Almost every "how to show up in AI" post is really a "how to show up in ChatGPT" post. I get why — ChatGPT is the one everyone pictures. But if that's where your effort stops, you're optimizing for maybe two-thirds of the problem and ignoring the rest.
I build FixAEO, a tool for tracking and improving whether AI engines recommend your brand, so I stare at this data a lot. The single most useful thing I've learned: the engines don't pull from the same places. What gets you named in ChatGPT is not the same as what gets you into a Google AI Overview or a Perplexity answer. So "get recommended by AI" isn't one game. It's five or six games that happen to look alike.
Let me show you what I mean, then give you something you can actually act on.
The traffic is already splitting
Start with where the attention goes. In B2B during early 2026, DigitalApplied found AI referral traffic split roughly ChatGPT 63%, Claude 18.5%, Gemini 10.6%, Perplexity 7.3%. ChatGPT leads, clearly — but well over a third of your AI-referred visitors are coming from somewhere else. If you only ever check ChatGPT, that third is a blind spot you can't even see, let alone fix.
And that's just the chat assistants. The quiet giant is Google's AI Overviews, which were serving around 2.5 billion users a month by mid-2026 and now appear on north of 20% of US searches. For a lot of brands, the AI answer that matters most isn't in a chatbot at all — it's sitting on top of the Google results page they were already trying to rank on.
The part nobody tells you: each engine sources differently
Here's the finding that reframed how I think about this. Profound analyzed roughly 30 million citations and found that Wikipedia made up about 47.9% of ChatGPT's top-10 sources — but only 0.6% of all AI Overview citations. AI Overviews lean heavily on Reddit and YouTube instead. Same question, completely different source diet.
Ahrefs adds another angle: across AI-cited URLs, only about 12% also rank in Google's top 10 — but that average hides big per-engine differences. Perplexity overlapped with Google's top results around 28.6% of the time, while ChatGPT, Gemini, and Copilot were closer to 8%. Translation: Perplexity behaves the most like classic search, so your SEO work carries over there. The others are their own thing.
Put those together and the strategy writes itself: you can't optimize "for AI." You optimize per engine, because each one is drinking from a different well.
Engine by engine (what pulls, and what to do)
A quick tour. I'm describing how each engine tends to source — not ranking them; they all matter depending on where your buyers are.
ChatGPT leans on established, reference-grade sources (Wikipedia looms large in its top sources). The reasonable read: being a well-referenced, authoritative entity — consistent facts about you across the web, a clean Wikipedia/knowledge presence if you qualify — helps here. It's also the engine sending the most referral traffic, so it earns real attention.
Google AI Overviews is the volume king and sources very differently — more Reddit, more YouTube, more of the messy open web. Getting named here often means being talked about in community threads and video, not just on your own site. It's less "polish your homepage," more "be part of the conversation."
Perplexity is the most search-like of the bunch (highest overlap with Google's top 10) and leans on fresh, citable pages. If your classic SEO is decent and your pages are genuinely quotable, Perplexity is where that shows up fastest.
Gemini and Google AI Mode live in Google's ecosystem. AI Mode is fascinating — huge user numbers (around a billion by mid-2026) but still a tiny slice of actual US search queries so far. Worth tracking, not worth betting the quarter on yet.
Copilot rides Bing's index, so being properly indexed and reputable on Bing (not just Google) quietly matters.
You don't have to master all of these on day one. But you do need to know which ones you're winning and losing — and that's exactly the thing a ChatGPT-only view can't tell you.
The moves that work across all of them
Per-engine nuance aside, a few things move the needle everywhere. These are the ones with real evidence behind them.
- Sample many prompts, per engine, over time. AI answers aren't repeatable — SparkToro and Gumshoe ran the same "best brand" prompt 2,961 times and got the identical list twice less than 1 in 100 times. One screenshot of any engine naming you is luck, not a measurement. You need a real hit rate per engine.
- Get mentioned across the web, not just linked. Ahrefs' correlations across 75,000 brands found web mentions (0.66) and YouTube mentions (0.73) tracked AI visibility far more than backlinks (0.22). It's correlation, not proof — but "get talked about" beats "build links" as a bet.
- Make your content quotable. A Princeton/IIT Delhi study (KDD 2024) found that adding citations, direct quotations, and statistics lifted content's AI visibility by up to 40%. Specific and sourced beats vague and salesy — across every engine.
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Make sure the citation crawlers can actually reach you. The bots that drive citations aren't the training crawlers. Let in
OAI-SearchBotandChatGPT-User,PerplexityBot,Google-Extended, and the Claude search agents. If your robots.txt blocks those, you can't be recommended, period.
And two things to stop wasting effort on: schema markup doesn't get you cited (Ahrefs added JSON-LD to 1,885 pages and saw roughly zero citation lift), and llms.txt is unproven for citations (Google says it doesn't use it). Add them if you like — they're harmless — but don't expect them to earn you a recommendation.
Why you need to measure all of it (and where I come in)
Here's the honest problem with everything above: you cannot run six engines by hand. Checking ChatGPT, Gemini, Perplexity, Copilot, AI Overviews, and AI Mode — repeatedly, across a set of real buyer questions, and actually keeping track of the sources behind each — is a full-time job with a spreadsheet that falls apart in a week.
That's the gap I built FixAEO to fill. It runs your prompts across six engines — ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Google AI Mode — and gives you a per-engine visibility score, so you can see at a glance that you're winning ChatGPT but invisible in AI Overviews, or vice versa. More importantly, it shows the citations and sources behind each answer and a competitor leaderboard, so you're not guessing why — you can see that AI Overviews is pulling from three Reddit threads you're not in, while Perplexity is quoting a competitor's comparison page. That's a per-engine to-do list, not a vague "improve your AI presence."
To be straight about the limits: on the entry plan those six engines refresh every 72 hours, not daily (daily is a higher tier), it's one seat and 15 tracked prompts, and it's early software — I'm still smoothing edges. Fair warning.
On price: most tools doing serious multi-engine tracking start somewhere around $95–$300 a month. FixAEO's free scan is $0 (it checks one engine, Gemini, so you can sanity-check fast), and the full six-engine tracking is $29/mo. I won't do a competitor teardown — their pricing pages are public — but the entry cost is the honest reason to look.
The takeaway
"Get recommended by ChatGPT" is a good goal that's too small. Your customers are asking Perplexity and Gemini, and reading AI Overviews on the results page, and each of those engines is choosing brands from a different set of sources. Optimize for one and you're flying blind on the rest.
So the move is simple to say and harder to do: measure across every engine, learn what each one pulls from, fix per-engine, and re-check. Start by finding out where you actually stand.
Run a free scan on your own domain and see whether AI names you today — then go find the engines where it doesn't.
And if you try it and something's off, tell me. At this stage that feedback is worth more than the $29.
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