Stop Treating Search Engines and AI Like People

There’s a mistake that even some smart marketers make again and again and again. And it’s less of a technical failure, more of a bad assumption: they keep treating search engines and AI systems as if they think and act the way a person does.

That single misunderstanding about how search actually works can wreck more SEO strategies than any algorithm update.

The problem is old in the SEO world, but it’s getting worse with AI. Look around and you’ll see people treating AI like a friend, a therapist, a life coach, and even a romantic partner. So it shouldn’t be that surprising that marketers are also projecting human motives onto AI systems.

As search tools start to feel more and more like people, the pull to humanize them grows stronger, and those conversational experiences can trick even capable marketers into thinking about these systems as more human than they really are.

What Is E-E-A-T?

E-E-A-T stands for experience, expertise, authoritativeness, and trustworthiness. It’s a framework from Google’s Search Quality Rater Guidelines that human raters use to judge the quality of a page. The acronym grew from the original E-A-T when Google added “Experience” as its own pillar in December 2022, treating firsthand knowledge as something separate from formal expertise.¹

It’s also the acronym everyone in SEO loves to argue about, and the argument usually starts with a detail that gets lost.

E-E-A-T is not a direct ranking factor of the Google search algorithm. It’s an evaluation framework applied by human quality raters, and those raters don’t move your rankings directly. Their aggregated feedback trains Google’s ranking systems over time.

So when someone says to “improve your E-E-A-T,” they’re pointing at a destination and calling it a setting you can adjust. There’s no E-E-A-T score sitting in a database with a company’s name on it. Google has confirmed there are no E-E-A-T scores at all, but rather classifiers that sort domains and documents into classes.

Inside the SEO world, some people say these things are just manipulative concepts Google uses to get brands and webmasters to create better content, so that Google looks like it’s delivering better results. Others believe E-E-A-T is the end-all-be-all, that you simply do E-E-A-T and you win. The truth sits between those poles, and it’s more interesting than either camp will admit.

The Real Problem Is Personification

E-E-A-T is a clean example of something the industry runs into constantly, where marketers begin to humanize or personify the underlying algorithms and technology they work around, with, or against. The words sound human. Trust. Authority. Expertise. Experience. So people assume the machine weighs them the way a human would.

It doesn’t. Google has no real, effective way of quantifying trustworthiness or authority or expertise the way humans might. It has to approximate those things through its algorithms, using data points it can actually interpret and score. The human-sounding label is just a wrapper around a pile of machine-readable signals.

Google put it about as plainly as it can be put: E-E-A-T isn’t specifically part of the algorithm, but Google’s algorithm is designed to mirror how a human evaluator would judge content quality, looking at various signals to estimate how much experience, expertise, authoritativeness, and trustworthiness a page demonstrates. Estimating is all it does. The understanding people imagine behind the score was never there.²

A thermostat is a fair comparison. It has no idea what “comfortable” feels like. It reads a number off a sensor and acts when that number crosses a line. Google is doing a version of the same thing, just with hundreds of sensors instead of one, and “trustworthy” is the comfortable it’s trying to approximate.

How the Approximation Actually Works

For a sense of how technical this gets, there are more than 200 known ranking factors, and Google won’t confirm the full list. Some of those signals are sophisticated machine learning models. Others are surprisingly old-fashioned. Even as machine learning grows in importance, many of Google’s signals are still hand-crafted by engineers who analyze data, apply functions, and set thresholds to tune them.

Authority makes a good case study, precisely because it sounds so human. What Google actually leans on is closer to geometry than reputation. PageRank is still an important signal, but its job is now framed as measuring distance from a known good source, starting from a set of trusted seed sites for a topic, where pages closer to those seeds in the link graph earn a stronger score. That score then feeds into a broader quality measure as one of its inputs.

So “authority” in the human sense quietly becomes “how few link-hops away are you from a site Google already trusts” in the machine sense. The two are related. They are not identical, and the space between them is where most strategy actually lives.The rest of the framework gets approximated the same way. Raters gauge authoritativeness through mentions and citations from reputable sources, testimonials from people Google considers experts, and links from respected sites. Trust carries the most weight of anything. Google’s own guidelines say trust is the most important member of the family, because a page nobody can trust scores low no matter how experienced or expert it might otherwise look.³

The Two Ways to Go Wrong as an SEO

Both extremes fail, and they fail in fairly predictable ways.

SEOs who lean too far into humanizing the machine tend to swing and miss when they try to scale across a lot of brands. They end up catering to large brands, because big brands already carry these E-E-A-T type factors on their own, and sometimes those SEOs don’t even fully understand why their clients rank. The strategy looks like it worked. The brand was going to rank regardless.

The opposite camp has its own failure mode. SEOs who deny E-E-A-T means anything get lost in purely technical work, and frankly a lot of them end up as substandard link builders, because link volume becomes the whole playbook. They treat the machine as nothing but a machine and forget there’s a person reading whatever it returns.

The middle is harder, and that’s the point. It means holding two ideas at once: that the work is guiding a computer system, and that a human being is the one who actually consumes what that system serves. Drop either one and the strategy comes apart.

How AI Search Works, and Why the Same Trap Repeats

Everything just described about Google is already repeating itself, because AI tools feel far more human than a search box ever did. That conversational quality really is different from the traditional Google experience, and it’s exactly what makes the personification trap so easy to fall into.

Underneath the friendly interface, though, the thing on the other end is still a computer approximating meaning. The dominant architecture behind cited AI answers makes that concrete. Most current AI platforms generate sourced responses through Retrieval-Augmented Generation, which pulls relevant text from outside knowledge bases and then writes an answer that incorporates what it pulled. That is how AI search works. The model isn’t recalling a brand from memory the way a friend would. It’s retrieving passages that look relevant and assembling something from them.

That’s why optimizing for AI search, often called AI SEO or GEO (generative engine optimization), behaves so differently from classic SEO. Large language models are non-deterministic, so the same question asked five times can produce five different answers, which means there’s no position number one the way there is on Google. Frequency is the better way to think about it. Visibility in AI search comes down to how often a brand shows up across many prompts and many responses, closer to a mention rate than a ranking.

Entities, Consistency, and Why the Machine Stays a Machine

If there’s one concept that proves AI systems aren’t thinking like people, it’s how they handle entities. An entity is just a clearly defined person, place, organization, product, or concept that a system can recognize and connect to other information. The machine doesn’t “know” a company. It assembles a picture of that company from scattered signals across the web.

That’s why consistency matters so much, and it’s a great tell that you’re dealing with a computer and not a colleague. AI systems cross-reference signals from multiple sources, so your brand description on LinkedIn should align with what appears on your site, and profiles on Crunchbase, review platforms, or industry directories should reinforce the same category and positioning. When these signals are consistent across sources, AI systems can categorize and reference your brand with greater confidence, and when they conflict, confidence drops and your brand is less likely to be mentioned.

A human friend who knew the brand would shrug off a small inconsistency, where a retrieval system might just drop it from its answer.

The good news is that GEO and SEO aren’t separate religions. Traditional SEO remains essential, and AI SEO enhances it rather than replacing it, because AIs rely on many of the same authority and relevance signals that traditional search algorithms use. How AI search works is not that different from how traditional search works. Strong content backed by references other people actually cite does double duty.

But Don’t Overcorrect Into Cold Mechanics

There’s a counterargument worth taking seriously here, because anyone who treats “it’s just a machine” too literally will build something nobody wants to read.

These frameworks reward trust and experience for a reason, and the reason is the actual humans on the other end. Google added experience specifically because there are searches where what matters most is content made by someone who has genuinely done the thing, like used the product or visited the place. The signals are stand-ins for real helpfulness, and content engineered only to satisfy the stand-in tends to age badly the moment a core update recalibrates.

So the real work is keeping both in tension at the same time. Coalition helps clients treat these things as technical factors a computer can measure, while making sure the work still reflects something a real person would find genuinely useful. That balance between the machine and the human consumer is a big part of why this approach has held up for years. The aim is to find the right places and the right openings to put a brand forward in a way that makes sense to the engine and to the person reading its answer.

Frequently Asked Questions

A Practical Way to Think About This

Treat search engines and AI as machines, systems that approximate human judgment through things they can measure, but not think. And keep the person reading the result in view the whole time, because what matters to them is the whole point. Build real experience and expertise into the work, then make that quality legible to a machine through clear authorship, references it can verify, and a footprint that tells the same story about a brand wherever it appears.

The complications are where it stops being so simple. The Search Quality Rater Guidelines run 182 pages, there are likely way over 200 ranking factors stacked into layered systems that few people outside Google fully see, and the AI engines on top shift their behavior on something closer to a weekly cadence.⁴ Grasping the idea that these systems approximate rather than understand takes an afternoon. Knowing which signals matter for a particular market, working out why a competitor keeps winning a query, and running all of it across both classic search and a handful of AI platforms is the part that takes years.

Untangling that ball of yarn is the kind of problem that rewards experience. Coalition Technologies has helped clients make sense of it across both Google and AI search for years, generating more than $588 million from SEO along the way, so if you want help getting your strategy to work in both places reach out to start that conversation.

Sources:

1. https://developers.google.com/search/blog/2022/12/google-raters-guidelines-e-e-a-t ↩︎
2. https://developers.google.com/search/docs/fundamentals/creating-helpful-content ↩︎
3. https://services.google.com/fh/files/misc/hsw-sqrg.pdf ↩︎
4. https://www.searchenginejournal.com/google-200-ranking-factors-facts/265085/ ↩︎