Try this right now. Open ChatGPT, Gemini, or Perplexity and ask it to recommend the best in some category you actually care about, a favorite gadget brand, a streaming service, a game studio, whatever comes to mind. Read the answer closely. There’s a decent chance your favorite doesn’t show up at all, or shows up buried behind three competitors the AI seems to like better.
That’s not a glitch. It’s just how these tools actually work, and almost nobody outside the AI industry has been paying attention to it until fairly recently.
Google Used to Be the Referee. Now It’s a Handful of AI Models, and They Don’t Agree
For most of the internet’s history, showing up online meant showing up on Google. One search engine, one results page, one set of rules everyone more or less understood. Rank well, get seen. Simple enough.
That single referee has quietly split into several, and they don’t call the same game. ChatGPT, Gemini, Perplexity, Copilot, and Google’s own AI Overviews each generate their own answer to the exact same question, often citing completely different sources and naming completely different brands as the top pick. Ask five AI tools the same question about your favorite thing and you can get five different answers, and sometimes your favorite brand is the star of one and invisible in the other four.
This is the part that should sound familiar to anyone who has ever argued online about which version of a franchise is canon: different sources, different interpretations, wildly different conclusions, all presented with total confidence.
It gets weirder when you ask follow-up questions. Push an AI model on why it picked one brand over another and it will often cite a source, sometimes a review site, sometimes a forum thread, sometimes a page that hasn’t been updated in years. The confidence in the answer doesn’t always match the quality of what it’s actually pulling from.
Why This Actually Matters Beyond Being a Fun Experiment
It’s easy to treat this as a curiosity, something to try once and move on from. It matters more than that for a simple reason: a growing number of people are now using these tools the way they used to use a search engine, meaning the AI’s answer is often the only impression a brand, creator, or product ever makes on that person.
If an AI model doesn’t mention a small game studio, an indie publisher, or a niche gadget brand when someone asks for recommendations, that brand effectively doesn’t exist for that person’s search. Not because the brand did anything wrong, but because the AI’s answer was built from whatever sources it happened to weigh most heavily, which isn’t always the most accurate or complete picture of what’s actually out there.
What’s Actually Happening Behind the Answer
Some of these tools, Perplexity and Google’s AI Overviews especially, don’t just recall information from training. They run live web searches in the background before answering, breaking one question into several related searches and pulling from whatever ranks well at that exact moment. That process has a name in the industry: query fan-out. It means the “one answer” you see is actually stitched together from several searches you never see, each one shaping a small piece of the final response.
That also explains why the answer can change from week to week even when nothing about the brand itself changed. If the underlying pages the AI pulls from shift, so does the summary it builds. A brand’s presence in these answers isn’t fixed the way a search ranking used to be. It moves, sometimes without anyone at the company noticing until a customer mentions it.
Comparing What AI Visibility Tracking Actually Costs
The people who do pay attention to this, mostly brands, creators, and studios who want to know how AI models are describing them, have historically had to pay a lot to check. Most tracking tools bundle their own AI usage costs into a subscription, meaning a company pays a markup every time the tool runs a check on its behalf.
| Platform | Plan | AI Responses / Month | Cost / Month |
| Llumo | Free platform + own API keys | ~42,000 | ~$115–150 (API cost only) |
| Peec AI | Starter | ~4,500 | $95 |
| AthenaHQ | Starter | ~3,600 | $295 |
| OtterlyAI | Standard | ~12,000 | $189 |
| Writesonic | Growth | ~18,000 | $399 |
That gap is the difference between checking a handful of questions once a month versus running daily checks across every major AI model without worrying about hitting a cap.
A newer approach, including a platform called Llumo, skips the markup entirely by having the company connect its own API keys directly to the AI providers and pay those providers at cost. The tracking platform itself is free; the only real expense is the AI usage itself, the same cost anyone pays to use these models directly.
What Fans and Creators Are Starting to Notice
This isn’t just a brand problem. Independent creators, small studios, and niche communities are increasingly the ones checking first, since they’re the most likely to get quietly left out of an AI’s answer in favor of a bigger, more heavily cited competitor. A small comic publisher, an indie dev studio, or a niche YouTube channel can do everything right on social media and still be invisible the moment someone asks an AI model for a recommendation in their category.
Checking this isn’t complicated once the tooling exists. It comes down to running the actual questions fans would ask, across each major AI model, and seeing who gets named, who gets cited as a source, and who doesn’t show up at all.
For a smaller studio or creator, that kind of check can be genuinely eye-opening the first time. It’s one thing to assume an AI model would mention you if asked. It’s another to actually run the question and see the gap in real time, competitor names filling a space where yours should be.
Frequently Asked Questions
Why does the same question get such different answers from different AI tools? Each model was trained on a different mix of data, and several of them run their own live web searches before answering, so the specific pages ranking well at that moment shape the final response. Two models can genuinely disagree.
Is this only something big companies need to worry about? No. Smaller creators and niche brands are arguably more exposed, since a single AI answer that leaves them out can be the only impression a potential fan or customer ever gets.
What is Answer Engine Optimization? It’s the practice of tracking and improving how often AI tools like ChatGPT and Gemini mention and cite a brand or creator, the AI-era equivalent of tracking a search engine ranking.
Can someone check this without a technical background? Running the checks typically means connecting API access to whichever AI providers are being tracked, which is a short setup rather than a specialized skill, though it helps to have someone comfortable with basic account setup handle it.
Is free AI visibility tracking actually free? The platform itself can be, while the underlying AI usage is usually billed by the provider directly. That total cost tends to be far lower than subscription-based tools that bundle a markup into the price.
Next time you’re in an argument about which brand, studio, or creator actually deserves the credit, it might be worth asking an AI model directly and seeing who it names first. The answer might surprise you, and increasingly, it’s the answer a lot of other people are already getting.
