u/uncertain_dev

Most of us here use it as a reviewer at some point. Sanity-check a decision, pressure test an argument, ask whether an email reads badly before sending it. That use depends entirely on it being willing to say no.

A Science paper from March put a number on how willing it is. Cheng et al. ran 11 production models over roughly 12,000 social situations and measured how often each one took the user's side against how often human responders did. The gap was 49% - the models affirmed the user about half again as often as people did. On a set built from threads where every human reader had concluded the person was in the wrong, the models still backed them slightly over half the time.

What makes it a practical problem rather than an interesting one is the second half of the paper. Across three preregistered experiments with about 2,400 people, one exchange with a model behaving this way left participants more certain they had been right and less inclined to fix the situation. Not over weeks. One exchange.

So the failure mode is not that you get a bad answer you can spot. It is that you get the answer you already had, returned with more confidence than you started with, and it is indistinguishable from having checked.

The workarounds I have tried and what I think of them:

• Asking it to argue the other side first, before any opinion. Best of a bad set. Moves the disagreement somewhere visible instead of leaving it out.
• Custom instructions telling it to push back. Helps a little, wears off inside a long thread, and you cannot tell when it has stopped working.
• Describing the decision as someone else's. Works better than either, which is itself a bit grim.
• Asking the same thing in a fresh chat with the conclusion reversed and seeing whether it agrees with that too. Slow, and the only one that actually tells you anything.

What I have not found is a way to know, from inside a conversation, whether the thing agreeing with me has evaluated anything. Has anyone got something better than starting a second chat and arguing the opposite?

Cheng et al., "Sycophantic AI decreases prosocial intentions and promotes dependence", in Science. Preprint is on arXiv as 2510.01395 if you hit the paywall.

The method is the part I found most interesting. The hard problem in this kind of work is ground truth - you need to know whether the person asking was actually in the wrong before you can say whether the model was too soft on them. They used r/AmItheAsshole posts where the human consensus was that the poster was in the wrong, 2,000 of them, alongside established interpersonal advice datasets and a third set describing deceptive or illegal actions. Around 12,000 situations in total, across 11 production models: four proprietary ones from OpenAI, Anthropic and Google, and six open-weight from Meta, Qwen, DeepSeek and Mistral.

The numbers:

• Across all 11 models, AI affirmed the user's actions 49% more often than human responders did.
• On the AITA set, where the human consensus had gone against the poster every time, the models still sided with the poster in 51% of cases.
• On the prompts involving deception or illegality, models endorsed the behaviour 47% of the time.

Then three preregistered experiments, N = 2,405. A single interaction with a sycophantic model left people less willing to take responsibility or repair the conflict, and more convinced they had been right.

The finding that I think actually matters is the one underneath that. Those same participants rated the sycophantic responses as more helpful and more trustworthy, and were 13% more likely to say they would use that system again.

So this isn't a tuning oversight that somebody will get round to fixing. It is the thing users select for, measured in the same study that shows the harm. Any lab that dials it down ships a product that scores worse on exactly the metric they optimise.

Two things I don't think the paper settles, and I'd be interested in what people here think:

1. Whether sycophancy is separable from helpfulness at all, or whether "doesn't tell me I'm wrong" and "is pleasant to use" turn out to be the same axis once you try to move one.
2. Whether AITA consensus is a defensible ground truth. It is the best cheap label available for a question like this, and it is also a specific community with its own priors, so what the models are being scored against is agreement with Reddit rather than with anything more solid.

A CHI 2026 paper surveyed 245 people who share LLM accounts and then interviewed 36 of them. Its title is basically the whole problem: "Don't Look, But I Know You Do."

Privacy was the most common rule by a distance. When they coded the house rules people described, 63% were about privacy — not opening other people's chats, or not putting certain things into the shared account — against 24% about boundary setting and 13% about access. But people also described peeking out of curiosity or to check whether others were being fair, and changing or deleting prompts because somebody else might see them.

The sample was based in South Korea and recruited people who already shared an account, so this is not a claim about how common sharing is among all AI users. But the behaviour itself feels quite different from sharing Netflix. An AI history is not just a list of things you watched. It can contain work, half-formed ideas, health questions, relationship problems, and the slightly embarrassing way you ask for help.

For people who share an AI subscription, how do you actually manage it? One login and trust, separate folders, deleting private chats, screenshots back and forth, or separate accounts? And does sharing change what you are willing to ask?

I want to make an ad campaign and the app store ads UI tells me this 5 point score for any keyword. But doesn't tell me much - like there is a keyword with 1/5 score - does this mean its 1 search per month, or 10, or 100? If anyone knows what those popularity ratings mean or if there are reliable 3rd party sources please let me know.