It started with frustration. Someone posted in Reddit’s r/selfhosted community about recommendation threads that felt off, a little too rehearsed, a little too convenient. Within hours, the replies pulled the conversation somewhere nobody expected: whether AI-generated posts are now quietly being fed into the pipelines that shape what ChatGPT, Gemini, Claude, and Perplexity tell their users.
Key Points
- Reddit users described what looked like coordinated, AI-generated discussions built to shift both Google rankings and the answers AI systems produce.
- Several claimed suspicious threads kept vanishing once criticism landed directly beneath the product names being pushed.
- The exchange also dragged a quieter industry into plain sight: GEO, a business now entirely built around making brands visible inside AI-generated search results.
Reddit Users Started Noticing the Same Script, Over and Over
The original post made a blunt case: Reddit had filled up with AI-generated SEO spam dressed as casual conversation. The logic behind it, the user explained, is straightforward. Reddit threads now surface prominently not just inside Google search but inside the answers AI systems hand back to users. That combination made it a target.
The shape of the threads felt instantly familiar to almost everyone who joined the discussion. A post opens with something like: “I have problem x, and have tried y and z. Curious what others are doing?” Then the replies arrive in sequence. One account casually drops a product name. Another reinforces it. A few more comments weigh it against well-known tools, which makes the whole thing feel balanced rather than planted. Users said the pattern had become easy to spot even when they struggled to name exactly what made it feel wrong.
One commenter captured the absurdity with a single mock post: “Wondering how other self-hosters have handled the common problem of petabyte-scale RAG for in-house LLM inference clusters serving 1000+ clients in their homelab?” People laughed, but the joke landed precisely because the style was so recognisable: language bloated with technical weight, a scale nobody in a homelab actually deals with, and a product recommendation tucked inside what looked like genuine troubleshooting.
Names came quickly after that. One commenter flagged r/homeassistant: “this is happening a bunch in r/homeassistant with multiple posts about robot vacuums.” Others listed psychotherapy forums, SaaS communities, VPS hosting threads, AI tooling subreddits. The sense was the same across each one: conversations that used to feel real now felt managed. Nobody claimed certainty. Nobody insisted every suspicious post was fabricated. What held the thread together was the number of people who had arrived at the same unease completely on their own.
The Conversation Shifted the Moment GEO Came Up
Somewhere in the middle of the thread, someone with a background in advertising dropped a term most users had never encountered: “There is an entire category of SaaS companies working in a space called GEO / AEO that is dedicated to this.” That single sentence stopped the discussion from being about spam and made it about something much more structural.
GEO stands for Generative Engine Optimisation. Where traditional SEO chases webpage rankings, GEO is built around something narrower: shaping how AI systems pull, cite, and piece together information when they generate an answer. It is not about appearing on a search page. It is about appearing inside the response itself.
The material provided shows how fast that market has moved. Companies including Profound, AthenaHQ, Scrunch, and Surfer now sell tools built specifically to track brand visibility across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Some track how often a brand gets cited. Others watch for brand mentions, recommendation placement, or what the industry calls “Share of Voice” inside AI-generated answers.
One GEO guide put the stakes plainly: “If your brand does not appear in those answers, you are not ranked third. You do not exist in the conversation at all.” A separate report cited Adobe Analytics figures showing AI-generated referral traffic to U.S. retail websites grew 393% year on year through Q1 2026. Numbers like that do not stay quietly in industry reports. They pull money and attention very fast.
Research cited throughout the uploaded material keeps returning to the same finding: AI systems favour quotations, citations, statistics, repeated mentions of the same entities, and structured information that looks reliable enough to pull with confidence. Reading that alongside what Reddit users were describing, the overlap was hard to miss.
The behaviours users had been complaining about, repeated product mentions wrapped in synthetic warmth, map almost directly onto what retrieval optimisation platforms now sell as a feature. One commenter compressed the whole strategy into a sentence: “seed Reddit and Quora with ‘authentic’ mentions, ride the LLM citations after.” After that, the thread stopped reading like a spam complaint. People were now talking about whether recommendation systems themselves had become the target.
Users Started Flipping the Strategy Around
The conversation turned tactical. Once people worked out that these campaigns depended on building positive, searchable associations around a product name, the obvious question followed: what happens when the association turns negative? The original poster had already noticed that suspicious threads often disappeared after criticism landed beneath them. If the point is shaping what AI systems retrieve, criticism attached directly to a product name might hollow out the campaign entirely.
One commenter offered a direct suggestion: “Suggestion, NAME THE PRODUCT while trashing it.”
Another pushed further: “Make sure when you do this you post the full name and website of the product/company. That way future Google results will pull it up.”
Some described it as poisoning the watering hole. Others said exposing dishonest marketing publicly was not poisoning anything. One reply settled the argument in five words: “is it poisoning when it’s true?”
Nobody produced hard evidence that the tactic works at scale. Still, several users reported that suspicious posts disappeared soon after criticism was tied to the product names being pushed. What made the exchange remarkable was how quickly people grasped the underlying logic without anyone laying it out formally. GEO systems depend on contextual relationships, citations, recurring entity mentions, the texture of recommendation patterns. The users were simply trying to run those relationships in reverse.
The Real Fear Was Not Spam – It Was Fake Consensus
At some point the thread stopped being about spam altogether. One commenter named what was actually unsettling: “Having your LLM pretend to be 10 different people and have full on discussions with itself on a post it created is weird af.” That landed differently from everything before it. The earlier comments were about fake product tips. This was about something bigger: the idea of fabricated participation, conversations that look real because they have the shape of disagreement and curiosity and back-and-forth, but contain none of it.
The GEO material clarifies why that reaction was so sharp. Several guides describe off-site trust signals and third-party discussions as increasingly important inputs for AI retrieval systems. Reddit, Quora, LinkedIn, review platforms, and niche communities come up repeatedly as citation environments that large language models treat as credible. The incentive that follows from that is not subtle: build conversations in the exact spaces where AI systems are already looking for signals of trust.
Some users shifted focus to the language itself. One commenter flagged a phrase they had started seeing everywhere in AI-generated posts: “And that’s when I realized, it wasn’t an x problem, it was a y problem.” Another noted that marketers are now deliberately removing em dashes, dropping Oxford commas, and loosening capitalisation, on the assumption that the imperfection reads as human. The original poster replied plainly: “Unfortunately it’s enough to fool a large majority of seemingly human users in basically every sub except this one.” The sarcasm had drained out by then. What remained sounded closer to resignation.
Because beneath every joke in the thread, the same question kept surfacing: how many conversations that feel real, actually are?
