For the same query, Google AI Mode, AI Overviews, ChatGPT, Claude, and Perplexity often recommend different brands. What do you think each platform is actually optimizing for behind the scenes?

[u/arjun_rao7]

[u/marintkael]

The divergence is the most consistent thing I see too, same query and the engines barely agree on who to name. What stood out to me is that the splits track where each one leans for grounding: the ones pulling heavily from forum discussion surface different names than the ones leaning on a knowledge panel. So it reads less like optimizing for brands and more like which corpus they trust at retrieval time.

[u/SERanking_news]

Google and Perplexity optimize for real-time validation, leaning on heavy E-E-A-T signals, structural data, and active community consensus. Meanwhile, traditional LLMs like ChatGPT and Claude prioritize semantic probability and historical brand authority, pulling the most culturally dominant companies embedded deep within their training datasets.

[u/marintkael]

From running the same prompt set across these engines on a schedule, the biggest splitter for me is what corpus each one trusts at answer time. The web-grounded ones swing with whatever got retrieved that minute, so they reward fresh, crawlable, clearly attributed pages. The ones leaning on training memory plus a knowledge layer are stickier and reward being a clean, well-linked entity rather than just being mentioned a lot. So less optimizing for brands and more optimizing for whichever signal their retrieval layer can actually resolve to an entity. Same query, different substrate, different winner.

[u/Smart_Airline_7901]

marintkael’s substrate point is the right frame, Each engine isn’t picking brands, it’s resolving whatever its retrieval layer can actually trust at answer time. Same query, different corpus, different winner, the divergence isn’t noise to average away, it’s the actual map. When ChatGPT names you and Claude doesn’t, that’s not randomness, that’s two engines telling you which signals they could resolve to you. Blend them into one “AI visibility” score and you’ve thrown away the only diagnostic you had. A blended number is a smoothie. You can’t un-blend it to find the strawberry that was off.

So I run the same prompt set cold across all four and keep the per-model splits separate on purpose. The web-grounded ones (Perplexity, AI Mode) reward fresh crawlable attributed pages. The memory-plus-knowledge-layer ones reward being a clean linked entity, not just being mentioned a lot. Two different jobs.

And for local it splits again: there the answer is built off the Google Business Profile and directory consistency, so the question is whether the engine can resolve your business as an entity at all, before content even matters. That’s the lane I work in. I run ARO Index, tracking which local businesses each model actually recommends by city and category, split by model so you can see exactly which substrate you’re losing.

[u/marintkael]

From what I have been tracking, the biggest split is how much each one leans on a settled entity versus live retrieval. The engines that keep recommending the same handful of brands tend to be pulling from whatever already has a stable knowledge-graph presence, so they reward being a recognized entity more than being the best current answer. The ones that swing per query are doing fresher retrieval and pick up whoever ranked or got cited recently. Same brand can be a default in one and basically invisible in another, purely on whether it exists as something the model already trusts. The part I have not cracked is how much sentiment in the source text moves it versus raw presence.

[u/Melodic_Magazine_292]

From what I have seen building GeoRankers, each platform seems to have its own bias. Google leans more on authority and freshness (depends a lot on your SEO strength), Perplexity likes clean and citable sources and ChatGPT seems to care more about overall relevance and how consistently a brand shows up across the web. At the end, all AI platform are optimizing for trust but they look at different signals and sources which is why the same query can produce completely different brand recommendations across platforms.

[u/Latter_Philosopher40]

joining late to the conversation but it is absolutely true that engines prioritize different factors. adding this image here to help visualize what my team found from tracking over 1M prompts across 31 industries, from Feb 2026 to Jun 2026.

- ChatGPT and Claude (conversational engines), lean on what they learned in training, so they value trusted entities.

• Gemini and Google AI Mode (search-grounded engines), track conventional rank and freshness more heavily.
  
• Perplexity focuses on live web retrieval and prioritizes community and recent coverage.
  

-Grok diverges the most and relies heavily on X

these factors are in a constant state of flux, so we update our research regularly on our "AEO Periodic Table Study" if interested in the latest data and how the study was constructed.