Today, I’m excited to discuss the latest development in the world of search engines: Google has just rolled out the May 2026 core update. This follows the previous update we saw in March.
I learned that the announcement was made by Google through their official status page. It’s a significant moment as it marks the second core update of the year after March’s update and the earlier Discover update in February.
What Google is sharing. According to Google’s updated Search Status Dashboard, the rollout might take up to two weeks to complete. They also made a LinkedIn post explaining the aim is to enhance the visibility of relevant content.
Core updates like these occur several times yearly. They bring broad, impactful changes to Google’s algorithms, and though they often aren’t announced, this one is attracted due attention.
If you’ve noticed changes. Experiencing shifts in your site’s rankings? Google typically suggests focusing on producing quality content. Even if hit, it may not indicate problems with your pages.
Reflection on past updates. Looking back, we’ve seen similar significant updates like the March 2026 and December 2025 rollouts, each influencing search result dynamics differently. Will this update continue that trend? Only time will tell.
Why this matters for us. Core updates can shake up the search engine landscape, causing noticeable volatility. It’s an opportunity for improved site visibility or a call to action to tweak your strategies if rankings dip. May this update bolster your SEO efforts, rewarding your dedication with increased organic traffic.
I’ve come to realize that my buyers often have a shortlist in mind even before hitting Google. It’s fascinating how these pre-search decisions form. Here’s my take on how I influence those vital conversations that put my brand on that list.
The customer journey used to kick off with a simple search, but it’s evolved beyond that point. By the time potential buyers type a query into Google, they usually have some brands in mind. They’ve watched Instagram Reels featuring a product repeatedly, read threads on Reddit with unanimous recommendations, and seen similar endorsements in Facebook groups.
Google is now more of a confirmation tool than a starting point. When someone searches, they’re looking to confirm their assumptions, not to browse aimlessly.
The key question is, did my brand make it onto their mental shortlist before they began searching? In most cases, being visible on comparison platforms is crucial for this.
So, where is this shortlist actually built? Peer-driven decisions are made in various industry-specific environments
By the time these interactions prompt a Google search, choices are often boiled down to specific comparisons like “brand X review” or “brand X vs. brand Y.” Being mentioned in those off-SERP discussions is usually more influential than ranking for a head term.
It’s worth noting that platforms like Reddit won’t hold the spotlight forever as visibility there is inherently temporary. Yet the basic behavior remains constant: people ask their peers before consulting search engines. My strategy focuses more on participating in these conversations rather than just chasing trending platforms.
Dig deeper into strategies to ensure pre-search visibility and why your brand might not be included in AI recommendation sets.
The two objectives of search everywhere optimization, or SEvO, form the backbone of my campaigns:
Direct visibility ensures my brand appears where buyers are narrowing options, measurable by direct search traffic and specific branded queries. Engine comprehension, on the other hand, leverages each brand mention next to relevant problems or solutions to enhance AI system recommendations.
Steve Jobs famously said, “You can’t connect the dots looking forward; you can only connect them looking backward.” I can’t see how these efforts gel until they start appearing in AI responses and the buyer conversations.
To measure effectively, I keep tabs on things like brand mention volume and trends in branded searches. These indicators suggest that pre-click visibility is working.
When it comes to Search Everywhere Optimization, the strategy I use is all about getting discovered where my buyers spend time, even before they think to search for brands like mine.
The Search Everywhere Optimization Pyramid organizes my efforts:
The groundwork is Audience Platform Research, guiding me to where my customers are likely making their decisions.
Setting up effective alert systems is key to knowing when relevant topics surface, helping me know when my brand should join the conversation.
Next up comes credibility through industry publications, earning my brand recognition in places potential buyers trust.
Then I focus on distribution, ensuring my content reaches my audiences effectively and keeps them engaged.
Finally, I create and refine my own content to support everything from below, nudging my brand into view when buyers are in that crucial decision-making phase.
Understanding that conversation is ongoing helps me navigate future shifts, even as specific platforms rise and fall in popularity.
If my goal is making it to the buyer’s shortlist, I need to ensure visibility not just on SERPs but across all the web spaces they engage with. Through consistent and deliberate steps, the pyramid ensures that my brand is more than just a search result — it’s part of the discussion.
I often find myself explaining Reddit’s role in AI search. It’s frequently underestimated, yet its influence extends well beyond training data.
Clients frequently ask how AI training, licensed access, and retrieval systems can affect SEOs and AI strategies, particularly concerning Reddit.
Here are the typical questions I receive:
Should I engage with Reddit to boost my brand visibility?
Is advertising on Reddit beneficial if AI uses Reddit for training?
Our CEO suggests creating a subreddit for each product. Is that wise?
Why does Google’s AI reference a Reddit thread criticizing my product?
These inquiries often conflate three separate but interrelated concepts:
Training data.
Licensed or real-time access.
Citation and retrieval systems.
Although connected, they serve different purposes. Understanding these distinctions impacts how we approach SEO and AI citations, especially as Reddit increasingly appears in AI-driven results.
Let’s demystify AI training, access, and citation. You might think, “ChatGPT was trained on Reddit,” means every post is directly stored in its memory—an incorrect assumption.
Training AI is akin to education. Kids learn concepts like using the Pythagorean theorem without remembering specific textbook answers. Similarly, AI learns conversational patterns, not individual Reddit posts.
AI doesn’t remember specific threads but discerns key discussion points from Reddit, like consumer preferences on r/RockTumbling.
Reddit partnerships with Google and OpenAI in 2024 enabled a transition from static datasets to ongoing access, allowing AI to stay updated on Reddit dialogs.
If AI training is like schooling, licensed access is a continuous flow of information akin to subscribing to a newspaper.
AI can cite Reddit, not because it’s preferential part of the training, but finds it useful for real-time querying, just like humans might refer to yesterday’s conversation.
Reddit’s prominence in AI results impacts my SEO strategy, yet it’s not only due to formal partnerships. Reddit’s depth in human experiences enhances its informational value.
Reddit offers what many websites lack: practical user insights and diverse opinions. Where official sites provide features, Reddit adds authentic experiences and user narratives.
Rather than mimicking Reddit, I focus on fostering authentic discussion by leveraging user insights from reviews, interviews, or forums, enhancing the context around my content.
I’ve realized that prioritizing nuanced details and showing reasoning can increase credibility, making my content more relatable in subjective decision-making scenarios.
Ultimately, integrating firsthand experiences and transparency can elevate content strategy, aiding systems that synthesize human input into AI insights.
When I think about AI search, I realize it’s more than just translating or localizing results. It’s about deciding which sources, narratives, and realities emerge on top. This complex system is incredibly fascinating to me, especially when I consider how multilingual regions like Catalonia challenge these AI search systems.
The unique geography of Catalonia, where Catalan and Spanish languages coexist, serves as an excellent stress test for AI technology. It’s intriguing to see the underlying patterns unfold when the same queries are entered in both languages across platforms like Google AI Overviews and ChatGPT.
In Catalonia, a query like Tradicions de Sant Jordi shows how AI systems can sometimes misidentify the language, often tagging Catalan as Occitan. This discovery was both surprising and revealing, shedding light on broader problems that transcend multilingual spaces.
Consider this: an AI system operating out of Barcelona with a local IP may choose the less prevalent language of Occitan over Catalan, a decision that feels bizarre given Catalonia’s linguistic and geographical context.
This issue isn’t isolated. In January 2023, Google acknowledged downgrading Catalan results in favor of Spanish, which sparked dissatisfaction among users. The subsequent updates improved things somewhat, but the root language-identification errors persist, affecting how AI synthesizes information today.
My journey into this topic has involved documenting AI search variations across Hispanic markets, observing how it often treats diverse Spanish-speaking regions as uniform, ignoring their unique contexts. However, in Catalonia, where geography remains constant, the retrieval patterns unfold in more distinct and educational ways.
For me, multilingual regions expose the foundational defaults in retrieval systems. Here, users can switch languages and observe firsthand how the system reallocates meaning, authority, and even the language of an answer.
The reality is, the same issues will likely emerge in seemingly monolingual markets, manifesting in different ways as AI technology advances.
I’ve just delved into Goodie’s enlightening AI search traffic report for early 2026, covering the period from January to April, and I’m excited to share my insights with you. This report dives into trends in usership, referral traffic, and marketing considerations, offering a comprehensive view of the shifting landscape.
You’ll want to pay particular attention to how ChatGPT’s dominance is starting to wane, with some surprising contenders like Claude and Gemini making waves. This shift could significantly impact how marketers strategize their efforts in AI-driven search optimization.
The data reveals fascinating patterns in user habits and referral traffic, which could inform future marketing strategies and the allocation of resources. For a full dive into these emerging trends and what they might mean for businesses, I encourage you to explore the detailed findings of the report.
I’ve recently discovered that Google has introduced a new feature in Chrome Lighthouse to check for llms.txt files. Though Google mentions that llms.txt isn’t necessary for AI search visibility, Lighthouse has started flagging sites based on their presence.
Google’s latest Lighthouse audits, under the “Agentic Browsing” category, now focus on a site’s usability for machine interaction. I find this interesting as it aligns with Google’s push towards better machine readability.
The new audits are part of Chrome’s evolving “Agentic Browsing” features, which analyze if sites are prepared for automated interaction. This concept came soon after Google issued guidance on AI search optimization, debunking the necessity of llms.txt files in their new guide on generative AI features.
What Lighthouse Evaluates Now. Lighthouse’s Agentic Browsing tests focus on how well my site is built for machine interactions, incorporating various deterministic audits as per Google’s documentation. These checks include:
– WebMCP integration.
– Accessibility tree integrity.
– Layout stability through CLS.
– Presence of an llms.txt file.
These audits help ensure that there’s a machine-readable summary at the site’s domain root. Google explains that without llms.txt, agents might take longer to understand a site’s main structure.
The impact of these audits doesn’t translate into a traditional Lighthouse score but into a fractional pass ratio related to agentic readiness signals.
The Tension. Interestingly, while these audits don’t directly affect SEO rankings, their mention in Google’s readiness checks could make SEOs reconsider their stance on llms.txt files.
Agentic Engine Optimization. Google’s approach aligns with insights shared by Addy Osmani from Google Cloud AI about Agentic Engine Optimization. Osmani emphasizes creating web content that is semantically structured, token-efficient, and easy for AI to process.
SEO vs. llms.txt. According to Google, creating llms.txt or similar files isn’t necessary for AI search success, as outlined in the guide on Mythbusting generative AI search. The AI systems can discover, crawl, and index a variety of file types encountered on the internet.
John Mueller from Google responded to concerns about the role of llms.txt in a discussion with Lily Ray on Bluesky, stating that the use of these files is more for functionality and not directly linked to search engine optimization.
Google’s Take on AI Agents. Besides llms.txt, Google’s Lighthouse guidelines place strong emphasis on accessibility and interface stability. The insight I gained is that AI agents heavily rely on the accessibility tree as their core data model, focusing on integrity and proper layout.
Ultimately, while Google indicates llms.txt isn’t needed for search, including such files might be beneficial for adapting to Google’s evolving tools that prioritize machine readability.
Entity optimization might sound like a complex term, but trust me, it’s incredibly powerful when you’re trying to make AI understand your brand better. Essentially, my goal is to help AI see exactly who I am and what I’m about. Let me share more about how you can do the same.
When I optimize entities related to my brand, I start by clarifying what my brand represents. This means ensuring that all my online content clearly reflects my brand’s identity and core values. By creating a strong, consistent message, AI can better understand and categorize my content.
Next, I focus on strengthening associations. This involves connecting my brand with relevant entities and concepts within my industry. When AI detects these connections, it increases my brand’s relevance in related searches.
Finally, driving accurate AI citations is crucial. I make sure that any references to my brand on different platforms are correct and consistent. This helps in building trust with AI, ensuring that it can reliably reference my brand in the right contexts.
I find it fascinating how Google Discover has evolved with the introduction of publisher profiles and follow features. These profiles have started making waves, yet they remain a bit enigmatic due to limited documentation.
More publishers, creators, and social-first accounts are now visible through these profiles. Let me take you through how these profiles work, how they connect with social accounts and the Knowledge Graph, and why some publishers already enjoy enhanced customization features.
As a technical SEO enthusiast, I’m quite accustomed to Google glossing over details in their documentation. And with Discover publisher profiles, that mystery deepens.
Google barely mentions these profiles in their official Discover documentation, though they seem to play an increasingly significant role in the visibility of publishers and creators.
It’s intriguing to see how Discover profiles let users manage the publishers they follow while gathering content from various websites and social platforms.
Because Google has been reticent about the inner workings of these profiles, I’ve taken upon myself to study their patterns across different accounts. Here’s what I’ve noticed about:
Google rolled out substantial updates to Discover in September 2025, vastly altering how we engage with content through publisher follows and profile pages.
The update granted publishers dedicated landing pages for content aggregation, offering users a streamlined way to interact with preferred publishers and seamlessly integrating social content into Discover.
The most eye-catching aspect of this update is how it empowers users to have greater control over publisher visibility while enabling brands to reach their audience more effectively.
Publishers can’t typically alter the layout of these pages, but some recently gained access to customize their profiles, an option part of a limited beta test.
Common to most publisher profiles are features like a profile photo, usually sourced from the Knowledge Graph or a YouTube profile, which also counts total social followers, and integrates various social media handles.
The social connections catered to include platforms like YouTube, TikTok, Instagram, Facebook, X, and LinkedIn. The ‘About’ section is succinct, often derived from a Wikipedia entry or something similar.
Some editable profiles offer additional features like customized banners, pinned posts, and external links that could direct users to apps or livestreams, further enhancing content reach.
There are two main types of Discover publisher profiles: ones for entities with websites and others solely focused on social media publishers.
Web-focused publishers’ profiles tend to be more comprehensive, often including the About section, logos, social accounts, and website links—although social links might sometimes need a manual push to be included.
On the other hand, profiles for social media publishers focus on prominent journalists, notable figures, and those solely identifiable through social media.
These profiles are generally less complete unless they are tied to a Knowledge Graph, missing elements like profile pictures or descriptions, frequently needing aid from connected YouTube accounts for better appearance.
Looking forward, I anticipate Google may broaden access to these editable profiles, though I suspect customization will remain selective, likely reserved for well-established publishers and creators.
I’m excited to share that Google has announced some transformative updates to its search capabilities. These updates include the introduction of information agents and enhanced agentic experiences that will elevate how we interact with search. Google’s AI will continuously scan the web, ensuring we receive the most current information, much like a personal assistant would.
In a recent announcement, Google revealed new search agents, focusing on information agents and additional agentic functionalities within Google Search. These information agents are designed to monitor the web for changes to our tasks, seamlessly supporting us on our journey through various challenges and questions.
Liz Reid, the head of Google Search, stated, “We’re entering the era of Search agents, where you can easily create, customize, and manage multiple AI agents for your many tasks, right in Search.” This new era provides a unique opportunity to tailor search experiences to our specific needs.
Information Agents. These agents are designed to keep us informed about our questions and tasks. Google’s agents will intelligently sift through the internet—exploring blogs, news sites, social posts, and accessing the freshest real-time data on finance, shopping, and sports, to ensure we receive the most relevant updates on our inquiries.
The information agents will then compile an “intelligent, synthesized update” that not only provides the necessary information but also enables us to take action.
The Example. Envision yourself apartment hunting. You can simply input all your specific requirements, and your agent will continuously scan listings, alerting you whenever a match surfaces. Similarly, if you’re keen on not missing any sneaker collaborations from your favorite athletes, your agent will notify you about new releases.
Availability. These exciting capabilities are set to roll out this summer, initially available to Google AI Pro & Ultra subscribers.
Agentic Experiences. Google is also extending its agentic booking capabilities within Google Search to encompass new tasks like finding local experiences and services. Imagine effortlessly booking a private karaoke room for an exact time and with specific food options, all handled by Google Search.
Google will provide the most current pricing and availability information, along with direct links for purchase, streamlining experiences across various services, including home, repair, beauty, and pet care. These features are expected in the U.S. this summer.
Personal Intelligence Expanding. In addition, Google has revealed plans to broaden its Personal Intelligence feature within AI Mode, now reaching around 200 countries and territories, supporting 98 languages.
Today, I’m excited to share that Google has announced the launch of its latest AI model, Gemini 3.5 Flash. This powerful update is now the default engine for Google’s AI Mode, transforming how we experience search every day.
At the recent Google I/O, I learned about Gemini 3.5 Flash directly from Google’s head of Search, Liz Reid. She described this model as Google’s “newest Flash model delivering sustained frontier performance for agents and coding.” It’s thrilling to know that this technology is now impacting users worldwide.
What really excites me is that 3.5 Flash doesn’t just enhance AI Mode in Google Search; it also powers the Gemini app for everyone, regardless of whether they are paid users or not. It’s great to see Google making such advancements widely accessible.
Developers, you’re in for a treat! 3.5 Flash is now integrated into Google Antigravity, Gemini API for Google AI Studio, Android Studio, and more. For those in enterprise, it’s now part of the Enterprise Agent Platform and Gemini Enterprise.
Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect, shared that Gemini 3.5 Flash rivals the intelligence of large flagship models while providing the speed we expect from the Flash series. It outshines previous models, making remarkable strides in agentic and coding performance benchmarks. I’m truly impressed by its capabilities in multimodal understanding too.
Why should I care? Well, with Gemini 3.5, Google Search’s AI Mode is smarter and more efficient than ever. I’m eager to explore how AI Mode’s responses evolve, especially for the queries that matter most to my site.
The rapid changes in search technology mean it’s crucial to stay informed and adaptable. This update reaffirms the importance of keeping pace with Google’s innovations.
