Citation Is the New Ranking

A fact-grounded examination of the shift from search results to AI-generated answers, and the technical signals engines now rely on to choose their sources.

Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and Large Language Model Optimization (LLMO) are all trying to name the same tectonic shift: search is no longer just “10 blue links.” It’s answers, summaries, citations, and side-by-side comparisons generated by models like ChatGPT, Claude, Gemini, Perplexity, and Google’s AI Overviews.

This isn’t replacing SEO. It’s running in parallel to it.

To understand what actually matters in this new layer, it helps to separate three things:

• how generative engines decide what to say
• how they decide whom to cite
• and what, realistically, a website can do about it.

1. From SEO to GEO/AEO/LLMO: a quick map

SEO is still about convincing traditional search engines (Google, Bing, etc.) that your page deserves a high ranking for a query.

AEO (Answer Engine Optimization) focuses on earning direct answers and mentions in systems that give conversational responses instead of just links—voice assistants, rich snippets, AI chats, and now AI search experiences like ChatGPT, Copilot, Perplexity, and Google AI Overviews.

GEO (Generative Engine Optimization) narrows this further: it’s about improving visibility in generative engines—AI systems that synthesize answers (not just rank URLs) and decide which sources to weave into those answers. The term was formalized in late 2023 by Gao et al., and is now widely used in both research and industry.

LLMO (Large Language Model Optimization) is the umbrella idea: shaping your content and data so that LLMs can understand it, trust it, and reuse it—whether in search-like interfaces, agents, copilots, or embedded workflows. Some sources treat LLMO and GEO as siblings; others use them interchangeably.

Meanwhile, analysts are already quantifying the shift: one widely cited forecast suggests traditional search volume could drop by around 25% by 2026 as AI chatbots and virtual agents soak up more “search-like” behavior.

So the question is no longer if you should care about GEO/AEO/LLMO. It’s how you participate in this new “answer layer” without abandoning everything you already know from SEO.

2. How generative engines actually pick sources

Every system has its own stack, but most of them follow the same high-level pattern:

1. Understand the query (intent, entities, constraints).
2. Retrieve candidate content from multiple places:
   • web indexes (like classic search)
   • curated sources (Wikipedia, news sites, documentation hubs)
   • licensed datasets (e.g., Reddit data deals)
   • proprietary or partner content.
3. Score and filter sources for relevance, safety, and redundancy.
4. Generate an answer using an LLM that conditions on the retrieved content.
5. Optionally show citations: a small subset of the sources that anchored the answer.

That last step—which links actually show up as citations—is where GEO lives.

Traditional SEO optimizes for “do I rank on page one?”
GEO optimizes for “am I one of the sources this system reaches for when building an answer—and do I get credited?”

3. The new citation economy: Reddit, Wikipedia, and friends

We now have early data on who is winning that citation game.

Several independent analyses over 2024–2025 show that Reddit and Wikipedia are consistently among the most cited domains across major AI platforms like ChatGPT, Perplexity, and Google’s AI Overviews.

The pattern is fairly consistent:

• Reddit dominates conversational, experiential, “how do real people handle this?” content.
• Wikipedia anchors factual, definitional, and entity-level information.
• YouTube and major news / docs sites supply tutorials, step-by-step walkthroughs, and up-to-date reporting.

One study found that by mid-2025, Reddit had become the single most cited domain across a range of AI platforms, with Wikipedia still highly prominent despite some volatility in specific tools.

The lesson isn’t “be Reddit” (good luck with that). It’s that generative engines privilege:

• Rich context over thin content
• Conversational and experiential perspectives alongside formal documentation
• Highly structured, consistent sources for core facts (Wikipedia-style).

In other words: the new answer layer rewards both structured data discipline and human, grounded perspectives.

4. What most GEO/AEO tools are doing right now

As the term GEO spread from research into marketing and product circles, an ecosystem of tools emerged quickly:

• Platforms like Otterly, Profound, and others track brand mentions and citations across AI search experiences (e.g., ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot).
• Tool roundups list a growing set of AEO/GEO products—AthenaHQ, Rankscale, Peec AI, Knowatoa, and more—focused on showing where your brand appears in AI answers and how that changes over time.

Most of these tools are, by design, observational:

• Which prompts mention my brand?
• In which AI tools do I appear?
• How often am I cited, and for what?

That’s valuable. It gives you an analytics lens on the new layer: share of voice, category positioning, competitive benchmarks.

But monitoring alone doesn’t change the underlying data feed the engines are consuming.

If classic SEO had only ever built “rank trackers” and never bothered with sitemaps, robots, and schema, we’d call that incomplete. The same applies here: GEO needs both measurement and infrastructure.

5. GEO as infrastructure: treating your site like a data product

This is where a different species of tool quietly emerged: instead of just watching what AI systems do, it reformats what they see.

Generative engines are far more comfortable with:

• Structured content (JSON-LD, YAML, arrays of clean text blocks)
• Clear machine-readable instructions (like
• llms.txt
• files with crawl and usage guidance)
• Consistent metadata (entities, relationships, and context tied together predictably).

Geordy is one of the very few platforms that start from that premise and work backwards from the model’s needs.

Instead of asking, “Where did I get mentioned?”, it effectively asks:

“If my site were a dataset designed for LLMs, what would that dataset look like—and can I generate it automatically?”

According to its public documentation, Geordy:

• Scans your pages and builds multiple structured mirrors: YAML configs, Markdown summaries,
• llms.txt
• , schema JSON-LD, RSS, manifest.json, humans.txt, OG.json, and more.
• Hosts these as stable, crawlable file paths under your domain (or a dedicated subdomain), turning your site into an always-updated data source.

It’s not a dashboard that tells you what AI did yesterday; it’s a formatting layer that tries to make your site irresistibly legible to generative systems.

That’s a fundamentally different stance:

• Monitoring tools say: “Here’s where you showed up.”
• A data-infrastructure tool says: “Here’s a better representation of you to read in the first place.”

And because those structured formats (especially schema JSON-LD and RSS) are still consumed by traditional search engines too, this approach tends to support classic SEO as a side effect rather than treating SEO as the primary goal and AI as an afterthought.

You don’t have to say “GEO-first, SEO-second” out loud for that hierarchy to be obvious.

6. Rules of the new game

Based on the early research, platform documentation, and tool behavior, a few working “rules” of GEO/LLMO/AEO are emerging:

6.1. Treat content as a dataset, not just pages

Generative engines prefer:

• Consistent entity descriptions (people, products, organizations, concepts).
• Clear relationships (this product belongs to this category, solves this use case, fits this audience).
• Clean, structured mirrors of your important pages.

That’s why formats like JSON-LD, YAML, and

llms.txt

are showing up in GEO discussions—they make your site behave more like a source of records than a loose bag of HTML.

6.2. Build for both training-time and retrieval-time

Some models may see your content at training time (as part of web-scale datasets or licensed corpora). Others hit you only at retrieval time (via browsing tools or AI Overviews). Often, both.

That means:

• You still want durable, crawlable, high-quality public URLs (classic SEO hygiene).
• But you also want explicit, machine-readable files that describe your content with less ambiguity—this is where GEO-style structured outputs matter most.

6.3. Reputation now includes where else you appear

GEO isn’t only about your own domain. Studies showing Reddit and Wikipedia dominating AI citations highlight a broader point: models lean on sites that are already part of their “mental map” of the web.

For many brands, that means:

• Contributing to high-authority ecosystems (Wikipedia, open documentation, standards bodies, credible forums).
• Ensuring your brand and domain appear in those contexts in a way that models can reliably associate with your core topics.

It’s still “off-page optimization,” just reframed for answer engines instead of PageRank.

6.4. Observability matters—but isn’t enough

GEO monitoring tools fill an important gap: without them, you’re effectively blind. You don’t know:

• Which prompts you’re winning.
• Which LLMs are leaning on you.
• When an update suddenly removes you from an answer pattern.

But observability should feed back into formatting and structuring, not just reporting. That’s where pairing monitoring platforms with infrastructure-style tools (like Geordy or custom pipelines that emit schema, RSS, YAML, llms.txt, etc.) becomes powerful: one tells you what is happening, the other gives you a lever to change it.

7. What the future likely holds

A few reasonable bets for the next few years:

1. GEO and SEO become parallel workstreams.
   For mature organizations, “visibility” will split into:
   • SEO: classic search rankings, organic sessions, crawl budgets.
   • GEO/AEO/LLMO: AI citations, answer coverage, model-level share of voice.
2. Structured “AI feeds” become normal.
   Just as sitemaps and schema became standard SEO hygiene, expect AI-specific feeds (llms.txt, AI-oriented JSON, vectorizable YAML) to become standard for serious sites. Tools that can generate and maintain those automatically—Geordy being one of the first at scale—essentially act as publishing infrastructure for this layer.
3. Citation landscapes will keep shifting.
   Semrush’s multi-platform analysis already shows meaningful volatility in which domains dominate citations from different AI tools, even if Reddit and Wikipedia stay near the top.
   That volatility will make continuous monitoring—and fast iteration on structured content—a core operational task.
4. Specialization will matter.
   Most GEO/AEO tools will continue to focus on analytics, dashboards, and monitoring. A smaller subset will focus on being the “data product layer” for AI, emitting the formats models prefer. Geordy is an early example of that latter category: it doesn’t try to be your analytics suite; it tries to be your AI-readable reflection.

8. Bringing it together

The short version:

• SEO is still vital. It’s how humans find you through traditional search.
• GEO/AEO/LLMO sit beside it, governing how machines cite you when they speak on your behalf.
• The rules of this new game are emerging, but some are already clear:
  • structure your content like data,
  • participate in trusted ecosystems,
  • measure where you show up,
  • and give AI systems a version of your site that’s built for them, not just for browsers.

Most of the market today is staring at dashboards, counting mentions. A smaller part is quietly reshaping the substrate itself—turning websites into clean, structured feeds that generative engines can actually use.

If GEO is “SEO for AI,” then tools like Geordy are less billboards and more plumbing. And in infrastructure games, the quiet, well-designed pipes often matter more than the loudest reports.