Research report · Published by MyDreamCompanion
The State of AI Companionship 2026
How many people use AI girlfriends, boyfriends and virtual partners? The answer is at least 4 different numbers, each measuring something else. This report shows what each one means, who uses AI companions and why, what the studies say about wellbeing, and what the public evidence cannot yet answer.
One question, 4 answers
How many people use AI companions? It depends on what you ask, who you ask and how.
use AI chatbots for companionship
Pew asked chatbot users. That is 4% of all U.S. adults, or 8% of chatbot users · probability panel · 17–23 Feb 2026
Representative survey · Pew topline (PDF) · exact question and sample ↓
used any AI for an undefined “companionship” purpose, at any listed frequency
U.S. adults · probability panel · 10–14 Jul 2025
Representative survey · AP-NORC topline (PDF) · exact question and sample ↓
had ever chatted with, or downloaded, an AI system meant to simulate a romantic partner
Respondents in a U.S. quota sample, n=2,969 · same-author Wheatley Institute report, not peer reviewed · fieldwork dates not reported
Quota sample · Wheatley report (PDF) · exact question and sample ↓
reported intimate conversations or interactions with an AI in the past year (the survey item asks about erotic or sexual communication with an AI)
Respondents in a German opt-in quota panel, adults 18–75 · Nov–Dec 2024
Peer-reviewed · Döring et al., journal article · exact question and sample ↓
These 4 figures are not directly comparable. They measure different behaviors, among different people, over different periods, so they cannot be added, averaged or ranked, and they are not a trend. Of the 4, 2 come from probability panels and 2 from quota samples. In a probability panel, people are chosen at random from a known population, so results can support population estimates (low response rates remain a limit). In a quota sample, volunteers are recruited until age, sex and similar targets are filled, so results describe the people in the sample, not national rates. Each dot grid shows the rounded share of 100 people in its own sample.
Bottom line
What the public evidence says
- How many? There is no single number: the 4 figures above answer 4 different questions. We found no national probability estimate for AI girlfriend or boyfriend use, and evidence on AI boyfriends is limited to 1 community analysis and 2 small interview studies.
- Who? Younger adults report more use than older adults in a U.S. survey. Men report somewhat more than women, but that gap is only a hint.
- Why? People describe support, availability, play, fantasy and intimacy. No single motive is established, and loneliness is not shown to be the main cause.
- Does it help? A short chat can lower reported loneliness for a moment. Lasting benefit is unproven, and heavier use goes with worse outcomes in some studies, though it is unclear which comes first.
- What is unknown? How long the average user spends, what people pay and how large the market is cannot be answered for the population from the public evidence we found. One quota sample reports a self-reported average of 51 minutes a week among people who had chatted with an AI romantic partner (Section 8).
- Safety. Teens and non-consensual imagery are kept apart from adult findings. Rules exist; we found no outcome evaluation showing whether they work.
Section 0 · Opening · Key findings
6 findings that stand out
These 6 results come from across the report. Each keeps its evidence label and links to the evidence behind it.
Reported AI companionship use is 17.9 points higher at ages 18–29 than at 65 and older
In one U.S. survey, 24.6% of adults aged 18–29 and 6.7% of those 65 and older said they use AI for “companionship”, a word the survey did not define. These are weighted shares, not adjusted for anything else. Men reported more use than women (18.8% vs 14.1%), but about 100 people in each group reported any use and the gap was not tested, so treat it as a hint.
The U.S. divorce rate was lower in 2024 than in 2008
Divorces per 1,000 married women were 20.5 in 2008 and 14.2 in 2024 (NCFMR analysis of American Community Survey data). This is social context only: the series does not measure AI use.
A 15-minute companion chat was followed by lower reported loneliness. Lasting benefit is unproven
In a 1-week randomized experiment, the average score on a 3-item loneliness scale (a lower score means less loneliness) was 36.64 before and 30.74 after daily 15-minute chats with an AI companion (314 people in the companion group completed the study; a small change, d=0.20). The control group completed only a short survey, and the study does not show a lasting benefit.
Few named companionship as their main purpose, yet about half described the chatbot in companion terms
Among 1,131 U.S. adult Character.AI users recruited on Prolific, 11.8% chose companionship as their main use, and 51.0% used companion terms when describing their relationship with the chatbot.
Adult AI is several different behaviors, not one
In one German quota sample, 14.1% reported intimate conversations or interactions with an AI, 20.2% viewing AI-generated 18+ material and 11.3% creating it, over the past 12 months.
About 6% reported daily-or-more companionship use of AI. Average time and spending are not known for the population
Adding AP-NORC’s 3 most frequent answers (“almost constantly”, “several times a day”, “about once a day”; rounded) gives about 6% of U.S. adults. We found no representative public estimate of average time, retention or spending (Section 8 reports one self-reported figure from a quota sample).
Explore the research
12 sections, 4 themes. Start where your question is.
Each section lists the question it answers. Open a preview for the short answer, or go straight to the section.
What it is, and who uses it
What counts as an AI companion, how everyday AI use differs, and who the users are.
From AI to Conversation
How does everyday AI use relate to AI companionship?
Go to section 1 →Everyday AI use is common. Companionship is a much narrower purpose, and none of the surveys here follows people from one to the other.
Inside: AP-NORC chart of information search vs companionship · Pew 49% vs 4%
What Counts as an AI Companion
What do studies mean by an AI companion, a romantic partner and adult AI?
Go to section 2 →People use “AI companionship” for 5 different things, from ordinary chatbot use to AI-generated 18+ imagery, and each is measured differently.
Inside: construct map · terms · adult AI behaviors chart (consenting adults only)
Who Uses Them
Who is most likely to use AI companions and virtual partners?
Go to section 3 →Younger adults report more use in both studies here, and men somewhat more than women, though the gender gap is only a hint. The U.S. data cover legal marital status but not current partnership status, and neither study can tell us about country or device.
Inside: MDC analysis of AP-NORC by age, gender and marital status · German age and gender chart
Why, and what it means
Motives, what people build, what studies say about wellbeing and the real-life context.
Why People Use AI Partners
Why do people form relationships or intimate interactions with AI?
Go to section 4 →No single reason. People describe support, availability, play, fantasy and intimacy, and some did not set out to form a relationship.
Inside: evidence-strength table for 11 motives and pathways
Personalization and What People Build
What do people customize, and is there an AI-girlfriend versus AI-boyfriend split?
Go to section 5 →Products document many customization controls, but the studies here cannot say what people choose. Evidence on AI boyfriends is thin: 1 community analysis and 2 small interview studies.
Inside: Character.AI bot-description chart (bots, not users) · what can be customized · what is known about AI boyfriends
Does It Help or Harm?
Do AI companions make people feel better, worse or both?
Go to section 6 →Both patterns appear, at different time scales. Lasting benefit is unproven.
Inside: study-design ladder from short experiments to community posts · what we can say now
Human Relationships: Myth vs Data
What does the evidence say about marriage, divorce, living arrangements and loneliness?
Go to section 7 →Public data on divorce, living alone and loneliness do not show that they explain AI-companion use. Divorce rates were lower in 2024 than in the first year shown (2008 or 2000, depending on the measure), loneliness shows no steady rise, and more one-person households say nothing about AI use.
Inside: 5 ideas checked against public data · divorce and loneliness charts · context only
The market and the outlook
What is and is not measured about use, time and money, and where MDC offers opinion.
The Market: What Is and Isn’t Measured
What is known about how often people use AI companions, how long they spend and what they pay?
Go to section 8 →Representative evidence shows how often adults report use, not how long they spend or what they pay; one quota sample adds a self-reported weekly time for people who chatted with an AI romantic partner. We found no market size we could verify, and this report publishes none.
Inside: a 100-adult frequency chart · what is measured and what is unknown
Outlook
Which product changes are visible, and which statements are opinion?
Go to section 9 →Some product signals are visible, but adoption and effects were not measured in the evidence reviewed. The outlook is MDC opinion only, with no numerical forecast.
Inside: MDC opinion, labelled · what the evidence supports for each signal
Rules, limits and evidence
Safety and boundaries, open questions, and how to check our work.
Safety and Boundaries
Which boundaries rest on evidence, and how are minors and non-consensual imagery kept apart from adult findings?
Go to section 10 →Rules exist; we found no evaluation of whether they work. Adults, minors and non-consensual imagery are kept apart.
Inside: minors · non-consensual imagery · official records · what is permitted, restricted and measured
Open Questions
What can the public evidence not yet answer?
Go to section 11 →The main gaps, collected from the sections above, each with the reason it is open.
Inside: 9 groups of open questions, each linked to its section
Methodology, Disclosure, Sources, How to Cite and FAQ
How was this built, and what should readers know before relying on it?
Go to section 12 →Every key figure links to its source, and the page says where that source is a summary rather than the original paper.
Inside: how the evidence was selected · labels · MDC analysis method · disclosure · access limits · how to cite · FAQ · sources
Section 0 · Opening
The 4 measures in detail
Open a figure to see the question (exact wording where accessible), the people asked, the sample and the period.
Pew’s narrower question gives 4% and AP-NORC’s broader one gives 16%: the gap reflects wording, not a decline in use. The 19% and the 14.1% come from quota samples, so they describe those samples, not national rates, and should not be used to rank the United States and Germany.
Not found in this selection: a probability-sample national estimate for AI girlfriend or boyfriend use as separate categories, and any figure for AI-generated 18+ video alone. Reddit posts, platform logs, downloads, visits and search interest are not used as prevalence estimates (see “How the evidence was selected” in Section 12).
Plain-English guide to the terms used in the figures
- Probability panel
- Respondents are recruited by random selection from a known population, so results can support population estimates. A low response rate remains a limit even after weighting.
- Quota sample
- Volunteers are recruited until age, sex and similar targets are filled. Results describe the people surveyed, not the whole country.
- Weighted share
- Responses are adjusted so the sample matches the population’s age, sex and similar makeup.
- Denominator
- The group a percentage is calculated from, for example all adults or only chatbot users.
- Preprint
- A research manuscript shared before, or without, peer review. This report uses the label when the version it checked was a preprint; where a journal version now exists, the page says so.
- Randomized experiment
- People are assigned to conditions by chance, the strongest design for showing cause and effect.
- Observational study
- Researchers record what people do or report without assigning them. It can show associations, not causes.
- AP-NORC
- The Associated Press-NORC Center for Public Affairs Research. NORC is at the University of Chicago; AmeriSpeak is NORC’s survey panel.
- Wheatley Institute, IFS
- The Wheatley Institute is a research institute at Brigham Young University; IFS is the Institute for Family Studies, which published a blog summary of the Wheatley report.
- NCFMR, CDC/NCHS
- The National Center for Family & Marriage Research (Bowling Green State University) and the U.S. Centers for Disease Control and Prevention’s National Center for Health Statistics: sources of the divorce-rate tables in Section 7.
- d, n
- n is the number of people or items counted. d is an effect size: how large a difference is, in standard-deviation units; 0.20 is conventionally called small.
The 4 definitions in full
4%Pew Research CenterExact question, sample and period
Whether people who use AI chatbots ever use them “for companionship”. Pew also reports the result on an all-adult base.
- Question asked
- Asked of chatbot users: “Do you ever use artificial intelligence (AI) chatbots for the following?” Item: “For companionship.” (Chatbot use overall: “Do you ever use an artificial intelligence (AI) chatbot like ChatGPT, Gemini or Copilot?” 49%.)
- Population
- U.S. adults 18+; n=5,119 (2,605 chatbot users)
- Sample
- Probability panel (American Trends Panel). Margin of error ±1.6 points overall; the topline gives no response rate
- Period
- 17–23 February 2026
- Reads as
- 4% of all adults; 8% of chatbot users. Not romantic-partner use
Representative survey · Pew topline
16%AP-NORCExact question, sample and period
How often people use any AI for “companionship”; any frequency from almost constantly to less often.
- Question asked
- “About how often do you use artificial intelligence (AI) for each of the following?” Item: “Companionship.” Companionship was not defined and no product or relationship type was named
- Population
- U.S. adults 18+; n=1,437
- Sample
- Probability panel (AmeriSpeak). Margin of error ±3.6 points overall. Cumulative response rate 2.7% (final-stage completion 13.2%), corrected by weighting
- Period
- 10–14 July 2025
- Reads as
- Any listed frequency, any AI. Not a dedicated companion app
Representative survey · AP-NORC topline · methodology · project page and data
19%Willoughby and colleagues, Wheatley Institute reportExact question, sample and period
Whether respondents had ever chatted with, or downloaded, an AI system meant to simulate a romantic partner.
- Question asked
- “Have you ever chatted online with an AI system or downloaded an AI chat app that was meant to simulate a romantic partner?” (item wording as printed in the Wheatley report, which does not list the answer options; the journal questionnaire was not accessible to this report)
- Population
- U.S. adults 18+; n=2,969
- Sample
- Online quota sample run by Qualtrics (quotas on sex, age and race/ethnicity); n=2,969 includes an added oversample of 1,000 adults aged 18–29, and the report does not describe weighting. The 19% comes from the authors’ Wheatley Institute report (not peer reviewed); the study itself is peer reviewed
- Period
- Fieldwork dates not reported
- Reads as
- Ever, within the sample. Not a national rate
Quota sample · Wheatley report (PDF) · IFS blog summary
14.1%Döring, Mikhailova and MohseniExact question, sample and period
Intimate conversations or interactions with an AI: the survey item asks about erotic or sexual communication with an AI, such as an adult chatbot or a virtual girlfriend/boyfriend app.
- Question asked
- “In the past 12 months, how often have you engaged in erotic or sexual communication with an AI (e.g., with an adult chatbot, a Replica, or a virtual girlfriend/boyfriend app)?”
- Population
- Adults 18–75 in Germany; n=2,658 (375 reporting)
- Sample
- Opt-in online quota panel (Bilendi). 99% confidence interval 12.6% to 15.6%
- Period
- 11 Nov–3 Dec 2024; past 12 months
- Reads as
- Past year, within the sample. Not a national rate
Peer-reviewed · Döring et al., Archives of Sexual Behavior: article · open-access PDF
Quote it correctly
Pick a figure to get a sentence that keeps its behavior, people, sample and period.
The Willoughby 19% comes from the authors’ own Wheatley Institute report, which is not peer reviewed. The journal article was not accessible to this report, and fieldwork dates and funding are unconfirmed. The report’s age and sex breakdowns are left out because they could not be checked against the journal article.
Why this research, and how to use it
Numbers about AI companionship are easy to misread without their definitions.
Published by MyDreamCompanion, a company in the AI-companion category. Public original sources only, no private company data, no product ranking.
- Read
Each section opens with a short answer. Start there, then go as deep as you like.
- Check
Each key figure shows who was asked, how, and an evidence label or source type, with its period where the source gives one. The main limits sit beside the number.
- Cite
Cite the original source shown beside a statistic and keep its context. The quote builder above writes a correct sentence for each headline figure, and Section 12 has the citation for this report.
What it is, and who uses it
From AI to Conversation#
How does everyday AI use relate to AI companionship?
Everyday AI use is common. Companionship is a much narrower purpose, and none of the surveys here follows people from one to the other.
Using AI for tasks is far more common than using it for companionship. The AP-NORC chart below compares 2 items from one survey. In Pew’s February 2026 survey, 49% of U.S. adults said they ever use an AI chatbot, and 4% of all adults use chatbots for companionship. These are broad measures of everyday use, not evidence of a personal relationship with AI, and none of the surveys here follows people from using an assistant to having an AI partner.
Figure 1 · Representative survey#
More U.S. adults report using AI to search for information than for companionship
2 items from AP-NORC’s AI-use grid · any listed frequency, from almost constantly to less often · U.S. adults 18+ · 10–14 July 2025 · n=1,437
Of every 100 U.S. adults asked, 60 said they use AI to search for information at least occasionally and 16 said they use AI for “companionship” at least occasionally.
Companionship is a much narrower use than information search, and the survey never defined the word. The 16% does not mean 16% of adults use a companion app or have an AI partner.
View the data as a table
| Purpose in AP-NORC’s AI-use grid | Almost constantly | Several times a day | About once a day | Several times a week | Less often | Any listed frequency |
|---|---|---|---|---|---|---|
| Searching for information | 5% | 9% | 8% | 19% | 19% | 60% |
| Companionship | 2% | 1% | 3% | 3% | 7% | 16% |
Purposes can overlap. The 4% (Pew, a narrower chatbot-purpose item) and the 16% are not a trend: different surveys, wording and scopes.
Sources: AP-NORC topline · methodology · project page and data · Pew topline
What it is, and who uses it
What Counts as an AI Companion#
What do studies mean by an AI companion, a romantic partner and adult AI?
People use “AI companionship” for 5 different things, from ordinary chatbot use to AI-generated 18+ imagery, and each is measured differently.
People use “AI companionship” for very different things: chatting with a general chatbot, using an app built to act as a partner, or having intimate conversations with an AI. A person may move between these, stay with one, or use several. Surveys measure each separately, so their numbers cannot be added together, and no study shows a standard path from one to the next.
The surveys do not separately identify dedicated companion chat services. One example of such a service is MyDreamCompanion’s own chat experience (the publisher’s product, not a source for any finding).
5 different things that get called “AI companionship”
Definitions#
A construct map, not a funnel
Each row is a different thing a survey can measure. A number from one row cannot stand in for another.
| Construct | What qualifies | Example public measure | What it must not be called |
|---|---|---|---|
| Ordinary AI or chatbot use | Any interaction with AI or chatbot tools, often for tasks | Pew “ever use”; AP-NORC purpose grid | AI companionship |
| Broad companionship use | Any AI or chatbot used “for companionship” | AP-NORC; Pew | A dedicated AI girlfriend/boyfriend account; romance; intimate use |
| Romantic or virtual-partner use | An item says the system simulates a romantic partner or AI girlfriend/boyfriend | Willoughby (Wheatley Institute report) | Broad companionship prevalence; a probability-based national rate |
| Intimate AI interaction | An item measures intimate conversations or interactions with an AI agent | Döring et al. | All romantic use; all AI-generated 18+ material use |
| AI-generated 18+ imagery | Viewing or creating synthetic 18+ imagery, video or audio, by study definition | Döring et al. | Intimate chat; a separate video prevalence unless video is measured separately |
An MDC synthesis of public measures. It is not original fieldwork and does not claim that users progress from one row to the next: no cited study follows a representative cohort through these categories, and the categories overlap.
Definitions of the terms used in this report
Definitions#
Terms used in this report
| Term | Meaning in this report | Boundary |
|---|---|---|
| AI used for companionship | A survey respondent reports using any AI for a companionship purpose | May include a general assistant; not automatically a dedicated companion, romance or intimate use |
| Chatbot used for companionship | A chatbot user reports companionship as a purpose | The denominator may be chatbot users or all adults. Both must be shown. |
| AI companion | A system used in an ongoing social, supportive or relational role | Defined by the source or report context; not one universal survey construct |
| Virtual or romantic partner | A source explicitly describes an AI girlfriend, boyfriend, spouse, romantic companion or a system meant to simulate a romantic partner | Must not be inferred from broad companionship alone |
| Intimate AI | Consenting-adult intimate conversations, interactions or roleplay with AI | Kept separate from romantic use, 18+ media, minors and non-consensual content |
| AI-generated 18+ material | Synthetic adult imagery, video or audio as defined by the source | Combined-media items cannot give image-only or video-only prevalence |
| Product capability | A feature or mode documented by a provider or dated walkthrough | An availability claim, not adoption, preference, quality or effect |
Each quantitative claim keeps its source’s own construct, population, denominator, geography, period and method. This is not a general “State of AI” report.
Adult AI: intimate conversations, roleplay and AI-generated 18+ imagery
“Adult AI” is not one behavior. It can mean intimate conversation, romantic roleplay, viewing AI-generated 18+ imagery, creating it, or using video. Each behavior is measured with a different question, and most are covered by only one survey (in Germany) or by one platform’s chat logs.
The clearest data come from one German survey (Figure 2). A U.S. study asked something different: whether people had ever chatted with or downloaded an AI meant to simulate a romantic partner (the 19% in the opening figures). Chat logs are a third kind of evidence: 68.0% of 4,664 donated Character.AI sessions from 237 selected users were labelled romantic or intimate roleplay. That describes what was in those chats, not how many adults use AI this way.
Some companion services also offer image and video generation, for example MyDreamCompanion’s image generation tool and video generation tool (both the publisher’s own). No figure here measures their use.
Figure 2 · Peer-reviewed#
Intimate conversation, viewing and creating are different adult-AI behaviors
Germany · adults 18–75 · n=2,658 · opt-in quota panel · past 12 months · fieldwork 11 Nov–3 Dec 2024 · “intimate conversation” is the survey’s item on erotic or sexual communication with an AI
In one German survey, 14.1% of respondents reported intimate conversations with an AI in the past year, 20.2% reported viewing AI-generated 18+ material and 11.3% reported creating it. 31.6% reported at least one of 6 AI-supported sexual activities, a composite that includes sexual information and counseling.
Adult AI is several different behaviors, so a single “adult AI” number would mislead. These percentages describe the people in the survey sample, not Germany as a whole.
View the data as a table
| Behavior (past 12 months) | Share of respondents |
|---|---|
| Intimate conversations or interactions with an AI | 14.1% |
| Viewing AI-generated 18+ material | 20.2% |
| Creating AI-generated 18+ material | 11.3% |
| Any of 6 AI-supported intimate activities (composite) | 31.6% |
Categories overlap, and the 31.6% composite covers 6 activities, including information and counseling. These are sample percentages, not national rates. “Viewing” covers synthetic images, video or audio combined, so this survey gives no video-only figure.
Sources: Döring et al., Archives of Sexual Behavior: article · open-access PDF
Evidence table#
Behavior and available evidence
One row per behavior. “Unit” shows what was counted: respondents and sessions are never combined.
| Behavior | Best verified measure | Unit | What remains unknown |
|---|---|---|---|
| Romantic-partner simulation | Willoughby, Wheatley report: 19% ever, U.S. quota sample | Respondents | Field date, subgroup bases, probability-based national prevalence, duration and depth |
| Intimate conversation / interaction | Döring: 14.1% past year, German quota sample | Respondents | Romantic vs non-romantic context; probability-sample precision |
| Intimate roleplay | Zhang: 68.0% of donated Character.AI sessions | Sessions | General-user prevalence; users per session type; population incidence |
| Viewing AI-generated 18+ material | Döring: 20.2% past year (images, video or audio) | Respondents | Image-only vs video-only prevalence; frequency and spend |
| Creating AI-generated 18+ material | Döring: 11.3% past year | Respondents | Sharing, platform, frequency, payment and legality |
| AI-generated 18+ video alone | No verified standalone estimate found | — | All prevalence dimensions |
Platform sessions describe content in selected chats and cannot estimate how many adults use intimate roleplay.
Sources: Peer-reviewed Döring, Mikhailova & Mohseni: article · open-access PDF Quota sample Willoughby, Carroll, Dover & Hakala, Wheatley Institute: Wheatley report (PDF) · IFS blog summary Peer-reviewed Zhang et al.: Nature Human Behaviour · open manuscript
What it is, and who uses it
Who Uses Them#
Who is most likely to use AI companions and virtual partners?
Younger adults report more use in both studies here, and men somewhat more than women, though the gender gap is only a hint. The U.S. data cover legal marital status but not current partnership status, and neither study can tell us about country or device.
What the U.S. survey shows
In AP-NORC’s survey, reported use of AI for companionship is lower in each older age group (Figure 3). Men reported more use than women (18.8% vs 14.1%), but only about 100 people in each group reported any use and the gap was not tested for statistical significance, so treat it as a hint, not a finding.
How to read the bars. Each bar is the share of everyone in the group, including people who answered “never”. Under each label, n is how many people were asked and “reported use” is how many of them said they use AI for companionship (raw counts). Small counts mean less precise percentages. Hover or tap a bar for exact values.
Figure 3 · MDC analysis#
Reported AI companionship use is lower in each older age group in one U.S. probability survey
U.S. adults 18+ · any listed frequency of AI use for an undefined “companionship” · weighted share of all respondents in each group · 10–14 July 2025
The share of adults who said they use AI for “companionship” at least occasionally falls from 24.6% at ages 18–29 to 6.7% at 65 and older.
Age is the clearest pattern in these data. It describes who says they use AI for an undefined “companionship” purpose, not who has an AI partner, and it is not adjusted for anything else.
View the data as a table
| Age group | Weighted share reporting any use | Respondents asked (unweighted) | Of whom reported any use (unweighted) |
|---|---|---|---|
| 18–29 | 24.6% | 386 | 90 |
| 30–39 | 21.6% | 183 | 36 |
| 40–59 | 16.7% | 341 | 48 |
| 60–64 | 11.4% | 125 | 11 |
| 65+ | 6.7% | 402 | 23 |
Overall margin of error ±3.6 points (±6.6 for ages 18–29); no subgroup significance tests were run.
Sources: AP-NORC data and codebook
Figure 4 · MDC analysis#
Gender and legal marital status in the same item
Same survey, item and weighting as Figure 3
Weighted shares reporting any use: men 18.8% and women 14.1%; married adults 12.1%, divorced adults 17.4% and never-married adults 22.5%.
Marital status here is legal status, and the file has no current-partner variable. “Never married” does not mean single, so this does not show that single people use AI companions more. The figures are not adjusted for age, and the male–female gap is indicative only.
Gender (released categories)
Legal marital status
View the data as a table
| Group | Weighted share reporting any use | Respondents asked (unweighted) | Of whom reported any use (unweighted) |
|---|---|---|---|
| Male | 18.8% | 671 | 108 |
| Female | 14.1% | 766 | 100 |
| Married | 12.1% | 676 | 66 |
| Divorced | 17.4% | 140 | 24 |
| Never married | 22.5% | 506 | 106 |
Legal marital status is not current partnership: “never married” does not mean single, and the rates do not show who the users are. The gender gap was not tested for significance. Widowed (n=67; 3 users) and separated (n=48; 9 users) adults are not foregrounded because too few reported use.
Sources: AP-NORC data and codebook
MDC analysis Descriptive gaps in one survey item: not adjusted, not tested for significance and not causal.
What a German survey adds
A German quota sample points the same way for a narrower behavior: past-year intimate conversations with an AI (Figure 5).
Figure 5 · Peer-reviewed#
Past-year intimate interaction with an AI by age and gender in one German quota sample
Germany · adults 18–75 · n=2,658 · opt-in quota panel · 11 Nov–3 Dec 2024 · separate comparisons on one scale
In the German sample, 33.6% of respondents aged 18–39 reported past-year intimate conversations with an AI, against 2.4% of those 60 and older. Among men the share was 20.5%, among women 7.7%.
Younger adults and men report more in this sample too. The German item is narrower than AP-NORC’s, so these numbers cannot be compared with the U.S. figures or used to rank countries.
View the data as a table
| Group | Past-year intimate interaction | n |
|---|---|---|
| Age 18–39 | 33.6% | 891 |
| Age 40–59 | 5.9% | 961 |
| Age 60+ | 2.4% | 806 |
| Men | 20.5% | 1,338 |
| Women | 7.7% | 1,320 |
Opt-in quota sample; each comparison looks at one characteristic at a time. The percentages describe the sample under the study’s method, not probability-based national rates. Ages and genders are separate comparisons, not a ranking of countries.
Sources: Döring et al., Archives of Sexual Behavior: article · open-access PDF
What these data can and cannot tell us
Evidence table#
What we know about who uses AI companions, and what we cannot infer
| Dimension | Best evidence | What it supports | What it does not support |
|---|---|---|---|
| Age | AP-NORC probability panel; Döring quota sample | Within each of these 2 studies, younger groups report more use. | A universal age rate, a causal age effect or a cross-country rank. |
| Gender / sex | AP-NORC; Döring | Male subgroup estimates exceed female in both studies (the AP-NORC gap was not tested for significance). Subgroup rates do not reveal the gender composition of all users. | Inferring user gender from bot or character gender, or explaining the gap. |
| Legal marital status | AP-NORC public-use file | Descriptive weighted shares for married, divorced and never-married adults. | “Single”, “currently partnered”, cohabiting, satisfaction, or adjusted effects. |
| Country | U.S. and German studies with unlike items | Side-by-side evidence that these behaviors are measurable in both countries. | A national ranking or pooled prevalence. |
| Device | No suitable study found | A documented evidence gap. | Using web or mobile traffic as a demographic proxy. |
Only within-study comparisons are interpreted. Measures, samples, age bands and periods differ between the U.S. and German studies.
Sources: MDC analysis MDC analysis of the AP-NORC public-use file: AP-NORC data and codebook Peer-reviewed Döring, Mikhailova & Mohseni: article · open-access PDF
Still unknown
- Current relationship status, cohabitation and satisfaction of users.
- Device preference: web traffic and downloads do not describe who the users are.
- Whether the gaps are caused by age or gender, or reflect other differences.
- Evidence on non-binary and gender-diverse users: the sources in this selection are inadequate.
Why, and what it means
Why People Use AI Partners#
Why do people form relationships or intimate interactions with AI?
No single reason. People describe support, availability, play, fantasy and intimacy, and some did not set out to form a relationship.
What stands out
What people say and what they do differ. In a study of 1,131 U.S. adult Character.AI users (a selected group recruited through Prolific), only 11.8% named companionship as their main use, yet 51.0% described their chatbot in companion terms. Among 237 people who donated chat histories, 92.9% had at least one companionship-oriented session. These figures come from different groups, so they are not steps in a funnel.
Some posts describe a relationship that grew out of another use. An analysis of 1,506 top posts in an AI-boyfriend community found 10.2% of posts describing such a path and 6.5% describing deliberately seeking an AI companion (2.3% described explicitly seeking a relationship). Those are shares of posts, not of people.
Loneliness is part of some users’ context, not a shown cause. 90% of 1,006 student Replika users scored as lonely, but with no comparison group and no way to tell whether loneliness came first.
How well supported is each motive?
The table grades how well the studies in this selection support each motive. Stronger: the theme recurs across several kinds of study. Moderate: direct evidence, mostly from selected or small samples. Limited: one narrow or conflicting study. Insufficient: not adequately measured here. The grades are MyDreamCompanion’s editorial summary, not scores. People, donors, sessions, reviews and posts are different units, so there is no motive bar chart, average or ranking.
Evidence table#
Why people use AI partners: repeatedly observed, sample-specific, and still assumed
For each motive: what the evidence shows (with its sources), how strong it is, and what it cannot show.
| Motive or pathway | What the evidence shows | Evidence strength | What it cannot show |
|---|---|---|---|
| Companionship and emotional support | Users describe companionship and support in surveys, reviews and chats. Relational behavior can exceed stated primary purpose.Sources and design: Zhang; Ta. Selected Character.AI users and log donors; selected Replika reviews and n=66 surveyed users | Stronger | The population share choosing AI primarily for support, or whether support improves long-term wellbeing. |
| Availability and nonjudgmental interaction | Constant availability, acceptance and safer disclosure recur in small qualitative studies and coded reviews.Sources and design: Ta; Skjuve; Brandtzaeg. Selected Replika users and reviews; interviews n=18 and n=19 | Moderate | How common either motive is, or whether AI is objectively safer or more understanding. |
| Curiosity, entertainment and gradual entry | Curiosity appears in interviews. Posts document functional or recreational use developing into relationships.Sources and design: Pataranutaporn; Skjuve; Zhang. Community posts, interviews, one-platform survey and logs | Moderate | The probability that a general-AI user will develop a relationship, or a conversion rate. |
| Romantic fantasy and intimate exploration | Fantasy was the strongest predictor of relationship intensity in a small romantic-user sample (N=92). Romantic or intimate roleplay appeared in many donated sessions.Sources and design: Ebner; Zhang. N=92 current romantic users plus n=15 interviews; donated sessions | Moderate | Fantasy as the main reason people start, or the population prevalence of intimate exploration. |
| Loneliness | Many selected Replika users score as lonely, with no non-user comparison. Loneliness did not predict intensity in Ebner’s sample.Sources and design: Maples; Ebner. Cross-sectional selected users; small specialist sample | Limited / conflicting | That loneliness is the main motive or causes adoption, or that AI use causes loneliness. |
| Intentional search for an AI companion | Intentional adoption of an AI companion in 6.5% of posts in one community (explicit relationship-seeking: 2.3%).Sources and design: Pataranutaporn. Top-ranked posts; preprint classifier | Limited | That 6.5% of users intentionally seek an AI companion. The denominator is posts. |
| Perceived control, predictability and customization | Users describe personalized, user-centered friendship. Fantasy can shape interactions.Sources and design: Brandtzaeg; Ebner. Small interviews; specialist romantic-user study | Limited | A prevalence estimate or causal effect of customization on attachment. |
| Fear of rejection or social anxiety | Nonjudgmental disclosure is suggestive, but none of the studies in this selection establishes fear of rejection as a motive.Sources and design: Skjuve; Brandtzaeg. Small qualitative studies | Insufficient | That socially anxious people are more likely to adopt AI partners. |
| Relationship dissatisfaction or dating fatigue | None of the studies in this selection measures either as a predictor of adoption.Sources and design: No study in this selection | Insufficient | Any link between dissatisfaction, app burnout and virtual-partner use. |
| Being single or divorced | Current single-vs-partnered uptake is not adequately measured. Some selected user samples include many partnered people.Sources and design: AP-NORC limits (Section 3); Zhang sample context. Legal-status descriptives; selected platform users | Insufficient | That AI-partner users are mostly single or that divorce causes use. |
| Privacy or anonymity | Safe disclosure and nonjudgment are measured qualitatively. Privacy as an adoption motive is not directly measured.Sources and design: Skjuve; Brandtzaeg. Small qualitative studies | Insufficient | That privacy is a leading motive or that companion services protect privacy better. |
Most motive evidence comes from selected users, chats, reviews or community posts, not representative surveys.
Sources: Peer-reviewed Zhang et al.: Nature Human Behaviour · open manuscript Preprint Pataranutaporn et al.: arXiv Peer-reviewed Ebner & Szczuka: preprint · published version Peer-reviewed Maples et al.: article Peer-reviewed Ta et al.: article Peer-reviewed Skjuve et al.: article Peer-reviewed Brandtzaeg, Skjuve & Følstad: article
Why, and what it means
Personalization and What People Build#
What do people customize, and is there an AI-girlfriend versus AI-boyfriend split?
Products document many customization controls, but the studies here cannot say what people choose. Evidence on AI boyfriends is thin: 1 community analysis and 2 small interview studies.
What one large public bot catalogue shows
The largest public view comes from a CHI 2026 study of Character.AI. The authors scraped 5.76 million public bot descriptions and closely coded a sample of 2,468 English-language ones. These describe bots: the data do not show the gender or other characteristics of the people who created or used them.
Figure 6 · Peer-reviewed#
Male-coded and intimate characters are common in one large public bot catalogue
Coded sample of 2,468 English-language Character.AI descriptions · Apr–Aug 2025 · bots, not users
In a coded sample of Character.AI bot descriptions, 87% of popular and 79% of general descriptions that gave a gender included at least one male character. Separately, across all coded descriptions, 63% of popular and 51% of general ones set up an intimate relationship with the user.
This shows what is being created and offered, not what users want. A male-coded character may have been created or used by a person of any gender.
Gender reportedshare of all descriptions
At least one male characterof descriptions that reported gender
At least one female characterof descriptions that reported gender; categories overlap
An intimate relationship set up with the userof coded descriptions
A violent or abusive dynamicof coded descriptions
View the data as a table
| Characteristic | Popular bots | General bots |
|---|---|---|
| Gender reported | 88% | 76% |
| At least one male character | 87% | 79% |
| At least one female character | 17% | 24% |
| An intimate relationship set up with the user | 63% | 51% |
| A violent or abusive dynamic | 22% | 12% |
Creator and user gender are unknown. Gender was reported in 88% (n=1,291) of popular and 76% (n=757) of general descriptions, so the male and female shares are of those descriptions only. Descriptions show creator intent, not conversations or effects. “Popular” and “general” are sampling groups, not a time trend.
Sources: Kieserman et al., CHI 2026: open preprint · published version
What users and products say
AI boyfriends. The evidence is thin. One analysis covered 1,506 top posts from an AI-boyfriend community: 15.1% of posts discussed customization or personality and 13.1% memory or continuity. These are shares of posts in a selected enthusiast community, not of people. Separately, 2 small interview studies of women in China looked at tailoring a partner’s appearance, personality and role for safety and control. We found no representative AI-girlfriend versus AI-boyfriend user split.
One peer-reviewed walkthrough of 4 platforms (Replika, Digi, Paradot and AvatarOne) reports controls for personality, communication style and appearance on all 4, with relationship and intimate features often behind paid tiers. That shows what the interfaces offered, not what people want. Only the abstract and publisher metadata were available to this report.
Character creation tools are one example of this customization layer, such as MyDreamCompanion’s character creator (the publisher’s own product; none of the studies cited here examined it).
Adult behavior surveys are in Section 2. They count people, while the Character.AI data count bot descriptions, so the 2 are not comparable.
Evidence table#
What can be customized versus what research observes
Each row is a layer of customization, the kind of evidence that exists for it, and what that evidence cannot show.
| Layer | Examples | Evidence available | What must not be inferred |
|---|---|---|---|
| Appearance and character gender | Avatar or image, gender, visual style | Product walkthroughs; bot-description coding; small qualitative studies | A feature’s presence is not proof most users choose it. Character gender is not user gender. |
| Personality and relationship role | Traits, backstory, friend/partner/spouse, communication style | 4-platform walkthrough; qualitative accounts; public bot descriptions | A representative ranking of preferred traits or a clean girlfriend/boyfriend split. |
| Memory and continuity | Saved facts, journals, story memory, long-term-memory claims | Selected community themes; product-change evidence | Memory quality, persistence and user control are separate. Benefit is not experimentally established. |
| Voice, image and video | Calls, voice notes, selfies and avatars, video | Product marketing and selected community posts. Not counted in this report. | Availability is not adoption, importance, engagement or safety. |
| Intimate settings | Relationship tiers, roleplay, 18+ features | Walkthrough and product documentation (adult behavior is in Section 2) | Paywall placement is not observed spending or prevalence. |
None of the studies in this selection gives a representative AI-girlfriend versus AI-boyfriend user split, and none is a companion-specific experiment on whether memory, voice or customization raise attachment or wellbeing. Product evidence is a dated, purposive snapshot, not a market census.
Sources: Peer-reviewed Kieserman et al.: open preprint · published version Preprint Pataranutaporn et al.: arXiv Preprint Zhang & Li: arXiv Peer-reviewed Lai & Cheon: open preprint · published version Peer-reviewed Wang & Dehnert: article
Teen signal. Some age-specified public descriptions place characters at ages 13–17. That is a minors-safety signal about descriptions, not users or incidents; it is handled in Section 10, with no percentage published.
Still unknown
- A representative AI-girlfriend versus AI-boyfriend user split (none found in this selection).
- Which controls people actually use, how often they change them, and which drive retention or payment.
- Whether memory, voice or deeper customization raises attachment or wellbeing.
Why, and what it means
Does It Help or Harm?#
Do AI companions make people feel better, worse or both?
Both patterns appear, at different time scales. Brief chats can lower reported loneliness. Lasting benefit is unproven, and heavier use travels with worse outcomes in some studies.
Attachment is not the same as harm. A bond, relational language, dependence and problematic use are different things. The risks to watch are overreliance, losing wanted human contact, and distress when a service changes. Which users benefit and which are harmed is still open.
What can we say now?
Evidence table#
What can we say now?
6 questions readers ask, the best current evidence, how confident the evidence allows us to be, and the bottom line.
| Question | Best current evidence | Confidence | Bottom line |
|---|---|---|---|
| Can AI companionship reduce loneliness immediately? | De Freitas’ randomized short-interaction studies and 7-day repeated sessions show small momentary reductions (d=.20 pooled pre/post in the companion condition). | Moderate for momentary state relief | Yes, a brief interaction can reduce reported state loneliness in some experimental settings. Not durable improvement. |
| Does it improve loneliness long-term? | A 21-day randomized preprint found no significant overall effect. A 4-week randomized trial found no robust assigned-condition effects and lacked a no-chatbot control. | Low / unresolved | Durable improvement has not been demonstrated. |
| Can heavy use be associated with worse wellbeing? | Fang’s naturally longer daily use was associated with more loneliness, dependence and problematic use and less socialization. Zhang’s longitudinal preprint also links sustained engagement to lower wellbeing. | Moderate for association; low for causation | Yes, heavier engagement can travel with worse outcomes. Direction is unclear. |
| Does AI companionship increase isolation? | Folk & Dunn found that more chatbot use predicted more emotional isolation, but chatbot use did not significantly predict decreases in broader social connection. Zhang’s preprint is consistent with lower in-person interaction as a pathway. | Limited-to-moderate, measure-dependent | A risk signal exists. A general causal claim is not supported. |
| Can sudden product changes bring acute distress? | In one Replika community, a disruptive update was followed by more mental-health-term and refund language. | Moderate for an observational distress signal in an affected community | Post shares are not population incidence or clinical diagnoses. |
| Is emotional attachment itself harmful? | Bond, intensity, dependence and problematic use are distinct. Qualitative work identifies dependence-related harms; short randomized trials do not show attachment worsens outcomes. | Low / heterogeneous | No. Risk depends on dependence, displacement, product design, user context and the outcome measured. |
Confidence words summarize how well the study designs support each answer; they are not scores. Short-term loneliness is not depression, anxiety, flourishing, clinical recovery or durable relationship functioning.
The evidence behind these answers: how each kind of study differs
Studies differ in what they can prove. A randomized experiment assigns people to conditions by chance and is the strongest design for showing cause and effect. A longitudinal study follows the same people over time, and a cross-sectional survey measures them once; both show associations that confounding or reverse causation could explain. Community posts show what people say online, and posts are not people. A preprint has not yet been peer reviewed. The ladder below runs from the design that can establish most to the one that can establish least.
Evidence by study design#
What each kind of study can say about wellbeing
8 kinds of study, from short randomized experiments to qualitative analyses of community posts, with what each can and cannot show.
Brief experiments show momentary relief. Longer randomized experiments found no significant overall effect, and studies of heavier or sustained use point to risk signals but cannot show cause.
- Short randomized experiment
De Freitas et al.
Average score on a 3-item loneliness scale (lower means less lonely) fell from 36.64 to 30.74 right after daily 15-minute chats over 7 days (d=0.20). The control group completed only a short daily survey.
It can showMomentary relief after a chat.
It cannot showLasting or clinical benefit, or an effect separate from any 15-minute activity.
Peer-reviewed · De Freitas et al., Journal of Consumer Research: journal article · open working paper
- Longer randomized experiments
Guingrich & Graziano (n=183, 21 days); Fang et al. (n=981 completers, 4 weeks)
No significant overall effect on loneliness or social health; no robust broad effect of the assigned chat mode.
It can showEffects of the assigned conditions.
It cannot showUse versus no use: Fang et al. had no no-chatbot group. Fang et al. were funded by OpenAI, and 4 authors were OpenAI employees.
Peer-reviewed · open preprint · published version · arXiv
- Observational within a randomized trial
Fang et al. (voluntary daily duration, 4 weeks)
Across all 981 completers, mean actual use was about 5.3 minutes a day (range 1 to 28). Participants who chose to spend more daily time with the chatbot reported slightly more loneliness, greater emotional dependence and problematic use, and less socialization at week 4.
It can showA risk marker.
It cannot showThat heavier use caused worse outcomes: duration was not randomly assigned.
Preprint · arXiv
- Longitudinal panel
Folk & Dunn (more than 2,000 adults, 4 waves over 12 months)
More chatbot use predicted more emotional isolation (a single-item measure), and lower social connection predicted later chatbot use; chatbot use did not significantly predict decreases in social connection.
It can showAssociations over time.
It cannot showCausation. Exploratory, observational.
Peer-reviewed · article
- Longitudinal, high attrition
Zhang et al. preprint (1,182 at baseline, 439 at follow-up)
More engagement was linked to less in-person interaction, which was linked to lower wellbeing.
It can showA pattern consistent with displacement.
It cannot showThat AI displaced human contact: the follow-up measures were contemporaneous, and 62.9% of baseline participants were lost to follow-up.
Preprint · arXiv
- Cross-sectional survey
Zhang et al. (n=1,131 Character.AI users)
Companionship as the main use was linked to lower wellbeing (β=−.48).
It can showWhere lower wellbeing may concentrate.
It cannot showDirection: lower wellbeing could come first.
Peer-reviewed · Nature Human Behaviour · open manuscript
- Community posts
Replika update study (12,793 posts)
Mental-health-term posts rose from 0.13% to 0.65% and “refund” posts from 0.10% to 3.30% after a disruptive update.
It can showWhat followed in one community.
It cannot showIncidence, diagnoses or proof of harm: posts are not people.
Preprint · arXiv
- Qualitative community analysis
Laestadius et al. (582 posts, 2017–2021)
A grounded-theory analysis of mental-health-relevant r/Replika posts described harms linked to emotional dependence.
It can showHow harm may arise.
It cannot showFrequency, population risk or causation.
Peer-reviewed · article
Short-term relief is not durable improvement, associations are not randomized effects, and posts are not people.
Sources: Peer-reviewed De Freitas et al.: journal article · open working paper Peer-reviewed Guingrich & Graziano: open preprint · published version Preprint Fang et al.: arXiv Peer-reviewed Folk & Dunn: article Preprint Zhang et al.: arXiv Peer-reviewed Zhang et al.: Nature Human Behaviour · open manuscript Preprint De Freitas et al.: arXiv Peer-reviewed Laestadius et al.: article
Still unknown
- Whether AI companionship improves or harms wellbeing over many months.
- Who benefits, who is vulnerable and which product features change outcomes.
- Whether AI companionship changes human relationships across a population.
Why, and what it means
Human Relationships: Myth vs Data#
What does the evidence say about marriage, divorce, living arrangements and loneliness?
Public data on divorce, living alone and loneliness do not show that they explain AI-companion use. Divorce rates were lower in 2024 than in the first year shown (2008 or 2000, depending on the measure), loneliness shows no steady rise, and more one-person households say nothing about AI use.
5 ideas, checked against public data
Myth versus data#
5 ideas checked against public data
Each idea is stated, then compared with what the data show and what they cannot show.
AI companions are growing because people are divorcing more.
UnsupportedU.S. divorces per 1,000 married women fell from 20.5 (2008) to 14.2 (2024). The crude U.S. rate (CDC provisional table; 4 states excluded in 2000 and 5 in 2024) fell from 4.0 (2000) to 2.3 (2024). The EU crude rate peaked in 2006 (2.1) and was 1.6 in 2024; Eurostat flags the recent EU values as imputed.
Neither series contains any AI measure, so neither can show what divorce does to AI use.
More people live alone, so AI relationships are inevitable.
Unsupported as an explanationOne-person households rose from 20% to 29% of U.S. households (1975–2025). In the EU, single-adult households without dependent children rose from 32.8% (2015, flagged as estimated) to 35.7% (2025).
These are household shares, not relationship status, loneliness or AI use.
Loneliness keeps rising, so people turn to AI.
UnsupportedAcross 31 Gallup observations, daily loneliness ranged from 17% to 25%: it peaked at 25% (3 times) in 2020–21 and was 17–20% in every observation from April 2021 to August 2024.
No steady rise, no pre-pandemic baseline, and no link to companion use in the same people.
AI-companion users are mostly single.
Not testable with current dataReported use was 12.1% among married, 17.4% among divorced and 22.5% among never-married adults (unadjusted).
Rates within groups do not give the makeup of all users, and “never married” is not “single”.
AI is replacing human relationships.
Unsupported as a general claimSome studies link heavier or sustained use with less socializing or lower wellbeing. A 21-day randomized study found no significant overall effect on social health.
No study shows net replacement of partners, friends or family across a population.
Different denominators and sources: the NCFMR and CDC divorce rates must not share a scale. Household categories are not relationship status. The AP-NORC rows are MDC analysis (Section 3).
Sources: Representative survey National Center for Family & Marriage Research: NCFMR table Official record CDC/NCHS: CDC/NCHS table Representative survey U.S. Census Bureau: Census release · Table HH-4 Representative survey Eurostat: Eurostat publication · dataset Official record Eurostat: Eurostat dataset demo_ndivind Representative survey Gallup: Gallup 2024 article · Gallup 2023 release MDC analysis MDC analysis of the AP-NORC public-use file: AP-NORC data and codebook
The 2 trends behind the ideas
Figure 7#
U.S. divorce rates depend on the denominator, and both were lower in 2024 than in the first year shown
2 measures from 2 sources (a survey-based table and an official record), each on its own scale. Do not compare bar lengths across panels. Context only.
2 different divorce measures, each lower in the later year shown: 20.5 divorces per 1,000 married women in 2008 and 14.2 in 2024, and 4.0 per 1,000 people in 2000 and 2.3 in 2024 (CDC provisional table; state coverage differs, see note).
The 2 rates use different denominators and must not be compared with each other.
Married women 15+, per 1,000 (NCFMR refined rate) Representative survey
Total population, per 1,000 (CDC crude rate) Official record
The CDC table is titled provisional and its state coverage differs by year: 2019 and 2024 exclude California, Hawaii, Indiana, Minnesota and New Mexico; 2000 excludes California, Indiana, Louisiana and Oklahoma. NCFMR also lists a 2020 estimate from experimental data without a margin of error; it is not charted.
Sources: Representative survey National Center for Family & Marriage Research: NCFMR table Official record CDC/NCHS: CDC/NCHS table
Figure 8 · Representative survey#
U.S. daily loneliness peaked at 25% in 2020–21 and has been between 17% and 20% since mid-2021
Felt lonely “a lot of the day yesterday” · U.S. adults · all 31 observations in Gallup’s published table · hover a point for its value
The share of U.S. adults who felt lonely “a lot of the day yesterday” peaked at 25% in 2020–21 and was between 17% and 20% in every observation from April 2021 to August 2024.
Loneliness has not risen steadily since 2020 in this series.
Axis starts at 10%. The one gap longer than 6 months (Feb to Sep 2023) is left open. The 25% peaks were in April 2020, December 2020 and March 2021. Tap a point or open the table for each value.
View the data as a table
| Observation | Felt lonely a lot of the day yesterday |
|---|---|
| Mar 2020 | 24% |
| Apr 2020 | 25% |
| May 2020 | 24% |
| Jun 2020 | 21% |
| Jul 2020 | 23% |
| Aug 2020 | 23% |
| Sep 2020 | 22% |
| Oct 2020 | 22% |
| Nov 2020 | 22% |
| Dec 2020 | 25% |
| Jan 2021 | 22% |
| Feb 2021 | 23% |
| Mar 2021 | 25% |
| Apr 2021 | 20% |
| May 2021 | 20% |
| Jun 2021 | 19% |
| Jul 2021 | 18% |
| Aug 2021 | 18% |
| Sep 2021 | 20% |
| Oct 2021 | 17% |
| Nov–Dec 2021 | 20% |
| Feb 2022 | 19% |
| Apr 2022 | 17% |
| Jul 2022 | 18% |
| Oct 2022 | 20% |
| Feb 2023 | 17% |
| Sep 2023 | 18% |
| Dec 2023 | 18% |
| Feb 2024 | 17% |
| May 2024 | 18% |
| Aug 2024 | 20% |
Single yes/no item from a web panel. Gallup has no pre-pandemic measure, so March 2020 is not a baseline. Nothing is interpolated or smoothed.
Sources: Gallup 2024 article · Gallup 2023 release
Still unknown
- A representative study linking current partnership, cohabitation, relationship quality, loneliness and clearly defined AI-companion behavior in the same adults (we found none).
- Whether AI companionship changes marriage, divorce or household formation: we found no population study that measures it.
The market and the outlook
The Market: What Is and Isn’t Measured#
What is known about how often people use AI companions, how long they spend and what they pay?
Representative evidence shows how often adults report use, not how long they spend or what they pay; one quota sample adds a self-reported weekly time for people who chatted with an AI romantic partner. We found no market size we could verify, and this report publishes none.
What adults report
Adding the 3 most frequent AP-NORC categories gives about 6% reporting daily-or-more use, and about 9% reporting use several times a week or more (Figure 9).
Figure 9 · Representative survey#
Most U.S. adults reported no AI companionship use; about 6% reported daily-or-more use
Each dot is one of 100 U.S. adults (rounded shares) · n=1,437 · 10–14 July 2025 · AmeriSpeak probability panel
Out of 100 U.S. adults, 75 said they never use AI for companionship, 8 did not know and 1 skipped the question. About 6 said almost constantly, several times a day or about once a day; 3 said several times a week and 7 said less often.
This is how often adults say they use AI for an undefined “companionship” purpose. It is not time spent, spending, or a count of people with an AI partner.
- Almost constantly2%
- Several times a day1%
- About once a day3%
- Several times a week3%
- Less often7%
- Never75%
- Don’t know8%
- Skipped or refused1%
View the data as a table
| Response | Share of U.S. adults |
|---|---|
| Almost constantly | 2% |
| Several times a day | 1% |
| About once a day | 3% |
| Several times a week | 3% |
| Less often | 7% |
| Never | 75% |
| Don’t know | 8% |
| Skipped or refused | 1% |
Daily-or-more (2% + 1% + 3%) and several-times-a-week-or-more (about 9%) are sums of rounded categories.
Sources: AP-NORC topline · methodology · project page and data
What platform data, a quota-sample survey and experiments add, and what they do not
Chat logs show how far some selected users go: the 237 Character.AI donors in the Nature Human Behaviour study sent 464,687 messages across 4,664 sessions, about 100 messages per session. Those are averages for self-selected donors, not typical use. Experiments that ask people to chat for 10 or 15 minutes a day show study dosage, not natural use, and list prices show what services charge, not what users spend.
A quota-sample survey adds one self-reported time figure. In the Wheatley Institute report (a U.S. quota sample of 2,969 adults that includes an added oversample of adults aged 18–29), respondents who said they had chatted with an AI romantic partner reported an average of 51 minutes a week. The report text does not give the number of these respondents or say how the average was calculated, and it does not describe weighting. This is a sample-specific, self-reported figure for one group of users, not a national average, and it says nothing about retention or spending.
Sources: Wheatley report (PDF) · IFS blog summary
What is measured and what is not
Evidence table#
What is actually measured
Each row is a question people ask, what public evidence exists for it, the unit counted and whether it is measured.
| Dimension | Publishable evidence | Unit | Status |
|---|---|---|---|
| Population frequency | AP-NORC original response distribution | U.S. adults | Measured |
| Selected-user sessions and messages | Zhang donated corpus | Donors, sessions, messages | Measured, selected sample |
| Experimental exposure | Replika, at least 10 min/day × 21 days; ChatGPT (GPT-4o), at least 5 min/day × 28 days | Assigned protocol | Measured dosage, not natural use |
| Natural average time spent | No representative estimate found. One self-reported figure: an average of 51 minutes per week among respondents who had chatted with an AI romantic partner (Willoughby / Wheatley report, U.S. quota sample) | Self-reported minutes per week, one group of respondents in one sample | Measured, selected sample; no population estimate |
| Relationship duration / retention | None found in this selection | Users retained over a defined period | Unknown |
| Paid conversion | No qualifying public dataset found | Payers per eligible user | Unknown |
| Average or median spending | No qualifying public dataset found | Currency per payer or user per period | Unknown |
| Modeled category revenue | Partial commercial references only | Estimated store in-app purchases | Held; not user spending |
This report publishes no market size, growth rate, CAGR, average spend or product ranking. Commercial store-revenue estimates were reviewed, but the snapshots could not be reconciled, so none is used or chained into a growth rate. No finding here carries the Market measurement label because no commercial figure passed the checks. Downloads, web visits and revenue estimates are not users.
Evidence table#
Unit guardrails
Rules this report follows so that one kind of number is never presented as another.
| If the source reports… | Label it as… | Never relabel it as… |
|---|---|---|
| Survey frequency | Share of a defined population in each response category | Minutes, sessions or retention |
| Donated sessions or messages | Corpus activity among selected donors | Typical user intensity or prevalence |
| Prescribed study minutes | Experimental exposure | Natural engagement |
| Self-reported weekly time in a quota sample | Average minutes per week reported by one group of respondents in that sample | A national average, natural use by all users, retention or spending |
| Subscription price | Listed price at a date and tier | Average spending or revenue per user |
| Store revenue estimate | Modeled covered-store in-app purchases under a vendor taxonomy | Total market revenue or consumer spending across channels |
| Downloads or visits | Downloads or visit events | Unique active users, subscribers or relationships |
Survey shares, self-reported minutes, donated sessions, prescribed minutes, prices, revenue estimates and downloads each keep their own unit.
Still unknown
- Average minutes per user in a representative population (the one self-reported figure comes from a quota sample), and how long relationships last.
- Paid conversion and average or median spending. List prices are not spending.
- Market size or growth. Commercial revenue snapshots could not be reconciled, so none is used or chained into a growth rate.
The market and the outlook
Outlook#
Which product changes are visible, and which statements are opinion?
Some product signals are visible, but adoption and effects were not measured in the evidence reviewed. The outlook is MDC opinion only, with no numerical forecast.
Voice, images, video, persistent memory and configurable identity are discussed in product and research materials. This edition does not count them: a product-by-product tally could not be reproduced consistently from public documentation, so none is published. A feature’s presence in marketing does not show how many people use it, whether it works well or what will dominate the market.
Memory is the clearest example of the gap between a product signal and an evidence-based forecast. Users discuss continuity, and several products market long-term memory. Yet “memory” may mean editable facts, pinned messages, summaries, journals or an opaque retrieval system. This report does not compare services on memory controls or privacy disclosures, and it ranks no service.
MDC opinion#
Signals and opinion, kept apart
For each product signal: what the evidence supports, and a separate, labelled MDC opinion.
| Signal | What the evidence supports | MDC opinion (labelled) |
|---|---|---|
| Multimodality | Voice, image and video features are discussed in product and research materials. This report does not count them. | MDC opinion Multimodal relationships are likely to become more prominent, but adoption and effects were not measured in the evidence reviewed. |
| Memory and persistence | Users discuss continuity, and the Replika update study (Section 6) shows distress after a product change. This report does not compare services on memory controls. | MDC opinion Control over persistent memory is likely to become a central trust issue. |
| Personalization | One platform walkthrough (Section 5) reports appearance, personality and relationship settings. | MDC opinion Personalization will probably deepen, but benefits and dependency effects require testing. |
| Platform integration | General assistants appear in relational-use research and community posts. | MDC opinion The boundary between assistant and companion is likely to remain porous. |
| Regulation | The New York statute, a California signing release, an Italian enforcement decision and a U.S. federal inquiry are documented in Section 10. | MDC opinion Compliance evidence and auditability are likely to matter more than feature lists. |
Opinion is labelled and separate from findings. No numeric market forecast, growth rate or adoption extrapolation is made in this table or anywhere in the report.
Rules, limits and evidence
Safety and Boundaries#
Which boundaries rest on evidence, and how are minors and non-consensual imagery kept apart from adult findings?
Rules exist; we found no evaluation of whether they work. Adults, minors and non-consensual imagery are kept apart.
Adult use, minors and non-consensual synthetic imagery need different evidence. Section 2 covers consenting adults. It does not show what minors can access or how often real people are targeted without consent.
Minors · separate from adult evidence
Common Sense Media’s survey of 1,060 U.S. teens (30 Apr–14 May 2025; ±4.2 points) combined 719 probability-panel and 341 nonprobability interviews. Its broad definition can include general assistants such as ChatGPT or Claude used as companions, so it cannot be compared with adult romantic or intimate-AI figures.
Hybrid design with no agreed variance method for the nonprobability part; self-reported; U.S. only. The publisher is a child and media-safety advocacy organization.
Sources: report page · full report (PDF)
Character descriptions at ages 13–17
Some age-specified public Character.AI character descriptions explicitly place characters at ages 13–17. The unit is a public character description, not a user, an incident or a verified minor’s interaction. No percentage is published.
Peer-reviewed Kieserman et al., CHI 2026: open preprint · published version
Non-consensual imagery
In a peer-reviewed analysis of 390 Reddit posts about AI-generated 18+ material, 39.5% (154 posts) concerned real-person deepfake content. This shows how prominent the issue is in that discussion. It does not measure victims, creators or incidents, or establish lack of consent in every post.
Peer-reviewed article
What rules exist, and whether they work
4 official records show what rules exist or were applied: New York’s Article 47 (crisis protocols and “not a human” notices), California’s SB 243 (as described in the Governor’s signing release), Italy’s Garante decision on Replika (privacy and age verification, for one company and period) and a U.S. FTC inquiry (not a finding). None shows whether the rules work. These 4 records are a selection, not a complete list of AI-companion rules: other U.S. states and other countries have adopted or proposed their own. A rule existing, a rule being enforced and a rule working are 3 different things.
- Step 1
Policy or legal duty exists
New York Article 47 notices and California’s SB 243 (as described in its signing release) are enacted requirements.
- Step 2
Implementation or enforcement is documented
The Italian Garante decision of 10 April 2025, for a historical period and one company.
- Step 3
Effectiveness is measured
No companion-wide age-assurance or crisis-protocol outcome evaluation was found in the reviewed evidence.
A legal duty or an age policy is not evidence that it works. Evidence may exist at one step and be absent at another; this is a ladder, not a maturity score. A minimum-age statement is not an age-assurance result, and no platform is ranked here.
Sources: Article 47 · Governor’s notice · decision · press release
The 4 official records in detail
New York · GBL Article 47
Operators of an “AI companion” (a system designed to simulate a sustained human-like relationship; customer-service and similar systems are excluded) must have a protocol to detect and address suicidal ideation or self-harm and refer users to crisis service providers. They must also give a clear notice that the user is not talking to a human at the start of an interaction (not required more than once a day) and at least every 3 hours of continuing interaction. The Governor’s office stated the requirements took effect on 5 November 2025. The Attorney General enforces, with civil penalties up to $15,000 per day.
Official record Article 47 · Governor’s notice
California · SB 243
Signed 13 October 2025. As the Governor’s signing release describes it, platforms must disclose that interactions are artificially generated; create protocols to identify and address users’ suicidal ideation or self-harm and share them, with crisis-notification statistics, with the Department of Public Health; give minors break reminders; and prevent minors from viewing sexually explicit images the chatbot generates. Statutory scope and effective date are not reported here. A later release (10 September 2026) lists SB 1119, “Companion chatbots: children’s safety”, and says it strengthens these protections; this report describes only the 2025 release.
Official record signing release
Italy · Garante decision on Replika
On 10 April 2025 the data-protection authority found that, as of 2 February 2023, Luka Inc. had no identified legal basis for processing, an inadequate privacy policy and no age verification. A press release of 19 May 2025 states a €5 million fine, under appeal. One company, one historical period.
Official record decision · press release
United States · FTC inquiry
On 11 September 2025 the FTC sent information orders to 7 companies about companion-chatbot safety, monetization, testing and child protections. An inquiry is not a finding of violation.
Official record FTC announcement
Evidence table#
Safety boundaries
Which evidence is allowed for each domain, and what must never be mixed with it.
| Domain | Evidence allowed | What each claim must state | What must stay separate |
|---|---|---|---|
| Consenting adult use | Adult surveys and adult platform/session studies | Adult behavior and exact construct | Minors and non-consensual real-person imagery |
| Minors | Teen surveys, child-safety research, official rules, measured access tests | Ages, definition, sample and method | Adult prevalence and adult 18+ behavior charts |
| Non-consensual synthetic imagery | Victim or incident research, official enforcement, clearly coded real-person/deepfake evidence | Consent status and unit | Consensual creation and consumption estimates |
| Age assurance and access | Product policy, regulator rules, implementation tests, bypass and effectiveness studies | Policy / enforcement / measured effectiveness | A minimum-age statement is not effectiveness evidence |
| Regulation | Enacted text, effective date, inquiry, decision, enforcement result | Exact legal status and jurisdiction | An inquiry is not a violation; a law is not proven safety |
Adult and minor evidence never share a chart or a total.
What is permitted, restricted and actually measured
Evidence table#
What is permitted, restricted and actually measured
Questions about rules and safety, what evidence can answer each, and where the evidence stops.
| Question | What the evidence can answer | Status | How this report treats it |
|---|---|---|---|
| Is a behavior offered or permitted? | Dated terms, age statements, product documentation, relationship-mode descriptions | Often documentable | Attribute to the provider and date it. Do not infer actual access or use. |
| Is a behavior restricted? | Terms, store and region rules, enacted legal duties | Partly documentable; scope varies | Separate product policy from law and jurisdiction. |
| Is a restriction enforced? | Regulator decisions, transparency reports, independent tests | Sparse and event-specific | Name the actor, period and finding. Do not generalize. |
| Does age assurance keep minors out? | Requires direct implementation and bypass/effectiveness measurement | Not established across the reviewed services | Do not call a service effectively age-gated from policy alone. |
| Does a crisis protocol improve outcomes? | Requires response accuracy, referral and downstream outcome data | Not established | A legal requirement or scripted response is not clinical effectiveness. |
| Are non-consensual harms common? | Requires incident or victim data with a defined population | Not established by the Reddit discourse sample | Do not turn post shares into prevalence. |
| Is one platform safest? | Requires comparable independent audits and outcome measures | Not supportable | No platform safety ranking. |
A policy documents a provider’s statement. Independent audits, regulator findings and measured outcomes are separate evidence. No companion-wide outcome evaluation of age assurance or crisis protocols was found.
Still unknown
- Whether companion age-assurance systems work, and how easily they are bypassed: public minimum-age terms do not establish actual exclusion of minors.
- How common non-consensual synthetic intimate imagery is: that needs consent-specific incident or victim data, not Reddit post shares (we found none).
- Whether crisis protocols improve outcomes, and how the FTC inquiry resolves.
Rules, limits and evidence
Open Questions#
What can the public evidence not yet answer?
The main gaps, collected from the sections above, each with the reason it is open.
Each item below marks where the public evidence we reviewed stops (see “How the evidence was selected” in Section 12). Each group links to the section where it arises.
Definitions and prevalence
- A probability-sample national estimate for AI girlfriend, AI boyfriend or virtual-spouse use as separate categories.
- A verified study that separates AI-generated 18+ video from images or audio.
- Fieldwork dates, weighting details and a confidence interval for the Willoughby 19% (none appears in the text of the Wheatley report, and a quota sample does not support a conventional margin of sampling error).
- A representative longitudinal study of how many everyday AI users later form companion, romantic or intimate relationships.
Who uses AI companions
- A representative survey that profiles users of purpose-built AI girlfriend, boyfriend or virtual-partner services by age, gender, education, income, race/ethnicity and current relationship status in one instrument.
- Current-partner, cohabitation, household-size, satisfaction and loneliness variables in the AP-NORC file, which also lacks replicate weights.
- Adequate evidence on non-binary and gender-diverse users, country rankings and device preference.
Why people use them
- A representative adult survey that asks companion users to select motives with one common instrument.
- Prospective research that measures motive before adoption and follows whether a relationship develops.
- More than sparse evidence on adult motives for AI-generated 18+ imagery and video.
What people build
- A representative AI-girlfriend versus AI-boyfriend user split.
- Public data on which controls people use, how often they change them and which features drive retention or payment.
- A companion-specific experiment comparing pre-built and custom characters, memory and no memory, or voice and text on attachment or wellbeing.
Help or harm
- A long-duration randomized study of whether AI companionship improves or harms adult wellbeing over many months.
- Evidence on who benefits, who is vulnerable and which product features change outcomes.
- Population-level measurement of net substitution of partners, friends or family.
Real-life context
- A representative adult study linking current partnership, cohabitation, relationship quality, loneliness and clearly defined AI-companion behavior in the same respondents.
- Any basis for testing whether divorce, marriage, household composition or population loneliness predicts AI-companion adoption: matching national trends to AI use would be invalid.
Time, money and the market
- A public dataset reporting average or median companion spending, paid conversion, renewal and refunds on the same basis.
- A representative panel or log reporting natural minutes per day or week for a defined companion-user population (the one self-reported figure so far comes from a quota sample), and a comparable retention series.
- Verified evidence for a market size, growth rate or long-range adoption projection.
Outlook
- Measurement of how many people use product features such as voice, video, memory and personalization, or what effects they have, so any outlook remains opinion.
Safety and boundaries
- A study of the effectiveness and bypass resistance of companion age-assurance systems.
- Purpose-built datasets on the prevalence of non-consensual synthetic intimate imagery, victimization and perpetration.
- Measured effects of crisis protocols and legal duties on outcomes; the FTC inquiry is also unresolved in the reviewed record.
Gaps are taken from the “still unknown” and gap statements of Sections 2 to 10. Legal summaries in Section 10 also remain without lawyer review.
Rules, limits and evidence
Methodology, Disclosure, Sources, How to Cite and FAQ#
How was this built, and what should readers know before relying on it?
Every key figure links to its source, and the page says where that source is a summary rather than the original paper. Where the evidence is thin, the page says so.
Evidence labels and comparison rules
Each key finding carries one of 8 evidence labels. Labels name the type of source. They are not quality scores: a peer-reviewed quota survey is still a quota survey. Where a table or chart draws on several sources, each source shows its own label.
Published in a peer-reviewed journal or proceedings.
Probability-based or explicitly population-weighted; hybrid designs are described.
Public manuscript not yet relied on as peer reviewed.
Nonprobability sample balanced to demographic quotas.
Commercial measurement with a stated unit, coverage and period (none is charted here).
Posts, reviews or donated chats from selected communities (not used as a headline label here).
A documented, reproducible MDC calculation from public inputs.
Available only through a secondary account, or kept only to explain an exclusion or gap (none is used as a statistic).
Government and regulatory records (vital-statistics tables, statutes, decisions and inquiries) are described as official records in the text. They are not an evidence label and are not treated as studies or surveys. The Census and Eurostat household figures come from named surveys (CPS ASEC and EU-SILC) and carry the Representative survey label. “MDC opinion” marks interpretation, is not an evidence label and appears only in Section 9.
Comparison rules. Comparisons between studies follow 4 classes:
| Class | Use | Example |
|---|---|---|
| Direct | Same or well-aligned construct, population, denominator, unit, method and period. A within-study comparison is usually direct. | Age groups answering the same AP-NORC item. |
| Qualified | Informative, but one material limitation stays adjacent. | Quota-sample results when the sampling frame or field dates are partly unavailable. |
| Side by side only | Related questions answered in separate labelled panels. | U.S. ever-romantic-AI use beside German past-year intimate-AI interaction. |
| Not valid | Would mislead because a core construct, denominator, unit or design differs. | Visits ranked with active users; posts turned into user prevalence. |
- Different constructs, denominators, populations, periods and methods are never merged into one figure, average, ranking or trend. “Ever”, “past year”, “any frequency” and “daily” stay separate, as do users, visits, downloads and revenue.
- Quota samples are reported as results within the sample. Posts, reviews, sessions and character descriptions keep their own units and are never described as people.
- Marriage, divorce, household and loneliness series are context only and are never plotted against AI use.
- This report publishes no market size, growth rate, forecast, average spend or product ranking.
How the evidence was selected
The studies, surveys and records on this page are a purposive selection assembled for this report from public original sources. Each was then checked against the original document where access allowed (see “Source access limits” below). They are not the result of a systematic literature search: the records available for this report do not document search terms, databases or inclusion criteria, so the search cannot be reproduced.
Statements such as “we found no study” therefore mean “none in this selection”, and a study that is not on this page is not evidence that it does not exist. What the report does document is the source of every key figure, the exact question and sample behind each headline number, the analyses that were considered and excluded, and the access limits for each source.
Final check, 20 October 2026. Every cited source was checked again for corrections, retractions, new versions and updated data. No retraction and no changed figure was found. Corrections exist for three cited papers: Döring et al. on AI-supported sexual activities (August 2026; it restored missing table headings and changed no values), the Döring, Le and Miller Reddit paper (December 2025; it changed one table percentage, 7.4 to 7.2, not the 39.5% used here) and Maples et al. (February 2024; acknowledgements only). The Zhang et al. preprint was revised on 18 October 2026 (version 2); the figures quoted here were re-checked in that version. A limited search of 10–20 October 2026 found one new U.S. adult survey with a related item: the University of Iowa’s CSSI (published 15 October 2026; fielded 17–28 April 2026 by Verasight; n=1,000) reports that 12% of AI users turn to AI for “companionship or conversation”. Its base and wording differ from the four measures above, and it was not added. A Pew short read of 16 October 2026 reuses the February 2026 data already cited.
Original MDC analysis: method, variables, weighting and reproduction
MDC used the AP-NORC July 2025 public-use file and the survey’s final adult-population weight. The survey used NORC’s AmeriSpeak probability panel and represents U.S. adults 18+ in the 50 states and D.C. The analytical sample has 1,437 respondents; the published overall margin of sampling error is ±3.6 points (±6.6 for ages 18–29), and fieldwork ran 10–14 July 2025. The results are in Figures 3 and 4 (Section 3). Each result carries the label MDC analysis.
| Variable | Use | Coding rule |
|---|---|---|
| AI1G | Frequency of using AI for “Companionship” | Responses 1–5 (almost constantly, several times a day, about once a day, several times a week, less often) count as use. Never, don’t know and skipped/refused count as 0 and stay in the all-respondent denominator. |
| AGEGRP | Released age bands | Report 18–29, 30–39, 40–59, 60–64 and 65+ separately. |
| GENDER | Released gender field | Report the 2 released categories, male and female. Do not infer others. |
| MARITAL | Legal marital status | Publish married, divorced and never married separately. Do not relabel as single or partnered. |
| FINALWT | Final adult-population weight | Use for every percentage. Unweighted shares are not estimates. |
Formula. For subgroup g: 100 × Σ(FINALWT × any_use) ÷ Σ(FINALWT) over all records in g, rounded to 1 decimal. The separately reported “about 6%” (daily or more) and “about 9%” (several times a week or more) are sums of rounded published categories, not unrounded estimates from the file.
| Analysis | Released bases (unweighted) | Weighted result | Derived |
|---|---|---|---|
| Age | 386 / 183 / 341 / 125 / 402 | 24.6 / 21.6 / 16.7 / 11.4 / 6.7% | 18–29 minus 65+ = 17.9 points |
| Gender | male 671; female 766 | 18.8% / 14.1% | 4.7 points |
| Legal marital status | married 676; divorced 140; never married 506 | 12.1 / 17.4 / 22.5% | never married minus married = 10.4 points |
How to reproduce it.
- Download the public-use file, codebook, topline and methodology from AP-NORC’s project page.
- Code any_use as 1 for AI1G responses 1–5 and 0 for “never”, “don’t know” and skipped/refused. Keep every respondent in the denominator.
- For each released age band, gender category and legal marital status, compute the formula above with FINALWT.
- Compare your results with the table. Small differences should come only from rounding.
Limits. The results are descriptive point estimates, not adjusted or causal. The file has no replicate weights or public PSU/strata fields, so no design-based subgroup intervals or tests are possible. It has no current-partnership, cohabitation, satisfaction or loneliness variables. AI1G covers any AI used for an undefined companionship purpose, not a dedicated app, romance or intimate interaction. Widowed (n=67; 3 unweighted users) and separated (n=48; 9) adults are not foregrounded because too few reported use. MyDreamCompanion operates in the category, so these controls reduce, but cannot eliminate, the risk of publisher bias. No private MDC user, revenue, conversion, product or behavioral data were used.
Analyses considered and excluded. A result appears only if MDC performed a documented transformation beyond source transcription, the inputs are public, the method can be repeated and the limits are stated.
| Proposed analysis | Reason it was excluded |
|---|---|
| AP-NORC “single vs partnered” comparison | The public file records legal marital status, not current partnership or cohabitation. |
| AP-NORC regression or subgroup significance tests | No replicate weights or public PSU/strata, and several cells are small. |
| AP-NORC age × gender × marital-status model | Positive-case cells too small, and the construct would still not measure current partnership. |
| Custom reanalysis of the Willoughby study | Raw data, subgroup denominators, field dates and the item-to-statistic mapping were not verified. |
| Cross-country romantic or intimate-AI ranking or pooled prevalence | Constructs, samples, recall windows, age bands, periods and denominators differ. |
| Marriage, divorce and household trends as explanations of AI use | No same-person dataset supports correlation or causation. |
| Market size, growth rate or CAGR, and chained commercial snapshots | Coverage and definitions are incomplete or incompatible. Downloads, traffic and store revenue are not users or total market revenue. |
| Google Trends as evidence of adoption or growth | Search interest is not measured adoption, revenue or causation. |
| Product “best”, safety or privacy rankings | Public claims and disclosure states are not audited behavior. |
Disclosure, evidence cutoff and conflicts of sources
Publisher: MyDreamCompanion, a company that operates in the AI-companion category. The report is not described as fully independent. It uses public original sources and no private MyDreamCompanion data, and compares and ranks no product.
Dates: the “evidence cutoff” at the top of the report is the end of the evidence review, and “sources last checked” is the day of the last editorial verification. Neither is a publication date. No external peer review has been done or is claimed.
Authorship and AI tools: This report was researched, compiled, and edited by Varazdat Samsonyan for MyDreamCompanion, with assistance from the AI tools Anthropic Claude, OpenAI Codex, Google Gemini and Perplexity AI. These tools supported the research and editorial workflow. They are not credited as authors. Checks made with AI tools are not independent verification.
The links to MyDreamCompanion’s own products in Sections 2 and 5 are product examples, not research sources, and no finding relies on them.
Funding and affiliation are shown where relevant. They are not treated as automatic invalidation, but they are not hidden when a source studies its own product, comes from an advocacy institution or has another material interest. Zhang et al. report academic funding and no competing interests.
| Source | Context | Treatment |
|---|---|---|
| Fang et al. | The paper states that OpenAI funded the research and identifies 4 co-authors as OpenAI employees. | Both facts are stated wherever the study is used. |
| Willoughby / Wheatley / IFS | The 19% and the 51-minute figure appear in a same-author institutional report (Wheatley Institute, summarized by an IFS blog) from organizations with a family-policy perspective. | Labelled Quota sample. The peer-reviewed journal study is distinguished from the non-peer-reviewed report. |
| Common Sense Media | A child and media-safety advocacy organization; calibrated hybrid probability/nonprobability design. | Labelled Representative survey, with the design and role stated. |
| “Lessons from an App Update at Replika AI” | One survey of 120 users in that paper was posted with assistance from the Replika CEO. | Applies to that recruitment only, not to the separate community-post counts used in Section 6. |
| MyDreamCompanion | The publisher operates in the AI-companion category. | Disclosed prominently. No private MDC data and no product comparison are used. |
Source verification, statistical handling and access limits
Source verification. Original studies, datasets, official statistics, regulator records and provider documents are preferred, and key figures link directly to the original source. Question wording, population, denominator, fieldwork period, sample design and result location were checked where access permitted. Abstract-only access is stated, and a journal citation alone does not imply that the exact headline number was visible. Corrections were checked: Döring et al.’s corrected article is used, and the publisher’s correction restored missing table headings without reporting changed values. The study authors’ own Wheatley Institute report, which is not peer reviewed, is the source of the Willoughby 19% and of the 51-minute time figure, because the journal article was not accessible. Secondary media, vendor marketing, search-interest charts and private SEO material are not primary evidence.
Statistical handling. Most calculations are descriptive: weighted percentages, arithmetic ratios, counts and percentage-point differences within one compatible source. No cross-study averages or pooled rates are calculated when questions, populations, denominators, units, recall periods or methods differ, and no ecological correlations are calculated between AI use and marriage, divorce, households or loneliness. Missing, unclear and not-applicable values are never converted to zero. Post, review, session and character-description counts keep those units. AP-NORC and NORC conducted the July 2025 survey (funded by The Associated Press and NORC); its AmeriSpeak panel is probability-based, with a 2.7% cumulative response rate corrected by weighting.
Source access limits (checked 20 October 2026). Where a publisher blocked automated access, the report says so and uses the best open copy. Nothing below is described as verified against text that was not read.
- Willoughby et al.: the journal article and questionnaire supplement were not accessible. The abstract was read through publisher metadata. The 19% and the 51-minute figure come from the authors’ Wheatley Institute report (text read in full; not peer reviewed), which the IFS blog summarizes. Fieldwork dates and funding could not be confirmed.
- Zhang et al. (Nature Human Behaviour): the published page was readable to the abstract. The 11.8%, 51.0%, 92.9% and 68.0% figures, the eligibility rules and the study period were checked in the authors’ open manuscript.
- De Freitas et al. (Journal of Consumer Research): the journal full text was paywalled. Numbers were checked in the open working paper, where this study is reported as Study 4 (the journal’s study numbering was not checked).
- Folk & Dunn: the journal page was blocked to automated access; the findings were checked in the authors’ accepted manuscript (open preprint server) and the published abstract. Guingrich & Graziano (AIES 2025 proceedings) and Ebner & Szczuka (Technology, Mind, and Behavior): checked in the open preprints; the published full texts were not opened, and the published abstracts keep the same sample sizes.
- Skjuve et al., Wang & Dehnert and Laestadius et al.: abstracts and publisher metadata only. Kieserman et al. and Lai & Cheon: checked in the open preprints; the published CHI and DIS 2026 versions were not opened.
- California SB 243: the Legislature’s bill pages blocked automated access, so only the Governor’s signing release was read and only what it states is reported. A later Governor’s release (10 September 2026) was also read; the text of SB 1119 was not. The Italian Garante decision was read except for sections that did not show the fine, which is taken from the Garante’s press release.
Corrections policy and legal notice
Corrections: if an error is found, the report is corrected in place, the version and date change, and the change is recorded with the prior value, the replacement, the source and the reason. Version 1.0 is the first publication, so no corrections have been issued.
Legal notice: this report is not legal advice. Legal and regulatory summaries were checked against primary official records and are presented for research purposes, not as legal advice.
How to cite
Samsonyan, V. (2026). The State of AI Companionship 2026. MyDreamCompanion. Evidence cutoff 20 October 2026. Available at the address of this page.
No DOI or ISBN is assigned. For a single statistic, cite the original source shown beside it and keep its population, denominator, period and label. The quote builder under the 4 measures writes a correct sentence for each headline figure.
Frequently asked questions
What counts as an AI companion?
Why do the prevalence figures differ so much?
Can quota-sample results be treated as national rates?
Why are Reddit posts, app reviews and donated chats not treated as prevalence?
Do AI companions cause loneliness, or cure it?
Is there a market-size estimate?
Why is evidence about minors kept separate?
Can the findings be compared across countries?
Does a documented product feature mean people use or value it?
Is this report independent?
Is any of this legal advice?
How were the studies chosen?
What does MyDreamCompanion add?
How often will the report be updated?
Original sources cited on this page (36)
- Pew Research Center (2026). Americans and AI, survey topline, 17–23 Feb 2026. Representative survey Pew topline
- AP-NORC (2025). Young Adults Leading the Way in AI Adoption, July 2025 topline, methodology and public-use file. Representative survey AP-NORC topline · methodology · project page and data
- MDC analysis of the AP-NORC public-use file (2026). Weighted subgroup shares from AP-NORC July 2025 item AI1G. MDC analysis AP-NORC data and codebook
- Willoughby, Dover, Hakala & Carroll (2025). Artificial connections: Romantic relationship engagement with artificial intelligence in the United States, Journal of Social and Personal Relationships, 42(12), 3363–3387. Quota sample journal abstract · DOI
- Willoughby, Carroll, Dover & Hakala, Wheatley Institute (2025). Counterfeit Connections: The Rise of Romantic AI Companions and AI Sexualized Media Among the Rising Generation, Wheatley Institute report by the study authors (not peer reviewed); the IFS blog summarizes it. Quota sample Wheatley report (PDF) · IFS blog summary
- Döring, Mikhailova & Mohseni (2026). Prevalence of AI-Supported Sexual Activities Among Adults in Germany, Archives of Sexual Behavior. Peer-reviewed article · open-access PDF
- Zhang et al. (2026). Interaction with AI companions and psychological well-being, Nature Human Behaviour. Peer-reviewed Nature Human Behaviour · open manuscript
- Pataranutaporn et al. (2025). “My Boyfriend is AI”: A Computational Analysis of Human-AI Companionship in Reddit’s AI Community. Preprint arXiv
- Ebner & Szczuka (2025; published 2026). Understanding romantic relationships between humans and chatbots: A qualitative and quantitative study on romantic fantasy and other interpersonal characteristics (arXiv title: Understanding Human-Chatbot Romance), Technology, Mind, and Behavior. Peer-reviewed preprint · published version
- Ta et al. (2020). User Experiences of Social Support From Companion Chatbots in Everyday Contexts, Journal of Medical Internet Research. Peer-reviewed article
- Skjuve et al. (2021). My Chatbot Companion: A Study of Human-Chatbot Relationships, International Journal of Human-Computer Studies. Peer-reviewed article
- Brandtzaeg, Skjuve & Følstad (2022). My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship, Human Communication Research. Peer-reviewed article
- Maples et al. (2024). Loneliness and suicide mitigation for students using GPT3-enabled chatbots, npj Mental Health Research. Peer-reviewed article
- National Center for Family & Marriage Research (2025). Refined Divorce Rate in the U.S., 2024, original table (ACS-based). Representative survey NCFMR table
- CDC/NCHS (2026). National Marriage and Divorce Rate Trends, 2000–2024. Official record CDC/NCHS table
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