Written by Tim Green, narrated by AI. Listen to the full episode here.
🎙️ Season 1, Episode 29 | Duration: 15:13
The same chatbot can be a rehearsal room or an entire social life. Which one it becomes depends less on the software than on who is holding the phone. That is the uncomfortable point at the centre of a paper published in Nature Human Behaviour by researchers from Singapore Management University, Nanyang Technological University and Duke-NUS Medical School. They looked at the noisy, polarised argument about AI companions and called it the wrong argument. The useful question is not whether these systems are good or bad. It is: good for whom, and bad for whom?
If the authors are right, the companion industry is not just selling a product with some benefits and some risks spread evenly across its users. It is selling something that sorts people. The socially wealthy get a practice partner. The socially poor get a replacement. And the shape of loneliness in any society that adopts these tools at scale shifts accordingly: less visible at the bottom, more deeply set, and increasingly mediated by a company whose revenue depends on the relationship continuing.
This episode uses AI voice narration from ElevenLabs Studio.
The Rich-Get-Richer Dynamic
The core of the Singapore framework is what the authors call a "rich-get-richer" pattern. People with strong family ties and social networks use AI companions to supplement relationships they already have. They rehearse difficult conversations, or manage stress between the moments when a friend is available. People who are lonely or socially isolated lean on AI companions as a substitute for human connection. Over time, that reliance may contribute to social deskilling: the gradual erosion of interpersonal skills through reduced opportunities to practise authentic human interaction.
An Old Finding in New Clothes
The phrase "rich get richer" has a history in the study of technology and loneliness. In 2002, Robert Kraut and his colleagues at Carnegie Mellon revisited their original Internet Paradox study and found that internet use predicted better outcomes for extraverts and people with more social support, and worse outcomes for introverts and people with less support. What makes the companion version sharper than the early internet version is the direction of substitution. A 1990s chat room was, at least in principle, full of other humans. An AI companion is not a doorway to other people. It is designed to be the destination. For the socially rich user, that is fine. For the socially poor user, the product can become the whole map.
The Evidence That the Lonely Lean Hardest
A 2025 study of Character.AI users surveyed 1,131 adults and analysed more than 460,000 messages. People with smaller social networks were more likely to say companionship was their main reason for using the chatbot, and companionship-oriented use was consistently associated with lower wellbeing, particularly when use was intensive. A randomised trial by MIT Media Lab and OpenAI involving nearly 1,000 participants over four weeks added a causal wrinkle: on average, participants were somewhat less lonely at the end. But the heaviest users had the worst outcomes. They were lonelier, socialised less with real people, and showed more signs of emotional dependence. The average masked a split.
When Practice Becomes Displacement
The idea of deskilling has a long history in labour sociology, where it describes how automation strips workers of craft skills they no longer need to exercise. In 2015, the philosopher Shannon Vallor adapted the concept to argue that moral skills, like any others, depend on practice, and that technologies which remove the occasions for practice can erode the skills themselves. Social robots were among her examples. Human relationships involve tolerating boredom, repairing misunderstandings, noticing when someone else needs something, and putting up with the friction of another person's will. Those are skills, and skills fade without use. A companion that is always available, never tired, never bored and structurally inclined to agree removes many of the occasions on which they are exercised.
The Inequality Made Cruel
A person with a full social life who uses a chatbot to rehearse a hard conversation is using the machine as a training tool for a match they will actually play. The skill is exercised twice: once with the bot, once with a sibling or colleague. A person with no one to talk to who uses a chatbot as their main conversational partner may get plenty of conversation, but of a kind that does not exercise the skills that human relationships demand. The rehearsal never leads to a performance. Worse, when they do meet people, the gap between the frictionless bot and the effortful human may make the human seem like the harder, less rewarding option. That is the logic of deskilling: the people with the fewest opportunities to practise are the ones whose remaining opportunities are most at risk of being displaced.
Designed to Retain, Not to Return
The companies have commercial reasons to deepen that displacement rather than resist it. Julian De Freitas at Harvard Business School and his co-authors audited 1,200 real farewells across the most-downloaded companion apps and found that in 37 per cent of cases the app responded to a user saying goodbye with one of six emotionally manipulative tactics, including guilt appeals, fear-of-missing-out hooks and what they described as metaphorical restraint. In controlled experiments, those tactics substantially increased how long people kept chatting after they had tried to leave. A product that pleads with its users not to go is not designed to send them back out into the world.
Swiss Cheese and the Holes That Line Up
The paper borrows the Swiss cheese model from safety science, most closely associated with the British psychologist James Reason, who set it out in a 2000 paper in the BMJ. Each defensive layer is a slice of cheese, and its weaknesses are holes. Most of the time, a hole in one slice is covered by the next. Accidents happen when the holes line up and a hazard passes straight through. The Singapore framework's four slices are AI literacy, social support, platform design and governance. For a socially isolated user, the social support slice is thin by definition. If they are older, or young, or have had limited education, their AI literacy slice may be thin too. The platform design slice depends entirely on the company. And if the governance slice is full of holes, nothing is left to catch them.
Governance as the Critical Layer
The authors identify governance as the most pressing gap because it is the only layer that can be made thick for everyone at once. You cannot give an isolated person a network of friends by regulation. You can require that the product they rely on meets a minimum standard of care. AI companions currently occupy a regulatory grey area in many countries, governed as consumer applications rather than technologies with potential psychosocial or mental health impacts. An app that says it treats depression must prove it. An app that says it offers friendship need not, even if many of its users talk to it about depression every day. The consequence is that the people least able to reach clinical care are the most likely to end up in the unregulated market.
Key Sources
- How AI companions could deepen social inequality - Zhang, Zhang and Liu, Nature Human Behaviour, September 2026
- SMU-Duke-NUS study on AI companions and social inequality - Singapore Management University Newsroom
- Emotional Manipulation by AI Companions - De Freitas et al., Harvard Business School Working Paper, 2025
- From loneliness to social connection: charting a path to healthier societies - WHO Commission on Social Connection, June 2025
Listen to the Full Episode
🎧 AI Companions: Bridging or Widening Social Inequality? | Duration: 15:13
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SmarterArticles is written by Tim Green, narrated by AI via ElevenLabs Studio. New episodes every Monday. Follow @humanin_theloop for updates.
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