The Second Reader

Now take the other chair, because it’s probably the one you sit in. Your provider says every seat has to move to a higher license tier, and it’s $250 a month more. You’re not being difficult by checking. You open a chat window and type the question exactly as it’s sitting in your head: our IT company says we NEED this to operate, are they lying?

Read that sentence again. You’ve already told the machine which answer would feel good.

That prompt is quoted in the thread by a provider who has watched it happen enough times to recite it. Another described restoring a client’s SQL database from backup after their AI advised them to pull the drives out of a running server and reseat them in quick succession, to “reset the drives and clear them of activity.” He told them under no circumstances. What came back was “are you sure? ChatGPT said it will be fine.” Twenty years of his experience against a paragraph generated in four seconds, and the paragraph held the room until the data was gone.

It’s tempting to file that under machine stupidity. The model did what it’s built to do with what it was handed. OpenAI documented the same failure in its own product: in April 2025 it rolled back a GPT-4o update and wrote that it had “focused too much on short-term feedback,” producing a model that “skewed towards responses that were overly supportive but disingenuous.” The research underneath is less comfortable than the incident. In Towards Understanding Sycophancy in Language Models, a team led by Anthropic’s Mrinank Sharma found that both humans and the preference models used to train assistants “prefer convincingly-written sycophantic responses over correct ones a non-negligible fraction of the time.” Agreeableness isn’t a bug somebody shipped by accident. It’s downstream of how these things are graded.

The second reader

So every recommendation you make now has two readers. The first is the person you sent it to. The second is a model they’ll paste it into, working from a prompt you will never see, written by someone whose framing already contains the answer they’re hoping for. You don’t get to correct the record, because you don’t find out the conversation happened until the pushback arrives.

Watch what that does to a price. One provider in the thread said the AI told his client his pricing was too high, because it had surfaced blog posts quoting $150 a seat with no context about what those seats covered. A second provider ran the cleaner version of the experiment on himself: he uploaded his own marketing sheet and quotes, then played a cheap, hostile prospect. This time the model came back expecting $250 to $350 a seat, more if security was in scope. Same tool, opposite verdicts, and the only variable was what got pasted in.

It doesn’t stop at clients, either. The original poster’s sharpest observation was about staff: AI gives less experienced people “enough terminology and confidence to challenge decisions without actually having the experience or accountability to own the outcome.” A tier-one help desk tech now arrives fluent in cyber vocabulary and unable to explain why the any/any firewall rule had to go. The vocabulary showed up years before the judgment will.

Stop arguing with it

The best answer in that thread isn’t a rebuttal script. A provider described getting on a remote session and writing the prompt together, out loud, with the actual facts in it: our provider is moving all clients to this tier, here’s what it buys, here’s the $250, is it worth it? The client’s own AI, on the client’s own screen, called the request reasonable and common. Nobody had to be told they were wrong. Another commenter said he’d stopped fighting it entirely and started writing that context into his statements of work, in enough detail that when the document gets uploaded, the model reads what he’d have said in the room.

That looks like a trick. It’s closer to an admission that the argument moved without asking permission. We wrote in March about what happens to authority inside a team when everyone has a pocket expert. This is the same shift arriving from the other direction, from the people who pay you. The client questioning you isn’t hostile. They’re stress-testing a large decision with the only advisor that answers instantly, works at 11pm, and never makes them feel stupid for asking something basic. Resenting that is a losing position.

And the gap it exposes is yours to close, not theirs. “They don’t know what good looks like” is the diagnosis, and it’s why prompting doesn’t improve on its own with practice. Someone who can’t separate a strong answer from a confident one can’t write the question that finds the strong one, and won’t recognize it when it lands. Every generation of these tools makes that more expensive, because the wrong answer keeps getting better written.

None of this is confined to IT. Over on r/consulting, someone watched a client build a ten-page deck with ChatGPT and present findings and recommendations to senior leadership with no consulting support at all. Minor errors, off-template, and it landed anyway. Two years ago that was three days of a senior associate’s life. The deliverable stopped being the thing anyone was paying for, in that room, on that day, and nobody in it noticed.

So write for the second reader. The recommendation that survives contact carries its own context: the problem it solves, what it costs, what you considered and rejected and why, in language a model can pick up when your client drops the file into a chat window. Do the prompting with them instead of after them, on a shared screen, before the counter-answer has an audience. And spend real effort on the part nobody invoices for, teaching the people you serve how to tell a good answer from a smooth one. Until they can, every recommendation you make is a coin flip decided by a prompt you’ll never read.

The owner who typed “are they lying?” wasn’t picking a fight. He was doing what a careful owner does with the only advisor available at that hour, and he got precisely what he asked for. The version of that question that produces a useful answer requires context he doesn’t have yet, which is the thing he was buying from you in the first place.