Google’s John Mueller re-posted the results of an experiment that tested if ecommerce sites were accessible by AI Agents, commenting that it may be useful to check if your ecommerce site works for AI agents that are shopping on behalf of actual customers.
AI Agent Experiment On Ecommerce Sites
Malte Polzin posted commentary on LinkedIn on an experiment he did to test if the top 50 Swiss ecommerce sites were open for business for users who are shopping online with ChatGPT agents.
They reported that most of the ecommerce stores were accessible to ChatGPT’s AI agent but he also found some stores were not for a few reasons.
Reasons Why ChatGPT’s AI Agent Couldn’t Shop
CAPTCHA prevented ChatGPT’s AI agent from shopping
Blocked by Cloudflare’s Turnstile tool that’s a CAPTCHA alternative.
Store blocked access with a maintenance page
Bot defense blocked access
Google’s John Mueller Offers Advice
Google’s John Mueller recommended checking if your ecommerce store is open for business to shoppers who use AI agents. It may become more commonplace that users employ agentic search for online shopping.
“Pro tip: check your ecommerce site to see if it works for shoppers using the common agents. (Or, if you’d prefer they go elsewhere because you have too much business, maybe don’t.)
Bot-detection sometimes triggers on users with agents, and it can be annoying for them to get through. (Insert philosophical discussion on whether agents are more like bots or more like users, and whether it makes more sense to differentiate by actions rather than user-agent.)”
Should SEOs Add Agentic AI Testing To Site Audits?
SEOs want to consider adding Agentic AI accessibility to their site audits for ecommerce sites. There may be other use cases where an AI agent may need access to filling out forms, for example on a local services website.
Unlike top search engines, ChatGPT does not maintain an index of global websites. It has relied instead on Bing’s index and search for training and sources. However, recent third-party tests suggest ChatGPT has switched to Google for that purpose.
An ex-Googler and web developer in India, Abhishek Iyer, summarized his test on X. He invented a meaningless word with a definition, placed them on a page that was neither linked internally nor externally, and submitted the page to Google through Search Console.
He then prompted ChatGPT to define the term. The response was “verbatim” from his web page. He searched for the same word on Bing, DuckDuckGo, and Yandex. None returned results.
Another test, by Aleyda Solís, a search engine consultant, produced similar results. But it also revealed that ChatGPT utilized Google’s search snippet to fetch information.
In a response to a Solís prompt, ChatGPT stated it used “a cached snippet via web search” to fetch the information, indicating that ChatGPT may have direct access to Google’s cache.
In short, ChatGPT appears to utilize Google’s index to find information and sources.
What does it mean for visibility in ChatGPT?
ChatGPT has apparently switched from using Bing’s search index to Google’s.
Google’s Index
Both tests reveal ChatGPT’s reliance on Google’s index, like Google’s own Gemini and AI Mode. Hence being indexed by Google is a key step for visibility in generative AI platforms.
Yet Google is now aggressively removing pages from its index. It’s essential to monitor the indexation status of your important pages. “Crawled but not indexed” statuses in Search Console are more frequent. There’s little chance unindexed pages will surface in genAI responses.
If you are experiencing indexing glitches:
Know when to ignore them. All sites have unindexed pages. There’s often no problem to solve. It could be near-duplicate pages, old or outdated pages, or pages generated by internal search or filtering. Unless it’s an important product or landing page, “crawled but not indexed” is likely temporary.
Improve internal linking. A site’s navigation structure is the first step to better indexation. AI-powered tools can help, but overall, tactics such as “Related products,” “Related categories or subcategories,” and product-bundling pages elevate deeper pages.
Produce unique content. Repeated content can prevent a page from being indexed. It often occurs on sites with extensive products and manufacturer-provided descriptions. Third-party tools can create unique descriptions. Merchants can also follow Amazon’s example and include unique summaries and takeaways on product pages for additional informative content.
Beyond Indexing
Indexation by Google is fundamental, but a strategy for visibility in AI answers is much more. I’ve seen no evidence that organic rankings impact answers in ChatGPT or Gemini. Higher organic rankings do not improve visibility.
GenAI algorithms rely on different signals than search engines, preferring pages that answer questions clearly and succinctly.
Thus ensure your pages:
Provide straightforward answers to frequent questions,
Have content easily crawled and accessed with JavaScript disabled — AI crawlers cannot render JavaScript.
Generative AI-driven search isn’t a trend; it’s the new baseline. Tools like Gemini and ChatGPT have already replaced traditional queries for millions of users.
Your audience doesn’t just search anymore: They ask. They expect answers. And those answers are being assembled, ranked, and cited by AI systems that don’t care about title tags or keyword placement. They care about trust, structure, and retrievability.
Most SEO training programs haven’t caught up. They’re still built around tactics designed for a ranking algorithm, not a generative model. The gap isn’t closing; it’s widening.
And this isn’t speculation. Research from multiple firms now shows that conversational AI is becoming a dominant discovery interface.
Microsoft, Google, Meta, OpenAI, and Amazon are all restructuring their product ecosystems around AI-powered answers, not just ranked links.
The tipping point has already passed. If your training still revolves around keyword targeting and domain authority, you’re falling behind, and not gradually, but right now.
The uncomfortable reality is that many marketers are now trained in a playbook from the early 2010s, while the engines have moved on to an entirely different game.
At this point, are we even optimizing for “search engines” anymore – or have they become “discovery assistants” or “search assistants” built to curate, cite, and synthesize?
How SEO Fell Behind (Historical Context)
Traditional SEO has always adapted, from Google’s Panda and Penguin algorithms, which prioritized content quality and penalized low-quality links, to Hummingbird’s semantic understanding of user intent.
But today’s generative search landscape is an entirely new paradigm. Google Gemini, ChatGPT, and other conversational interfaces don’t simply rank pages; they synthesize answers from the most retrievable chunks of content available.
This is not a gradual shift. This is the biggest leap in SEO’s history, and most training programs haven’t caught up yet.
The Old Curriculum: What We’re Still Teaching (And Shouldn’t Be)
Traditional SEO curriculums typically emphasize:
Title Tags & Meta Descriptions: Despite Google rewriting around 60-75% of these (source: Zyppy SEO study), these remain foundational to most SEO training programs.
Link Outreach & Link Building: Still focused on quantity and domain authority, even though AI-driven search systems focus more on contextual relevance and content (and author) trustworthiness.
Keyword-Focused Blogging & Content Calendars: Rigid editorial calendars and keyword-driven articles are becoming obsolete in an AI-driven search era.
Technical SEO: While still useful for traditional search engines, modern AI-based systems care far less about the technical structure of a webpage, and more about the accessibility of the content, and how it displays entities and relationships.
Example:
Take a common assignment from SEO training programs: “Write a blog post targeting the keyword ‘best hiking boots for 2025’.”
You’re taught to select a primary keyword, structure your headers around related phrases, and write a long-form post designed to rank in traditional SERPs.
That approach might still work for Google’s blue links, but in a generative AI context, it fails.
Ask Gemini or ChatGPT the same query, and your content likely won’t appear. Not because it’s low quality, but because it wasn’t structured to be retrieved.
It lacks semantic chunking, embedding alignment, and explicit trust signals.
The AI systems are selecting content blocks they can understand, rank by relevance, and cite. If your article is built to match human scan patterns instead of machine retrieval cues, it’s simply invisible.
Image credit: Duane Forrester
The New SEO Work: What Actually Drives Results Now
Real SEO today revolves around structured, retrievable, semantically rich content:
1. Semantic Chunking
Creating content structured into clearly defined, self-contained chunks optimized for large language models (LLMs).
2. Vector Modeling & Embeddings
Placing content into semantic clusters inside vector databases, ensuring each piece of content is closely aligned with user intent and query vectors.
3. Trust, Signal Engineering
Implementing structured citations, schema markup, clear attribution, and credibility signals that AI-driven models trust enough to cite explicitly.
4. Retrieval Simulation & Prediction
Using tools such as RankBee, SERPRecon, and Waikay.io to actively simulate how your content surfaces within AI-driven answers.
5. RRF Tuning & Model Optimization
Fine-tuning content performance across generative models like Perplexity, Gemini, ChatGPT, ensuring maximum retrievability in various conversational contexts.
6. Zero-Click Optimization
Optimizing content not just for clicks but to be featured directly in generative AI responses.
Backlinko’s guide on LLM Seeding introduces a practical framework for getting cited by large language models like ChatGPT and Gemini.
It emphasizes creating chunkable, trustworthy content designed to be surfaced in AI-generated answers – marking a fundamental shift from optimizing for rankings to optimizing for retrieval.
Consider leading brands engaging with AI-first discovery themes:
Zapier has published educational content on vector embeddings and how they underpin tools like ChatGPT and semantic search (source). While that article doesn’t detail their internal SEO strategies, it shows how marketing teams can start unpacking the concepts that underpin retrieval-based visibility. → Correction: An earlier version of this article suggested Zapier had implemented semantic chunking and retrieval optimization. That was an editing error on my part: there’s no public evidence to support that claim.
Shopify, meanwhile, uses its Shopify Magic tool to generate SEO-optimized product descriptions at scale, integrating generative workflows into day-to-day content ops (source). → Takeaway: Shopify ties generative tooling directly to scalable, structured content designed for discovery.
These examples don’t suggest perfect alignment – but they point to how modern teams are beginning to integrate AI thinking into real workflows. That’s the shift: from content creation to content retrieval architecture.
Why The Disconnect Exists (And Persists)
1. Educational Inertia
Updating curriculums is expensive, difficult, and risky for educators.
Many course creators and educational institutions are overwhelmed or ill-equipped to rapidly pivot their syllabi toward advanced semantic optimization and vector embeddings.
2. Hiring Practices & Organizational Habits
Job ads often still emphasize outdated skills, perpetuating the inertia by attracting talent trained in legacy SEO methods rather than future-oriented techniques.
3. Legacy Toolsets
Major SEO platforms like Moz, Semrush, and Ahrefs continue to emphasize metrics like domain authority, keyword volumes, and traditional backlink counts, reinforcing outdated optimization practices.
The Fix: An Outcome-Driven SEO Training Model
To address these problems, SEO training must now shift toward measurable KPIs, clear roles, and task-based learning:
New KPI, Driven Framework:
Embedding retrieval rate (AI-driven visibility).
GenAI attribution percentage (citations in AI outputs).
Vector presence and semantic alignment.
Trust-signal effectiveness (schema and structured data).
Re-ranking lift via Retrieval Rank Fusion (RRF).
New Roles And Responsibilities:
Digital GEOlogist: Optimizes content placement and semantic structure for retrieval. (I know, the title is a joke, but you get the point.)
Cheditor (Chunk Editor): Optimizes chunks of content specifically for LLM consumption and retrievability. If you’re an Editor, you need to be a Cheditor.
Task-Based SEO Education:
Simulate retrieval via ChatGPT/Perplexity prompt engineering.
Perform semantic embedding audits to measure content similarity against successful retrieval outputs.
Conduct regular A/B tests on chunk structures and semantic signals, evaluating real-world retrievability.
How To Take Charge: You Are The Resource Now
The reality is stark but empowering: No one’s coming to save your career. Not your company, which may move slowly, nor traditional schools, nor third-party platforms with outdated content.
You won’t find this in a course catalog. If your company hasn’t caught up (and most haven’t), it’s on you to take the lead.
Here’s a practical roadmap to start building your own AI-SEO expertise from the ground up:
Month 1: Build Your Foundation
Complete foundational AI courses:
Share key learnings internally.
Month 2: Tactical Skill, Building
Complete practical SEO, specific courses:
Start sharing actionable tips via Slack or internal newsletters.
Month 3: Community And Collaboration
Organize “Lunch & Learns” or internal SEO Labs, focused on semantic chunking, embeddings, trust, signal engineering.
Engage actively in external communities (Discord groups, LinkedIn SEO groups, online forums like Moz Q&A) to deepen your knowledge.
Month 4: Institutionalize Your Expertise
Formally propose and launch an internal “AI-SEO Center of Excellence.”
Run practical retrieval simulations, document results, and showcase tangible improvements to secure ongoing investment and visibility internally.
Turning Learning Into Leadership
Once you’ve built momentum with personal upskilling, don’t stop at silent improvement. Make your learning visible, and valuable, by creating change around you:
Host SEO-AI Micro Sessions: Run short, focused sessions (15-20 minutes) on topics like semantic chunking, retrieval testing, or schema design. Keep them informal, repeatable, and useful.
Run Retrieval Audits: Pick three to five high-priority URLs and test them in ChatGPT, Gemini, or Perplexity. Which content blocks surface? What gets ignored? Share your findings openly.
Build a Knowledge Hub: Use Notion, Google Docs, or Confluence to create a centralized space for SEO-AI strategies, test results, tools, and templates.
Create a Weekly AI Digest: Curate key updates from the field – citations appearing in generative answers, new tools, useful prompts – and circulate them internally.
Recruit Allies: Invite collaborators to contribute retrieval tests, co-host sessions, or flag examples of your content appearing in AI answers. Leadership scales faster with support.
This is how you shift from learner to leader. You’re no longer just upskilling, you’re operationalizing AI search inside your company.
You Are the Catalyst, Take Action Now
The roles of traditional SEO specialists will shift (or fade?), replaced by experts fluent in semantic optimization and retrievability.
Become the person who educates your company because you educated yourself first.
Your role isn’t just to keep up, it’s to lead. The responsibility, and the opportunity, sit with you right now.
Don’t wait for your company to catch up or for course platforms to get current. Take action. The new discovery systems are already here, and the people who learn to work with them will define the next era of visibility.
If you teach SEO, rewrite your courses around these new KPIs and roles.
If you hire SEO talent, demand modern optimization skills: semantic embeddings knowledge, chunk structuring experience, retrieval simulation approaches.
If you practice SEO, proactively shift your efforts toward retrieval testing, embedding audits, and semantic optimization immediately.
SEO isn’t dying, it’s evolving.
And you have an opportunity, right now, to be at the forefront of this evolution.
You’ve heard the predictions: AI will replace SEO, generative search will eliminate organic traffic, and marketers should start updating their resumes.
With 73% of marketing teams using generative AI, it’s easy to assume we’re witnessing SEO’s funeral.
Here’s what’s actually happening: AI isn’t replacing SEO. It’s expanding SEO into new territories with bigger opportunities.
While Google’s AI Overviews and tools like ChatGPT are changing how people find information, they’re also creating new ways for your content to get discovered, cited, and trusted by millions of searchers.
The game isn’t ending. You just need to learn the new rules.
How AI Search Actually Works (And Where Your Content Fits)
Generative search doesn’t eliminate the need for quality content; it amplifies it.
When someone asks ChatGPT about email marketing or searches with Google’s AI features, these systems scan thousands of webpages to synthesize comprehensive answers.
Your content isn’t competing for traditional rankings anymore. You’re competing to become the authoritative source that AI systems pull from when generating responses.
The Citation Game
Here’s what most marketers miss: AI systems still cite their sources.
Google’s AI Overviews include links to referenced websites, and ChatGPT and Perplexity provide source citations.
Getting featured as a cited source can drive more qualified traffic than a traditional No. 1 ranking because users already know your content contributed to the answer they received.
Google AIO Citation Example:
Screenshot from search for [email marketing courses beginners must try], Google, July 2025
ChatGPT Citation Example:
Screenshot from ChatGPT, July 2025
What AI systems look for in sources:
Factual accuracy and reliability (they cross-reference information).
Update older content with recent statistics and insights.
Structure information in clear, scannable sections.
From Rankings To Retrieval
Traditional SEO targeted specific keyword rankings. AI search introduces “retrieval” – your content gets pulled into responses for queries you never directly optimized for.
Your comprehensive project management guide might get cited when someone asks, “How can I keep my remote team organized without micromanaging?” even though you never targeted that exact phrase.
Optimizing for retrieval requires a different mindset than traditional keyword targeting.
Create content that covers topics from multiple angles rather than focusing on single keyword phrases.
Structure your articles around the actual questions your audience asks, using headings that mirror real user queries.
Build comprehensive topic clusters that demonstrate your expertise across related subjects, showing AI systems that you’re a reliable source for broad topic coverage.
The SEO Fundamentals That Still Matter (With New Twists)
AI systems are far less forgiving than Google’s crawlers.
While Google’s bots can render JavaScript, handle errors gracefully, and work around technical issues, most AI agents simply fetch raw HTML and move on.
If they find an empty page, wrong HTTP status, or tangled markup, they won’t see your content at all.
This makes technical SEO non-negotiable for AI visibility. Server-side rendering becomes absolutely critical since AI agents won’t execute JavaScript or wait for client-side rendering.
Your content must be immediately visible in raw HTML.
Clean, semantic markup with valid HTML and proper heading hierarchy helps AI systems parse content accurately, while efficient delivery ensures AI agents don’t abandon slow or bloated sites.
AI bot requirements:
Allow AI crawlers (GPTBot, ClaudeBot, PerplexityBot, etc.) through robots.txt.
Whitelist AI bot IP ranges rather than blocking with firewalls.
Ensure critical content loads without JavaScript dependencies.
Avoid “noindex” and “nosnippet” tags on valuable content.
Optimize server response times for efficient content retrieval.
It could direct AI models to your best content during inference.
Place this plain text file at your domain root using proper markdown structure, including only your highest-value, well-structured content that answers specific questions.
Content Strategy For AI Citations
Your content strategy needs a fundamental shift. Instead of writing for search engine rankings, you’re creating content that feeds AI knowledge bases.
The key to successful retrieval optimization means leading with clear, definitive answers to specific questions.
When addressing common queries like [how long do SEO results take?], start immediately with “SEO results typically appear within three to six months for new websites.”
Break complex topics into digestible, extractable sections that include comprehensive explanations with supporting context.
AI systems favor content that provides complete answers rather than surface-level information, so include relevant data and statistics that can be easily identified and cited.
AI systems don’t retrieve entire pages; they break content into passages or “chunks” and extract the most relevant segments.
This means each section of your content should work as a standalone snippet that’s independently understandable.
Keep one focused idea per section, staying tightly concentrated on single concepts.
Use structured HTML with clear H2 and H3 subheadings for every subtopic, making passages semantically tight and self-contained.
Start each section with direct, concise sentences that immediately address the core point.
Building topical authority requires understanding how Google’s AI uses “query fan-out” techniques.
Complex queries get automatically broken into multiple related subqueries and executed in parallel, rewarding sites with both topical breadth and depth.
Create comprehensive pillar pages that summarize main topics with strategic links to deeper cluster content.
Develop cluster pages targeting specific facets of your expertise, then cross-link between related cluster pages to establish semantic relationships.
Cover diverse angles and intents to increase your content’s surface area for AI retrieval across multiple query variations.
Working With AI Systems, Not Against Them
The most successful marketers are learning to optimize for AI inclusion rather than fighting against machine-generated answers.
Optimizing For AI Summaries
Structure your content so AI systems can’t ignore it by leading with clear answers and using scannable formatting.
Include concrete data and statistics that make content citation-worthy, and implement schema markup like FAQ, how-to, and article schemas to help AI understand your content structure.
Key formatting elements that AI systems prefer:
Bullet points and numbered lists for easy parsing.
Clear subheadings that mirror actual user questions.
Natural language Q&A format throughout the content.
Building citation-worthy authority requires meeting higher trust and clarity standards than basic content inclusion.
AI systems prioritize content perceived as factually accurate, up-to-date, and authoritative. Include specific, verifiable claims with source citations that link to studies and expert sources.
Refresh key content regularly with timestamps to signal updated information, and consider publishing original research, surveys, or industry studies that journalists and bloggers reference.
AI search systems increasingly retrieve and synthesize content beyond text, including images, charts, tables, and videos. This creates opportunities for more engaging, scannable answers.
Ensure images and videos are crawlable by avoiding JavaScript-only rendering, and use descriptive alt text that includes topic context for all images.
Add explanatory captions directly below or beside visual elements, and use proper HTML markup like
and
instead of images of tables to support AI bot parsing.
Monitor Your AI Presence
Traditional rank tracking won’t show your full search visibility anymore. You need to track how AI platforms reference your content across different systems.
Set up Google Alerts for your brand and key topics you cover to catch when AI systems cite your content in their responses.
Regularly check Perplexity AI, ChatGPT, and Google’s AI Overviews for appearances of your content, and screenshot these citations since they’re becoming your new success metrics.
Don’t just monitor your brand presence. Track how competitors appear in AI summaries to understand what type of content AI engines prefer.
This competitive intelligence helps you adjust your strategy based on what’s actually getting cited.
Pay attention to the context around your citations, too, since AI engines sometimes present information differently than you intended, providing valuable feedback for refining how you present information in future content.
The Future Of SEO Is Bigger, Not Smaller
SEO isn’t shrinking. It’s expanding into a multi-platform opportunity. Your content can now appear in traditional search results, AI Overviews, chatbot responses, and voice search answers.
Skills That Matter Most
The SEOs thriving in this new landscape are developing expertise in data analysis to understand how different AI systems crawl and categorize content.
Multi-platform optimization has become essential, requiring the ability to write for Google, ChatGPT, Perplexity, and emerging AI tools simultaneously.
Advanced technical skills around implementing schema markup that actually helps AI understanding are increasingly valuable, along with content strategy integration that aligns SEO with broader content marketing and brand positioning efforts.
As AI makes search more complex, companies need expert guidance to navigate multiple platforms and opportunities.
The brands trying to handle this evolution internally often get left behind while their competitors appear across every AI-powered search experience.
SEO leaders today aren’t just optimizing websites; they’re building strategies that work across traditional and generative search platforms, tracking brand mentions in AI search, and ensuring their companies stay visible as search continues evolving.
Your Next Steps
The shift to AI-powered search isn’t a threat; it’s a call to expand your reach.
Start by auditing your current content for AI citation potential, asking whether it answers specific questions clearly and directly.
Create topic clusters that demonstrate deep expertise in your field.
Monitor AI platforms for mentions of your brand and competitors.
Update older content with fresh data and improved structure for AI retrieval.
The brands dominating tomorrow’s search landscape are adapting now.
Your SEO skills aren’t becoming obsolete; they’re becoming more valuable as companies need experts who can navigate both traditional rankings and AI-generated responses.
The game hasn’t ended. It just got more interesting.
At the recent Search Central Live Deep Dive 2025, Kenichi Suzuki asked Google’s Gary Illyes how Google measures high quality and user satisfaction of traffic from AI Overviews. Illyes’ response, published by Suzuki on LinkedIn, covered multiple points.
Kenichi asked for specific data, and Gary’s answer offered an overview of how Google gathers external data to form internal opinions on how AI Overviews is perceived by users in terms of satisfaction. He said that the data informs public statements by Google, including those made by CEO Sundar Pichai.
Illyes began his answer by saying that he couldn’t share specifics about the user satisfaction data, but he still continued to offer his overview.
User Satisfaction Surveys
The first data point that Illyes mentioned was user satisfaction surveys to understand how people feel about AI Overviews. Kenichi wrote that Illyes said:
“The public statements made by company leaders, such as Sundar Pichai, are validated by this internal data before being made public.”
Observed User Behavior
The second user satisfaction data point that Illyes mentioned was inferring from the broader market. Kenichi wrote:
“Gary suggested that one can infer user preference by looking at the broader market. He pointed out that the rapidly growing user base for other AI tools (like ChatGPT and Copilot) likely consists of the same demographic that enjoys and finds value in AI Overviews.”
Motivated By User-Focus
This part means putting the user first as the motivation for introducing a new feature. Illyes specifically said that causing a disruption is not Google’s motivation for AI search features.
Acknowledged The Web Ecosystem
The last point he made was to explain that Google’s still figuring out how to balance their user-focused approach with the need to maintain a healthy web ecosystem.
“He finished by acknowledging that they are still figuring out how to balance this user-focused approach with the need to continue supporting the wider web ecosystem.”
Balancing The Needs Of The Web Ecosystem
At the dawn of modern SEO, Google did something extraordinary: they reached out to web publishers through the most popular SEO forum at the time, WebmasterWorld. Gary Illyes himself, before he joined Google, was a WebmasterWorld member. This outreach by Google was the initiative of one Googler, Matt Cutts. Other Googlers provided interviews, but Matt Cutts, under the WebmasterWorld nickname of GoogleGuy, held two-way conversations with the search and publisher community.
This is no longer the case at Google, which is largely back to one-way communication accompanied by intermittent social media outreach.
The SEO community may share in the blame for this situation, as some SEOs post abusive responses on social media. Fortunately, those people are in the minority, but that behavior nonetheless puts a chill on the few opportunities provided to have a constructive dialogue.
It’s encouraging to hear Illyes mention the web ecosystem, and it would be even further encouraging to hear Googlers, including the CEO, focus more on how they intend to balance the needs of the users with those of the creators who publish content, because many feel that Google’s current direction is not sustainable for publishers.
A company founder shared their experience with programmatic SEO, which they credited for initial success until it was deindexed by Google, calling it a big mistake they won’t repeat. The post, shared on LinkedIn, received scores of supportive comments.
The website didn’t receive a manual action, Google deindexed the web pages due to poor content quality.
Programmatic SEO (pSEO)
Programmatic SEO (aka pSEO) is a phrase that encompasses a wide range of tactics that have automation at the heart of it. Some of it can be very useful, like automating sitewide meta descriptions, titles, and alt text for images.
pSEO is also the practice of using AI automation to scale content creation sitewide, which is what the person did. They created fifty thousand pages targeting long tail phrases, phrases that are not commonly queried. The site initially received hundreds of clicks and millions of impressions but the success was not long-lived.
We learned the hard way that shortcuts don’t scale sustainably.
It was a huge mistake, but also a great lesson.
And it’s one of the reasons we rebranded to Tailride.”
Thin AI Content Was The Culprit
A follow-up post explained that they believe the AI generated content backfired was because it was thin content, which makes sense. Thin content, regardless of how it was authored, can be problematic.
One of the posts by Palet explained:
“We’re not sure, but probably not because AI. It was thin content and probably duplicated.”
Rasmus Sørensen (LinkedIn profile), an experienced digital marketer shared his opinion that he’s seen some marketers pushing shady practices under the banner of pSEO:
“Thanks for sharing and putting some real live experiences forward. Programmatic SEO had been touted as the next best thing in SEO. It’s not and I’ve seen soo much garbage published the last few months and agencies claiming that their pSEO is the silver bullet. It very rarely is.”
Joe Youngblood (LinkedIn profile) shared that SEO trends can be abused and implied that it is a viable strategy if done correctly:
“I would always do something like pSEO under the supervision of a seasoned SEO consultant. This tale happens all too frequently with an SEO trend…”
What They Did To Fix The site
The company founder shared that they rebranded the website to a new domain, redirecting the old domain to the new one, and focused their site on higher quality content that’s relevant to users.
They explained:
“Less pages + more quality”
A site: search for their domain shows that Google is now indexing their content, indicating that they are back on track.
Takeaways
Programmatic SEO can be useful if approached with an understanding of where the line is between good quality and “not-quality” content.
A new SEO plugin called SureRank, by Brainstorm Force, makers of the popular Astra theme, is rapidly growing in popularity. In beta for a few months, it was announced in July and has amassed over twenty thousand installations. That’s a pretty good start for an SEO plugin that has only been out of beta for a few weeks.
One possible reason that SureRank is quickly becoming popular is that it’s created by a trusted brand, much loved for its Astra WordPress theme.
SureRank By Brainstorm Force
SureRank is the creation of the publishers of many highly popular plugins and themes installed in many millions of websites, such as Astra theme, Ultimate Addons for Elementor, Spectra Gutenberg Blocks – Website Builder for the Block Editor, and Starter Templates – AI-Powered Templates for Elementor & Gutenberg, to name a few.
Why Another SEO Plugin?
The goal of SureRank is to provide an easy-to-use SEO solution that includes only the necessary features every site needs in order to avoid feature bloat. It positions itself as an SEO assistant that guides the user with an intuitive user interface.
What Does SureRank Do?
SureRank has an onboarding process that walks a user through the initial optimizations and setup. It then performs an analysis and offers suggestions for site-level improvements.
It currently enables users to handle the basics like:
Edit titles and meta descriptions
Custom write social media titles, descriptions, and featured images,
Tweak home page and, archive page meta data
Meta robot directives, canonicals, and sitemaps
Schema structured data
Site and page level SEO analysis
Automatic image alt text generation
Google Search Console integration
WooCommerce integration
SureRank also provides a built-in tool for migrating settings from other popular SEO plugins like Rank Math, Yoast, and AIOSEO.
Check out the SureRank SEO plugin at the official WordPress.org repository:
As SEO evolves with AI optimization, generative engine optimization, and answer engine optimization, brands and marketers must rethink their SEO strategies to stay competitive.
Instead of focusing solely on traditional SEO strategies and tactics, you need to be visible in AI-powered search and answer engines.
Showing the value of SEO in this new world means showcasing how optimized, structured, and intent-driven content can maximize visibility across generative platforms.
It can also enhance user trust and drive qualified engagement in a world where AI chatbots and platforms interpret a user’s intent, retrieve relevant information, and generate clear and concise answers.
In today’s competitive AI-powered results, it can be difficult to maximize your visibility.
With SEO becoming more challenging and the search engine results constantly changing to incorporate AI results, what metrics do you need to track, and how can you show the value of SEO in today’s AI-powered search results?
Let’s explore.
Proving The Value Of SEO
Proving SEO value depends on your client or prospective client’s goals and what will move the needle for them to get visibility in the search engine results pages (SERPs) and in AI chatbots and platforms.
This could include local search, app store optimization, content marketing, technical optimization, AI Overviews, etc.
That said, you must show performance improvements and drive revenue to secure more funding and make your client successful.
In my experience, here are some of the best metrics to track and measure to prove the SEO value in an AI world:
1. Monitor AI Results
With AI Overviews and generative AI changing SEO, it is important to track visibility as we move from ranking to relevance.
AI Overviews are not expected to go anywhere. During I/O 2025, Google announced that AI Overviews were expanding to over 200 countries and more than 40 languages.
AI Mode is now available to all users in the United States without the need to opt in via Search Labs.
To track AI Overviews:
Identify Which Queries Trigger AI Overviews
You can use tools like ZipTie.dev or Semrush to track which of your top-performing queries show AI Overviews and whether your site is included in those summaries.
Screenshot from Semrush, June 2025
Track AI Overview Queries
Once you have a list of queries that your site does or doesn’t appear in for an AI Overview, you should track those queries using keyword tracking tools and compare your traffic pre- and post-AI rollouts.
Strategize To Optimize Your Content For AI Overviews
Segment your traffic based on content type, as many informational queries are experiencing a decline in traffic due to users obtaining answers directly from AI Overviews.
This will help you identify which areas are most impacted and plan your strategy to optimize queries that have the potential to show AI Overviews.
Consider server-side analytics solutions (e.g., Writesonic’s AI Traffic Analytics) to track AI crawler visits, see which pages are accessed, and monitor trends over time.
2. Track AI Brand Mentions
Since AI platforms process information differently than traditional search engines, getting mentioned in ChatGPT, Perplexity, Claude, or Google’s AI Mode for relevant queries is a must.
AI platforms like ChatGPT and Google’s AI Overview generate answers from a mix of training data and some real-time retrieval, depending on the platform and setup.
In my experience, brands that are frequently mentioned across various platforms, including PR, blogs, social media, news coverage, YouTube forums (such as Reddit and Quora), and authoritative sites, tend to be mentioned by AI.
To track AI mentions, several tools like Brand24, Brand Radar from Ahrefs, and Mention.com use AI to monitor online conversations across various platforms, leveraging large datasets to provide insights into your brand’s perception and those of your competitors.
It’s imperative that you find out if your brand is mentioned, what people are saying about your brand (both positive and negative), what queries are used to describe it, and which websites mention your brand.
Screenshot from Brand Radar, Ahrefs, June 2025
3. Track AI Citations/References
Checking to see if your website is cited by large language models (LLMs) can help brands and marketers understand how their content is being used by AI and assess their brand’s authority and visibility.
Ahrefs now offers a free tool that tracks when your website is cited in the answers generated by AI-powered search tools like Google AIO, ChatGPT, and Perplexity. AI citations count how often a domain was linked in AI results.
Pages show how many unique URLs from this domain were linked.
Screenshot from Ahrefs, June 2025
This is one of my favorite audit tools to look to see if there are any citations in any brand that we’re reviewing.
If Ahrefs adds trend analysis to track whether you’re gaining more citations in Google AIO, ChatGPT, and other platforms over time, it would be a valuable way to assess whether your strategies are working.
4. Tracking Branded Searches
It’s extremely important to track your branded searches in this new SEO AI era. AI-powered search results are personalized, and LLMs like Gemini and ChatGPT, to name a few, heavily consider user intent and context.
Having strong brand signals could improve entity recognition, which can improve your visibility for related queries.
Tracking how AI-generated answers (e.g., featured snippets or AI Overviews) treat your brand helps you optimize for entity-driven SEO.
In the AI SEO era, where search engines prioritize context, trust, and relevance, tracking branded searches could inform you to refine strategies that help defend your SERP presence and maximize conversions.
Here are some tips to help enhance branded visibility:
Create unique, authoritative, and factual, conversational content because AI models prioritize reliable and accurate information. Focus on content that demonstrates expertise and includes verifiable data.
Structure content for AI readability by using clear headings (H1, H2, H3), bullet lists, numbered lists, and data tables. Also, create concise paragraphs that directly answer questions.
Leverage schema markup like Organization, Product, Service, FAQPage, and Review to provide structured data that AI models can easily understand and reference.
Build brand authority and expertise by getting consistent citations, mentions on authoritative third-party sites, and positive reviews, to contribute to AI’s perception of your brand’s credibility.
Optimize conversational queries by creating content that directly answers “who, what, why, and how” in your niche.
Be active on platforms like Reddit and Quora, where AI models often pull information. SEO becomes “Search Engine Everywhere.”
Regularly review your AI visibility data, identify gaps, and adjust your content and SEO strategies based on insights.
AI Mode groups the user’s question into subtopics and searches for each one simultaneously, and users can go deeper.
If a user asks a follow-up question within AI Mode, they are essentially performing a new query. All impression, position, and click data in the new response are counted as coming from this new user query.
AI Traffic In GA4
While Google Analytics 4 doesn’t explicitly label AI traffic, you can look for patterns. Create custom reports with “Session source/medium” and apply regex filters for known AI domains (e.g., .*ChatGPT.*|.*perplexity.*|.*openai.*|.*bard.*).
For specific content you hope AI will cite, create unique URLs with UTM parameters (e.g., utm_source=chatgpt, utm_medium=ai). This can help attribute some traffic directly.
If you can get more conversions from AI Overviews, like Ahrefs did, when it found that AI search visitors converted at a rate 23 times higher than traditional organic search traffic, despite representing only 0.5% of total website visits, then you will have discovered a conversion goldmine that makes AI optimization not just worthwhile, but essential for staying competitive.
Final Thoughts
The SEO landscape has shifted from optimizing search engines and traditional search to optimizing for AI-powered chatbots and solutions, such as ChatGPT, Perplexity, Claude, Google’s AI Overviews, and potentially OpenAI’s web browser “in the coming weeks,” according to Reuters.
OpenAI has 500 million weekly active users of ChatGPT and could disrupt a key component of rival Google’s ad-money source.
SEO is no longer about ranking on the first page of Google.
It’s about being relevant and visible across multiple AI platforms, getting mentioned in generative responses, and demonstrating value through AI-focused metrics outside of the traditional metrics like rankings and traffic.
Brands and marketers that prove the SEO value in this new era can deliver immediate, measurable value while building momentum for larger investments in the future.
Day three picked up from there, diving into how Google actually returns search results.
The serving infrastructure encompasses query understanding, result retrieval, index selection, ranking, and feature application, including rich results, before presenting them to the user.
Image from author, July 2025
Making Sense Of User Queries
Cherry Prommawin provided a detailed explanation of how Google interprets users’ queries.
Not all queries are straightforward.
In languages like Chinese or Japanese, there are no spaces between words, so Google has to learn where words start and end by looking at past queries and documents. This is known as segmenting, and not all languages require this.
After that, it removes stopwords unless they’re part of a meaningful phrase or entity, like “The Lord of the Rings.”
Then, it expands the query to include synonyms across all languages to better match what the user is actually looking for (see image above).
Context plays a significant role in how Google understands and responds to queries. A crucial aspect of this is the utilization of contextual synonyms.
Image from author, July 2025
These aren’t like the typical synonyms you’d find in an English dictionary. Instead, they’re created to help return better search results, based on how words are used in real-world searches and content.
Google might learn that people searching for “car hire” often click on pages that say “rental car,” so it treats the two terms as similar in the right context. This is what Google refers to as “siblings.”
These relationships are mostly invisible to users, but they help connect queries to the most relevant information, even when the exact words don’t match.
Image from author, July 2025
How Google Understands Quality
Alfin Hotario Ho provided a clear explanation of how Google evaluates quality in search results.
Over the years, Google has attempted to define what “quality” means, and it consistently returns to five key points:
Focus on people-first content.
Expertise.
Content and quality.
Presentation and production.
Avoid creating search engine-first content.
Image from author, July 2025
Ho highlighted the Quality Rater Guidelines as a useful resource. These guidelines don’t directly influence ranking, but they help explain how Google measures whether its systems are performing well.
When the guidelines change, they reflect updates in Google’s thinking about what constitutes good content.
There are four main pillars of quality in the guidelines:
Effort: Content should be made for people, not search engines. It should clearly show time, skill, and first-hand knowledge.
Originality: The content should offer something new – original research, fresh analysis, or reporting that goes beyond what’s already out there.
Talent or Skill: It should be well-written or produced free from obvious errors, and show a strong level of craft. You also don’t need to be an expert in something, as long as you can demonstrate verifiable first-hand experience.
Accuracy: It must be factually correct, supported by evidence, and consistent with expert or public consensus when possible.
Other key takeaways from Ho’s session include:
From E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness), it is clear that trust matters most.
Even if a topic isn’t about health, money, or safety (Your Money or Your Life), Google still prioritizes trustworthy content.
If a page strongly disagrees with general expert opinion, it may be seen as less reliable overall.
Lots of 404 or noindex pages on a website are not a quality issue. 404 is a technical issue, as is the “noindex” tag.
Image from author, July 2025
What Are Quality Updates?
Google updates its search systems for three primary reasons: to support new content formats, to enhance content quality, or to combat spam.
These updates help ensure that people receive useful and relevant results when they search.Supporting New Content Formats
As new content types become more popular, such as short videos or interactive visuals, users start to expect to find them in search results.
If enough people show interest, Google may launch new features to match that demand.
This could include new filters or information views in the results. These updates help keep Search useful and aligned with how people want to explore information.
Improving Content Breadth And Relevance
The internet is constantly growing, and many topics become saturated. That makes it harder to find the best content.
To improve this, Google rolls out core updates. These updates don’t target specific websites or pages.
Instead, they improve how Google ranks content across the web with the overarching goal of surfacing higher-quality results overall.
Combating Low-Quality And Spam Content
Some people try to game the system with low-effort content. Google isn’t perfect, and spammers look for gaps to exploit.
In response, Google launches targeted updates that adjust how its systems detect spam or low-quality signals. These changes aim to remove poor content from search results.
Recovering From Google Updates
Core Updates
You’re technically not penalized, so technically there’s no recovery like with spam updates.
Google recommends that you should:
Continue doing a great job, look at what your competitors are doing better, and learn from sites that are doing better than you.
Spam Updates
Remove the type of spam that Google has mentioned in its blog communications.
Caveats On Structured Data Usage
Google addressed some common myths surrounding structured data, particularly its connection to serving and ranking.
None of these are new, but the reiteration has been based on continued questions around the impact and value of adding structured data.
Not A Direct Ranking Factor
Adding structured data to your site won’t directly improve your rankings. But, it can make your listings more attractive in search results, which might lead to more clicks.
That added engagement could help your site over time.
No Guarantees
Just because you’ve added structured data doesn’t mean Google will show rich results. The algorithms decide when and where it makes sense to display them.
Google Can Add Rich Results On Its Own
Even without structured data, Google may still display enhanced results, such as your site name or breadcrumbs, if it can infer that information from your page content.
It Needs Ongoing Maintenance
Structured data isn’t a one-time task. You should check it regularly to ensure it remains accurate and error-free.
Keeping it up to date helps you stay eligible for enhanced search features.
That’s all from Google Search Central Live in Thailand. There have been a lot of insights and a big announcement over the last three days.
I recommend that SEOs review the last three articles and digest what Google has said. Then, consider how they can apply that to their strategies for 2025.
Aleyda Solís conducted an experiment to test how fast ChatGPT indexes a web page and unexpectedly discovered that ChatGPT appears to use Google’s search results as a fallback for web pages that it cannot access or that are not yet indexed on Bing.
According to Aleyda:
I’ve run a simple but straightforward to follow test that confirms the reliance of ChatGPT on Google SERPs snippets for its answers.
Created A New Web Page, Not Yet Indexed
Aleyda created a brand new page (titled “LLMs.txt Generators”) on her website, LearningAISearch.com. She immediately tested ChatGPT (with web search enabled) to see if it could access or locate the page but ChatGPT failed to find it. ChatGPT responded with the suggestion that the URL was not publicly indexed or possibly outdated.
She then asked Google Gemini about the web page, which successfully fetched and summarized the live page content.
Submitted Web Page For Indexing
She next submitted the web page for indexing via Google Search Console and Bing Webmaster Tools. Google successfully indexed the web page but Bing had problems with it.
After several hours elapsed Google started showing results for the page with the site: operator and with a direct search for the URL. But Bing continued to have trouble indexing the web page.
Checked ChatGPT Until It Used Google Search Snippet
Aleyda went back to ChatGPT and after several tries it gave her an incomplete summary of the page content, mentioning just one tool that was listed on it. When she asked ChatGPT for the origin of that incomplete snippet it responded that it was using a “cached snippet via web search””, likely from “search engine indexing.”
She confirmed that the snippet shown by ChatGPT matched Google’s search result snippet, not Bing’s (which still hadn’t indexed it).
Aleyda explained:
“A snippet from where?
When I followed up asking where was that snippet they grabbed the information being shown, the answer was that it had “located a cached snippet via web search that previews the page content – likely from search engine indexing.”
But I knew the page wasn’t indexed yet in Bing, so it had to be … Google search results? I went to check.
When I compared the text snippet provided by ChatGPT vs the one shown in Google Search Results for the specific Learning AI Search LLMs.txt Generators page, I could confirm it was the same information…”
Proof That Traditional SEO Remains Relevant For AI Search
Aleyda also documented what happened on a LinkedIn post where Kyle Atwater Morley shared his observation:
“So ChatGPT is basically piggybacking off Google snippets to generate answers?
What a wake-up call for anyone thinking traditional SEO is dead.”
Stéphane Bureau shared his opinion on what’s going on:
“If Bing’s results are insufficient, it appears to fall back to scraping Google SERP snippets.”
He elaborated on his post with more details later on in the discussion:
“Based on current evidence, here’s my refined theory:
When browsing is enabled, ChatGPT sends search requests via Bing first (as seen in DevTools logs).
However, if Bing’s results are insufficient or outdated, it appears to fall back to scraping Google SERP snippets—likely via an undocumented proxy or secondary API.
This explains why some replies contain verbatim Google snippets that never appear in Bing API responses.
I’ve seen multiple instances that align with this dual-source behavior.”
Takeaway
ChatGPT was initially unable to access the page directly, and it was only after the page began to appear in Google’s search results that it was able to respond to questions about the page. Once the snippet appeared in Google’s search results, ChatGPT began referencing it, revealing a reliance on publicly visible Google Search snippets as a fallback when the same data is unavailable in Bing.
What would be interesting to see is whether the server logs held a clue as to whether ChatGPT attempted to crawl the page and, if so, what error code was returned in response to the failure to retrieve the data. It’s curious that ChatGPT was unable to retrieve the page, and though it probably doesn’t have any bearing on the conclusions, it would still contribute to making the conclusions feel more complete to have that last bit of information crossed off.
Nevertheless, it appears that this is yet more proof that standard SEO is still applicable for AI-powered search, including for ChatGPT Search. This adds to recent comments by Gary Illyes that confirms that there is no need for specialized GEO or AEO in order to rank well in Google AI Overviews and AI Mode.
