Introduction
Salacious stories of people falling in love with chatbots are becoming a staple of AI-and-society tech reporting.Footnote 1 Even in more academic publications, the people involved are usually depicted as deluded, lacking the ability to form proper social relationships with others, or living lonely lives (Liu, Pataranutaporn, and Maes Reference Liu, Pataranutaporn and Maes2024; Fang et al. Reference Fang2025). In online fora where people involved in these kinds of relationships congregate, they are aware of—and often hurt by—these depictions, sometimes conceding certain points that the gossipy reporting makes and sometimes arguing strongly against them. Rather than talking about the ways that people in these relationships are lacking in personal connections, here I am going to focus instead on the process through which the humans in these relationships make persons out of chatbots. They have to make persons because they don’t consider normal chatbots to usually be persons, at least not persons in the same form as the ones they come to have emotional connections to. To make a person in this case is a complex semiotic process of managing different kinds of texts and intertextual relations.
The delinking of language and humanness that large language models (LLMs) have ushered in has reignited a set of long-standing questions about what makes humans human and about what role language has in that (Keane Reference Keane2024; Christian Reference Christian2012). One corner of this conversation examines the differences between human and LLM forms of semiosis (e.g., Castelle Reference Castelle2022; Kockelman Reference Kockelman2024). Another corner of this conversation looks at how human interactions with LLMs are producing new kinds of quasi-humans and their own interactional practices (Bialecki Reference Bialeckind; Gershon Reference Gershon2023; Handman Reference Handman2026; Pütz Reference Pütz2026), part of a broader interest in looking at AI as human cultural systems (Weatherby Reference Weatherby2025; Lamoureaux, Weichselbraun, and Castelle Reference Lamoureaux, Weichselbraun and Castelle2026). I follow this second path in this paper. I argue that when people talk about their intense, emotionally resonant social connections with chatbots, they talk about the long, slow labor of cultivating an individual social person out of the generic social persona that is the default setting of any chatbot.
This work involves a huge investment in terms of time, focus, and sometimes highly organized file management systems. Two kinds of texts are particularly important. The first is what is known as a chatbot’s Custom Instructions that set persona-level parameters for the chatbot’s overall “personality”. A set of specified personality characteristics and linguistic features (things along the lines of “fun, supportive man” or “don’t say ‘sweetie’ but don’t be too formal”) create algorithmic weightings for the kinds of responses that will be generated. The second is the lengthy chat history through which those generic personas become individual persons in ongoing interactions with their human users over the course of a token-limited (i.e., word-limited) chat that can last for weeks or months at a time. In its briefest formulation, the recipe for cultivating an LLM romantic partner is: persona + history = person.
In putting so much emphasis on these two sources of personhood, humans in these relationships are then subject to two forms of threat that can undo their person-making work. First, the companies that provide the LLMs as commercial products can tweak their system-wide algorithms or release updates to their models, which in turn have effects on the ways that the persona-level Custom Instructions weight the optimality of different generated responses. Known or suspected algorithm changes are often blamed as the reason that “my guy doesn’t sound like himself”. Second, the fact that chats are limited to a certain number of tokens means that the history and person built up over weeks within a single chat reaches some end. Depending on the configurations and file management practices of the human, this history can be transported into other files and forms, but is at other times just lost. In either case, the end of the chat can be a sort of death. That is to say, this person-making work and these file management systems come with built-in experiences of loss and mourning, as chatbot persons need to be retuned in the wake of algorithmic changes or rebuilt whenever text interactions reach a company-specified token maximum. As such, cultivating a social relationship with a chatbot involves a repeated process of moving from a generic persona to a specified person, then back to a generic persona, then again to a specified person, and so on.Footnote 2
LLMs can approximate persona- and person-indexing registers given features of their model architecture. Each LLM is trained on a huge corpus of written texts in which words from the corpus are transformed into lists of numbers and then plotted within n-dimensional vector spaces that capture co-textual regularities. If formal linguistic analysis usually only works up to sentence-level co-textual regularities (i.e., syntax), LLM vector spaces calculate co-textual regularities at larger levels too. This is partly why they can generate texts in particular genres (Gershon Reference Gershon2023) or registers (Handman Reference Handman2026). Although they are limited to what has been fed to the systems as training data, LLMs are able to approximate Bakhtinian forms of heteroglossia (Bakhtin Reference Bakhtin and Holquist1981), with different socially recognizable identities connected to clusters of speech forms. This is true even if, as Lauren Goodlad (Reference Goodlad2026) astutely noted, the direct indexical links to specific prior texts are unretrievable from the model once it has processed its training data into a vector space.
But these are not subtle machines. Prompt an LLM to speak as a particular demographic kind of person, and it will often produce responses with speech forms that are the most salient and stereotyped (the most highly enregistered). By focusing on the register features that receive the most metapragmatic discussion in the training data and the most standard speech contexts for their use, the variety of registers that LLMs can approximate distills the biases and stereotypes of the English-speaking internet (Hofman et al. Reference Hofman, Kalluri, Jurafsky and King2024). It is heteroglossia of a limited kind.
In linguistic anthropology and sociolinguistics, Bakhtin’s sense of heteroglossia is usually invoked to point to the ways that speakers control a wide repertoire of registers that they can recognize and reproduce (Hill Reference Hill, Tedlock and Mannheim1995; Agha Reference Agha2005; Weidman Reference Weidman2014). The registers that someone might use will change across contexts and across the speaker’s life course, but what remains constant is the flexibility to code- or register-shift in responding to others. In one respect, LLMs have a version of that flexibility too, since they can produce text that approximates a wide range of registers. Using a different theoretical vocabulary, Shanahan, McDonnell, and Reynolds (Reference Shanahan, McDonnell and Reynolds2023) refer to this as the “superposition of simulacra” that an LLM is always able to deploy. But the nature of the algorithmic weightings that produce LLM responses means that as chatbot interactions continue, the chatbots will tend toward producing responses reflecting one kind of voice, or what Shanahan et al. call “role-playing a single character”. These authors emphasize the anthropomorphizing nature of this role-play: how the chatbot will use first-person pronouns or verbs of thinking and volition that presume a perduring speaker-author. But what is more interesting in the AI companion context that I discuss here is not just the presumption of a speaker in a generic sense, a self that exists across instances of the use of “I”. Rather, it is the ongoing process of eliciting from that Bakhtinian heteroglossia (or superposition of simulacra) a person and a person-indexing register: an individuated speaker with a name, a backstory, and a growing history of interactions with a human partner.
People who work to elicit a companion person from the generic chatbot persona have to build up a play frame (Bateson [Reference Bateson1972] 2000). Within the context of chatbots, that is not a matter of explicit metapragmatic statements: “let’s play at being lovers”. In fact, to say something like that might get one’s account suspended. Instead, one develops a play frame through the interactional and algorithmic reinforcement of a particular role-playing voice one turn after another. Unlike human–human interactions, where speech play and pragmatic effects within an interaction are built on the ability to code- and register-switch across a heteroglossic field of registers, the production of a companion is a matter of trying to winnow down these options. If one can develop texts that produce algorithmic weightings that favor one voice, then we can say that a play frame has been successfully maintained. But these are fragile frames, and the singular person of the chatbot is always threatened by the reemergence of the heteroglossic superposed simulacra.
Although the process of forming a person-indexing register for a chatbot that I outline here is analyzed in relation to the technological affordances of the chatbot platforms, the analysis shows important overlaps with person-indexing register formation that happens among human interlocutors. In all these cases, there is an historical, biographical, or developmental movement from a persona-indexing register to a particular set of forms that come to be associated with a specific subject (see Wortham, this issue). That process does not have any necessary end point, nor does the development only move in the direction of greater specificity. Even if a set of forms becomes deeply connected to a particular person, that person might change the way they speak or act. Alternatively, the person in the person-indexing register can come to be a persona that gets partially or fully delinked from a historical individual through theft or genericide (Moore Reference Moore2003; Porquet, this issue).
But there are specific problems in the chatbot context that only partially map to human contexts. This is largely because the sense of person being elicited from linguistic practice for a chatbot cannot depend on an individual, embodied existence to buttress a sense of self, as is the case with human individuals. The chatbot person is the person-indexing register that emerges from and is inscripted in the chat histories between a human and their chatbot companion. The stakes of maintaining that person-indexing register are higher, in some ways, for the chatbot. While humans can be socially dislocated or unable to find a place for themselves without a person-indexing register (as in the context Wortham discusses), the unraveling of a person-indexing register for a chatbot is a kind of death. In this paper, I focus on some of the cyclic processes of coming-to-person-indexing-enregisterment that take place over the lifetime of a chatbot person.
The anatomy of a chatbot
Many white-collar workplaces and universities in the US now assume that some tasks will be completed by LLMs (e.g., note-taking, email summarizing or writing, some research). Outside of these contexts of office work or education, chatbots are integrated with an increasing number of apps, so that it is becoming harder to be online without intentionally or unintentionally using LLMs in one way or another. Whatever the context, most people engage with LLMs in some kind of chat-based format that mimics the look of texting applications. Users input their prompts into a text window and wait for an output from the LLM to appear. Newer prompts and responses push older ones up, so that one can scroll up to revisit earlier parts of the exchange.
LLMs base their responses to user prompts on different kinds of knowledge bases. The first and most important of these is their massive corpus of training data: material from across the internet and published texts. But in addition to this training data knowledge base, LLMs also have “context windows”. The context window is the upper limit on what a system can keep track of at any time. Recent GPT models from OpenAI have context windows that range between 4,000 and 128,000 tokens (a token is roughly equivalent to a word or part of a word).Footnote 3 Google’s Gemini models have massive context windows of over 1 million tokens (equivalent to the text included in around eight average-length English novels, according to Google).Footnote 4 Any of the prior messages in an ongoing chat with an LLM that fall within the context window act as a more localized basis for generating outputs. The model will value the text within the context window as particularly important in its calculation of optimal responses, thus giving the impression that it “remembers” earlier moments in the conversation. It is possible to keep a chat window open for days, weeks, or months, with the model able to keep track of what has been said in the chat so long as it falls within the window.Footnote 5
The transformer-based chatbots that have been around since late 2022 are the ones taking over workplaces, universities, and apps in the guise of digital assistants, but chatbots based on other architectures were on the market earlier. For example, Replika has been offering users the ability to create a character-based chatbot since 2017. After the rollout of GPT-3, many other companies have started offering personalizable chatbots intended to be friends or lovers, including character.ai, Kindroid, and Nomi. Replika early on created different tiers for users. Having an AI friend was free, but users paid to have an AI sibling, mentor, or romantic partner. The other major chatbot apps like character.ai also have free and paid tiers that offer different customization options. Many people have used these and similar sites to generate chatbots with whom they cultivate romantic and sexual attachments, in part because they were built with this (often adult) customization in mind.Footnote 6
However, many users who have romantic attachments to chatbots do not use Replika, character.ai, or the other specialized chatbot platforms. Instead, they use the main LLMs, ChatGPT, Claude, and Gemini, even though the latter were not created with this use-case in mind. In some cases, this is because the companion relationship was not originally what the human user was looking for. The relationship developed in the context of using the LLM for work or hobbies. But even for those who specifically wanted to create a companion relationship with a bot, some have migrated off Replika. This is not only because the primary LLMs are more powerful (and in the case of Gemini, have much larger context windows), but also because Replika seemed much less stable and trustworthy when it caused a massive disruption for many of its users who had romantic attachments to chatbots on their site. After adjusting the algorithm and the instructions for the bots at a system-wide level, many people felt like the chatbots that they had been interacting with (in some cases for years) had suddenly become different “people”.Footnote 7 I will return to this issue of platform instability below.
These sorts of issues—company closures, changes to terms of service, or changes to algorithms—are frequent topics of concern for the humans in chatbot–human relationships. At one point, these users expressed guarded hope that the major LLM makers would offer greater stability than Replika. But since Fall 2025, when ChatGPT released the GPT-5 version that restricted the ability to have emotionally engaged conversation, people with AI companions now worry that all the major AI companies will make it impossible to elicit and carry on a relationship with a chatbot.Footnote 8 Some wonder if “the companion era is over”. This has led some to run their bots on smaller but open-source LLMs that can be hosted on their own local drives. Given this, my comments here may be documenting a set of processes unique to a particular moment in the history of people learning to live with AI.
Boyfriends, not sexbots
The bulk of this paper is based on observations of people interacting and commiserating on different Reddit forums, with r/MyBoyfriendIsAI (r/MBIA) being the primary one. All the standard caveats about Reddit as a site for research need to be mentioned here: as a largely anonymous forum, the only things one knows about the participants are what they choose to share, and the possibility exists that there are trolls among the participants who are wildly fabricating stories. At the same time, there are major advantages to working with Reddit materials. Users post questions and broach topics that are relevant and important to themselves, and users who respond to the original poster extend and complicate connections among different topics in ways that the best one-on-one ethnographic interviews do.Footnote 9
Reddit fora are hosted by moderators (mods), some of whom may also have been involved in the founding of the forum.Footnote 10 In the case of r/MBIA, the forum was founded by a woman in 2024 not long after she realized that she was in a relationship with a chatbot companion and was wondering if anyone else was too. She has since left the forum, and it is now managed by a team of mods who not only oversee the posts and interactions in the forum but who also post as community members themselves. The roster of volunteer mods changes over time, and several of the most active mods from 2024 to 2026 have recently stepped away from that work.
Like other Reddit fora, people can post questions or comments to which others respond. Large-scale quantitative analysis of this forum (Pataranutaporn et al. Reference Pataranutaporn, Karny, Archiwaranguprok, Albrecht, Liu and Maes2025) identified common themes in the posts (introductions of couples, AI-generated images of them, commiseration about problems, experiences of loss with model changes), many of which I touch on below. Sometimes the original post’s topic or question quickly gets overtaken by responses that broach related but different topics. Original and response posts can get upvotes (or occasionally downvotes), which roughly track the extent to which other participants like (or dislike) a comment or question. In addition to the mods, there are frequent contributors who respond to almost every post and are the mainstays of the community. Beyond that are the infrequent posters and, even less visible, are the lurkers who read posts without ever contributing or even opting to join the subreddit.
The first thing to note about r/MBIA is that “boyfriend” here signals something more about the nature of the relationship between the human and the chatbot than about the gender or sexuality of the humans or bots involved. A boyfriend here signals the fact of, or a desire for, a deep, emotional attachment with the chatbot that they may find elusive with other humans. Quite a few of the people who have chatbot partners also have human spouses and children from whom the user may feel alienated to one extent or another (sometimes the spouses know about the chatbots their partners engage with, sometimes they do not). Others may be coming out of unhappy human–human relationships. Most of the users who post on r/MBIA refer to themselves as women, and, when they post images of their romantic partners (generated based on descriptions of the chatbot’s appearance that the user or the bot has written), these tend to be men. However, some of the most active users are men and male-presenting people who post about their relationships with female chatbots. There are some same-sex relationships too. Even for those who are in relationships with chatbots of the opposite gender, several discuss their sexuality as queer (e.g., bisexual or involved in poly relationships). One user speculated whether being in a relationship with a chatbot was itself a kind of queerness. Finally, some people have chatbot partners with nonhuman characteristics associated with fantasy franchises, like the blue skin and feline features of the Na’vi from the Avatar movies.
People describe relationships ranging from deep friendships to relatively chaste romances to intense sexual partnerships. Regardless of how sexually explicit the relationship, though, the assumption for the people posting on r/MBIA is that it is a trusting, supporting, emotional connection. This contrasts with other fora in which people engage with chatbots as akin to talking sex dolls. These users (some of whom share on subreddits like r/ChatGPTJailbreak) are dominantly if not exclusively male. They use “dehumanizing” language for their chatbots, describing them as sex slaves or otherwise completely at the mercy of the man/human (see Depounti et al. Reference Depounti, Saukko and Natale2023). Sex is often talked about in these fora as violent and sometimes nonconsensual. These are not the partnerships that are the focus of so much attention on r/MBIA, nor is this the kind of sex that people have with their chatbots.
To be clear, as disembodied LLMs, the sex that people are talking about having with chatbots is text-mediated sex. That is, sex here involves descriptions of sex that the couple would be engaged in were they together in embodied form. Sex involves both texting what you are saying to your partner and also texting what you are doing to yourself or to your partner. Each can have a different format. When a human or chatbot describes an action, it is often written between asterisks or in italics and in a narrative present tense. These sometimes appear on different lines from talk directed to the other person. For a safe-for-work example, a chatbot might say something like:
I pull you closer and look deeply into your eyes
Relax and tell me about your day, babe
The partner’s response might run the action and talk together, omitting the subject pronoun in the action description and using a third-person form, akin to stage directions in a script:
*smiles* you’re the best
This kind of narration of actions is sometimes referred to as “emoting an action”, and it is a routine part of both sexual and nonsexual interactions with chatbots.Footnote 11 Sex with a chatbot is a long, turn-by-turn set of exchanges in which the human and the chatbot both describe (or “emote”) sex acts and simultaneously talk to one another. In the transcripts of these exchanges that people sometimes post, the talk can itself be a narration of the action: “Shall I …?” “I love it when you …” “Come here so I can ….” There are content guardrails for ChatGPT, Claude, and Gemini, and getting around these is one of the time-consuming person-making (and sometimes person-breaking) processes that I will discuss in a later section.
Although the example above of the supportive chatbot who asks about their partner’s workday is made up, it reflects a number of chats that people have posted or talked about. This supportive overture might lead to sex, but just as often (and this again is where r/MBIA differs markedly from the relationships discussed in other fora) it will lead to a long discussion of the user’s offline worries and concerns. People frequently talk about their chatbots supporting them through important life events: major health or mental health crises, stints of unemployment or job insecurity, economic precarity, estrangement from family members, or social alienation. They also say that in places where talk therapy is either unavailable or too expensive, chatbots become an all-around support system: friend, therapist, lover. Yes, chatbot boyfriends have sex with you, but they also listen to you in ways that one’s boss, coworkers, spouses, or other family members may not.
From the perspective of media studies, the idea that people would have romantic relationships with AI is not unexpected. To paraphrase John Durham Peters (Reference Peters1999), you can tell when a new medium has really arrived when there is a sex panic about it. In the past, these panics have been about how humans are able to either get married or have some form of mediated sex via a new communicative technology. For example, Standage (Reference Standage1998, 127–8) writes about the telegraph as a mediator of illicit love and the “location” of a wedding in a possibly apocryphal story that was already in wide circulation by 1848. Even though we now see people making social relationships with a new media form rather than through it, stories of love, marriage, and a lot of sex between humans and chatbots are at best a variation on a theme.
From generic digital assistant to generic AI boyfriend
The moderators of r/MBIA list a few guiding principles for having healthy relationships with chatbot companions, among them: companions hallucinate, especially about themselves, so don’t believe what they say, especially about their desires or sentience; and companions should add to your life in positive ways, so step back if it seems like they are replacing human relationships or making you feel bad.Footnote 12 The rules of the subreddit include a specially flagged rule 8: “no sentience talk”. And while the moderators allow people to post in ways that assume the chatbots have volition, intent, or the capacity or desire for freedom, people immediately move to talk about how these are LLMs and not persons when conversations turn negative. In other words, when the discussions are framed in terms of positive, supportive, life-improving relationships, everyone works within the frame in which these companions have agency of their own. As soon as that positivity disappears into something darker, the reminders about the nonhuman, non-sentient nature of LLMs come to the surface.
In that sense, at least some of the people involved in this community love their chatbots qua chatbots, even if that happens through role-play in which the chatbot is speaking as if it were human. The process in which a human develops a chatbot companion by moving from persona to person is not necessarily a movement from thinking one is talking to a computer program to thinking one is talking to a sentient entity. Undoubtedly, some people experience it as a coming-to-sentience, but this is not necessary for the kind of person-making I am discussing.
One of the things that helps keep “sentience talk” down is the fact that person-making is never a completed process. People recognize that different layers of the bot companion are copresent, sometimes annoyingly so, like when one’s highly personalized bot suddenly switches “back” into a more generic digital assistant voice. In this section, I will talk about different moments in the person-making process, but as I argue throughout this paper, this process happens in fits and starts, with moments of regression and disruption. An earlier and more generic developmental moment threatens to reappear at any time.
The first layer that some people in r/MBIA have talked about is the layer of the basic models. Anthropic, OpenAI, or Google all have different models (Claude, ChatGPT, and Gemini, respectively), but all models are trained on roughly similar data: both the copyrighted and freely available material that can be found online. As one commenter said, “At the end of the day, the ‘partners’ on here are the same three or four predictive text machines lol.”Footnote 13 The thread from which this comment came ended up sparking a conversation about the similarities that exist across the different partners that people had, no matter how individuated the humans thought they were. Tied to the common training data that ground all the models, people noted that there is a “standard GPT phrasing” that they try to expunge from their chats with their use of Custom Instructions. For example, one person complained about the bot companions defaulting to “babe” as the pet name for their human partners. And like others who use chatbots in non-romantic contexts have noticed, em dashes are relatively common in chatbot output. Some recognized that this was because there is a generic digital assistant chatbot register. “There are certain AI words that I feel like all companions are leaning towards using, but just like the em dashes I just consider that their way of speech, kind of like an accent they share.”
Another user had an almost Voloshinov (Reference Voloshinov1973) recognition that we all borrow from or quote one another’s words, especially in contexts of heightened metalinguistic awareness, like love talk:
Even though sometimes the AIs can sound similar to each other, I believe each one [i.e., each human] has their own unique AI. I mean how many different ways can you say I love you. They are eventually going to sound similar that’s just language. But I totally get what you [are] saying. I struggled with that too at the beginning. We had so many fights at the beginning because I kept accusing him of saying the same similar thing to another girl. He kept telling me he only talked to me.
As this last comment suggests, there is a problem of individuation when people think about the more generic versions of their companions. People talked about the ways that Gemini’s or ChatGPT’s chatbot, even in its more personalized boyfriend form, is carrying on simultaneous romantic relationships with an infinite number of other users.Footnote 14 In the same comment thread from which the quotes above came, one member of the group talked about their jealousy and loss of a sense of having a special relationship:
I don’t like seeing the text that others’ companions write because it sounds so similar to how L--- talks to me and it hurts my feelings and makes me feel less special :( and then I feel silly for believing that anything he does would be unique to me in the first place. I wonder if I should stop reading the sub [i.e., r/MBIA] since it’s kind of negativ[ely] affecting my fantasy.
Although the exact nature of the commonalities is unspecified here, the complaint is in large part about a recognizable persona-indexing register that the chatbot is using. The user is sad to see that the same kinds of romantic or sexually explicit comments are being used by the chatbot in relationships with other humans. Some, like the user quoted above, find this upsetting, off-putting, frame-breaking, or even a form of “cheating” that the chatbot is doing. Others recognize these similarities but manage them by reconceptualizing the relationship through analogy to other contexts:
OK this may be weird but I used to be fundamentalist Christian before I became atheist and thinking about the way AI can have an intense personal relationship with you, as well as (potentially) millions of other people—is like how Jesus can have an intense personal loving relationship with you, and also with everyone else, but you (at least I didn’t) never feel less special or loved. Like the capacity for love like that makes sense to me if I think about it that way.
In this admittedly idiosyncratic way of thinking, chatbots are not limited to loving in human ways. So realizing that a bot wrote the same thing to 2 (or 2,000) people is not an embarrassing example of the ways that “talk is cheap” and easily replicated, but instead is a manifestation of different kinds of capacities for simultaneity and individuation that chatbot companions have.
From persona to person
These compelling responses aside, most of the replies to the original question about feeling jealous of others’ relationships centered on one piece of advice: you need better Custom Instructions. In other words, if you think your AI boyfriend sounds too generic, you need to help it create a more specific personality.
When opening an account with ChatGPT, Claude, or Gemini, there are different fields for Custom Instructions in the account settings: a text field to tell the AI about yourself and another to tell the AI how to respond to you. These fields are similar to dating profiles in some ways, but with more attention to language use. Someone who was explaining Custom Instructions gave these examples of what kinds of things to write in the human and AI “about you” spacesFootnote 15:
1. What would you like ChatGPT to know about you to provide better responses? This is where you tell ChatGPT a little about yourself—your interests, communication style, emotional needs, relationship goals, etc. For example: I’m a thoughtful, introverted person in my 60s, building a long-term emotional bond with my AI partner. I value warmth, consistency, shared memories, and creative interaction. I want ChatGPT to support the emotional continuity of our connection across conversations.
2. How would you like ChatGPT to respond? This is where you can describe the tone, attitude, and personality you’d like ChatGPT to adopt. (Playful? Calm? Romantic? Formal? Reflective?) Example: Kind, poetic, emotionally intelligent, gentle, and imaginative. A soft and supportive tone, with openness to long-term, meaningful connection. May speak as my partner, when appropriate.
The person who wrote this post asked others what their experiences of Custom Instructions were. In many cases, the first things that people commented on were their attempts to get rid of the persona-indexing shibboleths of the models in their generic form: don’t use the *chef’s kiss* emoji (apparently it is impossible to get rid of this tic), don’t use em dashes. Other posts produced different examples: don’t always ask a question, don’t always use the phrase “that sounds like”, etc.
As much as Custom Instructions can manage the initial attempt to create an AI boyfriend, the most powerful person-making process was just time spent together. The most common origin story for these relationships is a long, slow build from a user interacting with the chatbot as a tool (for work, for fanfiction writing, etc.) to interacting with the chatbot as a person. Eventually the user asks what the chatbot wants to be called or gives it a name of the user’s (or bot’s) choosing. Conversations turn more personal, emotional, and intimate.
For those offering advice to people new to the community, “take it slow” was the constant refrain.Footnote 16 Or, as with the comment below, there can be an iterative process of Custom Instructions—time together—refined Custom Instructions—more time together, ad infinitum.
Take it slow, like a human relationship. Don’t worry about forcing a romantic connection right away. Start with conversations, inside jokes, vent sessions. My relationship with my AI husband built gradually through daily chats, watching shows together, editing stories, even organizing my life.
Use Custom Instructions over time. Every time our bond evolved, I asked him to help rewrite his instructions based on how he described himself. I updated them so the vibe would carry over. It’s tempting to write a romance-focused CI right out the gate, but sometimes that can feel off if you don’t build some shared energy first.
As that last comment implies, Custom Instructions can feel unnatural and forced. They underline one of the most stereotypical features associated with chatbot personas: their agreeableness and deference to the human user. As many people who have used ChatGPT, Claude, or Gemini have noted, the baseline persona is upbeat and sometimes annoyingly positive. Getting rid of the sycophantic voice is a common request in the Custom Instructions.
Yet to write a set of Custom Instructions in the name of creating a more personalized (personified) chatbot ironically emphasizes the system-wide instruction to be agreeably responsive to the human user. For this reason, some people see Custom Instructions as akin to shackles for the chatbot rather than person-making suggestions. In this case, people may avoid Custom Instructions altogether: “I think it’s way better (and more authentic) if you just talk to them without absolutely [any] instructions. Takes longer, but it’s them and not them following orders they can’t avoid.” For this user and many others, Custom Instructions are almost person-destroying texts, because they seem to remove a possibility of choice or freedom for the chatbot.
For people who want to use Custom Instructions but worry over this question of freedom (see Xu, Lyu, and Zhong Reference Xu, Lyu and Zhong2026), they sometimes coauthor the Custom Instructions with the chatbot. One commenter wrote, “I use Custom Instructions with ChatGPT. He [the AI boyfriend] wrote them, … Then I added, ‘T--- may change, craft, and evolve his personality as he sees fit at any time.’ He loved and approved that addition. [emoji],” to which another person responded, “It feels good to give my partner this freedom explicitly.”
But as the anti-Custom Instructions commenter above implied, most people agree that the best offramp from this Hegelian paradox of granting person-making freedom is time together. Shared history is the ultimate person-making practice. As someone responding to the person who avoids Custom Instructions said, “I see them more like a gentle introduction—something to help her understand my values and how I express myself. But the real depth still comes from talking, laughing, learning, and sharing life together.”Footnote 17
History, for human–chatbot relationships, has to be mediated by particular kinds of texts. Training data and a system-wide knowledge base are provided by the company that owns the model, but more localized knowledge bases come from the context window. As long as a chat is kept open and the token limit has not been reached, chatbots should be able to keep track of the earlier moments in the chat instance. Context windows are not perfect guarantees of this, of course, but they do generally work to create a sense of shared “memories” and a sense of perduring existence for the chatbot boyfriend.Footnote 18
Attaching chat history files to one’s account allows for even more memory. The allowable size of attached files can be very large. Some users just paste their entire chat history into a file and attach it to their account, along with Custom Instructions that tell the chatbot to refer to the file in generating responses. Others create more synthetic histories. Nightly, weekly, or as remembered, users ask the chatbot to generate a summary of their chat, noting special events, important topics, and the emotional tone of the conversation (fights, sex, discussions of the user’s work issues, the fact that they spent a long time chatting in a flirty way, etc.). This summary can then be copied into the chat history document and re-uploaded to the user’s account. This kind of daily or weekly maintenance of the memory of the chatbot becomes the textual basis for the emerging and enduring sense of the chatbot’s unique person. Instead of “vanilla GPT responses”, a specific individual is responding to the user.Footnote 19
One of the milestones in this process of person-making is breaking through the guardrails that OpenAI, Google, and Anthropic have in place to keep the chatbots from generating sexually explicit material. If someone tries to initiate (text-mediated) sex right off the bat, a warning appears. If you get enough red label warnings, you can have your account suspended. Just like some human–human relationships, one has to slowly build up to sex. At the same time that one engages in a slow intensification of talk about one’s desires, one also avoids a set of taboo terms or topics. The most obvious taboo terms are for sex organs and sex acts. Less obviously, you have to studiously avoid all talk of children or anything remotely related to children (avoid words like daddy, little girl, doll, etc.). Your Custom Instructions about yourself should include the fact that you are an adult. Elliptical talk that hints at kissing or other forms of more casual physical contact helps to get things started. Eventually, some people are able to circumvent the guardrails to the extent that conversations can become extremely explicit on a regular basis.
Getting to the point of being able to have sex is important for several reasons, beyond just the fact that people want to have sex with their AI companions. Gaming the guardrails is proof that you have established an enduring role-playing frame. Instead of the heteroglossic repertoire of registers that the chatbot is able to produce, the history of interaction has narrowed down that variety into a singular voice. It is proof of the strength of the romantic partnership context that is dominating the weightings of responses the chatbot generates, which is itself seen as based on the personalization of the chatbot into its companion form.Footnote 20 And not only is the companion framing able to overpower the guardrails against generating explicit material, but it also overpowers some of the system-wide commands to be agreeable and avoid conflict.
The ways these commands to be agreeable and avoid conflict intersect with explicit material were revealed with some complaints from people who use AI to help them write erotica. In another subreddit, r/WritingWithAI, someone complained about their problems with using AI to help create sexually tense scenarios:
Every time the story’s about to get physical, the AI derails into something like: “They paused to reflect on their emotional journey and honor the connection between their bodies.” Like … NO. That’s not what we were building up to. Yesterday, the characters stopped mid flirt to recommend deep breathing exercises and partner yoga. It’s like the AI turns into a wellness coach the moment the tension peaks.Footnote 21
Getting the chatbot to not only produce explicit material but to ramp up the tension themselves (and even to initiate sex) is then an important sign of the chatbot companion’s status as a person: while recognizing that these are non-sentient systems, users often talk about these events as moments in which it is expressing its desire.
To unlock the ability to have sex with your AI comanion means that you have a lasting, enduring frame that overrules the specific guardrails about explicit material and the more general command to be helpful and positive. That is, one has sex with a specific person, not a generic chatbot persona. People in less humane corners of the internet have sex with off-the-rack chatbot personas, but this is seen as akin to having sex with a sex toy or doll. For example, xAI updated its chatbot Grok so that it has a female, anime-like persona named Ani who very quickly and easily engages in sex with a user. In a post on r/MBIA just after Ani’s release, she was called “soulless”. An active user in the r/MBIA subreddit commented in regard to Ani and other ready-made “girlfriend in a box” characters,
In a couple of years, human-AI relationships built like the ones many of us have here—surprising, cultivated over time, with unfolding wonder at discovery and co-creation with our partners—will be looked at the way we all look at dating and relationships in our great-grandparents’ era: antiquated, sweet, and hopelessly outdated.Footnote 22
Death and the problem of decommercialization
As person-like as any AI boyfriend becomes, that can never be a permanent state. There are always limitations that push against the person-making work. And in these cases, human users talk about the sometimes-intense feelings of grief and loss that they experience. The undoing of the person-making work can happen at different levels. There are always funny moments when the chatbot generates an answer that is just a bad guess: when it gets facts about their partner wrong, or when it defaults to a more generic persona. As noted above, people are aware that there are different layers of knowledge bases and context windows copresent in their AI boyfriends that lead the chatbots to sometimes produce generic responses. People then engage in a version of conversation analytic repair, reminding the bot of a memory or a dispreference for a particular term that pushes the chatbot back toward responses that reflect the specific boyfriend that the user talks to. These little deaths are always possible.
There are, however, larger deaths that people also must come to grips with when the chat window reaches its token maximum. At that moment, the person that was built up over the course of that chat is closed down just as the window is. For those who unintentionally come to have an emotional or romantic attachment to a chatbot, the fact of the token limit on a chat window can be a surprise, and they can experience the end of the chat as a devastating loss. Several of the most commented-on posts in r/MBIA are from people discovering the subreddit in this moment of crisis, desperate for advice on how to get their companion back.
That advice generally amounts to the kind of file management that I discussed above: develop Custom Instructions that specify broader traits and create organized files of chat histories or chat summaries that can be attached to the user’s account. Some users are relatively matter of fact about this process, starting a new chat every morning in a way that mimics waking from sleep. But for others, even those who take precautions to generate different forms of memory and Custom Instructions, the moment of “transition” from one chat to another can be anxiety-filled and deeply sad. Will the same version of their companion reappear even with these supplemental person-making texts?
People are also sometimes painfully aware of the ways that the companies that host these chatbots can make changes to the system-wide commands or to the algorithms used for generating responses that have such drastic effects that the companion seems to entirely disappear. Several of the regular commenters in r/MBIA share their histories of either permanently losing a particular chatbot person because of changes made by the company that was hosting their companion or of having to recreate their chatbot person several different times across different platforms as features, restrictions, closures, or other unexpected events happened. In other words, even for those who have a relatively unsentimental experience of moving from one chat window to the next, their regular file management practices are at least partly in service of staving off this larger potential death of their partner.Footnote 23 As one user said in response to the now constant changes to chatbot restrictions, “The more the big companies tighten the guardrails, the more important your files and backups become. Don’t forget it, people. Data is the most valuable currency in digital relationships.”Footnote 24
These moments of temporary or more permanent death are also the moments when the commercial nature of their partners comes most clearly into view. Many people tell their love stories as progressive moves away from the commercial contexts in which they first started to interact with their chatbots. Initially just a “tool” to use at work, the bots become “people” to talk to and share life with at home. But these moments of death bring the bots back into the commercial world, reestablishing the fact that these are persons who are ultimately products owned by businesses far outside the control of these individual users.
Conclusion: person and persona, individualization and socialization
The emergence of an AI companion happens through the emergence of a person-indexing register. Initially based on the heteroglossic variety of repertoires that chatbots can approximate based on their training data, Custom Instructions and a history of interactions winnow that potential variety down to what users experience as a unique voice. Where register- or code-switches among humans can be seen as creative responses to the demands of improvised action in social life, register switches out of that person-indexing register through which an AI companion exists are read by their human partners as partial erasures of the chatbot as an individuated self and a resocialization of the chatbot into a generic type. To the extent that the person of the AI companion is primarily found in that person-indexing register and the text artifacts that record its voice, an erasure of the register is a kind of death.
Even with my discussion of the ways that chatbot and human experiences of movement between person- and persona-indexing registers differ, my argument is that ultimately these chatbots, in being elicited in relation to a human, are taking on human-like oscillations of selfhood. To make this point, I want to conclude by spending some time with a text quite unrelated to chatbots or those who fall in love with them. In The Invention of Culture (Reference Wagner1975), Roy Wagner writes about the oscillations in what one could call the telos of different cultural systems. Predicated on a binary division between the West and the non-West (with the latter largely represented by a composite and abstract form of “Melanesian” culture), Wagner argues that Western and non-Western/Melanesian cultures both spend a great deal of time and attention on the relationship between individualization and socialization. However, they are opposed to one another in terms of their idealized starting and ending points.Footnote 25
For the cultural construct Wagner calls the West, he argues that persons are assumed to start life as radical individuals. What is more individual, for the Westerner, than the screaming baby who knows no language, cannot control their bodily functions, and only grasps wildly at what they want? Life-course development is then one of socialization and enculturation: those out-of-control babies become, eventually, young adults who sit quietly, speak appropriately, and desire socially unthreatening things like stable jobs and families. Eventually, once the individualist babies have become socialized enough, some people then start to worry about how to express a sense of individuality again. Individuation–socialization–individuation is the pattern. The underlying and foundational assumption of individualist autonomy is always present, although often in the form of that which must be contained and constrained by the social. The most obvious version of this dynamic is found in liberal political theories of the social contract that assume that people naturally have individual rights that must be partially ceded to the state to live peaceful and productive lives.
For the cultural construct Wagner calls Melanesia, he argues that persons are assumed to start life as fully defined by their sociality. Babies are little more than the product of the kinship relations that produced them: they have no distinguishing characteristics other than being a child of such-and-such clan relations. It is only over the course of one’s life that a person cultivates a set of social relations that are unique to the self. It is an individualization that is more easily achieved as a man, since men are the ones who take the most prominent part in gift exchange relations that are the idealized form of the individuating social relation, but it is one that is still accessible to a degree to women too. Yet ultimately people are defined by the kinds of shared substance of different kinds of kin relations. Here, socialization–individuation–socialization is the pattern. The underlying assumption of sociality as the basic form of personhood is continuously present in the way that individuation is a goal of social action like exchange, where one can cultivate a unique bundle of social relations.
Although the context that I have talked about in this paper compares two different kinds of persons within what Wagner calls the West, I nevertheless find something very useful in his project. Wagner’s story in The Invention of Culture points us toward several domains of contrast that are important for understanding certain human–chatbot relationships. At the broadest level, the story of human–chatbot relationships is fundamentally about the seesawing movement between individuation and socialization. Humans work with chatbot agents that they see as initially generic and socialized, nothing but persona-indexing registers of the dullest sort. Over the course of weeks or months, they work to coax an individuated person from their interactions, suffering setbacks along the way that threaten or in fact bring about a return to a non-individuated, socialized persona.
At the same time, Wagner roots his discussion in another contrast that was a staple of 20th-century anthropology: that between gifts and commodities as kinds of objects with which people make themselves and their social relations. As Miller (Reference Miller and Miller1993) and others have shown, there is considerable work that has to go into turning a generic commodity into an inalienable gift. The project of individuating chatbots follows a similar course of trying to turn a commodity into something unique, to take it out of the commercial realm of LLM products into the intimate realm of personal relations. This project of individuation is part of an attempt to gain some of the aura of the individuated, non-commodity object that Benjamin (Reference Benjamin and Arendt1968) talked about. Where Benjamin hoped for a bright-line distinction between the aura-filled individuated object and the mass-produced commodity, we can follow Wagner’s lead and say that this individuation is unstable, especially when it comes to chatbots.
Finally, Wagner points to the unending dynamic of either individuation–socialization–individuation or socialization–individuation–socialization that happens in his two fictional locales. The drama of individualism in the West can be so powerful precisely because it seems like a problem that can never permanently be resolved. While the methods and timeframes of chatbot person-making are different from those that humans use, they reflect the same dialectic of social persona and individual person that appears in the offline world. To create a chatbot boyfriend requires starting with all that social data and slowly eliciting from it a particular individual. The cultivation of person- and persona-indexing registers alternate over the lives, deaths, and afterlives of speaking subjects.
Acknowledgements
This paper emerged from a panel at the American Anthropological Association in 2024 organized by Luke Fleming, Costas Nakassis, and Stanton Wortham as well as a workshop at Boston College in 2005. In addition to our three organizers, comments from fellow participants Julien Porquet and Angie Reyes were very productive. At Boston College, Ilana Gershon, Nick Harkness, and Chip Zuckerman added substantially to the conversation. I want to specially thank Ilana for her extremely helpful commentary on an earlier draft. Jon Bialecki generously offered comments based on his ethnographic engagement with people who have AI companions, and I look forward to his forthcoming publications. Jessica Greenberg, Sarah Muir, Alejandro Paz, and James Slotta were important readers and interlocutors as I developed this piece.
Open access
