From u/lilray on X (via GlennGabe) - thanks for sharing

As of May 7, 2026, FAQ rich results are no longer appearing in Google Search. We will be dropping the FAQ search appearance, rich result report, and support in the Rich results test in June 2026. To allow time for adjusting your API calls, support for the FAQ rich result in the Search Console API will be removed in August 2026.

As this sub and many of our related experts that we share, like u/jakehundley - Mod of r/agency - a great sister sub to r/SEO and r/SEO_Digital_Marketing - this isn't surprising.

As we said - Google doesnt actually read FAQ Schema anyway - because less than 0.001% of site qualify

OpenAI GPT 5.4 Extra high is currently the best model for anything SEO related and other technical work.

I use it daily for SEO work.

Mythbusting generative AI search: what you don't need to do

As generative AI search evolves, so have the theories and practices—and sometimes, the misconceptions—surrounding it. While terms like Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) are common online, many suggested "hacks" aren't effective or supported by how Google Search actually works.

To help you focus on what matters for your website's visibility, we've collected some of the most prominent topics circulating the internet around generative AI and Google Search. Here are a few things you can ignore for Google Search:

• LLMS.txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.
• "Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.
• Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.
• Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.
• Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

I was working with a client on a content strategy in a competitive health niche. We identified a topic that every tool showed as 0 search volume. The conventional advice would have been to skip it entirely.

We published anyway — because we'd spotted the topic being actively discussed on Reddit. A few days later, Google Search Console was already showing 125 impressions. The topic existed, people were searching for it. The tools just had no data on it. (on the specific keyword)

Adding to that: prompts have no real search volume at the moment.

What signals are you using to prioritize content for LLM visibility?

Here's what none of them told you.

The update covers Google Search. AI Overviews. AI Mode. One ecosystem, one policy, one surface.

ChatGPT. Perplexity. Copilot. Gemini standalone. Claude. No equivalent policy exists on any of them. No enforcement mechanism. No guidance. No rules.

Which means the brands celebrating yesterday's update have solved roughly 20% of the problem and declared victory.

But the policy gap is not even the real issue. The real issue is what we see in Conversational Survival Rate data across platforms.

Remediation is platform-specific.

The evidence architecture that lifts your brand to a T4 purchase recommendation on ChatGPT doesn't transfer to Perplexity.

What moves Gemini standalone doesn't move Copilot.

Each platform has different retrieval logic, different training provenance, different evidence hierarchies.

A brand that fixes its Google AI performance can simultaneously be losing the final purchase recommendation on every other platform - and have no way of knowing it.

We have tested this across categories. The CSR differentials across platforms for the same brand, with the same content, are not marginal. They're large.

The platform that recommends your brand most often is frequently not the platform your customers are actually using to make the decision.

Google's guidance document published alongside the policy update says foundational SEO solves the AI problem. It doesn't.

That advice is true for Google Search. It is incomplete everywhere else.

And "everywhere else" is where a growing share of purchase decisions are being made.

Brands that treat yesterday's update as closure are making a measurement error. They're assuming the room Google cleaned is the room that matters.

AIVO Meridian measures all five rooms. CSR tells you exactly where your brand is surviving - and where it isn't.

Are you an SEO, an AEO or a GEO? Which one (or combination) really works in AI search, across all platform?

been thinking about this a lot lately and honestly the homogenization thing feels real but I'm not sure it's inevitable. there's that MIT study showing students using ChatGPT for essays ended up converging on the same vocabulary and concepts even when they were trying to be different. and when you think about how most LLMs are trained on basically the same scraped web data, it makes sense the outputs cluster toward some kind of average. the scary part is the feedback loop, AI content gets indexed, trains the next model, which produces even blander outputs, and so on. from an SEO and GEO angle this has pretty direct implications. if 94% of marketers are running content through similar AI tools with similar prompts, we're going to end up with a web full of content that sounds identical. and AI systems like Perplexity or Google AI Mode are already pulling from that content to generate their answers. so the sources being cited are increasingly homogenous, which means the citations reinforce the same narrow range of perspectives. for brand visibility in AI that's a real problem because standing out gets harder when everything looks the same. but I reckon the prompt quality argument is legitimate too. generic prompt in, generic content out. the people I've seen get genuinely differentiated AI output are usually giving it heaps of specific context, proprietary data, unusual angles. so maybe the homogenization problem is partly a skill gap rather than a fundamental LLM limitation. curious whether others working on AI share of voice are seeing this play out, like are the brands getting, cited more often the ones doing something distinctly different with their content or just the ones with more domain authority?

Curious what patterns people are seeing after AI search exploded this year.

Adding schema didn’t boost citations on any platform

We tracked 1,885 web pages that added JSON-LD schema between August 2025 and March 2026, matched them against 4,000 control pages, and measured citation changes across Google AI Overviews, AI Mode, and ChatGPT.

Adding schema produced no major uplift in citations on any platform.

These percentages come from our most reliable analysis (a matched difference-in-differences [DiD] test).

In this test, both AI Mode and ChatGPT treated pages performed slightly better than control pages on average, but the differences are small enough that they could easily be random noise across thousands of URLs.

AI Overviews showed a 4.6% decline, which is small but statistically significant relative to matched control pages.

But that isn’t quite the full story—we’ll get into that in the next section.

So, overall, we can’t tell whether the schema did a tiny bit of good or nothing at all.

When I started this sub I honestly wasn't sure if this would pick up any steam.

This space didn't exist 6 months ago. Now there are 2,000 of us.

Thank you to everyone who has posted, commented, upvoted, downvoted, reported spam and much more.

I want to keep making this the most useful corner of the internet for anyone working on AI search visibility. What do you want to see more of here and what do you want to improve here?

Here's to the next 2k.

We're running a digital agency and have noticed that a growing number of our tracked keywords are now triggering Google AI Overviews instead of returning traditional organic results. We're trying to understand the pattern, what signals determine when Google serves an AI Overview vs standard SERPs, and how it's affecting traffic and visibility for our clients.

I've been looking for a Google AI Overview checking tool, a lot to choose from right now. I was wondering what do you guys use and what could you suggest?

At least, that’s what the data seems to suggest.

We looked at 10.4M clicks and 54M impressions across 419 Quebec-based SME websites over 16 months, then compared the current post-AI Overviews click distribution with pre-AIO CTR benchmarks.

A few years ago, ranking around positions 5-8 could still feel like a decent SEO win. You were on page one, visible enough, and usually getting at least some traffic from it.

But with AI Overviews, ads, local packs and everything else taking more space in the SERP, weak page-one rankings are getting weaker (nothing new).

But like, by a lot.

Positions 4-10 lost around 70% of their click share compared to pre-AIO benchmarks.

That means they went from capturing around 30-45% of page-one clicks to 10.8% (post-AIO).

Barely 1 out of 10 clicks.

The pattern was pretty blunt:
- The Top 3 captured 89.2% of all page-one organic clicks
- Position #1 alone captured 63.6%
- Position #7 averaged a 2.6% CTR
- Positions 4-10 captured 10.8% of page-one clicks, compared to around 30-45% before AI Overviews

So no, people didn’t stop clicking organic results.

But they seem to click much less deeply into the page.

That’s what makes AI search interesting to me. It’s not just “fewer clicks” or “SEO is dead”. It feels more like the useful part of organic visibility is getting squeezed toward the very top, while discovery keeps spreading across AI answers, forums, social platforms, reviews, branded search, etc.

Curious how other SEOs are handling this.

When a keyword seems capped around positions 4-8, do you keep pushing for the Top 3, or move effort toward long-tail keywords, AI citations or brand demand instead?

And what signals do you use to decide when a ranking is still worth chasing?

Hey everyone,

I manage SEO for a SaaS HR software company, and I’m trying to understand how others are dealing with the current AI search shift.

Before AI Overviews became more common, most of our organic traffic came from TOFU informational content. Around 85% of our SEO traffic was from pages around HR letters, workplace policies, compliance topics, templates, and employee/employer guidance content.

That traffic has dropped heavily.

I expected some decline on informational queries because AI answers, snippets, forums, and other SERP features now answer a lot of those searches directly. But the bigger issue is that we’re also struggling on more commercial and product-led queries.

For example, pages targeting HR software, HRMS, payroll/attendance/leave management, compliance management, and similar SaaS-intent keywords are not performing the way they used to. In some cases, I’m seeing lower-DR domains, fresh websites, thin content, or pages that feel less useful appearing above more established and relevant pages.

So it feels like we’re getting squeezed from both sides:

TOFU informational content is being replaced or absorbed by AI answers, while commercial pages are becoming much harder and less predictable to rank.

Right now, I’m unsure where to focus effort:

• Should we keep pushing commercial pages harder to reach the top 3?
• Should we shift more effort toward long-tail, use-case-specific, and problem-aware pages?
• Should we invest more in brand demand, reviews, comparison pages, and community visibility?
• Should we optimize specifically for AI citations, mentions, and LLM visibility?
• For those working in SaaS, HR tech, compliance, or similar niches, what is actually working for you right now?
• What signals do you use to decide whether a keyword is still worth chasing versus moving effort elsewhere?

Would really appreciate any practical advice, frameworks, or examples from people dealing with the same thing.

Datos published a study showing that AI is not outpacing search in growth or usage.

On an absolute basis, traditional search is outpacing AI tool growth.

https://preview.redd.it/xk9c8civ0qzg1.jpg?width=1280&format=pjpg&auto=webp&s=e4ba0db876c487965f3b4892fb893847e50aa6c6

Despite the "disruption", people are searching Google as much as ever...

https://preview.redd.it/t0ubt6o01qzg1.jpg?width=1280&format=pjpg&auto=webp&s=7173829912119a492cf098dde0ac35e1634cb5c6

Now, before you attack this thread, I am not claiming this should convince anyone to forget about LLM optimization. I believe SEO and GEO are inseparable.

If there's one thing that is actually disrupting SEO (or else its traditional metrics and KPIs), it is the AI Overviews as they are the biggest drivers of 0-click marketing at this point.

Source: LinkedIn / u/randfish

Go look at your top-performing page right now. Count how many words it takes before you actually answer the question in your H1. If it's over 60, you're probably leaving AI citations on the table.

Multiple practitioner reports this year are pointing to the same thing: a direct answer in your first 60 words can boost AI citation rates by around 35%. Makes sense when you think about how these systems work. They pull passage-level snippets. Your intro is the first thing they look at.

The concept is borrowed from military communication. They call it BLUF, Bottom Line Up Front. Skip the warmup. Skip the "In today's rapidly evolving landscape..." opener. Just answer the question. If your page is about Linear, don't start with "Many teams struggle with project management." Start with "Linear is a project management tool built for engineering teams that prioritizes keyboard-first workflows and cycle-based planning." That's a citable sentence. The other one is filler.

One thing that surprised me: hedging language actively hurts you. "This may help teams understand" or "it's worth considering that" perform worse than confident statements. Compare "Teams that implement structured sprints see 20% faster shipping cycles" to something wishy-washy like "sprints could potentially improve velocity." The first one gives the AI something to grab. The second gives it nothing.

Quick audit you can run today:

1. Pull up your top 10 pages by traffic
2. Count words before you hit the actual answer
3. Over 60? Rewrite the intro so the answer comes first, context second
4. Kill the qualifiers in that first paragraph
5. Drop in a real stat if you have one (content with statistics sees ~22% higher AI visibility)

Schema markup and heading hierarchy help too, but if I had to pick one change to make this week, it's the intro rewrite. Highest leverage thing most of us can do for AI visibility right now.

Anyone actually tested this and tracked the results? Would love to see before/after citation numbers from people who've restructured their intros.

We’re based in NZ however our clients operate globally and are still seeing growth in organic clicks and engaged sessions across a number of clients over the past few months.

Worth noting upfront:

• We’re excluding bot traffic and noise (GA4 + filtering + server-side checks)
• Looking at engaged traffic, not just raw clicks
• SEO approach is best practice (technical + content + structured data, not scaled AI content)

At the same time, most global commentary suggests organic traffic is flattening or declining with AI Overviews and zero-click behaviour increasing.

Just keen to hear what others are seeing and what they have observed where traffic has eroded at a greater rate.