered that hobbies are hobbies. I don’t have to post them.

Those were different mistakes. One was a wrong fact. One was a wrong timeline. One had quietly chosen a measure of success I hadn’t agreed to. All of them would have shaped the answer if I’d gone along with the question.

The most instructive example involved a name. Speech-to-text had been turning Karl into Cole. A later review treated the different names as evidence that I’d avoided a question about one person by answering about another. It was a neat interpretation of a transcription error.

Once written down, that interpretation became material for the next interview. The system could have kept asking sharper questions about a pattern it had helped invent.

That’s the risk I care about. A bad answer is often easy to challenge. A bad premise can stay hidden inside a very good question.

And the better a system knows you, the more persuasive that question can sound. It can mention your projects, dates, goals and previous answers. Familiar details make the frame feel earned. They don’t prove it is.

I still want difficult questions. Some of these sessions have helped me say things I’d been circling for months. Being challenged is useful. I just don’t want challenge confused with a negative interpretation of every answer.

In August I pointed out that the whole set of questions opened with a gap. None asked what was working. That mattered because working things contain instructions too. A client who trusts me, a relationship that brings work, a training block I’ve adapted around real life. Those deserve attention for what they can teach me, rather than appearing only as exceptions to a diagnosis.

Now I try to separate three things before answering: what happened, what the question assumes it means, and whether I accept the measure being applied.

Sometimes the uncomfortable interpretation is right. Sometimes I’ve avoided something, and explaining the context is a sophisticated way of continuing to avoid it. Correcting a premise doesn’t give me immunity from that possibility. It gives the conversation a fair place to start.

If I’m going to use AI to question my judgement, questioning its judgement has to remain part of the work.

An answer is only as honest as the question allows it to be.