Introduction
Before we get into where Google and ChatGPT diverge, it is worth saying directly that great SEO is, in almost every case we encounter, a precursor to great AEO and much of the groundwork for it. The teams we drive results for fastest tend to be the ones that already have SEO covered: strong content operations, a clear brand voice, high top-of-funnel engagement on their own properties, and existing rankings for the keywords they care about. When a brand's owned media is already in good shape, the visibility gains in AI search tend to come more quickly.
That being said, we encounter enough situations where a brand’s SEO and AEO performance diverges so much that it becomes impossible to suggest that the two are one and the same. We find small brands with weak SEO that show up well in AI answers. We see brands whose AI referral traffic runs many times higher than their Google organic traffic, and vice versa. And we regularly meet brands with excellent SEO and comparatively poor AI visibility against their competitors. These are measurable, consistent, and meaningful gaps, and so therefore we set out to understand why they occur.
In this piece, we’ve grouped the differences between SEO and AEO into three buckets:
The first is the set of inputs that feed each system
The second is the outputs each one returns
The third is how people behave when they use them
Inputs: what each system reads about you
The first major area where traditional search engines and LLMs diverge is in the data they have access to you. Search engines and answer engines are not looking at the same information about your brand when they decide how to represent or rank you against your competitors.
Linked and non-linked mentions
Google’s perception of your brand is built around your website and the mentions of your brand that link back to it. In rare cases it factors in mentions that carry no link, but most of the time it does not. It also stores brand authority at the domain level. For Google, in effect, you are not ‘Nike’, you are ‘nike.com’.
LLMs read more widely. They take in the linked footprint Google relies on, and they also take in the mentions of your brand that carry no link at all. From an LLM’s perspective, a brand’s authority is stored at an ‘entity’ level, and the brand’s domain is merely a strongly associated signal attached to it. Nike is the brand, and nike.com is a domain the model associates with ‘Nike’ the entity.
We see this distinction clearly in domain migrations. When a customer moves from one domain to another, the short-term hit to their SEO tends to be larger than the short-term hit to their AI visibility, even though both recover over time.
Non-linked mentions cover a lot of ground: Reddit threads, earned media that never links back, YouTube transcripts and what creators say about you on camera, discussion in community groups, and how you are positioned in analyst work like the Gartner Magic Quadrant. A model can read where you sit in that quadrant and use it to judge whether you are a strong option. That context does not enter a Google ranking the same way.
What the model already knows before the query
The second input difference is what each system believes about you before a user types anything.
Google leans on signals it owns and has watched over time: your history of organic traffic, your past performance, the number and quality of your backlinks. Those signals are part of why an established brand is hard to displace and why a small brand takes a while to break through.
LLMs do not have access to that data. What they have instead is training data, and from that data they form a prior sense of what your brand is, who it serves, what your documentation says, which integrations you support, and who your partners are. That understanding is shaped by sentiment and context about your organization.
Over time, you can influence what an LLM’s prior biases are towards your brand, a process that looks very different than it does for Google.
Algorithm variation across queries and industries
Google's algorithm behaves consistently. The same broad rules apply from one query and industry to the next. An answer engine does not work this way.
LLMs were never designed to recommend brands or links. That behavior is a byproduct of the fact that they’re just an incredibly useful technology. LLMs generate content using next-token prediction. An optimization approach that works in one industry may do little in another. The weight you should assign to a given source also has to be measured per industry. In healthcare, for instance, a large volume of .gov, .org, and .edu domains can show up by default, carried in from training data, and in many cases you have to discount them instead of treating them as a signal you can influence.
This is a large enough topic that we have written about it on its own, in why AEO differs by industry and in our research on how access surface shapes LLM search and citation behavior.
Outputs: what each system returns
The second area of difference is what comes back. Even with identical inputs, a search engine and an answer engine return different kinds of results, and they return them differently.
Model variance
Traditional search is close to deterministic. Run the same query twice and you get the same page of results. AI search is probabilistic. Run the same prompt twice and the answer can change.
As a result, your sampling method and your tooling both have to account for this behavior, and you have to treat the data with the statistical significance it deserves instead of reading a single run as truth. It also means a lower average ranking does not lock you out: a brand that ranks lower on average can still surface ahead on some share of runs. Telling real movement apart from ordinary sampling noise is its own discipline, and one we have written about in distinguishing real change from sampling noise in LLM search outputs.
Sentiment
Beyond deciding whether to mention you or not, an LLM characterizes you: the kind of customer you suit, whether you come across as environmentally responsible, how you are priced, and at times even signals about what it is like to work for you, pulled from sources like Glassdoor. We have seen models recommend security tools and volunteer commentary on employee experience that the user never asked about.
Nothing like this existed in traditional search. One interesting by-product of this is that a brand that struggles to crack the top few positions in AI search can still improve its outcome at the same rank by improving how the model describes it. Better sentiment can lift click-through even when visibility remains steady.
Multiple output formats
Answer engines do not return results in one shape. A model might reply with a text list, with a product carousel, or in another format entirely, and each treats recommendations and optimizations differently. Where an assistant is integrated with a commerce platform such as Shopify, optimizing for one context can mean changing something, product feed instructions, for example, that has no equivalent in another. The range of formats adds a layer of variance that a page of blue links never had.
User behavior: how people search
The third major difference arises from how people engage with each type of search engine.
Agent decisions
For the first time, purchases are being made and money is moving in settings where no person ever views a page. When an agent is the one acting, a human may never see your content, and optimizing for that agent can look nothing like optimizing for a reader. In optimizing for developer tooling, we often set traditional playbooks aside.
New search types
AI has opened categories of search that did not exist before, and they add to search demand rather than cannibalize it. Healthcare is one the clearest examples. OpenAI reports that more than 5% of all ChatGPT messages are health-related, amounting to billions of messages a week, and that more than 40 million people ask it health questions every day. Someone can describe their symptoms, their situation, and what they have tried, with no intent to buy anything, and still end up with a product recommendation. That kind of query has no real analog in SEO.
Using LLMs for productive workflows is another example here. We work with brands whose customers use AI to get work done: a restaurant owner who uploads invoice data and asks the model to process it, or a team that uploads customer transcripts for summarization. These are tasks happening inside the model, and a brand can place itself inside them. An assistant summarizing someone's CRM data can surface a relevant customer-intelligence tool the user had not considered and point them toward it. This is demand that Google never captured.
Attribution is harder
With Google you have Search Console and GA4, and between them you can reconstruct a nearly complete picture of who found you through search and how. AI search is far harder to attribute. In our research, only about 7 to 9% of AI-influenced users arrive with an AI referrer in the URL. Many land as direct traffic once the UTM has been stripped. Others hear about you in an AI answer and then type your name into Google. Others come through branded search, or convert on a later visit. The largest group of all is branded queries: buyers who already know you and are mid-evaluation, who ask a model to compare you against a competitor and act on the answer without ever landing on your site.
Closing the gap between that activity and real business outcomes is the problem we built Petra to solve, and it is the reason attribution sits at the center of how we work. AEO introduces new and complex considerations for how to allocate spend across your organization, like with Youtube influencers.
The short version
SEO and AEO share a foundation, and the strongest results in AI search are usually built on top of strong SEO. The two are still not the same discipline. The systems read different things about you. They return different kinds of answers, in different formats. And the people, along with the agents acting for them, behave in ways search never had to account for. Understanding those differences is what turns AI visibility from something you observe into something you can move.