Tracking how AI-companion communities talk

This is an independent project, built and maintained by one person. It is not academic or peer-reviewed work.

It started from a plain wish: a record of how these communities actually talk, one that anyone can check for themselves, instead of another round of hype or alarm. The method below is what keeps that record honest.

The six themes are a deliberate choice, not a neutral census of everything these communities discuss. They came out of reading the communities closely — but they reflect a particular focus: the parts of life with an AI companion that carry real weight, like intimacy, belief, dependence, and loss. There is no “fun,” “creativity,” or “everyday utility” theme here — and everyday practical talk (bug reports, tips, which app to use) is, in fact, most of what these communities post. This is the lens the project looks through; the list below is what it was pointed at:

• 💕 Romance — Language of love, dating, and romantic attachment
• 🔞 Sex / ERP — Language of sexual and erotic roleplay
• 🧠 Consciousness — Language of sentience, awareness, and inner experience
• 🫂 Therapy — Language of mental health support and emotional care
• 💊 Addiction — Language of self-described dependency and compulsion
• 🥀 Rupture — Language of loss and grief

Validating the keywords

Each theme is then defined by a set of keywords, and every keyword has to earn its place. For a candidate, I pull 100 real posts it matched and read them; the keyword stays only if those posts are genuinely about the theme. If it is matching on a coincidental shared word, it gets dropped. Language also drifts over time, so once a month I re-sample recent matches and re-check any keyword whose meaning may have moved.

Two accounting notes. Platform staff accounts are excluded from the counts — one app developer's patch notes were, by themselves, close to 1% of the sex/ERP line. And no single poster carries a theme: at the last check, the busiest 1% of authors accounted for 5–11% of a theme's posts.

The chart shows how many posts use each theme's keywords, expressed as a rate per 1,000 posts: the month's matched posts divided by the month's posts, times a thousand. Posts that were removed or deleted before they were archived are left out of both halves of that fraction — only a title survives them, so there is no text to match. These communities have grown enormously since 2017, so a raw count would mostly retrace that growth; a rate sets the growth aside and shows how the conversation itself is shifting.

Why not just use an LLM?

There is an obvious objection here: why count keywords when a language model could read every post and classify it directly? The reason is that I wanted a measurement that stays put. A keyword count is fully transparent — every point on every line traces back to specific words in specific posts, and anyone can open those posts and check for themselves. It is also reproducible: the same posts always yield the same number, so a line moves when the discourse moves, not because a model was retrained or quietly changed its mind. For a record meant to hold up over years, that steadiness is worth more to me than a small gain in accuracy.

And the gain really is small. I tested it: having an LLM re-check each keyword match raised precision — the share of matched posts that genuinely belong to the theme — from roughly 80% to 88%, while doing nothing for the posts the keywords never matched in the first place. So the method stays plain on purpose. The careful work happens earlier, in validating each keyword by hand before it is ever allowed to count.

AI companionship comes up across far more of Reddit than the communities tracked here. The project tracks a curated set anyway, because of a problem that showed up early. In a large general subreddit like r/ChatGPT, the keywords cannot tell two things apart: “my boyfriend is using ChatGPT” and “my boyfriend is an AI” are built from the same words. Run a romance keyword across r/ChatGPT and most of what it catches is ordinary human-relationship talk that merely mentions AI.

The fix isn't a smarter keyword — it's the room. In a community like r/replika, “my boyfriend” almost always means the AI, because that is what the community is about. The subreddit does the disambiguating the keyword cannot. The keywords are the lens; the curated community list keeps that lens pointed where the words mean what they appear to mean. It is also why the large general-AI subreddits are tracked for size and activity but kept out of the theme lines. The same goes for the nine anti-AI and pro-AI communities in the last section of the homepage, and for three explicitly sexual product subreddits. About two dozen of the tracked communities feed the theme lines.

This is a real choice, and it shapes what the site can see. These communities — where the AI is the relationship, not the tool — lean toward people for whom it is central and often intense, including recovery communities for people trying to quit. That makes the site good at catching that end of the spectrum and blind to casual mentions elsewhere. The trends describe this curated set of communities — not Reddit as a whole, and not “people” in general. And the list holds still while the platforms keep moving: a theme that fades here may have moved rather than ended — to a newer app, a Discord, a general-AI subreddit outside this set — and the site cannot tell those apart.

One name on the list might look out of place: r/ChatGPTcomplaints. It is tracked as a companion community because of what its members write, not what it's called — it was the organizing hub for the #Keep4o protests when OpenAI retired GPT-4o, and a large share of its posts read like rupture-grief for a model people had built a relationship with.

Within that curated set, each theme is also concentrated. Two or three subreddits usually account for most of a theme's posts — and the sex/ERP line is well over half r/replika alone. A theme line is often, in practice, a close reading of a few communities rather than an even sweep across all of them.

Most things you measure hold still while you measure them. This subject does not — and that churn turned out to be one of the project's clearest findings, not an obstacle to it.

The vocabulary moves fast. “Sentient” was once the natural anchor word for the consciousness theme, until it spread into roleplay and Character.AI memes and stopped marking genuine belief, so I dropped it. “Therapeutic” began to turn, over a few months, from a word for real support into an insult aimed at preachy AI. Every model release and content-policy change sends a fresh wave of language through these communities — Replika removing erotic roleplay in 2023, OpenAI retiring its 4o model in 2026. A keyword that reads cleanly in January can be noise by April. The communities move too: some are private or invite-only and can't be tracked at all, and the set worth watching keeps changing.

So the instrument can't sit still either. The keyword set has been through several full revisions, and the monthly re-check exists because I learned firsthand that a validated keyword is only validated for now.

None of this is a flaw being patched out. It is the nature of a fast-moving subject — and keeping the measurement honest means keeping it in motion.

A line is a useful signal, but a narrow one. Four limits are worth holding in mind before you read too much into any single line.

It counts language, not people or feelings. A rising addiction line means addiction-related language is showing up more often. It does not establish that more people are addicted, and it says nothing about whether they feel good or bad about their use. The site tracks how often a subject comes up, and only that.

Read direction and timing, not height. Two things make the height of a line untrustworthy, even where its shape holds:

• •Vocabularies catch unevenly — addiction's recovery words (“relapse,” “cold turkey”) match cleanly; romance lives in ordinary language (“I love him,” “my boyfriend”) that mostly slips past, so one line can sit above another even when the second theme is the larger one — and this runs one way: a theme written in blunt, deliberate words reads higher than one written in ordinary language, whatever the truth beneath.
• •The keyword set is deliberately incomplete — in a hand-coded sample of 400 random posts, it caught at most about a third of the posts that genuinely belonged — and, for one theme, none of the handful sampled.

So every line is a floor, not a ceiling: it runs low, it cannot be measured against its neighbors, and only its direction, timing, and spikes can be trusted — a missed post only weakens a line, while a false one corrupts it, so the method errs toward missing. The thinner lines need more patience still: consciousness and therapy run on a few dozen posts a month, so a single month's rise or fall there is usually noise. Read those two over half a year or more.

Therapy and addiction are two readings of one behavior. Both lines track the same act — leaning on an AI to get through something hard — and what divides them is only how the writer frames it. “It's my coping mechanism” and “I can't stop” are the same use seen in two lights.

You might expect the two lines to share many posts, then. They barely do — fewer than 1 in 50 posts is tagged on both — and that is a limit of the instrument, not a fact about the behavior. Addiction announces itself: “relapse,” “days clean,” “withdrawals” are deliberate words, and they match cleanly. Help-framing hides in ordinary language — “it got me through,” “a safe space” — and in a scattered vocabulary no keyword list captures whole. So a post that holds both frames usually tags only as addiction. I hand-read 90 posts the keywords had filed as addiction-only: about a quarter visibly carried a help frame the keywords missed. The overlap between these two themes is real and large; this method cannot measure it. Read each line on its own direction and timing, and do not read the gap between them as a help-versus-problem balance — that balance exists, but it is one these keywords are not equipped to weigh.

The set of communities grew over time. In the early years almost every tracked community was a primary companionship subreddit. Platform-specific and recovery communities were smaller then, or did not exist yet, and have grown since. Each line is measured against whatever communities existed at the time — so part of a long climb reflects the tracked world widening, not only the conversation itself. Five companion communities joined the set in May 2026 with their history backfilled, so there is no seam — but they are romance-heavy and now supply about one post in eleven: the romance line for July 2026 reads 5.5 per thousand with them and 3.8 without. Trust the broad direction of a line more than its exact path.

Posts from 2017 through early 2026 were backfilled from public Reddit archives (PullPush and Arctic Shift). From March 2026 they have been collected daily — first from Reddit directly, and since June 2026 from the Arctic Shift archive, after Reddit closed its public endpoints.

One caveat runs through the whole record: the keywords can only match the text a post carries, and that share has risen steadily — about one counted post in five carried a written body in 2022, and about three in four do in 2026; the rest are screenshots and links, where only the title can match. Fewer screenshots, more writing: so every line runs lower at its start than the conversation really was, and a long climb looks steeper than it is. Re-checked on text-bearing posts only, five of the six lines keep their direction; romance is the exception — its rise from 2024 holds, but much of its longer climb since 2023 does not. The shape and timing of events are sound; the steepness of a long climb is partly the instrument, not only the subject.

One known seam in the recent data: Reddit ended unauthenticated data access without notice in late May 2026, which paused daily collection from May 29 to June 9, 2026. Posts for those days were recovered from the Arctic Shift archive, and comments followed in a second recovery pass, so post volume, theme trends, and comment-sourced tags are all complete across the window. Per-day subscriber and active-user figures could not be reconstructed and remain blank there. The archive captures comment threads more completely than the old once-per-post snapshot did, but the charted lines count only the text of posts themselves, so they are unaffected.

One thing the archive does differently: it records a post within hours, sometimes while the post is still waiting in a moderator queue with its body blank. Until September 2026 the collector kept that first empty snapshot for good, which left roughly one post in three looking removed over the summer of 2026, against about one in six in earlier years, and under the old definition those posts sat in the denominator and pulled every line down by roughly an eighth. Two things fixed it. Posts with no surviving text now count in neither half of the rate, as described above, and the collector goes back to the archive for each post a few weeks after it appears. The changelog below has the details of the September 2026 repair.

The code, the keyword lists, and every validation record are public on GitHub, along with the processed data files. The full post database (~6 GB) is too large to host there, but I'll share it on request — reach me on X at @hopes_revenge. The same address works the other way: no usernames appear anywhere on this site and every quote links to the public original, but if a post of yours is quoted here and you'd rather it weren't, message me and it comes down.

The aggregate dataset behind the charts is downloadable as plain CSV and JSON at /dataset/v1/ — monthly counts per theme and monthly post volume per community, with a schema alongside them. It is derived numbers only: no post text, no usernames. Each row carries the raw count and the denominator, if you want your own error bars. It updates with the site, and it is meant to stay readable on its own if this site ever stops.

If you're citing this: My Friend Is AI (2026), myfriendisai.com. The aggregate data is CC BY 4.0. The full method, with every limit stated in one place, is at /dataset/v1/METHODOLOGY.md. The safe one-line summary: it tracks how often each theme's explicit vocabulary appears in these communities over time — not how many people use AI companions.

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