One of the main reasons people cite for talking to AI is that it is always available. Our friends sleep, but AI doesn’t. People tend to judge us when we ask them the same question multiple times. AI doesn’t. As life seemingly demands more and more from us, something always being available without judgement seems like a godsend.
All that said, Character.ai's announcement about a possible shutdown gives us a reason to think that maybe AI won’t always be available. In addition, I also look into some research on AI answering surveys for people, and a writing course in which students received totally different feedback from real life classmates than they got from ChatGPT.
01 / THE RESEARCH
The company behind the companion is looking for a buyer
Company announcement, alongside a separate peer-reviewed survey of 666 adults
A separate, one-time survey in China. It did not test what happens when a service closes.
On October 12, Character.ai announced that it had entered Chapter 11 to pursue a sale. The company says the platform is still operating, but if it cannot find an actionable bid, it will wind the service down. It expects the sale process to finish in late November or early December. That is the company's current account, and a shutdown is still a possibility rather than a decision that has been made.
If somebody has been using a chatbot to help write emails, losing it may mean finding another tool. But if somebody has spent months telling a particular companion about their marriage, their fears, or the things they have been unable to tell another person, finding a replacement may feel very different. We don't have to believe that the system feels anything in return to take the person's attachment seriously.
A separate study published this week helps explain why the attachment deserves attention. Researchers surveyed 666 adult users of companion or conversational AI products in China. People who saw the system as more humanlike and felt it met their psychological needs also reported more signs of problematic dependence. The questionnaire included difficulty controlling use, distress, reliance on the system to manage emotions, and displacement of offline conversation.
This was a survey taken at one point in time, not a clinical diagnosis or an experiment showing that the chatbot caused dependence. It also did not study what happens when a service closes. People who were already struggling could have been more likely to turn to the system and experience it this way. That distinction matters because we should not use this research to dismiss a source of comfort that someone found helpful.
What I think we should ask is how that comfort fits into the rest of the person's life. Does talking to the companion help them get through a difficult evening and return to people the next day? Or have they gradually stopped bringing those people into the things they are going through? Those are very different situations if the service they have come to rely on becomes unavailable.
The people running a company may be trying to keep it alive, and there may be limits to what they can do. But the person who has been sharing their life with a companion has very little control over that process. We need to take that dependency seriously while the service is working, rather than only after someone finds out it might go away.
02 / THE RESEARCH
AI answered for people in 74 countries and missed much of the difference
Peer-reviewed research in Nature Human Behaviour
Not the percentage of individual people whose answers were correct.
Fadong Chen and colleagues compared AI-generated survey responses with answers from 67,201 people across 74 countries. They gave the models information about each respondent, including age, gender, cognitive ability, and country, then asked questions about preferences such as trust, patience, and willingness to take risks. The full project tested 20 model configurations.
The models reproduced some demographic patterns more reliably than cultural ones. Across the tested cultural comparisons, they matched 48.1 percent of the patterns. That number is not the percentage of people whose answers were correct. It describes how often the models reproduced the cultural differences the researchers were comparing. Alignment was also often weaker in countries with less economic development or digital infrastructure.
This does not tell us that simulated respondents have no research value. It tells us that these models, prompts, and survey questions did not reliably reproduce important differences in what real people said.
Think about why the substitute would be attractive. Finding people, translating questions, and waiting for replies all take time and money. A researcher with a small budget may be able to explore a question with AI that they could not otherwise afford to explore. There is a legitimate reason to investigate whether that can work.
But if we want to know what a group of people thinks, some of the value comes from giving them the opportunity to tell us something we didn't expect. A person can disagree with the assumption behind a question. They can have a reason for their answer that we would never have thought to put in their profile. That is one of the things we risk missing when we ask a system to speak for them.
I think this is worth remembering outside research too. Asking AI how a colleague or a customer might react can help us prepare. It doesn't mean we have learned what that particular person thinks. If we never ask them, they also lose the chance to influence what we decide to do.
03 / THE RESEARCH
A classmate had something different to offer the writer
Exploratory conference paper about 36 students over a 12-week semester
An exploratory classroom study, without a randomized comparison.
Hisami Tsuichibaru studied an English writing course for science students at a Japanese university. The students used both peer feedback and ChatGPT. End-of-course questionnaires and a review of drafts, comments, and revisions suggested that the two sources were doing different kinds of work.
Students mainly used ChatGPT for grammar, wording, and immediate changes to particular parts of the text. Peer feedback helped with developing the content, organizing it, and considering what a reader would understand. Some students also found the combination difficult to manage because they had to decide which of the overlapping comments to act on.
There was no randomized comparison, and this was one small course. It did not establish that students became better independent writers over time. The useful finding is more limited. In this class, using AI did not make the classmates' contribution interchangeable with what the tool provided.
We can understand that by thinking about a time someone read something we wrote and misunderstood it. We may have thought the point was obvious because we already knew what we meant. Having to explain it to a person can show us where we left something out. And the person reading may care about a part of the argument that we had barely considered.
AI can help us notice problems too, and it can make the draft easier for a classmate to read in the first place. But if the classmate is no longer involved, we have removed an opportunity for two people to understand how the other thinks. The writer loses that particular reader's response, and the reader loses a chance to help develop an idea and see what the writer does with it.
I like that this course left room for both kinds of help. The next thing I would want to know is whether students became better at deciding which comments to use, and whether giving feedback helped the classmates with their own writing. A cleaner final draft would not answer either question.
THE RECKONING
When we ask another person for help, we are usually trying to get something done or figure something out, but there is another side to that coin. It also gives them a chance to be helpful and share a lesson they have learned, or could very well be learning currently. And this makes the process dual sided and beneficial for two people.
In a world where we always turn to AI to answer those questions, we might get what we need but the friend loses out on an opportunity. And both people miss out on a chance to grow closer as well. Losses like these are harder to track because they involve something NOT happening, and that is always difficult to measure.
What I want us to consider is whether convenience is making that decision for us in situations where we would actually like the other person to remain involved. Think about the famous ‘Why Not Both?!’ meme. We can get help from AI and still ask a colleague what they think. We can have a useful conversation with a chatbot and still tell a friend what is going on. The answer we already have may be only part of what we would get from including them in the conversation.
Also worth your time
Feeling less dependent did not establish better writing. In a nine-week randomized study completed by 92 university students, personalized coaching prompts led to lower self-reported AI-dependence scores than template prompts, but the evidence did not establish different trajectories in unaided writing performance. The dependence questionnaire also included perceived usefulness and emotional engagement, not just harmful reliance.
Read the writing studyA referral is only the beginning. An unreviewed audit of 2,550 scripted crisis conversations found that 17 chatbots all referred the simulated user to outside help, but responded very differently when that help was repeatedly refused. Only 2 percent of the replies urging help asked about barriers to seeking it, and the audit did not test outcomes for real people.
Read the chatbot auditThe counselor still had to follow up. In a two-week, nonrandomized school pilot, a supervised chatbot used by 30 students generated nine dangerous-behavior alerts that prompted offline counselor review. After statistical adjustment, clinical outcomes did not differ significantly from the 32-student waitlist group. The study did not establish how many cases the alert system might miss.
Read the school pilotHundreds of studies can still leave important questions open. A scoping review of 394 empirical studies on generative AI in higher education found less attention to self-regulated learning, creativity, ethics, feedback quality, and equity than to several more commonly measured outcomes. It maps the literature rather than establishing one overall effect on learning.
Read the education reviewAI may also help people find a community. A four-wave survey beginning with 1,200 participants in Chinese digital sports communities linked feeling recognized by AI-curated content with later measures of identity, belonging, and inclusion. The associations could run in both directions, so the study does not show that the technology caused people to feel more included.
Read the sports-community studyMeasure what happens after the help. A new preprint uses secondary analyses of four earlier studies to argue that AI evaluations should track what remains in a person's competence, confidence, or bias afterward. It is a proposed framework drawing on different tasks and time periods, not a new experiment proving that AI consistently strengthens or weakens us.
Read the frameworkPRACTICE
Practice with someone you know
This week, try The Unrehearsed Hour in the Stay Raw studio at The Quiet Cost Practice. Sit down with someone you know well, draw a topic neither of you has worked out in advance, and see where the conversation goes. There is no polished response to prepare and no score for getting it right.
Part of the fun is finding out that someone you know has an answer you would never have guessed. Ask them how they got there. Give them time to ask you something too. You may disagree, change your mind, or spend a while talking about something that has never come up between you before.
This is an invitation to have a conversation, not a treatment or a tested way to prevent AI-related skill loss. You don't need the site to do it, but the prompts can give you somewhere to start.
Open The Quiet Cost Practice →Michael McNamara
Illustrations created with AI. They are editorial images, not depictions of research participants.