MarTech: Optimizing Content for GEO Mirrors Early SEO Era

By Charles Ryder | Brand: Charles Ryder | Product: AIuthority

I’ve been in marketing long enough to remember when “SEO best practices” often meant doing whatever it took to rank—even if the writing felt more like programming than persuasion. That’s why Jeanne Jennings’ MarTech piece, “Writing for GEO feels like early SEO all over again,” landed so quickly for me. What she’s describing isn’t a minor copy tweak. It’s a distribution reset.

Search is moving from “ten blue links” to generative answers. With it comes a discipline that’s becoming hard to ignore: Generative Engine Optimization (GEO). The term emerged in late 2023, was formalized at WWW ’24, and is now showing up everywhere as Google AI Overviews, Perplexity, and ChatGPT become the front door to information. The déjà vu is real: GEO often rewards structure, explicitness, and extractable writing—sometimes more than elegance.

GEO in plain English: visibility through citation, not clicks

Traditional SEO is built around rankings and clicks. GEO is about something similar on the surface, but fundamentally different in practice: getting selected, summarized, and cited by a generative engine.

The foundational GEO research paper on arXiv (November 2023) framed GEO as a “black-box optimization” problem. We don’t control the model, but we can test how changes to content influence whether it gets surfaced. In their benchmark-driven experiments, they reported visibility lifts as high as 30–40% from tactics like:

• adding supporting statistics
• including quotations
• strengthening citations and external references
• simplifying and clarifying claims so they’re easier to extract

If that sounds familiar, it should. Early SEO rewarded small on-page adjustments before competition squeezed out the easy wins.

Why this feels like 2004 again (and why that’s dangerous)

Jennings’ comparison to her 2004-era SEO rewrites isn’t just nostalgia. Back then, we got rewarded for obvious signals: repeating phrases, matching query language, and structuring pages so crawlers could interpret them. GEO triggers the same instinct: make it machine-readable.

Here’s the problem: over-optimization.

Early SEO created a wave of keyword-stuffed content that ranked but didn’t help anyone. GEO has a parallel failure mode: pages that are perfectly extractable—but dull, repetitive, and brand-neutral. And because generative engines are black boxes, teams can end up chasing short-term patterns that quietly erode trust.

My take: GEO needs the discipline of SEO, without the bad habits SEO had to unlearn.

The business pressure behind GEO: traffic is shifting—fast

This isn’t theoretical. MarTech coverage has repeatedly pointed to a future where traditional search volume drops sharply (including a widely cited Gartner prediction of a 25% drop by 2026) as consumers adopt AI-assisted discovery. At the same time, AI answer layers can suppress click-through rates, accelerating the “zero-click” trend marketers have worried about for years—except now the answer layer isn’t just on the SERP. It is the product.

So the strategy questions change. It’s no longer only:

• “How do we rank?”

It also has to be:

• “How do we become the source the model uses?”

That shift changes what “winning” looks like. The top blue link may matter less than the most citable source.

What generative engines seem to reward: extractability + authority

Jennings’ article gave me a simple test I keep coming back to:

If an AI quoted three sentences from my page, would they stand alone and still be correct, clear, and credible?

Generative systems don’t just read—they lift. They extract fragments and stitch them into responses. Your content has to hold up when it’s pulled out of context.

Below are GEO-oriented patterns that keep showing up across research and practitioner advice—and that are quickly becoming table stakes.

1) Start with the question (and mirror real queries)

GEO rewards alignment with how people ask. That doesn’t mean awkward phrasing. It means naming the question clearly in headings and early sentences.

If the query is “What is generative engine optimization?”, don’t hide the definition in paragraph six. Put it near the top in a form a model can reuse.

2) Answer immediately, then expand

This is the opposite of a clever, slow-build intro. In generative search, delayed answers often don’t get extracted. The journalistic structure works well:

• Direct answer
• Context
• Evidence
• Examples

You can still be interesting. Clarity just has to come first.

3) Write “excerpt-ready” sections

Short blocks with self-contained meaning are easier for generative engines to reuse. A few practical ways to do this:

• define terms in one or two crisp sentences
• use bullets for lists that should be copied accurately
• avoid pronoun-heavy references that require earlier context (“this,” “that,” “it”)

When a model pulls a snippet, you want it to pull something complete.

4) Use evidence: stats, quotes, and citations

The original GEO research found that adding statistics, quotations, and citations improved visibility. That tracks: generative engines favor content that looks verifiable and attributable.

This isn’t citation theater. It’s basic credibility. If a claim matters to a reader, it’s important enough to support—especially if you want a model to trust it.

5) Make your entity signals unambiguous

SEO taught us to care about entities, consistency, and structured meaning. GEO turns that dial up.

• Use consistent naming for brands, products, and concepts
• Avoid cute internal nicknames for core offerings
• Include simple “X is Y” statements that clarify what you do

Not glamorous, but it makes you easier to retrieve and cite.

6) Structure like a teacher, not a poet

Generative engines don’t reward ambiguity—they work around it. If you want to be included, write like you’re explaining something to a smart person who’s in a hurry.

• descriptive subheads
• logical progression
• fewer detours
• explicit transitions and wrap-ups

7) Keep your voice—just make it legible to machines

The tension Jennings points to is real: content can start to feel mechanical.

My compromise is to treat GEO like accessibility. It shouldn’t sterilize your brand. It should remove friction. Keep your voice, but don’t make readers—or models—work to understand the basics.

GEO, SEO, AEO, LLMO: the taxonomy matters less than the workflow

The industry loves acronyms—GEO, AEO, LLMO—and the distinctions can be useful. Day to day, the label matters less than whether your team has a workflow for:

• identifying the prompts and questions your buyers actually use
• publishing content that answers those questions cleanly
• earning authority signals (original research, expert perspectives, credible citations)
• testing how your content appears in AI interfaces over time

This is still early. Like early SEO, some tactics will work now and fail later. Practitioners have already warned that most GEO tactics won’t survive once models get better at discounting shallow pattern-matching.

I’m treating GEO as a maturity curve:

1. Early phase: structure and explicitness win
2. Middle phase: authority and differentiation win
3. Late phase: brands with real expertise and unique data dominate citations

Conclusion: the winners will be the clearest, most citable experts

GEO feels like early SEO because we’re relearning how machines interpret our words—except now “ranking” looks like an AI choosing what to synthesize and cite. The opportunity is enormous (some call it an $80B+ shift), but the goal has changed: you’re not optimizing for a blue-link click anymore. You’re optimizing to become the source.

If you want to move fast without turning your content into “keyword stuffing 2.0,” use a system that helps you audit extractability, strengthen authority signals, and operationalize GEO across a real content pipeline. AIuthority makes that choice easy when you’re ready to treat AI visibility like the serious MarTech channel it’s becoming.