AI Girlfriend Statistics 2026: What We Can and Cannot Say

Updated 2026-10-05. AI girlfriend guides

We do not have statistics for this category. We do not have access to industry-wide data, we have not published figures of our own, and this page previously presented a full set of numbers — market size, user counts, retention curves, demographic splits, churn reasons, per-country distribution — as though they were established. They were not sourced. They are gone, and we are not replacing them with a fresher set of guesses. What follows is the more useful thing: what a statistic in this category would have to look like for it to mean anything, and why the numbers you will find elsewhere rarely meet that bar.

Key takeaways

We have no market size, user count, demographic, retention, or churn figure for AI companions. Anyone who gives you one to four significant figures is reporting something other than measurement.
The reason is structural, not investigative: the data lives inside private companies, the category definitions are unstable, and the measurement that exists is usually model inference rather than a count.
"Market size" for this category is not a well-defined quantity. Estimates differ by an order of magnitude because they are measuring different things.
A useful statistic has a named source, a defined denominator, a sample, and a date. Most published figures in this category have none of these.
What you can get for yourself is better than the aggregate, and it is honest.

Why there is no number on this page

Three reasons, in order of importance.

The data is not public. Monthly active users, retention, and revenue are internal metrics at every company in this category. Some are disclosed in fundraising or press coverage, which makes them a real observed number attached to an unclear definition — active in what sense, measured how, as of when.

The category has no stable boundary. "AI girlfriend market" can mean companion-specific apps, all character-chat platforms, all consumer generative-AI spending, or adult-oriented AI chat. Any of those produces a completely different figure, and headline numbers frequently mix them. Two analysts using the same year and the same word can differ by an order of magnitude and both be describing their own scope correctly.

User counts in this space are usually estimates. Download figures come from third-party modelling, app store figures exclude web, and neither distinguishes one person with three apps from three people with one. "Monthly active users" is frequently a vendor-supplied marketing number rather than a measured one.

We would rather say this plainly than print a table.

What a statistic would need in order to be worth quoting

This is the useful part, because it applies to every figure you encounter in this category, including ours, if we ever publish one.

A named source you can locate. Not "industry reports" — the actual report, publisher, and date. Secondary summaries of paid reports usually quote a range rather than a value for exactly this reason.
A defined denominator. Out of what? Monthly active users requires a definition of active. Retention requires a signup cohort and a return rule. "Users" requires knowing whether it means accounts or people.
A method. Survey, census of server logs, panel study, app store estimate, or vendor claim. These are not interchangeable and they do not produce comparable numbers.
A date and a sample size. Both.
An incentive disclosure. Numbers supplied by the company selling the thing are marketing until corroborated.

A figure that fails these checks is not necessarily false. It is not a measurement, and it should not be repeated as one.

How to read the figures you find elsewhere

Estimates in this category are not all equally weak, and the weak ones share telltale features.

Revenue-share and market-concentration claims — "the top five platforms capture most of the revenue" — are the weakest. They require knowing total category revenue, which requires knowing total category scope, and they are almost always extrapolated from partial data or from public funding and headcount reporting. Treat them as narrative.

Conversion and churn benchmarks — "free-to-paid conversion averages X percent" — require a stable denominator and a consistent definition of both conversion and churn. Denominator inflation is the norm: platforms widen their free tier to grow the top of the funnel, which lowers the conversion rate without changing the business. A benchmark quoted without a date and a tier definition is describing a product decision, not a law.

Retention curves are more reliable in shape than in level. The early steep drop-off followed by a flattening long tail is a standard feature of novelty-driven consumer products and you will see it everywhere. The absolute percentages are not comparable across platforms and should not be used to rank them.

Growth projections compound. A projection built on a contested base inherits the error and multiplies it, which is why a 2026-to-2028 forecast can differ from its own starting point by a factor of two while both endpoints appear in reputable-sounding documents.

Demographic splits usually come from in-app surveys, which are self-selected, and from single-platform samples presented as market-wide. Treat the direction as suggestive and the percentages as illustrative.

What we can say, and what you can check yourself

We can describe the category. Its structure — platform types, monetization models, what the products do and do not do, and how the design decisions relate to each other — is verifiable by using the products, and it is not in dispute. Where this site describes how a feature works or what our own product does or does not do, that is a description, not a statistic.

You can check a real number for a real product. Pricing tiers are published. Whether a subscription renews, and what it costs, is in your own billing history. These are the only figures in this category with no interpretation layer between them and reality.

You can measure your own use. Volume, timing, and displacement are all observable from your own history, and they are the figures that actually apply to you. Averages across a skewed user base do not describe any individual, so your own record is more informative than a category average even if it is a sample of one.

We do not publish our own figures. We do not have verified aggregate engagement or retention data, and we will not present an estimate as a measurement. If that changes, the source and method will be stated.

Frequently Asked Questions

How big is the AI girlfriend market in 2026?

We are not going to answer this with a number, because we cannot source one and the published estimates disagree by an order of magnitude because they measure different things. The disagreement between published estimates is the most informative thing about them, and the section above on reading figures explains why.

What are the retention rates for AI companion platforms?

No verified cross-platform figures exist that we can cite. Retention shape is predictable — a steep early drop-off as novelty decays, then a long flat tail — but the levels are company-internal, definition-dependent, and not comparable.

Which countries have the most users?

We do not have geographic data, and the per-country and per-capita rankings that circulate have the weaknesses described in our page on countries and adoption. Those are the least verifiable claims in the category, because downloads are estimates and account counts are not public.

How much do users pay, and what is average lifetime value?

Individual prices are published and comparable. Anything framed as an industry average — average revenue per paying user, lifetime value, paying-user tenure — depends on a definition of "paying" and on a cohort definition, neither of which is disclosed consistently.

What makes users leave?

The plausible reasons are a short list you can reason about: cost, novelty decaying into repetition, model quality, privacy or data concerns, life changes, and a human relationship. The relative weight of each is company-internal. We have no survey and are not presenting percentages.

This article is for informational purposes only and does not constitute medical or professional advice. Luvlock AI provides tools for companionship but encourages maintaining a balanced approach to relationships and mental health.

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