I’m watching Google add a new layer of AI transparency to ads across Search, YouTube, and Discover. The company said its new How this ad was made section will appear inside My Ad Center, giving people a clearer view of whether AI played a role in the ad creative they see.
The panel will show whether an ad was created or modified with AI. I see this as a meaningful expansion of Google’s ad transparency tools, especially as more advertisers rely on generative AI to produce images, copy, and other campaign assets at scale.
What it looks like. I’ll be able to access the disclosure from the three-dot menu or the info icon on an ad. In the screenshot Google shared with Search Engine Land, the My Ad Center panel includes a dedicated section explaining how the ad was made.
Google will handle some disclosures. When advertisers use Google’s own generative AI ad tools, Google will automatically add the disclosure inside My Ad Center.
Google’s My Ad Center adds a clear AI disclosure, helping users see when ad creative may have been created or edited with generative AI.
For advertisers using third-party AI tools, Google said they will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.
Why I care. AI-generated ads are getting easier and faster to create, so disclosure matters more than ever. I want to know when creative was made or changed with AI because requirements can vary by market, platform, and ad format.
Existing ad rules still apply. Google said its ad policies still prohibit misleading or deceptive advertising, whether AI was involved or not. This update adds more visibility into how an ad was made, but it does not change the requirement that advertisers clearly identify who they are and what they are promoting.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers are also required to disclose synthetic or digitally altered content in political ads, under a policy Google introduced in 2023.
I’m tracking an important AMP update from Google Search: users who tap AMP results will now be sent directly to publisher-hosted AMP pages instead of cached AMP pages shown inside Google’s AMP viewer.
A Google spokesperson told Search Engine Land, “Starting today, we are updating how we connect users to AMP pages from Search, taking them directly to the AMP host pages.”
Google also made it clear that this is not a ranking change. AMP content will continue to rank like any other webpage, and Google said the serving and ranking of AMP content in Google Search and Google Discover will remain the same.
From my perspective, the practical value here is mostly on the publisher side. By sending searchers straight to the AMP host page, Google says publishers should have simpler analytics management and tracking, along with less maintenance work when creating and supporting AMP content.
Google told us it will continue to support the open-source AMPhtml format, and it also posted the update in its Search documentation.
I also think it’s worth noting how much AMP’s role has changed over time. AMP has not received preferential treatment in Google’s Top Stories for a while, and AMP pages are much less common to encounter than they once were. Search Engine Land even turned off AMP in 2021.
It has been a long time since I’ve had much reason to cover AMP closely, but this change matters because it shifts the user journey back to publisher-hosted pages while keeping AMP’s ranking treatment unchanged.
I see Google Discover’s “Tailor Your Feed,” now showing up as “Add topics to your feed,” as a meaningful shift in how people can shape what appears in their feed. Instead of relying only on Google’s inferred signals, such as clicks, dwell time, follows, and engagement history, I can now type what I want to see in natural language and let Google translate that request into feed instructions.
That matters because it creates a third visibility path for small and niche publishers. Until now, a smaller site usually needed either strong implicit affinity from a user or an explicit follow. With prompt-based tuning, a user can simply ask for a topic, creator, source, or type of content, and Google can retrieve matching material even when that content has barely appeared in Discover before.
In my tracking, the feature turns prompts into actions such as SEE_MORE and SEE_LESS. Those actions are applied after the user refreshes or updates the feed. The experience feels conversational, but underneath it appears to create persistent instructions that can affect both the current feed and future Discover sessions.
I also see signs of an LLM-style system behind the workflow. A user prompt is interpreted, converted into a readable assistant response, and returned with a structured result. In one observed example, the prompt “show me more content on seroundtable.com” produced an actionable SEE_MORE response and a persistent thread key, suggesting that feed tuning is treated as an ongoing conversation rather than a single isolated command.
The feature first appeared in Search Labs for US English accounts in December 2025. At that stage, the impact was subtle: after several refreshes, I could see a few on-topic cards, but the feed did not radically transform. By early 2026, Google started adding attribution, including labels such as “resulting from natural language tuning” and later “You asked to see,” making it easier to identify which cards were influenced by a prompt.
By spring 2026, “Tailor Your Feed” had effectively become “Add topics to your feed.” The interface moved toward a chat-style entry point with prompt starters such as “Show me content from…,” “I want videos about…,” and “Keep me updated…”. The same underlying verbs remained, but Google made them easier for everyday users to trigger.
The most important technical clue is the pipeline behind the feature. Discover cards influenced by these prompts can be associated with naturallanguagetuningcontent.f for current tuning and historicalnaturallanguagetuningcontent.f for older prompts that continue shaping the feed. I read that “historical” pipeline as evidence that these preferences are meant to last over time, not disappear after one refresh.
From the observed cards, I see two ways this content is selected. The first and dominant mode is entity or interest expansion. A prompt is mapped to related people, topics, publishers, or concepts, and Discover expands around that meaning. This is why asking for one source or creator may also surface related sources, related subjects, or nearby entities rather than only the exact name typed into the prompt box.
The second and more interesting mode is query-intent fan-out. In this mode, a prompt is decomposed into natural-language retrieval queries. A broad request about SEO, for example, can become query intents such as “SEO strategies algorithm changes,” “Google ranking system updates,” or “tips for getting content into google discover.” Those query intents then retrieve articles based on semantic relevance.
This is where the connection to Generative Engine Optimization becomes clear to me. The Discover fan-out behaves like the retrieval pattern we see in generative search: one user prompt becomes several more specific sub-queries, and content is selected because it answers one of those sub-queries well. Popularity can still matter in some cases, but it is not the only gatekeeper.
That distinction is what gives niche publishers a real opening. In the observed data, prompts surfaced examples such as vegan recipe creators, Mississippi Today, a LinkedIn post, niche Japanese-property blogs, and a gardening site tied to a seed-starting query. Some mainstream publishers still appeared, including Reuters and VentureBeat in certain contexts, but the pattern was not limited to the usual high-volume Discover winners.
In the most striking cases, the pipeline surfaced articles with no detectable prior Discover distribution in the tracking dataset. I am not using “distribution” here as an audience number or a Search Console metric. I mean that the article did not appear to have circulated previously in the Discover tracking data available for analysis.
That makes this pipeline different from classic Discover distribution. Traditional Discover systems often re-serve articles that already have engagement momentum. Prompt-based tuning can retrieve content because it matches what a user explicitly asked for, even if the article has not already built a Discover track record.
I would not treat this as a mass traffic channel yet. Google appears to promote these cards cautiously, and the pipeline does not seem to snowball the way broader Discover pipelines can. It serves the user who asked. It does not automatically broadcast the content to a much larger audience.
I would also be careful about false positives. In one Japanese-property cluster, relevant results such as guides to buying a home in Japan appeared alongside a video-game article about in-game home locations. That kind of loose match helps explain why Google may rank and distribute these cards conservatively.
For publishers, the practical implication is straightforward: I would optimize for both topical clarity and query-intent vocabulary. The entity-expansion mode rewards sites that are unmistakably about a topic users can name. The fan-out mode rewards titles, headings, and introductions that align with the natural-language questions and information needs Google derives from prompts.
That does not mean stuffing pages with raw keywords. The better move is to describe the content clearly in the language a real person would use when asking Discover for more of it. If a user might ask for “buying Japanese property guide,” “starting seeds indoors guide,” or “tips for getting content into google discover,” I want the page’s title, H1, and opening section to make that relevance obvious.
The strategic shift is that selection power moves closer to the user. In the classic feed, Google infers demand. In this model, the user declares it. Google then turns that declaration into entities, interests, and query intents that drive retrieval.
For small publishers, that is the opportunity. If the feature graduates from Search Labs and users adopt it at scale, a focused site with clear topical authority could appear because it directly satisfies declared demand, not because it already won the popularity contest inside Discover.
There are still real limits. The feature has been US English and Search Labs focused, with French feeds showing essentially no presence in the observed data. Adoption also appears early. A powerful prompt-based personalization system changes little if users do not actually use it.
What I am watching next is whether Google expands this beyond Search Labs, whether the current and historical tuning pipelines become more visible, and whether this behavior converges with broader generative retrieval systems. A nascent generativeretrieval.f pipeline has already appeared in tracking data, but that broader connection still needs confirmation.
My read is that Discover is moving from observed personalization toward declared personalization. Google still infers plenty, but users are beginning to write part of their own interest profile. If that model becomes mainstream, niche publishers with clear focus, strong entity signals, and natural-language relevance may gain a new route into Discover visibility.
Notes: In this analysis, a Discover pipeline means the selection circuit that chooses and serves cards. The .f suffix in identifiers such as historicalnaturallanguagetuningcontent.f is an observed internal marker attached to Discover card metadata. “Fan-out” refers to a mechanism where one prompt is broken into several retrieval sub-queries. “GEO” means Generative Engine Optimization, or the practice of optimizing content for visibility in generative search and answer systems. “AIO” refers to AI Overviews, and “AI Mode” refers to Google Search’s conversational interface.
Field tracking referenced here covers Google app Search Labs US English accounts from December 2025 through June 2026. Pipeline behavior is based on close observation of Discover feed cards and 1492.vision tracking data. The internal mechanisms described are my interpretation of observed data and public research, and approximate dates are treated as approximate.
I recently delved into a fascinating study on Google Discover headline formats, looking at a staggering 3.4 million articles. The results were eye-opening and showed that a simple headline rewrite often doesn’t yield the expected lift.
You might have come across these bold statements before:
Quote-led headlines outperform plain declarative ones by nearly 29%.
Question headlines underperform both, sometimes by 24%.
Format drives the result: Rewrite a statement as a quote, or add that magic word, and you should expect a real lift.
To put these claims to the test, I examined 1,674,518 English articles and 1,690,295 French articles from the 1492.vision Discover corpus. That’s quite a hefty sample size!
What I found was a deeper flaw than just numbers. It turns out that all three claims treat headline format as a leverage point for visibility. However, the data clearly shows that the impact of a headline’s format mainly reflects the publisher’s audience and the specific Discover surface used.
One striking analysis was Simpson’s paradox. An anomaly that, once noticed, appeared across the entire dataset.
Here’s what we’re really measuring:
Rather than clicks from Discover, our metric is hits per article: how often an article appears across the 1492.vision fleet. This serves as a proxy for visibility.
The dataset was limited to editorial articles, excluding platforms like YouTube because they have different headline norms. We’ll dive back into these at the end, as they bring more clarity than anything else.
Why is volume important? The crux of the argument depends on slicing this vast dataset by publisher, Discover surface, topic, and language while still keeping enough data in each segment for valid insights. This is where the real difference between numbers and insights, and between a genuine format effect and a statistical illusion, lies.
Here’s a sneak peek: when you pool all publishers together, a clear gradient appears with quote-led headlines leading the pack and statements trailing.
The frequently cited +29% is actually a conservative estimate for editorial pieces: quote-led headlines achieve a +37% lift in English and +48% in French. Even questions don’t lag behind as much as expected since they outperform statements to some extent (+7% EN, +16% FR).
Though claim 1 appears understated and claim 2 misguided at the aggregate level, these are the observations on which most headline advice leans. Let’s delve further to understand what the data is really revealing.
Let’s shift to the hidden aspects, starting with publishers. The raw comparison isn’t effectively between quotes and statements. It’s more about one set of publishers versus another because the publishers employing quotes often differ from those who don’t.
Some media, like celebrity-focused outlets, regional newspapers, and sites attuned to trending topics, gravitate towards quotes, and naturally earn more Discover hits compared to entities that prefer factual presentations.
This is a prime example of Simpson’s paradox: a strong trend at the aggregate level that fades or reverses when segmented into groups.
To focus on the format itself, publishers must each be their own baseline: comparing quotes with statements within the same publishing entities while controlling for audience and topic diversity.
So, the question is, how does each format fare on its own? Let me walk you through the rest of this journey as we unpack these layers.
Hey there, have you heard about Google’s latest feature within Google Discover? They’ve just launched Search profiles in the U.S., and it’s a game-changer for publishers like me. These profiles act as enhanced landing pages where my audience can not only follow me but also see a collection of my latest articles, videos, and social media posts all in one convenient spot.
Google has been working on this for quite some time, refining and testing it over several months. They’ve even made some tweaks, such as adding shortnames, which make it even easier to share these profiles.
“Search profiles give publishers and creators a central place to showcase their latest articles, videos, and social posts. People can easily follow sources from their profile, so they’re more likely to see that content on Discover, found on the home screen of the Google app.”
It’s described as a “new way for publishers and creators to shape their presence on Search. Search profiles are a dedicated, shareable space to highlight content across platforms and help audiences find accurate, up-to-date information about sources on Search.”
What it looks like: Curious to see it in action? Here’s a video demonstration:
Managing Your Search Profile: If you’re a publisher or creator with a significant following on a major social or video platform, you’re in luck! You’ll be able to claim your Search profile, personalize it with an avatar, bio, and links to your website and social media platforms.
Once you claim your profile, it might even create a Knowledge Panel for you, or enhance your existing one with updated details and a direct link to your profile.
If you’re interested in setting up your own Search profile, check out this guide for creating a profile, claiming an existing one, and managing it.
Availability: Currently, this feature is available in the U.S. for users and publishers who meet a certain follower threshold. Here’s what you need:
TikTok: 300,000 followers
YouTube: 100,000 subscribers
Instagram: 100,000 followers
X: 100,000 followers
Why This Matters: As a publisher, I’m always looking for ways to get more visibility. Google’s new feature allows us to increase our reach not just on Google platforms but across our entire digital presence. It’s an exciting time, though one has to ponder whether this will be enough in the fast-paced world where AI continues to evolve.
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.
Have you recently noticed a decline in clicks and impressions around May 7th to May 8th? Don’t worry; it’s just a reporting glitch.
I discovered that Google has confirmed a bug affecting the Discover report in Google Search Console. It turns out there was a ‘logging’ error with the data, which has resulted in a drop in clicks and impressions during May 7th to May 8th, 2026.
Google assures us that this is merely a ‘data logging only’ issue, and it hasn’t impacted the actual positioning in Google Discover.
The issue: Google stated once again that a data logging error caused the discrepancies in the Discover report between May 7th and 8th, 2026.
As per Google’s post, this bug might have caused a ‘decrease in clicks and impressions in the Discover performance report.’
Why it matters: Numerous publishers, possibly including myself, saw a dip in performance metrics. It’s crucial to note that this is likely due to this bug.
Make sure to annotate your reports and inform your stakeholders that the Discover data from May 7th to May 8th is inaccurate and should be disregarded.
I recently delved into the intricate world of Google Discover, uncovering how its 20 pipelines and 42 million cards shape the landscape for publishers. This exploration reveals how trends, news, videos, and advertisements flow through the digital pipelines, achieving broadcast-level reach for some content.
Metehan Yesilyurt’s SDK analysis brought the pipeline names to my attention, and I meticulously collected data over three months to decipher each pipeline’s function—including volume, reach, timing, and dominance. Let’s dive into what the examination of 42 million cards reveals about Discover’s inner framework.
Our journey took three months (December 2025 – February 2026), where I analyzed real Discover feeds from hundreds of devices. The result was the analysis of 42 million feed cards intricately linked to their selecting pipelines.
This analysis built on existing knowledge from the SDK, as you might have encountered in Metehan’s SDK Analysis. My objective was to illuminate what each pipeline actively accomplishes—how much content it picks, how many devices view it, the pace at which it operates, and which publishers it highlights. That’s the story my data tells.
Four metrics were computed for every pipeline:
Reach — the percentage of devices showing each URL daily
Speed — the median age of articles when they appear
Exclusivity — the percentage of URLs exclusive to the pipeline
Diving deeper, many believe Discover operates on just one recommendation algorithm. However, our results tell a different tale—a sophisticated system with six layers, each with its unique logic, pace, and audience.
The six layers include:
Core editorial — various content types leading with editorial consistency.
News urgency — swift distribution of must-see news content.
Trends — pipelines dedicated to detecting and maintaining trends.
Local/geo — focusing on geotargeted stories and content.
Social/video — elevating YouTube video content into prominence.
Commercial — enhancing advertisements’ reach through platforms like YouTube.
In my exploration, I found peculiarities unique to the English Discover feed, including a YouTube content journey expanding through three successive pipelines. This system brings significant amplification to the reach of content that passes through it.
English Discover has also incorporated AI Overviews, an AI-generated summary, although this has been limited to English feeds only. Furthermore, a surprising revelation was the systemic under-representation of Premier League content across numerous pipelines, unlike other sports.
In conclusion, the Discover ecosystem continually evolves. Observing these changes provides valuable insights into the system’s architecture and potential influential power for publishers.
Data Source: 42 million Discover cards from December 2025 to February 2026. Analysis by 1492.vision with recognition to Metehan Yesilyurt for his work on Google SDK analysis.
Seeing the shifts in Google’s search traffic firsthand, I’ve noticed publishers losing organic search traffic, yet there’s a silver lining with breaking news traffic soaring by 103%, and Google Discover clicks surging.
Google’s AI Overviews might be cutting into traditional search clicks, but I believe publishers can still find significant growth through breaking news and Google Discover according to recent insights from Define Media Group.
Organic search clicks have dropped 42% since AI Overviews began expanding in Google Search, based on Define Media Group’s analysis of Google Search Console data from 64 sites. It’s quite revealing!
Why we care. AI-generated answers are dramatically reshaping how search traffic is distributed. While evergreen content loses clicks, real-time news coverage and Discover distribution are becoming more potent channels for us publishers.
By the numbers. In Google Search, Discover, and Google News, breaking news traffic has grown 103% from November 2024 to early 2026 within the company’s dataset. However, losses have mainly hit informational and evergreen content.
Here are some figures to consider: organic search traffic averaged 1.7 billion clicks per quarter from Q1 2023 through Q1 2024. Post AI Overviews launch, traffic took a 16% plunge immediately and couldn’t recover. As Google expanded AI Overviews in May 2025, these declines accelerated. By Q4 2025, search traffic had fallen 42% from the pre-AI Overviews baseline.
Discover’s role: Google Discover, which has grown by 30% across the portfolio, is becoming a primary growth engine for breaking news distribution, rising steadily even as web search traffic dips. It’s the first time Discover and web search have driven almost equal traffic.
Interestingly, the report highlights a significant increase in Discover traffic following the December 2025 Google core update, although some gains eased after the February 2026 Discover core update. Yet, according to Chartbeat data, Discover was the main driver of Google referrals to news sites last summer.
Why is this happening? AI Overviews appear less frequently for news queries compared to other topics. Reports show that AI Overviews appeared for only about 15% of news queries, which is nearly three times less often than in categories like health and science.
It seems news queries frequently trigger the Top Stories carousel, linking directly to publisher articles, especially for major events such as international conflicts. Define Media Group suggests that Google may avoid AI summaries for breaking news due to rapid changes and high accuracy needs.
When it comes to ensuring my images stand out in Google Search and Discover, I’ve learned that it’s all about using both schema.org markup and the og:image meta tag effectively. Google recently revised its image SEO best practices and Discover guide to clarify how they utilize these elements to select thumbnails.
“Google’s selection of an image preview is entirely automated, considering various sources to display a suitable image on Google, such as a text result image or a preview image in Discover.”
So, how can I influence the thumbnails Google selects?
I can specify the primaryImageOfPage property with a URL or ImageObject in schema.org. Alternatively, linking an image URL or ImageObject to the main entity using the mainEntity or mainEntityOfPage properties could be beneficial. Another option is to define the og:image meta tag.
Overall best practices include choosing an image that truly represents the page, avoiding generic images or those containing text, steering clear of extremes in aspect ratios, and opting for high-resolution images whenever possible.
Google Discover Image Selection – In the Discover documentation, I found some insightful tips:
“Incorporate engaging, high-quality images in your content, especially large images, as they are more likely to attract visits from Discover. Images should be at least 1200px wide, high resolution of at least 300K, and maintain a 16×9 aspect ratio.”
Google attempts to crop images automatically for Discover. If I choose to crop images myself, they should be well-positioned for landscape use, ensuring vital details remain in the cropped version specified in the og:image meta tag.
Also important is enabling the max-image-preview:large setting or using AMP. Utilizing schema.org markup or the og:image meta tag allows specifying a large, relevant image as thumbnails in Discover.
Why It Matters – Images significantly impact click-through rates from Google Search and Discover. By understanding and applying these guidelines, I can better guide Google in selecting the right image thumbnails to boost visibility.
