Three Communities, One Question
In the space of a few months, the same question surfaced in three communities that otherwise have nothing in common.
In r/content_marketing, a thread with over a hundred comments started with a moment many teams are quietly having: “someone casually said they found us through ChatGPT, not Google. wasn’t even something we were tracking.” The thread’s most-cited practical advice was almost embarrassingly analog — every time a new customer signs up, ask how they found you, with ChatGPT and Perplexity as explicit options, because nothing in your analytics will tell you.
In r/B2BSaaS, a founder reported that “Claude has officially started recommending my SaaS over the < $1M incumbents” and reverse-engineered a playbook from it: dense, structured content plus community mentions as the validating signal.
And in r/InsuranceAgent (about as far from marketing Twitter as you can get), a broker with twelve years in the business described a long-standing client who “almost switched brokers because when she asked ChatGPT for insurance recommendations for her new business, our competitor came up and we didn’t.” A carrier rep confirmed it wasn’t a one-off: “I am hearing more and more agents telling me they were recommended to a customer by chatgpt.” The broker’s question to the sub: “is this something I should be more worried about?” The thread’s consensus was yes. One veteran’s reply captured the mood: “I hate that you are right. Watching seo happen in real time was a hard shift to make.”
Marketing practitioners, SaaS founders, and Main Street insurance agents are all watching the same shift: a growing share of “who should I buy from?” decisions now route through an AI assistant, and nobody handed out a playbook. The threads answering these questions are full of partial advice and point-tool pitches. So let’s do what those threads couldn’t: separate what’s mechanically knowable from what’s being sold.
How Assistants Actually Source Recommendations
When an assistant recommends a vendor, a product, or a broker, the answer comes from some combination of three mechanisms. This part is not speculation — it’s how these systems are built.
1. The training corpus. Every large model is trained on a snapshot of text: websites, documentation, books, forums, licensed datasets. If your company is discussed substantively across that corpus — described, compared, complained about, recommended by real people — the model has internalized an impression of you. This is slow-moving. It updates when models retrain, on a cadence you don’t control and can’t observe. You cannot “submit” yourself to a training corpus. You can only be the kind of thing that gets written about.
2. Live retrieval. Increasingly, assistants search the web at answer time. Perplexity does this by design; ChatGPT and Claude do it whenever the question benefits from current information — and “which vendor should I pick?” usually does. Retrieval looks a lot like search: the assistant issues queries, reads the top results, and synthesizes an answer from what it finds. This is the fast-moving layer, and it’s the one where the mechanics of classic SEO still substantially apply. If a page ranks for the question and directly answers it, it’s in the candidate set.
3. Citations and consensus signals. When assistants retrieve, they weight sources that look like credible answers: documentation, comparison pages that show real work, community threads where actual practitioners name actual products. The B2BSaaS founder’s observation about community mentions has a mechanical basis — AI labs have licensing arrangements with platforms like Reddit precisely because human discussion is a signal of what real people actually use and trust. An assistant asked “what do people recommend for X” is, in a real sense, summarizing the conversations it can see.
Here is the honest part, and it’s the part most vendors skip: the blend is not observable from outside. Nobody outside the labs knows how much weight any given answer places on trained knowledge versus retrieved pages versus which sources. It varies by model, by question, by phrasing, by day. The same question asked twice can produce different vendors. Anyone who claims to know your “ranking” inside a model’s weights is describing something that cannot be measured. That uncertainty is not a reason to ignore the shift. It’s a reason to be suspicious of anyone selling certainty about it.
What Durably Works
Strip away the mythology and the durable strategy is recognizable, because it’s the strategy that always worked — with a higher bar.
Be the page that answers the question practitioners actually ask, in their words. Not the question your positioning deck wishes they asked. The threads above are full of the real phrasing: “do I need to optimize for ChatGPT now?”, “how do I get recommended by Claude?”, “how do I actually do Reddit research?” An assistant fielding those questions retrieves pages, and the page that gets synthesized into the answer is the one that engages the question directly, with specifics, tradeoffs, and honest limits. This is classic SEO logic. What changed is the bar for substance.
Search engines ranked pages; a human then judged them. Answer engines read pages and decide what to repeat. A keyword-optimized page with no real content could win a ranking; it cannot survive being read. The model summarizes what the page actually says — and if the page says nothing, there is nothing to repeat. Thin content that gamed its way onto page one now gets read, judged shallow, and skipped in the synthesis.
Be discussed where practitioners discuss. The observable pattern across every citation-heavy assistant is that community threads and answer-shaped content get cited constantly. Not because of any secret deal, but because a thread where an insurance agent explains which tool actually handled their renewals is exactly the evidence an assistant wants when someone asks the same question. You cannot fake this at scale — communities have developed sharp antibodies to planted mentions, and coordinated shilling gets called out in-thread and discounted. What you can do is earn it: build something people mention, and show up in those conversations honestly when you have something real to add.
Measure the crude way, because it’s the only way. The r/content_marketing thread got this right: there is no analytics layer for AI-driven discovery yet. Referral data from assistants is sparse and inconsistent. The one reliable instrument is asking — put “ChatGPT / Claude / Perplexity” as explicit options on your “how did you hear about us?” question and watch the line over time. It’s unsatisfying. It’s also the only number in this entire category you can currently trust.
What Doesn’t Work
Keyword-stuffing for LLMs. A cottage industry now sells “LLM-optimized” content: pages salted with question phrasings, invisible text, and prompt-like instructions aimed at the model. This is a bet that answer engines will remain dumber than search engines, and it’s a bad bet. Models are trained specifically to distinguish substance from filler, and every lab treats manipulation of this kind as an adversarial problem to be engineered away. You’d be building on a bug and hoping nobody fixes it.
AEO tools that measure nothing verifiable. The category has attracted vendors selling dashboards of your “AI visibility score” — a number derived by asking models questions and tallying mentions. Understand what that is: a sample of a nondeterministic system whose weighting you can’t observe and whose behavior changes with every model release. It’s not that the tools are dishonest; it’s that the thing they claim to measure does not hold still enough to be a metric. The insurance thread had a vendor pitching exactly this within hours. When a category is new and anxious, the first products to arrive sell the anxiety, not the answer.
Waiting for the playbook. The veteran agents in that thread who lived through SEO’s arrival made the sharpest point: the ones who waited for the definitive guide watched competitors compound a head start for years. There will never be a settled playbook for answer-engine visibility, because the engines won’t hold still. There is only the durable behavior underneath.
The Operations Discipline Underneath
That durable behavior has a shape, and it’s worth naming plainly: this is a content operations problem, not a tooling problem.
The loop is listening → answering → maintaining. Listening: knowing which questions your buyers actually ask, in their words, in the places they ask them — which means systematically reading the communities where your customers think out loud, not skimming them once a quarter. Answering: producing pages that genuinely resolve those questions, with enough substance to survive being read by a machine and repeated to a stranger. Maintaining: keeping those answers current, because a retrieval-based assistant will happily cite your outdated pricing page or your abandoned comparison post.
None of that is a hack, and none of it is new. It’s the same work good content teams always did, run with more rigor and pointed at a reader that never skims. The teams that struggle with it don’t lack a tool — they lack the operational cadence to do the listening and maintaining continuously instead of in campaign bursts. That’s a discipline problem, and disciplines can be run for you. It’s part of what we operate for clients at JieGou: the standing research pipeline that reads the communities, the answer-shaped content built from what it finds, and the maintenance loop that keeps it true. We don’t sell an AEO score, because we don’t believe one exists. We run the work that the score-sellers are gesturing at.
One last note, offered as evidence rather than cleverness: this essay exists because that research pipeline surfaced the same question three times, in three unrelated communities, over ten weeks — and flagged that nobody had written a straight answer. The system that noticed the question is the system that produced the page you’re reading. If an assistant sent you here, that’s the loop working.