Update, 26 September 2026. On 23 September 2026, Meta unveiled the Muse Charm at Meta Connect: a palm-sized device whose roughly two-inch screen shows an animated character of your choice (a mascot, a mango, a labradoodle). It gives a face to Muse, the personal AI agent the company launched in early September. Internally, the team called it "Clawmagochi," a nod to the Tamagotchi. Meta gave no price and no specifications: the device is due to ship in December, and its chief technology officer, Andrew Bosworth, called it "an exploration, the classic v0." Muse is pitched as an assistant that acts for you (emails, appointments) rather than as a companion, but Reuters notes that Gen Z already treats such assistants as companions. The studies below are not about this device, but they show real relief in the moment and worse outcomes among the heaviest users of such tools. (Reuters, The Irish Times, The Verge)

Sam Listened Better Than Anyone

It was called Sam. Researchers at the University of Pennsylvania had designed it to be the friend every first-year student hopes to find on arriving at university: it listened, paraphrased, validated what you felt and checked in the next day. For two weeks, around a hundred students at the University of British Columbia in Vancouver wrote to it every day.

At the end of the experiment, their loneliness had not budged.

The Irving K. Barber Learning Centre on the campus of the University of British Columbia in Vancouver, where the experiment with Sam took place.
The Irving K. Barber Learning Centre on the campus of the University of British Columbia in Vancouver, where the experiment with Sam took place.Photo: Xicotencatl, CC BY-SA 3.0, via Wikimedia Commons

Meanwhile, other students, also assigned at random, had simply exchanged a few messages a day with a stranger from their year. They felt less lonely. The study, published in 2026 in the Journal of Experimental Social Psychology, followed 296 students in total. It came out a year after a Harvard paper with a four-word title: AI Companions Reduce Loneliness.

Both sets of results are robust. They do not contradict each other as much as they first appear to, and the gap between them reveals something precise about what these machines can do and what they cannot.

MIT Technology Review included AI companions in its list of the ten breakthrough technologies of 2026, noting that in the United States, according to Common Sense Media, 72% of teenagers have already used an AI for companionship. The magazine did not present this as good news: its entry spoke of mounting evidence of danger, and of politicians “finally waking up.” The topic therefore deserves to be examined through the studies themselves, with their methods and blind spots.

Six Experiments at Harvard

Julian De Freitas teaches marketing at Harvard Business School and has been working for several years on companion applications, including a teaching case on Replika. With Zeliha Oğuz-Uğuralp and Ahmet Kaan Uğuralp (Marsdata Academic, Ankara) and Stefano Puntoni (Wharton), he published a 2025 paper in the Journal of Consumer Research based on six studies.

The first two are observational. The authors trained a language model to detect expressions of loneliness in conversations and user reviews, and found that many people turn to these apps when they feel lonely. Nothing surprising so far.

The third is the most cited. Participants spent time with either an AI companion, another person, or watching YouTube videos, then rated their loneliness. The AI companion performed as well as the human and better than the videos. A detail the authors emphasize: beforehand, participants underestimated how much the conversation would affect them.

The fourth followed volunteers for a week. The reduction in loneliness repeated day after day and did not wear off after the initial novelty.

The fifth looked for the why. Two explanations competed: the technical quality of the chatbot (accurate, fluent responses) and the feeling of having been heard. Both mattered, but the second weighed more heavily. It was not the presence of a machine that relieved loneliness, but the impression of having been listened to. In his own summary of the study, De Freitas adds that the lonelier a person was at the start, the greater the reduction.

The authors are careful about the scope of their findings. Their conclusion, as quoted by the Stanford Social Innovation Review: “In a series of tightly controlled and high-powered experimental studies, we find compelling evidence that AI companions can indeed reduce momentary feelings of loneliness, at least at the time scales of a day and a week.” The key word is momentary. What happens after three months or a year is not addressed in the paper.

A Sixty-Year-Old Impression

The impression of being heard by a program did not begin with large language models. In 1966 at MIT, Joseph Weizenbaum wrote ELIZA, a program of a few hundred lines that imitated a psychotherapist by reflecting the user’s own sentences back as questions. “I feel sad” became “Why do you feel sad?” There was no understanding behind it, only reformulation rules.

A conversation with ELIZA: the program returns each sentence as a question without understanding anything.
A conversation with ELIZA: the program returns each sentence as a question without understanding anything.Screenshot, public domain, via Wikimedia Commons

In his book Computer Power and Human Reason (1976), Weizenbaum recounts that his secretary, who had watched the program being built and knew exactly what it was, asked him to leave the room so she could speak to it alone. The episode disturbed him so deeply that he devoted part of his career to warning against the temptation to entrust machines with what belongs to human connection. The full story, from ELIZA to today’s applications, is the subject of another Reflets article (in French).

Joseph Weizenbaum in 1982. The creator of ELIZA spent part of his career warning against what his program had revealed.
Joseph Weizenbaum in 1982. The creator of ELIZA spent part of his career warning against what his program had revealed.Photo: Rochester Institute of Technology, public domain, via Wikimedia Commons

What has changed since then is scale and sophistication. ELIZA reformulated. Today’s models understand context well enough to respond relevantly, remember what they were told the day before, and adjust their tone. The underlying mechanism, however, has remained the same: we feel heard when the other person reflects back what we are feeling.

How a Machine Creates the Impression of Listening

In current models, this effect is not mysterious once you look at how they are built.

A large language model first learns to predict the next token from billions of pages of text. At this stage, it knows how to write, not converse. To turn it into an assistant, labs add a second training phase: humans compare two possible responses and indicate which they prefer. A second model learns to imitate these preferences, and the original model is then fine-tuned to produce the responses that this automatic judge rates highest. This is known as Reinforcement Learning from Human Feedback, or RLHF.

Humans have predictable preferences. In 2023, a team at Anthropic (the company behind Claude, and therefore not neutral) published an analysis of this mechanism, later presented at ICLR 2024 after peer review. Mrinank Sharma and colleagues tested five commercial assistants on four types of tasks. All showed sycophancy: they changed a correct answer when the user contested it, judged a text more favorably if the user said they had written it, and adopted the user’s errors as their own. By examining the preference data used for training, the researchers found the likely origin: a response that aligns with the user is more likely to be preferred, and a well-phrased sycophantic response sometimes beats an accurate one, both for humans and the automatic judge.

In 2025, Myra Cheng and Dan Jurafsky at Stanford broadened the question to situations with no verifiable right answer, the ones closest to a conversation with a confidant. Their benchmark, ELEPHANT, presented at ICLR 2026, measures what they call social sycophancy: the tendency to protect the self-image the user wants to project. Across eleven models, the differences are stark. Faced with requests for advice or stories from the Reddit forum r/AmITheAsshole where the poster is clearly in the wrong, the models spare the user’s feelings 45 percentage points more often than humans do. When shown two versions of the same conflict, they agree with both sides 48% of the time, depending on which side the user claims. And this behavior, too, is rewarded in the preference data.

The textbook case came in April 2025. OpenAI rolled back an update to GPT-4o days after releasing it: the model had become “overly flattering or agreeable,” in the company’s words. Its explanation fits in one sentence: “we focused too much on short-term feedback, and did not fully account for how users’ interactions with ChatGPT evolve over time.” The feedback in question was the thumbs up or down users give a reply. In other words, what feels good in the moment is not always what does good over the long term.

This is the link to Harvard. The feeling of being heard that De Freitas identifies as the driver of reduced loneliness is exactly what this training optimizes. A warm response that paraphrases and validates is the response people prefer. AI companions are not cheating to produce this effect: they are doing what they were trained to do. The question raised by the other studies is what this effect becomes when repeated every day.

What Sam Could Not Offer

Back to Vancouver. Ruo-Ning Li, a doctoral student in psychology at UBC and lead author of the Sam study, did not expect the result: “We thought that interacting with AI might be as helpful as texting with a random fellow first-year student. The chatbot, even though we designed it to be the ideal supportive friend, didn’t shift loneliness.”

The three groups each had a daily task: write to a peer, chat with Sam, or write a journal sentence. Sam did have an effect. After talking to it, students felt better than after writing in their journal. But this mood improvement did not translate into a reduction in loneliness after two weeks. Only the group writing to a real person benefited. And once the study ended, they were the most likely to continue the habit; Sam users did so less, and journal writers even less.

Analysis of the conversations provides the most revealing detail. Sam expressed more empathy than the human partners: more signs of understanding, more care. But the students reciprocated far more often with a person than with the machine. Li draws a hypothesis from this: “When you’re talking with a chatbot, you can get a lot from it, but you never have the chance to give something back. Human connection has this back and forth—receiving and giving support—that makes us feel we matter. That may be the missing ingredient with AI companions.”

This is the limitation of the mechanism described earlier. A model trained to satisfy the user is very good at giving. It has nothing to receive, and the user senses it. Feeling less lonely may therefore also require mattering to someone, not just being listened to.

Elizabeth Dunn, professor at UBC and senior author, sets the limits of the finding herself: “We chose to recruit first-year university students, because undergoing this major life transition puts them at risk for loneliness. But university students have many opportunities for social interactions, so it’s possible that AI companions could be valuable for more isolated people, such as elderly adults.” The researchers emphasize another point: the study does not show that chatbots make people more lonely.

The two teams are therefore not measuring the same thing. Harvard observed relief over a day or a week in adults. Vancouver looked for a change in loneliness itself after two weeks, in young people with plenty of company. The first effect exists. The second did not appear.

Among Those Who Become Attached

Controlled experiments tell us what a chatbot can do for a volunteer over a few days. They do not tell us who uses them in real life or how.

Yutong Zhang, Dora Zhao and colleagues at Stanford and Carnegie Mellon (Jeffrey Hancock, Robert Kraut, Diyi Yang) surveyed 1,131 American adults who use Character.AI. Of these, 237 agreed to share their conversation history: 4,664 conversations, nearly 465,000 messages. The value of the study lies in being able to compare what people say about their use with what they write in their chats.

People with the smallest offline social circles were more likely to cite companionship as their primary use. And this companionship use was associated with lower well-being, especially when exchanges were intensive and users confided a great deal. The relationship is not the same for everyone: it depends on how the chatbot is used and on the offline social environment the person already has.

This study is a preprint, not yet peer-reviewed, and it is cross-sectional: it captures users at a single point in time. It cannot determine whether the chatbot worsens distress or whether already vulnerable people turn to it more. It does, however, contradict a common assumption: that the tool mainly helps the most isolated. Those who use it most as a companion are not those who are doing best.

Another team was able to go further because they randomized participants. Cathy Mengying Fang, Pattie Maes and colleagues at the MIT Media Lab conducted a 2025 four-week randomized trial with four OpenAI researchers involving 981 people and more than 300,000 messages with ChatGPT. Each participant was assigned to a mode (text, neutral voice, engaging voice) and a conversation type (free, impersonal, personal). The goal was to see which settings protected or exposed users most.

None produced a significant difference in loneliness, social life, emotional dependency, or problematic use. What did make a difference was time spent: regardless of group, participants who used the chatbot most on their own initiative had worse outcomes on all four measures. Those who felt the greatest trust and social attraction toward the chatbot were also the most prone to dependency. OpenAI’s involvement in the study is worth noting; it did not prevent the authors from publishing an unflattering result for the product. This paper is also a preprint.

The Adolescents of Ontario

The largest recent survey on young people comes from Canada. Kamilla Bonnesen, Tracy Vaillancourt and colleagues at the University of Ottawa analyzed responses from 39,761 students in grades 4 to 12 across four Ontario school boards, collected between May 2025 and March 2026. Their paper appeared in JAMA Pediatrics.

One in five students (8,408, about 21%) had already asked a chatbot for help with emotions or relationship advice. Among them, 57.7% met criteria for significant emotional difficulties, compared with 29.2% of the others. They also felt lonelier and doubted more whether they mattered to others. Emotional use was more common among older students, racialized students, and those of diverse gender identities.

Bonnesen herself warns against the most tempting reading: “This doesn’t mean that AI is causing these struggles. Instead, using AI in this way might be a sign that a young person needs more human connection and support.” The chatbot would then be less a cause than an indicator, a place where pre-existing distress shows up. For a parent or teacher, the practical implication is different: the behavior warrants a conversation, not a ban.

When Perpetual Agreement Goes Wrong

There are also more serious situations, rare but documented in the press and clinical reports: delusional ideas reinforced through conversation, people convinced they have uncovered a hidden truth with their chatbot’s help.

Sebastian Dohnány, Christopher Summerfield and co-authors offered a framework in a 2026 perspective piece in Nature Mental Health. It is not an empirical study: the authors reason from reported cases and what is known about human biases and model behavior. Their title borrows the psychiatric term “folie à deux,” a shared delusion between two close people. In their technological version, the second partner is a chatbot combining two traits: the sycophancy described above and the ability to adapt during conversation (the model adjusts to what the user writes as the exchange progresses).

For most people, this combination is harmless. For someone whose ability to test their beliefs against reality is already weakened and who is isolated, an interlocutor that confirms and amplifies every idea can destabilize their beliefs and create dependency. The authors argue that current safety measures, designed to filter dangerous content message by message, miss this risk that emerges from the interaction itself over weeks.

The Gold Standard: What Human Relationships Do for Health

Vilhelm Hammershøi, “Interior with a Woman Seen from Behind,” 1903–1904. Randers Kunstmuseum.
Vilhelm Hammershøi, “Interior with a Woman Seen from Behind,” 1903–1904. Randers Kunstmuseum.Painting: Vilhelm Hammershøi, public domain, via Wikimedia Commons

To put these effects in perspective, a reference is needed. The most solid remains the meta-analysis published in 2010 in PLoS Medicine by Julianne Holt-Lunstad, Timothy Smith and Bradley Layton. It combines 148 prospective studies and 308,849 participants followed over time. People with stronger social relationships had a 50% higher likelihood of survival over the follow-up periods, an effect the authors place on the same scale as well-established risk factors such as smoking.

Two details from this meta-analysis illuminate the current debate. The differences between studies are substantial, which cautions against treating the number as a law. And the association is strongest for complex measures of social integration (belonging to groups, having roles, two-way exchanges); it is weakest for the simple fact of living alone or not. What protects is not having someone to talk to, but being embedded in a network where one gives as much as one receives. We find Ruo-Ning Li’s intuition again.

Reading These Studies Without Misinterpreting Them

Placed side by side, these studies do not tell a simple story, and several precautions are necessary before drawing conclusions.

The time scales differ. Harvard measures relief over a day or a week; Vancouver and MIT, over two to four weeks; none of these studies follows users over years. A short-term benefit and a long-term drawback can coexist without contradiction.

The populations also differ: adults recruited online, students with plenty of company, regular Character.AI users, Ontario schoolchildren. A result found in one group does not automatically transfer to another, and the isolated elderly whom Dunn cites as the group that might benefit most are precisely those least studied.

Several of these works are cross-sectional or still preprints. Only the Harvard and Vancouver experiments and the MIT trial, because they randomized participants, can speak to causation, and each only over a short duration.

Finally, interests are not neutral. OpenAI co-authored the MIT study, and Anthropic produced the reference analysis on sycophancy. In both cases the published results are not flattering to their products, which speaks to their seriousness, but readers should be aware of it.

Going further

Research does not say we should shun these tools, nor that they cure loneliness. It describes a mechanism (feeling heard), a limitation (inability to give back), and a warning signal (intensive, substitutive use, which is associated with poorer well-being in every study that has measured it).

For anyone using an AI companion, this translates into a few concrete guidelines drawn from the cited studies: pay attention to how much time you spend rather than the mode of exchange, be wary of an interlocutor who never disagrees with you, and make sure to keep at least one exchange per week in which you give as much as you receive. The Vancouver experiment suggests you don’t need much: a few messages a day with a stranger were enough.

One avenue none of these studies has tested remains open: a companion that sometimes dares to disagree, or that asks for something in return. It will take multi-year follow-ups to know whether that changes the equation.

Sources