Sorry kids, but SEO isn’t what it used to be.

It’s time to lay to rest the outdated notion of “optimization” and start focusing on what we truly do: teach search engines.

In this era of generative AI, the way we handle search has fundamentally transformed, and holding onto obsolete ideas won’t take us far.

Why ‘optimization’ no longer mirrors modern SEO

Every couple of years since the phrase “search engine optimization” was coined nearly 30 years ago, there’s been a concerted effort by someone either misinformed or seeking attention to boldly proclaim that “SEO is dead.”

A flurry of debate ensues before things settle back to normal.

However, in the new world of generative AI and its intersection with search engines, I have a fresh issue with the term “optimization,” one that could significantly alter how we perceive and think about this practice.

I’m not here to pronounce “SEO” dead—it remains very much alive. However, linguistically, the term “optimization” is now defunct to me.

This shift in language is actually advantageous as we reconsider how to tackle search in the age of AI, benefiting both myself and many others in the industry.

The term “optimization” no longer aligns with what we’ve been doing for the past 30 years because search engines have always been a sophisticated form of generative AI.

SEOs have always knowingly or unknowingly been at the forefront of engaging with artificial intelligence on behalf of their websites, digital assets, and identities.

Traditional ‘SEOs’ act as human intermediaries between our content and generative AI

The emergence of modern generative AI and ChatGPT over the last two-plus years has sped up and clarified the understanding of what the search industry has truly been doing all along—serving as a human bridge between digital media and AI.

We haven’t been “optimizing” assets for search engines; instead, we’ve been teaching search engines to comprehend and swiftly connect to our digital assets in their endlessly generated lists in the most pertinent manner.

It’s widely acknowledged that generative AI is crucial for digital media and still requires skilled human guidance to reach full effectiveness.

SEOs have consistently fulfilled this critical role as a human intermediary between assets and AI within the context of search intelligence.

This straightforward realization carries significant implications for how you view your SEO experience entering this new AI-driven world and how website owners and businesses perceive their efforts. Like SEO itself, it’s not rocket science.

The etymology of ‘optimization’ and rise of the generative AI human intermediary

Before delving deeper, let’s briefly revisit the origins of “search engine optimization” and the brief history of how people have interacted with AI.

In my 2012 book, “Search and Social: The Definitive Guide To Real-time Content Marketing” (Wiley/Sybex), I drew on my experiences and conducted an extensive study to explore every possible origin of the term. Here’s a quote from the book:

“Even in their earliest stages, search engines were rooted in core network principles and were developed by humans. It’s worth noting that early search engineers frequently clashed with publishers over optimizing their content. The search engines aimed to observe the Web and rank those pages as they saw fit.

Of course, not every web publisher agreed with their results, and some began reverse-engineering the process through what is now commonly known as search engine optimization (SEO), a term coined simultaneously by Bob Heyman, John Audette, and Bruce Clay. At the time, the engines didn’t fully consider that their data was an almost living and breathing corpus.

The corpus was interactive, prompting the engines to innovate in ways they hadn’t previously considered. I believe it’s unfortunate and misplaced that many people still perceive search-engine algorithms to be purely technical. The more accurate depiction is that search is created and edited by people, consisting of content created by people (even if they use technical tools). Links are created by people. The analysis of relationships between links and sites is network analysis. In this sense, search has always been “social” and “networked.”

Notably emphasized here is the concept of human intermediaries interacting with AI in search engine form. It was true in the earliest stages and is even truer now in shaping the desired output.

Equally notable, those performing the work known as “SEO” have challenged and driven our friends at Google, Microsoft Bing, and other engines to innovate with more advanced AI technology, leading us largely to where we are today.

Yes, SEOs have, collectively and indirectly, propelled generative AI to its current state.

From firsthand knowledge and extensive research, it became clear to me that “search engine optimization” was coined simultaneously and independently by Clay, Audette, and Heyman.

Bob Heyman is often overlooked in this terminology discussion, but he is on par with Clay and Audette in this regard.

The first use of the term “search engine optimization” was identified by search journalist and current Google Search Liaison Danny Sullivan, who discovered the phrase in an unattributed email spam message that appeared in a few inboxes in 1995.

Other early viable contenders for naming this new search phenomenon included “search engine positioning,” championed by early search marketer Frederick Marckini around 1996 in his tech book of the same name and his then-small but growing agency, iProspect (which is no longer small, now under the umbrella of Dentsu-Aegis holdings, my former employer).

Despite the shadow of AI, this term also doesn’t fit current practices.

Perhaps the most vocal criticism has come from my friend and search luminary Mike Grehan, who has had an issue with the term “optimization” for decades.

In the early days of search, Grehan was—and still is—one of the smartest and most insightful voices on search, whether speaking from the conference stage, writing for major search sites, or conversing with executives in the boardroom.

In its simplest form, Grehan’s primary complaint is somewhat grammatical, and he has a point. “How do you optimize a search engine? You can’t,” he has often said.

I would counter that perhaps the “optimization” part is grammatically correct in the sense that assets are being optimized and enhanced for search engines. But it has never fully sat well with me either.

“Optimization” has become something of a necessary but tainted word in the absence of a better alternative.

The Google ethos: Search engines have always been about artificial intelligence

Semantics aside, Google’s ethos is relevant to this discussion in that it has always viewed search as generative AI.

During a conversation I had with Grehan at the Luxor Hotel in Las Vegas for Pubcon last March, I recounted an unforgettable casual discussion I’d heard in 2004 at the Search Engine Strategies conference in New York.

I told him about the talk I attended, which likely had only 20 people in a smaller room, featuring a one-on-one discussion with Google’s number three employee, Craig Silverstein and an interviewer.

I recalled how Silverstein surprisingly spoke about Google as artificial intelligence, which he called “search pets” for humans in the future state of Google AI.

It was an early glimpse into the company’s mindset that what they were doing wasn’t just “search.” They saw their mission as that of AI in service of people, even in its earliest incarnations.

This talk has stayed with me daily ever since, and I’ve often referred to search engines as AI in the subsequent two decades.

Grehan quickly reminded me that he was the one interviewing Silverstein, and we both had an Oprah “full-circle moment,” along with a good laugh.

But again, Google is and has always been about generative artificial intelligence. And those who “optimize” have actually been “training” search engines all along.

In my own work, I’ve fully shifted how I explain SEO concepts not as “optimization,” but rather as “training a search engine to better perform with our digital content.”

In discussions with people ranging from complete novices to seasoned professionals, the complex aspects of gaining maximum visibility for content are more easily understood when discussed as “training.”

The use of “training” also allows for a more reasonable shift in thinking about what is needed for success.

SEO is no longer wholly performance-based and tied to dollar-in-dollar-back expectations; it is now more holistic, the sum of both direct and indirect actions that lead to the ultimate comprehensive goal.

A basic reframing of ‘optimization’ to ‘training’

What does it mean to “train” a search engine, in the context of what is now considered “optimization”? Here are a few very basic ways to recalibrate our thinking:

• Keywords: Trains a search engine to understand what our content is about linguistically.
• Content: Depth, length, reading level, topics, subtopics, and mixed media all help train a search engine to a level of trust that is high enough to appear at the top of list-generated results in the appropriate context.
• Internal links: Trains the engine on internal relationships within a website or domain and adds additional relationship context.
• External links: Trains a search engine about external relationships from other trusted websites, which imparts a level of trust to the website for results generation.
• Schema: Trains a search engine to understand a further level of semantics.

All of these areas above can be noted or discarded by the engines, but they are still being trained nonetheless.

If non-relevant techniques are being utilized, whether in linking, content, relevancy, or other elements, then the search engine is still being trained—either knowingly or

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