I will go first.

Page views.

For months, we reported page views to our investors. The number went up every month. It looked like progress. It looked like growth. Everyone was happy.

Then we looked at what was actually happening. People were landing on the blog, reading one post, and leaving. They were not clicking on the call to action. They were not signing up for the trial. They were not coming back.

July 2026 has been a turning point for search. Two major updates landed within weeks of each other, and they change how visibility works.

1. Google: AI visibility is now part of Search Console

In early July, Google launched generative AI performance reports inside Search Console. The data is now isolated: impressions from AI Overviews, AI Mode, and generative AI in Discover are separated from classic search. For the first time, you can see whether your content is actually being surfaced inside Google's AI answers not just rankings, not just clicks, but presence.

The message is clear: AI visibility is not a separate discipline. It's search visibility. Google put it inside the tool SEOs already use.

Ahrefs just dropped a study that changes how we think about SEO. They analyzed 863,000 keywords and 4 million AI Overview URLs. The findings are clear: the old playbook is broken.

In July 2025, 76% of URLs cited in AI Overviews also ranked in Google's top 10 for the same query. Today, that number has collapsed to 38%. The drop is real. The overlap has been cut in half in less than a year.

Where are the citations coming from now? The remaining citations are split almost evenly: 31.2% come from positions 11 100, and 31.0% come from outside the top 100 entirely. A huge chunk of AI citations are coming from pages you wouldn t even see on page one.

Why is this happening? Query fan out. When a user searches, Google splits the query into multiple related sub-queries and pulls sources from all of them. The pages that appear most often across those sub-queries get cited in the AI Overview, even if they don t rank for the original keyword. The AI answer is not a summary of the SERP below it.

We're enhancing Rankfender's Content Generation Engine (RCGE) and v2.2 is coming in the next few weeks. Before we lock things in, we want to know what actually matters to people who use content generation tools.

Here's what RCGE already does:

• Intelligence. It analyzes the top 10 ranking articles for any keyword and identifies patterns. What structure do they use? What headers? What formatting? What makes them get cited by AI? Then it builds a brief based on what actually works, not guesswork.

• Structure control. You can add, remove, and reorganize H2s before generation. No fixed templates. You decide the flow.

• Inline images. Generated articles include images, not just text walls.

• Regeneration. Mess up one paragraph? Regenerate just that part. Not the whole article.

What we're adding in v2.2:

Yes, backlinks are still one of the strongest signals Google uses to judge whether a page deserves to rank. For competitive commercial queries, they remain one of the clearest ways to separate two pages with similar content.

But the math changed.

In the age of AI search, backlinks influence visibility differently. They act on the retrieval layer the live search index that grounds AI answers rather than the model's frozen training weights. That split explains why studies measuring pure ChatGPT answers sometimes find links matter less, while studies measuring AI Overviews or Perplexity find they still matter.

The shift in 2026:

For months, we were building features based on what users said they wanted. Feature requests.
Sales calls. "It would be great if you added X."

We built X. Nobody used it.

So we stopped trusting what people said and started tracking what they actually did.

The dataset

Two days ago, I shared the 10k mistake product owners make with AI search. The response was overwhelming.

Since then, we've more than doubled our dataset at Rankfender. And many found they were invisible.

But here's what scared me more:

Of those who WERE visible, 43% had incorrect information in AI answers.

For months, the narrative has been that AI search is a single channel. You optimize for ChatGPT. You win everywhere. The data says otherwise.

A 7-month analysis of citation behavior across ChatGPT, ChatGPT Search, Perplexity, Google AI Overviews, Google AI Mode, Gemini, and Claude found that every major AI engine has a persistent source preference .
Not just a trend. A consistent editorial identity that holds month after month.

The breakdown of editorial identities :

• ChatGPT Search is encyclopedic. It favors Wikipedia for Education, Recommendations, Comparison, and Purchase queries. If your content reads like a reference section, it surfaces here.

• Perplexity is video-anchored. It leads with YouTube for Education and Recommendations. Written content alone leaves citations on the table.

• Google AI Overviews is video-biased across six of seven intents. The one exception is Navigational queries, where brand-owned domains take the top slot.

• Google AI Mode is the most exploratory engine. It routes users back to Google properties for Purchase queries and cites LinkedIn for Education intent a signal none of the other engines produced .

• Gemini is YouTube-anchored across every single intent in the dataset. It is the most consistent YouTube-first engine.

• Claude bypasses the social and encyclopedic layers entirely. In early 2026 data, it never surfaced YouTube, Wikipedia, or Reddit. It goes straight to primary sources. Brand domains, institutional sources, and compliance-grade content .

We ran an experiment 2 weeks ago.

Control group: a two-hour roadmap review meeting. Six people in a room (virtual). We debated features. We argued about timelines. We discussed dependencies. We left feeling productive.

Test group: We fed the same roadmap into Claude. No slides. No politics. No one trying to protect their pet project. Just the raw plan. The prompt: "Analyze this roadmap. Identify the three most likely failure points. Use first principles reasoning. Assume we will follow your recommendations without ego. If you need more data, ask for it."

The results were not symmetrical.

The research on how AI systems decide what to trust is clearer than you might think. It comes down to a few core signals that are measurable and actionable.

Citations are the strongest signal. A study from the University of Notre Dame and Deloitte found that simply having citations in an AI response increases user trust significantly even when the citations themselves are random . The presence of sources signals credibility. The act of checking them signals distrust.

E-E-A-T is no longer just Google's framework. It has become the core principle AI systems use to decide what (and who) to trust . AI systems prioritize Experience, Expertise, Authoritativeness, and Trustworthiness when evaluating sources. They look for signs that a real practitioner stands behind the advice detailed bylines, credentials, first-hand narratives, and verifiable experience

Perceived gatekeeping and information completeness matter. Users trust Google because it performs credible gatekeeping . Wikipedia earns trust through collective curation. AI systems look for similar signals: is there evidence of editorial oversight? Is the information comprehensive enough to answer the query completely?