Humans: The Original AI

By Garry M. Callis Jr.

Humans: The Original AI

From a brief conversation from our developer, comes this thought-provoking article about how humans shouldn't underestimate their own experiences in the Age of AI. LLMs can only synthesize information. It can't feel what you have, or experience what you have. Use that to your advantage in marketing.

KEY TAKEAWAYS

  1. AI is trained on human knowledge, language and judgment, which means it is, at its core, trying to replicate and identify what humans do well

  2. The signals AI search engines use to determine citation-worthiness are proxies for human expertise: E-E-A-T, original perspective, first-hand experience and documented outcomes

  3. Content that only a human with genuine experience could write is the most citable content in any AI system

  4. Your specific experience, your opinion and your documented outcomes are not soft differentiators, they are the hard citation signals

  5. Brands that produce content assembled from publicly available information will lose to brands whose content sounds like someone who was actually there


Think about what a search engine is actually doing. It reads millions of pages, identifies patterns in what's authoritative and trustworthy, predicts what a user actually needs and surfaces the best answer. It synthesizes. It prioritizes. It learns from everything it has seen before.

That description also fits a very old, but very current technology: the human brain.

We have been doing information retrieval since before language existed. We categorized plants as edible or poisonous from accumulated experience, trial, and error. We identified patterns in weather and seasons that let us plan rather than react. We evaluated which members of our community were trustworthy based on signals built up over time. We made predictions about outcomes based on precedent. We were creating the pyramids, star charts, mapping constellations before electricity was even a thought. But we sometimes forget where we were, because we're too focused on where we are and where we want to go.

I want to give kudos to our Discover AIO developer, Drew Faithful, for giving us the subject for this article, and if you have ideas for an article you want to see us tackle, shoot us a message on LinkedIn or on Instagram.

What AI is Actually Trained On

Every large language model powering an AI search engine was trained on human-generated content: the writing, thinking, arguing and explaining that people have produced over decades. The model learned what a good answer looks like by reading millions of examples of humans answering questions well. It learned what expertise sounds like by reading content from people who actually knew their subject.

This has a direct implication for how AI search works. An AI Overview isn't constructed by a neutral algorithm computing relevance scores in a vacuum. It is a system that learned what humans find credible and is now applying that learning at scale to decide who gets cited.

When Google's AI Overview evaluates whether your content is worth citing, it is asking a very human question: does this sound like someone who actually knows this, or does it sound like someone who assembled a plausible answer from what other people have already said?

The answer to that question determines your citation rate more than any technical SEO factor.



The Oldest Ranking Signal

Long before Google, humans ranked information sources by the same criteria AI is now attempting to encode: experience, expertise, authoritativeness and trustworthiness. Google didn't invent this thought process, they quantified it in a way their systems can encode and codify.

When you asked a friend for a restaurant recommendation, you weighed their experience (have they been there?), their expertise (do they know food?), their authority (do others respect their taste?) and their trustworthiness (do they give honest opinions or just tell you what you want to hear?). You performed an E-E-A-T evaluation before the acronym existed. But now we live in a world where you perform a search, and you allow the AI search bots to render the decision for you. That Consideration stage of the traditional buyer's journey has now been augmented/hijacked, and a lot of people don't even realize how the shift has happened.

Word of mouth worked because human credibility signals are powerful. The village healer in the times of antiquity built authority not through a website, but through documented outcomes: people who got better. The trusted merchant maintained reputation through consistent, honest dealings. The village elder earned their reputation through demonstrating wisdom over time, and was willing to admit that they were not fallible. Hence the reason you see the disclaimer, "AI can make mistakes."

AI search is attempting to find the digital equivalent of these signals. It's looking for the content version of the trusted expert, the source that demonstrates it knows something from experience, not just from reading about it. The old saying goes, "word of mouth travels fast." But what if we found a way to synthesize that?



Why This Changes the Content Equation

The implication for content strategy in 2026 has always been one of scale, volume, and synthesized experiences for clout.


For most of SEO's history, the quality signal was indirect and muddy. You couldn't evaluate directly whether a piece of content was written by someone who genuinely understood the assignment or was a straight up poser. So what did most people do? ' They used proxies: inbound links, domain authority, keyword coverage, page structure, and all these other "citation slop methods." These signals correlated with quality but were also gameable, which is why SEO became an industry of optimizing proxies rather than the underlying quality they were meant to represent. This is basically the SEO equivalent of Obi-Wan Kenobi yelling at Anakin Skywalker about destroying the Sith, not joining them.


AI search changes the game just a smidge. Large language models can evaluate whether content reads like genuine expertise or assembled information at a level that simple keyword matching never could. The model learned from humans what expert writing actually sounds like: the specificity, the acknowledgment of nuance and edge cases, the examples that only someone with real experience would include, the honest admission of uncertainty where it exists. This is why you have humanizers in LLMs, to inject humanity in places it should be.

Content calibrated to rank by optimizing proxies rather than demonstrating genuine expertise has now been deemed as lower quality. Google's generative engine optimization update put this out for everyone to see. You can still use proxies effectively, but the ability to evaluate the underlying signal of trust has improved significantly. For the first time, the thing you were always supposed to be optimizing for is what actually gets measured. So now your content has to be better, not just for AI, but for the people you were supposed to be making content for in the first place.



What Makes Content Humanly Citable


The practical question this raises: What actually gets measured by AI search systems when we create content for humans?

A person with genuine and unique expertise on a particular subject doesn't just synthesize what everyone else already said. They add something: a direct experience, a data point others don't have, a framework developed through their own practice. I'm an example of this. As a professional FlingGolfer, a former youth golf coach, and a former golf fitter, I have expertise in the golf realm that a lot of people don't have. And so I created my own marketing framework for niche brands, known as Relief. This is something that can't be taken away from me, especially when I put pen to paper. An article that only recombines what's publicly available has no original perspective and therefore no substance, or reason to be cited. Any AI could have assembled that content itself. If you didn't add anything the model couldn't derive from five other sources, there's no reason to cite you. It's cookie-cutter, it's boring, it's unoriginal.

Specific Named Examples.

Generic framing is the hallmark of content assembled from sources rather than lived experience. "Companies that have adopted AI search optimization have seen improved visibility" is content the model can generate without your help. "A regional home services franchise we worked with saw a 40% increase in AI Overview citations within 90 days of restructuring their FAQ content" is content only someone with direct access to that outcome could write. The specificity is the signal. Being able to differentiate yourself from your competitors is the entire reason you're looking to use AI in the first place, but don't forget that you are the story. You are the source of truth. You are the one who drives the ship, not AI.


Honest Acknowledgment of Limits and Uncertainty.

As I mentioned with the village elder analogy, experts, or those people with authority are comfortable saying they don't know, that the data is mixed or that the right answer depends on context. As a matter of fact, I'd be more prone to trusting someone if they tell me off the bat the things they do know, and the things they don't. AI has a tendency to hallucinate, to put on airs in order to impress the user. Generic assembled content avoids these admissions of limitation because it's calibrated to appear comprehensive and authoritative. Experts include them because they care more about accuracy and integrity than appearing authoritative. AI models learned this pattern from reading thousands upon thousands of expert sources, which is why content that hedges appropriately reads as more credible, not less.

First-person Documented Outcomes.

The phrase "in my experience" used to be deemed as an unreliable opinion rather than solid evidence. In AI search, documented first-hand experience is the best version of evidence. The model recognizes first-person accounts of outcomes as a distinct class of content: the kind that a human who was actually there can produce. This content is non-replicable in a way that third-person synthesis is not, which is precisely why it gets cited. AI rewards unique insights in situations. There's a reason why you'll find that in a lot of cases, human-led content ranks higher than content that's synthesized entirely. There's an article by Semrush, that covers exactly that.



The Practical Checklist

If humans are the original AI, if AI is essentially trying to identify and amplify the best human expertise, then the most important question for your content strategy is whether your content proves that a real expert made it.

That Question Has a Checklist:


  1. Does each article include at least one thing only you could know, from direct experience, original data or community-sourced insight that isn't publicly available elsewhere?

  2. Does your author page and organizational About page establish the credentials and experience that make your citations credible to a system evaluating trustworthiness?

  3. Does your content include the honest acknowledgments of nuance that separate real expertise from assembled information?

  4. Is your perspective specific enough that a reader couldn't reach the same conclusion by summarizing five competitor articles?

  5. Do your examples include real outcomes, named contexts or documented results that a generalist aggregating sources couldn't replicate?


If the answer to any of these is no, the content may perform adequately in organic search. Ensure that your content is unique. If it isn't, then there's no reason for your content to be out in the world. Provide something with substance. Answer the question, "why should I care?" Make your content citable, not just by AI, but by humans. That's who's buying your products, or enlisting you for your services and expertise.



The Shift is Definitional, not Technical

The change AI search introduces is not a new set of technical requirements. It is a redefinition of what quality means in practice.

For most of SEO's history, quality and technical optimization were separate tracks. You could rank well with mediocre content if your technical signals were strong enough. AI search is collapsing those tracks. Quality now means content that only a genuinely knowledgeable source could produce. Technical signals still matter, but they are the floor, not the ceiling. Once again, Google's generative engine update covers that shift in detail.

The ceiling is expertise. And expertise at the end of the day, has always been human. Remember that even if you use AI to write your content, there has to be a human in the loop to provide context to reinforce your content.

For the full framework on what makes content citation-eligible, the complete guide to AI-eligible citation covers the structural and signal requirements in detail.



Frequently Asked Questions


Why does AI search favor human expertise signals over technical optimization?

AI search engines are trained on human-generated content, which means they learned what credibility and expertise look like from human examples. When evaluating content for citation, they apply that learned understanding of expert writing — specificity, original perspective, honest acknowledgment of uncertainty — rather than purely technical signals like keyword density or link counts. Human expertise signals are the standard the model was trained on.


What is the difference between genuine expertise and assembled information in content?

Genuine expertise includes things that only someone with real knowledge or direct experience could provide: specific named examples with actual outcomes, original frameworks developed through practice, honest acknowledgment of limits and edge cases and first-person documented results. Assembled information recombines what's publicly available without adding a distinct perspective. AI models, trained on large volumes of human-generated content, can increasingly distinguish between the two.


Does this mean AI SEO is just about writing better content?

It means the definition of "better content" has changed. Better content used to mean content that optimized the signals search engines could measure, keyword coverage, page structure, backlinks. Better content now means content that demonstrates genuine expertise, original perspective and documented experience. The optimization target has shifted from measurable proxies to the underlying quality those proxies were always meant to represent.


How do I make my existing content more humanly citable?

Start by auditing your content for what only you can say. Where does it include something a competitor couldn't have assembled from publicly available sources? Where does it reference a direct outcome, a client result, a community observation or an original framework? Those are the citable elements. For pages that have none of them, the update priority is adding a documented example, a first-person perspective or a specific outcome that the content currently lacks.


Does this apply to brand content or only individual expert content?

Both. A brand that produces content grounded in documented client outcomes, community research, proprietary data and named expert perspectives earns citation credibility the same way an individual expert does. The underlying signal is the same: is there something here that only this source could know? Brands that operate that way — publishing from genuine organizational knowledge rather than assembled topic coverage — build entity-level authority that compounds.



Where to Go From Here



AI search didn't change the rules. It made the original rules visible. The content that earns citations is the same content that would have earned trust in any era: specific, experienced, honest and genuinely useful. Discover AIO is where practitioners building that kind of content come to sharpen their thinking and connect with others doing the same work. Join Discover AIO and learn that in the Age of AI, your humanity isn't a limitation. It's your X-Factor.