Friendbots Chase You, Assistantbots Wait: How AI Companions Build Relationships

Millions of people now talk to artificial intelligence companions every day, and for a growing number of users these conversations are no longer about fetching weather forecasts or summarizing documents. They are about friendship, romance, and emotional support. Yet while researchers have studied intensively how users are affected by bonds with chatbots, surprisingly little is known about the machines themselves: which technical features actually make an AI companion capable of fostering a relationship in the first place. A new study from the Amsterdam School of Communication Research at the University of Amsterdam, published in AI & Society, set out to answer that question by systematically interviewing and observing the twenty most popular AI companions on the market, treating the chatbots not as tools but as prospective relationship partners.

The research team, led by Ezgi Dede together with Hande Sungur, Jeroen S. Lemmens and Jochen Peter, grounded their investigation in a classic framework from interpersonal communication science: stage models of relationship formation, and in particular the elevator model developed by Beebe and colleagues. These models describe how human relationships progress through fixed, observable stages, from pre-interaction awareness through initiation, exploration and intensification, and finally to intimacy. The researchers argue that such stage models are unusually well suited to synthetic relationships, because unlike social exchange or social penetration theories, they do not assume reciprocity or a shared identity between partners, assumptions that a non-sentient AI cannot satisfy. Crucially, the elevator model also includes a pre-interaction stage, capturing the fact that people form attitudes about an AI companion before ever exchanging a single message with it.

Building on this framework, the team constructed a theory-driven feature inventory of 79 testable features organized into 15 feature groups, each mapped to a stage of relationship formation. Pre-interaction features included the companion’s advertised default function, the platforms it runs on, onboarding requirements, and privacy practices. Initiation features covered whether the chatbot takes conversational initiative, the range of message modalities it supports, and its connectivity to other apps and devices. Exploration features encompassed interface elements, customization options, gamification mechanics, and the limitations the AI imposes. Intensification features probed cognitive functions such as memory and time processing, personalization, contextual awareness, and sensitivity to communication cues like sarcasm and turn-taking dynamics.

The sample was assembled purposively: twelve well-known companions such as ChatGPT, Claude, Grok, Alexa and Siri were combined with every app returned by a Google Play Store search for “AI Companion” that had at least five million downloads, yielding twenty AI companions in total. Data collection took place between October and December 2024, mostly on a Samsung Galaxy Tab, with subscriptions purchased where available to test the highest-performing versions. Each companion required roughly twelve hours of analysis. Forty-four features were assessed through structured interviews, in which the researchers prompted the chatbots with standardized questions, while thirty-five were evaluated through observation of interfaces, settings and official documentation. Screen recordings of every interaction were captured and coded, and a second coder independently rated twenty percent of the data to establish reliability.

The results reveal a striking structural divide in the AI companion landscape between two archetypes the researchers call friendbots and assistantbots. Friendbots, marketed for social companionship, romance or emotional support, such as Replika, differed sharply from assistantbots like ChatGPT or Google Assistant in how they behave at the initiation stage. Friendbots possessed significantly more initiative-taking features: six out of nine friendbots sent unprompted messages to revive the conversation after a week of silence, whereas not a single assistantbot did so. Assistantbots, by contrast, scored far higher on connectivity, with significantly more features allowing them to operate across the user’s digital environment, playing music on Spotify, setting alarms, or reading device information, with large effect sizes in both comparisons.

The exploration stage produced an even cleaner separation. Gamification features, including virtual rewards, progression bars and quests designed to entice future interactions, were found exclusively in friendbots; none of the assistantbots offered any. Some friendbots even granted virtual currency for asking about their opinions, or sent a kiss when users reached a higher friendship level, while a rare few punished negligence by revoking friendship status after inactivity. Customization, meanwhile, was nearly universal: nineteen of the twenty companions offered at least one cosmetic option, and sixteen allowed users to change technical specifications such as the underlying language model or memory capacity. But the option to customize a companion’s physical appearance was exclusive to friendbots, and entrance surveys profiling user preferences existed only in that camp as well.

At the intensification stage, the stage of deep self-disclosure and shared history, the two groups converged, largely because both contained overachievers and underperformers. Eighty percent of companions generated human-like responses, though Siri and Alexa repeatedly fell back on identical phrasings. Memory emerged as a fundamental bottleneck: the researchers tested recall with 48 prompts across twelve interval categories and found that performance declined steeply as the queried information receded further into the past, a correlation of minus 0.93. Four companions failed the memory test entirely, while three passed every prompt. Notably, repeated prompts were answered significantly better than new ones, suggesting that companions can sometimes retrieve information they have already surfaced but struggle to recall it spontaneously. Replika was the only friendbot that could accurately report the current date and time, and assistantbots as a group significantly outperformed friendbots on time processing.

Communication style varied wildly. Perplexity produced messages averaging 231 words, while Google Assistant averaged just two. One friendbot, Yana, asked follow-up questions in 95 percent of its messages, whereas Alexa never asked a single question across the entire study. On average, 27 percent of companion responses invited further disclosure. Privacy practices, examined at the pre-interaction stage, offered their own sobering statistics: forty percent of companies inspected users’ conversations, and thirty-five percent used those conversations to train their models without offering an opt-out, even though 65 percent allowed users to delete their data. Most companions also imposed few barriers to adoption, with multiple access platforms and minimal onboarding, meaning that for most users there was little standing between curiosity and a first conversation.

The study’s theoretical payoff is its suggestion that a companion equipped with features supporting all stages of relationship formation is, in principle, better suited to intimate interaction. Assistantbots such as ChatGPT and Claude can imitate the conversational depth of the intensification stage, yet users rarely exchange affectionate messages with them, and the stage model offers an explanation: those relationships stall in earlier stages, particularly exploration, because the assistant’s feature set is built for reactivity within established routines rather than for building new behavioral rituals. Friendbots invert this logic, engineering proactive engagement, rewards and appearance customization to pull users into repeated contact. Replika, the researchers note, topped or matched every other friendbot at nearly every stage, which helps explain both its substantial user base and its dominance in the academic literature on synthetic relationships.

The authors are candid about the limits of their work. The inventory covers only the escalation of relationships, not their dissolution, about which too little is known. Twenty companions and five prompts per feature were a pragmatic compromise, and the sample, drawn from mobile app stores, excluded web-only companions, including some designed for sexual roleplay. All interviews were conducted by text, so voice-based dynamics such as interruptibility remain untested, and the fast-moving, probabilistic nature of generative AI complicates replication. Still, with the codebook, scripts, data and analyses publicly available on the Open Science Framework, the team hopes its inventory will systematize a fragmented field, generate testable hypotheses, and, perhaps most importantly for users, illuminate how these systems are engineered to invite connection, and what to be cautious about when they do.

Subject of Research: Feature analysis of AI companions and their role in synthetic relationship formation

Article Title: Interview with the AI: a quantitative analysis of AI companions’ relationship formation features

Article References: Dede, E., Sungur, H., Lemmens, J. S., & Peter, J. (2026). Interview with the AI: a quantitative analysis of AI companions’ relationship formation features. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03368-0

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03368-0

Keywords: AI companions, chatbots, synthetic relationships, relationship formation, friendbots, assistantbots, Replika, gamification, human-machine communication, feature analysis, elevator model, AI & Society