I’ve been exploring some fantastic new features on Google Maps, and I’m excited to share how they’ve transformed my experience. With recent updates, sharing photos, reviews, and local insights has become more intuitive, thanks to the introduction of AI-generated captions powered by Gemini.
Local Guides Redesign. If you’re like me, who enjoys contributing to Google Maps, you’ll appreciate the revamped Local Guides profiles. Now, our total points and levels are prominently displayed, and the badges have received a fresh new look!
Top contributors like us can enjoy greater visibility in reviews, thanks to new gold profile indicators that help us stand out.
AI Caption Drafts. Another noteworthy addition is the AI-generated caption drafts. Gemini is there to assist us by analyzing selected images and suggesting text we can either edit or discard, offering a smoother captioning experience.
Currently, these caption suggestions are available in English on iOS in the U.S., with plans for broader availability on Android and globally.
Media Sharing. Sharing photos and videos has never been easier. Recent uploads are now showcased directly in the Contribute tab, speeding up the sharing process.
By allowing media access, Google Maps helps us by suggesting images from our camera roll that are ready for sharing with just a tap. This feature is live on iOS and Android across the globe.
Why We Care. These updates not only enhance content creation but also potentially boost our local content visibility and search rankings. This could influence which reviews we trust and which businesses receive more attention.
Recently, I’ve found myself pondering whether low-quality listicles are starting to lose their footing in Google Search. Imagining the golden days when simple top-ten lists ruled the web raises the question: will they continue to thrive or face obsolescence?
I’ve learned that Google has noticed these weak ‘best of’ lists and is actively working to combat this issue in both Search and Gemini. Interestingly, if I were to rank my own product as number one in my “best of” list, it could not only be a search-quality dilemma but also a possible violation of new FTC regulations that took effect in October 2024.
Driving the news. Lily Ray pointed out on LinkedIn that the FTC’s Consumer Review Rule (16 CFR Part 465) bans several deceptive practices involving reviews and testimonials. Examples include presenting company-managed content as independent reviews, publishing reviews of products never used, and attributing reviews to unwritten sources.
Presenting company-controlled content as independent reviews.
Publishing reviews of products or services never actually used.
Attributing reviews to people who didn’t write them.
Faced with penalties reaching up to $53,088 per violation, where each page could potentially be judged separately, it’s wise to rethink our approach. Lily also shared a reference table created alongside Claude, providing further insight.
Why now? Over the past couple of years, “Best X” and “Top 10 Y” listicles have become a popular GEO tactic, performing well in search and even influencing AI-generated answers. But their heyday may now be at risk.
The backstory. Before the FTC rule was finalized, some companies faced legal challenges for producing hundreds of “best of” pages that ranked their own services top, fabricated competitor reviews, and used counterfeit testimonials.
Ranked its own services #1.
Included fabricated competitor reviews.
Used fake reviews on third-party platforms.
The Better Business Bureau later reprimanded these companies for their unsubstantiated claims.
What’s happening. Today’s listicles frequently follow this pattern: publishing “best tools” lists, including untested competitors, applying subjective scoring, and placing their own brand at the top. They often give the illusion of independence or firsthand evaluation.
A brand publishes a “best tools” list.
Includes competitors it hasn’t tested.
Uses subjective or invented scoring systems.
Ranks itself #1.
The nuance. While it’s still possible to create comparison content featuring your own product, the FTC suggests heightened risk when implying objectivity, using non-genuine reviews, or failing to disclose material relationships.
You imply objectivity, but promote your own product.
You present reviews not based on real experience.
You fail to clearly disclose material relationships.
What Google is saying. Google acknowledges the trend towards low-quality listicles. A spokesperson informed The Verge that Google imposes protective measures against such manipulation in both Search and Gemini. They continue to advise creating content intended for real people, ensuring it’s comprehensible to search systems.
Why we care. The strategy that once provided high visibility might now bear risks, not only from regulatory authorities but also from possible changes to Google’s search algorithms. Consequently, this former GEO mainstay might see a rapid decline as its influence diminishes.
Caveat. I must emphasize I’m not a lawyer. It’s always best to consult your own legal counsel if you’re contemplating the continued use of this tactic.
I’ve come across some fascinating findings that demonstrate the prowess of human-written content on Google. According to data from Semrush, it turns out that content crafted by us, humans, stands a significant chance of claiming the top spot in Google’s search results, unlike its AI-generated counterpart.
The Semrush study, analyzing 42,000 blog posts, revealed that human-written content dominates the No. 1 position on Google 80% of the time. In comparison, purely AI-generated pages manage to capture this coveted spot only 9% of the time.
The details. Semrush conducted an analysis of 20,000 keywords and their top 10 results, utilizing an AI detector to classify the content.
Human-authored pages outshined both AI-generated and mixed content across all top 10 positions.
The gap was most pronounced at Position 1, where human content had an 8x higher likelihood of ranking.
Meanwhile, I noticed that AI-generated content tended to appear more frequently in the lower spots on Page 1, with a nearly double increase from Positions 1 to 4.
Yes, but. AI detection tools, as widely acknowledged, can be inconsistent. This inconsistency often leads to misclassifications between human and AI-generated content, introducing a degree of “fuzziness” in these classifications.Why we care. While AI-generated content can occasionally perform well, the data suggests that the insights and intuition of human writers still drive superior results. For competitive queries, originality, expertise, and sound editorial judgment remain valuable advantages.Perception vs. data. It’s intriguing that 72% of SEO professionals regard AI content as performing as well as or even better than human content. Yet, the actual ranking data clearly indicates a strong advantage for human-written content at the top.How teams use AI. It doesn’t surprise me to find that AI is widely adopted, especially in creating a hybrid workflow:
A substantial 87% of teams retain significant human involvement during content creation.
64% employ a human-led, AI-assisted approach.
AI proves most beneficial in research, drafting, and optimization stages.
However, AI usage noticeably declines for multimedia, localization, and tasks requiring heightened judgment.
What’s driving adoption. While AI speeds up output, it doesn’t consistently enhance content quality.
73% of respondents highlighted faster production as AI’s primary benefit.
Yet, only 19% asserted that it improves content quality.
About the data: The analysis’s foundation lies on 42,000 blog pages from 200,000 URLs associated with 20,000 keywords. GPTZero was used to classify content for this study, which also includes insights from a survey of 224 SEO professionals involved in content and search.The study. Does AI content rank well in search? [Survey + Data study]
Backlinks are still important, but today, authority also thrives on mentions and citations. I’m here to guide you on crafting content that garners both, significantly boosting your presence in AI search results.
In the past, links were the main authority signal in search. Creating backlinks was my go-to strategy for visibility, and earning placements was key for credibility. This still holds relevance, but it’s no longer the sole method.
In the realm of AI-driven search, my authority is now shaped by how frequently my brand is mentioned, cited, and associated with specific topics. Visibility is achieved through references in AI-generated answers.
With this in mind, my aim is to craft content that consistently earns brand mentions and citations, which are the new driving forces for AEO visibility.
The Philosophy Driving Content that Fuels AEO Growth
In 2026, organic discovery is driven by authority incorporating entity recognition. On platforms like Google and AI models such as ChatGPT, authority is strengthened through:
High-quality backlinks.
Brand mentions (linked or unlinked).
Consistent citations across trusted publications.
Clear entity associations (defining who I am, what I’m known for, and my core topics).
Since LLMs synthesize information rather than rank pages, I need repeatable, credible mentions across the web to enhance the probability of being cited or referenced in AI answers. Moreover, I’m focused on using my owned media to clearly define my brand entity.
Building authority has become more crucial as my content competes with AI results within the SERP and AI-generated content from other creators.
In short, I need to establish a clear brand identity and produce content so valuable that other experts, journalists, creators, and AI systems frequently reference my brand in discussions relevant to my business.
The Principles and Formatting of AEO-Friendly Content
I rely on many traditional SEO principles as a foundation for AEO-friendly content. Content aligned with Google’s helpful content guidelines, emphasizing value and user experience, appeals to both people and LLMs sourcing expert input.
However, to truly optimize AEO-friendly content, I incorporate formatting that facilitates LLM extraction.
Key formatting principles include:
Clear definitions: Provide concise, clear definitions high on the page:
“X is…”
“Y refers to…”
Structured formatting:
Use descriptive H2s and H3s.
Employ bullet points.
Keep paragraphs short.
Include direct answers under question-based headers.
Explicit context:
Avoid vague pronouns and implied references.
LLMs perform better with explicit, self-contained context.
Summary sections:
TL;DR blocks.
Key takeaways.
FAQs.
Entity reinforcement:
Brand name.
Author expertise and authority.
Brand and author credentials.
By keeping these principles in mind, I can effectively create content that resonates with both AEO requirements and user expectations.
The Specific Objectives for Your AEO Content to Address
To focus solely on AEO, I approach content with these objectives:
Be highly citable: Provide original data or perspectives that are valuable for media such as podcasts, expert roundups, or contributor columns.
Be highly quotable: Deliver at least one clear, insightful quote.
Be specific: Address specific questions that AI systems would seek to answer. Articulate and answer a question verbatim within the content.
Be clear: Clearly define topics for easy extraction.
To meet these goals, I think beyond blog posts to create “reference-grade” assets like:
Practical Steps to Build AEO Authority with Content
Here’s how I turn those principles into a repeatable process:
Research keywords where bloggers and journalists seek references (often including “statistics” or “reports”). I utilize resources like Reddit, Quora, X, Ahrefs, and Exploding Topics.
From those keywords, develop a list of topics my team can provide valuable insights on.
Compile a list of writers and journalists who cover those topics.
Conduct interviews with expert resources to gather content.
Refine content into contemporary insights using Google Trends and social listening.
Example: Collect tips from an expert to help hay fever sufferers (niche audience) sleep better (core topic) during high pollen periods (relevance).
Pitch to writers and journalists on the urgency and uniqueness of my content.
Engage with these writers on social media to build relationships for future opportunities.
Writing for AEO is aligned with writing for humans. It incorporates many of the SEO fundamentals meant to engage actual users.
Despite differences in how LLMs extract and process content, keeping these nuances in mind helps me refine my content approach for both AEO and human users.
With a well-defined brand on my owned media and a strong understanding of AEO principles, I’m ready to leverage my team’s expertise for superior visibility in the AI search landscape.
Recently, I discovered that Google is addressing a pesky bug in Search Console that has been inflating impression counts. Since May 13, 2025, there has been a logging error misreporting impression data, and Google has assured us that corrections will be rolling out in the coming weeks.
This bug has been a longstanding issue, and I was relieved to hear that Google is finally correcting it. They’ve updated their Data anomalies in Search Console page with the following message:
“A logging error is preventing Search Console from accurately reporting impressions from May 13, 2025 onward. This issue will be resolved over the next few weeks; as a result, you may notice a decrease in impressions in the Search Console Performance report. Clicks and other metrics were not affected by the error, and this issue affected data logging only.”
I also read a statement from a Google spokesperson who confirmed: “We identified a reporting error in Search Console that temporarily led to an over-reporting of impressions from May 13, 2025 onward. Bug fixes are being implemented to ensure accurate reporting.”
So, what’s changing? As Google works on these fixes, we can expect changes in how impressions are logged and reported. With this rollout, I anticipate seeing a drop in impression numbers in my Performance report, although clicks and other metrics remain unaffected.
The timeline of this issue stretches back to May 13, 2025, and it has persisted until now. Google mentioned that the complete correction will take several weeks for full implementation across various reporting areas.
Why is this important to me? If my Google Search Console impression numbers change in the near future, it’s likely due to this bug fix. Staying informed helps me understand these shifts better.
I was surprised when despite all the right moves—maintaining a fast website, creating comprehensive content, and achieving a top 10 ranking—my site didn’t show up in Google’s AI Overview. It turns out that high rankings don’t guarantee AI Overview visibility.
This issue isn’t about how well my content ranks, but rather how it’s retrieved. Understanding this distinction is vital for anyone involved in SEO today.
AI Overviews prioritize content that offers the clearest, most usable answers, rather than just relying on high-ranking signals.
If my content doesn’t meet this standard, my search ranking becomes irrelevant. I realized I needed to understand where things were going wrong to make sure my content appeared in more AI Overviews.
The ranking-citation gap is real — and growing
The overlap between AI Overview citations and organic rankings increased from 32.3% to 54.5% between May 2024 and September 2025, according to BrightEdge. Although positive, this means that many AI Overview citations still come from pages not ranked at the top. Google often chooses pages that better suit the AI Overview format.
This trend varies by industry. In ecommerce, the overlap stayed almost flat over time, while in YMYL categories like healthcare, insurance, and education, it remained between 68%-75%.
High ranking and visibility don’t always align. I’ve seen scenarios where I rank second but remain invisible, while sometimes ranking on the second page gets more visibility in an AI Overview.
1. Your content answers the wrong version of the question
AI Overviews are often triggered by long-tail, conversational searches. These drive 57% of AI Overviews, whereas commercial queries less so, according to Semrush.
Google’s AI looks for content matching user intent, not just the keywords. For instance, a query about managing remote teams may overlook my page if it primarily discusses “project management software.”
2. You’ve buried the answer
If I start with too much context and not enough answer, search systems move on. They extract clean, immediate information. If my response isn’t close to the top, it gets skipped.
3. Your structure is opaque to AI systems
AI systems need clear, self-contained answers with concise paragraph structure and heading hierarchies. Overly complex narratives confuse AI, even if the content is accurate.
4. Your E-E-A-T signals aren’t visible at the content level
Google emphasizes E-E-A-T signals for quality. These need to be explicit in the content, beyond domain authority. Each page needs to establish credibility independently.
Who wrote it?
Where did the data come from?
Does it demonstrate field expertise?
Such signals are crucial in YMYL content where misinformation risks are high.
5. You’re targeting queries that don’t trigger AI Overviews
Before optimizing for AI, I check if my queries trigger Overviews. As of late 2025, they appeared in 16% of searches, but not evenly across types.
Transactional queries, navigational searches, and local searches trigger fewer Overviews. If my traffic is commercial, the lack of a citation might not reflect my content quality but the nature of the query.
What the data tells us about the impact of this shift
The stakes are high. Seer Interactive found AI Overviews reduced CTRs for informational queries by 61% between June 2024 and September 2025. Brands featured in Overviews, however, experienced a 35% increase in CTR.
As Pew Research noted, only 8% of users clicked a traditional result when AI Overviews were present. Without being cited, I could miss not just the Overview visibility but also clicks from organic listings.
How to optimize for retrieval, not just rankings
Rewrite introductions: Provide a direct answer immediately. Context can follow later.
Restructure headings: Make them specific and complete. Each section should operate independently.
Add explicit expertise signals: Use author details, original insights, and reliable sources to enhance credibility.
Audit query triggers: Check if queries trigger AI Overviews and study cited source structures.
Expand topical coverage: Don’t focus excessively on a single page. Deliver comprehensive knowledge across your topic.
AI Overviews show the split between content quality and ranking signals. High rankings used to equal quality, but now they don’t guarantee AI compatibility.
Ranking still matters, but understanding AI identification and retrieval processes is critical for visibility today. We can no longer rely solely on top rankings to bring visibility.
To improve AI Overview inclusion, I focus on understanding how AI systems extract information, making content adjustments accordingly.
I’ve always been fascinated by how Google keeps improving its search capabilities. Recently, Gary Illyes from Google shared more about Googlebot’s operations, diving into its crawling ecosystem, fetching processes, and how it handles data.
Googlebot Reimagined. It’s intriguing to learn that Google uses multiple crawlers for diverse objectives. Referring to Googlebot as a singular entity might not capture this complexity anymore. You can find more details on the various crawlers and user agents here.
Understanding Limits. During a recent discussion, Google elaborated on its crawling limits. Gary Illyes provided these insights:
Googlebot fetches up to 2MB for any individual URL, except for PDFs.
This means it crawls only up to 2MB of a resource, encompassing the HTTP header.
For PDF files, the limit is notably higher at 64MB.
Image and video crawlers have varied threshold values, contingent on the product they serve.
By default, other crawlers have a 15MB limit, regardless of content type.
What exactly occurs when Google initiates crawling?
Partial Fetching: For HTML files exceeding 2MB, Googlebot will not dismiss the page. Instead, it halts the fetch exactly at the 2MB mark, including HTTP request headers.
Processing the Cutoff: The downloaded section is then forwarded to Google’s indexing systems and the Web Rendering Service (WRS) as if it were the entire file.
The Unseen Bytes: Any data beyond the 2MB cutoff won’t be fetched, rendered, or indexed.
Resource Handling: All referenced resources in the HTML, except media, fonts, and certain files, are fetched by WRS independently, with their own byte count not affecting the parent page’s size.
Rendering Bytes with Google. Once the crawler accesses these bytes, WRS takes over. It processes JavaScript and executes code like a modern browser to grasp the final visual and textual state of the page. This process doesn’t request images or videos but does respect the 2MB threshold for each resource.
Best Practices You might want to embrace these recommended practices:
Streamline Your HTML: Shift large CSS and JavaScript to external files. While the main HTML document is capped at 2MB, external scripts and stylesheets can be fetched separately, under their own constraints.
Prioritize Content: Position crucial elements like meta tags, <title>, <link>, canonicals, and vital structured data high in the HTML to ensure they’re not overlooked.
Monitor Server Logs: Keep track of server response times. If your server struggles to deliver data efficiently, our fetchers may slow down to avoid overloading, reducing crawl frequency.
Don’t Miss the Podcast! Google also released a podcast on this topic. Check it out:
I’ve recently delved into the world of AI search engines like ChatGPT, Google AI Mode, and Perplexity, and how they’re transforming the way consumers find and buy products online. It’s clear to me that if my product pages aren’t optimized for these AI assistants, I’m likely missing out on significant traffic and revenue.
What I’ve discovered is that AI assistants evaluate product pages differently than traditional search engines. They require a deep understanding of products to recommend them confidently to users with varied needs.
To ensure my product pages are AI-ready, I’ve crafted a simple scorecard focusing on six key factors:
1. Product specifications
Does the product page clearly display the product’s attributes and specifications?
AI assistants need explicit specifications to understand my products and match them with customer needs. For example, if someone asks for “an airline-friendly crate for a 115-pound dog,” the AI must see the weight limit clearly to recommend it.
Amazon excels at this, as their product pages display detailed specifications that likely boost their AI search performance.
Action item: I ensure all specifications are clearly presented on my product pages, ideally in a structured table or a list, rather than burying them in the description or marketing copy.
2. Unique selling points
Are the product’s unique benefits clearly described?
Highlighting what makes my products special gives AI a reason to recommend them over competitors. It’s crucial for AI to grasp these unique features to decide on recommendations.
Action item: I emphasize key features that set my products apart, avoiding vague claims like “high-quality craftsmanship” and instead focusing on specific differentiators.
3. Use cases and target audience
Discover everything you need to know about Mulch Glue, from safety and longevity to watering tips and delivery times.
Are the product’s intended use cases and audience clear?
AI matches products with people and their needs, not just keywords. Explicitly stating who the product is for and how it’s used makes it more likely to be recommended by AI.
Action item: I list the top use cases and audience segments for each product, considering situations, pain points, and goals.
4. FAQ section
Does the product page include an FAQ section answering common questions about the product?
FAQs can bolster AI’s confidence in recommending my products by showing they’re a good fit for specific queries. The more detailed the FAQ section, the more it helps in AI search contexts.
Action item: I gather and answer the most common questions from customer inquiries, reviews, and even competitor analysis to include on product pages.
5. Product reviews
Does the product page display customer ratings and review counts?
AI recommends products with proven reputations. Displaying a high rating and substantial number of reviews increases the chances of my products being recommended by AI.
Action item: I ensure high visibility for product ratings and review counts on every product page, possibly using third-party platforms to solicit reviews.
6. Product structured data
Does the product page include structured data for price, availability, reviews, and other key attributes?
Structured data helps AI understand my product information effortlessly and even feeds into knowledge graphs that power AI recommendations.
I understand that as AI agents engage more deeply in commerce, detailed product data becomes crucial for comparisons and purchasing.
Putting the scorecard to work
Here’s my concise strategy to audit and enhance my product pages for AI optimization, focusing on closing gaps where AI might overlook my products.
Prioritizing these optimizations means I’m not only engaging effectively but also increasing my competitiveness in the AI-driven market landscape.
Welcome to the ultimate guide on Generative Engine Optimization (GEO)! As we move into 2026, knowing how to optimize for AI-driven platforms like ChatGPT, Gemini, Perplexity, and Claude is crucial. This guide will help you ensure that your brand is easily discovered in AI-generated responses.
Imagine having the skills to make your brand the first choice for AI-powered searches. With our comprehensive insights, you’ll learn how to elevate your visibility across key AI platforms and gain a competitive edge.
Whether you’re a seasoned marketer or new to AI optimization, this guide offers strategies that align with both current trends and future predictions. By mastering Generative Engine Optimization, you’re setting the foundation for sustainable success in a rapidly evolving digital landscape.
As someone who closely follows advancements in technology, I was thrilled to learn about Google’s latest breakthrough with the TurboQuant algorithm. It’s designed to enhance the speed of vector searches, fundamentally changing the way we interact with AI-powered data searches.
If you’re like me and value precision in data retrieval, this algorithm is exciting news. A tiny error-correction signal maintains compressed vectors’ accuracy, enabling AI systems to retrieve data more broadly and precisely than ever before.
Google’s TurboQuant is a compression algorithm that can shrink and organize large AI datasets with nearly zero indexing time. This technology might just obliterate one of the major speed bottlenecks in modern search engines.
What TurboQuant Is. For me, TurboQuant represents a monumental way of handling the data behind AI and search by keeping it compact without losing precision. It significantly reduces memory usage and cuts down the time to build searchable AI indexes almost to zero, according to Google’s research paper.
How It Works. Modern search systems, which convert content into vectors, can be resource-heavy. These numeric representations cluster based on similarity, allowing searches to match the closest ideas. But let’s face it, these vectors are massive and expensive to store. That’s where TurboQuant steps in, using efficiently compressed data that mirrors the original extremely well through:
Smart Compression. It rotates data mathematically, organizing it like neatly packed boxes, an image that resonates with how I like to visualize innovative data solutions.
Error Correction. By introducing a 1-bit signal, it corrects minor compression mistakes, ensuring the data remains accurate, which is quite a comforting thought for anyone concerned about data integrity.
What This Means. For those of us deeply engaged with AI, TurboQuant signifies a shift. Vector search systems, the backbone of semantic search and AI-driven answers, have traditionally been slow and costly. Google claims TurboQuant makes these operations quicker and more cost-effective, enabling faster similarity searching, lower memory consumption, and real-time processing of colossal datasets.
Why It Matters to Us. Imagine Google being able to analyze far greater volumes of documents per query, not just a limited subset. Should Google implement this into its Search, AI Overviews could access a wider, more accurate range of sources, making instant summaries from large data sets far more accessible.
