LLM hallucinations are a known issue. But as frustrating as they may be, hallucinated AI citations are just a symptom.
Every time an AI assistant invents a URL to your site—confidently, wrongly—it’s doing so because a real person asked a real question. The model expects your site to have an answer, and when it can’t find one, it fills in the blank.
That’s the gap worth paying attention to.
Google AI Overviews are rapidly becoming the first thing users see for informational queries. That citation layer is increasingly shaping perception before a user even looks at the organic results.
But most teams have no idea whether they’re showing up in these citations or not.
SEO used to reward depth. If your article was an exhaustive piece on a topic that covered every angle and included a few keywords, clicks would usually follow.
Today, search rewards precision over sheer depth.
For the past two years, tracking AI visibility and citations has largely been a black box.
Third-party tools like Semrush and Ahrefs introduced ways to estimate AI visibility by monitoring search results and citations. But until recently, we didn’t have first-party data directly from the search engines themselves confirming when and how often content was used inside AI-generated answers.
That changed a few weeks ago.
The problem with competitor content analysis isn’t that it doesn’t work. It’s that doing it manually at any meaningful scale is unsustainable.
You either do it rarely enough that it’s useless, or you burn your team’s time on a process that collapses under its own weight the moment you try to run it across more than a handful of keywords.
Most educational content shows you the polished end result. The best practices, the final framework, the “what works.”
But what often gets left out are the setbacks, the failed experiments, and the messy process that actually leads to those results.
That’s the gap we’re closing with this new series.
AI won’t take your job. But someone who knows AI might.
That’s been the dominant narrative for the past two years. So instead of adding to the speculation, we pulled real data. We analyzed 1,000 US-based marketing job listings on LinkedIn to see what companies are actually hiring for, what they’re paying, and where AI fits in.
If there’s one thing recent SEO and GEO studies have confirmed, it’s that YouTube is now one of the top domains referenced by LLMs.
People would rather watch answers than read them, and according to Datos, YouTube searches aren’t plateauing. They’re growing significantly, steadily pulling search intent away from text-first platforms.
So why are so many marketing professionals avoiding this opportunity for YouTube growth?
Nobody disputes that SEO fundamentals still work. Valuable content. Quality backlinks. Domain authority. These aren’t going anywhere.
It’s how people find information, and the infrastructure determining what gets surfaced that has fundamentally shifted.
When it comes to AI, most of us are already using it in one way or another. While marketers may lean on it for data analysis, to clean up some copy, or automate repetitive tasks, most marketers primarily use it as a search engine.
Whether it’s ChatGPT, Claude, or Gemini, chances are you’ve already used one of these tools to research something. But what if, instead of using existing AI to gather insights on your audience, you could build your own AI research tool?